diff --git a/Exams/Final/Final.ipynb b/Exams/Final/Final.ipynb new file mode 100644 index 0000000..03c637d --- /dev/null +++ b/Exams/Final/Final.ipynb @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Final Exam\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github//afarbin/DATA1401-Spring-2020/blob/master/Exams/Final/Final.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall the drawing system from lecture 18:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class Canvas:\n", + " def __init__(self, width, height):\n", + " self.width = width\n", + " self.height = height\n", + " self.data = [[' '] * width for i in range(height)]\n", + "\n", + " def set_pixel(self, row, col, char='*'):\n", + " self.data[row][col] = char\n", + "\n", + " def get_pixel(self, row, col):\n", + " return self.data[row][col]\n", + " \n", + " def h_line(self, x, y, w, **kargs):\n", + " for i in range(x,x+w):\n", + " self.set_pixel(i,y, **kargs)\n", + "\n", + " def v_line(self, x, y, h, **kargs):\n", + " for i in range(y,y+h):\n", + " self.set_pixel(x,i, **kargs)\n", + " \n", + " def line(self, x1, y1, x2, y2, **kargs):\n", + " slope = (y2-y1) / (x2-x1)\n", + " for y in range(y1,y2):\n", + " x= int(slope * y)\n", + " self.set_pixel(x,y, **kargs)\n", + " \n", + " def display(self):\n", + " print(\"\\n\".join([\"\".join(row) for row in self.data]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class Shape:\n", + " def __init__(self, name=\"\", **kwargs):\n", + " self.name=name\n", + " self.kwargs=kwargs\n", + " \n", + " def paint(self, canvas): pass\n", + "\n", + "class Rectangle(Shape):\n", + " def __init__(self, x, y, w, h, **kwargs):\n", + " Shape.__init__(self, **kwargs)\n", + " self.x = x\n", + " self.y = y\n", + " self.w = w\n", + " self.h = h\n", + "\n", + " def paint(self, canvas):\n", + " canvas.h_line(self.x, self.y, self.w, **self.kwargs)\n", + " canvas.h_line(self.x, self.y + self.h, self.w, **self.kwargs)\n", + " canvas.v_line(self.x, self.y, self.h, **self.kwargs)\n", + " canvas.v_line(self.x + self.w, self.y, self.h, **self.kwargs)\n", + "\n", + "class Square(Rectangle):\n", + " def __init__(self, x, y, size, **kwargs):\n", + " Rectangle.__init__(self, x, y, size, size, **kwargs)\n", + "\n", + "class Line(Shape):\n", + " def __init__(self, x1, y1, x2, y2, **kwargs):\n", + " Shape.__init__(self, **kwargs)\n", + " self.x1=x1\n", + " self.y1=y1\n", + " self.x2=x2\n", + " self.y2=y2\n", + " \n", + " def paint(self, canvas):\n", + " canvas.line(self.x1,self.y1,self.x2,self.y2)\n", + " \n", + "class CompoundShape(Shape):\n", + " def __init__(self, shapes):\n", + " self.shapes = shapes\n", + "\n", + " def paint(self, canvas):\n", + " for s in self.shapes:\n", + " s.paint(canvas)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class RasterDrawing:\n", + " def __init__(self):\n", + " self.shapes=dict()\n", + " self.shape_names=list()\n", + " \n", + " def add_shape(self,shape):\n", + " if shape.name == \"\":\n", + " shape.name = self.assign_name()\n", + " \n", + " self.shapes[shape.name]=shape\n", + " self.shape_names.append(shape.name)\n", + " \n", + " def paint(self,canvas):\n", + " for shape_name in self.shape_names:\n", + " self.shapes[shape_name].paint(canvas)\n", + " \n", + " def assign_name(self):\n", + " name_base=\"shape\"\n", + " name = name_base+\"_0\"\n", + " \n", + " i=1\n", + " while name in self.shapes:\n", + " name = name_base+\"_\"+str(i)\n", + " \n", + " return name\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Add `Point` and `Triangle` classes and test them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Add an `Arc` class that is instantiated with a center location, two axis lengths, and starting and ending angles. If start and end are not specified or are the same angle, the `Arc` instance should draw an oval. If in addition the two axes are the same, the `Arc` instance should draw a circle. Create `Oval` and `Circle` classes that inherit from `Arc`. Test everything." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Use your classes to create a `RasterDrawing` that draws a happy face." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Add to the `Shape` base class a `__str__()` method. Overwrite the method in each shape to generate a string of the python code necessary to reinstantiate the object. For example, for a rectangle originally instantiated using `Square(5,5,20,char=\"^\")`, `__str__()` should return the string `'Square(5,5,20,char=\"^\")'`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Add to `RasterDrawing` two functions, `save(filename)` and `load(filename)`. The save function writes the `__str__()` of all of the shapes in the drawing to a file (one shape per line). The load function, reads the file, and instantiates each object using the python `eval(expression)` function, and adds each shape to the drawing, thereby recreating a \"saved\" raster drawing. Use this functionality to save and load your happy face.\n", + "\n", + " `eval` takes a string that contains a fragment of a python code and executes it. Consider the following examples: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eval(\"print('Hello')\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = eval('1+2')\n", + "print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Exams/Mid-term/Exam.ipynb b/Exams/Mid-term/Exam.ipynb new file mode 100644 index 0000000..5200376 --- /dev/null +++ b/Exams/Mid-term/Exam.ipynb @@ -0,0 +1,199 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Mid-term Exam\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github//afarbin/DATA1401-Spring-2020/blob/master/Exams/Mid-term/Exam.ipynb)\n", + "\n", + "Add cells to this notebook as you need for your solutions and your test of your solutions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Write a function `first_alphabetically(lst)` that takes a list `lst` of strings and returns the string that is alphabetically first. For example, calling your function with the list of states:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "states=['Mississippi', 'Maryland', 'Delaware', 'Connecticut', 'Virginia', 'Utah', 'Kansas',\n", + " 'Wyoming', 'Indiana', 'Louisiana', 'Missouri', 'Illinois', 'Minnesota', 'Vermont', \n", + " 'New Mexico', 'North Dakota', 'Wisconsin', 'Tennessee', 'New York', 'Oklahoma', \n", + " 'Colorado', 'Pennsylvania', 'West Virginia', 'Alabama', 'Montana', 'Texas', \n", + " 'Washington', 'Michigan', 'New Hampshire', 'Arkansas', 'Hawaii', 'Iowa', \n", + " 'Idaho', 'Kentucky', 'Ohio', 'Nebraska', 'Alaska', 'Oregon', 'South Dakota', \n", + " 'New Jersey', 'Florida', 'Georgia', 'Rhode Island', 'Arizona', 'Maine', \n", + " 'South Carolina', 'California', 'Nevada', 'Massachusetts', 'North Carolina']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "should return the string `\"Alabama\"`. Note that you can compare strings:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"A\">\"B\")\n", + "print(\"B\">\"A\")\n", + "print(\"A\">\"a\")\n", + "print(\"bca\">\"bbc\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make sure your implementation isn't case sensitive. Do not use python's built-in `min`, `max`, `sort` or any other sort function you find." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Write a function `arg_first_alphabetically(lst)`, which does the same thing as in exercise 1 but returns the index of the first string alphabetically." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Use your result in question 2 to implement a function `arg_sort_alphabetically(lst)` that returns a list that is alphabetically sorted. Sorting can be accomplished by successively applying the function in question 1 and removing the first element alphabetically. You can remove an element from a list using `pop()`. Make sure your implementation isn't case sensitive. Do not use python's built-in `min`, `max`, `sort` or any other sort function you find." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Implement a function `outer_product` that takes two one-dimensional lists of numbers and returns the two-dimensional outer product matrix defined as:\n", + "\n", + "\\begin{equation*}\n", + "\\begin{pmatrix} x_1\\\\x_2\\\\ \\vdots \\\\x_m \\end{pmatrix} \\begin{pmatrix} y_1&y_2& \\dots &y_n\\end{pmatrix} =\n", + "\\begin{pmatrix}\n", + "x_1y_1 & x_1y_2 & \\dots & x_1y_n\\\\\n", + "x_2y_1 & x_2y_2 & \\dots & x_2y_n\\\\\n", + "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", + "x_my_1 & x_my_2 & \\dots & x_my_n\n", + "\\end{pmatrix}\n", + "\\end{equation*}\n", + "\n", + "In other words the elements of matrix C which is the outer product of A and B are $c_{ij} = a_i b_j$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Implement a function `cumulative_sum(lst)` that takes a list of numbers and returns a list of same size where the element `i` is the sum of the elements `0` to `i` of the input list. For example given `[1,2,3]`, you should return [1,3,6]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "6. Imagine you have a normal distributed random variable `x`. For example `x` can be grades on this exam. Using the normal distribution generator and histogram functions from lecture (provided below) and `cumulative_sum` from previous question to compute what is the value of $x_{90}$ in $\\sigma$ such that 90% of the values $x$ are below $x_{90}$. In other words:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "\\int_{-\\infty}^{x_{90}} N(x;\\mu=0,\\sigma=1) dx = 0.9\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import math,random\n", + "\n", + "def arange(x_min,x_max,steps=10):\n", + " step_size=(x_max-x_min)/steps\n", + " x=x_min\n", + " out = list()\n", + " for i in range(steps):\n", + " out.append(x)\n", + " x+=step_size\n", + " return out\n", + "\n", + "def generate_normal(N,m=0,s=1):\n", + " out = list() \n", + " \n", + " while len(out)=bin_edges[i] and d0: \n", + " print (\"Type of Data Contents:\", type(data[0]))\n", + " print (\"Data Minimum:\", min(data))\n", + " print (\"Data Maximum:\", max(data))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 2a:* \n", + "Write a function that computes the mean of values in a list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Skeleton\n", + "def mean(Data):\n", + " m=0.\n", + " \n", + " ### BEGIN SOLUTION\n", + "\n", + " # Fill in your solution here \n", + " \n", + " ### END SOLUTION\n", + " \n", + " return m" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test your solution here\n", + "print (\"Mean of Data:\", mean(data))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 2b:* \n", + "Write a function that computes the variance of values in a list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Skeleton\n", + "def variance(Data):\n", + " m=0.\n", + " \n", + " ### BEGIN SOLUTION\n", + "\n", + " # Fill in your solution here \n", + " \n", + " ### END SOLUTION\n", + " \n", + " return m" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test your solution here\n", + "print (\"Variance of Data:\", variance(data))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Histogramming" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 3:* Write a function that bins the data so that you can create a histogram. An example of how to implement histogramming is the following logic:\n", + "\n", + "* User inputs a list of values `x` and optionally `n_bins` which defaults to 10.\n", + "* If not supplied, find the minimum and maximum (`x_min`,`x_max`) of the values in x.\n", + "* Determine the bin size (`bin_size`) by dividing the range of the function by the number of bins.\n", + "* Create an empty list of zeros of size `n_bins`, call it `hist`.\n", + "* Loop over the values in `x`\n", + " * Loop over the values in `hist` with index `i`:\n", + " * If x is between `x_min+i*bin_size` and `x_min+(i+1)*bin_size`, increment `hist[i].` \n", + " * For efficiency, try to use continue to goto the next bin and data point.\n", + "* Return `hist` and the list corresponding of the bin edges (i.e. of `x_min+i*bin_size`). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "def histogram(x,n_bins=10,x_min=None,x_max=None):\n", + " ### BEGIN SOLUTION\n", + "\n", + " # Fill in your solution here \n", + " \n", + " ### END SOLUTION\n", + "\n", + " return hist,bin_edges" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test your solution here\n", + "h,b=histogram(data,100)\n", + "print(h)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 4:* Write a function that uses the histogram function in the previous exercise to create a text-based \"graph\". For example the output could look like the following:\n", + "```\n", + "[ 0, 1] : ######\n", + "[ 1, 2] : #####\n", + "[ 2, 3] : ######\n", + "[ 3, 4] : ####\n", + "[ 4, 5] : ####\n", + "[ 5, 6] : ######\n", + "[ 6, 7] : #####\n", + "[ 7, 8] : ######\n", + "[ 8, 9] : ####\n", + "[ 9, 10] : #####\n", + "```\n", + "\n", + "Where each line corresponds to a bin and the number of `#`'s are proportional to the value of the data in the bin. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "def draw_histogram(x,n_bins,x_min=None,x_max=None,character=\"#\",max_character_per_line=20):\n", + " ### BEGIN SOLUTION\n", + "\n", + " # Fill in your solution here \n", + " \n", + " ### END SOLUTION\n", + "\n", + " return hist,bin_edges" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test your solution here\n", + "h,b=histogram(data,20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Functional Programming\n", + "\n", + "*Exercise 5:* Write a function the applies a booling function (that returns true/false) to every element in data, and return a list of indices of elements where the result was true. Use this function to find the indices of entries greater than 0.5. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def where(mylist,myfunc):\n", + " out= []\n", + " \n", + " ### BEGIN SOLUTION\n", + "\n", + " # Fill in your solution here \n", + " \n", + " ### END SOLUTION\n", + " \n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test your solution here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 6:* The inrange(mymin,mymax) function below returns a function that tests if it's input is between the specified values. Write corresponding functions that test:\n", + "* Even\n", + "* Odd\n", + "* Greater than\n", + "* Less than\n", + "* Equal\n", + "* Divisible by" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def in_range(mymin,mymax):\n", + " def testrange(x):\n", + " return x=mymin\n", + " return testrange\n", + "\n", + "# Examples:\n", + "F1=inrange(0,10)\n", + "F2=inrange(10,20)\n", + "\n", + "# Test of in_range\n", + "print (F1(0), F1(1), F1(10), F1(15), F1(20))\n", + "print (F2(0), F2(1), F2(10), F2(15), F2(20))\n", + "\n", + "print (\"Number of Entries passing F1:\", len(where(data,F1)))\n", + "print (\"Number of Entries passing F2:\", len(where(data,F2)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### BEGIN SOLUTION\n", + "\n", + " # Fill in your solution here \n", + " \n", + "### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test your solution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 7:* Repeat the previous exercise using `lambda` and the built-in python functions sum and map instead of your solution above. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### BEGIN SOLUTION\n", + "\n", + " # Fill in your solution here \n", + " \n", + "### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Monte Carlo\n", + "\n", + "*Exercise 7:* Write a \"generator\" function called `generate_function(func,x_min,x_max,N)`, that instead of generating a flat distribution, generates a distribution with functional form coded in `func`. Note that `func` will always be > 0. \n", + "\n", + "Use the test function below and your histogramming functions above to demonstrate that your generator is working properly.\n", + "\n", + "Hint: A simple, but slow, solution is to a draw random number test_x within the specified range and another number p between the min and max of the function (which you will have to determine). If p<=function(test_x), then place test_x on the output. If not, repeat the process, drawing two new numbers. Repeat until you have the specified number of generated numbers, N. For this problem, it's OK to determine the min and max by numerically sampling the function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_function(func,x_min,x_max,N=1000):\n", + " out = list()\n", + " ### BEGIN SOLUTION\n", + "\n", + " # Fill in your solution here \n", + " \n", + " ### END SOLUTION\n", + " \n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# A test function\n", + "def test_func(x,a=1,b=1):\n", + " return abs(a*x+b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 8:* Use your function to generate 1000 numbers that are normal distributed, using the `gaussian` function below. Confirm the mean and variance of the data is close to the mean and variance you specify when building the Gaussian. Histogram the data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "def gaussian(mean, sigma):\n", + " def f(x):\n", + " return math.exp(-((x-mean)**2)/(2*sigma**2))/math.sqrt(math.pi*sigma)\n", + " return f\n", + "\n", + "# Example Instantiation\n", + "g1=gaussian(0,1)\n", + "g2=gaussian(10,3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 9:* Combine your `generate_function`, `where`, and `in_range` functions above to create an integrate function. Use your integrate function to show that approximately 68% of Normal distribution is within one variance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def integrate(func, x_min, x_max, n_points=1000):\n", + " \n", + " return integral" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Labs/Lab-5/Lab-5.ipynb b/Labs/Lab-5/Lab-5.ipynb new file mode 100644 index 0000000..84b32cc --- /dev/null +++ b/Labs/Lab-5/Lab-5.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lab 5- Object Oriented Programming\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github//afarbin/DATA1401-Spring-2020/blob/master/Labs/Lab-5/Lab-5.ipynb)\n", + "\n", + "For all of the exercises below, make sure you provide tests of your solutions.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Write a \"counter\" class that can be incremented up to a specified maximum value, will print an error if an attempt is made to increment beyond that value, and allows reseting the counter. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "class counter:\n", + " def __init__(self,max_val):\n", + " self.max_val=max_val\n", + " self.cur_val=1\n", + " \n", + " def increment(self):\n", + " if self.cur_val>self.max_val:\n", + " print(\"Max value reached.\")\n", + " else:\n", + " self.cur_val+=1\n", + " \n", + " def reset(self):\n", + " self.cur_val=1\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "my_counter=counter(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max value reached.\n", + "Max value reached.\n" + ] + } + ], + "source": [ + "my_counter.increment()\n", + "my_counter.increment()\n", + "my_counter.increment()\n", + "my_counter.increment()\n", + "my_counter.increment()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "my_counter.cur_val=100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Copy and paste your solution to question 1 and modify it so that all the data held by the counter is private. Implement functions to check the value of the counter, check the maximum value, and check if the counter is at the maximum." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "class counter:\n", + " def __init__(self,max_val):\n", + " self.__max_val=max_val\n", + " self.__cur_val=1\n", + " \n", + " def increment(self):\n", + " if self.__cur_val>self.__max_val:\n", + " print(\"Max value reached.\")\n", + " else:\n", + " self.__cur_val+=1\n", + " \n", + " def reset(self):\n", + " self.__cur_val=1\n", + " \n", + " def cur_val(self):\n", + " return self.__cur_val\n", + "\n", + " def max_val(self):\n", + " return self.__max_val\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "my_counter=counter(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_counter.cur_val()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Implement a class to represent a rectangle, holding the length, width, and $x$ and $y$ coordinates of a corner of the object. Implement functions that compute the area and parameter of the rectangle. Make all data members private and privide accessors to retrieve values of data members. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class rectangle:\n", + " def __init__(self,width,length,x,y):\n", + " self.__width=width\n", + " self.__length=legth\n", + " self.__x=x\n", + " self.__y=y\n", + " \n", + " def area(self):\n", + " return self.__width*self.__length\n", + " \n", + " def perimeter(self):\n", + " return 2*(self.__width+self.__length)\n", + " \n", + " def x(self):\n", + " return self.__x\n", + " \n", + " ...\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Implement a class to represent a circle, holding the radius and $x$ and $y$ coordinates of center of the object. Implement functions that compute the area and parameter of the rectangle. Make all data members private and privide accessors to retrieve values of data members. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Implement a common base class for the classes implemented in 3 and 4 above which implements all common methods as dummy functions. Re-implement those classes to inherit from the base class and overload the functions accordingly. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "6. Implement an analogous triangle class." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "7. Add a function to the object classes that test if a given set of $x$ and $y$ coordinates are inside of the object." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "8. Add a function to the object classes that return a list of up to 16 pairs of $x$ and $y$ points on the parameter of the object.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "9. Add a function in the base class of the object classes that returns true/false testing that the object overlaps with another object." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Labs/Lab-6/Lab-6.ipynb b/Labs/Lab-6/Lab-6.ipynb new file mode 100644 index 0000000..0b9fcae --- /dev/null +++ b/Labs/Lab-6/Lab-6.ipynb @@ -0,0 +1,126 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lab 6\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Matrix Representation: In this lab you will be creating a simple linear algebra system. In memory, we will represent matrices as nested python lists as we have done in lecture. \n", + "\n", + "1. Create a `matrix` class with the following properties:\n", + " * It can be initialized in 2 ways:\n", + " 1. with arguments `n` and `m`, the size of the matrix. A newly instanciated matrix will contain all zeros.\n", + " 2. with a list of lists of values. Note that since we are using lists of lists to implement matrices, it is possible that not all rows have the same number of columns. Test explicitly that the matrix is properly specified.\n", + " * Matrix instances `M` can be indexed with `M[i][j]` and `M[i,j]`.\n", + " * Matrix assignment works in 2 ways:\n", + " 1. If `M_1` and `M_2` are `matrix` instances `M_1=M_2` sets the values of `M_1` to those of `M_2`, if they are the same size. Error otherwise.\n", + " 2. In example above `M_2` can be a list of lists of correct size.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Add the following methods:\n", + " * `shape()`: returns a tuple `(n,m)` of the shape of the matrix.\n", + " * `transpose()`: returns a new matrix instance which is the transpose of the matrix.\n", + " * `row(n)` and `column(n)`: that return the nth row or column of the matrix M as a new appropriately shaped matrix object.\n", + " * `to_list()`: which returns the matrix as a list of lists.\n", + " * `block(n_0,n_1,m_0,m_1)` that returns a smaller matrix located at the n_0 to n_1 columns and m_0 to m_1 rows. \n", + " * (Extra credit) Modify `__getitem__` implemented above to support slicing.\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Write functions that create special matrices (note these are standalone functions, not member functions of your `matrix` class):\n", + " * `constant(n,m,c)`: returns a `n` by `m` matrix filled with floats of value `c`.\n", + " * `zeros(n,m)` and `ones(n,m)`: return `n` by `m` matrices filled with floats of value `0` and `1`, respectively.\n", + " * `eye(n)`: returns the n by n identity matrix." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Add the following member functions to your class. Make sure to appropriately test the dimensions of the matrices to make sure the operations are correct.\n", + " * `M.scalarmul(c)`: a matrix that is scalar product $cM$, where every element of $M$ is multiplied by $c$.\n", + " * `M.add(N)`: adds two matrices $M$ and $N$. Don’t forget to test that the sizes of the matrices are compatible for this and all other operations.\n", + " * `M.sub(N)`: subtracts two matrices $M$ and $N$.\n", + " * `M.mat_mult(N)`: returns a matrix that is the matrix product of two matrices $M$ and $N$.\n", + " * `M.element_mult(N)`: returns a matrix that is the element-wise product of two matrices $M$ and $N$.\n", + " * `M.equals(N)`: returns true/false if $M==N$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Overload python operators to appropriately use your functions in 4 and allow expressions like:\n", + " * 2*M\n", + " * M*2\n", + " * M+N\n", + " * M-N\n", + " * M*N\n", + " * M==N\n", + " * M=N\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "6. Demonstrate the basic properties of matrices with your matrix class by creating two 2 by 2 example matrices using your Matrix class and illustrating the following:\n", + "\n", + "$$\n", + "(AB)C=A(BC)\n", + "$$\n", + "$$\n", + "A(B+C)=AB+AC\n", + "$$\n", + "$$\n", + "AB\\neq BA\n", + "$$\n", + "$$\n", + "AI=A\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Labs/Lab-7/Data1401-Grades.csv b/Labs/Lab-7/Data1401-Grades.csv new file mode 100644 index 0000000..ad6e2f6 --- /dev/null +++ b/Labs/Lab-7/Data1401-Grades.csv @@ -0,0 +1,17 @@ +l1_n,l1_1,12_n,l2_1,l2_2,l2_3,l2_4,l2_5,l2_6,l2_7,l3_n,l3_1,l3_2,l3_3,l3_4,l3_5,l3_6,l3_7,l3_8,l3_9,l3_10,l3_11,l3_12,l3_13,l3_14,l4_n,l4_1,l4_2,l4_3,l4_4,l4_5,l4_6,l4_7,l4_8,l4_9,l4_10,l4_11,q1_n,q1_1,e1_n,e1_1,e1_2,e1_3,e1_4,e1_5,e1_6,e1_7,e1_8,e1_9,e1_10,e1_11,e1_12,e1_13,e1_14,e1_15 +1,10,7,0,10,10,8,10,10,10,14,9,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,9.5,15,9,9,0,9,8,0,0,0,0,0,0,0,0,0,0 +1,10,7,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +1,10,7,0,0,0,0,0,0,0,14,9,10,10,10,7,10,3,6,3,3,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,5,15,5,5,5,5,0,0,0,0,0,0,0,0,0,0,0 +1,10,7,10,10,3,9.5,10,10,9.5,14,10,10,10,8,5,10,5,10,3,0,10,3,10,8,11,10,10,10,10,10,10,0,0,10,5,0,1,10,15,9,9,10,9,7,9,0,0,10,10,9,5,10,8,10 +1,10,7,10,10,9.5,0,10,10,0,14,9.5,0,0,10,0,10,5,10,7,0,10,6,10,0,11,10,10,6,0,0,0,0,0,0,0,0,1,0,15,0,0,0,0,5,0,7,0,3,3,3,0,3,0,0 +1,10,7,10,10,10,9.5,10,10,9.5,14,5,9.5,9.5,8,10,10,8,10,8,0,5,6,0,0,11,0,10,10,10,0,5,0,0,0,0,0,1,9.5,15,9,9,10,9,9,10,7,0,9,9,9,0,5,0,0 +1,10,7,10,10,0,5,10,10,9.5,14,9.5,10,10,8,10,8,9,0,0,0,0,0,0,0,11,0,10,10,0,0,10,0,0,0,0,0,1,10,15,9,9,10,9,0,0,0,0,0,0,0,0,0,0,0 +1,10,7,10,10,10,9.5,10,10,9.5,14,10,10,10,10,0,0,0,0,0,0,0,0,0,0,11,10,10,10,10,3,3,0,0,5,0,0,1,10,15,9,9,10,0,10,0,7,5,9,9,9,0,0,0,0 +1,10,7,0,10,9.5,0,10,10,0,14,10,10,10,10,0,0,0,0,0,0,0,0,0,0,11,10,10,10,10,5,3,0,3,10,7,0,1,9.5,15,9,9,10,5,10,0,9,9,9,9,9,10,5,0,0 +1,10,7,10,10,0,10,10,10,10,14,10,6,10,0,0,0,0,0,0,0,0,0,0,0,11,10,10,0,7,0,0,0,0,0,0,0,1,9.5,15,9,9,10,9,5,9,7,9,10,10,10,5,10,5,0 +1,10,7,10,10,0,0,10,10,7,14,10,10,10,10,7,10,6,3,10,10,10,10,10,10,11,10,10,10,10,10,5,10,10,10,10,10,1,0,15,9,9,9,9,9,10,9,9,10,10,10,10,10,5,10 +1,10,7,10,10,9.5,9.5,10,10,9.5,14,9.5,10,10,10,8,10,8,10,10,7,5,0,0,0,11,10,10,10,10,5,6,0,0,0,0,0,1,10,15,9,9,10,9,8,9,7,9,10,10,10,10,0,0,0 +1,10,7,10,10,5,9.5,10,10,9.5,14,5,9,9,10,7,10,10,10,10,7,10,3,5,10,11,0,0,0,0,0,0,0,0,0,0,0,1,10,15,9,9,9,8,7,10,0,9,10,9,10,9,5,0,0 +1,10,7,10,10,9.5,0,10,10,0,14,9.5,10,10,10,10,10,10,10,0,0,10,5,10,10,11,0,10,10,0,0,5,0,0,0,0,0,1,0,15,9,9,10,0,8,9,7,9,10,10,10,10,10,0,0 +1,10,7,10,10,9.5,9,10,10,9.5,14,10,10,10,10,10,10,9,10,3,0,3,3,5,2,11,0,0,0,0,0,0,0,0,0,0,0,1,0,15,9,9,10,5,5,0,0,10,10,10,10,0,10,5,10 +1,10,7,10,10,3,7,10,10,9,14,10,10,10,10,0,10,9,10,7,7,3,7,5,8,11,10,10,10,8,5,3,0,0,7,0,0,1,9.5,15,9,9,10,10,7,10,10,10,10,10,10,10,9,8,2 diff --git a/Labs/Lab-7/Lab-7.ipynb b/Labs/Lab-7/Lab-7.ipynb new file mode 100644 index 0000000..5a9197d --- /dev/null +++ b/Labs/Lab-7/Lab-7.ipynb @@ -0,0 +1,586 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lab 7\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github//afarbin/DATA1401-Spring-2020/blob/master/Labs/Lab-7/Lab-7.ipynb)\n", + "\n", + "Here are the \"Gradebook\" classes from lecture. For this lab, you will use these classes and are encouraged to modify them as you need." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import math\n", + "\n", + "# Create some virtual classes\n", + "class base:\n", + " __name=\"\"\n", + " \n", + " def __init__(self,name):\n", + " self.__name=name\n", + "\n", + " def name(self):\n", + " return self.__name\n", + "\n", + "class data(base):\n", + " def __init__(self,name):\n", + " base.__init__(self,name)\n", + " \n", + "class alg(base):\n", + " def __init__(self,name):\n", + " base.__init__(self,name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class grade(data):\n", + " __value=0\n", + " __numerical=True\n", + " __gradebook_name=str()\n", + " __letter_grades=[\"F-\",\"F\",\"F+\",\"D-\",\"D\",\"D+\",\"C-\",\"C\",\"C+\",\"B-\",\"B\",\"B+\",\"A-\",\"A\",\"A+\"]\n", + " \n", + " def __init__(self,name,numerical=True,value=None):\n", + " if value:\n", + " if isinstance(value,(int,float)):\n", + " self.__numerical=True\n", + " elif isinstance(value,str):\n", + " self.__numerical=False\n", + " self.set(value)\n", + " else: \n", + " self.__numerical=numerical\n", + " self.__gradebook_name=name\n", + " data.__init__(self,name+\" Grade Algorithm\") \n", + "\n", + " def set(self,value):\n", + " if isinstance(value,(int,float)) and self.__numerical:\n", + " self.__value=value\n", + " elif isinstance(value,str) and not self.__numerical:\n", + " if value in self.__letter_grades:\n", + " self.__value=value\n", + " else:\n", + " print (self.name()+\" Error: Bad Grade.\")\n", + " raise Exception\n", + " \n", + " def value(self):\n", + " return self.__value\n", + " \n", + " def numerical(self):\n", + " return self.__numerical\n", + " \n", + " def gradebook_name(self):\n", + " return self.__gradebook_name\n", + " \n", + " def __str__(self):\n", + " return self.__gradebook_name+\": \"+str(self.__value)\n", + "\n", + "class student(data):\n", + " __id_number=0\n", + " __grades=dict()\n", + " \n", + " def __init__(self,first_name, last_name,id_number):\n", + " self.__id_number=id_number\n", + " self.__grades=dict()\n", + " data.__init__(self,first_name+\" \"+last_name+\" Student Data\")\n", + "\n", + " def add_grade(self,a_grade,overwrite=False):\n", + " if overwrite or not a_grade.gradebook_name() in self.__grades:\n", + " self.__grades[a_grade.gradebook_name()]=a_grade\n", + " else:\n", + " print (self.name()+\" Error Adding Grade \"+a_grade.name()+\". Grade already exists.\")\n", + " raise Exception\n", + "\n", + " def id_number(self):\n", + " return self.__id_number\n", + " \n", + " def __getitem__(self,key):\n", + " return self.__grades[key]\n", + " \n", + " def print_grades(self):\n", + " for grade in self.__grades:\n", + " print (self.__grades[grade])\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class calculator(alg): \n", + " def __init__(self,name):\n", + " alg.__init__(self,name)\n", + "\n", + " def apply(self,a_grade_book):\n", + " raise NotImplementedError\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class grade_book(data):\n", + " # New member class to hold arbitrary data associated with the class\n", + "\n", + " __data=dict()\n", + " __students=dict()\n", + " \n", + " def __init__(self,name):\n", + " data.__init__(self,name+\" Course Grade Book\")\n", + " self.__students=dict()\n", + " self.__data=dict()\n", + " \n", + " # New method to access data\n", + " def __getitem__(self,key):\n", + " return self.__data[key]\n", + " \n", + " # New method to add data\n", + " def __setitem__(self, key, value):\n", + " self.__data[key] = value\n", + " \n", + " def add_student(self,a_student):\n", + " self.__students[a_student.id_number()]=a_student\n", + "\n", + " # New method to allow iterating over students\n", + " def get_students(self):\n", + " return self.__students\n", + " \n", + " def assign_grade(self,key,a_grade):\n", + " the_student=None\n", + " try:\n", + " the_student=self.__students[key]\n", + " except:\n", + " for id in self.__students:\n", + " if key == self.__students[id].name():\n", + " the_student=self.__students[id]\n", + " break\n", + " if the_student:\n", + " the_student.add_grade(a_grade)\n", + " else:\n", + " print (self.name()+\" Error: Did not find student.\")\n", + " \n", + " def apply_calculator(self,a_calculator,**kwargs):\n", + " a_calculator.apply(self,**kwargs)\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class uncurved_letter_grade_percent(calculator):\n", + " __grades_definition=[ (.97,\"A+\"),\n", + " (.93,\"A\"),\n", + " (.9,\"A-\"),\n", + " (.87,\"B+\"),\n", + " (.83,\"B\"),\n", + " (.8,\"B-\"),\n", + " (.77,\"C+\"),\n", + " (.73,\"C\"),\n", + " (.7,\"C-\"),\n", + " (.67,\"C+\"),\n", + " (.63,\"C\"),\n", + " (.6,\"C-\"),\n", + " (.57,\"F+\"),\n", + " (.53,\"F\"),\n", + " (0.,\"F-\")]\n", + " __max_grade=100.\n", + " __grade_name=str()\n", + " \n", + " def __init__(self,grade_name,max_grade=100.):\n", + " self.__max_grade=max_grade\n", + " self.__grade_name=grade_name\n", + " calculator.__init__(self,\n", + " \"Uncurved Percent Based Grade Calculator \"+self.__grade_name+\" Max=\"+str(self.__max_grade))\n", + " \n", + " def apply(self,a_grade_book,grade_name=None,**kwargs):\n", + " if grade_name:\n", + " pass\n", + " else:\n", + " grade_name=self.__grade_name\n", + " \n", + " \n", + " for k,a_student in a_grade_book.get_students().iteritems():\n", + " a_grade=a_student[grade_name]\n", + "\n", + " if not a_grade.numerical():\n", + " print (self.name()+ \" Error: Did not get a numerical grade as input.\")\n", + " raise Exception\n", + " \n", + " percent=a_grade.value()/self.__max_grade\n", + " \n", + " for i,v in enumerate(self.__grades_definition):\n", + " if percent>=v[0]:\n", + " break\n", + " \n", + " a_student.add_grade(grade(grade_name+\" Letter\",value=self.__grades_definition[i][1]))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class mean_std_calculator(calculator):\n", + " def __init__(self):\n", + " calculator.__init__(self,\"Mean and Standard Deviation Calculator\")\n", + " \n", + " def apply(self,a_grade_book,grade_name,**kwargs):\n", + " grades=list()\n", + " for k,a_student in a_grade_book.get_students().iteritems():\n", + " grades.append(a_student[grade_name].value())\n", + " \n", + " a_grade_book[grade_name+\" Mean\"] = np.mean(grades)\n", + " a_grade_book[grade_name+\" STD\"] = math.sqrt(np.var(grades))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## CSV Reader\n", + "\n", + "*Exercise 1*: The data for a class are stored in a \"camma separated values\" (CSV) file name `Data1401-Grades.csv` in the directory of this lab. You can see the contents using the `cat` shell command:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "l1_n,l1_1,12_n,l2_1,l2_2,l2_3,l2_4,l2_5,l2_6,l2_7,l3_n,l3_1,l3_2,l3_3,l3_4,l3_5,l3_6,l3_7,l3_8,l3_9,l3_10,l3_11,l3_12,l3_13,l3_14,l4_n,l4_1,l4_2,l4_3,l4_4,l4_5,l4_6,l4_7,l4_8,l4_9,l4_10,l4_11,q1_n,q1_1,e1_n,e1_1,e1_2,e1_3,e1_4,e1_5,e1_6,e1_7,e1_8,e1_9,e1_10,e1_11,e1_12,e1_13,e1_14,e1_15\r", + "\r\n", + "1,10,7,0,10,10,8,10,10,10,14,9,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,9.5,15,9,9,0,9,8,0,0,0,0,0,0,0,0,0,0\r", + "\r\n", + "1,10,7,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\r", + "\r\n", + "1,10,7,0,0,0,0,0,0,0,14,9,10,10,10,7,10,3,6,3,3,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,5,15,5,5,5,5,0,0,0,0,0,0,0,0,0,0,0\r", + "\r\n", + "1,10,7,10,10,3,9.5,10,10,9.5,14,10,10,10,8,5,10,5,10,3,0,10,3,10,8,11,10,10,10,10,10,10,0,0,10,5,0,1,10,15,9,9,10,9,7,9,0,0,10,10,9,5,10,8,10\r", + "\r\n", + "1,10,7,10,10,9.5,0,10,10,0,14,9.5,0,0,10,0,10,5,10,7,0,10,6,10,0,11,10,10,6,0,0,0,0,0,0,0,0,1,0,15,0,0,0,0,5,0,7,0,3,3,3,0,3,0,0\r", + "\r\n", + "1,10,7,10,10,10,9.5,10,10,9.5,14,5,9.5,9.5,8,10,10,8,10,8,0,5,6,0,0,11,0,10,10,10,0,5,0,0,0,0,0,1,9.5,15,9,9,10,9,9,10,7,0,9,9,9,0,5,0,0\r", + "\r\n", + "1,10,7,10,10,0,5,10,10,9.5,14,9.5,10,10,8,10,8,9,0,0,0,0,0,0,0,11,0,10,10,0,0,10,0,0,0,0,0,1,10,15,9,9,10,9,0,0,0,0,0,0,0,0,0,0,0\r", + "\r\n", + "1,10,7,10,10,10,9.5,10,10,9.5,14,10,10,10,10,0,0,0,0,0,0,0,0,0,0,11,10,10,10,10,3,3,0,0,5,0,0,1,10,15,9,9,10,0,10,0,7,5,9,9,9,0,0,0,0\r", + "\r\n", + "1,10,7,0,10,9.5,0,10,10,0,14,10,10,10,10,0,0,0,0,0,0,0,0,0,0,11,10,10,10,10,5,3,0,3,10,7,0,1,9.5,15,9,9,10,5,10,0,9,9,9,9,9,10,5,0,0\r", + "\r\n", + "1,10,7,10,10,0,10,10,10,10,14,10,6,10,0,0,0,0,0,0,0,0,0,0,0,11,10,10,0,7,0,0,0,0,0,0,0,1,9.5,15,9,9,10,9,5,9,7,9,10,10,10,5,10,5,0\r", + "\r\n", + "1,10,7,10,10,0,0,10,10,7,14,10,10,10,10,7,10,6,3,10,10,10,10,10,10,11,10,10,10,10,10,5,10,10,10,10,10,1,0,15,9,9,9,9,9,10,9,9,10,10,10,10,10,5,10\r", + "\r\n", + "1,10,7,10,10,9.5,9.5,10,10,9.5,14,9.5,10,10,10,8,10,8,10,10,7,5,0,0,0,11,10,10,10,10,5,6,0,0,0,0,0,1,10,15,9,9,10,9,8,9,7,9,10,10,10,10,0,0,0\r", + "\r\n", + "1,10,7,10,10,5,9.5,10,10,9.5,14,5,9,9,10,7,10,10,10,10,7,10,3,5,10,11,0,0,0,0,0,0,0,0,0,0,0,1,10,15,9,9,9,8,7,10,0,9,10,9,10,9,5,0,0\r", + "\r\n", + "1,10,7,10,10,9.5,0,10,10,0,14,9.5,10,10,10,10,10,10,10,0,0,10,5,10,10,11,0,10,10,0,0,5,0,0,0,0,0,1,0,15,9,9,10,0,8,9,7,9,10,10,10,10,10,0,0\r", + "\r\n", + "1,10,7,10,10,9.5,9,10,10,9.5,14,10,10,10,10,10,10,9,10,3,0,3,3,5,2,11,0,0,0,0,0,0,0,0,0,0,0,1,0,15,9,9,10,5,5,0,0,10,10,10,10,0,10,5,10\r", + "\r\n", + "1,10,7,10,10,3,7,10,10,9,14,10,10,10,10,0,10,9,10,7,7,3,7,5,8,11,10,10,10,8,5,3,0,0,7,0,0,1,9.5,15,9,9,10,10,7,10,10,10,10,10,10,10,9,8,2\r", + "\r\n" + ] + } + ], + "source": [ + "!cat Data1401-Grades.csv " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will note that the first line has the names of the \"columns\" of data, and that subsequent lines (or \"rows\") have the data for each student, separated by cammas.\n", + "\n", + "Recalling that in lecture we created a file reader, create a CSV reader function that takes a filename as input and returns data structure(s) that store the data in the file. Note that you are not allowed to use a library. The point here is for *you* to write the CSV reader. Some options for your data structures (pick one):\n", + "\n", + "* A list of dictionaries, where each element of the list is corresponds to a row of data and the dictionaries are keyed by the column name. For example `data[5][\"l3_5\"]` corresponds to the 6th student's grade on lab 3 question 5.\n", + "\n", + "* A list of lists (i.e. a 2-D array or matrix) and a dictionary, where each element of the \"matrix\" corresponds to a a specific grade for a specific student and the dictionary maps the name of the column to the column index. For example `data[5][column_names[\"l1_5\"]]` corresponds to the 6th student's grade on lab 3 question 5.\n", + "\n", + "* A dictionary of lists, where each element of the dictionary corresponds to a column of data and the lists contain the data in that column. For example `data[\"l3_5\"][5]` corresponds to the 6th student's grade on lab 3 question 5.\n", + "\n", + "* (Extra Credit) A class that simultaneously supports all of the above methods." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your solution here.\n", + "\n", + "def cvs_reader(filename):\n", + " data=list() # if you choose first option\n", + " \n", + " return data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating a Gradebook\n", + "\n", + "*Exercise 2:* In lecture we used pandas to read the CSV file and create the grade book. The example below works for the CSV file for this lab. Modify the code below to use your CSV reader instead." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "class_data=pd.read_csv(\"Data1401-Grades.csv\")\n", + "\n", + "a_grade_book=grade_book(\"Data 1401\")\n", + "\n", + "for student_i in range(class_data.shape[0]):\n", + " a_student_0=student(\"Student\",str(student_i),student_i)\n", + "\n", + " for k in class_data.keys():\n", + " a_student_0.add_grade(grade(k,value=class_data[k][student_i]))\n", + "\n", + " a_grade_book.add_student(a_student_0)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Grade Summing\n", + "\n", + "*Exercise 3:* In lectre we will change the design of our algorithm classes and then update the `uncurved_letter_grade_percent` calculator. In lecture we also created a `grade_summer` calcuator that takes a prefix (for example `e1_` and a number `n`) and sums all grades starting with that prefix up to `n` and creates a new sum grade. Update this calculator (below) to the new design of our algorithm classes. Test your updated calculator by using it to sum the grades for all labs, quizzes, and exams of each student." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Note this is the OLD design... you will need to modify it.\n", + "\n", + "class summary_calculator(alg): \n", + " def __init__(self,name):\n", + " alg.__init__(self,name)\n", + "\n", + " def apply(self,a_student):\n", + " raise NotImplementedError\n", + "\n", + "class grade_summer(summary_calculator):\n", + " def __init__(self,prefix,n):\n", + " self.__prefix=prefix\n", + " self.__n=n\n", + " summary_calculator.__init__(self,\"Sum Grades\")\n", + " \n", + " def apply(self,a_student):\n", + " labels=[self.__prefix+str(x) for x in range(1,self.__n)]\n", + " \n", + " grade_sum=0.\n", + " for label in labels:\n", + " grade_sum+=a_student[label].value()\n", + "\n", + " a_student.add_grade(grade(self.__prefix+\"sum\",value=grade_sum))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Curving Grades\n", + "\n", + "*Exercise 4:* Use the `mean_std_calculator` above to calculate the mean and standard deviation for every lab, quiz, and exam in the class. Add a new print function to the `grade_book` class to print out such information in a nice way, and use this function to show your results.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your solution here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 5:* In lecture we will change the design of our algorithms classes and then update the `uncurved_letter_grade_percent` calculator. Do the same for the `curved_letter_grade` calculator below and by curving all the lab, quiz, and exam grades." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class curved_letter_grade(grade_calculator):\n", + " __grades_definition=[ (.97,\"A+\"),\n", + " (.93,\"A\"),\n", + " (.9,\"A-\"),\n", + " (.87,\"B+\"),\n", + " (.83,\"B\"),\n", + " (.8,\"B-\"),\n", + " (.77,\"C+\"),\n", + " (.73,\"C\"),\n", + " (.7,\"C-\"),\n", + " (.67,\"C+\"),\n", + " (.63,\"C\"),\n", + " (.6,\"C-\"),\n", + " (.57,\"F+\"),\n", + " (.53,\"F\"),\n", + " (0.,\"F-\")]\n", + " __max_grade=100.\n", + " __grade_name=str()\n", + " \n", + " def __init__(self,grade_name,mean,std,max_grade=100.):\n", + " self.__max_grade=max_grade\n", + " self.__mean=mean\n", + " self.__std=std\n", + " self.__grade_name=grade_name\n", + " grade_calculator.__init__(self,\n", + " \"Curved Percent Based Grade Calculator \"+self.__grade_name+ \\\n", + " \" Mean=\"+str(self.__mean)+\\\n", + " \" STD=\"+str(self.__std)+\\\n", + " \" Max=\"+str(self.__max_grade))\n", + " \n", + "\n", + " def apply(self,a_grade):\n", + " if not isinstance(a_grade,grade):\n", + " print (self.name()+ \" Error: Did not get an proper grade as input.\")\n", + " raise Exception\n", + " if not a_grade.numerical():\n", + " print (self.name()+ \" Error: Did not get a numerical grade as input.\")\n", + " raise Exception\n", + " \n", + " # Rescale the grade\n", + " percent=a_grade.value()/self.__max_grade\n", + " shift_to_zero=percent-(self.__mean/self.__max_grade)\n", + " scale_std=0.1*shift_to_zero/(self.__std/self.__max_grade)\n", + " scaled_percent=scale_std+0.8\n", + " \n", + " for i,v in enumerate(self.__grades_definition):\n", + " if scaled_percent>=v[0]:\n", + " break\n", + " \n", + " return grade(self.__grade_name,value=self.__grades_definition[i][1])\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Final Course Grade\n", + "\n", + "*Exercise 6:* Write a new calculator that sums grades with a prefix, as in the `grade_summer` calculator, but drops `n` lowest grades. Apply the algorithm to drop the lowest lab grade in the data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your solution here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 7*: Write a new calculator that creates a new letter grade based on a weighted average of letter grades, by assigning the following numerical values to letter grades:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "GradeMap={\"A+\":12,\n", + " \"A\":11,\n", + " \"A-\":10,\n", + " \"B+\":9,\n", + " \"B\":8,\n", + " \"B-\":7,\n", + " \"C+\":6,\n", + " \"C\":5,\n", + " \"C-\":4,\n", + " \"D+\":3,\n", + " \"D\":2,\n", + " \"D-\":1,\n", + " \"F\":0}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test you calculator by applying the weights from the syllabus of this course and computing everyone's grade in the course." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your solution here" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Lecture-10/Lecture-10.ipynb b/Lectures/Lecture-10/Lecture-10.ipynb new file mode 100644 index 0000000..f7aec16 --- /dev/null +++ b/Lectures/Lecture-10/Lecture-10.ipynb @@ -0,0 +1,277 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 10\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n" + ] + } + ], + "source": [ + "#1\n", + "x=list(range(10))\n", + "print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 3, 4]\n" + ] + } + ], + "source": [ + "#2\n", + "print(x[2:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5, 4, 3]\n" + ] + } + ], + "source": [ + "#3\n", + "print(x[5:2:-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9\n" + ] + } + ], + "source": [ + "#4\n", + "print(x[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[7, 8]\n" + ] + } + ], + "source": [ + "#5\n", + "print(x[-3:-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[7, 8, 9]\n" + ] + } + ], + "source": [ + "print(x[-3:])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]\n" + ] + } + ], + "source": [ + "#6\n", + "print(x[::-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#7\n", + "def test_matrix_0(x):\n", + " n=len(x)\n", + " for i in range(n):\n", + " if not x[i][i]==x[0][0]:\n", + " return False\n", + " return True\n", + "\n", + "\n", + "def test_matrix_1(x):\n", + " n=len(x)\n", + " for i in range(n):\n", + " if not x[i][n-i-1]==x[0][n-1]:\n", + " return False\n", + " return True " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n", + "True\n", + "False\n" + ] + } + ], + "source": [ + "print (test_matrix_0( [[ 0,0,0],\n", + " [ 0,0,0],\n", + " [ 0,0,0]] ))\n", + "\n", + "print (test_matrix_0( [[ 1,0,0],\n", + " [ 0,0,0],\n", + " [ 0,0,0]] ))\n", + "\n", + "print (test_matrix_1( [[ 1,0,0],\n", + " [ 0,0,0],\n", + " [ 0,0,0]] ))\n", + "\n", + "print (test_matrix_1( [[ 1,0,1],\n", + " [ 0,0,0],\n", + " [ 0,0,0]] ))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def eye(n):\n", + " matrix=list()\n", + " for i in range(n):\n", + " row=list()\n", + " for j in range(n):\n", + " if i==j:\n", + " row.append(1.)\n", + " else:\n", + " row.append(0.)\n", + " matrix.append(row)\n", + " return matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eye(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Lecture-12/Lecture-12-Part-I.ipynb b/Lectures/Lecture-12/Lecture-12-Part-I.ipynb new file mode 100644 index 0000000..10fd558 --- /dev/null +++ b/Lectures/Lecture-12/Lecture-12-Part-I.ipynb @@ -0,0 +1,519 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 12\n", + "\n", + "## Python Libraries\n", + "\n", + "Until now we have been primarily used python built-ins. One of the great things about python is the incredible libraries that are either already installed with python or easily to install. If you want to do something in python, you can probably find a library or a GitHub page that already does it. \n", + "\n", + "Two resources of libraries are:\n", + "\n", + "* [The Python Standard Library reference](https://docs.python.org/2.7/library/): These packages are most likely already available in your python installation.\n", + "* [PyPI Repository](https://pypi.org): These packages can be installed easily using the `pip install` command.\n", + "\n", + "### `import`\n", + "\n", + "Up until now in this course we typically wrote everything in a jupyter notebook, which we sequentially executed. All of our variable assignments and function definitions are therefore executed in the python interperter that jupyter is running and available in the subsequent cells. In a real piece of software, the code would likely be organized into files. \n", + "\n", + "For example, consider the checkers example in lecture 6. We can copy all of the relevant code from that lecture into one file: [checkers.py](https://github.com/afarbin/DATA1401-Spring-2019/blob/master/Lectures/Lecture-8/checkers.py). \n", + "\n", + "Now we can run the checkers game by \"importing\" the checkers \"module\":" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import checkers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have an \"checkers\" object in our current context:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n" + ] + } + ], + "source": [ + "print(checkers)\n", + "print(type(checkers))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the contents of the module using the `dir` built-in:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__builtins__',\n", + " '__cached__',\n", + " '__doc__',\n", + " '__file__',\n", + " '__loader__',\n", + " '__name__',\n", + " '__package__',\n", + " '__spec__',\n", + " 'checkers_game',\n", + " 'count_pieces',\n", + " 'draw_board',\n", + " 'empty',\n", + " 'empty_space',\n", + " 'game_won',\n", + " 'left_move',\n", + " 'make_game_board',\n", + " 'move_piece',\n", + " 'moves',\n", + " 'nice_move_piece',\n", + " 'parse_location',\n", + " 'parse_move',\n", + " 'player_1',\n", + " 'player_1_left_move',\n", + " 'player_1_piece',\n", + " 'player_1_right_move',\n", + " 'player_2',\n", + " 'player_2_left_move',\n", + " 'player_2_piece',\n", + " 'player_2_right_move',\n", + " 'player_moves',\n", + " 'print_message',\n", + " 'right_move',\n", + " 'size',\n", + " 'space_character',\n", + " 'take_move']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(checkers)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that calling `dir()` without an argument will show you everything in your current context:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['In',\n", + " 'Out',\n", + " '_',\n", + " '_3',\n", + " '__',\n", + " '___',\n", + " '__builtin__',\n", + " '__builtins__',\n", + " '__doc__',\n", + " '__loader__',\n", + " '__name__',\n", + " '__package__',\n", + " '__spec__',\n", + " '_dh',\n", + " '_i',\n", + " '_i1',\n", + " '_i2',\n", + " '_i3',\n", + " '_i4',\n", + " '_ih',\n", + " '_ii',\n", + " '_iii',\n", + " '_oh',\n", + " 'checkers',\n", + " 'exit',\n", + " 'get_ipython',\n", + " 'quit',\n", + " 'x']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x=1\n", + "dir()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To call something from the checkers module, we simply do, for example:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[0, 1, 0, 1, 0, 1, 0, 1],\n", + " [1, 0, 1, 0, 1, 0, 1, 0],\n", + " [0, 1, 0, 1, 0, 1, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0],\n", + " [2, 0, 2, 0, 2, 0, 2, 0],\n", + " [0, 2, 0, 2, 0, 2, 0, 2],\n", + " [2, 0, 2, 0, 2, 0, 2, 0]]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "checkers.make_game_board()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This may be combersome, so we can import a specific part of the checkers into our current context:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0, 1, 0, 1, 0, 1, 0, 1], [1, 0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2], [2, 0, 2, 0, 2, 0, 2, 0]]\n" + ] + }, + { + "data": { + "text/plain": [ + "['In',\n", + " 'Out',\n", + " '_',\n", + " '_3',\n", + " '_4',\n", + " '_5',\n", + " '__',\n", + " '___',\n", + " '__builtin__',\n", + " '__builtins__',\n", + " '__doc__',\n", + " '__loader__',\n", + " '__name__',\n", + " '__package__',\n", + " '__spec__',\n", + " '_dh',\n", + " '_i',\n", + " '_i1',\n", + " '_i2',\n", + " '_i3',\n", + " '_i4',\n", + " '_i5',\n", + " '_i6',\n", + " '_ih',\n", + " '_ii',\n", + " '_iii',\n", + " '_oh',\n", + " 'checkers',\n", + " 'exit',\n", + " 'get_ipython',\n", + " 'make_game_board',\n", + " 'quit',\n", + " 'x']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from checkers import make_game_board\n", + "print(make_game_board())\n", + "dir()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can rename what we import in our current context:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1: {0: (1, 1), 1: (1, -1)}, 2: {0: (-1, -1), 1: (-1, 1)}}\n" + ] + } + ], + "source": [ + "from checkers import moves as checkers_moves\n", + "print(checkers_moves)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or we can import everything into our current context using `*`:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['In',\n", + " 'Out',\n", + " '_',\n", + " '_3',\n", + " '_4',\n", + " '_5',\n", + " '_6',\n", + " '__',\n", + " '___',\n", + " '__builtin__',\n", + " '__builtins__',\n", + " '__doc__',\n", + " '__loader__',\n", + " '__name__',\n", + " '__package__',\n", + " '__spec__',\n", + " '_dh',\n", + " '_i',\n", + " '_i1',\n", + " '_i2',\n", + " '_i3',\n", + " '_i4',\n", + " '_i5',\n", + " '_i6',\n", + " '_i7',\n", + " '_i8',\n", + " '_ih',\n", + " '_ii',\n", + " '_iii',\n", + " '_oh',\n", + " 'checkers',\n", + " 'checkers_game',\n", + " 'checkers_moves',\n", + " 'count_pieces',\n", + " 'draw_board',\n", + " 'empty',\n", + " 'empty_space',\n", + " 'exit',\n", + " 'game_won',\n", + " 'get_ipython',\n", + " 'left_move',\n", + " 'make_game_board',\n", + " 'move_piece',\n", + " 'moves',\n", + " 'nice_move_piece',\n", + " 'parse_location',\n", + " 'parse_move',\n", + " 'player_1',\n", + " 'player_1_left_move',\n", + " 'player_1_piece',\n", + " 'player_1_right_move',\n", + " 'player_2',\n", + " 'player_2_left_move',\n", + " 'player_2_piece',\n", + " 'player_2_right_move',\n", + " 'player_moves',\n", + " 'print_message',\n", + " 'quit',\n", + " 'right_move',\n", + " 'size',\n", + " 'space_character',\n", + " 'take_move',\n", + " 'x']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from checkers import *\n", + "dir()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you know how to import libraries. For example, the python math library:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__doc__',\n", + " '__file__',\n", + " '__loader__',\n", + " '__name__',\n", + " '__package__',\n", + " '__spec__',\n", + " 'acos',\n", + " 'acosh',\n", + " 'asin',\n", + " 'asinh',\n", + " 'atan',\n", + " 'atan2',\n", + " 'atanh',\n", + " 'ceil',\n", + " 'comb',\n", + " 'copysign',\n", + " 'cos',\n", + " 'cosh',\n", + " 'degrees',\n", + " 'dist',\n", + " 'e',\n", + " 'erf',\n", + " 'erfc',\n", + " 'exp',\n", + " 'expm1',\n", + " 'fabs',\n", + " 'factorial',\n", + " 'floor',\n", + " 'fmod',\n", + " 'frexp',\n", + " 'fsum',\n", + " 'gamma',\n", + " 'gcd',\n", + " 'hypot',\n", + " 'inf',\n", + " 'isclose',\n", + " 'isfinite',\n", + " 'isinf',\n", + " 'isnan',\n", + " 'isqrt',\n", + " 'ldexp',\n", + " 'lgamma',\n", + " 'log',\n", + " 'log10',\n", + " 'log1p',\n", + " 'log2',\n", + " 'modf',\n", + " 'nan',\n", + " 'perm',\n", + " 'pi',\n", + " 'pow',\n", + " 'prod',\n", + " 'radians',\n", + " 'remainder',\n", + " 'sin',\n", + " 'sinh',\n", + " 'sqrt',\n", + " 'tan',\n", + " 'tanh',\n", + " 'tau',\n", + " 'trunc']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import math\n", + "dir(math)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Lecture-12/Lecture-12-Part-II.ipynb b/Lectures/Lecture-12/Lecture-12-Part-II.ipynb new file mode 100644 index 0000000..1c7b55d --- /dev/null +++ b/Lectures/Lecture-12/Lecture-12-Part-II.ipynb @@ -0,0 +1,261 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 12 - Part II\n", + "\n", + "## Random Number Generator" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup to make historgrams later...\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def uniform_generator(seed=123124.):\n", + " a=1111111\n", + " b=2222222\n", + " m=6700417 # This is a large prime number\n", + " x=seed\n", + " \n", + " def random():\n", + " nonlocal x\n", + " x=(a*x+b)%m\n", + " return x/m # divide by m to set range to 0->1\n", + " \n", + " return random" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "my_uniform=uniform_generator()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "random_numbers=list()\n", + "for _ in range(10):\n", + " random_numbers.append(my_uniform())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.6326616686692783,\n", + " 0.6709910741376246,\n", + " 0.8950304137787245,\n", + " 0.46973837598465884,\n", + " 0.8083329739029675,\n", + " 0.9906205837636672,\n", + " 0.7591005156843224,\n", + " 0.26473680667934546,\n", + " 0.30966057784164774,\n", + " 0.6059604946975689]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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CDHdJKshwl6SCDHdJKuj/AZhT1pWKXax0AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "random_numbers=list()\n", + "for _ in range(10000):\n", + " random_numbers.append(my_uniform())\n", + "_=plt.hist(random_numbers,bins=10,range=(.5,.7))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([180., 204., 208., 219., 182., 215., 200., 218., 221., 195.]),\n", + " array([0.5 , 0.52, 0.54, 0.56, 0.58, 0.6 , 0.62, 0.64, 0.66, 0.68, 0.7 ]),\n", + " )" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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CDHdJKshwl6SCDHdJKuj/AZhT1pWKXax0AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(random_numbers,bins=10,range=(.5,.7))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exponential Generator" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "def generate_exp(tau,seed=32144):\n", + " my_uniform=uniform_generator(seed)\n", + " \n", + " def generator():\n", + " nonlocal my_uniform\n", + " nonlocal tau\n", + " u = my_uniform()\n", + " return -tau*(math.log(1.-u))\n", + " \n", + " return generator" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "my_exp_generator= generate_exp(10.)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11.372504436305544" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_exp_generator()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "random_numbers=list()\n", + "for _ in range(10000):\n", + " random_numbers.append(my_exp_generator())\n", + "_=plt.hist(random_numbers,bins=25)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Lecture-12/checkers.py b/Lectures/Lecture-12/checkers.py new file mode 100644 index 0000000..33dbd5d --- /dev/null +++ b/Lectures/Lecture-12/checkers.py @@ -0,0 +1,249 @@ +# Index assignment for the matrix representation of the game board +player_1 = 1 +player_2 = 2 +empty = 0 + +# Game board size +size = 8 + +def make_game_board(size=8): + # Make an empty board + board=[[empty]*size for i in range(size)] + + # Even Columns + for i in range(0,size,2): + board[1][i]=player_1 + board[-1][i]=player_2 + board[-3][i]=player_2 + + # Odd Columns + for i in range(1,size,2): + board[0][i]=player_1 + board[2][i]=player_1 + board[-2][i]=player_2 + + return board + +left_move=0 +right_move=1 + +player_1_left_move=(1,1) +player_1_right_move=(1,-1) + +player_2_left_move=(-1,-1) +player_2_right_move=(-1,1) + +def player_moves(player,direction): + if player==player_1: + if direction==left_move: + return (1,1) + elif direction==right_move: + return (1,-1) + if player==player_2: + if direction==left_move: + return (-1,-1) + elif direction==right_move: + return (-1,1) + +moves={ player_1: {left_move: player_1_left_move, + right_move:player_1_right_move}, + player_2: {left_move: player_2_left_move, + right_move: player_2_right_move}} + +def print_message(message,verbose=True): + if verbose: + print(message) + +def move_piece(board,player,location,move,verbose=True): + x,y=location + + # Check if player's piece is at location + if not board[x][y] == player: + print_message("Player does not have piece at location.",verbose) + return False + + # Fetch the offset for the move + x_offset,y_offset = moves[player][move] + + # Make sure the move is on the board: + move_possible= x+x_offset < size and \ + x+x_offset >= 0 and \ + y+y_offset < size and \ + y+y_offset >= 0 + + + jump_possible= x+2*x_offset < size and \ + x+2*x_offset >= 0 and \ + y+2*y_offset < size and \ + y+2*y_offset >= 0 + + if not (move_possible or jump_possible): + print_message("Move is off of board.",verbose) + return False + + # Try the move + # Is the target space empty + if move_possible and \ + board[x+x_offset][y+y_offset]==empty: + + # Make the move + # Empty the spot + board[x][y]=empty + # Place player in new spot + board[x+x_offset][y+y_offset]=player + print_message("Moved.",verbose) + + return True + # Does the target space have an opponent's piece, and the space after empty + elif jump_possible and \ + board[x+x_offset][y+y_offset]!=player and \ + board[x+2*x_offset][y+2*y_offset]==empty: + + # Make the move + # Empty the spot + board[x][y]=empty + # Remove the oppoent's piece + board[x+x_offset][y+y_offset]=empty + # Move player to new spot + board[x+2*x_offset][y+2*y_offset]=player + print_message("Took opponent's piece.",verbose) + + return True + else: + print_message("Move not possible.",verbose) + return False + +player_1_piece="X" +player_2_piece="O" +empty_space=" " + +space_character= { player_1: player_1_piece, + player_2: player_2_piece, + empty: empty_space } + + +def draw_board(board): + print(" ",end=" ") + for j in range(8): + print(column_names[j],end=" ") + print() + + for i in range(8): + print(row_names[i],end=" ") + for j in range(8): + print(space_character[board[i][j]],end=" ") + print() + +def parse_location(l_string): + if not isinstance(l_string,str): + print_message("Bad Input. Location must be string.") + return False + + if len(l_string)!=2: + print_message("Bad Input. Location must be 2 characters.") + return False + + row=l_string[0].upper() + col=l_string[1].upper() + + if not row in row_names: + print_message("Bad Row.") + return False + + if not col in column_names: + print_message("Bad Column.") + return False + + return row_map[row],column_map[col] + +def parse_move(m_string): + if not isinstance(m_string,str): + print_message("Bad Input. Location must be string.") + return -1 + + if len(m_string)!=1: + print_message("Bad Input. Location must be 1 character.") + return -1 + + if m_string.upper()=="L": + return left_move + + if m_string.upper()=="R": + return right_move + + print_message("Bad Move. must be R/L.") + + return -1 + + +def nice_move_piece(board,player,location,move): + loc=parse_location(location) + mov=parse_move(move) + + if loc and mov!=-1: + return move_piece(board,player,loc,mov) + else: + return print_message("Bad move.") + +def take_move(board,player): + good_move=False + + while not good_move: + loc_str =input("Input location:") + mov_str =input("Input move (L/R):") + + good_move = nice_move_piece(board,player,loc_str,mov_str) + +def count_pieces(board,player): + n=0 + for i in range(size): + for j in range(size): + if board[i][j]==player: + n+=1 + return n + + +def game_won(board): + player_1_n=count_pieces(board,player_1) + player_2_n=count_pieces(board,player_2) + + if player_1_n==0: + return player_2 + if player_1_n==0: + return player_1 + + return False + + +def checkers_game(): + + print ("Welcome to Checkers.") + print ("--------------------") + + # Make a game board + board_0=make_game_board() + + # Start with player 1 + player=player_1 + + this_game_won=False + while not this_game_won: + # Draw the board + draw_board(board_0) + + # Make a move + print("Player",player,"move:") + take_move(board_0,player) + + # Check if the game has been won + this_game_won=game_won(board_0) + + # Switch players + if player==player_1: + player=player_2 + else: + player=player_1 + + + print("Winner is player:",this_game_won) + diff --git a/Lectures/Lecture-14/Lecture-14.ipynb b/Lectures/Lecture-14/Lecture-14.ipynb new file mode 100644 index 0000000..7f692be --- /dev/null +++ b/Lectures/Lecture-14/Lecture-14.ipynb @@ -0,0 +1,819 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scaling/Shifting\n", + "\n", + "In the beginning of Lab 4 you are asked to take random numbers between 0 and 1 and scale and shift them to be between $x_{min}$ and $x_{max}$. The formula is pretty basic. If $x_0$ is between 0 and 1 then $x$ computed as:\n", + "$$\n", + "x= (x_{max}-x_{min}) x_0 + x_{min}\n", + "$$\n", + "will be between $x_{min}$ and $x_{max}$. \n", + "\n", + "In your solution, you'll most likely generate $x_0$ one by one, compute $x$, and store $x$ into a list to be returned from your function.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mean/Variance\n", + "\n", + "Also for lab 4, remember the equations for mean/variance. If you have a data sample ${x_1, x_2, ..., x_N}$ the mean is:\n", + "\n", + "$$ \n", + "\\bar{x} = \\frac{1}{N}\\sum_{i=1}^{N} x_i\n", + "$$\n", + "\n", + "and the variance is:\n", + "\n", + "$$\n", + " = \\frac{1}{N-1} \\sum_{i=1}^{N} (x_i - \\bar{x})^2\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Histogram\n", + "\n", + "In Lab 4 you are asked to write a histogram function:\n", + "\n", + "* User inputs a list of values `x` and optionally `n_bins` which defaults to 10.\n", + "* If not supplied, find the minimum and maximum (`x_min`,`x_max`) of the values in x.\n", + "* Determine the bin size (`bin_size`) by dividing the range of the function by the number of bins.\n", + "* Create an empty list of zeros of size `n_bins`, call it `hist`.\n", + "* Loop over the values in `x`\n", + " * Loop over the values in `hist` with index `i`:\n", + " * If x is between `x_min+i*bin_size` and `x_min+(i+1)*bin_size`, increment `hist[i].` \n", + " * For efficiency, try to use continue to goto the next bin and data point.\n", + "* Return `hist` and the list corresponding of the bin edges (i.e. of `x_min+i*bin_size`). \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Functional Programming\n", + "\n", + "In lab 3 you built a tic-tac-toe game by implementing a series of functions that performed various tasks, which you then combined in various ways to implement the game logic. What you wrote was a *structured program*, which consist of sequences of instructions, utilizing control flow (if/then/else), repetition (while and for), block structures, and function calls. \n", + "\n", + "*Functional Programming* is another style of programming that is not well suited to writing games, but is well suited to manipulating data. A function program performs computation by evaluating mathematical functions, where the output only depend on the input. Data passes through as inputs/outputs of functions, but is otherwise never changed. This paradigm is often used in data science because manipulation of data can othen be viewed as composition of functions:\n", + "\n", + "$$\n", + "D_{result} = f_n(f_{n-1}(...(f_0(D_{input}))))\n", + "$$\n", + "\n", + "Consider the `find_min` example from last lecture:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def a_function(x):\n", + " return (1+x)**2\n", + "\n", + "def find_min_0(f,x_min,x_max,steps=10):\n", + " step_size=(x_max-x_min)/steps\n", + " x=x_min\n", + " y_min=f(x_min)\n", + " x_min_val=x_min\n", + "\n", + " for i in range(steps):\n", + " y=f(x)\n", + " if y1:\n", + " return [x_min] + a_range(x_min+((x_max-x_min)/steps),x_max,steps-1)\n", + " else:\n", + " return [x_min]\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are not going to write functions this way, but the idea is to get familiar with seeing data manipulations as a composition of functions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear Algebra\n", + "\n", + "Linear Algebra was invented to solve equations like this:\n", + "\n", + "\\begin{align}\n", + "4 x_1 + 5 x_2 &=& 12\\\\\n", + "-2 x_1 - x_2 &=& 2\n", + "\\end{align}\n", + "\n", + "by representing them as matrices like this:\n", + "\n", + "\\begin{equation*}\n", + "\\begin{pmatrix}\n", + "4 & 5 & 12\\\\\n", + "-2 & -1 & 2\n", + "\\end{pmatrix}\n", + "\\end{equation*}\n", + "\n", + "or\n", + "\n", + "\\begin{equation*}\n", + "A = \\begin{pmatrix} 4 & 5 \\\\ -2 & -1\\end{pmatrix}, \n", + "\\vec{x} = \\begin{pmatrix} x_1 \\\\ x_2\\end{pmatrix}, \n", + "\\vec{y} = \\begin{pmatrix} 12 \\\\2 \\end{pmatrix},\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + "A\\vec{x}=\\vec{y} \\Rightarrow \\vec{x}=A^{-1} \\vec{y}\n", + "\\end{equation*}\n", + "\n", + "Where $A^{-1}$ is the inverse of $A$, which if the equations are not linearly dependent can be computed algorithmically.\n", + "\n", + "\\begin{equation*}\n", + "\\end{equation*}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Properties of Matrices\n", + "$$\n", + "(AB)C=A(BC)\n", + "$$\n", + "$$\n", + "A(B+C)=AB+AC\n", + "$$\n", + "$$\n", + "AB\\neq BA\n", + "$$\n", + "$$\n", + "AI=A\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Matrix Elements\n", + "Consider an arbitrary matrix $A$:\n", + "\n", + "\\begin{equation*}\n", + "A_{m,n} = \n", + " \\begin{pmatrix}\n", + " a_{11} & a_{12} & \\cdots & a_{1n} \\\\\n", + " a_{21} & a_{22} & \\cdots & a_{2n} \\\\\n", + " \\vdots & \\vdots & \\ddots & \\vdots\\\\\n", + " a_{m1} & a_{m2} & \\cdots & a_{mn} \n", + "\\end{pmatrix}\n", + "\\end{equation*}\n", + "\n", + "we define the columns as $a_j=A_{:,j}$:\n", + "\n", + "\\begin{pmatrix} \n", + "| & | & &|\\\\\n", + "a_1 & a_2 & \\dots &\\ a_n\\\\\n", + "| & | & &|\n", + "\\end{pmatrix}\n", + "\n", + "and rows $a^T_i = A_{i,:}$:\n", + "\n", + "\\begin{pmatrix} \n", + "- & a^T_1 & -\\\\\n", + "- & a^T_2 & -\\\\\n", + " & \\vdots & \\\\\n", + "- & a^T_3 & -\\\\\n", + "\\end{pmatrix}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Matrix Operations\n", + "\n", + "* Transpose: $(A^T)_{ij} = A_{ji}$\n", + "* Sum (elementwise): $C_{ij} = A_{ij} + B_{ij}$\n", + "* Elementwise product: $C_{ij} = A_{ij} B_{ij}$\n", + "* Matrix product: $C=A \\cdot B$: $C_{ij} = \\sum_{k} A_{ik} B_{kj}$.\n", + " * Note than if size of $A$ is $n \\times m$ then $B$ has to be of size $m \\times k$ and the resulting matrix will be of size $n \\times k$.\n", + " * Good way to visualize product:\n", + " \\begin{equation*}\n", + " AB=\n", + "\\begin{pmatrix} \n", + "- & a_1 & -\\\\\n", + "- & a_2 & -\\\\\n", + " & \\vdots & \\\\\n", + "- & a_m & -\\\\\n", + "\\end{pmatrix} \n", + "\\begin{pmatrix} \n", + "| & | & &|\\\\\n", + "b_1 & b_2 & \\dots &\\ b_n\\\\\n", + "| & | & &|\n", + "\\end{pmatrix}=\n", + "\\begin{pmatrix}\n", + "a^T_1b_1 & a^T_1b_2 & \\dots & a^T_1b_n\\\\\n", + "a^T_2b_1 & a^T_2b_2 & \\dots & a^T_2b_n\\\\\n", + "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", + "a^T_mb_1 & a^T_mb_2 & \\dots & a^T_mb_n\n", + "\\end{pmatrix}\n", + "\\end{equation*}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$A$ is $m_1$ by $n_1$ and $B$ is $m_2$ by $n_2$ then you can multiply ($AB$) if $n_1=m_2$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Vector Products\n", + "\n", + "* Dot product: $x\\cdot y = x^T y = \\sum_{i=1}^n x_i y_i$\n", + "* Other product: \n", + "\\begin{equation*}\n", + "\\begin{pmatrix} x_1\\\\x_2\\\\ \\vdots \\\\x_m \\end{pmatrix} \\begin{pmatrix} y_1&y_2& \\dots &y_n\\end{pmatrix} =\n", + "\\begin{pmatrix}\n", + "x_1y_1 & x_1y_2 & \\dots & x_1y_n\\\\\n", + "x_2y_1 & x_2y_2 & \\dots & x_2y_n\\\\\n", + "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", + "x_my_1 & x_my_2 & \\dots & x_my_n\n", + "\\end{pmatrix}\n", + "\\end{equation*}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Norms\n", + "* $l=1$ Norm: $\\parallel x \\parallel_1 = \\sum_{i=1}^{n}|x_i|$\n", + "* $l=2$ Norm: $\\parallel x \\parallel_2 = \\sqrt{\\sum_{i=1}^{n}x_i^2}$\n", + "* $l=p$ Norm: $\\parallel x \\parallel_p = \\left(\\sum_{i=1}^{n}x_i^p\\right)^\\frac{1}{p}$\n", + "* $l=\\infty$ Norm: $\\parallel x \\parallel_\\infty = \\max_i |x_i|$\n", + "* Law of cosines: $x \\cdot y = \\parallel x \\parallel_2 \\parallel y \\parallel_2 \\cos{\\theta}$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear Independence\n", + "Given vectors \n", + "$$\n", + "\\{\\vec{x}_1,\\vec{x}_2,\\dots,\\vec{x}_n\\},\n", + "$$\n", + "a linear combination of these vectors is\n", + "$$\n", + "\\sum_{i=0}^{n}=c_i \\vec{x}_i=\\begin{pmatrix} \n", + "| & | & &|\\\\\n", + "\\vec{x}_1 & \\vec{x}_2 & \\dots &\\ \\vec{x}_n\\\\\n", + "| & | & &|\n", + "\\end{pmatrix}\n", + "\\begin{pmatrix} \n", + "c_1\\\\\n", + "c_2\\\\\n", + "\\vdots\\\\\n", + "c_n\n", + "\\end{pmatrix}\n", + "$$\n", + "where $\\{c_1,c_2,\\dots,c_n\\}$ are a set of coefficients (a single number, not a vector). \n", + "\n", + "A vector $\\vec{y}$ is linearly independent from the set $\\{\\vec{x}_i\\}$ if $\\vec{y}$ cannot be written as a linear combination of $\\{\\vec{x}_i\\}$. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Matrix Inverse\n", + "\n", + "[Simple Algorithm For Inverse](http://www.irma-international.org/viewtitle/41011/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementing Matrix Operations" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "def zero_matrix(m,n):\n", + " return [ [0 for _ in range(m)] for _ in range(n)]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def zero_matrix(m,n):\n", + " out=list()\n", + " for i in range(m):\n", + " row=list()\n", + " for j in range(n):\n", + " row.append(0.)\n", + " out.append(row)\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "zero_matrix(5,10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def is_matrix(M):\n", + " if isinstance(M,list):\n", + " row_length=len(M[0])\n", + " for row in M:\n", + " if not row_length==len(row):\n", + " return False\n", + " else:\n", + " False\n", + " return True\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "is_matrix(zero_matrix(10,4))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "is_matrix( [[ 2,3],[1,2,3]])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def matrix_shape(M):\n", + " if is_matrix(M):\n", + " m=len(M)\n", + " n=len(M[0])\n", + " return m,n\n", + " else:\n", + " 0,0" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10, 5)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matrix_shape(zero_matrix(10,5))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def is_square_matrix(M):\n", + " m,n=matrix_shape(M)\n", + " if m==0 or n==0:\n", + " return False\n", + " return m==n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "is_square_matrix(zero_matrix(10,10))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def matrix_add(M1,M2):\n", + " m1,n1=matrix_shape(M1)\n", + " m2,n2=matrix_shape(M2)\n", + " \n", + " if m1==m2 and n1==n2:\n", + " M3=zero_matrix(m1,n1)\n", + " for i in range(m1):\n", + " for j in range(n1):\n", + " M3[i][j]=M1[i][j]+M2[i][j]\n", + " return M3\n", + " \n", + " return False" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[6, 4, 4], [3, 7, 7]]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A= [ [ 1,2,3],\n", + " [ 2,4,5]]\n", + "\n", + "B= [ [ 5,2,1],\n", + " [ 1,3,2]]\n", + "\n", + "matrix_add(A,B)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def matrix_scalar_multiply(M,a):\n", + " m,n=matrix_shape(M)\n", + " M_out=zero_matrix(m,n)\n", + " for i in range(m):\n", + " for j in range(n):\n", + " M_out[i][j]=a*M[i][j]\n", + " return M_out\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[4, 8, 12], [8, 16, 20]]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matrix_scalar_multiply(A,4)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def matrix_neg(M):\n", + " return matrix_scalar_multiply(M,-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[-1, -2, -3], [-2, -4, -5]]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matrix_neg(A)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def matrix_sub(M1,M2):\n", + " return matrix_add(M1, matrix_neg(M2))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[-4, 0, 2], [1, 1, 3]]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matrix_sub(A,B)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def matrix_transpose(M):\n", + " m,n=matrix_shape(M)\n", + " M_out=zero_matrix(n,m)\n", + " for i in range(m):\n", + " for j in range(n):\n", + " M_out[j][i]=M[i][j]\n", + " return M_out\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[1, 2], [2, 4], [3, 5]]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matrix_transpose(A)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Lecture-15/Lecture 15.pdf b/Lectures/Lecture-15/Lecture 15.pdf new file mode 100644 index 0000000..6ba5bc0 Binary files /dev/null and b/Lectures/Lecture-15/Lecture 15.pdf differ diff --git a/Lectures/Lecture-15/Lecture-15.ipynb b/Lectures/Lecture-15/Lecture-15.ipynb new file mode 100644 index 0000000..a3fd364 --- /dev/null +++ b/Lectures/Lecture-15/Lecture-15.ipynb @@ -0,0 +1,783 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 15\n", + "\n", + "* Labs: best way to study. Many didn't complete the assignments. Some had copied other's. \n", + " * New Rule: if you copy, you must site. Ideally link to GitHub in your solution.\n", + " * When you copy, the question is not longer counted in the demoninator of your grade... until 50%.\n", + " * Use this as a means of studying. Turn in Wednesday.\n", + " * Just submit new versions of the previous labs, and it will be regraded. \n", + " \n", + "\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve some problems together\n", + "\n", + "1. What is the output of the following cell?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 4\n" + ] + } + ], + "source": [ + "my_list=[1,2,3,4,5,4,3,2,1]\n", + "\n", + "def arg_max(d):\n", + " a_max=d[0]\n", + " i_max=0\n", + " for i,e in enumerate(d):\n", + " if e>a_max:\n", + " a_max=e\n", + " i_max=i\n", + " return i_max\n", + "\n", + "print(max(my_list),arg_max(my_list))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 0\n" + ] + } + ], + "source": [ + "def arg_min(d):\n", + " a_min=d[0]\n", + " i_min=0\n", + " for i,e in enumerate(d):\n", + " if e)" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data_1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Histogram\n", + "\n", + "In Lab 4 you are asked to write a histogram function:\n", + "\n", + "* User inputs a list of values `x` and optionally `n_bins` which defaults to 10.\n", + "* If not supplied, find the minimum and maximum (`x_min`,`x_max`) of the values in x.\n", + "* Determine the bin size (`bin_size`) by dividing the range of the function by the number of bins.\n", + "* Create an empty list of zeros of size `n_bins`, call it `hist`.\n", + "* Loop over the values in `x`\n", + " * Loop over the values in `hist` with index `i`:\n", + " * If x is between `x_min+i*bin_size` and `x_min+(i+1)*bin_size`, increment `hist[i].` \n", + " * For efficiency, try to use continue to goto the next bin and data point.\n", + "* Return `hist` and the list corresponding of the bin edges (i.e. of `x_min+i*bin_size`). \n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Alternative\n", + "* User inputs a list of values `x` and optionally `n_bins` which defaults to 10.\n", + "* If not supplied, find the minimum and maximum (`x_min`,`x_max`) of the values in x.\n", + "* Create an empty list of zeros of size `n_bins`, call it `hist`.\n", + "* Create a list of `bin_edges` using `arange`.\n", + "* Loop over the values in `x`\n", + " * Loop over the values in `hist` with index `i`:\n", + " * If x is between `bin_edge[i]` and `bin_edge[i+1]`, increment `hist[i].` \n", + " * For efficiency, try to use continue to goto the next bin and data point.\n", + "* Return `hist` and the list corresponding of the bin edges (i.e. of `x_min+i*bin_size`). \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def arange(x_min,x_max,steps=10):\n", + " step_size=(x_max-x_min)/steps\n", + " x=x_min\n", + " out = list()\n", + " for i in range(steps):\n", + " out.append(x)\n", + " x+=step_size\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def histogram(data, n_bins=10,x_min=None, x_max=None):\n", + " \n", + " return hist,bin_edges" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#histogram(data,10,0,10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Lecture-16/Common-Issues.ipynb b/Lectures/Lecture-16/Common-Issues.ipynb new file mode 100644 index 0000000..f374385 --- /dev/null +++ b/Lectures/Lecture-16/Common-Issues.ipynb @@ -0,0 +1,296 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comments from the TA\n", + "\n", + "List of items:\n", + "\n", + "* for loops: i in data\n", + "* if elif, if if\n", + "* using protected keywords (ex: min max) as variables\n", + "* consistent spacing\n", + "* parantheses syntax \n", + "* writing functions, then renaming it and now code that uses old function names don't work \n", + "* submission issues: how to git pull, create new folder, or uploading file to correct directory\n", + "* assignment description: watching out for return vs print\n", + "* list of lists by reference or value? matrix = [[0]*3]*3\n", + "* effective commenting: help me help you\n", + "* infinite loops (getting stuck when it shouldn't)\n", + "* trying to combine different types of data: print(str + int)\n", + "* indexing out of range: for i in range(len(x)): print(x[i+1])\n", + "* Assignment asks for functions, write it in a def function block\n", + "* taking user input when it should just be hard coded test-cases" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tips for Getting Full Credit on Assignments\n", + "\n", + "Some common reasons students don't get full-credit:\n", + "\n", + "- Not following directions:\n", + " - Carefully following exercise and submission directions will help you avoid most of the issues students have been running into, including the ones described below.\n", + " - Make sure to re-read and ask for clarification if you feel something may be ambiguous\n", + " - Make sure that you submit the assignments correctly and by the deadline. Check the google group for the submission process. Please contact the GTA if you're unclear on the process.\n", + "- Not returning the right data, or not returning anything at all when asked in exercise\n", + " - Some students will use 'print' instead of return keyword\n", + " - The exercise will ask for a list, and a set is returned, or string is requested and list of words is returned.\n", + "- Not testing solutions thoroughly\n", + " - When the exercise asks you to test your solution in a new cell, you should test your solution on several different input arguments. Just because your code works on one example, does not mean it will work on all perfectly reasonable inputs, which may be used when grading.\n", + " - You should strive to test your code with edgecase inputs (like negative numbers, different sized lists, zero/empty lists, etc). This depends on what your function is supposed to do.\n", + " - Another reason your code may not work is that you have defined variables that you later deleted, but were integral to running the code is your notebook. As long as your continue to run the notebook, the variables are still accessible, but the TA will not be able to run the code when a new instance of the notebook is opened because those variables don't exist anywhere. A good way to combat this is by exiting the notebook/restarting your kernel and clearing all your outputs. Try running all the cells in order now and see if your code runs into any errors.\n", + "- Hard-coding values into their functions\n", + " - The exercise may ask that your function uses arguments to generalize your code (On lab 3, for example, you had to make your create board function allow you to specify N so that it could create it NxN). \n", + " \n", + "- Not attempting the exercise or copy/pasting previous solution attempts\n", + " - Several of the exercises will build on each other but simply copy/pasting a previous solution (which does not address/or solve the current exercise) is not enough to get credit for the problem. Related exercises will of course have similar code so it is reasonable to copy/paste your old function and update it if needed, but you should not try to cheat the grading system by copy/pasting one of your solutions for each problem to get 'partial credit' for attempting them.\n", + "- Copying solutions from others without attribution\n", + " - The purpose of these labs is to help you develop fundamental datascience skills. Knowing how to use the internet to help you solve problems and resolve technical errors is certainly an important skill, and encouraged. That said, copy/pasting solutions from stackoverflow et al., can be counter-productive to your learning if you're not taking the time to understand all the details of the implementation. You should cite solutions by pasting a comment next to the code with a link to the source. \n", + " - You should abide by UTA's honor code and the syllabus by refraining from copying solutions from students in the class.\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## For Loops\n", + "\n", + "The different between looping over indices, elements \\[of a list\\], or both.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Example 1\n", + "0\n", + "323\n", + "1\n", + "34834\n", + "cats\n", + "-2.2\n", + "Example 2\n", + "0\n", + "0\n", + "1\n", + "323\n", + "2\n", + "1\n", + "3\n", + "34834\n", + "4\n", + "cats\n", + "5\n", + "-2.2\n", + "Example 3\n", + "0 0\n", + "1 323\n", + "2 1\n", + "3 34834\n", + "4 cats\n", + "5 -2.2\n" + ] + } + ], + "source": [ + "data = [0, 323, 1, 34834, 'cats', -2.2]\n", + "\n", + "print('Example 1')\n", + "# Prints elements of data, each iteration updates i to next item in list from left to right.\n", + "\n", + "for i in data:\n", + " print(i)\n", + " \n", + "print('Example 2')\n", + " \n", + "for i in range(len(data)):\n", + " print(i)\n", + " print(data[i])\n", + "\n", + "print('Example 3')\n", + "\n", + "for index, element in enumerate(data):\n", + " print(index, element)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Difference between if if else, and if elif else" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "def is_num_bigger(num):\n", + " if num > 10:\n", + " print('Num is bigger than 10')\n", + " if num > 20: \n", + " print('Num is bigger than 20')\n", + " \n", + " if num > 30: \n", + " print('Num is bigger than 30')\n", + " else:\n", + " print('Num is not bigger than 30')\n", + "\n", + "def is_num_bigger_two(num):\n", + " if num > 10:\n", + " print('Num is bigger than 10')\n", + " elif num > 20: \n", + " print('Num is bigger than 20')\n", + " elif num > 30: \n", + " print('Num is bigger than 30')\n", + " else:\n", + " print('Num is not bigger than 30')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "First Function\n", + "Number testing is: 6\n", + "Num is not bigger than 30\n", + "Number testing is: 15\n", + "Num is bigger than 10\n", + "Num is not bigger than 30\n", + "Number testing is: 35\n", + "Num is bigger than 10\n", + "Num is bigger than 20\n", + "Num is bigger than 30\n", + "Number testing is: 22\n", + "Num is bigger than 10\n", + "Num is bigger than 20\n", + "Num is not bigger than 30\n", + "Number testing is: -5\n", + "Num is not bigger than 30\n", + "Second Function\n", + "Number testing is: 6\n", + "Num is not bigger than 30\n", + "Number testing is: 15\n", + "Num is bigger than 10\n", + "Number testing is: 35\n", + "Num is bigger than 10\n", + "Number testing is: 22\n", + "Num is bigger than 10\n", + "Number testing is: -5\n", + "Num is not bigger than 30\n" + ] + } + ], + "source": [ + "numbers = [6, 15, 35, 22, -5]\n", + "\n", + "print('First Function')\n", + "\n", + "for number in numbers:\n", + " print('Number testing is:', number)\n", + " is_num_bigger(number)\n", + " \n", + "print('Second Function')\n", + "\n", + "for number in numbers:\n", + " print('Number testing is:', number)\n", + " is_num_bigger_two(number)\n", + " \n", + "# Illustrate function name changes, but old function persists" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Assigning to built-in functions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-5\n", + "-5\n" + ] + }, + { + "ename": "TypeError", + "evalue": "'int' object is not callable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mnew_numbers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m35\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m22\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_numbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# be careful about assigning to built-in functions.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;31m# If jupyter highlights the word, good chance it is a built-in/keyword.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: 'int' object is not callable" + ] + } + ], + "source": [ + "numbers = [6, 15, 35, 22, -5]\n", + "print(min(numbers))\n", + "\n", + "min = min(numbers)\n", + "\n", + "print(min)\n", + "\n", + "new_numbers = [-6, -15, -35, -22, 5]\n", + "\n", + "print(min(new_numbers)) # be careful about assigning to built-in functions.\n", + "# If jupyter highlights the word, good chance it is a built-in/keyword." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Lectures/Lecture-16/Lecture 16.ipynb b/Lectures/Lecture-16/Lecture 16.ipynb new file mode 100644 index 0000000..f4e8c86 --- /dev/null +++ b/Lectures/Lecture-16/Lecture 16.ipynb @@ -0,0 +1,739 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 16" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the python math and random libraries, implement the Box-Muller transform. This transform generates 2 random variables $Z_0$ and $Z_1$ that are Normal distributed from two uniformly distributed random variables $U_1$ and $U_2$ using the relation:\n", + "\n", + "$$\n", + "Z_0 = \\sqrt{-2 \\log{U_1}}\\cos{(2\\pi U_2)}\\\\\n", + "Z_1 = \\sqrt{-2 \\log{U_1}}\\sin{(2\\pi U_2)}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6128658286207223" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import math\n", + "import random\n", + "\n", + "x=.1\n", + "# Useful functions\n", + "math.pi\n", + "math.sin(x)\n", + "math.cos(x)\n", + "math.log(x)\n", + "math.sqrt(x)\n", + "random.random()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_normal(N,m=0,s=1):\n", + " out = list() \n", + " \n", + " while len(out))" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+bHwOeKJ7/GPfsg/RO6d3ELhq3FkXyP5OeufpXgSOA1+elux9ObfSu0rpW/RONY0904C8nwOOAf/b/dtfT+/c6T7gGeAB4KJx5zxD9rfQ+wDyyb73+9Ypyv964Otd/v3AX3Xjv0Hv4OUQ8C/A+ePOOuDnuBz40jRmH/Tw9gOS1KCJOi0jSVoelrskNchyl6QGWe6S1CDLXZIaZLlLUoMsd0lq0P8BJ7CUGOJE4S8AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(generate_normal(1000,10,10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lab 4\n", + "\n", + "*Exercise 5:* Write a function the applies a booling function (that returns true/false) to every element in data, and return a list of indices of elements where the result was true. Use this function to find the indices of entries greater than 0.5. " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "def where(mylist,myfunc):\n", + " out= list()\n", + " for i,v in enumerate(mylist):\n", + " if myfunc(v):\n", + " out.append(i)\n", + " \n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[5, 6, 7, 8]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Test your solution here\n", + "\n", + "def greater_than_five(x):\n", + " return x>5\n", + "\n", + "where(range(1,10),greater_than_five)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[5, 6, 7, 8]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "where(range(1,10),lambda x: x>5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 6:* The inrange(mymin,mymax) function below returns a function that tests if it's input is between the specified values. Write corresponding functions that test:\n", + "* Even\n", + "* Odd\n", + "* Greater than\n", + "* Less than\n", + "* Equal\n", + "* Divisible by" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5.007019136039755 14.880151850704772\n" + ] + } + ], + "source": [ + "import random\n", + "data= [ 10*random.random()+5 for _ in range(100) ]\n", + "print(min(data),max(data))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True True False False False\n", + "False False True True False\n", + "Number of Entries passing F1: 56\n", + "Number of Entries passing F2: 44\n" + ] + } + ], + "source": [ + "def in_range(mymin,mymax):\n", + " def testrange(x):\n", + " return x=mymin\n", + " return testrange\n", + "\n", + "# Examples:\n", + "F1=in_range(0,10)\n", + "F2=in_range(10,20)\n", + "\n", + "# Test of in_range\n", + "print (F1(0), F1(1), F1(10), F1(15), F1(20))\n", + "print (F2(0), F2(1), F2(10), F2(15), F2(20))\n", + "\n", + "print (\"Number of Entries passing F1:\", len(where(data,F1)))\n", + "print (\"Number of Entries passing F2:\", len(where(data,F2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "### BEGIN SOLUTION\n", + "\n", + "def even(x):\n", + " return x%2==0\n", + "\n", + "def odd(x):\n", + " return x%2==1\n", + " \n", + "def greater_than(y):\n", + " def func(x):\n", + " return x>y\n", + " return func\n", + " \n", + "def less_than(y):\n", + " def func(x):\n", + " return x10,d)))\n", + "print (\"Number of Entries passing less than 10:\", sum(map(lambda x: x<10,d)))\n", + "print (\"Number of Entries passing equal to 10:\", sum(map(lambda x: x==10,d)))\n", + "print (\"Number of Entries passing divisible by 10:\", sum(map(lambda x: x%10==0,d)))\n", + " \n", + "### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Monte Carlo\n", + "\n", + "*Exercise 7:* Write a \"generator\" function called `generate_function(func,x_min,x_max,N)`, that instead of generating a flat distribution, generates a distribution with functional form coded in `func`. Note that `func` will always be > 0. \n", + "\n", + "Use the test function below and your histogramming functions above to demonstrate that your generator is working properly.\n", + "\n", + "Hint: A simple, but slow, solution is to a draw random number test_x within the specified range and another number p between the min and max of the function (which you will have to determine). If p<=function(test_x), then place test_x on the output. If not, repeat the process, drawing two new numbers. Repeat until you have the specified number of generated numbers, N. For this problem, it's OK to determine the min and max by numerically sampling the function. " + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def arange(x_min,x_max,steps=10):\n", + " step_size=(x_max-x_min)/steps\n", + " x=x_min\n", + " out = list()\n", + " for i in range(steps):\n", + " out.append(x)\n", + " x+=step_size\n", + " return out\n", + "\n", + "def generate_function(func,x_min,x_max,N=1000):\n", + " out = list()\n", + " x_scan = arange(x_min,x_max,100)\n", + " y_scan = list(map(func,x_scan))\n", + " y_min = min(y_scan)\n", + " y_max = max(y_scan)\n", + " \n", + " while len(out)=bin_edges[i] and d)" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(generate_function(test_func,0,10,100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 8:* Use your function to generate 1000 numbers that are normal distributed, using the `gaussian` function below. Confirm the mean and variance of the data is close to the mean and variance you specify when building the Gaussian. Histogram the data. " + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "def gaussian(mean, sigma):\n", + " def f(x):\n", + " return math.exp(-((x-mean)**2)/(2*sigma**2))/math.sqrt(math.pi*sigma)\n", + " return f\n", + "\n", + "# Example Instantiation\n", + "g1=gaussian(0,1)\n", + "g2=gaussian(10,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "g1_data=generate_function(g1,-5,5,10000)\n", + "g2_data=generate_function(g2,0,20,1000)\n", + "\n", + "plt.hist(g1_data)\n", + "plt.show()\n", + "plt.hist(g2_data)\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.006324680309003644 1.0017820500663661\n", + "9.943126633736021 2.940006543089863\n" + ] + } + ], + "source": [ + "def mean(x):\n", + " return sum(x)/float(len(x))\n", + "\n", + "def variance(x):\n", + " m=mean(x)\n", + " return (sum(map(lambda y: (y-m)**2,x))/float(len(x)-1))\n", + "\n", + "import math\n", + "\n", + "print(mean(g1_data),math.sqrt(variance(g1_data)))\n", + "print(mean(g2_data),math.sqrt(variance(g2_data)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "571 ms ± 27.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%timeit g1_data=generate_function(g1,-50,50,10000)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8.77 ms ± 69.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%timeit generate_normal(10000,0,1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 9:* Combine your `generate_function`, `where`, and `in_range` functions above to create an integrate function. Use your integrate function to show that approximately 68% of Normal distribution is within one variance." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "def integrate(func, x_min, x_max, n_points=1000):\n", + " out = list()\n", + " x_scan = arange(x_min,x_max,100)\n", + " y_scan = list(map(func,x_scan))\n", + " y_min = min(y_scan)\n", + " y_max = max(y_scan)\n", + "\n", + " count=0\n", + " while len(out)\n", + "The student instance: \n" + ] + } + ], + "source": [ + "a_student=student()\n", + "\n", + "print(\"The student class:\", type(student))\n", + "print(\"The student instance:\", type(a_student))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Why would such a construction be helpful? An alternative way of keeping all doing all of the book-keeping for the students would have been to create a bunch of lists for each of the attributes and make sure that the first student's information is always at index 0, second student index 1, and so on. \n", + "\n", + "For exmaple:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "names=list()\n", + "id_numbers=list()\n", + "genders=list()\n", + "years=list()\n", + "grades=list()\n", + "\n", + "# Create an \"instance\" of a student\n", + "\n", + "names.append(str())\n", + "id_numbers.append(int())\n", + "genders.append(str())\n", + "years.append(str())\n", + "grades.append(list())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could write functions to help make all of this look nicer, but it would be cumbersome to manage and ugly to read. By encapsulating concepts into objects, you ultimately reduce the complexity of your code.\n", + "\n", + "\n", + "## Constructor / Destructor\n", + "\n", + "We created an instance of student in the example above, but we didn't take care to carefully make sure all that the student instance was carefully setup. The first important OO concept are **constructors** and **destructors**. These are optional methods that are called when an object is created or destroyed. Since python manages memory for us, we typically don't need to implement destructors, but constructors are always a good idea. \n", + "\n", + "In python the names of build-in methods of classes typically start and end with 2 underscores. `__init__(self,...)` and `__del__(self)` are class constructor and destructors, respectively. \n", + "\n", + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class student:\n", + " def __init__(self, name, id_number, gender, year):\n", + " self.name=name\n", + " self.id_number=id_number\n", + " self.gender=gender\n", + " self.year=year\n", + " self.grades=list()\n", + " \n", + " def add_grade(self,grade):\n", + " self.grades.append(grade)\n", + " \n", + " def average_grade(self):\n", + " return sum(self.grades)/len(self.grades)\n", + " \n", + " def print_grades(self):\n", + " for grade in self.grades:\n", + " print(grade)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now when you instantiate a student you would do:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "85\n", + "90\n", + "Average: 87.5\n" + ] + } + ], + "source": [ + "a_student=student(\"John Doe\", 111, \"Male\", 0)\n", + "\n", + "a_student.add_grade(85)\n", + "a_student.add_grade(90)\n", + "\n", + "a_student.print_grades()\n", + "\n", + "print(\"Average:\", a_student.average_grade())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And you can keep all of the information for all of your students in a list:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name: John Doe\n", + "Name: Jane Doe\n" + ] + } + ], + "source": [ + "students=list()\n", + "\n", + "students.append(student(\"John Doe\", 111, \"Male\", 0))\n", + "students.append(student(\"Jane Doe\", 112, \"Female\", 0))\n", + "\n", + "for student in students:\n", + " print(\"Name:\", student.name)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are lots of built-in methods for classes, some of which have default implementations that you can **overload**, others that you can optionally implement. For example, if you have objects that you want python to know how to add, you can implement `__add__(self,other)` method." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Object oriented programming allows you to establish and maintain abstractions more effectively. Once an object has been implemented, users of the object don't need to know how the internals work and how it stores data to use it. They simply use the objects methods. It also makes it easier to change implementations. As long as the class maintains the same methods, you can improve and evolve the data and methods as you like without effecting any application that uses your class." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classes and Instances\n", + "\n", + "Consider the following simple class and instances." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello, my name is Bob\n", + "Hello, my name is Bill\n" + ] + } + ], + "source": [ + "class person:\n", + " name = \"\"\n", + " \n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " def say_hello(self):\n", + " print(\"Hello, my name is \" + self.name)\n", + " \n", + "# create objects\n", + "bob = person(\"Bob\")\n", + "bill = person(\"Bill\")\n", + " \n", + "# call methods owned by virtual objects\n", + "bob.say_hello()\n", + "bill.say_hello()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "people = [person(\"Jane\"),person(\"John\")]\n", + "Jane = people[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inheritance\n", + "\n", + "A powerful feature of object-oriented programming is inheritance, which allows you to build a hierarchy of classes. For example what if we wanted to keep track of students and faculty at the University. There would be some aspects of students and faculty that would be in common, while other would be different. We can store the common atrributes and methods in a common class called \"person\" that both \"student\" and \"faculty\" **inherit** from. \n", + "\n", + "For example:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class person:\n", + " def __init__(self, name, id_number, gender):\n", + " self.name=name\n", + " self.id_number=id_number\n", + " self.gender=gender\n", + " \n", + " \n", + "class student(person):\n", + " def __init__(self, name, id_number, gender, year):\n", + " super(student,self).__init__(name,id_number,gender)\n", + " self.year=year\n", + " self.grades=list()\n", + " \n", + " def add_grade(self,grade):\n", + " self.grades.append(grade)\n", + " \n", + " def average_grade(self):\n", + " return sum(self.grades)/len(self.grades)\n", + " \n", + " def print_grades(self):\n", + " for grade in self.grades:\n", + " print(grade)\n", + "\n", + " \n", + "class faculty(person):\n", + " def __init__(self, name, id_number, gender):\n", + " super(faculty,self).__init__(name,id_number,gender)\n", + " self.courses=list()\n", + " \n", + " def add_courses(self,course):\n", + " self.grades.append(course)\n", + " \n", + " def print_courses(self):\n", + " for courses in self.courses:\n", + " print(course)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inheritance helps in reducing the need to copy and paste code and to make it easier to use your code by establishing common interface and behaviors between different objects." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Public and Private Methods\n", + "\n", + "By convention, methods that start with two underscores (`__`) are considered to be private and are meant to be only called by the class." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "updating software\n", + "operate\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'device' object has no attribute '__update'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;31m# This will fail\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0ma_device\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'device' object has no attribute '__update'" + ] + } + ], + "source": [ + "class device: \n", + " def __init__(self):\n", + " self.__update()\n", + " \n", + " def operate(self):\n", + " print('operate')\n", + " \n", + " def __update(self):\n", + " print('updating software')\n", + " \n", + "a_device= device()\n", + "\n", + "a_device.operate()\n", + "\n", + "# This will fail\n", + "a_device.__update()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Public and Private Data\n", + "\n", + "Usually a class needs to control the data it holds. If an external class or user changes a data member of a class in a unexpected way, then the class can fail.\n", + "\n", + "The way to control the data in your classes is to make the varibles holding the data private and create \"setter\" and \"accessor\" functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class car:\n", + " __name = \"\"\n", + " __n_doors = 0\n", + " __n_passangers = 0\n", + " __max_passangers = 4\n", + " \n", + " def __init__(self,name=\"Unnamed\",n_doors=4, max_passangers=4):\n", + " self.__name=name\n", + " self.__n_doors=n_doors\n", + " self.__max_passangers=max_passangers\n", + " \n", + " ## Accessors\n", + " def name(self):\n", + " return self.__name\n", + " \n", + " def n_doors(self):\n", + " return self.__n_doors\n", + " \n", + " def n_passangers(self):\n", + " return self.__n_passangers\n", + " \n", + " ## Setter\n", + " def set_name(self,name):\n", + " if isinstance(name,str):\n", + " self.__name=name\n", + " else:\n", + " print (\"Name must be a string.\")\n", + " \n", + " ## Can't change number of doors on a car... so no setter for __n_doors\n", + " \n", + " ## We can only add and remove passangers\n", + " def add_passanger(self,n=1):\n", + " if isinstance(n,(int,float)):\n", + " self.__n_passangers+=n\n", + " if self.__n_passangers>self.__max_passangers:\n", + " self.__n_passangers=self.__max_passangers\n", + " print (\"Car is full. \",n-self.max_passangers,\" passangers were left outside.\")\n", + " else:\n", + " print (\"Number of passangers must be an interger.\")\n", + " \n", + " def remove_passanger(self,n=1):\n", + " if isinstance(n,int):\n", + " self.__n_passangers-=n\n", + " if self.__n_passangers<0:\n", + " self.__n_passangers=0 \n", + " else:\n", + " print(\"Number of passangers must be an interger.\")\n", + "\n", + "\n", + "\n", + "my_car=car()\n", + "print (my_car.name())\n", + "my_car.set_name(\"My Car\")\n", + "print (my_car.name())\n", + "\n", + "my_car.add_passanger()\n", + "print (my_car.n_passangers())\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Method Overloading\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning...\n", + "Teaching...\n" + ] + } + ], + "source": [ + "\n", + "class person:\n", + " __name=\"\"\n", + " __gender=\"\"\n", + " def __init__(self, name,gender):\n", + " self.__name=name\n", + " self.__gender=gender\n", + " \n", + " # This is a virtual method\n", + " def do_work(self):\n", + " raise NotImplementedError\n", + " \n", + "class student(person):\n", + " __year=0\n", + " __grades=list()\n", + " \n", + " def __init__(self, name, gender, year):\n", + " person.__init__(self,name,gender)\n", + " self.__year=year\n", + " \n", + " def add_grade(self,grade):\n", + " self.__grades.append(grade)\n", + " \n", + " def average_grade(self):\n", + " return sum(self.__grades)/len(self.__grades)\n", + " \n", + " def print_grades(self):\n", + " for grade in self.__grades:\n", + " print (grade)\n", + " \n", + " def do_work(self):\n", + " print (\"Learning...\")\n", + "\n", + " \n", + "class faculty(person):\n", + " __courses=list()\n", + " \n", + " def __init__(self, name, gender):\n", + " person.__init__(self,name,gender)\n", + " \n", + " def add_courses(self,course):\n", + " self.__courses.append(course)\n", + " \n", + " def print_courses(self):\n", + " for courses in self.__courses:\n", + " print (course)\n", + "\n", + " def do_work(self):\n", + " print (\"Teaching...\")\n", + "\n", + " \n", + " \n", + "a_student=student(\"Bob\",\"Male\",2)\n", + "a_teacher=faculty(\"Mary\",\"Female\")\n", + "\n", + "a_student.do_work()\n", + "a_teacher.do_work()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Polymorphism\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning...\n", + "Teaching...\n" + ] + } + ], + "source": [ + "people= [student(\"Bob\",\"Male\",2), faculty(\"Mary\",\"Female\")]\n", + "\n", + "for person in people:\n", + " person.do_work()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "isinstance(a_student,student)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100.0\n" + ] + } + ], + "source": [ + "for person in people:\n", + " if isinstance(person,student):\n", + " person.add_grade(100.)\n", + " if isinstance(person,faculty):\n", + " person.add_courses(\"Data 1401\")\n", + "\n", + "people[0].print_grades()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overloading Built-ins\n", + "\n", + "I found [this](https://realpython.com/operator-function-overloading/) walk through of overloading python operators to be very well done, so lets go through it.\n", + "\n", + "For a complete list of operators, look at the table at the bottom of the [Operator Library referece](https://docs.python.org/3.7/library/operator.html).\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Lecture-18/Lecture-18.ipynb b/Lectures/Lecture-18/Lecture-18.ipynb new file mode 100644 index 0000000..dcfd247 --- /dev/null +++ b/Lectures/Lecture-18/Lecture-18.ipynb @@ -0,0 +1,970 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 18" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Drawing Program Example\n", + "\n", + "First two examples are inspired by [Python Practice Book](https://anandology.com/python-practice-book/object_oriented_programming.html).\n", + "\n", + "### Canvas" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "class Canvas:\n", + " def __init__(self, width, height):\n", + " self.width = width\n", + " self.height = height\n", + " self.data = [[' '] * width for i in range(height)]\n", + "\n", + " def set_pixel(self, row, col, char='*'):\n", + " self.data[row][col] = char\n", + "\n", + " def get_pixel(self, row, col):\n", + " return self.data[row][col]\n", + " \n", + " def h_line(self, x, y, w, **kargs):\n", + " for i in range(x,x+w):\n", + " self.set_pixel(i,y, **kargs)\n", + "\n", + " def v_line(self, x, y, h, **kargs):\n", + " for i in range(y,y+h):\n", + " self.set_pixel(x,i, **kargs)\n", + " \n", + " def line(self, x1, y1, x2, y2, **kargs):\n", + " slope = (y2-y1) / (x2-x1)\n", + " for y in range(y1,y2):\n", + " x= int(slope * y)\n", + " self.set_pixel(x,y, **kargs)\n", + " \n", + " def display(self):\n", + " print(\"\\n\".join([\"\".join(row) for row in self.data]))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "** \n", + " ***** \n", + " ** \n", + " * ** \n", + " * ** \n", + " * ** \n", + " * ** \n", + " ** \n", + " ** \n", + " ** \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n" + ] + } + ], + "source": [ + "c1=Canvas(40,20)\n", + "c1.v_line(1,1,5)\n", + "c1.h_line(3,3,4)\n", + "c1.line(0,0,40,20)\n", + "c1.display()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Shapes" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class Shape:\n", + " def __init__(self, name=\"\", **kwargs):\n", + " self.name=name\n", + " self.kwargs=kwargs\n", + " \n", + " def paint(self, canvas): pass\n", + "\n", + "class Rectangle(Shape):\n", + " def __init__(self, x, y, w, h, **kwargs):\n", + " Shape.__init__(self, **kwargs)\n", + " self.x = x\n", + " self.y = y\n", + " self.w = w\n", + " self.h = h\n", + "\n", + " def paint(self, canvas):\n", + " canvas.h_line(self.x, self.y, self.w, **self.kwargs)\n", + " canvas.h_line(self.x, self.y + self.h, self.w, **self.kwargs)\n", + " canvas.v_line(self.x, self.y, self.h, **self.kwargs)\n", + " canvas.v_line(self.x + self.w, self.y, self.h, **self.kwargs)\n", + "\n", + "class Square(Rectangle):\n", + " def __init__(self, x, y, size, **kwargs):\n", + " Rectangle.__init__(self, x, y, size, size, **kwargs)\n", + "\n", + "class Line(Shape):\n", + " def __init__(self, x1, y1, x2, y2, **kwargs):\n", + " Shape.__init__(self, **kwargs)\n", + " self.x1=x1\n", + " self.y1=y1\n", + " self.x2=x2\n", + " self.y2=y2\n", + " \n", + " def paint(self, canvas):\n", + " canvas.line(self.x1,self.y1,self.x2,self.y2)\n", + " \n", + "class CompoundShape(Shape):\n", + " def __init__(self, shapes):\n", + " self.shapes = shapes\n", + "\n", + " def paint(self, canvas):\n", + " for s in self.shapes:\n", + " s.paint(canvas)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "c1=Canvas(50,40)\n", + "s1=Square(5,5,20,char=\"^\")\n", + "s1.paint(c1)\n", + "l1=Line(2,2,13,19)\n", + "l1.paint(c1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "c1=Canvas(50,40)\n", + "s1=CompoundShape([Square(5,5,20,char=\"^\"),\n", + " Line(2,2,13,19)])\n", + "s1.paint(c1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + " \n", + " * \n", + " * \n", + " ^^^^^^^^^^^^^^^^^^^^^ \n", + " *^ ^ \n", + " * ^ \n", + " ^ ^ \n", + " ^* ^ \n", + " ^ * ^ \n", + " ^ ^ \n", + " ^ * ^ \n", + " ^ * ^ \n", + " ^ ^ \n", + " ^ * ^ \n", + " ^ ^ \n", + " ^ * ^ \n", + " ^ * ^ \n", + " ^ ^ \n", + " ^ * ^ \n", + " ^ * ^ \n", + " ^ ^ \n", + " ^ * ^ \n", + " ^ * ^ \n", + " ^^^^^^^^^^^^^^^^^^^^ \n", + " * \n", + " * \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n" + ] + } + ], + "source": [ + "c1.display()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Drawing" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "class RasterDrawing:\n", + " def __init__(self):\n", + " self.shapes=dict()\n", + " self.shape_names=list()\n", + " \n", + " def add_shape(self,shape):\n", + " if shape.name == \"\":\n", + " shape.name = self.assign_name()\n", + " \n", + " self.shapes[shape.name]=shape\n", + " self.shape_names.append(shape.name)\n", + " \n", + " def paint(self,canvas):\n", + " for shape_name in self.shape_names:\n", + " self.shapes[shape_name].paint(canvas)\n", + " \n", + " def assign_name(self):\n", + " name_base=\"shape\"\n", + " name = name_base+\"_0\"\n", + " \n", + " i=1\n", + " while name in self.shapes:\n", + " name = name_base+\"_\"+str(i)\n", + " \n", + " return name\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + " \n", + " * \n", + " * \n", + " ^^^^^^^^^^^^^^^^^^^^^ \n", + " *^ ^ \n", + " * ^ \n", + " ^ ^ \n", + " ^* ^ \n", + " ^ * ^ \n", + " ^ ^ \n", + " ^ * ^ \n", + " ^ * ^ \n", + " ^ ^ \n", + " ^ * ^ \n", + " ^ ^ \n", + " ^ * ^ \n", + " ^ * ^ \n", + " ^ ^ \n", + " ^ * ^ \n", + " ^ * ^ \n", + " ^ ^ \n", + " ^ * ^ \n", + " ^ * ^ \n", + " ^^^^^^^^^^^^^^^^^^^^ \n", + " * \n", + " * \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n" + ] + } + ], + "source": [ + "c1=Canvas(50,40)\n", + "rd=RasterDrawing()\n", + "\n", + "rd.add_shape(Square(5,5,20,char=\"^\"))\n", + "rd.add_shape(Line(2,2,13,19))\n", + "\n", + "rd.paint(c1)\n", + "\n", + "c1.display()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "rd.shapes[\"shape_0\"].w=10" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + " \n", + " * \n", + " * \n", + " ^^^^^^^^^^^^^^^^^^^^^ \n", + " *^ ^ \n", + " * ^ \n", + " ^ ^ \n", + " ^* ^ \n", + " ^ * ^ \n", + " ^ ^ \n", + " ^ * ^ \n", + " ^ * ^ \n", + " ^ ^ \n", + " ^^^^^*^^^^^^^^^^^^^^ \n", + " \n", + " * \n", + " * \n", + " \n", + " * \n", + " * \n", + " \n", + " * \n", + " * \n", + " \n", + " * \n", + " * \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n" + ] + } + ], + "source": [ + "c2=Canvas(40,40)\n", + "rd.paint(c2)\n", + "c2.display()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rational Numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "class RationalNumber:\n", + " \"\"\"\n", + " Rational Numbers with support for arthmetic operations.\n", + "\n", + " >>> a = RationalNumber(1, 2)\n", + " >>> b = RationalNumber(1, 3)\n", + " >>> a + b\n", + " 5/6\n", + " >>> a - b\n", + " 1/6\n", + " >>> a * b\n", + " 1/6\n", + " >>> a/b\n", + " 3/2\n", + " \"\"\"\n", + " def __init__(self, numerator, denominator=1):\n", + " self.n = numerator\n", + " self.d = denominator\n", + "\n", + " def __add__(self, other):\n", + " if not isinstance(other, RationalNumber):\n", + " other = RationalNumber(other)\n", + "\n", + " n = self.n * other.d + self.d * other.n\n", + " d = self.d * other.d\n", + " return RationalNumber(n, d)\n", + "\n", + " def __sub__(self, other):\n", + " if not isinstance(other, RationalNumber):\n", + " other = RationalNumber(other)\n", + "\n", + " n1, d1 = self.n, self.d\n", + " n2, d2 = other.n, other.d\n", + " return RationalNumber(n1*d2 - n2*d1, d1*d2)\n", + "\n", + " def __mul__(self, other):\n", + " if not isinstance(other, RationalNumber):\n", + " other = RationalNumber(other)\n", + "\n", + " n1, d1 = self.n, self.d\n", + " n2, d2 = other.n, other.d\n", + " return RationalNumber(n1*n2, d1*d2)\n", + "\n", + " def __div__(self, other):\n", + " if not isinstance(other, RationalNumber):\n", + " other = RationalNumber(other)\n", + "\n", + " n1, d1 = self.n, self.d\n", + " n2, d2 = other.n, other.d\n", + " return RationalNumber(n1*d2, d1*n2)\n", + "\n", + " def __str__(self):\n", + " return \"%s/%s\" % (self.n, self.d)\n", + "\n", + " __repr__ = __str__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Particle Physics Example\n", + "\n", + "Let's try to do something more meaningful and introduce some particle physics basics. In relativistic mechanics, the Energy and Momentum of particles are different in every frame, but obey $m^2=E^2-\\vec{p}^2$ or $E^2=\\vec{p}^2$ for massless particles, where we set the speed of light $c=1$. It is therefore convenient to express the Energy and Momentum of a particle as a 4-vector, for example in Euclidean coordiates: $p= (E,p_{x},p_{y},p_{z}) = (E,\\vec{p})$.\n", + "\n", + "Energy and momentum are concerved with a particle decays into two other particles, for example a $Z$ boson to two electrons, $Z\\rightarrow e^+ e^-$, or a Higgs Boson to two photons, $H\\rightarrow \\gamma\\gamma$. In 4-vectors we can express conservations, for example in the Higgs decay, as $p_H = p_{\\gamma1}+p_{\\gamma2}$. In a two body decay, it's easy to fully solve for the momenta daughter particles in the rest frame of the parent:\n", + "\n", + "$$\n", + "m_H = 125 GeV\\\\\n", + "p_H = (m_{H},0,0,0)\n", + "$$\n", + "\n", + "Momentum conservation tells us that $\\vec{p_{H}} = 0 = \\vec{p_{\\gamma1}} + \\vec{p_{\\gamma2}} \\Rightarrow \\vec{p_{\\gamma1}} = - \\vec{p_{\\gamma2}} = p_\\gamma$, i.e. the daughters travel in opposite directions. The 4-vector of the photons are\n", + "\n", + "$$\n", + "E_H = m_H = E_{\\gamma1}+E_{\\gamma2} = |\\vec{p_{\\gamma1}}| + |\\vec{p_{\\gamma2}}|=2|p_\\gamma|\\\\\n", + "\\Rightarrow p_{\\gamma1}= (m_H/2, \\vec{p_{\\gamma}})\\\\\n", + "\\Rightarrow p_{\\gamma2}= (m_H/2, -\\vec{p_{\\gamma}})\n", + "$$\n", + "\n", + "If we select that direction to be aligned with one of our axes, then we can write:\n", + "$$\n", + "p_{\\gamma1}= (m_H/2, 0,0, m_H/2)\\\\\n", + "p_{\\gamma2}= (m_H/2, 0,0, -m_H/2).\n", + "$$\n", + "\n", + "We can compute these 4-vectors in the case that the parent particle is not at rest by relavistic boosting. \n", + "\n", + "We will begin by representing 4-vectors as python lists. For example the first photon in the rest frame can be written as:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[62.5, 0, 0, 62.5]\n" + ] + } + ], + "source": [ + "m_H= 125.\n", + "p_g1= [m_H/2,0,0,m_H/2]\n", + "print(p_g1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get the second photon, lets write a function that negates 4-vectors:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[62.5, 0, 0, -62.5]\n" + ] + } + ], + "source": [ + "def neg_4v(p):\n", + " return [p[0], -p[1], -p[2] , -p[3]]\n", + "\n", + "p_g2=neg_4v(p_g1)\n", + "print(p_g2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Other useful functions:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sum: [125.0, 0, 0, 0.0]\n", + "Difference: [125.0, 0, 0, 125.0]\n" + ] + } + ], + "source": [ + "def add_v4(p1,p2):\n", + " return [p1[0]+p2[0], p1[1]+p2[1], p1[2]+p2[2] , p1[3]+p2[3]]\n", + "\n", + "def sub_v4(p1,p2):\n", + " return add_v4(p1,neg_4v(p2))\n", + "\n", + "print (\"Sum:\", add_v4(p_g1,p_g2))\n", + "print (\"Difference:\", sub_v4(p_g1,p_g2))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dot: 88.38834764831844\n", + "Gamma Mass: 0.0\n", + "Higgs Mass: 125.0\n" + ] + } + ], + "source": [ + "import math\n", + "\n", + "def dot_v4(p1,p2):\n", + " return math.sqrt(sum([p1[0]*p2[0], -p1[1]*p2[1], -p1[2]*p2[2] , -p1[3]*p2[3]]))\n", + "\n", + "def mass_v4(p):\n", + " return dot_v4(p,p)\n", + " \n", + "print (\"Dot:\", dot_v4(p_g1,p_g2))\n", + "print (\"Gamma Mass:\", mass_v4(p_g1))\n", + "print (\"Higgs Mass:\", mass_v4(add_v4(p_g1,p_g2)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are lots of ways to write the same thing, for example:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zip: [(62.5, 62.5), (0, 0), (0, 0), (62.5, -62.5)]\n", + "Sum: [125.0, 0, 0, 0.0]\n" + ] + } + ], + "source": [ + "def add_v4_1(p1,p2):\n", + " out=list()\n", + " for i in range(4):\n", + " out.append(p1[i]+p2[i])\n", + " return out\n", + "\n", + "def add_v4_2(p1,p2):\n", + " return map(lambda x: x[0]+x[1],zip(p1,p2))\n", + "\n", + "def add_v4_3(p1,p2):\n", + " return [sum(x) for x in zip(p1,p2)]\n", + "\n", + "print (\"zip:\", list(zip(p_g1,p_g2)))\n", + "\n", + "print (\"Sum:\", add_v4_3(p_g1,p_g2))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def boost_matrix(beta_in):\n", + " Lambda= [[0,0,0,0],\n", + " [0,0,0,0],\n", + " [0,0,0,0],\n", + " [0,0,0,0]]\n", + " \n", + " beta=[0]+beta_in\n", + "\n", + " beta2=sum(x**2 for x in beta)\n", + " gamma=1./math.sqrt(1.-beta2)\n", + " \n", + " for i in range(4):\n", + " for j in range(4):\n", + " if j==0:\n", + " Lambda[i][0]=-gamma*beta[i]\n", + " elif i==0:\n", + " Lambda[0][j]=-gamma*beta[j]\n", + " else:\n", + " Lambda[i][j]= (gamma-1)*beta[i]*beta[j]/beta2 + float(i==j)\n", + "\n", + " Lambda[0][0]=gamma\n", + "\n", + " return Lambda\n", + " \n", + "def boost(p,beta):\n", + " Lambda=boost_matrix(beta)\n", + " out=4*[0.]\n", + " for j in range(4):\n", + " out[j]=sum(map(lambda x: x[0]*x[1],zip(p,Lambda[j])))\n", + " return out\n", + "\n", + "def decay(p):\n", + " m=mass_v4(p)\n", + " p1=[m/2.,0.,0.,m/2.]\n", + " p2=[m/2.,0.,0.,-m/2.]\n", + " # We should now rotate by 2 arbitrary angles...\n", + " beta=[p[1]/p[0],p[2]/p[0],p[3]/p[0]]\n", + " \n", + " p1b=boost(p1,beta)\n", + " p2b=boost(p2,beta)\n", + "\n", + " return p1b,p2b\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Boosted Higgs: [144.33756729740645, 0.0, -72.16878364870323, 0.0]\n", + "Mass of Boosted Higgs: 125.00000000000001\n", + "p1: [72.16878364870324, 0.0, 36.08439182435162, 62.50000000000001]\n", + "p2: [72.16878364870324, 0.0, 36.08439182435162, -62.50000000000001]\n", + "Higgs from daughters: [144.33756729740648, 0.0, 72.16878364870324, 0.0]\n", + "Higgs mass from daughters: 125.00000000000003\n" + ] + } + ], + "source": [ + "# Start with a Higgs at rest\n", + "p_H=[m_H,0.,0.,0.]\n", + "\n", + "# Now boost it (along y for example)\n", + "p_Hb=boost(p_H,[0.,.5,0.])\n", + "print (\"Boosted Higgs:\", p_Hb)\n", + "print (\"Mass of Boosted Higgs:\", mass_v4(p_Hb))\n", + "\n", + "# Decay the boosted Higgs\n", + "p1,p2=decay(p_Hb)\n", + "\n", + "print(\"p1:\",p1)\n", + "print(\"p2:\",p2)\n", + "\n", + "# Make sure the decay products add back to the Higgs\n", + "print (\"Higgs from daughters:\", add_v4(p1,p2))\n", + "print (\"Higgs mass from daughters:\", mass_v4(add_v4(p1,p2)))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets write a function that gives us the 4-vectors of 2 daughter particles given a parent particle 4 vector." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Object Oriented Programming\n", + "\n", + "Lets write a 4-vector class to do the same thing:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "class four_vector(object):\n", + " def __init__(self, p=None):\n", + " if p:\n", + " self.v=p\n", + " else:\n", + " self.v=[0.,0.,0.,0.]\n", + "\n", + " def setval(self,l):\n", + " self.v=l\n", + " \n", + " def __add__(self,other):\n", + " return four_vector([sum(x) for x in zip(self.v,other)])\n", + " \n", + " def neg(self,p):\n", + " return four_vector([p[0], -p[1], -p[2] , -p[3]])\n", + "\n", + " def __sub__(self,other):\n", + " return self.__add__(self.v,self.neg(other))\n", + " \n", + " def __mul__(self,other):\n", + " return math.sqrt(sum([self.v[0]*other[0], \n", + " -self.v[1]*other[1],\n", + " -self.v[2]*other[2],\n", + " -self.v[3]*other[3]]))\n", + "\n", + " def boost(self,beta):\n", + " Lambda=boost_matrix(beta)\n", + " out=4*[0.]\n", + " for j in range(4):\n", + " out[j]=sum(map(lambda x: x[0]*x[1],zip(self.v,Lambda[j])))\n", + " return four_vector(out)\n", + "\n", + " def mass(self):\n", + " return self.__mul__(self.v)\n", + "\n", + " def __getitem__(self,i):\n", + " return self.v[i]\n", + "\n", + " \n", + " def __str__(self):\n", + " return \"({0}, {1}, {2}, {3})\".format(self.v[0],self.v[1],self.v[2],self.v[3])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def decay(p):\n", + " m=p.mass()\n", + " p1=four_vector([m/2.,0.,0.,m/2.])\n", + " p2=four_vector([m/2.,0.,0.,-m/2.])\n", + " # We should now rotate by 2 arbitrary angles...\n", + " beta=[p[1]/p[0],p[2]/p[0],p[3]/p[0]]\n", + " \n", + " p1b=p1.boost(beta)\n", + " p2b=p2.boost(beta)\n", + "\n", + " return p1b,p2b" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial Higgs: (125.0, 0.0, 0.0, 0.0)\n", + "Boosted Higgs: (144.33756729740645, 0.0, -72.16878364870323, 0.0)\n", + "Mass of Boosted Higgs: 125.00000000000001\n", + "Higgs from daughters: (144.33756729740648, 0.0, 72.16878364870324, 0.0)\n", + "Higgs mass from daughters: 125.00000000000003\n" + ] + } + ], + "source": [ + "# Start with a Higgs at rest\n", + "p_H=four_vector([m_H,0.,0.,0.])\n", + "print (\"Initial Higgs:\", p_H)\n", + "\n", + "# Now boost it (along y for example)\n", + "p_Hb=p_H.boost([0.,.5,0.])\n", + "print (\"Boosted Higgs:\", p_Hb)\n", + "print (\"Mass of Boosted Higgs:\", p_Hb.mass())\n", + "\n", + "# Decay the boosted Higgs\n", + "p1,p2=decay(p_Hb)\n", + "\n", + "# Make sure the decay products add back to the Higgs\n", + "print (\"Higgs from daughters:\", p1+p2)\n", + "print (\"Higgs mass from daughters:\", (p1+p2).mass())" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "class composite(four_vector):\n", + " def __init__(self,daughters):\n", + " super(composite, self).__init__()\n", + " self.daughters=daughters\n", + "\n", + " tmp=four_vector()\n", + " for d in self.daughters:\n", + " tmp=tmp+d\n", + " \n", + " self.setval(tmp.v)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Composite Higgs: (144.33756729740648, 0.0, 72.16878364870324, 0.0)\n", + "Mass: 125.00000000000003\n" + ] + } + ], + "source": [ + "H_reco=composite([p1,p2])\n", + "print (\"Composite Higgs:\", H_reco)\n", + "print (\"Mass:\", H_reco.mass())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating Events" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Events=[]\n", + "\n", + "for i in range(1,11):\n", + " p_H=four_vector([m_H,0.,0.,0.])\n", + " my_boost= float(i)/11.\n", + " p_Hb=p_H.boost([0.,my_boost,0.])\n", + " p1,p2=decay(p_Hb)\n", + " \n", + " Event= {\"Higgs\":composite([p1,p2]),\n", + " \"Boost\":my_boost}\n", + " \n", + " Events.append(Event)\n", + " \n", + "# Make sure the decay products add back to the Higgs\n", + "for i,Event in enumerate(Events):\n", + " print (\"Event:\",i)\n", + " print (\"Higgs 4-vector:\",Event[\"Higgs\"])\n", + " print (\"Boost:\",Event[\"Boost\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Lecture-19/Lecture-19.ipynb b/Lectures/Lecture-19/Lecture-19.ipynb new file mode 100644 index 0000000..ed19dc4 --- /dev/null +++ b/Lectures/Lecture-19/Lecture-19.ipynb @@ -0,0 +1,3210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 19\n", + "\n", + "## Reading Data\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lecture-19.ipynb Scores.csv\r\n" + ] + } + ], + "source": [ + "!ls " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "l1_n,l1_1,12_n,l2_1,l2_2,l2_3,l2_4,l2_5,l2_6,l2_7,l3_n,l3_1,l3_2,l3_3,l3_4,l3_5,l3_6,l3_7,l3_8,l3_9,l3_10,l3_11,l3_12,l3_13,l3_14,l4_n,l4_1,l4_2,l4_3,l4_4,l4_5,l4_6,l4_7,l4_8,l4_9,l4_10,l4_11,q1_n,q1_1,e1_n,e1_1,e1_2,e1_3,e1_4,e1_5,e1_6,e1_7,e1_8,e1_9,e1_10,e1_11,e1_12,e1_13,e1_14,e1_15\r", + "\r\n", + "1,10,7,10,10,9.5,9,10,10,9.5,14,10,10,10,10,10,10,9,10,3,0,3,3,5,2,11,0,0,0,0,0,0,0,0,0,0,0,1,0,15,9,9,10,5,5,0,0,10,10,10,10,0,10,5,10\r", + "\r\n", + "1,10,7,0,10,9.5,0,10,10,0,14,10,10,10,10,0,0,0,0,0,0,0,0,0,0,11,10,10,10,10,5,3,0,3,10,7,0,1,9.5,15,9,9,10,5,10,0,9,9,9,9,9,10,5,0,0\r", + "\r\n", + "1,10,7,10,10,0,10,10,10,10,14,10,6,10,0,0,0,0,0,0,0,0,0,0,0,11,10,10,0,7,0,0,0,0,0,0,0,1,9.5,15,9,9,10,9,5,9,7,9,10,10,10,5,10,5,0\r", + "\r\n", + "1,10,7,10,10,10,9.5,10,10,9.5,14,10,10,10,10,0,0,0,0,0,0,0,0,0,0,11,10,10,10,10,3,3,0,0,5,0,0,1,10,15,9,9,10,0,10,0,7,5,9,9,9,0,0,0,0\r", + "\r\n", + "1,10,7,10,10,5,9.5,10,10,9.5,14,5,9,9,10,7,10,10,10,10,7,10,3,5,10,11,0,0,0,0,0,0,0,0,0,0,0,1,10,15,9,9,9,8,7,10,0,9,10,9,10,9,5,0,0\r", + "\r\n", + "1,10,7,10,10,3,7,10,10,9,14,10,10,10,10,0,10,9,10,7,7,3,7,5,8,11,10,10,10,8,5,3,0,0,7,0,0,1,9.5,15,9,9,10,10,7,10,10,10,10,10,10,10,9,8,2\r", + "\r\n", + "1,10,7,10,10,3,9.5,10,10,9.5,14,10,10,10,8,5,10,5,10,3,0,10,3,10,8,11,10,10,10,10,10,10,0,0,10,5,0,1,10,15,9,9,10,9,7,9,0,0,10,10,9,5,10,8,0\r", + "\r\n", + "1,10,7,10,10,0,5,10,10,9.5,14,9.5,10,10,8,10,8,9,0,0,0,0,0,0,0,11,0,10,10,0,0,10,0,0,0,0,0,1,10,15,9,9,10,9,0,0,0,0,0,0,0,0,0,0,0\r", + "\r\n", + "1,10,7,0,0,0,0,0,0,0,14,9,10,10,10,7,10,3,6,3,3,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,5,15,5,5,5,5,0,0,0,0,0,0,0,0,0,0,0\r", + "\r\n", + "1,10,7,10,10,10,9.5,10,10,9.5,14,5,9.5,9.5,8,10,10,8,10,8,0,5,6,0,0,11,0,10,10,10,0,5,0,0,0,0,0,1,9.5,15,9,9,10,9,9,10,7,0,9,9,9,0,5,0,0\r", + "\r\n", + "1,10,7,10,10,9.5,0,10,10,0,14,9.5,10,10,10,10,10,10,10,0,0,10,5,10,10,11,0,10,10,0,0,5,0,0,0,0,0,1,0,15,9,9,10,0,8,9,7,9,10,10,10,10,10,0,0\r", + "\r\n", + "1,10,7,0,10,10,8,10,10,10,14,9,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,9.5,15,9,9,0,9,8,0,0,0,0,0,0,0,0,0,0\r", + "\r\n", + "1,10,7,10,10,9.5,9.5,10,10,9.5,14,9.5,10,10,10,8,10,8,10,10,7,5,0,0,0,11,10,10,10,10,5,6,0,0,0,0,0,1,10,15,9,9,10,9,8,9,7,9,10,10,10,10,0,0,0\r", + "\r\n", + "1,10,7,10,10,0,0,10,10,7,14,10,10,10,10,7,10,6,3,10,10,10,10,10,10,11,10,10,10,10,10,5,10,10,10,10,10,1,0,15,9,9,9,9,9,10,9,9,10,10,10,10,10,5,10\r", + "\r\n", + "1,10,7,10,10,9.5,0,10,10,0,14,9.5,0,0,10,0,10,5,10,7,0,10,6,10,0,11,10,10,6,0,0,0,0,0,0,0,0,1,0,15,0,0,0,0,5,0,7,0,3,3,3,0,3,0,0\r", + "\r\n", + "1,10,7,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\r", + "\r\n" + ] + } + ], + "source": [ + "!cat Scores.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comma Separated Values (CSV) File Format\n", + "\n", + "The simplest and most common file format for storing data is called Comma Separated Values (CSV). Generally a CSV file represents a table, with the top row (first line of the file) consisting of the labels of the columns (separated by commas). Generally each column keeps a different feature or field. For example for student data, the first column could be the name, second the ID, third major, etc. After the first line, each row hold the data for one data point or example. In the case of student data, each row could correspond to one student." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reading CSV Files\n", + "\n", + "There are lots of libraries for reading CSV files into memory. Before we start using them, lets write our own. We'll need two things:\n", + "\n", + "* Means of reading and interpreting the file.\n", + "* A representation of the read data in memory.\n", + "\n", + "We have written a simple CSV reader before. Lets recall one of the ways in python to read a file." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "l1_n,l1_1,12_n,l2_1,l2_2,l2_3,l2_4,l2_5,l2_6,l2_7,l3_n,l3_1,l3_2,l3_3,l3_4,l3_5,l3_6,l3_7,l3_8,l3_9,l3_10,l3_11,l3_12,l3_13,l3_14,l4_n,l4_1,l4_2,l4_3,l4_4,l4_5,l4_6,l4_7,l4_8,l4_9,l4_10,l4_11,q1_n,q1_1,e1_n,e1_1,e1_2,e1_3,e1_4,e1_5,e1_6,e1_7,e1_8,e1_9,e1_10,e1_11,e1_12,e1_13,e1_14,e1_15\n", + "\n", + "1,10,7,10,10,9.5,9,10,10,9.5,14,10,10,10,10,10,10,9,10,3,0,3,3,5,2,11,0,0,0,0,0,0,0,0,0,0,0,1,0,15,9,9,10,5,5,0,0,10,10,10,10,0,10,5,10\n", + "\n", + "1,10,7,0,10,9.5,0,10,10,0,14,10,10,10,10,0,0,0,0,0,0,0,0,0,0,11,10,10,10,10,5,3,0,3,10,7,0,1,9.5,15,9,9,10,5,10,0,9,9,9,9,9,10,5,0,0\n", + "\n", + "1,10,7,10,10,0,10,10,10,10,14,10,6,10,0,0,0,0,0,0,0,0,0,0,0,11,10,10,0,7,0,0,0,0,0,0,0,1,9.5,15,9,9,10,9,5,9,7,9,10,10,10,5,10,5,0\n", + "\n", + "1,10,7,10,10,10,9.5,10,10,9.5,14,10,10,10,10,0,0,0,0,0,0,0,0,0,0,11,10,10,10,10,3,3,0,0,5,0,0,1,10,15,9,9,10,0,10,0,7,5,9,9,9,0,0,0,0\n", + "\n", + "1,10,7,10,10,5,9.5,10,10,9.5,14,5,9,9,10,7,10,10,10,10,7,10,3,5,10,11,0,0,0,0,0,0,0,0,0,0,0,1,10,15,9,9,9,8,7,10,0,9,10,9,10,9,5,0,0\n", + "\n", + "1,10,7,10,10,3,7,10,10,9,14,10,10,10,10,0,10,9,10,7,7,3,7,5,8,11,10,10,10,8,5,3,0,0,7,0,0,1,9.5,15,9,9,10,10,7,10,10,10,10,10,10,10,9,8,2\n", + "\n", + "1,10,7,10,10,3,9.5,10,10,9.5,14,10,10,10,8,5,10,5,10,3,0,10,3,10,8,11,10,10,10,10,10,10,0,0,10,5,0,1,10,15,9,9,10,9,7,9,0,0,10,10,9,5,10,8,0\n", + "\n", + "1,10,7,10,10,0,5,10,10,9.5,14,9.5,10,10,8,10,8,9,0,0,0,0,0,0,0,11,0,10,10,0,0,10,0,0,0,0,0,1,10,15,9,9,10,9,0,0,0,0,0,0,0,0,0,0,0\n", + "\n", + "1,10,7,0,0,0,0,0,0,0,14,9,10,10,10,7,10,3,6,3,3,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,5,15,5,5,5,5,0,0,0,0,0,0,0,0,0,0,0\n", + "\n", + "1,10,7,10,10,10,9.5,10,10,9.5,14,5,9.5,9.5,8,10,10,8,10,8,0,5,6,0,0,11,0,10,10,10,0,5,0,0,0,0,0,1,9.5,15,9,9,10,9,9,10,7,0,9,9,9,0,5,0,0\n", + "\n", + "1,10,7,10,10,9.5,0,10,10,0,14,9.5,10,10,10,10,10,10,10,0,0,10,5,10,10,11,0,10,10,0,0,5,0,0,0,0,0,1,0,15,9,9,10,0,8,9,7,9,10,10,10,10,10,0,0\n", + "\n", + "1,10,7,0,10,10,8,10,10,10,14,9,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,9.5,15,9,9,0,9,8,0,0,0,0,0,0,0,0,0,0\n", + "\n", + "1,10,7,10,10,9.5,9.5,10,10,9.5,14,9.5,10,10,10,8,10,8,10,10,7,5,0,0,0,11,10,10,10,10,5,6,0,0,0,0,0,1,10,15,9,9,10,9,8,9,7,9,10,10,10,10,0,0,0\n", + "\n", + "1,10,7,10,10,0,0,10,10,7,14,10,10,10,10,7,10,6,3,10,10,10,10,10,10,11,10,10,10,10,10,5,10,10,10,10,10,1,0,15,9,9,9,9,9,10,9,9,10,10,10,10,10,5,10\n", + "\n", + "1,10,7,10,10,9.5,0,10,10,0,14,9.5,0,0,10,0,10,5,10,7,0,10,6,10,0,11,10,10,6,0,0,0,0,0,0,0,0,1,0,15,0,0,0,0,5,0,7,0,3,3,3,0,3,0,0\n", + "\n", + "1,10,7,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n", + "\n" + ] + } + ], + "source": [ + "f=open(\"Scores.csv\",\"r\")\n", + "first_line = f.readline()\n", + "print(first_line)\n", + "line = f.readline()\n", + "while line:\n", + " print(line)\n", + " line = f.readline()\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's work on the first line, which is special:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['l1_n', 'l1_1', '12_n', 'l2_1', 'l2_2', 'l2_3', 'l2_4', 'l2_5', 'l2_6', 'l2_7', 'l3_n', 'l3_1', 'l3_2', 'l3_3', 'l3_4', 'l3_5', 'l3_6', 'l3_7', 'l3_8', 'l3_9', 'l3_10', 'l3_11', 'l3_12', 'l3_13', 'l3_14', 'l4_n', 'l4_1', 'l4_2', 'l4_3', 'l4_4', 'l4_5', 'l4_6', 'l4_7', 'l4_8', 'l4_9', 'l4_10', 'l4_11', 'q1_n', 'q1_1', 'e1_n', 'e1_1', 'e1_2', 'e1_3', 'e1_4', 'e1_5', 'e1_6', 'e1_7', 'e1_8', 'e1_9', 'e1_10', 'e1_11', 'e1_12', 'e1_13', 'e1_14', 'e1_15\\n']\n" + ] + } + ], + "source": [ + "f=open(\"Scores.csv\",\"r\")\n", + "first_line = f.readline()\n", + "print(first_line.split(\",\"))\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It appears that each line ends with `\\n`. Here's how we can remove these newlines." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['l1_n', 'l1_1', '12_n', 'l2_1', 'l2_2', 'l2_3', 'l2_4', 'l2_5', 'l2_6', 'l2_7', 'l3_n', 'l3_1', 'l3_2', 'l3_3', 'l3_4', 'l3_5', 'l3_6', 'l3_7', 'l3_8', 'l3_9', 'l3_10', 'l3_11', 'l3_12', 'l3_13', 'l3_14', 'l4_n', 'l4_1', 'l4_2', 'l4_3', 'l4_4', 'l4_5', 'l4_6', 'l4_7', 'l4_8', 'l4_9', 'l4_10', 'l4_11', 'q1_n', 'q1_1', 'e1_n', 'e1_1', 'e1_2', 'e1_3', 'e1_4', 'e1_5', 'e1_6', 'e1_7', 'e1_8', 'e1_9', 'e1_10', 'e1_11', 'e1_12', 'e1_13', 'e1_14', 'e1_15']\n" + ] + } + ], + "source": [ + "f=open(\"Scores.csv\",\"r\")\n", + "first_line = f.readline().rstrip()\n", + "print(first_line.split(\",\"))\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally lets store the first line, which is a list of the column names:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "f=open(\"Scores.csv\",\"r\")\n", + "first_line = f.readline().rstrip()\n", + "fields=first_line.split(\",\")\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "f=open(\"Scores.csv\",\"r\")\n", + "first_line = f.readline().rstrip()\n", + "fields=first_line.split(\",\")\n", + "\n", + "data=list()\n", + "\n", + "line = f.readline().rstrip()\n", + "while line:\n", + " data.append(line.split(\",\"))\n", + " line = f.readline().rstrip()\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "55" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "26" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fields.index(\"l4_1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(data[10][fields.index(\"l4_1\")])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building a CSV Reader" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have the basics down, but we have more things to consider:\n", + "* We have written some example code, we should now write something that is general and we could use in different instances. \n", + "* The fields can be different types: strings, numbers (integer or floating point). We should store the fields as the correct data type.\n", + "* Still need to figure out how we will store the data in memory.\n", + "\n", + "\n", + "We have some options on how to proceed:\n", + " * We could write a CSV reader function that given the filename of a CSV file, reads the data and returns it as a standard python data object. There are various suitable such representations, so we'll either have to pick one or provide some options to allow for other ones.\n", + " * Instead of a CSV reader function, we could create a CSV reader class. It will be instantiated with a CSV filename, so each instance would be uniquely connected to a specific file. It'll read the data into some representation that is kept private. We provide accessor methods to get to keep retrieve specific parts of the data, or the whole data as standard python data.\n", + " * We can separate the concepts of a CSV reader and how we store the data. In this way, we could write other readers (e.g. Excel file reader) that would still use the same data storage.\n", + " * We might want to also be able to write out CSV files." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "class DataFileHandler:\n", + " def __init__(self,extensions):\n", + " self.__extensions=extensions\n", + " \n", + " def check_extension(self,filename):\n", + " file_extension=filename.split(\".\")[-1]\n", + " return file_extension in self.__extensions\n", + "\n", + " def _readfile(self,filename):\n", + " raise NotImplementedError \n", + " \n", + " def readfile(self,filename,check_extension=True):\n", + " if not check_extension or self.check_extension(filename):\n", + " return self._readfile(filename) \n", + " else:\n", + " print(\"Error: filename {} does not match acceptable extensions.\".format(filename))\n", + " \n", + " def _writefile(self,filename,data):\n", + " raise NotImplementedError\n", + " \n", + " def writefile(self,filename,data):\n", + " return self._writefile(filename,data)\n", + " \n", + " \n", + "class CSVHandler(DataFileHandler):\n", + " def __init__(self):\n", + " #super(CSVHandler,self).__init__([\"csv\",\"CSV\"])\n", + " DataFileHandler.__init__(self,[\"csv\",\"CSV\"])\n", + " \n", + " def _readfile(self,filename):\n", + " f=open(filename,\"r\")\n", + " first_line = f.readline().rstrip()\n", + " fields=first_line.split(\",\")\n", + "\n", + " data=list()\n", + "\n", + " line = f.readline().rstrip()\n", + " while line:\n", + " data.append(line.split(\",\"))\n", + " line = f.readline().rstrip()\n", + "\n", + " f.close()\n", + " \n", + " return fields,data\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "my_handler=CSVHandler()\n", + "fields,data=my_handler.readfile(\"Scores.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Handling Different Types" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "class DataFileHandler:\n", + " def __init__(self,extensions):\n", + " self.__extensions=extensions\n", + " \n", + " def check_extension(self,filename):\n", + " file_extension=filename.split(\".\")[-1]\n", + " return file_extension in self.__extensions\n", + "\n", + " def _readfile(self,filename):\n", + " raise NotImplementedError \n", + " \n", + " def readfile(self,filename,check_extension=True):\n", + " if not check_extension or self.check_extension(filename):\n", + " return self._readfile(filename) \n", + " else:\n", + " print(\"Error: filename {} does not match acceptable extensions.\".format(filename))\n", + " \n", + " def _writefile(self,filename,data):\n", + " raise NotImplementedError\n", + " \n", + " def writefile(self,filename,data):\n", + " return self._writefile(filename,data)\n", + " \n", + " \n", + "class CSVHandler(DataFileHandler):\n", + " def __init__(self):\n", + " #super(CSVHandler,self).__init__([\"csv\",\"CSV\"])\n", + " DataFileHandler.__init__(self,[\"csv\",\"CSV\"])\n", + " \n", + " def _readfile(self,filename):\n", + " f=open(filename,\"r\")\n", + " first_line = f.readline().rstrip()\n", + " fields=first_line.split(\",\")\n", + "\n", + " data=list()\n", + "\n", + " line = f.readline().rstrip()\n", + " while line:\n", + " items=line.split(\",\")\n", + " \n", + " row=list()\n", + " for item in items:\n", + " try:\n", + " d=float(item)\n", + " except ValueError:\n", + " d=item\n", + " row.append(d)\n", + " \n", + " data.append(row)\n", + " \n", + " line = f.readline().rstrip()\n", + "\n", + " f.close()\n", + " \n", + " return fields,data\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "my_handler=CSVHandler()\n", + "fields,data=my_handler.readfile(\"Scores.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[0][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Representation" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "float" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[10][fields.index(\"l4_1\")]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'L1': 1, 'L2': 2}" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "foo=dict()\n", + "foo[\"L1\"]=1\n", + "foo[\"L2\"]=2\n", + "\n", + "foo\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "foo[\"L1\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'L1': 1, 'L2': 2}" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dict([ (\"L1\",1), (\"L2\",2) ] )" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "first_row=dict(list(zip(fields,data[0])))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first_row[\"l4_1\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "new_data=list()\n", + "\n", + "for row in data:\n", + " new_data.append(dict(list(zip(fields,row))))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'l1_n': 1.0,\n", + " 'l1_1': 10.0,\n", + " '12_n': 7.0,\n", + " 'l2_1': 10.0,\n", + " 'l2_2': 10.0,\n", + " 'l2_3': 9.5,\n", + " 'l2_4': 9.0,\n", + " 'l2_5': 10.0,\n", + " 'l2_6': 10.0,\n", + " 'l2_7': 9.5,\n", + " 'l3_n': 14.0,\n", + " 'l3_1': 10.0,\n", + " 'l3_2': 10.0,\n", + " 'l3_3': 10.0,\n", + " 'l3_4': 10.0,\n", + " 'l3_5': 10.0,\n", + " 'l3_6': 10.0,\n", + " 'l3_7': 9.0,\n", + " 'l3_8': 10.0,\n", + " 'l3_9': 3.0,\n", + " 'l3_10': 0.0,\n", + " 'l3_11': 3.0,\n", + " 'l3_12': 3.0,\n", + " 'l3_13': 5.0,\n", + " 'l3_14': 2.0,\n", + " 'l4_n': 11.0,\n", + " 'l4_1': 0.0,\n", + " 'l4_2': 0.0,\n", + " 'l4_3': 0.0,\n", + " 'l4_4': 0.0,\n", + " 'l4_5': 0.0,\n", + " 'l4_6': 0.0,\n", + " 'l4_7': 0.0,\n", + " 'l4_8': 0.0,\n", + " 'l4_9': 0.0,\n", + " 'l4_10': 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'e1_2': 0.0,\n", + " 'e1_3': 0.0,\n", + " 'e1_4': 0.0,\n", + " 'e1_5': 0.0,\n", + " 'e1_6': 0.0,\n", + " 'e1_7': 0.0,\n", + " 'e1_8': 0.0,\n", + " 'e1_9': 0.0,\n", + " 'e1_10': 0.0,\n", + " 'e1_11': 0.0,\n", + " 'e1_12': 0.0,\n", + " 'e1_13': 0.0,\n", + " 'e1_14': 0.0,\n", + " 'e1_15': 0.0}]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_data" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "class DataRow:\n", + " def __init__(self,fields,data):\n", + " self.__fields=fields\n", + " self.__data=data\n", + " \n", + " def __getitem__(self,key):\n", + " return self.__data[self.__fields.index(key)]\n", + "\n", + "\n", + "class Data:\n", + " def __init__(self):\n", + " self.__fields=list()\n", + " self.__data=list()\n", + " \n", + " def set_fields(self,fields):\n", + " self.__fields=fields\n", + " \n", + " def add_data_point(self,data_point):\n", + " if isinstance(data_point,list):\n", + " if len(data_point) == len(self.__fields):\n", + " self.__data.append(DataRow(self.__fields,data_point))\n", + " else:\n", + " print(\"Expected {} fields, got {} fields.\".format(len(self.__fields),len(fields)))\n", + " else:\n", + " print(\"Data Point must be given as a list.\")\n", + "\n", + " def add_data_points(self,data_points):\n", + " for data_point in data_points:\n", + " self.add_data_point(data_point)\n", + " \n", + " def fields(self):\n", + " return self.__fields\n", + " \n", + " def __getitem__(self,key):\n", + " return self.__data[key]\n", + "\n", + " def __str__(self):\n", + " return self.__fields" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "my_data=Data()\n", + "my_data.set_fields(fields)\n", + "my_data.add_data_points(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_data[10][\"l4_1\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "class DataFileHandler:\n", + " def __init__(self,extensions):\n", + " self.__extensions=extensions\n", + " \n", + " def check_extension(self,filename):\n", + " file_extension=filename.split(\".\")[-1]\n", + " return file_extension in self.__extensions\n", + "\n", + " def _readfile(self,filename):\n", + " raise NotImplementedError \n", + " \n", + " def readfile(self,filename,check_extension=True):\n", + " if not check_extension or self.check_extension(filename):\n", + " return self._readfile(filename) \n", + " else:\n", + " print(\"Error: filename {} does not match acceptable extensions.\".format(filename))\n", + " \n", + " def _writefile(self,filename,data):\n", + " raise NotImplementedError\n", + " \n", + " def writefile(self,filename,data):\n", + " return self._writefile(filename,data)\n", + " \n", + " \n", + "class CSVHandler(DataFileHandler):\n", + " def __init__(self):\n", + " #super(CSVHandler,self).__init__([\"csv\",\"CSV\"])\n", + " DataFileHandler.__init__(self,[\"csv\",\"CSV\"])\n", + " \n", + " def _readfile(self,filename):\n", + " f=open(filename,\"r\")\n", + " first_line = f.readline().rstrip()\n", + " fields=first_line.split(\",\")\n", + "\n", + " data=list()\n", + "\n", + " line = f.readline().rstrip()\n", + " while line:\n", + " items=line.split(\",\")\n", + " \n", + " row=list()\n", + " for item in items:\n", + " try:\n", + " d=float(item)\n", + " except ValueError:\n", + " d=item\n", + " row.append(d)\n", + " \n", + " data.append(row)\n", + " \n", + " line = f.readline().rstrip()\n", + "\n", + " f.close()\n", + " \n", + " my_data=Data()\n", + " my_data.set_fields(fields)\n", + " my_data.add_data_points(data)\n", + " \n", + " return my_data\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "my_handler=CSVHandler()\n", + "my_data=my_handler.readfile(\"Scores.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_data[10][\"l4_1\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "Data=pd.read_csv(\"Scores.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(Data)" + ] + }, + { + "cell_type": "code", + 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+1,10,7,10,10,9.5,9.5,10,10,9.5,14,9.5,10,10,10,8,10,8,10,10,7,5,0,0,0,11,10,10,10,10,5,6,0,0,0,0,0,1,10,15,9,9,10,9,8,9,7,9,10,10,10,10,0,0,0 +1,10,7,10,10,0,0,10,10,7,14,10,10,10,10,7,10,6,3,10,10,10,10,10,10,11,10,10,10,10,10,5,10,10,10,10,10,1,0,15,9,9,9,9,9,10,9,9,10,10,10,10,10,5,10 +1,10,7,10,10,9.5,0,10,10,0,14,9.5,0,0,10,0,10,5,10,7,0,10,6,10,0,11,10,10,6,0,0,0,0,0,0,0,0,1,0,15,0,0,0,0,5,0,7,0,3,3,3,0,3,0,0 +1,10,7,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/Lectures/Lecture-20/Lecture-20.ipynb b/Lectures/Lecture-20/Lecture-20.ipynb new file mode 100644 index 0000000..48e5829 --- /dev/null +++ b/Lectures/Lecture-20/Lecture-20.ipynb @@ -0,0 +1,4816 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 20\n", + "\n", + "Use pandas to read in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data-1401-Grades-Fixed.csv Data-1401-Grades.csv\r\n", + "Data-1401-Grades-Fixed.csv~ Lecture-20.ipynb\r\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Grade Key,No attempt/wrong: -5,\"No attempt: -5, wrong: -2\",\"No attempt: -10, wrong: -1..-7, bonus: 0..10\",\"No attempt: -10, wrong: -1 ..-8. no bonus\",\"No attempt: -20, wrong: -1..-15, bonus: 0..20\",,,\r", + "\r\n", + "Student ID,Lab 1,Lab 2,Lab 3,Lab 4,Exam 1,Lab 5,Lab 6,Lab 7\r", + "\r\n", + "1000954113,100,81,25,35,0,,,\r", + "\r\n", + ",Stopped at ex 5,\"-ex1: functions print not return even/odd bool\r\n", + "-ex3: does not solve problem posed in ex.\r\n", + "-ex8: no solution\r\n", + "-ex9: no solution\r\n", + "-ex10: no solution\",\"-ex2: index errors, code ostensibly only checks for 3x3 case: -5 pts\r\n", + "-ex3: board doesn't draw top/bottom borders, hard coded inputs: -5 pts\r\n", + "-ex4: Not attempted: -10 pts\r\n", + "-ex5: Does not work (no test output provided): -5 pts\r\n", + "-ex6 - ex10: Not attempted: -50 pts\r\n", + "-ex11: Not attempted: +0 pts\r\n", + "\r\n", + "\",\"-ex2b: calculates the mean every time functions is called. logic is correct but instead of recomputing the mean every time it loops over, you could store the mean in another variable (which is only calculated once). correct though. no points deducted.\r\n", + "-ex3: does not run: -5pts\r\n", + "-ex4 - ex9: not attempted: -60pts\",- did not submit exam,,,\r", + "\r\n", + "1000625629,95,87,0,88,120,,,\r", + "\r\n", + ",\"- Did not do ex 4 - count lines of 'w'\r\n", + "\",\"-ex2: does not return list, prints\r\n", + "-ex3: does not solve problem. just takes max of list and gets all other nums less than that.\r\n", + "-ex7: flipped condition on rocks and scissors\r\n", + "-ex9: uses list of words, not string of words\r\n", + "-ex10: did not attempt \",- did not submit lab 3,\"-ex2: enhancement: could have just called mean() function from earlier instead of re-writting. \r\n", + "-ex4: does not normalize (what happens to the plot if you input 2000 data points?): -4pts\r\n", + "-ex6: funcs should return funcs, odd/even not really important but should have implemented for others: -3pts\r\n", + "-ex9: supposed to use previous functions, trapezoidal is good approach but not what assignment requests: -5pts \r\n", + "\",\"- excellent\r\n", + "-q6: +20pts bonus\",,,\r", + "\r\n", + "1000847686,100,0,0,0,90,,,\r", + "\r\n", + ",Stopped at ex 5,- did not submit lab 2,- did not submit lab 3,- did not submit lab 4,\"-submitted in wrong folder: -5pts\r\n", + "-q4: does not attribute lecture 15 code: -5pts\r\n", + "-q6: not correct: +5pts\r\n", + "\",,,\r", + "\r\n", + "1001108137,90,96,0,35,90,,,\r", + "\r\n", + ",\"-ex1: did not echo $SHELL\r\n", + "-ex4: did not count passwd\",\"-ex3: does not return function, using additional argument\r\n", + "-ex8: outputs fib of n+1 and not n.\r\n", + "ACADEMIC DISHONESTY. Matches with 1001428148 \",- did not submit lab 3,\"-ex1: does not normalize between x_min and x_max: -5pts\r\n", + "-ex3: doesn't work: -5pts\r\n", + "-ex4: doesn't work: -5pts\r\n", + "-ex5 - ex9: not attempted: -50pts \",\"-q2: incorrect output: -15pts\r\n", + "-q6: incomplete: +5pts\",,,\r", + "\r\n", + "1000999851,95,94,43,0,100,,,\r", + "\r\n", + ",-ex4: did not count passwd,\"-ex3: does not return function, using additional argument\r\n", + "-ex5: does not check for duplicates when appending intersection\r\n", + "-ex7: does not loop when p1 and p2 draw\r\n", + "\",\"-ex1: Each row is referenced to each other: -5 pts\r\n", + "-ex2: Does not return -1 if incomplete: -2 pts\r\n", + "-ex6 - ex10: Not attemped: -50 pts \r\n", + "-ex11: Not attempted: +0 pts\",- did not submit lab 4,-q6: not attempted: +0pts,,,\r", + "\r\n", + "1001214491,80,82,30,0,90,,,\r", + "\r\n", + ",did not complete any assignment correctly,\"-ex1: prints not return\r\n", + "-ex2: not a function, just block of code\r\n", + "-ex3: not a function, does not return a function, does not return anything for that matter\r\n", + "-ex4: not a function, logic correct\r\n", + "-ex5: not a function, logic correct\r\n", + "-ex6: not a function, logic correct\r\n", + "-ex7: not a function, logic correct\r\n", + "-ex8: not a function, logic correct\r\n", + "-ex9: not a function, logic NOT correct. uses list not string\r\n", + "\",\"-ex1: Not a function: -5 pts\r\n", + "-ex2: Not a function. Hard-code prints for 3x3: -5 pts\r\n", + "-ex4: Same code as ex3, does not solve problem: -5 pts\r\n", + "-ex5: Does not work, takes user input but does not cast it to use it for indexing: -5 pts\r\n", + "-ex6 - ex10: Not attempted. -50 pts\r\n", + "-ex11: Not attempted: +0 pts\r\n", + "\",- did not submit lab 4,\"-q4: Does not work: -10pts\r\n", + "-q6: Not attempted: 0pts\",,,\r", + "\r\n", + "1001428148,80,90,24,0,80,,,\r", + "\r\n", + ",\"Did not do assignment.\r\n", + "\r\n", + "EDIT: He did the git stuff\r\n", + " which was not part of the grading.\r\n", + "\r\n", + "- did not do ex1-ex4. \",\"-ex3: does not return function, using additional argument\r\n", + "-ex5: doesn't return common elements, prints\r\n", + "-ex8: outputs fib of n+1 and not n.\r\n", + "-ex9: reverses entire string, not words\r\n", + "-ex10: wrote a guessing game where human guesses computers number.\r\n", + "ACADEMIC DISHONESTY. Matches with 1001108137 \",\"-ex2: did not attempt: -10pts\r\n", + "-ex3: hard-coded prints: -7pts\r\n", + "-ex4: does not solve problem: -7pts\r\n", + "-ex5: Did not do problem: -7pts\r\n", + "-ex6: Code does not run, several flow issues: -5pts\r\n", + "-ex7 - ex10: Did not attempt: -40pts\r\n", + "\",- did not submit lab 4,\"- Submitted in wrong folder: -5pts\r\n", + "-q2: does not run: -10pts\r\n", + "-q4: only works for matrixes of size 5x5: -5pts\r\n", + "-q6: not attempted: +0pts\",,,\r", + "\r\n", + "1001397199,100,94,87,12,105,,,\r", + "\r\n", + ",stopped at ex5,\"-ex3: does not return a function\r\n", + "-ex8: returns fib value at n, not the list til n, pretty obvious that user is copying recursive code off the internet without understanding what it does\r\n", + "-ex9: takes a list as input, not string\r\n", + "\",\"-ex2: hard coded for 3x3 boards: -2pts\r\n", + "-ex3: breakes for boards, i.e 6x3: -2pts\r\n", + "-ex4: does not handle NxN boards: -2pts\r\n", + "-ex5: does not handle NxN boards: -2pts\r\n", + "-ex9: good!\r\n", + "-ex10: does not work: -5pts\r\n", + "-ex11: did not attempt: +0pts\r\n", + "\",\"-ex2b: not supposed to use other package: -8pts\r\n", + "-ex3 -ex9: not attempted: -80pts\r\n", + "\",\"-q6: Incorrect output. Does not use previous functions or provided code: +5pts\r\n", + "\",,,\r", + "\r\n", + "1001372854,100,70,17,35,20,,,\r", + "\r\n", + ",stopped at ex5,- did not attempt exercise 4-10,\"-ex2: does not handle NxN boards: -3pts\r\n", + "-ex3 - ex10: did not attempt: -80pts\r\n", + "-ex11: did not attempt: +0pts\",\"-ex2b: could have just called mean function instead of copy/pasting the code into variance\r\n", + "-ex3: hard codes bin, not correct, should be appending bin_edges to list: -5pts\r\n", + "-ex4 - ex9: -60pts\",\"-q1: should only return one state, not a list of states\r\n", + "-q2 -q5: not attempted: -20*4pts\r\n", + "-q6: not attempted: +0pts\",,,\r", + "\r\n", + "1001558073,95,86,0,0,84,,,\r", + "\r\n", + ",-ex4: did not use wc to count,\"-ex3: did not return a function\r\n", + "-ex4: not a function, logic correct\r\n", + "-ex5: not a function, user input for both lists.\r\n", + "-ex6: not a function, logic correct\r\n", + "-ex7: not a function, exits if draw\r\n", + "-ex9: not a function, logic correct\r\n", + "-ex10: Reversed: Instead of the computer guessing the user is guessing.\",- did not submit lab 3,- did not submit lab 4,\"-q2: should return, not print, does not provide correct output: -3pts\r\n", + "-q3: should sort everything using q2, not sorted (you used python's sorted function in one of your first cells, i think that confused your output since list of states was sorted after that, if you remove the sorted you'll see that these functions aren't doing what is asked for in questions): -10pts\r\n", + "-q4: your numbers are correct, but it should be returned in a list of size MxN (2d matrix), not a list that is (N+M)x1 (1d list): -5pts\r\n", + "-q6: Not complete: +2pts\",,,\r", + "\r\n", + "1001593219,100,96,96,85,120,,,\r", + "\r\n", + ",-excellent. completed everything.,\"-ex1: prints, doesn't return\r\n", + "-ex3: doesn't exactly mimic ex2 as it instead takes user input.\r\n", + "\r\n", + "\",\"-ex2: should use sample input as provided: -3pts\r\n", + "-ex4: Does not draw board in correct format: -1pts (edit, I see you did this later on so I changed this from -5 to -1 for being incorrectly placed).\r\n", + "-ex11: did not attempt: +0pts\",\"-ex3: should only update xmin and xmax if none provided (someone might want to discard outliers with this function, for ex): -2pts\r\n", + "you only really need to keep track of the counts for the histogram, but won't count off points for this.\r\n", + "-ex4: looks good but didn't normalize number of '#' to 20 max (several arguments were removed from drawhist function): -4pts\r\n", + "-ex5: should pass check is an argument to 'truth' function: -2pts\r\n", + "-ex6: not using nested functions, see GTA example solutions: -5pts\r\n", + "-ex9: should not have to redefine functions to do integration: -2pts\",\"-excellent. full credit +20pts for q6 bonus.\r\n", + "-note: q1-q3: you didn't need to use alphabet to compare letters if you wanted to make strings upper or lower, you could use entry.lower().\r\n", + "\",,,\r", + "\r\n", + "1001596311,90,92,71,57,100,,,\r", + "\r\n", + ",\"-ex2: did not attempt\r\n", + "-ex4: did not count passwd\",\"-ex2: returns numbers less than or equal, and not strictly less than\r\n", + "-ex3: uses argument instead of return function\r\n", + "-ex5: does not check for duplicates\r\n", + "-ex7: does not handle draw case\r\n", + "\",\"-ex2: tie when it should have been incomplete. only works for 3x3: -2pts\r\n", + "-ex3: Does not work for boards of size: ex. 6x8: -5pts\r\n", + "-ex4: Incorrect print with sizes 4x4 for example: -2pts\r\n", + "-ex5: Logic looks okay but could not reproduce results when running cells: -5pts\r\n", + "-ex6: Logic looks okay but running the code gave errors: placeInput() missing player: -5pts\r\n", + "-ex10: Did not attempt: -10pts\r\n", + "-ex11: Did not attempt: +0pts\",\"-ex3: should check x_min and x_max seperately, won't deduct for this though. bin_edges not updated correctly: -3pts\r\n", + "-ex5: look at common-issues.ipynb, you are providing the element as index, incorrect: -5pts\r\n", + "-ex6: should be func returning func like example: -5pts\r\n", + "-ex7 -ex9: not attempted: -30pts\r\n", + "\r\n", + " \",\"-q1: should have returned 'Alabama': -5pts\r\n", + "-q4: not attributing lecture 15 for code: -5pts\r\n", + "-q6: Does not calculate x90 using functions: +10pts\r\n", + "\",,,\r", + "\r\n", + "1001608680,100,94,36,61,95,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: uses argument instead of return function\r\n", + "-ex6: not a function, logic incomplete and not correct\r\n", + "-ex9: reverses string, not words in string\r\n", + "\",\"-ex2: Does not work on most of included examples: -5pts\r\n", + "-ex3: Did not construct board: -7pts\r\n", + "-ex4: Does not construct board: -7pts\r\n", + "-ex5: Did not return or place board: -5pts\r\n", + "-ex6: Hard-coded: -5pts\r\n", + "-ex7: Errors when ran, demonstrate to TA: -5pts\r\n", + "-ex8 - ex10: Did not attempt (copying and pasting ex7 does not count): -30pts\r\n", + "-ex11: Did not attempt: +0pts\r\n", + "\",\"-ex2b: should not use numpy functions: -8pts\r\n", + "-ex4: did not attempt (copied q3): -10pts\r\n", + "-ex5: supposed to pass in function, not a value, supposed to give index, not value: -6pts\r\n", + "-ex6: supposed to have func that returns func (like provided example), see gta solutions: -5pts\r\n", + "-ex8: should be returning values up to length N, instead returns one zero: -5pts\r\n", + "-ex9: does not work: -5pts\",\"-q2: does not return correct index (states[24] == montana): -5pts\r\n", + "-q4: not attributing lecture 15 for code: -5pts\r\n", + "-q6: incomplete: +5pts\r\n", + "\",,,\r", + "\r\n", + "1001618032,100,96,97,86,110,,,\r", + "\r\n", + ",stopped at ex5,\"-ex3: uses argument instead of return function\r\n", + "-ex9: input is list not string\r\n", + "\",\"-ex2: output 0 should be -1: -1pts\r\n", + "-ex3: size should be n x m: -2pts\r\n", + "-ex6: does not use pretty print with border: -5pts\r\n", + "-ex7: does not use \"\"A3\"\" notation: -5pts\r\n", + "-ex11: +10pts\",\"-ex3: should check x_min and x_max seperately, won't deduct for this though.\r\n", + "-ex4: should be normalizing by max_character_per_line: -4pts\r\n", + "-ex6: should have funcs that return funcs, see GTA example solutions: -5pts\r\n", + "-ex9: should have completed solution using generate_function, where, and in_range: -5pts\r\n", + "\",\"-q4: not attributing lecture 15 for code: -5pts\r\n", + "-q6: Nice output and plot: +20pts\",,,\r", + "\r\n", + "1001652624,100,90,0,0,0,,,\r", + "\r\n", + ",stopped at ex5,\"- in general, well commented\r\n", + "-ex3: uses argument instead of return function\r\n", + "-ex5: does not check for duplicates\r\n", + "-ex7: did not attempt\r\n", + "-ex9: uses list as input, not string\r\n", + "-ex10: did not attempt\r\n", + "\",- ACADEMIC DISHONESTY. Matches with 1001496565. I suspect this student copied off of 1001496565 since the other student had the solutions on github for several days before the deadline. This student did not have a working solution during the last lab period and 1001496565's solution is unique.,- did not submit lab 4,- did not submit exam,no longer on canvas,,\r", + "\r\n", + "1001658617,100,94,96,92,100,,,\r", + "\r\n", + ",stopped at ex5,\"-ex3: pretty close on finishing correctly. returns called function\r\n", + "-ex6: prints, doesn't return true/false if palindrome\r\n", + "-ex7: does not call again if draw\r\n", + "-ex10: logic is there but automated, user inputs number and random guessing ensues by itself. i'd count this as a correct solution though different since it doesn't allow user to explic indicate higher/lower.\r\n", + "\",\"-ex2: draw condition should be 0: -1pts\r\n", + "-ex3: output incorrect for larger boards: eg 6x8: -3pts\r\n", + "-overall: excellent!\",-ex4: not correct: -8pts,\"-excellent\r\n", + "-q6: not attempted: +0pts\",,,\r", + "\r\n", + "1001663166,95,94,38,0,82,,,\r", + "\r\n", + ",-ex2: did not complete,\"-ex3: uses argument instead of return function\r\n", + "-ex7: does not handle draw correctly\r\n", + "-ex9: returns a list not a string, though input is correct in being a string\r\n", + "\r\n", + "\",\"-ex2: does not return 2,1,0,-1, just prints, not checked against other states: -5pts\r\n", + "-ex3: does not print board (copied ex1): -10pts\r\n", + "-ex4: could not test. demonstrate to TA: -5pts\r\n", + "-ex5: does not place block: -7pts\r\n", + "-ex6: not complete: -7pts\r\n", + "-ex7: not complete: -7pts\r\n", + "-ex8: not complete: -7pts\r\n", + "-ex9: does not run, demonstrate to TA: -7pts\r\n", + "-ex10: does not run, demonstrate to TA: -7pts\",- did not submit lab 4,\"-q4: should not be using numpy for this (to use built-in functions), also does not work for lists with >3 elements: -15pts\r\n", + "-q5: does not return value: -3pts\r\n", + "-q6: not attempted: +0pts\r\n", + " \",,,\r", + "\r\n", + "1001674179,95,90,23,0,0,,,\r", + "\r\n", + ",\"-ex4: incorrect solution, did not grep for w, just counted all lines\",\"-ex2: used lambda function, not defined\r\n", + "-ex3: not correctly implemented, harcoded list\r\n", + "-ex7: does not handle tie/draw correctly\r\n", + "-ex9: incorrect, reverses full string and not the words\r\n", + "-ex10: does not work\",\"-ex1: does not return board: -3pts\r\n", + "-ex2: did not attempt: -10pts\r\n", + "-ex3: does not print correctly: -7pts\r\n", + "-ex4: does not print correctly: -7pts\r\n", + "-ex6 - ex10: not attempted: -50pts\r\n", + " \r\n", + "\",- did not submit lab 4,- did not submit exam,,,\r", + "\r\n", + "1001722244,95,100,0,0,0,,,\r", + "\r\n", + ",\"-ex4: incorrect solution, did not grep for w, just counted all lines\",-perfect. all correct.,- did not submit lab 3,- did not submit lab 4,- did not submit exam,,,\r", + "\r\n", + "1001543608,100,96,19,51,111,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: uses argument instead of return function\r\n", + "-ex10: incorrect, reversed\",\"-ex1: not a function, does not work: -7pts\r\n", + "-ex2: does not work: -5pts\r\n", + "-ex3: does not print correctly: -5pts\r\n", + "-ex4: does not run, logic not correct: -7pts\r\n", + "-ex5: gives errors, demonstrate to TA for credit: -7pts\r\n", + "-ex6 - ex10: not attempted: -50pts\",\"-ex4: does not normalize based num_chars: -4pts\r\n", + "-ex5: myfunc should be an arg to where, not written in: -5pts\r\n", + "-ex6 -ex9: not attempted: -40pts\",\"-q1-q3: should return the value, not print: -9pts\r\n", + "-q6: nicely done: +20pts\",,,\r", + "\r\n", + "1001547659,100,94,86,75,100,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: incorrect\r\n", + "-ex4: print instead of return list\r\n", + "-ex5: does not handle dupes, prints\r\n", + "-ex9: just reveses entire string\r\n", + "\",\"-ex2: incorrect output on examples (no_winner)): -2pts\r\n", + "-ex6: hard-coded size (1 to 3): -2pts\r\n", + " -ex10: not attempted: -10pts\r\n", + "- well done overall!\",\"-ex3: not caclulating bin edges correctly: should be n_bins+1 in length: -5pts\r\n", + "-ex4: not attempted: -10pts\r\n", + "-ex5: nicely done\r\n", + "-ex9: not attempted: -10pts\",\"-q6: close but output is not correct: +10pts \r\n", + "-late: -10pts\",,,\r", + "\r\n", + "1001458157,85,88,21,37,50,,,\r", + "\r\n", + ",\"-ex2: did not create any dirs\r\n", + "-ex3: did not show work\r\n", + "-ex4: did not attempt\r\n", + "\",\"-ex3: incorrect\r\n", + "-ex4: not a function\r\n", + "-ex5: not a function, incorrect\r\n", + "-ex7: does not handle draw correctly\r\n", + "-ex8: not a function\r\n", + "-ex10: does not allow user input to specify h/l\r\n", + "\r\n", + "\",\"-ex1: not a function, not attempted: -5pts\r\n", + "-ex2: not a function, hard-coded for 3x3: -7pts\r\n", + "-ex3: should be n x m, only n x n: -2pts\r\n", + "-ex4: not attempted: -10pts\r\n", + "-ex5: does not run: -5pts\r\n", + "-ex6 - ex10: not attempted: -50pts\",\"-ex2b: not supposed to use statistics functions: -8pts\r\n", + "-ex3: does not work: -5pts\r\n", + "-ex4: not attempted: -10pts\r\n", + "-ex5: supposed to take a function as argument to where: -5pts\r\n", + "-ex6: incomplete: -5pts\r\n", + "-ex7-ex9: not attempted: -30pts \r\n", + "\",\"- did not submit exam (found something in lab 4, but it was empty). Resubmitted correct format. Re-graded on 4/17/2020\r\n", + "\r\n", + "-q1: Used sort, not supposed to use sort: -15pts\r\n", + "-q2: You're supposed to find the index based on the list, not hardcoding it: -15pts\r\n", + "-q3: Again, not supposed to use built in sort function: -15pts\r\n", + "-q4: Should attribute source code: https://stackoverflow.com/a/57337824: -5pts\r\n", + "-q6: Not attempted: +0pts\",,,\r", + "\r\n", + "1001496565,100,98,100,100,120,,,\r", + "\r\n", + ",-excellent. completed everything.,\"-ex5: does not handle duplicates\r\n", + "\r\n", + "code is clean and refreshing to read\",\"-excellent.\r\n", + "\r\n", + "- SOLUTION MATCHES WITH ANOTHER STUDENT.\",-excellent.,\"- broke my grader assistant: please make sure you perform a pull request, autograder searches for dr. farbin's committed version of the exam/lab and since your submission's exam dir only had your solution (with the same name as his) it got flagged as not submitted.\r\n", + "- excellent\r\n", + "-q6: +20pts\",,,\r", + "\r\n", + "1001519928,100,94,85,50,115,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: uses argument instead of return function\r\n", + "-ex8: recursive function that returns fib(n) not a list up to n.\r\n", + "-ex9: reverses full string, incorrect\r\n", + "\",\"-ex2: doesn't handle NxN: -2pts\r\n", + "-ex4: should print 'X', 'O': -3pts\r\n", + "-ex6: nice use of ascii increment!\r\n", + "-ex10: did not attempt: -10pts\",\"-ex2b: should not be using statistics function: -8pts\r\n", + "-ex4: should be normalizing, not clipping: -2pts\r\n", + "-ex5: should be a simple booling function, where should do the logic check with the data: -5pts\r\n", + "-ex6: should be returning func (func returns func): -5pts\r\n", + "-ex7 - ex9: not attempted: -30pts\r\n", + "\r\n", + "\",\"-excellent\r\n", + "-q6: close, incorrect outputs: +15pts\r\n", + "\",,,\r", + "\r\n", + "1001774305,100,94,20,79,120,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: uses argument instead of return function\r\n", + "-ex7: draws don't auto replay, though in infinite look with prompts after each game. \r\n", + "-ex9: uses list of strings instead of string\r\n", + "-ex10: incorrect\",-ex3 - ex10: not attempted: -80pts,\"-ex3: should only update xmin and xmax if none provided (someone might want to discard outliers with this function, for ex): -2pts\r\n", + "-ex4: doesn't normalize to char_per_line in function args: -4pts\r\n", + "-ex7 - ex9: copies without attribution: -15pts\",\"-excellent\r\n", + "\",,,\r", + "\r\n", + "1001696890,0,96,0,40,100,,,\r", + "\r\n", + ",- did not submit lab 1,\"-ex5: does not handle dupes\r\n", + "-ex7: nice approach with dicts but doens't handle draw correctly\r\n", + "\",- did not submit lab 3,-ex4-ex9: not attempted: -60pts,\"-excellent\r\n", + "-q6: not attempted: +0pts\r\n", + "\",,,\r", + "\r\n", + "1001232602,80,94,79,60,90,,,\r", + "\r\n", + ",- submitted late: 2/24/2020 ,\"-ex3: uses argument instead of return function\r\n", + "-ex8: multiple fibs by n, not give fibs to n\r\n", + "-ex10: not fully correct, doesn't respect 'halving property'\",\"-ex1: not a function: -5pts\r\n", + "-ex3: does not handle n x m, ex: 3x6: -2pts\r\n", + "-ex4: does not handle nxn: -2pts\r\n", + "-ex6: does not handle nxn; -2pts\r\n", + "-ex10: did not attempt: -10pts\",\"-ex3: wrong number of bin edges: -5pts\r\n", + "-ex4: should normalize, not clip max_characters_per_line, should use bin edges, not recompute: -5pts\r\n", + "-ex5: should append indices, not values: -3pts\r\n", + "-ex6: should be a func returning a func, don't harcode values: -5pts\r\n", + "-ex7: not attempted: -8pts\r\n", + "-ex8: does not confirm mean/var: -4pts\r\n", + "-ex9: not attempted: -10pts\",\"-q1-q5: Should return, not print: -15pts\r\n", + "q6: attempted but does not run: +5pts\",,,\r", + "\r\n", + "1001464999,95,94,0,0,105,,,\r", + "\r\n", + ",- submitted lab 1 in wrong folder,\"-ex3: uses argument instead of return function\r\n", + "-ex7: does not handle draw\r\n", + "-ex10: incorrect\",- did not submit lab 3,- did not submit lab 4,\"-submitted in wrong folder: -5pts\r\n", + "-q6: not correct: +10pts\",,,\r", + "\r\n", + "1001644554,80,80,10,0,0,,,\r", + "\r\n", + ",- submitted late: 2/21/2020,\"- submitted late: 2/21/2020\r\n", + "-ex3: uses argument instead of return function\r\n", + "-ex4: not a function\r\n", + "-ex5: doesn't handle duplicates\r\n", + "-ex7: does not handle draw correctly\r\n", + "-ex10: does not allow user to indicate higher/lower\",\"-ex1: not a function, also copies rows by reference: -6pts\r\n", + "-ex2: bruteforce, does not work: -7pts\r\n", + "-ex3: hard-coded, output is error: -7pts\r\n", + "-ex4 - ex10: did not attempt: -70pts\",- did not submit lab 4,- did not submit exam,,,\r", + "\r\n", + "1001684648,0,0,0,0,0,,,\r", + "\r\n", + ",- did not submit lab 1,- did not submit lab 2,- did not submit lab 3,- did not submit lab 4,- did not submit exam,,," + ] + } + ], + "source": [ + "!cat Data-1401-Grades.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"Data-1401-Grades.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Grade KeyNo attempt/wrong: -5No attempt: -5, wrong: -2No attempt: -10, wrong: -1..-7, bonus: 0..10No attempt: -10, wrong: -1 ..-8. no bonusNo attempt: -20, wrong: -1..-15, bonus: 0..20Unnamed: 6Unnamed: 7Unnamed: 8
0Student IDLab 1Lab 2Lab 3Lab 4Exam 1Lab 5Lab 6Lab 7
110009541131008125350NaNNaNNaN
2NaNStopped at ex 5-ex1: functions print not return even/odd bool...-ex2: index errors, code ostensibly only check...-ex2b: calculates the mean every time function...- did not submit examNaNNaNNaN
310006256299587088120NaNNaNNaN
4NaN- Did not do ex 4 - count lines of 'w'\\n-ex2: does not return list, prints\\n-ex3: does...- did not submit lab 3-ex2: enhancement: could have just called mean...- excellent\\n-q6: +20pts bonusNaNNaNNaN
..............................
56NaN- submitted lab 1 in wrong folder-ex3: uses argument instead of return function...- did not submit lab 3- did not submit lab 4-submitted in wrong folder: -5pts\\n-q6: not c...NaNNaNNaN
57100164455480801000NaNNaNNaN
58NaN- submitted late: 2/21/2020- submitted late: 2/21/2020\\n-ex3: uses argume...-ex1: not a function, also copies rows by refe...- did not submit lab 4- did not submit examNaNNaNNaN
59100168464800000NaNNaNNaN
60NaN- did not submit lab 1- did not submit lab 2- did not submit lab 3- did not submit lab 4- did not submit examNaNNaNNaN
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61 rows Ă— 9 columns

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" + ], + "text/plain": [ + " Grade Key No attempt/wrong: -5 \\\n", + "0 Student ID Lab 1 \n", + "1 1000954113 100 \n", + "2 NaN Stopped at ex 5 \n", + "3 1000625629 95 \n", + "4 NaN - Did not do ex 4 - count lines of 'w'\\n \n", + ".. ... ... \n", + "56 NaN - submitted lab 1 in wrong folder \n", + "57 1001644554 80 \n", + "58 NaN - submitted late: 2/21/2020 \n", + "59 1001684648 0 \n", + "60 NaN - did not submit lab 1 \n", + "\n", + " No attempt: -5, wrong: -2 \\\n", + "0 Lab 2 \n", + "1 81 \n", + "2 -ex1: functions print not return even/odd bool... \n", + "3 87 \n", + "4 -ex2: does not return list, prints\\n-ex3: does... \n", + ".. ... \n", + "56 -ex3: uses argument instead of return function... \n", + "57 80 \n", + "58 - submitted late: 2/21/2020\\n-ex3: uses argume... \n", + "59 0 \n", + "60 - did not submit lab 2 \n", + "\n", + " No attempt: -10, wrong: -1..-7, bonus: 0..10 \\\n", + "0 Lab 3 \n", + "1 25 \n", + "2 -ex2: index errors, code ostensibly only check... \n", + "3 0 \n", + "4 - did not submit lab 3 \n", + ".. ... \n", + "56 - did not submit lab 3 \n", + "57 10 \n", + "58 -ex1: not a function, also copies rows by refe... \n", + "59 0 \n", + "60 - did not submit lab 3 \n", + "\n", + " No attempt: -10, wrong: -1 ..-8. no bonus \\\n", + "0 Lab 4 \n", + "1 35 \n", + "2 -ex2b: calculates the mean every time function... \n", + "3 88 \n", + "4 -ex2: enhancement: could have just called mean... \n", + ".. ... \n", + "56 - did not submit lab 4 \n", + "57 0 \n", + "58 - did not submit lab 4 \n", + "59 0 \n", + "60 - did not submit lab 4 \n", + "\n", + " No attempt: -20, wrong: -1..-15, bonus: 0..20 Unnamed: 6 Unnamed: 7 \\\n", + "0 Exam 1 Lab 5 Lab 6 \n", + "1 0 NaN NaN \n", + "2 - did not submit exam NaN NaN \n", + "3 120 NaN NaN \n", + "4 - excellent\\n-q6: +20pts bonus NaN NaN \n", + ".. ... ... ... \n", + "56 -submitted in wrong folder: -5pts\\n-q6: not c... NaN NaN \n", + "57 0 NaN NaN \n", + "58 - did not submit exam NaN NaN \n", + "59 0 NaN NaN \n", + "60 - did not submit exam NaN NaN \n", + "\n", + " Unnamed: 8 \n", + "0 Lab 7 \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + ".. ... \n", + "56 NaN \n", + "57 NaN \n", + "58 NaN \n", + "59 NaN \n", + "60 NaN \n", + "\n", + "[61 rows x 9 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 363 Data-1401-Grades.csv\r\n" + ] + } + ], + "source": [ + "!wc -l Data-1401-Grades.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Student ID,Lab 1,Lab 2,Lab 3,Lab 4,Exam 1,Lab 5,Lab 6,Lab 7\r", + "\r\n", + "1000954113,100,81,25,35,0,,,\r", + "\r\n", + ",Stopped at ex 5,\"-ex1: functions print not return even/odd bool\r\n", + "-ex3: does not solve problem posed in ex.\r\n", + "-ex8: no solution\r\n", + "-ex9: no solution\r\n", + "-ex10: no solution\",\"-ex2: index errors, code ostensibly only checks for 3x3 case: -5 pts\r\n", + "-ex3: board doesn't draw top/bottom borders, hard coded inputs: -5 pts\r\n", + "-ex4: Not attempted: -10 pts\r\n", + "-ex5: Does not work (no test output provided): -5 pts\r\n", + "-ex6 - ex10: Not attempted: -50 pts\r\n", + "-ex11: Not attempted: +0 pts\r\n", + "\r\n", + "\",\"-ex2b: calculates the mean every time functions is called. logic is correct but instead of recomputing the mean every time it loops over, you could store the mean in another variable (which is only calculated once). correct though. no points deducted.\r\n", + "-ex3: does not run: -5pts\r\n", + "-ex4 - ex9: not attempted: -60pts\",- did not submit exam,,,\r", + "\r\n", + "1000625629,95,87,0,88,120,,,\r", + "\r\n", + ",\"- Did not do ex 4 - count lines of 'w'\r\n", + "\",\"-ex2: does not return list, prints\r\n", + "-ex3: does not solve problem. just takes max of list and gets all other nums less than that.\r\n", + "-ex7: flipped condition on rocks and scissors\r\n", + "-ex9: uses list of words, not string of words\r\n", + "-ex10: did not attempt \",- did not submit lab 3,\"-ex2: enhancement: could have just called mean() function from earlier instead of re-writting. \r\n", + "-ex4: does not normalize (what happens to the plot if you input 2000 data points?): -4pts\r\n", + "-ex6: funcs should return funcs, odd/even not really important but should have implemented for others: -3pts\r\n", + "-ex9: supposed to use previous functions, trapezoidal is good approach but not what assignment requests: -5pts \r\n", + "\",\"- excellent\r\n", + "-q6: +20pts bonus\",,,\r", + "\r\n", + "1000847686,100,0,0,0,90,,,\r", + "\r\n", + ",Stopped at ex 5,- did not submit lab 2,- did not submit lab 3,- did not submit lab 4,\"-submitted in wrong folder: -5pts\r\n", + "-q4: does not attribute lecture 15 code: -5pts\r\n", + "-q6: not correct: +5pts\r\n", + "\",,,\r", + "\r\n", + "1001108137,90,96,0,35,90,,,\r", + "\r\n", + ",\"-ex1: did not echo $SHELL\r\n", + "-ex4: did not count passwd\",\"-ex3: does not return function, using additional argument\r\n", + "-ex8: outputs fib of n+1 and not n.\r\n", + "ACADEMIC DISHONESTY. Matches with 1001428148 \",- did not submit lab 3,\"-ex1: does not normalize between x_min and x_max: -5pts\r\n", + "-ex3: doesn't work: -5pts\r\n", + "-ex4: doesn't work: -5pts\r\n", + "-ex5 - ex9: not attempted: -50pts \",\"-q2: incorrect output: -15pts\r\n", + "-q6: incomplete: +5pts\",,,\r", + "\r\n", + "1000999851,95,94,43,0,100,,,\r", + "\r\n", + ",-ex4: did not count passwd,\"-ex3: does not return function, using additional argument\r\n", + "-ex5: does not check for duplicates when appending intersection\r\n", + "-ex7: does not loop when p1 and p2 draw\r\n", + "\",\"-ex1: Each row is referenced to each other: -5 pts\r\n", + "-ex2: Does not return -1 if incomplete: -2 pts\r\n", + "-ex6 - ex10: Not attemped: -50 pts \r\n", + "-ex11: Not attempted: +0 pts\",- did not submit lab 4,-q6: not attempted: +0pts,,,\r", + "\r\n", + "1001214491,80,82,30,0,90,,,\r", + "\r\n", + ",did not complete any assignment correctly,\"-ex1: prints not return\r\n", + "-ex2: not a function, just block of code\r\n", + "-ex3: not a function, does not return a function, does not return anything for that matter\r\n", + "-ex4: not a function, logic correct\r\n", + "-ex5: not a function, logic correct\r\n", + "-ex6: not a function, logic correct\r\n", + "-ex7: not a function, logic correct\r\n", + "-ex8: not a function, logic correct\r\n", + "-ex9: not a function, logic NOT correct. uses list not string\r\n", + "\",\"-ex1: Not a function: -5 pts\r\n", + "-ex2: Not a function. Hard-code prints for 3x3: -5 pts\r\n", + "-ex4: Same code as ex3, does not solve problem: -5 pts\r\n", + "-ex5: Does not work, takes user input but does not cast it to use it for indexing: -5 pts\r\n", + "-ex6 - ex10: Not attempted. -50 pts\r\n", + "-ex11: Not attempted: +0 pts\r\n", + "\",- did not submit lab 4,\"-q4: Does not work: -10pts\r\n", + "-q6: Not attempted: 0pts\",,,\r", + "\r\n", + "1001428148,80,90,24,0,80,,,\r", + "\r\n", + ",\"Did not do assignment.\r\n", + "\r\n", + "EDIT: He did the git stuff\r\n", + " which was not part of the grading.\r\n", + "\r\n", + "- did not do ex1-ex4. \",\"-ex3: does not return function, using additional argument\r\n", + "-ex5: doesn't return common elements, prints\r\n", + "-ex8: outputs fib of n+1 and not n.\r\n", + "-ex9: reverses entire string, not words\r\n", + "-ex10: wrote a guessing game where human guesses computers number.\r\n", + "ACADEMIC DISHONESTY. Matches with 1001108137 \",\"-ex2: did not attempt: -10pts\r\n", + "-ex3: hard-coded prints: -7pts\r\n", + "-ex4: does not solve problem: -7pts\r\n", + "-ex5: Did not do problem: -7pts\r\n", + "-ex6: Code does not run, several flow issues: -5pts\r\n", + "-ex7 - ex10: Did not attempt: -40pts\r\n", + "\",- did not submit lab 4,\"- Submitted in wrong folder: -5pts\r\n", + "-q2: does not run: -10pts\r\n", + "-q4: only works for matrixes of size 5x5: -5pts\r\n", + "-q6: not attempted: +0pts\",,,\r", + "\r\n", + "1001397199,100,94,87,12,105,,,\r", + "\r\n", + ",stopped at ex5,\"-ex3: does not return a function\r\n", + "-ex8: returns fib value at n, not the list til n, pretty obvious that user is copying recursive code off the internet without understanding what it does\r\n", + "-ex9: takes a list as input, not string\r\n", + "\",\"-ex2: hard coded for 3x3 boards: -2pts\r\n", + "-ex3: breakes for boards, i.e 6x3: -2pts\r\n", + "-ex4: does not handle NxN boards: -2pts\r\n", + "-ex5: does not handle NxN boards: -2pts\r\n", + "-ex9: good!\r\n", + "-ex10: does not work: -5pts\r\n", + "-ex11: did not attempt: +0pts\r\n", + "\",\"-ex2b: not supposed to use other package: -8pts\r\n", + "-ex3 -ex9: not attempted: -80pts\r\n", + "\",\"-q6: Incorrect output. Does not use previous functions or provided code: +5pts\r\n", + "\",,,\r", + "\r\n", + "1001372854,100,70,17,35,20,,,\r", + "\r\n", + ",stopped at ex5,- did not attempt exercise 4-10,\"-ex2: does not handle NxN boards: -3pts\r\n", + "-ex3 - ex10: did not attempt: -80pts\r\n", + "-ex11: did not attempt: +0pts\",\"-ex2b: could have just called mean function instead of copy/pasting the code into variance\r\n", + "-ex3: hard codes bin, not correct, should be appending bin_edges to list: -5pts\r\n", + "-ex4 - ex9: -60pts\",\"-q1: should only return one state, not a list of states\r\n", + "-q2 -q5: not attempted: -20*4pts\r\n", + "-q6: not attempted: +0pts\",,,\r", + "\r\n", + "1001558073,95,86,0,0,84,,,\r", + "\r\n", + ",-ex4: did not use wc to count,\"-ex3: did not return a function\r\n", + "-ex4: not a function, logic correct\r\n", + "-ex5: not a function, user input for both lists.\r\n", + "-ex6: not a function, logic correct\r\n", + "-ex7: not a function, exits if draw\r\n", + "-ex9: not a function, logic correct\r\n", + "-ex10: Reversed: Instead of the computer guessing the user is guessing.\",- did not submit lab 3,- did not submit lab 4,\"-q2: should return, not print, does not provide correct output: -3pts\r\n", + "-q3: should sort everything using q2, not sorted (you used python's sorted function in one of your first cells, i think that confused your output since list of states was sorted after that, if you remove the sorted you'll see that these functions aren't doing what is asked for in questions): -10pts\r\n", + "-q4: your numbers are correct, but it should be returned in a list of size MxN (2d matrix), not a list that is (N+M)x1 (1d list): -5pts\r\n", + "-q6: Not complete: +2pts\",,,\r", + "\r\n", + "1001593219,100,96,96,85,120,,,\r", + "\r\n", + ",-excellent. completed everything.,\"-ex1: prints, doesn't return\r\n", + "-ex3: doesn't exactly mimic ex2 as it instead takes user input.\r\n", + "\r\n", + "\",\"-ex2: should use sample input as provided: -3pts\r\n", + "-ex4: Does not draw board in correct format: -1pts (edit, I see you did this later on so I changed this from -5 to -1 for being incorrectly placed).\r\n", + "-ex11: did not attempt: +0pts\",\"-ex3: should only update xmin and xmax if none provided (someone might want to discard outliers with this function, for ex): -2pts\r\n", + "you only really need to keep track of the counts for the histogram, but won't count off points for this.\r\n", + "-ex4: looks good but didn't normalize number of '#' to 20 max (several arguments were removed from drawhist function): -4pts\r\n", + "-ex5: should pass check is an argument to 'truth' function: -2pts\r\n", + "-ex6: not using nested functions, see GTA example solutions: -5pts\r\n", + "-ex9: should not have to redefine functions to do integration: -2pts\",\"-excellent. full credit +20pts for q6 bonus.\r\n", + "-note: q1-q3: you didn't need to use alphabet to compare letters if you wanted to make strings upper or lower, you could use entry.lower().\r\n", + "\",,,\r", + "\r\n", + "1001596311,90,92,71,57,100,,,\r", + "\r\n", + ",\"-ex2: did not attempt\r\n", + "-ex4: did not count passwd\",\"-ex2: returns numbers less than or equal, and not strictly less than\r\n", + "-ex3: uses argument instead of return function\r\n", + "-ex5: does not check for duplicates\r\n", + "-ex7: does not handle draw case\r\n", + "\",\"-ex2: tie when it should have been incomplete. only works for 3x3: -2pts\r\n", + "-ex3: Does not work for boards of size: ex. 6x8: -5pts\r\n", + "-ex4: Incorrect print with sizes 4x4 for example: -2pts\r\n", + "-ex5: Logic looks okay but could not reproduce results when running cells: -5pts\r\n", + "-ex6: Logic looks okay but running the code gave errors: placeInput() missing player: -5pts\r\n", + "-ex10: Did not attempt: -10pts\r\n", + "-ex11: Did not attempt: +0pts\",\"-ex3: should check x_min and x_max seperately, won't deduct for this though. bin_edges not updated correctly: -3pts\r\n", + "-ex5: look at common-issues.ipynb, you are providing the element as index, incorrect: -5pts\r\n", + "-ex6: should be func returning func like example: -5pts\r\n", + "-ex7 -ex9: not attempted: -30pts\r\n", + "\r\n", + " \",\"-q1: should have returned 'Alabama': -5pts\r\n", + "-q4: not attributing lecture 15 for code: -5pts\r\n", + "-q6: Does not calculate x90 using functions: +10pts\r\n", + "\",,,\r", + "\r\n", + "1001608680,100,94,36,61,95,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: uses argument instead of return function\r\n", + "-ex6: not a function, logic incomplete and not correct\r\n", + "-ex9: reverses string, not words in string\r\n", + "\",\"-ex2: Does not work on most of included examples: -5pts\r\n", + "-ex3: Did not construct board: -7pts\r\n", + "-ex4: Does not construct board: -7pts\r\n", + "-ex5: Did not return or place board: -5pts\r\n", + "-ex6: Hard-coded: -5pts\r\n", + "-ex7: Errors when ran, demonstrate to TA: -5pts\r\n", + "-ex8 - ex10: Did not attempt (copying and pasting ex7 does not count): -30pts\r\n", + "-ex11: Did not attempt: +0pts\r\n", + "\",\"-ex2b: should not use numpy functions: -8pts\r\n", + "-ex4: did not attempt (copied q3): -10pts\r\n", + "-ex5: supposed to pass in function, not a value, supposed to give index, not value: -6pts\r\n", + "-ex6: supposed to have func that returns func (like provided example), see gta solutions: -5pts\r\n", + "-ex8: should be returning values up to length N, instead returns one zero: -5pts\r\n", + "-ex9: does not work: -5pts\",\"-q2: does not return correct index (states[24] == montana): -5pts\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-q4: not attributing lecture 15 for code: -5pts\r\n", + "-q6: incomplete: +5pts\r\n", + "\",,,\r", + "\r\n", + "1001618032,100,96,97,86,110,,,\r", + "\r\n", + ",stopped at ex5,\"-ex3: uses argument instead of return function\r\n", + "-ex9: input is list not string\r\n", + "\",\"-ex2: output 0 should be -1: -1pts\r\n", + "-ex3: size should be n x m: -2pts\r\n", + "-ex6: does not use pretty print with border: -5pts\r\n", + "-ex7: does not use \"\"A3\"\" notation: -5pts\r\n", + "-ex11: +10pts\",\"-ex3: should check x_min and x_max seperately, won't deduct for this though.\r\n", + "-ex4: should be normalizing by max_character_per_line: -4pts\r\n", + "-ex6: should have funcs that return funcs, see GTA example solutions: -5pts\r\n", + "-ex9: should have completed solution using generate_function, where, and in_range: -5pts\r\n", + "\",\"-q4: not attributing lecture 15 for code: -5pts\r\n", + "-q6: Nice output and plot: +20pts\",,,\r", + "\r\n", + "1001652624,100,90,0,0,0,,,\r", + "\r\n", + ",stopped at ex5,\"- in general, well commented\r\n", + "-ex3: uses argument instead of return function\r\n", + "-ex5: does not check for duplicates\r\n", + "-ex7: did not attempt\r\n", + "-ex9: uses list as input, not string\r\n", + "-ex10: did not attempt\r\n", + "\",- ACADEMIC DISHONESTY. Matches with 1001496565. I suspect this student copied off of 1001496565 since the other student had the solutions on github for several days before the deadline. This student did not have a working solution during the last lab period and 1001496565's solution is unique.,- did not submit lab 4,- did not submit exam,no longer on canvas,,\r", + "\r\n", + "1001658617,100,94,96,92,100,,,\r", + "\r\n", + ",stopped at ex5,\"-ex3: pretty close on finishing correctly. returns called function\r\n", + "-ex6: prints, doesn't return true/false if palindrome\r\n", + "-ex7: does not call again if draw\r\n", + "-ex10: logic is there but automated, user inputs number and random guessing ensues by itself. i'd count this as a correct solution though different since it doesn't allow user to explic indicate higher/lower.\r\n", + "\",\"-ex2: draw condition should be 0: -1pts\r\n", + "-ex3: output incorrect for larger boards: eg 6x8: -3pts\r\n", + "-overall: excellent!\",-ex4: not correct: -8pts,\"-excellent\r\n", + "-q6: not attempted: +0pts\",,,\r", + "\r\n", + "1001663166,95,94,38,0,82,,,\r", + "\r\n", + ",-ex2: did not complete,\"-ex3: uses argument instead of return function\r\n", + "-ex7: does not handle draw correctly\r\n", + "-ex9: returns a list not a string, though input is correct in being a string\r\n", + "\r\n", + "\",\"-ex2: does not return 2,1,0,-1, just prints, not checked against other states: -5pts\r\n", + "-ex3: does not print board (copied ex1): -10pts\r\n", + "-ex4: could not test. demonstrate to TA: -5pts\r\n", + "-ex5: does not place block: -7pts\r\n", + "-ex6: not complete: -7pts\r\n", + "-ex7: not complete: -7pts\r\n", + "-ex8: not complete: -7pts\r\n", + "-ex9: does not run, demonstrate to TA: -7pts\r\n", + "-ex10: does not run, demonstrate to TA: -7pts\",- did not submit lab 4,\"-q4: should not be using numpy for this (to use built-in functions), also does not work for lists with >3 elements: -15pts\r\n", + "-q5: does not return value: -3pts\r\n", + "-q6: not attempted: +0pts\r\n", + " \",,,\r", + "\r\n", + "1001674179,95,90,23,0,0,,,\r", + "\r\n", + ",\"-ex4: incorrect solution, did not grep for w, just counted all lines\",\"-ex2: used lambda function, not defined\r\n", + "-ex3: not correctly implemented, harcoded list\r\n", + "-ex7: does not handle tie/draw correctly\r\n", + "-ex9: incorrect, reverses full string and not the words\r\n", + "-ex10: does not work\",\"-ex1: does not return board: -3pts\r\n", + "-ex2: did not attempt: -10pts\r\n", + "-ex3: does not print correctly: -7pts\r\n", + "-ex4: does not print correctly: -7pts\r\n", + "-ex6 - ex10: not attempted: -50pts\r\n", + " \r\n", + "\",- did not submit lab 4,- did not submit exam,,,\r", + "\r\n", + "1001722244,95,100,0,0,0,,,\r", + "\r\n", + ",\"-ex4: incorrect solution, did not grep for w, just counted all lines\",-perfect. all correct.,- did not submit lab 3,- did not submit lab 4,- did not submit exam,,,\r", + "\r\n", + "1001543608,100,96,19,51,111,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: uses argument instead of return function\r\n", + "-ex10: incorrect, reversed\",\"-ex1: not a function, does not work: -7pts\r\n", + "-ex2: does not work: -5pts\r\n", + "-ex3: does not print correctly: -5pts\r\n", + "-ex4: does not run, logic not correct: -7pts\r\n", + "-ex5: gives errors, demonstrate to TA for credit: -7pts\r\n", + "-ex6 - ex10: not attempted: -50pts\",\"-ex4: does not normalize based num_chars: -4pts\r\n", + "-ex5: myfunc should be an arg to where, not written in: -5pts\r\n", + "-ex6 -ex9: not attempted: -40pts\",\"-q1-q3: should return the value, not print: -9pts\r\n", + "-q6: nicely done: +20pts\",,,\r", + "\r\n", + "1001547659,100,94,86,75,100,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: incorrect\r\n", + "-ex4: print instead of return list\r\n", + "-ex5: does not handle dupes, prints\r\n", + "-ex9: just reveses entire string\r\n", + "\",\"-ex2: incorrect output on examples (no_winner)): -2pts\r\n", + "-ex6: hard-coded size (1 to 3): -2pts\r\n", + " -ex10: not attempted: -10pts\r\n", + "- well done overall!\",\"-ex3: not caclulating bin edges correctly: should be n_bins+1 in length: -5pts\r\n", + "-ex4: not attempted: -10pts\r\n", + "-ex5: nicely done\r\n", + "-ex9: not attempted: -10pts\",\"-q6: close but output is not correct: +10pts \r\n", + "-late: -10pts\",,,\r", + "\r\n", + "1001458157,85,88,21,37,50,,,\r", + "\r\n", + ",\"-ex2: did not create any dirs\r\n", + "-ex3: did not show work\r\n", + "-ex4: did not attempt\r\n", + "\",\"-ex3: incorrect\r\n", + "-ex4: not a function\r\n", + "-ex5: not a function, incorrect\r\n", + "-ex7: does not handle draw correctly\r\n", + "-ex8: not a function\r\n", + "-ex10: does not allow user input to specify h/l\r\n", + "\r\n", + "\",\"-ex1: not a function, not attempted: -5pts\r\n", + "-ex2: not a function, hard-coded for 3x3: -7pts\r\n", + "-ex3: should be n x m, only n x n: -2pts\r\n", + "-ex4: not attempted: -10pts\r\n", + "-ex5: does not run: -5pts\r\n", + "-ex6 - ex10: not attempted: -50pts\",\"-ex2b: not supposed to use statistics functions: -8pts\r\n", + "-ex3: does not work: -5pts\r\n", + "-ex4: not attempted: -10pts\r\n", + "-ex5: supposed to take a function as argument to where: -5pts\r\n", + "-ex6: incomplete: -5pts\r\n", + "-ex7-ex9: not attempted: -30pts \r\n", + "\",\"- did not submit exam (found something in lab 4, but it was empty). Resubmitted correct format. Re-graded on 4/17/2020\r\n", + "\r\n", + "-q1: Used sort, not supposed to use sort: -15pts\r\n", + "-q2: You're supposed to find the index based on the list, not hardcoding it: -15pts\r\n", + "-q3: Again, not supposed to use built in sort function: -15pts\r\n", + "-q4: Should attribute source code: https://stackoverflow.com/a/57337824: -5pts\r\n", + "-q6: Not attempted: +0pts\",,,\r", + "\r\n", + "1001496565,100,98,100,100,120,,,\r", + "\r\n", + ",-excellent. completed everything.,\"-ex5: does not handle duplicates\r\n", + "\r\n", + "code is clean and refreshing to read\",\"-excellent.\r\n", + "\r\n", + "- SOLUTION MATCHES WITH ANOTHER STUDENT.\",-excellent.,\"- broke my grader assistant: please make sure you perform a pull request, autograder searches for dr. farbin's committed version of the exam/lab and since your submission's exam dir only had your solution (with the same name as his) it got flagged as not submitted.\r\n", + "- excellent\r\n", + "-q6: +20pts\",,,\r", + "\r\n", + "1001519928,100,94,85,50,115,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: uses argument instead of return function\r\n", + "-ex8: recursive function that returns fib(n) not a list up to n.\r\n", + "-ex9: reverses full string, incorrect\r\n", + "\",\"-ex2: doesn't handle NxN: -2pts\r\n", + "-ex4: should print 'X', 'O': -3pts\r\n", + "-ex6: nice use of ascii increment!\r\n", + "-ex10: did not attempt: -10pts\",\"-ex2b: should not be using statistics function: -8pts\r\n", + "-ex4: should be normalizing, not clipping: -2pts\r\n", + "-ex5: should be a simple booling function, where should do the logic check with the data: -5pts\r\n", + "-ex6: should be returning func (func returns func): -5pts\r\n", + "-ex7 - ex9: not attempted: -30pts\r\n", + "\r\n", + "\",\"-excellent\r\n", + "-q6: close, incorrect outputs: +15pts\r\n", + "\",,,\r", + "\r\n", + "1001774305,100,94,20,79,120,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: uses argument instead of return function\r\n", + "-ex7: draws don't auto replay, though in infinite look with prompts after each game. \r\n", + "-ex9: uses list of strings instead of string\r\n", + "-ex10: incorrect\",-ex3 - ex10: not attempted: -80pts,\"-ex3: should only update xmin and xmax if none provided (someone might want to discard outliers with this function, for ex): -2pts\r\n", + "-ex4: doesn't normalize to char_per_line in function args: -4pts\r\n", + "-ex7 - ex9: copies without attribution: -15pts\",\"-excellent\r\n", + "\",,,\r", + "\r\n", + "1001696890,0,96,0,40,100,,,\r", + "\r\n", + ",- did not submit lab 1,\"-ex5: does not handle dupes\r\n", + "-ex7: nice approach with dicts but doens't handle draw correctly\r\n", + "\",- did not submit lab 3,-ex4-ex9: not attempted: -60pts,\"-excellent\r\n", + "-q6: not attempted: +0pts\r\n", + "\",,,\r", + "\r\n", + "1001232602,80,94,79,60,90,,,\r", + "\r\n", + ",- submitted late: 2/24/2020 ,\"-ex3: uses argument instead of return function\r\n", + "-ex8: multiple fibs by n, not give fibs to n\r\n", + "-ex10: not fully correct, doesn't respect 'halving property'\",\"-ex1: not a function: -5pts\r\n", + "-ex3: does not handle n x m, ex: 3x6: -2pts\r\n", + "-ex4: does not handle nxn: -2pts\r\n", + "-ex6: does not handle nxn; -2pts\r\n", + "-ex10: did not attempt: -10pts\",\"-ex3: wrong number of bin edges: -5pts\r\n", + "-ex4: should normalize, not clip max_characters_per_line, should use bin edges, not recompute: -5pts\r\n", + "-ex5: should append indices, not values: -3pts\r\n", + "-ex6: should be a func returning a func, don't harcode values: -5pts\r\n", + "-ex7: not attempted: -8pts\r\n", + "-ex8: does not confirm mean/var: -4pts\r\n", + "-ex9: not attempted: -10pts\",\"-q1-q5: Should return, not print: -15pts\r\n", + "q6: attempted but does not run: +5pts\",,,\r", + "\r\n", + "1001464999,95,94,0,0,105,,,\r", + "\r\n", + ",- submitted lab 1 in wrong folder,\"-ex3: uses argument instead of return function\r\n", + "-ex7: does not handle draw\r\n", + "-ex10: incorrect\",- did not submit lab 3,- did not submit lab 4,\"-submitted in wrong folder: -5pts\r\n", + "-q6: not correct: +10pts\",,,\r", + "\r\n", + "1001644554,80,80,10,0,0,,,\r", + "\r\n", + ",- submitted late: 2/21/2020,\"- submitted late: 2/21/2020\r\n", + "-ex3: uses argument instead of return function\r\n", + "-ex4: not a function\r\n", + "-ex5: doesn't handle duplicates\r\n", + "-ex7: does not handle draw correctly\r\n", + "-ex10: does not allow user to indicate higher/lower\",\"-ex1: not a function, also copies rows by reference: -6pts\r\n", + "-ex2: bruteforce, does not work: -7pts\r\n", + "-ex3: hard-coded, output is error: -7pts\r\n", + "-ex4 - ex10: did not attempt: -70pts\",- did not submit lab 4,- did not submit exam,,,\r", + "\r\n", + "1001684648,0,0,0,0,0,,,\r", + "\r\n", + ",- did not submit lab 1,- did not submit lab 2,- did not submit lab 3,- did not submit lab 4,- did not submit exam,,," + ] + } + ], + "source": [ + "!tail -363 Data-1401-Grades.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "!tail -363 Data-1401-Grades.csv > Data-1401-Grades-Fixed.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Student ID,Lab 1,Lab 2,Lab 3,Lab 4,Exam 1,Lab 5,Lab 6,Lab 7\r", + "\r\n", + "1000954113,100,81,25,35,0,,,\r", + "\r\n", + ",Stopped at ex 5,\"-ex1: functions print not return even/odd bool\r\n", + "-ex3: does not solve problem posed in ex.\r\n", + "-ex8: no solution\r\n", + "-ex9: no solution\r\n", + "-ex10: no solution\",\"-ex2: index errors, code ostensibly only checks for 3x3 case: -5 pts\r\n", + "-ex3: board doesn't draw top/bottom borders, hard coded inputs: -5 pts\r\n", + "-ex4: Not attempted: -10 pts\r\n", + "-ex5: Does not work (no test output provided): -5 pts\r\n", + "-ex6 - ex10: Not attempted: -50 pts\r\n", + "-ex11: Not attempted: +0 pts\r\n", + "\r\n", + "\",\"-ex2b: calculates the mean every time functions is called. logic is correct but instead of recomputing the mean every time it loops over, you could store the mean in another variable (which is only calculated once). correct though. no points deducted.\r\n", + "-ex3: does not run: -5pts\r\n", + "-ex4 - ex9: not attempted: -60pts\",- did not submit exam,,,\r", + "\r\n", + "1000625629,95,87,0,88,120,,,\r", + "\r\n", + ",\"- Did not do ex 4 - count lines of 'w'\r\n", + "\",\"-ex2: does not return list, prints\r\n", + "-ex3: does not solve problem. just takes max of list and gets all other nums less than that.\r\n", + "-ex7: flipped condition on rocks and scissors\r\n", + "-ex9: uses list of words, not string of words\r\n", + "-ex10: did not attempt \",- did not submit lab 3,\"-ex2: enhancement: could have just called mean() function from earlier instead of re-writting. \r\n", + "-ex4: does not normalize (what happens to the plot if you input 2000 data points?): -4pts\r\n", + "-ex6: funcs should return funcs, odd/even not really important but should have implemented for others: -3pts\r\n", + "-ex9: supposed to use previous functions, trapezoidal is good approach but not what assignment requests: -5pts \r\n", + "\",\"- excellent\r\n", + "-q6: +20pts bonus\",,,\r", + "\r\n", + "1000847686,100,0,0,0,90,,,\r", + "\r\n", + ",Stopped at ex 5,- did not submit lab 2,- did not submit lab 3,- did not submit lab 4,\"-submitted in wrong folder: -5pts\r\n", + "-q4: does not attribute lecture 15 code: -5pts\r\n", + "-q6: not correct: +5pts\r\n", + "\",,,\r", + "\r\n", + "1001108137,90,96,0,35,90,,,\r", + "\r\n", + ",\"-ex1: did not echo $SHELL\r\n", + "-ex4: did not count passwd\",\"-ex3: does not return function, using additional argument\r\n", + "-ex8: outputs fib of n+1 and not n.\r\n", + "ACADEMIC DISHONESTY. Matches with 1001428148 \",- did not submit lab 3,\"-ex1: does not normalize between x_min and x_max: -5pts\r\n", + "-ex3: doesn't work: -5pts\r\n", + "-ex4: doesn't work: -5pts\r\n", + "-ex5 - ex9: not attempted: -50pts \",\"-q2: incorrect output: -15pts\r\n", + "-q6: incomplete: +5pts\",,,\r", + "\r\n", + "1000999851,95,94,43,0,100,,,\r", + "\r\n", + ",-ex4: did not count passwd,\"-ex3: does not return function, using additional argument\r\n", + "-ex5: does not check for duplicates when appending intersection\r\n", + "-ex7: does not loop when p1 and p2 draw\r\n", + "\",\"-ex1: Each row is referenced to each other: -5 pts\r\n", + "-ex2: Does not return -1 if incomplete: -2 pts\r\n", + "-ex6 - ex10: Not attemped: -50 pts \r\n", + "-ex11: Not attempted: +0 pts\",- did not submit lab 4,-q6: not attempted: +0pts,,,\r", + "\r\n", + "1001214491,80,82,30,0,90,,,\r", + "\r\n", + ",did not complete any assignment correctly,\"-ex1: prints not return\r\n", + "-ex2: not a function, just block of code\r\n", + "-ex3: not a function, does not return a function, does not return anything for that matter\r\n", + "-ex4: not a function, logic correct\r\n", + "-ex5: not a function, logic correct\r\n", + "-ex6: not a function, logic correct\r\n", + "-ex7: not a function, logic correct\r\n", + "-ex8: not a function, logic correct\r\n", + "-ex9: not a function, logic NOT correct. uses list not string\r\n", + "\",\"-ex1: Not a function: -5 pts\r\n", + "-ex2: Not a function. Hard-code prints for 3x3: -5 pts\r\n", + "-ex4: Same code as ex3, does not solve problem: -5 pts\r\n", + "-ex5: Does not work, takes user input but does not cast it to use it for indexing: -5 pts\r\n", + "-ex6 - ex10: Not attempted. -50 pts\r\n", + "-ex11: Not attempted: +0 pts\r\n", + "\",- did not submit lab 4,\"-q4: Does not work: -10pts\r\n", + "-q6: Not attempted: 0pts\",,,\r", + "\r\n", + "1001428148,80,90,24,0,80,,,\r", + "\r\n", + ",\"Did not do assignment.\r\n", + "\r\n", + "EDIT: He did the git stuff\r\n", + " which was not part of the grading.\r\n", + "\r\n", + "- did not do ex1-ex4. \",\"-ex3: does not return function, using additional argument\r\n", + "-ex5: doesn't return common elements, prints\r\n", + "-ex8: outputs fib of n+1 and not n.\r\n", + "-ex9: reverses entire string, not words\r\n", + "-ex10: wrote a guessing game where human guesses computers number.\r\n", + "ACADEMIC DISHONESTY. Matches with 1001108137 \",\"-ex2: did not attempt: -10pts\r\n", + "-ex3: hard-coded prints: -7pts\r\n", + "-ex4: does not solve problem: -7pts\r\n", + "-ex5: Did not do problem: -7pts\r\n", + "-ex6: Code does not run, several flow issues: -5pts\r\n", + "-ex7 - ex10: Did not attempt: -40pts\r\n", + "\",- did not submit lab 4,\"- Submitted in wrong folder: -5pts\r\n", + "-q2: does not run: -10pts\r\n", + "-q4: only works for matrixes of size 5x5: -5pts\r\n", + "-q6: not attempted: +0pts\",,,\r", + "\r\n", + "1001397199,100,94,87,12,105,,,\r", + "\r\n", + ",stopped at ex5,\"-ex3: does not return a function\r\n", + "-ex8: returns fib value at n, not the list til n, pretty obvious that user is copying recursive code off the internet without understanding what it does\r\n", + "-ex9: takes a list as input, not string\r\n", + "\",\"-ex2: hard coded for 3x3 boards: -2pts\r\n", + "-ex3: breakes for boards, i.e 6x3: -2pts\r\n", + "-ex4: does not handle NxN boards: -2pts\r\n", + "-ex5: does not handle NxN boards: -2pts\r\n", + "-ex9: good!\r\n", + "-ex10: does not work: -5pts\r\n", + "-ex11: did not attempt: +0pts\r\n", + "\",\"-ex2b: not supposed to use other package: -8pts\r\n", + "-ex3 -ex9: not attempted: -80pts\r\n", + "\",\"-q6: Incorrect output. Does not use previous functions or provided code: +5pts\r\n", + "\",,,\r", + "\r\n", + "1001372854,100,70,17,35,20,,,\r", + "\r\n", + ",stopped at ex5,- did not attempt exercise 4-10,\"-ex2: does not handle NxN boards: -3pts\r\n", + "-ex3 - ex10: did not attempt: -80pts\r\n", + "-ex11: did not attempt: +0pts\",\"-ex2b: could have just called mean function instead of copy/pasting the code into variance\r\n", + "-ex3: hard codes bin, not correct, should be appending bin_edges to list: -5pts\r\n", + "-ex4 - ex9: -60pts\",\"-q1: should only return one state, not a list of states\r\n", + "-q2 -q5: not attempted: -20*4pts\r\n", + "-q6: not attempted: +0pts\",,,\r", + "\r\n", + "1001558073,95,86,0,0,84,,,\r", + "\r\n", + ",-ex4: did not use wc to count,\"-ex3: did not return a function\r\n", + "-ex4: not a function, logic correct\r\n", + "-ex5: not a function, user input for both lists.\r\n", + "-ex6: not a function, logic correct\r\n", + "-ex7: not a function, exits if draw\r\n", + "-ex9: not a function, logic correct\r\n", + "-ex10: Reversed: Instead of the computer guessing the user is guessing.\",- did not submit lab 3,- did not submit lab 4,\"-q2: should return, not print, does not provide correct output: -3pts\r\n", + "-q3: should sort everything using q2, not sorted (you used python's sorted function in one of your first cells, i think that confused your output since list of states was sorted after that, if you remove the sorted you'll see that these functions aren't doing what is asked for in questions): -10pts\r\n", + "-q4: your numbers are correct, but it should be returned in a list of size MxN (2d matrix), not a list that is (N+M)x1 (1d list): -5pts\r\n", + "-q6: Not complete: +2pts\",,,\r", + "\r\n", + "1001593219,100,96,96,85,120,,,\r", + "\r\n", + ",-excellent. completed everything.,\"-ex1: prints, doesn't return\r\n", + "-ex3: doesn't exactly mimic ex2 as it instead takes user input.\r\n", + "\r\n", + "\",\"-ex2: should use sample input as provided: -3pts\r\n", + "-ex4: Does not draw board in correct format: -1pts (edit, I see you did this later on so I changed this from -5 to -1 for being incorrectly placed).\r\n", + "-ex11: did not attempt: +0pts\",\"-ex3: should only update xmin and xmax if none provided (someone might want to discard outliers with this function, for ex): -2pts\r\n", + "you only really need to keep track of the counts for the histogram, but won't count off points for this.\r\n", + "-ex4: looks good but didn't normalize number of '#' to 20 max (several arguments were removed from drawhist function): -4pts\r\n", + "-ex5: should pass check is an argument to 'truth' function: -2pts\r\n", + "-ex6: not using nested functions, see GTA example solutions: -5pts\r\n", + "-ex9: should not have to redefine functions to do integration: -2pts\",\"-excellent. full credit +20pts for q6 bonus.\r\n", + "-note: q1-q3: you didn't need to use alphabet to compare letters if you wanted to make strings upper or lower, you could use entry.lower().\r\n", + "\",,,\r", + "\r\n", + "1001596311,90,92,71,57,100,,,\r", + "\r\n", + ",\"-ex2: did not attempt\r\n", + "-ex4: did not count passwd\",\"-ex2: returns numbers less than or equal, and not strictly less than\r\n", + "-ex3: uses argument instead of return function\r\n", + "-ex5: does not check for duplicates\r\n", + "-ex7: does not handle draw case\r\n", + "\",\"-ex2: tie when it should have been incomplete. only works for 3x3: -2pts\r\n", + "-ex3: Does not work for boards of size: ex. 6x8: -5pts\r\n", + "-ex4: Incorrect print with sizes 4x4 for example: -2pts\r\n", + "-ex5: Logic looks okay but could not reproduce results when running cells: -5pts\r\n", + "-ex6: Logic looks okay but running the code gave errors: placeInput() missing player: -5pts\r\n", + "-ex10: Did not attempt: -10pts\r\n", + "-ex11: Did not attempt: +0pts\",\"-ex3: should check x_min and x_max seperately, won't deduct for this though. bin_edges not updated correctly: -3pts\r\n", + "-ex5: look at common-issues.ipynb, you are providing the element as index, incorrect: -5pts\r\n", + "-ex6: should be func returning func like example: -5pts\r\n", + "-ex7 -ex9: not attempted: -30pts\r\n", + "\r\n", + " \",\"-q1: should have returned 'Alabama': -5pts\r\n", + "-q4: not attributing lecture 15 for code: -5pts\r\n", + "-q6: Does not calculate x90 using functions: +10pts\r\n", + "\",,,\r", + "\r\n", + "1001608680,100,94,36,61,95,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: uses argument instead of return function\r\n", + "-ex6: not a function, logic incomplete and not correct\r\n", + "-ex9: reverses string, not words in string\r\n", + "\",\"-ex2: Does not work on most of included examples: -5pts\r\n", + "-ex3: Did not construct board: -7pts\r\n", + "-ex4: Does not construct board: -7pts\r\n", + "-ex5: Did not return or place board: -5pts\r\n", + "-ex6: Hard-coded: -5pts\r\n", + "-ex7: Errors when ran, demonstrate to TA: -5pts\r\n", + "-ex8 - ex10: Did not attempt (copying and pasting ex7 does not count): -30pts\r\n", + "-ex11: Did not attempt: +0pts\r\n", + "\",\"-ex2b: should not use numpy functions: -8pts\r\n", + "-ex4: did not attempt (copied q3): -10pts\r\n", + "-ex5: supposed to pass in function, not a value, supposed to give index, not value: -6pts\r\n", + "-ex6: supposed to have func that returns func (like provided example), see gta solutions: -5pts\r\n", + "-ex8: should be returning values up to length N, instead returns one zero: -5pts\r\n", + "-ex9: does not work: -5pts\",\"-q2: does not return correct index (states[24] == montana): -5pts\r\n", + "-q4: not attributing lecture 15 for code: -5pts\r\n", + "-q6: incomplete: +5pts\r\n", + "\",,,\r", + "\r\n", + "1001618032,100,96,97,86,110,,,\r", + "\r\n", + ",stopped at ex5,\"-ex3: uses argument instead of return function\r\n", + "-ex9: input is list not string\r\n", + "\",\"-ex2: output 0 should be -1: -1pts\r\n", + "-ex3: size should be n x m: -2pts\r\n", + "-ex6: does not use pretty print with border: -5pts\r\n", + "-ex7: does not use \"\"A3\"\" notation: -5pts\r\n", + "-ex11: +10pts\",\"-ex3: should check x_min and x_max seperately, won't deduct for this though.\r\n", + "-ex4: should be normalizing by max_character_per_line: -4pts\r\n", + "-ex6: should have funcs that return funcs, see GTA example solutions: -5pts\r\n", + "-ex9: should have completed solution using generate_function, where, and in_range: -5pts\r\n", + "\",\"-q4: not attributing lecture 15 for code: -5pts\r\n", + "-q6: Nice output and plot: +20pts\",,,\r", + "\r\n", + "1001652624,100,90,0,0,0,,,\r", + "\r\n", + ",stopped at ex5,\"- in general, well commented\r\n", + "-ex3: uses argument instead of return function\r\n", + "-ex5: does not check for duplicates\r\n", + "-ex7: did not attempt\r\n", + "-ex9: uses list as input, not string\r\n", + "-ex10: did not attempt\r\n", + "\",- ACADEMIC DISHONESTY. Matches with 1001496565. I suspect this student copied off of 1001496565 since the other student had the solutions on github for several days before the deadline. This student did not have a working solution during the last lab period and 1001496565's solution is unique.,- did not submit lab 4,- did not submit exam,no longer on canvas,,\r", + "\r\n", + "1001658617,100,94,96,92,100,,,\r", + "\r\n", + ",stopped at ex5,\"-ex3: pretty close on finishing correctly. returns called function\r\n", + "-ex6: prints, doesn't return true/false if palindrome\r\n", + "-ex7: does not call again if draw\r\n", + "-ex10: logic is there but automated, user inputs number and random guessing ensues by itself. i'd count this as a correct solution though different since it doesn't allow user to explic indicate higher/lower.\r\n", + "\",\"-ex2: draw condition should be 0: -1pts\r\n", + "-ex3: output incorrect for larger boards: eg 6x8: -3pts\r\n", + "-overall: excellent!\",-ex4: not correct: -8pts,\"-excellent\r\n", + "-q6: not attempted: +0pts\",,,\r", + "\r\n", + "1001663166,95,94,38,0,82,,,\r", + "\r\n", + ",-ex2: did not complete,\"-ex3: uses argument instead of return function\r\n", + "-ex7: does not handle draw correctly\r\n", + "-ex9: returns a list not a string, though input is correct in being a string\r\n", + "\r\n", + "\",\"-ex2: does not return 2,1,0,-1, just prints, not checked against other states: -5pts\r\n", + "-ex3: does not print board (copied ex1): -10pts\r\n", + "-ex4: could not test. demonstrate to TA: -5pts\r\n", + "-ex5: does not place block: -7pts\r\n", + "-ex6: not complete: -7pts\r\n", + "-ex7: not complete: -7pts\r\n", + "-ex8: not complete: -7pts\r\n", + "-ex9: does not run, demonstrate to TA: -7pts\r\n", + "-ex10: does not run, demonstrate to TA: -7pts\",- did not submit lab 4,\"-q4: should not be using numpy for this (to use built-in functions), also does not work for lists with >3 elements: -15pts\r\n", + "-q5: does not return value: -3pts\r\n", + "-q6: not attempted: +0pts\r\n", + " \",,,\r", + "\r\n", + "1001674179,95,90,23,0,0,,,\r", + "\r\n", + ",\"-ex4: incorrect solution, did not grep for w, just counted all lines\",\"-ex2: used lambda function, not defined\r\n", + "-ex3: not correctly implemented, harcoded list\r\n", + "-ex7: does not handle tie/draw correctly\r\n", + "-ex9: incorrect, reverses full string and not the words\r\n", + "-ex10: does not work\",\"-ex1: does not return board: -3pts\r\n", + "-ex2: did not attempt: -10pts\r\n", + "-ex3: does not print correctly: -7pts\r\n", + "-ex4: does not print correctly: -7pts\r\n", + "-ex6 - ex10: not attempted: -50pts\r\n", + " \r\n", + "\",- did not submit lab 4,- did not submit exam,,,\r", + "\r\n", + "1001722244,95,100,0,0,0,,,\r", + "\r\n", + ",\"-ex4: incorrect solution, did not grep for w, just counted all lines\",-perfect. all correct.,- did not submit lab 3,- did not submit lab 4,- did not submit exam,,,\r", + "\r\n", + "1001543608,100,96,19,51,111,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: uses argument instead of return function\r\n", + "-ex10: incorrect, reversed\",\"-ex1: not a function, does not work: -7pts\r\n", + "-ex2: does not work: -5pts\r\n", + "-ex3: does not print correctly: -5pts\r\n", + "-ex4: does not run, logic not correct: -7pts\r\n", + "-ex5: gives errors, demonstrate to TA for credit: -7pts\r\n", + "-ex6 - ex10: not attempted: -50pts\",\"-ex4: does not normalize based num_chars: -4pts\r\n", + "-ex5: myfunc should be an arg to where, not written in: -5pts\r\n", + "-ex6 -ex9: not attempted: -40pts\",\"-q1-q3: should return the value, not print: -9pts\r\n", + "-q6: nicely done: +20pts\",,,\r", + "\r\n", + "1001547659,100,94,86,75,100,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: incorrect\r\n", + "-ex4: print instead of return list\r\n", + "-ex5: does not handle dupes, prints\r\n", + "-ex9: just reveses entire string\r\n", + "\",\"-ex2: incorrect output on examples (no_winner)): -2pts\r\n", + "-ex6: hard-coded size (1 to 3): -2pts\r\n", + " -ex10: not attempted: -10pts\r\n", + "- well done overall!\",\"-ex3: not caclulating bin edges correctly: should be n_bins+1 in length: -5pts\r\n", + "-ex4: not attempted: -10pts\r\n", + "-ex5: nicely done\r\n", + "-ex9: not attempted: -10pts\",\"-q6: close but output is not correct: +10pts \r\n", + "-late: -10pts\",,,\r", + "\r\n", + "1001458157,85,88,21,37,50,,,\r", + "\r\n", + ",\"-ex2: did not create any dirs\r\n", + "-ex3: did not show work\r\n", + "-ex4: did not attempt\r\n", + "\",\"-ex3: incorrect\r\n", + "-ex4: not a function\r\n", + "-ex5: not a function, incorrect\r\n", + "-ex7: does not handle draw correctly\r\n", + "-ex8: not a function\r\n", + "-ex10: does not allow user input to specify h/l\r\n", + "\r\n", + "\",\"-ex1: not a function, not attempted: -5pts\r\n", + "-ex2: not a function, hard-coded for 3x3: -7pts\r\n", + "-ex3: should be n x m, only n x n: -2pts\r\n", + "-ex4: not attempted: -10pts\r\n", + "-ex5: does not run: -5pts\r\n", + "-ex6 - ex10: not attempted: -50pts\",\"-ex2b: not supposed to use statistics functions: -8pts\r\n", + "-ex3: does not work: -5pts\r\n", + "-ex4: not attempted: -10pts\r\n", + "-ex5: supposed to take a function as argument to where: -5pts\r\n", + "-ex6: incomplete: -5pts\r\n", + "-ex7-ex9: not attempted: -30pts \r\n", + "\",\"- did not submit exam (found something in lab 4, but it was empty). Resubmitted correct format. Re-graded on 4/17/2020\r\n", + "\r\n", + "-q1: Used sort, not supposed to use sort: -15pts\r\n", + "-q2: You're supposed to find the index based on the list, not hardcoding it: -15pts\r\n", + "-q3: Again, not supposed to use built in sort function: -15pts\r\n", + "-q4: Should attribute source code: https://stackoverflow.com/a/57337824: -5pts\r\n", + "-q6: Not attempted: +0pts\",,,\r", + "\r\n", + "1001496565,100,98,100,100,120,,,\r", + "\r\n", + ",-excellent. completed everything.,\"-ex5: does not handle duplicates\r\n", + "\r\n", + "code is clean and refreshing to read\",\"-excellent.\r\n", + "\r\n", + "- SOLUTION MATCHES WITH ANOTHER STUDENT.\",-excellent.,\"- broke my grader assistant: please make sure you perform a pull request, autograder searches for dr. farbin's committed version of the exam/lab and since your submission's exam dir only had your solution (with the same name as his) it got flagged as not submitted.\r\n", + "- excellent\r\n", + "-q6: +20pts\",,,\r", + "\r\n", + "1001519928,100,94,85,50,115,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: uses argument instead of return function\r\n", + "-ex8: recursive function that returns fib(n) not a list up to n.\r\n", + "-ex9: reverses full string, incorrect\r\n", + "\",\"-ex2: doesn't handle NxN: -2pts\r\n", + "-ex4: should print 'X', 'O': -3pts\r\n", + "-ex6: nice use of ascii increment!\r\n", + "-ex10: did not attempt: -10pts\",\"-ex2b: should not be using statistics function: -8pts\r\n", + "-ex4: should be normalizing, not clipping: -2pts\r\n", + "-ex5: should be a simple booling function, where should do the logic check with the data: -5pts\r\n", + "-ex6: should be returning func (func returns func): -5pts\r\n", + "-ex7 - ex9: not attempted: -30pts\r\n", + "\r\n", + "\",\"-excellent\r\n", + "-q6: close, incorrect outputs: +15pts\r\n", + "\",,,\r", + "\r\n", + "1001774305,100,94,20,79,120,,,\r", + "\r\n", + ",\"-attempted ex5, completed everything before\",\"-ex3: uses argument instead of return function\r\n", + "-ex7: draws don't auto replay, though in infinite look with prompts after each game. \r\n", + "-ex9: uses list of strings instead of string\r\n", + "-ex10: incorrect\",-ex3 - ex10: not attempted: -80pts,\"-ex3: should only update xmin and xmax if none provided (someone might want to discard outliers with this function, for ex): -2pts\r\n", + "-ex4: doesn't normalize to char_per_line in function args: -4pts\r\n", + "-ex7 - ex9: copies without attribution: -15pts\",\"-excellent\r\n", + "\",,,\r", + "\r\n", + "1001696890,0,96,0,40,100,,,\r", + "\r\n", + ",- did not submit lab 1,\"-ex5: does not handle dupes\r\n", + "-ex7: nice approach with dicts but doens't handle draw correctly\r\n", + "\",- did not submit lab 3,-ex4-ex9: not attempted: -60pts,\"-excellent\r\n", + "-q6: not attempted: +0pts\r\n", + "\",,,\r", + "\r\n", + "1001232602,80,94,79,60,90,,,\r", + "\r\n", + ",- submitted late: 2/24/2020 ,\"-ex3: uses argument instead of return function\r\n", + "-ex8: multiple fibs by n, not give fibs to n\r\n", + "-ex10: not fully correct, doesn't respect 'halving property'\",\"-ex1: not a function: -5pts\r\n", + "-ex3: does not handle n x m, ex: 3x6: -2pts\r\n", + "-ex4: does not handle nxn: -2pts\r\n", + "-ex6: does not handle nxn; -2pts\r\n", + "-ex10: did not attempt: -10pts\",\"-ex3: wrong number of bin edges: -5pts\r\n", + "-ex4: should normalize, not clip max_characters_per_line, should use bin edges, not recompute: -5pts\r\n", + "-ex5: should append indices, not values: -3pts\r\n", + "-ex6: should be a func returning a func, don't harcode values: -5pts\r\n", + "-ex7: not attempted: -8pts\r\n", + "-ex8: does not confirm mean/var: -4pts\r\n", + "-ex9: not attempted: -10pts\",\"-q1-q5: Should return, not print: -15pts\r\n", + "q6: attempted but does not run: +5pts\",,,\r", + "\r\n", + "1001464999,95,94,0,0,105,,,\r", + "\r\n", + ",- submitted lab 1 in wrong folder,\"-ex3: uses argument instead of return function\r\n", + "-ex7: does not handle draw\r\n", + "-ex10: incorrect\",- did not submit lab 3,- did not submit lab 4,\"-submitted in wrong folder: -5pts\r\n", + "-q6: not correct: +10pts\",,,\r", + "\r\n", + "1001644554,80,80,10,0,0,,,\r", + "\r\n", + ",- submitted late: 2/21/2020,\"- submitted late: 2/21/2020\r\n", + "-ex3: uses argument instead of return function\r\n", + "-ex4: not a function\r\n", + "-ex5: doesn't handle duplicates\r\n", + "-ex7: does not handle draw correctly\r\n", + "-ex10: does not allow user to indicate higher/lower\",\"-ex1: not a function, also copies rows by reference: -6pts\r\n", + "-ex2: bruteforce, does not work: -7pts\r\n", + "-ex3: hard-coded, output is error: -7pts\r\n", + "-ex4 - ex10: did not attempt: -70pts\",- did not submit lab 4,- did not submit exam,,,\r", + "\r\n", + "1001684648,0,0,0,0,0,,,\r", + "\r\n", + ",- did not submit lab 1,- did not submit lab 2,- did not submit lab 3,- did not submit lab 4,- did not submit exam,,," + ] + } + ], + "source": [ + "!cat Data-1401-Grades-Fixed.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"Data-1401-Grades-Fixed.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Student IDLab 1Lab 2Lab 3Lab 4Exam 1Lab 5Lab 6Lab 7
01.000954e+091008125350NaNNaNNaN
1NaNStopped at ex 5-ex1: functions print not return even/odd bool...-ex2: index errors, code ostensibly only check...-ex2b: calculates the mean every time function...- did not submit examNaNNaNNaN
21.000626e+099587088120NaNNaNNaN
3NaN- Did not do ex 4 - count lines of 'w'\\n-ex2: does not return list, prints\\n-ex3: does...- did not submit lab 3-ex2: enhancement: could have just called mean...- excellent\\n-q6: +20pts bonusNaNNaNNaN
41.000848e+0910000090NaNNaNNaN
5NaNStopped at ex 5- did not submit lab 2- did not submit lab 3- did not submit lab 4-submitted in wrong folder: -5pts\\n-q4: does n...NaNNaNNaN
61.001108e+09909603590NaNNaNNaN
7NaN-ex1: did not echo $SHELL\\n-ex4: did not count...-ex3: does not return function, using addition...- did not submit lab 3-ex1: does not normalize between x_min and x_m...-q2: incorrect output: -15pts\\n-q6: incomplete...NaNNaNNaN
81.001000e+099594430100NaNNaNNaN
9NaN-ex4: did not count passwd-ex3: does not return function, using addition...-ex1: Each row is referenced to each other: -5...- did not submit lab 4-q6: not attempted: +0ptsNaNNaNNaN
101.001214e+09808230090NaNNaNNaN
11NaNdid not complete any assignment correctly-ex1: prints not return\\n-ex2: not a function,...-ex1: Not a function: -5 pts\\n-ex2: Not a func...- did not submit lab 4-q4: Does not work: -10pts\\n-q6: Not attempted...NaNNaNNaN
121.001428e+09809024080NaNNaNNaN
13NaNDid not do assignment.\\n\\nEDIT: He did the git...-ex3: does not return function, using addition...-ex2: did not attempt: -10pts\\n-ex3: hard-code...- did not submit lab 4- Submitted in wrong folder: -5pts\\n-q2: does ...NaNNaNNaN
141.001397e+09100948712105NaNNaNNaN
15NaNstopped at ex5-ex3: does not return a function\\n-ex8: return...-ex2: hard coded for 3x3 boards: -2pts\\n-ex3: ...-ex2b: not supposed to use other package: -8pt...-q6: Incorrect output. Does not use previous f...NaNNaNNaN
161.001373e+0910070173520NaNNaNNaN
17NaNstopped at ex5- did not attempt exercise 4-10-ex2: does not handle NxN boards: -3pts\\n-ex3 ...-ex2b: could have just called mean function in...-q1: should only return one state, not a list ...NaNNaNNaN
181.001558e+0995860084NaNNaNNaN
19NaN-ex4: did not use wc to count-ex3: did not return a function\\n-ex4: not a f...- did not submit lab 3- did not submit lab 4-q2: should return, not print, does not provid...NaNNaNNaN
201.001593e+09100969685120NaNNaNNaN
21NaN-excellent. completed everything.-ex1: prints, doesn't return\\n-ex3: doesn't ex...-ex2: should use sample input as provided: -3p...-ex3: should only update xmin and xmax if none...-excellent. full credit +20pts for q6 bonus.\\n...NaNNaNNaN
221.001596e+0990927157100NaNNaNNaN
23NaN-ex2: did not attempt\\n-ex4: did not count passwd-ex2: returns numbers less than or equal, and ...-ex2: tie when it should have been incomplete....-ex3: should check x_min and x_max seperately,...-q1: should have returned 'Alabama': -5pts\\n-q...NaNNaNNaN
241.001609e+0910094366195NaNNaNNaN
25NaN-attempted ex5, completed everything before-ex3: uses argument instead of return function...-ex2: Does not work on most of included exampl...-ex2b: should not use numpy functions: -8pts\\n...-q2: does not return correct index (states[24]...NaNNaNNaN
261.001618e+09100969786110NaNNaNNaN
27NaNstopped at ex5-ex3: uses argument instead of return function...-ex2: output 0 should be -1: -1pts\\n-ex3: size...-ex3: should check x_min and x_max seperately,...-q4: not attributing lecture 15 for code: -5pt...NaNNaNNaN
281.001653e+0910090000NaNNaNNaN
29NaNstopped at ex5- in general, well commented\\n-ex3: uses argum...- ACADEMIC DISHONESTY. Matches with 1001496565...- did not submit lab 4- did not submit examno longer on canvasNaNNaN
301.001659e+09100949692100NaNNaNNaN
31NaNstopped at ex5-ex3: pretty close on finishing correctly. ret...-ex2: draw condition should be 0: -1pts\\n-ex3:...-ex4: not correct: -8pts-excellent\\n-q6: not attempted: +0ptsNaNNaNNaN
321.001663e+09959438082NaNNaNNaN
33NaN-ex2: did not complete-ex3: uses argument instead of return function...-ex2: does not return 2,1,0,-1, just prints, n...- did not submit lab 4-q4: should not be using numpy for this (to us...NaNNaNNaN
341.001674e+0995902300NaNNaNNaN
35NaN-ex4: incorrect solution, did not grep for w, ...-ex2: used lambda function, not defined\\n-ex3:...-ex1: does not return board: -3pts\\n-ex2: did ...- did not submit lab 4- did not submit examNaNNaNNaN
361.001722e+0995100000NaNNaNNaN
37NaN-ex4: incorrect solution, did not grep for w, ...-perfect. all correct.- did not submit lab 3- did not submit lab 4- did not submit examNaNNaNNaN
381.001544e+09100961951111NaNNaNNaN
39NaN-attempted ex5, completed everything before-ex3: uses argument instead of return function...-ex1: not a function, does not work: -7pts\\n-e...-ex4: does not normalize based num_chars: -4pt...-q1-q3: should return the value, not print: -9...NaNNaNNaN
401.001548e+09100948675100NaNNaNNaN
41NaN-attempted ex5, completed everything before-ex3: incorrect\\n-ex4: print instead of return...-ex2: incorrect output on examples (no_winner)...-ex3: not caclulating bin edges correctly: sho...-q6: close but output is not correct: +10pts \\...NaNNaNNaN
421.001458e+098588213750NaNNaNNaN
43NaN-ex2: did not create any dirs\\n-ex3: did not s...-ex3: incorrect\\n-ex4: not a function\\n-ex5: n...-ex1: not a function, not attempted: -5pts\\n-e...-ex2b: not supposed to use statistics function...- did not submit exam (found something in lab ...NaNNaNNaN
441.001497e+0910098100100120NaNNaNNaN
45NaN-excellent. completed everything.-ex5: does not handle duplicates\\n\\ncode is cl...-excellent.\\n\\n- SOLUTION MATCHES WITH ANOTHER...-excellent.- broke my grader assistant: please make sure ...NaNNaNNaN
461.001520e+09100948550115NaNNaNNaN
47NaN-attempted ex5, completed everything before-ex3: uses argument instead of return function...-ex2: doesn't handle NxN: -2pts\\n-ex4: should ...-ex2b: should not be using statistics function...-excellent\\n-q6: close, incorrect outputs: +15...NaNNaNNaN
481.001774e+09100942079120NaNNaNNaN
49NaN-attempted ex5, completed everything before-ex3: uses argument instead of return function...-ex3 - ex10: not attempted: -80pts-ex3: should only update xmin and xmax if none...-excellent\\nNaNNaNNaN
501.001697e+09096040100NaNNaNNaN
51NaN- did not submit lab 1-ex5: does not handle dupes\\n-ex7: nice approa...- did not submit lab 3-ex4-ex9: not attempted: -60pts-excellent\\n-q6: not attempted: +0pts\\nNaNNaNNaN
521.001233e+098094796090NaNNaNNaN
53NaN- submitted late: 2/24/2020-ex3: uses argument instead of return function...-ex1: not a function: -5pts\\n-ex3: does not ha...-ex3: wrong number of bin edges: -5pts\\n-ex4: ...-q1-q5: Should return, not print: -15pts\\nq6: ...NaNNaNNaN
541.001465e+09959400105NaNNaNNaN
55NaN- submitted lab 1 in wrong folder-ex3: uses argument instead of return function...- did not submit lab 3- did not submit lab 4-submitted in wrong folder: -5pts\\n-q6: not c...NaNNaNNaN
561.001645e+0980801000NaNNaNNaN
57NaN- submitted late: 2/21/2020- submitted late: 2/21/2020\\n-ex3: uses argume...-ex1: not a function, also copies rows by refe...- did not submit lab 4- did not submit examNaNNaNNaN
581.001685e+0900000NaNNaNNaN
59NaN- did not submit lab 1- did not submit lab 2- did not submit lab 3- did not submit lab 4- did not submit examNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " Student ID Lab 1 \\\n", + "0 1.000954e+09 100 \n", + "1 NaN Stopped at ex 5 \n", + "2 1.000626e+09 95 \n", + "3 NaN - Did not do ex 4 - count lines of 'w'\\n \n", + "4 1.000848e+09 100 \n", + "5 NaN Stopped at ex 5 \n", + "6 1.001108e+09 90 \n", + "7 NaN -ex1: did not echo $SHELL\\n-ex4: did not count... \n", + "8 1.001000e+09 95 \n", + "9 NaN -ex4: did not count passwd \n", + "10 1.001214e+09 80 \n", + "11 NaN did not complete any assignment correctly \n", + "12 1.001428e+09 80 \n", + "13 NaN Did not do assignment.\\n\\nEDIT: He did the git... \n", + "14 1.001397e+09 100 \n", + "15 NaN stopped at ex5 \n", + "16 1.001373e+09 100 \n", + "17 NaN stopped at ex5 \n", + "18 1.001558e+09 95 \n", + "19 NaN -ex4: did not use wc to count \n", + "20 1.001593e+09 100 \n", + "21 NaN -excellent. completed everything. \n", + "22 1.001596e+09 90 \n", + "23 NaN -ex2: did not attempt\\n-ex4: did not count passwd \n", + "24 1.001609e+09 100 \n", + "25 NaN -attempted ex5, completed everything before \n", + "26 1.001618e+09 100 \n", + "27 NaN stopped at ex5 \n", + "28 1.001653e+09 100 \n", + "29 NaN stopped at ex5 \n", + "30 1.001659e+09 100 \n", + "31 NaN stopped at ex5 \n", + "32 1.001663e+09 95 \n", + "33 NaN -ex2: did not complete \n", + "34 1.001674e+09 95 \n", + "35 NaN -ex4: incorrect solution, did not grep for w, ... \n", + "36 1.001722e+09 95 \n", + "37 NaN -ex4: incorrect solution, did not grep for w, ... \n", + "38 1.001544e+09 100 \n", + "39 NaN -attempted ex5, completed everything before \n", + "40 1.001548e+09 100 \n", + "41 NaN -attempted ex5, completed everything before \n", + "42 1.001458e+09 85 \n", + "43 NaN -ex2: did not create any dirs\\n-ex3: did not s... \n", + "44 1.001497e+09 100 \n", + "45 NaN -excellent. completed everything. \n", + "46 1.001520e+09 100 \n", + "47 NaN -attempted ex5, completed everything before \n", + "48 1.001774e+09 100 \n", + "49 NaN -attempted ex5, completed everything before \n", + "50 1.001697e+09 0 \n", + "51 NaN - did not submit lab 1 \n", + "52 1.001233e+09 80 \n", + "53 NaN - submitted late: 2/24/2020 \n", + "54 1.001465e+09 95 \n", + "55 NaN - submitted lab 1 in wrong folder \n", + "56 1.001645e+09 80 \n", + "57 NaN - submitted late: 2/21/2020 \n", + "58 1.001685e+09 0 \n", + "59 NaN - did not submit lab 1 \n", + "\n", + " Lab 2 \\\n", + "0 81 \n", + "1 -ex1: functions print not return even/odd bool... \n", + "2 87 \n", + "3 -ex2: does not return list, prints\\n-ex3: does... \n", + "4 0 \n", + "5 - did not submit lab 2 \n", + "6 96 \n", + "7 -ex3: does not return function, using addition... \n", + "8 94 \n", + "9 -ex3: does not return function, using addition... \n", + "10 82 \n", + "11 -ex1: prints not return\\n-ex2: not a function,... \n", + "12 90 \n", + "13 -ex3: does not return function, using addition... \n", + "14 94 \n", + "15 -ex3: does not return a function\\n-ex8: return... \n", + "16 70 \n", + "17 - did not attempt exercise 4-10 \n", + "18 86 \n", + "19 -ex3: did not return a function\\n-ex4: not a f... \n", + "20 96 \n", + "21 -ex1: prints, doesn't return\\n-ex3: doesn't ex... \n", + "22 92 \n", + "23 -ex2: returns numbers less than or equal, and ... \n", + "24 94 \n", + "25 -ex3: uses argument instead of return function... \n", + "26 96 \n", + "27 -ex3: uses argument instead of return function... \n", + "28 90 \n", + "29 - in general, well commented\\n-ex3: uses argum... \n", + "30 94 \n", + "31 -ex3: pretty close on finishing correctly. ret... \n", + "32 94 \n", + "33 -ex3: uses argument instead of return function... \n", + "34 90 \n", + "35 -ex2: used lambda function, not defined\\n-ex3:... \n", + "36 100 \n", + "37 -perfect. all correct. \n", + "38 96 \n", + "39 -ex3: uses argument instead of return function... \n", + "40 94 \n", + "41 -ex3: incorrect\\n-ex4: print instead of return... \n", + "42 88 \n", + "43 -ex3: incorrect\\n-ex4: not a function\\n-ex5: n... \n", + "44 98 \n", + "45 -ex5: does not handle duplicates\\n\\ncode is cl... \n", + "46 94 \n", + "47 -ex3: uses argument instead of return function... \n", + "48 94 \n", + "49 -ex3: uses argument instead of return function... \n", + "50 96 \n", + "51 -ex5: does not handle dupes\\n-ex7: nice approa... \n", + "52 94 \n", + "53 -ex3: uses argument instead of return function... \n", + "54 94 \n", + "55 -ex3: uses argument instead of return function... \n", + "56 80 \n", + "57 - submitted late: 2/21/2020\\n-ex3: uses argume... \n", + "58 0 \n", + "59 - did not submit lab 2 \n", + "\n", + " Lab 3 \\\n", + "0 25 \n", + "1 -ex2: index errors, code ostensibly only check... \n", + "2 0 \n", + "3 - did not submit lab 3 \n", + "4 0 \n", + "5 - did not submit lab 3 \n", + "6 0 \n", + "7 - did not submit lab 3 \n", + "8 43 \n", + "9 -ex1: Each row is referenced to each other: -5... \n", + "10 30 \n", + "11 -ex1: Not a function: -5 pts\\n-ex2: Not a func... \n", + "12 24 \n", + "13 -ex2: did not attempt: -10pts\\n-ex3: hard-code... \n", + "14 87 \n", + "15 -ex2: hard coded for 3x3 boards: -2pts\\n-ex3: ... \n", + "16 17 \n", + "17 -ex2: does not handle NxN boards: -3pts\\n-ex3 ... \n", + "18 0 \n", + "19 - did not submit lab 3 \n", + "20 96 \n", + "21 -ex2: should use sample input as provided: -3p... \n", + "22 71 \n", + "23 -ex2: tie when it should have been incomplete.... \n", + "24 36 \n", + "25 -ex2: Does not work on most of included exampl... \n", + "26 97 \n", + "27 -ex2: output 0 should be -1: -1pts\\n-ex3: size... \n", + "28 0 \n", + "29 - ACADEMIC DISHONESTY. Matches with 1001496565... \n", + "30 96 \n", + "31 -ex2: draw condition should be 0: -1pts\\n-ex3:... \n", + "32 38 \n", + "33 -ex2: does not return 2,1,0,-1, just prints, n... \n", + "34 23 \n", + "35 -ex1: does not return board: -3pts\\n-ex2: did ... \n", + "36 0 \n", + "37 - did not submit lab 3 \n", + "38 19 \n", + "39 -ex1: not a function, does not work: -7pts\\n-e... \n", + "40 86 \n", + "41 -ex2: incorrect output on examples (no_winner)... \n", + "42 21 \n", + "43 -ex1: not a function, not attempted: -5pts\\n-e... \n", + "44 100 \n", + "45 -excellent.\\n\\n- SOLUTION MATCHES WITH ANOTHER... \n", + "46 85 \n", + "47 -ex2: doesn't handle NxN: -2pts\\n-ex4: should ... \n", + "48 20 \n", + "49 -ex3 - ex10: not attempted: -80pts \n", + "50 0 \n", + "51 - did not submit lab 3 \n", + "52 79 \n", + "53 -ex1: not a function: -5pts\\n-ex3: does not ha... \n", + "54 0 \n", + "55 - did not submit lab 3 \n", + "56 10 \n", + "57 -ex1: not a function, also copies rows by refe... \n", + "58 0 \n", + "59 - did not submit lab 3 \n", + "\n", + " Lab 4 \\\n", + "0 35 \n", + "1 -ex2b: calculates the mean every time function... \n", + "2 88 \n", + "3 -ex2: enhancement: could have just called mean... \n", + "4 0 \n", + "5 - did not submit lab 4 \n", + "6 35 \n", + "7 -ex1: does not normalize between x_min and x_m... \n", + "8 0 \n", + "9 - did not submit lab 4 \n", + "10 0 \n", + "11 - did not submit lab 4 \n", + "12 0 \n", + "13 - did not submit lab 4 \n", + "14 12 \n", + "15 -ex2b: not supposed to use other package: -8pt... \n", + "16 35 \n", + "17 -ex2b: could have just called mean function in... \n", + "18 0 \n", + "19 - did not submit lab 4 \n", + "20 85 \n", + "21 -ex3: should only update xmin and xmax if none... \n", + "22 57 \n", + "23 -ex3: should check x_min and x_max seperately,... \n", + "24 61 \n", + "25 -ex2b: should not use numpy functions: -8pts\\n... \n", + "26 86 \n", + "27 -ex3: should check x_min and x_max seperately,... \n", + "28 0 \n", + "29 - did not submit lab 4 \n", + "30 92 \n", + "31 -ex4: not correct: -8pts \n", + "32 0 \n", + "33 - did not submit lab 4 \n", + "34 0 \n", + "35 - did not submit lab 4 \n", + "36 0 \n", + "37 - did not submit lab 4 \n", + "38 51 \n", + "39 -ex4: does not normalize based num_chars: -4pt... \n", + "40 75 \n", + "41 -ex3: not caclulating bin edges correctly: sho... \n", + "42 37 \n", + "43 -ex2b: not supposed to use statistics function... \n", + "44 100 \n", + "45 -excellent. \n", + "46 50 \n", + "47 -ex2b: should not be using statistics function... \n", + "48 79 \n", + "49 -ex3: should only update xmin and xmax if none... \n", + "50 40 \n", + "51 -ex4-ex9: not attempted: -60pts \n", + "52 60 \n", + "53 -ex3: wrong number of bin edges: -5pts\\n-ex4: ... \n", + "54 0 \n", + "55 - did not submit lab 4 \n", + "56 0 \n", + "57 - did not submit lab 4 \n", + "58 0 \n", + "59 - did not submit lab 4 \n", + "\n", + " Exam 1 Lab 5 \\\n", + "0 0 NaN \n", + "1 - did not submit exam NaN \n", + "2 120 NaN \n", + "3 - excellent\\n-q6: +20pts bonus NaN \n", + "4 90 NaN \n", + "5 -submitted in wrong folder: -5pts\\n-q4: does n... NaN \n", + "6 90 NaN \n", + "7 -q2: incorrect output: -15pts\\n-q6: incomplete... NaN \n", + "8 100 NaN \n", + "9 -q6: not attempted: +0pts NaN \n", + "10 90 NaN \n", + "11 -q4: Does not work: -10pts\\n-q6: Not attempted... NaN \n", + "12 80 NaN \n", + "13 - Submitted in wrong folder: -5pts\\n-q2: does ... NaN \n", + "14 105 NaN \n", + "15 -q6: Incorrect output. Does not use previous f... NaN \n", + "16 20 NaN \n", + "17 -q1: should only return one state, not a list ... NaN \n", + "18 84 NaN \n", + "19 -q2: should return, not print, does not provid... NaN \n", + "20 120 NaN \n", + "21 -excellent. full credit +20pts for q6 bonus.\\n... NaN \n", + "22 100 NaN \n", + "23 -q1: should have returned 'Alabama': -5pts\\n-q... NaN \n", + "24 95 NaN \n", + "25 -q2: does not return correct index (states[24]... NaN \n", + "26 110 NaN \n", + "27 -q4: not attributing lecture 15 for code: -5pt... NaN \n", + "28 0 NaN \n", + "29 - did not submit exam no longer on canvas \n", + "30 100 NaN \n", + "31 -excellent\\n-q6: not attempted: +0pts NaN \n", + "32 82 NaN \n", + "33 -q4: should not be using numpy for this (to us... NaN \n", + "34 0 NaN \n", + "35 - did not submit exam NaN \n", + "36 0 NaN \n", + "37 - did not submit exam NaN \n", + "38 111 NaN \n", + "39 -q1-q3: should return the value, not print: -9... NaN \n", + "40 100 NaN \n", + "41 -q6: close but output is not correct: +10pts \\... NaN \n", + "42 50 NaN \n", + "43 - did not submit exam (found something in lab ... NaN \n", + "44 120 NaN \n", + "45 - broke my grader assistant: please make sure ... NaN \n", + "46 115 NaN \n", + "47 -excellent\\n-q6: close, incorrect outputs: +15... NaN \n", + "48 120 NaN \n", + "49 -excellent\\n NaN \n", + "50 100 NaN \n", + "51 -excellent\\n-q6: not attempted: +0pts\\n NaN \n", + "52 90 NaN \n", + "53 -q1-q5: Should return, not print: -15pts\\nq6: ... NaN \n", + "54 105 NaN \n", + "55 -submitted in wrong folder: -5pts\\n-q6: not c... NaN \n", + "56 0 NaN \n", + "57 - did not submit exam NaN \n", + "58 0 NaN \n", + "59 - did not submit exam NaN \n", + "\n", + " Lab 6 Lab 7 \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 NaN NaN \n", + "14 NaN NaN \n", + "15 NaN NaN \n", + "16 NaN NaN \n", + "17 NaN NaN \n", + "18 NaN NaN \n", + "19 NaN NaN \n", + "20 NaN NaN \n", + "21 NaN NaN \n", + "22 NaN NaN \n", + "23 NaN NaN \n", + "24 NaN NaN \n", + "25 NaN NaN \n", + "26 NaN NaN \n", + "27 NaN NaN \n", + "28 NaN NaN \n", + "29 NaN NaN \n", + "30 NaN NaN \n", + "31 NaN NaN \n", + "32 NaN NaN \n", + "33 NaN NaN \n", + "34 NaN NaN \n", + "35 NaN NaN \n", + "36 NaN NaN \n", + "37 NaN NaN \n", + "38 NaN NaN \n", + "39 NaN NaN \n", + "40 NaN NaN \n", + "41 NaN NaN \n", + "42 NaN NaN \n", + "43 NaN NaN \n", + "44 NaN NaN \n", + "45 NaN 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Student IDLab 1Lab 2Lab 3Lab 4Exam 1Lab 5Lab 6Lab 7
01.000954e+091008125350NaNNaNNaN
21.000626e+099587088120NaNNaNNaN
41.000848e+0910000090NaNNaNNaN
61.001108e+09909603590NaNNaNNaN
81.001000e+099594430100NaNNaNNaN
101.001214e+09808230090NaNNaNNaN
121.001428e+09809024080NaNNaNNaN
141.001397e+09100948712105NaNNaNNaN
161.001373e+0910070173520NaNNaNNaN
181.001558e+0995860084NaNNaNNaN
201.001593e+09100969685120NaNNaNNaN
221.001596e+0990927157100NaNNaNNaN
241.001609e+0910094366195NaNNaNNaN
261.001618e+09100969786110NaNNaNNaN
281.001653e+0910090000NaNNaNNaN
301.001659e+09100949692100NaNNaNNaN
321.001663e+09959438082NaNNaNNaN
341.001674e+0995902300NaNNaNNaN
361.001722e+0995100000NaNNaNNaN
381.001544e+09100961951111NaNNaNNaN
401.001548e+09100948675100NaNNaNNaN
421.001458e+098588213750NaNNaNNaN
441.001497e+0910098100100120NaNNaNNaN
461.001520e+09100948550115NaNNaNNaN
481.001774e+09100942079120NaNNaNNaN
501.001697e+09096040100NaNNaNNaN
521.001233e+098094796090NaNNaNNaN
541.001465e+09959400105NaNNaNNaN
561.001645e+0980801000NaNNaNNaN
581.001685e+0900000NaNNaNNaN
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{ + "data": { + "text/plain": [ + "'0'" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_0[\"Exam 1\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "2 120\n", + "4 90\n", + "6 90\n", + "8 100\n", + "10 90\n", + "12 80\n", + "14 105\n", + "16 20\n", + "18 84\n", + "20 120\n", + "22 100\n", + "24 95\n", + "26 110\n", + "28 0\n", + "30 100\n", + "32 82\n", + "34 0\n", + "36 0\n", + "38 111\n", + "40 100\n", + "42 50\n", + "44 120\n", + "46 115\n", + "48 120\n", + "50 100\n", + "52 90\n", + "54 105\n", + "56 0\n", + "58 0\n", + "Name: Exam 1, dtype: int32" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_0[\"Exam 1\"].astype('int32')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(df_0[\"Exam 1\"].astype('int32'))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "exam_1=df_0[\"Exam 1\"].astype('int32').to_list()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0,\n", + " 120,\n", + " 90,\n", + " 90,\n", + " 100,\n", + " 90,\n", + " 80,\n", + " 105,\n", + " 20,\n", + " 84,\n", + " 120,\n", + " 100,\n", + " 95,\n", + " 110,\n", + " 0,\n", + " 100,\n", + " 82,\n", + " 0,\n", + " 0,\n", + " 111,\n", + " 100,\n", + " 50,\n", + " 120,\n", + " 115,\n", + " 120,\n", + " 100,\n", + " 90,\n", + " 105,\n", + " 0,\n", + " 0]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exam_1" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 76.56666666666666\n", + "Std: 43.17690998155792\n" + ] + } + ], + "source": [ + "print(\"Mean:\",np.mean(exam_1))\n", + "print(\"Std:\",np.std(exam_1))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "min_exam_1=60\n", + "exam_1_df=df_0[\"Exam 1\"].astype('int32')\n", + "exam_1=exam_1_df[exam_1_df>min_exam_1].to_list()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "2 True\n", + "4 True\n", + "6 True\n", + "8 True\n", + "10 True\n", + "12 True\n", + "14 True\n", + "16 False\n", + "18 True\n", + "20 True\n", + "22 True\n", + "24 True\n", + "26 True\n", + "28 False\n", + "30 True\n", + "32 True\n", + "34 False\n", + "36 False\n", + "38 True\n", + "40 True\n", + "42 False\n", + "44 True\n", + "46 True\n", + "48 True\n", + "50 True\n", + "52 True\n", + "54 True\n", + "56 False\n", + "58 False\n", + "Name: Exam 1, dtype: bool" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exam_1_df>60" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([2., 1., 4., 1., 0., 5., 2., 2., 1., 4.]),\n", + " array([ 80., 84., 88., 92., 96., 100., 104., 108., 112., 116., 120.]),\n", + " )" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(exam_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 101.22727272727273\n", + "Std: 12.555085236240183\n" + ] + } + ], + "source": [ + "print(\"Mean:\",np.mean(exam_1))\n", + "print(\"Std:\",np.std(exam_1))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(exam_1_df>100)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([8, 0, 3, 5, 8]), array([ 0, 60, 80, 90, 100, 110]))" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.histogram(exam_1_df,bins=[0,60,80,90,100,110])" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([8., 0., 3., 5., 8.]),\n", + " array([ 0, 60, 80, 90, 100, 110]),\n", + " )" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
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"text/plain": [ + "{100: 'A+', 90: 'A', 80: 'B', 70: 'C', 60: 'D', 0: 'F'}" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grades_inverse" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import OrderedDict " + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "grades_inverse=OrderedDict(zip(list(grades_0.values())[::-1],\n", + " list(grades_0.keys())[::-1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OrderedDict([(0, 'F'),\n", + " (60, 'D'),\n", + " (70, 'C'),\n", + " (80, 'B'),\n", + " (90, 'A'),\n", + " (100, 'A+')])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grades_inverse" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "grades=OrderedDict(zip(list(grades_0.keys())[::-1],\n", + " list(grades_0.values())[::-1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OrderedDict([('F', 0),\n", + " ('D', 60),\n", + " ('C', 70),\n", + " ('B', 80),\n", + " ('A', 90),\n", + " ('A+', 100)])" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grades" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 8, 0, 0, 3, 10]), array([ 0, 60, 70, 80, 90, 100]))" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.histogram(exam_1_df,bins=list(grades.values()))" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 8., 0., 0., 3., 10.]),\n", + " array([ 0, 60, 70, 80, 90, 100]),\n", + " )" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(exam_1_df,bins=list(grades.values()))" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([6., 1., 0., 0., 1., 0., 2., 6., 7., 7.]),\n", + " array([ 0., 12., 24., 36., 48., 60., 72., 84., 96., 108., 120.]),\n", + " )" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(exam_1_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "def grade_summary(df_0,grades,min_grade=0.):\n", + " df=df_0[df_0>min_grade]\n", + " N_total=sum(df>min_grade)\n", + " hist,bins=np.histogram(df,bins=list(grades.values()))\n", + "\n", + " print(\"Mean:\",np.mean(df))\n", + " print(\"Std:\",np.std(df))\n", + " \n", + " for grade_letter,bin_count in zip(grades.keys(),hist):\n", + " print(grade_letter+\": \"+str(bin_count),\n", + " float(bin_count/N_total) )\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 95.70833333333333\n", + "Std: 22.322408708639745\n", + "F: 2 0.08333333333333333\n", + "D: 0 0.0\n", + "C: 0 0.0\n", + "B: 3 0.125\n", + "A: 10 0.4166666666666667\n" + ] + } + ], + "source": [ + "grade_summary(exam_1_df,grades)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 101.22727272727273\n", + "Std: 12.555085236240183\n", + "F: 0 0.0\n", + "D: 0 0.0\n", + "C: 0 0.0\n", + "B: 3 0.13636363636363635\n", + "A: 10 0.45454545454545453\n" + ] + } + ], + "source": [ + "grade_summary(exam_1_df,grades,60)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_curve(df_0, min_grade=None):\n", + "\n", + " if min_grade:\n", + " pass\n", + " else:\n", + " min_grade=np.max(df_0)/2.\n", + " \n", + " print(\"Min grade:\",min_grade)\n", + "\n", + " df=df_0[df_0>min_grade]\n", + " N_total=sum(df_0>min_grade)\n", + " \n", + " print(\"N Total (post cut):\",N_total)\n", + " print(\"N Total (pre cut):\",df_0.shape)\n", + " \n", + " mean=np.mean(df)\n", + " std=np.std(df)\n", + "\n", + " print(\"Mean:\",mean)\n", + " print(\"Std:\",std)\n", + " \n", + " grade_def=[(\"A+\",np.max(df)),\n", + " (\"A\", min(np.max(df),mean+std)),\n", + " (\"B\",mean), \n", + " (\"C\",mean-std),\n", + " (\"D\",mean-2*std),\n", + " (\"F\",0.)]\n", + " \n", + " grade_def.reverse()\n", + " grades=OrderedDict( grade_def )\n", + " \n", + " \n", + " hist,bins=np.histogram(df,bins=list(grades.values()))\n", + " \n", + " _=plt.hist(df)\n", + " plt.show()\n", + " \n", + " for grade_letter,bin_count,min_grade in zip(grades.keys(),hist,grades.values()):\n", + " print(\"{:2.2f}\".format(min_grade),\n", + " grade_letter+\": \"+str(bin_count),\n", + " \"{:2.2f}%\".format(100.*float(bin_count/N_total)) )\n", + " return grades\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min grade: 60\n", + "N Total (post cut): 22\n", + "N Total (pre cut): (30,)\n", + "Mean: 101.22727272727273\n", + "Std: 12.555085236240183\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.00 F: 0 0.00%\n", + "76.12 D: 3 13.64%\n", + "88.67 C: 10 45.45%\n", + "101.23 B: 4 18.18%\n", + "113.78 A: 5 22.73%\n" + ] + }, + { + "data": { + "text/plain": [ + "OrderedDict([('F', 0.0),\n", + " ('D', 76.11710225479237),\n", + " ('C', 88.67218749103255),\n", + " ('B', 101.22727272727273),\n", + " ('A', 113.78235796351292),\n", + " ('A+', 120)])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "compute_curve(exam_1_df,60)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lab 1\n", + "Min grade: 50.0\n", + "N Total (post cut): 28\n", + "N Total (pre cut): (30,)\n", + "Mean: 94.64285714285714\n", + "Std: 7.06204283331614\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.00 F: 4 14.29%\n", + "80.52 D: 1 3.57%\n", + "87.58 C: 2 7.14%\n", + "94.64 B: 7 25.00%\n", + "100.00 A: 14 50.00%\n", + "Lab 2\n", + "Min grade: 50.0\n", + "N Total (post cut): 28\n", + "N Total (pre cut): (30,)\n", + "Mean: 91.21428571428571\n", + "Std: 6.3827733720043165\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.00 F: 1 3.57%\n", + "78.45 D: 3 10.71%\n", + "84.83 C: 6 21.43%\n", + "91.21 B: 16 57.14%\n", + "97.60 A: 2 7.14%\n", + "Lab 3\n", + "Min grade: 50.0\n", + "N Total (post cut): 9\n", + "N Total (pre cut): (30,)\n", + "Mean: 88.55555555555556\n", + "Std: 9.032178414622923\n" + ] + }, + { + "data": { + "image/png": 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LvgWcn+TU7lHIFwJ3AXfQe7wDzN4+GtTT/kUHWuhd25yVfUSSn+3+bqR3rflvmfHjaFBPM3wcPWLYPtkBvLq72+V8epcBDw96g3FM7ZeiSZ5I7wD7+ao62i07nd6dBxuBbwK/V1X9Xy6ctIb0dBO9j4kF3A38wUp22ImU5J3A7wPHgC8Db6B3fe8j9C5NfBl4ZXdmOxOG9HQbMEfvToM9wB9W1UNTK3IJknye3qOqfwz8SVX9SwPH0aCeZuY4SnIzcAG9x/7eC7wd+CQD9kl3EnE1vcuZDwOvq6qFZW97WoEuSZqsaV9ykSRNiIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGvF/ToH6s8kd1xcAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.00 F: 0 0.00%\n", + "70.49 D: 2 22.22%\n", + "79.52 C: 3 33.33%\n", + "88.56 B: 3 33.33%\n", + "97.59 A: 1 11.11%\n", + "Lab 4\n", + "Min grade: 50.0\n", + "N Total (post cut): 11\n", + "N Total (pre cut): (30,)\n", + "Mean: 75.81818181818181\n", + "Std: 15.473250712222878\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.00 F: 0 0.00%\n", + "44.87 D: 3 27.27%\n", + "60.34 C: 2 18.18%\n", + "75.82 B: 4 36.36%\n", + "91.29 A: 2 18.18%\n" + ] + } + ], + "source": [ + "for name in [\"Lab 1\",\"Lab 2\",\"Lab 3\",\"Lab 4\"]:\n", + " df=df_0[name].astype('int32')\n", + " print(name)\n", + " compute_curve(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "120\n", + "90\n", + "90\n", + "100\n", + "90\n", + "80\n", + "105\n", + "20\n", + "84\n", + "120\n", + "100\n", + "95\n", + "110\n", + "0\n", + "100\n", + "82\n", + "0\n", + "0\n", + "111\n", + "100\n", + "50\n", + "120\n", + "115\n", + "120\n", + "100\n", + "90\n", + "105\n", + "0\n", + "0\n" + ] + } + ], + "source": [ + "for item in df_0[\"Exam 1\"].astype('int32'):\n", + " print(item)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [], + "source": [ + "def assign_grade(numeric,grades):\n", + " previous_grade=\"F\"\n", + " for grade_letter,grade_boundry in grades.items():\n", + " #print(numeric, grade_letter,grade_boundry)\n", + " if numeric < grade_boundry:\n", + " return previous_grade\n", + " break\n", + " else:\n", + " previous_grade=grade_letter\n", + " return previous_grade\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 F\n", + "120 A+\n", + "90 A\n", + "90 A\n", + "100 A+\n", + "90 A\n", + "80 B\n", + "105 A+\n", + "20 F\n", + "84 B\n", + "120 A+\n", + "100 A+\n", + "95 A\n", + "110 A+\n", + "0 F\n", + "100 A+\n", + "82 B\n", + "0 F\n", + "0 F\n", + "111 A+\n", + "100 A+\n", + "50 F\n", + "120 A+\n", + "115 A+\n", + "120 A+\n", + "100 A+\n", + "90 A\n", + "105 A+\n", + "0 F\n", + "0 F\n" + ] + } + ], + "source": [ + "for item in df_0[\"Exam 1\"].astype('int32'):\n", + " print(item,assign_grade(item, grades))" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'A'" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "assign_grade(90,grades)" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "def assign_grades(name,grades):\n", + " letter_grades=list()\n", + " for item in df_0[name].astype('int32'):\n", + " letter_grades.append(assign_grade(item, grades))\n", + " return letter_grades" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['F',\n", + " 'A+',\n", + " 'A',\n", + " 'A',\n", + " 'A+',\n", + " 'A',\n", + " 'B',\n", + " 'A+',\n", + " 'F',\n", + " 'B',\n", + " 'A+',\n", + " 'A+',\n", + " 'A',\n", + " 'A+',\n", + " 'F',\n", + " 'A+',\n", + " 'B',\n", + " 'F',\n", + " 'F',\n", + " 'A+',\n", + " 'A+',\n", + " 'F',\n", + " 'A+',\n", + " 'A+',\n", + " 'A+',\n", + " 'A+',\n", + " 'A',\n", + " 'A+',\n", + " 'F',\n", + " 'F']" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "assign_grades(\"Exam 1\",grades)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [], + "source": [ + "letter_grades=dict()\n", + "for name in [\"Lab 1\",\"Lab 2\",\"Lab 3\",\"Lab 4\"]:\n", + " letter_grades[name]=assign_grades(name,grades)" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['F',\n", + " 'F',\n", + " 'F',\n", + " 'F',\n", + " 'F',\n", + " 'F',\n", + " 'F',\n", + " 'B',\n", + " 'F',\n", + " 'F',\n", + " 'A',\n", + " 'C',\n", + " 'F',\n", + " 'A',\n", + " 'F',\n", + " 'A',\n", + " 'F',\n", + " 'F',\n", + " 'F',\n", + " 'F',\n", + " 'B',\n", + " 'F',\n", + " 'A+',\n", + " 'B',\n", + " 'F',\n", + " 'F',\n", + " 'C',\n", + " 'F',\n", + " 'F',\n", + " 'F']" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "letter_grades[\"Lab 3\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Lecture-21/Lecture-21.ipynb b/Lectures/Lecture-21/Lecture-21.ipynb new file mode 100644 index 0000000..cb01816 --- /dev/null +++ b/Lectures/Lecture-21/Lecture-21.ipynb @@ -0,0 +1,1425 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 21\n", + "\n", + "From previous lecture:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import libraries we will use\n", + "import pandas as pd\n", + "import numpy as np\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from collections import OrderedDict" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Read Data into a Pandas DataFrame\n", + "df = pd.read_csv(\"Data-1401-Grades-Fixed.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df_0=df[np.logical_not(np.isnan(df[\"Student ID\"]))]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Student IDLab 1Lab 2Lab 3Lab 4Exam 1Lab 5Lab 6Lab 7
01.000954e+091008125350NaNNaNNaN
21.000626e+099587088120NaNNaNNaN
41.000848e+0910000090NaNNaNNaN
61.001108e+09909603590NaNNaNNaN
81.001000e+099594430100NaNNaNNaN
101.001214e+09808230090NaNNaNNaN
121.001428e+09809024080NaNNaNNaN
141.001397e+09100948712105NaNNaNNaN
161.001373e+0910070173520NaNNaNNaN
181.001558e+0995860084NaNNaNNaN
201.001593e+09100969685120NaNNaNNaN
221.001596e+0990927157100NaNNaNNaN
241.001609e+0910094366195NaNNaNNaN
261.001618e+09100969786110NaNNaNNaN
281.001653e+0910090000NaNNaNNaN
301.001659e+09100949692100NaNNaNNaN
321.001663e+09959438082NaNNaNNaN
341.001674e+0995902300NaNNaNNaN
361.001722e+0995100000NaNNaNNaN
381.001544e+09100961951111NaNNaNNaN
401.001548e+09100948675100NaNNaNNaN
421.001458e+098588213750NaNNaNNaN
441.001497e+0910098100100120NaNNaNNaN
461.001520e+09100948550115NaNNaNNaN
481.001774e+09100942079120NaNNaNNaN
501.001697e+09096040100NaNNaNNaN
521.001233e+098094796090NaNNaNNaN
541.001465e+09959400105NaNNaNNaN
561.001645e+0980801000NaNNaNNaN
581.001685e+0900000NaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " Student ID Lab 1 Lab 2 Lab 3 Lab 4 Exam 1 Lab 5 Lab 6 Lab 7\n", + "0 1.000954e+09 100 81 25 35 0 NaN NaN NaN\n", + "2 1.000626e+09 95 87 0 88 120 NaN NaN NaN\n", + "4 1.000848e+09 100 0 0 0 90 NaN NaN NaN\n", + "6 1.001108e+09 90 96 0 35 90 NaN NaN NaN\n", + "8 1.001000e+09 95 94 43 0 100 NaN NaN NaN\n", + "10 1.001214e+09 80 82 30 0 90 NaN NaN NaN\n", + "12 1.001428e+09 80 90 24 0 80 NaN NaN NaN\n", + "14 1.001397e+09 100 94 87 12 105 NaN NaN NaN\n", + "16 1.001373e+09 100 70 17 35 20 NaN NaN NaN\n", + "18 1.001558e+09 95 86 0 0 84 NaN NaN NaN\n", + "20 1.001593e+09 100 96 96 85 120 NaN NaN NaN\n", + "22 1.001596e+09 90 92 71 57 100 NaN NaN NaN\n", + "24 1.001609e+09 100 94 36 61 95 NaN NaN NaN\n", + "26 1.001618e+09 100 96 97 86 110 NaN NaN NaN\n", + "28 1.001653e+09 100 90 0 0 0 NaN NaN NaN\n", + "30 1.001659e+09 100 94 96 92 100 NaN NaN NaN\n", + "32 1.001663e+09 95 94 38 0 82 NaN NaN NaN\n", + "34 1.001674e+09 95 90 23 0 0 NaN NaN NaN\n", + "36 1.001722e+09 95 100 0 0 0 NaN NaN NaN\n", + "38 1.001544e+09 100 96 19 51 111 NaN NaN NaN\n", + "40 1.001548e+09 100 94 86 75 100 NaN NaN NaN\n", + "42 1.001458e+09 85 88 21 37 50 NaN NaN NaN\n", + "44 1.001497e+09 100 98 100 100 120 NaN NaN NaN\n", + "46 1.001520e+09 100 94 85 50 115 NaN NaN NaN\n", + "48 1.001774e+09 100 94 20 79 120 NaN NaN NaN\n", + "50 1.001697e+09 0 96 0 40 100 NaN NaN NaN\n", + "52 1.001233e+09 80 94 79 60 90 NaN NaN NaN\n", + "54 1.001465e+09 95 94 0 0 105 NaN NaN NaN\n", + "56 1.001645e+09 80 80 10 0 0 NaN NaN NaN\n", + "58 1.001685e+09 0 0 0 0 0 NaN NaN NaN" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_0" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "exam_1_df=df_0[\"Exam 1\"].astype('int32')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "grade_def = [(\"A+\",100),\n", + " (\"A\",90),\n", + " (\"B\",80), \n", + " (\"C\",70),\n", + " (\"D\",60),\n", + " (\"F\",0) ]\n", + "\n", + "grades=OrderedDict(grade_def[::-1])\n", + "\n", + "grades_inverse=OrderedDict(zip(list(grades.values())[::-1],\n", + " list(grades.keys())[::-1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grades[\"F\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def grade_summary(df_0,grades,min_grade=0.):\n", + " df=df_0[df_0>min_grade]\n", + " N_total=sum(df>min_grade)\n", + " hist,bins=np.histogram(df,bins=list(grades.values()))\n", + "\n", + " print(\"Mean:\",np.mean(df))\n", + " print(\"Std:\",np.std(df))\n", + " \n", + " for grade_letter,bin_count in zip(grades.keys(),hist):\n", + " print(grade_letter+\": \"+str(bin_count),\n", + " float(bin_count/N_total) )\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 95.70833333333333\n", + "Std: 22.322408708639745\n", + "F: 2 0.08333333333333333\n", + "D: 0 0.0\n", + "C: 0 0.0\n", + "B: 3 0.125\n", + "A: 10 0.4166666666666667\n" + ] + } + ], + "source": [ + "grade_summary(exam_1_df,grades)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_curve(df_0, min_grade=None):\n", + "\n", + " if min_grade:\n", + " pass\n", + " else:\n", + " min_grade=np.max(df_0)/2.\n", + " \n", + " print(\"Min grade:\",min_grade)\n", + "\n", + " df=df_0[df_0>min_grade]\n", + " N_total=sum(df_0>min_grade)\n", + " \n", + " print(\"N Total (post cut):\",N_total)\n", + " print(\"N Total (pre cut):\",df_0.shape)\n", + " \n", + " mean=np.mean(df)\n", + " std=np.std(df)\n", + "\n", + " print(\"Mean:\",mean)\n", + " print(\"Std:\",std)\n", + " \n", + " grade_def=[(\"A+\",np.max(df)),\n", + " (\"A\", min(np.max(df),mean+std)),\n", + " (\"B\",mean), \n", + " (\"C\",mean-std),\n", + " (\"D\",mean-2*std),\n", + " (\"F\",0.)]\n", + " \n", + " grade_def.reverse()\n", + " grades=OrderedDict( grade_def )\n", + " \n", + " \n", + " hist,bins=np.histogram(df,bins=list(grades.values()))\n", + " \n", + " _=plt.hist(df)\n", + " plt.show()\n", + " \n", + " for grade_letter,bin_count,min_grade in zip(grades.keys(),hist,grades.values()):\n", + " print(\"{:2.2f}\".format(min_grade),\n", + " grade_letter+\": \"+str(bin_count),\n", + " \"{:2.2f}%\".format(100.*float(bin_count/N_total)) )\n", + " return grades\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min grade: 60\n", + "N Total (post cut): 22\n", + "N Total (pre cut): (30,)\n", + "Mean: 101.22727272727273\n", + "Std: 12.555085236240183\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.00 F: 0 0.00%\n", + "76.12 D: 3 13.64%\n", + "88.67 C: 10 45.45%\n", + "101.23 B: 4 18.18%\n", + "113.78 A: 5 22.73%\n" + ] + }, + { + "data": { + "text/plain": [ + "OrderedDict([('F', 0.0),\n", + " ('D', 76.11710225479237),\n", + " ('C', 88.67218749103255),\n", + " ('B', 101.22727272727273),\n", + " ('A', 113.78235796351292),\n", + " ('A+', 120)])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "compute_curve(exam_1_df,60)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Grading Strategies\n", + "\n", + "Thinking about what we did, we noticed that we have 2 grading strategies:\n", + "1. \"Standard\": 70, 80, 90 scheme.\n", + "2. \"Curved\": Using mean and standard deviation.\n", + "\n", + "There is a third strategy that I often apply when the grade distribution doesn't look like a normal distribution. When we curve the letter grades mean the following, based on the fact that 68% of normal distribution lies between -1 and 1 $\\sigma$ of the mean.\n", + "\n", + "* B and C: comprise of the 68% of the students withing 1 $\\sigma$ of the mean, with the top 34% subset assigned a B and the bottom 34% assigned a C. Nearly all of the remaining 32% of the students will be within 2 $\\sigma$ of the mean.\n", + "* A: The approximately 16% that are more than 1 $\\sigma$ above the mean are assigned an A.\n", + "* D: The approximately 16% that are more are between 1 $\\sigma$ and 2 $\\sigma$ below the mean are assigned a D.\n", + "\n", + "All of this assume a normal distribution. If the grade distribution isn't normal, we can still assign grades in a way that we could interpret in the same way as the curved grades by simply assigning the top 16% an A, next 32% B, then assign C to the 32% and D to the the 16% after.\n", + "\n", + "\n", + "## Disecting the Task at Hand\n", + "\n", + "We can also note that all the stragegies are defined by the boundries between letter grades. Therefore we can factorize the operation of assigning a letter grade into several steps:\n", + "* Perform statistical analysis of the grades to obtain basic parameters.\n", + " * Minimum cut-off\n", + " * Mean / Sigma\n", + "* Establish grade boundries.\n", + " * Potentially apply several strategies.\n", + " * Select a strategy appropriate for given distribution of grades.\n", + "* Use grade boundries to assign grades.\n", + "* Validate the assigned grades.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Organizing Our Code\n", + "\n", + "It is possible to perform the steps above without any (or with minimal) generalization or abstraction (ie using functions). We could work out a grade with a series of steps... then copy and paste for the other grades. The benefits would be that without generalization, we could fine tune every grade easily. But it could become tedious and our results would be error prone.\n", + "\n", + "Alternatively, we can recognize that each of the steps above can be done with a set of functions that have the same inputs and outputs:\n", + "\n", + "* Statistical Analysis Functions: Input list of grades. Output mean, standard deviation, and minium grade.\n", + "* Grade Boundries Functions: Input stats (and list of grades). Output boundries.\n", + "* Assign Grades Function: Input boundries and list grades. Output corresponding list of assigned letter grades.\n", + "* Validate Function: Input assigned letter grades, output percentage of each grade.\n", + "\n", + "In addition, we will need to do a weighted sum of the grades to assign a class grade. \n", + "\n", + "We also have to note that beyond the procedural steps, we will have to make choices about how we represent the input/output data of each step. For example, previously we represented grade boundries as an `OrderedDict`. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building a System\n", + "\n", + "We are starting to recognize that depending on the context, we may choose different levels of sophistication for our code to solve a problem:\n", + "\n", + "* Quickly solve a problem once: Cut and paste can be sufficient.\n", + "* Scale or solve the problem on many data points: Implement functions that perform tasks.\n", + "* Solve the same problem with different data: Identify tasks and generalize functions with defined input/outputs\n", + "* Enable others to use your code to solve the same problem.\n", + "* Build a production system or service that solves the problem.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## An Object Oriented Approach\n", + "\n", + "Object Oriented programming is not required to solve problems. But in many cases, OO can greatly simplify the development process and ability to further add functionality to the code. Even within OO, there are many approaches to solving the same problem, and while some may have advatanges, often it takes several iterations and revisions to approach optimality... and even then, there are stylistic and historical choices.\n", + "\n", + "Lets explore one OO approach to building a Grade Book system.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Base Classes" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Create some virtual classes\n", + "\n", + "class base:\n", + " __name=\"\"\n", + " \n", + " def __init__(self,name):\n", + " self.__name=name\n", + "\n", + " def name(self):\n", + " return self.__name\n", + " \n", + "\n", + "class data(base):\n", + " def __init__(self,name):\n", + " base.__init__(self,name)\n", + " \n", + "class alg(base):\n", + " def __init__(self,name):\n", + " base.__init__(self,name)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hello'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a=alg(\"Hello\")\n", + "a.name()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " '_base__name',\n", + " 'name']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Grade Representation\n", + "\n", + "We note that we have two types of grades: numerical and letter. We have a choice of how to represent grades. Below is one. What might be another way?" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "class grade(data):\n", + " __letter_grades=[\"F-\",\"F\",\"F+\",\"D-\",\"D\",\"D+\",\"C-\",\"C\",\"C+\",\"B-\",\"B\",\"B+\",\"A-\",\"A\",\"A+\"]\n", + " \n", + " def __init__(self,name,numerical=True,value=None):\n", + " self.__value=value\n", + " self.__numerical=numerical\n", + " self.__gradebook_name=str()\n", + " \n", + " if value:\n", + " if isinstance(value,(int,float)):\n", + " self.__numerical=True\n", + " elif isinstance(value,str):\n", + " self.__numerical=False\n", + " self.set(value)\n", + " else: \n", + " self.__numerical=numerical\n", + " self.__gradebook_name=name\n", + " data.__init__(self,name+\" Grade Data Object\") \n", + "\n", + " def set(self,value):\n", + " if isinstance(value,(int,float)) and self.__numerical:\n", + " self.__value=value\n", + " elif isinstance(value,str) and not self.__numerical:\n", + " if value in self.__letter_grades:\n", + " self.__value=value\n", + " else:\n", + " print( self.name()+\" Error: Bad Grade.\")\n", + " raise Exception\n", + " \n", + " def value(self):\n", + " return self.__value\n", + " \n", + " def numerical(self):\n", + " return self.__numerical\n", + " \n", + " def gradebook_name(self):\n", + " return self.__gradebook_name\n", + " \n", + " def __str__(self):\n", + " return self.__gradebook_name+\": \"+str(self.__value)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Student Representation" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "class student(data):\n", + " \n", + " def __init__(self, first_name, last_name, id_number):\n", + " self.__grades=dict()\n", + " self.__id_number=id_number\n", + " data.__init__(self,first_name+\" \"+last_name+\" Student Data\")\n", + "\n", + " def add_grade(self,a_grade,overwrite=False):\n", + " if overwrite or not a_grade.gradebook_name() in self.__grades:\n", + " self.__grades[a_grade.gradebook_name()]=a_grade\n", + " else:\n", + " print (self.name()+\" Error Adding Grade \"+a_grade.name()+\". Grade already exists.\")\n", + " raise Exception\n", + "\n", + " def id_number(self):\n", + " return __id_number\n", + " \n", + " def __getitem__(self,key):\n", + " return self.__grades[key]\n", + " \n", + " def print_grades(self):\n", + " for grade in self.__grades:\n", + " print (self.__grades[grade])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exam 1: 95\n", + "Exam 1 Letter: A\n" + ] + } + ], + "source": [ + "a_student=student(\"John\",\"Doe\",111)\n", + "\n", + "a_student.add_grade(grade(\"Exam 1\",value=95))\n", + "a_student.add_grade(grade(\"Exam 1 Letter\",value=\"A\"))\n", + "\n", + "a_student.print_grades()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Grade Calculator" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "class grade_calculator(alg): \n", + " def __init__(self,name,stats):\n", + " self.__stats=stats\n", + " alg.__init__(self,name)\n", + "\n", + " def apply(self,a_grade):\n", + " raise NotImplementedError\n", + " \n", + "\n", + "class uncurved_letter_grade_percent(grade_calculator):\n", + " __grades_definition=[ (.97,\"A+\"),\n", + " (.93,\"A\"),\n", + " (.9,\"A-\"),\n", + " (.87,\"B+\"),\n", + " (.83,\"B\"),\n", + " (.8,\"B-\"),\n", + " (.77,\"C+\"),\n", + " (.73,\"C\"),\n", + " (.7,\"C-\"),\n", + " (.67,\"C+\"),\n", + " (.63,\"C\"),\n", + " (.6,\"C-\"),\n", + " (.57,\"F+\"),\n", + " (.53,\"F\"),\n", + " (0.,\"F-\")]\n", + " __max_grade=100.\n", + " __grade_name=str()\n", + " \n", + " def __init__(self,grade_name,max_grade=100.):\n", + " self.__max_grade=max_grade\n", + " self.__grade_name=grade_name\n", + " grade_calculator.__init__(self,\n", + " \"Uncurved Percent Based Grade Calculator \"+self.__grade_name+\" Max=\"+str(self.__max_grade),\n", + " False)\n", + " \n", + "\n", + " def apply(self,a_grade):\n", + " if not isinstance(a_grade,grade):\n", + " print (self.name()+ \" Error: Did not get an proper grade as input.\")\n", + " raise Exception\n", + " if not a_grade.numerical():\n", + " print (self.name()+ \" Error: Did not get a numerical grade as input.\")\n", + " raise Exception\n", + "\n", + " percent=a_grade.value()/self.__max_grade\n", + " \n", + " for i,v in enumerate(self.__grades_definition):\n", + " if percent>=v[0]:\n", + " break\n", + " \n", + " return grade(self.__grade_name,value=self.__grades_definition[i][1])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exam 1 Test Grade: A\n" + ] + } + ], + "source": [ + "a_grader=uncurved_letter_grade_percent(grade_name=\"Exam 1 Test Grade\")\n", + "print(a_grader.apply(a_student[\"Exam 1\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exam 1 Test Grade: B-\n" + ] + } + ], + "source": [ + "print(a_grader.apply(grade(\"Test Grade\",value=81.)))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "class curved_letter_grade(grade_calculator):\n", + " __grades_definition=[ (.97,\"A+\"),\n", + " (.93,\"A\"),\n", + " (.9,\"A-\"),\n", + " (.87,\"B+\"),\n", + " (.83,\"B\"),\n", + " (.8,\"B-\"),\n", + " (.77,\"C+\"),\n", + " (.73,\"C\"),\n", + " (.7,\"C-\"),\n", + " (.67,\"C+\"),\n", + " (.63,\"C\"),\n", + " (.6,\"C-\"),\n", + " (.57,\"F+\"),\n", + " (.53,\"F\"),\n", + " (0.,\"F-\")]\n", + " __max_grade=100.\n", + " __grade_name=str()\n", + " \n", + " def __init__(self,grade_name,mean,std,max_grade=100.):\n", + " self.__max_grade=max_grade\n", + " self.__mean=mean\n", + " self.__std=std\n", + " self.__grade_name=grade_name\n", + " grade_calculator.__init__(self,\n", + " \"Curved Percent Based Grade Calculator \"+self.__grade_name+ \\\n", + " \" Mean=\"+str(self.__mean)+\\\n", + " \" STD=\"+str(self.__std)+\\\n", + " \" Max=\"+str(self.__max_grade),\n", + " False)\n", + " \n", + "\n", + " def apply(self,a_grade):\n", + " if not isinstance(a_grade,grade):\n", + " print (self.name()+ \" Error: Did not get an proper grade as input.\")\n", + " raise Exception\n", + " if not a_grade.numerical():\n", + " print (self.name()+ \" Error: Did not get a numerical grade as input.\")\n", + " raise Exception\n", + " \n", + " # Rescale the grade\n", + " percent=a_grade.value()/self.__max_grade\n", + " shift_to_zero=percent-(self.__mean/self.__max_grade)\n", + " scale_std=0.1*shift_to_zero/(self.__std/self.__max_grade)\n", + " scaled_percent=scale_std+0.8\n", + " \n", + " for i,v in enumerate(self.__grades_definition):\n", + " if scaled_percent>=v[0]:\n", + " break\n", + " \n", + " return grade(self.__grade_name,value=self.__grades_definition[i][1])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exam 1: 70\n", + "Exam 1 Letter: A\n" + ] + } + ], + "source": [ + "a_student=student(\"John\",\"Doe\",111)\n", + "\n", + "a_student.print_grades()\n", + "\n", + "a_student.add_grade(grade(\"Exam 1\",value=70))\n", + "a_student.add_grade(grade(\"Exam 1 Letter\",value=\"A\"))\n", + "\n", + "a_student.print_grades()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exam 1 Test Grade: A+\n" + ] + } + ], + "source": [ + "a_grader=curved_letter_grade(grade_name=\"Exam 1 Test Grade\",mean=50.,std=10.)\n", + "print( a_grader.apply(a_student[\"Exam 1\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exam 1 Test Grade: A+\n" + ] + } + ], + "source": [ + "print( a_grader.apply(grade(\"Test Grade\",value=75.)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stats Computation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import math\n", + "\n", + "class statistics_calculator(alg): \n", + " def __init__(self,name):\n", + " alg.__init__(self,name)\n", + "\n", + " def apply(self,grades):\n", + " raise NotImplementedError\n", + " \n", + "class mean_std_calculator(statistics_calculator):\n", + " def __init__(self):\n", + " statistics_calculator.__init__(self,\"Mean and Standard Deviation Calculator\")\n", + " \n", + " def apply(self,grades):\n", + " return np.mean(grades),math.sqrt(np.var(grades))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Grade Summing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class summary_calculator(alg): \n", + " def __init__(self,name):\n", + " alg.__init__(self,name)\n", + "\n", + " def apply(self,a_student):\n", + " raise NotImplementedError\n", + " \n", + "class grade_summer(summary_calculator):\n", + " def __init__(self,prefix,n):\n", + " self.__prefix=prefix\n", + " self.__n=n\n", + " statistics_calculator.__init__(self,\"Sum Grades\")\n", + " \n", + " def apply(self,a_student):\n", + " labels=[prefix+str(x) for x in range(1,n)]\n", + " \n", + " grade_sum=0.\n", + " for label in labels:\n", + " grade_sum+=a_student[label]\n", + " \n", + " a_student.add_grade(grade(prefix+\"sum\",value=grade_sum))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gradebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class grade_book(data):\n", + " \n", + " def __init__(self,name):\n", + " data.__init__(self,name+\" Course Grade Book\")\n", + " self.__students=dict()\n", + " \n", + " def add_student(self,a_student):\n", + " self.__students[a_student.id_number]=a_student\n", + " \n", + " def assign_grade(self,key,a_grade):\n", + " the_student=None\n", + " try:\n", + " the_student=self.__students[key]\n", + " except:\n", + " for id in self.__students:\n", + " if key == self.__students[id].name():\n", + " the_student=self.__students[id]\n", + " break\n", + " if the_student:\n", + " the_student.add_grade(a_grade)\n", + " else:\n", + " print (self.name()+\" Error: Did not find student.\")\n", + " \n", + " def apply_grader(self,a_grader,grade_name):\n", + " for k,a_student in self.__students.items():\n", + " a_student.add_grade(a_grader.apply(a_student[grade_name]))\n", + " \n", + " def apply_stats(self,a_stat_comp,grade_name):\n", + " grades=list()\n", + " for k,a_student in self.__students.iteritems():\n", + " grades.append(a_student[grade_name].value())\n", + " \n", + " return a_stat_comp.apply(grades)\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building a Gradebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_0.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for d in df_0.iterrows():\n", + " print(type(d))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_0[\"Lab 1\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a_grade_book=grade_book(\"Data 1401\")\n", + "\n", + "for student_i in range(df_0.shape[0]):\n", + " a_student_0=student(\"Student\",str(student_i),student_i)\n", + "\n", + " for k in df_0.keys():\n", + " a_student_0.add_grade(grade(k,value=df_0[k][student_i]))\n", + "\n", + " a_grade_book.add_student(a_student_0)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a_grade_book.apply_stats(mean_std_calculator(),\"l2_3\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Lecture-22/Lecture-22.ipynb b/Lectures/Lecture-22/Lecture-22.ipynb new file mode 100644 index 0000000..c654782 --- /dev/null +++ b/Lectures/Lecture-22/Lecture-22.ipynb @@ -0,0 +1,4000 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 22\n", + "\n", + "From previous lecture:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import libraries we will use\n", + "import pandas as pd\n", + "import numpy as np\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from collections import OrderedDict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Base Classes" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Create some virtual classes\n", + "class base:\n", + " __name=\"\"\n", + " \n", + " def __init__(self,name):\n", + " self.__name=name\n", + "\n", + " def name(self):\n", + " return self.__name\n", + "\n", + "class data(base):\n", + " def __init__(self,name):\n", + " base.__init__(self,name)\n", + " \n", + "class alg(base):\n", + " def __init__(self,name):\n", + " base.__init__(self,name)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Grade Representation\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class grade(data):\n", + " __letter_grades=[\"F-\",\"F\",\"F+\",\"D-\",\"D\",\"D+\",\"C-\",\"C\",\"C+\",\"B-\",\"B\",\"B+\",\"A-\",\"A\",\"A+\"]\n", + " \n", + " def __init__(self,name,numerical=True,value=None):\n", + " self.__value=value\n", + " self.__numerical=numerical\n", + " self.__gradebook_name=str()\n", + " \n", + " if value:\n", + " if isinstance(value,(int,float)):\n", + " self.__numerical=True\n", + " elif isinstance(value,str):\n", + " self.__numerical=False\n", + " self.set(value)\n", + " else: \n", + " self.__numerical=numerical\n", + " self.__gradebook_name=name\n", + " data.__init__(self,name+\" Grade Data Object\") \n", + "\n", + " def set(self,value):\n", + " if isinstance(value,(int,float)) and self.__numerical:\n", + " self.__value=value\n", + " elif isinstance(value,str) and not self.__numerical:\n", + " if value in self.__letter_grades:\n", + " self.__value=value\n", + " else:\n", + " print( self.name()+\" Error: Bad Grade.\")\n", + " raise Exception\n", + " \n", + " def value(self):\n", + " return self.__value\n", + " \n", + " def numerical(self):\n", + " return self.__numerical\n", + " \n", + " def gradebook_name(self):\n", + " return self.__gradebook_name\n", + " \n", + " def __str__(self):\n", + " return self.__gradebook_name+\": \"+str(self.__value)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Student Representation" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class student(data):\n", + " def __init__(self, first_name, last_name, id_number):\n", + " self.__grades=dict()\n", + " self.__id_number=id_number\n", + " data.__init__(self,first_name+\" \"+last_name+\" Student Data\")\n", + "\n", + " def add_grade(self,a_grade,overwrite=False):\n", + " if overwrite or not a_grade.gradebook_name() in self.__grades:\n", + " self.__grades[a_grade.gradebook_name()]=a_grade\n", + " else:\n", + " print (self.name()+\" Error Adding Grade \"+a_grade.name()+\". Grade already exists.\")\n", + " raise Exception\n", + "\n", + " def id_number(self):\n", + " return self.__id_number\n", + " \n", + " def grade_names(self):\n", + " return self.__grades.keys()\n", + " \n", + " def grades(self):\n", + " return self.__grades\n", + " \n", + " def __getitem__(self,key):\n", + " return self.__grades[key]\n", + " \n", + " def print_grades(self):\n", + " for grade in self.__grades:\n", + " print (self.__grades[grade])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Grade Calculator" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class grade_calculator(alg): \n", + " def __init__(self,name,stats):\n", + " self.__stats=stats\n", + " alg.__init__(self,name)\n", + "\n", + " def apply(self,a_grade):\n", + " raise NotImplementedError\n", + " \n", + "\n", + "class uncurved_letter_grade_percent(grade_calculator):\n", + " __grades_definition=[ (.97,\"A+\"),\n", + " (.93,\"A\"),\n", + " (.9,\"A-\"),\n", + " (.87,\"B+\"),\n", + " (.83,\"B\"),\n", + " (.8,\"B-\"),\n", + " (.77,\"C+\"),\n", + " (.73,\"C\"),\n", + " (.7,\"C-\"),\n", + " (.67,\"C+\"),\n", + " (.63,\"C\"),\n", + " (.6,\"C-\"),\n", + " (.57,\"F+\"),\n", + " (.53,\"F\"),\n", + " (0.,\"F-\")]\n", + " __max_grade=100.\n", + " __grade_name=str()\n", + " \n", + " def __init__(self,grade_name,max_grade=100.):\n", + " self.__max_grade=max_grade\n", + " self.__grade_name=grade_name\n", + " grade_calculator.__init__(self,\n", + " \"Uncurved Percent Based Grade Calculator \"+self.__grade_name+\" Max=\"+str(self.__max_grade),\n", + " False)\n", + " \n", + "\n", + " def apply(self,a_grade):\n", + " if not isinstance(a_grade,grade):\n", + " print (self.name()+ \" Error: Did not get an proper grade as input.\")\n", + " raise Exception\n", + " if not a_grade.numerical():\n", + " print (self.name()+ \" Error: Did not get a numerical grade as input.\")\n", + " raise Exception\n", + "\n", + " percent=a_grade.value()/self.__max_grade\n", + " \n", + " for i,v in enumerate(self.__grades_definition):\n", + " if percent>=v[0]:\n", + " break\n", + " \n", + " return grade(self.__grade_name,value=self.__grades_definition[i][1])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "class curved_letter_grade(grade_calculator):\n", + " __grades_definition=[ (.97,\"A+\"),\n", + " (.93,\"A\"),\n", + " (.9,\"A-\"),\n", + " (.87,\"B+\"),\n", + " (.83,\"B\"),\n", + " (.8,\"B-\"),\n", + " (.77,\"C+\"),\n", + " (.73,\"C\"),\n", + " (.7,\"C-\"),\n", + " (.67,\"C+\"),\n", + " (.63,\"C\"),\n", + " (.6,\"C-\"),\n", + " (.57,\"F+\"),\n", + " (.53,\"F\"),\n", + " (0.,\"F-\")]\n", + " __max_grade=100.\n", + " __grade_name=str()\n", + " \n", + " def __init__(self,grade_name,mean,std,max_grade=100.):\n", + " self.__max_grade=max_grade\n", + " self.__mean=mean\n", + " self.__std=std\n", + " self.__grade_name=grade_name\n", + " grade_calculator.__init__(self,\n", + " \"Curved Percent Based Grade Calculator \"+self.__grade_name+ \\\n", + " \" Mean=\"+str(self.__mean)+\\\n", + " \" STD=\"+str(self.__std)+\\\n", + " \" Max=\"+str(self.__max_grade),\n", + " False)\n", + " \n", + "\n", + " def apply(self,a_grade):\n", + " if not isinstance(a_grade,grade):\n", + " print (self.name()+ \" Error: Did not get an proper grade as input.\")\n", + " raise Exception\n", + " if not a_grade.numerical():\n", + " print (self.name()+ \" Error: Did not get a numerical grade as input.\")\n", + " raise Exception\n", + " \n", + " # Rescale the grade\n", + " percent=a_grade.value()/self.__max_grade\n", + " shift_to_zero=percent-(self.__mean/self.__max_grade)\n", + " scale_std=0.1*shift_to_zero/(self.__std/self.__max_grade)\n", + " scaled_percent=scale_std+0.8\n", + " \n", + " for i,v in enumerate(self.__grades_definition):\n", + " if scaled_percent>=v[0]:\n", + " break\n", + " \n", + " return grade(self.__grade_name,value=self.__grades_definition[i][1])\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stats Computation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import math\n", + "\n", + "class statistics_calculator(alg): \n", + " def __init__(self,name):\n", + " alg.__init__(self,name)\n", + "\n", + " def apply(self,grades):\n", + " raise NotImplementedError\n", + " \n", + "class mean_std_calculator(statistics_calculator):\n", + " def __init__(self):\n", + " statistics_calculator.__init__(self,\"Mean and Standard Deviation Calculator\")\n", + " \n", + " def apply(self,grades):\n", + " return np.mean(grades),math.sqrt(np.var(grades))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Grade Summing" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "class summary_calculator(alg): \n", + " def __init__(self,name):\n", + " alg.__init__(self,name)\n", + "\n", + " def apply(self,a_student):\n", + " raise NotImplementedError\n", + " \n", + "class grade_summer(summary_calculator):\n", + " def __init__(self,prefix,n):\n", + " self.__prefix=prefix\n", + " self.__n=n\n", + " statistics_calculator.__init__(self,\"Sum Grades\")\n", + " \n", + " def apply(self,a_student):\n", + " labels=[self.__prefix+str(x) for x in range(1,self.__n)]\n", + " \n", + " grade_sum=0.\n", + " for label in labels:\n", + " grade_sum+=a_student[label].value()\n", + " \n", + " return grade(self.__prefix+\"sum\",value=grade_sum)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gradebook" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "class grade_book(data):\n", + " # New member class to hold arbitrary data associated with the class \n", + " def __init__(self,name):\n", + " data.__init__(self,name+\" Course Grade Book\")\n", + " self.__students=dict() \n", + " \n", + " # New method to add data \n", + " def add_student(self,a_student):\n", + " self.__students[a_student.id_number()]=a_student\n", + "\n", + " # New method to allow iterating over students\n", + " def get_students(self):\n", + " return self.__students\n", + " \n", + " def assign_grade(self,key,a_grade):\n", + " the_student=None\n", + " try:\n", + " the_student=self.__students[key]\n", + " except:\n", + " for id in self.__students:\n", + " if key == self.__students[id].name():\n", + " the_student=self.__students[id]\n", + " break\n", + " if the_student:\n", + " the_student.add_grade(a_grade)\n", + " else:\n", + " print (self.name()+\" Error: Did not find student.\")\n", + " \n", + " def apply_summary(self,a_grader):\n", + " for k,a_student in self.__students.items():\n", + " a_student.add_grade(a_grader.apply(a_student))\n", + " \n", + " def apply_grader(self,a_grader,grade_name):\n", + " for k,a_student in self.__students.items():\n", + " a_student.add_grade(a_grader.apply(a_student[grade_name]))\n", + " \n", + " def apply_stats(self,a_stat_comp,grade_name):\n", + " grades=list()\n", + " for k,a_student in self.__students.items():\n", + " grades.append(a_student[grade_name].value())\n", + " return a_stat_comp.apply(grades)\n", + " \n", + " def students(self):\n", + " return self.__students\n", + " \n", + " def print_grades(self,grade_name):\n", + " if isinstance(grade_name,str):\n", + " grade_names=list()\n", + " grade_names.append(grade_name)\n", + " else:\n", + " grade_names=grade_name\n", + " \n", + " for k,a_student in self.__students.items():\n", + " print (a_student.name(),end=\"\")\n", + " for a_grade_name in grade_names:\n", + " print (a_student[a_grade_name],end=\"\")\n", + " print()\n", + " \n", + " def print_students(self): \n", + " for k,a_student in self.__students.items():\n", + " print (k, a_student.name())\n", + " a_student.print_grades()\n", + " print (\"_______________________________________\")\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building a Gradebook" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Read Data into a Pandas DataFrame\n", + "df = pd.read_csv(\"Data-1401-Grades-Fixed.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "df_0=df[np.logical_not(np.isnan(df[\"Student ID\"]))]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Student ID', 'Lab 1', 'Lab 2', 'Lab 3', 'Lab 4', 'Exam 1', 'Lab 5',\n", + " 'Lab 6', 'Lab 7'],\n", + " dtype='object')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_0.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Student ID 1000954113.0\n", + "Lab 1 100\n", + "Lab 2 81\n", + "Lab 3 25\n", + "Lab 4 35\n", + "Exam 1 0\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1000625629.0\n", + "Lab 1 95\n", + "Lab 2 87\n", + "Lab 3 0\n", + "Lab 4 88\n", + "Exam 1 120\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1000847686.0\n", + "Lab 1 100\n", + "Lab 2 0\n", + "Lab 3 0\n", + "Lab 4 0\n", + "Exam 1 90\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001108137.0\n", + "Lab 1 90\n", + "Lab 2 96\n", + "Lab 3 0\n", + "Lab 4 35\n", + "Exam 1 90\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1000999851.0\n", + "Lab 1 95\n", + "Lab 2 94\n", + "Lab 3 43\n", + "Lab 4 0\n", + "Exam 1 100\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001214491.0\n", + "Lab 1 80\n", + "Lab 2 82\n", + "Lab 3 30\n", + "Lab 4 0\n", + "Exam 1 90\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001428148.0\n", + "Lab 1 80\n", + "Lab 2 90\n", + "Lab 3 24\n", + "Lab 4 0\n", + "Exam 1 80\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001397199.0\n", + "Lab 1 100\n", + "Lab 2 94\n", + "Lab 3 87\n", + "Lab 4 12\n", + "Exam 1 105\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001372854.0\n", + "Lab 1 100\n", + "Lab 2 70\n", + "Lab 3 17\n", + "Lab 4 35\n", + "Exam 1 20\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001558073.0\n", + "Lab 1 95\n", + "Lab 2 86\n", + "Lab 3 0\n", + "Lab 4 0\n", + "Exam 1 84\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001593219.0\n", + "Lab 1 100\n", + "Lab 2 96\n", + "Lab 3 96\n", + "Lab 4 85\n", + "Exam 1 120\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001596311.0\n", + "Lab 1 90\n", + "Lab 2 92\n", + "Lab 3 71\n", + "Lab 4 57\n", + "Exam 1 100\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001608680.0\n", + "Lab 1 100\n", + "Lab 2 94\n", + "Lab 3 36\n", + "Lab 4 61\n", + "Exam 1 95\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001618032.0\n", + "Lab 1 100\n", + "Lab 2 96\n", + "Lab 3 97\n", + "Lab 4 86\n", + "Exam 1 110\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001652624.0\n", + "Lab 1 100\n", + "Lab 2 90\n", + "Lab 3 0\n", + "Lab 4 0\n", + "Exam 1 0\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001658617.0\n", + "Lab 1 100\n", + "Lab 2 94\n", + "Lab 3 96\n", + "Lab 4 92\n", + "Exam 1 100\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001663166.0\n", + "Lab 1 95\n", + "Lab 2 94\n", + "Lab 3 38\n", + "Lab 4 0\n", + "Exam 1 82\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001674179.0\n", + "Lab 1 95\n", + "Lab 2 90\n", + "Lab 3 23\n", + "Lab 4 0\n", + "Exam 1 0\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001722244.0\n", + "Lab 1 95\n", + "Lab 2 100\n", + "Lab 3 0\n", + "Lab 4 0\n", + "Exam 1 0\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001543608.0\n", + "Lab 1 100\n", + "Lab 2 96\n", + "Lab 3 19\n", + "Lab 4 51\n", + "Exam 1 111\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001547659.0\n", + "Lab 1 100\n", + "Lab 2 94\n", + "Lab 3 86\n", + "Lab 4 75\n", + "Exam 1 100\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001458157.0\n", + "Lab 1 85\n", + "Lab 2 88\n", + "Lab 3 21\n", + "Lab 4 37\n", + "Exam 1 50\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001496565.0\n", + "Lab 1 100\n", + "Lab 2 98\n", + "Lab 3 100\n", + "Lab 4 100\n", + "Exam 1 120\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001519928.0\n", + "Lab 1 100\n", + "Lab 2 94\n", + "Lab 3 85\n", + "Lab 4 50\n", + "Exam 1 115\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001774305.0\n", + "Lab 1 100\n", + "Lab 2 94\n", + "Lab 3 20\n", + "Lab 4 79\n", + "Exam 1 120\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001696890.0\n", + "Lab 1 0\n", + "Lab 2 96\n", + "Lab 3 0\n", + "Lab 4 40\n", + "Exam 1 100\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001232602.0\n", + "Lab 1 80\n", + "Lab 2 94\n", + "Lab 3 79\n", + "Lab 4 60\n", + "Exam 1 90\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001464999.0\n", + "Lab 1 95\n", + "Lab 2 94\n", + "Lab 3 0\n", + "Lab 4 0\n", + "Exam 1 105\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001644554.0\n", + "Lab 1 80\n", + "Lab 2 80\n", + "Lab 3 10\n", + "Lab 4 0\n", + "Exam 1 0\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n", + "Student ID 1001684648.0\n", + "Lab 1 0\n", + "Lab 2 0\n", + "Lab 3 0\n", + "Lab 4 0\n", + "Exam 1 0\n", + "Lab 5 nan\n", + "Lab 6 nan\n", + "Lab 7 nan\n" + ] + } + ], + "source": [ + "for d in df_0.iterrows():\n", + " for x in d[1].iteritems():\n", + " print(x[0],x[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "a_grade_book=grade_book(\"Data 1401\")\n", + "\n", + "for student_i, d in enumerate(df_0.iterrows()):\n", + " a_student_0=student(\"Student\",str(student_i),int(d[1][\"Student ID\"]))\n", + "\n", + " for k,v in d[1].iteritems():\n", + " a_student_0.add_grade(grade(k,value=float(v)))\n", + "\n", + " a_grade_book.add_student(a_student_0)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000954113 Student 0 Student Data\n", + "Student ID: 1000954113.0\n", + "Lab 1: 100.0\n", + "Lab 2: 81.0\n", + "Lab 3: 25.0\n", + "Lab 4: 35.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1000625629 Student 1 Student Data\n", + "Student ID: 1000625629.0\n", + "Lab 1: 95.0\n", + "Lab 2: 87.0\n", + "Lab 3: 0.0\n", + "Lab 4: 88.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1000847686 Student 2 Student Data\n", + "Student ID: 1000847686.0\n", + "Lab 1: 100.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001108137 Student 3 Student Data\n", + "Student ID: 1001108137.0\n", + "Lab 1: 90.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 35.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1000999851 Student 4 Student Data\n", + "Student ID: 1000999851.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 43.0\n", + "Lab 4: 0.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001214491 Student 5 Student Data\n", + "Student ID: 1001214491.0\n", + "Lab 1: 80.0\n", + "Lab 2: 82.0\n", + "Lab 3: 30.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001428148 Student 6 Student Data\n", + "Student ID: 1001428148.0\n", + "Lab 1: 80.0\n", + "Lab 2: 90.0\n", + "Lab 3: 24.0\n", + "Lab 4: 0.0\n", + "Exam 1: 80.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001397199 Student 7 Student Data\n", + "Student ID: 1001397199.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 87.0\n", + "Lab 4: 12.0\n", + "Exam 1: 105.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001372854 Student 8 Student Data\n", + "Student ID: 1001372854.0\n", + "Lab 1: 100.0\n", + "Lab 2: 70.0\n", + "Lab 3: 17.0\n", + "Lab 4: 35.0\n", + "Exam 1: 20.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001558073 Student 9 Student Data\n", + "Student ID: 1001558073.0\n", + "Lab 1: 95.0\n", + "Lab 2: 86.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 84.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001593219 Student 10 Student Data\n", + "Student ID: 1001593219.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 96.0\n", + "Lab 4: 85.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001596311 Student 11 Student Data\n", + "Student ID: 1001596311.0\n", + "Lab 1: 90.0\n", + "Lab 2: 92.0\n", + "Lab 3: 71.0\n", + "Lab 4: 57.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001608680 Student 12 Student Data\n", + "Student ID: 1001608680.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 36.0\n", + "Lab 4: 61.0\n", + "Exam 1: 95.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001618032 Student 13 Student Data\n", + "Student ID: 1001618032.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 97.0\n", + "Lab 4: 86.0\n", + "Exam 1: 110.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001652624 Student 14 Student Data\n", + "Student ID: 1001652624.0\n", + "Lab 1: 100.0\n", + "Lab 2: 90.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001658617 Student 15 Student Data\n", + "Student ID: 1001658617.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 96.0\n", + "Lab 4: 92.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001663166 Student 16 Student Data\n", + "Student ID: 1001663166.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 38.0\n", + "Lab 4: 0.0\n", + "Exam 1: 82.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001674179 Student 17 Student Data\n", + "Student ID: 1001674179.0\n", + "Lab 1: 95.0\n", + "Lab 2: 90.0\n", + "Lab 3: 23.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001722244 Student 18 Student Data\n", + "Student ID: 1001722244.0\n", + "Lab 1: 95.0\n", + "Lab 2: 100.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001543608 Student 19 Student Data\n", + "Student ID: 1001543608.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 19.0\n", + "Lab 4: 51.0\n", + "Exam 1: 111.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001547659 Student 20 Student Data\n", + "Student ID: 1001547659.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 86.0\n", + "Lab 4: 75.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001458157 Student 21 Student Data\n", + "Student ID: 1001458157.0\n", + "Lab 1: 85.0\n", + "Lab 2: 88.0\n", + "Lab 3: 21.0\n", + "Lab 4: 37.0\n", + "Exam 1: 50.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001496565 Student 22 Student Data\n", + "Student ID: 1001496565.0\n", + "Lab 1: 100.0\n", + "Lab 2: 98.0\n", + "Lab 3: 100.0\n", + "Lab 4: 100.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001519928 Student 23 Student Data\n", + "Student ID: 1001519928.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 85.0\n", + "Lab 4: 50.0\n", + "Exam 1: 115.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001774305 Student 24 Student Data\n", + "Student ID: 1001774305.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 20.0\n", + "Lab 4: 79.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001696890 Student 25 Student Data\n", + "Student ID: 1001696890.0\n", + "Lab 1: 0.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 40.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001232602 Student 26 Student Data\n", + "Student ID: 1001232602.0\n", + "Lab 1: 80.0\n", + "Lab 2: 94.0\n", + "Lab 3: 79.0\n", + "Lab 4: 60.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001464999 Student 27 Student Data\n", + "Student ID: 1001464999.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 105.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001644554 Student 28 Student Data\n", + "Student ID: 1001644554.0\n", + "Lab 1: 80.0\n", + "Lab 2: 80.0\n", + "Lab 3: 10.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n", + "1001684648 Student 29 Student Data\n", + "Student ID: 1001684648.0\n", + "Lab 1: 0.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "_______________________________________\n" + ] + } + ], + "source": [ + "a_grade_book.print_students()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(85.13333333333334, 23.57361990776036)\n" + ] + } + ], + "source": [ + "Lab_2_stats=a_grade_book.apply_stats(mean_std_calculator(),\"Lab 2\")\n", + "print(Lab_2_stats)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "a_grade_book.apply_grader(curved_letter_grade(\"Lab 2 Letter\",Lab_2_stats[0],Lab_2_stats[1]), \"Lab 2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000954113 Student 0 Student Data\n", + "Student ID: 1000954113.0\n", + "Lab 1: 100.0\n", + "Lab 2: 81.0\n", + "Lab 3: 25.0\n", + "Lab 4: 35.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: C+\n", + "_______________________________________\n", + "1000625629 Student 1 Student Data\n", + "Student ID: 1000625629.0\n", + "Lab 1: 95.0\n", + "Lab 2: 87.0\n", + "Lab 3: 0.0\n", + "Lab 4: 88.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "_______________________________________\n", + "1000847686 Student 2 Student Data\n", + "Student ID: 1000847686.0\n", + "Lab 1: 100.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: F-\n", + "_______________________________________\n", + "1001108137 Student 3 Student Data\n", + "Student ID: 1001108137.0\n", + "Lab 1: 90.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 35.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1000999851 Student 4 Student Data\n", + "Student ID: 1000999851.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 43.0\n", + "Lab 4: 0.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001214491 Student 5 Student Data\n", + "Student ID: 1001214491.0\n", + "Lab 1: 80.0\n", + "Lab 2: 82.0\n", + "Lab 3: 30.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: C+\n", + "_______________________________________\n", + "1001428148 Student 6 Student Data\n", + "Student ID: 1001428148.0\n", + "Lab 1: 80.0\n", + "Lab 2: 90.0\n", + "Lab 3: 24.0\n", + "Lab 4: 0.0\n", + "Exam 1: 80.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "_______________________________________\n", + "1001397199 Student 7 Student Data\n", + "Student ID: 1001397199.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 87.0\n", + "Lab 4: 12.0\n", + "Exam 1: 105.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001372854 Student 8 Student Data\n", + "Student ID: 1001372854.0\n", + "Lab 1: 100.0\n", + "Lab 2: 70.0\n", + "Lab 3: 17.0\n", + "Lab 4: 35.0\n", + "Exam 1: 20.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: C\n", + "_______________________________________\n", + "1001558073 Student 9 Student Data\n", + "Student ID: 1001558073.0\n", + "Lab 1: 95.0\n", + "Lab 2: 86.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 84.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "_______________________________________\n", + "1001593219 Student 10 Student Data\n", + "Student ID: 1001593219.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 96.0\n", + "Lab 4: 85.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001596311 Student 11 Student Data\n", + "Student ID: 1001596311.0\n", + "Lab 1: 90.0\n", + "Lab 2: 92.0\n", + "Lab 3: 71.0\n", + "Lab 4: 57.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "_______________________________________\n", + "1001608680 Student 12 Student Data\n", + "Student ID: 1001608680.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 36.0\n", + "Lab 4: 61.0\n", + "Exam 1: 95.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001618032 Student 13 Student Data\n", + "Student ID: 1001618032.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 97.0\n", + "Lab 4: 86.0\n", + "Exam 1: 110.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001652624 Student 14 Student Data\n", + "Student ID: 1001652624.0\n", + "Lab 1: 100.0\n", + "Lab 2: 90.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "_______________________________________\n", + "1001658617 Student 15 Student Data\n", + "Student ID: 1001658617.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 96.0\n", + "Lab 4: 92.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001663166 Student 16 Student Data\n", + "Student ID: 1001663166.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 38.0\n", + "Lab 4: 0.0\n", + "Exam 1: 82.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001674179 Student 17 Student Data\n", + "Student ID: 1001674179.0\n", + "Lab 1: 95.0\n", + "Lab 2: 90.0\n", + "Lab 3: 23.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "_______________________________________\n", + "1001722244 Student 18 Student Data\n", + "Student ID: 1001722244.0\n", + "Lab 1: 95.0\n", + "Lab 2: 100.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001543608 Student 19 Student Data\n", + "Student ID: 1001543608.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 19.0\n", + "Lab 4: 51.0\n", + "Exam 1: 111.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001547659 Student 20 Student Data\n", + "Student ID: 1001547659.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 86.0\n", + "Lab 4: 75.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001458157 Student 21 Student Data\n", + "Student ID: 1001458157.0\n", + "Lab 1: 85.0\n", + "Lab 2: 88.0\n", + "Lab 3: 21.0\n", + "Lab 4: 37.0\n", + "Exam 1: 50.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "_______________________________________\n", + "1001496565 Student 22 Student Data\n", + "Student ID: 1001496565.0\n", + "Lab 1: 100.0\n", + "Lab 2: 98.0\n", + "Lab 3: 100.0\n", + "Lab 4: 100.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001519928 Student 23 Student Data\n", + "Student ID: 1001519928.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 85.0\n", + "Lab 4: 50.0\n", + "Exam 1: 115.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001774305 Student 24 Student Data\n", + "Student ID: 1001774305.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 20.0\n", + "Lab 4: 79.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001696890 Student 25 Student Data\n", + "Student ID: 1001696890.0\n", + "Lab 1: 0.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 40.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001232602 Student 26 Student Data\n", + "Student ID: 1001232602.0\n", + "Lab 1: 80.0\n", + "Lab 2: 94.0\n", + "Lab 3: 79.0\n", + "Lab 4: 60.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001464999 Student 27 Student Data\n", + "Student ID: 1001464999.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 105.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001644554 Student 28 Student Data\n", + "Student ID: 1001644554.0\n", + "Lab 1: 80.0\n", + "Lab 2: 80.0\n", + "Lab 3: 10.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: C+\n", + "_______________________________________\n", + "1001684648 Student 29 Student Data\n", + "Student ID: 1001684648.0\n", + "Lab 1: 0.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: F-\n", + "_______________________________________\n" + ] + } + ], + "source": [ + "a_grade_book.print_students()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "a_grade_book.apply_summary(grade_summer(\"Lab \",5))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000954113 Student 0 Student Data\n", + "Student ID: 1000954113.0\n", + "Lab 1: 100.0\n", + "Lab 2: 81.0\n", + "Lab 3: 25.0\n", + "Lab 4: 35.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: C+\n", + "Lab sum: 241.0\n", + "_______________________________________\n", + "1000625629 Student 1 Student Data\n", + "Student ID: 1000625629.0\n", + "Lab 1: 95.0\n", + "Lab 2: 87.0\n", + "Lab 3: 0.0\n", + "Lab 4: 88.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "Lab sum: 270.0\n", + "_______________________________________\n", + "1000847686 Student 2 Student Data\n", + "Student ID: 1000847686.0\n", + "Lab 1: 100.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: F-\n", + "Lab sum: 100.0\n", + "_______________________________________\n", + "1001108137 Student 3 Student Data\n", + "Student ID: 1001108137.0\n", + "Lab 1: 90.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 35.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 221.0\n", + "_______________________________________\n", + "1000999851 Student 4 Student Data\n", + "Student ID: 1000999851.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 43.0\n", + "Lab 4: 0.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 232.0\n", + "_______________________________________\n", + "1001214491 Student 5 Student Data\n", + "Student ID: 1001214491.0\n", + "Lab 1: 80.0\n", + "Lab 2: 82.0\n", + "Lab 3: 30.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: C+\n", + "Lab sum: 192.0\n", + "_______________________________________\n", + "1001428148 Student 6 Student Data\n", + "Student ID: 1001428148.0\n", + "Lab 1: 80.0\n", + "Lab 2: 90.0\n", + "Lab 3: 24.0\n", + "Lab 4: 0.0\n", + "Exam 1: 80.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "Lab sum: 194.0\n", + "_______________________________________\n", + "1001397199 Student 7 Student Data\n", + "Student ID: 1001397199.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 87.0\n", + "Lab 4: 12.0\n", + "Exam 1: 105.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 293.0\n", + "_______________________________________\n", + "1001372854 Student 8 Student Data\n", + "Student ID: 1001372854.0\n", + "Lab 1: 100.0\n", + "Lab 2: 70.0\n", + "Lab 3: 17.0\n", + "Lab 4: 35.0\n", + "Exam 1: 20.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: C\n", + "Lab sum: 222.0\n", + "_______________________________________\n", + "1001558073 Student 9 Student Data\n", + "Student ID: 1001558073.0\n", + "Lab 1: 95.0\n", + "Lab 2: 86.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 84.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "Lab sum: 181.0\n", + "_______________________________________\n", + "1001593219 Student 10 Student Data\n", + "Student ID: 1001593219.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 96.0\n", + "Lab 4: 85.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 377.0\n", + "_______________________________________\n", + "1001596311 Student 11 Student Data\n", + "Student ID: 1001596311.0\n", + "Lab 1: 90.0\n", + "Lab 2: 92.0\n", + "Lab 3: 71.0\n", + "Lab 4: 57.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "Lab sum: 310.0\n", + "_______________________________________\n", + "1001608680 Student 12 Student Data\n", + "Student ID: 1001608680.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 36.0\n", + "Lab 4: 61.0\n", + "Exam 1: 95.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 291.0\n", + "_______________________________________\n", + "1001618032 Student 13 Student Data\n", + "Student ID: 1001618032.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 97.0\n", + "Lab 4: 86.0\n", + "Exam 1: 110.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 379.0\n", + "_______________________________________\n", + "1001652624 Student 14 Student Data\n", + "Student ID: 1001652624.0\n", + "Lab 1: 100.0\n", + "Lab 2: 90.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "Lab sum: 190.0\n", + "_______________________________________\n", + "1001658617 Student 15 Student Data\n", + "Student ID: 1001658617.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 96.0\n", + "Lab 4: 92.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 382.0\n", + "_______________________________________\n", + "1001663166 Student 16 Student Data\n", + "Student ID: 1001663166.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 38.0\n", + "Lab 4: 0.0\n", + "Exam 1: 82.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 227.0\n", + "_______________________________________\n", + "1001674179 Student 17 Student Data\n", + "Student ID: 1001674179.0\n", + "Lab 1: 95.0\n", + "Lab 2: 90.0\n", + "Lab 3: 23.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "Lab sum: 208.0\n", + "_______________________________________\n", + "1001722244 Student 18 Student Data\n", + "Student ID: 1001722244.0\n", + "Lab 1: 95.0\n", + "Lab 2: 100.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 195.0\n", + "_______________________________________\n", + "1001543608 Student 19 Student Data\n", + "Student ID: 1001543608.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 19.0\n", + "Lab 4: 51.0\n", + "Exam 1: 111.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 266.0\n", + "_______________________________________\n", + "1001547659 Student 20 Student Data\n", + "Student ID: 1001547659.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 86.0\n", + "Lab 4: 75.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 355.0\n", + "_______________________________________\n", + "1001458157 Student 21 Student Data\n", + "Student ID: 1001458157.0\n", + "Lab 1: 85.0\n", + "Lab 2: 88.0\n", + "Lab 3: 21.0\n", + "Lab 4: 37.0\n", + "Exam 1: 50.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "Lab sum: 231.0\n", + "_______________________________________\n", + "1001496565 Student 22 Student Data\n", + "Student ID: 1001496565.0\n", + "Lab 1: 100.0\n", + "Lab 2: 98.0\n", + "Lab 3: 100.0\n", + "Lab 4: 100.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 398.0\n", + "_______________________________________\n", + "1001519928 Student 23 Student Data\n", + "Student ID: 1001519928.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 85.0\n", + "Lab 4: 50.0\n", + "Exam 1: 115.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 329.0\n", + "_______________________________________\n", + "1001774305 Student 24 Student Data\n", + "Student ID: 1001774305.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 20.0\n", + "Lab 4: 79.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 293.0\n", + "_______________________________________\n", + "1001696890 Student 25 Student Data\n", + "Student ID: 1001696890.0\n", + "Lab 1: 0.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 40.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 136.0\n", + "_______________________________________\n", + "1001232602 Student 26 Student Data\n", + "Student ID: 1001232602.0\n", + "Lab 1: 80.0\n", + "Lab 2: 94.0\n", + "Lab 3: 79.0\n", + "Lab 4: 60.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 313.0\n", + "_______________________________________\n", + "1001464999 Student 27 Student Data\n", + "Student ID: 1001464999.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 105.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab sum: 189.0\n", + "_______________________________________\n", + "1001644554 Student 28 Student Data\n", + "Student ID: 1001644554.0\n", + "Lab 1: 80.0\n", + "Lab 2: 80.0\n", + "Lab 3: 10.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: C+\n", + "Lab sum: 170.0\n", + "_______________________________________\n", + "1001684648 Student 29 Student Data\n", + "Student ID: 1001684648.0\n", + "Lab 1: 0.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: F-\n", + "Lab sum: 0.0\n", + "_______________________________________\n" + ] + } + ], + "source": [ + "a_grade_book.print_students()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Algorithms\n", + "\n", + "So far, we ended up with three different types of algorithms:\n", + "\n", + "* `grade_calcuator` algorithms: Take grade from the student and return another grade. We use this for assigning letter grades and curving.\n", + "* `statistics_calculator` algorithms: Take a set of grades for all students and return numbers associated with the class. We used these to compute the mean and standard deviation for a class.\n", + "* `summary_calculator` algorithms: Take many grades from a student and return a another grade. We used these to sum grades of a student.\n", + "\n", + "Here are the base classes:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class grade_calculator(alg): \n", + " def __init__(self,name, stats):\n", + " self.__stats=stats\n", + " alg.__init__(self,name)\n", + "\n", + " def apply(self,a_grade):\n", + " raise NotImplementedError\n", + " # Returns a grade\n", + " \n", + "class statistics_calculator(alg): \n", + " def __init__(self,name):\n", + " alg.__init__(self,name)\n", + "\n", + " def apply(self,grades):\n", + " raise NotImplementedError\n", + " # returns numbers\n", + " \n", + "class summary_calculator(alg): \n", + " def __init__(self,name):\n", + " alg.__init__(self,name)\n", + "\n", + " def apply(self,a_student):\n", + " raise NotImplementedError\n", + " # returns a grade\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While this approach works, users will need to be aware of how to use each type of algorithm and it would be difficult to create a system that would automate tasks, like curving a grade, that would require running several algorithms. We may be able to find a more general implementation that can unify all of these types of algorithms. There are several issues to consider:\n", + "\n", + "* **Input**: Each of these algorithms take different inputs, either a single grade, all grades of a specific type for all students, or different grades for the same student.\n", + "* **Output**: Some of these algorithms generate new grades for each student, while others provide compute over the whole class.\n", + "* **Information storage**: While we can store grades with students, other information like means and standard deviation have to do with the class and not any individual student. \n", + "\n", + "Lets first address the information storage issue. We simply need a mechanism to store information that has to do with a class instead of a student. The gradebook is what holds all of the students in the class, so we just add new data members to keep additional information. The only type of information that we are holding for now are specific exam means and standard deviations. We could choose to create new data objects for such information. Or we could simply stuff the information into a dictionary. The latter is easier and may suffice, so let's try it.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "class grade_book(data):\n", + " # New member class to hold arbitrary data associated with the class \n", + " def __init__(self,name):\n", + " data.__init__(self,name+\" Course Grade Book\")\n", + " self.__students=dict()\n", + " self.__data=dict()\n", + " \n", + " # New method to access data\n", + " def __getitem__(self,key):\n", + " return self.__data[key]\n", + " \n", + " # New method to add data\n", + " def __setitem__(self, key, value):\n", + " self.__data[key] = value\n", + " \n", + " def add_student(self,a_student):\n", + " self.__students[a_student.id_number()]=a_student\n", + "\n", + " def get_students(self):\n", + " return self.__students\n", + " \n", + " def assign_grade(self,key,a_grade):\n", + " the_student=None\n", + " try:\n", + " the_student=self.__students[key]\n", + " except:\n", + " for id in self.__students:\n", + " if key == self.__students[id].name():\n", + " the_student=self.__students[id]\n", + " break\n", + " if the_student:\n", + " the_student.add_grade(a_grade)\n", + " else:\n", + " print (self.name()+\" Error: Did not find student.\")\n", + " \n", + " def apply_summary(self,a_grader):\n", + " for k,a_student in self.__students.items():\n", + " a_student.add_grade(a_grader.apply(a_student))\n", + " \n", + " def apply_grader(self,a_grader,grade_name):\n", + " for k,a_student in self.__students.items():\n", + " a_student.add_grade(a_grader.apply(a_student[grade_name]))\n", + " \n", + " def apply_stats(self,a_stat_comp,grade_name):\n", + " grades=list()\n", + " for k,a_student in self.__students.items():\n", + " grades.append(a_student[grade_name].value())\n", + " return a_stat_comp.apply(grades)\n", + "\n", + " # Accessors\n", + " \n", + " def data(self):\n", + " return self.__data\n", + "\n", + " def students(self):\n", + " return self.__students\n", + " \n", + " \n", + " def get_data(self,key=None):\n", + " a_data=dict()\n", + " for k,v in self.__data.items():\n", + " if key:\n", + " if key in k:\n", + " a_data[k]=v\n", + " else:\n", + " a_data[k]=v\n", + "\n", + " return a_data\n", + " \n", + " # Print functions\n", + " def print_data(self):\n", + " for k,v in self.__data.items():\n", + " print (k,\":\",v)\n", + " \n", + " def print_grades(self,grade_name):\n", + " if isinstance(grade_name,str):\n", + " grade_names=list()\n", + " grade_names.append(grade_name)\n", + " else:\n", + " grade_names=grade_name\n", + " \n", + " for k,a_student in self.__students.items():\n", + " print (a_student.name(),end=\"\")\n", + " for a_grade_name in grade_names:\n", + " print (a_student[a_grade_name],end=\"\")\n", + " print()\n", + " \n", + " def print_students(self): \n", + " for k,a_student in self.__students.items():\n", + " print (k, a_student.name())\n", + " a_student.print_grades()\n", + " print (\"_______________________________________\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100\n" + ] + } + ], + "source": [ + "a_grade_book=grade_book(\"Test Gradebook\")\n", + "a_grade_book[\"Example Data\"]=100\n", + "print (a_grade_book[\"Example Data\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now lets address the input/output issue. We have several options:\n", + "\n", + "1. Keep things the same. We keep 3 different types of algorithms. Note that necessarily needed to create separate apply methods in the `grade_book` class (`apply_grader`, `apply_stats`, ...) for each type of algorithm with a different method name. Here the user has to know what type of algorithm he/she is applying and then use the right apply method.\n", + "\n", + "2. Keep the 3 differnt types of algorithms, but create a general apply method to `grade_book` that checks the type of the algorithm and then uses the right apply method.\n", + "\n", + "3. Unify the 3 types of algorithms, pass into them the `grade_book` instance and let them decide what to do.\n", + "\n", + "Option 2 is a reasonable solution, but let's try option 3 because it provides more flexibility for future algorithms that may take other input/output combinations. \n", + "\n", + "First turn our three different `calculator` algorithm base classes into one:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "class calculator(alg): \n", + " def __init__(self,name):\n", + " alg.__init__(self,name)\n", + "\n", + " def apply(self,a_grade_book):\n", + " raise NotImplementedError\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, lets combine the `apply` methods in the `grade_book` class:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "class grade_book(data):\n", + " # New member class to hold arbitrary data associated with the class \n", + " def __init__(self,name):\n", + " data.__init__(self,name+\" Course Grade Book\")\n", + " self.__students=dict()\n", + " self.__data=dict()\n", + " \n", + " # New method to access data\n", + " def __getitem__(self,key):\n", + " return self.__data[key]\n", + " \n", + " # New method to add data\n", + " def __setitem__(self, key, value):\n", + " self.__data[key] = value\n", + " \n", + " def add_student(self,a_student):\n", + " self.__students[a_student.id_number()]=a_student\n", + "\n", + " def get_students(self):\n", + " return self.__students\n", + " \n", + " def assign_grade(self,key,a_grade):\n", + " the_student=None\n", + " try:\n", + " the_student=self.__students[key]\n", + " except:\n", + " for id in self.__students:\n", + " if key == self.__students[id].name():\n", + " the_student=self.__students[id]\n", + " break\n", + " if the_student:\n", + " the_student.add_grade(a_grade)\n", + " else:\n", + " print (self.name()+\" Error: Did not find student.\")\n", + " \n", + "\n", + " # Accessors\n", + " def data(self):\n", + " return self.__data\n", + "\n", + " def students(self):\n", + " return self.__students\n", + " \n", + " \n", + " def get_data(self,key=None):\n", + " a_data=dict()\n", + " for k,v in self.__data.items():\n", + " if key:\n", + " if key in k:\n", + " a_data[k]=v\n", + " else:\n", + " a_data[k]=v\n", + "\n", + " return a_data\n", + " \n", + " # Print functions\n", + " def print_data(self):\n", + " for k,v in self.__data.items():\n", + " print (k,\":\",v)\n", + " \n", + " def print_grades(self,grade_name):\n", + " if isinstance(grade_name,str):\n", + " grade_names=list()\n", + " grade_names.append(grade_name)\n", + " else:\n", + " grade_names=grade_name\n", + " \n", + " for k,a_student in self.__students.items():\n", + " print (a_student.name(),end=\"\")\n", + " for a_grade_name in grade_names:\n", + " print (a_student[a_grade_name],end=\"\")\n", + " print()\n", + " \n", + " def print_students(self): \n", + " for k,a_student in self.__students.items():\n", + " print (k, a_student.name())\n", + " a_student.print_grades()\n", + " print (\"_______________________________________\")\n", + " \n", + " \n", + " def apply_calculator(self,a_calculator,**kwargs):\n", + " a_calculator.apply(self,**kwargs)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have to move the logic of what each calculator algorithm uses into the algorithm. For example: " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "class uncurved_letter_grade_percent(calculator):\n", + " __grades_definition=[ (.97,\"A+\"),\n", + " (.93,\"A\"),\n", + " (.9,\"A-\"),\n", + " (.87,\"B+\"),\n", + " (.83,\"B\"),\n", + " (.8,\"B-\"),\n", + " (.77,\"C+\"),\n", + " (.73,\"C\"),\n", + " (.7,\"C-\"),\n", + " (.67,\"C+\"),\n", + " (.63,\"C\"),\n", + " (.6,\"C-\"),\n", + " (.57,\"F+\"),\n", + " (.53,\"F\"),\n", + " (0.,\"F-\")]\n", + " __max_grade=100.\n", + " __grade_name=str()\n", + " \n", + " def __init__(self,grade_name,max_grade=100.):\n", + " self.__max_grade=max_grade\n", + " self.__grade_name=grade_name\n", + " calculator.__init__(self,\n", + " \"Uncurved Percent Based Grade Calculator \"+self.__grade_name+\" Max=\"+str(self.__max_grade))\n", + " \n", + " def apply(self,a_grade_book,grade_name=None,**kwargs):\n", + " if grade_name:\n", + " pass\n", + " else:\n", + " grade_name=self.__grade_name\n", + " \n", + " \n", + " for k,a_student in a_grade_book.get_students().items():\n", + " a_grade=a_student[grade_name]\n", + "\n", + " if not a_grade.numerical():\n", + " print (self.name()+ \" Error: Did not get a numerical grade as input.\")\n", + " raise Exception\n", + " \n", + " percent=a_grade.value()/self.__max_grade\n", + " \n", + " for i,v in enumerate(self.__grades_definition):\n", + " if percent>=v[0]:\n", + " break\n", + " \n", + " a_student.add_grade(grade(grade_name+\" Letter\",value=self.__grades_definition[i][1]))\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's test with our class data:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "a_grade_book=grade_book(\"Data 1401\")\n", + "\n", + "for student_i, d in enumerate(df_0.iterrows()):\n", + " a_student_0=student(\"Student\",str(student_i),int(d[1][\"Student ID\"]))\n", + "\n", + " for k,v in d[1].iteritems():\n", + " a_student_0.add_grade(grade(k,value=float(v)))\n", + "\n", + " a_grade_book.add_student(a_student_0)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000954113 Lab 2: 81.0 Lab 2 Letter: B-\n", + "1000625629 Lab 2: 87.0 Lab 2 Letter: B+\n", + "1000847686 Lab 2: 0.0 Lab 2 Letter: F-\n", + "1001108137 Lab 2: 96.0 Lab 2 Letter: A\n", + "1000999851 Lab 2: 94.0 Lab 2 Letter: A\n", + "1001214491 Lab 2: 82.0 Lab 2 Letter: B-\n", + "1001428148 Lab 2: 90.0 Lab 2 Letter: A-\n", + "1001397199 Lab 2: 94.0 Lab 2 Letter: A\n", + "1001372854 Lab 2: 70.0 Lab 2 Letter: C-\n", + "1001558073 Lab 2: 86.0 Lab 2 Letter: B\n", + "1001593219 Lab 2: 96.0 Lab 2 Letter: A\n", + "1001596311 Lab 2: 92.0 Lab 2 Letter: A-\n", + "1001608680 Lab 2: 94.0 Lab 2 Letter: A\n", + "1001618032 Lab 2: 96.0 Lab 2 Letter: A\n", + "1001652624 Lab 2: 90.0 Lab 2 Letter: A-\n", + "1001658617 Lab 2: 94.0 Lab 2 Letter: A\n", + "1001663166 Lab 2: 94.0 Lab 2 Letter: A\n", + "1001674179 Lab 2: 90.0 Lab 2 Letter: A-\n", + "1001722244 Lab 2: 100.0 Lab 2 Letter: A+\n", + "1001543608 Lab 2: 96.0 Lab 2 Letter: A\n", + "1001547659 Lab 2: 94.0 Lab 2 Letter: A\n", + "1001458157 Lab 2: 88.0 Lab 2 Letter: B+\n", + "1001496565 Lab 2: 98.0 Lab 2 Letter: A+\n", + "1001519928 Lab 2: 94.0 Lab 2 Letter: A\n", + "1001774305 Lab 2: 94.0 Lab 2 Letter: A\n", + "1001696890 Lab 2: 96.0 Lab 2 Letter: A\n", + "1001232602 Lab 2: 94.0 Lab 2 Letter: A\n", + "1001464999 Lab 2: 94.0 Lab 2 Letter: A\n", + "1001644554 Lab 2: 80.0 Lab 2 Letter: B-\n", + "1001684648 Lab 2: 0.0 Lab 2 Letter: F-\n" + ] + } + ], + "source": [ + "a_grade_book.apply_calculator(uncurved_letter_grade_percent(\"Lab 2\",max_grade=100))\n", + "for k,a_student in a_grade_book.get_students().items():\n", + " print (a_student.id_number(),a_student[\"Lab 2\"],a_student[\"Lab 2 Letter\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000954113 Student 0 Student Data\n", + "Student ID: 1000954113.0\n", + "Lab 1: 100.0\n", + "Lab 2: 81.0\n", + "Lab 3: 25.0\n", + "Lab 4: 35.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "_______________________________________\n", + "1000625629 Student 1 Student Data\n", + "Student ID: 1000625629.0\n", + "Lab 1: 95.0\n", + "Lab 2: 87.0\n", + "Lab 3: 0.0\n", + "Lab 4: 88.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B+\n", + "_______________________________________\n", + "1000847686 Student 2 Student Data\n", + "Student ID: 1000847686.0\n", + "Lab 1: 100.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: F-\n", + "_______________________________________\n", + "1001108137 Student 3 Student Data\n", + "Student ID: 1001108137.0\n", + "Lab 1: 90.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 35.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1000999851 Student 4 Student Data\n", + "Student ID: 1000999851.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 43.0\n", + "Lab 4: 0.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001214491 Student 5 Student Data\n", + "Student ID: 1001214491.0\n", + "Lab 1: 80.0\n", + "Lab 2: 82.0\n", + "Lab 3: 30.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "_______________________________________\n", + "1001428148 Student 6 Student Data\n", + "Student ID: 1001428148.0\n", + "Lab 1: 80.0\n", + "Lab 2: 90.0\n", + "Lab 3: 24.0\n", + "Lab 4: 0.0\n", + "Exam 1: 80.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "_______________________________________\n", + "1001397199 Student 7 Student Data\n", + "Student ID: 1001397199.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 87.0\n", + "Lab 4: 12.0\n", + "Exam 1: 105.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001372854 Student 8 Student Data\n", + "Student ID: 1001372854.0\n", + "Lab 1: 100.0\n", + "Lab 2: 70.0\n", + "Lab 3: 17.0\n", + "Lab 4: 35.0\n", + "Exam 1: 20.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: C-\n", + "_______________________________________\n", + "1001558073 Student 9 Student Data\n", + "Student ID: 1001558073.0\n", + "Lab 1: 95.0\n", + "Lab 2: 86.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 84.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001593219 Student 10 Student Data\n", + "Student ID: 1001593219.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 96.0\n", + "Lab 4: 85.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001596311 Student 11 Student Data\n", + "Student ID: 1001596311.0\n", + "Lab 1: 90.0\n", + "Lab 2: 92.0\n", + "Lab 3: 71.0\n", + "Lab 4: 57.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "_______________________________________\n", + "1001608680 Student 12 Student Data\n", + "Student ID: 1001608680.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 36.0\n", + "Lab 4: 61.0\n", + "Exam 1: 95.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001618032 Student 13 Student Data\n", + "Student ID: 1001618032.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 97.0\n", + "Lab 4: 86.0\n", + "Exam 1: 110.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001652624 Student 14 Student Data\n", + "Student ID: 1001652624.0\n", + "Lab 1: 100.0\n", + "Lab 2: 90.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "_______________________________________\n", + "1001658617 Student 15 Student Data\n", + "Student ID: 1001658617.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 96.0\n", + "Lab 4: 92.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001663166 Student 16 Student Data\n", + "Student ID: 1001663166.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 38.0\n", + "Lab 4: 0.0\n", + "Exam 1: 82.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001674179 Student 17 Student Data\n", + "Student ID: 1001674179.0\n", + "Lab 1: 95.0\n", + "Lab 2: 90.0\n", + "Lab 3: 23.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "_______________________________________\n", + "1001722244 Student 18 Student Data\n", + "Student ID: 1001722244.0\n", + "Lab 1: 95.0\n", + "Lab 2: 100.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A+\n", + "_______________________________________\n", + "1001543608 Student 19 Student Data\n", + "Student ID: 1001543608.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 19.0\n", + "Lab 4: 51.0\n", + "Exam 1: 111.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001547659 Student 20 Student Data\n", + "Student ID: 1001547659.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 86.0\n", + "Lab 4: 75.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001458157 Student 21 Student Data\n", + "Student ID: 1001458157.0\n", + "Lab 1: 85.0\n", + "Lab 2: 88.0\n", + "Lab 3: 21.0\n", + "Lab 4: 37.0\n", + "Exam 1: 50.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B+\n", + "_______________________________________\n", + "1001496565 Student 22 Student Data\n", + "Student ID: 1001496565.0\n", + "Lab 1: 100.0\n", + "Lab 2: 98.0\n", + "Lab 3: 100.0\n", + "Lab 4: 100.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A+\n", + "_______________________________________\n", + "1001519928 Student 23 Student Data\n", + "Student ID: 1001519928.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 85.0\n", + "Lab 4: 50.0\n", + "Exam 1: 115.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001774305 Student 24 Student Data\n", + "Student ID: 1001774305.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 20.0\n", + "Lab 4: 79.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001696890 Student 25 Student Data\n", + "Student ID: 1001696890.0\n", + "Lab 1: 0.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 40.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001232602 Student 26 Student Data\n", + "Student ID: 1001232602.0\n", + "Lab 1: 80.0\n", + "Lab 2: 94.0\n", + "Lab 3: 79.0\n", + "Lab 4: 60.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001464999 Student 27 Student Data\n", + "Student ID: 1001464999.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 105.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001644554 Student 28 Student Data\n", + "Student ID: 1001644554.0\n", + "Lab 1: 80.0\n", + "Lab 2: 80.0\n", + "Lab 3: 10.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "_______________________________________\n", + "1001684648 Student 29 Student Data\n", + "Student ID: 1001684648.0\n", + "Lab 1: 0.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: F-\n", + "_______________________________________\n" + ] + } + ], + "source": [ + "a_grade_book.print_students()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stats Computation" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "class mean_std_calculator(calculator):\n", + " def __init__(self):\n", + " calculator.__init__(self,\"Mean and Standard Deviation Calculator\")\n", + " \n", + " def apply(self,a_grade_book,grade_name,**kwargs):\n", + " grades=list()\n", + " for k,a_student in a_grade_book.get_students().items():\n", + " grades.append(a_student[grade_name].value())\n", + " \n", + " a_grade_book[grade_name+\" Mean\"] = np.mean(grades)\n", + " a_grade_book[grade_name+\" STD\"] = math.sqrt(np.var(grades))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "36.766666666666666\n", + "36.31774895128949\n" + ] + } + ], + "source": [ + "a_grade_book.apply_calculator(mean_std_calculator(),grade_name=\"Lab 3\")\n", + "print (a_grade_book[\"Lab 3 Mean\"])\n", + "print (a_grade_book[\"Lab 3 STD\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next Steps\n", + "\n", + "* Migrate `grade_summer` and `curved_letter_grade`\n", + "* remove n requirement from `grade_summer`\n", + "* Update `mean_std_calculator`, adding cut_off and grade_name to constructor\n", + "* Use `**kwargs` for passing overwrite\n", + "* Add to grade_book\n", + " * get data\n", + " * print grades\n", + " * print data\n", + "* Add curve function to gradebook\n", + "* Create Letter Grade Counter Calculator\n", + "* Create bell curve grade distributor" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "class mean_std_calculator(calculator):\n", + " def __init__(self,grade_name,cut_off=None):\n", + " self.__grade_name=grade_name\n", + " self.__cut_off=cut_off\n", + " calculator.__init__(self,\"Mean and Standard Deviation Calculator\")\n", + " \n", + " def apply(self,a_grade_book,grade_name=None,cut_off=None,**kwargs):\n", + " if grade_name:\n", + " pass\n", + " else:\n", + " grade_name=self.__grade_name\n", + " \n", + " if cut_off:\n", + " pass\n", + " else:\n", + " cut_off=self.__cut_off\n", + " \n", + " grades=list()\n", + " for k,a_student in a_grade_book.get_students().items():\n", + " a_grade_val=a_student[grade_name].value()\n", + " if cut_off:\n", + " if a_grade_val>cut_off:\n", + " grades.append(a_student[grade_name].value())\n", + " else:\n", + " grades.append(a_student[grade_name].value())\n", + " \n", + " a_grade_book[grade_name+\" Mean\"] = np.mean(grades)\n", + " a_grade_book[grade_name+\" STD\"] = math.sqrt(np.var(grades))\n", + " a_grade_book[grade_name+\" Max\"] = max(grades)\n", + " a_grade_book[grade_name+\" Min\"] = min(grades)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "class grade_summer(calculator):\n", + " def __init__(self,prefix,n=None):\n", + " self.__prefix=prefix\n", + " self.__n=n\n", + " calculator.__init__(self,\"Sum Grades\")\n", + " \n", + " def apply(self,a_gradebook,**kwargs):\n", + " first=True\n", + " \n", + " for k,a_student in a_grade_book.get_students().items():\n", + " if first:\n", + " first=False \n", + " if self.__n:\n", + " labels=[self.__prefix+str(x) for x in range(1,self.__n)]\n", + " else:\n", + " labels=list()\n", + " for i in range(1,1000):\n", + " label=self.__prefix+str(i)\n", + " try:\n", + " a_grade=a_student[label]\n", + " labels.append(label)\n", + " except:\n", + " break \n", + "\n", + " grade_sum=0.\n", + " for label in labels:\n", + " grade_sum+=a_student[label].value()\n", + "\n", + " a_student.add_grade(grade(self.__prefix+\"sum\",value=grade_sum),**kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "class curved_letter_grade(calculator):\n", + " __grades_definition=[ (.97,\"A+\"),\n", + " (.93,\"A\"),\n", + " (.9,\"A-\"),\n", + " (.87,\"B+\"),\n", + " (.83,\"B\"),\n", + " (.8,\"B-\"),\n", + " (.77,\"C+\"),\n", + " (.73,\"C\"),\n", + " (.7,\"C-\"),\n", + " (.67,\"C+\"),\n", + " (.63,\"C\"),\n", + " (.6,\"C-\"),\n", + " (.57,\"F+\"),\n", + " (.53,\"F\"),\n", + " (0.,\"F-\")]\n", + " __max_grade=100.\n", + " __grade_name=str()\n", + " \n", + " def __init__(self,grade_name,mean,std,max_grade=100.):\n", + " self.__max_grade=max_grade\n", + " self.__mean=mean\n", + " self.__std=std\n", + " self.__grade_name=grade_name\n", + " calculator.__init__(self,\n", + " \"Curved Percent Based Grade Calculator \"+self.__grade_name+ \\\n", + " \" Mean=\"+str(self.__mean)+\\\n", + " \" STD=\"+str(self.__std)+\\\n", + " \" Max=\"+str(self.__max_grade))\n", + "\n", + "\n", + " def apply(self,a_grade_book,grade_name=None,**kwargs):\n", + " if grade_name:\n", + " pass\n", + " else:\n", + " grade_name=self.__grade_name\n", + "\n", + " for k,a_student in a_grade_book.get_students().items():\n", + " a_grade=a_student[grade_name]\n", + "\n", + " if not a_grade.numerical():\n", + " print (self.name()+ \" Error: Did not get a numerical grade as input.\")\n", + " raise Exception\n", + "\n", + " # Rescale the grade\n", + " percent=a_grade.value()/self.__max_grade\n", + " shift_to_zero=percent-(self.__mean/self.__max_grade)\n", + " scale_std=0.1*shift_to_zero/(self.__std/self.__max_grade)\n", + " scaled_percent=scale_std+0.8\n", + "\n", + " for i,v in enumerate(self.__grades_definition):\n", + " if scaled_percent>=v[0]:\n", + " break\n", + " \n", + " a_student.add_grade(grade(grade_name+\" Letter\",value=self.__grades_definition[i][1]),**kwargs)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "a_grade_book.apply_calculator(curved_letter_grade(\"Lab 3\",a_grade_book[\"Lab 3 Mean\"],a_grade_book[\"Lab 3 STD\"]),overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000954113 Student 0 Student Data\n", + "Student ID: 1000954113.0\n", + "Lab 1: 100.0\n", + "Lab 2: 81.0\n", + "Lab 3: 25.0\n", + "Lab 4: 35.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "Lab 3 Letter: C\n", + "_______________________________________\n", + "1000625629 Student 1 Student Data\n", + "Student ID: 1000625629.0\n", + "Lab 1: 95.0\n", + "Lab 2: 87.0\n", + "Lab 3: 0.0\n", + "Lab 4: 88.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B+\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n", + "1000847686 Student 2 Student Data\n", + "Student ID: 1000847686.0\n", + "Lab 1: 100.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: F-\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n", + "1001108137 Student 3 Student Data\n", + "Student ID: 1001108137.0\n", + "Lab 1: 90.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 35.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n", + "1000999851 Student 4 Student Data\n", + "Student ID: 1000999851.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 43.0\n", + "Lab 4: 0.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: B-\n", + "_______________________________________\n", + "1001214491 Student 5 Student Data\n", + "Student ID: 1001214491.0\n", + "Lab 1: 80.0\n", + "Lab 2: 82.0\n", + "Lab 3: 30.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n", + "1001428148 Student 6 Student Data\n", + "Student ID: 1001428148.0\n", + "Lab 1: 80.0\n", + "Lab 2: 90.0\n", + "Lab 3: 24.0\n", + "Lab 4: 0.0\n", + "Exam 1: 80.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "Lab 3 Letter: C\n", + "_______________________________________\n", + "1001397199 Student 7 Student Data\n", + "Student ID: 1001397199.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 87.0\n", + "Lab 4: 12.0\n", + "Exam 1: 105.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: A\n", + "_______________________________________\n", + "1001372854 Student 8 Student Data\n", + "Student ID: 1001372854.0\n", + "Lab 1: 100.0\n", + "Lab 2: 70.0\n", + "Lab 3: 17.0\n", + "Lab 4: 35.0\n", + "Exam 1: 20.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: C-\n", + "Lab 3 Letter: C\n", + "_______________________________________\n", + "1001558073 Student 9 Student Data\n", + "Student ID: 1001558073.0\n", + "Lab 1: 95.0\n", + "Lab 2: 86.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 84.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n", + "1001593219 Student 10 Student Data\n", + "Student ID: 1001593219.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 96.0\n", + "Lab 4: 85.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: A\n", + "_______________________________________\n", + "1001596311 Student 11 Student Data\n", + "Student ID: 1001596311.0\n", + "Lab 1: 90.0\n", + "Lab 2: 92.0\n", + "Lab 3: 71.0\n", + "Lab 4: 57.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "Lab 3 Letter: B+\n", + "_______________________________________\n", + "1001608680 Student 12 Student Data\n", + "Student ID: 1001608680.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 36.0\n", + "Lab 4: 61.0\n", + "Exam 1: 95.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n", + "1001618032 Student 13 Student Data\n", + "Student ID: 1001618032.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 97.0\n", + "Lab 4: 86.0\n", + "Exam 1: 110.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: A\n", + "_______________________________________\n", + "1001652624 Student 14 Student Data\n", + "Student ID: 1001652624.0\n", + "Lab 1: 100.0\n", + "Lab 2: 90.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n", + "1001658617 Student 15 Student Data\n", + 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+ "Lab 2: 100.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A+\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n", + "1001543608 Student 19 Student Data\n", + "Student ID: 1001543608.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 19.0\n", + "Lab 4: 51.0\n", + "Exam 1: 111.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: C\n", + "_______________________________________\n", + "1001547659 Student 20 Student Data\n", + "Student ID: 1001547659.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 86.0\n", + "Lab 4: 75.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: A\n", + "_______________________________________\n", + "1001458157 Student 21 Student Data\n", + "Student ID: 1001458157.0\n", + "Lab 1: 85.0\n", + "Lab 2: 88.0\n", + "Lab 3: 21.0\n", + "Lab 4: 37.0\n", + "Exam 1: 50.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B+\n", + "Lab 3 Letter: C\n", + "_______________________________________\n", + "1001496565 Student 22 Student Data\n", + "Student ID: 1001496565.0\n", + "Lab 1: 100.0\n", + "Lab 2: 98.0\n", + "Lab 3: 100.0\n", + "Lab 4: 100.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A+\n", + "Lab 3 Letter: A+\n", + "_______________________________________\n", + "1001519928 Student 23 Student Data\n", + "Student ID: 1001519928.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 85.0\n", + "Lab 4: 50.0\n", + "Exam 1: 115.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: A\n", + "_______________________________________\n", + "1001774305 Student 24 Student Data\n", + "Student ID: 1001774305.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 20.0\n", + "Lab 4: 79.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: C\n", + "_______________________________________\n", + "1001696890 Student 25 Student Data\n", + "Student ID: 1001696890.0\n", + "Lab 1: 0.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 40.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n", + "1001232602 Student 26 Student Data\n", + "Student ID: 1001232602.0\n", + "Lab 1: 80.0\n", + "Lab 2: 94.0\n", + "Lab 3: 79.0\n", + "Lab 4: 60.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: A-\n", + "_______________________________________\n", + "1001464999 Student 27 Student Data\n", + "Student ID: 1001464999.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 105.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n", + "1001644554 Student 28 Student Data\n", + "Student ID: 1001644554.0\n", + "Lab 1: 80.0\n", + "Lab 2: 80.0\n", + "Lab 3: 10.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "Lab 3 Letter: C-\n", + "_______________________________________\n", + "1001684648 Student 29 Student Data\n", + "Student ID: 1001684648.0\n", + "Lab 1: 0.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: F-\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n" + ] + } + ], + "source": [ + "a_grade_book.print_students()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "algs=[grade_summer(\"Lab \",n=5),\n", + " mean_std_calculator(\"Lab sum\",300)]\n", + "\n", + "# curved_letter_grade(\"Lab sum\",\n", + "# a_grade_book[\"Lab sum Mean\"],\n", + "# a_grade_book[\"Lab sum STD\"]) ]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[None, None]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(map(lambda x: a_grade_book.apply_calculator(x,overwrite=True), algs))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "a_grade_book.apply_calculator(curved_letter_grade(\"Lab sum\",\n", + " a_grade_book[\"Lab sum Mean\"],\n", + " a_grade_book[\"Lab sum STD\"]) )" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000954113 Student 0 Student Data\n", + "Student ID: 1000954113.0\n", + "Lab 1: 100.0\n", + "Lab 2: 81.0\n", + "Lab 3: 25.0\n", + "Lab 4: 35.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "Lab 3 Letter: C\n", + "Lab sum: 241.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1000625629 Student 1 Student Data\n", + "Student ID: 1000625629.0\n", + "Lab 1: 95.0\n", + "Lab 2: 87.0\n", + "Lab 3: 0.0\n", + "Lab 4: 88.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B+\n", + "Lab 3 Letter: C+\n", + "Lab sum: 270.0\n", + "Lab sum Letter: F\n", + "_______________________________________\n", + "1000847686 Student 2 Student Data\n", + "Student ID: 1000847686.0\n", + "Lab 1: 100.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: F-\n", + "Lab 3 Letter: C+\n", + "Lab sum: 100.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001108137 Student 3 Student Data\n", + "Student ID: 1001108137.0\n", + "Lab 1: 90.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 35.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: C+\n", + "Lab sum: 221.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1000999851 Student 4 Student Data\n", + "Student ID: 1000999851.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 43.0\n", + "Lab 4: 0.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: B-\n", + "Lab sum: 232.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001214491 Student 5 Student Data\n", + "Student ID: 1001214491.0\n", + "Lab 1: 80.0\n", + "Lab 2: 82.0\n", + "Lab 3: 30.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "Lab 3 Letter: C+\n", + "Lab sum: 192.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001428148 Student 6 Student Data\n", + "Student ID: 1001428148.0\n", + "Lab 1: 80.0\n", + "Lab 2: 90.0\n", + "Lab 3: 24.0\n", + "Lab 4: 0.0\n", + "Exam 1: 80.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "Lab 3 Letter: C\n", + "Lab sum: 194.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001397199 Student 7 Student Data\n", + "Student ID: 1001397199.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 87.0\n", + "Lab 4: 12.0\n", + "Exam 1: 105.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: A\n", + "Lab sum: 293.0\n", + "Lab sum Letter: C-\n", + "_______________________________________\n", + "1001372854 Student 8 Student Data\n", + "Student ID: 1001372854.0\n", + "Lab 1: 100.0\n", + "Lab 2: 70.0\n", + "Lab 3: 17.0\n", + "Lab 4: 35.0\n", + "Exam 1: 20.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: C-\n", + "Lab 3 Letter: C\n", + "Lab sum: 222.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001558073 Student 9 Student Data\n", + "Student ID: 1001558073.0\n", + "Lab 1: 95.0\n", + "Lab 2: 86.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 84.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "Lab 3 Letter: C+\n", + "Lab sum: 181.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001593219 Student 10 Student Data\n", + "Student ID: 1001593219.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 96.0\n", + "Lab 4: 85.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: A\n", + "Lab sum: 377.0\n", + "Lab sum Letter: B\n", + "_______________________________________\n", + "1001596311 Student 11 Student Data\n", + "Student ID: 1001596311.0\n", + "Lab 1: 90.0\n", + "Lab 2: 92.0\n", + "Lab 3: 71.0\n", + "Lab 4: 57.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "Lab 3 Letter: B+\n", + "Lab sum: 310.0\n", + "Lab sum Letter: C\n", + "_______________________________________\n", + "1001608680 Student 12 Student Data\n", + "Student ID: 1001608680.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 36.0\n", + "Lab 4: 61.0\n", + "Exam 1: 95.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: C+\n", + "Lab sum: 291.0\n", + "Lab sum Letter: F+\n", + "_______________________________________\n", + "1001618032 Student 13 Student Data\n", + "Student ID: 1001618032.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 97.0\n", + "Lab 4: 86.0\n", + "Exam 1: 110.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: A\n", + "Lab sum: 379.0\n", + "Lab sum Letter: B+\n", + "_______________________________________\n", + "1001652624 Student 14 Student Data\n", + "Student ID: 1001652624.0\n", + "Lab 1: 100.0\n", + "Lab 2: 90.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "Lab 3 Letter: C+\n", + "Lab sum: 190.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001658617 Student 15 Student Data\n", + "Student ID: 1001658617.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 96.0\n", + "Lab 4: 92.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: A\n", + "Lab sum: 382.0\n", + "Lab sum Letter: B+\n", + "_______________________________________\n", + "1001663166 Student 16 Student Data\n", + "Student ID: 1001663166.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 38.0\n", + "Lab 4: 0.0\n", + "Exam 1: 82.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: B-\n", + "Lab sum: 227.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001674179 Student 17 Student Data\n", + "Student ID: 1001674179.0\n", + "Lab 1: 95.0\n", + "Lab 2: 90.0\n", + "Lab 3: 23.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "Lab 3 Letter: C\n", + "Lab sum: 208.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001722244 Student 18 Student Data\n", + "Student ID: 1001722244.0\n", + "Lab 1: 95.0\n", + "Lab 2: 100.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A+\n", + "Lab 3 Letter: C+\n", + "Lab sum: 195.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001543608 Student 19 Student Data\n", + "Student ID: 1001543608.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 19.0\n", + "Lab 4: 51.0\n", + "Exam 1: 111.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: C\n", + "Lab sum: 266.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001547659 Student 20 Student Data\n", + "Student ID: 1001547659.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 86.0\n", + "Lab 4: 75.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: A\n", + "Lab sum: 355.0\n", + "Lab sum Letter: C+\n", + "_______________________________________\n", + "1001458157 Student 21 Student Data\n", + "Student ID: 1001458157.0\n", + "Lab 1: 85.0\n", + "Lab 2: 88.0\n", + "Lab 3: 21.0\n", + "Lab 4: 37.0\n", + "Exam 1: 50.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B+\n", + "Lab 3 Letter: C\n", + "Lab sum: 231.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001496565 Student 22 Student Data\n", + "Student ID: 1001496565.0\n", + "Lab 1: 100.0\n", + "Lab 2: 98.0\n", + "Lab 3: 100.0\n", + "Lab 4: 100.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A+\n", + "Lab 3 Letter: A+\n", + "Lab sum: 398.0\n", + "Lab sum Letter: A\n", + "_______________________________________\n", + "1001519928 Student 23 Student Data\n", + "Student ID: 1001519928.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 85.0\n", + "Lab 4: 50.0\n", + "Exam 1: 115.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: A\n", + "Lab sum: 329.0\n", + "Lab sum Letter: C-\n", + "_______________________________________\n", + "1001774305 Student 24 Student Data\n", + "Student ID: 1001774305.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 20.0\n", + "Lab 4: 79.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: C\n", + "Lab sum: 293.0\n", + "Lab sum Letter: C-\n", + "_______________________________________\n", + "1001696890 Student 25 Student Data\n", + "Student ID: 1001696890.0\n", + "Lab 1: 0.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 40.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: C+\n", + "Lab sum: 136.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001232602 Student 26 Student Data\n", + "Student ID: 1001232602.0\n", + "Lab 1: 80.0\n", + "Lab 2: 94.0\n", + "Lab 3: 79.0\n", + "Lab 4: 60.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: A-\n", + "Lab sum: 313.0\n", + "Lab sum Letter: C\n", + "_______________________________________\n", + "1001464999 Student 27 Student Data\n", + "Student ID: 1001464999.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 105.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "Lab 3 Letter: C+\n", + "Lab sum: 189.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001644554 Student 28 Student Data\n", + "Student ID: 1001644554.0\n", + "Lab 1: 80.0\n", + "Lab 2: 80.0\n", + "Lab 3: 10.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "Lab 3 Letter: C-\n", + "Lab sum: 170.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n", + "1001684648 Student 29 Student Data\n", + "Student ID: 1001684648.0\n", + "Lab 1: 0.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: F-\n", + "Lab 3 Letter: C+\n", + "Lab sum: 0.0\n", + "Lab sum Letter: F-\n", + "_______________________________________\n" + ] + } + ], + "source": [ + "a_grade_book.print_students()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Student 0 Student DataLab 3 Letter: C\n", + "Student 1 Student DataLab 3 Letter: C+\n", + "Student 2 Student DataLab 3 Letter: C+\n", + "Student 3 Student DataLab 3 Letter: C+\n", + "Student 4 Student DataLab 3 Letter: B-\n", + "Student 5 Student DataLab 3 Letter: C+\n", + "Student 6 Student DataLab 3 Letter: C\n", + "Student 7 Student DataLab 3 Letter: A\n", + "Student 8 Student DataLab 3 Letter: C\n", + "Student 9 Student DataLab 3 Letter: C+\n", + "Student 10 Student DataLab 3 Letter: A\n", + "Student 11 Student DataLab 3 Letter: B+\n", + "Student 12 Student DataLab 3 Letter: C+\n", + "Student 13 Student DataLab 3 Letter: A\n", + "Student 14 Student DataLab 3 Letter: C+\n", + "Student 15 Student DataLab 3 Letter: A\n", + "Student 16 Student DataLab 3 Letter: B-\n", + "Student 17 Student DataLab 3 Letter: C\n", + "Student 18 Student DataLab 3 Letter: C+\n", + "Student 19 Student DataLab 3 Letter: C\n", + "Student 20 Student DataLab 3 Letter: A\n", + "Student 21 Student DataLab 3 Letter: C\n", + "Student 22 Student DataLab 3 Letter: A+\n", + "Student 23 Student DataLab 3 Letter: A\n", + "Student 24 Student DataLab 3 Letter: C\n", + "Student 25 Student DataLab 3 Letter: C+\n", + "Student 26 Student DataLab 3 Letter: A-\n", + "Student 27 Student DataLab 3 Letter: C+\n", + "Student 28 Student DataLab 3 Letter: C-\n", + "Student 29 Student DataLab 3 Letter: C+\n" + ] + } + ], + "source": [ + "a_grade_book.print_grades(\"Lab 3 Letter\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lab 3 Mean : 36.766666666666666\n", + "Lab 3 STD : 36.31774895128949\n", + "Lab sum Mean : 355.375\n", + "Lab sum STD : 31.823487788110214\n", + "Lab sum Max : 398.0\n", + "Lab sum Min : 310.0\n" + ] + } + ], + "source": [ + "a_grade_book.print_data()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Lecture-23/Lecture-23.ipynb b/Lectures/Lecture-23/Lecture-23.ipynb new file mode 100644 index 0000000..df7a74d --- /dev/null +++ b/Lectures/Lecture-23/Lecture-23.ipynb @@ -0,0 +1,1628 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 23\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github//afarbin/DATA1401-Spring-2020/blob/master/Lectures/Lecture-23/Lecture-23.ipynb)\n", + "\n", + "\n", + "From previous lecture:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import libraries we will use\n", + "import pandas as pd\n", + "import numpy as np\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from collections import OrderedDict\n", + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Create some virtual classes\n", + "class base:\n", + " __name=\"\"\n", + " \n", + " def __init__(self,name):\n", + " self.__name=name\n", + "\n", + " def name(self):\n", + " return self.__name\n", + "\n", + "class data(base):\n", + " def __init__(self,name):\n", + " base.__init__(self,name)\n", + " \n", + "class alg(base):\n", + " def __init__(self,name):\n", + " base.__init__(self,name)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Classes" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class grade(data):\n", + " __letter_grades=[\"F-\",\"F\",\"F+\",\"D-\",\"D\",\"D+\",\"C-\",\"C\",\"C+\",\"B-\",\"B\",\"B+\",\"A-\",\"A\",\"A+\"]\n", + " \n", + " def __init__(self,name,numerical=True,value=None):\n", + " self.__value=value\n", + " self.__numerical=numerical\n", + " self.__gradebook_name=str()\n", + " \n", + " if value:\n", + " if isinstance(value,(int,float)):\n", + " self.__numerical=True\n", + " elif isinstance(value,str):\n", + " self.__numerical=False\n", + " self.set(value)\n", + " else: \n", + " self.__numerical=numerical\n", + " self.__gradebook_name=name\n", + " data.__init__(self,name+\" Grade Data Object\") \n", + "\n", + " def set(self,value):\n", + " if isinstance(value,(int,float)) and self.__numerical:\n", + " self.__value=value\n", + " elif isinstance(value,str) and not self.__numerical:\n", + " if value in self.__letter_grades:\n", + " self.__value=value\n", + " else:\n", + " print( self.name()+\" Error: Bad Grade.\")\n", + " raise Exception\n", + " \n", + " def value(self):\n", + " return self.__value\n", + " \n", + " def numerical(self):\n", + " return self.__numerical\n", + " \n", + " def gradebook_name(self):\n", + " return self.__gradebook_name\n", + " \n", + " def __str__(self):\n", + " return self.__gradebook_name+\": \"+str(self.__value)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class student(data):\n", + " def __init__(self, first_name, last_name, id_number):\n", + " self.__grades=dict()\n", + " self.__id_number=id_number\n", + " data.__init__(self,first_name+\" \"+last_name+\" Student Data\")\n", + "\n", + " def add_grade(self,a_grade,overwrite=False):\n", + " if overwrite or not a_grade.gradebook_name() in self.__grades:\n", + " self.__grades[a_grade.gradebook_name()]=a_grade\n", + " else:\n", + " print (self.name()+\" Error Adding Grade \"+a_grade.name()+\". Grade already exists.\")\n", + " raise Exception\n", + "\n", + " def id_number(self):\n", + " return self.__id_number\n", + " \n", + " def grade_names(self):\n", + " return self.__grades.keys()\n", + " \n", + " def grades(self):\n", + " return self.__grades\n", + " \n", + " def __getitem__(self,key):\n", + " return self.__grades[key]\n", + " \n", + " def print_grades(self):\n", + " for grade in self.__grades:\n", + " print (self.__grades[grade])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class grade_book(data):\n", + " # New member class to hold arbitrary data associated with the class \n", + " def __init__(self,name):\n", + " data.__init__(self,name+\" Course Grade Book\")\n", + " self.__students=dict()\n", + " self.__data=dict()\n", + " \n", + " # New method to access data\n", + " def __getitem__(self,key):\n", + " return self.__data[key]\n", + " \n", + " # New method to add data\n", + " def __setitem__(self, key, value):\n", + " self.__data[key] = value\n", + " \n", + " def add_student(self,a_student):\n", + " self.__students[a_student.id_number()]=a_student\n", + "\n", + " def get_students(self):\n", + " return self.__students\n", + " \n", + " def assign_grade(self,key,a_grade):\n", + " the_student=None\n", + " try:\n", + " the_student=self.__students[key]\n", + " except:\n", + " for id in self.__students:\n", + " if key == self.__students[id].name():\n", + " the_student=self.__students[id]\n", + " break\n", + " if the_student:\n", + " the_student.add_grade(a_grade)\n", + " else:\n", + " print (self.name()+\" Error: Did not find student.\")\n", + " \n", + "\n", + " # Accessors\n", + " def data(self):\n", + " return self.__data\n", + "\n", + " def students(self):\n", + " return self.__students\n", + " \n", + " \n", + " def get_data(self,key=None):\n", + " a_data=dict()\n", + " for k,v in self.__data.items():\n", + " if key:\n", + " if key in k:\n", + " a_data[k]=v\n", + " else:\n", + " a_data[k]=v\n", + "\n", + " return a_data\n", + " \n", + " # Print functions\n", + " def print_data(self):\n", + " for k,v in self.__data.items():\n", + " print (k,\":\",v)\n", + " \n", + " def print_grades(self,grade_name):\n", + " if isinstance(grade_name,str):\n", + " grade_names=list()\n", + " grade_names.append(grade_name)\n", + " else:\n", + " grade_names=grade_name\n", + " \n", + " for k,a_student in self.__students.items():\n", + " print (a_student.name(),end=\"\")\n", + " for a_grade_name in grade_names:\n", + " print (a_student[a_grade_name],end=\"\")\n", + " print()\n", + " \n", + " def print_students(self): \n", + " for k,a_student in self.__students.items():\n", + " print (k, a_student.name())\n", + " a_student.print_grades()\n", + " print (\"_______________________________________\")\n", + " \n", + " \n", + " def apply_calculator(self,a_calculator,**kwargs):\n", + " a_calculator.apply(self,**kwargs)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building a Gradebook" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Read Data into a Pandas DataFrame\n", + "df = pd.read_csv(\"Data-1401-Grades-Fixed.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df_0=df[np.logical_not(np.isnan(df[\"Student ID\"]))]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Algorithm Classes\n", + "### Migrating Algorithms\n", + "\n", + "We have redesigned the calculator part of our Grade Book framework. We now have to migrate the existing calculator algorithms to work with this redesign. The main thing to notice is that previously the `apply_xxx` methods\n", + "\n", + "* found the data they needed in the grade book\n", + "* looped over that data\n", + "* applied the algorithm\n", + "* placed the results back into grade book\n", + "\n", + "here are the methods of the old implementation:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + " def apply_summary(self,a_grader):\n", + " for k,a_student in self.__students.items():\n", + " a_student.add_grade(a_grader.apply(a_student))\n", + " \n", + " def apply_grader(self,a_grader,grade_name):\n", + " for k,a_student in self.__students.items():\n", + " a_student.add_grade(a_grader.apply(a_student[grade_name]))\n", + " \n", + " def apply_stats(self,a_stat_comp,grade_name):\n", + " grades=list()\n", + " for k,a_student in self.__students.items():\n", + " grades.append(a_student[grade_name].value())\n", + " return a_stat_comp.apply(grades)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "which were replaced with:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + " def apply_calculator(self,a_calculator,**kwargs):\n", + " a_calculator.apply(self,**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Therefore, what ever was in the different `apply_xxx` functions of the old `grade_book` implementation have to be moved into the `apply` function of the calculators. \n", + "\n", + "For example compare the apply methods of the old and new implementation of this `grader` algorithm: " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + " # Old implementation\n", + " def apply(self,a_grade):\n", + " if not isinstance(a_grade,grade):\n", + " print (self.name()+ \" Error: Did not get an proper grade as input.\")\n", + " raise Exception\n", + " if not a_grade.numerical():\n", + " print (self.name()+ \" Error: Did not get a numerical grade as input.\")\n", + " raise Exception\n", + " \n", + " # Rescale the grade\n", + " percent=a_grade.value()/self.__max_grade\n", + " shift_to_zero=percent-(self.__mean/self.__max_grade)\n", + " scale_std=0.1*shift_to_zero/(self.__std/self.__max_grade)\n", + " scaled_percent=scale_std+0.8\n", + " \n", + " for i,v in enumerate(self.__grades_definition):\n", + " if scaled_percent>=v[0]:\n", + " break\n", + " \n", + " return grade(self.__grade_name,value=self.__grades_definition[i][1])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + " # migrated implementation\n", + " def apply(self,a_grade_book,grade_name=None,**kwargs):\n", + " if grade_name:\n", + " pass\n", + " else:\n", + " grade_name=self.__grade_name\n", + " \n", + " for k,a_student in a_grade_book.get_students().items():\n", + " a_grade=a_student[grade_name]\n", + "\n", + " if not a_grade.numerical():\n", + " print (self.name()+ \" Error: Did not get a numerical grade as input.\")\n", + " raise Exception\n", + " \n", + " percent=a_grade.value()/self.__max_grade\n", + " \n", + " for i,v in enumerate(self.__grades_definition):\n", + " if percent>=v[0]:\n", + " break\n", + " \n", + " a_student.add_grade(grade(grade_name+\" Letter\",value=self.__grades_definition[i][1]))\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's test with our class data:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "a_grade_book=grade_book(\"Data 1401\")\n", + "\n", + "for student_i, d in enumerate(df_0.iterrows()):\n", + " a_student_0=student(\"Student\",str(student_i),int(d[1][\"Student ID\"]))\n", + "\n", + " for k,v in d[1].iteritems():\n", + " a_student_0.add_grade(grade(k,value=float(v)))\n", + "\n", + " a_grade_book.add_student(a_student_0)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "class calculator(alg): \n", + " def __init__(self,name):\n", + " alg.__init__(self,name)\n", + "\n", + " def apply(self,a_grade_book):\n", + " raise NotImplementedError\n", + "\n", + "class uncurved_letter_grade_percent(calculator):\n", + " __grades_definition=[ (.97,\"A+\"),\n", + " (.93,\"A\"),\n", + " (.9,\"A-\"),\n", + " (.87,\"B+\"),\n", + " (.83,\"B\"),\n", + " (.8,\"B-\"),\n", + " (.77,\"C+\"),\n", + " (.73,\"C\"),\n", + " (.7,\"C-\"),\n", + " (.67,\"C+\"),\n", + " (.63,\"C\"),\n", + " (.6,\"C-\"),\n", + " (.57,\"F+\"),\n", + " (.53,\"F\"),\n", + " (0.,\"F-\")]\n", + " __max_grade=100.\n", + " __grade_name=str()\n", + " \n", + " def __init__(self,grade_name,max_grade=100.):\n", + " self.__max_grade=max_grade\n", + " self.__grade_name=grade_name\n", + " calculator.__init__(self,\n", + " \"Uncurved Percent Based Grade Calculator \"+self.__grade_name+\" Max=\"+str(self.__max_grade))\n", + " \n", + " def apply(self,a_grade_book,grade_name=None,**kwargs):\n", + " if grade_name:\n", + " pass\n", + " else:\n", + " grade_name=self.__grade_name\n", + " \n", + " \n", + " for k,a_student in a_grade_book.get_students().items():\n", + " a_grade=a_student[grade_name]\n", + "\n", + " if not a_grade.numerical():\n", + " print (self.name()+ \" Error: Did not get a numerical grade as input.\")\n", + " raise Exception\n", + " \n", + " percent=a_grade.value()/self.__max_grade\n", + " \n", + " for i,v in enumerate(self.__grades_definition):\n", + " if percent>=v[0]:\n", + " break\n", + " \n", + " a_student.add_grade(grade(grade_name+\" Letter\",value=self.__grades_definition[i][1]))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "a_grade_book.apply_calculator(uncurved_letter_grade_percent(\"Lab 2\",max_grade=100))\n", + "# for k,a_student in a_grade_book.get_students().items():\n", + "# print (a_student.id_number(),a_student[\"Lab 2\"],a_student[\"Lab 2 Letter\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000954113 Student 0 Student Data\n", + "Student ID: 1000954113.0\n", + "Lab 1: 100.0\n", + "Lab 2: 81.0\n", + "Lab 3: 25.0\n", + "Lab 4: 35.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "_______________________________________\n", + "1000625629 Student 1 Student Data\n", + "Student ID: 1000625629.0\n", + "Lab 1: 95.0\n", + "Lab 2: 87.0\n", + "Lab 3: 0.0\n", + "Lab 4: 88.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B+\n", + "_______________________________________\n", + "1000847686 Student 2 Student Data\n", + "Student ID: 1000847686.0\n", + "Lab 1: 100.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: F-\n", + "_______________________________________\n", + "1001108137 Student 3 Student Data\n", + "Student ID: 1001108137.0\n", + "Lab 1: 90.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 35.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1000999851 Student 4 Student Data\n", + "Student ID: 1000999851.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 43.0\n", + "Lab 4: 0.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001214491 Student 5 Student Data\n", + "Student ID: 1001214491.0\n", + "Lab 1: 80.0\n", + "Lab 2: 82.0\n", + "Lab 3: 30.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "_______________________________________\n", + "1001428148 Student 6 Student Data\n", + "Student ID: 1001428148.0\n", + "Lab 1: 80.0\n", + "Lab 2: 90.0\n", + "Lab 3: 24.0\n", + "Lab 4: 0.0\n", + "Exam 1: 80.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "_______________________________________\n", + "1001397199 Student 7 Student Data\n", + "Student ID: 1001397199.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 87.0\n", + "Lab 4: 12.0\n", + "Exam 1: 105.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001372854 Student 8 Student Data\n", + "Student ID: 1001372854.0\n", + "Lab 1: 100.0\n", + "Lab 2: 70.0\n", + "Lab 3: 17.0\n", + "Lab 4: 35.0\n", + "Exam 1: 20.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: C-\n", + "_______________________________________\n", + "1001558073 Student 9 Student Data\n", + "Student ID: 1001558073.0\n", + "Lab 1: 95.0\n", + "Lab 2: 86.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 84.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B\n", + "_______________________________________\n", + "1001593219 Student 10 Student Data\n", + "Student ID: 1001593219.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 96.0\n", + "Lab 4: 85.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001596311 Student 11 Student Data\n", + "Student ID: 1001596311.0\n", + "Lab 1: 90.0\n", + "Lab 2: 92.0\n", + "Lab 3: 71.0\n", + "Lab 4: 57.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "_______________________________________\n", + "1001608680 Student 12 Student Data\n", + "Student ID: 1001608680.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 36.0\n", + "Lab 4: 61.0\n", + "Exam 1: 95.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001618032 Student 13 Student Data\n", + "Student ID: 1001618032.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 97.0\n", + "Lab 4: 86.0\n", + "Exam 1: 110.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001652624 Student 14 Student Data\n", + "Student ID: 1001652624.0\n", + "Lab 1: 100.0\n", + "Lab 2: 90.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "_______________________________________\n", + "1001658617 Student 15 Student Data\n", + "Student ID: 1001658617.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 96.0\n", + "Lab 4: 92.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001663166 Student 16 Student Data\n", + "Student ID: 1001663166.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 38.0\n", + "Lab 4: 0.0\n", + "Exam 1: 82.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001674179 Student 17 Student Data\n", + "Student ID: 1001674179.0\n", + "Lab 1: 95.0\n", + "Lab 2: 90.0\n", + "Lab 3: 23.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A-\n", + "_______________________________________\n", + "1001722244 Student 18 Student Data\n", + "Student ID: 1001722244.0\n", + "Lab 1: 95.0\n", + "Lab 2: 100.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A+\n", + "_______________________________________\n", + "1001543608 Student 19 Student Data\n", + "Student ID: 1001543608.0\n", + "Lab 1: 100.0\n", + "Lab 2: 96.0\n", + "Lab 3: 19.0\n", + "Lab 4: 51.0\n", + "Exam 1: 111.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001547659 Student 20 Student Data\n", + "Student ID: 1001547659.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 86.0\n", + "Lab 4: 75.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001458157 Student 21 Student Data\n", + "Student ID: 1001458157.0\n", + "Lab 1: 85.0\n", + "Lab 2: 88.0\n", + "Lab 3: 21.0\n", + "Lab 4: 37.0\n", + "Exam 1: 50.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B+\n", + "_______________________________________\n", + "1001496565 Student 22 Student Data\n", + "Student ID: 1001496565.0\n", + "Lab 1: 100.0\n", + "Lab 2: 98.0\n", + "Lab 3: 100.0\n", + "Lab 4: 100.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A+\n", + "_______________________________________\n", + "1001519928 Student 23 Student Data\n", + "Student ID: 1001519928.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 85.0\n", + "Lab 4: 50.0\n", + "Exam 1: 115.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001774305 Student 24 Student Data\n", + "Student ID: 1001774305.0\n", + "Lab 1: 100.0\n", + "Lab 2: 94.0\n", + "Lab 3: 20.0\n", + "Lab 4: 79.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001696890 Student 25 Student Data\n", + "Student ID: 1001696890.0\n", + "Lab 1: 0.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 40.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001232602 Student 26 Student Data\n", + "Student ID: 1001232602.0\n", + "Lab 1: 80.0\n", + "Lab 2: 94.0\n", + "Lab 3: 79.0\n", + "Lab 4: 60.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001464999 Student 27 Student Data\n", + "Student ID: 1001464999.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 105.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: A\n", + "_______________________________________\n", + "1001644554 Student 28 Student Data\n", + "Student ID: 1001644554.0\n", + "Lab 1: 80.0\n", + "Lab 2: 80.0\n", + "Lab 3: 10.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: B-\n", + "_______________________________________\n", + "1001684648 Student 29 Student Data\n", + "Student ID: 1001684648.0\n", + "Lab 1: 0.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 2 Letter: F-\n", + "_______________________________________\n" + ] + } + ], + "source": [ + "a_grade_book.print_students()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Examples of other types of calculators" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "class mean_std_calculator(calculator):\n", + " def __init__(self,grade_name,cut_off=None):\n", + " self.__grade_name=grade_name\n", + " self.__cut_off=cut_off\n", + " calculator.__init__(self,\"Mean and Standard Deviation Calculator\")\n", + " \n", + " def apply(self,a_grade_book,grade_name=None,cut_off=None,**kwargs):\n", + " if grade_name:\n", + " pass\n", + " else:\n", + " grade_name=self.__grade_name\n", + " \n", + " if cut_off:\n", + " pass\n", + " else:\n", + " cut_off=self.__cut_off\n", + " \n", + " grades=list()\n", + " for k,a_student in a_grade_book.get_students().items():\n", + " a_grade_val=a_student[grade_name].value()\n", + " if cut_off:\n", + " if a_grade_val>cut_off:\n", + " grades.append(a_student[grade_name].value())\n", + " else:\n", + " grades.append(a_student[grade_name].value())\n", + " \n", + " a_grade_book[grade_name+\" Mean\"] = np.mean(grades)\n", + " a_grade_book[grade_name+\" STD\"] = math.sqrt(np.var(grades))\n", + " a_grade_book[grade_name+\" Max\"] = max(grades)\n", + " a_grade_book[grade_name+\" Min\"] = min(grades)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "class grade_summer(calculator):\n", + " def __init__(self,prefix,n=None):\n", + " self.__prefix=prefix\n", + " self.__n=n\n", + " calculator.__init__(self,\"Sum Grades\")\n", + " \n", + " def apply(self,a_gradebook,**kwargs):\n", + " first=True\n", + " \n", + " for k,a_student in a_grade_book.get_students().items():\n", + " if first:\n", + " first=False \n", + " if self.__n:\n", + " labels=[self.__prefix+str(x) for x in range(1,self.__n)]\n", + " else:\n", + " labels=list()\n", + " for i in range(1,1000):\n", + " label=self.__prefix+str(i)\n", + " try:\n", + " a_grade=a_student[label]\n", + " labels.append(label)\n", + " except:\n", + " break \n", + "\n", + " grade_sum=0.\n", + " for label in labels:\n", + " grade_sum+=a_student[label].value()\n", + "\n", + " a_student.add_grade(grade(self.__prefix+\"sum\",value=grade_sum),**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Migration Exercise\n", + "\n", + "Migrate the following algorithm from old to new calculator implementation. Use the example above to guide you. You will have to:\n", + "\n", + "* Change the arguments of the apply function\n", + "* Insert loop over students (copy it from the analgous place above)\n", + "* Instead of returning the result, update the data within the grade book." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "class grade_calculator(alg): \n", + " def __init__(self,name, stats):\n", + " self.__stats=stats\n", + " alg.__init__(self,name)\n", + "\n", + " def apply(self,a_grade):\n", + " raise NotImplementedError\n", + " # Returns a grade\n", + " \n", + "class curved_letter_grade(grade_calculator):\n", + " __grades_definition=[ (.97,\"A+\"),\n", + " (.93,\"A\"),\n", + " (.9,\"A-\"),\n", + " (.87,\"B+\"),\n", + " (.83,\"B\"),\n", + " (.8,\"B-\"),\n", + " (.77,\"C+\"),\n", + " (.73,\"C\"),\n", + " (.7,\"C-\"),\n", + " (.67,\"C+\"),\n", + " (.63,\"C\"),\n", + " (.6,\"C-\"),\n", + " (.57,\"F+\"),\n", + " (.53,\"F\"),\n", + " (0.,\"F-\")]\n", + " __max_grade=100.\n", + " __grade_name=str()\n", + " \n", + " def __init__(self,grade_name,mean,std,max_grade=100.):\n", + " self.__max_grade=max_grade\n", + " self.__mean=mean\n", + " self.__std=std\n", + " self.__grade_name=grade_name\n", + " grade_calculator.__init__(self,\n", + " \"Curved Percent Based Grade Calculator \"+self.__grade_name+ \\\n", + " \" Mean=\"+str(self.__mean)+\\\n", + " \" STD=\"+str(self.__std)+\\\n", + " \" Max=\"+str(self.__max_grade))\n", + " \n", + "\n", + " def apply(self,a_grade):\n", + " if not isinstance(a_grade,grade):\n", + " print (self.name()+ \" Error: Did not get an proper grade as input.\")\n", + " raise Exception\n", + " if not a_grade.numerical():\n", + " print (self.name()+ \" Error: Did not get a numerical grade as input.\")\n", + " raise Exception\n", + " \n", + " # Rescale the grade\n", + " percent=a_grade.value()/self.__max_grade\n", + " shift_to_zero=percent-(self.__mean/self.__max_grade)\n", + " scale_std=0.1*shift_to_zero/(self.__std/self.__max_grade)\n", + " scaled_percent=scale_std+0.8\n", + " \n", + " for i,v in enumerate(self.__grades_definition):\n", + " if scaled_percent>=v[0]:\n", + " break\n", + " \n", + " return grade(self.__grade_name,value=self.__grades_definition[i][1])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution here\n", + " \n", + "class curved_letter_grade(calculator):\n", + " __grades_definition=[ (.97,\"A+\"),\n", + " (.93,\"A\"),\n", + " (.9,\"A-\"),\n", + " (.87,\"B+\"),\n", + " (.83,\"B\"),\n", + " (.8,\"B-\"),\n", + " (.77,\"C+\"),\n", + " (.73,\"C\"),\n", + " (.7,\"C-\"),\n", + " (.67,\"C+\"),\n", + " (.63,\"C\"),\n", + " (.6,\"C-\"),\n", + " (.57,\"F+\"),\n", + " (.53,\"F\"),\n", + " (0.,\"F-\")]\n", + " __max_grade=100.\n", + " __grade_name=str()\n", + " \n", + " def __init__(self,grade_name,mean,std,max_grade=100.):\n", + " self.__max_grade=max_grade\n", + " self.__mean=mean\n", + " self.__std=std\n", + " self.__grade_name=grade_name\n", + " calculator.__init__(self,\n", + " \"Curved Percent Based Grade Calculator \"+self.__grade_name+ \\\n", + " \" Mean=\"+str(self.__mean)+\\\n", + " \" STD=\"+str(self.__std)+\\\n", + " \" Max=\"+str(self.__max_grade))\n", + " \n", + "\n", + " def apply(self,a_grade_book,grade_name=None,\n", + " overwrite=False,**kwargs):\n", + " if grade_name:\n", + " pass\n", + " else:\n", + " grade_name=self.__grade_name\n", + " \n", + " for k,a_student in a_grade_book.get_students().items():\n", + " a_grade=a_student[grade_name]\n", + " \n", + " if not isinstance(a_grade,grade):\n", + " print (self.name()+ \" Error: Did not get an proper grade as input.\")\n", + " raise Exception\n", + " if not a_grade.numerical():\n", + " print (self.name()+ \" Error: Did not get a numerical grade as input.\")\n", + " raise Exception\n", + "\n", + " # Rescale the grade\n", + " percent=a_grade.value()/self.__max_grade\n", + " shift_to_zero=percent-(self.__mean/self.__max_grade)\n", + " scale_std=0.1*shift_to_zero/(self.__std/self.__max_grade)\n", + " scaled_percent=scale_std+0.8\n", + "\n", + " for i,v in enumerate(self.__grades_definition):\n", + " if scaled_percent>=v[0]:\n", + " break\n", + " \n", + " #a_student.add_grade(grade(grade_name+\" Letter\",value=self.__grades_definition[i][1]))\n", + " \n", + " a_student.add_grade(grade(self.__grade_name+\" Letter\",value=self.__grades_definition[i][1]),\n", + " overwrite=overwrite)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "a_grade_book.apply_calculator(mean_std_calculator(grade_name=\"Lab 3\"))\n", + "a_grade_book.apply_calculator(curved_letter_grade(\"Lab 3\",\n", + " a_grade_book[\"Lab 3 Mean\"],\n", + " a_grade_book[\"Lab 3 STD\"]),\n", + " overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000954113 Student 0 Student Data\n", + "Student ID: 1000954113.0\n", + "Lab 1: 100.0\n", + "Lab 2: 81.0\n", + "Lab 3: 25.0\n", + "Lab 4: 35.0\n", + "Exam 1: 0.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 3 Letter: C\n", + "_______________________________________\n", + "1000625629 Student 1 Student Data\n", + "Student ID: 1000625629.0\n", + "Lab 1: 95.0\n", + "Lab 2: 87.0\n", + "Lab 3: 0.0\n", + "Lab 4: 88.0\n", + "Exam 1: 120.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n", + "1000847686 Student 2 Student Data\n", + "Student ID: 1000847686.0\n", + "Lab 1: 100.0\n", + "Lab 2: 0.0\n", + "Lab 3: 0.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n", + "1001108137 Student 3 Student Data\n", + "Student ID: 1001108137.0\n", + "Lab 1: 90.0\n", + "Lab 2: 96.0\n", + "Lab 3: 0.0\n", + "Lab 4: 35.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n", + "1000999851 Student 4 Student Data\n", + "Student ID: 1000999851.0\n", + "Lab 1: 95.0\n", + "Lab 2: 94.0\n", + "Lab 3: 43.0\n", + "Lab 4: 0.0\n", + "Exam 1: 100.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 3 Letter: B-\n", + "_______________________________________\n", + "1001214491 Student 5 Student Data\n", + "Student ID: 1001214491.0\n", + "Lab 1: 80.0\n", + "Lab 2: 82.0\n", + "Lab 3: 30.0\n", + "Lab 4: 0.0\n", + "Exam 1: 90.0\n", + "Lab 5: nan\n", + "Lab 6: nan\n", + "Lab 7: nan\n", + "Lab 3 Letter: C+\n", + "_______________________________________\n", + "1001428148 Student 6 Student Data\n", + "Student ID: 1001428148.0\n", + "Lab 1: 80.0\n", + "Lab 2: 90.0\n", + "Lab 3: 24.0\n", + "Lab 4: 0.0\n", + "Exam 1: 80.0\n", + "Lab 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"cell_type": "markdown", + "metadata": {}, + "source": [ + "### More Exercises\n", + "\n", + "* Write an algorithm that removes grades that are less than a value (default 50% of max possible grade), and distributes grades as follows:\n", + " * 16% A\n", + " * 34% B\n", + " * 34% C\n", + " * 16% D\n", + "\n", + "* Write new algorithms:\n", + " * Write an algorithm that uses the grade boundries to assign grades, including +/-. \n", + " * Write an algorithm that sums up a provided list of grades, dropping the lowest $n$.\n", + "\n", + " For each:\n", + " * Determine what type of algorithm.\n", + " * Copy paste analogous algorithm.\n", + " * Change name.\n", + " * Remove code.\n", + " * Add new code.\n", + " * Test.\n", + " \n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "class forced_curve(calculator):\n", + " def __init__(self,grade_name,cut_off=None):\n", + " self.__grade_name=grade_name\n", + " self.__cut_off=cut_off\n", + " calculator.__init__(self,\"Forced Curve Boundry Statistical Calculator\")\n", + " \n", + " def apply(self,a_grade_book,grade_name=None,cut_off=None,**kwargs):\n", + " if grade_name:\n", + " pass\n", + " else:\n", + " grade_name=self.__grade_name\n", + " \n", + " if cut_off:\n", + " pass\n", + " else:\n", + " cut_off=self.__cut_off\n", + " \n", + " grades=list()\n", + " for k,a_student in a_grade_book.get_students().items():\n", + " a_grade_val=a_student[grade_name].value()\n", + " if cut_off:\n", + " if a_grade_val>cut_off:\n", + " grades.append(a_student[grade_name].value())\n", + " else:\n", + " grades.append(a_student[grade_name].value())\n", + " \n", + " # Possible Algorithm Logic\n", + " # Sort the grades\n", + " # Top 16% -> A\n", + " # How many is 16%? Count all students (after min grade cut) -> .16 * n\n", + "\n", + " grades = sorted(grades)[::-1]\n", + " n_students = len(grades)\n", + " \n", + " n_16 = int(0.16 * float(n_students))\n", + " n_34 = int(0.34 * float(n_students))\n", + " \n", + " boundries= dict()\n", + " boundries[\"A\"]=grades[n_16-1]\n", + " boundries[\"B\"]=grades[n_16+n_34-1]\n", + " boundries[\"C\"]=grades[n_16+n_34+n_34-1]\n", + " boundries[\"D\"]=grades[min(n_students-1,n_16+n_34+n_34+n_16-1)]\n", + " \n", + " a_grade_book[grade_name+\" Boundries\"] = boundries\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "a_grade_book.apply_calculator(forced_curve(grade_name=\"Exam 1\"))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lab 3 Mean : 36.766666666666666\n", + "Lab 3 STD : 36.31774895128949\n", + "Lab 3 Max : 100.0\n", + "Lab 3 Min : 0.0\n", + "Lab 3 Boundries : {'A': 96.0, 'B': 25.0, 'C': 0.0, 'D': 0.0}\n", + "Exam 1 Boundries : {'A': 120.0, 'B': 100.0, 'C': 20.0, 'D': 0.0}\n" + ] + } + ], + "source": [ + "a_grade_book.print_data()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Lecture-24/2012_Workplace_Fatalities_by_State.csv b/Lectures/Lecture-24/2012_Workplace_Fatalities_by_State.csv new file mode 100644 index 0000000..b216157 --- /dev/null +++ b/Lectures/Lecture-24/2012_Workplace_Fatalities_by_State.csv @@ -0,0 +1,105 @@ +State,"Number of Fatalities, 2012","Rate of Fatalities, 2012","State Rank, Fatalities 2012",Number of Injuries/Illnesses 2012,Injuries/Illnesses 2012 Rate,Penalties FY 2013 (Average $),Penalties FY 2013 (Rank),Inspectors,Years to Inspect Each Workplace Once,State or Federal Program +"South Carolina +(33.99882060100049, -81.04536765699964)",63,3.5,25,36200,3,492,49,24,111,State +"West Virginia +(38.665511497000466, -80.71263935099967)",49,6.9,46,19800,4.1,1798,27,7,173,Federal +"Massachusetts +(42.27687306500047, -72.08268985899963)",44,1.4,1,69700,3.1,1929,21,33,123,Federal +"Tennessee +(35.680943063000484, -85.77448642199965)",101,3.8,30,65100,3.5,727,45,30,82,State +"Oklahoma +(35.472034350000456, -97.52106845499969)",97,6.1,42,39000,3.6,1872,24,19,131,Federal +"Illinois +(40.48501278700047, -88.99770813999965)",146,2.5,10,124900,3.2,1876,23,74,137,Federal +"Nebraska +(41.64104043900045, -99.36571864599966)",48,5.2,38,24300,3.9,2565,5,9,128,Federal +"Delaware +(39.00883351400046, -75.57773943699965)",14,3.1,18,7900,2.8,2406,6,5,175,Federal +"Hawaii +(21.30485166200043, -157.85774691599974)",20,3.4,22,13700,3.8,964,39,20,79,State +"Iowa +(42.469404401000475, -93.81648936699969)",97,6.6,44,45600,4.5,790,43,26,98,State +"Arizona +(34.865973091000455, -111.76380949799972)",60,2.3,6,54400,3.2,891,40,30,126,State +"Florida +(28.932042899000464, -81.92895558499964)",218,2.7,15,,,1821,25,60,238,Federal +"Virginia +(37.54268075100049, -78.45788924199968)",149,3.8,30,66200,2.7,726,46,48,82,State +"Missouri +(38.63579372300046, -92.56629737199967)",88,3.3,21,60300,3.3,1931,20,26,118,Federal +"Michigan +(44.66131575600048, -84.71438724399968)",137,3.4,22,105500,4,542,48,63,45,State +"Indiana +(39.76691364600049, -86.14995579899966)",115,4.2,33,77900,3.9,1054,34,39,104,State +"North Carolina +(35.46622388600048, -79.15924924699965)",146,3.5,25,75900,2.9,996,38,104,60,State +"New Hampshire +(43.6559537330005, -71.50035726399966)",14,2.2,4,,,2243,8,7,119,Federal +"New Mexico +(34.52088247800049, -106.24057768899968)",39,4.8,35,19900,3.9,998,37,9,191,State +"Pennsylvania +(40.79373106100047, -77.86069775999965)",194,3.4,22,155300,3.9,1916,22,57,125,Federal +"South Dakota +(44.35313342000046, -100.37352811899967)",31,6.7,45,,,2346,7,,521,Federal +"New York +(42.82700023900048, -75.54396639699968)",202,2.4,8,146300,2.5,2016,17,105,184,Federal +"Utah +(39.36070374600047, -111.5871285339997)",39,3,17,27700,3.4,1053,35,22,81,State +"Maine +(45.254228663000504, -68.98502952999962)",19,3.2,20,21200,5.6,2083,14,8,80,Federal +"Montana +(47.066526051000494, -109.42441687999968)",34,7.3,47,13300,5,1983,18,7,135,Federal +"Vermont +(43.62538292400046, -72.51763944499965)",11,3.5,25,9900,5,1008,36,9,68,State +"Arkansas +(34.748651751000466, -92.27448794899965)",63,5.4,39,26600,3.2,2569,4,9,237,Federal +"Nevada +(39.49324126500045, -117.07183978499972)",42,3.6,29,32400,4.1,2133,13,44,49,State +"Kentucky +(37.645973909000475, -84.77496612599964)",91,4.9,37,48900,4.1,3254,2,39,124,State +"Maryland +(39.2905806980005, -76.60925970899967)",72,2.6,12,51900,3.1,685,47,48,108,State +"Alabama +(32.84057327200048, -86.63185803899967)",84,4.3,34,41200,3.3,1803,26,24,94,Federal +"Connecticut +(41.56266394200048, -72.64983753699966)",36,2.1,3,43800,3.9,1735,30,24,107,Federal +"Oregon +(44.567446178000466, -120.15502977999972)",43,2.6,12,42900,3.9,363,50,75,31,State +"Colorado +(38.84384047000049, -106.13360888799969)",82,3.5,25,,,1649,31,28,122,Federal +"Ohio +(40.06021029700048, -82.40425685299965)",161,3.1,18,113600,3.2,2156,11,53,112,Federal +"Wyoming +(43.23554147100049, -108.10982744299969)",35,12.2,49,6500,3.5,1777,28,9,101,State +"Minnesota +(46.35564867700049, -94.79419697699967)",70,2.6,12,67500,3.8,768,44,58,57,State +"Kansas +(38.34774033400049, -98.20077655499966)",76,5.7,41,33400,3.6,1971,19,16,110,Federal +"Idaho +(43.682630058000484, -114.3637261449997)",19,2.7,15,,,1449,33,9,108,Federal +"Washington +(47.522287905000496, -120.47002746299972)",67,2.2,4,89300,4.8,791,42,111,50,State +"Wisconsin +(44.3931903350005, -89.81636715299965)",114,4,32,72900,4,2207,9,36,104,Federal +"Mississippi +(32.74551123200047, -89.53802764499966)",63,5.5,40,,,1515,32,14,112,Federal +"Louisiana +(31.312662564000448, -92.44567554599968)",116,6.4,43,30600,2.3,1765,29,16,206,Federal +"Georgia +(32.83968004200045, -83.62757601199968)",101,2.5,10,74800,2.8,2061,15,49,138,Federal +"Rhode Island +(41.70828281900049, -71.52246918099962)",8,1.7,2,,,2023,16,7,103,Federal +"Alaska +(64.84507923900048, -147.72205669099972)",31,8.9,48,9700,4.6,889,41,11,58,State +"New Jersey +(40.1305700530005, -74.27368565099965)",92,2.4,8,80900,3.1,2151,12,67,123,Federal +"North Dakota +(47.47531738700047, -100.11842599699969)",65,17.7,50,,,3045,3,8,111,Federal +"Texas +(31.827243635000457, -99.4267664729997)",536,4.8,35,203200,2.7,2187,10,98,136,Federal +"California +(37.638640488000476, -120.99999889499969)",375,2.3,6,345400,3.5,6422,1,216,179,State +,,,,,,,,,, +,,,,,,,,,, +,,,,,,,,,, +"Total or National, Average",4628,3.4,,,3.4,,,,, diff --git a/Lectures/Lecture-24/Lecture-24-Part-II.ipynb b/Lectures/Lecture-24/Lecture-24-Part-II.ipynb new file mode 100644 index 0000000..9fefb43 --- /dev/null +++ b/Lectures/Lecture-24/Lecture-24-Part-II.ipynb @@ -0,0 +1,2277 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 24\n", + "\n", + "## Basic Data Manipulation\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Workplace Fatalities by State Data\n", + "\n", + "Lets look at some workforce data from [source](https://opendata.socrata.com/Government/2012-Workplace-Fatalities-by-State/vcx3-xxtb)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2012_Workplace_Fatalities_by_State.csv random-matrix.csv\r\n", + "Lecture-24-Part-II.ipynb random-matrix.npy\r\n", + "Lecture-24.ipynb stockholm_td_adj.dat\r\n", + "heart.csv\r\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "State,\"Number of Fatalities, 2012\",\"Rate of Fatalities, 2012\",\"State Rank, Fatalities 2012\",Number of Injuries/Illnesses 2012,Injuries/Illnesses 2012 Rate,Penalties FY 2013 (Average $),Penalties FY 2013 (Rank),Inspectors,Years to Inspect Each Workplace Once,State or Federal Program\r\n", + "\"South Carolina\r\n", + "(33.99882060100049, -81.04536765699964)\",63,3.5,25,36200,3,492,49,24,111,State\r\n", + "\"West Virginia\r\n", + "(38.665511497000466, -80.71263935099967)\",49,6.9,46,19800,4.1,1798,27,7,173,Federal\r\n", + "\"Massachusetts\r\n", + "(42.27687306500047, -72.08268985899963)\",44,1.4,1,69700,3.1,1929,21,33,123,Federal\r\n", + "\"Tennessee\r\n", + "(35.680943063000484, -85.77448642199965)\",101,3.8,30,65100,3.5,727,45,30,82,State\r\n", + "\"Oklahoma\r\n" + ] + } + ], + "source": [ + "!head 2012_Workplace_Fatalities_by_State.csv " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.read_csv(\"2012_Workplace_Fatalities_by_State.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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StateNumber of Fatalities, 2012Rate of Fatalities, 2012State Rank, Fatalities 2012Number of Injuries/Illnesses 2012Injuries/Illnesses 2012 RatePenalties FY 2013 (Average $)Penalties FY 2013 (Rank)InspectorsYears to Inspect Each Workplace OnceState or Federal Program
0South Carolina\\n(33.99882060100049, -81.045367...63.03.525.036200.03.0492.049.024.0111.0State
1West Virginia\\n(38.665511497000466, -80.712639...49.06.946.019800.04.11798.027.07.0173.0Federal
2Massachusetts\\n(42.27687306500047, -72.0826898...44.01.41.069700.03.11929.021.033.0123.0Federal
3Tennessee\\n(35.680943063000484, -85.7744864219...101.03.830.065100.03.5727.045.030.082.0State
4Oklahoma\\n(35.472034350000456, -97.52106845499...97.06.142.039000.03.61872.024.019.0131.0Federal
5Illinois\\n(40.48501278700047, -88.99770813999965)146.02.510.0124900.03.21876.023.074.0137.0Federal
6Nebraska\\n(41.64104043900045, -99.36571864599966)48.05.238.024300.03.92565.05.09.0128.0Federal
7Delaware\\n(39.00883351400046, -75.57773943699965)14.03.118.07900.02.82406.06.05.0175.0Federal
8Hawaii\\n(21.30485166200043, -157.85774691599974)20.03.422.013700.03.8964.039.020.079.0State
9Iowa\\n(42.469404401000475, -93.81648936699969)97.06.644.045600.04.5790.043.026.098.0State
10Arizona\\n(34.865973091000455, -111.76380949799...60.02.36.054400.03.2891.040.030.0126.0State
11Florida\\n(28.932042899000464, -81.92895558499964)218.02.715.0NaNNaN1821.025.060.0238.0Federal
12Virginia\\n(37.54268075100049, -78.45788924199968)149.03.830.066200.02.7726.046.048.082.0State
13Missouri\\n(38.63579372300046, -92.56629737199967)88.03.321.060300.03.31931.020.026.0118.0Federal
14Michigan\\n(44.66131575600048, -84.71438724399968)137.03.422.0105500.04.0542.048.063.045.0State
15Indiana\\n(39.76691364600049, -86.14995579899966)115.04.233.077900.03.91054.034.039.0104.0State
16North Carolina\\n(35.46622388600048, -79.159249...146.03.525.075900.02.9996.038.0104.060.0State
17New Hampshire\\n(43.6559537330005, -71.50035726...14.02.24.0NaNNaN2243.08.07.0119.0Federal
18New Mexico\\n(34.52088247800049, -106.240577688...39.04.835.019900.03.9998.037.09.0191.0State
19Pennsylvania\\n(40.79373106100047, -77.86069775...194.03.422.0155300.03.91916.022.057.0125.0Federal
20South Dakota\\n(44.35313342000046, -100.3735281...31.06.745.0NaNNaN2346.07.0NaN521.0Federal
21New York\\n(42.82700023900048, -75.54396639699968)202.02.48.0146300.02.52016.017.0105.0184.0Federal
22Utah\\n(39.36070374600047, -111.5871285339997)39.03.017.027700.03.41053.035.022.081.0State
23Maine\\n(45.254228663000504, -68.98502952999962)19.03.220.021200.05.62083.014.08.080.0Federal
24Montana\\n(47.066526051000494, -109.42441687999...34.07.347.013300.05.01983.018.07.0135.0Federal
25Vermont\\n(43.62538292400046, -72.51763944499965)11.03.525.09900.05.01008.036.09.068.0State
26Arkansas\\n(34.748651751000466, -92.27448794899...63.05.439.026600.03.22569.04.09.0237.0Federal
27Nevada\\n(39.49324126500045, -117.07183978499972)42.03.629.032400.04.12133.013.044.049.0State
28Kentucky\\n(37.645973909000475, -84.77496612599...91.04.937.048900.04.13254.02.039.0124.0State
29Maryland\\n(39.2905806980005, -76.60925970899967)72.02.612.051900.03.1685.047.048.0108.0State
30Alabama\\n(32.84057327200048, -86.63185803899967)84.04.334.041200.03.31803.026.024.094.0Federal
31Connecticut\\n(41.56266394200048, -72.649837536...36.02.13.043800.03.91735.030.024.0107.0Federal
32Oregon\\n(44.567446178000466, -120.15502977999972)43.02.612.042900.03.9363.050.075.031.0State
33Colorado\\n(38.84384047000049, -106.13360888799...82.03.525.0NaNNaN1649.031.028.0122.0Federal
34Ohio\\n(40.06021029700048, -82.40425685299965)161.03.118.0113600.03.22156.011.053.0112.0Federal
35Wyoming\\n(43.23554147100049, -108.10982744299969)35.012.249.06500.03.51777.028.09.0101.0State
36Minnesota\\n(46.35564867700049, -94.79419697699...70.02.612.067500.03.8768.044.058.057.0State
37Kansas\\n(38.34774033400049, -98.20077655499966)76.05.741.033400.03.61971.019.016.0110.0Federal
38Idaho\\n(43.682630058000484, -114.3637261449997)19.02.715.0NaNNaN1449.033.09.0108.0Federal
39Washington\\n(47.522287905000496, -120.47002746...67.02.24.089300.04.8791.042.0111.050.0State
40Wisconsin\\n(44.3931903350005, -89.81636715299965)114.04.032.072900.04.02207.09.036.0104.0Federal
41Mississippi\\n(32.74551123200047, -89.538027644...63.05.540.0NaNNaN1515.032.014.0112.0Federal
42Louisiana\\n(31.312662564000448, -92.4456755459...116.06.443.030600.02.31765.029.016.0206.0Federal
43Georgia\\n(32.83968004200045, -83.62757601199968)101.02.510.074800.02.82061.015.049.0138.0Federal
44Rhode Island\\n(41.70828281900049, -71.52246918...8.01.72.0NaNNaN2023.016.07.0103.0Federal
45Alaska\\n(64.84507923900048, -147.72205669099972)31.08.948.09700.04.6889.041.011.058.0State
46New Jersey\\n(40.1305700530005, -74.27368565099...92.02.48.080900.03.12151.012.067.0123.0Federal
47North Dakota\\n(47.47531738700047, -100.1184259...65.017.750.0NaNNaN3045.03.08.0111.0Federal
48Texas\\n(31.827243635000457, -99.4267664729997)536.04.835.0203200.02.72187.010.098.0136.0Federal
49California\\n(37.638640488000476, -120.99999889...375.02.36.0345400.03.56422.01.0216.0179.0State
50NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
51NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
52NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
53Total or National, Average4628.03.4NaNNaN3.4NaNNaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " State \\\n", + "0 South Carolina\\n(33.99882060100049, -81.045367... \n", + "1 West Virginia\\n(38.665511497000466, -80.712639... \n", + "2 Massachusetts\\n(42.27687306500047, -72.0826898... \n", + "3 Tennessee\\n(35.680943063000484, -85.7744864219... \n", + "4 Oklahoma\\n(35.472034350000456, -97.52106845499... \n", + "5 Illinois\\n(40.48501278700047, -88.99770813999965) \n", + "6 Nebraska\\n(41.64104043900045, -99.36571864599966) \n", + "7 Delaware\\n(39.00883351400046, -75.57773943699965) \n", + "8 Hawaii\\n(21.30485166200043, -157.85774691599974) \n", + "9 Iowa\\n(42.469404401000475, -93.81648936699969) \n", + "10 Arizona\\n(34.865973091000455, -111.76380949799... \n", + "11 Florida\\n(28.932042899000464, -81.92895558499964) \n", + "12 Virginia\\n(37.54268075100049, -78.45788924199968) \n", + "13 Missouri\\n(38.63579372300046, -92.56629737199967) \n", + "14 Michigan\\n(44.66131575600048, -84.71438724399968) \n", + "15 Indiana\\n(39.76691364600049, -86.14995579899966) \n", + "16 North Carolina\\n(35.46622388600048, -79.159249... \n", + "17 New Hampshire\\n(43.6559537330005, -71.50035726... \n", + "18 New Mexico\\n(34.52088247800049, -106.240577688... \n", + "19 Pennsylvania\\n(40.79373106100047, -77.86069775... \n", + "20 South Dakota\\n(44.35313342000046, -100.3735281... \n", + "21 New York\\n(42.82700023900048, -75.54396639699968) \n", + "22 Utah\\n(39.36070374600047, -111.5871285339997) \n", + "23 Maine\\n(45.254228663000504, -68.98502952999962) \n", + "24 Montana\\n(47.066526051000494, -109.42441687999... \n", + "25 Vermont\\n(43.62538292400046, -72.51763944499965) \n", + "26 Arkansas\\n(34.748651751000466, -92.27448794899... \n", + "27 Nevada\\n(39.49324126500045, -117.07183978499972) \n", + "28 Kentucky\\n(37.645973909000475, -84.77496612599... \n", + "29 Maryland\\n(39.2905806980005, -76.60925970899967) \n", + "30 Alabama\\n(32.84057327200048, -86.63185803899967) \n", + "31 Connecticut\\n(41.56266394200048, -72.649837536... \n", + "32 Oregon\\n(44.567446178000466, -120.15502977999972) \n", + "33 Colorado\\n(38.84384047000049, -106.13360888799... \n", + "34 Ohio\\n(40.06021029700048, -82.40425685299965) \n", + "35 Wyoming\\n(43.23554147100049, -108.10982744299969) \n", + "36 Minnesota\\n(46.35564867700049, -94.79419697699... \n", + "37 Kansas\\n(38.34774033400049, -98.20077655499966) \n", + "38 Idaho\\n(43.682630058000484, -114.3637261449997) \n", + "39 Washington\\n(47.522287905000496, -120.47002746... \n", + "40 Wisconsin\\n(44.3931903350005, -89.81636715299965) \n", + "41 Mississippi\\n(32.74551123200047, -89.538027644... \n", + "42 Louisiana\\n(31.312662564000448, -92.4456755459... \n", + "43 Georgia\\n(32.83968004200045, -83.62757601199968) \n", + "44 Rhode Island\\n(41.70828281900049, -71.52246918... \n", + "45 Alaska\\n(64.84507923900048, -147.72205669099972) \n", + "46 New Jersey\\n(40.1305700530005, -74.27368565099... \n", + "47 North Dakota\\n(47.47531738700047, -100.1184259... \n", + "48 Texas\\n(31.827243635000457, -99.4267664729997) \n", + "49 California\\n(37.638640488000476, -120.99999889... \n", + "50 NaN \n", + "51 NaN \n", + "52 NaN \n", + "53 Total or National, Average \n", + "\n", + " Number of Fatalities, 2012 Rate of Fatalities, 2012 \\\n", + "0 63.0 3.5 \n", + "1 49.0 6.9 \n", + "2 44.0 1.4 \n", + "3 101.0 3.8 \n", + "4 97.0 6.1 \n", + "5 146.0 2.5 \n", + "6 48.0 5.2 \n", + "7 14.0 3.1 \n", + "8 20.0 3.4 \n", + "9 97.0 6.6 \n", + "10 60.0 2.3 \n", + "11 218.0 2.7 \n", + "12 149.0 3.8 \n", + "13 88.0 3.3 \n", + "14 137.0 3.4 \n", + "15 115.0 4.2 \n", + "16 146.0 3.5 \n", + "17 14.0 2.2 \n", + "18 39.0 4.8 \n", + "19 194.0 3.4 \n", + "20 31.0 6.7 \n", + "21 202.0 2.4 \n", + "22 39.0 3.0 \n", + "23 19.0 3.2 \n", + "24 34.0 7.3 \n", + "25 11.0 3.5 \n", + "26 63.0 5.4 \n", + "27 42.0 3.6 \n", + "28 91.0 4.9 \n", + "29 72.0 2.6 \n", + "30 84.0 4.3 \n", + "31 36.0 2.1 \n", + "32 43.0 2.6 \n", + "33 82.0 3.5 \n", + "34 161.0 3.1 \n", + "35 35.0 12.2 \n", + "36 70.0 2.6 \n", + "37 76.0 5.7 \n", + "38 19.0 2.7 \n", + "39 67.0 2.2 \n", + "40 114.0 4.0 \n", + "41 63.0 5.5 \n", + "42 116.0 6.4 \n", + "43 101.0 2.5 \n", + "44 8.0 1.7 \n", + "45 31.0 8.9 \n", + "46 92.0 2.4 \n", + "47 65.0 17.7 \n", + "48 536.0 4.8 \n", + "49 375.0 2.3 \n", + "50 NaN NaN \n", + "51 NaN NaN \n", + "52 NaN NaN \n", + "53 4628.0 3.4 \n", + "\n", + " State Rank, Fatalities 2012 Number of Injuries/Illnesses 2012 \\\n", + "0 25.0 36200.0 \n", + "1 46.0 19800.0 \n", + "2 1.0 69700.0 \n", + "3 30.0 65100.0 \n", + "4 42.0 39000.0 \n", + "5 10.0 124900.0 \n", + "6 38.0 24300.0 \n", + "7 18.0 7900.0 \n", + "8 22.0 13700.0 \n", + "9 44.0 45600.0 \n", + "10 6.0 54400.0 \n", + "11 15.0 NaN \n", + "12 30.0 66200.0 \n", + "13 21.0 60300.0 \n", + "14 22.0 105500.0 \n", + "15 33.0 77900.0 \n", + "16 25.0 75900.0 \n", + "17 4.0 NaN \n", + "18 35.0 19900.0 \n", + "19 22.0 155300.0 \n", + "20 45.0 NaN \n", + "21 8.0 146300.0 \n", + "22 17.0 27700.0 \n", + "23 20.0 21200.0 \n", + "24 47.0 13300.0 \n", + "25 25.0 9900.0 \n", + "26 39.0 26600.0 \n", + "27 29.0 32400.0 \n", + "28 37.0 48900.0 \n", + "29 12.0 51900.0 \n", + "30 34.0 41200.0 \n", + "31 3.0 43800.0 \n", + "32 12.0 42900.0 \n", + "33 25.0 NaN \n", + "34 18.0 113600.0 \n", + "35 49.0 6500.0 \n", + "36 12.0 67500.0 \n", + "37 41.0 33400.0 \n", + "38 15.0 NaN \n", + "39 4.0 89300.0 \n", + "40 32.0 72900.0 \n", + "41 40.0 NaN \n", + "42 43.0 30600.0 \n", + "43 10.0 74800.0 \n", + "44 2.0 NaN \n", + "45 48.0 9700.0 \n", + "46 8.0 80900.0 \n", + "47 50.0 NaN \n", + "48 35.0 203200.0 \n", + "49 6.0 345400.0 \n", + "50 NaN NaN \n", + "51 NaN NaN \n", + "52 NaN NaN \n", + "53 NaN NaN \n", + "\n", + " Injuries/Illnesses 2012 Rate Penalties FY 2013 (Average $) \\\n", + "0 3.0 492.0 \n", + "1 4.1 1798.0 \n", + "2 3.1 1929.0 \n", + "3 3.5 727.0 \n", + "4 3.6 1872.0 \n", + "5 3.2 1876.0 \n", + "6 3.9 2565.0 \n", + "7 2.8 2406.0 \n", + "8 3.8 964.0 \n", + "9 4.5 790.0 \n", + "10 3.2 891.0 \n", + "11 NaN 1821.0 \n", + "12 2.7 726.0 \n", + "13 3.3 1931.0 \n", + "14 4.0 542.0 \n", + "15 3.9 1054.0 \n", + "16 2.9 996.0 \n", + "17 NaN 2243.0 \n", + "18 3.9 998.0 \n", + "19 3.9 1916.0 \n", + "20 NaN 2346.0 \n", + "21 2.5 2016.0 \n", + "22 3.4 1053.0 \n", + "23 5.6 2083.0 \n", + "24 5.0 1983.0 \n", + "25 5.0 1008.0 \n", + "26 3.2 2569.0 \n", + "27 4.1 2133.0 \n", + "28 4.1 3254.0 \n", + "29 3.1 685.0 \n", + "30 3.3 1803.0 \n", + "31 3.9 1735.0 \n", + "32 3.9 363.0 \n", + "33 NaN 1649.0 \n", + "34 3.2 2156.0 \n", + "35 3.5 1777.0 \n", + "36 3.8 768.0 \n", + "37 3.6 1971.0 \n", + "38 NaN 1449.0 \n", + "39 4.8 791.0 \n", + "40 4.0 2207.0 \n", + "41 NaN 1515.0 \n", + "42 2.3 1765.0 \n", + "43 2.8 2061.0 \n", + "44 NaN 2023.0 \n", + "45 4.6 889.0 \n", + "46 3.1 2151.0 \n", + "47 NaN 3045.0 \n", + "48 2.7 2187.0 \n", + "49 3.5 6422.0 \n", + "50 NaN NaN \n", + "51 NaN NaN \n", + "52 NaN NaN \n", + "53 3.4 NaN \n", + "\n", + " Penalties FY 2013 (Rank) Inspectors \\\n", + "0 49.0 24.0 \n", + "1 27.0 7.0 \n", + "2 21.0 33.0 \n", + "3 45.0 30.0 \n", + "4 24.0 19.0 \n", + "5 23.0 74.0 \n", + "6 5.0 9.0 \n", + "7 6.0 5.0 \n", + "8 39.0 20.0 \n", + "9 43.0 26.0 \n", + "10 40.0 30.0 \n", + "11 25.0 60.0 \n", + "12 46.0 48.0 \n", + "13 20.0 26.0 \n", + "14 48.0 63.0 \n", + "15 34.0 39.0 \n", + "16 38.0 104.0 \n", + "17 8.0 7.0 \n", + "18 37.0 9.0 \n", + "19 22.0 57.0 \n", + "20 7.0 NaN \n", + "21 17.0 105.0 \n", + "22 35.0 22.0 \n", + "23 14.0 8.0 \n", + "24 18.0 7.0 \n", + "25 36.0 9.0 \n", + "26 4.0 9.0 \n", + "27 13.0 44.0 \n", + "28 2.0 39.0 \n", + "29 47.0 48.0 \n", + "30 26.0 24.0 \n", + "31 30.0 24.0 \n", + "32 50.0 75.0 \n", + "33 31.0 28.0 \n", + "34 11.0 53.0 \n", + "35 28.0 9.0 \n", + "36 44.0 58.0 \n", + "37 19.0 16.0 \n", + "38 33.0 9.0 \n", + "39 42.0 111.0 \n", + "40 9.0 36.0 \n", + "41 32.0 14.0 \n", + "42 29.0 16.0 \n", + "43 15.0 49.0 \n", + "44 16.0 7.0 \n", + "45 41.0 11.0 \n", + "46 12.0 67.0 \n", + "47 3.0 8.0 \n", + "48 10.0 98.0 \n", + "49 1.0 216.0 \n", + "50 NaN NaN \n", + "51 NaN NaN \n", + "52 NaN NaN \n", + "53 NaN NaN \n", + "\n", + " Years to Inspect Each Workplace Once State or Federal Program \n", + "0 111.0 State \n", + "1 173.0 Federal \n", + "2 123.0 Federal \n", + "3 82.0 State \n", + "4 131.0 Federal \n", + "5 137.0 Federal \n", + "6 128.0 Federal \n", + "7 175.0 Federal \n", + "8 79.0 State \n", + "9 98.0 State \n", + "10 126.0 State \n", + "11 238.0 Federal \n", + "12 82.0 State \n", + "13 118.0 Federal \n", + "14 45.0 State \n", + "15 104.0 State \n", + "16 60.0 State \n", + "17 119.0 Federal \n", + "18 191.0 State \n", + "19 125.0 Federal \n", + "20 521.0 Federal \n", + "21 184.0 Federal \n", + "22 81.0 State \n", + "23 80.0 Federal \n", + "24 135.0 Federal \n", + "25 68.0 State \n", + "26 237.0 Federal \n", + "27 49.0 State \n", + "28 124.0 State \n", + "29 108.0 State \n", + "30 94.0 Federal \n", + "31 107.0 Federal \n", + "32 31.0 State \n", + "33 122.0 Federal \n", + "34 112.0 Federal \n", + "35 101.0 State \n", + "36 57.0 State \n", + "37 110.0 Federal \n", + "38 108.0 Federal \n", + "39 50.0 State \n", + "40 104.0 Federal \n", + "41 112.0 Federal \n", + "42 206.0 Federal \n", + "43 138.0 Federal \n", + "44 103.0 Federal \n", + "45 58.0 State \n", + "46 123.0 Federal \n", + "47 111.0 Federal \n", + "48 136.0 Federal \n", + "49 179.0 State \n", + "50 NaN NaN \n", + "51 NaN NaN \n", + "52 NaN NaN \n", + "53 NaN NaN " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 63.0\n", + "1 49.0\n", + "2 44.0\n", + "3 101.0\n", + "4 97.0\n", + "5 146.0\n", + "6 48.0\n", + "7 14.0\n", + "8 20.0\n", + "9 97.0\n", + "10 60.0\n", + "11 218.0\n", + "12 149.0\n", + "13 88.0\n", + "14 137.0\n", + "15 115.0\n", + "16 146.0\n", + "17 14.0\n", + "18 39.0\n", + "19 194.0\n", + "20 31.0\n", + "21 202.0\n", + "22 39.0\n", + "23 19.0\n", + "24 34.0\n", + "25 11.0\n", + "26 63.0\n", + "27 42.0\n", + "28 91.0\n", + "29 72.0\n", + "30 84.0\n", + "31 36.0\n", + "32 43.0\n", + "33 82.0\n", + "34 161.0\n", + "35 35.0\n", + "36 70.0\n", + "37 76.0\n", + "38 19.0\n", + "39 67.0\n", + "40 114.0\n", + "41 63.0\n", + "42 116.0\n", + "43 101.0\n", + "44 8.0\n", + "45 31.0\n", + "46 92.0\n", + "47 65.0\n", + "48 536.0\n", + "49 375.0\n", + "Name: Number of Fatalities, 2012, dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# To access a column\n", + "df[df.columns[1]][:50]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "536.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Max of a column\n", + "# Note that the last 4 rows are not states\n", + "np.max(df[df.columns[1]][0:50])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/fromnumeric.py:61: FutureWarning: \n", + "The current behaviour of 'Series.argmax' is deprecated, use 'idxmax'\n", + "instead.\n", + "The behavior of 'argmax' will be corrected to return the positional\n", + "maximum in the future. For now, use 'series.values.argmax' or\n", + "'np.argmax(np.array(values))' to get the position of the maximum\n", + "row.\n", + " return bound(*args, **kwds)\n" + ] + }, + { + "data": { + "text/plain": [ + "48" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# What was the index of the max?\n", + "np.argmax(df[df.columns[1]][0:50])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Texas\\n(31.827243635000457, -99.4267664729997)'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# What was the state?\n", + "df[df.columns[0]][np.argmax(df[df.columns[1]][0:50])]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 South Carolina\\n(33.99882060100049, -81.045367...\n", + "1 West Virginia\\n(38.665511497000466, -80.712639...\n", + "2 Massachusetts\\n(42.27687306500047, -72.0826898...\n", + "3 Tennessee\\n(35.680943063000484, -85.7744864219...\n", + "4 Oklahoma\\n(35.472034350000456, -97.52106845499...\n", + "5 Illinois\\n(40.48501278700047, -88.99770813999965)\n", + "6 Nebraska\\n(41.64104043900045, -99.36571864599966)\n", + "7 Delaware\\n(39.00883351400046, -75.57773943699965)\n", + "8 Hawaii\\n(21.30485166200043, -157.85774691599974)\n", + "9 Iowa\\n(42.469404401000475, -93.81648936699969)\n", + "10 Arizona\\n(34.865973091000455, -111.76380949799...\n", + "11 Florida\\n(28.932042899000464, -81.92895558499964)\n", + "12 Virginia\\n(37.54268075100049, -78.45788924199968)\n", + "13 Missouri\\n(38.63579372300046, -92.56629737199967)\n", + "14 Michigan\\n(44.66131575600048, -84.71438724399968)\n", + "15 Indiana\\n(39.76691364600049, -86.14995579899966)\n", + "16 North Carolina\\n(35.46622388600048, -79.159249...\n", + "17 New Hampshire\\n(43.6559537330005, -71.50035726...\n", + "18 New Mexico\\n(34.52088247800049, -106.240577688...\n", + "19 Pennsylvania\\n(40.79373106100047, -77.86069775...\n", + "20 South Dakota\\n(44.35313342000046, -100.3735281...\n", + "21 New York\\n(42.82700023900048, -75.54396639699968)\n", + "22 Utah\\n(39.36070374600047, -111.5871285339997)\n", + "23 Maine\\n(45.254228663000504, -68.98502952999962)\n", + "24 Montana\\n(47.066526051000494, -109.42441687999...\n", + "25 Vermont\\n(43.62538292400046, -72.51763944499965)\n", + "26 Arkansas\\n(34.748651751000466, -92.27448794899...\n", + "27 Nevada\\n(39.49324126500045, -117.07183978499972)\n", + "28 Kentucky\\n(37.645973909000475, -84.77496612599...\n", + "29 Maryland\\n(39.2905806980005, -76.60925970899967)\n", + "30 Alabama\\n(32.84057327200048, -86.63185803899967)\n", + "31 Connecticut\\n(41.56266394200048, -72.649837536...\n", + "32 Oregon\\n(44.567446178000466, -120.15502977999972)\n", + "33 Colorado\\n(38.84384047000049, -106.13360888799...\n", + "34 Ohio\\n(40.06021029700048, -82.40425685299965)\n", + "35 Wyoming\\n(43.23554147100049, -108.10982744299969)\n", + "36 Minnesota\\n(46.35564867700049, -94.79419697699...\n", + "37 Kansas\\n(38.34774033400049, -98.20077655499966)\n", + "38 Idaho\\n(43.682630058000484, -114.3637261449997)\n", + "39 Washington\\n(47.522287905000496, -120.47002746...\n", + "40 Wisconsin\\n(44.3931903350005, -89.81636715299965)\n", + "41 Mississippi\\n(32.74551123200047, -89.538027644...\n", + "42 Louisiana\\n(31.312662564000448, -92.4456755459...\n", + "43 Georgia\\n(32.83968004200045, -83.62757601199968)\n", + "44 Rhode Island\\n(41.70828281900049, -71.52246918...\n", + "45 Alaska\\n(64.84507923900048, -147.72205669099972)\n", + "46 New Jersey\\n(40.1305700530005, -74.27368565099...\n", + "47 North Dakota\\n(47.47531738700047, -100.1184259...\n", + "48 Texas\\n(31.827243635000457, -99.4267664729997)\n", + "49 California\\n(37.638640488000476, -120.99999889...\n", + "Name: State, dtype: object" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# State names:\n", + "df[df.columns[0]][0:50]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Clean it up\n", + "state_names=list(map(lambda x: x.split(\"\\n\")[0],df[df.columns[0]][0:50]))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['South Carolina',\n", + " 'West Virginia',\n", + " 'Massachusetts',\n", + " 'Tennessee',\n", + " 'Oklahoma',\n", + " 'Illinois',\n", + " 'Nebraska',\n", + " 'Delaware',\n", + " 'Hawaii',\n", + " 'Iowa',\n", + " 'Arizona',\n", + " 'Florida',\n", + " 'Virginia',\n", + " 'Missouri',\n", + " 'Michigan',\n", + " 'Indiana',\n", + " 'North Carolina',\n", + " 'New Hampshire',\n", + " 'New Mexico',\n", + " 'Pennsylvania',\n", + " 'South Dakota',\n", + " 'New York',\n", + " 'Utah',\n", + " 'Maine',\n", + " 'Montana',\n", + " 'Vermont',\n", + " 'Arkansas',\n", + " 'Nevada',\n", + " 'Kentucky',\n", + " 'Maryland',\n", + " 'Alabama',\n", + " 'Connecticut',\n", + " 'Oregon',\n", + " 'Colorado',\n", + " 'Ohio',\n", + " 'Wyoming',\n", + " 'Minnesota',\n", + " 'Kansas',\n", + " 'Idaho',\n", + " 'Washington',\n", + " 'Wisconsin',\n", + " 'Mississippi',\n", + " 'Louisiana',\n", + " 'Georgia',\n", + " 'Rhode Island',\n", + " 'Alaska',\n", + " 'New Jersey',\n", + " 'North Dakota',\n", + " 'Texas',\n", + " 'California']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_names" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax= plt.subplots(1,1,figsize=(10,10))\n", + "\n", + "ax.bar(range(50),df[df.columns[1]][0:50])\n", + "ax.set_xticks(range(50))\n", + "_=ax.set_xticklabels(state_names, rotation='vertical', fontsize=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax= plt.subplots(1,1,figsize=(10,10))\n", + "\n", + "ax.bar(range(50),df[df.columns[2]][0:50])\n", + "ax.set_xticks(range(50))\n", + "_=ax.set_xticklabels(state_names, rotation='vertical', fontsize=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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303 rows Ă— 14 columns

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" + ], + "text/plain": [ + " age sex cp trestbps chol fbs restecg thalach exang oldpeak \\\n", + "0 63 1 3 145 233 1 0 150 0 2.3 \n", + "1 37 1 2 130 250 0 1 187 0 3.5 \n", + "2 41 0 1 130 204 0 0 172 0 1.4 \n", + "3 56 1 1 120 236 0 1 178 0 0.8 \n", + "4 57 0 0 120 354 0 1 163 1 0.6 \n", + ".. ... ... .. ... ... ... ... ... ... ... \n", + "298 57 0 0 140 241 0 1 123 1 0.2 \n", + "299 45 1 3 110 264 0 1 132 0 1.2 \n", + "300 68 1 0 144 193 1 1 141 0 3.4 \n", + "301 57 1 0 130 131 0 1 115 1 1.2 \n", + "302 57 0 1 130 236 0 0 174 0 0.0 \n", + "\n", + " slope ca thal target \n", + "0 0 0 1 1 \n", + "1 0 0 2 1 \n", + "2 2 0 2 1 \n", + "3 2 0 2 1 \n", + "4 2 0 2 1 \n", + ".. ... .. ... ... \n", + "298 1 0 3 0 \n", + "299 1 0 3 0 \n", + "300 1 2 3 0 \n", + "301 1 1 3 0 \n", + "302 1 1 2 0 \n", + "\n", + "[303 rows x 14 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_heart" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(303, 14)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np_heart=np.array(df_heart)\n", + "np_heart.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'age': 0,\n", + " 'sex': 1,\n", + " 'cp': 2,\n", + " 'trestbps': 3,\n", + " 'chol': 4,\n", + " 'fbs': 5,\n", + " 'restecg': 6,\n", + " 'thalach': 7,\n", + " 'exang': 8,\n", + " 'oldpeak': 9,\n", + " 'slope': 10,\n", + " 'ca': 11,\n", + " 'thal': 12,\n", + " 'target': 13}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h_c_tmp=df_heart.columns.tolist()\n", + "h_columns=dict(zip(h_c_tmp,range(len(h_c_tmp))))\n", + "h_columns" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['age',\n", + " 'sex',\n", + " 'cp',\n", + " 'trestbps',\n", + " 'chol',\n", + " 'fbs',\n", + " 'restecg',\n", + " 'thalach',\n", + " 'exang',\n", + " 'oldpeak',\n", + " 'slope',\n", + " 'ca',\n", + " 'thal',\n", + " 'target']" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h_c_tmp" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "index=np.where(np_heart[:,h_columns[\"cp\"]]==0.)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(143,)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np_heart[index,h_columns[\"chol\"]].flatten().shape" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "_=plt.hist(np_heart[index,h_columns[\"chol\"]].flatten(),bins=50)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "index=np.where(np_heart[:,h_columns[\"cp\"]]==0)\n", + "_=plt.hist(np_heart[index,h_columns[\"chol\"]].flatten(),color=\"r\",bins=50,density=1.)\n", + "index=np.where(np_heart[:,h_columns[\"cp\"]]!=0)\n", + "_=plt.hist(np_heart[index,h_columns[\"chol\"]].flatten(),color=\"b\",bins=50,density=1.)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "250.13286713286712\n", + "242.80625\n" + ] + } + ], + "source": [ + "index=np.where(np_heart[:,h_columns[\"cp\"]]==0)\n", + "print (np.mean(np_heart[index,h_columns[\"chol\"]].flatten()))\n", + "index=np.where(np_heart[:,h_columns[\"cp\"]]!=0)\n", + "print (np.mean(np_heart[index,h_columns[\"chol\"]].flatten()))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'age': -0.06865301584014483,\n", + " 'sex': -0.049352875346989386,\n", + " 'cp': 1.0,\n", + " 'trestbps': 0.04760776064464842,\n", + " 'chol': -0.07690439103320763,\n", + " 'fbs': 0.09444403499533158,\n", + " 'restecg': 0.04442059251016377,\n", + " 'thalach': 0.2957621245879105,\n", + " 'exang': -0.39428026849502146,\n", + " 'oldpeak': -0.14923015809708068,\n", + " 'slope': 0.11971658853470613,\n", + " 'ca': -0.18105302605349535,\n", + " 'thal': -0.1617355705100218,\n", + " 'target': 0.4337982615068934}" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dict(zip(h_c_tmp,np.corrcoef(np.array(np_heart).transpose())[h_columns[\"cp\"]]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Lecture-24/Lecture-24.ipynb b/Lectures/Lecture-24/Lecture-24.ipynb new file mode 100644 index 0000000..c09456a --- /dev/null +++ b/Lectures/Lecture-24/Lecture-24.ipynb @@ -0,0 +1,4703 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 24\n", + "\n", + "## Numpy " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Parts of this lecture are adapted from [http://github.com/jrjohansson/scientific-python-lectures](http://github.com/jrjohansson/scientific-python-lectures).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `numpy` package (module) is used in almost all numerical computation using Python. It is a package that provide high-performance vector, matrix and higher-dimensional data structures for Python. It is implemented in C and Fortran so when calculations are vectorized (formulated with vectors and matrices), performance is very good. \n", + "\n", + "To use `numpy` you need to import the module, using for example:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the `numpy` package the terminology used for vectors, matrices and higher-dimensional data sets is *array*. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating `numpy` arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are a number of ways to initialize new numpy arrays, for example from\n", + "\n", + "* a Python list or tuples\n", + "* using functions that are dedicated to generating numpy arrays, such as `arange`, `linspace`, etc.\n", + "* reading data from files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### From lists" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example, to create new vector and matrix arrays from Python lists we can use the `numpy.array` function." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# a vector: the argument to the array function is a Python list\n", + "v = np.array([1,2,3,4])\n", + "\n", + "v" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# a matrix: the argument to the array function is a nested Python list\n", + "M = np.array([[1, 2], [3, 4]])\n", + "\n", + "M" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `v` and `M` objects are both of the type `ndarray` that the `numpy` module provides." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(numpy.ndarray, numpy.ndarray)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(v), type(M)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The difference between the `v` and `M` arrays is only their shapes. We can get information about the shape of an array by using the `ndarray.shape` property." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4,)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 2)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The number of elements in the array is available through the `ndarray.size` property:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Equivalently, we could use the function `numpy.shape` and `numpy.size`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 2)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.shape(M)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.size(M)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far the `numpy.ndarray` looks awefully much like a Python list (or nested list). Why not simply use Python lists for computations instead of creating a new array type? \n", + "\n", + "There are several reasons:\n", + "\n", + "* Python lists are very general. They can contain any kind of object. They are dynamically typed. They do not support mathematical functions such as matrix and dot multiplications, etc. Implementing such functions for Python lists would not be very efficient because of the dynamic typing.\n", + "* Numpy arrays are **statically typed** and **homogeneous**. The type of the elements is determined when the array is created.\n", + "* Numpy arrays are memory efficient.\n", + "* Because of the static typing, fast implementation of mathematical functions such as multiplication and addition of `numpy` arrays can be implemented in a compiled language (C and Fortran is used).\n", + "\n", + "Using the `dtype` (data type) property of an `ndarray`, we can see what type the data of an array has:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('int64')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.dtype" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We get an error if we try to assign a value of the wrong type to an element in a numpy array:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "invalid literal for int() with base 10: 'hello'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hello\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'hello'" + ] + } + ], + "source": [ + "M[0,0] = \"hello\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want, we can explicitly define the type of the array data when we create it, using the `dtype` keyword argument: " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.+0.j, 2.+0.j],\n", + " [3.+0.j, 4.+0.j]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M = np.array([[1, 2], [3, 4]], dtype=complex)\n", + "\n", + "M" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Common data types that can be used with `dtype` are: `int`, `float`, `complex`, `bool`, `object`, etc.\n", + "\n", + "We can also explicitly define the bit size of the data types, for example: `int64`, `int16`, `float128`, `complex128`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using array-generating functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For larger arrays it is inpractical to initialize the data manually, using explicit python lists. Instead we can use one of the many functions in `numpy` that generate arrays of different forms. Some of the more common are:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### arange" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create a range\n", + "\n", + "x = np.arange(0, 10, 1) # arguments: start, stop, step\n", + "\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-1.00000000e+00, -9.00000000e-01, -8.00000000e-01, -7.00000000e-01,\n", + " -6.00000000e-01, -5.00000000e-01, -4.00000000e-01, -3.00000000e-01,\n", + " -2.00000000e-01, -1.00000000e-01, -2.22044605e-16, 1.00000000e-01,\n", + " 2.00000000e-01, 3.00000000e-01, 4.00000000e-01, 5.00000000e-01,\n", + " 6.00000000e-01, 7.00000000e-01, 8.00000000e-01, 9.00000000e-01])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.arange(-1, 1, 0.1)\n", + "\n", + "x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### linspace and logspace" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 0.41666667, 0.83333333, 1.25 , 1.66666667,\n", + " 2.08333333, 2.5 , 2.91666667, 3.33333333, 3.75 ,\n", + " 4.16666667, 4.58333333, 5. , 5.41666667, 5.83333333,\n", + " 6.25 , 6.66666667, 7.08333333, 7.5 , 7.91666667,\n", + " 8.33333333, 8.75 , 9.16666667, 9.58333333, 10. ])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# using linspace, both end points ARE included\n", + "np.linspace(0, 10, 25)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.00000000e+00, 3.03773178e+00, 9.22781435e+00, 2.80316249e+01,\n", + " 8.51525577e+01, 2.58670631e+02, 7.85771994e+02, 2.38696456e+03,\n", + " 7.25095809e+03, 2.20264658e+04])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from math import e\n", + "np.logspace(0, 10, 10, base=e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### mgrid" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "x, y = np.mgrid[0:5, 0:5] # similar to meshgrid in MATLAB" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 0, 0, 0, 0],\n", + " [1, 1, 1, 1, 1],\n", + " [2, 2, 2, 2, 2],\n", + " [3, 3, 3, 3, 3],\n", + " [4, 4, 4, 4, 4]])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4]])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### random data" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.63112997, 0.13574817, 0.74303704, 0.502363 , 0.79405927],\n", + " [0.81827903, 0.95889016, 0.94296117, 0.75706362, 0.06519533],\n", + " [0.2031896 , 0.04010831, 0.79287743, 0.84231938, 0.78992637],\n", + " [0.34726123, 0.89875878, 0.4950607 , 0.89109261, 0.74353815],\n", + " [0.74882519, 0.33852538, 0.88550848, 0.13775374, 0.82998075]])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# uniform random numbers in [0,1]\n", + "np.random.rand(5,5)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.23639361, -0.54285209, 1.52931601, -1.45424374, 0.35703921],\n", + " [ 1.0274817 , -0.564539 , -1.34690528, 0.37877 , 0.69705654],\n", + " [-0.39125945, 1.49676014, -1.00223801, 1.62323919, -0.05757404],\n", + " [ 0.40243793, -1.65216426, -1.20963184, 2.89594655, -0.8400074 ],\n", + " [ 0.21597637, 1.54423065, -0.13224831, -0.37995534, 1.12949491]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# standard normal distributed random numbers\n", + "np.random.randn(5,5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### diag" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 0, 0],\n", + " [0, 2, 0],\n", + " [0, 0, 3]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# a diagonal matrix\n", + "np.diag([1,2,3])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 0, 0],\n", + " [0, 0, 2, 0],\n", + " [0, 0, 0, 3],\n", + " [0, 0, 0, 0]])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# diagonal with offset from the main diagonal\n", + "np.diag([1,2,3], k=1) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### zeros and ones" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.]])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.zeros((3,3))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.]])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.ones((3,3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## File I/O" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comma-separated values (CSV)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A very common file format for data files is comma-separated values (CSV), or related formats such as TSV (tab-separated values). To read data from such files into Numpy arrays we can use the `numpy.genfromtxt` function. For example, " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1800 1 1 -6.1 -6.1 -6.1 1\r\n", + "1800 1 2 -15.4 -15.4 -15.4 1\r\n", + "1800 1 3 -15.0 -15.0 -15.0 1\r\n", + "1800 1 4 -19.3 -19.3 -19.3 1\r\n", + "1800 1 5 -16.8 -16.8 -16.8 1\r\n", + "1800 1 6 -11.4 -11.4 -11.4 1\r\n", + "1800 1 7 -7.6 -7.6 -7.6 1\r\n", + "1800 1 8 -7.1 -7.1 -7.1 1\r\n", + "1800 1 9 -10.1 -10.1 -10.1 1\r\n", + "1800 1 10 -9.5 -9.5 -9.5 1\r\n" + ] + } + ], + "source": [ + "!head stockholm_td_adj.dat" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.genfromtxt('stockholm_td_adj.dat')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(77431, 7)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(14,4))\n", + "ax.plot(data[:,0]+data[:,1]/12.0+data[:,2]/365, data[:,5])\n", + "ax.axis('tight')\n", + "ax.set_title('tempeatures in Stockholm')\n", + "ax.set_xlabel('year')\n", + "ax.set_ylabel('temperature (C)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `numpy.savetxt` we can store a Numpy array to a file in CSV format:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.70398304, 0.98898545, 0.32264658],\n", + " [0.90084036, 0.36902765, 0.74232302],\n", + " [0.75028389, 0.30695582, 0.33015886]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M = np.random.rand(3,3)\n", + "\n", + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "np.savetxt(\"random-matrix.csv\", M)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7.039830359649775771e-01 9.889854525993927448e-01 3.226465761315283620e-01\r\n", + "9.008403594820075799e-01 3.690276521440528645e-01 7.423230185694473793e-01\r\n", + "7.502838869099542896e-01 3.069558247267092366e-01 3.301588630950162973e-01\r\n" + ] + } + ], + "source": [ + "!cat random-matrix.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.70398 0.98899 0.32265\r\n", + "0.90084 0.36903 0.74232\r\n", + "0.75028 0.30696 0.33016\r\n" + ] + } + ], + "source": [ + "np.savetxt(\"random-matrix.csv\", M, fmt='%.5f') # fmt specifies the format\n", + "\n", + "!cat random-matrix.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Numpy's native file format" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Useful when storing and reading back numpy array data. Use the functions `numpy.save` and `numpy.load`:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "random-matrix.npy: NumPy array, version 1.0, header length 118\r\n" + ] + } + ], + "source": [ + "np.save(\"random-matrix.npy\", M)\n", + "\n", + "!file random-matrix.npy" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.70398304, 0.98898545, 0.32264658],\n", + " [0.90084036, 0.36902765, 0.74232302],\n", + " [0.75028389, 0.30695582, 0.33015886]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.load(\"random-matrix.npy\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## More properties of the numpy arrays" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.itemsize # bytes per element" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "72" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.nbytes # number of bytes" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.ndim # number of dimensions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Manipulating arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Indexing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can index elements in an array using square brackets and indices:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# v is a vector, and has only one dimension, taking one index\n", + "v[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.36902765214405286" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# M is a matrix, or a 2 dimensional array, taking two indices \n", + "M[1,1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we omit an index of a multidimensional array it returns the whole row (or, in general, a N-1 dimensional array) " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.70398304, 0.98898545, 0.32264658],\n", + " [0.90084036, 0.36902765, 0.74232302],\n", + " [0.75028389, 0.30695582, 0.33015886]])" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.90084036, 0.36902765, 0.74232302])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The same thing can be achieved with using `:` instead of an index: " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.90084036, 0.36902765, 0.74232302])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M[1,:] # row 1" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.98898545, 0.36902765, 0.30695582])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M[:,1] # column 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can assign new values to elements in an array using indexing:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "M[0,0] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1. , 0.98898545, 0.32264658],\n", + " [0.90084036, 0.36902765, 0.74232302],\n", + " [0.75028389, 0.30695582, 0.33015886]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# also works for rows and columns\n", + "M[1,:] = 0\n", + "M[:,2] = -1" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1. , 0.98898545, -1. ],\n", + " [ 0. , 0. , -1. ],\n", + " [ 0.75028389, 0.30695582, -1. ]])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Index slicing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Index slicing is the technical name for the syntax `M[lower:upper:step]` to extract part of an array:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.array([1,2,3,4,5])\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 3])" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[1:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Array slices are *mutable*: if they are assigned a new value the original array from which the slice was extracted is modified:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, -2, -3, 4, 5])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[1:3] = [-2,-3]\n", + "\n", + "A" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can omit any of the three parameters in `M[lower:upper:step]`:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, -2, -3, 4, 5])" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[::] # lower, upper, step all take the default values" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, -3, 5])" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[::2] # step is 2, lower and upper defaults to the beginning and end of the array" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, -2, -3])" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[:3] # first three elements" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4, 5])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[3:] # elements from index 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Negative indices counts from the end of the array (positive index from the begining):" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "A = np.array([1,2,3,4,5])" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[-1] # the last element in the array" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3, 4, 5])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[-3:] # the last three elements" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Index slicing works exactly the same way for multidimensional arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4],\n", + " [10, 11, 12, 13, 14],\n", + " [20, 21, 22, 23, 24],\n", + " [30, 31, 32, 33, 34],\n", + " [40, 41, 42, 43, 44]])" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.array([[n+m*10 for n in range(5)] for m in range(5)])\n", + "\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[11, 12, 13],\n", + " [21, 22, 23],\n", + " [31, 32, 33]])" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# a block from the original array\n", + "A[1:4, 1:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 2, 4],\n", + " [20, 22, 24],\n", + " [40, 42, 44]])" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# strides\n", + "A[::2, ::2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fancy indexing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fancy indexing is the name for when an array or list is used in-place of an index: " + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[10, 11, 12, 13, 14],\n", + " [20, 21, 22, 23, 24],\n", + " [30, 31, 32, 33, 34]])" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row_indices = [1, 2, 3]\n", + "A[row_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([11, 22, 34])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "col_indices = [1, 2, -1] # remember, index -1 means the last element\n", + "A[row_indices, col_indices]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use index masks: If the index mask is an Numpy array of data type `bool`, then an element is selected (True) or not (False) depending on the value of the index mask at the position of each element: " + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "B = np.array([n for n in range(5)])\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 2])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row_mask = np.array([True, False, True, False, False])\n", + "B[row_mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 2])" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# same thing\n", + "row_mask = np.array([1,0,1,0,0], dtype=bool)\n", + "B[row_mask]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This feature is very useful to conditionally select elements from an array, using for example comparison operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. ,\n", + " 6.5, 7. , 7.5, 8. , 8.5, 9. , 9.5])" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.arange(0, 10, 0.5)\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, False, False, False, False, False, False, False, False,\n", + " False, False, True, True, True, True, False, False, False,\n", + " False, False])" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mask = (5 < x) * (x < 7.5)\n", + "\n", + "mask" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5.5, 6. , 6.5, 7. ])" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[mask]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Functions for extracting data from arrays and creating arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### where" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The index mask can be converted to position index using the `where` function" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([11, 12, 13, 14]),)" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indices = np.where(mask)\n", + "\n", + "indices" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5.5, 6. , 6.5, 7. ])" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[indices] # this indexing is equivalent to the fancy indexing x[mask]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### diag" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the diag function we can also extract the diagonal and subdiagonals of an array:" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 11, 22, 33, 44])" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.diag(A)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([10, 21, 32, 43])" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.diag(A, -1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### take" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `take` function is similar to fancy indexing described above:" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-3, -2, -1, 0, 1, 2])" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v2 = np.arange(-3,3)\n", + "v2" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-2, 0, 2])" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row_indices = [1, 3, 5]\n", + "v2[row_indices] # fancy indexing" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-2, 0, 2])" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v2.take(row_indices)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But `take` also works on lists and other objects:" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-2, 0, 2])" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.take([-3, -2, -1, 0, 1, 2], row_indices)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### choose" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Constructs an array by picking elements from several arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 5, -2, 5, -2])" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "which = [1, 0, 1, 0]\n", + "choices = [[-2,-2,-2,-2], [5,5,5,5]]\n", + "\n", + "np.choose(which, choices)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear algebra" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vectorizing code is the key to writing efficient numerical calculation with Python/Numpy. That means that as much as possible of a program should be formulated in terms of matrix and vector operations, like matrix-matrix multiplication." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scalar-array operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use the usual arithmetic operators to multiply, add, subtract, and divide arrays with scalar numbers." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "v1 = np.arange(0, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 2, 4, 6, 8])" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v1 * 2" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 3, 4, 5, 6])" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v1 + 2" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[ 0, 2, 4, 6, 8],\n", + " [20, 22, 24, 26, 28],\n", + " [40, 42, 44, 46, 48],\n", + " [60, 62, 64, 66, 68],\n", + " [80, 82, 84, 86, 88]]), array([[ 2, 3, 4, 5, 6],\n", + " [12, 13, 14, 15, 16],\n", + " [22, 23, 24, 25, 26],\n", + " [32, 33, 34, 35, 36],\n", + " [42, 43, 44, 45, 46]]))" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A * 2, A + 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Element-wise array-array operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we add, subtract, multiply and divide arrays with each other, the default behaviour is **element-wise** operations:" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 4, 9, 16],\n", + " [ 100, 121, 144, 169, 196],\n", + " [ 400, 441, 484, 529, 576],\n", + " [ 900, 961, 1024, 1089, 1156],\n", + " [1600, 1681, 1764, 1849, 1936]])" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A * A # element-wise multiplication" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 4, 9, 16])" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v1 * v1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we multiply arrays with compatible shapes, we get an element-wise multiplication of each row:" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((5, 5), (5,))" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.shape, v1.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 4, 9, 16],\n", + " [ 0, 11, 24, 39, 56],\n", + " [ 0, 21, 44, 69, 96],\n", + " [ 0, 31, 64, 99, 136],\n", + " [ 0, 41, 84, 129, 176]])" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A * v1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Matrix algebra" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What about matrix mutiplication? There are two ways. We can either use the `dot` function, which applies a matrix-matrix, matrix-vector, or inner vector multiplication to its two arguments: " + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 300, 310, 320, 330, 340],\n", + " [1300, 1360, 1420, 1480, 1540],\n", + " [2300, 2410, 2520, 2630, 2740],\n", + " [3300, 3460, 3620, 3780, 3940],\n", + " [4300, 4510, 4720, 4930, 5140]])" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.dot(A, A)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 30, 130, 230, 330, 430])" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.dot(A, v1)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.dot(v1, v1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can cast the array objects to the type `matrix`. This changes the behavior of the standard arithmetic operators `+, -, *` to use matrix algebra." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [], + "source": [ + "M = np.matrix(A)\n", + "v = np.matrix(v1).T # make it a column vector" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0],\n", + " [1],\n", + " [2],\n", + " [3],\n", + " [4]])" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[ 300, 310, 320, 330, 340],\n", + " [1300, 1360, 1420, 1480, 1540],\n", + " [2300, 2410, 2520, 2630, 2740],\n", + " [3300, 3460, 3620, 3780, 3940],\n", + " [4300, 4510, 4720, 4930, 5140]])" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M * M" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[ 30],\n", + " [130],\n", + " [230],\n", + " [330],\n", + " [430]])" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M * v" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[30]])" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# inner product\n", + "v.T * v" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[ 30],\n", + " [131],\n", + " [232],\n", + " [333],\n", + " [434]])" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# with matrix objects, standard matrix algebra applies\n", + "v + M*v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we try to add, subtract or multiply objects with incomplatible shapes we get an error:" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "v = np.matrix([1,2,3,4,5,6]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((5, 5), (6, 1))" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.shape(M), np.shape(v)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "shapes (5,5) and (6,1) not aligned: 5 (dim 1) != 6 (dim 0)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/matrixlib/defmatrix.py\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 218\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0;31m# This promotes 1-D vectors to row vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masmatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 221\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__rmul__'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: shapes (5,5) and (6,1) not aligned: 5 (dim 1) != 6 (dim 0)" + ] + } + ], + "source": [ + "M * v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See also the related functions: `inner`, `outer`, `cross`, `kron`, `tensordot`. Try for example `help(kron)`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Array/Matrix transformations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above we have used the `.T` to transpose the matrix object `v`. We could also have used the `transpose` function to accomplish the same thing. \n", + "\n", + "Other mathematical functions that transform matrix objects are:" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0.+1.j, 0.+2.j],\n", + " [0.+3.j, 0.+4.j]])" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "C = np.matrix([[1j, 2j], [3j, 4j]])\n", + "C" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0.-1.j, 0.-2.j],\n", + " [0.-3.j, 0.-4.j]])" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.conjugate(C)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hermitian conjugate: transpose + conjugate" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0.-1.j, 0.-3.j],\n", + " [0.-2.j, 0.-4.j]])" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "C.H" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can extract the real and imaginary parts of complex-valued arrays using `real` and `imag`:" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0., 0.],\n", + " [0., 0.]])" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.real(C) # same as: C.real" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[1., 2.],\n", + " [3., 4.]])" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.imag(C) # same as: C.imag" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or the complex argument and absolute value" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0.78539816, 1.10714872],\n", + " [1.24904577, 1.32581766]])" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.angle(C+1) # heads up MATLAB Users, angle is used instead of arg" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[1., 2.],\n", + " [3., 4.]])" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.abs(C)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Matrix computations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Inverse" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0.+2.j , 0.-1.j ],\n", + " [0.-1.5j, 0.+0.5j]])" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.inv(C) # equivalent to C.I " + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[1.00000000e+00+0.j, 0.00000000e+00+0.j],\n", + " [1.11022302e-16+0.j, 1.00000000e+00+0.j]])" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "C.I * C" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Determinant" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.0000000000000004+0j)" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.det(C)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.49999999999999967+0j)" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.det(C.I)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Often it is useful to store datasets in Numpy arrays. Numpy provides a number of functions to calculate statistics of datasets in arrays. \n", + "\n", + "For example, let's calculate some properties from the Stockholm temperature dataset used above." + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(77431, 7)" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reminder, the tempeature dataset is stored in the data variable:\n", + "np.shape(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### mean" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.197109684751585" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the temperature data is in column 3\n", + "np.mean(data[:,3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The daily mean temperature in Stockholm over the last 200 years has been about 6.2 C." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### standard deviations and variance" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8.282271621340573, 68.59602320966341)" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.std(data[:,3]), np.var(data[:,3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### min and max" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-25.8" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lowest daily average temperature\n", + "data[:,3].min()" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "28.3" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# highest daily average temperature\n", + "data[:,3].max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### sum, prod, and trace" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = np.arange(0, 10)\n", + "d" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "45" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sum up all elements\n", + "np.sum(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3628800" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# product of all elements\n", + "np.prod(d+1)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 3, 6, 10, 15, 21, 28, 36, 45])" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# cummulative sum\n", + "np.cumsum(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 2, 6, 24, 120, 720, 5040,\n", + " 40320, 362880, 3628800])" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# cummulative product\n", + "np.cumprod(d+1)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "110" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# same as: diag(A).sum()\n", + "np.trace(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computations on subsets of arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can compute with subsets of the data in an array using indexing, fancy indexing, and the other methods of extracting data from an array (described above).\n", + "\n", + "For example, let's go back to the temperature dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1800 1 1 -6.1 -6.1 -6.1 1\r\n", + "1800 1 2 -15.4 -15.4 -15.4 1\r\n", + "1800 1 3 -15.0 -15.0 -15.0 1\r\n" + ] + } + ], + "source": [ + "!head -n 3 stockholm_td_adj.dat" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataformat is: year, month, day, daily average temperature, low, high, location.\n", + "\n", + "If we are interested in the average temperature only in a particular month, say February, then we can create a index mask and use it to select only the data for that month using:" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.])" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(data[:,1]) # the month column takes values from 1 to 12" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "mask_feb = data[:,1] == 2" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-3.212109570736596" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the temperature data is in column 3\n", + "np.mean(data[mask_feb,3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With these tools we have very powerful data processing capabilities at our disposal. For example, to extract the average monthly average temperatures for each month of the year only takes a few lines of code: " + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "months = np.arange(1,13)\n", + "monthly_mean = [np.mean(data[data[:,1] == month, 3]) for month in months]\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.bar(months, monthly_mean)\n", + "ax.set_xlabel(\"Month\")\n", + "ax.set_ylabel(\"Monthly avg. temp.\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculations with higher-dimensional data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When functions such as `min`, `max`, etc. are applied to a multidimensional arrays, it is sometimes useful to apply the calculation to the entire array, and sometimes only on a row or column basis. Using the `axis` argument we can specify how these functions should behave: " + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.24069276, 0.31177791, 0.28573237],\n", + " [0.16521973, 0.76434069, 0.96119093],\n", + " [0.92194894, 0.01893095, 0.80473947]])" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = np.random.rand(3,3)\n", + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9611909250933929" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# global max\n", + "m.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.92194894, 0.76434069, 0.96119093])" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# max in each column\n", + "m.max(axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.31177791, 0.96119093, 0.92194894])" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# max in each row\n", + "m.max(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many other functions and methods in the `array` and `matrix` classes accept the same (optional) `axis` keyword argument." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reshaping, resizing and stacking arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The shape of an Numpy array can be modified without copying the underlaying data, which makes it a fast operation even for large arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4],\n", + " [10, 11, 12, 13, 14],\n", + " [20, 21, 22, 23, 24],\n", + " [30, 31, 32, 33, 34],\n", + " [40, 41, 42, 43, 44]])" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [], + "source": [ + "n, m = A.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30,\n", + " 31, 32, 33, 34, 40, 41, 42, 43, 44]])" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "B = A.reshape((1,n*m))\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5, 5, 5, 5, 5, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30,\n", + " 31, 32, 33, 34, 40, 41, 42, 43, 44]])" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "B[0,0:5] = 5 # modify the array\n", + "\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5, 5, 5, 5, 5],\n", + " [10, 11, 12, 13, 14],\n", + " [20, 21, 22, 23, 24],\n", + " [30, 31, 32, 33, 34],\n", + " [40, 41, 42, 43, 44]])" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A # and the original variable is also changed. B is only a different view of the same data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use the function `flatten` to make a higher-dimensional array into a vector. But this function create a copy of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 5, 5, 5, 5, 5, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31,\n", + " 32, 33, 34, 40, 41, 42, 43, 44])" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "B = A.flatten()\n", + "\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([10, 10, 10, 10, 10, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31,\n", + " 32, 33, 34, 40, 41, 42, 43, 44])" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "B[0:5] = 10\n", + "\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5, 5, 5, 5, 5],\n", + " [10, 11, 12, 13, 14],\n", + " [20, 21, 22, 23, 24],\n", + " [30, 31, 32, 33, 34],\n", + " [40, 41, 42, 43, 44]])" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A # now A has not changed, because B's data is a copy of A's, not refering to the same data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding a new dimension: newaxis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With `newaxis`, we can insert new dimensions in an array, for example converting a vector to a column or row matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3])" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v = np.array([1,2,3])\n", + "v" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3,)" + ] + }, + "execution_count": 209, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.shape(v)" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1],\n", + " [2],\n", + " [3]])" + ] + }, + "execution_count": 210, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make a column matrix of the vector v\n", + "v[:, np.newaxis]" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 1)" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# column matrix\n", + "v[:,np.newaxis].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 3, 4]])" + ] + }, + "execution_count": 207, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# row matrix\n", + "v[np.newaxis,:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stacking and repeating arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using function `repeat`, `tile`, `vstack`, `hstack`, and `concatenate` we can create larger vectors and matrices from smaller ones:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### tile and repeat" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [], + "source": [ + "a = np.array([[1, 2], [3, 4]])" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4])" + ] + }, + "execution_count": 158, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# repeat each element 3 times\n", + "np.repeat(a, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 1, 2, 1, 2],\n", + " [3, 4, 3, 4, 3, 4]])" + ] + }, + "execution_count": 159, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# tile the matrix 3 times \n", + "np.tile(a, 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### concatenate" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [], + "source": [ + "b = np.array([[5, 6]])" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4],\n", + " [5, 6]])" + ] + }, + "execution_count": 161, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.concatenate((a, b), axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 5],\n", + " [3, 4, 6]])" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.concatenate((a, b.T), axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### hstack and vstack" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4],\n", + " [5, 6]])" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.vstack((a,b))" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 5],\n", + " [3, 4, 6]])" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.hstack((a,b.T))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Copy and \"deep copy\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To achieve high performance, assignments in Python usually do not copy the underlaying objects. This is important for example when objects are passed between functions, to avoid an excessive amount of memory copying when it is not necessary (technical term: pass by reference). " + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4]])" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.array([[1, 2], [3, 4]])\n", + "\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [], + "source": [ + "# now B is referring to the same array data as A \n", + "B = A " + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[10, 2],\n", + " [ 3, 4]])" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# changing B affects A\n", + "B[0,0] = 10\n", + "\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[10, 2],\n", + " [ 3, 4]])" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to avoid this behavior, so that when we get a new completely independent object `B` copied from `A`, then we need to do a so-called \"deep copy\" using the function `copy`:" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [], + "source": [ + "B = np.copy(A)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-5, 2],\n", + " [ 3, 4]])" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now, if we modify B, A is not affected\n", + "B[0,0] = -5\n", + "\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[10, 2],\n", + " [ 3, 4]])" + ] + }, + "execution_count": 171, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Iterating over array elements" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generally, we want to avoid iterating over the elements of arrays whenever we can (at all costs). The reason is that in a interpreted language like Python (or MATLAB), iterations are really slow compared to vectorized operations. \n", + "\n", + "However, sometimes iterations are unavoidable. For such cases, the Python `for` loop is the most convenient way to iterate over an array:" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "3\n", + "4\n" + ] + } + ], + "source": [ + "v = np.array([1,2,3,4])\n", + "\n", + "for element in v:\n", + " print(element)" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "row [1 2]\n", + "1\n", + "2\n", + "row [3 4]\n", + "3\n", + "4\n" + ] + } + ], + "source": [ + "M = np.array([[1,2], [3,4]])\n", + "\n", + "for row in M:\n", + " print(\"row\", row)\n", + " \n", + " for element in row:\n", + " print(element)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we need to iterate over each element of an array and modify its elements, it is convenient to use the `enumerate` function to obtain both the element and its index in the `for` loop: " + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "row_idx 0 row [1 4]\n", + "col_idx 0 element 1\n", + "col_idx 1 element 4\n", + "row_idx 1 row [ 9 16]\n", + "col_idx 0 element 9\n", + "col_idx 1 element 16\n" + ] + } + ], + "source": [ + "for row_idx, row in enumerate(M):\n", + " print(\"row_idx\", row_idx, \"row\", row)\n", + " \n", + " for col_idx, element in enumerate(row):\n", + " print(\"col_idx\", col_idx, \"element\", element)\n", + " \n", + " # update the matrix M: square each element\n", + " M[row_idx, col_idx] = element ** 2" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1, 16],\n", + " [ 81, 256]])" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# each element in M is now squared\n", + "M" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Vectorizing functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned several times by now, to get good performance we should try to avoid looping over elements in our vectors and matrices, and instead use vectorized algorithms. The first step in converting a scalar algorithm to a vectorized algorithm is to make sure that the functions we write work with vector inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [], + "source": [ + "def Theta(x):\n", + " \"\"\"\n", + " Scalar implemenation of the Heaviside step function.\n", + " \"\"\"\n", + " if x >= 0:\n", + " return 1\n", + " else:\n", + " return 0" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mTheta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mTheta\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mScalar\u001b[0m \u001b[0mimplemenation\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mHeaviside\u001b[0m \u001b[0mstep\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \"\"\"\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" + ] + } + ], + "source": [ + "Theta(np.array([-3,-2,-1,0,1,2,3]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OK, that didn't work because we didn't write the `Theta` function so that it can handle a vector input... \n", + "\n", + "To get a vectorized version of Theta we can use the Numpy function `vectorize`. In many cases it can automatically vectorize a function:" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [], + "source": [ + "Theta_vec = np.vectorize(Theta)" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 1, 1, 1, 1])" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Theta_vec(np.array([-3,-2,-1,0,1,2,3]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also implement the function to accept a vector input from the beginning (requires more effort but might give better performance):" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [], + "source": [ + "def Theta(x):\n", + " \"\"\"\n", + " Vector-aware implemenation of the Heaviside step function.\n", + " \"\"\"\n", + " return 1 * (x >= 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 1, 1, 1, 1])" + ] + }, + "execution_count": 186, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Theta(np.array([-3,-2,-1,0,1,2,3]))" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 1)" + ] + }, + "execution_count": 187, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# still works for scalars as well\n", + "Theta(-1.2), Theta(2.6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using arrays in conditions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When using arrays in conditions,for example `if` statements and other boolean expressions, one needs to use `any` or `all`, which requires that any or all elements in the array evalutes to `True`:" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1, 16],\n", + " [ 81, 256]])" + ] + }, + "execution_count": 188, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "at least one element in M is larger than 5\n" + ] + } + ], + "source": [ + "if (M > 5).any():\n", + " print(\"at least one element in M is larger than 5\")\n", + "else:\n", + " print(\"no element in M is larger than 5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "all elements in M are not larger than 5\n" + ] + } + ], + "source": [ + "if (M > 5).all():\n", + " print(\"all elements in M are larger than 5\")\n", + "else:\n", + " print(\"all elements in M are not larger than 5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Type casting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since Numpy arrays are *statically typed*, the type of an array does not change once created. But we can explicitly cast an array of some type to another using the `astype` functions (see also the similar `asarray` function). This always create a new array of new type:" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('int64')" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 16.],\n", + " [ 81., 256.]])" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M2 = M.astype(float)\n", + "\n", + "M2" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M2.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ True, True],\n", + " [ True, True]])" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M3 = M.astype(bool)\n", + "\n", + "M3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Vectorizing" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [], + "source": [ + "def hatFunc(x):\n", + " if x < 0:\n", + " return 0.0\n", + " elif 0 <= x < 1:\n", + " return x\n", + " elif 1 <= x < 2:\n", + " return 2 - x\n", + " elif x >= 2:\n", + " return 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. ,\n", + " 6.5, 7. , 7.5, 8. , 8.5, 9. , 9.5])" + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [], + "source": [ + "y=list()\n", + "for i in range(len(x)):\n", + " y.append(hatFunc(x[i]))" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.0,\n", + " 0.5,\n", + " 1.0,\n", + " 0.5,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0]" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 200, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x=np.linspace(-10.,10,100)\n", + "y=list(map(hatFunc,x))\n", + "plt.plot(x,y)" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [], + "source": [ + "y=x.copy()\n", + "y[x<0]=0.\n", + "index=np.logical_and(0.<=x,x<1)\n", + "y[index]=x[index]" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def hatFunc_vect(x):\n", + " y=x.copy()\n", + " y[x<0]=0.\n", + " index=np.logical_and(0.<=x,x<1.)\n", + " y[index]=x[index]\n", + " index=np.logical_and(1.<=x,x<2.)\n", + " y[index]=2.-x[index]\n", + " y[x>=2]=0.\n", + " \n", + " return y\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x=np.linspace(-5.,5.,100)\n", + "\n", + "plt.plot(x,hatFunc_vect(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 µs ± 486 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n" + ] + } + ], + "source": [ + "%timeit hatFunc_vect(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "76.2 µs ± 2.02 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" + ] + } + ], + "source": [ + "xx=x.tolist()\n", + "\n", + "%timeit list(map(hatFunc,x))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Lectures/Lecture-24/heart.csv b/Lectures/Lecture-24/heart.csv new file mode 100644 index 0000000..08b5462 --- /dev/null +++ b/Lectures/Lecture-24/heart.csv @@ -0,0 +1,304 @@ +age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target +63,1,3,145,233,1,0,150,0,2.3,0,0,1,1 +37,1,2,130,250,0,1,187,0,3.5,0,0,2,1 +41,0,1,130,204,0,0,172,0,1.4,2,0,2,1 +56,1,1,120,236,0,1,178,0,0.8,2,0,2,1 +57,0,0,120,354,0,1,163,1,0.6,2,0,2,1 +57,1,0,140,192,0,1,148,0,0.4,1,0,1,1 +56,0,1,140,294,0,0,153,0,1.3,1,0,2,1 +44,1,1,120,263,0,1,173,0,0,2,0,3,1 +52,1,2,172,199,1,1,162,0,0.5,2,0,3,1 +57,1,2,150,168,0,1,174,0,1.6,2,0,2,1 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