M3ExploratoryDataAnalysis-lab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Exploratory Data Analysis Lab**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **30** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this module you get to work with the cleaned dataset from the previous module.\n",
    "\n",
    "In this assignment you will perform the task of exploratory data analysis.\n",
    "You will find out the distribution of data, presence of outliers and also determine the correlation between different columns in the dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab you will perform the following:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*   Identify the distribution of data in the dataset.\n",
    "\n",
    "*   Identify outliers in the dataset.\n",
    "\n",
    "*   Remove outliers from the dataset.\n",
    "\n",
    "*   Identify correlation between features in the dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hands on Lab\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the pandas module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset into a dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m2_survey_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine how the data is distributed\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column `ConvertedComp` contains Salary converted to annual USD salaries using the exchange rate on 2019-02-01.\n",
    "\n",
    "This assumes 12 working months and 50 working weeks.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distribution curve for the column `ConvertedComp`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.ConvertedComp.plot(kind='kde')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the histogram for the column `ConvertedComp`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.ConvertedComp.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the median of the column `ConvertedComp`?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57745.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ConvertedComp.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many responders identified themselves only as a **Man**?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10480"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Gender.value_counts().Man"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out the  median ConvertedComp of responders identified themselves only as a **Woman**?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57708.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Gender=='Woman'].ConvertedComp.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Give the five number summary for the column `Age`?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Double click here for hint**.\n",
    "\n",
    "<!--\n",
    "min,q1,median,q3,max of a column are its five number summary.\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min    16.0\n",
       "q1     25.0\n",
       "mid    29.0\n",
       "q3     35.0\n",
       "max    99.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def five_summary(series): \n",
    "    summary = series.describe().rename(index={'25%':'q1', '50%':'mid', '75%':'q3'})\n",
    "    return summary[['min', 'q1', 'mid', 'q3', 'max']]\n",
    "\n",
    "five_summary(df.Age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a histogram of the column `Age`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "df.Age.plot(kind='hist')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "df.Age.plot(kind='box', figsize=(12,5))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outliers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding outliers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out if outliers exist in the column `ConvertedComp` using a box plot?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.ConvertedComp.plot(kind='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out the Inter Quartile Range for the column `ConvertedComp`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min          0.0\n",
       "q1       26868.0\n",
       "mid      57745.0\n",
       "q3      100000.0\n",
       "max    2000000.0\n",
       "Name: ConvertedComp, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "converted_comp_5 = five_summary(df.ConvertedComp)\n",
    "converted_comp_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73132.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr = converted_comp_5.q3 - converted_comp_5.q1\n",
    "iqr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out the upper and lower bounds.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Lower Bound: -82830.0, Upper Bound: 209698.0'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_bound, upper_bound = (converted_comp_5.q1 - iqr*1.5, converted_comp_5.q3 + iqr*1.5)\n",
    "f'Lower Bound: {lower_bound}, Upper Bound: {upper_bound}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify how many outliers are there in the `ConvertedComp` column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'There are 879 outliers (out of 11398).'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_filter = df.ConvertedComp.le(lower_bound) | df.ConvertedComp.ge(upper_bound)\n",
    "num_outliers = len(df[outlier_filter])\n",
    "f'There are {num_outliers} outliers (out of {len(df)}).'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new dataframe by removing the outliers from the `ConvertedComp` column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'There are 0 outliers (out of 10519).'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.drop(df[outlier_filter].index)\n",
    "\n",
    "outlier_filter = df2.ConvertedComp.le(lower_bound) | df2.ConvertedComp.ge(upper_bound)\n",
    "num_outliers = len(df2[outlier_filter])\n",
    "f'There are {num_outliers} outliers (out of {len(df2)}).'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min         0.0\n",
       "q1      24060.0\n",
       "mid     52704.0\n",
       "q3      85574.5\n",
       "max    209356.0\n",
       "Name: ConvertedComp, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "five_summary(df2.ConvertedComp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59883.20838915799"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.ConvertedComp.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding correlation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the correlation between `Age` and all other numerical columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Respondent       0.004041\n",
       "CompTotal        0.006970\n",
       "ConvertedComp    0.105386\n",
       "WorkWeekHrs      0.036518\n",
       "CodeRevHrs      -0.020469\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr().Age.drop(index=['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Respondent       0.004041\n",
       "CompTotal        0.006970\n",
       "ConvertedComp    0.105386\n",
       "WorkWeekHrs      0.036518\n",
       "CodeRevHrs      -0.020469\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr().Age.drop(index=['Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ramesh Sannareddy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Contributors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rav Ahuja\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By        | Change Description                 |\n",
    "| ----------------- | ------- | ----------------- | ---------------------------------- |\n",
    "| 2020-10-17        | 0.1     | Ramesh Sannareddy | Created initial version of the lab |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2022-01-01&cm_mmc=Email_Newsletter-\\_-Developer_Ed%2BTech-\\_-WW_WW-\\_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"
   ]
  }
 ],
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