Intro_to_data_vizualization.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sBBfyELRQEfJ"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/porterjenkins/CS180/blob/main/data_science_labs/data_science_lab_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a><p><b>After clicking the \"Open in Colab\" link, copy the notebook to your own Google Drive before getting started, or it will not save your work</b></p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TeSX7lLboUt0"
      },
      "source": [
        "# BYU CS 180 Lab 4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BnhWEScNUxrT"
      },
      "source": [
        "## Introduction:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QDUbedV0ot3Z"
      },
      "source": [
        "Numpy shaping works like vector shaping, with the first dimension representing the number of rows, and the second dimension representing the number of columns.\n",
        "\n",
        "See A [Visual Intro to NumPy and Data Representation](https://jalammar.github.io/visual-numpy/)  for a nice guide to dimensions and other operations.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "esxsgdDu3QaI"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import statsmodels.api as sm\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TzJtJpN5pGow",
        "outputId": "fcd2ecbc-51be-440e-d6c7-fa89d610e2b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100, 2)\n"
          ]
        }
      ],
      "source": [
        "data = np.random.random((100, 2))\n",
        "print(data.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gWtsiBarpIjJ"
      },
      "source": [
        "![image-20210816-193817.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0y1bHggrHRD"
      },
      "source": [
        "For the data we generated randomly, we can treat the first column as x values and the second column as y values in a scatter plot."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "nZUC7x83rGmV",
        "outputId": "b2539b96-cfd9-4450-ad9e-10ce67fc5ef7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "x = data[:, 0] # slicing to get the first column of data\n",
        "y = data[:, 1] # slicing to get the second column of data\n",
        "\n",
        "plt.scatter(x, y)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lf60slwPrMhk"
      },
      "source": [
        "We could also reverse the order."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Vx8lGRBvrRit",
        "outputId": "38faa806-711d-4511-e0c1-ea35f77530bb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(y, x)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g7m3tLrQofZb"
      },
      "source": [
        "## Exercise 1: y=mx+b"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6EdMde3VUq4m"
      },
      "source": [
        "### Exercise Question"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MI3_grQWres_"
      },
      "source": [
        "Create a scatter plot where the data forms a single diagonal line from the top left to the bottom right corner. You can use numpy, but it can also be done without numpy. If you have any questions about the syntax of matplotlib, visit the [docs](https://matplotlib.org/stable/tutorials/introductory/usage.html).\n",
        "\n",
        "Hint: create the plot using only the x values from the pre-lab examples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "BNQPsVFyrhZK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "62e6f8be-4489-44da-a0ac-69267d04c6f5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Write the code for the scatter plot here:\n",
        "\n",
        "plt.scatter(x, -100*x+2)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hgdZ9FW3ohi8"
      },
      "source": [
        "## Exercise 2: Flower Power"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHUhcW0AUPKM"
      },
      "source": [
        "### Pre-Exercise Information"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZnstOIHr7TU"
      },
      "source": [
        "We can load in various datasets using statsmodels. The list of datasets available through statsmodels is [here](https://github.com/vincentarelbundock/Rdatasets/blob/master/datasets.csv)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2FDJrJstr7Bn",
        "outputId": "19b7be99-cf59-45ab-b8dc-0b170e618313"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-76a4673c-9d1b-4048-99f8-3790291697eb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sepal.Length</th>\n",
              "      <th>Sepal.Width</th>\n",
              "      <th>Petal.Length</th>\n",
              "      <th>Petal.Width</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-76a4673c-9d1b-4048-99f8-3790291697eb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-76a4673c-9d1b-4048-99f8-3790291697eb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-76a4673c-9d1b-4048-99f8-3790291697eb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df = sm.datasets.get_rdataset('iris').data\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qbwuhjRJyQUd"
      },
      "source": [
        "We can use seaborn to plot two attributes together and color the points by the flower's species."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "KBHH1gIdyDn2",
        "outputId": "3d6f2f22-3218-4a5b-950b-19ee9f2424f4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.scatterplot(data=df, x='Sepal.Length', y='Sepal.Width', hue='Species')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sh8_TkJCSfs2"
      },
      "source": [
        "### Exercise Question"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ICo8NYWvMGzF"
      },
      "source": [
        "Plot various combinations of columns, then comment of which one you think shows the best separation between classes of flower (subjective)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "_Z2hDlL1MGBX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "outputId": "c5406a3f-e37a-4168-afa5-ba369aaa2354"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Write code to plot various combinations of flower attributes that will help determine the best combination of columns:\n",
        "sns.scatterplot(data=df, x='Petal.Length', y='Petal.Width', hue='Species')\n",
        "plt.show()\n",
        "sns.scatterplot(data=df, x='Petal.Width', y='Sepal.Width', hue='Species')\n",
        "plt.show()\n",
        "sns.scatterplot(data=df, x='Petal.Length', y='Sepal.Width', hue='Species')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HA4xAdqhM34I"
      },
      "source": [
        "Write a few sentences in the  text cell below explaining your choice."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rBgObOdxODyK"
      },
      "source": [
        "I think the first scatterplot with Petal length and petal width is the most indicative of the relationship between length and width - as length increases width also increases and vice versa. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "siisKlzZois6"
      },
      "source": [
        "## Exercise 3: What Goes In Must Come Out"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3P7hvyZvT7kA"
      },
      "source": [
        "### Pre-Exercise Information"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j4imRBlbRKHo"
      },
      "source": [
        "Run this code to download the lady_potty.csv file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "T3KZauONRIWF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55550517-9b80-4c39-c090-b81797839d16"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-01-30 21:34:05--  https://andrewnc.github.io/lady_potty.csv\n",
            "Resolving andrewnc.github.io (andrewnc.github.io)... 185.199.108.153, 185.199.109.153, 185.199.110.153, ...\n",
            "Connecting to andrewnc.github.io (andrewnc.github.io)|185.199.108.153|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3255 (3.2K) [text/csv]\n",
            "Saving to: ‘lady_potty.csv’\n",
            "\n",
            "\rlady_potty.csv        0%[                    ]       0  --.-KB/s               \rlady_potty.csv      100%[===================>]   3.18K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-01-30 21:34:05 (57.8 MB/s) - ‘lady_potty.csv’ saved [3255/3255]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# You may need to use !curl -O instead of !wget to download the data on a mac\n",
        "!wget https://andrewnc.github.io/lady_potty.csv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0VC997U3RDf4"
      },
      "source": [
        "This data comes from a friend’s cat named Lady. We recorded her bathroom trips over the course of a few weeks to see if there was a trend to her business.\n",
        "\n",
        "It was mostly because she liked to go out multiple times per day, and we wanted to see if there was a better time for us to take her out.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P4Q0YUevT-P5"
      },
      "source": [
        "### Exercise Question"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74WzZxeZ3QaP"
      },
      "source": [
        "Load lady_potty.csv into a pandas data frame.\n",
        "\n",
        "All you have to do for this exercise is read in the data into a pandas dataframe, and print out the dataframe head using the following syntax:\n",
        "```\n",
        "dataFrame.head()\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "jXr5h8gUREAb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6624f819-dcba-4ee7-ca2a-3ded9c60a993"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ind                     dt  dow  duration  pooped  peed\n",
              "0    1  2020-05-23 18:55:00.0  sat         2       0     1\n",
              "1    2  2020-05-23 20:30:00.0  sat         2       1     1\n",
              "2    3  2020-05-23 21:25:00.0  sat         2       0     1\n",
              "3    4  2020-05-24 09:38:00.0  sun         1       0     1\n",
              "4    5  2020-05-24 11:01:00.0  sun         3       1     0"
            ],
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              "\n",
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              "      <th>dt</th>\n",
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              "      <td>sat</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2020-05-24 09:38:00.0</td>\n",
              "      <td>sun</td>\n",
              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2020-05-24 11:01:00.0</td>\n",
              "      <td>sun</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d933c2ca-609f-4d4b-a940-e31c2e327ae2')\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d933c2ca-609f-4d4b-a940-e31c2e327ae2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d933c2ca-609f-4d4b-a940-e31c2e327ae2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Read in the lady_potty data file, and print out the dataframe header:\n",
        "data = pd.read_csv('lady_potty.csv')\n",
        "dataFrame = pd.DataFrame(data)\n",
        "dataFrame.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QvNfCzPfoj_f"
      },
      "source": [
        "## Exercise 4: Litter Box"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aSIujZiUTu0y"
      },
      "source": [
        "### Pre-Exercise Information"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5e_7Aen3SmN2"
      },
      "source": [
        "Often times in data science, you want to compare sets of data to one another. For example, you may want to compare how something changes over time.\n",
        "\n",
        "One great way to compare these values is with a box plot. The box plot shows you how the data is distributed from the minimal values to the 75% range all the way to the maximal outlier values.\n",
        "\n",
        "![image-20210816-194401.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4VgsomNKS50h",
        "outputId": "8e5849f7-566c-439e-dec5-ebf6b18f78a6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   time  value\n",
              "0  1912   49.9\n",
              "1  1913   52.3\n",
              "2  1914   49.4\n",
              "3  1915   51.1\n",
              "4  1916   49.4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f872162-b5a2-4be5-b48e-78ab283b707c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1912</td>\n",
              "      <td>49.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1913</td>\n",
              "      <td>52.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1914</td>\n",
              "      <td>49.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1915</td>\n",
              "      <td>51.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1916</td>\n",
              "      <td>49.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f872162-b5a2-4be5-b48e-78ab283b707c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5f872162-b5a2-4be5-b48e-78ab283b707c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5f872162-b5a2-4be5-b48e-78ab283b707c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df = sm.datasets.get_rdataset('nhtemp').data\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KAsOXn-iTJyS"
      },
      "source": [
        "Here we have the average yearly temperature from New Haven Connecticut from around 1910 to 1970.\n",
        "\n",
        "A box plot is an excellent way to visualize this data over time. We first need to massage the data into a form with decades as a column. We do this by using the built in pandas cut function and choose 6 decades.\n",
        "\n",
        "We then can generate a boxplot on a certain column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "kgXv0sRRTK_Y",
        "outputId": "22a34ee6-9701-4f1f-e200-3a54c26e16cc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df['Decade'] = pd.cut(df.time, bins=6) #new column for by decade\n",
        "df[['Decade', 'value']].boxplot(by='Decade') #boxplot of decade and value x = decade\n",
        "plt.xticks(range(7), [str(x) for x in range(1910, 1980, 10)]) #string of x by 10 from 1910-1980\n",
        "plt.xticks(ha='left')\n",
        "plt.title('Temperature Over the Decades')\n",
        "plt.suptitle('')\n",
        "plt.xlabel('Decade')\n",
        "plt.ylabel('Average Yearly Temperature (F)')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dbj9hopbTjWA"
      },
      "source": [
        "### Exercise Question"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9NP3L_BMR7rS"
      },
      "source": [
        "Create a box plot for each 24 hour period to visualize the trend of Lady's potty breaks.\n",
        "\n",
        "Make sure the xticks, xlabel, ylabel, and title are all tidy and well labeled.\n",
        "\n",
        "The y-axis should be bathroom break duration, and the x-axis should be the day of the month. The plot should have a box plot for every single day.\n",
        "\n",
        "Hint: Much of the code can be used from the temperature box plot example above."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "XOPoohJS3QaT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "3514cab2-b928-43a9-8d76-4207d75f5276"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    ind                     dt  dow  duration  pooped  peed    day\n",
              "0     1  2020-05-23 18:55:00.0  sat         2       0     1  05-23\n",
              "1     2  2020-05-23 20:30:00.0  sat         2       1     1  05-23\n",
              "2     3  2020-05-23 21:25:00.0  sat         2       0     1  05-23\n",
              "3     4  2020-05-24 09:38:00.0  sun         1       0     1  05-24\n",
              "4     5  2020-05-24 11:01:00.0  sun         3       1     0  05-24\n",
              "..  ...                    ...  ...       ...     ...   ...    ...\n",
              "84   85  2020-06-01 08:16:00.0  mon         3       1     1  06-01\n",
              "85   86  2020-06-01 10:30:00.0  mon         1       0     1  06-01\n",
              "86   87  2020-06-01 11:18:00.0  mon         3       1     0  06-01\n",
              "87   88  2020-06-01 11:45:00.0  mon         1       0     1  06-01\n",
              "88   89  2020-06-01 13:48:00.0  mon         4       1     1  06-01\n",
              "\n",
              "[89 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e7d3417f-0e0c-41f3-bf73-dd3c91185297\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ind</th>\n",
              "      <th>dt</th>\n",
              "      <th>dow</th>\n",
              "      <th>duration</th>\n",
              "      <th>pooped</th>\n",
              "      <th>peed</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-05-23 18:55:00.0</td>\n",
              "      <td>sat</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>05-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-05-23 20:30:00.0</td>\n",
              "      <td>sat</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>05-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2020-05-23 21:25:00.0</td>\n",
              "      <td>sat</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>05-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-05-24 09:38:00.0</td>\n",
              "      <td>sun</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>05-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2020-05-24 11:01:00.0</td>\n",
              "      <td>sun</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>05-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>85</td>\n",
              "      <td>2020-06-01 08:16:00.0</td>\n",
              "      <td>mon</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>06-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>86</td>\n",
              "      <td>2020-06-01 10:30:00.0</td>\n",
              "      <td>mon</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>06-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>87</td>\n",
              "      <td>2020-06-01 11:18:00.0</td>\n",
              "      <td>mon</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>06-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>88</td>\n",
              "      <td>2020-06-01 11:45:00.0</td>\n",
              "      <td>mon</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>06-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>89</td>\n",
              "      <td>2020-06-01 13:48:00.0</td>\n",
              "      <td>mon</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>06-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>89 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7d3417f-0e0c-41f3-bf73-dd3c91185297')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e7d3417f-0e0c-41f3-bf73-dd3c91185297 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e7d3417f-0e0c-41f3-bf73-dd3c91185297');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# Here is a function that will split the datetime values up into month-day:\n",
        "def getDay(dt):\n",
        "  value = dt.split(' ')\n",
        "  values = value[0].split('-')\n",
        "  return '{}-{}'.format(values[1], values[2])\n",
        "\n",
        "# This will apply the function to the datetime column and assign that to a new column:\n",
        "dataFrame['day'] = dataFrame['dt'].map(getDay)\n",
        "dataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "0hf6lML4RywH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "888e1372-b8e2-4031-f127-97a48f0e6e10"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Enter all of your data parsing and plotting code here. Like the hint above says, much of the temperature box plot code can be reused here:\n",
        " #new column for by decade\n",
        " #axs[1].set_xticks(np.arange(0, 100, 30), ['zero', '30', 'sixty', '90'])\n",
        "dataFrame[['day', 'duration']].boxplot(by='day') #boxplot of decade and value x = decade\n",
        "#string of x by 10 from 1910-1980\n",
        "plt.xticks()\n",
        "plt.title(\"Lady's Weekly Potty Breaks\")\n",
        "plt.suptitle('')\n",
        "plt.xlabel('day of month')\n",
        "plt.ylabel('bathroom break duration')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4XUjaSrFWJjO"
      },
      "source": [
        "## Exercise 5: Oh Shoot!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYH_OOSOWT5T"
      },
      "source": [
        "Write a few sentences about what you observe from the boxplot in the empty cell below."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uWjl8ostWYjC"
      },
      "source": [
        "While almost each day of the month has its own set of outliers, the median for each day seems to sit close to 2 minutes which indicates that is about how much time is needed for her bathroom breaks. The first two days of May have very opposite behavior which could indicate some kind of issue with the way the data was collected, but the rest of the days of the month follow a more regular range of times. "
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.11.0 64-bit ('3.11.0')",
      "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": "3.11.0"
    },
    "vscode": {
      "interpreter": {
        "hash": "921445e9336842bf3da7ce0cce684b71b836ed2d0c3aed21b6777cfb0e4f3429"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}