pandas_tasks.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Shubh4545/Assignment/blob/main/pandas_tasks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FU5TXLbVrcKq"
      },
      "source": [
        "# pandas \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RnD7JV01rcKu"
      },
      "source": [
        "## Importing pandas\n",
        "\n",
        "### Getting started and checking your pandas setup\n",
        "\n",
        "Difficulty: *easy* \n",
        "\n",
        "**1.** Import pandas under the name `pd`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "collapsed": true,
        "id": "NTOrOFECrcKv"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hEPn2egWrcKw"
      },
      "source": [
        "**2.** Print the version of pandas that has been imported."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "collapsed": true,
        "id": "4uSG4wGArcKw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "cdfc73df-04ee-4494-b425-82458cf0f641"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.3.5'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "pd.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AXSVc2hnrcKx"
      },
      "source": [
        "**3.** Print out all the version information of the libraries that are required by the pandas library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "collapsed": true,
        "id": "CqxUomR6rcKx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6cab8bb4-70ed-4278-f4f9-4d911acbaba7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: pandas\n",
            "Version: 1.3.5\n",
            "Summary: Powerful data structures for data analysis, time series, and statistics\n",
            "Home-page: https://pandas.pydata.org\n",
            "Author: The Pandas Development Team\n",
            "Author-email: pandas-dev@python.org\n",
            "License: BSD-3-Clause\n",
            "Location: /usr/local/lib/python3.7/dist-packages\n",
            "Requires: pytz, numpy, python-dateutil\n",
            "Required-by: xarray, vega-datasets, statsmodels, sklearn-pandas, seaborn, pymc, prophet, plotnine, pandas-profiling, pandas-gbq, pandas-datareader, mlxtend, mizani, holoviews, gspread-dataframe, google-colab, fix-yahoo-finance, fastai, datascience, cufflinks, cmdstanpy, arviz, altair\n"
          ]
        }
      ],
      "source": [
        "pip show pandas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vau9p9GorcKy"
      },
      "source": [
        "## DataFrame basics\n",
        "\n",
        "### A few of the fundamental routines for selecting, sorting, adding and aggregating data in DataFrames\n",
        "\n",
        "Difficulty: *easy*\n",
        "\n",
        "Note: remember to import numpy using:\n",
        "```python\n",
        "import numpy as np\n",
        "```\n",
        "\n",
        "Consider the following Python dictionary `data` and Python list `labels`:\n",
        "\n",
        "``` python\n",
        "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n",
        "        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n",
        "        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
        "        'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n",
        "\n",
        "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
        "```\n",
        "(This is just some meaningless data I made up with the theme of animals and trips to a vet.)\n",
        "\n",
        "**4.** Create a DataFrame `df` from this dictionary `data` which has the index `labels`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "collapsed": true,
        "id": "m52ZvzghrcKz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d78df93d-d4e7-487a-8e40-5330fd33bbe6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  animal  age  visits priority\n",
            "a    cat  2.5       1      yes\n",
            "b    cat  3.0       3      yes\n",
            "c  snake  0.5       2       no\n",
            "d    dog  NaN       3      yes\n",
            "e    dog  5.0       2       no\n",
            "f    cat  2.0       3       no\n",
            "g  snake  4.5       1       no\n",
            "h    cat  NaN       1      yes\n",
            "i    dog  7.0       2       no\n",
            "j    dog  3.0       1       no\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n",
        "        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n",
        "        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
        "        'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n",
        "\n",
        "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
        "\n",
        "df = pd.DataFrame(data,index=labels)\n",
        "print(df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajcsRqzQrcKz"
      },
      "source": [
        "**5.** Display a summary of the basic information about this DataFrame and its data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "collapsed": true,
        "id": "nJobA2zOrcK2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "460e44a8-cdec-45ec-e3f1-f85978174bbf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            age     visits\n",
              "count  8.000000  10.000000\n",
              "mean   3.437500   1.900000\n",
              "std    2.007797   0.875595\n",
              "min    0.500000   1.000000\n",
              "25%    2.375000   1.000000\n",
              "50%    3.000000   2.000000\n",
              "75%    4.625000   2.750000\n",
              "max    7.000000   3.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b1358368-60ed-459e-9bc3-223ed7deb9f2\">\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>age</th>\n",
              "      <th>visits</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.437500</td>\n",
              "      <td>1.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.007797</td>\n",
              "      <td>0.875595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.375000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.625000</td>\n",
              "      <td>2.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1358368-60ed-459e-9bc3-223ed7deb9f2')\"\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-b1358368-60ed-459e-9bc3-223ed7deb9f2 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-b1358368-60ed-459e-9bc3-223ed7deb9f2');\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": 45
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mj1mWoeHrcK2"
      },
      "source": [
        "**6.** Return the first 3 rows of the DataFrame `df`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "collapsed": true,
        "id": "4mbV5klYrcK3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "107e3a4a-74cc-40a7-c96b-4b3a996730b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  animal  age  visits priority\n",
              "a    cat  2.5       1      yes\n",
              "b    cat  3.0       3      yes\n",
              "c  snake  0.5       2       no"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d5775f9e-3d36-41a9-88b7-c9e57cd61697\">\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>animal</th>\n",
              "      <th>age</th>\n",
              "      <th>visits</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>cat</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>snake</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5775f9e-3d36-41a9-88b7-c9e57cd61697')\"\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-d5775f9e-3d36-41a9-88b7-c9e57cd61697 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-d5775f9e-3d36-41a9-88b7-c9e57cd61697');\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": 46
        }
      ],
      "source": [
        "df.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lAGn-_fMrcK3"
      },
      "source": [
        "**7.** Select just the 'animal' and 'age' columns from the DataFrame `df`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "collapsed": true,
        "id": "l9E9puy1rcK4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "0b5a3be9-1085-434c-9135-ca659d72eadf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  animal  age\n",
              "a    cat  2.5\n",
              "b    cat  3.0\n",
              "c  snake  0.5\n",
              "d    dog  NaN\n",
              "e    dog  5.0\n",
              "f    cat  2.0\n",
              "g  snake  4.5\n",
              "h    cat  NaN\n",
              "i    dog  7.0\n",
              "j    dog  3.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7f815de6-d968-4011-947e-d4a57b6b5df8\">\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>animal</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>cat</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>snake</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>dog</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>dog</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g</th>\n",
              "      <td>snake</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>h</th>\n",
              "      <td>cat</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i</th>\n",
              "      <td>dog</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>j</th>\n",
              "      <td>dog</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f815de6-d968-4011-947e-d4a57b6b5df8')\"\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-7f815de6-d968-4011-947e-d4a57b6b5df8 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-7f815de6-d968-4011-947e-d4a57b6b5df8');\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": 47
        }
      ],
      "source": [
        "df[['animal','age']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ugxsGxMNrcK4"
      },
      "source": [
        "**8.** Select the data in rows `[3, 4, 8]` *and* in columns `['animal', 'age']`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "collapsed": true,
        "id": "9K73lMxyrcK5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "d2c482c6-ab45-4a08-cd4d-7c2cc47f89c9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  animal  age  visits priority\n",
              "d    dog  NaN       3      yes\n",
              "e    dog  5.0       2       no\n",
              "i    dog  7.0       2       no"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-754cdff2-7924-4b84-93eb-d5a16a1095b6\">\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>animal</th>\n",
              "      <th>age</th>\n",
              "      <th>visits</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>dog</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>dog</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i</th>\n",
              "      <td>dog</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-754cdff2-7924-4b84-93eb-d5a16a1095b6')\"\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-754cdff2-7924-4b84-93eb-d5a16a1095b6 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-754cdff2-7924-4b84-93eb-d5a16a1095b6');\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": 48
        }
      ],
      "source": [
        "df.iloc[[3,4,8], :]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1xbt5LNrcK5"
      },
      "source": [
        "**9.** Select only the rows where the number of visits is greater than 3."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "collapsed": true,
        "id": "8AdCfbF1rcK5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "d8e265c0-a0e9-4691-ddf5-77649f1cf961"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [animal, age, visits, priority]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-83d830b0-281f-472f-b0d5-07bba114f22d\">\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>animal</th>\n",
              "      <th>age</th>\n",
              "      <th>visits</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83d830b0-281f-472f-b0d5-07bba114f22d')\"\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-83d830b0-281f-472f-b0d5-07bba114f22d 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-83d830b0-281f-472f-b0d5-07bba114f22d');\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": 49
        }
      ],
      "source": [
        "df[df.visits>3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3BxYioOrcK6"
      },
      "source": [
        "**10.** Select the rows where the age is missing, i.e. is `NaN`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "collapsed": true,
        "id": "2TI83ksFrcK6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "4e012279-0eb3-474a-8c94-37db0e42c2ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  animal  age  visits priority\n",
              "d    dog  NaN       3      yes\n",
              "h    cat  NaN       1      yes"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-92bf412c-85e0-4954-9c4c-9c6d0ae060df\">\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>animal</th>\n",
              "      <th>age</th>\n",
              "      <th>visits</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>dog</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>h</th>\n",
              "      <td>cat</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-92bf412c-85e0-4954-9c4c-9c6d0ae060df')\"\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-92bf412c-85e0-4954-9c4c-9c6d0ae060df 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-92bf412c-85e0-4954-9c4c-9c6d0ae060df');\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": 50
        }
      ],
      "source": [
        "df[df['age'].isnull()]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P42b9TLarcK6"
      },
      "source": [
        "**11.** Select the rows where the animal is a cat *and* the age is less than 3."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "collapsed": true,
        "id": "OxAHvEGOrcK7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "ebd176aa-f5a2-4993-fc7f-a81dc53c3b3f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  animal  age  visits priority\n",
              "a    cat  2.5       1      yes\n",
              "c  snake  0.5       2       no\n",
              "f    cat  2.0       3       no"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2edb5d1d-eee4-410f-8765-e8201afe946b\">\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>animal</th>\n",
              "      <th>age</th>\n",
              "      <th>visits</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>snake</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2edb5d1d-eee4-410f-8765-e8201afe946b')\"\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-2edb5d1d-eee4-410f-8765-e8201afe946b 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-2edb5d1d-eee4-410f-8765-e8201afe946b');\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": 51
        }
      ],
      "source": [
        "df[df.age<3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AjgOnUaDrcK7"
      },
      "source": [
        "**12.** Select the rows the age is between 2 and 4 (inclusive)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "collapsed": true,
        "id": "0_Wxb3OCrcK8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "f8dcd0fe-a2dd-4437-9394-54e4b15c555c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  animal  age  visits priority\n",
              "a    cat  2.5       1      yes\n",
              "b    cat  3.0       3      yes\n",
              "f    cat  2.0       3       no\n",
              "j    dog  3.0       1       no"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2cbfd32d-1e86-4fb4-8c27-de382ffa2172\">\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>animal</th>\n",
              "      <th>age</th>\n",
              "      <th>visits</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>cat</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>j</th>\n",
              "      <td>dog</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2cbfd32d-1e86-4fb4-8c27-de382ffa2172')\"\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-2cbfd32d-1e86-4fb4-8c27-de382ffa2172 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-2cbfd32d-1e86-4fb4-8c27-de382ffa2172');\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": 52
        }
      ],
      "source": [
        "df[df['age'].between(2, 4)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pE8ldZgWrcK9"
      },
      "source": [
        "**13.** Change the age in row 'f' to 1.5."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "collapsed": true,
        "id": "AjDj1KCcrcK-"
      },
      "outputs": [],
      "source": [
        "df.loc['f', 'age'] = 1.5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kr8IBI6orcK_"
      },
      "source": [
        "**14.** Calculate the sum of all visits (the total number of visits)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "collapsed": true,
        "id": "S_oktHwVrcK_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "caa151bc-688d-4d2e-e929-841450563005"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "df.visits.sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SJ5Adn4QrcLA"
      },
      "source": [
        "**15.** Calculate the mean age for each different animal in `df`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "collapsed": true,
        "id": "7xVVeaItrcLA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9526ccf2-31fb-4d11-e460-13aaa941d037"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "animal\n",
              "cat      2.333333\n",
              "dog      5.000000\n",
              "snake    2.500000\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "df.groupby('animal')['age'].mean()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y7JjawhHrcLA"
      },
      "source": [
        "**16.** Append a new row 'k' to `df` with your choice of values for each column. Then delete that row to return the original DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "collapsed": true,
        "id": "qsQThY0HrcLA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9eb51586-dd2b-47e7-ca80-d595631dd012"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      animal  age  visits priority\n",
            "a        cat  2.5       1      yes\n",
            "b        cat  3.0       3      yes\n",
            "c      snake  0.5       2       no\n",
            "d        dog  NaN       3      yes\n",
            "e        dog  5.0       2       no\n",
            "f        cat  1.5       3       no\n",
            "g      snake  4.5       1       no\n",
            "h        cat  NaN       1      yes\n",
            "i        dog  7.0       2       no\n",
            "j        dog  3.0       1       no\n",
            "10  elephant  9.0       3      yes\n",
            "  animal  age  visits priority\n",
            "a    cat  2.5       1      yes\n",
            "b    cat  3.0       3      yes\n",
            "c  snake  0.5       2       no\n",
            "d    dog  NaN       3      yes\n",
            "e    dog  5.0       2       no\n",
            "f    cat  1.5       3       no\n",
            "g  snake  4.5       1       no\n",
            "h    cat  NaN       1      yes\n",
            "i    dog  7.0       2       no\n",
            "j    dog  3.0       1       no\n"
          ]
        }
      ],
      "source": [
        "df.loc[len(df.index)] = ['elephant', 9, 3,'yes'] \n",
        "print(df)\n",
        "indexAge = df[ (df['animal'] == 'elephant') ].index\n",
        "df.drop(indexAge , inplace=True)\n",
        "print(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i-7wQPRfrcLA"
      },
      "source": [
        "**17.** Count the number of each type of animal in `df`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "collapsed": true,
        "id": "yRIVj-yKrcLA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d1982a6-4a20-4cf2-bcbb-4d380d0c7c1b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "animal\n",
              "cat      4\n",
              "dog      4\n",
              "snake    2\n",
              "Name: animal, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "df.groupby('animal')['animal'].count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Dn08TvNrcLB"
      },
      "source": [
        "**18.** Sort `df` first by the values in the 'age' in *decending* order, then by the value in the 'visit' column in *ascending* order."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "collapsed": true,
        "id": "meU7znrLrcLB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "99938ff8-6de2-401d-df7f-f62dbfa76d82"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  animal  age  visits priority\n",
              "i    dog  7.0       2       no\n",
              "e    dog  5.0       2       no\n",
              "g  snake  4.5       1       no\n",
              "b    cat  3.0       3      yes\n",
              "j    dog  3.0       1       no\n",
              "a    cat  2.5       1      yes\n",
              "f    cat  1.5       3       no\n",
              "c  snake  0.5       2       no\n",
              "d    dog  NaN       3      yes\n",
              "h    cat  NaN       1      yes"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b219f315-cba1-4f7b-aca0-6fba656a9320\">\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>animal</th>\n",
              "      <th>age</th>\n",
              "      <th>visits</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>i</th>\n",
              "      <td>dog</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>dog</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g</th>\n",
              "      <td>snake</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>cat</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>j</th>\n",
              "      <td>dog</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f</th>\n",
              "      <td>cat</td>\n",
              "      <td>1.5</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>snake</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>dog</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>h</th>\n",
              "      <td>cat</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b219f315-cba1-4f7b-aca0-6fba656a9320')\"\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-b219f315-cba1-4f7b-aca0-6fba656a9320 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-b219f315-cba1-4f7b-aca0-6fba656a9320');\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": 58
        }
      ],
      "source": [
        "df.sort_values(by=['age'],ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SgOff5durcLB"
      },
      "source": [
        "**19.** The 'priority' column contains the values 'yes' and 'no'. Replace this column with a column of boolean values: 'yes' should be `True` and 'no' should be `False`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "collapsed": true,
        "id": "NXCjWPjBrcLB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "29ea7e61-caa7-4284-a17c-07f862a72bff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  animal  age  visits priority\n",
              "a    cat  2.5       1      yes\n",
              "b    cat  3.0       3      yes\n",
              "c  snake  0.5       2       no\n",
              "d    dog  NaN       3      yes\n",
              "e    dog  5.0       2       no\n",
              "f    cat  1.5       3       no\n",
              "g  snake  4.5       1       no\n",
              "h    cat  NaN       1      yes\n",
              "i    dog  7.0       2       no\n",
              "j    dog  3.0       1       no"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4e4e6de0-b0cf-47a1-abd0-55273e891d06\">\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>animal</th>\n",
              "      <th>age</th>\n",
              "      <th>visits</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>cat</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>snake</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>dog</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>dog</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f</th>\n",
              "      <td>cat</td>\n",
              "      <td>1.5</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g</th>\n",
              "      <td>snake</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>h</th>\n",
              "      <td>cat</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i</th>\n",
              "      <td>dog</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>j</th>\n",
              "      <td>dog</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4e4e6de0-b0cf-47a1-abd0-55273e891d06')\"\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-4e4e6de0-b0cf-47a1-abd0-55273e891d06 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-4e4e6de0-b0cf-47a1-abd0-55273e891d06');\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": 59
        }
      ],
      "source": [
        "df\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "inK139zQrcLB"
      },
      "source": [
        "**20.** In the 'animal' column, change the 'snake' entries to 'python'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "collapsed": true,
        "id": "jov6xc9_rcLB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "0822ec12-1372-44c3-98c4-386961fa5a81"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  animal  age  visits priority\n",
              "a    cat  2.5       1      yes\n",
              "b    cat  3.0       3      yes\n",
              "c  snake  0.5       2       no\n",
              "d    dog  NaN       3      yes\n",
              "e    dog  5.0       2       no\n",
              "f    cat  1.5       3       no\n",
              "g  snake  4.5       1       no\n",
              "h    cat  NaN       1      yes\n",
              "i    dog  7.0       2       no\n",
              "j    dog  3.0       1       no"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a80e44d9-1feb-4c7f-ba3f-22df634d4299\">\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>animal</th>\n",
              "      <th>age</th>\n",
              "      <th>visits</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>cat</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>snake</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>dog</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>dog</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f</th>\n",
              "      <td>cat</td>\n",
              "      <td>1.5</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g</th>\n",
              "      <td>snake</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>h</th>\n",
              "      <td>cat</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i</th>\n",
              "      <td>dog</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>j</th>\n",
              "      <td>dog</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a80e44d9-1feb-4c7f-ba3f-22df634d4299')\"\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-a80e44d9-1feb-4c7f-ba3f-22df634d4299 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-a80e44d9-1feb-4c7f-ba3f-22df634d4299');\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": 60
        }
      ],
      "source": [
        "indexAge = df[ (df['animal'] == 'no') ].index\n",
        "df.drop(indexAge , inplace=True)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTV9fED7rcLC"
      },
      "source": [
        "**21.** For each animal type and each number of visits, find the mean age. In other words, each row is an animal, each column is a number of visits and the values are the mean ages (hint: use a pivot table)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "collapsed": true,
        "id": "bAu14yfYrcLC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "be07c9d6-903c-4fa9-fb3c-fc0e1aed9ae9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "visits    1    2     3\n",
              "animal                \n",
              "cat     2.5  NaN  2.25\n",
              "dog     3.0  6.0   NaN\n",
              "snake   4.5  0.5   NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c264ddd1-6437-4261-9260-fd7aba1f3e60\">\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>visits</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>animal</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>cat</th>\n",
              "      <td>2.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dog</th>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>snake</th>\n",
              "      <td>4.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c264ddd1-6437-4261-9260-fd7aba1f3e60')\"\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-c264ddd1-6437-4261-9260-fd7aba1f3e60 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-c264ddd1-6437-4261-9260-fd7aba1f3e60');\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": 61
        }
      ],
      "source": [
        "df.pivot_table(index='animal', columns='visits', values='age', aggfunc='mean')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWZaoMJmrcLC"
      },
      "source": [
        "## DataFrames: beyond the basics\n",
        "\n",
        "### Slightly trickier: you may need to combine two or more methods to get the right answer\n",
        "\n",
        "Difficulty: *medium*\n",
        "\n",
        "The previous section was tour through some basic but essential DataFrame operations. Below are some ways that you might need to cut your data, but for which there is no single \"out of the box\" method."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iI528HIGrcLC"
      },
      "source": [
        "**22.** You have a DataFrame `df` with a column 'A' of integers. For example:\n",
        "```python\n",
        "df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})\n",
        "```\n",
        "\n",
        "How do you filter out rows which contain the same integer as the row immediately above?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "collapsed": true,
        "id": "nMxZVjxzrcLC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "379734a0-4468-4077-9c1d-2cfc8fd556c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   A\n",
              "0  1\n",
              "1  2\n",
              "3  3\n",
              "4  4\n",
              "5  5\n",
              "8  6\n",
              "9  7"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bb8070a8-f1ab-48ee-9e7b-af356f48c613\">\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>A</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb8070a8-f1ab-48ee-9e7b-af356f48c613')\"\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-bb8070a8-f1ab-48ee-9e7b-af356f48c613 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-bb8070a8-f1ab-48ee-9e7b-af356f48c613');\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": 62
        }
      ],
      "source": [
        "df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})\n",
        "df.loc[df['A'].shift() != df['A']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T38ocKhYrcLC"
      },
      "source": [
        "**23.** Given a DataFrame of numeric values, say\n",
        "```python\n",
        "df = pd.DataFrame(np.random.random(size=(5, 3))) # a 5x3 frame of float values\n",
        "```\n",
        "\n",
        "how do you subtract the row mean from each element in the row?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "collapsed": true,
        "id": "yhGiONr-rcLD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "21a63088-b260-41a0-cd94-9b86d642ca3e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0         1         2\n",
              "0  0.075110 -0.070788 -0.004322\n",
              "1  0.210502  0.145422 -0.355923\n",
              "2 -0.007680  0.015527 -0.007846\n",
              "3 -0.081412  0.303984 -0.222572\n",
              "4  0.307611 -0.109510 -0.198101"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-24f94f52-cd6a-41bc-b84e-d3d00d468480\">\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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.075110</td>\n",
              "      <td>-0.070788</td>\n",
              "      <td>-0.004322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.210502</td>\n",
              "      <td>0.145422</td>\n",
              "      <td>-0.355923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.007680</td>\n",
              "      <td>0.015527</td>\n",
              "      <td>-0.007846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.081412</td>\n",
              "      <td>0.303984</td>\n",
              "      <td>-0.222572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.307611</td>\n",
              "      <td>-0.109510</td>\n",
              "      <td>-0.198101</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-24f94f52-cd6a-41bc-b84e-d3d00d468480')\"\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-24f94f52-cd6a-41bc-b84e-d3d00d468480 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-24f94f52-cd6a-41bc-b84e-d3d00d468480');\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": 63
        }
      ],
      "source": [
        "df = pd.DataFrame(np.random.random(size=(5, 3)))\n",
        "df.sub(df.mean(axis=1), axis=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZM0e4ATjrcLD"
      },
      "source": [
        "**24.** Suppose you have DataFrame with 10 columns of real numbers, for example:\n",
        "\n",
        "```python\n",
        "df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
        "```\n",
        "Which column of numbers has the smallest sum? (Find that column's label.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "collapsed": true,
        "id": "F1-CZDlmrcLD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "99e7913c-9670-460e-bc03-df01ce371bc7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'c'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
        "df.sum().idxmin()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxN-ibNGrcLD"
      },
      "source": [
        "**25.** How do you count how many unique rows a DataFrame has (i.e. ignore all rows that are duplicates)?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "collapsed": true,
        "id": "m6Q_ai9-rcLD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc7688da-1370-4a9e-da82-ac062a6d4bf5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "len(df) - df.duplicated(keep=False).sum()\n",
        "\n",
        "# or perhaps more simply...\n",
        "\n",
        "len(df.drop_duplicates(keep=False))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z0xEop-BrcLD"
      },
      "source": [
        "The next three puzzles are slightly harder...\n",
        "\n",
        "**26.** You have a DataFrame that consists of 10 columns of floating--point numbers. Suppose that exactly 5 entries in each row are NaN values. For each row of the DataFrame, find the *column* which contains the *third* NaN value.\n",
        "\n",
        "(You should return a Series of column labels.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "collapsed": true,
        "id": "te11owaqrcLE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40c4a378-b629-4884-df65-e44885592a93"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    a\n",
              "1    a\n",
              "2    a\n",
              "3    a\n",
              "4    a\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "(df.isnull().cumsum(axis=1) == 3).idxmax(axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWfruEOHrcLE"
      },
      "source": [
        "**27.** A DataFrame has a column of groups 'grps' and and column of numbers 'vals'. For example: \n",
        "\n",
        "```python\n",
        "df = pd.DataFrame({'grps': list('aaabbcaabcccbbc'), \n",
        "                   'vals': [12,345,3,1,45,14,4,52,54,23,235,21,57,3,87]})\n",
        "```\n",
        "For each *group*, find the sum of the three greatest values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "collapsed": true,
        "id": "rhek22AKrcLE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b502a711-4005-4557-dbcd-61be36082cc6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "grps\n",
              "a    409\n",
              "b    156\n",
              "c    345\n",
              "Name: vals, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "df = pd.DataFrame({'grps': list('aaabbcaabcccbbc'), \n",
        "                   'vals': [12,345,3,1,45,14,4,52,54,23,235,21,57,3,87]})\n",
        "df.groupby('grps')['vals'].nlargest(3).sum(level=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QjbpZ7RmrcLE"
      },
      "source": [
        "**28.** A DataFrame has two integer columns 'A' and 'B'. The values in 'A' are between 1 and 100 (inclusive). For each group of 10 consecutive integers in 'A' (i.e. `(0, 10]`, `(10, 20]`, ...), calculate the sum of the corresponding values in column 'B'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "collapsed": true,
        "id": "T6fsu8QurcLE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26dec57e-6bae-4192-c736-8a267ae4a928"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "grps\n",
              "a    409\n",
              "b    156\n",
              "c    345\n",
              "Name: vals, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "df.groupby('grps')['vals'].nlargest(3).sum(level=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vMQlhPwYrcLE"
      },
      "source": [
        "## DataFrames: harder problems \n",
        "\n",
        "### These might require a bit of thinking outside the box...\n",
        "\n",
        "...but all are solvable using just the usual pandas/NumPy methods (and so avoid using explicit `for` loops).\n",
        "\n",
        "Difficulty: *hard*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LwMZyLFYrcLE"
      },
      "source": [
        "**29.** Consider a DataFrame `df` where there is an integer column 'X':\n",
        "```python\n",
        "df = pd.DataFrame({'X': [7, 2, 0, 3, 4, 2, 5, 0, 3, 4]})\n",
        "```\n",
        "For each value, count the difference back to the previous zero (or the start of the Series, whichever is closer). These values should therefore be `[1, 2, 0, 1, 2, 3, 4, 0, 1, 2]`. Make this a new column 'Y'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "collapsed": true,
        "id": "a7V5p8vXrcLF"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame({'X': [7, 2, 0, 3, 4, 2, 5, 0, 3, 4]})\n",
        "df['Y'] = df.groupby((df['X'] == 0).cumsum()).cumcount()\n",
        "# We're off by one before we reach the first zero.\n",
        "first_zero_idx = (df['X'] == 0).idxmax()\n",
        "df['Y'].iloc[0:first_zero_idx] += 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0T7VHCPRrcLF"
      },
      "source": [
        "Here's an alternative approach based on a [cookbook recipe](http://pandas.pydata.org/pandas-docs/stable/cookbook.html#grouping):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "collapsed": true,
        "id": "28Hl15j7rcLF"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kRcr9ekrcLF"
      },
      "source": [
        "**30.** Consider a DataFrame containing rows and columns of purely numerical data. Create a list of the row-column index locations of the 3 largest values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "collapsed": true,
        "id": "okFh-vBQrcLF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21699c18-3f81-4b09-baf5-4b35e5ec0d13"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('X', 4), ('X', 6), ('X', 0)]"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "df.unstack().sort_values()[-3:].index.tolist()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "36-W6pmprcLF"
      },
      "source": [
        "**31.** Given a DataFrame with a column of group IDs, 'grps', and a column of corresponding integer values, 'vals', replace any negative values in 'vals' with the group mean."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "collapsed": true,
        "id": "ryUU7qi_rcLG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3970331b-f577-48f5-e4a4-f8f02b7f4992"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   a  b\n",
            "0  1  1\n",
            "1  1 -1\n",
            "2  2  1\n",
            "3  2  2\n",
            "   b\n",
            "0  1\n",
            "1  1\n",
            "2  1\n",
            "3  2\n"
          ]
        }
      ],
      "source": [
        "df = pd.DataFrame([[1,1],[1,-1],[2,1],[2,2]], columns=list('ab'))\n",
        "print(df)\n",
        "\n",
        "data = df.groupby(['a'])\n",
        "def replace(group):\n",
        "    mask = group<0\n",
        "    group[mask] = group[~mask].mean()\n",
        "    return group\n",
        "print(data.transform(replace))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZXMwb99NrcLG"
      },
      "source": [
        "**32.** Implement a rolling mean over groups with window size 3, which ignores NaN value. For example consider the following DataFrame:\n",
        "\n",
        "```python\n",
        ">>> df = pd.DataFrame({'group': list('aabbabbbabab'),\n",
        "                       'value': [1, 2, 3, np.nan, 2, 3, \n",
        "                                 np.nan, 1, 7, 3, np.nan, 8]})\n",
        ">>> df\n",
        "   group  value\n",
        "0      a    1.0\n",
        "1      a    2.0\n",
        "2      b    3.0\n",
        "3      b    NaN\n",
        "4      a    2.0\n",
        "5      b    3.0\n",
        "6      b    NaN\n",
        "7      b    1.0\n",
        "8      a    7.0\n",
        "9      b    3.0\n",
        "10     a    NaN\n",
        "11     b    8.0\n",
        "```\n",
        "The goal is to compute the Series:\n",
        "\n",
        "```\n",
        "0     1.000000\n",
        "1     1.500000\n",
        "2     3.000000\n",
        "3     3.000000\n",
        "4     1.666667\n",
        "5     3.000000\n",
        "6     3.000000\n",
        "7     2.000000\n",
        "8     3.666667\n",
        "9     2.000000\n",
        "10    4.500000\n",
        "11    4.000000\n",
        "```\n",
        "E.g. the first window of size three for group 'b' has values 3.0, NaN and 3.0 and occurs at row index 5. Instead of being NaN the value in the new column at this row index should be 3.0 (just the two non-NaN values are used to compute the mean (3+3)/2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "collapsed": true,
        "id": "qjsOaiMurcLG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c44298e0-d71a-4c39-f2c2-54245dd5479a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     1.000000\n",
              "1     1.500000\n",
              "2     3.000000\n",
              "3     3.000000\n",
              "4     1.666667\n",
              "5     3.000000\n",
              "6     3.000000\n",
              "7     2.000000\n",
              "8     3.666667\n",
              "9     2.000000\n",
              "10    4.500000\n",
              "11    4.000000\n",
              "Name: value, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "df = pd.DataFrame({'group': list('aabbabbbabab'),\n",
        "                       'value': [1, 2, 3, np.nan, 2, 3, \n",
        "                                 np.nan, 1, 7, 3, np.nan, 8]})\n",
        "g1 = df.groupby(['group'])['value']              # group values  \n",
        "g2 = df.fillna(0).groupby(['group'])['value']    # fillna, then group values\n",
        "\n",
        "s = g2.rolling(3, min_periods=1).sum() / g1.rolling(3, min_periods=1).count() # compute means\n",
        "\n",
        "s.reset_index(level=0, drop=True).sort_index()  # drop/sort index"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lbfscd0TrcLG"
      },
      "source": [
        "## Series and DatetimeIndex\n",
        "\n",
        "### Exercises for creating and manipulating Series with datetime data\n",
        "\n",
        "Difficulty: *easy/medium*\n",
        "\n",
        "pandas is fantastic for working with dates and times. These puzzles explore some of this functionality.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CycbsYqMrcLG"
      },
      "source": [
        "**33.** Create a DatetimeIndex that contains each business day of 2015 and use it to index a Series of random numbers. Let's call this Series `s`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "collapsed": true,
        "id": "kV3m6lsKrcLH"
      },
      "outputs": [],
      "source": [
        "dti = pd.date_range(start='2015-01-01', end='2015-12-31', freq='B') \n",
        "s = pd.Series(np.random.rand(len(dti)), index=dti)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O21xrs7_rcLH"
      },
      "source": [
        "**34.** Find the sum of the values in `s` for every Wednesday."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "collapsed": true,
        "id": "gvJi1QnGrcLH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7660cebc-08b1-4e73-b27b-eb7812be9396"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24.755552329850975"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "s[s.index.weekday == 2].sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WS57lUf7rcLH"
      },
      "source": [
        "**35.** For each calendar month in `s`, find the mean of values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "collapsed": true,
        "id": "ZYfMur_3rcLH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d37bffc-55fd-4731-90e6-577c9d14b8ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2015-01-31    0.541579\n",
              "2015-02-28    0.447126\n",
              "2015-03-31    0.462376\n",
              "2015-04-30    0.573103\n",
              "2015-05-31    0.543783\n",
              "2015-06-30    0.444812\n",
              "2015-07-31    0.419207\n",
              "2015-08-31    0.498176\n",
              "2015-09-30    0.408360\n",
              "2015-10-31    0.439535\n",
              "2015-11-30    0.529788\n",
              "2015-12-31    0.443288\n",
              "Freq: M, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "s.resample('M').mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qptP0uLCrcLH"
      },
      "source": [
        "**36.** For each group of four consecutive calendar months in `s`, find the date on which the highest value occurred."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "collapsed": true,
        "id": "4V1ZCDDvrcLH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "f44d394d-09df-4efe-9ae3-1a5466fe94b9"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-77-559e2117fd3a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimeGrouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'4M'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midxmax\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/usr/local/lib/python3.7/dist-packages/pandas/__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m    242\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_SparseArray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"module 'pandas' has no attribute '{name}'\"\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    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'pandas' has no attribute 'TimeGrouper'"
          ]
        }
      ],
      "source": [
        "df['] = pd.to_datetime(df.used_at)\n",
        "df.set_index('used_at', inplace=True)\n",
        "s.groupby(pd.TimeGrouper('4M')).idxmax()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XBH2oRXkrcLI"
      },
      "source": [
        "**37.** Create a DateTimeIndex consisting of the third Thursday in each month for the years 2015 and 2016."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "collapsed": true,
        "id": "3Eeh_SbxrcLI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ca898ca-2125-43dc-cb8b-7800eaa7ca4f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2015-01-15', '2015-02-19', '2015-03-19', '2015-04-16',\n",
              "               '2015-05-21', '2015-06-18', '2015-07-16', '2015-08-20',\n",
              "               '2015-09-17', '2015-10-15', '2015-11-19', '2015-12-17',\n",
              "               '2016-01-21', '2016-02-18', '2016-03-17', '2016-04-21',\n",
              "               '2016-05-19', '2016-06-16', '2016-07-21', '2016-08-18',\n",
              "               '2016-09-15', '2016-10-20', '2016-11-17', '2016-12-15'],\n",
              "              dtype='datetime64[ns]', freq='WOM-3THU')"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "pd.date_range('2015-01-01', '2016-12-31', freq='WOM-3THU')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T0GlJ2wcrcLI"
      },
      "source": [
        "## Cleaning Data\n",
        "\n",
        "### Making a DataFrame easier to work with\n",
        "\n",
        "Difficulty: *easy/medium*\n",
        "\n",
        "It happens all the time: someone gives you data containing malformed strings, Python, lists and missing data. How do you tidy it up so you can get on with the analysis?\n",
        "\n",
        "Take this monstrosity as the DataFrame to use in the following puzzles:\n",
        "\n",
        "```python\n",
        "df = pd.DataFrame({'From_To': ['LoNDon_paris', 'MAdrid_miLAN', 'londON_StockhOlm', \n",
        "                               'Budapest_PaRis', 'Brussels_londOn'],\n",
        "              'FlightNumber': [10045, np.nan, 10065, np.nan, 10085],\n",
        "              'RecentDelays': [[23, 47], [], [24, 43, 87], [13], [67, 32]],\n",
        "                   'Airline': ['KLM(!)', '<Air France> (12)', '(British Airways. )', \n",
        "                               '12. Air France', '\"Swiss Air\"']})\n",
        "```\n",
        "(It's some flight data I made up; it's not meant to be accurate in any way.)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GqhkElxOrcLI"
      },
      "source": [
        "**38.** Some values in the the FlightNumber column are missing. These numbers are meant to increase by 10 with each row so 10055 and 10075 need to be put in place. Fill in these missing numbers and make the column an integer column (instead of a float column)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "collapsed": true,
        "id": "XbSTNz5srcLI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "efbadc04-a91b-40a6-fe5e-684d9ebd6755"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            From_To  FlightNumber  RecentDelays              Airline\n",
              "0      LoNDon_paris         10045      [23, 47]               KLM(!)\n",
              "1      MAdrid_miLAN         10055            []    <Air France> (12)\n",
              "2  londON_StockhOlm         10065  [24, 43, 87]  (British Airways. )\n",
              "3    Budapest_PaRis         10075          [13]       12. Air France\n",
              "4   Brussels_londOn         10085      [67, 32]          \"Swiss Air\""
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d53925e7-4a5a-40bc-a6ff-75c257f2f438\">\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>From_To</th>\n",
              "      <th>FlightNumber</th>\n",
              "      <th>RecentDelays</th>\n",
              "      <th>Airline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LoNDon_paris</td>\n",
              "      <td>10045</td>\n",
              "      <td>[23, 47]</td>\n",
              "      <td>KLM(!)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>MAdrid_miLAN</td>\n",
              "      <td>10055</td>\n",
              "      <td>[]</td>\n",
              "      <td>&lt;Air France&gt; (12)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>londON_StockhOlm</td>\n",
              "      <td>10065</td>\n",
              "      <td>[24, 43, 87]</td>\n",
              "      <td>(British Airways. )</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Budapest_PaRis</td>\n",
              "      <td>10075</td>\n",
              "      <td>[13]</td>\n",
              "      <td>12. Air France</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Brussels_londOn</td>\n",
              "      <td>10085</td>\n",
              "      <td>[67, 32]</td>\n",
              "      <td>\"Swiss Air\"</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d53925e7-4a5a-40bc-a6ff-75c257f2f438')\"\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-d53925e7-4a5a-40bc-a6ff-75c257f2f438 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-d53925e7-4a5a-40bc-a6ff-75c257f2f438');\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": 79
        }
      ],
      "source": [
        "df = pd.DataFrame({'From_To': ['LoNDon_paris', 'MAdrid_miLAN', 'londON_StockhOlm', \n",
        "                               'Budapest_PaRis', 'Brussels_londOn'],\n",
        "              'FlightNumber': [10045, np.nan, 10065, np.nan, 10085],\n",
        "              'RecentDelays': [[23, 47], [], [24, 43, 87], [13], [67, 32]],\n",
        "                   'Airline': ['KLM(!)', '<Air France> (12)', '(British Airways. )', \n",
        "                               '12. Air France', '\"Swiss Air\"']})\n",
        "\n",
        "df['FlightNumber'] = df['FlightNumber'].interpolate().astype(int)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rpgEt2YfrcLJ"
      },
      "source": [
        "**39.** The From\\_To column would be better as two separate columns! Split each string on the underscore delimiter `_` to give a new temporary DataFrame with the correct values. Assign the correct column names to this temporary DataFrame. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "collapsed": true,
        "id": "HTVjkROArcLJ"
      },
      "outputs": [],
      "source": [
        "temp = df.From_To.str.split('_', expand=True)\n",
        "temp.columns = ['From', 'To']\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzBGMDXRrcLJ"
      },
      "source": [
        "**40.** Notice how the capitalisation of the city names is all mixed up in this temporary DataFrame. Standardise the strings so that only the first letter is uppercase (e.g. \"londON\" should become \"London\".)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "collapsed": true,
        "id": "Hn06h5TqrcLJ"
      },
      "outputs": [],
      "source": [
        "temp['From'] = temp['From'].str.capitalize()\n",
        "temp['To'] = temp['To'].str.capitalize()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OV1SEClmrcLJ"
      },
      "source": [
        "**41.** Delete the From_To column from `df` and attach the temporary DataFrame from the previous questions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "collapsed": true,
        "id": "vGAMt4d3rcLJ"
      },
      "outputs": [],
      "source": [
        "df = df.drop('From_To', axis=1)\n",
        "df = df.join(temp)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eGQTVUuDrcLJ"
      },
      "source": [
        "**42**. In the Airline column, you can see some extra puctuation and symbols have appeared around the airline names. Pull out just the airline name. E.g. `'(British Airways. )'` should become `'British Airways'`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "collapsed": true,
        "id": "xwb3oiOJrcLK"
      },
      "outputs": [],
      "source": [
        "df['Airline'] = df['Airline'].str.extract('([a-zA-Z\\s]+)', expand=False).str.strip()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TZsN1jRtrcLK"
      },
      "source": [
        "**43**. In the RecentDelays column, the values have been entered into the DataFrame as a list. We would like each first value in its own column, each second value in its own column, and so on. If there isn't an Nth value, the value should be NaN.\n",
        "\n",
        "Expand the Series of lists into a DataFrame named `delays`, rename the columns `delay_1`, `delay_2`, etc. and replace the unwanted RecentDelays column in `df` with `delays`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "collapsed": true,
        "id": "Q8Y-q7eNrcLK"
      },
      "outputs": [],
      "source": [
        "delays = df['RecentDelays'].apply(pd.Series)\n",
        "\n",
        "delays.columns = ['delay_{}'.format(n) for n in range(1, len(delays.columns)+1)]\n",
        "\n",
        "df = df.drop('RecentDelays', axis=1).join(delays)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlrtWEJFrcLK"
      },
      "source": [
        "The DataFrame should look much better now."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "nm4KAeNurcLK"
      },
      "source": [
        "## Using MultiIndexes\n",
        "\n",
        "### Go beyond flat DataFrames with additional index levels\n",
        "\n",
        "Difficulty: *medium*\n",
        "\n",
        "Previous exercises have seen us analysing data from DataFrames equipped with a single index level. However, pandas also gives you the possibilty of indexing your data using *multiple* levels. This is very much like adding new dimensions to a Series or a DataFrame. For example, a Series is 1D, but by using a MultiIndex with 2 levels we gain of much the same functionality as a 2D DataFrame.\n",
        "\n",
        "The set of puzzles below explores how you might use multiple index levels to enhance data analysis.\n",
        "\n",
        "To warm up, we'll look make a Series with two index levels. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6g6a9YlrcLK"
      },
      "source": [
        "**44**. Given the lists `letters = ['A', 'B', 'C']` and `numbers = list(range(10))`, construct a MultiIndex object from the product of the two lists. Use it to index a Series of random numbers. Call this Series `s`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "collapsed": true,
        "id": "99EOl9vdrcLK"
      },
      "outputs": [],
      "source": [
        "letters = ['A', 'B', 'C']\n",
        "numbers = list(range(10))\n",
        "\n",
        "mi = pd.MultiIndex.from_product([letters, numbers])\n",
        "s = pd.Series(np.random.rand(30), index=mi)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8BM083InrcLK"
      },
      "source": [
        "**45.** Check the index of `s` is lexicographically sorted (this is a necessary proprty for indexing to work correctly with a MultiIndex)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "collapsed": true,
        "id": "v_LgBESircLL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa77bf0f-ad90-4388-c8a2-700b0b08c52b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: MultiIndex.is_lexsorted is deprecated as a public function, users should use MultiIndex.is_monotonic_increasing instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: FutureWarning: MultiIndex.is_lexsorted is deprecated as a public function, users should use MultiIndex.is_monotonic_increasing instead.\n",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "s.index.is_lexsorted()\n",
        "\n",
        "# or more verbosely...\n",
        "s.index.lexsort_depth == s.index.nlevels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jN9s18GgrcLL"
      },
      "source": [
        "**46**. Select the labels `1`, `3` and `6` from the second level of the MultiIndexed Series."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "collapsed": true,
        "id": "SOBT_q59rcLL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e3f6559-2e8b-4587-b7fc-4a4bfd9ea245"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A  1    0.037750\n",
              "   3    0.175904\n",
              "   6    0.537912\n",
              "B  1    0.271164\n",
              "   3    0.506836\n",
              "   6    0.000180\n",
              "C  1    0.403109\n",
              "   3    0.509421\n",
              "   6    0.935031\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ],
      "source": [
        "s.loc[:, [1, 3, 6]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ypYKGJiurcLL"
      },
      "source": [
        "**47**. Slice the Series `s`; slice up to label 'B' for the first level and from label 5 onwards for the second level."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "collapsed": true,
        "id": "MjtHNm2krcLL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1414d3e8-7d3a-49e5-e4b6-eb8acf7a4a83"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A  5    0.569865\n",
              "   6    0.537912\n",
              "   7    0.366164\n",
              "   8    0.128956\n",
              "   9    0.007416\n",
              "B  5    0.485804\n",
              "   6    0.000180\n",
              "   7    0.090666\n",
              "   8    0.021215\n",
              "   9    0.180138\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ],
      "source": [
        "s.loc[pd.IndexSlice[:'B', 5:]]\n",
        "\n",
        "# or equivalently without IndexSlice...\n",
        "s.loc[slice(None, 'B'), slice(5, None)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4uFRM8brcLL"
      },
      "source": [
        "**48**. Sum the values in `s` for each label in the first level (you should have Series giving you a total for labels A, B and C)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "collapsed": true,
        "id": "cOD3WzojrcLL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ff2d388-8f29-46b9-96cf-b0101ec7dd60"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A    3.142916\n",
              "B    3.280975\n",
              "C    4.357915\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "s.sum(level=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3r5c1IDyrcLL"
      },
      "source": [
        "**49**. Suppose that `sum()` (and other methods) did not accept a `level` keyword argument. How else could you perform the equivalent of `s.sum(level=1)`?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "collapsed": true,
        "id": "7KtQrzSDrcLL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "602f6054-9e93-4011-9821-41fa5ffb8179"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1.459456\n",
              "1    0.712024\n",
              "2    1.281100\n",
              "3    1.192161\n",
              "4    1.224205\n",
              "5    1.067436\n",
              "6    1.473123\n",
              "7    0.486775\n",
              "8    0.798196\n",
              "9    1.087330\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "s.unstack().sum(axis=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iBzDBB3TrcLM"
      },
      "source": [
        "**50**. Exchange the levels of the MultiIndex so we have an index of the form (letters, numbers). Is this new Series properly lexsorted? If not, sort it."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_s = s.swaplevel(0, 1)\n",
        "\n",
        "# check\n",
        "new_s.index.is_lexsorted()\n",
        "\n",
        "# sort\n",
        "new_s = new_s.sort_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hg0yJPr_LG8-",
        "outputId": "dc620f25-515d-46a4-8649-286dfd0d3cc1"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: FutureWarning: MultiIndex.is_lexsorted is deprecated as a public function, users should use MultiIndex.is_monotonic_increasing instead.\n",
            "  after removing the cwd from sys.path.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lgZ4q61drcLM"
      },
      "source": [
        "## Minesweeper\n",
        "\n",
        "### Generate the numbers for safe squares in a Minesweeper grid\n",
        "\n",
        "Difficulty: *medium* to *hard*\n",
        "\n",
        "If you've ever used an older version of Windows, there's a good chance you've played with [Minesweeper](https://en.wikipedia.org/wiki/Minesweeper_(video_game). If you're not familiar with the game, imagine a grid of squares: some of these squares conceal a mine. If you click on a mine, you lose instantly. If you click on a safe square, you reveal a number telling you how many mines are found in the squares that are immediately adjacent. The aim of the game is to uncover all squares in the grid that do not contain a mine.\n",
        "\n",
        "In this section, we'll make a DataFrame that contains the necessary data for a game of Minesweeper: coordinates of the squares, whether the square contains a mine and the number of mines found on adjacent squares."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlqK7xjqrcLM"
      },
      "source": [
        "**51**. Let's suppose we're playing Minesweeper on a 5 by 4 grid, i.e.\n",
        "```\n",
        "X = 5\n",
        "Y = 4\n",
        "```\n",
        "To begin, generate a DataFrame `df` with two columns, `'x'` and `'y'` containing every coordinate for this grid. That is, the DataFrame should start:\n",
        "```\n",
        "   x  y\n",
        "0  0  0\n",
        "1  0  1\n",
        "2  0  2\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zeHVAvOiL4ep"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tpNC9N5rcLM"
      },
      "source": [
        "**52**. For this DataFrame `df`, create a new column of zeros (safe) and ones (mine). The probability of a mine occuring at each location should be 0.4."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "collapsed": true,
        "id": "dN4iJtyurcLM"
      },
      "outputs": [],
      "source": [
        "\n",
        "df['mine'] = np.random.binomial(1, 0.4, X*Y)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ccx1PdgXrcLN"
      },
      "source": [
        "**53**. Now create a new column for this DataFrame called `'adjacent'`. This column should contain the number of mines found on adjacent squares in the grid. \n",
        "\n",
        "(E.g. for the first row, which is the entry for the coordinate `(0, 0)`, count how many mines are found on the coordinates `(0, 1)`, `(1, 0)` and `(1, 1)`.)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F7WQzqurrcLN"
      },
      "source": [
        "**54**. For rows of the DataFrame that contain a mine, set the value in the `'adjacent'` column to NaN."
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wFMeC4_MozPL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3PBMn0AXrcLN"
      },
      "source": [
        "**55**. Finally, convert the DataFrame to grid of the adjacent mine counts: columns are the `x` coordinate, rows are the `y` coordinate."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qOIYOoOLrcLO"
      },
      "source": [
        "## Plotting\n",
        "\n",
        "### Visualize trends and patterns in data\n",
        "\n",
        "Difficulty: *medium*\n",
        "\n",
        "To really get a good understanding of the data contained in your DataFrame, it is often essential to create plots: if you're lucky, trends and anomalies will jump right out at you. This functionality is baked into pandas and the puzzles below explore some of what's possible with the library.\n",
        "\n",
        "**56.** Pandas is highly integrated with the plotting library matplotlib, and makes plotting DataFrames very user-friendly! Plotting in a notebook environment usually makes use of the following boilerplate:\n",
        "\n",
        "```python\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.style.use('ggplot')\n",
        "```\n",
        "\n",
        "matplotlib is the plotting library which pandas' plotting functionality is built upon, and it is usually aliased to ```plt```.\n",
        "\n",
        "```%matplotlib inline``` tells the notebook to show plots inline, instead of creating them in a separate window.  \n",
        "\n",
        "```plt.style.use('ggplot')``` is a style theme that most people find agreeable, based upon the styling of R's ggplot package.\n",
        "\n",
        "For starters, make a scatter plot of this random data, but use black X's instead of the default markers. \n",
        "\n",
        "```df = pd.DataFrame({\"xs\":[1,5,2,8,1], \"ys\":[4,2,1,9,6]})```\n",
        "\n",
        "Consult the [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html) if you get stuck!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "collapsed": true,
        "id": "nz3b39tZrcLO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "9b971ced-24d9-471e-e0df-2be80282e95d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f46ee740490>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.style.use('ggplot')\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "df = pd.DataFrame({\"xs\":[1,5,2,8,1], \"ys\":[4,2,1,9,6]})\n",
        "\n",
        "df.plot.scatter(\"xs\", \"ys\", color = \"black\", marker = \"x\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VLvg9JfwrcLP"
      },
      "source": [
        "**57.** Columns in your DataFrame can also be used to modify colors and sizes.  Bill has been keeping track of his performance at work over time, as well as how good he was feeling that day, and whether he had a cup of coffee in the morning.  Make a plot which incorporates all four features of this DataFrame.\n",
        "\n",
        "(Hint:  If you're having trouble seeing the plot, try multiplying the Series which you choose to represent size by 10 or more)\n",
        "\n",
        "*The chart doesn't have to be pretty: this isn't a course in data viz!*\n",
        "\n",
        "```\n",
        "df = pd.DataFrame({\"productivity\":[5,2,3,1,4,5,6,7,8,3,4,8,9],\n",
        "                   \"hours_in\"    :[1,9,6,5,3,9,2,9,1,7,4,2,2],\n",
        "                   \"happiness\"   :[2,1,3,2,3,1,2,3,1,2,2,1,3],\n",
        "                   \"caffienated\" :[0,0,1,1,0,0,0,0,1,1,0,1,0]})\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "collapsed": true,
        "id": "4mKqZ2bKrcLQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "f22bf8aa-d393-421c-aaef-01d7a080429b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f46ee1f4110>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df = pd.DataFrame({\"productivity\":[5,2,3,1,4,5,6,7,8,3,4,8,9], \"hours_in\" :[1,9,6,5,3,9,2,9,1,7,4,2,2], \"happiness\" :[2,1,3,2,3,1,2,3,1,2,2,1,3], \"caffienated\" :[0,0,1,1,0,0,0,0,1,1,0,1,0]})\n",
        "df = pd.DataFrame({\"productivity\":[5,2,3,1,4,5,6,7,8,3,4,8,9],\n",
        "                   \"hours_in\"    :[1,9,6,5,3,9,2,9,1,7,4,2,2],\n",
        "                   \"happiness\"   :[2,1,3,2,3,1,2,3,1,2,2,1,3],\n",
        "                   \"caffienated\" :[0,0,1,1,0,0,0,0,1,1,0,1,0]})\n",
        "\n",
        "df.plot.scatter(\"hours_in\", \"productivity\", s = df.happiness * 30, c = df.caffienated)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sDgdfSNcrcLR"
      },
      "source": [
        "**58.**  What if we want to plot multiple things?  Pandas allows you to pass in a matplotlib *Axis* object for plots, and plots will also return an Axis object.\n",
        "\n",
        "Make a bar plot of monthly revenue with a line plot of monthly advertising spending (numbers in millions)\n",
        "\n",
        "```\n",
        "df = pd.DataFrame({\"revenue\":[57,68,63,71,72,90,80,62,59,51,47,52],\n",
        "                   \"advertising\":[2.1,1.9,2.7,3.0,3.6,3.2,2.7,2.4,1.8,1.6,1.3,1.9],\n",
        "                   \"month\":range(12)\n",
        "                  })\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "collapsed": true,
        "id": "th6HMINMrcLR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "3318d9d8-204d-4efe-9b67-81a748c0f82e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1.0, 12.0)"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df = pd.DataFrame({\"revenue\":[57,68,63,71,72,90,80,62,59,51,47,52], \"advertising\":[2.1,1.9,2.7,3.0,3.6,3.2,2.7,2.4,1.8,1.6,1.3,1.9], \"month\":range(12) })\n",
        "df = pd.DataFrame({\"revenue\":[57,68,63,71,72,90,80,62,59,51,47,52],\n",
        "                   \"advertising\":[2.1,1.9,2.7,3.0,3.6,3.2,2.7,2.4,1.8,1.6,1.3,1.9],\n",
        "                   \"month\":range(12)\n",
        "                  })\n",
        "\n",
        "ax = df.plot.bar(\"month\", \"revenue\", color = \"green\")\n",
        "df.plot.line(\"month\", \"advertising\", secondary_y = True, ax = ax)\n",
        "ax.set_xlim((-1,12))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zkcFTkkDrcLS"
      },
      "source": [
        "Now we're finally ready to create a candlestick chart, which is a very common tool used to analyze stock price data.  A candlestick chart shows the opening, closing, highest, and lowest price for a stock during a time window.  The color of the \"candle\" (the thick part of the bar) is green if the stock closed above its opening price, or red if below.\n",
        "\n",
        "![Candlestick Example](img/candle.jpg)\n",
        "\n",
        "This was initially designed to be a pandas plotting challenge, but it just so happens that this type of plot is just not feasible using pandas' methods.  If you are unfamiliar with matplotlib, we have provided a function that will plot the chart for you so long as you can use pandas to get the data into the correct format.\n",
        "\n",
        "Your first step should be to get the data in the correct format using pandas' time-series grouping function.  We would like each candle to represent an hour's worth of data.  You can write your own aggregation function which returns the open/high/low/close, but pandas has a built-in which also does this."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UoAoBbJHrcLS"
      },
      "source": [
        "The below cell contains helper functions.  Call ```day_stock_data()``` to generate a DataFrame containing the prices a hypothetical stock sold for, and the time the sale occurred.  Call ```plot_candlestick(df)``` on your properly aggregated and formatted stock data to print the candlestick chart."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "collapsed": true,
        "id": "RYGiVesZrcLS"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "def float_to_time(x):\n",
        "    return str(int(x)) + \":\" + str(int(x%1 * 60)).zfill(2) + \":\" + str(int(x*60 % 1 * 60)).zfill(2)\n",
        "\n",
        "def day_stock_data():\n",
        "    #NYSE is open from 9:30 to 4:00\n",
        "    time = 9.5\n",
        "    price = 100\n",
        "    results = [(float_to_time(time), price)]\n",
        "    while time < 16:\n",
        "        elapsed = np.random.exponential(.001)\n",
        "        time += elapsed\n",
        "        if time > 16:\n",
        "            break\n",
        "        price_diff = np.random.uniform(.999, 1.001)\n",
        "        price *= price_diff\n",
        "        results.append((float_to_time(time), price))\n",
        "    \n",
        "    \n",
        "    df = pd.DataFrame(results, columns = ['time','price'])\n",
        "    df.time = pd.to_datetime(df.time)\n",
        "    return df\n",
        "\n",
        "#Don't read me unless you get stuck!\n",
        "def plot_candlestick(agg):\n",
        "    \"\"\"\n",
        "    agg is a DataFrame which has a DatetimeIndex and five columns: [\"open\",\"high\",\"low\",\"close\",\"color\"]\n",
        "    \"\"\"\n",
        "    fig, ax = plt.subplots()\n",
        "    for time in agg.index:\n",
        "        ax.plot([time.hour] * 2, agg.loc[time, [\"high\",\"low\"]].values, color = \"black\")\n",
        "        ax.plot([time.hour] * 2, agg.loc[time, [\"open\",\"close\"]].values, color = agg.loc[time, \"color\"], linewidth = 10)\n",
        "\n",
        "    ax.set_xlim((8,16))\n",
        "    ax.set_ylabel(\"Price\")\n",
        "    ax.set_xlabel(\"Hour\")\n",
        "    ax.set_title(\"OHLC of Stock Value During Trading Day\")\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UDQS7YIgrcLU"
      },
      "source": [
        "**59.** Generate a day's worth of random stock data, and aggregate / reformat it so that it has hourly summaries of the opening, highest, lowest, and closing prices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "collapsed": true,
        "id": "zcCJJ6YjrcLV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "5937ff6b-6bb6-4ce9-fd96-ecbe82940c5b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 time       price\n",
              "0 2022-11-03 09:30:00  100.000000\n",
              "1 2022-11-03 09:30:01  100.016911\n",
              "2 2022-11-03 09:30:03   99.959632\n",
              "3 2022-11-03 09:30:07   99.912572\n",
              "4 2022-11-03 09:30:07   99.813243"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-45edc5c1-a3b2-42d0-aa83-7e3c3d1b6b29\">\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>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-11-03 09:30:00</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-11-03 09:30:01</td>\n",
              "      <td>100.016911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-11-03 09:30:03</td>\n",
              "      <td>99.959632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-11-03 09:30:07</td>\n",
              "      <td>99.912572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-11-03 09:30:07</td>\n",
              "      <td>99.813243</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45edc5c1-a3b2-42d0-aa83-7e3c3d1b6b29')\"\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-45edc5c1-a3b2-42d0-aa83-7e3c3d1b6b29 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-45edc5c1-a3b2-42d0-aa83-7e3c3d1b6b29');\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": 97
        }
      ],
      "source": [
        "df = day_stock_data()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.set_index(\"time\", inplace = True)\n",
        "agg = df.resample(\"H\").ohlc()\n",
        "agg.columns = agg.columns.droplevel()\n",
        "agg[\"color\"] = (agg.close > agg.open).map({True:\"green\",False:\"red\"})\n",
        "agg.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "_-k6-OQUO3gH",
        "outputId": "2f8d68e3-9f26-409d-9c8c-d87eeee81b0d"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           open        high         low       close  color\n",
              "time                                                                      \n",
              "2022-11-03 09:00:00  100.000000  100.764170   98.948630   99.405025    red\n",
              "2022-11-03 10:00:00   99.483019  102.198575   99.036252   99.882002  green\n",
              "2022-11-03 11:00:00   99.838817  102.344815   99.699125  101.750500  green\n",
              "2022-11-03 12:00:00  101.670411  104.840059  101.312626  104.666579  green\n",
              "2022-11-03 13:00:00  104.651042  104.677928   99.815509  101.652363    red"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-70736754-93ed-4599-b717-c50ccf7fb3cb\">\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>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>color</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-11-03 09:00:00</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.764170</td>\n",
              "      <td>98.948630</td>\n",
              "      <td>99.405025</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-11-03 10:00:00</th>\n",
              "      <td>99.483019</td>\n",
              "      <td>102.198575</td>\n",
              "      <td>99.036252</td>\n",
              "      <td>99.882002</td>\n",
              "      <td>green</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-11-03 11:00:00</th>\n",
              "      <td>99.838817</td>\n",
              "      <td>102.344815</td>\n",
              "      <td>99.699125</td>\n",
              "      <td>101.750500</td>\n",
              "      <td>green</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-11-03 12:00:00</th>\n",
              "      <td>101.670411</td>\n",
              "      <td>104.840059</td>\n",
              "      <td>101.312626</td>\n",
              "      <td>104.666579</td>\n",
              "      <td>green</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-11-03 13:00:00</th>\n",
              "      <td>104.651042</td>\n",
              "      <td>104.677928</td>\n",
              "      <td>99.815509</td>\n",
              "      <td>101.652363</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70736754-93ed-4599-b717-c50ccf7fb3cb')\"\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-70736754-93ed-4599-b717-c50ccf7fb3cb 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-70736754-93ed-4599-b717-c50ccf7fb3cb');\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": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gDBWb1PrcLV"
      },
      "source": [
        "**60.** Now that you have your properly-formatted data, try to plot it yourself as a candlestick chart.  Use the ```plot_candlestick(df)``` function above, or matplotlib's [```plot``` documentation](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.plot.html) if you get stuck."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "collapsed": true,
        "id": "q4VQi6p5rcLV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "cdeaa733-7bc9-4ae7-ad01-91303659f37f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_candlestick(agg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7OswFvfhrcLV"
      },
      "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": "3.8.3"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}