Task_3_Pandas_tasks_(DW21).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x1zrotoBLzXl"
      },
      "source": [
        "# pandas \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IrJnME2WLzXo"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "BQuYPjGQLzXp"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9AZqG6vLzXp"
      },
      "source": [
        "**2.** Print the version of pandas that has been imported."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "6tSt5HflLzXp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8d1c1f8-85aa-4ba9-8bb9-45c68d5031a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.3.5\n"
          ]
        }
      ],
      "source": [
        "print(pd.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qByx05MvLzXq"
      },
      "source": [
        "**3.** Print out all the version information of the libraries that are required by the pandas library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "UKuRy-hpLzXq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97bd5ba9-dba9-47ad-a301-d9f7a1c082e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "INSTALLED VERSIONS\n",
            "------------------\n",
            "commit           : 66e3805b8cabe977f40c05259cc3fcf7ead5687d\n",
            "python           : 3.7.13.final.0\n",
            "python-bits      : 64\n",
            "OS               : Linux\n",
            "OS-release       : 5.4.188+\n",
            "Version          : #1 SMP Sun Apr 24 10:03:06 PDT 2022\n",
            "machine          : x86_64\n",
            "processor        : x86_64\n",
            "byteorder        : little\n",
            "LC_ALL           : None\n",
            "LANG             : en_US.UTF-8\n",
            "LOCALE           : en_US.UTF-8\n",
            "\n",
            "pandas           : 1.3.5\n",
            "numpy            : 1.21.6\n",
            "pytz             : 2022.1\n",
            "dateutil         : 2.8.2\n",
            "pip              : 21.1.3\n",
            "setuptools       : 57.4.0\n",
            "Cython           : 0.29.32\n",
            "pytest           : 3.6.4\n",
            "hypothesis       : None\n",
            "sphinx           : 1.8.6\n",
            "blosc            : None\n",
            "feather          : 0.4.1\n",
            "xlsxwriter       : None\n",
            "lxml.etree       : 4.9.1\n",
            "html5lib         : 1.0.1\n",
            "pymysql          : None\n",
            "psycopg2         : 2.9.3 (dt dec pq3 ext lo64)\n",
            "jinja2           : 2.11.3\n",
            "IPython          : 7.9.0\n",
            "pandas_datareader: 0.9.0\n",
            "bs4              : 4.6.3\n",
            "bottleneck       : None\n",
            "fsspec           : None\n",
            "fastparquet      : None\n",
            "gcsfs            : None\n",
            "matplotlib       : 3.2.2\n",
            "numexpr          : 2.8.3\n",
            "odfpy            : None\n",
            "openpyxl         : 3.0.10\n",
            "pandas_gbq       : 0.13.3\n",
            "pyarrow          : 6.0.1\n",
            "pyxlsb           : None\n",
            "s3fs             : None\n",
            "scipy            : 1.7.3\n",
            "sqlalchemy       : 1.4.40\n",
            "tables           : 3.7.0\n",
            "tabulate         : 0.8.10\n",
            "xarray           : 0.20.2\n",
            "xlrd             : 1.1.0\n",
            "xlwt             : 1.3.0\n",
            "numba            : 0.56.0\n"
          ]
        }
      ],
      "source": [
        "pd.show_versions()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aBEdzw-eLzXr"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "Fr2Uz1J3LzXr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "94e1a496-0373-48a5-83a1-52f3757b8fc1"
      },
      "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  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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f62ad793-25e4-4486-a7b6-ac6d16785a4a\">\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>2.0</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-f62ad793-25e4-4486-a7b6-ac6d16785a4a')\"\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-f62ad793-25e4-4486-a7b6-ac6d16785a4a 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-f62ad793-25e4-4486-a7b6-ac6d16785a4a');\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": 54
        }
      ],
      "source": [
        "import pandas as pd \n",
        "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",
        "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
        "\n",
        "df=pd.DataFrame(data=data, index=labels)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U43wMyyzLzXs"
      },
      "source": [
        "**5.** Display a summary of the basic information about this DataFrame and its data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "lF2-1I9JLzXs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "outputId": "116386ec-ff6a-4f85-a9d0-6a753d7d36d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 10 entries, a to j\n",
            "Data columns (total 4 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   animal    10 non-null     object \n",
            " 1   age       8 non-null      float64\n",
            " 2   visits    10 non-null     int64  \n",
            " 3   priority  10 non-null     object \n",
            "dtypes: float64(1), int64(1), object(2)\n",
            "memory usage: 700.0+ bytes\n"
          ]
        },
        {
          "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-a5b72260-8b18-46f2-b273-23b8a265167e\">\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-a5b72260-8b18-46f2-b273-23b8a265167e')\"\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-a5b72260-8b18-46f2-b273-23b8a265167e 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-a5b72260-8b18-46f2-b273-23b8a265167e');\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": 17
        }
      ],
      "source": [
        "df.info()\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hUhZKpArLzXs"
      },
      "source": [
        "**6.** Return the first 3 rows of the DataFrame `df`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "tseESOl0LzXt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "f8e0866b-16a5-4c64-8e76-097bc14a35f4"
      },
      "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-6b0f529f-0d18-4309-abf7-7ea4fec88279\">\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-6b0f529f-0d18-4309-abf7-7ea4fec88279')\"\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-6b0f529f-0d18-4309-abf7-7ea4fec88279 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-6b0f529f-0d18-4309-abf7-7ea4fec88279');\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": 18
        }
      ],
      "source": [
        "df.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HCFqV0AdLzXt"
      },
      "source": [
        "**7.** Select just the 'animal' and 'age' columns from the DataFrame `df`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "JKoVHKqOLzXt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "82d894f2-1103-444b-c6bd-c8b18d176e36"
      },
      "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-955124ab-b902-40a5-83b2-470b2a115b73\">\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-955124ab-b902-40a5-83b2-470b2a115b73')\"\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-955124ab-b902-40a5-83b2-470b2a115b73 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-955124ab-b902-40a5-83b2-470b2a115b73');\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": 32
        }
      ],
      "source": [
        "df[['animal', 'age']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLRZY6KVLzXu"
      },
      "source": [
        "**8.** Select the data in rows `[3, 4, 8]` *and* in columns `['animal', 'age']`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "7bhhfnuSLzXu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52fb6ab2-3d7c-44d1-eee0-b8f221ec7959"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  animal  age\n",
            "d    dog  NaN\n",
            "e    dog  5.0\n",
            "i    dog  7.0\n"
          ]
        }
      ],
      "source": [
        "print(df.iloc[[ 3,4,8], [0, 1]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kwTBMRANLzXu"
      },
      "source": [
        "**9.** Select only the rows where the number of visits is greater than 3."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "fj2ZNxPHLzXu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "89513786-528b-4b3e-fe98-a4c0ca543da4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [animal, age, visits, priority]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b90106bb-9143-417f-81e6-a4e43e853a3d\">\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-b90106bb-9143-417f-81e6-a4e43e853a3d')\"\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-b90106bb-9143-417f-81e6-a4e43e853a3d 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-b90106bb-9143-417f-81e6-a4e43e853a3d');\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": 41
        }
      ],
      "source": [
        "df[df['visits'] > 3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RhTyp-VMLzXu"
      },
      "source": [
        "**10.** Select the rows where the age is missing, i.e. is `NaN`.\n",
        "\n",
        "1.   List item\n",
        "2.   List item\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "ybdMFpIELzXv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef6dc001-c210-45b3-bc7a-88b2355da6b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  animal  age  visits priority\n",
            "d    dog  NaN       3      yes\n",
            "h    cat  NaN       1      yes\n"
          ]
        }
      ],
      "source": [
        "print(df[df['age'].isnull()])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-QYvlcqLzXv"
      },
      "source": [
        "**11.** Select the rows where the animal is a cat *and* the age is less than 3."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df[(df['animal'] ==\"cat\" ) & (df['age'] < 3)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-v9CKGtBVHK4",
        "outputId": "68b0a75b-88fc-4e0a-fad2-724fb1e164b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  animal  age  visits priority\n",
            "a    cat  2.5       1      yes\n",
            "f    cat  2.0       3       no\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0zYXTSGLzXv"
      },
      "source": [
        "**12.** Select the rows the age is between 2 and 4 (inclusive)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "XI29bqMuLzXv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "8236c916-823f-4f38-be03-c52c023b0134"
      },
      "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-6faee776-a84c-4c9c-b8ab-4abb266cfe66\">\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-6faee776-a84c-4c9c-b8ab-4abb266cfe66')\"\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-6faee776-a84c-4c9c-b8ab-4abb266cfe66 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-6faee776-a84c-4c9c-b8ab-4abb266cfe66');\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[df['age'].between(2, 4)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9muGX_lxLzXv"
      },
      "source": [
        "**13.** Change the age in row 'f' to 1.5."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "yA5kfcNtLzXw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "e262a8d5-9bed-4232-81a0-794400119b0d"
      },
      "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-baedd246-0917-49cf-9ef3-edb063e1b141\">\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-baedd246-0917-49cf-9ef3-edb063e1b141')\"\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-baedd246-0917-49cf-9ef3-edb063e1b141 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-baedd246-0917-49cf-9ef3-edb063e1b141');\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": 66
        }
      ],
      "source": [
        "df.loc['f', 'age']=1.5\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Do5_KxiZLzXw"
      },
      "source": [
        "**14.** Calculate the sum of all visits (the total number of visits)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "E77cAaudLzXw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e11a05de-e873-442a-f0db-a6fce317c9c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "df['visits'].sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "epin7BzdLzXw"
      },
      "source": [
        "**15.** Calculate the mean age for each different animal in `df`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "yLNH_6XRLzXw"
      },
      "outputs": [],
      "source": [
        "df.['animal'].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMvBso0eLzXw"
      },
      "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",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "_SWPn_UrX02T",
        "outputId": "7e2a7c19-8521-46f3-c1cd-932b854cb7ca"
      },
      "execution_count": null,
      "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-d08925d1-c6a0-432a-b9d0-c9b341b1c664\">\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-d08925d1-c6a0-432a-b9d0-c9b341b1c664')\"\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-d08925d1-c6a0-432a-b9d0-c9b341b1c664 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-d08925d1-c6a0-432a-b9d0-c9b341b1c664');\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": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "MMH-8eymLzXw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95c7ada5-fc2a-4063-aaec-7c5c69046022"
      },
      "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",
            "k  mouse  60.0      60      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['k'] = ['mouse', 60, 60, 'yes']\n",
        "print(df)\n",
        "df = df.drop(['k'])\n",
        "print(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0U643FHILzXw"
      },
      "source": [
        "**17.** Count the number of each type of animal in `df`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "UDuykiRHLzXx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f2d6397-125a-4288-b38c-4a86c6070a5d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "animal\n",
              "cat      4\n",
              "dog      4\n",
              "snake    2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "df.groupby('animal').size()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MM67ldGXLzXx"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "J7GXpAN7LzXy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "e39330cf-e910-4044-a64c-7603246a512f"
      },
      "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",
              "j    dog  3.0       1       no\n",
              "b    cat  3.0       3      yes\n",
              "a    cat  2.5       1      yes\n",
              "f    cat  1.5       3       no\n",
              "c  snake  0.5       2       no\n",
              "h    cat  NaN       1      yes\n",
              "d    dog  NaN       3      yes"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8151e057-8de4-482e-9d12-fd8a2d68a20d\">\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>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>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>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>h</th>\n",
              "      <td>cat</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8151e057-8de4-482e-9d12-fd8a2d68a20d')\"\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-8151e057-8de4-482e-9d12-fd8a2d68a20d 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-8151e057-8de4-482e-9d12-fd8a2d68a20d');\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": 83
        }
      ],
      "source": [
        "df.sort_values(by=['age', 'visits'], ascending=[False, True])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VjuNHJiVLzXy"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "5SIVQ6h4LzXy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "85e0843d-ee40-457b-b8dd-63181115df4f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  animal  age  visits priority\n",
              "a    cat  2.5       1      NaN\n",
              "b    cat  3.0       3      NaN\n",
              "c  snake  0.5       2      NaN\n",
              "d    dog  NaN       3      NaN\n",
              "e    dog  5.0       2      NaN\n",
              "f    cat  1.5       3      NaN\n",
              "g  snake  4.5       1      NaN\n",
              "h    cat  NaN       1      NaN\n",
              "i    dog  7.0       2      NaN\n",
              "j    dog  3.0       1      NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1c0c9b99-efaa-4f84-b902-809360bd0110\">\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>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>cat</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>snake</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>dog</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>dog</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f</th>\n",
              "      <td>cat</td>\n",
              "      <td>1.5</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g</th>\n",
              "      <td>snake</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>h</th>\n",
              "      <td>cat</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i</th>\n",
              "      <td>dog</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>j</th>\n",
              "      <td>dog</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1c0c9b99-efaa-4f84-b902-809360bd0110')\"\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-1c0c9b99-efaa-4f84-b902-809360bd0110 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-1c0c9b99-efaa-4f84-b902-809360bd0110');\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": 85
        }
      ],
      "source": [
        "df['priority'] = df['priority'].map({'yes':True ,'no':False})\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BX3gkTfvLzXy"
      },
      "source": [
        "**20.** In the 'animal' column, change the 'snake' entries to 'python'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "Q2d4_8dCLzXy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "ad2debde-b365-4c1c-c83f-e44af95adfee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   animal  age  visits priority\n",
              "a     cat  2.5     1.0      NaN\n",
              "b     cat  3.0     3.0      NaN\n",
              "c  python  0.5     2.0      NaN\n",
              "d     dog  NaN     3.0      NaN\n",
              "e     dog  5.0     2.0      NaN\n",
              "f     cat  1.5     3.0      NaN\n",
              "g  python  4.5     1.0      NaN\n",
              "h     cat  NaN     1.0      NaN\n",
              "i     dog  7.0     2.0      NaN\n",
              "j     dog  3.0     1.0      NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-85437a3d-758a-45a6-b4bb-9030229b97a8\">\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>1.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>cat</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>python</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>dog</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>dog</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f</th>\n",
              "      <td>cat</td>\n",
              "      <td>1.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g</th>\n",
              "      <td>python</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>h</th>\n",
              "      <td>cat</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i</th>\n",
              "      <td>dog</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>j</th>\n",
              "      <td>dog</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85437a3d-758a-45a6-b4bb-9030229b97a8')\"\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-85437a3d-758a-45a6-b4bb-9030229b97a8 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-85437a3d-758a-45a6-b4bb-9030229b97a8');\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": 91
        }
      ],
      "source": [
        "df['animal'] = df['animal'].replace('snake', 'python')\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mmbzQQsXLzXy"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "BO3i-wJfLzXy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a03c0eea-8224-4423-e9d2-76a7324e973c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "visits  1.0  2.0   3.0\n",
            "animal                \n",
            "cat     2.5  NaN  2.25\n",
            "dog     3.0  6.0   NaN\n",
            "python  4.5  0.5   NaN\n"
          ]
        }
      ],
      "source": [
        "print(df.pivot_table(index='animal', columns='visits', values='age', aggfunc='mean'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZkEpIyGmLzXz"
      },
      "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": "iVmWWnUhLzXz"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "oNJsG7IELzXz"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9WSi2hmaLzXz"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "WVvCltzlLzXz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "ecc0ac22-8cbd-48d7-bb65-1c858bcb3851"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      A\n",
              "2   2.0\n",
              "6   5.0\n",
              "7   5.0\n",
              "10  7.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9112e283-3660-451c-ad81-4056be9cf93b\">\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>2</th>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9112e283-3660-451c-ad81-4056be9cf93b')\"\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-9112e283-3660-451c-ad81-4056be9cf93b 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-9112e283-3660-451c-ad81-4056be9cf93b');\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": 99
        }
      ],
      "source": [
        "df1 = pd.DataFrame()\n",
        "for i in range(1,len(df)):\n",
        "    if (df.loc[i,'A'] == df.loc[i-1,'A']):\n",
        "        df1.loc[i,'A'] =df.loc[i,'A']\n",
        "df1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbCJPA7rLzX0"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "oxgZem0ILzX0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3fd41cbd-0fe5-4eb8-c879-07db0722d88d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "f\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
        "x=df.sum(axis=0)\n",
        "\n",
        "min=x.min()\n",
        "\n",
        "values=x[x == min].index[0]\n",
        "print(values)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T5qO4UqcjSmD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hKEofnfqLzX0"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "5VgtfcSaLzX0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "923b7cf8-f095-4414-d0f2-9c1811d76a51"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-66ee31a94c08>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mduplicated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\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[0;31mNameError\u001b[0m: name 'df' is not defined"
          ]
        }
      ],
      "source": [
        "df1=df.copy()\n",
        "df[df1.duplicated()]['a'].count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KMf81JJ4LzX0"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "pqgHcyJuLzX0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "e0045955-017f-4f78-e424-12537ccb7f1c"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\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   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 0",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-364558586c70>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m   \u001b[0;31m#for i in columns:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m   \u001b[0;32mif\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNaN\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     15\u001b[0m     \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    923\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    924\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_takeable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 925\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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    926\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    927\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1098\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1099\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIndexingError\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[0;32m-> 1100\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\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   1101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m         \u001b[0;31m# no multi-index, so validate all of the indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    860\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0msection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m                 \u001b[0;31m# This is an elided recursive call to iloc/loc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 862\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_key\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    863\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    864\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"not applicable\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1162\u001b[0m         \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1163\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1164\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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   1165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1166\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_slice_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\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[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m   1111\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1112\u001b[0m         \u001b[0;31m# GH#5667 this will fail if the label is not present in the axis.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1113\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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   1114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1115\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_handle_lowerdim_multi_index_axis0\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mxs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m   3774\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Expected label or tuple of labels, got {key}\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3775\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3776\u001b[0;31m             \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3777\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3778\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\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[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 0"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random\n",
        "x = np.random.randint(1,11,(5,10))\n",
        "df =pd.DataFrame(x, columns =['a','b','c','d','e','f','g','h','i','j'])\n",
        "df\n",
        "columns =['a','b','c','d','e','f','g','h','i','j']\n",
        "for i in range(5):\n",
        "  for j in range(5):\n",
        "    k=random.choice(columns)\n",
        "l=[]\n",
        "for i in range(5):\n",
        "  #for i in columns:\n",
        "  if df.loc[3, i] ==np.NaN:\n",
        "    l.append(i)\n",
        "print(*l)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HAfq3B1NLzX0"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "Li0zbWsLLzX0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68e598f8-0683-4149-afe3-1b9b228047ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "grps    \n",
            "a     1     345\n",
            "      7      52\n",
            "      0      12\n",
            "b     12     57\n",
            "      8      54\n",
            "      4      45\n",
            "c     10    235\n",
            "      14     87\n",
            "      9      23\n",
            "Name: vals, dtype: int64\n"
          ]
        }
      ],
      "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",
        "c = df.groupby('grps')['vals'].nlargest(3)\n",
        "print(c)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lZtpKGJ6LzX0"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "2U8dO9rcLzX1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2a836d7-4f0a-431f-e677-770f71b3b305"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A\n",
              "(0, 10]      49\n",
              "(10, 20]     52\n",
              "(20, 30]     59\n",
              "(30, 40]     67\n",
              "(40, 50]     55\n",
              "(50, 60]     50\n",
              "(60, 70]     35\n",
              "(70, 80]     60\n",
              "(80, 90]     61\n",
              "(90, 100]    52\n",
              "Name: B, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "x = np.arange(1,101)\n",
        "df= pd.DataFrame(data ={'A': x} )\n",
        "df['B'] = np.random.randint(1,10,size=100)\n",
        "\n",
        "df.groupby(pd.cut(df['A'], np.arange(0, 101, 10)))['B'].sum()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJBYyPZwLzX1"
      },
      "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": "fcoE9ystLzX1"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "g1tXnvCnLzX1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "cacbc4de-f3c7-4f38-9d2b-aa8f4e000aad"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-912659771b07>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Y'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'X'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfirst_zero_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'X'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\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[1;32m      4\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Y'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mfirst_zero_idx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
          ]
        }
      ],
      "source": [
        "df['Y'] = df.groupby((df['X'] == 0).cumsum()).cumcount()\n",
        "\n",
        "first_zero_idx = (df['X'] == 0).idxmax()\n",
        "df['Y'].iloc[0:first_zero_idx] += 1\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4izfRbWcLzX1"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "lY_kh_BILzX1"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sq3u_BrcLzX1"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "cCTADTvVLzX2"
      },
      "outputs": [],
      "source": [
        "x = np.random.randint(1,100,(5,7))\n",
        "df= pd.DataFrame(x)\n",
        "df.unstack().sort_values()[-3:].index.tolist()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xo2pFCQbLzX2"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "vMG0poH_LzX2"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame({'grps': list('abcdefghiklmnop'), \n",
        "                   'vals': [11,-22,33,44,55,66,-77,88,-54,89,23,23,43,2331,34]})\n",
        "df['vals'] = df.groupby('grps')['vals'].transform(lambda x: np.where(x < 0, x.mean(), x))\n",
        "ans= df.groupby('grps')['vals'].mean()\n",
        "print(ans)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cj3kgLQvLzX2"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "Pn2gYwa7LzX2"
      },
      "outputs": [],
      "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",
        "a1 = df.groupby(['group'])['value']              \n",
        "a2 = df.fillna(0).groupby(['group'])['value']    \n",
        "\n",
        "s = a2.rolling(3, min_periods=1).sum() / a1.rolling(3, min_periods=1).count() \n",
        "\n",
        "s.reset_index(level=0, drop=True).sort_index()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ScwX-95LzX2"
      },
      "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": "laK1YnpQLzX2"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "FueDHR2bLzX3"
      },
      "outputs": [],
      "source": [
        "ref=pd.date_range(start='2015-01-01', end='2015-12-31', freq='C') \n",
        "s = pd.Series(np.random.rand(len(ref)), index=ref)\n",
        "s"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WoaHUlv3LzX3"
      },
      "source": [
        "**34.** Find the sum of the values in `s` for every Wednesday."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "xyTX1WlALzX3"
      },
      "outputs": [],
      "source": [
        "s[s.index.weekday == 2].sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmq4ed8nLzX3"
      },
      "source": [
        "**35.** For each calendar month in `s`, find the mean of values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "SF82olGMLzX3"
      },
      "outputs": [],
      "source": [
        "s.resample('M').mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dXvda4D0LzX3"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "BN6ZXXvaLzX3"
      },
      "outputs": [],
      "source": [
        "s.groupby(pd.Grouper(freq='4M')).max()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wm_-A1zbLzX3"
      },
      "source": [
        "**37.** Create a DateTimeIndex consisting of the third Thursday in each month for the years 2015 and 2016."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "uZjTQPhRLzX3"
      },
      "outputs": [],
      "source": [
        "renge= pd.date_range(start='01/01/2015', end='31/12/2016', freq='WOM-3THU')\n",
        "range"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fwey36nELzX4"
      },
      "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": "6cSJl1jULzX4"
      },
      "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": 7,
      "metadata": {
        "collapsed": true,
        "id": "L6Lo5d_8LzX4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "02f98158-91fc-49c7-acf7-1ff679ef55e2"
      },
      "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-8d9dbabe-2660-49ba-a9ba-af2e43f9632f\">\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-8d9dbabe-2660-49ba-a9ba-af2e43f9632f')\"\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-8d9dbabe-2660-49ba-a9ba-af2e43f9632f 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-8d9dbabe-2660-49ba-a9ba-af2e43f9632f');\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": 7
        }
      ],
      "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",
        "df['FlightNumber'].interpolate(inplace = True)\n",
        "df['FlightNumber'] = df['FlightNumber'].astype(int)\n",
        "df\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zM3n4_jkLzX4"
      },
      "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": 8,
      "metadata": {
        "collapsed": true,
        "id": "RGiRsYKaLzX4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "34d5e52a-f98d-4dd4-a96c-36ce8d48b896"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   FlightNumber  RecentDelays              Airline      From         To\n",
              "0       10045.0      [23, 47]               KLM(!)    LoNDon      paris\n",
              "1           NaN            []    <Air France> (12)    MAdrid      miLAN\n",
              "2       10065.0  [24, 43, 87]  (British Airways. )    londON  StockhOlm\n",
              "3           NaN          [13]       12. Air France  Budapest      PaRis\n",
              "4       10085.0      [67, 32]          \"Swiss Air\"  Brussels     londOn"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-73f47a05-7072-4633-9f8d-8ed7de2d45d4\">\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>FlightNumber</th>\n",
              "      <th>RecentDelays</th>\n",
              "      <th>Airline</th>\n",
              "      <th>From</th>\n",
              "      <th>To</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10045.0</td>\n",
              "      <td>[23, 47]</td>\n",
              "      <td>KLM(!)</td>\n",
              "      <td>LoNDon</td>\n",
              "      <td>paris</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>[]</td>\n",
              "      <td>&lt;Air France&gt; (12)</td>\n",
              "      <td>MAdrid</td>\n",
              "      <td>miLAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10065.0</td>\n",
              "      <td>[24, 43, 87]</td>\n",
              "      <td>(British Airways. )</td>\n",
              "      <td>londON</td>\n",
              "      <td>StockhOlm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>[13]</td>\n",
              "      <td>12. Air France</td>\n",
              "      <td>Budapest</td>\n",
              "      <td>PaRis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10085.0</td>\n",
              "      <td>[67, 32]</td>\n",
              "      <td>\"Swiss Air\"</td>\n",
              "      <td>Brussels</td>\n",
              "      <td>londOn</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73f47a05-7072-4633-9f8d-8ed7de2d45d4')\"\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-73f47a05-7072-4633-9f8d-8ed7de2d45d4 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-73f47a05-7072-4633-9f8d-8ed7de2d45d4');\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": 8
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\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",
        "temp = df.copy()\n",
        "temp['From'] = df['From_To'].apply(lambda x:x.split('_')[0])\n",
        "temp['To'] = df['From_To'].apply(lambda x:x.split('_')[1])\n",
        "temp.drop('From_To', axis=1, inplace=True)\n",
        "temp\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5TazwZiLzX4"
      },
      "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": 9,
      "metadata": {
        "collapsed": true,
        "id": "C62Eq49pLzX4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8b9c610f-9e3a-42f4-87cb-2c2330e5d791"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   FlightNumber  RecentDelays              Airline      From         To\n",
              "0       10045.0      [23, 47]               KLM(!)    London      Paris\n",
              "1           NaN            []    <Air France> (12)    Madrid      Milan\n",
              "2       10065.0  [24, 43, 87]  (British Airways. )    London  Stockholm\n",
              "3           NaN          [13]       12. Air France  Budapest      Paris\n",
              "4       10085.0      [67, 32]          \"Swiss Air\"  Brussels     London"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f74955ed-06dc-4fef-b4ab-dbf50703162b\">\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>FlightNumber</th>\n",
              "      <th>RecentDelays</th>\n",
              "      <th>Airline</th>\n",
              "      <th>From</th>\n",
              "      <th>To</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10045.0</td>\n",
              "      <td>[23, 47]</td>\n",
              "      <td>KLM(!)</td>\n",
              "      <td>London</td>\n",
              "      <td>Paris</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>[]</td>\n",
              "      <td>&lt;Air France&gt; (12)</td>\n",
              "      <td>Madrid</td>\n",
              "      <td>Milan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10065.0</td>\n",
              "      <td>[24, 43, 87]</td>\n",
              "      <td>(British Airways. )</td>\n",
              "      <td>London</td>\n",
              "      <td>Stockholm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>[13]</td>\n",
              "      <td>12. Air France</td>\n",
              "      <td>Budapest</td>\n",
              "      <td>Paris</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10085.0</td>\n",
              "      <td>[67, 32]</td>\n",
              "      <td>\"Swiss Air\"</td>\n",
              "      <td>Brussels</td>\n",
              "      <td>London</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f74955ed-06dc-4fef-b4ab-dbf50703162b')\"\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-f74955ed-06dc-4fef-b4ab-dbf50703162b 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-f74955ed-06dc-4fef-b4ab-dbf50703162b');\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": 9
        }
      ],
      "source": [
        "temp['From'] = temp['From'].str.capitalize()\n",
        "temp['To'] = temp['To'].str.capitalize()\n",
        "temp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6KDKyHffLzX4"
      },
      "source": [
        "**41.** Delete the From_To column from `df` and attach the temporary DataFrame from the previous questions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "id": "gRcf9XNNLzX4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e8e84f5d-781d-4972-fccd-b1cc829806c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   FlightNumber  RecentDelays              Airline      From         To\n",
              "0       10045.0      [23, 47]               KLM(!)    London      Paris\n",
              "1           NaN            []    <Air France> (12)    Madrid      Milan\n",
              "2       10065.0  [24, 43, 87]  (British Airways. )    London  Stockholm\n",
              "3           NaN          [13]       12. Air France  Budapest      Paris\n",
              "4       10085.0      [67, 32]          \"Swiss Air\"  Brussels     London"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-961dd381-ea17-43e3-a4f9-b38901fdd9bd\">\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>FlightNumber</th>\n",
              "      <th>RecentDelays</th>\n",
              "      <th>Airline</th>\n",
              "      <th>From</th>\n",
              "      <th>To</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10045.0</td>\n",
              "      <td>[23, 47]</td>\n",
              "      <td>KLM(!)</td>\n",
              "      <td>London</td>\n",
              "      <td>Paris</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>[]</td>\n",
              "      <td>&lt;Air France&gt; (12)</td>\n",
              "      <td>Madrid</td>\n",
              "      <td>Milan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10065.0</td>\n",
              "      <td>[24, 43, 87]</td>\n",
              "      <td>(British Airways. )</td>\n",
              "      <td>London</td>\n",
              "      <td>Stockholm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>[13]</td>\n",
              "      <td>12. Air France</td>\n",
              "      <td>Budapest</td>\n",
              "      <td>Paris</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10085.0</td>\n",
              "      <td>[67, 32]</td>\n",
              "      <td>\"Swiss Air\"</td>\n",
              "      <td>Brussels</td>\n",
              "      <td>London</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-961dd381-ea17-43e3-a4f9-b38901fdd9bd')\"\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-961dd381-ea17-43e3-a4f9-b38901fdd9bd 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-961dd381-ea17-43e3-a4f9-b38901fdd9bd');\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": 10
        }
      ],
      "source": [
        "df.drop('From_To', axis=1, inplace=True)\n",
        "df = pd.concat([df, temp[['From', 'To']]], axis=1)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mwRBz8GELzX5"
      },
      "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": 14,
      "metadata": {
        "collapsed": true,
        "id": "m7PBYWMSLzX5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "51ba0510-e195-48ef-a7a7-eed961d8e940"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   FlightNumber  RecentDelays          Airline      From         To\n",
              "0       10045.0      [23, 47]              KLM    London      Paris\n",
              "1           NaN            []       Air France    Madrid      Milan\n",
              "2       10065.0  [24, 43, 87]  British Airways    London  Stockholm\n",
              "3           NaN          [13]       Air France  Budapest      Paris\n",
              "4       10085.0      [67, 32]        Swiss Air  Brussels     London"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-06540bb0-6029-405e-a52e-7fffd3a90721\">\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>FlightNumber</th>\n",
              "      <th>RecentDelays</th>\n",
              "      <th>Airline</th>\n",
              "      <th>From</th>\n",
              "      <th>To</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10045.0</td>\n",
              "      <td>[23, 47]</td>\n",
              "      <td>KLM</td>\n",
              "      <td>London</td>\n",
              "      <td>Paris</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>[]</td>\n",
              "      <td>Air France</td>\n",
              "      <td>Madrid</td>\n",
              "      <td>Milan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10065.0</td>\n",
              "      <td>[24, 43, 87]</td>\n",
              "      <td>British Airways</td>\n",
              "      <td>London</td>\n",
              "      <td>Stockholm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>[13]</td>\n",
              "      <td>Air France</td>\n",
              "      <td>Budapest</td>\n",
              "      <td>Paris</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10085.0</td>\n",
              "      <td>[67, 32]</td>\n",
              "      <td>Swiss Air</td>\n",
              "      <td>Brussels</td>\n",
              "      <td>London</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-06540bb0-6029-405e-a52e-7fffd3a90721')\"\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-06540bb0-6029-405e-a52e-7fffd3a90721 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-06540bb0-6029-405e-a52e-7fffd3a90721');\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": 14
        }
      ],
      "source": [
        "df['Airline'] = df['Airline'].str.extract('([a-zA-Z\\s]+)', expand=False).str.strip()\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JK5-gTxbLzX5"
      },
      "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": 18,
      "metadata": {
        "collapsed": true,
        "id": "zouBaCyBLzX5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "76f544b0-0831-4203-dd3f-ff2bbc22d44c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   delay_1  delay_2  delay_3\n",
              "0     23.0     47.0      NaN\n",
              "1      NaN      NaN      NaN\n",
              "2     24.0     43.0     87.0\n",
              "3     13.0      NaN      NaN\n",
              "4     67.0     32.0      NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8dc6f499-6c9c-473d-a09d-005010d8cc3a\">\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>delay_1</th>\n",
              "      <th>delay_2</th>\n",
              "      <th>delay_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>24.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>87.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>67.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8dc6f499-6c9c-473d-a09d-005010d8cc3a')\"\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-8dc6f499-6c9c-473d-a09d-005010d8cc3a 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-8dc6f499-6c9c-473d-a09d-005010d8cc3a');\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": 18
        }
      ],
      "source": [
        "delays = df['RecentDelays'].apply(pd.Series)\n",
        "delays.columns = ['delay_'+str(n) for n in range(1,len(delays.columns)+1)]\n",
        "delays"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aY4nGcxSLzX5"
      },
      "source": [
        "The DataFrame should look much better now."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "3NEFZAHKLzX5"
      },
      "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": "-q7zXDzkLzX5"
      },
      "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": 19,
      "metadata": {
        "collapsed": true,
        "id": "7tzOSP9qLzX5"
      },
      "outputs": [],
      "source": [
        "letters = ['A', 'B', 'C']\n",
        "numbers = list(range(10))\n",
        "multi_ind = pd.MultiIndex.from_product([letters, numbers])\n",
        "s = pd.Series(np.random.rand(30), index=multi_ind)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kwrz8064LzX6"
      },
      "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": 20,
      "metadata": {
        "collapsed": true,
        "id": "Pu_q4NPwLzX6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0ca72dc-f6d8-4093-eb59-01181da951bf"
      },
      "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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "multi_ind.is_lexsorted()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OkuZuZOeLzX6"
      },
      "source": [
        "**46**. Select the labels `1`, `3` and `6` from the second level of the MultiIndexed Series."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "collapsed": true,
        "id": "0IBTH_UsLzX6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e7c0be5-2fdf-4ef1-c132-680019933460"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A  1    0.251039\n",
              "   3    0.039200\n",
              "   6    0.049995\n",
              "B  1    0.125121\n",
              "   3    0.584585\n",
              "   6    0.703501\n",
              "C  1    0.264383\n",
              "   3    0.299589\n",
              "   6    0.324623\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "\n",
        "s.loc[:,[1,3,6]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NK7Ro3dmLzX6"
      },
      "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": 22,
      "metadata": {
        "collapsed": true,
        "id": "z-8DIfxoLzX6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3057a8d8-3929-48b1-a474-10fdc3c90720"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A  5    0.313495\n",
              "   6    0.049995\n",
              "   7    0.970759\n",
              "   8    0.978216\n",
              "   9    0.717606\n",
              "B  5    0.920511\n",
              "   6    0.703501\n",
              "   7    0.325224\n",
              "   8    0.481650\n",
              "   9    0.152603\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "s.loc[pd.IndexSlice[:'B', 5:]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jS4XBMsCLzX6"
      },
      "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": 23,
      "metadata": {
        "collapsed": true,
        "id": "bbh3-EeNLzX6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90cf96d9-138d-43ee-d924-1ab514ada5ae"
      },
      "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    4.908391\n",
              "B    4.591035\n",
              "C    4.454759\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "s.sum(level=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ak_1luz0LzX7"
      },
      "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": 24,
      "metadata": {
        "collapsed": true,
        "id": "1cZJ3beeLzX7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9c4a22a-414e-4dbe-de2c-a9f9472cdbef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1.011046\n",
              "1    0.640544\n",
              "2    1.547907\n",
              "3    0.923374\n",
              "4    1.456695\n",
              "5    2.021307\n",
              "6    1.078119\n",
              "7    1.387931\n",
              "8    2.147817\n",
              "9    1.739446\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "\n",
        "s.unstack().sum(axis=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R4VVqSYrLzX7"
      },
      "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": [],
      "metadata": {
        "id": "nPe3xzVB2_Ea"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_G2rbLTLzX7"
      },
      "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": "XfJ6dgLaLzX7"
      },
      "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",
      "execution_count": 28,
      "metadata": {
        "collapsed": true,
        "id": "b2_5nZRMLzX7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "b0c0bf82-6c60-44ff-cbaa-ce968b9f12f1"
      },
      "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-28-22788e4963e9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcartesian_product\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'y'\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[0;32m/usr/local/lib/python3.7/dist-packages/numpy/__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m             raise AttributeError(\"module {!r} has no attribute \"\n\u001b[0;32m--> 314\u001b[0;31m                                  \"{!r}\".format(__name__, attr))\n\u001b[0m\u001b[1;32m    315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'tools'"
          ]
        }
      ],
      "source": [
        "p = np.tools.util.cartesian_product([np.arange(X), np.arange(Y)])\n",
        "df = pd.DataFrame(np.asarray(p).T, columns=['x', 'y'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XmJjnijDLzX7"
      },
      "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": null,
      "metadata": {
        "collapsed": true,
        "id": "t3Mk2ohILzX8"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dXGBgWiSLzX8"
      },
      "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": "code",
      "execution_count": 29,
      "metadata": {
        "collapsed": true,
        "id": "3leiRpADLzX8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "3fbfa100-dc1e-42ed-c1de-0f9ae74cc51c"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\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   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'mine'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-29-bd29bca15cbf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mine'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'adjacent'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\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/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'mine'"
          ]
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HyJh0QCGLzX8"
      },
      "source": [
        "**54**. For rows of the DataFrame that contain a mine, set the value in the `'adjacent'` column to NaN."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "q_FhdufYLzX8"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZjpN5V1ELzX8"
      },
      "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": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "asieumk3LzX8"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b17jWfyhLzX8"
      },
      "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": 31,
      "metadata": {
        "collapsed": true,
        "id": "WARdMWifLzX9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "e1974255-5def-42c1-f7bc-96335908359b"
      },
      "outputs": [
        {
          "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",
        "df = pd.DataFrame({\"xs\":[1,5,2,8,1], \"ys\":[4,2,1,9,6]})\n",
        "plt.scatter(df['xs'], df['ys'], marker='x', color='green')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f-nn80ljLzX9"
      },
      "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": 36,
      "metadata": {
        "collapsed": true,
        "id": "B4ypRhTmLzX9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "5110697d-bac6-4567-a000-d343f99667e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee9cae5d90>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEJCAYAAAB11IfBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de3hU5Z0H8O+ZOXPNkMxMJoEmATEQpGCFR1cBgSyXUKUorVRx9cEVqYKAZRVLRbfdeKVRSEU0XHRdbdkK+pRWretlO1wWiixFgXItYCDAEiHJJJNkMpn72T94MhhzO2fuyXw/fyU5l/fLJPzmzHve876CJEkSiIioT1MlOwAREcUfiz0RURpgsSciSgMs9kREaYDFnogoDbDYExGlATHZAbpTXV0d0XE2mw11dXUxThMZvV4Pg8EAQRCg1Wrh8XjgcrkQCASSHS0slV6vb2IuZZhLmb6YKy8vr8ttKV3seytBENCvXz/odDqIoghBEMLbjEYj9Ho9/H4/Wltb4Xa7k5iUiNIFi32MqdVqWCwWaLXaLvdRqVTQ6XTQaDTQ6XRoaGhIYEIiSkfss48hlUoFq9XabaH/9v56vR4WiyXOyYgo3bHYx5DZbIZGo1F0jCAI0Ol0yMjIiFMqIiIW+5hRq9WKC30blUoFg8EQ40RERFckrM/+448/xtatWyFJEqZOnYoZM2YkqumE6NevH9RqdcTHt71Z+P3+GKYiIrosIVf2586dw9atW7FixQqsXLkS+/fvx8WLFxPRdMJEelXfRq1Ww2QyxSgNEVF7CSn2Fy5cwNChQ6HT6aBWq/Hd734Xe/fuTUTTCfPN4ZXJPAcRUWcS0o0zcOBAbN68Gc3NzdBqtThw4ACGDBnSYT+73Q673Q4AKCsrg81mi6g9URQjPjZSKlX075tarTbhuYHkvF5yMJcyzKVMuuUSErV4ybZt2/DZZ59Br9ejoKAAGo0Gc+fO7faY3vQEbW5uLkQxuvdOj8eD+vr6GCWSry8+SRhPzKUMcynT65+gnTJlCqZMmQIAeOedd5CdnZ2ophMiGAxGXex5c5aI4iVhQy8bGxsBAHV1dfjrX/+KCRMmJKrphGhtbUU0H5ICgQBcLlcMExERXZGwK/vy8nI0NzdDFEX85Cc/6XMPEbndbmRkZEQ8Ksfv90f1ZkFE1J2EFftnn302UU0ljdvthslkUjzePhAIoKmpKU6piIj4BG1MtbS0oLW1FcFgUPYxgUAATqdT0TFEREpx1ssYa2pqQjAYREZGBtRqdZdj50OhULjQp9Lc9kTUN7HYx0FLSwvcbjeMRiMMBkO4W0elUiEYDCIQCKClpQVerzfJSYkoXbDYx4kkSWhpaUFLSwsEQYBKpYLFYknJcb1E1Pexzz4BJElinzwRJRWLPRFRGmCxJyJKAyz2RERpgMWeiCgNsNgTEaUBFnsiojTAYk9ElAZY7ImI0kCfeoL2/Pnz+Pvf/468vDwMGTIEer0+2ZEAXF6D9/jx4+jfvz+GDh0Kg8GQ7EhElGb6RLE/ePAgXnjhBZw8eRJ1dXVQqVS4+uqrMW7cODz33HPQarVJyXX8+HE888wzOHHiBGpqaqBSqXDVVVfhxhtvxIoVK1j0iShhen2x37dvHxYvXowLFy6EfxYKhVBZWYnTp0/j7Nmz2LhxY8SLikTqyJEjeOihh3Du3Ll2uc6cOYMzZ87g7Nmz2LRpE3Q6XUJzEVF6Slif/UcffYSlS5fi8ccfx+rVq+Hz+aI+pyRJKC0tbVfov7199+7deP3116NuS6l//dd/bVfov23v3r14+eWXE5iIiNJZQop9fX09PvnkE5SVlaG8vByhUAiff/551Ofdu3cvKisru90nFArhk08+ibotJY4ePYpTp071uN/27du5FCERJUTCruxDoRB8Ph+CwSB8Ph8sFkvU59y2bZusRbpra2vh9/ujbk+uHTt2hBdY705dXR2XIySihEhIn73VasXtt9+OhQsXQqvVYtSoURg1alSH/ex2O+x2OwCgrKwMNput2/PKXbRcpVIhNzdX8dqwkerXr5+s/VQqFXJycpCZmRnnRN0TRbHH1zoZmEsZ5lIm3XIlpNi7XC7s27cPFRUVMBqN+PWvf42dO3eiuLi43X4lJSUoKSkJf9/TQh+TJ0/Ghg0beryKzs3NRUNDQ+T/AIXGjx+P7OxsOByObvfLycmB1+tN+oImNpst6Rk6w1zKMJcyfTFXXl5el9sS0o1z+PBh5ObmIjMzE6IoYsyYMTh58mTU573uuutQVFTU7T5arRazZs2Kui0lhgwZgmuuuabbfURRxPTp07tco5aIKJYSUuxtNhtOnToFr9cLSZJw+PBh5Ofnx+Tcq1atwtVXX93pNo1Gg6lTp+K+++6LSVtKlJeXY8iQIZ1uE0URxcXFWLRoUYJTEVG6Skg3TlFREcaOHYsnnngCarUagwcPbtddE+25f/e73+GZZ57BsWPH4HA4oNVqkZ+fj2nTpuHxxx+HSpX4WSEGDRqEzZs3o7S0FEeOHEFdXR00Gg3y8vIwefLk8GtBRJQIgpTCY/+qq6sV7d/U1ITz589jwIABsFgsSSnynXG5XDh37hxyc3NhsVhSrsj3xb7LeGIuZZhLmXj12ff6J2i/KTMzEyNHjky5X6LJZMKIESNSLhcRpY/UuPQlIqK4YrEnIkoDLPZERGmAxZ6IKA2w2BMRpQEWeyKiNMBiT0SUBljsiYjSAIs9EVEKUKvV0Gg0EAQBohj751371BO0RES9jdFohMFggCiKEAQBgiDAZrMhGAzC6/XC5XIhFApF3Q6LPRFREmi1WmRlZYWL/DcJggCVSgWNRgO9Xo/W1lY0NzdH1R6LPRFRgun1emRlZcmaFFEURWRkZEClUsla7rQr7LMnIkogURSRmZmpaPZblUoFg8EAk8kUcbss9kRECdS2Yp9SbQU/Uiz2REQJolKpohppI4oijEZjZMdG3KoC1dXVePnll8Pf19TUYPbs2ZgxY0YimiciSgkmkymqYi8IAoxGI9xut+JjE1Ls8/LysHLlSgBAKBTCggULcNNNNyWiaSKilBGL8fORrsCX8G6cw4cPY8CAAcjJyUl000RESfXtIZaJPE/C16Bdu3YtCgsLceutt3bYZrfbYbfbAQBlZWXw+XwRtSGKIgKBQFQ544G5lGEuZZhLmWTkEkUx6rWxQ6FQl7m1Wm2XxyW02AcCASxYsADl5eUwm8097q90wfE2qbrWK3Mpw1zKMJcyychlsViiGlEDAH6/H7W1tZ1u627B8YR24xw4cABXX321rEJPRNTXxGLqg0h7PBJa7Hfv3o3x48cnskkiopTh9/uj6joKBoMRT5uQsGLv8Xhw6NAhjBkzJlFNEhGlHLfbHfHVvc/ni/jYhM2No9fr8R//8R+Jao6IKCW53W5oNBoYjUZFo2p8Ph+cTmfE7XIiNCKiBGtsbIQkSTAYDD3OkSNJEvx+P+rr6xHNeBoWeyKiJGhqaoLX60VGRgY0Gk2Hoh8MBhEMBtHa2oqWlpao22OxJyJKEq/XC6/XC5VKhYyMDKjVauh0Ong8Hrjdbvj9/pi1xWJPRJRkoVAoPMrGZrNFNW99VzjrJRFRGmCxJyJKAyz2RERpgMWeiCgNsNgTEaUBFnsiojTAYk9ElAZkF/t9+/YhGAzGM0ufo1KpkJmZCZvNhpycHIiiiOzs7IgXDCYiipTsh6ree+89rF+/HjfffDOKi4tRVFQUz1y9XlZWFnQ6XYc1J3U6HbRaLTIyMsKPSxMRxZvsYr9y5UpUVVVh165dKC8vh06nQ3FxMSZOnIjc3Nx4Zux1zGYzDAZDlzPaCYIAjUYDs9mMpqYmtLa2JjghEaUbRdMlDB48GIMHD8acOXNw+PBhbNy4Ee+99x6GDx+OkpISjB8/Pur1FXu7jIwM6PV6WVOXqtVq9OvXD16vN+rVa4iIuqN4bpyLFy9i165d2LVrFwRBwN133w2bzYZPP/0Ue/fuxc9+9rNOj2tpacH69etx/vx5CIKAhQsXYtiwYVH/A1KNwWBQ9IYniiJMJhOamprimIqI0p3sYv/pp59i165d+Prrr3HzzTfjkUceaVesx4wZgwcffLDL49966y2MHj0ajz/+OAKBQJ/sq+5smlI5dDpdHNIQEV0hu9gfPHgQt912G/7hH/4BGo2mw3adTtflVb3b7cbx48exePHiy42KYocbl32BXq+PqNirVCqoVCp25RBR3AiSzKVPPvzwQ8ycObPDzz/66CPcdttt3R5bVVWFDRs2oKCgAGfPnkVhYSHmzp0LvV7fbj+73Q673Q4AKCsri3gVdVEUo1rUN1JqtTqiYt+2Ek2yJOv16glzKcNcyvTFXFqttuvzyj3Jli1bOi32W7Zs6bHYB4NBnDlzBvPmzUNRURHeeustvP/++/inf/qndvuVlJSgpKQk/H1dXZ3ceO3YbLaIj42GyWRCZmam4uNCoRDq6+uTdmWfrNerJ8ylDHMp0xdz5eXldbmtx2J/5MgRAJcLdtvXbS5dugSDwdBjgOzsbGRnZ4fH5o8dOxbvv/9+j8f1Nq2treHVZpQIBoPswiGiuOqx2K9btw4A4Pf7w18Dl8eKm81mzJs3r8dGzGYzsrOzUV1djby8PBw+fBgFBQVRxE5NwWAQfr9fUbGXJAlutzuOqYiIZBT7iooKAMBrr72GRx55JOKG5s2bhzVr1iAQCCA3NxeLFi2K+FypzOVyKRqV4/f7WeyJKO5k99lHU+iByw9klZWVRXWO3sDn88HlcsFkMvVY8AOBABoaGhKUjIjSWbfF/rHHHsPLL78MAFi4cGGX+32ze4cuP0AWDAbDBf/bRb+tu8fpdLKvnogSottiv2DBgvDXP/3pT+Mepi/xeDzweDzQaDQwmUwQBAFarRZutxsul4tFnogSqttiP3z48PDXBQUFEQ0rTHd+vz/cVWOz2TgtAhElhew++0WLFmHkyJEYP348brrppg4PRBERUeqSPWPX2rVrcf311+PPf/4z5s+fj9WrV+OLL77ggiZERL2A7Cv7zMxM3HLLLbjllltQW1uL3bt3Y/PmzVi3bh3efPPNeGYkIqIoRTT5fGNjI5xOJ5qbm5GRkRHrTEREFGOyr+z/7//+D3/5y1+we/du+Hw+jBs3DsuWLcPQoUPjmY+IiGJAdrH/5S9/iTFjxmD+/PkYOXJk2q9IRUTUm8gu9m+88UafnIOeiCgddFu9d+7cieLi4vDXXZkyZUpsUxERUUx1W+x3794dLva7du3qcr9UKfZqtRoqlQqCIEAQBMhcl4WIqM/rttg/+eST4a9LS0vjHiYSgiDAaDTCYDCEiz0A5OTkwO/3w+VyJXUVKCKiVCD7LuvPf/7zTn++fPnymIVRSqVSITs7G5mZmdBqtVCr1eGrelEUYTAYwtuJiFJdJMuayiX7juvFixc7/EySJFy6dCmmgeQSBAFWq7XbNReBy28IRqMRADgvDRGlpH79+kGn04V7J3Jzc+Hz+dDU1BSzSRN7LPavvfYagMtzr7d93aa2thYDBw6MSRClsrKyeiz0bVQqFQwGQ3jqYSKiVGGxWKDX6yEIQvhnoihCFEVoNBo4HI6YFPwei33//v07/VoQBFxzzTUYN26crIYWL14MvV4PlUoFtVod1UImbdMFK6FWq9GvXz84nc6I2yUiiqWMjIwOhf6bNBoNzGYz6uvro26rx2J/1113AQCKioowevToqBorLS2NSf95281YpTQaTdRtExHFisFg6LLQt2lb5jTaXgnZN2irqqrw1VdftfvZV199hQ8++CCqAJHQaDQ9vkCdaRuWSUSUbIIgyJqJQK1WQ6fTRd+eJHMw+vz587FmzZp289h7PB78y7/8CzZs2NDj8YsXL4bJZAIATJs2DSUlJR32sdvtsNvtAICysjL4fL5Oz9XZUn9yhEIhBAIBxcfFiiiKSW2/K8ylDHMpw1zdZ5BT8AOBgKx+++66t2WPxgkEAh2mSxBFscuC/G3PPfccrFYrGhsb8fzzzyMvLw8jRoxot09JSUm7N4G6urpOz2UymSLqDgqFQl2eMxFsNltS2+8KcynDXMowV/cZerr/GAqFUF9fL+uNKS8vr8ttsrtxCgsL8dlnn7X72X//93+jsLBQ1vFWqxXA5VE0N954Y4cuISVaWloiekfmw1VElEq8Xm+P+/j9/ph8ApF9ZX///ffj+eefx86dO9G/f39cunQJTqcTv/zlL3s81uPxQJIkGAwGeDweHDp0CHfeeWfEoSVJgt/vVzQxWygUgsvlirhNIqJYc7lc0Ol0XV7dBwIBNDY2xqQt2dVy4MCBeOWVV/Dll1/C4XBgzJgxuOGGG2StRdvY2IhVq1YBAILBICZMmBD1yJ6mpiZoNBpZBV+SJHi9Xl7ZE1FKkSQJDocj/NxQWz0LBoPhQh+r+wqK5izW6/UYP3684kb69++PlStXKj6uO8FgEA0NDTCbzd0OqWwr9A0NDTFtn4goFiRJgtPpDD8/lJmZifr6+pg/ACq72P/bv/1bl8MWn3nmmZgFUsLv98PhcCAzMzN8ld+WsW3kjdvthtvtTko+IiK52i5MJUmKy5P+sov9t6cxdjqd2L59OyZOnBjzUEqEQqHwU7F6vR5qtRoZGRmy714TEaUD2cV+0qRJHX42duxYrF27NqqbrbHk8XgAXH4qjYWeiOiKqBaStVqtOHv2bKyyEBFRnMi+st+2bVu7730+H/bu3Ythw4bFPBQREcWW7GL/7WUJdTodrrnmGsyYMSPmoYiIKLZkF/tUXZaQiIh61m2xl7sK1TfnuSciotTTbbFfsmSJrJO8++67MQlDRETx0W2x/2YR3759Ow4fPoy77roLOTk5qK2txe9//3t873vfi3tIih8506sSUe8n+3/6u+++i4cffhjf+c53IIoivvOd72D+/PnYvHlzPPNRHBiNRmRnZyM3Nxc5OTkQRRG5ubmyFnAnot5J9g1aSZJQU1ODgoKC8M9qa2tjtvI5xZ/BYIDJZGo3rUQblUoFURSh1WoRCATgdDqT8mCaIAjIyMgIL1BjNBrR2toKmWvsEFEXZBf7GTNm4Nlnn8WkSZPCk/7/z//8D4de9hIZGRkwmUw9rvClUqmg1WphtVrhdDplL04TLbVa3W6OozZZWVkwmUzw+XxobGxk0SeKkOxiP3PmTAwaNAh79uxBVVUVzGYzFi5cGPVUxRR/bVf0SpZyFEURZrMZDocjLpMyfbstq9Xa6XTVgiBAFEWIogiNRgOHw8FPk0QRUDTF8ejRo1nce6G2bhGlRFFEZmZmXKeHVqlUsFgsstYl0Gg0sFqtcDgcvMInUkjRGrR/+MMfsHPnTjQ0NMBisaC4uBizZs1StGIUJZZer4/q96PRaKBSqeJ2Nd2vX79u1yPoLI/RaERLS0tc8hD1VbKrwH/+53+isrISDz30UHjo5ZYtW+B2uzF37lxZ5wiFQli+fDmsViuWL18eaWZSwGg0RjW8UhRFZGRkoLm5OYaprlA6+kcQBBgMBhZ7IoVkV4H//d//xc9//nOMGjUKeXl5GDVqFH72s59hz549shv7+OOPkZ+fH1FQikwk3TffpuTKW4lIP3W0jdQhIvlkF/to+0gdDgf279+PqVOnRnUeUqar1cUSfY7OqNXqiM6tUqlY7IkUkn1ZNW7cOLz44ou48847w0Mvt2zZgnHjxsk6/u2338acOXPQ2tra5T52ux12ux0AUFZWBpvNJjdeO6IoRnxsPCUjV6yu7OORO5rupaysrKTdpOXflzLMpUy8csku9nPmzMGWLVvw5ptvoqGhAVarFTfffDN+/OMf93jsl19+iaysLBQWFuLo0aNd7ldSUoKSkpLw93V1dXLjtdP2ZpRqkpErJycn6m6YeC3Y3vZHrbToB4NB1NXVJa3Y8+9LGeZSJppceXl5XW6TVexDoRDWr1+PBQsW4O6771Yc4MSJE/jiiy9w4MAB+Hw+tLa2Ys2aNbInWqPI+Xy+qIp9KBTq9tNYNAKBAAKBgOKbtH6/n0MviRSSVexVKhUOHToUcd/tvffei3vvvRcAcPToUfzpT39ioU8Ql8sVXog9EoFAILy2bzy43W5oNBrZf1uhUAgulytueYj6Ktmfn2fMmIH33nuPC3n3MsFgEH6/P6JjJUmC1+uNcaL23G43PB6PrCv1UCgEt9sd8b+HKJ3J7rP/9NNP4XQ68V//9V/IzMxst23dunWyGxw5ciRGjhwpPyFFzel0Ijs7W3F3jtfrjdv4+m9qe0hPp9N12X8fDAbR2tqKpqamuOch6otkF/uf/vSn8cxBcRQKhVBfXw+LxSKrf7ztir6+vj4B6S5raGiARqOByWQKd+sIghD+ZNLc3Bz3OXqI+jLZxX7YsGHYsmULdu/eHb4Su/nmmzFr1qx45qMYCQaDcDgc6NevH3Q6XafTHAeDQQSDQXg8nqT0i/v9/vCoH5VKBavVmpKjJYh6I9nF/o033kB1dTUeeOCB8HQJf/zjH1FfX49FixbFMyPFiCRJ4W4Qg8EAvV4PQRCg1Wrh9XrR0tKSsCmNe8KZLYliS3ax37dvH1599VVkZGQAAAoKClBUVMTunV6qtbU1PKTSZrPFdWZLIko+2aNxzGZzh5EZPp8PFosl5qGIiCi2ZF/ZFxcXY8WKFbj11luRnZ0Nh8OBzz77DMXFxThy5Eh4v2uvvTYuQYmIKHKyi/2f//xnAMAf//jHDj9v2yYIAl577bUYxiMioliQXewrKirimYOIiOIo8mkHiYio1+B6gkS9WEtLC7Zu3YoLFy7AYDDAbDZj6tSp6NevX7KjUYphsSfqhU6cOIHVq1fj0KFDqKqqarftqquuwve+9z0sWbKEU5NQGIs9US8iSRJKS0vDDzR25uzZszh79iz+8pe/4Pbbb8eKFSuiWihGqVAoBLvdjo0bN6KpqQlqtRo6nQ4/+tGPMGvWrLgtc0ndY7En6kWWLVuGP/zhD7JmI3U6nXj33XfR0tKCNWvWxG15yW/aunUrXnzxRVRWVnaYGnvPnj1Yv3495s2bh/vuuy/uWag93qAl6iU2bNiADz/8UNG00z6fD5988gnWrFkTx2SXffDBB1i2bBmOHj3a6RoIfr8fJ0+eRFlZGYdoJwGLPVEvEAqFsGXLFrS0tCg+trW1FR9++GFcZw29cOECXnjhBVy6dKnHfZ1OJ15//XXs2bMnbnmoo4R04/h8PpSWliIQCCAYDGLs2LGYPXt2Ipom6hPsdjtOnz4d8fGnT5/Ghx9+iDvuuCOGqa749a9/jQsXLsje3+FwYO3atRg3blxc8lBHCbmy12g0KC0txcqVK/HSSy/h4MGDOHnyZCKaJuoTfvOb30S1FrDP58OmTZtimOgKr9eLL774QvFxx48fR21tbRwSUWcSUuwFQYBerwdwZc70RNwsIuornE5n1OdobGyMQZKOTp06herqasXHff3119ixY0fsA1GnEjYaJxQK4YknnsDFixdxyy23oKioqMM+drsddrsdAFBWVgabzRZRW6IoRnxsPDGXMsx1RSzm9w8Gg3HJrVarI16U3uv1Ju13nG5/Xwkr9iqVCitXrkRLSwtWrVqFc+fOYdCgQe32KSkpQUlJSfj7SFcpstlsKbnCEXMpw1xXxGKcvFqtjkvuYDAIvV4Pt9ut+FidTpe033Ff/PvKy8vrclvCR+NkZGRg5MiROHjwYKKbJuq1zGZz1OfIysqKQZKOioqKui0yXcnLy8OkSZNiH4g6lZBi39TUFB4y5vP5cOjQIeTn5yeiaaI+4f7774fBYIj4eK1Wi3vuuSeGia7Q6XS48cYbFR83fPhw5OTkxCERdSYh3TgNDQ2oqKhAKBSCJEkYN24cbrjhhkQ0TdQnlJSUYMiQIe0WClKisLAQM2fOjHGqKx577DHs3LlT9vDL7OxsLF68OG55qKOEFPurrroKL730UiKaIuqTVCoVfvzjH+PMmTOKH6wyGAyYOXMm1Gp1nNIB+fn5+MUvfoGnn366xwerzGYzFixYgLFjx8YtD3XEJ2iJeon58+fjhz/8YXgYsxxarRbTp0/HkiVL4pjsspkzZ2LVqlUYMWJEp11OGo0Gw4YNw5NPPsmr+iTgRGhEvchLL70Ek8mE3//+913OetnGYrGEZ71M1HMtU6ZMweTJk2G32/Hb3/4Wzc3N4Vkv77jjDtxxxx0QRZadZOCrTtSLCIKA0tJS3HPPPXjllVdw8OBBnD17FpIkhfe56qqrcN1112HJkiUYMWJEUjJOmzYN06ZNA5C6QxzTDYs9US80bNgwVFRUwO12Y9u2beGVqiwWCyZPngyTyZTsiJRiWOyJejGj0YjbbrsNAK+gqXu8QUtElAZY7ImI0gCLPRFRGmCxJyJKAyz2RERpgMWeiCgNsNgTEaUBFnsiojTAYk9ElAb4BC0RUZLp9XpkZGRArVZDpVLBZrPB6/XC5XK1m/coGiz2RERJlJWVBYPB0G6dYa1WC61WC51OB4fDEZOCn5BiX1dXh4qKCjidTgiCgJKSEvzgBz9IRNNERCnLYDB0KPTfpNVqYTab0dDQEHVbCSn2arUa9913HwoLC9Ha2orly5fjuuuuQ0FBQSKaJyJKSUajsctC30aj0UClUiEUCkXVVkJu0FosFhQWFgK4/E6Wn5/f48ILRER9mSAIspaKFEVR0epkXZ4n6jMoVFNTgzNnzmDo0KEdttntdtjtdgBAWVkZbDZbRG2IohjxsfHEXMowlzLMpUwq5Orpqr6NyWSC0WiMqi1BitWtXhk8Hg9KS0sxa9YsjBkzpsf9q6urI2onVef1Zi5lmEsZ5lImFXLl5ORAo9F0u08oFILD4YDf7+/xfHl5eV1uS9g4+0AggPLyckycOFFWoSci6ut8Pl+P+wQCAVmFvicJKfaSJGH9+vXIz88Pr6pDRJTumpqaui3kwWAQzc3NMWkrIX32J06cwM6dOzFo0CAsW7YMAHDPPffg+uuvT0TzREQpSZIkOBwOmM1maDSa8A3bUCiEYDCIpqYmeL3emLSVkGI/fPhwvPfee4loioioVwmFQqivr4darYbRaITRaITT6YxZkW/DuXGIiFJAW5dNMBiMeaEHWOyJiNICi+hK+F0AAAs8SURBVD0RURpgsSciSgMs9kREaYDFnogoDbDYExGlARZ7IqI0wGJPRJQGWOyJiNIAiz0RURrgguNEMgSDQezYsQMnT56EyWTCwIEDUVxcLHvxCaJkY7En6kZzczNWrlyJ3bt3o7KyMjwdrU6nw5AhQ/CP//iPWLp0adSrCBHFG4s9UReqq6sxd+5cHD16tMM2r9eLY8eO4dixY/j888+xceNGZGdnJyElkTz8DErUCZfLhQceeKDTQv9tf/vb3/DP//zP8Hg8CUhGFBkWe6JOvPrqqzhy5Ijs/f/2t7/hzTffjGMiougkpNivXbsWDz74IB5//PFENEcUlVAohO3btys6RpIkfPzxx5AkKU6piKKTkGI/adIkPPXUU4loiihq+/fvR2VlpeLjTp8+jZMnT8YhEVH0ElLsR4wYAZPJlIimiKJ2+vTpiPrfm5qacOHChTgkIopeSo3GsdvtsNvtAICysjLYbLaIziOKYsTHxhNzKZOsXFarNeJjbTZb0l5L/h6VSbdcKVXsS0pKUFJSEv6+rq4uovPYbLaIj40n5lImWbny8vKQmZmJpqYmRcdlZ2fDbDYn7bXk71GZvpgrLy+vy20cjUP0LSNGjMDQoUMVH1dUVIRBgwbFIRFR9FjsiTpx++23Q61Wy95fq9Xi7rvvjmMiougkpNivXr0av/jFL1BdXY2HH34Y27ZtS0SzRBGbN28exo4dK3v/CRMm4M4774xjIqLoJKTP/tFHH01EM0QxI4oi3n77bcybNw979+6Fz+frdD+dTocJEybg9ddf56RolNJS6gYtUSoxGo343e9+hw8++ADvvPMOKisr0djYCEEQkJWVhaKiItx///249dZbWegp5bHYE3VDrVZj1qxZmDVrFi5evIiLFy/CYrHAYDAgNzc32fGIZGOxJ5JpwIABGDBgQMoO2SPqDj97EhGlAUHizE1ERH1en7yyX758ebIjdIq5lGEuZZhLmXTL1SeLPRERtcdiT0SUBtRPP/3008kOEQ+FhYXJjtAp5lKGuZRhLmXSKRdv0BIRpQF24xARpQEWeyKiNNCnnqBdu3Yt9u/fj6ysLJSXlyc7DoDLC7BUVFTA6XRCEASUlJTgBz/4QbJjwefzobS0FIFAAMFgEGPHjsXs2bOTHSssFAph+fLlsFqtKTVEbvHixdDr9VCpVFCr1SgrK0t2JABAS0sL1q9fj/Pnz0MQBCxcuBDDhg1Laqbq6mq8/PLL4e9ramowe/ZszJgxI4mpLvvoo4+wbds2CIKAgQMHYtGiRdBqtcmOhY8//hhbt26FJEmYOnVqbF8rqQ85evSoVFlZKS1dujTZUcLq6+ulyspKSZIkye12S0uWLJHOnz+f5FSSFAqFpNbWVkmSJMnv90tPPvmkdOLEiSSnuuJPf/qTtHr1aulXv/pVsqO0s2jRIqmxsTHZMTp49dVXJbvdLknS5d+ny+VKcqL2gsGg9OCDD0o1NTXJjiI5HA5p0aJFktfrlSRJksrLy6Xt27cnN5QkSWfPnpWWLl0qeTweKRAISM8++6z09ddfx+z8faobJxUXNrdYLOE76waDAfn5+aivr09yKkAQBOj1egBAMBhEMBiEIAhJTnWZw+HA/v37MXXq1GRH6RXcbjeOHz+OKVOmALg8PXNGRkaSU7V3+PBhDBgwADk5OcmOAuDyJ0efz4dgMAifzweLxZLsSLhw4QKGDh0KnU4HtVqN7373u9i7d2/Mzt+nunFSXU1NDc6cORPRknfxEAqF8MQTT+DixYu45ZZbUFRUlOxIAIC3334bc+bMQWtra7KjdOqFF14AAEybNq3dmsnJUlNTg8zMTKxduxZnz55FYWEh5s6dG34zTwW7d+/G+PHjkx0DwOUF5W+//XYsXLgQWq0Wo0aNwqhRo5IdCwMHDsTmzZvR3NwMrVaLAwcOYMiQITE7f5+6sk9lHo8H5eXlmDt3LoxGY7LjAABUKhVWrlyJ9evXo7KyEufOnUt2JHz55ZfIyspK2fHPzz33HF588UU89dRT+Oyzz3Ds2LFkR0IwGMSZM2fw/e9/Hy+99BJ0Oh3ef//9ZMcKCwQC+PLLLxWt/BVPLpcL+/btQ0VFBTZs2ACPx4OdO3cmOxYKCgrwwx/+EM8//zxWrFiBwYMHx3SdBF7ZJ0AgEEB5eTkmTpyIMWPGJDtOBxkZGRg5ciQOHjyY9AWzT5w4gS+++AIHDhyAz+dDa2sr1qxZgyVLliQ1Vxur1QoAyMrKwo033oivvvoKI0aMSGqm7OxsZGdnhz+ZjR07NqWK/YEDB3D11VfDbDYnOwqAy11Kubm5yMzMBACMGTMGJ0+eRHFxcZKTAVOmTAl3x73zzjvIzs6O2bl5ZR9nkiRh/fr1yM/Px2233ZbsOGFNTU1oaWkBcHlkzqFDh5Cfn5/kVMC9996L9evXo6KiAo8++iiuvfbalCn0Ho8n3LXk8Xhw6NChpL85AoDZbEZ2djaqq6sBXC5mBQUFSU51RSp14QCAzWbDqVOn4PV6IUkSDh8+nBJ/+wDQ2NgI4PIovr/+9a+YMGFCzM7dp67sV69ejWPHjqG5uRkPP/wwZs+eHX6XTJYTJ05g586dGDRoEJYtWwYAuOeee3D99dcnNVdDQwMqKioQCoUgSRLGjRuHG264IamZUl1jYyNWrVoF4HLXyYQJEzB69Ogkp7ps3rx5WLNmDQKBAHJzc7Fo0aJkRwJw5U1x/vz5yY4SVlRUhLFjx+KJJ56AWq3G4MGDU+LeCwCUl5ejubkZoijiJz/5SUxvtHO6BCKiNMBuHCKiNMBiT0SUBljsiYjSAIs9EVEaYLEnIkoDLPZERGmAxZ76nMWLF+PQoUPJjtGp++67D5cuXUp2DEpDfeqhKqJUt3HjxmRHoDTFK3uiCASDwWRHIFKEV/bUJ1VVVeG3v/0tamtrMXr0aCxevBharRZ2ux0ffPABXC4Xhg8fjoceeghWqxU1NTV45JFHsGnTJqjVagDA008/jYkTJ2Lq1KnYsWMHtm7diiFDhmDnzp34/ve/j0mTJmHdunWoqqqCKIq49tpr8dhjj3Wba/bs2VizZg0GDBiAiooK6HQ61NbW4vjx4ygoKMCSJUswYMCARLxElGZ4ZU990p49e/DUU0+hoqIC586dw44dO3DkyBFs2rQJjz32GF5//XXk5OTglVdekX3OU6dOoX///njjjTcwa9YsbN68GaNGjcJbb72FdevWYfr06Ypzfv7557jrrrvw1ltvYcCAAdi8ebPicxDJwWJPfdL06dNhtVphMplwww03oKqqCrt27cLkyZNRWFgIjUaDe++9FydPnkRNTY2sc1osFkyfPh1qtRparRaiKKK2thYNDQ3QarUYPny44pw33XQThg4dCrVajQkTJqCqqkrxOYjkYLGnPumbc6drtVp4PB40NDTAZrOFf67X62EymWQvE/nNYwFgzpw5kCQJTz31FJYuXYpt27ZFlVOn08Hj8Sg+B5Ec7LOntGGxWFBXVxf+3uPxwOVywWq1hpfw83q94ZXEnE5nt+czm814+OGHAQB///vf8dxzz2HEiBHsc6eUxCt7Shvjx4/H9u3bUVVVBb/fj02bNmHo0KHhVYusVit27dqFUCiEbdu29Tgefs+ePXA4HAAQnnc8VRZtJ/o2XtlT2rjuuutw9913o7y8HC6XC9dccw0effTR8PYFCxbg3//937Fp0yZMmTIFw4YN6/Z8lZWVePvtt+F2u2E2m/HAAw+gf//+8f5nEEWEi5cQEaUBduMQEaUBduMQxdDx48exYsWKTrdxqgRKJnbjEBGlAXbjEBGlARZ7IqI0wGJPRJQGWOyJiNLA/wMOXXmxNw0gFQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "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*100 , c = df.caffienated*100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dEnIOMsqLzX9"
      },
      "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": 39,
      "metadata": {
        "collapsed": true,
        "id": "ZfdiwGlaLzX-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "f5295194-8b9b-4f73-8be5-1b2037c3b291"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee9cc7bd50>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "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],\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",
        "df\n",
        "ax = df.plot.bar(\"month\", \"revenue\", color = \"black\")\n",
        "df.plot.line(\"month\", \"advertising\", secondary_y = True, ax = ax)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CCTlUZpVLzX-"
      },
      "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": "veIG3WZULzX-"
      },
      "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": 41,
      "metadata": {
        "collapsed": true,
        "id": "mMMzJNx9LzX-"
      },
      "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": "8kkJ0JncLzX_"
      },
      "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": 43,
      "metadata": {
        "collapsed": true,
        "id": "-uIWB6mgLzX_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "4932c249-7833-4d01-ee97-e7eb5ddd5838"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           open        high         low       close  color\n",
              "time                                                                      \n",
              "2022-08-25 09:00:00  100.000000  100.579836   99.302593  100.232962  green\n",
              "2022-08-25 10:00:00  100.270889  103.559786  100.270889  101.793647  green\n",
              "2022-08-25 11:00:00  101.863890  104.275772  101.682745  102.337099  green\n",
              "2022-08-25 12:00:00  102.270398  106.266509  102.214871  105.366024  green\n",
              "2022-08-25 13:00:00  105.348741  106.989945  104.852771  105.983349  green"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-af908e20-e8a1-4008-850b-954fc9437de8\">\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>2022-08-25 09:00:00</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.579836</td>\n",
              "      <td>99.302593</td>\n",
              "      <td>100.232962</td>\n",
              "      <td>green</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-08-25 10:00:00</th>\n",
              "      <td>100.270889</td>\n",
              "      <td>103.559786</td>\n",
              "      <td>100.270889</td>\n",
              "      <td>101.793647</td>\n",
              "      <td>green</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-08-25 11:00:00</th>\n",
              "      <td>101.863890</td>\n",
              "      <td>104.275772</td>\n",
              "      <td>101.682745</td>\n",
              "      <td>102.337099</td>\n",
              "      <td>green</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-08-25 12:00:00</th>\n",
              "      <td>102.270398</td>\n",
              "      <td>106.266509</td>\n",
              "      <td>102.214871</td>\n",
              "      <td>105.366024</td>\n",
              "      <td>green</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-08-25 13:00:00</th>\n",
              "      <td>105.348741</td>\n",
              "      <td>106.989945</td>\n",
              "      <td>104.852771</td>\n",
              "      <td>105.983349</td>\n",
              "      <td>green</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-af908e20-e8a1-4008-850b-954fc9437de8')\"\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-af908e20-e8a1-4008-850b-954fc9437de8 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-af908e20-e8a1-4008-850b-954fc9437de8');\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": 43
        }
      ],
      "source": [
        "df = day_stock_data()\n",
        "df.head()\n",
        "\n",
        "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()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IqgQKdL6LzX_"
      },
      "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": 44,
      "metadata": {
        "collapsed": true,
        "id": "a6u27MXKLzX_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "94c0b69e-ddb9-4cb4-8d27-7b78de5ef723"
      },
      "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": "p35ELAwcLzX_"
      },
      "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": {
      "name": "Task-3 Pandas_tasks (DW21).ipynb",
      "provenance": [],
      "collapsed_sections": [
        "wJBYyPZwLzX1",
        "0ScwX-95LzX2",
        "fwey36nELzX4",
        "3NEFZAHKLzX5",
        "A_G2rbLTLzX7",
        "b17jWfyhLzX8"
      ]
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}