Martin_Lukan_Pandas_Pivot_tables.ipynb

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  "metadata": {
    "colab": {
      "name": "Martin Lukan  Pandas Pivot tables.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mlukan/GDA3B2021/blob/main/Martin_Lukan_Pandas_Pivot_tables.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vRqCcqj8ytV"
      },
      "source": [
        "# Execute the code below"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIvGKLrY7TkI"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "link = \"https://raw.githubusercontent.com/murpi/wilddata/master/quests/weather2019.csv\"\n",
        "df_weather = pd.read_csv(link)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B4h1onKe87Eh"
      },
      "source": [
        "# You have now a DataFrame with lots of weather data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRlt_-A088O9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "7b54543c-a360-40b7-ea55-b5e368fbed23"
      },
      "source": [
        "df_weather.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>MAX_TEMPERATURE_C</th>\n",
              "      <th>MIN_TEMPERATURE_C</th>\n",
              "      <th>WINDSPEED_MAX_KMH</th>\n",
              "      <th>TEMPERATURE_MORNING_C</th>\n",
              "      <th>TEMPERATURE_NOON_C</th>\n",
              "      <th>TEMPERATURE_EVENING_C</th>\n",
              "      <th>PRECIP_TOTAL_DAY_MM</th>\n",
              "      <th>HUMIDITY_MAX_PERCENT</th>\n",
              "      <th>VISIBILITY_AVG_KM</th>\n",
              "      <th>PRESSURE_MAX_MB</th>\n",
              "      <th>CLOUDCOVER_AVG_PERCENT</th>\n",
              "      <th>HEATINDEX_MAX_C</th>\n",
              "      <th>DEWPOINT_MAX_C</th>\n",
              "      <th>WINDTEMP_MAX_C</th>\n",
              "      <th>WEATHER_CODE_MORNING</th>\n",
              "      <th>WEATHER_CODE_NOON</th>\n",
              "      <th>WEATHER_CODE_EVENING</th>\n",
              "      <th>TOTAL_SNOW_MM</th>\n",
              "      <th>UV_INDEX</th>\n",
              "      <th>SUNHOUR</th>\n",
              "      <th>OPINION</th>\n",
              "      <th>MONTH</th>\n",
              "      <th>DAY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>94</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1037</td>\n",
              "      <td>62.250</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>116</td>\n",
              "      <td>143</td>\n",
              "      <td>176</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>very bad</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>18</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>90</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1040</td>\n",
              "      <td>58.125</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>119</td>\n",
              "      <td>116</td>\n",
              "      <td>116</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8.7</td>\n",
              "      <td>very bad</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>88</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1040</td>\n",
              "      <td>37.000</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>-4</td>\n",
              "      <td>116</td>\n",
              "      <td>116</td>\n",
              "      <td>116</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8.7</td>\n",
              "      <td>very bad</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>5</td>\n",
              "      <td>-1</td>\n",
              "      <td>15</td>\n",
              "      <td>-1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>91</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1040</td>\n",
              "      <td>42.625</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>-4</td>\n",
              "      <td>116</td>\n",
              "      <td>116</td>\n",
              "      <td>122</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>very bad</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>6</td>\n",
              "      <td>-1</td>\n",
              "      <td>8</td>\n",
              "      <td>-1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>91</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1040</td>\n",
              "      <td>17.250</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>-2</td>\n",
              "      <td>143</td>\n",
              "      <td>116</td>\n",
              "      <td>116</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8.7</td>\n",
              "      <td>very bad</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE  MAX_TEMPERATURE_C  MIN_TEMPERATURE_C  ...   OPINION  MONTH  DAY\n",
              "0  2019-01-01                  9                  4  ...  very bad      1    1\n",
              "1  2019-01-02                  8                  5  ...  very bad      1    2\n",
              "2  2019-01-03                  6                  0  ...  very bad      1    3\n",
              "3  2019-01-04                  5                 -1  ...  very bad      1    4\n",
              "4  2019-01-05                  6                 -1  ...  very bad      1    5\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xo876W3C9beL"
      },
      "source": [
        "# PIVOT TABLE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2-rtqpX9eCL"
      },
      "source": [
        "## It's like in Excel, but with Pandas\n",
        "\n",
        "There are 2 syntaxes, choose your preferred :\n",
        "- As a method : YOUR_DATAFRAME.pivot_table()\n",
        "- As a function : pd.crosstab()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OgFJ52E79ita"
      },
      "source": [
        "# Example\n",
        "df_weather.pivot_table(values = 'MAX_TEMPERATURE_C', index = 'MONTH', aggfunc = 'mean')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q6tAEF7M_dKg"
      },
      "source": [
        "## Create a Pivot table with :\n",
        "- Months on rows\n",
        "- the maximum of Max Temperature on values\n",
        "- Opinion on columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GPX5qzOE92p4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "34a9e258-06d6-4fc3-9771-4edaa124848a"
      },
      "source": [
        "df_weather.pivot_table(values = 'MAX_TEMPERATURE_C', index = 'MONTH',columns='OPINION', aggfunc = 'max')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>OPINION</th>\n",
              "      <th>bad</th>\n",
              "      <th>good</th>\n",
              "      <th>not good not bad</th>\n",
              "      <th>very bad</th>\n",
              "      <th>very good</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MONTH</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>1</th>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>18.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>19.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>23.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>31.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>24.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>21.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>20.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>17.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "OPINION   bad  good  not good not bad  very bad  very good\n",
              "MONTH                                                     \n",
              "1        11.0   NaN               NaN      10.0        NaN\n",
              "2        13.0  20.0              17.0      12.0        NaN\n",
              "3        14.0  19.0              17.0      12.0        NaN\n",
              "4        18.0  24.0              19.0      14.0        NaN\n",
              "5        19.0  25.0              20.0      15.0        NaN\n",
              "6        23.0  32.0              34.0      15.0       29.0\n",
              "7        31.0  33.0              39.0      22.0       33.0\n",
              "8        24.0  33.0              30.0       NaN       29.0\n",
              "9        21.0  31.0              20.0      20.0        NaN\n",
              "10       20.0  21.0              20.0      14.0        NaN\n",
              "11       17.0   NaN              13.0      14.0        NaN\n",
              "12       13.0   NaN              13.0      14.0        NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WsC3wPVZACMr"
      },
      "source": [
        "## Pivot table with :\n",
        "- Days on rows\n",
        "- Precipitation on values\n",
        "- Months on columns\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pz7aPcTiANpK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d9b2779f-8747-4775-926b-18f27bbb5ab3"
      },
      "source": [
        "df_weather.pivot_table(values = 'PRECIP_TOTAL_DAY_MM', index = 'DAY',columns='MONTH')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>MONTH</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DAY</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.2</td>\n",
              "      <td>7.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.7</td>\n",
              "      <td>14.5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.4</td>\n",
              "      <td>5.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>18.6</td>\n",
              "      <td>11.1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>6.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>1.1</td>\n",
              "      <td>4.2</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.1</td>\n",
              "      <td>10.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.0</td>\n",
              "      <td>9.4</td>\n",
              "      <td>47.6</td>\n",
              "      <td>6.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>7.5</td>\n",
              "      <td>4.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>27.3</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>4.3</td>\n",
              "      <td>11.4</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.2</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.6</td>\n",
              "      <td>17.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.9</td>\n",
              "      <td>3.2</td>\n",
              "      <td>9.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.0</td>\n",
              "      <td>6.3</td>\n",
              "      <td>5.3</td>\n",
              "      <td>0.8</td>\n",
              "      <td>4.4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.5</td>\n",
              "      <td>8.4</td>\n",
              "      <td>2.6</td>\n",
              "      <td>10.1</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.1</td>\n",
              "      <td>29.1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>2.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>7.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>5.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>18.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>11.2</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>6.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>12.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.1</td>\n",
              "      <td>10.3</td>\n",
              "      <td>16.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.1</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>4.4</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.6</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>7.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.1</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>9.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>8.5</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>7.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>9.9</td>\n",
              "      <td>3.8</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>18.9</td>\n",
              "      <td>1.6</td>\n",
              "      <td>14.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.1</td>\n",
              "      <td>9.5</td>\n",
              "      <td>2.7</td>\n",
              "      <td>16.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>4.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>4.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>8.6</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.7</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.8</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2.6</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.7</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>15.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>6.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>3.2</td>\n",
              "      <td>13.7</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>4.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "MONTH    1     2     3     4     5     6     7    8     9     10    11    12\n",
              "DAY                                                                         \n",
              "1       0.2   7.3   0.0   2.0   6.0   0.0   1.0  0.0   0.0   5.7  14.5   0.0\n",
              "2       0.0   1.0   4.9   1.8   0.4   0.0   0.0  0.0   0.0   0.2  11.0   0.0\n",
              "3       0.0   0.0   5.4   5.2   0.5   0.5   0.0  0.0   0.0   0.3  30.0   0.0\n",
              "4       0.0  18.6  11.1   1.1   0.6   6.3   0.0  0.0   0.3   1.1   4.2   0.0\n",
              "5       0.0   0.8   1.1  10.1   0.3   1.5   0.6  0.1   0.0   0.4   8.0   0.0\n",
              "6       0.0   9.4  47.6   6.1   0.0   2.7   7.5  4.4   0.0   1.5   1.1   0.3\n",
              "7       0.0   4.0   3.4   3.0   1.2  27.3   1.7  0.1   0.2   4.3  11.4   0.4\n",
              "8       0.2   1.6   0.2   1.6  17.8   3.4   0.0  0.0   0.0   3.9   3.2   9.1\n",
              "9       0.0   6.3   5.3   0.8   4.4   4.0   0.0  5.5   8.4   2.6  10.1   0.6\n",
              "10      0.1  29.1   1.2   1.9   1.7   2.7   0.0  0.2   7.6   0.5   2.1   0.9\n",
              "11      0.2   0.1   0.1   0.1   2.0   3.4   0.0  1.1   0.0   0.0   3.8   5.9\n",
              "12      0.0   0.0   4.2   0.0   0.0   5.1   0.2  2.4   0.0   0.0   1.8  18.2\n",
              "13      0.1   0.0   0.9   0.0   0.0   2.3   0.0  0.0   0.0   1.6   4.8   2.2\n",
              "14      0.1   0.0   0.5   2.3   0.0   0.0   0.0  0.8   0.0   2.7  11.2   4.8\n",
              "15      0.0   0.0   1.1   6.2   0.0   0.6   0.0  0.7   0.0   1.2   2.3   5.0\n",
              "16      1.3   0.0   0.5  12.3   0.0   0.3   0.0  0.0   0.0  13.1  10.3  16.1\n",
              "17      1.2   0.0   5.0   0.0   4.6   0.0   0.0  7.3   0.0   1.3   0.0   2.6\n",
              "18      0.1   0.9   1.1   0.0   6.0   0.0   0.0  9.9   0.0   5.3   0.0   0.0\n",
              "19      4.4   2.5   1.0   0.0   3.6   0.9   0.0  0.5   0.0   7.7   0.0  31.0\n",
              "20      0.2   0.0   0.6   1.6   7.0   0.0   2.1  0.0   0.0   1.2   0.0  35.1\n",
              "21      0.0   0.0   0.1   7.8   0.8   0.5   0.0  0.0   0.2   1.1  16.0  16.4\n",
              "22      9.8   0.0   0.0   6.0   1.8   0.0   0.0  0.0   6.9   1.5   8.5   3.0\n",
              "23      1.3   0.0   0.0   2.7   7.5   0.0   0.0  0.0   1.4   9.9   3.8   9.0\n",
              "24      2.4   0.0   0.1  18.9   1.6  14.1   0.0  0.0  24.1   9.5   2.7  16.7\n",
              "25      4.6   0.0   0.1   4.4   0.5   0.9   0.8  0.0   2.0   0.8   8.6   0.0\n",
              "26      2.1   0.0   0.0   2.0   0.5   0.0  16.3  0.0   0.5   0.0   9.7   0.2\n",
              "27      5.9   0.0   0.0   1.4   1.4   0.0   2.8  0.2   1.3   4.6   3.8   0.0\n",
              "28      2.6   0.9   0.0   0.4   1.2   0.0   0.0  0.0   1.1   1.7   1.9   0.0\n",
              "29     15.1   NaN   0.1   0.0   0.2   0.0   1.2  0.0   5.3   2.3   0.1   0.0\n",
              "30      2.0   NaN   2.5   0.0   0.0   0.7   6.1  0.0   0.8   3.2  13.7   0.0\n",
              "31      4.3   NaN   5.3   NaN   0.0   NaN   0.3  0.0   NaN   8.9   NaN   0.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yH9rvaARAM1k"
      },
      "source": [
        "## Pivot table with :\n",
        "- Months on rows\n",
        "- the sum of Precipitations on values\n",
        "\n",
        "**Then display it as a column chart.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sVA8CtR1AzIc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "b5b225b0-fde9-4705-ea1f-a6565ee5eff2"
      },
      "source": [
        "df_weather.pivot_table(values = 'PRECIP_TOTAL_DAY_MM', index = 'MONTH',aggfunc='sum').plot.bar()\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc3eb3b050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aEkcMiXOA07D"
      },
      "source": [
        "## Pivot table with :\n",
        "- Months on rows\n",
        "- the average of Max Temperature on a first column of values\n",
        "- the average of Sunhours on a second column of values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E84QfvQkBNNn"
      },
      "source": [
        "monthly_means=df_weather.pivot_table(values = ['MAX_TEMPERATURE_C','SUNHOUR'], index = 'MONTH',aggfunc='mean')"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQRj7LgUHrQO"
      },
      "source": [
        "# Chart\n",
        "Draw a chart from the previous pivot table with :\n",
        "- Temperature in a blue column chart\n",
        "- Sunhours in a red line plot\n",
        "\n",
        "Tip : Once your pivot table created, you can help yourself with the `.reset_index()`  method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cD-Bod_9IK_c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "d5bfcb78-9024-407a-d290-6eb5cbddcdf6"
      },
      "source": [
        "temp = monthly_means['MAX_TEMPERATURE_C'].plot.bar(color='blue',label='Inline_label')\n",
        "sunhour =monthly_means['SUNHOUR'].plot(secondary_y=True,color='red',label='Sun hour')\n",
        "temp.set_ylabel('Maximum temperature °C')\n",
        "sunhour.set_ylabel('Sun hour')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgqxrB8eFC_t"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}