Practice2.ipynb

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      "authorship_tag": "ABX9TyMJs8aRloUB3oGkqu/B2jCV",
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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/baepigyu/Machine-Learning-For-AI/blob/main/Practice2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 1000
        },
        "id": "4uSiDju5fLQ4",
        "outputId": "7d508dff-f165-41e2-d554-d515017b5f4f"
      },
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-10-16 13:21:17--  https://raw.githubusercontent.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/master/ch_03/data/bitcoin.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 48089 (47K) [text/plain]\n",
            "Saving to: ‘bitcoin.csv’\n",
            "\n",
            "\rbitcoin.csv           0%[                    ]       0  --.-KB/s               \rbitcoin.csv         100%[===================>]  46.96K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2024-10-16 13:21:17 (3.91 MB/s) - ‘bitcoin.csv’ saved [48089/48089]\n",
            "\n",
            "--2024-10-16 13:21:17--  https://raw.githubusercontent.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/master/ch_03/data/dirty_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 46422 (45K) [text/plain]\n",
            "Saving to: ‘dirty_data.csv’\n",
            "\n",
            "dirty_data.csv      100%[===================>]  45.33K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2024-10-16 13:21:17 (3.90 MB/s) - ‘dirty_data.csv’ saved [46422/46422]\n",
            "\n",
            "--2024-10-16 13:21:17--  https://raw.githubusercontent.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/master/ch_03/data/long_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5494 (5.4K) [text/plain]\n",
            "Saving to: ‘long_data.csv’\n",
            "\n",
            "long_data.csv       100%[===================>]   5.37K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-10-16 13:21:17 (56.6 MB/s) - ‘long_data.csv’ saved [5494/5494]\n",
            "\n",
            "--2024-10-16 13:21:17--  https://raw.githubusercontent.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/master/ch_03/data/nyc_temperatures.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5412 (5.3K) [text/plain]\n",
            "Saving to: ‘nyc_temperatures.csv’\n",
            "\n",
            "nyc_temperatures.cs 100%[===================>]   5.29K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-10-16 13:21:18 (64.1 MB/s) - ‘nyc_temperatures.csv’ saved [5412/5412]\n",
            "\n",
            "--2024-10-16 13:21:18--  https://raw.githubusercontent.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/master/ch_03/data/sp500.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 53010 (52K) [text/plain]\n",
            "Saving to: ‘sp500.csv’\n",
            "\n",
            "sp500.csv           100%[===================>]  51.77K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2024-10-16 13:21:18 (4.07 MB/s) - ‘sp500.csv’ saved [53010/53010]\n",
            "\n",
            "--2024-10-16 13:21:18--  https://raw.githubusercontent.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/master/ch_03/data/wide_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 794 [text/plain]\n",
            "Saving to: ‘wide_data.csv’\n",
            "\n",
            "wide_data.csv       100%[===================>]     794  --.-KB/s    in 0s      \n",
            "\n",
            "2024-10-16 13:21:18 (44.1 MB/s) - ‘wide_data.csv’ saved [794/794]\n",
            "\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "read_csv() missing 1 required positional argument: 'filepath_or_buffer'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-99ed467b17ba>\u001b[0m in \u001b[0;36m<cell line: 21>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mlong_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: read_csv() missing 1 required positional argument: 'filepath_or_buffer'"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "!wget https://raw.githubusercontent.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/master/ch_03/data/bitcoin.csv\n",
        "\n",
        "!wget https://raw.githubusercontent.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/master/ch_03/data/dirty_data.csv\n",
        "\n",
        "!wget https://raw.githubusercontent.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/master/ch_03/data/long_data.csv\n",
        "\n",
        "!wget https://raw.githubusercontent.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/master/ch_03/data/nyc_temperatures.csv\n",
        "\n",
        "!wget https://raw.githubusercontent.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/master/ch_03/data/sp500.csv\n",
        "\n",
        "!wget https://raw.githubusercontent.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/master/ch_03/data/wide_data.csv\n",
        "\n",
        "!mkdir data\n",
        "\n",
        "!mv *.csv data\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "long_df = pd.read_csv('data/long_data.csv',usecols=['date','datatype','value']\n",
        "                      ).rename(\n",
        "                          columns ={'value':'temp_C'}\n",
        ").assign(date=lambda x: pd.to_datetime(x.date),\n",
        "         temp_F=lambda x:(x.temp_C*9/5) + 32)\n",
        "long_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ENhtRzxGfTAH",
        "outputId": "67334b40-af50-4dca-83de-a9043319142e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  datatype       date  temp_C  temp_F\n",
              "0     TMAX 2018-10-01    21.1   69.98\n",
              "1     TMIN 2018-10-01     8.9   48.02\n",
              "2     TOBS 2018-10-01    13.9   57.02\n",
              "3     TMAX 2018-10-02    23.9   75.02\n",
              "4     TMIN 2018-10-02    13.9   57.02"
            ],
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              "summary": "{\n  \"name\": \"long_df\",\n  \"rows\": 93,\n  \"fields\": [\n    {\n      \"column\": \"datatype\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"TMAX\",\n          \"TMIN\",\n          \"TOBS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-10-01 00:00:00\",\n        \"max\": \"2018-10-31 00:00:00\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"2018-10-28 00:00:00\",\n          \"2018-10-16 00:00:00\",\n          \"2018-10-24 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp_C\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.362354016345017,\n        \"min\": -1.1,\n        \"max\": 26.7,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          6.7,\n          13.3,\n          22.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp_F\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.252237229421025,\n        \"min\": 30.02,\n        \"max\": 80.06,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          44.06,\n          55.94,\n          73.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
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        "long_df.set_index('date').head(6)"
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              "           datatype  temp_C  temp_F\n",
              "date                               \n",
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              "2018-10-01     TMIN     8.9   48.02\n",
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              "summary": "{\n  \"name\": \"long_df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-10-01 00:00:00\",\n        \"max\": \"2018-10-02 00:00:00\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"2018-10-02 00:00:00\",\n          \"2018-10-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datatype\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"TMAX\",\n          \"TMIN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp_C\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.434856637176979,\n        \"min\": 8.9,\n        \"max\": 23.9,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8.9,\n          17.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp_F\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.782741946918561,\n        \"min\": 48.02,\n        \"max\": 75.02,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          48.02,\n          62.959999999999994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pivoted_df = long_df.pivot(\n",
        "    index='date', columns = 'datatype', values = 'temp_C'\n",
        ")\n",
        "pivoted_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
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        "id": "fduOtLR_gA76",
        "outputId": "72b7ccff-237f-406e-c09b-da7e9ac2020f"
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "datatype    TMAX  TMIN  TOBS\n",
              "date                        \n",
              "2018-10-01  21.1   8.9  13.9\n",
              "2018-10-02  23.9  13.9  17.2\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-46e50106-19b2-4f37-b051-f1b640dc5d36');\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",
              "\n",
              "\n",
              "<div id=\"df-56848b6a-4302-4795-84bf-5bee407b1344\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-56848b6a-4302-4795-84bf-5bee407b1344')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-56848b6a-4302-4795-84bf-5bee407b1344 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pivoted_df",
              "summary": "{\n  \"name\": \"pivoted_df\",\n  \"rows\": 31,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-10-01 00:00:00\",\n        \"max\": \"2018-10-31 00:00:00\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"2018-10-28 00:00:00\",\n          \"2018-10-16 00:00:00\",\n          \"2018-10-24 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TMAX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.714962440727387,\n        \"min\": 7.8,\n        \"max\": 26.7,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          16.7,\n          26.1,\n          21.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TMIN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.513251999800349,\n        \"min\": -1.1,\n        \"max\": 17.8,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          8.9,\n          2.8,\n          -1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TOBS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5965500921526825,\n        \"min\": -1.1,\n        \"max\": 21.7,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          13.9,\n          7.8,\n          21.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pivoted_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "qdEPV-4lgSja",
        "outputId": "5f67fed3-e1d1-4dd1-def2-886e4e6d1b30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datatype       TMAX       TMIN       TOBS\n",
              "count     31.000000  31.000000  31.000000\n",
              "mean      16.829032   7.561290  10.022581\n",
              "std        5.714962   6.513252   6.596550\n",
              "min        7.800000  -1.100000  -1.100000\n",
              "25%       12.750000   2.500000   5.550000\n",
              "50%       16.100000   6.700000   8.300000\n",
              "75%       21.950000  13.600000  16.100000\n",
              "max       26.700000  17.800000  21.700000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4715ad44-fbf4-4c49-8bed-9c67a4108709\" 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>datatype</th>\n",
              "      <th>TMAX</th>\n",
              "      <th>TMIN</th>\n",
              "      <th>TOBS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>31.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>16.829032</td>\n",
              "      <td>7.561290</td>\n",
              "      <td>10.022581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.714962</td>\n",
              "      <td>6.513252</td>\n",
              "      <td>6.596550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>7.800000</td>\n",
              "      <td>-1.100000</td>\n",
              "      <td>-1.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12.750000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>5.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>16.100000</td>\n",
              "      <td>6.700000</td>\n",
              "      <td>8.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>21.950000</td>\n",
              "      <td>13.600000</td>\n",
              "      <td>16.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>26.700000</td>\n",
              "      <td>17.800000</td>\n",
              "      <td>21.700000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4715ad44-fbf4-4c49-8bed-9c67a4108709')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-4715ad44-fbf4-4c49-8bed-9c67a4108709 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-4715ad44-fbf4-4c49-8bed-9c67a4108709');\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",
              "\n",
              "\n",
              "<div id=\"df-c627c7fc-2f55-4d27-a3c2-266c8527354f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c627c7fc-2f55-4d27-a3c2-266c8527354f')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c627c7fc-2f55-4d27-a3c2-266c8527354f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"pivoted_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"TMAX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.819420651948674,\n        \"min\": 5.714962440727387,\n        \"max\": 31.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          16.829032258064515,\n          16.1,\n          31.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TMIN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.146349884319612,\n        \"min\": -1.1,\n        \"max\": 31.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.561290322580645,\n          6.7,\n          31.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TOBS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.22127904487259,\n        \"min\": -1.1,\n        \"max\": 31.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.02258064516129,\n          8.3,\n          31.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pivoted_df = long_df.pivot(\n",
        "    index='date', columns='datatype', values = ['temp_C','temp_F']\n",
        ")\n",
        "pivoted_df.head()"
      ],
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      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "           temp_C             temp_F              \n",
              "datatype     TMAX  TMIN  TOBS   TMAX   TMIN   TOBS\n",
              "date                                              \n",
              "2018-10-01   21.1   8.9  13.9  69.98  48.02  57.02\n",
              "2018-10-02   23.9  13.9  17.2  75.02  57.02  62.96\n",
              "2018-10-03   25.0  15.6  16.1  77.00  60.08  60.98\n",
              "2018-10-04   22.8  11.7  11.7  73.04  53.06  53.06\n",
              "2018-10-05   23.3  11.7  18.9  73.94  53.06  66.02"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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              "summary": "{\n  \"name\": \"pivoted_df\",\n  \"rows\": 31,\n  \"fields\": [\n    {\n      \"column\": [\n        \"date\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-10-01 00:00:00\",\n        \"max\": \"2018-10-31 00:00:00\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"2018-10-28 00:00:00\",\n          \"2018-10-16 00:00:00\",\n          \"2018-10-24 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_C\",\n        \"TMAX\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.714962440727387,\n        \"min\": 7.8,\n        \"max\": 26.7,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          16.7,\n          26.1,\n          21.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_C\",\n        \"TMIN\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.513251999800349,\n        \"min\": -1.1,\n        \"max\": 17.8,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          8.9,\n          2.8,\n          -1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_C\",\n        \"TOBS\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5965500921526825,\n        \"min\": -1.1,\n        \"max\": 21.7,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          13.9,\n          7.8,\n          21.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_F\",\n        \"TMAX\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.286932393309298,\n        \"min\": 46.04,\n        \"max\": 80.06,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          62.059999999999995,\n          78.98,\n          69.98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_F\",\n        \"TMIN\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.723853599640625,\n        \"min\": 30.02,\n        \"max\": 64.04,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          48.02,\n          37.04,\n          30.02\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_F\",\n        \"TOBS\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.87379016587483,\n        \"min\": 30.02,\n        \"max\": 71.06,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          57.02,\n          46.04,\n          71.06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pivoted_df['temp_F']['TMIN']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BialNYsjg4h-",
        "outputId": "15802824-85d6-417f-fdaf-a2c46780c562"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date\n",
              "2018-10-01    48.02\n",
              "2018-10-02    57.02\n",
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              "2018-10-04    53.06\n",
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              "      <td>55.94</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-07</th>\n",
              "      <td>60.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-08</th>\n",
              "      <td>64.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-09</th>\n",
              "      <td>62.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-10</th>\n",
              "      <td>62.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-11</th>\n",
              "      <td>64.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-12</th>\n",
              "      <td>57.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-13</th>\n",
              "      <td>44.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-14</th>\n",
              "      <td>42.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-15</th>\n",
              "      <td>44.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-16</th>\n",
              "      <td>46.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-17</th>\n",
              "      <td>37.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-18</th>\n",
              "      <td>39.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-19</th>\n",
              "      <td>30.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-20</th>\n",
              "      <td>30.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-21</th>\n",
              "      <td>46.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-22</th>\n",
              "      <td>30.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-23</th>\n",
              "      <td>30.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-24</th>\n",
              "      <td>39.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-25</th>\n",
              "      <td>37.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-26</th>\n",
              "      <td>30.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-27</th>\n",
              "      <td>30.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-28</th>\n",
              "      <td>41.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-29</th>\n",
              "      <td>44.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-30</th>\n",
              "      <td>35.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-31</th>\n",
              "      <td>32.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "multi_index_df = long_df.set_index(['date','datatype'])\n",
        "multi_index_df.head().index"
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultiIndex([('2018-10-01', 'TMAX'),\n",
              "            ('2018-10-01', 'TMIN'),\n",
              "            ('2018-10-01', 'TOBS'),\n",
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              "            ('2018-10-02', 'TMIN')],\n",
              "           names=['date', 'datatype'])"
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          "metadata": {},
          "execution_count": 12
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      "execution_count": null,
      "outputs": [
        {
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              "                     temp_C  temp_F\n",
              "date       datatype                \n",
              "2018-10-01 TMAX        21.1   69.98\n",
              "           TMIN         8.9   48.02\n",
              "           TOBS        13.9   57.02\n",
              "2018-10-02 TMAX        23.9   75.02\n",
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              "variable_name": "multi_index_df",
              "summary": "{\n  \"name\": \"multi_index_df\",\n  \"rows\": 93,\n  \"fields\": [\n    {\n      \"column\": \"temp_C\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.362354016345017,\n        \"min\": -1.1,\n        \"max\": 26.7,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          6.7,\n          13.3,\n          22.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp_F\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.252237229421025,\n        \"min\": 30.02,\n        \"max\": 80.06,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          44.06,\n          55.94,\n          73.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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    {
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      "source": [
        "unstacked_df = multi_index_df.unstack()\n",
        "unstacked_df.head()"
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        "colab": {
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          "height": 269
        },
        "id": "cnakzaUiiFE3",
        "outputId": "778de394-693b-4431-e553-beeafbc46f60"
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "           temp_C             temp_F              \n",
              "datatype     TMAX  TMIN  TOBS   TMAX   TMIN   TOBS\n",
              "date                                              \n",
              "2018-10-01   21.1   8.9  13.9  69.98  48.02  57.02\n",
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              "      <td>16.1</td>\n",
              "      <td>77.00</td>\n",
              "      <td>60.08</td>\n",
              "      <td>60.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-04</th>\n",
              "      <td>22.8</td>\n",
              "      <td>11.7</td>\n",
              "      <td>11.7</td>\n",
              "      <td>73.04</td>\n",
              "      <td>53.06</td>\n",
              "      <td>53.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-05</th>\n",
              "      <td>23.3</td>\n",
              "      <td>11.7</td>\n",
              "      <td>18.9</td>\n",
              "      <td>73.94</td>\n",
              "      <td>53.06</td>\n",
              "      <td>66.02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "    border-radius: 50%;\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "unstacked_df",
              "summary": "{\n  \"name\": \"unstacked_df\",\n  \"rows\": 31,\n  \"fields\": [\n    {\n      \"column\": [\n        \"date\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-10-01 00:00:00\",\n        \"max\": \"2018-10-31 00:00:00\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"2018-10-28 00:00:00\",\n          \"2018-10-16 00:00:00\",\n          \"2018-10-24 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_C\",\n        \"TMAX\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.714962440727387,\n        \"min\": 7.8,\n        \"max\": 26.7,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          16.7,\n          26.1,\n          21.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_C\",\n        \"TMIN\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.513251999800349,\n        \"min\": -1.1,\n        \"max\": 17.8,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          8.9,\n          2.8,\n          -1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_C\",\n        \"TOBS\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5965500921526825,\n        \"min\": -1.1,\n        \"max\": 21.7,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          13.9,\n          7.8,\n          21.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_F\",\n        \"TMAX\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.286932393309298,\n        \"min\": 46.04,\n        \"max\": 80.06,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          62.059999999999995,\n          78.98,\n          69.98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_F\",\n        \"TMIN\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.723853599640625,\n        \"min\": 30.02,\n        \"max\": 64.04,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          48.02,\n          37.04,\n          30.02\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_F\",\n        \"TOBS\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.87379016587483,\n        \"min\": 30.02,\n        \"max\": 71.06,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          57.02,\n          46.04,\n          71.06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "extra_data = long_df.append([{\n",
        "    'datatype': 'TAVG',\n",
        "    'date': '2018-10-01',\n",
        "    'temp_C': 10,\n",
        "    'temp_F': 50\n",
        "}]).set_index(['date', 'datatype']).sort_index()\n",
        "\n",
        "extra_data['2018-10-01':'2018-10-02']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "oLm3VpYGiK9c",
        "outputId": "6d027e1d-4447-4f9a-8682-a8870660cd9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'DataFrame' object has no attribute 'append'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-515a4e4ec9fd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m extra_data = long_df.append([{\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0;34m'datatype'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'TAVG'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;34m'date'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'2018-10-01'\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[0;34m'temp_C'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;34m'temp_F'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   6297\u001b[0m         ):\n\u001b[1;32m   6298\u001b[0m             \u001b[0;32mreturn\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6299\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6301\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'append'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wide_df = pd.read_csv('data/wide_data.csv')\n",
        "wide_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fvG_rmzEjAHP",
        "outputId": "dc9b23e2-2b39-43a5-ca2b-858eb97f6223"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date  TMAX  TMIN  TOBS\n",
              "0  2018-10-01  21.1   8.9  13.9\n",
              "1  2018-10-02  23.9  13.9  17.2\n",
              "2  2018-10-03  25.0  15.6  16.1\n",
              "3  2018-10-04  22.8  11.7  11.7\n",
              "4  2018-10-05  23.3  11.7  18.9"
            ],
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              "      <td>2018-10-02</td>\n",
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              "      <td>17.2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-10-03</td>\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-94dc8c8c-9640-4962-818e-1d31bf7af9c7 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-94dc8c8c-9640-4962-818e-1d31bf7af9c7');\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",
              "\n",
              "\n",
              "<div id=\"df-7d6ca7cf-4b94-485f-9b2b-4fd9cded18e6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7d6ca7cf-4b94-485f-9b2b-4fd9cded18e6')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7d6ca7cf-4b94-485f-9b2b-4fd9cded18e6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "wide_df",
              "summary": "{\n  \"name\": \"wide_df\",\n  \"rows\": 31,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"2018-10-28\",\n          \"2018-10-16\",\n          \"2018-10-24\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TMAX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.714962440727387,\n        \"min\": 7.8,\n        \"max\": 26.7,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          16.7,\n          26.1,\n          21.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TMIN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.513251999800349,\n        \"min\": -1.1,\n        \"max\": 17.8,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          8.9,\n          2.8,\n          -1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TOBS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5965500921526825,\n        \"min\": -1.1,\n        \"max\": 21.7,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          13.9,\n          7.8,\n          21.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "melted_df = wide_df.melt(\n",
        "    id_vars = 'date',\n",
        "    value_vars=['TMAX','TMIN','TOBS'],\n",
        "    value_name='temp_C',\n",
        "    var_name='measurement'\n",
        ")\n",
        "melted_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "W_rBqRxOjUrx",
        "outputId": "be259229-b1c6-4316-b3de-d98676310751"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date measurement  temp_C\n",
              "0  2018-10-01        TMAX    21.1\n",
              "1  2018-10-02        TMAX    23.9\n",
              "2  2018-10-03        TMAX    25.0\n",
              "3  2018-10-04        TMAX    22.8\n",
              "4  2018-10-05        TMAX    23.3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bf825404-ea54-4777-9849-e0f6d5e98b97\" 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>date</th>\n",
              "      <th>measurement</th>\n",
              "      <th>temp_C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-10-01</td>\n",
              "      <td>TMAX</td>\n",
              "      <td>21.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-10-02</td>\n",
              "      <td>TMAX</td>\n",
              "      <td>23.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-10-03</td>\n",
              "      <td>TMAX</td>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-10-04</td>\n",
              "      <td>TMAX</td>\n",
              "      <td>22.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-10-05</td>\n",
              "      <td>TMAX</td>\n",
              "      <td>23.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf825404-ea54-4777-9849-e0f6d5e98b97')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-bf825404-ea54-4777-9849-e0f6d5e98b97 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-bf825404-ea54-4777-9849-e0f6d5e98b97');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-13706bc5-6f06-41be-ae87-48996972b774\">\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-13706bc5-6f06-41be-ae87-48996972b774 button');\n",
              "      quickchartButtonEl.style.display =\n",
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          "metadata": {},
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        "wide_df.set_index('date',inplace=True)\n",
        "wide_df.head()"
      ],
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            "text/plain": [
              "            TMAX  TMIN  TOBS\n",
              "date                        \n",
              "2018-10-01  21.1   8.9  13.9\n",
              "2018-10-02  23.9  13.9  17.2\n",
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              "variable_name": "wide_df",
              "summary": "{\n  \"name\": \"wide_df\",\n  \"rows\": 31,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"2018-10-28\",\n          \"2018-10-16\",\n          \"2018-10-24\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TMAX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.714962440727387,\n        \"min\": 7.8,\n        \"max\": 26.7,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          16.7,\n          26.1,\n          21.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TMIN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.513251999800349,\n        \"min\": -1.1,\n        \"max\": 17.8,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          8.9,\n          2.8,\n          -1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TOBS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5965500921526825,\n        \"min\": -1.1,\n        \"max\": 21.7,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          13.9,\n          7.8,\n          21.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stacked_series = wide_df.stack()\n",
        "stacked_series.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 272
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      "execution_count": null,
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              "date            \n",
              "2018-10-01  TMAX    21.1\n",
              "            TMIN     8.9\n",
              "            TOBS    13.9\n",
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    {
      "cell_type": "code",
      "source": [
        "stacked_series.to_frame('values')\n",
        "stacked_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "DhsHv73PkIPg",
        "outputId": "2f4c989c-8af4-480a-b042-f1716d5d57b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'stacked_df' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-c8de7ce563ca>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mstacked_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'values'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstacked_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'stacked_df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "long_df = pd.read_csv(\n",
        "    'data/long_data.csv',usecols = ['date','datatype','value']\n",
        ").rename(columns={'value':'temp_C'}).assign(\n",
        "    date=lambda x : pd.to_datetime(x.date),\n",
        "    temp_F = lambda x: (x.temp_C* 9/5) + 32\n",
        ")\n",
        "long_df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9xGHexVikQAp",
        "outputId": "8dc433b4-90b2-4b40-82ac-96226c8bc9e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  datatype       date  temp_C  temp_F\n",
              "0     TMAX 2018-10-01    21.1   69.98\n",
              "1     TMIN 2018-10-01     8.9   48.02\n",
              "2     TOBS 2018-10-01    13.9   57.02\n",
              "3     TMAX 2018-10-02    23.9   75.02\n",
              "4     TMIN 2018-10-02    13.9   57.02"
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              "      <th>0</th>\n",
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              "      <td>57.02</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "long_df",
              "summary": "{\n  \"name\": \"long_df\",\n  \"rows\": 93,\n  \"fields\": [\n    {\n      \"column\": \"datatype\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"TMAX\",\n          \"TMIN\",\n          \"TOBS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-10-01 00:00:00\",\n        \"max\": \"2018-10-31 00:00:00\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"2018-10-28 00:00:00\",\n          \"2018-10-16 00:00:00\",\n          \"2018-10-24 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp_C\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.362354016345017,\n        \"min\": -1.1,\n        \"max\": 26.7,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          6.7,\n          13.3,\n          22.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp_F\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.252237229421025,\n        \"min\": 30.02,\n        \"max\": 80.06,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          44.06,\n          55.94,\n          73.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pivoted_df = long_df.pivot(\n",
        "    index='date', columns = 'datatype',values=['temp_C','temp_F']\n",
        ")\n",
        "pivoted_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lZwFBvUlmHWs",
        "outputId": "6bcb7562-045d-480e-a99f-0d83abd2be44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           temp_C             temp_F              \n",
              "datatype     TMAX  TMIN  TOBS   TMAX   TMIN   TOBS\n",
              "date                                              \n",
              "2018-10-01   21.1   8.9  13.9  69.98  48.02  57.02\n",
              "2018-10-02   23.9  13.9  17.2  75.02  57.02  62.96\n",
              "2018-10-03   25.0  15.6  16.1  77.00  60.08  60.98\n",
              "2018-10-04   22.8  11.7  11.7  73.04  53.06  53.06\n",
              "2018-10-05   23.3  11.7  18.9  73.94  53.06  66.02\n",
              "2018-10-06   20.0  13.3  16.1  68.00  55.94  60.98\n",
              "2018-10-07   20.0  16.1  20.0  68.00  60.98  68.00\n",
              "2018-10-08   26.7  17.8  17.8  80.06  64.04  64.04\n",
              "2018-10-09   18.9  17.2  17.8  66.02  62.96  64.04\n",
              "2018-10-10   24.4  17.2  18.3  75.92  62.96  64.94\n",
              "2018-10-11   26.1  17.8  21.7  78.98  64.04  71.06\n",
              "2018-10-12   22.8  14.4  15.6  73.04  57.92  60.08\n",
              "2018-10-13   15.6   7.2   8.3  60.08  44.96  46.94\n",
              "2018-10-14   13.3   5.6   6.7  55.94  42.08  44.06\n",
              "2018-10-15   13.3   6.7  10.0  55.94  44.06  50.00\n",
              "2018-10-16   18.9   7.8   7.8  66.02  46.04  46.04\n",
              "2018-10-17   13.3   3.3   5.0  55.94  37.94  41.00\n",
              "2018-10-18   16.1   4.4   5.0  60.98  39.92  41.00\n",
              "2018-10-19   10.0  -1.1   0.0  50.00  30.02  32.00\n",
              "2018-10-20   15.0  -0.6  10.6  59.00  30.92  51.08\n",
              "2018-10-21   16.7   7.8   7.8  62.06  46.04  46.04\n",
              "2018-10-22    7.8  -1.1  -1.1  46.04  30.02  30.02\n",
              "2018-10-23   15.6  -1.1  10.0  60.08  30.02  50.00\n",
              "2018-10-24   16.7   4.4   6.7  62.06  39.92  44.06\n",
              "2018-10-25   11.7   2.8   2.8  53.06  37.04  37.04\n",
              "2018-10-26    9.4  -0.6  -0.6  48.92  30.92  30.92\n",
              "2018-10-27    8.9  -0.6   6.1  48.02  30.92  42.98\n",
              "2018-10-28    8.3   5.0   7.2  46.94  41.00  44.96\n",
              "2018-10-29   10.6   6.7   8.3  51.08  44.06  46.94\n",
              "2018-10-30   13.3   2.2   5.0  55.94  35.96  41.00\n",
              "2018-10-31   12.2   0.0   0.0  53.96  32.00  32.00"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">temp_C</th>\n",
              "      <th colspan=\"3\" halign=\"left\">temp_F</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datatype</th>\n",
              "      <th>TMAX</th>\n",
              "      <th>TMIN</th>\n",
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              "      <th>TMAX</th>\n",
              "      <th>TMIN</th>\n",
              "      <th>TOBS</th>\n",
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              "    <tr>\n",
              "      <th>date</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-10-01</th>\n",
              "      <td>21.1</td>\n",
              "      <td>8.9</td>\n",
              "      <td>13.9</td>\n",
              "      <td>69.98</td>\n",
              "      <td>48.02</td>\n",
              "      <td>57.02</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-02</th>\n",
              "      <td>23.9</td>\n",
              "      <td>13.9</td>\n",
              "      <td>17.2</td>\n",
              "      <td>75.02</td>\n",
              "      <td>57.02</td>\n",
              "      <td>62.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-03</th>\n",
              "      <td>25.0</td>\n",
              "      <td>15.6</td>\n",
              "      <td>16.1</td>\n",
              "      <td>77.00</td>\n",
              "      <td>60.08</td>\n",
              "      <td>60.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-04</th>\n",
              "      <td>22.8</td>\n",
              "      <td>11.7</td>\n",
              "      <td>11.7</td>\n",
              "      <td>73.04</td>\n",
              "      <td>53.06</td>\n",
              "      <td>53.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-05</th>\n",
              "      <td>23.3</td>\n",
              "      <td>11.7</td>\n",
              "      <td>18.9</td>\n",
              "      <td>73.94</td>\n",
              "      <td>53.06</td>\n",
              "      <td>66.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-06</th>\n",
              "      <td>20.0</td>\n",
              "      <td>13.3</td>\n",
              "      <td>16.1</td>\n",
              "      <td>68.00</td>\n",
              "      <td>55.94</td>\n",
              "      <td>60.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-07</th>\n",
              "      <td>20.0</td>\n",
              "      <td>16.1</td>\n",
              "      <td>20.0</td>\n",
              "      <td>68.00</td>\n",
              "      <td>60.98</td>\n",
              "      <td>68.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-08</th>\n",
              "      <td>26.7</td>\n",
              "      <td>17.8</td>\n",
              "      <td>17.8</td>\n",
              "      <td>80.06</td>\n",
              "      <td>64.04</td>\n",
              "      <td>64.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-09</th>\n",
              "      <td>18.9</td>\n",
              "      <td>17.2</td>\n",
              "      <td>17.8</td>\n",
              "      <td>66.02</td>\n",
              "      <td>62.96</td>\n",
              "      <td>64.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-10</th>\n",
              "      <td>24.4</td>\n",
              "      <td>17.2</td>\n",
              "      <td>18.3</td>\n",
              "      <td>75.92</td>\n",
              "      <td>62.96</td>\n",
              "      <td>64.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-11</th>\n",
              "      <td>26.1</td>\n",
              "      <td>17.8</td>\n",
              "      <td>21.7</td>\n",
              "      <td>78.98</td>\n",
              "      <td>64.04</td>\n",
              "      <td>71.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-12</th>\n",
              "      <td>22.8</td>\n",
              "      <td>14.4</td>\n",
              "      <td>15.6</td>\n",
              "      <td>73.04</td>\n",
              "      <td>57.92</td>\n",
              "      <td>60.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-13</th>\n",
              "      <td>15.6</td>\n",
              "      <td>7.2</td>\n",
              "      <td>8.3</td>\n",
              "      <td>60.08</td>\n",
              "      <td>44.96</td>\n",
              "      <td>46.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-14</th>\n",
              "      <td>13.3</td>\n",
              "      <td>5.6</td>\n",
              "      <td>6.7</td>\n",
              "      <td>55.94</td>\n",
              "      <td>42.08</td>\n",
              "      <td>44.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-15</th>\n",
              "      <td>13.3</td>\n",
              "      <td>6.7</td>\n",
              "      <td>10.0</td>\n",
              "      <td>55.94</td>\n",
              "      <td>44.06</td>\n",
              "      <td>50.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-16</th>\n",
              "      <td>18.9</td>\n",
              "      <td>7.8</td>\n",
              "      <td>7.8</td>\n",
              "      <td>66.02</td>\n",
              "      <td>46.04</td>\n",
              "      <td>46.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-17</th>\n",
              "      <td>13.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>55.94</td>\n",
              "      <td>37.94</td>\n",
              "      <td>41.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-18</th>\n",
              "      <td>16.1</td>\n",
              "      <td>4.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>60.98</td>\n",
              "      <td>39.92</td>\n",
              "      <td>41.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-19</th>\n",
              "      <td>10.0</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.00</td>\n",
              "      <td>30.02</td>\n",
              "      <td>32.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-20</th>\n",
              "      <td>15.0</td>\n",
              "      <td>-0.6</td>\n",
              "      <td>10.6</td>\n",
              "      <td>59.00</td>\n",
              "      <td>30.92</td>\n",
              "      <td>51.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-21</th>\n",
              "      <td>16.7</td>\n",
              "      <td>7.8</td>\n",
              "      <td>7.8</td>\n",
              "      <td>62.06</td>\n",
              "      <td>46.04</td>\n",
              "      <td>46.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-22</th>\n",
              "      <td>7.8</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>46.04</td>\n",
              "      <td>30.02</td>\n",
              "      <td>30.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-23</th>\n",
              "      <td>15.6</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>10.0</td>\n",
              "      <td>60.08</td>\n",
              "      <td>30.02</td>\n",
              "      <td>50.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-24</th>\n",
              "      <td>16.7</td>\n",
              "      <td>4.4</td>\n",
              "      <td>6.7</td>\n",
              "      <td>62.06</td>\n",
              "      <td>39.92</td>\n",
              "      <td>44.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-25</th>\n",
              "      <td>11.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>2.8</td>\n",
              "      <td>53.06</td>\n",
              "      <td>37.04</td>\n",
              "      <td>37.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-26</th>\n",
              "      <td>9.4</td>\n",
              "      <td>-0.6</td>\n",
              "      <td>-0.6</td>\n",
              "      <td>48.92</td>\n",
              "      <td>30.92</td>\n",
              "      <td>30.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-27</th>\n",
              "      <td>8.9</td>\n",
              "      <td>-0.6</td>\n",
              "      <td>6.1</td>\n",
              "      <td>48.02</td>\n",
              "      <td>30.92</td>\n",
              "      <td>42.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-28</th>\n",
              "      <td>8.3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.2</td>\n",
              "      <td>46.94</td>\n",
              "      <td>41.00</td>\n",
              "      <td>44.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-29</th>\n",
              "      <td>10.6</td>\n",
              "      <td>6.7</td>\n",
              "      <td>8.3</td>\n",
              "      <td>51.08</td>\n",
              "      <td>44.06</td>\n",
              "      <td>46.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-30</th>\n",
              "      <td>13.3</td>\n",
              "      <td>2.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>55.94</td>\n",
              "      <td>35.96</td>\n",
              "      <td>41.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-31</th>\n",
              "      <td>12.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>53.96</td>\n",
              "      <td>32.00</td>\n",
              "      <td>32.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "summary": "{\n  \"name\": \"pivoted_df\",\n  \"rows\": 31,\n  \"fields\": [\n    {\n      \"column\": [\n        \"date\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-10-01 00:00:00\",\n        \"max\": \"2018-10-31 00:00:00\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"2018-10-28 00:00:00\",\n          \"2018-10-16 00:00:00\",\n          \"2018-10-24 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_C\",\n        \"TMAX\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.714962440727387,\n        \"min\": 7.8,\n        \"max\": 26.7,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          16.7,\n          26.1,\n          21.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_C\",\n        \"TMIN\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.513251999800349,\n        \"min\": -1.1,\n        \"max\": 17.8,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          8.9,\n          2.8,\n          -1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_C\",\n        \"TOBS\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5965500921526825,\n        \"min\": -1.1,\n        \"max\": 21.7,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          13.9,\n          7.8,\n          21.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_F\",\n        \"TMAX\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.286932393309298,\n        \"min\": 46.04,\n        \"max\": 80.06,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          62.059999999999995,\n          78.98,\n          69.98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_F\",\n        \"TMIN\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.723853599640625,\n        \"min\": 30.02,\n        \"max\": 64.04,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          48.02,\n          37.04,\n          30.02\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"temp_F\",\n        \"TOBS\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.87379016587483,\n        \"min\": 30.02,\n        \"max\": 71.06,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          57.02,\n          46.04,\n          71.06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pivoted_df['temp_F']['TMAX']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sEkAO1dbn4gH",
        "outputId": "7b5c9c00-a710-4ab6-b157-990568a60f92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date\n",
              "2018-10-01    69.98\n",
              "2018-10-02    75.02\n",
              "2018-10-03    77.00\n",
              "2018-10-04    73.04\n",
              "2018-10-05    73.94\n",
              "2018-10-06    68.00\n",
              "2018-10-07    68.00\n",
              "2018-10-08    80.06\n",
              "2018-10-09    66.02\n",
              "2018-10-10    75.92\n",
              "2018-10-11    78.98\n",
              "2018-10-12    73.04\n",
              "2018-10-13    60.08\n",
              "2018-10-14    55.94\n",
              "2018-10-15    55.94\n",
              "2018-10-16    66.02\n",
              "2018-10-17    55.94\n",
              "2018-10-18    60.98\n",
              "2018-10-19    50.00\n",
              "2018-10-20    59.00\n",
              "2018-10-21    62.06\n",
              "2018-10-22    46.04\n",
              "2018-10-23    60.08\n",
              "2018-10-24    62.06\n",
              "2018-10-25    53.06\n",
              "2018-10-26    48.92\n",
              "2018-10-27    48.02\n",
              "2018-10-28    46.94\n",
              "2018-10-29    51.08\n",
              "2018-10-30    55.94\n",
              "2018-10-31    53.96\n",
              "Name: TMAX, dtype: float64"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TMAX</th>\n",
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              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
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              "      <th>2018-10-01</th>\n",
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              "      <th>2018-10-02</th>\n",
              "      <td>75.02</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-03</th>\n",
              "      <td>77.00</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-04</th>\n",
              "      <td>73.04</td>\n",
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              "      <th>2018-10-05</th>\n",
              "      <td>73.94</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-06</th>\n",
              "      <td>68.00</td>\n",
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              "      <th>2018-10-07</th>\n",
              "      <td>68.00</td>\n",
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              "      <th>2018-10-08</th>\n",
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              "      <th>2018-10-09</th>\n",
              "      <td>66.02</td>\n",
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              "      <th>2018-10-10</th>\n",
              "      <td>75.92</td>\n",
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              "      <td>78.98</td>\n",
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              "      <th>2018-10-12</th>\n",
              "      <td>73.04</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-13</th>\n",
              "      <td>60.08</td>\n",
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              "      <th>2018-10-14</th>\n",
              "      <td>55.94</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-15</th>\n",
              "      <td>55.94</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-16</th>\n",
              "      <td>66.02</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-17</th>\n",
              "      <td>55.94</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-18</th>\n",
              "      <td>60.98</td>\n",
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              "      <th>2018-10-19</th>\n",
              "      <td>50.00</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-20</th>\n",
              "      <td>59.00</td>\n",
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              "      <th>2018-10-21</th>\n",
              "      <td>62.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-22</th>\n",
              "      <td>46.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-23</th>\n",
              "      <td>60.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-24</th>\n",
              "      <td>62.06</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-25</th>\n",
              "      <td>53.06</td>\n",
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              "      <th>2018-10-26</th>\n",
              "      <td>48.92</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-27</th>\n",
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              "      <th>2018-10-28</th>\n",
              "      <td>46.94</td>\n",
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              "      <th>2018-10-29</th>\n",
              "      <td>51.08</td>\n",
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              "      <td>53.96</td>\n",
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              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
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          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "multi_index_df = long_df.set_index(['date','datatype'])\n",
        "multi_index_df.head().index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BL6CU_asoOqx",
        "outputId": "3da2ac0e-5df2-41b5-c68d-ac6a35078d23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultiIndex([('2018-10-01', 'TMAX'),\n",
              "            ('2018-10-01', 'TMIN'),\n",
              "            ('2018-10-01', 'TOBS'),\n",
              "            ('2018-10-02', 'TMAX'),\n",
              "            ('2018-10-02', 'TMIN')],\n",
              "           names=['date', 'datatype'])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "multi_index_df.unstack()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NohD2_GCoX6f",
        "outputId": "accfba24-c01e-46c0-add4-7116535367cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           temp_C             temp_F              \n",
              "datatype     TMAX  TMIN  TOBS   TMAX   TMIN   TOBS\n",
              "date                                              \n",
              "2018-10-01   21.1   8.9  13.9  69.98  48.02  57.02\n",
              "2018-10-02   23.9  13.9  17.2  75.02  57.02  62.96\n",
              "2018-10-03   25.0  15.6  16.1  77.00  60.08  60.98\n",
              "2018-10-04   22.8  11.7  11.7  73.04  53.06  53.06\n",
              "2018-10-05   23.3  11.7  18.9  73.94  53.06  66.02\n",
              "2018-10-06   20.0  13.3  16.1  68.00  55.94  60.98\n",
              "2018-10-07   20.0  16.1  20.0  68.00  60.98  68.00\n",
              "2018-10-08   26.7  17.8  17.8  80.06  64.04  64.04\n",
              "2018-10-09   18.9  17.2  17.8  66.02  62.96  64.04\n",
              "2018-10-10   24.4  17.2  18.3  75.92  62.96  64.94\n",
              "2018-10-11   26.1  17.8  21.7  78.98  64.04  71.06\n",
              "2018-10-12   22.8  14.4  15.6  73.04  57.92  60.08\n",
              "2018-10-13   15.6   7.2   8.3  60.08  44.96  46.94\n",
              "2018-10-14   13.3   5.6   6.7  55.94  42.08  44.06\n",
              "2018-10-15   13.3   6.7  10.0  55.94  44.06  50.00\n",
              "2018-10-16   18.9   7.8   7.8  66.02  46.04  46.04\n",
              "2018-10-17   13.3   3.3   5.0  55.94  37.94  41.00\n",
              "2018-10-18   16.1   4.4   5.0  60.98  39.92  41.00\n",
              "2018-10-19   10.0  -1.1   0.0  50.00  30.02  32.00\n",
              "2018-10-20   15.0  -0.6  10.6  59.00  30.92  51.08\n",
              "2018-10-21   16.7   7.8   7.8  62.06  46.04  46.04\n",
              "2018-10-22    7.8  -1.1  -1.1  46.04  30.02  30.02\n",
              "2018-10-23   15.6  -1.1  10.0  60.08  30.02  50.00\n",
              "2018-10-24   16.7   4.4   6.7  62.06  39.92  44.06\n",
              "2018-10-25   11.7   2.8   2.8  53.06  37.04  37.04\n",
              "2018-10-26    9.4  -0.6  -0.6  48.92  30.92  30.92\n",
              "2018-10-27    8.9  -0.6   6.1  48.02  30.92  42.98\n",
              "2018-10-28    8.3   5.0   7.2  46.94  41.00  44.96\n",
              "2018-10-29   10.6   6.7   8.3  51.08  44.06  46.94\n",
              "2018-10-30   13.3   2.2   5.0  55.94  35.96  41.00\n",
              "2018-10-31   12.2   0.0   0.0  53.96  32.00  32.00"
            ],
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              "\n",
              "  <div id=\"df-4df39c7a-98ed-4874-89b3-5a129378dd9f\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">temp_C</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>datatype</th>\n",
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              "      <th>TMAX</th>\n",
              "      <th>TMIN</th>\n",
              "      <th>TOBS</th>\n",
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              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></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>2018-10-01</th>\n",
              "      <td>21.1</td>\n",
              "      <td>8.9</td>\n",
              "      <td>13.9</td>\n",
              "      <td>69.98</td>\n",
              "      <td>48.02</td>\n",
              "      <td>57.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-02</th>\n",
              "      <td>23.9</td>\n",
              "      <td>13.9</td>\n",
              "      <td>17.2</td>\n",
              "      <td>75.02</td>\n",
              "      <td>57.02</td>\n",
              "      <td>62.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-03</th>\n",
              "      <td>25.0</td>\n",
              "      <td>15.6</td>\n",
              "      <td>16.1</td>\n",
              "      <td>77.00</td>\n",
              "      <td>60.08</td>\n",
              "      <td>60.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-04</th>\n",
              "      <td>22.8</td>\n",
              "      <td>11.7</td>\n",
              "      <td>11.7</td>\n",
              "      <td>73.04</td>\n",
              "      <td>53.06</td>\n",
              "      <td>53.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-05</th>\n",
              "      <td>23.3</td>\n",
              "      <td>11.7</td>\n",
              "      <td>18.9</td>\n",
              "      <td>73.94</td>\n",
              "      <td>53.06</td>\n",
              "      <td>66.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-06</th>\n",
              "      <td>20.0</td>\n",
              "      <td>13.3</td>\n",
              "      <td>16.1</td>\n",
              "      <td>68.00</td>\n",
              "      <td>55.94</td>\n",
              "      <td>60.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-07</th>\n",
              "      <td>20.0</td>\n",
              "      <td>16.1</td>\n",
              "      <td>20.0</td>\n",
              "      <td>68.00</td>\n",
              "      <td>60.98</td>\n",
              "      <td>68.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-08</th>\n",
              "      <td>26.7</td>\n",
              "      <td>17.8</td>\n",
              "      <td>17.8</td>\n",
              "      <td>80.06</td>\n",
              "      <td>64.04</td>\n",
              "      <td>64.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-09</th>\n",
              "      <td>18.9</td>\n",
              "      <td>17.2</td>\n",
              "      <td>17.8</td>\n",
              "      <td>66.02</td>\n",
              "      <td>62.96</td>\n",
              "      <td>64.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-10</th>\n",
              "      <td>24.4</td>\n",
              "      <td>17.2</td>\n",
              "      <td>18.3</td>\n",
              "      <td>75.92</td>\n",
              "      <td>62.96</td>\n",
              "      <td>64.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-11</th>\n",
              "      <td>26.1</td>\n",
              "      <td>17.8</td>\n",
              "      <td>21.7</td>\n",
              "      <td>78.98</td>\n",
              "      <td>64.04</td>\n",
              "      <td>71.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-12</th>\n",
              "      <td>22.8</td>\n",
              "      <td>14.4</td>\n",
              "      <td>15.6</td>\n",
              "      <td>73.04</td>\n",
              "      <td>57.92</td>\n",
              "      <td>60.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-13</th>\n",
              "      <td>15.6</td>\n",
              "      <td>7.2</td>\n",
              "      <td>8.3</td>\n",
              "      <td>60.08</td>\n",
              "      <td>44.96</td>\n",
              "      <td>46.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-14</th>\n",
              "      <td>13.3</td>\n",
              "      <td>5.6</td>\n",
              "      <td>6.7</td>\n",
              "      <td>55.94</td>\n",
              "      <td>42.08</td>\n",
              "      <td>44.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-15</th>\n",
              "      <td>13.3</td>\n",
              "      <td>6.7</td>\n",
              "      <td>10.0</td>\n",
              "      <td>55.94</td>\n",
              "      <td>44.06</td>\n",
              "      <td>50.00</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-16</th>\n",
              "      <td>18.9</td>\n",
              "      <td>7.8</td>\n",
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              "      <td>66.02</td>\n",
              "      <td>46.04</td>\n",
              "      <td>46.04</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-17</th>\n",
              "      <td>13.3</td>\n",
              "      <td>3.3</td>\n",
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              "      <td>37.94</td>\n",
              "      <td>41.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-18</th>\n",
              "      <td>16.1</td>\n",
              "      <td>4.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>60.98</td>\n",
              "      <td>39.92</td>\n",
              "      <td>41.00</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-19</th>\n",
              "      <td>10.0</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.00</td>\n",
              "      <td>30.02</td>\n",
              "      <td>32.00</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-20</th>\n",
              "      <td>15.0</td>\n",
              "      <td>-0.6</td>\n",
              "      <td>10.6</td>\n",
              "      <td>59.00</td>\n",
              "      <td>30.92</td>\n",
              "      <td>51.08</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-21</th>\n",
              "      <td>16.7</td>\n",
              "      <td>7.8</td>\n",
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              "      <td>62.06</td>\n",
              "      <td>46.04</td>\n",
              "      <td>46.04</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-22</th>\n",
              "      <td>7.8</td>\n",
              "      <td>-1.1</td>\n",
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              "      <td>46.04</td>\n",
              "      <td>30.02</td>\n",
              "      <td>30.02</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-23</th>\n",
              "      <td>15.6</td>\n",
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              "      <td>60.08</td>\n",
              "      <td>30.02</td>\n",
              "      <td>50.00</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-24</th>\n",
              "      <td>16.7</td>\n",
              "      <td>4.4</td>\n",
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              "      <td>62.06</td>\n",
              "      <td>39.92</td>\n",
              "      <td>44.06</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-25</th>\n",
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              "      <td>2.8</td>\n",
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              "      <td>53.06</td>\n",
              "      <td>37.04</td>\n",
              "      <td>37.04</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-26</th>\n",
              "      <td>9.4</td>\n",
              "      <td>-0.6</td>\n",
              "      <td>-0.6</td>\n",
              "      <td>48.92</td>\n",
              "      <td>30.92</td>\n",
              "      <td>30.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-27</th>\n",
              "      <td>8.9</td>\n",
              "      <td>-0.6</td>\n",
              "      <td>6.1</td>\n",
              "      <td>48.02</td>\n",
              "      <td>30.92</td>\n",
              "      <td>42.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-28</th>\n",
              "      <td>8.3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.2</td>\n",
              "      <td>46.94</td>\n",
              "      <td>41.00</td>\n",
              "      <td>44.96</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-29</th>\n",
              "      <td>10.6</td>\n",
              "      <td>6.7</td>\n",
              "      <td>8.3</td>\n",
              "      <td>51.08</td>\n",
              "      <td>44.06</td>\n",
              "      <td>46.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-30</th>\n",
              "      <td>13.3</td>\n",
              "      <td>2.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>55.94</td>\n",
              "      <td>35.96</td>\n",
              "      <td>41.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-31</th>\n",
              "      <td>12.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>53.96</td>\n",
              "      <td>32.00</td>\n",
              "      <td>32.00</td>\n",
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            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wide_df = pd.read_csv('data/wide_data.csv')\n",
        "wide_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RqIRqsopoeo1",
        "outputId": "8f62c31f-72a8-4ecf-f3d4-5287214d9af0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date  TMAX  TMIN  TOBS\n",
              "0  2018-10-01  21.1   8.9  13.9\n",
              "1  2018-10-02  23.9  13.9  17.2\n",
              "2  2018-10-03  25.0  15.6  16.1\n",
              "3  2018-10-04  22.8  11.7  11.7\n",
              "4  2018-10-05  23.3  11.7  18.9"
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              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-4141eeef-2aed-4d61-89da-ef67ee171f03 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-4141eeef-2aed-4d61-89da-ef67ee171f03');\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",
              "\n",
              "\n",
              "<div id=\"df-11feef90-4985-4aa5-b47c-194564bb6df7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-11feef90-4985-4aa5-b47c-194564bb6df7')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-11feef90-4985-4aa5-b47c-194564bb6df7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "wide_df",
              "summary": "{\n  \"name\": \"wide_df\",\n  \"rows\": 31,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"2018-10-28\",\n          \"2018-10-16\",\n          \"2018-10-24\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TMAX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.714962440727387,\n        \"min\": 7.8,\n        \"max\": 26.7,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          16.7,\n          26.1,\n          21.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TMIN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.513251999800349,\n        \"min\": -1.1,\n        \"max\": 17.8,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          8.9,\n          2.8,\n          -1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TOBS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5965500921526825,\n        \"min\": -1.1,\n        \"max\": 21.7,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          13.9,\n          7.8,\n          21.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wide_df.melt(\n",
        "    id_vars = 'date',\n",
        "    value_vars = ['TMAX','TMIN','TOBS'],\n",
        "    value_name = 'temp_C',\n",
        "    var_name = 'measurement'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ZYqsD3s6qvVH",
        "outputId": "d7815de0-ccb6-4661-ec0b-9514f1cab843"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          date measurement  temp_C\n",
              "0   2018-10-01        TMAX    21.1\n",
              "1   2018-10-02        TMAX    23.9\n",
              "2   2018-10-03        TMAX    25.0\n",
              "3   2018-10-04        TMAX    22.8\n",
              "4   2018-10-05        TMAX    23.3\n",
              "..         ...         ...     ...\n",
              "88  2018-10-27        TOBS     6.1\n",
              "89  2018-10-28        TOBS     7.2\n",
              "90  2018-10-29        TOBS     8.3\n",
              "91  2018-10-30        TOBS     5.0\n",
              "92  2018-10-31        TOBS     0.0\n",
              "\n",
              "[93 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2f39e44-9c15-4cf6-88e2-b0123ad916d8\" 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>date</th>\n",
              "      <th>measurement</th>\n",
              "      <th>temp_C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-10-01</td>\n",
              "      <td>TMAX</td>\n",
              "      <td>21.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-10-02</td>\n",
              "      <td>TMAX</td>\n",
              "      <td>23.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-10-03</td>\n",
              "      <td>TMAX</td>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-10-04</td>\n",
              "      <td>TMAX</td>\n",
              "      <td>22.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-10-05</td>\n",
              "      <td>TMAX</td>\n",
              "      <td>23.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>2018-10-27</td>\n",
              "      <td>TOBS</td>\n",
              "      <td>6.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>2018-10-28</td>\n",
              "      <td>TOBS</td>\n",
              "      <td>7.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>2018-10-29</td>\n",
              "      <td>TOBS</td>\n",
              "      <td>8.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>2018-10-30</td>\n",
              "      <td>TOBS</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>2018-10-31</td>\n",
              "      <td>TOBS</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>93 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2f39e44-9c15-4cf6-88e2-b0123ad916d8')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c2f39e44-9c15-4cf6-88e2-b0123ad916d8 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-c2f39e44-9c15-4cf6-88e2-b0123ad916d8');\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",
              "\n",
              "\n",
              "<div id=\"df-05832084-c920-49c1-8f9a-813090d8257f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-05832084-c920-49c1-8f9a-813090d8257f')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
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              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
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              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \")\",\n  \"rows\": 93,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"2018-10-28\",\n          \"2018-10-16\",\n          \"2018-10-24\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"measurement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"TMAX\",\n          \"TMIN\",\n          \"TOBS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp_C\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.362354016345017,\n        \"min\": -1.1,\n        \"max\": 26.7,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          17.8,\n          10.0,\n          24.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wide_df.set_index('date',inplace=True)\n",
        "wide_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "3jfzcARNrE6Z",
        "outputId": "9c36f674-c725-4a72-f5f6-87ec9ea51151"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            TMAX  TMIN  TOBS\n",
              "date                        \n",
              "2018-10-01  21.1   8.9  13.9\n",
              "2018-10-02  23.9  13.9  17.2\n",
              "2018-10-03  25.0  15.6  16.1\n",
              "2018-10-04  22.8  11.7  11.7\n",
              "2018-10-05  23.3  11.7  18.9"
            ],
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    padding: 0;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "wide_df",
              "summary": "{\n  \"name\": \"wide_df\",\n  \"rows\": 31,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"2018-10-28\",\n          \"2018-10-16\",\n          \"2018-10-24\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TMAX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.714962440727387,\n        \"min\": 7.8,\n        \"max\": 26.7,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          16.7,\n          26.1,\n          21.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TMIN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.513251999800349,\n        \"min\": -1.1,\n        \"max\": 17.8,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          8.9,\n          2.8,\n          -1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TOBS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5965500921526825,\n        \"min\": -1.1,\n        \"max\": 21.7,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          13.9,\n          7.8,\n          21.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wide_df.stack()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "xH1UUZKKrSYw",
        "outputId": "409adab4-b400-4b90-d6d0-3611f7b70966"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date            \n",
              "2018-10-01  TMAX    21.1\n",
              "            TMIN     8.9\n",
              "            TOBS    13.9\n",
              "2018-10-02  TMAX    23.9\n",
              "            TMIN    13.9\n",
              "                    ... \n",
              "2018-10-30  TMIN     2.2\n",
              "            TOBS     5.0\n",
              "2018-10-31  TMAX    12.2\n",
              "            TMIN     0.0\n",
              "            TOBS     0.0\n",
              "Length: 93, dtype: float64"
            ],
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              "      <th rowspan=\"3\" valign=\"top\">2018-10-01</th>\n",
              "      <th>TMAX</th>\n",
              "      <td>21.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TMIN</th>\n",
              "      <td>8.9</td>\n",
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              "    <tr>\n",
              "      <th>TOBS</th>\n",
              "      <td>13.9</td>\n",
              "    </tr>\n",
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              "      <th rowspan=\"2\" valign=\"top\">2018-10-02</th>\n",
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              "    <tr>\n",
              "      <th>TMIN</th>\n",
              "      <td>13.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2018-10-30</th>\n",
              "      <th>TMIN</th>\n",
              "      <td>2.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TOBS</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">2018-10-31</th>\n",
              "      <th>TMAX</th>\n",
              "      <td>12.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TMIN</th>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>TOBS</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>93 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stacked_series = wide_df.stack()\n",
        "stacked_series.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "Fp8LV2qerVbL",
        "outputId": "d8ee9dd6-8439-4538-c33e-306e1054d578"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date            \n",
              "2018-10-01  TMAX    21.1\n",
              "            TMIN     8.9\n",
              "            TOBS    13.9\n",
              "2018-10-02  TMAX    23.9\n",
              "            TMIN    13.9\n",
              "dtype: float64"
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              "      <th>0</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th rowspan=\"3\" valign=\"top\">2018-10-01</th>\n",
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              "      <td>21.1</td>\n",
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              "      <th>TMIN</th>\n",
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              "      <td>13.9</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2018-10-02</th>\n",
              "      <th>TMAX</th>\n",
              "      <td>23.9</td>\n",
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              "      <th>TMIN</th>\n",
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              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('data/dirty_data.csv')"
      ],
      "metadata": {
        "id": "K4IP0mq3syKH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df[df.inclement_weather == 'NaN'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJGrkWKqwC4J",
        "outputId": "fdd9fc49-53f5-4932-8347-b61bb5ca6f5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Python ≥3.5 is required\n",
        "import sys\n",
        "assert sys.version_info >= (3, 5)\n",
        "\n",
        "# Scikit-Learn ≥0.20 is required\n",
        "import sklearn\n",
        "assert sklearn.__version__ >= \"0.20\"\n",
        "\n",
        "# Common imports\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "# To plot pretty figures\n",
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "mpl.rc('axes', labelsize=14)\n",
        "mpl.rc('xtick', labelsize=12)\n",
        "mpl.rc('ytick', labelsize=12)\n",
        "\n",
        "# Where to save the figures\n",
        "PROJECT_ROOT_DIR = \".\"\n",
        "CHAPTER_ID = \"end_to_end_project\"\n",
        "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
        "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
        "\n",
        "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
        "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
        "    print(\"Saving figure\", fig_id)\n",
        "    if tight_layout:\n",
        "        plt.tight_layout()\n",
        "    plt.savefig(path, format=fig_extension, dpi=resolution)"
      ],
      "metadata": {
        "id": "OmMBaANnwLl7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import tarfile\n",
        "import urllib.request\n",
        "\n",
        "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
        "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
        "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
        "\n",
        "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
        "    if not os.path.isdir(housing_path):\n",
        "        os.makedirs(housing_path)\n",
        "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
        "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
        "    housing_tgz = tarfile.open(tgz_path)\n",
        "    housing_tgz.extractall(path=housing_path)\n",
        "    housing_tgz.close()"
      ],
      "metadata": {
        "id": "0w4kYyPayYHU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fetch_housing_data()"
      ],
      "metadata": {
        "id": "33S92U71yZ3W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "def load_housing_data(housing_path=HOUSING_PATH):\n",
        "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
        "    return pd.read_csv(csv_path)"
      ],
      "metadata": {
        "id": "STsd2OOSybcI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "housing = load_housing_data()\n",
        "housing.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "Igy38Hokyc0w",
        "outputId": "c5005392-2e12-48c0-bf7a-77ed2e7f02ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0    -122.23     37.88                41.0        880.0           129.0   \n",
              "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
              "2    -122.24     37.85                52.0       1467.0           190.0   \n",
              "3    -122.25     37.85                52.0       1274.0           235.0   \n",
              "4    -122.25     37.85                52.0       1627.0           280.0   \n",
              "\n",
              "   population  households  median_income  median_house_value ocean_proximity  \n",
              "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
              "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
              "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
              "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
              "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
            ],
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              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
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              "      <th>total_rooms</th>\n",
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              "      <th>median_house_value</th>\n",
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              "      <th>1</th>\n",
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              "      <td>21.0</td>\n",
              "      <td>7099.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>1138.0</td>\n",
              "      <td>8.3014</td>\n",
              "      <td>358500.0</td>\n",
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              "      <td>177.0</td>\n",
              "      <td>7.2574</td>\n",
              "      <td>352100.0</td>\n",
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              "      <th>3</th>\n",
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              "      <td>341300.0</td>\n",
              "      <td>NEAR BAY</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>3.8462</td>\n",
              "      <td>342200.0</td>\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "housing",
              "summary": "{\n  \"name\": \"housing\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.003531723502581,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 844,\n        \"samples\": [\n          -118.63,\n          -119.86,\n          -121.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1359523974571117,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          33.7,\n          34.41,\n          38.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.585557612111637,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          35.0,\n          25.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2181.615251582787,\n        \"min\": 2.0,\n        \"max\": 39320.0,\n        \"num_unique_values\": 5926,\n        \"samples\": [\n          699.0,\n          1544.0,\n          3966.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 421.3850700740322,\n        \"min\": 1.0,\n        \"max\": 6445.0,\n        \"num_unique_values\": 1923,\n        \"samples\": [\n          1538.0,\n          1298.0,\n          1578.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.4621217653375,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3888,\n        \"samples\": [\n          4169.0,\n          636.0,\n          3367.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 382.3297528316099,\n        \"min\": 1.0,\n        \"max\": 6082.0,\n        \"num_unique_values\": 1815,\n        \"samples\": [\n          21.0,\n          750.0,\n          1447.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8998217179452732,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 12928,\n        \"samples\": [\n          5.0286,\n          2.0433,\n          6.1228\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115395.6158744132,\n        \"min\": 14999.0,\n        \"max\": 500001.0,\n        \"num_unique_values\": 3842,\n        \"samples\": [\n          194300.0,\n          379000.0,\n          230100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"<1H OCEAN\",\n          \"ISLAND\",\n          \"INLAND\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing.hist(bins=50, figsize=(20,15))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "uxYVEb7Byeew",
        "outputId": "f4c123fe-e442-4143-a8fc-0756cfe4ecc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'longitude'}>,\n",
              "        <Axes: title={'center': 'latitude'}>,\n",
              "        <Axes: title={'center': 'housing_median_age'}>],\n",
              "       [<Axes: title={'center': 'total_rooms'}>,\n",
              "        <Axes: title={'center': 'total_bedrooms'}>,\n",
              "        <Axes: title={'center': 'population'}>],\n",
              "       [<Axes: title={'center': 'households'}>,\n",
              "        <Axes: title={'center': 'median_income'}>,\n",
              "        <Axes: title={'center': 'median_house_value'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 9 Axes>"
            ],
            "image/png": 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AAUzCAAAAAAAAAAAAOIBJGAAAAAAAAAAAAAcwCQMAAAAAAAAAAOAAJmEAAAAAAAAAAAAcwCQMAAAAAAAAAACAA5iEAQAAAAAAAAAAcACTMAAAAAAAAAAAAA5gEgYAAAAAAAAAAMABTMIAAAAAAAAAAAA4gEkYAAAAAAAAAAAAB/w/gGbemgyh4qcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(42)"
      ],
      "metadata": {
        "id": "RmiX3wpIylJ9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def split_train_test(data, test_ratio):\n",
        "    shuffled_indices = np.random.permutation(len(data))\n",
        "    test_set_size = int(len(data)*test_ratio)\n",
        "    test_indices = shuffled_indices[:test_set_size]\n",
        "    train_indices = shuffled_indices[test_set_size:]\n",
        "    return data.iloc[train_indices], data.iloc[test_indices]"
      ],
      "metadata": {
        "id": "kRr5esOMzodl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "housing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "i6ERqreA0MB-",
        "outputId": "94ff85c5-c98d-4a03-c4d2-d15208a1dd3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0        -122.23     37.88                41.0        880.0           129.0   \n",
              "1        -122.22     37.86                21.0       7099.0          1106.0   \n",
              "2        -122.24     37.85                52.0       1467.0           190.0   \n",
              "3        -122.25     37.85                52.0       1274.0           235.0   \n",
              "4        -122.25     37.85                52.0       1627.0           280.0   \n",
              "...          ...       ...                 ...          ...             ...   \n",
              "20635    -121.09     39.48                25.0       1665.0           374.0   \n",
              "20636    -121.21     39.49                18.0        697.0           150.0   \n",
              "20637    -121.22     39.43                17.0       2254.0           485.0   \n",
              "20638    -121.32     39.43                18.0       1860.0           409.0   \n",
              "20639    -121.24     39.37                16.0       2785.0           616.0   \n",
              "\n",
              "       population  households  median_income  median_house_value  \\\n",
              "0           322.0       126.0         8.3252            452600.0   \n",
              "1          2401.0      1138.0         8.3014            358500.0   \n",
              "2           496.0       177.0         7.2574            352100.0   \n",
              "3           558.0       219.0         5.6431            341300.0   \n",
              "4           565.0       259.0         3.8462            342200.0   \n",
              "...           ...         ...            ...                 ...   \n",
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              "type": "dataframe",
              "variable_name": "housing",
              "summary": "{\n  \"name\": \"housing\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.003531723502581,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 844,\n        \"samples\": [\n          -118.63,\n          -119.86,\n          -121.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1359523974571117,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          33.7,\n          34.41,\n          38.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.585557612111637,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          35.0,\n          25.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2181.615251582787,\n        \"min\": 2.0,\n        \"max\": 39320.0,\n        \"num_unique_values\": 5926,\n        \"samples\": [\n          699.0,\n          1544.0,\n          3966.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 421.3850700740322,\n        \"min\": 1.0,\n        \"max\": 6445.0,\n        \"num_unique_values\": 1923,\n        \"samples\": [\n          1538.0,\n          1298.0,\n          1578.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.4621217653375,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3888,\n        \"samples\": [\n          4169.0,\n          636.0,\n          3367.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 382.3297528316099,\n        \"min\": 1.0,\n        \"max\": 6082.0,\n        \"num_unique_values\": 1815,\n        \"samples\": [\n          21.0,\n          750.0,\n          1447.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8998217179452732,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 12928,\n        \"samples\": [\n          5.0286,\n          2.0433,\n          6.1228\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115395.6158744132,\n        \"min\": 14999.0,\n        \"max\": 500001.0,\n        \"num_unique_values\": 3842,\n        \"samples\": [\n          194300.0,\n          379000.0,\n          230100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"<1H OCEAN\",\n          \"ISLAND\",\n          \"INLAND\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set, test_set = split_train_test(housing,0.2)"
      ],
      "metadata": {
        "id": "idkKDCHM0aG1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "82vLgAfw0diK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_set.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "m-OWox-m0tG6",
        "outputId": "0f2784ef-85a1-4b18-df1d-e3d8d4394776"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "20046    -119.01     36.06                25.0       1505.0             NaN   \n",
              "3024     -119.46     35.14                30.0       2943.0             NaN   \n",
              "15663    -122.44     37.80                52.0       3830.0             NaN   \n",
              "20484    -118.72     34.28                17.0       3051.0             NaN   \n",
              "9814     -121.93     36.62                34.0       2351.0             NaN   \n",
              "\n",
              "       population  households  median_income  median_house_value  \\\n",
              "20046      1392.0       359.0         1.6812             47700.0   \n",
              "3024       1565.0       584.0         2.5313             45800.0   \n",
              "15663      1310.0       963.0         3.4801            500001.0   \n",
              "20484      1705.0       495.0         5.7376            218600.0   \n",
              "9814       1063.0       428.0         3.7250            278000.0   \n",
              "\n",
              "      ocean_proximity  \n",
              "20046          INLAND  \n",
              "3024           INLAND  \n",
              "15663        NEAR BAY  \n",
              "20484       <1H OCEAN  \n",
              "9814       NEAR OCEAN  "
            ],
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              "      <td>1705.0</td>\n",
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              "      <td>5.7376</td>\n",
              "      <td>218600.0</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
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              "    <tr>\n",
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              "type": "dataframe",
              "variable_name": "test_set",
              "summary": "{\n  \"name\": \"test_set\",\n  \"rows\": 4128,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9944698265286813,\n        \"min\": -124.19,\n        \"max\": -114.58,\n        \"num_unique_values\": 646,\n        \"samples\": [\n          -119.07,\n          -122.49,\n          -121.22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.132759532626421,\n        \"min\": 32.54,\n        \"max\": 41.81,\n        \"num_unique_values\": 622,\n        \"samples\": [\n          41.01,\n          38.52,\n          38.47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.518304085546678,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          8.0,\n          49.0,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2209.3591011675617,\n        \"min\": 6.0,\n        \"max\": 37937.0,\n        \"num_unique_values\": 2764,\n        \"samples\": [\n          1350.0,\n          2105.0,\n          1260.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 431.29970430603225,\n        \"min\": 2.0,\n        \"max\": 5471.0,\n        \"num_unique_values\": 1178,\n        \"samples\": [\n          640.0,\n          404.0,\n          787.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1114.0231364419826,\n        \"min\": 8.0,\n        \"max\": 16122.0,\n        \"num_unique_values\": 2125,\n        \"samples\": [\n          603.0,\n          4071.0,\n          3612.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 387.77057023921293,\n        \"min\": 2.0,\n        \"max\": 5189.0,\n        \"num_unique_values\": 1156,\n        \"samples\": [\n          2329.0,\n          129.0,\n          155.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8815152640981057,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 3412,\n        \"samples\": [\n          2.9562,\n          4.1985,\n          6.2871\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 114486.96428830529,\n        \"min\": 14999.0,\n        \"max\": 500001.0,\n        \"num_unique_values\": 2198,\n        \"samples\": [\n          185700.0,\n          184800.0,\n          438300.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"NEAR BAY\",\n          \"ISLAND\",\n          \"<1H OCEAN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing['median_income'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "eTzVeHV90uZA",
        "outputId": "46ac2b8e-79dd-4fac-9653-1a565bc25c55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.cut(housing['median_income'],\n",
        "       bins = [0.,1.5,3.0,4.5,6.,np.inf],\n",
        "       labels = [1,2,3,4,5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "730wpjxt0x4e",
        "outputId": "09e7e44d-7ece-45e7-cb5c-3abb3101fe00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        5\n",
              "1        5\n",
              "2        5\n",
              "3        4\n",
              "4        3\n",
              "        ..\n",
              "20635    2\n",
              "20636    2\n",
              "20637    2\n",
              "20638    2\n",
              "20639    2\n",
              "Name: median_income, Length: 20640, dtype: category\n",
              "Categories (5, int64): [1 < 2 < 3 < 4 < 5]"
            ],
            "text/html": [
              "<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>median_income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20635</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20636</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20637</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20638</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20639</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20640 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> category</label>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w-07jrUu0_vT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}