Midterm_ai4ml_2023의_사본.ipynb

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      "source": [
        "# 2023 Mid-term exam on Machine Learning for AI\n",
        "\n",
        "- Name:  조다영\n",
        "- ID Number:  20210955\n",
        "\n",
        "------------------------------------------\n",
        "Copy this notebook to your google drive.  \n",
        "Good luck! :)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-6p6VRmTmAPZ"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "Downloading datasets"
      ],
      "metadata": {
        "id": "ap5lI7Z3uTdB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown --id 1FepWmpic9gRNg-90sijQKLWT2jVc4VDP"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1FepWmpic9gRNg-90sijQKLWT2jVc4VDP\n",
            "To: /content/nyc_weather_2018.csv\n",
            "100% 4.36M/4.36M [00:00<00:00, 189MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weather = pd.read_csv('nyc_weather_2018.csv').assign(date=lambda x: pd.to_datetime(x.date) )"
      ],
      "metadata": {
        "id": "U5HMpz1LzY8E"
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      "outputs": []
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "### Q1 [10 points] From weather data, try to set 'date' as index.\n",
        "'date' 변수를 index로 바꿔보세요."
      ],
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        "id": "JAjkBqfIzrEe"
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        "weather = weather.set_index('date')\n",
        "weather.head()"
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              "           datatype            station attributes  value\n",
              "date                                                    \n",
              "2018-01-01     PRCP  GHCND:US1CTFR0039       ,,N,    0.0\n",
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              "      <th>2018-01-01</th>\n",
              "      <td>PRCP</td>\n",
              "      <td>GHCND:US1NJBG0015</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>SNOW</td>\n",
              "      <td>GHCND:US1NJBG0015</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>PRCP</td>\n",
              "      <td>GHCND:US1NJBG0017</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>SNOW</td>\n",
              "      <td>GHCND:US1NJBG0017</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</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-c1e9daaa-e90e-4af1-9d00-10d2526b238c')\"\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-c1e9daaa-e90e-4af1-9d00-10d2526b238c 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-c1e9daaa-e90e-4af1-9d00-10d2526b238c');\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-e4461cdb-51d0-47d2-a006-79a251b1a788\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e4461cdb-51d0-47d2-a006-79a251b1a788')\"\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-e4461cdb-51d0-47d2-a006-79a251b1a788 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q2 [10 points] How many unique categories in datatype variable?\n",
        "datatype 변수에 얼마나 많은 unique한 값들이 있나요?\n"
      ],
      "metadata": {
        "id": "fUWDyByfhlJ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weather.datatype.describe()  # 27개의 unique한 값들이 존재한다."
      ],
      "metadata": {
        "id": "FGuOTiBl0FDT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23cc3c4d-7cdb-48f0-9b57-b752b9fa7403"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     78780\n",
              "unique       27\n",
              "top        PRCP\n",
              "freq      27228\n",
              "Name: datatype, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q3 [15 points] Follow the steps below to get information about SNOW.(아래의 절차들을 수행하여 눈 관련 정보를 알아봅시다.)\n",
        " - [5 points] Select only data with datatype 'SNOW'. (datatype 이 'SNOW' 인 데이터들만 선택합니다.)\n",
        " - [5 points] Average the numbers ('value') for each day to get the daily snowfall. (각 일자별로 수치('value')들의 평균을 구해서 일별 강설량을 구해보세요.)\n",
        " - [5 points] Find the sum of the snowfall for each month and paste it into the daily data. (각 월별로 강설량의 합을 구해서 일별데이터에 붙여보세요.)\n",
        "\n",
        " The result should look something like below:\n",
        "\n",
        " 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      ],
      "metadata": {
        "id": "vEXN-yq7W58q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "q3 = weather.query('datatype == \"SNOW\"')\n",
        "q3.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "7TruFYJLW5Vn",
        "outputId": "a4f05242-36e9-4f75-fe2f-1d1ed0e4178e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           datatype            station attributes  value\n",
              "date                                                    \n",
              "2018-01-01     SNOW  GHCND:US1NJBG0015       ,,N,    0.0\n",
              "2018-01-01     SNOW  GHCND:US1NJBG0017       ,,N,    0.0\n",
              "2018-01-01     SNOW  GHCND:US1NJBG0018       ,,N,    0.0\n",
              "2018-01-01     SNOW  GHCND:US1NJBG0023       ,,N,    0.0\n",
              "2018-01-01     SNOW  GHCND:US1NJBG0039       ,,N,    0.0"
            ],
            "text/html": [
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              "      <th>datatype</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>SNOW</td>\n",
              "      <td>GHCND:US1NJBG0015</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>SNOW</td>\n",
              "      <td>GHCND:US1NJBG0017</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>SNOW</td>\n",
              "      <td>GHCND:US1NJBG0018</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>SNOW</td>\n",
              "      <td>GHCND:US1NJBG0023</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>SNOW</td>\n",
              "      <td>GHCND:US1NJBG0039</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9eb9a854-a9ea-45a5-8538-7fa6f7f050d8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "      border: none;\n",
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              "\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-9eb9a854-a9ea-45a5-8538-7fa6f7f050d8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-9eb9a854-a9ea-45a5-8538-7fa6f7f050d8');\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-da2c3bf8-87d4-4b47-a28d-79775fedb233\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da2c3bf8-87d4-4b47-a28d-79775fedb233')\"\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-da2c3bf8-87d4-4b47-a28d-79775fedb233 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "q3_3 = q3.groupby(level=0).mean()\n",
        "q3_3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "PXEQNU8DegTo",
        "outputId": "bbda2982-e7cd-44ef-9791-d67a3cc8692e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-913940435d5b>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  q3_3 = q3.groupby(level=0).mean()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 value\n",
              "date                  \n",
              "2018-01-01    0.510204\n",
              "2018-01-02    0.000000\n",
              "2018-01-03    0.000000\n",
              "2018-01-04   68.458333\n",
              "2018-01-05  155.957447\n",
              "...                ...\n",
              "2018-12-27    0.000000\n",
              "2018-12-28    0.000000\n",
              "2018-12-29    0.000000\n",
              "2018-12-30    0.121951\n",
              "2018-12-31    0.000000\n",
              "\n",
              "[365 rows x 1 columns]"
            ],
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>value</th>\n",
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              "      <th>2018-01-01</th>\n",
              "      <td>0.510204</td>\n",
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              "      <th>2018-01-02</th>\n",
              "      <td>0.000000</td>\n",
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              "      <th>2018-01-03</th>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>2018-12-27</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-28</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-29</th>\n",
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              "    <tr>\n",
              "      <th>2018-12-30</th>\n",
              "      <td>0.121951</td>\n",
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              "    <tr>\n",
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              "      <td>0.000000</td>\n",
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              "  </tbody>\n",
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              "<p>365 rows × 1 columns</p>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      width: 32px;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fc802c31-0786-42d1-929a-9e424497a1bd 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-fc802c31-0786-42d1-929a-9e424497a1bd');\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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              "\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    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",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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",
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              "    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-8ba9ed46-da2a-429e-8d00-b05f94aad601 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weather\\\n",
        "    .query('datatype == \"SNOW\"')\\\n",
        "    .groupby(level=0).mean()\\\n",
        "    .assign(\n",
        "      monthly_total = lambda x: \\\n",
        "            x.groupby(pd.Grouper(freq='M')).transform(np.sum)\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "QpgsBJsEggGv",
        "outputId": "b5c92c80-c33d-4ad9-8ca8-d26acce51404"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-7d3ceb100924>:3: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  .groupby(level=0).mean()\\\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 value  monthly_total\n",
              "date                                 \n",
              "2018-01-01    0.510204     285.326082\n",
              "2018-01-02    0.000000     285.326082\n",
              "2018-01-03    0.000000     285.326082\n",
              "2018-01-04   68.458333     285.326082\n",
              "2018-01-05  155.957447     285.326082\n",
              "...                ...            ...\n",
              "2018-12-27    0.000000       5.560521\n",
              "2018-12-28    0.000000       5.560521\n",
              "2018-12-29    0.000000       5.560521\n",
              "2018-12-30    0.121951       5.560521\n",
              "2018-12-31    0.000000       5.560521\n",
              "\n",
              "[365 rows x 2 columns]"
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              "\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>0.510204</td>\n",
              "      <td>285.326082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>285.326082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-03</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>285.326082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-04</th>\n",
              "      <td>68.458333</td>\n",
              "      <td>285.326082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05</th>\n",
              "      <td>155.957447</td>\n",
              "      <td>285.326082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-27</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.560521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-28</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.560521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-29</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.560521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-30</th>\n",
              "      <td>0.121951</td>\n",
              "      <td>5.560521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.560521</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>365 rows × 2 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-dc61b912-b94c-426f-85a7-58c6a83c2a73')\"\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-dc61b912-b94c-426f-85a7-58c6a83c2a73 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-dc61b912-b94c-426f-85a7-58c6a83c2a73');\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-2c5169d3-1f6e-4233-a9ae-3f9bde38d2f1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2c5169d3-1f6e-4233-a9ae-3f9bde38d2f1')\"\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-2c5169d3-1f6e-4233-a9ae-3f9bde38d2f1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q4 [15 points] Follow the steps below to check for snow days.(아래의 절차들을 수행하여 눈이 온 날들을 확인해 봅시다.)\n",
        " - [5 points] Select only data with datatype 'SNOW'. (datatype 이 'SNOW' 인 데이터들만 선택합니다.)\n",
        " - [5 points] For each date, indicate if the number ('value') is greater than zero, True, otherwise False. (각 일자별로 수치('value')들이 0보다 큰 경우 True, 아닌경우 False 로 나타내세요.)\n",
        "\n",
        "The result should look like below:\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        " - [5 points] Calculate the number of snow days per month. (월별로 눈이 온 날들의 수를 계산해보세요.)\n",
        "\n",
        "The result should look like below:\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "a76foYfnVgBg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "q4 = weather.query('datatype == \"SNOW\"')"
      ],
      "metadata": {
        "id": "3iBhj99lWmoF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "q4['value'] > 0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N0YGyYqPjZzI",
        "outputId": "b03423ca-6759-4dda-e8a6-b0b4478aa8e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2        False\n",
              "4        False\n",
              "7        False\n",
              "10       False\n",
              "14       False\n",
              "         ...  \n",
              "78699    False\n",
              "78703    False\n",
              "78715    False\n",
              "78744    False\n",
              "78769    False\n",
              "Name: value, Length: 12834, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weather.query('datatype == \"SNOW\"').groupby(pd.Grouper(freq='M'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "F5PTQh7IZ_bm",
        "outputId": "672f1f9a-fa41-40d4-e039-2a81f00d432a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-72-0caed862b057>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mweather\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'datatype == \"SNOW\"'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGrouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'M'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)\u001b[0m\n\u001b[1;32m   8400\u001b[0m         \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8401\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 8402\u001b[0;31m         return DataFrameGroupBy(\n\u001b[0m\u001b[1;32m   8403\u001b[0m             \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8404\u001b[0m             \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)\u001b[0m\n\u001b[1;32m    963\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_grouper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 965\u001b[0;31m             grouper, exclusions, obj = get_grouper(\n\u001b[0m\u001b[1;32m    966\u001b[0m                 \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m                 \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, mutated, validate, dropna)\u001b[0m\n\u001b[1;32m    784\u001b[0m     \u001b[0;31m# a passed-in Grouper, directly convert\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 786\u001b[0;31m         \u001b[0mbinner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrouper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_grouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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    787\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mgrouper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrozenset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\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/resample.py\u001b[0m in \u001b[0;36m_get_grouper\u001b[0;34m(self, obj, validate)\u001b[0m\n\u001b[1;32m   1731\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_grouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1732\u001b[0m         \u001b[0;31m# create the resampler and return our binner\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1733\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_resampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1734\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/resample.py\u001b[0m in \u001b[0;36m_get_resampler\u001b[0;34m(self, obj, kind)\u001b[0m\n\u001b[1;32m   1723\u001b[0m             )\n\u001b[1;32m   1724\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1725\u001b[0;31m         raise TypeError(\n\u001b[0m\u001b[1;32m   1726\u001b[0m             \u001b[0;34m\"Only valid with DatetimeIndex, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1727\u001b[0m             \u001b[0;34m\"TimedeltaIndex or PeriodIndex, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Int64Index'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q5 [20 points] Let's perform the following tasks on the weather data. (weather 데이터에 대해 다음 작업을 수행해 봅시다.)\n",
        "- [5 points] Release the date index via reset_index(). (reset_index()를 통해 날짜인덱스를 해제하세요.)\n",
        "- [5 points] Organize your data in a wide format for values of a datatype. (datatype의 값들에 대해 wide format 으로 데이터를 정리해 보세요.)\n",
        "\n",
        "The result should look like below:\n",
        "\n",
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        "\n",
        "- [5 points] Try performing an aggregation that gets the maximum value for TMAX and minimum value for TMIN by date. (날짜별로 TMAX에 대해서는 최대값, TMIN에 대해서는 최솟값을 가져오는 aggregation을 수행해 보세요. )\n",
        "\n",
        "The result should look like below:\n",
        "\n",
        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)\n",
        "\n",
        "- [5 points] Create a tmax_tmin variable that has a result of TMAX - TMIN. Print 10 results in large order for the tmax_tmin variable. (TMAX - TMIN 의 결과를 갖는 tmax_tmin 변수를 만들어 보세요. tmax_tmin 변수에 대해 큰 순서로 10개 출력해 보세요.)\n",
        "\n",
        "The result should look like below:\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "H2KA5ALubeU_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weather.reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "oqisejDRm_s6",
        "outputId": "478e3465-7008-40a5-aff7-1adf48dbf825"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            date datatype            station attributes  value\n",
              "0     2018-01-01     PRCP  GHCND:US1CTFR0039       ,,N,    0.0\n",
              "1     2018-01-01     PRCP  GHCND:US1NJBG0015       ,,N,    0.0\n",
              "2     2018-01-01     SNOW  GHCND:US1NJBG0015       ,,N,    0.0\n",
              "3     2018-01-01     PRCP  GHCND:US1NJBG0017       ,,N,    0.0\n",
              "4     2018-01-01     SNOW  GHCND:US1NJBG0017       ,,N,    0.0\n",
              "...          ...      ...                ...        ...    ...\n",
              "78775 2018-12-31     WDF5  GHCND:USW00094789       ,,W,  130.0\n",
              "78776 2018-12-31     WSF2  GHCND:USW00094789       ,,W,    9.8\n",
              "78777 2018-12-31     WSF5  GHCND:USW00094789       ,,W,   12.5\n",
              "78778 2018-12-31     WT01  GHCND:USW00094789       ,,W,    1.0\n",
              "78779 2018-12-31     WT02  GHCND:USW00094789       ,,W,    1.0\n",
              "\n",
              "[78780 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fff32284-4b02-492b-9bdd-4aebda2d6334\" 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>datatype</th>\n",
              "      <th>station</th>\n",
              "      <th>attributes</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>PRCP</td>\n",
              "      <td>GHCND:US1CTFR0039</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>PRCP</td>\n",
              "      <td>GHCND:US1NJBG0015</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>SNOW</td>\n",
              "      <td>GHCND:US1NJBG0015</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>PRCP</td>\n",
              "      <td>GHCND:US1NJBG0017</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>SNOW</td>\n",
              "      <td>GHCND:US1NJBG0017</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78775</th>\n",
              "      <td>2018-12-31</td>\n",
              "      <td>WDF5</td>\n",
              "      <td>GHCND:USW00094789</td>\n",
              "      <td>,,W,</td>\n",
              "      <td>130.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78776</th>\n",
              "      <td>2018-12-31</td>\n",
              "      <td>WSF2</td>\n",
              "      <td>GHCND:USW00094789</td>\n",
              "      <td>,,W,</td>\n",
              "      <td>9.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78777</th>\n",
              "      <td>2018-12-31</td>\n",
              "      <td>WSF5</td>\n",
              "      <td>GHCND:USW00094789</td>\n",
              "      <td>,,W,</td>\n",
              "      <td>12.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78778</th>\n",
              "      <td>2018-12-31</td>\n",
              "      <td>WT01</td>\n",
              "      <td>GHCND:USW00094789</td>\n",
              "      <td>,,W,</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78779</th>\n",
              "      <td>2018-12-31</td>\n",
              "      <td>WT02</td>\n",
              "      <td>GHCND:USW00094789</td>\n",
              "      <td>,,W,</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>78780 rows × 5 columns</p>\n",
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              "        const element = document.querySelector('#df-fff32284-4b02-492b-9bdd-4aebda2d6334');\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",
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              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      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-top-color: var(--fill-color);\n",
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              "    60% {\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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a93490a1-7a86-4e87-ba91-f3a59cb62bc9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weather.pivot(index = ['date', 'station'], columns='datatype', values = 'value')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "RE0Oo4Wrmuiv",
        "outputId": "1e90858f-97aa-4403-8391-89ba8d148538"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'date'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-84f2d199bdc2>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mweather\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'station'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'datatype'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'value'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\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/frame.py\u001b[0m in \u001b[0;36mpivot\u001b[0;34m(self, index, columns, values)\u001b[0m\n\u001b[1;32m   8565\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpivot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 8567\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpivot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\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   8568\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8569\u001b[0m     _shared_docs[\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\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/reshape/pivot.py\u001b[0m in \u001b[0;36mpivot\u001b[0;34m(data, index, columns, values)\u001b[0m\n\u001b[1;32m    521\u001b[0m                 \u001b[0mindex_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    522\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 523\u001b[0;31m             \u001b[0mindex_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    525\u001b[0m         \u001b[0mdata_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcolumns_listlike\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/reshape/pivot.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    521\u001b[0m                 \u001b[0mindex_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    522\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 523\u001b[0;31m             \u001b[0mindex_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    525\u001b[0m         \u001b[0mdata_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcolumns_listlike\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'date'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "zD280lIDbhD2",
        "outputId": "25aecfc6-6670-49d8-b953-014d2b1c289d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datatype                      AWND  DAPR  MDPR    PGTM  PRCP  SNOW  SNWD  \\\n",
              "date       station                                                         \n",
              "2018-01-01 GHCND:US1CTFR0039   NaN   NaN   NaN     NaN   0.0   NaN   NaN   \n",
              "           GHCND:US1NJBG0015   NaN   NaN   NaN     NaN   0.0   0.0   NaN   \n",
              "           GHCND:US1NJBG0017   NaN   NaN   NaN     NaN   0.0   0.0   0.0   \n",
              "           GHCND:US1NJBG0018   NaN   NaN   NaN     NaN   0.0   0.0   3.0   \n",
              "           GHCND:US1NJBG0023   NaN   NaN   NaN     NaN   0.0   0.0  25.0   \n",
              "...                            ...   ...   ...     ...   ...   ...   ...   \n",
              "2018-12-31 GHCND:USW00054787   5.0   NaN   NaN  2052.0  28.7   NaN   NaN   \n",
              "           GHCND:USW00094728   NaN   NaN   NaN     NaN  25.9   0.0   0.0   \n",
              "           GHCND:USW00094741   1.7   NaN   NaN  1954.0  29.2   NaN   NaN   \n",
              "           GHCND:USW00094745   2.7   NaN   NaN  2212.0  24.4   NaN   NaN   \n",
              "           GHCND:USW00094789   4.1   NaN   NaN     NaN  31.2   0.0   0.0   \n",
              "\n",
              "datatype                      TAVG  TMAX  TMIN  ...  WSF5  WT01  WT02  WT03  \\\n",
              "date       station                              ...                           \n",
              "2018-01-01 GHCND:US1CTFR0039   NaN   NaN   NaN  ...   NaN   NaN   NaN   NaN   \n",
              "           GHCND:US1NJBG0015   NaN   NaN   NaN  ...   NaN   NaN   NaN   NaN   \n",
              "           GHCND:US1NJBG0017   NaN   NaN   NaN  ...   NaN   NaN   NaN   NaN   \n",
              "           GHCND:US1NJBG0018   NaN   NaN   NaN  ...   NaN   NaN   NaN   NaN   \n",
              "           GHCND:US1NJBG0023   NaN   NaN   NaN  ...   NaN   NaN   NaN   NaN   \n",
              "...                            ...   ...   ...  ...   ...   ...   ...   ...   \n",
              "2018-12-31 GHCND:USW00054787   NaN   8.3  -2.7  ...  15.7   NaN   NaN   NaN   \n",
              "           GHCND:USW00094728   NaN   8.9   2.2  ...   NaN   1.0   NaN   NaN   \n",
              "           GHCND:USW00094741   NaN   8.3  -1.0  ...   8.9   NaN   NaN   NaN   \n",
              "           GHCND:USW00094745   NaN   6.1  -3.2  ...  11.2   NaN   NaN   NaN   \n",
              "           GHCND:USW00094789   2.7   9.4  -1.0  ...  12.5   1.0   1.0   NaN   \n",
              "\n",
              "datatype                      WT04  WT05  WT06  WT08  WT09  WT11  \n",
              "date       station                                                \n",
              "2018-01-01 GHCND:US1CTFR0039   NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "           GHCND:US1NJBG0015   NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "           GHCND:US1NJBG0017   NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "           GHCND:US1NJBG0018   NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "           GHCND:US1NJBG0023   NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "...                            ...   ...   ...   ...   ...   ...  \n",
              "2018-12-31 GHCND:USW00054787   NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "           GHCND:USW00094728   NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "           GHCND:USW00094741   NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "           GHCND:USW00094745   NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "           GHCND:USW00094789   NaN   NaN   NaN   NaN   NaN   NaN  \n",
              "\n",
              "[27835 rows x 27 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-74d6298e-a391-4d97-88ed-2294712ef5cb\" 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",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>datatype</th>\n",
              "      <th>AWND</th>\n",
              "      <th>DAPR</th>\n",
              "      <th>MDPR</th>\n",
              "      <th>PGTM</th>\n",
              "      <th>PRCP</th>\n",
              "      <th>SNOW</th>\n",
              "      <th>SNWD</th>\n",
              "      <th>TAVG</th>\n",
              "      <th>TMAX</th>\n",
              "      <th>TMIN</th>\n",
              "      <th>...</th>\n",
              "      <th>WSF5</th>\n",
              "      <th>WT01</th>\n",
              "      <th>WT02</th>\n",
              "      <th>WT03</th>\n",
              "      <th>WT04</th>\n",
              "      <th>WT05</th>\n",
              "      <th>WT06</th>\n",
              "      <th>WT08</th>\n",
              "      <th>WT09</th>\n",
              "      <th>WT11</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th>station</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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 rowspan=\"5\" valign=\"top\">2018-01-01</th>\n",
              "      <th>GHCND:US1CTFR0039</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GHCND:US1NJBG0015</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GHCND:US1NJBG0017</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GHCND:US1NJBG0018</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GHCND:US1NJBG0023</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2018-12-31</th>\n",
              "      <th>GHCND:USW00054787</th>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2052.0</td>\n",
              "      <td>28.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.3</td>\n",
              "      <td>-2.7</td>\n",
              "      <td>...</td>\n",
              "      <td>15.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GHCND:USW00094728</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.9</td>\n",
              "      <td>2.2</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GHCND:USW00094741</th>\n",
              "      <td>1.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1954.0</td>\n",
              "      <td>29.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.3</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>8.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GHCND:USW00094745</th>\n",
              "      <td>2.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2212.0</td>\n",
              "      <td>24.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.1</td>\n",
              "      <td>-3.2</td>\n",
              "      <td>...</td>\n",
              "      <td>11.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GHCND:USW00094789</th>\n",
              "      <td>4.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>9.4</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>12.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>27835 rows × 27 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-74d6298e-a391-4d97-88ed-2294712ef5cb')\"\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-74d6298e-a391-4d97-88ed-2294712ef5cb 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-74d6298e-a391-4d97-88ed-2294712ef5cb');\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-f6d275cc-8382-48eb-b058-9e3b2455fd51\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f6d275cc-8382-48eb-b058-9e3b2455fd51')\"\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-f6d275cc-8382-48eb-b058-9e3b2455fd51 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "N4DgHXKpm85n",
        "outputId": "0c98c269-8dc3-48a6-d891-6a11cb076afe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datatype    TMAX  TMIN\n",
              "date                  \n",
              "2018-01-01  -6.6 -22.2\n",
              "2018-01-02  -1.6 -20.0\n",
              "2018-01-03  -1.0 -16.1\n",
              "2018-01-04  -1.1 -15.6\n",
              "2018-01-05  -2.2 -17.2\n",
              "...          ...   ...\n",
              "2018-12-27   9.4  -2.8\n",
              "2018-12-28  15.6  -2.8\n",
              "2018-12-29  15.6   1.1\n",
              "2018-12-30  13.3  -2.1\n",
              "2018-12-31   9.4  -3.3\n",
              "\n",
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              "      <td>15.6</td>\n",
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              "    <tr>\n",
              "      <th>2018-12-29</th>\n",
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              "      <td>1.1</td>\n",
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              "      <th>2018-12-30</th>\n",
              "      <td>13.3</td>\n",
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              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>9.4</td>\n",
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              "\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
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          "metadata": {},
          "execution_count": 123
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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          "height": 394
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        "id": "3qATd8vPaySq",
        "outputId": "e2597ea3-d2d5-45ea-a05d-489c53f6cdc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datatype    TMAX  TMIN  tmax_tmin\n",
              "date                             \n",
              "2018-01-15   0.0 -72.8       72.8\n",
              "2018-05-15  36.1   4.4       31.7\n",
              "2018-05-01  30.0  -1.7       31.7\n",
              "2018-05-02  33.3   3.9       29.4\n",
              "2018-02-11  17.8 -10.6       28.4\n",
              "2018-01-08   6.7 -21.1       27.8\n",
              "2018-04-15  29.4   1.7       27.7\n",
              "2018-11-01  24.4  -3.3       27.7\n",
              "2018-04-13  29.4   2.2       27.2\n",
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              "<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-7a573637-3667-418a-a813-18b2416f50e3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q6 [5 points] From the weather data, find the subset of data with 'station' ID names ending in 0039. How many data are there? (weather 데이터에서 station 이름이 0039로 끝나는 데이터들의 부분집합을 구해보세요. 몇 개의 데이터가 있나요? )\n",
        "\n",
        "The result should look like below:\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "CkLjzpADrgQL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weather[weather.station.str.endswith('0039')]  # 694개"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "2k7a89rEdR2T",
        "outputId": "234c511e-e621-4f94-f617-5b2d4eb73f18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           datatype            station attributes  value\n",
              "date                                                    \n",
              "2018-01-01     PRCP  GHCND:US1CTFR0039       ,,N,    0.0\n",
              "2018-01-01     PRCP  GHCND:US1NJBG0039       ,,N,    0.0\n",
              "2018-01-01     SNOW  GHCND:US1NJBG0039       ,,N,    0.0\n",
              "2018-01-02     PRCP  GHCND:US1CTFR0039       ,,N,    0.0\n",
              "2018-01-02     PRCP  GHCND:US1NJBG0039       ,,N,    0.0\n",
              "...             ...                ...        ...    ...\n",
              "2018-12-30     PRCP  GHCND:US1CTFR0039       ,,N,    0.0\n",
              "2018-12-30     PRCP  GHCND:US1NJBG0039      T,,N,    0.0\n",
              "2018-12-31     PRCP  GHCND:US1CTFR0039      T,,N,    0.0\n",
              "2018-12-31     PRCP  GHCND:US1NJBG0039       ,,N,    0.0\n",
              "2018-12-31     SNOW  GHCND:US1NJBG0039       ,,N,    0.0\n",
              "\n",
              "[694 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4927e517-4482-4e09-bfab-fd1762da40c0\" 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>datatype</th>\n",
              "      <th>station</th>\n",
              "      <th>attributes</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>PRCP</td>\n",
              "      <td>GHCND:US1CTFR0039</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>PRCP</td>\n",
              "      <td>GHCND:US1NJBG0039</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>SNOW</td>\n",
              "      <td>GHCND:US1NJBG0039</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02</th>\n",
              "      <td>PRCP</td>\n",
              "      <td>GHCND:US1CTFR0039</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02</th>\n",
              "      <td>PRCP</td>\n",
              "      <td>GHCND:US1NJBG0039</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-30</th>\n",
              "      <td>PRCP</td>\n",
              "      <td>GHCND:US1CTFR0039</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-30</th>\n",
              "      <td>PRCP</td>\n",
              "      <td>GHCND:US1NJBG0039</td>\n",
              "      <td>T,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>PRCP</td>\n",
              "      <td>GHCND:US1CTFR0039</td>\n",
              "      <td>T,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>PRCP</td>\n",
              "      <td>GHCND:US1NJBG0039</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>SNOW</td>\n",
              "      <td>GHCND:US1NJBG0039</td>\n",
              "      <td>,,N,</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>694 rows × 4 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-4927e517-4482-4e09-bfab-fd1762da40c0')\"\n",
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              "    </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-4927e517-4482-4e09-bfab-fd1762da40c0 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-4927e517-4482-4e09-bfab-fd1762da40c0');\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-6e08a99d-0a8c-444f-8dc5-a30e099df318\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6e08a99d-0a8c-444f-8dc5-a30e099df318')\"\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-6e08a99d-0a8c-444f-8dc5-a30e099df318 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Let's download another dataset (expdata)"
      ],
      "metadata": {
        "id": "_PhNSR25nj7M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown --id 1gFNrqxEhdFa42AShaqCJbq3kjGXmBXaj\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tRO6Dj5_dRlr",
        "outputId": "dd4accf8-e7b8-4a79-e790-9621295f345d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1gFNrqxEhdFa42AShaqCJbq3kjGXmBXaj\n",
            "To: /content/proformer.csv\n",
            "100% 664/664 [00:00<00:00, 1.94MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "expdata = pd.read_csv('proformer.csv')\n",
        "expdata"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "XJJWLXdEdkwp",
        "outputId": "418ed4f0-806b-4915-c634-b673866acdaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    dim  AttentionHeads  MacaronBlocks  AvgPooling      H1      H8     H16  \\\n",
              "0    64               2              1      0.7026  0.7046  0.7011  0.6977   \n",
              "1    64               2              2      0.7094  0.7086  0.7122  0.7119   \n",
              "2    64               2              4      0.7140  0.7196  0.7162  0.7184   \n",
              "3    64               2              8      0.7151  0.7198  0.7223  0.7138   \n",
              "4   128               4              1      0.7033  0.7153  0.7137  0.7075   \n",
              "5   128               4              2      0.7164  0.7209  0.7142  0.7197   \n",
              "6   128               4              4      0.7189  0.7224  0.7207  0.7218   \n",
              "7   128               4              8      0.0145  0.6627  0.7223  0.7200   \n",
              "8   256               8              1      0.7109  0.7177  0.7104  0.7124   \n",
              "9   256               8              2      0.7157  0.7219  0.7197  0.7185   \n",
              "10  256               8              4      0.6406  0.6616  0.7210  0.7186   \n",
              "11  256               8              8      0.0165  0.0603  0.7188  0.7194   \n",
              "\n",
              "       H32     H64  \n",
              "0   0.6977  0.6943  \n",
              "1   0.7136  0.7088  \n",
              "2   0.7190  0.7200  \n",
              "3   0.7191  0.7214  \n",
              "4   0.7069  0.7047  \n",
              "5   0.7147  0.7175  \n",
              "6   0.7139  0.7192  \n",
              "7   0.7207  0.7226  \n",
              "8   0.7058  0.7152  \n",
              "9   0.7207  0.7177  \n",
              "10  0.7213  0.7211  \n",
              "11  0.7222  0.7173  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-15968918-5920-4631-83d2-ef5b5978fbaf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dim</th>\n",
              "      <th>AttentionHeads</th>\n",
              "      <th>MacaronBlocks</th>\n",
              "      <th>AvgPooling</th>\n",
              "      <th>H1</th>\n",
              "      <th>H8</th>\n",
              "      <th>H16</th>\n",
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              "      <th>H64</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
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              "      <td>0.7026</td>\n",
              "      <td>0.7046</td>\n",
              "      <td>0.7011</td>\n",
              "      <td>0.6977</td>\n",
              "      <td>0.6977</td>\n",
              "      <td>0.6943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.7094</td>\n",
              "      <td>0.7086</td>\n",
              "      <td>0.7122</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.7136</td>\n",
              "      <td>0.7088</td>\n",
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              "    <tr>\n",
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              "      <td>2</td>\n",
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              "      <td>0.7196</td>\n",
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              "      <td>0.7190</td>\n",
              "      <td>0.7200</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>0.7151</td>\n",
              "      <td>0.7198</td>\n",
              "      <td>0.7223</td>\n",
              "      <td>0.7138</td>\n",
              "      <td>0.7191</td>\n",
              "      <td>0.7214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.7033</td>\n",
              "      <td>0.7153</td>\n",
              "      <td>0.7137</td>\n",
              "      <td>0.7075</td>\n",
              "      <td>0.7069</td>\n",
              "      <td>0.7047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
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              "      <td>0.7164</td>\n",
              "      <td>0.7209</td>\n",
              "      <td>0.7142</td>\n",
              "      <td>0.7197</td>\n",
              "      <td>0.7147</td>\n",
              "      <td>0.7175</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0.7189</td>\n",
              "      <td>0.7224</td>\n",
              "      <td>0.7207</td>\n",
              "      <td>0.7218</td>\n",
              "      <td>0.7139</td>\n",
              "      <td>0.7192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>0.0145</td>\n",
              "      <td>0.6627</td>\n",
              "      <td>0.7223</td>\n",
              "      <td>0.7200</td>\n",
              "      <td>0.7207</td>\n",
              "      <td>0.7226</td>\n",
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              "      <td>0.7104</td>\n",
              "      <td>0.7124</td>\n",
              "      <td>0.7058</td>\n",
              "      <td>0.7152</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>256</td>\n",
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              "      <td>0.7157</td>\n",
              "      <td>0.7219</td>\n",
              "      <td>0.7197</td>\n",
              "      <td>0.7185</td>\n",
              "      <td>0.7207</td>\n",
              "      <td>0.7177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>0.6406</td>\n",
              "      <td>0.6616</td>\n",
              "      <td>0.7210</td>\n",
              "      <td>0.7186</td>\n",
              "      <td>0.7213</td>\n",
              "      <td>0.7211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
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              "      <td>0.7173</td>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\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-15968918-5920-4631-83d2-ef5b5978fbaf 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-15968918-5920-4631-83d2-ef5b5978fbaf');\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-051f91e2-5184-42d9-a547-a00f67d8d708\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-051f91e2-5184-42d9-a547-a00f67d8d708')\"\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-051f91e2-5184-42d9-a547-a00f67d8d708 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q7 [15 points]  The data is represented in wide format for the variables 'AvgPooling', 'H1', 'H8', 'H16', 'H32', and 'H64'. Let's organize them in long format: the values of 'AvgPooling', 'H1', 'H8', 'H16', 'H32', 'H64' have a variable named 'method', and the value name is 'PearsonR'. (데이터는 'AvgPooling', 'H1', 'H8', 'H16', 'H32', 'H64' 변수들에 대해 wide format으로 표현되어있습니다. 해당 변수들에 대해 long format으로 정리해 봅시다. 'AvgPooling', 'H1', 'H8', 'H16', 'H32', 'H64'의 값들은 method 라는 변수이름을 갖도록 하고, 값의 이름으로는 'PearsonR'로 설정해 주세요. )\n",
        "\n",
        "The result should look like below:\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "M1hHOiYGnpDW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "q7 = expdata.melt(\n",
        "    id_vars = ['dim','AttentionHeads', 'MacaronBlocks'],\n",
        "    value_vars = ['AvgPooling', 'H1', 'H8', 'H16', 'H32', 'H64'],\n",
        "    value_name = 'PearsonR',\n",
        "    var_name = 'method'\n",
        ")\n",
        "q7"
      ],
      "metadata": {
        "id": "PrFgpseAdkod",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "71bcaf5d-f528-451a-9a91-dd5f8888aa0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    dim  AttentionHeads  MacaronBlocks      method  PearsonR\n",
              "0    64               2              1  AvgPooling    0.7026\n",
              "1    64               2              2  AvgPooling    0.7094\n",
              "2    64               2              4  AvgPooling    0.7140\n",
              "3    64               2              8  AvgPooling    0.7151\n",
              "4   128               4              1  AvgPooling    0.7033\n",
              "..  ...             ...            ...         ...       ...\n",
              "67  128               4              8         H64    0.7226\n",
              "68  256               8              1         H64    0.7152\n",
              "69  256               8              2         H64    0.7177\n",
              "70  256               8              4         H64    0.7211\n",
              "71  256               8              8         H64    0.7173\n",
              "\n",
              "[72 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c89e99b8-1a50-42d3-bb3e-cb449636bbaa\" 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>dim</th>\n",
              "      <th>AttentionHeads</th>\n",
              "      <th>MacaronBlocks</th>\n",
              "      <th>method</th>\n",
              "      <th>PearsonR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>AvgPooling</td>\n",
              "      <td>0.7026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>AvgPooling</td>\n",
              "      <td>0.7094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>AvgPooling</td>\n",
              "      <td>0.7140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>AvgPooling</td>\n",
              "      <td>0.7151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>AvgPooling</td>\n",
              "      <td>0.7033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7173</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>72 rows × 5 columns</p>\n",
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              "\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\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",
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              "\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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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
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              "      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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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J92jlK55e3cJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q8 [10 points] Take a subset of the cases where the variable 'method' has the values 'H8', 'H16', 'H32', and 'H64' and save it as data named 'res1'. ('method' 변수가 'H8', 'H16', 'H32', 'H64' 값을 갖는 경우의 데이터로 부분집합을 취해서 'res1'이라는 데이터로 저장해 보세요.)\n",
        "\n",
        "If you don't get the answer to Q7, assume you did, and write down the code. I will give you credit if the answer is correct. (Q7의 답을 구하지 못한 경우도, 답을 구했다고 가정하고, 코드를 적어서 코드가 맞으면 정답으로 인정해줍니다. )\n"
      ],
      "metadata": {
        "id": "TFC5IWvXo4kB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "res1 = q7.loc[q7.method.isin(['H8', 'H16', 'H32', 'H64'])]\n",
        "res1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "A3pEtN08fLzU",
        "outputId": "7e471f4e-cb01-4a00-d72f-77ef543d449c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    dim  AttentionHeads  MacaronBlocks method  PearsonR\n",
              "24   64               2              1     H8    0.7011\n",
              "25   64               2              2     H8    0.7122\n",
              "26   64               2              4     H8    0.7162\n",
              "27   64               2              8     H8    0.7223\n",
              "28  128               4              1     H8    0.7137\n",
              "29  128               4              2     H8    0.7142\n",
              "30  128               4              4     H8    0.7207\n",
              "31  128               4              8     H8    0.7223\n",
              "32  256               8              1     H8    0.7104\n",
              "33  256               8              2     H8    0.7197\n",
              "34  256               8              4     H8    0.7210\n",
              "35  256               8              8     H8    0.7188\n",
              "36   64               2              1    H16    0.6977\n",
              "37   64               2              2    H16    0.7119\n",
              "38   64               2              4    H16    0.7184\n",
              "39   64               2              8    H16    0.7138\n",
              "40  128               4              1    H16    0.7075\n",
              "41  128               4              2    H16    0.7197\n",
              "42  128               4              4    H16    0.7218\n",
              "43  128               4              8    H16    0.7200\n",
              "44  256               8              1    H16    0.7124\n",
              "45  256               8              2    H16    0.7185\n",
              "46  256               8              4    H16    0.7186\n",
              "47  256               8              8    H16    0.7194\n",
              "48   64               2              1    H32    0.6977\n",
              "49   64               2              2    H32    0.7136\n",
              "50   64               2              4    H32    0.7190\n",
              "51   64               2              8    H32    0.7191\n",
              "52  128               4              1    H32    0.7069\n",
              "53  128               4              2    H32    0.7147\n",
              "54  128               4              4    H32    0.7139\n",
              "55  128               4              8    H32    0.7207\n",
              "56  256               8              1    H32    0.7058\n",
              "57  256               8              2    H32    0.7207\n",
              "58  256               8              4    H32    0.7213\n",
              "59  256               8              8    H32    0.7222\n",
              "60   64               2              1    H64    0.6943\n",
              "61   64               2              2    H64    0.7088\n",
              "62   64               2              4    H64    0.7200\n",
              "63   64               2              8    H64    0.7214\n",
              "64  128               4              1    H64    0.7047\n",
              "65  128               4              2    H64    0.7175\n",
              "66  128               4              4    H64    0.7192\n",
              "67  128               4              8    H64    0.7226\n",
              "68  256               8              1    H64    0.7152\n",
              "69  256               8              2    H64    0.7177\n",
              "70  256               8              4    H64    0.7211\n",
              "71  256               8              8    H64    0.7173"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fdedc6d7-b83a-48ef-ba39-b32afcc2e03d\" 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>dim</th>\n",
              "      <th>AttentionHeads</th>\n",
              "      <th>MacaronBlocks</th>\n",
              "      <th>method</th>\n",
              "      <th>PearsonR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>H8</td>\n",
              "      <td>0.7011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>H8</td>\n",
              "      <td>0.7122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>H8</td>\n",
              "      <td>0.7162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>H8</td>\n",
              "      <td>0.7223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>H8</td>\n",
              "      <td>0.7137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>H8</td>\n",
              "      <td>0.7142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>H8</td>\n",
              "      <td>0.7207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>H8</td>\n",
              "      <td>0.7223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>H8</td>\n",
              "      <td>0.7104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>H8</td>\n",
              "      <td>0.7197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>H8</td>\n",
              "      <td>0.7210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>H8</td>\n",
              "      <td>0.7188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>H16</td>\n",
              "      <td>0.6977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>H16</td>\n",
              "      <td>0.7119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>H16</td>\n",
              "      <td>0.7184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>H16</td>\n",
              "      <td>0.7138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>H16</td>\n",
              "      <td>0.7075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>H16</td>\n",
              "      <td>0.7197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>H16</td>\n",
              "      <td>0.7218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>H16</td>\n",
              "      <td>0.7200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>H16</td>\n",
              "      <td>0.7124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>H16</td>\n",
              "      <td>0.7185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>H16</td>\n",
              "      <td>0.7186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>H16</td>\n",
              "      <td>0.7194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>H32</td>\n",
              "      <td>0.6977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>H32</td>\n",
              "      <td>0.7136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>H32</td>\n",
              "      <td>0.7190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>H32</td>\n",
              "      <td>0.7191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>H32</td>\n",
              "      <td>0.7069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>H32</td>\n",
              "      <td>0.7147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>H32</td>\n",
              "      <td>0.7139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>H32</td>\n",
              "      <td>0.7207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>H32</td>\n",
              "      <td>0.7058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>H32</td>\n",
              "      <td>0.7207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>H32</td>\n",
              "      <td>0.7213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>H32</td>\n",
              "      <td>0.7222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.6943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>256</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>H64</td>\n",
              "      <td>0.7173</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-fdedc6d7-b83a-48ef-ba39-b32afcc2e03d')\"\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-fdedc6d7-b83a-48ef-ba39-b32afcc2e03d 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-fdedc6d7-b83a-48ef-ba39-b32afcc2e03d');\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-3a6d3e83-543a-4845-8c92-29435a695740\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3a6d3e83-543a-4845-8c92-29435a695740')\"\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-3a6d3e83-543a-4845-8c92-29435a695740 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If we calculated res1 correctly, we can use seaborn to visualize it in a violin plot as shown below. (res1을 바르게 계산했다면 seaborn을 이용하여 아래와 같이 violin plot으로 시각화 시킬 수 있습니다.)"
      ],
      "metadata": {
        "id": "phoTs6-Iota7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.violinplot(res1, x = 'MacaronBlocks', y = 'PearsonR')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "HreMOYCNdkf2",
        "outputId": "5b9cd779-45a9-4f4c-d4c6-946188520922"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='MacaronBlocks', ylabel='PearsonR'>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "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": [],
      "metadata": {
        "id": "UNs5zQJVs7ji"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}