Exploring_WattTime_API.ipynb

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    "colab": {
      "provenance": [],
      "gpuType": "T4"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "7aGMkAqDfdxk"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "from requests.auth import HTTPBasicAuth\n",
        "import json\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### `/register`\n",
        "\n",
        "`one time run with your username, password, email`"
      ],
      "metadata": {
        "id": "1NOnD4e9vShk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "register_url = 'https://api.watttime.org/register'\n",
        "params = {'username': 'your_username',\n",
        "         'password': 'your_password',\n",
        "         'email': 'your_email_id',\n",
        "         'org': 'optional_param'}\n",
        "rsp = requests.post(register_url, json=params)\n",
        "print(rsp.text)"
      ],
      "metadata": {
        "id": "GadSBkZvuW2h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### `/login`"
      ],
      "metadata": {
        "id": "waY_TxrIvdxb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "login_url = 'https://api.watttime.org/login'\n",
        "rsp = requests.get(login_url, auth=HTTPBasicAuth('your_username', 'your_password'))\n",
        "TOKEN = rsp.json()['token']\n",
        "print(TOKEN)"
      ],
      "metadata": {
        "id": "mNgTK2h1ussb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Checking Access - `/v3/my-access`"
      ],
      "metadata": {
        "id": "zu0_HmWuvz4L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://api.watttime.org/v3/my-access\"\n",
        "headers = {\"Authorization\": f\"Bearer {TOKEN}\"}\n",
        "params = {}\n",
        "response = requests.get(url, headers=headers, params=params)\n",
        "response.raise_for_status()\n",
        "print(response.json())\n",
        "'''\n",
        "for i in response.json()['signal_types']:\n",
        "    for key, value in i.items():\n",
        "      print(f\"{key} \\n\\n {value} \\n\\n\")\n",
        "'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "4wI3pejjvuwG",
        "outputId": "3d1029b0-e170-4896-a109-46ef7ee94991"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'signal_types': [{'signal_type': 'co2_moer', 'regions': [{'region': 'CAISO_NORTH', 'region_full_name': 'California ISO Northern', 'parent': 'CAISO', 'data_point_period_seconds': 300, 'endpoints': [{'endpoint': 'v3/historical', 'models': [{'model': '2022-10-01', 'data_start': '2020-01-01', 'train_start': '2020-03-01', 'train_end': '2022-03-01', 'type': 'binned_regression'}, {'model': '2023-03-01', 'data_start': '2020-03-01', 'train_start': '2021-01-01', 'train_end': '2023-03-01', 'type': 'binned_regression'}]}, {'endpoint': 'v3/forecast', 'models': [{'model': '2022-10-01', 'data_start': '2021-12-31', 'train_start': '2020-01-01', 'train_end': '2021-12-31'}, {'model': '2023-03-01', 'data_start': '2023-01-01', 'train_start': '2021-01-01', 'train_end': '2023-01-01'}]}]}]}, {'signal_type': 'health_damage', 'regions': [{'region': 'CAISO_NORTH', 'region_full_name': 'California ISO Northern', 'parent': 'CAISO', 'data_point_period_seconds': 300, 'endpoints': [{'endpoint': 'v3/historical', 'models': [{'model': '2022-10-01', 'data_start': '2020-01-01', 'train_start': '2020-03-01', 'train_end': '2022-03-01', 'type': 'binned_regression'}]}, {'endpoint': 'v3/forecast', 'models': [{'model': '2022-06-03', 'data_start': '2019-12-31', 'train_start': '2019-01-01', 'train_end': '2019-12-31'}, {'model': '2022-10-01', 'data_start': '2021-12-31', 'train_start': '2020-01-01', 'train_end': '2021-12-31'}]}]}]}]}\n"
          ]
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            "text/plain": [
              "'\\nfor i in response.json()[\\'signal_types\\']:\\n    for key, value in i.items():\\n      print(f\"{key} \\n\\n {value} \\n\\n\")\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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          "execution_count": 4
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      "cell_type": "code",
      "source": [
        "# Assuming response.json() returns the JSON data\n",
        "json_data = response.json()\n",
        "\n",
        "# Extract the 'signal_types' key\n",
        "data = json_data['signal_types']\n",
        "\n",
        "\n",
        "# Normalize the JSON data\n",
        "# Normalize the JSON data\n",
        "df = pd.json_normalize(data, record_path=['regions', 'endpoints', 'models'],\n",
        "                       meta=['signal_type', ['regions', 'region'],\n",
        "                             ['regions', 'region_full_name'], ['regions', 'parent'],\n",
        "                             ['regions', 'data_point_period_seconds'],\n",
        "                             ['regions', 'endpoints', 'endpoint']])\n",
        "\n",
        "# Display the DataFrame\n",
        "for i in df.columns:\n",
        "    print(f\"\\n----{i}------\\n {df[i].value_counts()} \\n----------\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dwNaqLjXvyj9",
        "outputId": "da69dbcc-aa08-4e81-fbcd-640ed1a051a5"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "----model------\n",
            " model\n",
            "2022-10-01    4\n",
            "2023-03-01    2\n",
            "2022-06-03    1\n",
            "Name: count, dtype: int64 \n",
            "----------\n",
            "\n",
            "\n",
            "----data_start------\n",
            " data_start\n",
            "2020-01-01    2\n",
            "2021-12-31    2\n",
            "2020-03-01    1\n",
            "2023-01-01    1\n",
            "2019-12-31    1\n",
            "Name: count, dtype: int64 \n",
            "----------\n",
            "\n",
            "\n",
            "----train_start------\n",
            " train_start\n",
            "2020-03-01    2\n",
            "2021-01-01    2\n",
            "2020-01-01    2\n",
            "2019-01-01    1\n",
            "Name: count, dtype: int64 \n",
            "----------\n",
            "\n",
            "\n",
            "----train_end------\n",
            " train_end\n",
            "2022-03-01    2\n",
            "2021-12-31    2\n",
            "2023-03-01    1\n",
            "2023-01-01    1\n",
            "2019-12-31    1\n",
            "Name: count, dtype: int64 \n",
            "----------\n",
            "\n",
            "\n",
            "----type------\n",
            " type\n",
            "binned_regression    3\n",
            "Name: count, dtype: int64 \n",
            "----------\n",
            "\n",
            "\n",
            "----signal_type------\n",
            " signal_type\n",
            "co2_moer         4\n",
            "health_damage    3\n",
            "Name: count, dtype: int64 \n",
            "----------\n",
            "\n",
            "\n",
            "----regions.region------\n",
            " regions.region\n",
            "CAISO_NORTH    7\n",
            "Name: count, dtype: int64 \n",
            "----------\n",
            "\n",
            "\n",
            "----regions.region_full_name------\n",
            " regions.region_full_name\n",
            "California ISO Northern    7\n",
            "Name: count, dtype: int64 \n",
            "----------\n",
            "\n",
            "\n",
            "----regions.parent------\n",
            " regions.parent\n",
            "CAISO    7\n",
            "Name: count, dtype: int64 \n",
            "----------\n",
            "\n",
            "\n",
            "----regions.data_point_period_seconds------\n",
            " regions.data_point_period_seconds\n",
            "300    7\n",
            "Name: count, dtype: int64 \n",
            "----------\n",
            "\n",
            "\n",
            "----regions.endpoints.endpoint------\n",
            " regions.endpoints.endpoint\n",
            "v3/forecast      4\n",
            "v3/historical    3\n",
            "Name: count, dtype: int64 \n",
            "----------\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Grid Region: `/v3/region-from-loc`"
      ],
      "metadata": {
        "id": "jJJjWjVl0zRC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://api.watttime.org/v3/region-from-loc\"\n",
        "\n",
        "headers = {\"Authorization\": f\"Bearer {TOKEN}\"}\n",
        "params = {\"latitude\": \"32.372\", \"longitude\": \"-87.519\", \"signal_type\": \"co2_moer\"}\n",
        "response = requests.get(url, headers=headers, params=params)\n",
        "response.raise_for_status()\n",
        "print(response.json())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "g70RVP520hs2",
        "outputId": "6a52ba59-a9cd-4895-fbd1-7e1e7e0693c6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'region': 'SOCO', 'region_full_name': 'Southern Co Services Inc', 'signal_type': 'co2_moer'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://api.watttime.org/v3/region-from-loc\"\n",
        "\n",
        "headers = {\"Authorization\": f\"Bearer {TOKEN}\"}\n",
        "params = {\"latitude\": \"42.372\", \"longitude\": \"-76.519\", \"signal_type\": \"health_damage\"}\n",
        "response = requests.get(url, headers=headers, params=params)\n",
        "response.raise_for_status()\n",
        "print(response.json())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "zCapzv3O1TXX",
        "outputId": "6b9010e0-dd2e-4283-8b21-f1828ee5562c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'region': 'NYISO_CENTRAL', 'region_full_name': 'NYISO Central', 'signal_type': 'health_damage'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Forecasting: `/v3/forecast`"
      ],
      "metadata": {
        "id": "hA09EbkA2a77"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### Forecast data of region `CAISO_NORTH` and singal_type `co2_moer`"
      ],
      "metadata": {
        "id": "O4Ktq9acBTmK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://api.watttime.org/v3/forecast\"\n",
        "\n",
        "headers = {\"Authorization\": f\"Bearer {TOKEN}\"}\n",
        "params = {\n",
        "    \"region\": \"CAISO_NORTH\",\n",
        "    \"signal_type\": \"co2_moer\",\n",
        "}\n",
        "response = requests.get(url, headers=headers, params=params)\n",
        "response.raise_for_status()\n",
        "\n",
        "json_data = response.json()\n",
        "data = json_data['data']\n",
        "data\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "df['Date'] = pd.to_datetime(df['point_time']).dt.date\n",
        "df['Time'] = pd.to_datetime(df['point_time']).dt.time\n",
        "df['Hour'] = pd.to_datetime(df['point_time']).dt.hour\n",
        "df.drop(columns=['point_time'], inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "pGnim6gD2UdQ",
        "outputId": "f33ecc05-8eab-4785-b09a-e84a4b982783"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     value        Date      Time  Hour\n",
              "0    930.6  2024-10-03  05:50:00     5\n",
              "1    930.6  2024-10-03  05:55:00     5\n",
              "2    930.3  2024-10-03  06:00:00     6\n",
              "3    930.2  2024-10-03  06:05:00     6\n",
              "4    931.1  2024-10-03  06:10:00     6\n",
              "..     ...         ...       ...   ...\n",
              "283  933.7  2024-10-04  05:25:00     5\n",
              "284  934.1  2024-10-04  05:30:00     5\n",
              "285  931.7  2024-10-04  05:35:00     5\n",
              "286  931.1  2024-10-04  05:40:00     5\n",
              "287  931.1  2024-10-04  05:45:00     5\n",
              "\n",
              "[288 rows x 4 columns]"
            ],
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              "    <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>Hour</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>930.6</td>\n",
              "      <td>2024-10-03</td>\n",
              "      <td>05:50:00</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>930.6</td>\n",
              "      <td>2024-10-03</td>\n",
              "      <td>05:55:00</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>930.3</td>\n",
              "      <td>2024-10-03</td>\n",
              "      <td>06:00:00</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>930.2</td>\n",
              "      <td>2024-10-03</td>\n",
              "      <td>06:05:00</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>931.1</td>\n",
              "      <td>2024-10-03</td>\n",
              "      <td>06:10:00</td>\n",
              "      <td>6</td>\n",
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              "      <th>283</th>\n",
              "      <td>933.7</td>\n",
              "      <td>2024-10-04</td>\n",
              "      <td>05:25:00</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284</th>\n",
              "      <td>934.1</td>\n",
              "      <td>2024-10-04</td>\n",
              "      <td>05:30:00</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>931.7</td>\n",
              "      <td>2024-10-04</td>\n",
              "      <td>05:35:00</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>286</th>\n",
              "      <td>931.1</td>\n",
              "      <td>2024-10-04</td>\n",
              "      <td>05:40:00</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>931.1</td>\n",
              "      <td>2024-10-04</td>\n",
              "      <td>05:45:00</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
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              "<div id=\"df-d0101e55-9a03-420a-a9d0-f8c6d7288069\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d0101e55-9a03-420a-a9d0-f8c6d7288069')\"\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-d0101e55-9a03-420a-a9d0-f8c6d7288069 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_d5e3f54f-8cad-4a63-bec8-d0adf48c6202\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_d5e3f54f-8cad-4a63-bec8-d0adf48c6202 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 288,\n  \"fields\": [\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47.0821789696916,\n        \"min\": 802.9,\n        \"max\": 1049.8,\n        \"num_unique_values\": 236,\n        \"samples\": [\n          948.8,\n          1030.6,\n          1020.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-10-03\",\n        \"max\": \"2024-10-04\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"2024-10-04\",\n          \"2024-10-03\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 288,\n        \"samples\": [\n          \"09:35:00\",\n          \"18:55:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hour\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          13,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Date'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "w0zJAq1v2pvQ",
        "outputId": "b864ed1f-d921-450d-b0f8-8e7425b761c5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2024-10-03    218\n",
              "2024-10-04     70\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2024-10-03</th>\n",
              "      <td>218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-10-04</th>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_dates = df['Date'].unique()\n",
        "unique_dates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "R_qUXoTCn6oH",
        "outputId": "1d62e151-44b5-41a7-b9c0-b5a4737a90a3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([datetime.date(2024, 10, 3), datetime.date(2024, 10, 4)],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### For 1st Date of Prediction"
      ],
      "metadata": {
        "id": "9Sb6jzbtBKKf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_date_01 = df[pd.to_datetime(df['Date']) == pd.to_datetime(unique_dates[0])]\n",
        "df_date_01.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "I9gGKBvc7GMv",
        "outputId": "164a1f35-772d-46d2-bf25-1c326c166626"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(218, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(df_date_01['Hour'], df_date_01['value'], marker='o', linestyle=\"--\", color='red', label='Value')\n",
        "\n",
        "# Rotate date labels for better readability\n",
        "plt.gcf().autofmt_xdate()\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Time in Hour')\n",
        "plt.ylabel('Value')\n",
        "plt.title('Value over Time')\n",
        "\n",
        "# Show the plot\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "7WaUFOtX7o9j",
        "outputId": "97d29751-70e4-451d-9e57-39e1f14251b2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### For 2nd Date of Forecast"
      ],
      "metadata": {
        "id": "aEzeLxGrET-n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_date_02 = df[pd.to_datetime(df['Date']) == pd.to_datetime(unique_dates[1])]\n",
        "df_date_02.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "66NgUQFFEYjz",
        "outputId": "02f4382b-aeae-4e5a-c4c6-b530f5233d19"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(df_date_02['Hour'], df_date_02['value'], marker='o', linestyle=\"--\", color='red', label='Value')\n",
        "\n",
        "# Rotate date labels for better readability\n",
        "plt.gcf().autofmt_xdate()\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Time in Hour')\n",
        "plt.ylabel('Value')\n",
        "plt.title('Value over Time')\n",
        "\n",
        "# Show the plot\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "YgQKr_W9Ec10",
        "outputId": "cc484bbf-542a-4ec1-f8cb-68fe4624f213"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ffrOvCQ_7j0D"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}