NG_EPF_exercise.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "mount_file_id": "1-0rtWLzQsw7RBblNlyY6pDIMorq7jjOo",
      "authorship_tag": "ABX9TyNlnsm8S2id7F1FwbaEMCD3",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nifemi-alonge/EPF/blob/main/NG_EPF_exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set up"
      ],
      "metadata": {
        "id": "IAY-T-MRNeD_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Packages"
      ],
      "metadata": {
        "id": "sVAZS_5fNhrX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from statsmodels.tsa.arima.model import ARIMA\n",
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
        "import xgboost as xgb\n",
        "import statsmodels.api as sm"
      ],
      "metadata": {
        "id": "NDbUcMjK98tC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "IEzVO3NeOqpP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set paths"
      ],
      "metadata": {
        "id": "jSCpK9rY_xKr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Note: the paths are specific to my file directory\n",
        "data_path = \"/content/drive/MyDrive/REDACTED/data/\"\n",
        "output_path = \"/content/drive/MyDrive/REDACTED/output/\""
      ],
      "metadata": {
        "id": "J-MqfXIP9_eK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.max_rows', None)"
      ],
      "metadata": {
        "id": "XyWJ5bFAIjHW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load in data"
      ],
      "metadata": {
        "id": "xRYKbYdWNjZu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data pre-processing function"
      ],
      "metadata": {
        "id": "G4Eq4N9nv92X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to convert date to datetime type\n",
        "def preprocess_datetime_column(df, datetime_column):\n",
        "    # Check the data type of the 'Datetime (UTC)' column\n",
        "    original_dtype = df[datetime_column].dtypes\n",
        "\n",
        "    # Convert the datetime_column  to datetime with UTC timezone\n",
        "    df[datetime_column] = pd.to_datetime(df[datetime_column], utc=True)\n",
        "\n",
        "    # Check the data type of the datetime_column after conversion\n",
        "    new_dtype = df[datetime_column].dtypes\n",
        "\n",
        "    return original_dtype, new_dtype"
      ],
      "metadata": {
        "id": "GjI7kxlbFb28"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Demand"
      ],
      "metadata": {
        "id": "tNOJnEbTeSeT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# demand data\n",
        "demand_df = pd.read_csv(data_path + 'demand/' + 'demand.csv')\n",
        "demand_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "kLgasrFrNk-c",
        "outputId": "506792ab-b4f4-48d1-95b5-ae489c408d2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Datetime Country  Source  Demand (MW)\n",
              "0  2015-01-01 00:00:00+00:00      BE  ENTSOe       9555.0\n",
              "1  2015-01-01 01:00:00+00:00      BE  ENTSOe       9094.0\n",
              "2  2015-01-01 02:00:00+00:00      BE  ENTSOe       8691.0\n",
              "3  2015-01-01 03:00:00+00:00      BE  ENTSOe       8354.0\n",
              "4  2015-01-01 04:00:00+00:00      BE  ENTSOe       8080.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-29c1a3d7-be8c-4a4d-853f-2249b2306641\" 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>Datetime</th>\n",
              "      <th>Country</th>\n",
              "      <th>Source</th>\n",
              "      <th>Demand (MW)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-01 00:00:00+00:00</td>\n",
              "      <td>BE</td>\n",
              "      <td>ENTSOe</td>\n",
              "      <td>9555.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-01 01:00:00+00:00</td>\n",
              "      <td>BE</td>\n",
              "      <td>ENTSOe</td>\n",
              "      <td>9094.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-01 02:00:00+00:00</td>\n",
              "      <td>BE</td>\n",
              "      <td>ENTSOe</td>\n",
              "      <td>8691.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-01 03:00:00+00:00</td>\n",
              "      <td>BE</td>\n",
              "      <td>ENTSOe</td>\n",
              "      <td>8354.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-01 04:00:00+00:00</td>\n",
              "      <td>BE</td>\n",
              "      <td>ENTSOe</td>\n",
              "      <td>8080.0</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-29c1a3d7-be8c-4a4d-853f-2249b2306641')\"\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-29c1a3d7-be8c-4a4d-853f-2249b2306641 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-29c1a3d7-be8c-4a4d-853f-2249b2306641');\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-499ec7e5-c6fe-417f-8b14-ff74c49d8eb0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-499ec7e5-c6fe-417f-8b14-ff74c49d8eb0')\"\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-499ec7e5-c6fe-417f-8b14-ff74c49d8eb0 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": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert date to datetime type\n",
        "original_dtype, new_dtype = preprocess_datetime_column(demand_df, 'Datetime')\n",
        "\n",
        "print(f\"Original datetime column data type: {original_dtype}\")\n",
        "print(f\"New datetime column data type: {new_dtype}\")\n",
        "\n",
        "demand_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "Gtb2fn6nFi6j",
        "outputId": "60a58c08-658e-4bdf-df78-92cb2a0db5ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original datetime column data type: object\n",
            "New datetime column data type: datetime64[ns, UTC]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Datetime Country  Source  Demand (MW)\n",
              "0 2015-01-01 00:00:00+00:00      BE  ENTSOe       9555.0\n",
              "1 2015-01-01 01:00:00+00:00      BE  ENTSOe       9094.0\n",
              "2 2015-01-01 02:00:00+00:00      BE  ENTSOe       8691.0\n",
              "3 2015-01-01 03:00:00+00:00      BE  ENTSOe       8354.0\n",
              "4 2015-01-01 04:00:00+00:00      BE  ENTSOe       8080.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e68b1cf-149a-4f83-bbc7-b3eaf31063a9\" 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>Datetime</th>\n",
              "      <th>Country</th>\n",
              "      <th>Source</th>\n",
              "      <th>Demand (MW)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-01 00:00:00+00:00</td>\n",
              "      <td>BE</td>\n",
              "      <td>ENTSOe</td>\n",
              "      <td>9555.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-01 01:00:00+00:00</td>\n",
              "      <td>BE</td>\n",
              "      <td>ENTSOe</td>\n",
              "      <td>9094.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-01 02:00:00+00:00</td>\n",
              "      <td>BE</td>\n",
              "      <td>ENTSOe</td>\n",
              "      <td>8691.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-01 03:00:00+00:00</td>\n",
              "      <td>BE</td>\n",
              "      <td>ENTSOe</td>\n",
              "      <td>8354.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-01 04:00:00+00:00</td>\n",
              "      <td>BE</td>\n",
              "      <td>ENTSOe</td>\n",
              "      <td>8080.0</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-6e68b1cf-149a-4f83-bbc7-b3eaf31063a9')\"\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-6e68b1cf-149a-4f83-bbc7-b3eaf31063a9 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-6e68b1cf-149a-4f83-bbc7-b3eaf31063a9');\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-3aee6178-d475-4f9d-80dd-d397ea75b6db\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3aee6178-d475-4f9d-80dd-d397ea75b6db')\"\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-3aee6178-d475-4f9d-80dd-d397ea75b6db 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": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CHECK dates\n",
        "print(demand_df['Datetime'].min())\n",
        "print(demand_df['Datetime'].max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tV3WrAx8K5Hm",
        "outputId": "bb84356d-e276-4b12-87c6-28b871775475"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2015-01-01 00:00:00+00:00\n",
            "2020-12-31 23:00:00+00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### GB Demand"
      ],
      "metadata": {
        "id": "RpWS-CseeVN3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# subset data to create GB demand\n",
        "gb_demand_df = demand_df[demand_df['Country'] == 'GB']"
      ],
      "metadata": {
        "id": "nbs4iTDWb1a1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Other Demand"
      ],
      "metadata": {
        "id": "gR_Ol2Ojkts-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# subset\n",
        "be_demand_df = demand_df[demand_df['Country'] == 'BE']\n",
        "dk_demand_df = demand_df[demand_df['Country'] == 'DK']\n",
        "fr_demand_df = demand_df[demand_df['Country'] == 'FR']\n",
        "de_demand_df = demand_df[demand_df['Country'] == 'DE']\n",
        "ie_demand_df = demand_df[demand_df['Country'] == 'IE']\n",
        "nl_demand_df = demand_df[demand_df['Country'] == 'NL']\n",
        "# Note: no Norway demand data"
      ],
      "metadata": {
        "id": "qjM3FMMekvbL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# List of DataFrames\n",
        "add_demand = [be_demand_df, dk_demand_df, fr_demand_df, de_demand_df,\n",
        "                   ie_demand_df, nl_demand_df]\n",
        "\n",
        "# Define a list of country names corresponding to each DataFrame\n",
        "country_names = ['belgium', 'denmark', 'france', 'germany', 'ireland',\n",
        "                 'netherlands']\n",
        "\n",
        "# Initialize an empty DataFrame to store the merged data\n",
        "add_demand_df = pd.DataFrame()\n",
        "\n",
        "# Iterate through the list of DataFrames\n",
        "for df, country_name in zip(add_demand, country_names):\n",
        "    # Remove duplicate datetime values within each DataFrame\n",
        "    df = df.drop_duplicates(subset='Datetime', keep='first')\n",
        "\n",
        "    # Subset only the 'Datetime' and 'Demand (MW)' columns\n",
        "    subset_df = df[['Datetime', 'Demand (MW)']]\n",
        "\n",
        "    # Rename the 'Demand (MW)' column to the country-specific name\n",
        "    subset_df.rename(columns={'Demand (MW)': f'{country_name}_demand'}, inplace=True)\n",
        "\n",
        "    # Merge the subset DataFrame with the merged DataFrame\n",
        "    if add_demand_df.empty:\n",
        "        add_demand_df = subset_df\n",
        "    else:\n",
        "        # Perform a left merge to avoid duplicate rows\n",
        "        add_demand_df = pd.merge(add_demand_df, subset_df, on='Datetime', how='left')\n",
        "\n",
        "add_demand_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XJNZ-EN_m0Ku",
        "outputId": "0758baf1-5115-4f12-e55b-59285e8811ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Datetime  belgium_demand  denmark_demand  france_demand  \\\n",
              "0 2015-01-01 00:00:00+00:00          9555.0          3337.9      71506.350   \n",
              "1 2015-01-01 01:00:00+00:00          9094.0          3223.2      71134.370   \n",
              "2 2015-01-01 02:00:00+00:00          8691.0          3090.7      68840.400   \n",
              "3 2015-01-01 03:00:00+00:00          8354.0          2953.4      65749.414   \n",
              "4 2015-01-01 04:00:00+00:00          8080.0          2888.7      64041.330   \n",
              "\n",
              "   germany_demand  ireland_demand  netherlands_demand  \n",
              "0       47445.360          2849.0             11586.0  \n",
              "1       45923.710          2651.0             11338.0  \n",
              "2       44295.875          2412.0             10917.0  \n",
              "3       42868.043          2256.0             10473.0  \n",
              "4       42230.930          2170.0             10200.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7627d814-f3e9-4b49-a95e-3cf13711154b\" 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>Datetime</th>\n",
              "      <th>belgium_demand</th>\n",
              "      <th>denmark_demand</th>\n",
              "      <th>france_demand</th>\n",
              "      <th>germany_demand</th>\n",
              "      <th>ireland_demand</th>\n",
              "      <th>netherlands_demand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-01 00:00:00+00:00</td>\n",
              "      <td>9555.0</td>\n",
              "      <td>3337.9</td>\n",
              "      <td>71506.350</td>\n",
              "      <td>47445.360</td>\n",
              "      <td>2849.0</td>\n",
              "      <td>11586.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-01 01:00:00+00:00</td>\n",
              "      <td>9094.0</td>\n",
              "      <td>3223.2</td>\n",
              "      <td>71134.370</td>\n",
              "      <td>45923.710</td>\n",
              "      <td>2651.0</td>\n",
              "      <td>11338.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-01 02:00:00+00:00</td>\n",
              "      <td>8691.0</td>\n",
              "      <td>3090.7</td>\n",
              "      <td>68840.400</td>\n",
              "      <td>44295.875</td>\n",
              "      <td>2412.0</td>\n",
              "      <td>10917.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-01 03:00:00+00:00</td>\n",
              "      <td>8354.0</td>\n",
              "      <td>2953.4</td>\n",
              "      <td>65749.414</td>\n",
              "      <td>42868.043</td>\n",
              "      <td>2256.0</td>\n",
              "      <td>10473.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-01 04:00:00+00:00</td>\n",
              "      <td>8080.0</td>\n",
              "      <td>2888.7</td>\n",
              "      <td>64041.330</td>\n",
              "      <td>42230.930</td>\n",
              "      <td>2170.0</td>\n",
              "      <td>10200.0</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-7627d814-f3e9-4b49-a95e-3cf13711154b')\"\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-7627d814-f3e9-4b49-a95e-3cf13711154b 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-7627d814-f3e9-4b49-a95e-3cf13711154b');\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-90210f12-58ae-4085-a0f2-5a23a1958ead\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-90210f12-58ae-4085-a0f2-5a23a1958ead')\"\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-90210f12-58ae-4085-a0f2-5a23a1958ead 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": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### GB Prices"
      ],
      "metadata": {
        "id": "RRMcPVnaeW2G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# GB price data\n",
        "gb_prices_df = pd.read_csv(data_path + 'prices/' + 'Great Britain.csv')\n",
        "gb_prices_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "mGWQ6j-5HTY8",
        "outputId": "217775bf-1c61-4efd-c6a1-5017d4fd5868"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Country ISO3 Code    Datetime (UTC) Price (GBP/MWhe)\n",
              "0  Great Britain       GBR  01/01/2015 00:00                0\n",
              "1  Great Britain       GBR  01/01/2015 01:00                0\n",
              "2  Great Britain       GBR  01/01/2015 02:00                0\n",
              "3  Great Britain       GBR  01/01/2015 03:00                0\n",
              "4  Great Britain       GBR  01/01/2015 04:00                0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e600cc0f-a460-4ac7-9bcb-95ab80976117\" 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>Country</th>\n",
              "      <th>ISO3 Code</th>\n",
              "      <th>Datetime (UTC)</th>\n",
              "      <th>Price (GBP/MWhe)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>GBR</td>\n",
              "      <td>01/01/2015 00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>GBR</td>\n",
              "      <td>01/01/2015 01:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>GBR</td>\n",
              "      <td>01/01/2015 02:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>GBR</td>\n",
              "      <td>01/01/2015 03:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>GBR</td>\n",
              "      <td>01/01/2015 04:00</td>\n",
              "      <td>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-e600cc0f-a460-4ac7-9bcb-95ab80976117')\"\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-e600cc0f-a460-4ac7-9bcb-95ab80976117 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-e600cc0f-a460-4ac7-9bcb-95ab80976117');\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-dab10cee-e4fa-44bc-9901-01261831fa1b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dab10cee-e4fa-44bc-9901-01261831fa1b')\"\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-dab10cee-e4fa-44bc-9901-01261831fa1b 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": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert date to datetime type\n",
        "original_dtype, new_dtype = preprocess_datetime_column(gb_prices_df, 'Datetime (UTC)')\n",
        "\n",
        "print(f\"Original datetime column data type: {original_dtype}\")\n",
        "print(f\"New datetime column data type: {new_dtype}\")\n",
        "\n",
        "gb_prices_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "6RcKGE5dLUaX",
        "outputId": "1f6da622-1e66-4e5f-f181-3d892a4ca56c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original datetime column data type: object\n",
            "New datetime column data type: datetime64[ns, UTC]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Country ISO3 Code            Datetime (UTC) Price (GBP/MWhe)\n",
              "0  Great Britain       GBR 2015-01-01 00:00:00+00:00                0\n",
              "1  Great Britain       GBR 2015-01-01 01:00:00+00:00                0\n",
              "2  Great Britain       GBR 2015-01-01 02:00:00+00:00                0\n",
              "3  Great Britain       GBR 2015-01-01 03:00:00+00:00                0\n",
              "4  Great Britain       GBR 2015-01-01 04:00:00+00:00                0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2d95a0e2-1a5e-4e2f-b324-4c2ec7731ca3\" 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>Country</th>\n",
              "      <th>ISO3 Code</th>\n",
              "      <th>Datetime (UTC)</th>\n",
              "      <th>Price (GBP/MWhe)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>GBR</td>\n",
              "      <td>2015-01-01 00:00:00+00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>GBR</td>\n",
              "      <td>2015-01-01 01:00:00+00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>GBR</td>\n",
              "      <td>2015-01-01 02:00:00+00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>GBR</td>\n",
              "      <td>2015-01-01 03:00:00+00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>GBR</td>\n",
              "      <td>2015-01-01 04:00:00+00:00</td>\n",
              "      <td>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-2d95a0e2-1a5e-4e2f-b324-4c2ec7731ca3')\"\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-2d95a0e2-1a5e-4e2f-b324-4c2ec7731ca3 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-2d95a0e2-1a5e-4e2f-b324-4c2ec7731ca3');\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-099aaee7-4751-4a10-90ad-f95ca3dd00dc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-099aaee7-4751-4a10-90ad-f95ca3dd00dc')\"\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-099aaee7-4751-4a10-90ad-f95ca3dd00dc 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": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CHECK dates\n",
        "print(gb_prices_df['Datetime (UTC)'].min())\n",
        "print(gb_prices_df['Datetime (UTC)'].max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2ECX_WCMN5a",
        "outputId": "675e9608-7fff-463e-f014-6776369349f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2015-01-01 00:00:00+00:00\n",
            "2020-12-31 23:00:00+00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Other Prices"
      ],
      "metadata": {
        "id": "e1Z7trhiebXi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load in other prices\n",
        "be_prices_df = pd.read_csv(data_path + 'prices/' + 'Belgium.csv')\n",
        "dk_prices_df = pd.read_csv(data_path + 'prices/' + 'Denmark.csv')\n",
        "fr_prices_df = pd.read_csv(data_path + 'prices/' + 'France.csv')\n",
        "de_prices_df = pd.read_csv(data_path + 'prices/' + 'Germany.csv')\n",
        "ie_prices_df = pd.read_csv(data_path + 'prices/' + 'Ireland.csv')\n",
        "nl_prices_df = pd.read_csv(data_path + 'prices/' + 'Netherlands.csv')\n",
        "no_prices_df = pd.read_csv(data_path + 'prices/' + 'Norway.csv')"
      ],
      "metadata": {
        "id": "VECLNGlRH6qJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# convert date to datetime type\n",
        "\n",
        "# List of DataFrames\n",
        "add_prices = [be_prices_df, dk_prices_df, fr_prices_df, de_prices_df,\n",
        "                   ie_prices_df, nl_prices_df, no_prices_df]\n",
        "\n",
        "# Specify the column name\n",
        "column_name = 'Datetime (UTC)'\n",
        "\n",
        "# Apply the function to each DataFrame in the list\n",
        "for df in add_prices:\n",
        "    original_dtype, new_dtype = preprocess_datetime_column(df, column_name)\n",
        "    print(f\"Original 'Datetime (UTC)' column data type: {original_dtype}\")\n",
        "    print(f\"New 'Datetime (UTC)' column data type: {new_dtype}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MT0wi_MVFS4Y",
        "outputId": "780842c3-31b2-4a1d-c3e2-9b55b0dc1f65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original 'Datetime (UTC)' column data type: object\n",
            "New 'Datetime (UTC)' column data type: datetime64[ns, UTC]\n",
            "Original 'Datetime (UTC)' column data type: object\n",
            "New 'Datetime (UTC)' column data type: datetime64[ns, UTC]\n",
            "Original 'Datetime (UTC)' column data type: object\n",
            "New 'Datetime (UTC)' column data type: datetime64[ns, UTC]\n",
            "Original 'Datetime (UTC)' column data type: object\n",
            "New 'Datetime (UTC)' column data type: datetime64[ns, UTC]\n",
            "Original 'Datetime (UTC)' column data type: object\n",
            "New 'Datetime (UTC)' column data type: datetime64[ns, UTC]\n",
            "Original 'Datetime (UTC)' column data type: object\n",
            "New 'Datetime (UTC)' column data type: datetime64[ns, UTC]\n",
            "Original 'Datetime (UTC)' column data type: object\n",
            "New 'Datetime (UTC)' column data type: datetime64[ns, UTC]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a list of country names corresponding to each DataFrame\n",
        "country_names = ['belgium', 'denmark', 'france', 'germany', 'ireland',\n",
        "                 'netherlands', 'norway']\n",
        "\n",
        "# Initialize an empty DataFrame to store the merged data\n",
        "add_prices_df = pd.DataFrame()\n",
        "\n",
        "# Iterate through the list of DataFrames\n",
        "for df, country_name in zip(add_prices, country_names):\n",
        "    # Subset only the 'datetime' and 'price_' columns\n",
        "    subset_df = df[['Datetime (UTC)', 'Price (EUR/MWhe)']]\n",
        "\n",
        "    # Rename the 'price_' column to the country-specific name\n",
        "    subset_df.rename(columns={'Price (EUR/MWhe)': f'{country_name}_price'}, inplace=True)\n",
        "\n",
        "    # Merge the subset DataFrame with the merged DataFrame\n",
        "    if add_prices_df.empty:\n",
        "        add_prices_df = subset_df\n",
        "    else:\n",
        "        add_prices_df = pd.merge(add_prices_df, subset_df, on='Datetime (UTC)', how='outer')\n",
        "\n",
        "add_prices_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Q-568iZYILh9",
        "outputId": "5c24d5d2-c917-4850-c162-57f5ff5ca0a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Datetime (UTC)  belgium_price  denmark_price  france_price  \\\n",
              "0 2015-01-01 00:00:00+00:00          34.94          18.29         34.94   \n",
              "1 2015-01-01 01:00:00+00:00          32.19          16.04         32.19   \n",
              "2 2015-01-01 02:00:00+00:00          28.05          14.60         23.53   \n",
              "3 2015-01-01 03:00:00+00:00          28.04          14.95         22.92   \n",
              "4 2015-01-01 04:00:00+00:00          34.26          14.50         34.26   \n",
              "\n",
              "   germany_price  ireland_price  netherlands_price  norway_price  \n",
              "0          17.93         138.97              34.94         27.36  \n",
              "1          15.17          74.78              32.19         27.24  \n",
              "2          16.38          72.99              28.05         27.16  \n",
              "3          17.38          78.79              28.04         27.15  \n",
              "4          16.38          84.79              34.26         27.30  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc1f1471-8f95-4ce5-94af-5712adac05de\" 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>Datetime (UTC)</th>\n",
              "      <th>belgium_price</th>\n",
              "      <th>denmark_price</th>\n",
              "      <th>france_price</th>\n",
              "      <th>germany_price</th>\n",
              "      <th>ireland_price</th>\n",
              "      <th>netherlands_price</th>\n",
              "      <th>norway_price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-01 00:00:00+00:00</td>\n",
              "      <td>34.94</td>\n",
              "      <td>18.29</td>\n",
              "      <td>34.94</td>\n",
              "      <td>17.93</td>\n",
              "      <td>138.97</td>\n",
              "      <td>34.94</td>\n",
              "      <td>27.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-01 01:00:00+00:00</td>\n",
              "      <td>32.19</td>\n",
              "      <td>16.04</td>\n",
              "      <td>32.19</td>\n",
              "      <td>15.17</td>\n",
              "      <td>74.78</td>\n",
              "      <td>32.19</td>\n",
              "      <td>27.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-01 02:00:00+00:00</td>\n",
              "      <td>28.05</td>\n",
              "      <td>14.60</td>\n",
              "      <td>23.53</td>\n",
              "      <td>16.38</td>\n",
              "      <td>72.99</td>\n",
              "      <td>28.05</td>\n",
              "      <td>27.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-01 03:00:00+00:00</td>\n",
              "      <td>28.04</td>\n",
              "      <td>14.95</td>\n",
              "      <td>22.92</td>\n",
              "      <td>17.38</td>\n",
              "      <td>78.79</td>\n",
              "      <td>28.04</td>\n",
              "      <td>27.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-01 04:00:00+00:00</td>\n",
              "      <td>34.26</td>\n",
              "      <td>14.50</td>\n",
              "      <td>34.26</td>\n",
              "      <td>16.38</td>\n",
              "      <td>84.79</td>\n",
              "      <td>34.26</td>\n",
              "      <td>27.30</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-dc1f1471-8f95-4ce5-94af-5712adac05de')\"\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-dc1f1471-8f95-4ce5-94af-5712adac05de 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-dc1f1471-8f95-4ce5-94af-5712adac05de');\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-142e4c6e-e18a-4a5b-9aa1-9ac54018503a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-142e4c6e-e18a-4a5b-9aa1-9ac54018503a')\"\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-142e4c6e-e18a-4a5b-9aa1-9ac54018503a 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": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### GB Weather"
      ],
      "metadata": {
        "id": "Ljr9BTwneZfC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# GB weather data\n",
        "# Note: daily not hourly\n",
        "weather_df = pd.read_excel(data_path + 'weather/' + 'UK_daily_temp.xlsx')\n",
        "weather_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CZc1J8XjHhNo",
        "outputId": "61c29bbe-7b71-4073-a01e-d979e1a9c6fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Datetime  Temperature (Celsius)\n",
              "0  20150101                    7.5\n",
              "1  20150102                    6.9\n",
              "2  20150103                    3.7\n",
              "3  20150104                    1.5\n",
              "4  20150105                    3.9"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-365b476d-7054-43d2-af45-f67d8bf49e6d\" 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>Datetime</th>\n",
              "      <th>Temperature (Celsius)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20150101</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20150102</td>\n",
              "      <td>6.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20150103</td>\n",
              "      <td>3.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20150104</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20150105</td>\n",
              "      <td>3.9</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-365b476d-7054-43d2-af45-f67d8bf49e6d')\"\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-365b476d-7054-43d2-af45-f67d8bf49e6d 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-365b476d-7054-43d2-af45-f67d8bf49e6d');\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-89714bf8-6e7a-4579-a10f-a8ce00706848\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-89714bf8-6e7a-4579-a10f-a8ce00706848')\"\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-89714bf8-6e7a-4579-a10f-a8ce00706848 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": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CHECK dates\n",
        "print(weather_df['Datetime'].min())\n",
        "print(weather_df['Datetime'].max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E4GbeaUW2QDM",
        "outputId": "80ecc1ae-0e2b-43ce-a682-3687d140c01d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20150101\n",
            "20201231\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert date to datetime type\n",
        "# Convert the integer column to a string\n",
        "weather_df['Datetime'] = weather_df['Datetime'].astype(str)\n",
        "\n",
        "# Use string formatting to create the desired format\n",
        "weather_df['Datetime'] = pd.to_datetime(weather_df['Datetime'], format='%Y%m%d')\n",
        "\n",
        "# Set the UTC timezone to the datetime column\n",
        "weather_df['Datetime'] = weather_df['Datetime'].dt.tz_localize('UTC')"
      ],
      "metadata": {
        "id": "57q2q15CdsW5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# CHECK dates\n",
        "print(weather_df['Datetime'].min())\n",
        "print(weather_df['Datetime'].max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I19nJpan2hYB",
        "outputId": "68804264-0a7a-43aa-8176-b843daa11f61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2015-01-01 00:00:00+00:00\n",
            "2020-12-31 00:00:00+00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating combined dataset"
      ],
      "metadata": {
        "id": "G0BtYGBp_9BO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# merging datasets into one gb_dataset\n",
        "gb_dataset = pd.merge(gb_prices_df, gb_demand_df[['Demand (MW)', 'Datetime']], left_on='Datetime (UTC)', right_on='Datetime', how='left')\n",
        "gb_dataset = pd.merge(gb_dataset, weather_df, left_on='Datetime (UTC)', right_on='Datetime', how='left')\n",
        "gb_dataset = pd.merge(gb_dataset, add_prices_df, on='Datetime (UTC)', how='left')\n",
        "gb_dataset = pd.merge(gb_dataset, add_demand_df, left_on='Datetime (UTC)', right_on='Datetime', how='left')\n",
        "\n",
        "# forward fill temperature\n",
        "gb_dataset['Temperature (Celsius)'].ffill(inplace=True)\n",
        "\n",
        "# drop columns\n",
        "columns_to_drop = ['Datetime_x', 'Datetime_y','Country', 'ISO3 Code', 'Datetime']\n",
        "\n",
        "# drop the specified columns\n",
        "gb_dataset.drop(columns=columns_to_drop, inplace=True)\n",
        "\n",
        "# Define a dictionary to map old column names to new column names\n",
        "column_rename_mapping = {\n",
        "    'Datetime (UTC)': 'Datetime',\n",
        "    'Price (GBP/MWhe)': 'gb_price',\n",
        "    'Demand (MW)': 'gb_demand',\n",
        "    'Temperature (Celsius)': 'gb_temp'\n",
        "}\n",
        "\n",
        "# rename the specified columns\n",
        "gb_dataset.rename(columns=column_rename_mapping, inplace=True)\n",
        "\n",
        "gb_dataset.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "1aHcqQN2cYKv",
        "outputId": "c43912a6-c150-458b-de77-bc755654e5ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Datetime gb_price  gb_demand  gb_temp  belgium_price  \\\n",
              "52603 2020-12-31 19:00:00+00:00    61.04    38697.5     -1.3          56.79   \n",
              "52604 2020-12-31 20:00:00+00:00    60.39    35833.0     -1.3          52.44   \n",
              "52605 2020-12-31 21:00:00+00:00    57.96    33026.5     -1.3          51.86   \n",
              "52606 2020-12-31 22:00:00+00:00    55.02    30714.5     -1.3          52.26   \n",
              "52607 2020-12-31 23:00:00+00:00      n/e    28975.0     -1.3          50.87   \n",
              "\n",
              "       denmark_price  france_price  germany_price  ireland_price  \\\n",
              "52603          56.70         56.75          56.70          64.28   \n",
              "52604          52.44         52.44          52.44          62.66   \n",
              "52605          51.86         51.86          51.86          56.90   \n",
              "52606          52.26         52.26          52.26          50.70   \n",
              "52607          50.87         50.87          50.87          55.09   \n",
              "\n",
              "       netherlands_price  norway_price  belgium_demand  denmark_demand  \\\n",
              "52603              56.64         23.88         9794.50          4091.0   \n",
              "52604              52.44         23.80         9386.25          3874.0   \n",
              "52605              51.86         23.67         9582.00          3674.0   \n",
              "52606              52.26         23.00         9683.50          3506.0   \n",
              "52607              50.87         23.79         9296.00          3482.0   \n",
              "\n",
              "       france_demand  germany_demand  ireland_demand  netherlands_demand  \n",
              "52603        66763.0        51019.75          4259.0            12977.25  \n",
              "52604        64102.0        48681.25          3954.0            12278.75  \n",
              "52605        65097.0        47451.00          3689.5            11835.50  \n",
              "52606        66913.0        45235.25          3467.5            11573.75  \n",
              "52607        65761.0        44586.00          3423.5            11409.50  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-808b3257-ad02-4dd7-aa6b-4ab15a9f6968\" 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>Datetime</th>\n",
              "      <th>gb_price</th>\n",
              "      <th>gb_demand</th>\n",
              "      <th>gb_temp</th>\n",
              "      <th>belgium_price</th>\n",
              "      <th>denmark_price</th>\n",
              "      <th>france_price</th>\n",
              "      <th>germany_price</th>\n",
              "      <th>ireland_price</th>\n",
              "      <th>netherlands_price</th>\n",
              "      <th>norway_price</th>\n",
              "      <th>belgium_demand</th>\n",
              "      <th>denmark_demand</th>\n",
              "      <th>france_demand</th>\n",
              "      <th>germany_demand</th>\n",
              "      <th>ireland_demand</th>\n",
              "      <th>netherlands_demand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>52603</th>\n",
              "      <td>2020-12-31 19:00:00+00:00</td>\n",
              "      <td>61.04</td>\n",
              "      <td>38697.5</td>\n",
              "      <td>-1.3</td>\n",
              "      <td>56.79</td>\n",
              "      <td>56.70</td>\n",
              "      <td>56.75</td>\n",
              "      <td>56.70</td>\n",
              "      <td>64.28</td>\n",
              "      <td>56.64</td>\n",
              "      <td>23.88</td>\n",
              "      <td>9794.50</td>\n",
              "      <td>4091.0</td>\n",
              "      <td>66763.0</td>\n",
              "      <td>51019.75</td>\n",
              "      <td>4259.0</td>\n",
              "      <td>12977.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52604</th>\n",
              "      <td>2020-12-31 20:00:00+00:00</td>\n",
              "      <td>60.39</td>\n",
              "      <td>35833.0</td>\n",
              "      <td>-1.3</td>\n",
              "      <td>52.44</td>\n",
              "      <td>52.44</td>\n",
              "      <td>52.44</td>\n",
              "      <td>52.44</td>\n",
              "      <td>62.66</td>\n",
              "      <td>52.44</td>\n",
              "      <td>23.80</td>\n",
              "      <td>9386.25</td>\n",
              "      <td>3874.0</td>\n",
              "      <td>64102.0</td>\n",
              "      <td>48681.25</td>\n",
              "      <td>3954.0</td>\n",
              "      <td>12278.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52605</th>\n",
              "      <td>2020-12-31 21:00:00+00:00</td>\n",
              "      <td>57.96</td>\n",
              "      <td>33026.5</td>\n",
              "      <td>-1.3</td>\n",
              "      <td>51.86</td>\n",
              "      <td>51.86</td>\n",
              "      <td>51.86</td>\n",
              "      <td>51.86</td>\n",
              "      <td>56.90</td>\n",
              "      <td>51.86</td>\n",
              "      <td>23.67</td>\n",
              "      <td>9582.00</td>\n",
              "      <td>3674.0</td>\n",
              "      <td>65097.0</td>\n",
              "      <td>47451.00</td>\n",
              "      <td>3689.5</td>\n",
              "      <td>11835.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52606</th>\n",
              "      <td>2020-12-31 22:00:00+00:00</td>\n",
              "      <td>55.02</td>\n",
              "      <td>30714.5</td>\n",
              "      <td>-1.3</td>\n",
              "      <td>52.26</td>\n",
              "      <td>52.26</td>\n",
              "      <td>52.26</td>\n",
              "      <td>52.26</td>\n",
              "      <td>50.70</td>\n",
              "      <td>52.26</td>\n",
              "      <td>23.00</td>\n",
              "      <td>9683.50</td>\n",
              "      <td>3506.0</td>\n",
              "      <td>66913.0</td>\n",
              "      <td>45235.25</td>\n",
              "      <td>3467.5</td>\n",
              "      <td>11573.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52607</th>\n",
              "      <td>2020-12-31 23:00:00+00:00</td>\n",
              "      <td>n/e</td>\n",
              "      <td>28975.0</td>\n",
              "      <td>-1.3</td>\n",
              "      <td>50.87</td>\n",
              "      <td>50.87</td>\n",
              "      <td>50.87</td>\n",
              "      <td>50.87</td>\n",
              "      <td>55.09</td>\n",
              "      <td>50.87</td>\n",
              "      <td>23.79</td>\n",
              "      <td>9296.00</td>\n",
              "      <td>3482.0</td>\n",
              "      <td>65761.0</td>\n",
              "      <td>44586.00</td>\n",
              "      <td>3423.5</td>\n",
              "      <td>11409.50</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-808b3257-ad02-4dd7-aa6b-4ab15a9f6968')\"\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-808b3257-ad02-4dd7-aa6b-4ab15a9f6968 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-808b3257-ad02-4dd7-aa6b-4ab15a9f6968');\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-5f8e29e3-08e3-4a2c-9962-7a07a4eb53c8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5f8e29e3-08e3-4a2c-9962-7a07a4eb53c8')\"\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-5f8e29e3-08e3-4a2c-9962-7a07a4eb53c8 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": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# handling missing price data\n",
        "\n",
        "# price has 73 missing values in 2016\n",
        "print(gb_dataset['gb_price'].isna().sum())\n",
        "\n",
        "# dealing with missing price value fpr 2020-12-31 23:00:00+00:00\n",
        "# Replace 'n/e' with NaN for consistent handling\n",
        "gb_dataset['gb_price'].replace('n/e', np.nan, inplace=True)\n",
        "\n",
        "# Convert the 'gb_price' column to float\n",
        "gb_dataset['gb_price'] = gb_dataset['gb_price'].astype(float)\n",
        "\n",
        "# Find the indices of NaN values in the 'gb_price' column\n",
        "nan_indices = gb_dataset.index[gb_dataset['gb_price'].isna()]\n",
        "\n",
        "# Define the number of previous values to consider for imputation\n",
        "num_previous_values = 5\n",
        "\n",
        "# Loop through each NaN index and impute the value\n",
        "for index_to_replace in nan_indices:\n",
        "    # Calculate the imputed value based on the previous 5 valid values\n",
        "    previous_values = gb_dataset.loc[index_to_replace - num_previous_values:index_to_replace - 1, 'gb_price']\n",
        "    imputed_value = np.mean(previous_values)\n",
        "\n",
        "    imputed_value = round(imputed_value, 2)\n",
        "\n",
        "    # Replace NaN with the imputed value\n",
        "    gb_dataset.at[index_to_replace, 'gb_price'] = imputed_value\n",
        "\n",
        "# Verify that all missing values have been imputed\n",
        "print(\"Number of remaining NaN values:\", gb_dataset['gb_price'].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SY-lvzglwGKl",
        "outputId": "2cf5b022-3d6b-4710-9746-b3036b929764"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "73\n",
            "Number of remaining NaN values: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# handing issing data in france_demand ireland_demand\n",
        "def impute_demand_columns(df, column_name, num_previous_values=5):\n",
        "    \"\"\"\n",
        "    Impute missing values in the specified column using the mean of the previous valid values.\n",
        "\n",
        "    Parameters:\n",
        "    - df: DataFrame containing the dataset.\n",
        "    - column_name: Name of the column to impute.\n",
        "    - num_previous_values: Number of previous valid values to consider for imputation.\n",
        "\n",
        "    Returns:\n",
        "    - df: The DataFrame with missing values imputed in the specified column.\n",
        "    \"\"\"\n",
        "\n",
        "    # Find the indices of NaN values in the specified column\n",
        "    nan_indices = df.index[df[column_name].isna()]\n",
        "\n",
        "    # Loop through each NaN index and impute the value\n",
        "    for index_to_replace in nan_indices:\n",
        "        # Calculate the imputed value based on the previous valid values\n",
        "        previous_values = df.loc[index_to_replace - num_previous_values:index_to_replace - 1, column_name]\n",
        "        imputed_value = np.mean(previous_values)\n",
        "\n",
        "        imputed_value = round(imputed_value, 2)\n",
        "\n",
        "        # Replace NaN with the imputed value\n",
        "        df.at[index_to_replace, column_name] = imputed_value\n",
        "\n",
        "    # Verify that all missing values have been imputed\n",
        "    remaining_nan_count = df[column_name].isna().sum()\n",
        "    if remaining_nan_count == 0:\n",
        "        print(f\"All missing values in '{column_name}' have been imputed.\")\n",
        "    else:\n",
        "        print(f\"Number of remaining NaN values in '{column_name}': {remaining_nan_count}\")\n",
        "\n",
        "    return df"
      ],
      "metadata": {
        "id": "i4MFJ20P2cf1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# imputing for demand data\n",
        "gb_dataset = impute_demand_columns(gb_dataset, 'france_demand')\n",
        "gb_dataset = impute_demand_columns(gb_dataset, 'ireland_demand')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "swcNQkEtcBsj",
        "outputId": "3d720983-c4b3-44de-c77b-5bd2d2124161"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All missing values in 'france_demand' have been imputed.\n",
            "All missing values in 'ireland_demand' have been imputed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modelling"
      ],
      "metadata": {
        "id": "XQFyM1d8NndH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define train test split and features for all models\n",
        "\n",
        "# Split the data into training and testing datasets based on date\n",
        "train_data = gb_dataset[gb_dataset['Datetime'].dt.year < 2020]  # Data from 2015-2019\n",
        "test_data = gb_dataset[gb_dataset['Datetime'].dt.year == 2020]  # Data for 2020\n",
        "\n",
        "# Define  feature columns\n",
        "\n",
        "# full list of features available\n",
        "features = ['gb_demand', 'gb_price', 'gb_temp', 'belgium_price',\n",
        "                   'denmark_price', 'france_price', 'germany_price',\n",
        "                   'ireland_price',\t'netherlands_price',\t'norway_price',\n",
        "                   'belgium_demand',\t'denmark_demand',\t'france_demand',\n",
        "                   'germany_demand',\t'ireland_demand',\t'netherlands_demand']\n",
        "\n",
        "# final features after feature selection\n",
        "features_final = ['gb_price']"
      ],
      "metadata": {
        "id": "T2kvP9UuDFJO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 1 - Linear Regression"
      ],
      "metadata": {
        "id": "kTpm_9X-NtIP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# CREATE FUNCTION\n",
        "\n",
        "def train_and_predict_linear_regression(features,\n",
        "                                        output_name,\n",
        "                                        save_csv,\n",
        "                                        show_feature_importance):\n",
        "    # Initialize and train a Linear Regression model\n",
        "    model = LinearRegression()\n",
        "    model.fit(train_data[features], train_data['gb_price'])\n",
        "\n",
        "    # Make predictions on the test set\n",
        "    y_pred = model.predict(test_data[features])\n",
        "\n",
        "    # Evaluate the model\n",
        "    rmse = mean_squared_error(test_data['gb_price'], y_pred, squared=False)\n",
        "    print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
        "\n",
        "    r2 = r2_score(test_data['gb_price'], y_pred)\n",
        "    print(f\"R-squared: {r2:.2f}\")\n",
        "\n",
        "    mae = mean_absolute_error(test_data['gb_price'], y_pred)\n",
        "    print(f\"Mean Absolute Error (MAE): {mae}\")\n",
        "\n",
        "    # Train the final model on the full dataset\n",
        "    model.fit(gb_dataset[features], gb_dataset['gb_price'])  # Using the entire dataset for training\n",
        "\n",
        "    # Predict electricity prices for 2020\n",
        "    lr_predicted_2020_prices = model.predict(test_data[features])\n",
        "\n",
        "    # Round predicted prices to 1 decimal place\n",
        "    lr_predicted_2020_prices = np.round(lr_predicted_2020_prices, 1)\n",
        "\n",
        "    # Create a DataFrame with datetime and predicted prices\n",
        "    lr_results_df = pd.DataFrame({'Datetime': test_data['Datetime'],\n",
        "                                  'Price': lr_predicted_2020_prices})\n",
        "\n",
        "    # Save results to CSV\n",
        "    if save_csv:\n",
        "      lr_results_df.to_csv(output_path + output_name, index=False)\n",
        "\n",
        "    # Calculate feature importance using coefs\n",
        "    coefficients = model.coef_\n",
        "    feature_importance = pd.DataFrame({'Feature': features, 'Coefficient': coefficients})\n",
        "\n",
        "    # Sort by absolute coefficient value (importance)\n",
        "    feature_importance = feature_importance.reindex(feature_importance['Coefficient'].abs().sort_values(ascending=False).index)\n",
        "\n",
        "    # Visualize feature importance\n",
        "    if show_feature_importance:\n",
        "      plt.figure(figsize=(10, 6))\n",
        "      sns.barplot(x='Coefficient', y='Feature', data=feature_importance, orient='h')\n",
        "      plt.xlabel('Coefficient (Feature Importance)')\n",
        "      plt.ylabel('Feature')\n",
        "      plt.title('Feature Importance in Linear Regression')\n",
        "      plt.show()\n",
        "\n",
        "    return lr_predicted_2020_prices"
      ],
      "metadata": {
        "id": "bka7kiJ9GHWH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# run on features\n",
        "train_and_predict_linear_regression(features,\n",
        "                                    'predicted_prices_2020_linear_reg.csv',\n",
        "                                    save_csv=False,\n",
        "                                    show_feature_importance=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "1trT_3A2New2",
        "outputId": "0f6358a2-1e6a-4c43-9333-0e5fab574aa9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Squared Error (RMSE): 9.476164552868568e-13\n",
            "R-squared: 1.00\n",
            "Mean Absolute Error (MAE): 7.819933517086373e-13\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([43.4, 41.1, 30. , ..., 58. , 55. , 61.1])"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# run on features_final\n",
        "lr_predicted_2020_prices = train_and_predict_linear_regression(features_final,\n",
        "                                                               'predicted_prices_2020_linear_reg.csv',\n",
        "                                                               save_csv=True,\n",
        "                                                               show_feature_importance=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aa5oJa2CItWR",
        "outputId": "a8b44abf-c2d6-4ff3-e848-30cd294ffc93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Squared Error (RMSE): 9.608225582062903e-15\n",
            "R-squared: 1.00\n",
            "Mean Absolute Error (MAE): 7.439655486987495e-15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 2 - XGBoost"
      ],
      "metadata": {
        "id": "BTDeF8RDNqzI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# CREATE FUNCTION\n",
        "\n",
        "def train_and_predict_xgboost_model(features,\n",
        "                                    output_name,\n",
        "                                    save_csv,\n",
        "                                    show_feature_importance):\n",
        "    # Initialize and train an XGBoost Regressor model\n",
        "    model = xgb.XGBRegressor(objective='reg:squarederror')\n",
        "    model.fit(train_data[features], train_data['gb_price'])\n",
        "\n",
        "    # Get feature importances\n",
        "    importances_xgb = model.feature_importances_\n",
        "\n",
        "    # Match feature importances with feature names\n",
        "    feature_names = train_data[features].columns\n",
        "    feature_importance_xgb = dict(zip(feature_names, importances_xgb))\n",
        "\n",
        "    # Sort feature importances by importance value\n",
        "    sorted_feature_importance_xgb = sorted(feature_importance_xgb.items(),\n",
        "                                           key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # Print or visualize the sorted feature importances\n",
        "    if show_feature_importance:\n",
        "      print(\"Feature Importances:\")\n",
        "      for feature, importance in sorted_feature_importance_xgb:\n",
        "          print(f\"{feature}: {importance:.4f}\")\n",
        "\n",
        "    # Make predictions on the test set\n",
        "    y_pred = model.predict(test_data[features])\n",
        "\n",
        "    # Evaluate the model\n",
        "    rmse = mean_squared_error(test_data['gb_price'], y_pred, squared=False)\n",
        "    print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
        "\n",
        "    r2 = r2_score(test_data['gb_price'], y_pred)\n",
        "    print(f\"R-squared: {r2:.2f}\")\n",
        "\n",
        "    mae = mean_absolute_error(test_data['gb_price'], y_pred)\n",
        "    print(f\"Mean Absolute Error (MAE): {mae}\")\n",
        "\n",
        "    # Train the final model on the full dataset\n",
        "    model.fit(gb_dataset[features], gb_dataset['gb_price'])  # Using the entire dataset for training\n",
        "\n",
        "    # Predict electricity prices for 2020\n",
        "    xgb_predicted_2020_prices = model.predict(test_data[features])\n",
        "\n",
        "    # Round predicted prices to 1 decimal place\n",
        "    xgb_predicted_2020_prices = np.round(xgb_predicted_2020_prices, 1)\n",
        "\n",
        "    # Create a DataFrame with datetime and predicted prices\n",
        "    xgb_results_df = pd.DataFrame({'Datetime': test_data['Datetime'],\n",
        "                                   'Price': xgb_predicted_2020_prices})\n",
        "\n",
        "    # Save results to CSV\n",
        "    if save_csv:\n",
        "      xgb_results_df.to_csv(output_path + output_name, index=False)\n",
        "\n",
        "    return xgb_predicted_2020_prices"
      ],
      "metadata": {
        "id": "hIUWg10bAR8A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# run on features\n",
        "train_and_predict_xgboost_model(features,\n",
        "                                'predicted_prices_2020_xgboost.csv',\n",
        "                                save_csv=False,\n",
        "                                show_feature_importance=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uMidLimuRzQB",
        "outputId": "d9e74af4-5ab5-4d79-c6d0-8f2ec3b2a11b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature Importances:\n",
            "gb_price: 0.9978\n",
            "belgium_price: 0.0003\n",
            "netherlands_price: 0.0003\n",
            "gb_demand: 0.0003\n",
            "denmark_price: 0.0002\n",
            "germany_price: 0.0002\n",
            "gb_temp: 0.0002\n",
            "germany_demand: 0.0001\n",
            "netherlands_demand: 0.0001\n",
            "ireland_demand: 0.0001\n",
            "norway_price: 0.0001\n",
            "ireland_price: 0.0001\n",
            "belgium_demand: 0.0001\n",
            "denmark_demand: 0.0001\n",
            "france_demand: 0.0000\n",
            "france_price: 0.0000\n",
            "Root Mean Squared Error (RMSE): 1.2678749443505957\n",
            "R-squared: 0.99\n",
            "Mean Absolute Error (MAE): 0.13640504453733707\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([43.5, 41.1, 30. , ..., 58. , 55. , 61.1], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# run on features_final\n",
        "xgb_predicted_2020_prices = train_and_predict_xgboost_model(features_final,\n",
        "                                                            'predicted_prices_2020_xgboost.csv',\n",
        "                                                            save_csv=False,\n",
        "                                                            show_feature_importance=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j7YMDHoRIz0p",
        "outputId": "60d8b72d-a312-477c-e432-b469f2d76e0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Squared Error (RMSE): 1.2506469201081556\n",
            "R-squared: 0.99\n",
            "Mean Absolute Error (MAE): 0.14002089380040655\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 3 - Random Forest"
      ],
      "metadata": {
        "id": "MPaGM_i-Nvvb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# CREATE FUNCTION\n",
        "\n",
        "def train_and_predict_random_forest_model(features, output_name, save_csv,show_feature_importance):\n",
        "    # Initialize and train a Random Forest Regressor model\n",
        "    model = RandomForestRegressor(random_state=42)\n",
        "    model.fit(train_data[features], train_data['gb_price'])\n",
        "\n",
        "    # Get feature importances\n",
        "    importances_rf = model.feature_importances_\n",
        "\n",
        "    # Match feature importances with feature names\n",
        "    feature_names = train_data[features].columns\n",
        "    feature_importance_rf = dict(zip(feature_names, importances_rf))\n",
        "\n",
        "    # Sort feature importances by importance value\n",
        "    sorted_feature_importance_rf = sorted(feature_importance_rf.items(),\n",
        "                                          key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # Print or visualize the sorted feature importances\n",
        "    if show_feature_importance:\n",
        "      print(\"Feature Importances:\")\n",
        "      for feature, importance in sorted_feature_importance_rf:\n",
        "          print(f\"{feature}: {importance:.4f}\")\n",
        "\n",
        "    # Make predictions on the test set\n",
        "    y_pred = model.predict(test_data[features])\n",
        "\n",
        "    # Evaluate the model\n",
        "    rmse = mean_squared_error(test_data['gb_price'], y_pred, squared=False)\n",
        "    print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
        "\n",
        "    r2 = r2_score(test_data['gb_price'], y_pred)\n",
        "    print(f\"R-squared: {r2:.2f}\")\n",
        "\n",
        "    mae = mean_absolute_error(test_data['gb_price'], y_pred)\n",
        "    print(f\"Mean Absolute Error (MAE): {mae}\")\n",
        "\n",
        "    # Train the final model on the full dataset\n",
        "    model.fit(gb_dataset[features], gb_dataset['gb_price'])  # Using the entire dataset for training\n",
        "\n",
        "    # Predict electricity prices for 2020\n",
        "    rf_predicted_2020_prices = model.predict(test_data[features])\n",
        "\n",
        "    # Round predicted prices to 1 decimal place\n",
        "    rf_predicted_2020_prices = np.round(rf_predicted_2020_prices, 1)\n",
        "\n",
        "    # Create a DataFrame with datetime and predicted prices\n",
        "    rf_results_df = pd.DataFrame({'Datetime': test_data['Datetime'],\n",
        "                                  'Price': rf_predicted_2020_prices})\n",
        "\n",
        "    # Save results to CSV\n",
        "    if save_csv:\n",
        "      rf_results_df.to_csv(output_path + 'predicted_prices_2020_random_forest.csv',\n",
        "                           index=False)\n",
        "\n",
        "    return rf_predicted_2020_prices"
      ],
      "metadata": {
        "id": "OyCAUaeA_lgS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_and_predict_random_forest_model(features,\n",
        "                                      'predicted_prices_2020_random_forest.csv',\n",
        "                                      save_csv=False,\n",
        "                                      show_feature_importance=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7RiUtBsTaEL",
        "outputId": "799cca1c-23a6-461d-953e-55b47ddf0c75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature Importances:\n",
            "gb_price: 0.9952\n",
            "germany_price: 0.0007\n",
            "france_price: 0.0006\n",
            "ireland_demand: 0.0005\n",
            "gb_demand: 0.0005\n",
            "netherlands_demand: 0.0005\n",
            "ireland_price: 0.0004\n",
            "belgium_demand: 0.0003\n",
            "france_demand: 0.0002\n",
            "gb_temp: 0.0002\n",
            "denmark_price: 0.0002\n",
            "belgium_price: 0.0002\n",
            "germany_demand: 0.0001\n",
            "norway_price: 0.0001\n",
            "denmark_demand: 0.0001\n",
            "netherlands_price: 0.0001\n",
            "Root Mean Squared Error (RMSE): 1.4296778744379195\n",
            "R-squared: 0.99\n",
            "Mean Absolute Error (MAE): 0.10324602686704352\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([43.4, 41.1, 30. , ..., 58. , 55. , 61.1])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_predicted_2020_prices = train_and_predict_random_forest_model(features_final,\n",
        "                                                                 'predicted_prices_2020_random_forest.csv',\n",
        "                                                                 save_csv=False,\n",
        "                                                                 show_feature_importance=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JAcq5eokI2-9",
        "outputId": "e4eb89a8-4129-47c0-e22f-026ea762813f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Squared Error (RMSE): 1.2803030797337154\n",
            "R-squared: 0.99\n",
            "Mean Absolute Error (MAE): 0.09043760245903902\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualisations (EDA and Evaluation)"
      ],
      "metadata": {
        "id": "lE5S7D_laHpg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### GB 2015-2020 Time Series"
      ],
      "metadata": {
        "id": "hT8U-HINKW-U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the GB Prices time series\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(gb_dataset.index, gb_dataset['gb_price'], color='blue', label='GB Electricity Prices')\n",
        "plt.title('GB Electricity Prices Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price (GBP/MWhe)')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "gQZo1kbWIBOX",
        "outputId": "c546ba55-2b49-475b-8f45-b6329088ed9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### GB 1 month Time Series"
      ],
      "metadata": {
        "id": "2v87f1JQKarC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter the DataFrame to include only data for January 2019\n",
        "january_2019_prices = gb_dataset[(gb_dataset['Datetime'] >= '2019-01-01') &\n",
        "                                 (gb_dataset['Datetime'] <= '2019-01-31')]\n",
        "\n",
        "# Plot the time series for January 2019\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(january_2019_prices['Datetime'], january_2019_prices['gb_price'],\n",
        "         color='blue', label='GB Electricity Prices (Jan 2019)')\n",
        "plt.title('GB Electricity Prices for January 2019')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "KkOkcIToKdzQ",
        "outputId": "8279056b-3013-426d-8b08-d4ed5da95e43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### GB Multiple Days Hourly Time Series"
      ],
      "metadata": {
        "id": "KUnuFJxOLtSC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# List of dates to plot\n",
        "dates_to_plot = ['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-23', '2019-01-30']\n",
        "\n",
        "# Create a figure for the plot\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Loop through each date and plot hourly prices\n",
        "for date in dates_to_plot:\n",
        "    # Filter the DataFrame for the specific date\n",
        "    daily_prices = gb_dataset[gb_dataset['Datetime'].dt.date == pd.to_datetime(date).date()]\n",
        "\n",
        "    # Extract the hour from the 'Datetime' column\n",
        "    daily_prices['Hour'] = daily_prices['Datetime'].dt.hour\n",
        "\n",
        "    # Plot hourly prices for the day\n",
        "    plt.plot(daily_prices['Hour'], daily_prices['gb_price'],\n",
        "             label=f'GB Electricity Prices ({date})')\n",
        "\n",
        "# Customize the plot\n",
        "plt.title('GB Electricity Prices for Selected Days in January 2019')\n",
        "plt.xlabel('Hour of the Day')\n",
        "plt.ylabel('Price')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "\n",
        "# Show more x-axis ticks\n",
        "plt.xticks(range(24))  # Show ticks for all 24 hours\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "i6RiTOwhPUG7",
        "outputId": "7b6e7de4-2087-4413-8405-229d133c9026"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### GB Multiple Years Seasonality"
      ],
      "metadata": {
        "id": "UbtymKlwQ_tk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter the DataFrame to include data for the years 2019 and 2020\n",
        "gb_dataset_2019 = gb_dataset[gb_dataset['Datetime'].dt.year == 2019]\n",
        "gb_dataset_2020 = gb_dataset[gb_dataset['Datetime'].dt.year == 2020]\n",
        "\n",
        "# Combine the data for both years\n",
        "combined_data = pd.concat([gb_dataset_2019, gb_dataset_2020])\n",
        "\n",
        "# Set the 'Datetime' column as the index for the combined dataset\n",
        "combined_data.set_index('Datetime', inplace=True)\n",
        "\n",
        "# Fill missing values with linear interpolation\n",
        "combined_data['gb_price'].interpolate(method='linear', inplace=True)\n",
        "\n",
        "# Perform seasonal decomposition for the combined data\n",
        "decomposition_combined = sm.tsa.seasonal_decompose(combined_data['gb_price'],\n",
        "                                                   model='additive', period=365)\n",
        "\n",
        "# Plot the decomposed components for both years\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "plt.subplot(411)\n",
        "plt.plot(decomposition_combined.trend, label='Trend (2019-2020)')\n",
        "plt.legend(loc='upper left')\n",
        "plt.title('Trend Component')\n",
        "\n",
        "plt.subplot(412)\n",
        "plt.plot(decomposition_combined.seasonal, label='Seasonality (2019-2020)')\n",
        "plt.legend(loc='upper left')\n",
        "plt.title('Seasonality Component')\n",
        "\n",
        "plt.subplot(413)\n",
        "plt.plot(decomposition_combined.resid, label='Residuals (2019-2020)')\n",
        "plt.legend(loc='upper left')\n",
        "plt.title('Residuals Component')\n",
        "\n",
        "plt.subplot(414)\n",
        "plt.plot(combined_data['gb_price'], label='Original Data (2019-2020)')\n",
        "plt.legend(loc='upper left')\n",
        "plt.title('Original Data')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        },
        "id": "Tc5IpOExWPPt",
        "outputId": "11b2fb84-c397-4c00-8958-9539a5b71c77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter the DataFrame to include data from 2015 to 2020\n",
        "gb_dataset_seasonal = gb_dataset[(gb_dataset['Datetime'].dt.year >= 2015) &\n",
        "                                 (gb_dataset['Datetime'].dt.year <= 2020)]\n",
        "\n",
        "# Extract the month from the 'Datetime' column\n",
        "gb_dataset_seasonal['Month'] = gb_dataset_seasonal['Datetime'].dt.month\n",
        "\n",
        "# Define the months of interest (January and July)\n",
        "months_of_interest = [1, 7]\n",
        "\n",
        "# Filter the data for the specified months\n",
        "selected_months_data = gb_dataset_seasonal[gb_dataset_seasonal['Month'].isin(months_of_interest)]\n",
        "\n",
        "# Group the data by year and month, and calculate the average price for each group\n",
        "monthly_avg_prices = selected_months_data.groupby(['Datetime', 'Month'])['gb_price'].mean().reset_index()\n",
        "\n",
        "# Pivot the data to have years as columns and months as rows\n",
        "pivot_table = monthly_avg_prices.pivot(index='Month', columns='Datetime',\n",
        "                                       values='gb_price')\n",
        "\n",
        "# Create a line plot to show electricity prices for each January and July\n",
        "plt.figure(figsize=(12, 6))\n",
        "for month in months_of_interest:\n",
        "    plt.plot(pivot_table.columns, pivot_table.loc[month],\n",
        "             label=f'Month {month}', marker='o')\n",
        "\n",
        "plt.title('Electricity Prices for January and July (2015-2020)')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Price (GBP/MWhe)')\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "voZY-OwmT-Qu",
        "outputId": "fb53c521-5e1a-47d3-d549-5d1663b02454"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Predicted Prices vs. True Prices - LR model"
      ],
      "metadata": {
        "id": "UID8Nu64Q74T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# true 2020 hourly prices\n",
        "true_prices = test_data['gb_price']\n",
        "\n",
        "# LR predicted prices\n",
        "linear_regression_predictions = lr_predicted_2020_prices\n",
        "\n",
        "# Create a list of hours\n",
        "hours = range(1, len(true_prices) + 1)\n",
        "\n",
        "# Create a figure and axis\n",
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "\n",
        "# Plot the predicted prices for each model\n",
        "ax.plot(hours, linear_regression_predictions, label='Linear Regression',\n",
        "        linestyle='-',  color='g')\n",
        "\n",
        "# Plot the true 2020 hourly prices\n",
        "ax.plot(hours, true_prices, label='True Prices', linestyle='-', color='b')\n",
        "\n",
        "# Set plot labels and title\n",
        "ax.set_xlabel('Hourly Time Period')\n",
        "ax.set_ylabel('Price (GBP/MWhe)')\n",
        "ax.set_title('True vs. Predicted Prices in 2020')\n",
        "\n",
        "# Add a legend\n",
        "ax.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "AaYXalrxQ9DX",
        "outputId": "1ca200b8-7322-4a2a-b6c9-f7adfaf5b877"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Predicted Prices vs. True Prices - all models"
      ],
      "metadata": {
        "id": "4YxaCOX7KewB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# true 2020 hourly prices\n",
        "true_prices = test_data['gb_price']\n",
        "\n",
        "# predicted prices by each model\n",
        "linear_regression_predictions = lr_predicted_2020_prices\n",
        "xgboost_predictions = xgb_predicted_2020_prices\n",
        "random_forest_predictions = rf_predicted_2020_prices\n",
        "\n",
        "# Create a list of hours\n",
        "hours = range(1, len(true_prices) + 1)\n",
        "\n",
        "# Create a figure and axis\n",
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "\n",
        "# Plot the predicted prices for each model\n",
        "ax.plot(hours, linear_regression_predictions, label='Linear Regression',\n",
        "        linestyle='-',  color='g')\n",
        "ax.plot(hours, xgboost_predictions, label='XGBoost',\n",
        "        linestyle='-', color='r')\n",
        "ax.plot(hours, random_forest_predictions, label='Random Forest',\n",
        "        linestyle='-',  color='m')\n",
        "\n",
        "# Plot the true 2020 hourly prices\n",
        "ax.plot(hours, true_prices, label='True Prices', linestyle='-', color='b')\n",
        "\n",
        "# Set plot labels and title\n",
        "ax.set_xlabel('Hourly Time Period')\n",
        "ax.set_ylabel('Price (GBP/MWhe)')\n",
        "ax.set_title('True vs. Predicted Prices in 2020')\n",
        "\n",
        "# Add a legend\n",
        "ax.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "00IjfmgYebWT",
        "outputId": "1b7a4640-87ce-43b3-8256-5f3bdd3d7a80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Appendix"
      ],
      "metadata": {
        "id": "dVEDaVqzJxeo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.   \n",
        "  - LSTM requires vast amounts of data,\n",
        "  - LSTM requires data to exhibit stationarity (EDA visualisation showed the stationarity requirement is unlikely to be filled; i.e. mean and variance did not look constant over the time period),\n",
        "  - LSTM requires data preprocessing, setting sequence length, hyperparameter tuning etc... to get the most out of the technique, without spending time doing this (not possible for this project), the benefits over other techniques are unlikely to be seen\n",
        "  - Price spikes (such as those seen in 2016) to too rare to be captured in the model accurately,\n",
        "  - I concluded that ARIMA/machine learning techniques could give good enough results and have fewer requirements.\n",
        "  \n",
        "  A commonly used time-series technique is ARIMA modelling - this technique uses its own historic data to forecast a time series. ARIMA modelling does not make use of the additional features provided. In addition, it requires data to be stationary (no trends or seasonality). I created some visualisations to show the time-series, they show how the data exhibits trends and seasonality where daily prices are highest between 16.00 and 20.00 - this is a trend, another plot shows how typically July prices are cheaper than January prices and a seasonality decomposition shows there is seasonality in the data. For these reasons, ARIMA modelling is not suitable. I did however run an ARIMA model during exploration (see below) and the evaluation metrics were poor (low R squared, high RMSE, high MAE - both in absolute terms relative to the models above) confirming that this technique is ill-suited to this task.\n",
        "\n",
        "  Therefore machine learning methods were the most suitable. They also allow for multivariate relationships to be explored. I decided to explore linear regression, XGBoost and Random Forest modelling. Linear regression has the advantage of being efficient to run and easy to interpret (with the ability to isolate the impacts of each feature) however it does require several assumptions to be met - linear relationships, lack of outliers so it does require some pre-analysis to be done. XGBoost is a model created to be highly accurate, it works with various types of relationships between outcome and predictor variables, is less sensitive to changing data characteristics and outliers and its model prevents overfitting. It provides is more computationally intensive but running it on Google Colab mitigates this risk. Lastly, I explored Random Forest which shares a number of the advantages XGBoost has."
      ],
      "metadata": {
        "id": "gbLZFp8OJ1pE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 4 - ARIMA"
      ],
      "metadata": {
        "id": "wlln21I7xsS9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ARIMA model\n",
        "\n",
        "electricity_data = gb_dataset\n",
        "\n",
        "# Split the data into training and testing datasets based on date\n",
        "train_data = electricity_data[electricity_data['Datetime'].dt.year < 2020]\n",
        "test_data = electricity_data[electricity_data['Datetime'].dt.year == 2020]\n",
        "\n",
        "# Train the ARIMA model\n",
        "def train_arima_model(train_data, features, order):\n",
        "    # Create a DataFrame with only the selected features\n",
        "    train_data = train_data[['Datetime'] + features]\n",
        "\n",
        "    # Set the 'Datetime' column as the index\n",
        "    train_data.set_index('Datetime', inplace=True)\n",
        "\n",
        "    # Create and fit the ARIMA model\n",
        "    model = ARIMA(train_data['gb_price'], order=order)\n",
        "    model_fit = model.fit()\n",
        "\n",
        "    return model_fit\n",
        "\n",
        "# Define the ARIMA model order (p, d, q)\n",
        "# un-tuned\n",
        "p, d, q = 1, 1, 1\n",
        "\n",
        "arima_model = train_arima_model(train_data, features, order=(p, d, q))\n",
        "\n",
        "# Make predictions on the test set\n",
        "def predict_arima_model(model_fit, test_data, features):\n",
        "    # Create a DataFrame with only the selected features\n",
        "    test_data = test_data[['Datetime'] + features]\n",
        "\n",
        "    # Set the 'Datetime' column as the index\n",
        "    test_data.set_index('Datetime', inplace=True)\n",
        "\n",
        "    # Make predictions\n",
        "    y_pred = model_fit.forecast(steps=len(test_data))\n",
        "\n",
        "    return y_pred\n",
        "\n",
        "arima_predictions = predict_arima_model(arima_model, test_data, features)\n",
        "\n",
        "# Evaluate the ARIMA model\n",
        "def evaluate_arima_model(test_data, predictions):\n",
        "    # Calculate RMSE, R-squared, and MAE\n",
        "    rmse = np.sqrt(mean_squared_error(test_data['gb_price'], predictions))\n",
        "    r2 = r2_score(test_data['gb_price'], predictions)\n",
        "    mae = mean_absolute_error(test_data['gb_price'], predictions)\n",
        "\n",
        "    return rmse, r2, mae\n",
        "\n",
        "rmse, r2, mae = evaluate_arima_model(test_data, arima_predictions)\n",
        "\n",
        "# Print evaluation metrics\n",
        "print(f\"Root Mean Squared Error (RMSE): {rmse:.2f}\")\n",
        "print(f\"R-squared: {r2:.2f}\")\n",
        "print(f\"Mean Absolute Error (MAE): {mae:.2f}\")\n",
        "\n",
        "# train the final ARIMA model on the full dataset\n",
        "final_arima_model = train_arima_model(electricity_data, features, order=(p, d, q))\n",
        "\n",
        "# Predict electricity prices for 2020\n",
        "final_arima_predictions = predict_arima_model(final_arima_model, test_data, features)\n",
        "\n",
        "# Round predicted prices to 1 decimal place\n",
        "final_arima_predictions = np.round(final_arima_predictions, 1)\n",
        "\n",
        "# Create a DataFrame with datetime and predicted prices\n",
        "arima_results_df = pd.DataFrame({'Datetime': test_data.index,\n",
        "                                 'Price': final_arima_predictions})\n",
        "\n",
        "# Save results to CSV\n",
        "save_outputs = False\n",
        "\n",
        "if save_outputs==True:\n",
        "  arima_results_df.to_csv(output_path + 'predicted_prices_2020_arima.csv',\n",
        "                          index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MuumZgRYxqrQ",
        "outputId": "60d6e337-84e9-4ddd-c8f0-014ed29a6938"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Squared Error (RMSE): 16.87\n",
            "R-squared: -0.02\n",
            "Mean Absolute Error (MAE): 11.49\n"
          ]
        }
      ]
    }
  ]
}