grid_power.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "910izCft33Bj",
        "outputId": "a15e90c5-1b78-49de-b3f2-afb1d4771745"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "File: grid_stability_2019_2023.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 46007 entries, 0 to 46006\n",
            "Data columns (total 2 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   date       46007 non-null  object\n",
            " 1   stability  46007 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 719.0+ KB\n",
            "None\n",
            "\n",
            "File: price_per_unit_2019.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8759 entries, 0 to 8758\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8759 non-null   object \n",
            " 1   p1      8759 non-null   float64\n",
            " 2   p2      8759 non-null   float64\n",
            " 3   p3      8759 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.8+ KB\n",
            "None\n",
            "\n",
            "File: price_per_unit_2020.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8784 entries, 0 to 8783\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8784 non-null   object \n",
            " 1   p1      8784 non-null   float64\n",
            " 2   p2      8784 non-null   float64\n",
            " 3   p3      8784 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 274.6+ KB\n",
            "None\n",
            "\n",
            "File: price_per_unit_2021.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8760 entries, 0 to 8759\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8760 non-null   object \n",
            " 1   p1      8760 non-null   float64\n",
            " 2   p2      8760 non-null   float64\n",
            " 3   p3      8760 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.9+ KB\n",
            "None\n",
            "\n",
            "File: price_per_unit_2022.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8760 entries, 0 to 8759\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8760 non-null   object \n",
            " 1   p1      8760 non-null   float64\n",
            " 2   p2      8760 non-null   float64\n",
            " 3   p3      8760 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.9+ KB\n",
            "None\n",
            "\n",
            "File: price_per_unit_2023.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8760 entries, 0 to 8759\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8760 non-null   object \n",
            " 1   p1      8760 non-null   float64\n",
            " 2   p2      8760 non-null   float64\n",
            " 3   p3      8760 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.9+ KB\n",
            "None\n",
            "\n",
            "File: unit_consumption_2019.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8759 entries, 0 to 8758\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8759 non-null   object \n",
            " 1   c1      8759 non-null   float64\n",
            " 2   c2      8759 non-null   float64\n",
            " 3   c3      8759 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.8+ KB\n",
            "None\n",
            "\n",
            "File: unit_consumption_2020.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8784 entries, 0 to 8783\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8784 non-null   object \n",
            " 1   c1      8784 non-null   float64\n",
            " 2   c2      8784 non-null   float64\n",
            " 3   c3      8784 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 274.6+ KB\n",
            "None\n",
            "\n",
            "File: unit_consumption_2021.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8760 entries, 0 to 8759\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8760 non-null   object \n",
            " 1   c1      8760 non-null   float64\n",
            " 2   c2      8760 non-null   float64\n",
            " 3   c3      8760 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.9+ KB\n",
            "None\n",
            "\n",
            "File: unit_consumption_2022.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8760 entries, 0 to 8759\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8760 non-null   object \n",
            " 1   c1      8760 non-null   float64\n",
            " 2   c2      8760 non-null   float64\n",
            " 3   c3      8760 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.9+ KB\n",
            "None\n",
            "\n",
            "File: unit_consumption_2023.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8760 entries, 0 to 8759\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8760 non-null   object \n",
            " 1   c1      8760 non-null   float64\n",
            " 2   c2      8760 non-null   float64\n",
            " 3   c3      8760 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.9+ KB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "file_paths = [\n",
        "    'grid_stability_2019_2023.csv',\n",
        "    'price_per_unit_2019.csv',\n",
        "    'price_per_unit_2020.csv',\n",
        "    'price_per_unit_2021.csv',\n",
        "    'price_per_unit_2022.csv',\n",
        "    'price_per_unit_2023.csv',\n",
        "    'unit_consumption_2019.csv',\n",
        "    'unit_consumption_2020.csv',\n",
        "    'unit_consumption_2021.csv',\n",
        "    'unit_consumption_2022.csv',\n",
        "    'unit_consumption_2023.csv'\n",
        "]\n",
        "\n",
        "for file_path in file_paths:\n",
        "    df = pd.read_csv(file_path)\n",
        "    print(f\"\\nFile: {file_path}\")\n",
        "    print(df.info())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "def analyze_grid_dataset(files):\n",
        "    for file in files:\n",
        "        print(\"Analyzing file:\", file)\n",
        "        df = pd.read_csv(file)\n",
        "        print(\"File:\", os.path.basename(file))\n",
        "        print(df.info())\n",
        "        print(\"Shape:\", df.shape)\n",
        "        print(\"Columns:\", df.columns.tolist())\n",
        "        print(\"Missing Values:\")\n",
        "        print(df.isnull().sum())\n",
        "        print(\"Basic Statistics:\")\n",
        "        print(df.describe())\n",
        "        print(\"\\n\")\n"
      ],
      "metadata": {
        "id": "O3PRKD6U6cwI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "analyze_grid_dataset(file_paths)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iPyWkP1k_gKu",
        "outputId": "c66725a5-bc0a-4e48-965f-9317d19fd661"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Analyzing file: grid_stability_2019_2023.csv\n",
            "File: grid_stability_2019_2023.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 46007 entries, 0 to 46006\n",
            "Data columns (total 2 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   date       46007 non-null  object\n",
            " 1   stability  46007 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 719.0+ KB\n",
            "None\n",
            "Shape: (46007, 2)\n",
            "Columns: ['date', 'stability']\n",
            "Missing Values:\n",
            "date         0\n",
            "stability    0\n",
            "dtype: int64\n",
            "Basic Statistics:\n",
            "                    date stability\n",
            "count              46007     46007\n",
            "unique             46007         2\n",
            "top     01-01-2019 01:00  unstable\n",
            "freq                   1     29357\n",
            "\n",
            "\n",
            "Analyzing file: price_per_unit_2019.csv\n",
            "File: price_per_unit_2019.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8759 entries, 0 to 8758\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8759 non-null   object \n",
            " 1   p1      8759 non-null   float64\n",
            " 2   p2      8759 non-null   float64\n",
            " 3   p3      8759 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.8+ KB\n",
            "None\n",
            "Shape: (8759, 4)\n",
            "Columns: ['date', 'p1', 'p2', 'p3']\n",
            "Missing Values:\n",
            "date    0\n",
            "p1      0\n",
            "p2      0\n",
            "p3      0\n",
            "dtype: int64\n",
            "Basic Statistics:\n",
            "                p1           p2           p3\n",
            "count  8759.000000  8759.000000  8759.000000\n",
            "mean      0.524991     0.525702     0.524985\n",
            "std       0.274154     0.274757     0.273621\n",
            "min       0.050053     0.050054     0.050028\n",
            "25%       0.288442     0.287353     0.288458\n",
            "50%       0.525182     0.525567     0.526306\n",
            "75%       0.762418     0.764494     0.761170\n",
            "max       0.999944     0.999982     0.999930\n",
            "\n",
            "\n",
            "Analyzing file: price_per_unit_2020.csv\n",
            "File: price_per_unit_2020.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8784 entries, 0 to 8783\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8784 non-null   object \n",
            " 1   p1      8784 non-null   float64\n",
            " 2   p2      8784 non-null   float64\n",
            " 3   p3      8784 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 274.6+ KB\n",
            "None\n",
            "Shape: (8784, 4)\n",
            "Columns: ['date', 'p1', 'p2', 'p3']\n",
            "Missing Values:\n",
            "date    0\n",
            "p1      0\n",
            "p2      0\n",
            "p3      0\n",
            "dtype: int64\n",
            "Basic Statistics:\n",
            "                p1           p2           p3\n",
            "count  8784.000000  8784.000000  8784.000000\n",
            "mean      0.522732     0.523013     0.526332\n",
            "std       0.273633     0.273771     0.274413\n",
            "min       0.050028     0.050130     0.050054\n",
            "25%       0.285257     0.286248     0.290555\n",
            "50%       0.522806     0.522735     0.525558\n",
            "75%       0.758717     0.759116     0.765216\n",
            "max       0.999882     0.999944     0.999982\n",
            "\n",
            "\n",
            "Analyzing file: price_per_unit_2021.csv\n",
            "File: price_per_unit_2021.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8760 entries, 0 to 8759\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8760 non-null   object \n",
            " 1   p1      8760 non-null   float64\n",
            " 2   p2      8760 non-null   float64\n",
            " 3   p3      8760 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.9+ KB\n",
            "None\n",
            "Shape: (8760, 4)\n",
            "Columns: ['date', 'p1', 'p2', 'p3']\n",
            "Missing Values:\n",
            "date    0\n",
            "p1      0\n",
            "p2      0\n",
            "p3      0\n",
            "dtype: int64\n",
            "Basic Statistics:\n",
            "                p1           p2           p3\n",
            "count  8760.000000  8760.000000  8760.000000\n",
            "mean      0.528645     0.523993     0.521090\n",
            "std       0.274482     0.273287     0.274900\n",
            "min       0.050054     0.050028     0.050130\n",
            "25%       0.294040     0.288796     0.281086\n",
            "50%       0.529116     0.522301     0.518267\n",
            "75%       0.768314     0.758835     0.757671\n",
            "max       0.999982     0.999893     0.999944\n",
            "\n",
            "\n",
            "Analyzing file: price_per_unit_2022.csv\n",
            "File: price_per_unit_2022.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8760 entries, 0 to 8759\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8760 non-null   object \n",
            " 1   p1      8760 non-null   float64\n",
            " 2   p2      8760 non-null   float64\n",
            " 3   p3      8760 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.9+ KB\n",
            "None\n",
            "Shape: (8760, 4)\n",
            "Columns: ['date', 'p1', 'p2', 'p3']\n",
            "Missing Values:\n",
            "date    0\n",
            "p1      0\n",
            "p2      0\n",
            "p3      0\n",
            "dtype: int64\n",
            "Basic Statistics:\n",
            "                p1           p2           p3\n",
            "count  8760.000000  8760.000000  8760.000000\n",
            "mean      0.521622     0.523794     0.527627\n",
            "std       0.274647     0.274675     0.273142\n",
            "min       0.050130     0.050028     0.050053\n",
            "25%       0.280492     0.285558     0.291774\n",
            "50%       0.520075     0.522410     0.529399\n",
            "75%       0.758947     0.761818     0.764103\n",
            "max       0.999745     0.999930     0.999982\n",
            "\n",
            "\n",
            "Analyzing file: price_per_unit_2023.csv\n",
            "File: price_per_unit_2023.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8760 entries, 0 to 8759\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8760 non-null   object \n",
            " 1   p1      8760 non-null   float64\n",
            " 2   p2      8760 non-null   float64\n",
            " 3   p3      8760 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.9+ KB\n",
            "None\n",
            "Shape: (8760, 4)\n",
            "Columns: ['date', 'p1', 'p2', 'p3']\n",
            "Missing Values:\n",
            "date    0\n",
            "p1      0\n",
            "p2      0\n",
            "p3      0\n",
            "dtype: int64\n",
            "Basic Statistics:\n",
            "                p1           p2           p3\n",
            "count  8760.000000  8760.000000  8760.000000\n",
            "mean      0.523946     0.528957     0.524212\n",
            "std       0.273855     0.273649     0.274497\n",
            "min       0.050028     0.050054     0.050104\n",
            "25%       0.287959     0.293213     0.285568\n",
            "50%       0.522300     0.530805     0.523578\n",
            "75%       0.758835     0.766414     0.761870\n",
            "max       0.999944     0.999930     0.999745\n",
            "\n",
            "\n",
            "Analyzing file: unit_consumption_2019.csv\n",
            "File: unit_consumption_2019.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8759 entries, 0 to 8758\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8759 non-null   object \n",
            " 1   c1      8759 non-null   float64\n",
            " 2   c2      8759 non-null   float64\n",
            " 3   c3      8759 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.8+ KB\n",
            "None\n",
            "Shape: (8759, 4)\n",
            "Columns: ['date', 'c1', 'c2', 'c3']\n",
            "Missing Values:\n",
            "date    0\n",
            "c1      0\n",
            "c2      0\n",
            "c3      0\n",
            "dtype: int64\n",
            "Basic Statistics:\n",
            "                c1           c2           c3\n",
            "count  8759.000000  8759.000000  8759.000000\n",
            "mean     -1.251741    -1.249599    -1.250977\n",
            "std       0.432906     0.433972     0.432081\n",
            "min      -1.999891    -1.999715    -1.999926\n",
            "25%      -1.626826    -1.626614    -1.625815\n",
            "50%      -1.252820    -1.248094    -1.250497\n",
            "75%      -0.875722    -0.874295    -0.877578\n",
            "max      -0.500108    -0.500072    -0.500025\n",
            "\n",
            "\n",
            "Analyzing file: unit_consumption_2020.csv\n",
            "File: unit_consumption_2020.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8784 entries, 0 to 8783\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8784 non-null   object \n",
            " 1   c1      8784 non-null   float64\n",
            " 2   c2      8784 non-null   float64\n",
            " 3   c3      8784 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 274.6+ KB\n",
            "None\n",
            "Shape: (8784, 4)\n",
            "Columns: ['date', 'c1', 'c2', 'c3']\n",
            "Missing Values:\n",
            "date    0\n",
            "c1      0\n",
            "c2      0\n",
            "c3      0\n",
            "dtype: int64\n",
            "Basic Statistics:\n",
            "                c1           c2           c3\n",
            "count  8784.000000  8784.000000  8784.000000\n",
            "mean     -1.248479    -1.252285    -1.248476\n",
            "std       0.432274     0.429470     0.434914\n",
            "min      -1.999926    -1.999945    -1.999715\n",
            "25%      -1.621306    -1.622838    -1.625488\n",
            "50%      -1.245695    -1.253249    -1.249985\n",
            "75%      -0.876805    -0.881910    -0.870947\n",
            "max      -0.500025    -0.500108    -0.500072\n",
            "\n",
            "\n",
            "Analyzing file: unit_consumption_2021.csv\n",
            "File: unit_consumption_2021.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8760 entries, 0 to 8759\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8760 non-null   object \n",
            " 1   c1      8760 non-null   float64\n",
            " 2   c2      8760 non-null   float64\n",
            " 3   c3      8760 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.9+ KB\n",
            "None\n",
            "Shape: (8760, 4)\n",
            "Columns: ['date', 'c1', 'c2', 'c3']\n",
            "Missing Values:\n",
            "date    0\n",
            "c1      0\n",
            "c2      0\n",
            "c3      0\n",
            "dtype: int64\n",
            "Basic Statistics:\n",
            "                c1           c2           c3\n",
            "count  8760.000000  8760.000000  8760.000000\n",
            "mean     -1.249133    -1.246670    -1.250146\n",
            "std       0.434600     0.434918     0.429337\n",
            "min      -1.999715    -1.999926    -1.999945\n",
            "25%      -1.627204    -1.620063    -1.620800\n",
            "50%      -1.251937    -1.243380    -1.247343\n",
            "75%      -0.870110    -0.869040    -0.880719\n",
            "max      -0.500290    -0.500025    -0.500108\n",
            "\n",
            "\n",
            "Analyzing file: unit_consumption_2022.csv\n",
            "File: unit_consumption_2022.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8760 entries, 0 to 8759\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8760 non-null   object \n",
            " 1   c1      8760 non-null   float64\n",
            " 2   c2      8760 non-null   float64\n",
            " 3   c3      8760 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.9+ KB\n",
            "None\n",
            "Shape: (8760, 4)\n",
            "Columns: ['date', 'c1', 'c2', 'c3']\n",
            "Missing Values:\n",
            "date    0\n",
            "c1      0\n",
            "c2      0\n",
            "c3      0\n",
            "dtype: int64\n",
            "Basic Statistics:\n",
            "                c1           c2           c3\n",
            "count  8760.000000  8760.000000  8760.000000\n",
            "mean     -1.248181    -1.252329    -1.247948\n",
            "std       0.430064     0.432405     0.437501\n",
            "min      -1.999945    -1.999926    -1.999891\n",
            "25%      -1.621712    -1.628760    -1.629725\n",
            "50%      -1.243819    -1.253563    -1.248727\n",
            "75%      -0.879854    -0.878496    -0.863863\n",
            "max      -0.500072    -0.500025    -0.500274\n",
            "\n",
            "\n",
            "Analyzing file: unit_consumption_2023.csv\n",
            "File: unit_consumption_2023.csv\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8760 entries, 0 to 8759\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8760 non-null   object \n",
            " 1   c1      8760 non-null   float64\n",
            " 2   c2      8760 non-null   float64\n",
            " 3   c3      8760 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 273.9+ KB\n",
            "None\n",
            "Shape: (8760, 4)\n",
            "Columns: ['date', 'c1', 'c2', 'c3']\n",
            "Missing Values:\n",
            "date    0\n",
            "c1      0\n",
            "c2      0\n",
            "c3      0\n",
            "dtype: int64\n",
            "Basic Statistics:\n",
            "                c1           c2           c3\n",
            "count  8760.000000  8760.000000  8760.000000\n",
            "mean     -1.255204    -1.246643    -1.250217\n",
            "std       0.433785     0.435104     0.429511\n",
            "min      -1.999926    -1.999336    -1.999945\n",
            "25%      -1.627790    -1.627158    -1.622300\n",
            "50%      -1.257745    -1.247736    -1.247528\n",
            "75%      -0.880152    -0.863591    -0.884702\n",
            "max      -0.500108    -0.500290    -0.500072\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "VqqsdQNXBIR8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def visualize_data(df, column_name):\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.histplot(df[column_name], bins=20, kde=True)\n",
        "    plt.title(f\"Distribution of {column_name}\")\n",
        "    plt.xlabel(column_name)\n",
        "    plt.ylabel(\"Frequency\")\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "id": "lSwdp9orBLAn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def visualize_price_per_unit_datasets(files):\n",
        "    for file in files:\n",
        "        print(\"Visualizing file:\", file)\n",
        "        df = pd.read_csv(file)\n",
        "\n",
        "        # Plotting p1, p2, p3 columns\n",
        "        plt.figure(figsize=(10, 6))\n",
        "        plt.plot(df['date'], df['p1'], label='p1')\n",
        "        plt.plot(df['date'], df['p2'], label='p2')\n",
        "        plt.plot(df['date'], df['p3'], label='p3')\n",
        "        plt.title(\"Price per Unit Data - \" + file)\n",
        "        plt.xlabel('Date')\n",
        "        plt.ylabel('Price')\n",
        "        plt.legend()\n",
        "        plt.grid(True)\n",
        "        plt.xticks(rotation=45)\n",
        "        plt.show()\n",
        "\n",
        "file_paths = [\n",
        "    'price_per_unit_2019.csv',\n",
        "    'price_per_unit_2020.csv',\n",
        "    'price_per_unit_2021.csv',\n",
        "    'price_per_unit_2022.csv',\n",
        "    'price_per_unit_2023.csv'\n",
        "]\n",
        "\n",
        "visualize_price_per_unit_datasets(file_paths)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gli6uWeBBNlq",
        "outputId": "e98d6a3e-0e45-4513-f036-1d16570c3ae4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Visualizing file: price_per_unit_2019.csv\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Visualizing file: price_per_unit_2020.csv\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Visualizing file: price_per_unit_2021.csv\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Visualizing file: price_per_unit_2022.csv\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Visualizing file: price_per_unit_2023.csv\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Define the file paths\n",
        "file_paths = [\n",
        "    'price_per_unit_2019.csv',\n",
        "    'price_per_unit_2020.csv',\n",
        "    'price_per_unit_2021.csv',\n",
        "    'price_per_unit_2022.csv',\n",
        "    'price_per_unit_2023.csv'\n",
        "]\n",
        "\n",
        "# Load datasets into a list of DataFrames\n",
        "dfs = []\n",
        "for file in file_paths:\n",
        "    df = pd.read_csv(file)\n",
        "    dfs.append(df)\n",
        "\n",
        "# Concatenate the DataFrames\n",
        "merged_df = pd.concat(dfs, ignore_index=True)\n",
        "\n",
        "# Display information about the merged DataFrame\n",
        "print(\"Merged DataFrame:\")\n",
        "print(merged_df.info())\n",
        "\n",
        "# Optionally, you can save the merged DataFrame to a new CSV file\n",
        "merged_df.to_csv('/content/merged_price_per_unit.csv', index=False)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0lHxwH-EX6V",
        "outputId": "fbd2898b-7525-4f85-df58-b3a3d3f5a5af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Merged DataFrame:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 43823 entries, 0 to 43822\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    43823 non-null  object \n",
            " 1   p1      43823 non-null  float64\n",
            " 2   p2      43823 non-null  float64\n",
            " 3   p3      43823 non-null  float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 1.3+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Define the file paths for unit consumption datasets\n",
        "unit_consumption_files = [\n",
        "    'unit_consumption_2019.csv',\n",
        "    'unit_consumption_2020.csv',\n",
        "    'unit_consumption_2021.csv',\n",
        "    'unit_consumption_2022.csv',\n",
        "    'unit_consumption_2023.csv'\n",
        "]\n",
        "\n",
        "# Load datasets into a list of DataFrames\n",
        "unit_consumption_dfs = []\n",
        "for file in unit_consumption_files:\n",
        "    df = pd.read_csv(file)\n",
        "    unit_consumption_dfs.append(df)\n",
        "\n",
        "# Concatenate the DataFrames\n",
        "merged_unit_consumption_df = pd.concat(unit_consumption_dfs, ignore_index=True)\n",
        "\n",
        "# Display information about the merged DataFrame\n",
        "print(\"Merged Unit Consumption DataFrame:\")\n",
        "print(merged_unit_consumption_df.info())\n",
        "\n",
        "# Optionally, you can save the merged DataFrame to a new CSV file\n",
        "merged_unit_consumption_df.to_csv('/content/merged_unit_consumption.csv', index=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BV6HKFqwJJkl",
        "outputId": "ba9a78cf-7516-40f5-e9bf-fa05b59acc7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Merged Unit Consumption DataFrame:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 43823 entries, 0 to 43822\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    43823 non-null  object \n",
            " 1   c1      43823 non-null  float64\n",
            " 2   c2      43823 non-null  float64\n",
            " 3   c3      43823 non-null  float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 1.3+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the previously merged datasets\n",
        "merged_price_per_unit_df = pd.read_csv('merged_price_per_unit.csv')\n",
        "merged_unit_consumption_df = pd.read_csv('merged_unit_consumption.csv')\n",
        "\n",
        "# Load the new dataset\n",
        "grid_stability_df = pd.read_csv('grid_stability_2019_2023.csv')\n",
        "\n",
        "# Merge all three datasets based on the 'date' column\n",
        "final_merged_df = pd.merge(merged_price_per_unit_df, merged_unit_consumption_df, on='date')\n",
        "final_merged_df = pd.merge(final_merged_df, grid_stability_df, on='date')\n",
        "\n",
        "# Display information about the final merged DataFrame\n",
        "print(\"Final Merged DataFrame:\")\n",
        "print(final_merged_df.info())\n",
        "\n",
        "# Optionally, you can save the final merged DataFrame to a new CSV file\n",
        "final_merged_df.to_csv('/content/final_merged_data.csv', index=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qVuRXPuZzrr_",
        "outputId": "eb2ea953-0aa2-48f5-f6bc-2dcdd9c9048c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Merged DataFrame:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 43823 entries, 0 to 43822\n",
            "Data columns (total 8 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   date       43823 non-null  object \n",
            " 1   p1         43823 non-null  float64\n",
            " 2   p2         43823 non-null  float64\n",
            " 3   p3         43823 non-null  float64\n",
            " 4   c1         43823 non-null  float64\n",
            " 5   c2         43823 non-null  float64\n",
            " 6   c3         43823 non-null  float64\n",
            " 7   stability  43823 non-null  object \n",
            "dtypes: float64(6), object(2)\n",
            "memory usage: 2.7+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the dataset\n",
        "final_merged_data = pd.read_csv('/content/final_merged_data.csv')\n",
        "\n",
        "# Display the first few rows of the dataset to verify it's loaded correctly\n",
        "print(final_merged_data.head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uoy-Yr_f4UcE",
        "outputId": "81e70a38-2e3e-413d-a80b-897101d535fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               date        p1        p2        p3        c1        c2  \\\n",
            "0  01-01-2019 01:00  0.859578  0.887445  0.958034 -0.782604 -1.257395   \n",
            "1  01-01-2019 02:00  0.862414  0.562139  0.781760 -1.940058 -1.872742   \n",
            "2  01-01-2019 03:00  0.766689  0.839444  0.109853 -1.207456 -1.277210   \n",
            "3  01-01-2019 04:00  0.976744  0.929381  0.362718 -1.027473 -1.938944   \n",
            "4  01-01-2019 05:00  0.455450  0.656947  0.820923 -1.125531 -1.845975   \n",
            "\n",
            "         c3 stability  \n",
            "0 -1.723086  unstable  \n",
            "1 -1.255012    stable  \n",
            "2 -0.920492  unstable  \n",
            "3 -0.997374  unstable  \n",
            "4 -0.554305  unstable  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Assuming 'final_merged_data' contains your merged dataset and 'stability' is the target variable\n",
        "\n",
        "# Feature Engineering: Convert date column to numerical features\n",
        "final_merged_data['date'] = pd.to_datetime(final_merged_data['date'], format='%d-%m-%Y %H:%M')\n",
        "final_merged_data['year'] = final_merged_data['date'].dt.year\n",
        "final_merged_data['month'] = final_merged_data['date'].dt.month\n",
        "final_merged_data['day'] = final_merged_data['date'].dt.day\n",
        "final_merged_data['hour'] = final_merged_data['date'].dt.hour\n",
        "\n",
        "# Drop the original date column\n",
        "final_merged_data.drop(columns=['date'], inplace=True)\n",
        "\n",
        "# Splitting the data into features (X) and target variable (y)\n",
        "X = final_merged_data.drop(columns=['stability'])\n",
        "y = final_merged_data['stability']\n",
        "\n",
        "# Splitting the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initializing the Random Forest classifier\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# Training the Random Forest classifier\n",
        "rf_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Making predictions on the test set\n",
        "y_pred = rf_classifier.predict(X_test)\n",
        "\n",
        "# Evaluating the model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CHI53Wxp0ZIC",
        "outputId": "384b5170-fb54-4410-a0e5-d62cc7712b4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7106674272675414\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "\n",
        "# Initializing the AdaBoost classifier with a base estimator (Random Forest)\n",
        "adaboost_classifier = AdaBoostClassifier(base_estimator=rf_classifier, n_estimators=50, random_state=42)\n",
        "# You can adjust hyperparameters as needed\n",
        "\n",
        "# Training the AdaBoost classifier\n",
        "adaboost_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Making predictions on the test set\n",
        "y_pred_adaboost = adaboost_classifier.predict(X_test)\n",
        "\n",
        "# Evaluating the AdaBoost classifier\n",
        "accuracy_adaboost = accuracy_score(y_test, y_pred_adaboost)\n",
        "print(\"AdaBoost Accuracy:\", accuracy_adaboost)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6gmXwXxx3r6F",
        "outputId": "96a33f19-419f-47ad-f1b0-510bcd7076c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AdaBoost Accuracy: 0.7134055904164289\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Define the parameter grid\n",
        "param_grid = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'max_depth': [10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10]\n",
        "}\n",
        "\n",
        "# Instantiate the Random Forest classifier\n",
        "rf_classifier = RandomForestClassifier(random_state=42)\n",
        "\n",
        "# Instantiate the GridSearchCV object\n",
        "grid_search = GridSearchCV(rf_classifier, param_grid, cv=5, scoring='accuracy')\n",
        "\n",
        "# Fit the GridSearchCV object\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Retrieve the best hyperparameters\n",
        "best_params = grid_search.best_params_\n",
        "\n",
        "# Rebuild the model with the best hyperparameters\n",
        "best_rf_classifier = RandomForestClassifier(**best_params, random_state=42)\n",
        "\n",
        "# Fit the model on the training data\n",
        "best_rf_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate on the test set\n",
        "y_pred = best_rf_classifier.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XwAhf5q56h-Y",
        "outputId": "129be9b8-2aa3-4d19-bbaf-3360a5db4ffd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7150028522532801\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Fit the model on the training data\n",
        "best_rf_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Save the trained model to a file using joblib\n",
        "joblib.dump(best_rf_classifier, 'best_rf_classifier.pkl')\n",
        "\n",
        "# Evaluate on the test set and display accuracy\n",
        "y_pred = best_rf_classifier.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eP4dMdHhrFT5",
        "outputId": "e3a38fe6-2b66-4224-c3b5-e86e4855746f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7150028522532801\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "# Define a custom loss function if needed (replace compute_loss with your custom function)\n",
        "def compute_loss(y_true, y_pred):\n",
        "    # Define your custom loss function here\n",
        "    pass\n",
        "\n",
        "# Evaluate on the test set and display loss function\n",
        "# Since this is a classification problem, we'll skip the loss function calculation\n",
        "# loss_function = compute_loss(y_test, y_pred)  # Replace compute_loss with your custom loss function\n",
        "# print(\"Loss Function:\", loss_function)\n",
        "\n",
        "# Calculate and display the F1 score\n",
        "f1 = f1_score(y_test, y_pred, average='weighted')\n",
        "print(\"F1 Score:\", f1)\n",
        "\n",
        "# Now, let's remove the calculation of mean squared error since it's not relevant for classification problems\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2xpOCRIG8ObI",
        "outputId": "6490de66-247c-47cd-efa1-6b03d9d8721d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F1 Score: 0.691228881270547\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, roc_curve, roc_auc_score\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Calculate and display accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "\n",
        "# Calculate and display recall\n",
        "recall = recall_score(y_test, y_pred, pos_label='unstable')\n",
        "print(\"Recall:\", recall)\n",
        "\n",
        "# Calculate and display precision\n",
        "precision = precision_score(y_test, y_pred, pos_label='unstable')\n",
        "print(\"Precision:\", precision)\n",
        "\n",
        "# Calculate ROC curve\n",
        "y_prob = best_rf_classifier.predict_proba(X_test)[:, 1]\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_prob, pos_label='unstable')\n",
        "\n",
        "# Calculate ROC AUC score\n",
        "roc_auc = roc_auc_score(y_test, y_prob)\n",
        "print(\"ROC AUC Score:\", roc_auc)\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, color='blue', lw=2, label='ROC Curve (AUC = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='red', linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "8-t6vGRYP6vW",
        "outputId": "c02bcb6e-8cff-41f0-ea5a-52cd2e7ac708"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7150028522532801\n",
            "Recall: 0.8977272727272727\n",
            "Precision: 0.7245629120091717\n",
            "ROC AUC Score: 0.7312528223203145\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you have a DataFrame called merged_data with columns 'price_per_unit', 'C1', 'C2', and 'C3'\n",
        "import pandas as pd\n",
        "\n",
        "# Load the CSV file into a DataFrame\n",
        "df = pd.read_csv('final_merged_data.csv')\n",
        "\n",
        "# Calculate total cost of power consumption for each node\n",
        "df['total_cost_node1'] = df['p1'] * df['c1']\n",
        "df['total_cost_node2'] = df['p2'] * df['c2']\n",
        "df['total_cost_node3'] = df['p3'] * df['c3']\n",
        "\n",
        "# Calculate total power generated (p) for each node\n",
        "df['p_node1'] = df['total_cost_node1'] / df['p1']\n",
        "df['p_node2'] = df['total_cost_node2'] / df['p2']\n",
        "df['p_node3'] = df['total_cost_node3'] / df['p3']\n",
        "\n",
        "# Calculate total power generated (p) across all nodes\n",
        "df['p'] = df['p_node1'] + df['p_node2'] + df['p_node3']\n"
      ],
      "metadata": {
        "id": "LcycC0WDTDm7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read the dataset and print the first few rows (head) of the DataFrame in one line\n",
        "print(pd.read_csv('final_merged_data.csv').head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0NQ-nED47pYd",
        "outputId": "ba512f86-41d4-450f-8e46-102d6b8c8458"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               date        p1        p2        p3        c1        c2  \\\n",
            "0  01-01-2019 01:00  0.859578  0.887445  0.958034 -0.782604 -1.257395   \n",
            "1  01-01-2019 02:00  0.862414  0.562139  0.781760 -1.940058 -1.872742   \n",
            "2  01-01-2019 03:00  0.766689  0.839444  0.109853 -1.207456 -1.277210   \n",
            "3  01-01-2019 04:00  0.976744  0.929381  0.362718 -1.027473 -1.938944   \n",
            "4  01-01-2019 05:00  0.455450  0.656947  0.820923 -1.125531 -1.845975   \n",
            "\n",
            "         c3 stability  \n",
            "0 -1.723086  unstable  \n",
            "1 -1.255012    stable  \n",
            "2 -0.920492  unstable  \n",
            "3 -0.997374  unstable  \n",
            "4 -0.554305  unstable  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you have a DataFrame called merged_data with columns 'p', 'p_node1', 'p_node2', and 'p_node3'\n",
        "\n",
        "# Calculate the power distribution to each consumption node\n",
        "final_merged_data['p_node1'] = final_merged_data['p'] * 0.20  # 20% to Node 1\n",
        "final_merged_data['p_node2'] = final_merged_data['p'] * 0.45  # 45% to Node 2\n",
        "final_merged_data['p_node3'] = final_merged_data['p'] * 0.35  # 35% to Node 3\n"
      ],
      "metadata": {
        "id": "14t08QSz6ylw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read the dataset and print the first few rows (head) of the DataFrame in one line\n",
        "print(pd.read_csv('final_merged_data.csv').info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Thh1If8J8Usr",
        "outputId": "77922f23-3c32-4f8a-bea9-e824fdc83e63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 43823 entries, 0 to 43822\n",
            "Data columns (total 8 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   date       43823 non-null  object \n",
            " 1   p1         43823 non-null  float64\n",
            " 2   p2         43823 non-null  float64\n",
            " 3   p3         43823 non-null  float64\n",
            " 4   c1         43823 non-null  float64\n",
            " 5   c2         43823 non-null  float64\n",
            " 6   c3         43823 non-null  float64\n",
            " 7   stability  43823 non-null  object \n",
            "dtypes: float64(6), object(2)\n",
            "memory usage: 2.7+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read the dataset specifying column names and separator\n",
        "df = pd.read_csv('final_merged_data.csv', header=None, sep=';')  # Adjust parameters as necessary\n",
        "\n",
        "# Print the first few rows (head) of the DataFrame\n",
        "print(df.head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jkSrFTQh8aUz",
        "outputId": "727879c0-e83e-4f5b-c2d5-928c299ef8e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                   0\n",
            "0                   date,p1,p2,p3,c1,c2,c3,stability\n",
            "1  01-01-2019 01:00,0.859578106,0.887444921,0.958...\n",
            "2  01-01-2019 02:00,0.862414076,0.562139051,0.781...\n",
            "3  01-01-2019 03:00,0.766688657,0.839444015,0.109...\n",
            "4  01-01-2019 04:00,0.976744083,0.929380523,0.362...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Nwa61WUY87pN"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}