Exploring_ashrae_dataset_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Exploring_ashrae_dataset_.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qlAk0SnMYyA"
      },
      "source": [
        "# first upload your kaggle.json - from your profile create an API token - and then run the following"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8fxPne6IVsO"
      },
      "source": [
        "## To download the data using kaggle\n",
        "#!kaggle competitions download -c ashrae-energy-prediction"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mNx3RNtXMATD"
      },
      "source": [
        "## Importing our libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYJj7-TEMIlo"
      },
      "source": [
        "import pandas as pd , numpy as np , matplotlib.pyplot as plt , seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yc3y81rpMUyy"
      },
      "source": [
        "## Loading the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "U34fhJdlMtR1",
        "outputId": "38749f98-ab17-45c6-d8d1-93f6a25fb80d"
      },
      "source": [
        "train_df= pd.read_csv('content/train.csv',  )\n",
        "building_df= pd.read_csv('content/building_metadata.csv' )\n",
        "weather_df= pd.read_csv('content/weather_train.csv')\n",
        "train_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12560/2560017564.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../input/ashrae-energy-prediction/train.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mweather_train_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../input/ashrae-energy-prediction/weather_train.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mtest_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../input/ashrae-energy-prediction/test.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mweather_test_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../input/ashrae-energy-prediction/weather_test.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mbuilding_meta_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../input/ashrae-energy-prediction/building_metadata.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m                 )\n\u001b[1;32m--> 311\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    584\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    585\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 586\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    587\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    588\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    480\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    481\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 482\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    483\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    484\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    809\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    810\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 811\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    812\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    813\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1038\u001b[0m             )\n\u001b[0;32m   1039\u001b[0m         \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1040\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1041\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1042\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m     49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m         \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m    220\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \"\"\"\n\u001b[1;32m--> 222\u001b[1;33m         self.handles = get_handle(\n\u001b[0m\u001b[0;32m    223\u001b[0m             \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m             \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    700\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m\"b\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    701\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m             handle = open(\n\u001b[0m\u001b[0;32m    703\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../input/ashrae-energy-prediction/train.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10b3R3nZTaHD"
      },
      "source": [
        "## specifying only 10 buildings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqe6RdeITdKX"
      },
      "source": [
        "building_df= building_df[  building_df['building_id'] < 11 ]\n",
        "train_df= train_df[ train_df['building_id'] < 11 ]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3vEE1pMENw7",
        "outputId": "444766d9-8c44-4e9c-d4bb-eee25043a27b"
      },
      "source": [
        "train_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 111156 entries, 0 to 20213746\n",
            "Data columns (total 4 columns):\n",
            " #   Column         Non-Null Count   Dtype  \n",
            "---  ------         --------------   -----  \n",
            " 0   building_id    111156 non-null  int64  \n",
            " 1   meter          111156 non-null  int64  \n",
            " 2   timestamp      111156 non-null  object \n",
            " 3   meter_reading  111156 non-null  float64\n",
            "dtypes: float64(1), int64(2), object(1)\n",
            "memory usage: 4.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QUmmkcv3MX3p"
      },
      "source": [
        "## Parsing the timestamp column as date format"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZG6lPytQMATK"
      },
      "source": [
        "train_df['timestamp']= pd.to_datetime(train_df['timestamp'])\n",
        "weather_df['timestamp']= pd.to_datetime(weather_df['timestamp'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xllHWDOPMb1Z"
      },
      "source": [
        "## printing the columns and the info of train_df"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUSLY_tINI_D",
        "outputId": "c91e8ca2-8cf6-474c-8bd0-0cbfb3215e9b"
      },
      "source": [
        "print ( train_df.columns,'\\n' )\n",
        "print( building_df.columns,'\\n' )\n",
        "print( weather_df.columns,'\\n' )\n",
        "train_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['building_id', 'meter', 'timestamp', 'meter_reading'], dtype='object') \n",
            "\n",
            "Index(['site_id', 'building_id', 'primary_use', 'square_feet', 'year_built',\n",
            "       'floor_count'],\n",
            "      dtype='object') \n",
            "\n",
            "Index(['site_id', 'timestamp', 'air_temperature', 'cloud_coverage',\n",
            "       'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure',\n",
            "       'wind_direction', 'wind_speed'],\n",
            "      dtype='object') \n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 111156 entries, 0 to 20213746\n",
            "Data columns (total 4 columns):\n",
            " #   Column         Non-Null Count   Dtype         \n",
            "---  ------         --------------   -----         \n",
            " 0   building_id    111156 non-null  int64         \n",
            " 1   meter          111156 non-null  int64         \n",
            " 2   timestamp      111156 non-null  datetime64[ns]\n",
            " 3   meter_reading  111156 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(1), int64(2)\n",
            "memory usage: 4.2 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjZ1QY0_MkKR"
      },
      "source": [
        "## merging weather_df and building_df on the site column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "eViURDiPNP_X",
        "outputId": "ca929806-3631-451d-f538-c6419de4454a"
      },
      "source": [
        "building_weather= pd.merge( weather_df , building_df , on='site_id')\n",
        "building_weather.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   site_id  timestamp  air_temperature  cloud_coverage  dew_temperature  \\\n",
              "0        0 2016-01-01             25.0             6.0             20.0   \n",
              "1        0 2016-01-01             25.0             6.0             20.0   \n",
              "2        0 2016-01-01             25.0             6.0             20.0   \n",
              "3        0 2016-01-01             25.0             6.0             20.0   \n",
              "4        0 2016-01-01             25.0             6.0             20.0   \n",
              "\n",
              "   precip_depth_1_hr  sea_level_pressure  wind_direction  wind_speed  \\\n",
              "0                NaN              1019.7             0.0         0.0   \n",
              "1                NaN              1019.7             0.0         0.0   \n",
              "2                NaN              1019.7             0.0         0.0   \n",
              "3                NaN              1019.7             0.0         0.0   \n",
              "4                NaN              1019.7             0.0         0.0   \n",
              "\n",
              "   building_id primary_use  square_feet  year_built  floor_count  \n",
              "0            0   Education         7432      2008.0          NaN  \n",
              "1            1   Education         2720      2004.0          NaN  \n",
              "2            2   Education         5376      1991.0          NaN  \n",
              "3            3   Education        23685      2002.0          NaN  \n",
              "4            4   Education       116607      1975.0          NaN  "
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>site_id</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>cloud_coverage</th>\n",
              "      <th>dew_temperature</th>\n",
              "      <th>precip_depth_1_hr</th>\n",
              "      <th>sea_level_pressure</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>building_id</th>\n",
              "      <th>primary_use</th>\n",
              "      <th>square_feet</th>\n",
              "      <th>year_built</th>\n",
              "      <th>floor_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>25.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1019.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>25.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1019.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Education</td>\n",
              "      <td>2720</td>\n",
              "      <td>2004.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>25.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1019.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Education</td>\n",
              "      <td>5376</td>\n",
              "      <td>1991.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>25.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1019.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Education</td>\n",
              "      <td>23685</td>\n",
              "      <td>2002.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>25.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1019.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>Education</td>\n",
              "      <td>116607</td>\n",
              "      <td>1975.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yRIIFntiMymx"
      },
      "source": [
        "releasing some memo space"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07ZiROE1MuVT"
      },
      "source": [
        "del weather_df\n",
        "del building_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGRGWpQZUU68",
        "outputId": "6014ab46-67cf-4bb3-f5ee-ad22859223e5"
      },
      "source": [
        "len( building_weather )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96624"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZeadLwA2M1Gw"
      },
      "source": [
        "## merging what's left from the data together\n",
        "### reshaping the dataframe as for each building and each meter type and then sorting by time stamp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "Dc_3yWjrNdVP",
        "outputId": "27f42c84-0291-4f2b-8c78-81c75749cefd"
      },
      "source": [
        "all_data_files= pd.merge(train_df , building_weather , on=['building_id', 'timestamp']).drop(columns='site_id').set_index(['building_id','meter','timestamp']).sort_index()\n",
        "all_data_files.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       meter_reading  air_temperature  \\\n",
              "building_id meter timestamp                                             \n",
              "0           0     2016-01-01 00:00:00            0.0             25.0   \n",
              "                  2016-01-01 01:00:00            0.0             24.4   \n",
              "                  2016-01-01 02:00:00            0.0             22.8   \n",
              "                  2016-01-01 03:00:00            0.0             21.1   \n",
              "                  2016-01-01 04:00:00            0.0             20.0   \n",
              "\n",
              "                                       cloud_coverage  dew_temperature  \\\n",
              "building_id meter timestamp                                              \n",
              "0           0     2016-01-01 00:00:00             6.0             20.0   \n",
              "                  2016-01-01 01:00:00             NaN             21.1   \n",
              "                  2016-01-01 02:00:00             2.0             21.1   \n",
              "                  2016-01-01 03:00:00             2.0             20.6   \n",
              "                  2016-01-01 04:00:00             2.0             20.0   \n",
              "\n",
              "                                       precip_depth_1_hr  sea_level_pressure  \\\n",
              "building_id meter timestamp                                                    \n",
              "0           0     2016-01-01 00:00:00                NaN              1019.7   \n",
              "                  2016-01-01 01:00:00               -1.0              1020.2   \n",
              "                  2016-01-01 02:00:00                0.0              1020.2   \n",
              "                  2016-01-01 03:00:00                0.0              1020.1   \n",
              "                  2016-01-01 04:00:00               -1.0              1020.0   \n",
              "\n",
              "                                       wind_direction  wind_speed primary_use  \\\n",
              "building_id meter timestamp                                                     \n",
              "0           0     2016-01-01 00:00:00             0.0         0.0   Education   \n",
              "                  2016-01-01 01:00:00            70.0         1.5   Education   \n",
              "                  2016-01-01 02:00:00             0.0         0.0   Education   \n",
              "                  2016-01-01 03:00:00             0.0         0.0   Education   \n",
              "                  2016-01-01 04:00:00           250.0         2.6   Education   \n",
              "\n",
              "                                       square_feet  year_built  floor_count  \n",
              "building_id meter timestamp                                                  \n",
              "0           0     2016-01-01 00:00:00         7432      2008.0          NaN  \n",
              "                  2016-01-01 01:00:00         7432      2008.0          NaN  \n",
              "                  2016-01-01 02:00:00         7432      2008.0          NaN  \n",
              "                  2016-01-01 03:00:00         7432      2008.0          NaN  \n",
              "                  2016-01-01 04:00:00         7432      2008.0          NaN  "
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>meter_reading</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>cloud_coverage</th>\n",
              "      <th>dew_temperature</th>\n",
              "      <th>precip_depth_1_hr</th>\n",
              "      <th>sea_level_pressure</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>primary_use</th>\n",
              "      <th>square_feet</th>\n",
              "      <th>year_built</th>\n",
              "      <th>floor_count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>building_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>timestamp</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
              "      <th>2016-01-01 00:00:00</th>\n",
              "      <td>0.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1019.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Education</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-01 01:00:00</th>\n",
              "      <td>0.0</td>\n",
              "      <td>24.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.1</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1020.2</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Education</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-01 02:00:00</th>\n",
              "      <td>0.0</td>\n",
              "      <td>22.8</td>\n",
              "      <td>2.0</td>\n",
              "      <td>21.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Education</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-01 03:00:00</th>\n",
              "      <td>0.0</td>\n",
              "      <td>21.1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>20.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Education</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-01 04:00:00</th>\n",
              "      <td>0.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>Education</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ewNkJlRpUYm3",
        "outputId": "ea2cba89-346a-4053-e8a6-d225c4e05ca4"
      },
      "source": [
        "len( all_data_files )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "111156"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UKd9JpupR4TA"
      },
      "source": [
        "del building_weather\n",
        "del train_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQmNREUePe4Y",
        "outputId": "07bcf012-a120-4fc0-a243-832bbf19c3d4"
      },
      "source": [
        "all_data_files.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "MultiIndex: 20125605 entries, (0, 0, Timestamp('2016-01-01 00:00:00')) to (1448, 0, Timestamp('2016-12-31 23:00:00'))\n",
            "Data columns (total 12 columns):\n",
            " #   Column              Dtype  \n",
            "---  ------              -----  \n",
            " 0   meter_reading       float64\n",
            " 1   air_temperature     float64\n",
            " 2   cloud_coverage      float64\n",
            " 3   dew_temperature     float64\n",
            " 4   precip_depth_1_hr   float64\n",
            " 5   sea_level_pressure  float64\n",
            " 6   wind_direction      float64\n",
            " 7   wind_speed          float64\n",
            " 8   primary_use         object \n",
            " 9   square_feet         int64  \n",
            " 10  year_built          float64\n",
            " 11  floor_count         float64\n",
            "dtypes: float64(10), int64(1), object(1)\n",
            "memory usage: 1.9+ GB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yeq7DCUPP56o"
      },
      "source": [
        "### converting the primary_use to dummy variable\n",
        "### and then concatinating the dummy columns into our data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcvLmnxhP-sh"
      },
      "source": [
        "all_data_files=all_data_files.join( pd.get_dummies( all_data_files['primary_use'] , prefix='usage_'  )).drop(columns='primary_use')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Av7uHMFRVb3",
        "outputId": "3cc4ca28-8ffb-4abc-b212-616afce9bca8"
      },
      "source": [
        "all_data_files.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "MultiIndex: 111156 entries, (0, 0, Timestamp('2016-01-01 00:00:00')) to (10, 0, Timestamp('2016-12-31 23:00:00'))\n",
            "Data columns (total 15 columns):\n",
            " #   Column                                Non-Null Count   Dtype  \n",
            "---  ------                                --------------   -----  \n",
            " 0   meter_reading                         111156 non-null  float64\n",
            " 1   air_temperature                       111123 non-null  float64\n",
            " 2   cloud_coverage                        62832 non-null   float64\n",
            " 3   dew_temperature                       111123 non-null  float64\n",
            " 4   precip_depth_1_hr                     111145 non-null  float64\n",
            " 5   sea_level_pressure                    110089 non-null  float64\n",
            " 6   wind_direction                        107942 non-null  float64\n",
            " 7   wind_speed                            111156 non-null  float64\n",
            " 8   square_feet                           111156 non-null  int64  \n",
            " 9   year_built                            111156 non-null  float64\n",
            " 10  floor_count                           0 non-null       float64\n",
            " 11  usage__Education                      111156 non-null  uint8  \n",
            " 12  usage__Entertainment/public assembly  111156 non-null  uint8  \n",
            " 13  usage__Lodging/residential            111156 non-null  uint8  \n",
            " 14  usage__Office                         111156 non-null  uint8  \n",
            "dtypes: float64(10), int64(1), uint8(4)\n",
            "memory usage: 14.5 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3W_P86ZPNuqF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "553cdd69-26c7-4add-b415-ca218e5bdc86"
      },
      "source": [
        "all_data_files.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       meter_reading  air_temperature  cloud_coverage  dew_temperature  \\\n",
              "count  111156.000000    111123.000000    62832.000000    111123.000000   \n",
              "mean      498.081374        23.025007        3.048685        17.011486   \n",
              "std       982.588532         5.935097        2.106536         6.394679   \n",
              "min         0.000000         1.700000        0.000000        -9.400000   \n",
              "25%         0.000000        19.400000        2.000000        13.300000   \n",
              "50%        85.593000        23.900000        2.000000        18.300000   \n",
              "75%       485.983000        27.200000        4.000000        22.200000   \n",
              "max      8442.070000        36.100000        9.000000        25.600000   \n",
              "\n",
              "       precip_depth_1_hr  sea_level_pressure  wind_direction     wind_speed  \\\n",
              "count      111145.000000       110089.000000   107942.000000  111156.000000   \n",
              "mean            1.388493         1017.961614      155.492394       3.360759   \n",
              "std            13.065341            3.997847      117.966058       2.149582   \n",
              "min            -1.000000          992.000000        0.000000       0.000000   \n",
              "25%             0.000000         1015.500000       60.000000       2.100000   \n",
              "50%             0.000000         1018.000000      140.000000       3.100000   \n",
              "75%             0.000000         1020.500000      260.000000       4.600000   \n",
              "max           343.000000         1030.200000      360.000000      15.400000   \n",
              "\n",
              "         square_feet     year_built  floor_count  usage__Education  \\\n",
              "count  111156.000000  111156.000000          0.0     111156.000000   \n",
              "mean    70639.416010    1996.299336          NaN          0.697569   \n",
              "std     97881.471493      10.783587          NaN          0.459313   \n",
              "min      2720.000000    1975.000000          NaN          0.000000   \n",
              "25%      8000.000000    1989.000000          NaN          0.000000   \n",
              "50%     27000.000000    2000.000000          NaN          1.000000   \n",
              "75%    116607.000000    2004.000000          NaN          1.000000   \n",
              "max    370773.000000    2010.000000          NaN          1.000000   \n",
              "\n",
              "       usage__Entertainment/public assembly  usage__Lodging/residential  \\\n",
              "count                         111156.000000               111156.000000   \n",
              "mean                               0.079024                    0.079024   \n",
              "std                                0.269778                    0.269778   \n",
              "min                                0.000000                    0.000000   \n",
              "25%                                0.000000                    0.000000   \n",
              "50%                                0.000000                    0.000000   \n",
              "75%                                0.000000                    0.000000   \n",
              "max                                1.000000                    1.000000   \n",
              "\n",
              "       usage__Office  \n",
              "count  111156.000000  \n",
              "mean        0.144383  \n",
              "std         0.351479  \n",
              "min         0.000000  \n",
              "25%         0.000000  \n",
              "50%         0.000000  \n",
              "75%         0.000000  \n",
              "max         1.000000  "
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>meter_reading</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>cloud_coverage</th>\n",
              "      <th>dew_temperature</th>\n",
              "      <th>precip_depth_1_hr</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>111156.000000</td>\n",
              "      <td>111123.000000</td>\n",
              "      <td>62832.000000</td>\n",
              "      <td>111123.000000</td>\n",
              "      <td>111145.000000</td>\n",
              "      <td>110089.000000</td>\n",
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              "      <td>111156.000000</td>\n",
              "      <td>111156.000000</td>\n",
              "      <td>111156.000000</td>\n",
              "      <td>111156.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>498.081374</td>\n",
              "      <td>23.025007</td>\n",
              "      <td>3.048685</td>\n",
              "      <td>17.011486</td>\n",
              "      <td>1.388493</td>\n",
              "      <td>1017.961614</td>\n",
              "      <td>155.492394</td>\n",
              "      <td>3.360759</td>\n",
              "      <td>70639.416010</td>\n",
              "      <td>1996.299336</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.697569</td>\n",
              "      <td>0.079024</td>\n",
              "      <td>0.079024</td>\n",
              "      <td>0.144383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>982.588532</td>\n",
              "      <td>5.935097</td>\n",
              "      <td>2.106536</td>\n",
              "      <td>6.394679</td>\n",
              "      <td>13.065341</td>\n",
              "      <td>3.997847</td>\n",
              "      <td>117.966058</td>\n",
              "      <td>2.149582</td>\n",
              "      <td>97881.471493</td>\n",
              "      <td>10.783587</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.459313</td>\n",
              "      <td>0.269778</td>\n",
              "      <td>0.269778</td>\n",
              "      <td>0.351479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.700000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-9.400000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>992.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2720.000000</td>\n",
              "      <td>1975.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>19.400000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>13.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1015.500000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>2.100000</td>\n",
              "      <td>8000.000000</td>\n",
              "      <td>1989.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>85.593000</td>\n",
              "      <td>23.900000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>18.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1018.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>3.100000</td>\n",
              "      <td>27000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>485.983000</td>\n",
              "      <td>27.200000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>22.200000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1020.500000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>4.600000</td>\n",
              "      <td>116607.000000</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>max</th>\n",
              "      <td>8442.070000</td>\n",
              "      <td>36.100000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>25.600000</td>\n",
              "      <td>343.000000</td>\n",
              "      <td>1030.200000</td>\n",
              "      <td>360.000000</td>\n",
              "      <td>15.400000</td>\n",
              "      <td>370773.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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          },
          "metadata": {},
          "execution_count": 16
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      "source": [
        "all_data_files"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       meter_reading  air_temperature  \\\n",
              "building_id meter timestamp                                             \n",
              "0           0     2016-01-01 00:00:00           0.00             25.0   \n",
              "                  2016-01-01 01:00:00           0.00             24.4   \n",
              "                  2016-01-01 02:00:00           0.00             22.8   \n",
              "                  2016-01-01 03:00:00           0.00             21.1   \n",
              "                  2016-01-01 04:00:00           0.00             20.0   \n",
              "...                                              ...              ...   \n",
              "10          0     2016-12-31 19:00:00        2036.42             22.8   \n",
              "                  2016-12-31 20:00:00        1987.62             23.3   \n",
              "                  2016-12-31 21:00:00        1952.80             23.3   \n",
              "                  2016-12-31 22:00:00        1874.65             22.8   \n",
              "                  2016-12-31 23:00:00        1863.73             20.6   \n",
              "\n",
              "                                       cloud_coverage  dew_temperature  \\\n",
              "building_id meter timestamp                                              \n",
              "0           0     2016-01-01 00:00:00             6.0             20.0   \n",
              "                  2016-01-01 01:00:00             NaN             21.1   \n",
              "                  2016-01-01 02:00:00             2.0             21.1   \n",
              "                  2016-01-01 03:00:00             2.0             20.6   \n",
              "                  2016-01-01 04:00:00             2.0             20.0   \n",
              "...                                               ...              ...   \n",
              "10          0     2016-12-31 19:00:00             NaN             10.0   \n",
              "                  2016-12-31 20:00:00             NaN              8.9   \n",
              "                  2016-12-31 21:00:00             NaN             10.0   \n",
              "                  2016-12-31 22:00:00             NaN             10.0   \n",
              "                  2016-12-31 23:00:00             NaN             10.0   \n",
              "\n",
              "                                       precip_depth_1_hr  sea_level_pressure  \\\n",
              "building_id meter timestamp                                                    \n",
              "0           0     2016-01-01 00:00:00                NaN              1019.7   \n",
              "                  2016-01-01 01:00:00               -1.0              1020.2   \n",
              "                  2016-01-01 02:00:00                0.0              1020.2   \n",
              "                  2016-01-01 03:00:00                0.0              1020.1   \n",
              "                  2016-01-01 04:00:00               -1.0              1020.0   \n",
              "...                                                  ...                 ...   \n",
              "10          0     2016-12-31 19:00:00                0.0              1021.7   \n",
              "                  2016-12-31 20:00:00                0.0              1021.0   \n",
              "                  2016-12-31 21:00:00                0.0              1021.1   \n",
              "                  2016-12-31 22:00:00                0.0              1021.1   \n",
              "                  2016-12-31 23:00:00                0.0              1021.1   \n",
              "\n",
              "                                       wind_direction  wind_speed  \\\n",
              "building_id meter timestamp                                         \n",
              "0           0     2016-01-01 00:00:00             0.0         0.0   \n",
              "                  2016-01-01 01:00:00            70.0         1.5   \n",
              "                  2016-01-01 02:00:00             0.0         0.0   \n",
              "                  2016-01-01 03:00:00             0.0         0.0   \n",
              "                  2016-01-01 04:00:00           250.0         2.6   \n",
              "...                                               ...         ...   \n",
              "10          0     2016-12-31 19:00:00           140.0         5.7   \n",
              "                  2016-12-31 20:00:00           170.0         4.1   \n",
              "                  2016-12-31 21:00:00           150.0         4.1   \n",
              "                  2016-12-31 22:00:00           160.0         3.1   \n",
              "                  2016-12-31 23:00:00             0.0         0.0   \n",
              "\n",
              "                                       square_feet  year_built  floor_count  \\\n",
              "building_id meter timestamp                                                   \n",
              "0           0     2016-01-01 00:00:00         7432      2008.0          NaN   \n",
              "                  2016-01-01 01:00:00         7432      2008.0          NaN   \n",
              "                  2016-01-01 02:00:00         7432      2008.0          NaN   \n",
              "                  2016-01-01 03:00:00         7432      2008.0          NaN   \n",
              "                  2016-01-01 04:00:00         7432      2008.0          NaN   \n",
              "...                                            ...         ...          ...   \n",
              "10          0     2016-12-31 19:00:00       370773      1991.0          NaN   \n",
              "                  2016-12-31 20:00:00       370773      1991.0          NaN   \n",
              "                  2016-12-31 21:00:00       370773      1991.0          NaN   \n",
              "                  2016-12-31 22:00:00       370773      1991.0          NaN   \n",
              "                  2016-12-31 23:00:00       370773      1991.0          NaN   \n",
              "\n",
              "                                       usage__Education  \\\n",
              "building_id meter timestamp                               \n",
              "0           0     2016-01-01 00:00:00                 1   \n",
              "                  2016-01-01 01:00:00                 1   \n",
              "                  2016-01-01 02:00:00                 1   \n",
              "                  2016-01-01 03:00:00                 1   \n",
              "                  2016-01-01 04:00:00                 1   \n",
              "...                                                 ...   \n",
              "10          0     2016-12-31 19:00:00                 0   \n",
              "                  2016-12-31 20:00:00                 0   \n",
              "                  2016-12-31 21:00:00                 0   \n",
              "                  2016-12-31 22:00:00                 0   \n",
              "                  2016-12-31 23:00:00                 0   \n",
              "\n",
              "                                       usage__Entertainment/public assembly  \\\n",
              "building_id meter timestamp                                                   \n",
              "0           0     2016-01-01 00:00:00                                     0   \n",
              "                  2016-01-01 01:00:00                                     0   \n",
              "                  2016-01-01 02:00:00                                     0   \n",
              "                  2016-01-01 03:00:00                                     0   \n",
              "                  2016-01-01 04:00:00                                     0   \n",
              "...                                                                     ...   \n",
              "10          0     2016-12-31 19:00:00                                     1   \n",
              "                  2016-12-31 20:00:00                                     1   \n",
              "                  2016-12-31 21:00:00                                     1   \n",
              "                  2016-12-31 22:00:00                                     1   \n",
              "                  2016-12-31 23:00:00                                     1   \n",
              "\n",
              "                                       usage__Lodging/residential  \\\n",
              "building_id meter timestamp                                         \n",
              "0           0     2016-01-01 00:00:00                           0   \n",
              "                  2016-01-01 01:00:00                           0   \n",
              "                  2016-01-01 02:00:00                           0   \n",
              "                  2016-01-01 03:00:00                           0   \n",
              "                  2016-01-01 04:00:00                           0   \n",
              "...                                                           ...   \n",
              "10          0     2016-12-31 19:00:00                           0   \n",
              "                  2016-12-31 20:00:00                           0   \n",
              "                  2016-12-31 21:00:00                           0   \n",
              "                  2016-12-31 22:00:00                           0   \n",
              "                  2016-12-31 23:00:00                           0   \n",
              "\n",
              "                                       usage__Office  \n",
              "building_id meter timestamp                           \n",
              "0           0     2016-01-01 00:00:00              0  \n",
              "                  2016-01-01 01:00:00              0  \n",
              "                  2016-01-01 02:00:00              0  \n",
              "                  2016-01-01 03:00:00              0  \n",
              "                  2016-01-01 04:00:00              0  \n",
              "...                                              ...  \n",
              "10          0     2016-12-31 19:00:00              0  \n",
              "                  2016-12-31 20:00:00              0  \n",
              "                  2016-12-31 21:00:00              0  \n",
              "                  2016-12-31 22:00:00              0  \n",
              "                  2016-12-31 23:00:00              0  \n",
              "\n",
              "[111156 rows x 15 columns]"
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              "      <th>sea_level_pressure</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>square_feet</th>\n",
              "      <th>year_built</th>\n",
              "      <th>floor_count</th>\n",
              "      <th>usage__Education</th>\n",
              "      <th>usage__Entertainment/public assembly</th>\n",
              "      <th>usage__Lodging/residential</th>\n",
              "      <th>usage__Office</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>building_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>timestamp</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
              "      <th>2016-01-01 00:00:00</th>\n",
              "      <td>0.00</td>\n",
              "      <td>25.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1019.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-01 01:00:00</th>\n",
              "      <td>0.00</td>\n",
              "      <td>24.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.1</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1020.2</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-01 02:00:00</th>\n",
              "      <td>0.00</td>\n",
              "      <td>22.8</td>\n",
              "      <td>2.0</td>\n",
              "      <td>21.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-01 03:00:00</th>\n",
              "      <td>0.00</td>\n",
              "      <td>21.1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>20.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-01 04:00:00</th>\n",
              "      <td>0.00</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">10</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
              "      <th>2016-12-31 19:00:00</th>\n",
              "      <td>2036.42</td>\n",
              "      <td>22.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1021.7</td>\n",
              "      <td>140.0</td>\n",
              "      <td>5.7</td>\n",
              "      <td>370773</td>\n",
              "      <td>1991.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 20:00:00</th>\n",
              "      <td>1987.62</td>\n",
              "      <td>23.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1021.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>370773</td>\n",
              "      <td>1991.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 21:00:00</th>\n",
              "      <td>1952.80</td>\n",
              "      <td>23.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1021.1</td>\n",
              "      <td>150.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>370773</td>\n",
              "      <td>1991.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 22:00:00</th>\n",
              "      <td>1874.65</td>\n",
              "      <td>22.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1021.1</td>\n",
              "      <td>160.0</td>\n",
              "      <td>3.1</td>\n",
              "      <td>370773</td>\n",
              "      <td>1991.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 23:00:00</th>\n",
              "      <td>1863.73</td>\n",
              "      <td>20.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1021.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>370773</td>\n",
              "      <td>1991.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>111156 rows × 15 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYbdLh-KW1K5"
      },
      "source": [
        "Showing only building 1 and meter 1 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 827
        },
        "id": "nEOMda9uWjYR",
        "outputId": "6cd9eaee-366a-4c48-cc35-ac36f7e231c2"
      },
      "source": [
        "all_data_files.loc[0,0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     meter_reading  air_temperature  cloud_coverage  \\\n",
              "timestamp                                                             \n",
              "2016-01-01 00:00:00          0.000             25.0             6.0   \n",
              "2016-01-01 01:00:00          0.000             24.4             NaN   \n",
              "2016-01-01 02:00:00          0.000             22.8             2.0   \n",
              "2016-01-01 03:00:00          0.000             21.1             2.0   \n",
              "2016-01-01 04:00:00          0.000             20.0             2.0   \n",
              "...                            ...              ...             ...   \n",
              "2016-12-31 19:00:00        232.071             22.8             NaN   \n",
              "2016-12-31 20:00:00        189.069             23.3             NaN   \n",
              "2016-12-31 21:00:00        169.958             23.3             NaN   \n",
              "2016-12-31 22:00:00        169.958             22.8             NaN   \n",
              "2016-12-31 23:00:00        175.418             20.6             NaN   \n",
              "\n",
              "                     dew_temperature  precip_depth_1_hr  sea_level_pressure  \\\n",
              "timestamp                                                                     \n",
              "2016-01-01 00:00:00             20.0                NaN              1019.7   \n",
              "2016-01-01 01:00:00             21.1               -1.0              1020.2   \n",
              "2016-01-01 02:00:00             21.1                0.0              1020.2   \n",
              "2016-01-01 03:00:00             20.6                0.0              1020.1   \n",
              "2016-01-01 04:00:00             20.0               -1.0              1020.0   \n",
              "...                              ...                ...                 ...   \n",
              "2016-12-31 19:00:00             10.0                0.0              1021.7   \n",
              "2016-12-31 20:00:00              8.9                0.0              1021.0   \n",
              "2016-12-31 21:00:00             10.0                0.0              1021.1   \n",
              "2016-12-31 22:00:00             10.0                0.0              1021.1   \n",
              "2016-12-31 23:00:00             10.0                0.0              1021.1   \n",
              "\n",
              "                     wind_direction  wind_speed  square_feet  year_built  \\\n",
              "timestamp                                                                  \n",
              "2016-01-01 00:00:00             0.0         0.0         7432      2008.0   \n",
              "2016-01-01 01:00:00            70.0         1.5         7432      2008.0   \n",
              "2016-01-01 02:00:00             0.0         0.0         7432      2008.0   \n",
              "2016-01-01 03:00:00             0.0         0.0         7432      2008.0   \n",
              "2016-01-01 04:00:00           250.0         2.6         7432      2008.0   \n",
              "...                             ...         ...          ...         ...   \n",
              "2016-12-31 19:00:00           140.0         5.7         7432      2008.0   \n",
              "2016-12-31 20:00:00           170.0         4.1         7432      2008.0   \n",
              "2016-12-31 21:00:00           150.0         4.1         7432      2008.0   \n",
              "2016-12-31 22:00:00           160.0         3.1         7432      2008.0   \n",
              "2016-12-31 23:00:00             0.0         0.0         7432      2008.0   \n",
              "\n",
              "                     floor_count  usage__Education  \\\n",
              "timestamp                                            \n",
              "2016-01-01 00:00:00          NaN                 1   \n",
              "2016-01-01 01:00:00          NaN                 1   \n",
              "2016-01-01 02:00:00          NaN                 1   \n",
              "2016-01-01 03:00:00          NaN                 1   \n",
              "2016-01-01 04:00:00          NaN                 1   \n",
              "...                          ...               ...   \n",
              "2016-12-31 19:00:00          NaN                 1   \n",
              "2016-12-31 20:00:00          NaN                 1   \n",
              "2016-12-31 21:00:00          NaN                 1   \n",
              "2016-12-31 22:00:00          NaN                 1   \n",
              "2016-12-31 23:00:00          NaN                 1   \n",
              "\n",
              "                     usage__Entertainment/public assembly  \\\n",
              "timestamp                                                   \n",
              "2016-01-01 00:00:00                                     0   \n",
              "2016-01-01 01:00:00                                     0   \n",
              "2016-01-01 02:00:00                                     0   \n",
              "2016-01-01 03:00:00                                     0   \n",
              "2016-01-01 04:00:00                                     0   \n",
              "...                                                   ...   \n",
              "2016-12-31 19:00:00                                     0   \n",
              "2016-12-31 20:00:00                                     0   \n",
              "2016-12-31 21:00:00                                     0   \n",
              "2016-12-31 22:00:00                                     0   \n",
              "2016-12-31 23:00:00                                     0   \n",
              "\n",
              "                     usage__Lodging/residential  usage__Office  \n",
              "timestamp                                                       \n",
              "2016-01-01 00:00:00                           0              0  \n",
              "2016-01-01 01:00:00                           0              0  \n",
              "2016-01-01 02:00:00                           0              0  \n",
              "2016-01-01 03:00:00                           0              0  \n",
              "2016-01-01 04:00:00                           0              0  \n",
              "...                                         ...            ...  \n",
              "2016-12-31 19:00:00                           0              0  \n",
              "2016-12-31 20:00:00                           0              0  \n",
              "2016-12-31 21:00:00                           0              0  \n",
              "2016-12-31 22:00:00                           0              0  \n",
              "2016-12-31 23:00:00                           0              0  \n",
              "\n",
              "[8784 rows x 15 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>meter_reading</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>cloud_coverage</th>\n",
              "      <th>dew_temperature</th>\n",
              "      <th>precip_depth_1_hr</th>\n",
              "      <th>sea_level_pressure</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>square_feet</th>\n",
              "      <th>year_built</th>\n",
              "      <th>floor_count</th>\n",
              "      <th>usage__Education</th>\n",
              "      <th>usage__Entertainment/public assembly</th>\n",
              "      <th>usage__Lodging/residential</th>\n",
              "      <th>usage__Office</th>\n",
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              "    <tr>\n",
              "      <th>timestamp</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-01-01 00:00:00</th>\n",
              "      <td>0.000</td>\n",
              "      <td>25.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1019.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-01 01:00:00</th>\n",
              "      <td>0.000</td>\n",
              "      <td>24.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.1</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1020.2</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-01 02:00:00</th>\n",
              "      <td>0.000</td>\n",
              "      <td>22.8</td>\n",
              "      <td>2.0</td>\n",
              "      <td>21.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-01 03:00:00</th>\n",
              "      <td>0.000</td>\n",
              "      <td>21.1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>20.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-01 04:00:00</th>\n",
              "      <td>0.000</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2016-12-31 19:00:00</th>\n",
              "      <td>232.071</td>\n",
              "      <td>22.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1021.7</td>\n",
              "      <td>140.0</td>\n",
              "      <td>5.7</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 20:00:00</th>\n",
              "      <td>189.069</td>\n",
              "      <td>23.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1021.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 21:00:00</th>\n",
              "      <td>169.958</td>\n",
              "      <td>23.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1021.1</td>\n",
              "      <td>150.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 22:00:00</th>\n",
              "      <td>169.958</td>\n",
              "      <td>22.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1021.1</td>\n",
              "      <td>160.0</td>\n",
              "      <td>3.1</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 23:00:00</th>\n",
              "      <td>175.418</td>\n",
              "      <td>20.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1021.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8784 rows × 15 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "d7Z9O7HOWymr",
        "outputId": "80f6f87c-8cf3-4200-e44a-e737b4ca151a"
      },
      "source": [
        "sns.lineplot(all_data_files.loc[0,0].index , all_data_files.loc[0,0].meter_reading )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='timestamp', ylabel='meter_reading'>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CGSV5KizdeqQ"
      },
      "source": [
        "# Creating a plot for each building"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uk1gF3KfXfBI",
        "outputId": "c3feb3a1-114a-4e21-8207-824e262a13d5"
      },
      "source": [
        "fig , ax = plt.subplots(11,1 , figsize=(20,25 ))\n",
        "for building in np.arange(11):\n",
        "    sns.lineplot(all_data_files.loc[building,0].index ,\n",
        "                 all_data_files.loc[building,0].meter_reading , \n",
        "                 ax=ax[building])\n",
        "    ax[building].set_title(f'Plot for building no. {building}')\n",
        "plt.subplots_adjust(left=0.1,\n",
        "                    bottom=0.1, \n",
        "                    right=0.9, \n",
        "                    top=0.9, \n",
        "                    wspace=3, \n",
        "                    hspace=1)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1800 with 11 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IoH22HpJZ-MC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}