RNN_With_LSTM.ipynb

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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/codingCapricorn/RNN_With_LSTM/blob/master/RNN_With_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HzqKfH3d0gWV",
        "colab_type": "text"
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      "source": [
        "#:::: Time-series data analysis using LSTM ::::\n",
        "\n",
        "##Time-series prediction using LSTM recurrent neural network(RNN) with Keras\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kqZTgB1E0rQX",
        "colab_type": "text"
      },
      "source": [
        "Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning.\n",
        "\n",
        "DataSet :: Household Electric Power Consumption\n",
        "\n",
        "The description of data can be found here:\n",
        "http://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption\n",
        "\n",
        "1.This archive contains 1485328 measurements gathered in a house located in Sceaux (7km of Paris, France) between December 2006 and November 2010 (47 months).\n",
        "\n",
        "2.The dataset contains some missing values in the measurements (nearly 1,25% of the rows).\n",
        "\n",
        "Data Set Characteristics:  Multivariate, Time-Series\n",
        "\n",
        "Number of Instances:1485328\n",
        "\n",
        "Attribute Characteristics:Real\n",
        "\n",
        "Number of Attributes:9\n",
        "\n",
        "Associated Tasks:Regression, Clustering\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-nBU9Oamqxp",
        "colab_type": "code",
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      },
      "source": [
        "# Let`s import all packages that we may need:\n",
        "\n",
        "import sys \n",
        "import numpy as np # linear algebra\n",
        "from scipy.stats import randint\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv), data manipulation as in SQL\n",
        "import matplotlib.pyplot as plt # this is used for the plot the graph \n",
        "import seaborn as sns # used for plot interactive graph. \n",
        "from sklearn.model_selection import train_test_split # to split the data into two parts\n",
        "from sklearn.model_selection import KFold # use for cross validation\n",
        "from sklearn.preprocessing import StandardScaler # for normalization\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.pipeline import Pipeline # pipeline making\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "from sklearn import metrics # for the check the error and accuracy of the model\n",
        "from sklearn.metrics import mean_squared_error,r2_score\n",
        "\n",
        "## for Deep-learing:\n",
        "import keras\n",
        "from keras.layers import Dense\n",
        "from keras.models import Sequential\n",
        "from keras.utils import to_categorical\n",
        "from keras.optimizers import SGD \n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras.utils import np_utils\n",
        "import itertools\n",
        "from keras.layers import LSTM\n",
        "from keras.layers.convolutional import Conv1D\n",
        "from keras.layers.convolutional import MaxPooling1D\n",
        "from keras.layers import Dropout"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vt3aHLy7m9IY",
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          "height": 442
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        "outputId": "fda598b6-8ab6-4b26-8b32-eb9f7fd40f47"
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      "source": [
        "## Data can be downloaded from: http://archive.ics.uci.edu/ml/machine-learning-databases/00235/\n",
        "## Just open the zip file and grab the file 'household_power_consumption.txt' put it in the directory \n",
        "## that you would like to run the code. \n",
        "\n",
        "df = pd.read_csv('household_power_consumption.txt',sep=';',\n",
        "                 parse_dates={'dt' : ['Date', 'Time']}, infer_datetime_format=True, \n",
        "                 low_memory=False, na_values=['nan','?'], index_col='dt')"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ParserError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mParserError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-141-5726c9a4c544>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m df = pd.read_csv('household_power_consumption.txt',sep=';',\n\u001b[1;32m      6\u001b[0m                  \u001b[0mparse_dates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'dt'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Date'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfer_datetime_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m                  low_memory=False, na_values=['nan','?'], index_col='dt')\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;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, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    674\u001b[0m         )\n\u001b[1;32m    675\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 676\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m         \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1131\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m         \u001b[0mnrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_validate_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"nrows\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1135\u001b[0m         \u001b[0;31m# May alter columns / col_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   2035\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2036\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2037\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2038\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2039\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_first_chunk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.read\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mParserError\u001b[0m: Error tokenizing data. C error: Expected 9 fields in line 48505, saw 11\n"
          ]
        }
      ]
    },
    {
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        },
        "outputId": "36169cba-dc5c-44d2-c1b1-b9c7f3bf0996"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Global_active_power</th>\n",
              "      <th>Global_reactive_power</th>\n",
              "      <th>Voltage</th>\n",
              "      <th>Global_intensity</th>\n",
              "      <th>Sub_metering_1</th>\n",
              "      <th>Sub_metering_2</th>\n",
              "      <th>Sub_metering_3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:24:00</th>\n",
              "      <td>4.216</td>\n",
              "      <td>0.418</td>\n",
              "      <td>234.84</td>\n",
              "      <td>18.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.0</td>\n",
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              "    <tr>\n",
              "      <th>2006-12-16 17:25:00</th>\n",
              "      <td>5.360</td>\n",
              "      <td>0.436</td>\n",
              "      <td>233.63</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:26:00</th>\n",
              "      <td>5.374</td>\n",
              "      <td>0.498</td>\n",
              "      <td>233.29</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:27:00</th>\n",
              "      <td>5.388</td>\n",
              "      <td>0.502</td>\n",
              "      <td>233.74</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:28:00</th>\n",
              "      <td>3.666</td>\n",
              "      <td>0.528</td>\n",
              "      <td>235.68</td>\n",
              "      <td>15.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.0</td>\n",
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              "    <tr>\n",
              "      <th>2006-12-16 17:29:00</th>\n",
              "      <td>3.520</td>\n",
              "      <td>0.522</td>\n",
              "      <td>235.02</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>17.0</td>\n",
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              "    <tr>\n",
              "      <th>2006-12-16 17:30:00</th>\n",
              "      <td>3.702</td>\n",
              "      <td>0.520</td>\n",
              "      <td>235.09</td>\n",
              "      <td>15.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:31:00</th>\n",
              "      <td>3.700</td>\n",
              "      <td>0.520</td>\n",
              "      <td>235.22</td>\n",
              "      <td>15.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:32:00</th>\n",
              "      <td>3.668</td>\n",
              "      <td>0.510</td>\n",
              "      <td>233.99</td>\n",
              "      <td>15.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:33:00</th>\n",
              "      <td>3.662</td>\n",
              "      <td>0.510</td>\n",
              "      <td>233.86</td>\n",
              "      <td>15.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "                     Global_active_power  ...  Sub_metering_3\n",
              "dt                                        ...                \n",
              "2006-12-16 17:24:00                4.216  ...            17.0\n",
              "2006-12-16 17:25:00                5.360  ...            16.0\n",
              "2006-12-16 17:26:00                5.374  ...            17.0\n",
              "2006-12-16 17:27:00                5.388  ...            17.0\n",
              "2006-12-16 17:28:00                3.666  ...            17.0\n",
              "2006-12-16 17:29:00                3.520  ...            17.0\n",
              "2006-12-16 17:30:00                3.702  ...            17.0\n",
              "2006-12-16 17:31:00                3.700  ...            17.0\n",
              "2006-12-16 17:32:00                3.668  ...            17.0\n",
              "2006-12-16 17:33:00                3.662  ...            16.0\n",
              "\n",
              "[10 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRJQdzTDnMjC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "a4227a15-5f54-4eab-94a4-489b04777896"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 1485328 entries, 2006-12-16 17:24:00 to 2009-10-13 04:51:00\n",
            "Data columns (total 7 columns):\n",
            " #   Column                 Non-Null Count    Dtype  \n",
            "---  ------                 --------------    -----  \n",
            " 0   Global_active_power    1485328 non-null  float64\n",
            " 1   Global_reactive_power  1485328 non-null  float64\n",
            " 2   Voltage                1485328 non-null  float64\n",
            " 3   Global_intensity       1485328 non-null  float64\n",
            " 4   Sub_metering_1         1485328 non-null  float64\n",
            " 5   Sub_metering_2         1485328 non-null  float64\n",
            " 6   Sub_metering_3         1485328 non-null  float64\n",
            "dtypes: float64(7)\n",
            "memory usage: 90.7 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78ObEDtMnWvE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "677b35ce-59da-4cfe-c2fe-35e4e3708097"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Global_active_power      float64\n",
              "Global_reactive_power    float64\n",
              "Voltage                  float64\n",
              "Global_intensity         float64\n",
              "Sub_metering_1           float64\n",
              "Sub_metering_2           float64\n",
              "Sub_metering_3           float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nqDw1G3CnZaS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6aa68d80-2743-4514-c1b7-df9df1e66b6e"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1485328, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vB_5_J67nRZb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "53d14288-0ec4-4a36-d7c9-376e9bc4cc9d"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>Global_active_power</th>\n",
              "      <th>Global_reactive_power</th>\n",
              "      <th>Voltage</th>\n",
              "      <th>Global_intensity</th>\n",
              "      <th>Sub_metering_1</th>\n",
              "      <th>Sub_metering_2</th>\n",
              "      <th>Sub_metering_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.485328e+06</td>\n",
              "      <td>1.485328e+06</td>\n",
              "      <td>1.485328e+06</td>\n",
              "      <td>1.485328e+06</td>\n",
              "      <td>1.485328e+06</td>\n",
              "      <td>1.485328e+06</td>\n",
              "      <td>1.485328e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.085553e+00</td>\n",
              "      <td>1.216527e-01</td>\n",
              "      <td>2.404983e+02</td>\n",
              "      <td>4.614729e+00</td>\n",
              "      <td>1.148076e+00</td>\n",
              "      <td>1.365138e+00</td>\n",
              "      <td>6.094011e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.084842e+00</td>\n",
              "      <td>1.110129e-01</td>\n",
              "      <td>3.260359e+00</td>\n",
              "      <td>4.563437e+00</td>\n",
              "      <td>6.235748e+00</td>\n",
              "      <td>6.025610e+00</td>\n",
              "      <td>8.259896e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>7.600000e-02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.232000e+02</td>\n",
              "      <td>2.000000e-01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.980000e-01</td>\n",
              "      <td>4.600000e-02</td>\n",
              "      <td>2.386300e+02</td>\n",
              "      <td>1.400000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.600000e-01</td>\n",
              "      <td>1.000000e-01</td>\n",
              "      <td>2.407100e+02</td>\n",
              "      <td>2.400000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.516000e+00</td>\n",
              "      <td>1.920000e-01</td>\n",
              "      <td>2.426100e+02</td>\n",
              "      <td>6.400000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.700000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.112200e+01</td>\n",
              "      <td>1.390000e+00</td>\n",
              "      <td>2.539400e+02</td>\n",
              "      <td>4.840000e+01</td>\n",
              "      <td>8.200000e+01</td>\n",
              "      <td>7.800000e+01</td>\n",
              "      <td>3.100000e+01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Global_active_power  ...  Sub_metering_3\n",
              "count         1.485328e+06  ...    1.485328e+06\n",
              "mean          1.085553e+00  ...    6.094011e+00\n",
              "std           1.084842e+00  ...    8.259896e+00\n",
              "min           7.600000e-02  ...    0.000000e+00\n",
              "25%           2.980000e-01  ...    0.000000e+00\n",
              "50%           5.600000e-01  ...    1.000000e+00\n",
              "75%           1.516000e+00  ...    1.700000e+01\n",
              "max           1.112200e+01  ...    3.100000e+01\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CDscSMQInfpr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "0bd337dd-6db2-4fc2-a0e6-38a542a6cef1"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Global_active_power', 'Global_reactive_power', 'Voltage',\n",
              "       'Global_intensity', 'Sub_metering_1', 'Sub_metering_2',\n",
              "       'Sub_metering_3'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rP3KvD2JnkdG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "83f9d1a1-17db-471e-cb30-7c76f968ff67"
      },
      "source": [
        "for j in range(1,7):\n",
        "       print(df.iloc[:, j].unique())"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.418      0.436      0.498      0.502      0.528      0.522\n",
            " 0.52       0.51       0.47       0.478      0.398      0.422\n",
            " 0.282      0.152      0.156      0.         0.076      0.09\n",
            " 0.2        0.058      0.18       0.144      0.118      0.108\n",
            " 0.202      0.192      0.186      0.116      0.136      0.148\n",
            " 0.16       0.158      0.1        0.082      0.05       0.052\n",
            " 0.162      0.086      0.048      0.054      0.068      0.166\n",
            " 0.174      0.178      0.188      0.088      0.08       0.07\n",
            " 0.084      0.074      0.106      0.092      0.064      0.046\n",
            " 0.138      0.134      0.132      0.12       0.13       0.154\n",
            " 0.078      0.142      0.14       0.146      0.15       0.17\n",
            " 0.102      0.072      0.06       0.056      0.062      0.112\n",
            " 0.066      0.172      0.168      0.194      0.184      0.096\n",
            " 0.164      0.182      0.094      0.098      0.176      0.19\n",
            " 0.204      0.22       0.198      0.208      0.244      0.334\n",
            " 0.298      0.296      0.286      0.278      0.104      0.258\n",
            " 0.238      0.256      0.214      0.23       0.21       0.24\n",
            " 0.124      0.122      0.27       0.302      0.272      0.25\n",
            " 0.294      0.3        0.128      0.126      0.234      0.242\n",
            " 0.316      0.28       0.288      0.224      0.11       0.248\n",
            " 0.254      0.222      0.246      0.216      0.212      0.114\n",
            " 0.206      0.228      0.226      0.196      0.218      0.368\n",
            " 0.338      0.31       0.304      0.292      0.268      0.266\n",
            " 0.26       0.396      0.38       0.4        0.384      0.416\n",
            " 0.404      0.382      0.232      0.314      0.312      0.262\n",
            " 0.252      0.236      0.348      0.342      0.34       0.35\n",
            " 0.504      0.344      0.346      0.366      0.574      0.582\n",
            " 0.37       0.364      0.36       0.362      0.372      0.392\n",
            " 0.354      0.356      0.358      0.402      0.394      0.39\n",
            " 0.386      0.274      0.264      0.324      0.352      0.276\n",
            " 0.306      0.376      0.562      0.44       0.378      0.426\n",
            " 0.45       0.374      0.388      0.284      0.29       0.308\n",
            " 0.318      0.32       0.322      0.326      0.336      0.414\n",
            " 0.408      0.472      0.464      0.462      0.578      0.576\n",
            " 0.58       0.474      0.514      0.646      0.636      0.598\n",
            " 0.586      0.566      0.406      0.43       0.412      0.442\n",
            " 0.452      0.432      0.33       0.328      0.538      0.564\n",
            " 0.552      0.556      0.606      0.424      0.492      0.526\n",
            " 0.512      0.482      0.446      0.332      0.12165271 0.548\n",
            " 0.534      0.532      0.688      0.664      0.66       0.53\n",
            " 0.516      0.572      0.54       0.614      0.568      0.444\n",
            " 0.456      0.448      0.48       0.434      0.438      0.494\n",
            " 0.496      0.468      0.56       0.622      0.57       0.618\n",
            " 0.624      0.466      0.42       0.5        0.602      0.632\n",
            " 0.724      0.706      0.484      0.458      0.428      0.486\n",
            " 0.698      0.708      0.686      0.666      0.658      0.49\n",
            " 0.454      0.64       0.59       0.524      0.634      0.476\n",
            " 0.694      0.7        0.794      0.592      0.46       0.656\n",
            " 0.644      0.488      0.41       0.55       0.616      0.8\n",
            " 0.79       0.788      0.67       0.506      0.6        0.68\n",
            " 0.678      0.682      0.69       0.508      0.604      0.608\n",
            " 0.554      0.518      0.65       0.648      0.642      0.654\n",
            " 0.754      0.75       0.742      0.594      0.536      0.544\n",
            " 0.63       0.542      0.672      0.588      0.596      0.584\n",
            " 0.674      0.726      0.768      0.774      0.712      0.856\n",
            " 0.862      0.848      0.73       0.71       0.662      0.722\n",
            " 0.704      0.546      0.638      0.628      0.62       0.716\n",
            " 0.778      0.702      0.676      0.834      0.802      0.772\n",
            " 0.558      0.626      0.652      0.76       0.78       0.784\n",
            " 0.786      0.792      0.782      0.832      0.842      0.744\n",
            " 0.822      0.612      0.692      0.684      0.874      0.804\n",
            " 0.696      0.61       0.718      0.732      0.72       0.748\n",
            " 0.796      0.736      0.82       0.808      0.756      0.776\n",
            " 0.806      0.764      0.818      0.77       0.758      0.858\n",
            " 0.86       0.85       0.752      0.714      0.84       0.83\n",
            " 0.838      0.738      0.828      0.74       0.746      0.988\n",
            " 0.966      0.96       0.956      0.854      0.912      0.734\n",
            " 0.728      0.826      0.88       0.878      0.876      0.93\n",
            " 0.986      0.972      0.946      0.902      1.046      1.148\n",
            " 1.054      0.89       0.668      1.092      0.996      0.974\n",
            " 1.04       1.06       1.028      0.908      0.898      0.95\n",
            " 0.964      0.94       0.928      0.896      0.868      0.87\n",
            " 0.866      0.864      0.872      0.812      0.836      0.824\n",
            " 0.814      0.762      0.994      1.032      0.816      0.944\n",
            " 0.968      0.916      1.014      1.008      1.002      0.81\n",
            " 1.         1.006      1.012      1.034      0.852      0.924\n",
            " 0.942      0.982      0.906      0.932      0.914      0.846\n",
            " 0.844      1.222      1.19       1.18       1.118      1.082\n",
            " 1.08       0.91       1.102      1.274      1.364      1.39\n",
            " 0.918      1.198      1.192      0.936      0.954      0.98\n",
            " 0.99       0.904      0.798      0.766      1.09       1.016\n",
            " 0.922      1.036      0.892      0.894      0.9        0.886\n",
            " 1.096      0.92       0.962      0.958      0.998      1.044\n",
            " 1.048      0.938      0.934      1.13       1.218      0.888\n",
            " 0.882     ]\n",
            "[234.84 233.63 233.29 ... 224.94 225.54 225.41]\n",
            "[18.4        23.         15.8        15.         19.6        23.2\n",
            " 22.4        22.6        17.6        14.2        13.8        14.4\n",
            " 16.4        25.4        33.2        30.6        22.         19.4\n",
            " 13.6        13.4        11.6        19.2        10.4        11.8\n",
            " 11.         21.         26.2        29.         27.8        27.\n",
            " 19.          9.6        10.         21.4        17.8        12.\n",
            " 12.4        15.2        20.8        15.6        26.4        18.8\n",
            " 13.          9.8         9.2        13.2        18.         10.8\n",
            " 10.6        18.2        17.         17.4        14.8        14.6\n",
            " 15.4        14.          8.4         9.4         7.8         8.2\n",
            "  8.6        11.4        12.6         9.          7.6        12.8\n",
            "  7.4         5.6         4.6         7.2         1.6         2.4\n",
            "  2.8         1.2         4.4         4.         10.2         6.8\n",
            "  5.2         6.          6.6         8.          2.          4.8\n",
            "  5.4        16.6         3.          1.8         8.8         1.4\n",
            " 21.8        20.2        16.         18.6        16.8        11.2\n",
            "  2.6        16.2         4.2         7.          3.8         3.2\n",
            "  5.          3.6         0.8         5.8         1.          6.4\n",
            "  6.2         3.4         2.2        12.2        30.         29.4\n",
            " 28.2        25.8        20.         24.8        24.6        25.2\n",
            " 25.         23.4        23.8        20.6        21.6        19.8\n",
            " 20.4        26.6        17.2        21.2        23.6        29.2\n",
            " 33.6        28.         30.2        33.4        24.2        24.\n",
            " 22.2        24.4        22.8         4.61472938 30.4        34.2\n",
            " 32.8        33.         32.6        31.6        28.6        25.6\n",
            " 31.         28.4        27.2        29.8        29.6        37.\n",
            " 36.8        32.4        27.4        35.         34.8        32.2\n",
            " 26.         35.2        36.2        27.6        26.8        34.4\n",
            " 31.4        30.8        31.2        39.         39.2        39.4\n",
            " 28.8        38.8        35.6        32.         33.8        36.\n",
            " 37.2        34.         31.8        36.6        40.4        37.4\n",
            " 38.2        36.4        35.4        34.6        35.8        38.\n",
            " 40.2        41.         41.4        41.2        41.8        38.6\n",
            " 44.4        46.4        43.2        39.8        40.8         0.6\n",
            "  0.4        37.8        40.6        38.4        42.         42.2\n",
            " 39.6        42.6        44.2        37.6        40.          0.2\n",
            " 44.6        43.4        43.         45.8        48.4       ]\n",
            "[ 0.          1.          2.          6.         37.         36.\n",
            " 38.         25.         12.         11.          9.          4.\n",
            " 33.          5.         34.          8.         26.         27.\n",
            " 15.         20.         21.         23.         16.         10.\n",
            " 39.         32.          1.14807648 19.         35.         30.\n",
            " 40.          7.         31.         17.          3.         29.\n",
            " 24.         14.         41.         53.         76.         75.\n",
            " 77.         74.         28.         68.         58.         62.\n",
            " 13.         18.         22.         64.         71.         56.\n",
            " 72.         45.         42.         59.         66.         43.\n",
            " 44.         48.         46.         73.         55.         60.\n",
            " 70.         63.         67.         47.         65.         51.\n",
            " 50.         69.         78.         57.         49.         61.\n",
            " 52.         54.         79.         80.         81.         82.        ]\n",
            "[ 1.          2.          0.         16.         37.         36.\n",
            " 22.         12.         27.         20.          9.          4.\n",
            "  3.          5.         13.         35.         28.         26.\n",
            " 24.         21.         17.         33.         25.         23.\n",
            " 18.         30.         31.          7.         38.         29.\n",
            " 50.         71.         59.         62.         70.         40.\n",
            " 34.          8.         15.          6.         32.         19.\n",
            " 14.         49.         72.         73.         61.         39.\n",
            " 42.         10.         56.         11.          1.36513832 43.\n",
            " 67.         63.         53.         65.         52.         69.\n",
            " 48.         74.         41.         47.         58.         68.\n",
            " 64.         75.         46.         57.         60.         66.\n",
            " 44.         78.         77.         76.         51.         55.\n",
            " 45.         54.        ]\n",
            "[17.         16.         18.          9.          0.          5.\n",
            " 19.         10.         20.          8.          4.          2.\n",
            "  3.         11.         13.          6.         15.          6.09401143\n",
            " 12.          1.         14.          7.         23.         25.\n",
            " 26.         28.         27.         21.         22.         29.\n",
            " 24.         30.         31.        ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NTWHBvf0no7d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "72436b31-bfb0-4de9-ee08-a3c04f8e9002"
      },
      "source": [
        "## finding all columns that have nan:\n",
        "\n",
        "droping_list_all=[]\n",
        "for j in range(0,7):\n",
        "    if not df.iloc[:, j].notnull().all():\n",
        "        droping_list_all.append(j)        \n",
        "        #print(df.iloc[:,j].unique())\n",
        "droping_list_all"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzSCDdVonuJl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# filling nan with mean in any columns\n",
        "\n",
        "for j in range(0,7):        \n",
        "        df.iloc[:,j]=df.iloc[:,j].fillna(df.iloc[:,j].mean())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kf_SMFDinz9S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "bd4db59e-e34c-4b60-a054-28b05b1a50e9"
      },
      "source": [
        "\n",
        "# another sanity check to make sure that there are not more any nan\n",
        "df.isnull().sum()"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Global_active_power      0\n",
              "Global_reactive_power    0\n",
              "Voltage                  0\n",
              "Global_intensity         0\n",
              "Sub_metering_1           0\n",
              "Sub_metering_2           0\n",
              "Sub_metering_3           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7rVqyHDnzuT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "ff3e3bb0-4b2d-452a-8a94-c83e233d8180"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>Global_active_power</th>\n",
              "      <th>Global_reactive_power</th>\n",
              "      <th>Voltage</th>\n",
              "      <th>Global_intensity</th>\n",
              "      <th>Sub_metering_1</th>\n",
              "      <th>Sub_metering_2</th>\n",
              "      <th>Sub_metering_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.485328e+06</td>\n",
              "      <td>1.485328e+06</td>\n",
              "      <td>1.485328e+06</td>\n",
              "      <td>1.485328e+06</td>\n",
              "      <td>1.485328e+06</td>\n",
              "      <td>1.485328e+06</td>\n",
              "      <td>1.485328e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.085553e+00</td>\n",
              "      <td>1.216527e-01</td>\n",
              "      <td>2.404983e+02</td>\n",
              "      <td>4.614729e+00</td>\n",
              "      <td>1.148076e+00</td>\n",
              "      <td>1.365138e+00</td>\n",
              "      <td>6.094011e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.084842e+00</td>\n",
              "      <td>1.110129e-01</td>\n",
              "      <td>3.260359e+00</td>\n",
              "      <td>4.563437e+00</td>\n",
              "      <td>6.235748e+00</td>\n",
              "      <td>6.025610e+00</td>\n",
              "      <td>8.259896e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>7.600000e-02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.232000e+02</td>\n",
              "      <td>2.000000e-01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.980000e-01</td>\n",
              "      <td>4.600000e-02</td>\n",
              "      <td>2.386300e+02</td>\n",
              "      <td>1.400000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.600000e-01</td>\n",
              "      <td>1.000000e-01</td>\n",
              "      <td>2.407100e+02</td>\n",
              "      <td>2.400000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.516000e+00</td>\n",
              "      <td>1.920000e-01</td>\n",
              "      <td>2.426100e+02</td>\n",
              "      <td>6.400000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.700000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.112200e+01</td>\n",
              "      <td>1.390000e+00</td>\n",
              "      <td>2.539400e+02</td>\n",
              "      <td>4.840000e+01</td>\n",
              "      <td>8.200000e+01</td>\n",
              "      <td>7.800000e+01</td>\n",
              "      <td>3.100000e+01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Global_active_power  ...  Sub_metering_3\n",
              "count         1.485328e+06  ...    1.485328e+06\n",
              "mean          1.085553e+00  ...    6.094011e+00\n",
              "std           1.084842e+00  ...    8.259896e+00\n",
              "min           7.600000e-02  ...    0.000000e+00\n",
              "25%           2.980000e-01  ...    0.000000e+00\n",
              "50%           5.600000e-01  ...    1.000000e+00\n",
              "75%           1.516000e+00  ...    1.700000e+01\n",
              "max           1.112200e+01  ...    3.100000e+01\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SN78xaEhnzc3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "outputId": "cf0c8288-ca3e-49e6-ea84-c52b3c21db16"
      },
      "source": [
        "\n",
        "df['Global_active_power'].resample('M').sum()"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dt\n",
              "2006-12-31    41817.624213\n",
              "2007-01-31    69014.033107\n",
              "2007-02-28    56491.057107\n",
              "2007-03-31    58863.277553\n",
              "2007-04-30    39222.980864\n",
              "2007-05-31    44008.872000\n",
              "2007-06-30    35729.506791\n",
              "2007-07-31    29846.037480\n",
              "2007-08-31    34120.342172\n",
              "2007-09-30    41874.777107\n",
              "2007-10-31    49278.541107\n",
              "2007-11-30    55920.815107\n",
              "2007-12-31    72605.255553\n",
              "2008-01-31    65170.467553\n",
              "2008-02-29    49334.328660\n",
              "2008-03-31    55591.679553\n",
              "2008-04-30    48209.992000\n",
              "2008-05-31    45724.031107\n",
              "2008-06-30    42945.057553\n",
              "2008-07-31    35479.589107\n",
              "2008-08-31    12344.051107\n",
              "2008-09-30    42667.792000\n",
              "2008-10-31    50743.138791\n",
              "2008-11-30    59918.529980\n",
              "2008-12-31    56910.986283\n",
              "2009-01-31    62951.093553\n",
              "2009-02-28    50291.565410\n",
              "2009-03-31    54761.157107\n",
              "2009-04-30    49277.695107\n",
              "2009-05-31    45214.172213\n",
              "2009-06-30    37129.733593\n",
              "2009-07-31    27594.786213\n",
              "2009-08-31    30043.631973\n",
              "2009-09-30    42631.820660\n",
              "2009-10-31    18674.263553\n",
              "Freq: M, Name: Global_active_power, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2cgGymmg2pYW",
        "colab_type": "text"
      },
      "source": [
        "#Data visualization\n",
        "\n",
        "sum and mean of Global_active_power -->>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SEeTM_UVn_1l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "outputId": "a35cda0c-31d4-4fcd-ce6a-9dcc690b7650"
      },
      "source": [
        "df.Global_active_power.resample('D').sum().plot(title='Global_active_power resampled over day for sum') \n",
        "#df.Global_active_power.resample('D').mean().plot(title='Global_active_power resampled over day', color='red') \n",
        "plt.tight_layout()\n",
        "plt.show()   \n",
        "\n",
        "df.Global_active_power.resample('D').mean().plot(title='Global_active_power resampled over day for mean', color='red') \n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6M4IfK-X2xuy",
        "colab_type": "text"
      },
      "source": [
        "Mean and std of 'Global_intensity' resampled over day -->>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEBckxJxoDyf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "49cdd54c-843b-42c5-bb96-5524a7523c1f"
      },
      "source": [
        "r = df.Global_intensity.resample('D').agg(['mean', 'std'])\n",
        "r.plot(subplots = True, title='Global_intensity resampled over day')\n",
        "plt.show()"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "alITcwMr3H73",
        "colab_type": "text"
      },
      "source": [
        "Mean and std of 'Global_reactive_power' resampled over day -->>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqsaLp7poKVZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "008250c9-a92e-4bf3-9d87-3e262fc249b5"
      },
      "source": [
        "\n",
        "r2 = df.Global_reactive_power.resample('D').agg(['mean', 'std'])\n",
        "r2.plot(subplots = True, title='Global_reactive_power resampled over day', color='red')\n",
        "plt.show()"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JOpCAbtG3TF4",
        "colab_type": "text"
      },
      "source": [
        "Sum of 'Global_active_power' resampled over month -->>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzcTdxxqoQ8m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "3bf62c81-28d4-4741-c094-1edd4c5b5ca8"
      },
      "source": [
        "# Sum of 'Global_active_power' resampled over month\n",
        "df['Global_active_power'].resample('M').mean().plot(kind='bar')\n",
        "plt.xticks(rotation=60)\n",
        "plt.ylabel('Global_active_power')\n",
        "plt.title('Global_active_power per month (averaged over month)')\n",
        "plt.show()"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jy1Qxf2T3f2Z",
        "colab_type": "text"
      },
      "source": [
        "Mean of 'Global_active_power' resampled over quarter -->>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbJFCnJgoU1G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "3739fbde-63c8-45ba-d088-9db5f3f8d717"
      },
      "source": [
        "df['Global_active_power'].resample('Q').mean().plot(kind='bar')\n",
        "plt.xticks(rotation=60)\n",
        "plt.ylabel('Global_active_power')\n",
        "plt.title('Global_active_power per quarter (averaged over quarter)')\n",
        "plt.show()"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dkgWnbqV3nnk",
        "colab_type": "text"
      },
      "source": [
        "Mean of 'Voltage' resampled over month -->>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SKNgc7aOoYoT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "b44665c5-ac90-4fce-c0df-4b04b9c14abf"
      },
      "source": [
        "df['Voltage'].resample('M').mean().plot(kind='bar', color='red')\n",
        "plt.xticks(rotation=60)\n",
        "plt.ylabel('Voltage')\n",
        "plt.title('Voltage per quarter (summed over quarter)')\n",
        "plt.show()"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVpHt18Ooc2t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "4c131446-3a5e-474a-9aab-111a10e96d1d"
      },
      "source": [
        "df['Sub_metering_1'].resample('M').mean().plot(kind='bar', color='brown')\n",
        "plt.xticks(rotation=60)\n",
        "plt.ylabel('Sub_metering_1')\n",
        "plt.title('Sub_metering_1 per quarter (summed over quarter)')\n",
        "plt.show()"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_HTQsEf9old-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "outputId": "adf988ac-8afc-4fca-dcbc-6cb908cc920b"
      },
      "source": [
        "## resampling over week and computing mean\n",
        "df.Global_reactive_power.resample('W').mean().plot(color='y', legend=True)\n",
        "df.Global_active_power.resample('W').mean().plot(color='r', legend=True)\n",
        "df.Sub_metering_1.resample('W').mean().plot(color='b', legend=True)\n",
        "df.Global_intensity.resample('W').mean().plot(color='g', legend=True)\n",
        "plt.show()"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yH_VIIXaoxLf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "7113920e-cd6e-41cd-98c2-a59c31fbd67f"
      },
      "source": [
        "# Below I show hist plot of the mean of different feature resampled over month \n",
        "df.Global_active_power.resample('M').mean().plot(kind='hist', color='r', legend=True )\n",
        "df.Global_reactive_power.resample('M').mean().plot(kind='hist',color='b', legend=True)\n",
        "#df.Voltage.resample('M').sum().plot(kind='hist',color='g', legend=True)\n",
        "df.Global_intensity.resample('M').mean().plot(kind='hist', color='g', legend=True)\n",
        "df.Sub_metering_1.resample('M').mean().plot(kind='hist', color='y', legend=True)\n",
        "plt.show()"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O3Ihz80v3zDl",
        "colab_type": "text"
      },
      "source": [
        "Correlations between 'Global_intensity' and 'Global_active_power' -->>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CIFGXuw8o03B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "15a852ec-4c60-4838-eee0-de9a0fb08acf"
      },
      "source": [
        "## The correlations between 'Global_intensity', 'Global_active_power'\n",
        "data_returns = df.pct_change()\n",
        "sns.jointplot(x='Global_intensity', y='Global_active_power', data=data_returns)  \n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Nnz93oL9mO+sN/QEhiFNm6Idmdoekw1QatvuUuz/Z5Dkn1Tj8zXhNBDqj2Um8PGRX3UOq9dhq1c8tD/dJmlYwNbJ86fwp7a0e4gNCE2f13TWSjpL0I3f/QbNAAvKm2TxNrSG7eo+tlsVwXytDfEBo4gzf/YNK21WsMrPbVdrC4gfu/vtUWgZ0WPlkXW+IrdHQ3GCT4bis5neaDfEBoYkzfPdjST82sz5JR0o6VdJFkl6TUtuAjmt0Eq83vDc40K9bVxzZ8HWZ3wFaE6uiQ7T67n2SPi7pjyVdnEajgBC1sgw7jecCvaTlnpKZXa7SNUXXS/pHST+OrjUCekKz4b20ngv0EnOfUty79gPNlkr6D3evP9ObosWLF/umTZuyeGsASENLxQe69NxX97M37SmZ2ZHuvl7SbEnHmk1+LXdfO+3mAQCg1obv/lTSeknvqXGfSyKUAACJaBpK7n5O9Ovn3f2XlfeZ2b6ptArISNJVFwDEE2f13ZU1jl2RVEOArGVdZBVAa3NKB0g6UNKuZla5Z9JrJO2cVsOATmtUdYHeEtAZrcwpzZf0F5IGNHle6TmVLqAFugJVtYHstTKndJWkq8zsbe5+WwfaBGRi1/6CRkbHah4H0Blx5pQ+bmYD5RtmtpuZXZRCm4BMWJ0rJ+odB5C8OAVZ3+ruI+Ub7v6MmS1KoU1A6mqtsht5YWovSVLd4wCSFyeUZpjZbu7+jCSZ2e4xnw90TKOl3fX2NhqYVdAzNQJoYFaBpeJAh8QJlS9Jus3MvqdSiYjjJZ2fSquAaWi2oV69VXavmjlDhT7T2Pjk0lvPvjCm5VfcOXG8Exv0Ab2q5Tkld/+2ShXCH5f0G0nHuft30moY0K5mG+rVW0337OiYZu809d9pO6QpQZX2Bn1Ar4o1/ObuW81su6Lrk8xsnrs/nErLgDY1W9pdb2+jXfsLerbG6ru47wOgfXG2Qz/GzO6X9EtJP5b0kKR/T6ldQFuGhouaUWe5XHlDveVL56swY+pjnn/pZQ3Man35Nxv0AcmLsyT8C5IOk/Sf7r6vpHdK2pBKq4A2lOeSxmtsx1K5od6yRYPaZeepgwRj4y53TdmMrzDDVOibHGJs0AekI04ojbn7Uyqtwpvh7jdJWpxSu5BDQ8NFLVm1XvuuuFZLVq3veM24WnNJktRnppXHLZi0KKHeMu9nR8e08rgFGhzol6m01fnq9x+k1ccfNOlY9esBSEacOaURM9tF0s2S1pjZE5KeT6dZyJtmK946od4czw73KW2oN680d6BfyxYN1mxznM/BEnKgPXFC6VhJo5LOkHSypF0lfT6NRiF/Qihm2ug6o2rLl86fFKLS9IbkKkNo1/6Cnn/pZZaQA21oOZTcvdwr2iHp4ur7zew2d39bUg1DviRVzHQ6PYwaU0l1j5dfM4neTHUvsVb9PKqNA61JsiID21j0sEbDYa2a7hBgveXc9Y7XG6aLq95cVjWWkAPNxVno0Eydf6ciVEkuTFi+dP6UVWtxh8OaXfTaTL0ATHvpdqthwxJyoDlq1/WI6mGxIw6YoyvvKCa2MCGJ4bDpDgHWmicq9Jmef/Fl7bvi2tQWHNTrJVZiCTnQmiRDiQL/gao1LLZmw8NTurbTnfeY7nDYdIcAq4NxYFZBv/v9yxNzPGktOKgZhjNMu+w8UyMvjLH6DoghyVD6UIKvhQTVGharN9aa5bxHEiviKoNxyar1U1bjjY6N68zL75x4bBKSXDQB9LqmoWRmz6n2Ocwkubu/RqVf7k64bUhInKDJct4j6ZN7vc897p54jympRRNAr2tlO/RXd6IhSFblHNIMs5qld0yT/7URwrxHkif3RnM91UOVXOwKhCH26jsz28PM5pV/0mgUpqc8h1QcGZVLdWvBnXzYvK4unVNrRWClck+q+u+rPPfU6TJJAGLMKZnZMSpt9DdX0hOSXi/pXkkHptM0tKtRDbgd7rnuCcTp0ZSPn3n5nTWDuTxUGUI1CgAlcRY6lKuE/4e7LzKzIySdkk6zMB2NasD9ctW7O9ya5LRzcW35eKMFFElVo+gkhhvRragS3oWyuog0ba1eXFt9UbCkKZW/K4cq8/b3xXAjulk7VcJvEVXCg5Z0sdFQtNKjqdebWnncAt264sgpzx0aLur5F1+ecjzkvy+GG9HN4vSUylXCT5d0vaQHJL0njUZhepYtGmzYM8ibcs+n3rVVM8wmegmt9KbKr7fPimt1xmWbpxRQnb1Tn141c4bOuGxzJvtCNZPH4UagVbGqhJvZf5F0iKSnJa2LhvMQoCyvm0lyvqO651NL5XVHzU7Y1a9XK+heeGlcruz2hWomieK3QKjirL7775I+J2m9Spe4fM3MPu/uF6XVOIQv7Zp6rVbgLveGmp2wW3m9pMsvJa1bh2cBKd7w3XJJi9z9L939w5IOlvS/Gj3BzC4ysyfM7O6KY7ub2Q1mdn/0527tNR1ZqzXhvmbDw21V+q5XsTzOkNSjI6NNq5W3O8QV0tBYtw3PApXiLHR4StJzFbefi4418i1J/yjp2xXHVki60d1XmdmK6HbDcEN4hoaLNa//aaemXqOl3q1U4C4rb2Uu1S9V1Oz1qqtcVL52SChrhG7VSu27T0e//kLSRjO7SqX/b4+VdFej57r7zWa2T9XhYyUdHv1+saQfiVDKlXKI1LogtZ5GJ/VGixNqDVXVc8QBcyQ1PmHXer1yEA3WGH6UGBoDOqmVnlK59t0D0U/ZVW2+557u/lj0+28k7VnvgWZ2mqTTJGnePCoaheLcq7c2DInq3obplcCopdHihHK4nH7Z5qbtuum+7U0f00rR18Wv350LU5GpXj73tVKQ9bzK29G1SnL33033zd3dzazuP7fd/QJJF0jS4sWL2dk2AEPDxSlLqCv1F/r0R/N21U8feHoimFzSlXcUtfj1u9c8uTdanDA0XNRnv7+lpba1Ou/TbOiLoTFkrZfPfS0vdDCzt5jZsKStkraa2R1m1k7du8fNbK/oNfdSqY4ecqLRgoU+M73v4EFtePCZuivYaqm3OOGIA+bozO/dqedfaj50J4U37wMgvjgLHS6Q9OmovJDM7HBJF0p6e8z3vFrShyWtiv5sdxgQGWjUGznp0L313Q0Pt/Tc6qXk7zt4UDfdt33SkNnqdds0vqO1fyQW+ox5H6ALxAml2eVAkiR3/5GZzW70BDO7VKVFDa81s0cknaNSGF1uZh+V9CtJJ8RuNTJTb6htt1kFXfaz+oEkSTJp3xXXTmxTPhYFTnFkVFfeUdTK4xZIKvXGzrhsc92VfDX11AAH0L3ihNKDZvZ3kr4T3T5F0oONnuDuJ9W5650x3hcZq+zVDMwqqDDDJgJFKg21nfOeA5suRigv1qveolwqDe+dd81W/X5sR0sr7aqN7fCWL3ClwjYQrjih9FeSzpO0Nrp9S3QMXaz6GqJnXhhToc800F/Qs6Njk07qrayQa6RWWMXRykKHdra/KD8vyyDL+v2BTolT++4ZSZ9MsS0IUK1riMbGXbNfNVObzzkqo1bV5pKWrFrf8ITdToXtdoMsKVm/P9BJrVw8e40ajNi7+zGJtgixpfmv6DgVqfffY7bufyLb3UyanbDbqbCd9VYRWb8/0Emt9JT+ocaxckhZgm1BG9L+V3ScitRP/u6lab9fEhqdsNupsJ31VhFZvz/QSa1cpzQg6S3u/mN3/7Gk1SqVB/qWpD1SbBtaUO9f0acntBdQrWuICn2m5198eVLx1KHh4rTnhJJU74TdrGBrLVnvTJv1+wOd1Eoo/U+Vri0q20mlbdAPl/TxFNqEGBoVF01im+zqitS7zSpILo2Mjk1UBl9+xZ369DQXOSSt3gm7nQrb7QRZkrJ+f6CTWhm+28ndf11x+yfR5n5PNbtOCekaGi7WrWpdNjo2rnOv3jqtOafKsjtLVq2f0iMaG493kdCrZs5Qf6FPz0bBlrRmJ+y4ZYRaqZeXpqzfH+gk8yaVns3sF+7+h3Xue8Dd90ulZVUWL17smzZt6sRb5caSVetb3tahUn+hr2nvoN7iiX1XXDvtIBkc6NetK46UJC0874cNa+m189pHHDBnSnUITuAIUEtz8l167qv72VsZvttoZqdOeUWzj0n62XRahelpd6K72aZ7tTbvO2vtFp09tEUzbPprWyrb/WyCgTTQX9DypfN15R3FKW2f7twagM5oZfjuDElDZvZBST+Pjh0s6VWSlqXVMDQXZwO8avUCrd7mfaNj41qz4eFEhtsqrycamFVIbIHEyOgYy6eBnGtl64onJL3dzI6UVK4Kfq27r0+1ZWhoaLioF156ecrxwgzTLjvP1MgLpWoLL7z0cs2Tfq2FAM0270ty/ueVpevJzirVC2mWTwP5EKeiw3pJBFEAzh7aUrPXYpJOPGRvfXHZgolj1dcxSfUXAtTqZaSpk+/F8mkgH+LUvkMAhoaLdYfRam2mV71ya9f+gsxKO7lWVuKevVNfy/sW5Q3Lp4H8aHmTP3TO0HBRS1atn3RxatnqdduaLgGvXsSwbNGgbl1xpL5y4kK9+PKOieG8ytdJO5ASWB8R/z3V2nVIAMJBTykw9coGbfrV07rpvu0tLWyoN3/S6eG5Sk2uPKhphkkt7vE3ReWy8ySdPbRFl278tcbd1Wemkw6dPFwKYHroKQWm3uqx7254uOWVdvUWMbS7Ui8LgwP9+vIJC1Xoa9zFKsywKY9Ja7ju7KEt+u6GhycWgoy767sbHtbZQ1sSfy+gVxFKgZnuKrHqE/LQcFGLPv/Dae911Enlz7Bs0aBWH3/QpJJApxw2b9Lt1e8/aMpj0hquW1Nnq/d6xwHEx/BdYOJeezR7pz698NK4XFKfmd538CsldGqtvMuDylBptSRQJ+aM6o0kshM7kBx6SoFZvnR+0yGrsoH+gnb4KyfFcXddeUdxYmFElnNI05HFooSzh7Zov7Ou0z4rrtV+Z13HkByQEUIpMMsWDWr2Ts07sIUZJrOp1/pUrr7L4wWju80qdPT9hoaLetPf/TtzRUAgCKUAtVKgtFy1oZZHR0Y1NFxMpE5dp715r1d37L1eGd7cUfP+SzZOniuavVNfzcfVOw4gPuaUOijJbcvLZYRqzT8NzCo0LBcUsg0PPtOx92o2vFm9HL3etVzdetExkAVCqUOqSwNVX39UGVStOuKAObryjuKUEkLunS3hk6ROBmnc4c0+s5rt68thjxQIFcN3HVCvNFC58nb1NgutnOLKJYXed/CgBqPrkvrMNDo2nuj+RJ3WyRN83Hp49QIzjz1SIFSEUgc0Kg1UK6haPcWNjo3rpvu2T6zY64aT40mH7t2x96q1zXgjg3VCrN5xAPERSh2Q5iq4R0dGdd41W2NvSR6Kcs+oz0ynHDavoyV7li0a1MrjWn+/WiFGsVcgWcwpdcCu/YXUhtSS3CSv00zSAyvflWkbli0abLnaRXXFdbZaB5JHKKVsaLio52tsxpeUZ3MaSJI0M4f99FYrTABoTw5PC/myet22VIfWal9hkw9jOzRla46kNdoGBEB46CmlrN35pP5CX26XdcfxyrboyZcXGhouThqaK46MTtymtwOEiZ5SytrdhnvlcQumVMfuVrU2JkzCmZfXniuqPl6o839BveMA0sP/dimLu+y47MzL71SxYjL9pvu2p9C6cJR7lEkOt9UbNa0+vsvOtevt1TsOID0M36WsPEwUdz+j8jVH5eGtbhjKG2ywLcfcgf66u+5K6Q631ashWO84gPTQU+qAZYsGp3WB5ejYeFeUsrl1xZH66okL617rU2/X3TSG9irVG2Jtd+gVQPsIpQ454oA503p+3qs1lLekKF+wWmun2HqLQtLegqPedzPd7wxAfAzfdUi3zwk1UugznfOeAydu17vWp17V87R7LPW+m17+zoCsEEoJqrU1hVS6VinOFufdYHCgf8rfw5JV6xtWQjjigDn67oaHp7xW2j2Wet9Nr31nQAgIpYTUmqRffsWdkktj1Rvz9IBbVxw58XurCxiy6rGwJQUQDuaUElJrkn5s3HsykKq1uoAhqzkltqQAwkEoJSTtE2feVF5f1GrYxFkFl+T1TGxJAYQjs1Ays4fMbIuZbTazTVm1IyksH57srLVbJoKi1bBpdWuIoTz+dfcAABBiSURBVOGiPn3Z5kmbI376ss1tBxNbUgDhyLqndIS7L3T3xRm3Y9r2+QNCqVLl8Fyzk36513PGZZv1qpkztNuswpTl4pXOWnvXlEK0O6LjlcrL0KtVH2+0TB1AZ7HQIQFDw0X99IGns25GcMrDc432IapeBDEyOqb+Qp++cuLCuqEwOla7Nnr18XPec6CWX3HnpCrt1cvTy9iSAghDlqHkkn5oZi7pX9z9guoHmNlpkk6TpHnzwi1I2mi7815WOTxX76TfaBHEdEOCTfmQV3k596Uhy1B6h7sXzWwPSTeY2X3ufnPlA6KgukCSFi9eHOx5v9cXORRmmGSa1CNpdU4m7RV39ICQR3k596Uhszkldy9Gfz4h6fuSDsmqLdO1a3/vVpPebVZBq99/kFYff1BbczL1FkG4pH1WXKuTL7wt2QYDCFomPSUzmy1phrs/F/1+lKTPZ9GWJHRDBe927DaroOHPHTVxu50eyfKl8xtWQb/1gad18oW3ac2pb2u7nQDyI6vhuz0lfd9KV8zPlHSJu1+fUVum7cWX87wpeXv6C301FwzEVTnvU6+sz60sIgF6RibDd+7+oLsfFP0c6O7nZ9GOJJw9tCXrJnTcrMIM7VyYoTMu2zztC1elUjBVliUC0Luyvk4p99bUKCDarQb6CzrlsHlymZ55YWziwtXKC2U7Yae+2jXp6h0HkB+E0jT1wrKYwYF+ffXEhdp8zlG66b7tLW/El2QpoEon/PHesY4DyA8unp2GTvYOQtHqEu40tzZn/yOge9FTatPQcFFnfu/OrJvREcWRUS3/3p0aGi62XMeu3kWxpycwD5VVNXEA6SOU2nTu1Vs13kPbUoztcJ179daWi5c2CojKkKtUb0ao+nicauIA8oVQasPQcFEjo2NZN6Pjyp+5leKlzQKiHHKVTj6sdjmV6uNU9Qa6F3NKbag1qd8rzlq7RSuPW9B0CXezi2IlTQn2Ly5bIEm6dOOvNe6uPjOddOjeE8fLqGkHdC9CqQ29PHfRarHUVi6KreWLyxZMCaF6r08IAd2H4bs29HKtO6n1UOaiWABxEUoxDQ0X9dyLL2fdjNQVZpiszsoDFhQASAvDdzF99vtbunrVnUkTczSSpswLVS8oGBouMrcDIDGEUgxDw0U9/1L3VgQf6C9o8zlHTTleL3SGhouTdnYtjoxq+RWla7cIJgDtIJRi+Mzau7JuQmoKM0znHhNvm/Dzrtk6aWM/qbTR33nXbCWUALSFOaUWnT20RS+MdecWFTNMWv3+g2IHyTMv1L5Wq95xAGiGnlKLvtvF1cDdpw63JTlXNHunvprDnrN36qvxaAC9jJ5SC7p9z6Tq1XTluaLiyOjE9hTLr5haFqhV5793gfpmTF7K1zfDdP57m1+PBKC3EEotuGRj9/aSapXnaTRX1I5liwb1pfcfNKk00ZfaGC4E0P0YvmtiaLioblsBPjjQ33BYLo25IiowAGgFodRAeRirmwz0FxKrsmCqvckh+78CaBfDdw2sXrdtyjBWntVb9t2uen8z3fM3BqDTCKUG4hQSDV2fWVvLvhsZrFNuqN5xAGiGUOoRO9y1bNGghoaLWrJqvfZdce20d4BlXyMASWNOqY7pnKxDNHegf2IL93LtvuLI6MSW7u30oNjXCEDSCKU6ln9vc9ZNSEy591KrmOz4Dtdnv7+l7SBhVR2AJDF8V0e3VBSq3K68XjHZbi4yCyBf6Cn1gDMu29zTW7gDyA96SjXss+LarJuQmMpSQa3qq7O7X73jAJAUQqlKXhY4FPrSC4iTDt071nEASArDd1VOvywfCxzSvKj3i8tKhVIv3fhrjburz0wnHbr3xHEASAuhVOHkC2/LugnB+OKyBYQQEICnn38p6yZ0FMN3kbOHtujWB57OuhmZYKYIQCgIpcilG3+ddRNiqa6kMB0nHzYvsdcCgOno6eG7Q8+/QY8/l8+u8crjFkxUUmh1dqm/0Kc/mrerNjz4DHNFAILUs6GU50CSJldSaLSEvdneSQAQkp4NpTwHUhxJ7Z0EAJ3AnFIXYAsJoLtdsvFhXbLx4ayb0RE9GUqHnn9D1k1IFFtIAOgWPTd8l/e5pFrYQgJAt+i5UOq2QCpjCwkA3SCz4TszO9rMtpnZL8xsRdrvNzRc1IGfuz7tt0nM4EC/9t9jds376h0HgLzLpKdkZn2Svi7pzyQ9Iul2M7va3e9J4/2GhouZ17QzKdb1ROXhtz/78o90/xPPT9y3/x6zdcOnD0+jiQACl/fFDh88tPmF+lkN3x0i6Rfu/qAkmdm/STpWUuKhFEIgSaWqCWs2PNw0mAar5oMIIAC9JKvhu0FJlXV9HomOTWJmp5nZJjPbtH379thvEkogSaUCp630lG5dcSRzQ0CPqzz3PTfSWzU5g14S7u4XuPtid188Z86c2M//zNq7UmhV+5pdN7Rkv9071BIAIas89716oLfOC1mFUlFS5Y5xr4uOJeqFsR1Jv+QU/YUZ+uqJC/XQqnfrqycurPmY8vFa1xOVLdlvd6059W2ptRMA8iCrOaXbJe1vZvuqFEYfkPTBJN/g7KEtSb5cTV89ceGkobZm1wtxPREANGbu6e1g2vCNzd4l6auS+iRd5O7nN3r84sWLfdOmTS2/fqMipXEN9Bd07jEHEh4AktTSVmZveNNb/cF7w5qKSEDdz57ZxbPufp2k67J6/1YM9Be0+Zyjsm4GAPSMoBc6tCuJXlJhhuncYw5MoDUAgFb1XJmhRgozpJd3iLkeAMgIoRR5aNW7s24CAPS8rhy+i6veUm4AQGf1fChVL+sGgJDsPnunrJvQUT09fMeQHQCEpStDacl+u+vWB+rXiyKMACBMXTl8t+bUt02pI7dkv9310Kp3E0gAELCu7ClJoo4cAORQV/aUAAD5RCgBAIJBKAEAgkEoAQCCQSgBAIJBKAEAgkEoAQCCQSgBAIJBKAEAgmHunnUbWmJm2yX9KqWXf62kJ1N67U7rls/C5wgLnyN5T7r70c0eZGbXt/K4bpGbUEqTmW1y98VZtyMJ3fJZ+Bxh4XOgUxi+AwAEg1ACAASDUCq5IOsGJKhbPgufIyx8DkiSzOwiM3vCzO5u8fEnmNk9ZrbVzC5p+njmlAAArTKzP5H0O0nfdve3NHns/pIul3Skuz9jZnu4+xONnkNPCQDQMne/WdKkrb3NbD8zu97M7jCzW8zsgOiuUyV93d2fiZ7bMJAkQgkAMH0XSPobdz9Y0t9K+qfo+BslvdHMbjWzDWbWdGl7z4eSmR1tZtvM7BdmtiLr9rTLzB4ysy1mttnMNmXdnjhqjVGb2e5mdoOZ3R/9uVuWbWxFnc9xrpkVo+9ls5m9K8s2tsLM9jazmyrmAT4VHc/Vd9Lgc+TuOwmZme0i6e2SvmdmmyX9i6S9ortnStpf0uGSTpJ0oZkNNHq9ng4lM+uT9HVJfy7pzZJOMrM3Z9uqaTnC3Rfm8DqMb0mq/hfUCkk3uvv+km6MbofuW5r6OSTpK9H3stDdr+twm9rxsqQz3f3Nkg6T9Ino/4u8fSf1PoeUv+8kZDMkjVT8fS509zdF9z0i6Wp3H3P3X0r6T5VCquGL9bJDJP3C3R9095ck/ZukYzNuU8+pNUat0vdwcfT7xZKWdbRRbajzOXLH3R9z959Hvz8n6V5Jg8rZd9LgcyBB7v5bSb80s/dLkpUcFN09pFIvSWb2WpWG8x5s9Hq9HkqDkn5dcfsR5fc/Wpf0w2ii8bSsG5OAPd39sej330jaM8vGTNNfm9ld0fBe0ENe1cxsH0mLJG1Ujr+Tqs8h5fg7yZqZXSrpNknzzewRM/uopJMlfdTM7pS0Va/8436dpKfM7B5JN0la7u5PNXr9mek1HR32Dncvmtkekm4ws/uif7nnnru7meX12oVvSPqCSv9o+IKkL0n6q0xb1KJoruBKSae7+2/NbOK+PH0nNT5Hbr+TELj7SXXumjJ07aVrjj4d/bSk13tKRUl7V9x+XXQsd9y9GP35hKTvqzQ0mWePm9lekhT92XQpaYjc/XF3H3f3HZIuVE6+FzMrqHQiX+Pua6PDuftOan2OvH4nvaLXQ+l2Sfub2b5mtpOkD0i6OuM2xWZms83s1eXfJR0lqaWrrQN2taQPR79/WNJVGbalbeWTeOS9ysH3YqUu0Tcl3evuX664K1ffSb3PkcfvpJf0fEWHaDnoVyX1SbrI3c/PuEmxmdkbVOodSaUh2Uvy9DmiMerDVdpW4HFJ56g0QXq5pHkqbVlygrsHvYigzuc4XNJClYaKHpL0sYp5mSCZ2Tsk3SJpi6Qd0eHPqDQfk5vvpMHnOEk5+056Sc+HEgAgHL0+fAcACAihBAAIBqEEAAgGoQQACAahBAAIBqEEAAgGoYRMmNmeZnaJmT0Y1eu7zczea2aHm9kPmjz3XDP725jv97sG9801sytaeI3PxHnPVpjZYjP7v9Hvh5vZ25N+DyBPCCV0XHSl/ZCkm939DdHGYB9QqcxTx7n7o+5+fAsPTTyU3H2Tu38yunm4SvvSAD2LUEIWjpT0krv/c/mAu//K3b9W+aBoU7mhqJrzBjN7a8XdB0W9q/vN7NTo8buY2Y1m9nMrbXjY0jYkZrZPeWM+M/tLM1trpa2d7zezv4+Or5LUH20KtyY6doqZ/Sw69i/R/lwys9+Z2flmdmfU7j2j4+83s7uj4zdHxw43sx9EVaw/LumM6PX+q5n9MqrdJjN7TeVtoFsRSsjCgZJ+3sLjzpM07O5vVamX8u2K+96qUri9TdLnzGyupN9Leq+7/5GkIyR9ySpLW7duoaQTJS2QdKKZ7e3uKySNRhuYnWxmb4oes8TdF0oaV6l8vyTNlrTB3Q+SdLOkU6Pjn5O0NDp+TOUbuvtDkv5Zr2w+d4ukH0l6d/SQD0ha6+5jbXweIDcIJWTOzL4e9R5ur7rrHZK+I0nuvl7SH5jZa6L7rnL3UXd/UqV9Wg6RZJL+t5ndJek/VNobq509f25092fd/feS7pH0+hqPeaekgyXdbqUtoN8p6Q3RfS9JKs+L3SFpn+j3WyV9K+rZ9bXQjv8n6SPR7x+R9K8xPweQO+ynhCxslfS+8g13/0S0K+WmGK9RXbTRVeqpzJF0sLuPmdlDknZuo30vVvw+rtr/n5iki939rBr3jfkrRSUnnu/uHzezQ1Xq/dxhZgc3aoS73xoNLR4uqc/dqWaNrkdPCVlYL2lnM/sfFcdm1XjcLYqGxKIT85PR1suSdKyZ7Wxmf6DSAoHbJe0q6YkokI5Q7R7OdIxVzOncKOn4aFPF8vxXw/czs/3cfaO7f07Sdk3ey0uSnpP06qpj35Z0iegloUcQSui4qBexTNKfRpP3P5N0saT/VfXQcyUdHA3HrdIre/lI0l0qDdttkPQFd39U0hpJi81si6T/Jum+hJt+gaS7zGyNu98j6WyVtqC/S9INkvZq+GxpdbQA425JP5V0Z9X910h6b3mhQ3RsjaTdJF2a2KcAAsbWFUDAzOx4Sce6+4eybgvQCcwpAYEys69J+nNJ78q6LUCn0FNCzzCzBYpW81V40d0PzaI9AKYilAAAwWChAwAgGIQSACAYhBIAIBiEEgAgGP8fU0vN5YAEci8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6kJBEDpo30s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "6afa6e28-1d4f-4d91-8366-dbdaf7393b61"
      },
      "source": [
        "## The correlations between 'Voltage' and  'Global_active_power'\n",
        "sns.jointplot(x='Voltage', y='Global_active_power', data=data_returns)  \n",
        "plt.show()"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3jmnrkto6yC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "4466445e-b6d7-4882-88dc-438c6995b2a9"
      },
      "source": [
        "# Correlations among columns\n",
        "plt.matshow(df.corr(method='spearman'),vmax=1,vmin=-1,cmap='PRGn')\n",
        "plt.title('without resampling', size=15)\n",
        "plt.colorbar()\n",
        "plt.show()"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W5HNXjyRPrlGY1UGp6VFlqaDT2KNFbwAui4hVuXC4DNh/vd5TgQsKsxo0ahRVFuqLPfo3km6SdKGkxnSVleOWdqJKXI8zgbcAKyNi15EmaNaP1Nl0/XXEHv0R8O2IeELS/wHOAeaN8JrDqvLOzqaGqotZX1NHNYoypRNaR8T9EfFEXj0deHnVc9dHaUEREVcBq0aakFk/awyP1tRHURp7VNKswuoBwNL8+hJgvxyDdHNgv7xtRDzqYVaDxvBoHSrGHv0HSQcAa0lf5Efkc1dJ+hypsAE4PiJG/EXflQBAz529dV2XNRsXUmdm72KPRsSxwLHDnHsmcGZtmaHGgqIYAOhFu+3sAEA2sUi+hdvM2hNM7Fu4JX0buBp4oaTlko7sfrbMxhvVOeox5lSJFHZYLzJiNp7Js3CbWRkhpk6qZ9RjLHJBYVYDuTPTzKoY6OPOTBcUZjUQckFhZuVcUJhZW8IFhZmVkZseHdMjk5m6ZMtuXHodvQjOs/k7X9z1NACuP/rSrqcxZeeNup4GwCEzDu96Gg/d/mjX0xh6vPqTCEJMVv9+7/bvOzPrMdcozKwt91GYWQXuozCzMnKNwsxKCDHQx5Pau6Awq4lrFGbWlhCTB/r3z6l/i0CzHhvQQKWligqxRz8q6Tc5ANDlkrYr7BssxCS9qPnc9dG/RaBZD6nGOzMrxh69AZgbEY9Keh/wb8Ahed9jEbFbLZnJqkyFt62kRbn0ulXSh+rMgFm/GKj4r4LS2KMRsSgiGrenXkMK9NM1VXK9FvjHiNgFeBXwAUm7dDNTZuNN44arik2PumKPNhwJ/LSwvkG+7jWS3lrH+6syZ+Y9wD359UOSlpIy3RyC3WwC66jpUUfs0ZSq9A5gLvC6wubtImKFpB2AKyTdHBG/G0k6HfVRSJoD7A5cO5JEzfpRjcOjleKHStoH+BTwukIcUiJiRf7/TklXkv5mR1RQVH5nkjYBvgt8OCIebLH/qEZVas2Dq0eSJ7NxR0rDo1WWCqrEHt0dOBU4ICJWFrZvLmlafj0T2JMaav+Vci1pCqmQODcivtfqmGKksF122tWRwmxCEdR2Z2bF2KNfADYBviMJ4I8RcQDwIuBUSUOkisCJTaMl66W0oFDKxRnA0og4eaQJmvWneh8KqxB7dJ9hzvsV8JLaMpJVeWd7Au8E5hVu4nhT3RkxG+/qvOFqrKky6vFLUs3KzIYh0m3c/cp3ZprVQmic1haqcEFhVpMB1yjMrJ00H4VDCppZO0r3UvQrFxRmNVEfz9rggsKsJh71MLO2PGfmelh67+3sfvLe3bj0On7+jbO7nkYvIngBvOKU/bqexk/P/GrX0wCYs9Gcrqdxwh++3PU0Vjz5p46Odx+FmZXwqIeZVeAahZm1lW7hdh+FmZXwnZlmVkKuUZhZOfdRmFlbfszczCoQk/p4eLRKAKANJF0n6dc5ANBne5Exs/FEgDRQaal0vfKQgtMknZ/3X5tnyG/sOzZv/62kN9Tx/qrk+glgXkS8FNgN2F/Sq+pI3Kx/qPK/0is9E1LwjcAuwGEtgm4dCayOiB2BBcBJ+dxdSLN2vxjYH/hqvt6IlBYUkTycV6fkxbNsmzWpq6CgQkjBvH5Ofn0hsHeeCPtA4LyIeCIi7gKW5euNSKV6kKRJkm4EVgKXRYQDAJkVqdamR5WQgk8fExFrgQeALSqe27FKuY6IwRwdeTawh6Rdm48pBgDiqaGR5sts3OmgRlEWe3TM6WjUIyLWSFpEavvc0rTv6QBAmj7VTRObUBRCUXl4tCz2aJWQgo1jlkuaDDwHuL/iuR2rMuqxpaTN8usNgX2B20aasFk/CYKhoWpLBaUhBfP6/Pz6bcAVERF5+6F5VGR7YCfgupG+vyo1ilnAObnndAC4ICJ+PNKEzfpOtUKgVMWQgmcA35S0DFhFKkzIx11Aije6FvhARAyONE9VAgDdRIqGbGbDCUhf6DVdrjyk4OPA24c59wTghNoyg+/MNKtPH/fhu6Awq0mdNYqxxgWFWR0C+riccEFhVpuaOjPHIhcUZjVoDI/2KxcUZnUIXKMws3Luo+jQTnOez5fPPLkbl17HDpvs2PU0puy8UdfTgN4E53nj372/62kA3Pv967uexvHzPtH1NG7ctMNnH12jMLO2ar7haqxxQWFWg8BNDzMrFW56mFmJwMOjZlaBn/UwszLuzDSz9nzDlZlV0ccVChcUZnVIw6P9W1JULijyVHiLgRUR8ZbuZclsHIogBl1QAHwIWApM71JezMa16OM+iqoBgGYDbwZO7252zMavGmfhbkvSDEmXSboj/795i2N2k3R1jhd8k6RDCvvOlnSXpBvzsltZmtUipsIXgY/T1yPFZusvItUoqiw1+CRweUTsBFye15s9CrwrIhoxSL/YCLuRfSwidsvLjWUJVonr8RZgZUQsKTnu6UhhD6x6sOyyZn1nKKLSUoNi3NFzgLc2HxARt0fEHfn1n0jhQLdc3wSr1Cj2BA6Q9HtSsNR5kr7VImOnRcTciJj7nBnuxrCJplptoqYaxdYRcU9+/Wdg63YHS9oDmAr8rrD5hNwkWSBpWlmCVaKZHxsRsyNiDinIyBUR8Y6y88wmkoiO+ihKY49K+pmkW1osB66bbgRpdLYlSbOAbwLvjohG18GxwM7AK4AZQOnkHr6PwqwOAYNrK3fhlcUeJSL2GW6fpHslzYqIe3JBsHKY46YDC4FPRcQ1hWs3aiNPSDoLOKYsw1U7MxsJXOl7KMxa62HToxh3dD7ww+YDcszS7wPfiIgLm/bNyv+L1L9xS/P5zToqKMystaBaR2ZNnZknAvtKugPYJ68jaa6kxi0MBwOvBY5oMQx6rqSbgZuBmcC/lCXopodZHaJ3N1xFxP3A3i22Lwbek19/C3jWoEPeN6/TNF1QmNWkn+/MdEFhVoPAM1yZWZkeNj1GgwsKs1oEg4P9+4SDCwqzGoQn1+3ctEnTeMH0F3Tj0utY+IeFXU/jkBmHdz0NgDkbzel6Gr2I4AWw9UGv6Hoaq394U9fTmKxJHR3vpoeZtecahZmVCWq763JMckFhVhMXFGbWnpseZlYmwMOjZlYi3EdhZhW46WFmbYVv4YY8X+ZDwCCwtmx2HrOJaGjIfRQAr4+I+7qWE7PxLOqJ2TFWuelhVoPATQ9In8OlkgI4NSJO62KezMafgMGnBkc7F11TtaB4TUSskLQVcJmk2yLiquIBecrxowCet+2smrNpNrYF0dd9FJUm142IFfn/laSZffdocczTAYBmbPGsUIhm/a2zuB4jUiX2aD5usDCx7kWF7dtLulbSMknn5xm726oSUnBjSZs2XgP7UWF6b7OJZozFHgV4rBBf9IDC9pOABRGxI7AaOLIswSo1iq2BX0r6NXAdsDAiLq5wntmEEZGaHlWWGpTGHh1OjuUxD2jE+qh0fmkfRUTcCby0akbMJqoejnpUjT26gaTFwFrgxIj4AbAFsCYi1uZjlgPblCXo4VGzGkTA2rWVRz1m5j/ghtOaRxIl/Qx4botzP7VuuhF5NLKV7fIgxA7AFTnozwNVM1nkgsKsFh31P/Qk9mhhEOJOSVcCuwPfBTaTNDnXKmYDK8oy7JCCZnUIetlHUSX26OaSpuXXM4E9gd/k6OeLgLe1O7+ZCwqzGjQCAPVieJRqsUdfBCzOgxCLSH0Uv8n7PgF8VNIyUp/FGWUJuulhVosgenTDVcXYo78CXjLM+XfS4l6odlxQmNXBU+GZWRXhqfA686eHVnL8ogXduPQ6Fuz7r11P46HbH+16GgAn/OHLXU/j+Hmf6Hoa0JvgPJsf+D+7ngbLWg4mtBQRrF3rgsLM2kgzXLmgMLMS7qMwsxK9G/UYDS4ozGrgaOZmVolHPcysPdcozKxMRDD0pOfMNLN2Aobc9DCz9vo79milp0clbSbpQkm3SVoq6dXdzpjZeBK5RlFlGY+q1ii+BFwcEW/LM/Zu1MU8mY1DMbFHPSQ9B3gtcARARDwJPNndbJmNMwExOLGbHtsDfwHOknSDpNPztP1mVtDDGa56rkpBMRl4GfC1iNgdeIQWcQQkHSVpsaTFTzz4WM3ZNBvbYigNj1ZZxqMqBcVyYHlEXJvXLyQVHOsoRgqbNn3DOvNoNi4MDUalZTwqLSgi4s/A3ZJemDftDfymzSlmE09+zLzKMh5VnVz3g8C5km4CdgO6P2OM2TgSRM+GR6vEHpX0+kLc0RslPS7prXnf2ZLuKuzbrSzNSsOjEXEj0DYOgdmE1ttRj0bs0RMlfTKvrzN9WUQsIn2pI2kGsAy4tHDIxyLiQirydP1mNYnBoUpLDTqNPfo24KcRsd7zOrqgMKtBClLcs7geVWOPNhwKfLtp2wmSbpK0oBEoqB0/62FWh6CToc9exR4lhxx8CXBJYfOxpAJmKnAaqdlyfLsMu6Awq0NEJx2VPYk9mh0MfD8inipcu1EbeULSWcAxZRl208OsBkFPh0dLY48WHEZTsyMXLkgSqX/jlrIEXaMwq0PQy5upTgQukHQk8AdSrQFJc4H3RsR78vocYFvg503nnytpS0DAjcB7yxJ0QWFWi949PVol9mhe/z2wTYvj5nWaplIU9HpJ+guppKtqJnBf7RkZnXT8XsZmOuuTxnYRsWWVA2dtuE3Mf8H7K130pFs/vaSsj2Ks6UqNouqH2yBpcS8+uF6k4/cyNtPpdhoB4/bJ0Crc9DCrQUSwdnDtaGeja1xQmNUiGBoan4+QVzFWCorTyg8ZN+n4vYzNdLqaRgCD0b9Nj650ZppNNFtNmxVv32Z++YHAV+86yZ2ZZhNTuDPTzNoLYKiPmx4uKMxqEBGsXftU+YHjlAsKs1qEaxRmViJ8w5WZlfCdmWZWQTAYvuHKzNoINz3MrJzvozCzEn4ozMxKpRuu3EdhZm256WFmZfq8M9OzcJvVoPGYeZVlpCS9XdKtkobyhLrDHbe/pN9KWpZDDza2by/p2rz9fElTy9J0QWFWi9T0qLLU4Bbgr4GrhjtA0iTgK8AbgV2AwyTtknefBCyIiB2B1cCRZQm6oDCrQQopOFhpqSGtpRHx25LD9gCWRcSdEfEkcB5wYI7lMQ9oBCiuErvUfRRmdQgYa8Oj2wB3F9aXA68EtgDWRMTawvZnTenfzAWFWQ0e5oFLrmThzIqHbzCS2KMR0S4yWFe4oDCrQUTsX/P1ho09WtEKUpSwhtl52/3AZpIm51pFY3tb7qMw60/XAzvlEY6pwKHARZEmyV0EvC0fVxa7FHBBYTbuSDpI0nLg1cBCSZfk7c+T9BOAXFs4GrgEWApcEBG35kt8AviopGWkPoszStP0LNxmVsY1CjMr5YLCzEq5oDCzUi4ozKyUCwozK+WCwsxKuaAws1IuKMys1H8DJKx1oeNCg0QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jt0rEFXFo-Qw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "f3719983-3fd0-4e5f-be89-85d0b59e0dcc"
      },
      "source": [
        "# Correlations of mean of features resampled over months\n",
        "\n",
        "\n",
        "plt.matshow(df.resample('M').mean().corr(method='spearman'),vmax=1,vmin=-1,cmap='PRGn')\n",
        "plt.title('resampled over month', size=15)\n",
        "plt.colorbar()\n",
        "plt.margins(0.02)\n",
        "plt.matshow(df.resample('A').mean().corr(method='spearman'),vmax=1,vmin=-1,cmap='PRGn')\n",
        "plt.title('resampled over year', size=15)\n",
        "plt.colorbar()\n",
        "plt.show()"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fDv5d7kE4IS2",
        "colab_type": "text"
      },
      "source": [
        "LSTM  :::\n",
        "\n",
        "Taking supervised learning problem as predicting the Global_active_power at the current time (t) given the Global_active_power measurement and other features at the prior time step."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8drBEzq8pB8T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
        "\tn_vars = 1 if type(data) is list else data.shape[1]\n",
        "\tdff = pd.DataFrame(data)\n",
        "\tcols, names = list(), list()\n",
        "\t# input sequence (t-n, ... t-1)\n",
        "\tfor i in range(n_in, 0, -1):\n",
        "\t\tcols.append(dff.shift(i))\n",
        "\t\tnames += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
        "\t# forecast sequence (t, t+1, ... t+n)\n",
        "\tfor i in range(0, n_out):\n",
        "\t\tcols.append(dff.shift(-i))\n",
        "\t\tif i == 0:\n",
        "\t\t\tnames += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
        "\t\telse:\n",
        "\t\t\tnames += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
        "\t# put it all together\n",
        "\tagg = pd.concat(cols, axis=1)\n",
        "\tagg.columns = names\n",
        "\t# drop rows with NaN values\n",
        "\tif dropnan:\n",
        "\t\tagg.dropna(inplace=True)\n",
        "\treturn agg"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wfC3GRXzpGSp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dfa8cb02-6a48-4a85-d0dc-135e8acb4015"
      },
      "source": [
        "## resampling of data over hour\n",
        "df_resample = df.resample('h').mean() \n",
        "df_resample.shape"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(24756, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDtDU1DnpKQh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "d566fb29-f7b0-4299-e6bc-f092a4bd1a6f"
      },
      "source": [
        "## If you would like to train based on the resampled data (over hour), then used below\n",
        "values = df_resample.values \n",
        "\n",
        "\n",
        "## full data without resampling\n",
        "#values = df.values\n",
        "\n",
        "# integer encode direction\n",
        "# ensure all data is float\n",
        "#values = values.astype('float32')\n",
        "# normalize features\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "scaled = scaler.fit_transform(values)\n",
        "# frame as supervised learning\n",
        "reframed = series_to_supervised(scaled, 1, 1)\n",
        "\n",
        "# drop columns we don't want to predict\n",
        "reframed.drop(reframed.columns[[8,9,10,11,12,13]], axis=1, inplace=True)\n",
        "print(reframed.head())"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   var1(t-1)  var2(t-1)  var3(t-1)  ...  var6(t-1)  var7(t-1)   var1(t)\n",
            "1   0.636816   0.276074   0.337945  ...   0.011366   0.824504  0.545045\n",
            "2   0.545045   0.078322   0.335501  ...   0.144652   0.824776  0.509006\n",
            "3   0.509006   0.085225   0.283802  ...   0.030869   0.815811  0.488550\n",
            "4   0.488550   0.071773   0.315987  ...   0.000000   0.820701  0.455597\n",
            "5   0.455597   0.073853   0.434417  ...   0.008973   0.841891  0.322555\n",
            "\n",
            "[5 rows x 8 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2CvzWVq3pN5h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c67fde38-9b25-42e0-b600-776f7530845a"
      },
      "source": [
        "# split into train and test sets\n",
        "values = reframed.values\n",
        "\n",
        "n_train_time = 365*24\n",
        "train = values[:n_train_time, :]\n",
        "test = values[n_train_time:, :]\n",
        "##test = values[n_train_time:n_test_time, :]\n",
        "# split into input and outputs\n",
        "train_X, train_y = train[:, :-1], train[:, -1]\n",
        "test_X, test_y = test[:, :-1], test[:, -1]\n",
        "# reshape input to be 3D [samples, timesteps, features]\n",
        "train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))\n",
        "test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))\n",
        "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape) \n",
        "# We reshaped the input into the 3D format as expected by LSTMs, namely [samples, timesteps, features]."
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(8760, 1, 7) (8760,) (15995, 1, 7) (15995,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2zNlokSx4fyW",
        "colab_type": "text"
      },
      "source": [
        "Model architecture ::::\n",
        "\n",
        "1) LSTM with 100 neurons in the first visible layer\n",
        "\n",
        "2) dropout 20%\n",
        "\n",
        "3) 1 neuron in the output layer for predicting Global_active_power.\n",
        "\n",
        "4) The input shape will be 1 time step with 7 features.\n",
        "\n",
        "5) I use the Mean Absolute Error (MAE) loss function and the efficient Adam version of stochastic gradient descent.\n",
        "\n",
        "6) The model will be fit for 20 training epochs with a batch size of 70."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uNG8893pThd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ad306f8b-80bf-4313-c004-9c21bc1af9cf"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(100, input_shape=(train_X.shape[1], train_X.shape[2])))\n",
        "model.add(Dropout(0.2))\n",
        "#    model.add(LSTM(70))\n",
        "#    model.add(Dropout(0.3))\n",
        "model.add(Dense(1))\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "\n",
        "\n",
        "\n",
        "# fit network\n",
        "history = model.fit(train_X, train_y, epochs=20, batch_size=70, validation_data=(test_X, test_y), verbose=2, shuffle=False)\n",
        "\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper right')\n",
        "plt.show()\n",
        "\n",
        "# make a prediction\n",
        "yhat = model.predict(test_X)\n",
        "test_X = test_X.reshape((test_X.shape[0], 7))\n",
        "# invert scaling for forecast\n",
        "inv_yhat = np.concatenate((yhat, test_X[:, -6:]), axis=1)\n",
        "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
        "inv_yhat = inv_yhat[:,0]\n",
        "# invert scaling for actual\n",
        "test_y = test_y.reshape((len(test_y), 1))\n",
        "inv_y = np.concatenate((test_y, test_X[:, -6:]), axis=1)\n",
        "inv_y = scaler.inverse_transform(inv_y)\n",
        "inv_y = inv_y[:,0]\n",
        "# calculate RMSE\n",
        "rmse = np.sqrt(mean_squared_error(inv_y, inv_yhat))\n",
        "print('Test RMSE: %.3f' % rmse)"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 8760 samples, validate on 15995 samples\n",
            "Epoch 1/20\n",
            " - 1s - loss: 0.0227 - val_loss: 0.0133\n",
            "Epoch 2/20\n",
            " - 1s - loss: 0.0132 - val_loss: 0.0117\n",
            "Epoch 3/20\n",
            " - 1s - loss: 0.0118 - val_loss: 0.0105\n",
            "Epoch 4/20\n",
            " - 1s - loss: 0.0110 - val_loss: 0.0099\n",
            "Epoch 5/20\n",
            " - 1s - loss: 0.0107 - val_loss: 0.0097\n",
            "Epoch 6/20\n",
            " - 1s - loss: 0.0106 - val_loss: 0.0097\n",
            "Epoch 7/20\n",
            " - 1s - loss: 0.0106 - val_loss: 0.0097\n",
            "Epoch 8/20\n",
            " - 1s - loss: 0.0105 - val_loss: 0.0097\n",
            "Epoch 9/20\n",
            " - 1s - loss: 0.0104 - val_loss: 0.0096\n",
            "Epoch 10/20\n",
            " - 1s - loss: 0.0104 - val_loss: 0.0096\n",
            "Epoch 11/20\n",
            " - 1s - loss: 0.0104 - val_loss: 0.0096\n",
            "Epoch 12/20\n",
            " - 1s - loss: 0.0104 - val_loss: 0.0098\n",
            "Epoch 13/20\n",
            " - 1s - loss: 0.0104 - val_loss: 0.0095\n",
            "Epoch 14/20\n",
            " - 1s - loss: 0.0104 - val_loss: 0.0097\n",
            "Epoch 15/20\n",
            " - 1s - loss: 0.0104 - val_loss: 0.0096\n",
            "Epoch 16/20\n",
            " - 1s - loss: 0.0104 - val_loss: 0.0096\n",
            "Epoch 17/20\n",
            " - 1s - loss: 0.0104 - val_loss: 0.0097\n",
            "Epoch 18/20\n",
            " - 1s - loss: 0.0104 - val_loss: 0.0096\n",
            "Epoch 19/20\n",
            " - 1s - loss: 0.0103 - val_loss: 0.0095\n",
            "Epoch 20/20\n",
            " - 1s - loss: 0.0103 - val_loss: 0.0097\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Test RMSE: 0.632\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Av39j4Sf43o3",
        "colab_type": "text"
      },
      "source": [
        "Note that in order to improve the model, one has to adjust epochs and batch_size."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wk42fOkRpZBE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}