Telematics_Week_2.ipynb

{
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  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "name": "Telematics Week 2.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZibdGgjzEeyJ"
      },
      "source": [
        "# Telematics Data Analytics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFrbviPXEeyQ"
      },
      "source": [
        "    This notebook is for Internship at Xen.AI. The aim is to apply AI/ML on truck data to reduce the downtime/maintenance time by predicting Engine Health Score (EHS).\n",
        "    We have to generate an equation and perform time series prediction for the EHS using available data.\n",
        "\n",
        "    Data Credits  : Dr. Dmitry"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_RJwZ-9EeyS"
      },
      "source": [
        "import warnings                         #'do not disturb' mode\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import numpy as np                      # vectors and matrices\n",
        "import pandas as pd                     # tables and data manipulations\n",
        "import matplotlib.pyplot as plt         # plots\n",
        "import seaborn as sns                   # more plots\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GylVlMkfEeyU"
      },
      "source": [
        "# data = pd.read_csv('telematics_data.csv')   # data importing"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCeMLdcYEhm9",
        "outputId": "af76e9c7-32df-411e-c132-420ba2b97402"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zNaV7Q5E1Di"
      },
      "source": [
        "train_path='/content/drive/My Drive/Xen.AI/'"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hmfGylz0E1cc"
      },
      "source": [
        "data = pd.read_csv('/content/drive/My Drive/Xen.AI/telematics_data.csv')   # data importing"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OM_7WdakEeyV"
      },
      "source": [
        "pd.options.display.max_rows = 200\n",
        "pd.options.display.max_columns =50     #to show max rows and columns"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "n1tV_-IlEeyX",
        "outputId": "243452e2-96dc-423b-eda5-9dedc5185525"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>vin</th>\n",
              "      <th>wmi</th>\n",
              "      <th>engine_cd</th>\n",
              "      <th>device_type</th>\n",
              "      <th>msg_ts</th>\n",
              "      <th>vin_utc_key</th>\n",
              "      <th>packet_id</th>\n",
              "      <th>reason_code</th>\n",
              "      <th>script_version</th>\n",
              "      <th>deviceid</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>driveridcode1</th>\n",
              "      <th>obdvin</th>\n",
              "      <th>vehiclebattvolts</th>\n",
              "      <th>fuel_total</th>\n",
              "      <th>engine_rpm</th>\n",
              "      <th>coolant_tmp</th>\n",
              "      <th>fuel_lvl_pct</th>\n",
              "      <th>driving_tme_sec</th>\n",
              "      <th>cruise_tme_sec</th>\n",
              "      <th>idle_tme_sec</th>\n",
              "      <th>idle_fuel_total</th>\n",
              "      <th>pto_tme_total</th>\n",
              "      <th>pto_fuel_total</th>\n",
              "      <th>gear</th>\n",
              "      <th>fuel_tmp</th>\n",
              "      <th>oil_tmpl</th>\n",
              "      <th>throt_pos</th>\n",
              "      <th>mpg</th>\n",
              "      <th>acel_pos</th>\n",
              "      <th>engine_pct_load</th>\n",
              "      <th>torque</th>\n",
              "      <th>oil_lvl</th>\n",
              "      <th>oil_psi</th>\n",
              "      <th>coolant_psi</th>\n",
              "      <th>intak_air_tmp</th>\n",
              "      <th>manifold_tmp</th>\n",
              "      <th>vehiclespeed</th>\n",
              "      <th>heading</th>\n",
              "      <th>odometerinmeters</th>\n",
              "      <th>odometer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020</td>\n",
              "      <td>4</td>\n",
              "      <td>18</td>\n",
              "      <td>3HSDZTZR9KN352697</td>\n",
              "      <td>3HS</td>\n",
              "      <td>TZ</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>2020-04-18 06:50:40</td>\n",
              "      <td>3HSDZTZR9KN3526972020-04-18 06:50:40</td>\n",
              "      <td>36</td>\n",
              "      <td>9</td>\n",
              "      <td>204.0</td>\n",
              "      <td>173101817</td>\n",
              "      <td>38.799149</td>\n",
              "      <td>-90.603680</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3HSDZTZR9KN352697</td>\n",
              "      <td>14.2</td>\n",
              "      <td>106165.0</td>\n",
              "      <td>1144.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2167380.0</td>\n",
              "      <td>1503.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1005.0</td>\n",
              "      <td>960.0</td>\n",
              "      <td>380663000.0</td>\n",
              "      <td>380663.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>3HSDZTZR9LN055320</td>\n",
              "      <td>3HS</td>\n",
              "      <td>TZ</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>2020-05-20 03:01:09</td>\n",
              "      <td>3HSDZTZR9LN0553202020-05-20 03:01:09</td>\n",
              "      <td>36</td>\n",
              "      <td>9</td>\n",
              "      <td>204.0</td>\n",
              "      <td>172301945</td>\n",
              "      <td>33.618963</td>\n",
              "      <td>-84.333847</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3HSDZTZR9LN055320</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>584.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>228.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>700.0</td>\n",
              "      <td>88781990.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3HSDZTZR0KN354211</td>\n",
              "      <td>3HS</td>\n",
              "      <td>TZ</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>2020-04-01 20:02:39</td>\n",
              "      <td>3HSDZTZR0KN3542112020-04-01 20:02:39</td>\n",
              "      <td>36</td>\n",
              "      <td>9</td>\n",
              "      <td>204.0</td>\n",
              "      <td>173511306</td>\n",
              "      <td>41.139005</td>\n",
              "      <td>-101.173123</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3HSDZTZR0KN354211</td>\n",
              "      <td>NaN</td>\n",
              "      <td>113134.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1788120.0</td>\n",
              "      <td>1172.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>320.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2709.0</td>\n",
              "      <td>416436440.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3HSDWTZR8LN845362</td>\n",
              "      <td>3HS</td>\n",
              "      <td>TZ</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>2020-05-05 06:17:36</td>\n",
              "      <td>3HSDWTZR8LN8453622020-05-05 06:17:36</td>\n",
              "      <td>36</td>\n",
              "      <td>9</td>\n",
              "      <td>204.0</td>\n",
              "      <td>173404340</td>\n",
              "      <td>38.856184</td>\n",
              "      <td>-98.139251</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3HSDWTZR8LN845362</td>\n",
              "      <td>14.0</td>\n",
              "      <td>149410.0</td>\n",
              "      <td>1368.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>786600.0</td>\n",
              "      <td>497.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>424.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1095.0</td>\n",
              "      <td>838.0</td>\n",
              "      <td>426460520.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>3HSDZTZRXLN085409</td>\n",
              "      <td>3HS</td>\n",
              "      <td>TZ</td>\n",
              "      <td>N2</td>\n",
              "      <td>2020-04-07 20:14:07</td>\n",
              "      <td>3HSDZTZRXLN0854092020-04-07 20:14:07</td>\n",
              "      <td>36</td>\n",
              "      <td>9</td>\n",
              "      <td>204.0</td>\n",
              "      <td>190815-0009</td>\n",
              "      <td>33.351086</td>\n",
              "      <td>-87.017387</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3HSDZTZRXLN085409</td>\n",
              "      <td>14.1</td>\n",
              "      <td>33886.0</td>\n",
              "      <td>1167.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4755420.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>504.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>88.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>65.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>320.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1090.0</td>\n",
              "      <td>458.0</td>\n",
              "      <td>107975440.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   year  month  day                vin  wmi engine_cd device_type  \\\n",
              "0  2020      4   18  3HSDZTZR9KN352697  3HS        TZ       Xirgo   \n",
              "1  2020      5   20  3HSDZTZR9LN055320  3HS        TZ       Xirgo   \n",
              "2  2020      4    1  3HSDZTZR0KN354211  3HS        TZ       Xirgo   \n",
              "3  2020      5    5  3HSDWTZR8LN845362  3HS        TZ       Xirgo   \n",
              "4  2020      4    7  3HSDZTZRXLN085409  3HS        TZ          N2   \n",
              "\n",
              "                msg_ts                           vin_utc_key  packet_id  \\\n",
              "0  2020-04-18 06:50:40  3HSDZTZR9KN3526972020-04-18 06:50:40         36   \n",
              "1  2020-05-20 03:01:09  3HSDZTZR9LN0553202020-05-20 03:01:09         36   \n",
              "2  2020-04-01 20:02:39  3HSDZTZR0KN3542112020-04-01 20:02:39         36   \n",
              "3  2020-05-05 06:17:36  3HSDWTZR8LN8453622020-05-05 06:17:36         36   \n",
              "4  2020-04-07 20:14:07  3HSDZTZRXLN0854092020-04-07 20:14:07         36   \n",
              "\n",
              "   reason_code  script_version     deviceid   latitude   longitude  \\\n",
              "0            9           204.0    173101817  38.799149  -90.603680   \n",
              "1            9           204.0    172301945  33.618963  -84.333847   \n",
              "2            9           204.0    173511306  41.139005 -101.173123   \n",
              "3            9           204.0    173404340  38.856184  -98.139251   \n",
              "4            9           204.0  190815-0009  33.351086  -87.017387   \n",
              "\n",
              "   driveridcode1             obdvin  vehiclebattvolts  fuel_total  engine_rpm  \\\n",
              "0            0.0  3HSDZTZR9KN352697              14.2    106165.0      1144.0   \n",
              "1            0.0  3HSDZTZR9LN055320               NaN         NaN       584.0   \n",
              "2            0.0  3HSDZTZR0KN354211               NaN    113134.0      1145.0   \n",
              "3            0.0  3HSDWTZR8LN845362              14.0    149410.0      1368.0   \n",
              "4            0.0  3HSDZTZRXLN085409              14.1     33886.0      1167.0   \n",
              "\n",
              "   coolant_tmp  fuel_lvl_pct  driving_tme_sec  cruise_tme_sec  idle_tme_sec  \\\n",
              "0         79.0          41.0              NaN             NaN     2167380.0   \n",
              "1         61.0           NaN              NaN             NaN           NaN   \n",
              "2         81.0           NaN              NaN             NaN     1788120.0   \n",
              "3         81.0          54.0              NaN             NaN      786600.0   \n",
              "4         82.0           NaN        4755420.0             NaN           NaN   \n",
              "\n",
              "   idle_fuel_total  pto_tme_total  pto_fuel_total  gear  fuel_tmp  oil_tmpl  \\\n",
              "0           1503.0            0.0             0.0   1.0       0.0      76.0   \n",
              "1              NaN            NaN             0.0   2.0       0.0      56.0   \n",
              "2           1172.0            NaN             0.0   1.0       0.0       NaN   \n",
              "3            497.0            0.0             0.0   1.0       0.0      86.0   \n",
              "4            504.0            0.0             0.0   1.0       NaN      88.0   \n",
              "\n",
              "   throt_pos  mpg  acel_pos  engine_pct_load  torque  oil_lvl  oil_psi  \\\n",
              "0        0.0  NaN       0.0             11.0    12.0      0.0    340.0   \n",
              "1        1.0  NaN       0.0             26.0    11.0      0.0    228.0   \n",
              "2        0.0  NaN       NaN             41.0    42.0      NaN    320.0   \n",
              "3        0.0  NaN       0.0             19.0    19.0      0.0    424.0   \n",
              "4        0.0  NaN       NaN             65.0    67.0      NaN    320.0   \n",
              "\n",
              "   coolant_psi  intak_air_tmp  manifold_tmp  vehiclespeed  heading  \\\n",
              "0          0.0            4.0          10.0        1005.0    960.0   \n",
              "1          0.0           24.0           NaN          11.0    700.0   \n",
              "2          0.0           25.0           NaN           NaN   2709.0   \n",
              "3          0.0           13.0          30.0        1095.0    838.0   \n",
              "4          NaN            NaN           NaN        1090.0    458.0   \n",
              "\n",
              "   odometerinmeters  odometer  \n",
              "0       380663000.0  380663.0  \n",
              "1        88781990.0       NaN  \n",
              "2       416436440.0       NaN  \n",
              "3       426460520.0       NaN  \n",
              "4       107975440.0       NaN  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLqPKUjIEeyZ"
      },
      "source": [
        "# data.info()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9yNP72qrEeya"
      },
      "source": [
        "# data.describe().T"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPvS6d3SEeyb"
      },
      "source": [
        "# data.isnull().sum()/len(data) * 100   #percentage of missing values"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XH0vQqS3Eeyc",
        "outputId": "c6094a23-38a3-4d1c-89fc-662e549c1513"
      },
      "source": [
        "print('Total rows = {} \\n'.format(len(data)))\n",
        "\n",
        "for i in data.columns:   #lets see value counts in each column, print value & count if unique value < 106 (105 in deviceid)\n",
        "    \n",
        "    print('{}'.format(i))\n",
        "    \n",
        "    if len(data[i].value_counts())<106:\n",
        "        print(data[i].value_counts())\n",
        "        \n",
        "    else:\n",
        "        print(\"{} has More than 106 values\".format(i))\n",
        "        \n",
        "    print('Number of values =', data[i].nunique())   #Unique values\n",
        "    print('Percentage of Missing values = %.2f'%(data[i].isnull().sum()/len(data) * 100), '%')  #Missing value percentage\n",
        "    print('Percentage of Zero = %.2f'%(np.shape(np.where(data[i]==0))[1] / len(data) * 100), '%', '\\n') #percetnage of 0"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total rows = 514994 \n",
            "\n",
            "year\n",
            "2020    514994\n",
            "Name: year, dtype: int64\n",
            "Number of values = 1\n",
            "Percentage of Missing values = 0.00 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "month\n",
            "4    299339\n",
            "5    215655\n",
            "Name: month, dtype: int64\n",
            "Number of values = 2\n",
            "Percentage of Missing values = 0.00 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "day\n",
            "8     30136\n",
            "4     26772\n",
            "5     23166\n",
            "14    22010\n",
            "15    21944\n",
            "1     20784\n",
            "29    20444\n",
            "7     20259\n",
            "13    20209\n",
            "27    19883\n",
            "6     19324\n",
            "20    19160\n",
            "18    18087\n",
            "11    16606\n",
            "23    16270\n",
            "9     16004\n",
            "24    15687\n",
            "19    15648\n",
            "3     15644\n",
            "25    15228\n",
            "2     15182\n",
            "21    14887\n",
            "12    13911\n",
            "30    12962\n",
            "10    12318\n",
            "28    11887\n",
            "26    11585\n",
            "16    10745\n",
            "22    10605\n",
            "17     4596\n",
            "31     3051\n",
            "Name: day, dtype: int64\n",
            "Number of values = 31\n",
            "Percentage of Missing values = 0.00 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "vin\n",
            "3HSDZTZR5LN134290    34636\n",
            "1HSERTZRXLH809470     9962\n",
            "3HSDZTZRXLN395656     7263\n",
            "3HSDZTZRXLN085409     6657\n",
            "3HSDZTZR3LN154022     6639\n",
            "3HCDZTZR1LL880100     6583\n",
            "3HCDZTZR9LL880099     6401\n",
            "3HCDZTZR3LL880101     6267\n",
            "3HSDZTZR8KN398425     6048\n",
            "3HSDZTZR1KN174476     5873\n",
            "3HSDZTZR3MN096740     5791\n",
            "3HSDZTZR4LN056083     5586\n",
            "3HCDZTZR6ML684705     5362\n",
            "3HSDZTZR5LN164115     5343\n",
            "3HCDZTZR5LL880102     5316\n",
            "3HCDZTZR7LL880103     5315\n",
            "3HSDZTZR3KN315175     5291\n",
            "3HSDZTZR2LN230944     5289\n",
            "3HSDZTZR0LN880114     5239\n",
            "3HSDZTZR6LN056148     5208\n",
            "3HSDZTZR3LN833708     5132\n",
            "3HSDZTZR4KN126504     5116\n",
            "3HSDZTZR1LN871454     5097\n",
            "3HSDWTZR9LN844849     5074\n",
            "3HSDZTZR6LN065318     5051\n",
            "3HSDZTZR0LN437333     5039\n",
            "3HCDZTZR1ML681274     5030\n",
            "3HSDZTZR2LN075067     4976\n",
            "3HSDZTZR4KN547798     4961\n",
            "3HSDWTZR6LN844811     4957\n",
            "3HCDZTZR0ML681251     4932\n",
            "3HCDZTZRXLL056502     4924\n",
            "3HSDWTZR8LN845362     4916\n",
            "3HSDZTZR7LN630689     4896\n",
            "3HSDWTZR5LN844783     4891\n",
            "3HSDZTZR5KN140444     4878\n",
            "3HSDWTZR7LN064378     4869\n",
            "3HSDZTZR3LN164114     4843\n",
            "3HSDWTZR2LN845423     4830\n",
            "3HSDWTZR1LN845090     4809\n",
            "3HSDWTZR9LN810071     4794\n",
            "3HSDZTZR8KN537162     4773\n",
            "3HSDWTZR1LN887792     4749\n",
            "3HSDWTZRXKN315831     4736\n",
            "3HSDZTZR4LN822992     4710\n",
            "3HSDZTZR1LN081863     4708\n",
            "3HSDWTZR3LN845348     4694\n",
            "3HSDWTZR2LN844773     4681\n",
            "3HSDZTZR0LN384388     4663\n",
            "3HSDZTZR1LN125327     4653\n",
            "3HSDWTZRXLN844861     4614\n",
            "3HSDWTZR2LN845308     4612\n",
            "3HSDZTZR9LN055320     4596\n",
            "3HSDZTZR9LN085496     4568\n",
            "3HSDZTZR9KN136686     4556\n",
            "3HSDZTZR2LN112232     4550\n",
            "3HSDZTZR0KN354211     4539\n",
            "3HSDZTZR3LN852131     4535\n",
            "3HSDZTZR0LN630677     4533\n",
            "3HSDWTZR8LN845216     4532\n",
            "3HCDYTZN8LL296583     4489\n",
            "3HSDZTZR2LN817113     4475\n",
            "3HSDWTZR8LN845149     4466\n",
            "3HSDWTZR6LN854884     4460\n",
            "3HSDWTZRXLN845024     4455\n",
            "3HSDWTZR7LN844803     4434\n",
            "3HSDWTZR5LN845092     4426\n",
            "3HSDZTZR0KN279915     4421\n",
            "3HSDZTZR0LN062656     4417\n",
            "3HSDWTZR9LN845466     4402\n",
            "3HSDWTZR0LN845338     4399\n",
            "3HSDWTZR3LN211277     4393\n",
            "3HSDZTZR8LN139435     4387\n",
            "3HCDZTZR1LL158318     4377\n",
            "3HSDWTZR2LN854882     4361\n",
            "3HCDZTZR6LL833595     4356\n",
            "3HSDWTZR3KN308686     4349\n",
            "3HSDWTZR8KN308697     4345\n",
            "3HSDWTZR9LN845094     4343\n",
            "3HSDZTZRXLN443043     4325\n",
            "3HSDZTZR0LN291791     4324\n",
            "3HSDWTZR5LN845352     4321\n",
            "3HSDWTZRXKN310631     4313\n",
            "3HSDZTZR3LN054647     4313\n",
            "3HSDZTZR4KN227364     4291\n",
            "3HSDZTZR1LN290908     4273\n",
            "3HSDZTZR2LN630681     4260\n",
            "3HCDZTZR6LL833600     4256\n",
            "3HSDZTZR6LN056070     4242\n",
            "3HCDZTZR2LL070362     4233\n",
            "3HSDWTZR7LN810070     4232\n",
            "3HSDZTZR6LN112511     4228\n",
            "3HSDZTZR9KN352697     4211\n",
            "3HSDWTZR9LN845337     4210\n",
            "3HSDZTZR7KN314305     4205\n",
            "3HSDWTZR9KN310622     4204\n",
            "3HSDWTZR5LN845108     4185\n",
            "3HSDWTZR1LN845199     4181\n",
            "3HSDZTZR6KN540223     4177\n",
            "3HSDWTZR0LN844822     4169\n",
            "Name: vin, dtype: int64\n",
            "Number of values = 100\n",
            "Percentage of Missing values = 0.00 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "wmi\n",
            "3HS    433191\n",
            "3HC     71841\n",
            "1HS      9962\n",
            "Name: wmi, dtype: int64\n",
            "Number of values = 3\n",
            "Percentage of Missing values = 0.00 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "engine_cd\n",
            "TZ    514994\n",
            "Name: engine_cd, dtype: int64\n",
            "Number of values = 1\n",
            "Percentage of Missing values = 0.00 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "device_type\n",
            "Xirgo    414156\n",
            "N2       100838\n",
            "Name: device_type, dtype: int64\n",
            "Number of values = 2\n",
            "Percentage of Missing values = 0.00 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "msg_ts\n",
            "msg_ts has More than 106 values\n",
            "Number of values = 472918\n",
            "Percentage of Missing values = 0.00 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "vin_utc_key\n",
            "vin_utc_key has More than 106 values\n",
            "Number of values = 503424\n",
            "Percentage of Missing values = 0.00 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "packet_id\n",
            "36    514994\n",
            "Name: packet_id, dtype: int64\n",
            "Number of values = 1\n",
            "Percentage of Missing values = 0.00 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "reason_code\n",
            "9    514994\n",
            "Name: reason_code, dtype: int64\n",
            "Number of values = 1\n",
            "Percentage of Missing values = 0.00 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "script_version\n",
            "204.0    368108\n",
            "203.0      9777\n",
            "202.0      9196\n",
            "205.0      2488\n",
            "Name: script_version, dtype: int64\n",
            "Number of values = 4\n",
            "Percentage of Missing values = 24.35 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "deviceid\n",
            "173406713      33776\n",
            "173609885       9767\n",
            "190912-0002     6385\n",
            "190912-0010     6208\n",
            "190930-0104     6074\n",
            "200129-0308     5646\n",
            "173101356       5556\n",
            "172304411       5236\n",
            "190925-0048     5181\n",
            "191008-0057     5178\n",
            "190830-0128     5156\n",
            "190930-0120     5153\n",
            "190820-0094     5139\n",
            "173101835       4974\n",
            "191030-0107     4968\n",
            "191019-0067     4883\n",
            "173502918       4875\n",
            "173502526       4869\n",
            "173100734       4819\n",
            "180502617       4786\n",
            "191019-0061     4782\n",
            "172304208       4755\n",
            "190828-0133     4688\n",
            "173101045       4661\n",
            "172106709       4659\n",
            "172303105       4651\n",
            "173403603       4635\n",
            "173103744       4632\n",
            "173407376       4619\n",
            "173511297       4613\n",
            "190720-0099     4594\n",
            "172304180       4579\n",
            "172302416       4571\n",
            "173404340       4558\n",
            "173405767       4538\n",
            "180503386       4508\n",
            "180500129       4499\n",
            "172407237       4490\n",
            "180502953       4466\n",
            "190729-0230     4434\n",
            "172304488       4432\n",
            "190720-0091     4424\n",
            "173511724       4423\n",
            "173102863       4413\n",
            "172301384       4395\n",
            "173406114       4362\n",
            "173511248       4357\n",
            "173101443       4325\n",
            "173511364       4320\n",
            "191115-0040     4313\n",
            "172300794       4310\n",
            "172107479       4305\n",
            "172301945       4281\n",
            "173501805       4280\n",
            "173511306       4266\n",
            "190705-0188     4243\n",
            "180500300       4211\n",
            "173403726       4211\n",
            "191115-0035     4196\n",
            "173405742       4195\n",
            "173511477       4187\n",
            "172501043       4136\n",
            "172409433       4133\n",
            "173502990       4131\n",
            "173101778       4129\n",
            "180500323       4119\n",
            "172407933       4105\n",
            "172201914       4101\n",
            "173103073       4091\n",
            "172303011       4080\n",
            "172300632       4071\n",
            "173404679       4069\n",
            "172106430       4060\n",
            "173511493       4057\n",
            "173103076       4033\n",
            "173502468       4030\n",
            "173104829       4024\n",
            "172302291       4024\n",
            "180502072       4019\n",
            "173103913       4019\n",
            "173407203       4015\n",
            "173502509       4005\n",
            "172502479       4001\n",
            "173503925       4000\n",
            "173103580       3997\n",
            "192608020       3984\n",
            "180501226       3970\n",
            "173404992       3966\n",
            "173101751       3954\n",
            "173101817       3929\n",
            "173511871       3904\n",
            "180501458       3889\n",
            "173103873       3880\n",
            "172200480       3879\n",
            "172302772       3873\n",
            "192606648       3843\n",
            "172500680       3602\n",
            "172300721       3222\n",
            "172201012       3194\n",
            "173104028       3134\n",
            "192607076       3055\n",
            "190815-0009     2865\n",
            "172300092       2795\n",
            "190913-0132     2328\n",
            "172501784       2028\n",
            "Name: deviceid, dtype: int64\n",
            "Number of values = 105\n",
            "Percentage of Missing values = 5.29 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "latitude\n",
            "latitude has More than 106 values\n",
            "Number of values = 440731\n",
            "Percentage of Missing values = 5.30 %\n",
            "Percentage of Zero = 0.20 % \n",
            "\n",
            "longitude\n",
            "longitude has More than 106 values\n",
            "Number of values = 450452\n",
            "Percentage of Missing values = 5.29 %\n",
            "Percentage of Zero = 0.22 % \n",
            "\n",
            "driveridcode1\n",
            "0.0    487608\n",
            "Name: driveridcode1, dtype: int64\n",
            "Number of values = 1\n",
            "Percentage of Missing values = 5.32 %\n",
            "Percentage of Zero = 94.68 % \n",
            "\n",
            "obdvin\n",
            "3HSDZTZR5LN134290    33780\n",
            "1HSERTZRXLH809470     9766\n",
            "3HSDZTZRXLN395656     7016\n",
            "3HSDZTZRXLN085409     6449\n",
            "3HCDZTZR1LL880100     6387\n",
            "3HSDZTZR3LN154022     6264\n",
            "3HCDZTZR9LL880099     6210\n",
            "3HCDZTZR3LL880101     6059\n",
            "3HSDZTZR8KN398425     5917\n",
            "3HSDZTZR3MN096740     5646\n",
            "3HSDZTZR1KN174476     5544\n",
            "3HSDZTZR4LN056083     5256\n",
            "3HCDZTZR5LL880102     5182\n",
            "3HCDZTZR7LL880103     5173\n",
            "3HCDZTZR6ML684705     5172\n",
            "3HSDZTZR5LN164115     5171\n",
            "3HSDZTZR2LN230944     5126\n",
            "3HSDZTZR3KN315175     5014\n",
            "3HSDZTZR1LN871454     4975\n",
            "3HSDZTZR6LN056148     4896\n",
            "3HSDZTZR3LN833708     4858\n",
            "3HCDZTZR1ML681274     4843\n",
            "3HSDZTZR4KN126504     4830\n",
            "3HSDZTZR0LN880114     4771\n",
            "3HCDZTZR0ML681251     4763\n",
            "3HSDZTZR6LN065318     4761\n",
            "3HSDWTZR9LN844849     4683\n",
            "3HSDZTZR3LN164114     4669\n",
            "3HSDZTZR2LN075067     4658\n",
            "3HSDZTZR4KN547798     4657\n",
            "3HSDZTZR0LN437333     4654\n",
            "3HCDZTZRXLL056502     4646\n",
            "3HSDZTZR7LN630689     4634\n",
            "3HSDZTZR5KN140444     4620\n",
            "3HSDWTZR6LN844811     4607\n",
            "3HSDWTZR1LN887792     4606\n",
            "3HSDWTZR7LN064378     4572\n",
            "3HSDWTZR8LN845362     4551\n",
            "3HSDWTZR5LN844783     4538\n",
            "3HSDWTZR9LN810071     4514\n",
            "3HSDWTZR2LN845423     4490\n",
            "3HSDWTZR1LN845090     4473\n",
            "3HSDZTZR8KN537162     4449\n",
            "3HSDZTZR9LN085496     4443\n",
            "3HSDZTZR1LN081863     4436\n",
            "3HSDWTZRXKN315831     4415\n",
            "3HSDZTZR4LN822992     4409\n",
            "3HSDZTZR0LN384388     4408\n",
            "3HSDZTZR3LN852131     4399\n",
            "3HSDWTZR2LN844773     4346\n",
            "3HCDYTZN8LL296583     4341\n",
            "3HSDWTZR3LN845348     4340\n",
            "3HSDZTZR9KN136686     4334\n",
            "3HSDZTZR2LN112232     4322\n",
            "3HSDWTZR6LN854884     4309\n",
            "3HSDWTZR2LN845308     4307\n",
            "3HSDZTZR9LN055320     4301\n",
            "3HSDWTZRXLN844861     4294\n",
            "3HSDZTZR1LN125327     4271\n",
            "3HSDZTZR0KN354211     4247\n",
            "3HSDWTZR3LN211277     4240\n",
            "3HSDWTZR8LN845216     4228\n",
            "3HSDZTZR0LN630677     4216\n",
            "3HSDZTZR2LN817113     4207\n",
            "3HSDWTZR2LN854882     4207\n",
            "3HSDZTZR8LN139435     4187\n",
            "3HSDZTZR0LN062656     4153\n",
            "3HSDWTZR7LN844803     4128\n",
            "3HSDZTZR0KN279915     4127\n",
            "3HSDWTZRXLN845024     4121\n",
            "3HSDWTZR8LN845149     4119\n",
            "3HSDZTZR6LN056070     4096\n",
            "3HSDWTZR9LN845466     4091\n",
            "3HSDWTZR5LN845092     4086\n",
            "3HSDZTZR4KN227364     4084\n",
            "3HSDZTZR0LN291791     4078\n",
            "3HCDZTZR1LL158318     4075\n",
            "3HSDZTZRXLN443043     4064\n",
            "3HSDWTZR0LN845338     4061\n",
            "3HSDWTZR8KN308697     4038\n",
            "3HCDZTZR6LL833595     4038\n",
            "3HSDWTZR9LN845094     4024\n",
            "3HCDZTZR2LL070362     4023\n",
            "3HSDZTZR1LN290908     4012\n",
            "3HSDWTZRXKN310631     4002\n",
            "3HSDZTZR6LN112511     3999\n",
            "3HSDWTZR5LN845352     3997\n",
            "3HSDZTZR3LN054647     3992\n",
            "3HSDZTZR2LN630681     3992\n",
            "3HSDZTZR7KN314305     3977\n",
            "3HSDWTZR7LN810070     3977\n",
            "3HCDZTZR6LL833600     3974\n",
            "3HSDWTZR3KN308686     3974\n",
            "3HSDZTZR6KN540223     3967\n",
            "3HSDZTZR9KN352697     3950\n",
            "3HSDWTZR1LN845199     3901\n",
            "3HSDWTZR9LN845337     3899\n",
            "3HSDWTZR9KN310622     3893\n",
            "3HSDWTZR0LN844822     3864\n",
            "3HSDWTZR5LN845108     3856\n",
            "Name: obdvin, dtype: int64\n",
            "Number of values = 100\n",
            "Percentage of Missing values = 5.30 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "vehiclebattvolts\n",
            "14.10    138277\n",
            "14.00    100141\n",
            "14.20     76251\n",
            "13.90     47612\n",
            "13.80     28677\n",
            "14.30     12883\n",
            "13.70     12407\n",
            "13.85     11797\n",
            "14.05      8718\n",
            "13.95      6701\n",
            "13.75      6289\n",
            "13.60      2924\n",
            "13.65      1341\n",
            "13.50       971\n",
            "14.40       817\n",
            "13.40       578\n",
            "14.15       371\n",
            "13.30       301\n",
            "13.55       234\n",
            "13.20       218\n",
            "13.10       117\n",
            "13.00        65\n",
            "12.90        42\n",
            "13.45        25\n",
            "12.80        25\n",
            "12.70        23\n",
            "12.60        11\n",
            "12.40         6\n",
            "12.50         5\n",
            "12.85         4\n",
            "13.15         4\n",
            "12.20         3\n",
            "14.50         3\n",
            "12.35         2\n",
            "12.65         2\n",
            "12.95         2\n",
            "13.35         2\n",
            "11.60         1\n",
            "12.30         1\n",
            "12.10         1\n",
            "11.80         1\n",
            "14.80         1\n",
            "14.60         1\n",
            "13.25         1\n",
            "Name: vehiclebattvolts, dtype: int64\n",
            "Number of values = 44\n",
            "Percentage of Missing values = 11.09 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "fuel_total\n",
            "fuel_total has More than 106 values\n",
            "Number of values = 153159\n",
            "Percentage of Missing values = 11.81 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "engine_rpm\n",
            "engine_rpm has More than 106 values\n",
            "Number of values = 1529\n",
            "Percentage of Missing values = 0.00 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "coolant_tmp\n",
            "coolant_tmp has More than 106 values\n",
            "Number of values = 108\n",
            "Percentage of Missing values = 11.10 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "fuel_lvl_pct\n",
            "100.0    51886\n",
            "99.0     23172\n",
            "66.0     15157\n",
            "74.0     13080\n",
            "92.0     10477\n",
            "34.0      9252\n",
            "72.0      8726\n",
            "68.0      7735\n",
            "60.0      6998\n",
            "79.0      6900\n",
            "47.0      6549\n",
            "80.0      6346\n",
            "54.0      6247\n",
            "86.0      6093\n",
            "53.0      5931\n",
            "48.0      5555\n",
            "93.0      5292\n",
            "59.0      5208\n",
            "40.0      5042\n",
            "41.0      4108\n",
            "62.0      3994\n",
            "28.0      3912\n",
            "76.0      3841\n",
            "87.0      3786\n",
            "64.0      3728\n",
            "90.0      3686\n",
            "70.0      3582\n",
            "85.0      2922\n",
            "44.0      2147\n",
            "78.0      1929\n",
            "88.0      1851\n",
            "26.0      1666\n",
            "46.0      1569\n",
            "98.0      1558\n",
            "21.0      1555\n",
            "22.0      1113\n",
            "81.0      1093\n",
            "77.0      1024\n",
            "52.0      1009\n",
            "84.0       993\n",
            "97.0       986\n",
            "58.0       875\n",
            "83.0       873\n",
            "82.0       852\n",
            "94.0       845\n",
            "15.0       826\n",
            "96.0       816\n",
            "55.0       792\n",
            "89.0       739\n",
            "95.0       690\n",
            "36.0       664\n",
            "57.0       662\n",
            "39.0       639\n",
            "49.0       622\n",
            "56.0       591\n",
            "50.0       550\n",
            "51.0       533\n",
            "29.0       458\n",
            "42.0       453\n",
            "20.0       448\n",
            "32.0       401\n",
            "14.0       378\n",
            "38.0       359\n",
            "37.0       329\n",
            "31.0       257\n",
            "30.0       207\n",
            "25.0       190\n",
            "23.0       162\n",
            "16.0       162\n",
            "24.0       147\n",
            "8.0        100\n",
            "10.0        70\n",
            "17.0        69\n",
            "7.0         68\n",
            "18.0        53\n",
            "45.0        31\n",
            "61.0        31\n",
            "43.0        24\n",
            "12.0        23\n",
            "11.0        23\n",
            "33.0        17\n",
            "35.0        17\n",
            "4.0         11\n",
            "27.0        11\n",
            "91.0         8\n",
            "2.0          7\n",
            "75.0         7\n",
            "71.0         6\n",
            "69.0         6\n",
            "19.0         5\n",
            "6.0          5\n",
            "63.0         3\n",
            "73.0         2\n",
            "5.0          2\n",
            "13.0         2\n",
            "67.0         1\n",
            "65.0         1\n",
            "Name: fuel_lvl_pct, dtype: int64\n",
            "Number of values = 97\n",
            "Percentage of Missing values = 46.83 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "driving_tme_sec\n",
            "driving_tme_sec has More than 106 values\n",
            "Number of values = 49290\n",
            "Percentage of Missing values = 81.86 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "cruise_tme_sec\n",
            "Series([], Name: cruise_tme_sec, dtype: int64)\n",
            "Number of values = 0\n",
            "Percentage of Missing values = 100.00 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "idle_tme_sec\n",
            "idle_tme_sec has More than 106 values\n",
            "Number of values = 23621\n",
            "Percentage of Missing values = 11.88 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "idle_fuel_total\n",
            "idle_fuel_total has More than 106 values\n",
            "Number of values = 4148\n",
            "Percentage of Missing values = 11.92 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "pto_tme_total\n",
            "pto_tme_total has More than 106 values\n",
            "Number of values = 601\n",
            "Percentage of Missing values = 11.90 %\n",
            "Percentage of Zero = 81.65 % \n",
            "\n",
            "pto_fuel_total\n",
            "pto_fuel_total has More than 106 values\n",
            "Number of values = 461\n",
            "Percentage of Missing values = 11.92 %\n",
            "Percentage of Zero = 82.38 % \n",
            "\n",
            "gear\n",
            "1.0    401218\n",
            "3.0     31642\n",
            "2.0      2523\n",
            "0.0      2311\n",
            "Name: gear, dtype: int64\n",
            "Number of values = 4\n",
            "Percentage of Missing values = 15.01 %\n",
            "Percentage of Zero = 0.45 % \n",
            "\n",
            "fuel_tmp\n",
            " 0.0     364034\n",
            "-40.0       388\n",
            "Name: fuel_tmp, dtype: int64\n",
            "Number of values = 2\n",
            "Percentage of Missing values = 29.24 %\n",
            "Percentage of Zero = 70.69 % \n",
            "\n",
            "oil_tmpl\n",
            "oil_tmpl has More than 106 values\n",
            "Number of values = 121\n",
            "Percentage of Missing values = 11.08 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "throt_pos\n",
            "0.0      244172\n",
            "100.0     92048\n",
            "58.0       2036\n",
            "56.0       2011\n",
            "64.0       1976\n",
            "52.0       1974\n",
            "62.0       1963\n",
            "60.0       1963\n",
            "54.0       1904\n",
            "68.0       1849\n",
            "66.0       1846\n",
            "50.0       1793\n",
            "48.0       1720\n",
            "46.0       1697\n",
            "98.0       1651\n",
            "70.0       1637\n",
            "99.0       1637\n",
            "76.0       1634\n",
            "44.0       1594\n",
            "78.0       1583\n",
            "72.0       1571\n",
            "74.0       1570\n",
            "42.0       1562\n",
            "84.0       1554\n",
            "80.0       1527\n",
            "82.0       1507\n",
            "96.0       1482\n",
            "40.0       1453\n",
            "94.0       1438\n",
            "92.0       1423\n",
            "32.0       1414\n",
            "90.0       1402\n",
            "86.0       1399\n",
            "22.0       1390\n",
            "38.0       1388\n",
            "18.0       1373\n",
            "16.0       1367\n",
            "34.0       1359\n",
            "88.0       1353\n",
            "24.0       1347\n",
            "36.0       1340\n",
            "59.0       1329\n",
            "20.0       1324\n",
            "61.0       1316\n",
            "28.0       1307\n",
            "26.0       1306\n",
            "53.0       1305\n",
            "30.0       1294\n",
            "63.0       1283\n",
            "69.0       1277\n",
            "67.0       1276\n",
            "57.0       1276\n",
            "65.0       1242\n",
            "55.0       1224\n",
            "14.0       1209\n",
            "51.0       1199\n",
            "75.0       1140\n",
            "47.0       1134\n",
            "45.0       1113\n",
            "71.0       1111\n",
            "43.0       1095\n",
            "49.0       1095\n",
            "73.0       1094\n",
            "97.0       1054\n",
            "77.0       1048\n",
            "12.0       1020\n",
            "2.0         999\n",
            "41.0        987\n",
            "39.0        983\n",
            "83.0        981\n",
            "81.0        955\n",
            "79.0        954\n",
            "35.0        949\n",
            "21.0        946\n",
            "95.0        941\n",
            "37.0        932\n",
            "31.0        924\n",
            "87.0        923\n",
            "27.0        917\n",
            "91.0        904\n",
            "85.0        902\n",
            "25.0        900\n",
            "93.0        891\n",
            "33.0        887\n",
            "19.0        886\n",
            "29.0        870\n",
            "17.0        855\n",
            "89.0        850\n",
            "23.0        840\n",
            "15.0        834\n",
            "4.0         823\n",
            "10.0        813\n",
            "8.0         792\n",
            "13.0        774\n",
            "6.0         693\n",
            "11.0        647\n",
            "9.0         594\n",
            "3.0         592\n",
            "5.0         457\n",
            "7.0         456\n",
            "1.0         344\n",
            "Name: throt_pos, dtype: int64\n",
            "Number of values = 101\n",
            "Percentage of Missing values = 11.07 %\n",
            "Percentage of Zero = 47.41 % \n",
            "\n",
            "mpg\n",
            "Series([], Name: mpg, dtype: int64)\n",
            "Number of values = 0\n",
            "Percentage of Missing values = 100.00 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "acel_pos\n",
            "0.0     363205\n",
            "96.0       957\n",
            "46.0       103\n",
            "1.0         99\n",
            "14.0        29\n",
            "97.0        12\n",
            "42.0         9\n",
            "40.0         8\n",
            "38.0         6\n",
            "33.0         4\n",
            "Name: acel_pos, dtype: int64\n",
            "Number of values = 10\n",
            "Percentage of Missing values = 29.24 %\n",
            "Percentage of Zero = 70.53 % \n",
            "\n",
            "engine_pct_load\n",
            "engine_pct_load has More than 106 values\n",
            "Number of values = 128\n",
            "Percentage of Missing values = 7.59 %\n",
            "Percentage of Zero = 9.94 % \n",
            "\n",
            "torque\n",
            "torque has More than 106 values\n",
            "Number of values = 114\n",
            "Percentage of Missing values = 7.70 %\n",
            "Percentage of Zero = 9.98 % \n",
            "\n",
            "oil_lvl\n",
            "0.0    364709\n",
            "Name: oil_lvl, dtype: int64\n",
            "Number of values = 1\n",
            "Percentage of Missing values = 29.18 %\n",
            "Percentage of Zero = 70.82 % \n",
            "\n",
            "oil_psi\n",
            "oil_psi has More than 106 values\n",
            "Number of values = 231\n",
            "Percentage of Missing values = 11.08 %\n",
            "Percentage of Zero = 0.01 % \n",
            "\n",
            "coolant_psi\n",
            "0.0    364519\n",
            "Name: coolant_psi, dtype: int64\n",
            "Number of values = 1\n",
            "Percentage of Missing values = 29.22 %\n",
            "Percentage of Zero = 70.78 % \n",
            "\n",
            "intak_air_tmp\n",
            " 19.0    20963\n",
            " 21.0    20667\n",
            " 22.0    20582\n",
            " 23.0    20340\n",
            " 20.0    20113\n",
            " 18.0    19764\n",
            " 24.0    19235\n",
            " 17.0    18950\n",
            " 16.0    17748\n",
            " 15.0    17101\n",
            " 14.0    16550\n",
            " 13.0    15402\n",
            " 26.0    15008\n",
            " 25.0    14776\n",
            " 27.0    14097\n",
            " 12.0    14034\n",
            " 11.0    13048\n",
            " 28.0    12866\n",
            " 10.0    11951\n",
            " 29.0    10856\n",
            " 30.0    10850\n",
            " 9.0     10643\n",
            " 31.0    10214\n",
            " 8.0      8899\n",
            " 32.0     8462\n",
            " 33.0     8071\n",
            " 7.0      7388\n",
            " 34.0     7015\n",
            " 6.0      5996\n",
            " 35.0     5689\n",
            " 5.0      4749\n",
            " 36.0     4498\n",
            " 37.0     4146\n",
            " 4.0      3782\n",
            " 38.0     3321\n",
            " 3.0      3040\n",
            " 39.0     2414\n",
            " 2.0      2015\n",
            " 0.0      1749\n",
            " 40.0     1736\n",
            " 1.0      1559\n",
            " 41.0     1415\n",
            " 42.0     1093\n",
            " 43.0      825\n",
            " 44.0      592\n",
            "-2.0       566\n",
            " 45.0      508\n",
            "-3.0       396\n",
            " 46.0      354\n",
            "-4.0       307\n",
            "-5.0       261\n",
            " 47.0      249\n",
            "-6.0       216\n",
            "-1.0       171\n",
            " 48.0      151\n",
            "-7.0       142\n",
            " 49.0      115\n",
            "-8.0        89\n",
            "-9.0        77\n",
            " 50.0       65\n",
            "-10.0       53\n",
            " 51.0       52\n",
            " 52.0       44\n",
            "-11.0       34\n",
            " 55.0       27\n",
            " 54.0       24\n",
            "-13.0       21\n",
            "-12.0       19\n",
            " 53.0       18\n",
            "-14.0       14\n",
            " 56.0       13\n",
            "-15.0       12\n",
            " 57.0       12\n",
            "-16.0       10\n",
            " 58.0        7\n",
            " 61.0        5\n",
            " 59.0        3\n",
            "-18.0        3\n",
            "-17.0        3\n",
            "-19.0        3\n",
            "-20.0        2\n",
            " 60.0        2\n",
            " 63.0        2\n",
            " 64.0        1\n",
            "-21.0        1\n",
            " 66.0        1\n",
            " 62.0        1\n",
            " 65.0        1\n",
            "Name: intak_air_tmp, dtype: int64\n",
            "Number of values = 88\n",
            "Percentage of Missing values = 11.02 %\n",
            "Percentage of Zero = 0.34 % \n",
            "\n",
            "manifold_tmp\n",
            " 38.0    19878\n",
            " 37.0    19841\n",
            " 40.0    19825\n",
            " 36.0    19392\n",
            " 39.0    19216\n",
            " 41.0    18775\n",
            " 35.0    18709\n",
            " 34.0    17996\n",
            " 42.0    17873\n",
            " 33.0    16779\n",
            " 43.0    16347\n",
            " 32.0    15502\n",
            " 44.0    15232\n",
            " 45.0    14133\n",
            " 31.0    14063\n",
            " 30.0    12688\n",
            " 46.0    12676\n",
            " 47.0    11666\n",
            " 48.0    10684\n",
            " 29.0    10484\n",
            " 49.0     9557\n",
            " 28.0     9264\n",
            " 50.0     8528\n",
            " 27.0     8356\n",
            " 26.0     7404\n",
            " 51.0     7197\n",
            " 25.0     6522\n",
            " 52.0     6208\n",
            " 24.0     5750\n",
            " 53.0     5485\n",
            " 23.0     5204\n",
            " 54.0     4734\n",
            " 22.0     4560\n",
            " 55.0     4292\n",
            " 21.0     3886\n",
            " 56.0     3578\n",
            " 20.0     3359\n",
            " 57.0     3137\n",
            " 58.0     2728\n",
            " 59.0     2297\n",
            " 19.0     2213\n",
            " 18.0     2053\n",
            " 17.0     1758\n",
            " 60.0     1726\n",
            " 16.0     1696\n",
            " 61.0     1418\n",
            " 15.0     1418\n",
            " 62.0     1187\n",
            " 14.0     1182\n",
            " 63.0     1099\n",
            " 13.0     1003\n",
            " 64.0      875\n",
            " 65.0      828\n",
            " 12.0      790\n",
            " 66.0      763\n",
            " 11.0      714\n",
            " 10.0      580\n",
            " 67.0      452\n",
            " 9.0       440\n",
            " 68.0      407\n",
            " 8.0       348\n",
            " 69.0      342\n",
            " 70.0      253\n",
            " 7.0       247\n",
            " 6.0       225\n",
            " 5.0       171\n",
            " 71.0      147\n",
            " 4.0       136\n",
            " 3.0       109\n",
            " 72.0       79\n",
            " 2.0        66\n",
            " 1.0        51\n",
            " 0.0        48\n",
            " 73.0       48\n",
            " 74.0       25\n",
            " 75.0       23\n",
            " 76.0       14\n",
            "-2.0        11\n",
            "-3.0        10\n",
            " 77.0        9\n",
            " 78.0        7\n",
            "-5.0         6\n",
            "-7.0         5\n",
            "-1.0         4\n",
            "-4.0         3\n",
            " 81.0        3\n",
            "-6.0         3\n",
            " 79.0        2\n",
            " 80.0        2\n",
            "-9.0         2\n",
            " 84.0        2\n",
            " 82.0        1\n",
            "-8.0         1\n",
            "-11.0        1\n",
            "Name: manifold_tmp, dtype: int64\n",
            "Number of values = 94\n",
            "Percentage of Missing values = 10.91 %\n",
            "Percentage of Zero = 0.01 % \n",
            "\n",
            "vehiclespeed\n",
            "vehiclespeed has More than 106 values\n",
            "Number of values = 1332\n",
            "Percentage of Missing values = 5.31 %\n",
            "Percentage of Zero = 7.54 % \n",
            "\n",
            "heading\n",
            "heading has More than 106 values\n",
            "Number of values = 3599\n",
            "Percentage of Missing values = 8.13 %\n",
            "Percentage of Zero = 0.00 % \n",
            "\n",
            "odometerinmeters\n",
            "odometerinmeters has More than 106 values\n",
            "Number of values = 445613\n",
            "Percentage of Missing values = 5.28 %\n",
            "Percentage of Zero = 0.03 % \n",
            "\n",
            "odometer\n",
            "odometer has More than 106 values\n",
            "Number of values = 9400\n",
            "Percentage of Missing values = 97.98 %\n",
            "Percentage of Zero = 0.05 % \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSaXG01eEeye"
      },
      "source": [
        "# data.columns"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lOkfxwHEeyf"
      },
      "source": [
        "# Data frame with important variables only\n",
        "\n",
        "# df = pd.DataFrame(data, columns = ['msg_ts','vin','device_type','deviceid','fuel_total','engine_rpm','coolant_tmp',\n",
        "#                                 'oil_tmpl','throt_pos','torque','oil_psi','manifold_tmp','vehiclespeed','odometerinmeters'])\n",
        "\n",
        "\n",
        "# lets not use device type and device id \n",
        "df = pd.DataFrame(data, columns = ['vin','msg_ts','device_type','fuel_total','engine_rpm','coolant_tmp','oil_tmpl',\n",
        "                       'throt_pos','torque','oil_psi','manifold_tmp','vehiclespeed','odometerinmeters'])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "id": "bpPINDxuEeyg",
        "outputId": "70329764-9448-4391-aed1-b14f8fe6d7f1"
      },
      "source": [
        "df"
      ],
      "execution_count": 14,
      "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>vin</th>\n",
              "      <th>msg_ts</th>\n",
              "      <th>device_type</th>\n",
              "      <th>fuel_total</th>\n",
              "      <th>engine_rpm</th>\n",
              "      <th>coolant_tmp</th>\n",
              "      <th>oil_tmpl</th>\n",
              "      <th>throt_pos</th>\n",
              "      <th>torque</th>\n",
              "      <th>oil_psi</th>\n",
              "      <th>manifold_tmp</th>\n",
              "      <th>vehiclespeed</th>\n",
              "      <th>odometerinmeters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3HSDZTZR9KN352697</td>\n",
              "      <td>2020-04-18 06:50:40</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>106165.0</td>\n",
              "      <td>1144.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1005.0</td>\n",
              "      <td>380663000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3HSDZTZR9LN055320</td>\n",
              "      <td>2020-05-20 03:01:09</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>NaN</td>\n",
              "      <td>584.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>228.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>88781990.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3HSDZTZR0KN354211</td>\n",
              "      <td>2020-04-01 20:02:39</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>113134.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>416436440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3HSDWTZR8LN845362</td>\n",
              "      <td>2020-05-05 06:17:36</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>149410.0</td>\n",
              "      <td>1368.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>424.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1095.0</td>\n",
              "      <td>426460520.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3HSDZTZRXLN085409</td>\n",
              "      <td>2020-04-07 20:14:07</td>\n",
              "      <td>N2</td>\n",
              "      <td>33886.0</td>\n",
              "      <td>1167.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1090.0</td>\n",
              "      <td>107975440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>514989</th>\n",
              "      <td>3HSDZTZR1LN125327</td>\n",
              "      <td>2020-04-29 12:06:30</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>95643.0</td>\n",
              "      <td>1165.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>86.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>288.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1095.0</td>\n",
              "      <td>278538120.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>514990</th>\n",
              "      <td>3HSDZTZR6LN065318</td>\n",
              "      <td>2020-05-11 15:30:59</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>83040.0</td>\n",
              "      <td>601.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>295970500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>514991</th>\n",
              "      <td>3HSDWTZR5LN845352</td>\n",
              "      <td>2020-05-09 11:12:30</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>140542.0</td>\n",
              "      <td>1369.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>328.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>417229440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>514992</th>\n",
              "      <td>3HSDZTZR5LN134290</td>\n",
              "      <td>2020-04-16 04:27:08</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>86642.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>248.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>274880000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>514993</th>\n",
              "      <td>3HSDZTZR4KN547798</td>\n",
              "      <td>2020-04-18 13:15:09</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>92497.0</td>\n",
              "      <td>1146.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>328.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1002.0</td>\n",
              "      <td>333465420.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>514994 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      vin               msg_ts device_type  fuel_total  \\\n",
              "0       3HSDZTZR9KN352697  2020-04-18 06:50:40       Xirgo    106165.0   \n",
              "1       3HSDZTZR9LN055320  2020-05-20 03:01:09       Xirgo         NaN   \n",
              "2       3HSDZTZR0KN354211  2020-04-01 20:02:39       Xirgo    113134.0   \n",
              "3       3HSDWTZR8LN845362  2020-05-05 06:17:36       Xirgo    149410.0   \n",
              "4       3HSDZTZRXLN085409  2020-04-07 20:14:07          N2     33886.0   \n",
              "...                   ...                  ...         ...         ...   \n",
              "514989  3HSDZTZR1LN125327  2020-04-29 12:06:30       Xirgo     95643.0   \n",
              "514990  3HSDZTZR6LN065318  2020-05-11 15:30:59       Xirgo     83040.0   \n",
              "514991  3HSDWTZR5LN845352  2020-05-09 11:12:30       Xirgo    140542.0   \n",
              "514992  3HSDZTZR5LN134290  2020-04-16 04:27:08       Xirgo     86642.0   \n",
              "514993  3HSDZTZR4KN547798  2020-04-18 13:15:09       Xirgo     92497.0   \n",
              "\n",
              "        engine_rpm  coolant_tmp  oil_tmpl  throt_pos  torque  oil_psi  \\\n",
              "0           1144.0         79.0      76.0        0.0    12.0    340.0   \n",
              "1            584.0         61.0      56.0        1.0    11.0    228.0   \n",
              "2           1145.0         81.0       NaN        0.0    42.0    320.0   \n",
              "3           1368.0         81.0      86.0        0.0    19.0    424.0   \n",
              "4           1167.0         82.0      88.0        0.0    67.0    320.0   \n",
              "...            ...          ...       ...        ...     ...      ...   \n",
              "514989      1165.0          NaN      86.0      100.0    59.0    288.0   \n",
              "514990       601.0          NaN      80.0        0.0    10.0    148.0   \n",
              "514991      1369.0         84.0      92.0        0.0    86.0    328.0   \n",
              "514992      1000.0         95.0     101.0        0.0    11.0    248.0   \n",
              "514993      1146.0         80.0      80.0        0.0    38.0    328.0   \n",
              "\n",
              "        manifold_tmp  vehiclespeed  odometerinmeters  \n",
              "0               10.0        1005.0       380663000.0  \n",
              "1                NaN          11.0        88781990.0  \n",
              "2                NaN           NaN       416436440.0  \n",
              "3               30.0        1095.0       426460520.0  \n",
              "4                NaN        1090.0       107975440.0  \n",
              "...              ...           ...               ...  \n",
              "514989          35.0        1095.0       278538120.0  \n",
              "514990          67.0           0.0       295970500.0  \n",
              "514991           NaN           NaN       417229440.0  \n",
              "514992           NaN           0.0       274880000.0  \n",
              "514993          24.0        1002.0       333465420.0  \n",
              "\n",
              "[514994 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "u6l5rdioEeyh",
        "outputId": "6cd9e351-23d3-41a2-f5e8-11bedee6283b"
      },
      "source": [
        "df.describe().T"
      ],
      "execution_count": 15,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fuel_total</th>\n",
              "      <td>454170.0</td>\n",
              "      <td>7.267131e+04</td>\n",
              "      <td>4.471582e+04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40812.0</td>\n",
              "      <td>68874.0</td>\n",
              "      <td>94454.0</td>\n",
              "      <td>257814.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>engine_rpm</th>\n",
              "      <td>514994.0</td>\n",
              "      <td>1.162784e+03</td>\n",
              "      <td>2.508765e+02</td>\n",
              "      <td>458.0</td>\n",
              "      <td>1098.0</td>\n",
              "      <td>1198.0</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>8191.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>coolant_tmp</th>\n",
              "      <td>457846.0</td>\n",
              "      <td>8.055149e+01</td>\n",
              "      <td>7.013731e+00</td>\n",
              "      <td>-40.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>103.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oil_tmpl</th>\n",
              "      <td>457956.0</td>\n",
              "      <td>8.614560e+01</td>\n",
              "      <td>9.936019e+00</td>\n",
              "      <td>-273.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>114.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>throt_pos</th>\n",
              "      <td>457973.0</td>\n",
              "      <td>3.441681e+01</td>\n",
              "      <td>4.220618e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>torque</th>\n",
              "      <td>475351.0</td>\n",
              "      <td>4.264863e+01</td>\n",
              "      <td>3.296667e+01</td>\n",
              "      <td>-125.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>104.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oil_psi</th>\n",
              "      <td>457907.0</td>\n",
              "      <td>3.263757e+02</td>\n",
              "      <td>8.667653e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>292.0</td>\n",
              "      <td>332.0</td>\n",
              "      <td>372.0</td>\n",
              "      <td>920.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manifold_tmp</th>\n",
              "      <td>458811.0</td>\n",
              "      <td>3.829099e+01</td>\n",
              "      <td>1.009876e+01</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>84.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vehiclespeed</th>\n",
              "      <td>487650.0</td>\n",
              "      <td>8.563832e+02</td>\n",
              "      <td>3.706731e+02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>847.0</td>\n",
              "      <td>1023.0</td>\n",
              "      <td>1092.0</td>\n",
              "      <td>1437.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>odometerinmeters</th>\n",
              "      <td>487801.0</td>\n",
              "      <td>2.300427e+08</td>\n",
              "      <td>1.348677e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>129819230.0</td>\n",
              "      <td>226549935.0</td>\n",
              "      <td>301607180.0</td>\n",
              "      <td>611547720.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     count          mean           std    min          25%  \\\n",
              "fuel_total        454170.0  7.267131e+04  4.471582e+04    1.0      40812.0   \n",
              "engine_rpm        514994.0  1.162784e+03  2.508765e+02  458.0       1098.0   \n",
              "coolant_tmp       457846.0  8.055149e+01  7.013731e+00  -40.0         80.0   \n",
              "oil_tmpl          457956.0  8.614560e+01  9.936019e+00 -273.0         85.0   \n",
              "throt_pos         457973.0  3.441681e+01  4.220618e+01    0.0          0.0   \n",
              "torque            475351.0  4.264863e+01  3.296667e+01 -125.0          8.0   \n",
              "oil_psi           457907.0  3.263757e+02  8.667653e+01    0.0        292.0   \n",
              "manifold_tmp      458811.0  3.829099e+01  1.009876e+01  -11.0         32.0   \n",
              "vehiclespeed      487650.0  8.563832e+02  3.706731e+02    0.0        847.0   \n",
              "odometerinmeters  487801.0  2.300427e+08  1.348677e+08    0.0  129819230.0   \n",
              "\n",
              "                          50%          75%          max  \n",
              "fuel_total            68874.0      94454.0     257814.0  \n",
              "engine_rpm             1198.0       1326.0       8191.0  \n",
              "coolant_tmp              81.0         83.0        103.0  \n",
              "oil_tmpl                 88.0         90.0        114.0  \n",
              "throt_pos                 0.0         81.0        100.0  \n",
              "torque                   42.0         67.0        104.0  \n",
              "oil_psi                 332.0        372.0        920.0  \n",
              "manifold_tmp             38.0         45.0         84.0  \n",
              "vehiclespeed           1023.0       1092.0       1437.0  \n",
              "odometerinmeters  226549935.0  301607180.0  611547720.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTjrNuPXEeyi"
      },
      "source": [
        "df['msg_ts']=pd.to_datetime(df['msg_ts'])  # Converting msg_ts to date time"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2mug9aVBEeyj"
      },
      "source": [
        "df = df.sort_values(['vin','msg_ts'])"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
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      "source": [
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      "execution_count": 18,
      "outputs": [
        {
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>150524</th>\n",
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              "      <td>2020-05-31 23:11:52</td>\n",
              "      <td>Xirgo</td>\n",
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              "      <td>296.0</td>\n",
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              "      <td>38.0</td>\n",
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              "<p>514994 rows × 13 columns</p>\n",
              "</div>"
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            "text/plain": [
              "                      vin              msg_ts device_type  fuel_total  \\\n",
              "159159  1HSERTZRXLH809470 2020-04-01 00:34:45       Xirgo         NaN   \n",
              "305957  1HSERTZRXLH809470 2020-04-01 00:45:44       Xirgo      8821.0   \n",
              "85501   1HSERTZRXLH809470 2020-04-01 00:46:17       Xirgo      8821.0   \n",
              "166563  1HSERTZRXLH809470 2020-04-01 00:48:17       Xirgo      8822.0   \n",
              "114931  1HSERTZRXLH809470 2020-04-01 00:48:21       Xirgo      8822.0   \n",
              "...                   ...                 ...         ...         ...   \n",
              "144123  3HSDZTZRXLN443043 2020-05-30 23:07:32       Xirgo         NaN   \n",
              "62736   3HSDZTZRXLN443043 2020-05-30 23:12:32       Xirgo     73207.0   \n",
              "324654  3HSDZTZRXLN443043 2020-05-30 23:22:55       Xirgo         NaN   \n",
              "414388  3HSDZTZRXLN443043 2020-05-31 22:58:36       Xirgo     73208.0   \n",
              "150524  3HSDZTZRXLN443043 2020-05-31 23:11:52       Xirgo         NaN   \n",
              "\n",
              "        engine_rpm  coolant_tmp  oil_tmpl  throt_pos  torque  oil_psi  \\\n",
              "159159       649.0         72.0      75.0        NaN     NaN    184.0   \n",
              "305957       649.0         71.0      73.0        0.0     5.0    188.0   \n",
              "85501        649.0          NaN      73.0        0.0     NaN      NaN   \n",
              "166563      1084.0         74.0      74.0        8.0     NaN    400.0   \n",
              "114931       751.0         74.0      74.0       10.0     6.0      NaN   \n",
              "...            ...          ...       ...        ...     ...      ...   \n",
              "144123      1078.0         81.0      93.0        NaN     NaN    300.0   \n",
              "62736       1758.0         88.0      97.0        0.0     0.0    532.0   \n",
              "324654      1051.0         86.0      93.0       38.0    12.0    380.0   \n",
              "414388       588.0         21.0      21.0        0.0     NaN      NaN   \n",
              "150524       601.0         48.0      41.0        NaN     5.0    296.0   \n",
              "\n",
              "        manifold_tmp  vehiclespeed  odometerinmeters  \n",
              "159159          32.0           9.0        21344555.0  \n",
              "305957           NaN           0.0        21344615.0  \n",
              "85501            NaN           0.0        21344615.0  \n",
              "166563          31.0         249.0               NaN  \n",
              "114931          31.0         198.0        21344865.0  \n",
              "...              ...           ...               ...  \n",
              "144123          44.0         952.0       250518380.0  \n",
              "62736           57.0         309.0               NaN  \n",
              "324654          59.0         102.0       250522360.0  \n",
              "414388          20.0           0.0       250522720.0  \n",
              "150524          29.0          38.0       250523260.0  \n",
              "\n",
              "[514994 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJYeEkVmEeyk"
      },
      "source": [
        "df = df.reset_index()\n",
        "df = df.drop('index', axis =1)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RUJi9aCqEeyl",
        "outputId": "a8098e35-23ab-43e1-8214-71e3d70edce8"
      },
      "source": [
        "df.isnull().sum()/len(df) * 100 "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "vin                  0.000000\n",
              "msg_ts               0.000000\n",
              "device_type          0.000000\n",
              "fuel_total          11.810623\n",
              "engine_rpm           0.000000\n",
              "coolant_tmp         11.096828\n",
              "oil_tmpl            11.075469\n",
              "throt_pos           11.072168\n",
              "torque               7.697760\n",
              "oil_psi             11.084984\n",
              "manifold_tmp        10.909447\n",
              "vehiclespeed         5.309576\n",
              "odometerinmeters     5.280256\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WI20VMgrEeym"
      },
      "source": [
        "df = df.dropna(axis = 0, how = 'any')"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "id": "drKnuwLOEeym",
        "outputId": "3cf97694-f9f1-4152-ad7b-4161519c5beb"
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      "source": [
        "df"
      ],
      "execution_count": 22,
      "outputs": [
        {
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              "      <td>2020-04-01 02:12:08</td>\n",
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              "    <tr>\n",
              "      <th>514985</th>\n",
              "      <td>3HSDZTZRXLN443043</td>\n",
              "      <td>2020-05-30 22:37:32</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>73192.0</td>\n",
              "      <td>1078.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>288.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>777.0</td>\n",
              "      <td>250471320.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>514987</th>\n",
              "      <td>3HSDZTZRXLN443043</td>\n",
              "      <td>2020-05-30 22:47:32</td>\n",
              "      <td>Xirgo</td>\n",
              "      <td>73198.0</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>352.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>851.0</td>\n",
              "      <td>250487140.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>327222 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      vin              msg_ts device_type  fuel_total  \\\n",
              "8       1HSERTZRXLH809470 2020-04-01 01:11:49       Xirgo      8822.0   \n",
              "16      1HSERTZRXLH809470 2020-04-01 01:21:23       Xirgo      8823.0   \n",
              "24      1HSERTZRXLH809470 2020-04-01 01:36:23       Xirgo      8830.0   \n",
              "29      1HSERTZRXLH809470 2020-04-01 02:11:55       Xirgo      8831.0   \n",
              "30      1HSERTZRXLH809470 2020-04-01 02:12:08       Xirgo      8831.0   \n",
              "...                   ...                 ...         ...         ...   \n",
              "514973  3HSDZTZRXLN443043 2020-05-30 20:52:10       Xirgo     73155.0   \n",
              "514974  3HSDZTZRXLN443043 2020-05-30 21:12:52       Xirgo     73156.0   \n",
              "514984  3HSDZTZRXLN443043 2020-05-30 22:32:32       Xirgo     73190.0   \n",
              "514985  3HSDZTZRXLN443043 2020-05-30 22:37:32       Xirgo     73192.0   \n",
              "514987  3HSDZTZRXLN443043 2020-05-30 22:47:32       Xirgo     73198.0   \n",
              "\n",
              "        engine_rpm  coolant_tmp  oil_tmpl  throt_pos  torque  oil_psi  \\\n",
              "8            650.0         73.0      75.0        0.0     5.0    184.0   \n",
              "16          1107.0         81.0      82.0       30.0    24.0    368.0   \n",
              "24          1202.0         81.0      92.0        0.0     0.0    372.0   \n",
              "29          1155.0         81.0      81.0       96.0    98.0    468.0   \n",
              "30           646.0         81.0      82.0        0.0     5.0    160.0   \n",
              "...            ...          ...       ...        ...     ...      ...   \n",
              "514973       599.0         78.0      82.0        0.0     9.0    148.0   \n",
              "514974       600.0         79.0      86.0        0.0     6.0    144.0   \n",
              "514984      1278.0         79.0      89.0       69.0    62.0    380.0   \n",
              "514985      1078.0         81.0      93.0       92.0    90.0    288.0   \n",
              "514987      1261.0         83.0      93.0       83.0    91.0    352.0   \n",
              "\n",
              "        manifold_tmp  vehiclespeed  odometerinmeters  \n",
              "8               31.0           0.0        21345125.0  \n",
              "16              28.0         649.0        21347170.0  \n",
              "24              32.0         383.0        21367125.0  \n",
              "29              40.0         277.0        21369085.0  \n",
              "30              35.0         135.0        21369195.0  \n",
              "...              ...           ...               ...  \n",
              "514973          61.0         101.0       250353980.0  \n",
              "514974          52.0           0.0       250354380.0  \n",
              "514984          35.0         863.0       250466060.0  \n",
              "514985          41.0         777.0       250471320.0  \n",
              "514987          47.0         851.0       250487140.0  \n",
              "\n",
              "[327222 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9JQPYvNFEeyn"
      },
      "source": [
        "df_xirgo = df[df[\"device_type\"] == 'Xirgo']"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qtLg5shEeyo"
      },
      "source": [
        "df_xirgo = df_xirgo[df_xirgo[\"engine_rpm\"] >= 1000]"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GOzCvo3sEeyp",
        "outputId": "19429907-0b27-4e16-bd19-e0adc6035587"
      },
      "source": [
        "df_xirgo['device_type'].value_counts()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Xirgo    215269\n",
              "Name: device_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ET1qu9LuEeyp"
      },
      "source": [
        "df_xirgo = df_xirgo.drop('device_type', axis =1)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJwghDNaEeyq"
      },
      "source": [
        "df_xirgo = df_xirgo.reset_index()\n",
        "df_xirgo = df_xirgo.drop('index', axis =1)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "id": "y4fkoH1pEeyr",
        "outputId": "c34dccd3-57b3-429e-a225-f87cacfb2d77"
      },
      "source": [
        "df_xirgo"
      ],
      "execution_count": 28,
      "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>vin</th>\n",
              "      <th>msg_ts</th>\n",
              "      <th>fuel_total</th>\n",
              "      <th>engine_rpm</th>\n",
              "      <th>coolant_tmp</th>\n",
              "      <th>oil_tmpl</th>\n",
              "      <th>throt_pos</th>\n",
              "      <th>torque</th>\n",
              "      <th>oil_psi</th>\n",
              "      <th>manifold_tmp</th>\n",
              "      <th>vehiclespeed</th>\n",
              "      <th>odometerinmeters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1HSERTZRXLH809470</td>\n",
              "      <td>2020-04-01 01:21:23</td>\n",
              "      <td>8823.0</td>\n",
              "      <td>1107.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>368.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>649.0</td>\n",
              "      <td>21347170.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1HSERTZRXLH809470</td>\n",
              "      <td>2020-04-01 01:36:23</td>\n",
              "      <td>8830.0</td>\n",
              "      <td>1202.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>372.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>383.0</td>\n",
              "      <td>21367125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1HSERTZRXLH809470</td>\n",
              "      <td>2020-04-01 02:11:55</td>\n",
              "      <td>8831.0</td>\n",
              "      <td>1155.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>468.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>21369085.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1HSERTZRXLH809470</td>\n",
              "      <td>2020-04-01 02:16:09</td>\n",
              "      <td>8833.0</td>\n",
              "      <td>1487.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>436.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>21374645.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1HSERTZRXLH809470</td>\n",
              "      <td>2020-04-01 02:27:17</td>\n",
              "      <td>8837.0</td>\n",
              "      <td>1238.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>420.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>467.0</td>\n",
              "      <td>21388380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215264</th>\n",
              "      <td>3HSDZTZRXLN443043</td>\n",
              "      <td>2020-05-30 20:16:04</td>\n",
              "      <td>73150.0</td>\n",
              "      <td>1152.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>356.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>250336560.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215265</th>\n",
              "      <td>3HSDZTZRXLN443043</td>\n",
              "      <td>2020-05-30 20:34:29</td>\n",
              "      <td>73155.0</td>\n",
              "      <td>1085.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>328.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>250353460.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215266</th>\n",
              "      <td>3HSDZTZRXLN443043</td>\n",
              "      <td>2020-05-30 22:32:32</td>\n",
              "      <td>73190.0</td>\n",
              "      <td>1278.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>380.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>863.0</td>\n",
              "      <td>250466060.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215267</th>\n",
              "      <td>3HSDZTZRXLN443043</td>\n",
              "      <td>2020-05-30 22:37:32</td>\n",
              "      <td>73192.0</td>\n",
              "      <td>1078.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>288.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>777.0</td>\n",
              "      <td>250471320.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215268</th>\n",
              "      <td>3HSDZTZRXLN443043</td>\n",
              "      <td>2020-05-30 22:47:32</td>\n",
              "      <td>73198.0</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>352.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>851.0</td>\n",
              "      <td>250487140.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>215269 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      vin              msg_ts  fuel_total  engine_rpm  \\\n",
              "0       1HSERTZRXLH809470 2020-04-01 01:21:23      8823.0      1107.0   \n",
              "1       1HSERTZRXLH809470 2020-04-01 01:36:23      8830.0      1202.0   \n",
              "2       1HSERTZRXLH809470 2020-04-01 02:11:55      8831.0      1155.0   \n",
              "3       1HSERTZRXLH809470 2020-04-01 02:16:09      8833.0      1487.0   \n",
              "4       1HSERTZRXLH809470 2020-04-01 02:27:17      8837.0      1238.0   \n",
              "...                   ...                 ...         ...         ...   \n",
              "215264  3HSDZTZRXLN443043 2020-05-30 20:16:04     73150.0      1152.0   \n",
              "215265  3HSDZTZRXLN443043 2020-05-30 20:34:29     73155.0      1085.0   \n",
              "215266  3HSDZTZRXLN443043 2020-05-30 22:32:32     73190.0      1278.0   \n",
              "215267  3HSDZTZRXLN443043 2020-05-30 22:37:32     73192.0      1078.0   \n",
              "215268  3HSDZTZRXLN443043 2020-05-30 22:47:32     73198.0      1261.0   \n",
              "\n",
              "        coolant_tmp  oil_tmpl  throt_pos  torque  oil_psi  manifold_tmp  \\\n",
              "0              81.0      82.0       30.0    24.0    368.0          28.0   \n",
              "1              81.0      92.0        0.0     0.0    372.0          32.0   \n",
              "2              81.0      81.0       96.0    98.0    468.0          40.0   \n",
              "3              83.0      89.0       62.0    46.0    436.0          42.0   \n",
              "4              81.0      88.0       64.0    53.0    420.0          37.0   \n",
              "...             ...       ...        ...     ...      ...           ...   \n",
              "215264         81.0      89.0       15.0     6.0    356.0          42.0   \n",
              "215265         82.0      91.0       18.0    12.0    328.0          51.0   \n",
              "215266         79.0      89.0       69.0    62.0    380.0          35.0   \n",
              "215267         81.0      93.0       92.0    90.0    288.0          41.0   \n",
              "215268         83.0      93.0       83.0    91.0    352.0          47.0   \n",
              "\n",
              "        vehiclespeed  odometerinmeters  \n",
              "0              649.0        21347170.0  \n",
              "1              383.0        21367125.0  \n",
              "2              277.0        21369085.0  \n",
              "3                9.0        21374645.0  \n",
              "4              467.0        21388380.0  \n",
              "...              ...               ...  \n",
              "215264        1017.0       250336560.0  \n",
              "215265         144.0       250353460.0  \n",
              "215266         863.0       250466060.0  \n",
              "215267         777.0       250471320.0  \n",
              "215268         851.0       250487140.0  \n",
              "\n",
              "[215269 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajSp5lWmEeys",
        "outputId": "27c0ef38-33be-4972-9078-482fb533f640"
      },
      "source": [
        "df_xirgo.isnull().sum()/len(df) * 100 "
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "vin                 0.0\n",
              "msg_ts              0.0\n",
              "fuel_total          0.0\n",
              "engine_rpm          0.0\n",
              "coolant_tmp         0.0\n",
              "oil_tmpl            0.0\n",
              "throt_pos           0.0\n",
              "torque              0.0\n",
              "oil_psi             0.0\n",
              "manifold_tmp        0.0\n",
              "vehiclespeed        0.0\n",
              "odometerinmeters    0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "0xzT46VUEeys",
        "scrolled": true,
        "outputId": "805ef10e-163b-4571-9e0b-7405e59db566"
      },
      "source": [
        "# Distribution of important features - plot\n",
        "\n",
        "for i in ['fuel_total']:\n",
        "    \n",
        "    g = sns.FacetGrid(df_xirgo[['vin',i]], hue=\"vin\", height = 6, aspect = 1.5)\n",
        "    g = (g.map(sns.distplot, i , hist=False, rug=True))\n",
        "    plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a29raXuCeSpIk/fSYTADcASwds72kXjZhnYgoADOA7qMdGxEN1MLfF1JK/zymzu6IWFivsxDYM9nBSJIk6dgmEwDvA1ZGxIqIaKS2qGPNuDprgPfUX78duKs+e7cGuLq+SngFsBJYW78/8EbgsZTSXx2lrfcAXz3eQUmSJOnICseqkFIqR8QHgDuAPHBTSml9RHwE6EgpraEW5m6JiE6gh1pIpF7vNmADtZW/708pVSLiEuBdwCMRsa5+qv+ZUvo68GfAbRFxDbAV+KUTOWBJkqSsi9pE3amtvb09dXR0nOxuSJIkvdjERIV+EogkSVLGGAAlSZIyxgAoSZKUMQZASZKkjDEASpIkZYwBUJIkKWMMgJIkSRljAJQkScoYA6AkSVLGGAAlSZIyxgAoSZKUMQZASZKkjDEASpIkZYwBUJIkKWMMgJIkSRljAJQkScoYA6AkSVLGGAAlSZIyxgAoSZKUMQZASZKkjDEASpIkZYwBUJIkKWMMgJIkSRljAJQkScoYA6AkSVLGGAAlSZIyxgAoSZKUMQZASZKkjDEASpIkZYwBUJIkKWMMgJIkSRljAJQkScoYA6AkSVLGGAAlSZIyxgAoSZKUMQZASZKkjJlUAIyIKyNiY0R0RsR1E+xviogv1fffGxHLx+z7cL18Y0RcMab8pojYExGPjmvrDyNiR0Ssq3+96bkPT5IkSeMdMwBGRB74OPBGYBXwzohYNa7aNcC+lNIZwA3A9fVjVwFXA6uBK4FP1NsD+Hy9bCI3pJQurH99/fiGJEmSpKOZzAzgxUBnSmlTSqkI3ApcNa7OVcDN9de3A5dHRNTLb00pjaaUNgOd9fZIKX0f6DkBY5AkSdJxmEwAXAxsG7O9vV42YZ2UUhnoA+ZM8tiJfCAiHq5fJp41ifqSJEmapBfjIpBPAi8BLgR2An85UaWIuDYiOiKio6ur6yfZP0mSpFPaZALgDmDpmO0l9bIJ60REAZgBdE/y2MOklHanlCoppSrw99QvGU9Q7zMppfaUUntbW9skhiFJkiSYXAC8D1gZESsiopHaoo414+qsAd5Tf/124K6UUqqXX11fJbwCWAmsPdrJImLhmM1fAB49Ul1JkiQdv8KxKqSUyhHxAeAOIA/clFJaHxEfATpSSmuAG4FbIqKT2sKOq+vHro+I24ANQBl4f0qpAhARXwQuBeZGxHbgD1JKNwJ/HhEXAgnYAvzaiRywJElS1kVtou7U1t7enjo6Ok52NyRJkl5sYqLCF+MiEEmSJL2ADICSJEkZYwCUJEnKGAOgJElSxhgAJUmSMsYAKEmSlDEGQEmSpIwxAEqSJGWMAVCSJCljDICSJEkZYwCUJEnKGAOgJElSxhgAJUmSMsYAKEmSlDEGQEmSpIwxAEqSJGWMAVCSJCljDICSJEkZYwCUJEnKGAOgJElSxhgAJUmSMsYAKEmSlDEGQEmSpIwxAEqSJGWMAVCSJCljDICSJEkZYwCUJEnKGAOgJElSxhgAJUmSMsYAKEmSlDEGQEmSpIwxAEqSJGWMAVCSJCljDICSJEkZYwCUJEnKGAOgJElSxhgAJUmSMmZSATAiroyIjRHRGRHXTbC/KSK+VN9/b0QsH7Pvw/XyjRFxxZjymyJiT0Q8Oq6t2RFxZ0Q8Wf8+67kPT5IkSeMdMwBGRB74OPBGYBXwzohYNa7aNcC+lNIZwA3A9fVjVwFXA6uBK4FP1NsD+Hy9bLzrgO+klFYC36lvS5Ik6QSZzAzgxUBnSmlTSqkI3ApcNa7OVcDN9de3A5dHRNTLb00pjaaUNgOd9fZIKX0f6JngfGPbuhl463GMR5IkSccwmQC4GNg2Znt7vWzCOimlMtAHzJnksePNTyntrL/eBcyfqFJEXBsRHRHR0dXVNYlhSJIkCV7ki0BSSglIR9j3mZRSe0qpva2t7SfcM0mSpFPXZALgDmDpmO0l9bIJ60REAZgBdE/y2PF2R8TCelsLgT2T6KMkSZImaTIB8D5gZUSsiIhGaos61oyrswZ4T/3124G76rN3a4Cr66uEVwArgbXHON/Ytt4DfHUSfZQkSdIkHTMA1u/p+wBwB/AYcFtKaX1EfCQi3lKvdiMwJyI6gd+ivnI3pbQeuA3YAHwTeH9KqQIQEV8E7gHOiojtEXFNva0/A14fEU8Cr6tvS5Ik6QSJ2kTdqa29vT11dHSc7G5IkiS92MREhS/qRSCSJEk68QyAkiRJGWMAlCRJyhgDoCRJUsYYACVJkjLGAChJkpQxBkBJkqSMMQBKkiRljAFQkiQpYwyAkiRJGWMAlCRJyhgDoCRJUsYYACVJkjLGAChJkpQxBkBJkqSMMQBKkiRljAFQkiQpYwyAkiRJGWMAlCRJyhgDoCRJUsYYACVJkjLGAChJkpQxBkAdt+r2XaR9/Se7G5Ik6TkyAOq4Ff/qHxj9s8+e7G5IkqTnyACo56ZUPtk9kCRJz5EBUJIkKWMMgDouKaWT3QVJkvQ8GQB1fKoGQEmSTnUGQB2fVD3ZPZAkSc+TAVDHxxlASZJOeQZAHZ+qM4CSJJ3qDIA6Ps4ASpJ0yjMA6vg4AyhJ0inPAKjj42NgJEk65RkAdXycAZQk6ZRnANTx8R5ASZJOeQZAHZfkDKAkSae8SQXAiLgyIjZGRGdEXDfB/qaI+FJ9/70RsXzMvg/XyzdGxBXHajMiPh8RmyNiXf3rwuc3RJ1QzgBKknTKKxyrQkTkgY8Drwe2A/dFxJqU0oYx1a4B9qWUzoiIq4HrgXdExCrgamA1sAj4dkScWT/maG3+j5TS7SdgfDrRnAGUJOmUN5kZwIuBzpTSppRSEbgVuGpcnauAm+uvbwcuj4iol9+aUhpNKW0GOuvtTaZNvRi5CliSpFPeZALgYmDbmO3t9bIJ66SUykAfMOcoxx6rzY9GxMMRcUNENE3UqYi4NiI6IqKjq6trEsPQCeEMoCRJp7wX4yKQDwNnAy8HZgO/O1GllNJnUkrtKaX2tra2n2T/ss17ACVJOuVNJgDuAJaO2V5SL5uwTkQUgBlA91GOPWKbKaWdqWYU+By1y8V6sRgzA5i8HCxJ0ilpMgHwPmBlRKyIiEZqizrWjKuzBnhP/fXbgbtSLR2sAa6urxJeAawE1h6tzYhYWP8ewFuBR5/PAHWCjZ0BdDZQkqRT0jFXAaeUyhHxAeAOIA/clFJaHxEfATpSSmuAG4FbIqIT6KEW6KjXuw3YAJSB96eUKgATtVk/5Rciog0IYB3wvhM3XD1vacw9gJUK5F+MdxFIkqSjiZ+Gy3jt7e2po6PjZHcjE6pPbaP48S8C0PTH/52Y2nKSeyRJko4iJip0+kbHZewngYz+/t+exJ5IkqTnygCo4zPuvr/kfYCSJJ1yDIA6PuOfAzi0/+T0Q5IkPWcGQB2fcfeMpt7+k9QRSZL0XBkAdXzGzQBWHnz8JHVEkiQ9VwZAHZ/x9/yNFk9OPyRJ0nNmANTxGTcDmIqlk9QRSZL0XBkAdXzGBcDq/RtIvQMnqTOSJOm5MADq+Ezw2JfyD+4/CR2RJEnPlQFQx2eCT46JluaT0BFJkvRcGQB1XNL45wACNB7zI6UlSdKLiAFQx2dMAIzF82ovSuWT1BlJkvRcGAB1fMbcA5g/70wAUqlMqlQofeUbVHd1nayeSZKkSTIA6vjUA2DDf307+de9EhoKUCyRntlN5QdrKf/z10l9A1Q3bT3JHZUkSUdiANTxqV8Czi1bRORy0NgApTJpb09t955uSrf9K8W/+zxpZPSYzVUe6SP1+yxBSZJ+kgyAOj6pfg9gLmrfGwq1ADg8Utsulag+9mStave+ozc1WGb0bzsZ/fyWF6izkiRpIgZAHZ9K/R7AXO2tEw0FUqkEB2b7ioc+Gi71H/0B0am3Vrf6aP+J76ckSToiA6COz8EZwPpbp5qoPvg4aXCotl05tEo49Q8evam+Q5d+U7kWLCtPDDB60+aD25Ik6cQzAOr4VA6/BJy6ewGobn3m2XUHJh8Aq5trAbL0lR1UftxDddv+E9BZSZI0EQOgjks68EkgEYfvKD17IUcaGKK6ZRtpgo+Pg8MD4Oj/v5FUSaT9ldq+nuKEx0iSpOfPAJhB1a4+qruOvkDjyAdXIRfE+ABYrjyrauW+dRQ/dhOVf187YVOprwRNOWJJS217+6FZP1cGS5L0wjEAZkyqVCn+9b9S/NPbSf3P4TJrNUEcetvE0gW1F6USMW/OoXqF/MGFIZWHHqPyxKZn96WvTMxsoOl9p9ea3jZ88DmDqd9PF5Ek6YViAMyY9Ew3DA4DUHn06eNvoFqF/KG3TcO731Jrt1QiZs86WB6LFhw656atlD51C2nw8MCZ+krEjAZibhM05qjuGCYN1oKfM4CSJL1wDIAZU93efej1hucYAMdc/s3NmQnNjbXPA25pPlS+aP6zDk17x5x7+36qnYPE7EYiF+QWt1DdMgRD9UvJAy/MDGC1e5Di579HdUfP4X3r20/5hxtJY1YxS5L008oAmDGpZwByQf7iM6l27iRVjzPwVNOhR8Ac0NgI5TLR1EgsmEfMmQXTW5997n19B1+P/HXtYdExvaH2fUkL1aeGDtUdeGFmAMt3PEzlgS2U/vWBw8qLt95D6bYfU7m38wU5ryRJLyYGwIxJ3YPEzKnkzl4Mw0XStu5jHzRWfRHIWNHUAJUKNDfR+N/eReNvXENMnVLbt2QhsXRR7dxjHwtTv8ev8MrZAOQWtxza15QjvQAzgKlYpvLgltowOncfnO1LKVHd3AVA5eHnMCsqSdIpxgCYMalngJg9jdzKWiirPrHj+BqYYAYwNRSgWiWam4hprcS0qeSWLwWgcPklNH7wvZDLkQZqM3ypXIWAwpsXkltSC4q5ZVMOtpc7bcqEAbBUTnz9/iKbdz97xfGxlL6+jtE/+RcYLZH/mTOhWKZ89zeorP8eae8A7B+FfO5gMEylEcrf+ASVx/79uM91NPtGe+ge6TqhbWpi1a0jVDaPHLVOqiaqzwwc8VFFkvTTygCYMalnkJg9jZg+hVg4i+oTz5D691P6+v307+jnG4+U2L5v4svCqZpIlcqzZgApFGrfm5sOFuWWLqLpo79L/oJVRC6gderBB0OnvhIkyM1qPFR/2dQxr6fAcIVUOrwfP9hQ5o4HS3zmWyOMFBOPb69w35PlQ88mPIJqVz/lbz5E6hkid/o8Cm84D4DKN75C+R+uo/zD79eGcfnqWjD83lOUv/VlKt+9hfIt15GGeo/a/rGUqiV+sPM7/N7aD/JLd17B1Xe+kS9v+sKR62+oMHTjKJXdp8b9iIOlEt97Zgel472d4AVUeWiIkT/exuj/2Ub5h31UOnuoPLTrWUGvdMs6Rj/6PYo33n9c7f94106u/NpX+Oj9a5/1/ksp8fnHN/A79/yA7YNH/zhESTpZCie7A/rJSeUK9A8Rs2v35+XOXEzlhxsofvoO0va97O54hi8tuYwv3w+XrCzQNVBlalPwzosbmRklin+zhjS0HxoP/39DtZBnV9Mclo1ZBAIQY7ZjeuuhGcDe2v19MbPh0P5CkP+ZOVS37Se3oHZcGiwTY0Lius1lgtpk3cf+bYQd3bXAUSw38upzDrU1XrVzNwBNv/dWom06BNBYIaVF5BYVqfzoh1BYTf6Ssyh/6xHKX34EYjoxeyExupPK/d+g8Jp3Ttj2SLlMYz5Prr4wJqXE9qF9DJZHOHvGQvqKvfz2PdeyZeAp5jS38fbTf5mtA5v41PobOHvmuayefUHtuGoickEqJoY+PUIagNJjFVqvbSKmBbl5kPb2Em0zifH3YE5C6ev9lH80RNOvzWF4QfBIdw8XzJ1DS6HAYKnEms1bOG/ObM6bc+hRPtWUGCyVmN5Y+xlUUuKTjz7CMwNDXLP4AlpKjVTLcMO2+/hh/w7edvpL+J2LXnrMvpT3J6ojicbZuYN/ZyPbqzTNz5FrDFJKbH2gzPD9ZVqnBkve2VT7T8QkVfeWGL1xF7GwkZiSo/iFzURuOwD5ly+m4d0XErmgsmEPlbU7YHoT1XW7GP3sZhqvWU73rip7n6my7OwCLVOffd5KqvJXDz3AvtFR1mzZxGsWLuZnFy0+uP8rm5/ik+sfBmDP8DCfe+3rn/3cTEk6yQyAGT6xTJwAACAASURBVJJ6hyABs6cBkH/FmVS+9yhp+17SzFaWdu/iP1xS5JlqC9/dWGbetODJPVUe2znMdY0bmLOrPhM28/AA8g9NL+XHK5fy7oEeLjtwrv1lKATRmK/da5drpdpXD4D76gFw1uGhrfHdywCoPFRfLDJQhnoALJYT2/ZWufyCBrr7qzy4ucKythzlKnz7oRKvOqtArh4Stu2tcF9nmcvOa2Dm1BzVnfugsUC0TT8UJAp7obyEwn96L5WP3g6UKX76YUgFaC3DQDMx871Ew+1U190BYwLgxn0D/O8fP85IucyekUFaC020NjRQjr30l/YxXN1Pyu3hNQvPZGHTU2wb3Mr/ftn1XLLgteRzBfaXh7jm7l/kY49czydecwvl71bZ/09FWn6xEQqQBqDl6kaGv1Jk4M9GgArFtjXMKHYTS+fRcM1boNREKgTVrYnC+XkigpFylf3lKrObD/+1rnaVKf3f2s9u/xd7+K8XPcqm/n7OmDGDj//sJfz+2vtYu2cPDbkc/3j55SyfPo2UEn98Zwd379vOb7z8fK5adDrf/tZOHtjby6t7z2FdOQG15zy+PM7lmflF/nXTZn5l+irmntZEruHwwNO/tULf5grTlubY86Ui5cHE8g8007O3Ctur9N1dZsaCYOr8HD37E527qywZgH5g4IIy0889csA/YLRc5Yldw5z+mb1EFZrevxAag5HffwJyjeQvXULlrk3E3CkU3nAGpS89SsybSuFNF1H83I+odGxj78o2vv1AUK3CY/eXeN0vNTNt3Pt9fU8PWwcH+P2XXcxNj6/ns489yiULFxER9BeLfGr9w7x0bhtvPG05H33gPu7bs5uL5y84Qq9fPKop0TU8wozGRpoL+ZPdHUkvsDjW5bNTQXt7e+ro6DjZ3XjRq2zcQekTX2ein/j9M5bS3reNfY0jzCx3M8wCWip5djZN5zPLL+HXt3yfFrpoLTYz2rSNxuGVUF5GMZfjQxddQDmXZ+H+Xv7XhnuJ1AzU7+mLAYhS/ckxVVLKQaUVqnOgsK3+UOlx9/tVm6CyAPK7IWr3+21pncKnz3wlb9r5fV62bwubppzOyv5h/uD81xEE7970AOf0d9Hd0MRfrP45atN8tZH+ycNf5vA4Mkqu8ACp/CpSqtUkNQNlUpQhKuxoGmLR0Jk80PYoL9//Laojb4PURDXgfRctpaspGChUSRRIUSUYhjTl4EOyF4wMs2PaWgrVFpYOtfJn6xYxf6SZvzt9BdPSRv7x9CdpbryF965/G1c88UGCIJEIgnIukavC1qlFvn5aP+98uovFhR/RW1nJ9FwnUW0hDVxGlQJlZjDQeg8/mrefz592Ov2NeX6++y7+6+YSU0YXQqGbfdUzmDkyk7vmTufSndN5x+XreGNXD7cumQ7AG7Yt4L2Pz+fOxb08OKePj3asZONsmDfSSq5a4a7Fezln/yLIFUgkdk2p8uXFj/COJxZw77xu5qUlzCtOY5Buzu1vI6UqUa6wsK9MLnL0tDYy2BSUCtBQhqn1Bd7FqPLMzByLemH2fmioQJVEjmCkkBhuCggo5hLz+o/+3i5F4n9duJe3by1x+U648QzY0gqzyn38j87dUJ7NflqZkttB5Cq1n3tASkB5HsQo5PvZ1rSKx+a3cmHXE9w3fxX5apVze54iUpU5I/00V4rcdPooX1he4s8fPptdjZv4i3OKXL+uhe0zL+e7sx/h0WlbecfOn2V2sZXPL72LuaPTuWrPK47Y93+ae/SxUX83Q+3vJ3j2bGIiUYoSQVBIhSPWmai8QoVff2ojf3pe7Xc1X4W3bGvl2o0zaEzB7qZg/uj4fzUSb7h0Dt/6bs+z2juwH2Aon8gBXU0Vlu7PT3j+w8aZO8rnhwc8NLPCBb2HAnkxKjSl2vsSYN3s7VzUs/Tg9oGeBHDpFQ/z3TvOP1j++Zfs5leemuBxVcBQvkprJXew3oU9razubSGRaEwTh+NLr3j44Ou1v/A+Lv7Kpw7bnwMO3CTx3rNfxmcfv/+wug2Ro5SqB8t+2r3ve3fyYPce7v2Pv3yw7BX/fORbYwDee/Z5fLHzMYbKR14keKC9idpa0DKFr77xF454nrE/o7HtTVS/IYJSPT9NLRQO69OB87yITPiL5z2AGZJ6avcjxQRfP5q9mJ6GFmaWKkSuhyn59URhEwtH9/KhzV9kXnGQNfNeTTk/DCT6Z90JVHmitZVyLs8F/U/y2q4d5HIQuTIRowRl4sA/+dEP0V8LgvkhiGGIKhFlIhjzlaBQe0QM+Z1E4wNEUwfbZtT6/tL+B5jK05w/fDfNhQc5c3AT04sj/HjuUoYaB9gybTqH3uu10eXGjze3G/Lb6zVytcLcPv7+zMf4yzPzBHk+dsZiRnNwWv9qKkO/SVSWs7/QQMfcxO9tqPDrm3ppSj380a6/YX5pOyk3zN8+2MeaH4zwocdL7G5qpnn01aTKxTzdvIrfOb+ZBLxt+yJ+5elBqpWXUqku48dzdxEEA607Kedr/d46N3j4NOg4vZGrts1hUXULqdLKtH2XkAZeC7lhmLqWIGiknzmDq/n5TS/nY/fOZPH+4N9mv5oPXfwtblr1I+6d08zM3BZyUzs4p3oPuSjy0UeKvGvHbt694wnauxr57YfOYGYpzy9uOo1Ld87n0dmDrOhrYeZQianFxH/snEPj6Cg3LVtHOQcLhnKMVnv55DmPs2X6ECOj25kxDOf2t1HMVYnIsXt6gXtWNLN3eiMpF0wpJVY9PcTU0SpVIKUqeXIs6SkxY6QW/oaa4Km2oLcZmstBczFRzFUIYO+0XqgHmIn+PDG9xEiUeMPO4MHZ8Mgs6G2E+cUSAdxy2lRy1WYonk6qHFpwFAFUp0JqJCIxb6SHVT2bmT+yj1fvfIhqLsd981ezdsF5fOu0V7Jt2nzWzqmwqi9He/d23rCrgfnDeT63YpQnpzzDI9O2cv7ACuYVZ1KgwLkDy3i6pYu+wv4J+1094ogO/3PovZybYB8M5fYzkh9lOD/CUH7/wbA39s9ExwZBseEZ/vT8HkgNfHDDTK7cMZWvLBvkDy7qphqJBaPpYA8qkQ6G0Kj/J2tXS4V/WzzMH1zQx2+39/Jvi4epAkGwu6XKuy7p4Vcv6eUvVg/Vf+eO8uewfwvGfQEX9uYPq9+UCoe1eVHP0med48Cox5f/6lO1WdnhfPWwfTmCaZX8YfUu2tdKY8rTVA/XE/+Mjm5ssDgQ/sY6EP6y4sHuPcd9zGcff+So4e9Ydg0f/dOvjucnUBozeTa+T8c6z4uFl4Az5MAzABl3I/z+fInHpy1jT9PjzC5NgfI5pFwXpVyFhtK5TK3uJOUG6WxZwubmpZyxv8Bovnb5b+XgAOfu6+GXdt3D7OH5pFQAypQbn6GaK9NUXFSb0aNENDxDKrYCjVDoYl/TNMq5PFPKI7SWR0i5zVA9jW0zeli4d5RtM3sYnFKbBXxgdjNTqnvZNW2IRb0rmVKqQn4H/2XbF7h5WTuPT38TH3v5Vxna/8fk2AnRRbV6PlPLowfH2Vto4Ltz2nhw5iK2Nb+WP9r4DEtHhkkJ/nbFEr6yaAkzi4kPPdnDS/ZX+OjqCr+yOcc9cxJR2M4bti/mFbuDx6fBG3ZOZe5oN48tytHbvBXSeayblWd1P7xpV4Vp5cQnVvbQM2UDucoiBgor+dhZW3nJ0Fd4mG7etGcve5peyRk95wDQOXsu26bex/fnf5JX7Hkri0Z/nvP2wul9AzD7GTa3XETK51jWO4fKyHkUWh6m2riJNHwuBwLvwuH9fPbfp1PKTadQ/Xu6m5qZX/g2kevnzlnL+Lm+rRSn3cPM0TZufNlt/Pt0+M0frWZ/wwCfuuhfmNt3Ae/ZWLsnsZxL/ONZO3jXxiXsnjLIS7pbecvOMl0twaKhCr+x5Ryi2kYh5chV9xIpMdxQ5aFZA7xizzRO60sUIxEUGCkkmsvB07NK5KOFlEoMNzYwtQQp5WgqwUATFBtg9jC0joxSLORoLuXpn/UvNOx/G9unz2Jv263MHX6Gmd2/SENx6aH3NYlz+5r48PoWYIT2nqC9PjEVzT0kEq8eXMtvvOIiVvW28BuPLSYVuikUekjVBkiN7G4qMZMGmqq9fHdOFyMLRmmq9jBrdDcr+2azaLiR9bNfQse8VcwpN3B27yaqBI/MOYt37F7EtuYu/nXefcwpTeNV+84+2LfzBpbRMeNJHp6+mdf0nPus38nHW55VdFCBLirMrI2DIlWaac3dSynNYzS9BIiDM10t1WaoQorEcG6YwfwQLdVmcilHAnL1ADheMd/NcONWLt1TodC8lgtTD+0901hSWsmnT4cbz+jn2idnAHDz6UN84fT9FKqwbKhAuTDAu19d5Od2NzN3NMeckRzPTCnxV6sH+cbiEX5+ewufOnOQxmpwxY5Gvrl4hJ/Z08glXU3P6sdPUpXE/nyZm8/o4huLexhoqNBczrG6dwpXPDOLy3fNopC8Z1M//QyAGXLgGYCp5/DLLI/MmEE18kwt1y6WVKNK1+x7mLPr/RQbn6aRYahM57c3rufORadx5lA303uvYLjlcfrSBfzaU09BQxNVgtGYQXHG7czoey0Aldwg+cpsiixkc+s8nm45i4t2DjO7upVivsD3Fr6UNz/97+xtmsG3F1/Ny3o6WN5/Pv3Ne2kaXcKKPbW7Cv95zjRyhUf49ku+SWMVFvYv5axdL+PbKzbTNeV+cgOXMjj4WarRRL7lo+Savgmjb2B+168BcOvCpdy8dDmlXI6FI92cVprGt+bP4le3rOOJqbNYs2AV+eIQv7XxSSrA1duG+dg5T/M/X9rK3vwiUsznU8tKNJdb6Gka5N1P9/BfNi1m7ZxXs7/QT6G8h5tXTOf80a+xuWk5+5phbu4efuWp07h05xKayruBA4ti5nPlToC5wH5KU+5m0chivnjaQzSVXsZXlwzxs93B67pGSC1PALBu+kJoHWJFb5n+tJrm0jDNUx6ip6mRKf3nUsznaCnlyTc9CVOeJF+axoIokit0Uxm8mI5lBdbOf5jf2wjzitP4Rv6/MaW/wEW7XsEtZz7Ovy6cRVr4CA/MfZxf3vRmGvMzuXnlJr61bC0Xj3TwjofexSWbzmb9kk2MzvgirT3/jb7GjexvfJQZ+99IY6nEk3P+gc8tW8IZwyVm7b+K5tIuEvPpaklMGy0xsx5m/vrsH/D2Z36GKaUCU0p5ILHmjNt59e5LmD6ykC3zBpg7NJfWapm5XVdRKsDSffvZPvNq9rR8mf6XvJvFWz5O0/A5BMGmqSWenF7k7VsrjBRGgBINlWY2Lbibl/Qu5r4FT/I3Zw3RG1W2TP0Zzu5NXLpnNn+z+mFeu2MxF+2CPzxvPv/f5lHO6t9CNTXwT8t3Ujm48GQnZ/c18etPdNFbOJf2/pW0VObxvUXBvubpLBraQ6KNS3vO4s6273J3W+Ln9r6K5mozUyvNnDm0mEdat3JB/woaC+sYmPovlBq2UE15Hqt8mqmxi/n5b9JVuZSBdD4HAn2ZNqbk1rG06U/IxRCbR25gqPpyCtEDBPl4hn0xjYZUoCW1UKVCJYYoxDAlmtifHz7s9zyXKhRigEQfkasQaQr7G7qYU+7nstxfki9W6Z45g6U738abt09h3sgQf7UyOH24j3JlHj+YP8p7n5hKCtgwo8TqfY1c09lKc5WDITOR+Pe5Rf7i3AH+/NwBFu7P8ef3z2T+cI7LdzbTNpqjQiI/iRmzRGJ/vkhTtUCkYGtrNwOFUWaVprBw/wwajnAp9sjtVfn0WT/gy8seYjQugDSNV+9p5dy+6expKnLf3AH+5PxtfO6M3VzTuYDLd84kN4l+juaqfHf+83tSgHQyGAAz5MAzAA8LgDHI/TMvZGZpkMUjfSQSudRI68AryVenkUu7iAhSDDCl3Mabt++FAgw19tDd0MjfvGQ1v7/h+8wszaRCE59aeTrv3DfC1Bihu7WbJ2a3sGLPNBYO5nhi6ioGWrppqo7Q0wQLhvfx89u+xpRyCz9aupHhKT1sLJ/Fyt5OiDks6ju0svL03umsW/I4XfUZkwdbt/H1RdsO7s/zJzQNfYhy410MtXyTKs30NSYuHN0JwL/Nn8cr+jfy7q39PDW7l+/M+c9Ucr3kKm18v22Iav4Zmsoz+KOzLuQPn1hLe+9+7ppTpBrdFCr7KMcMhgtzGclPh2jg5hUllu7v4X1PrmZ380a2TH+ULU2X8aGzXs9r9ua5cleJd3UuJwdsa93F1+fBhtZlbG99lDfsLnDH/AJLhke4fu2lRMMelg1t5C/WLeDutmk8MvNMWotVzuwpUJnTyc6Wxeyduohzdtd+bk3FZlLpEoqtRWbn76M8fROVYjt7Z+5mYXUduUorNPYQUSGNnEWxfDrvf6SZay/byR2zK1zR8zTXbVzMYHUZ1UjccVoPuVQkMZvHZiUKjTN4YMYeKrkRdjTP4yvNb+KRl8Kfrs1z7vb5DO35VZ6YsYfHW8sUqudwZVeJGcVgev9/ZPnch/m/SzZx7RP7qDKfYj7xg7lbubRrBTOL0NswypTqDFK+gSem7eUVg3PZMKub2+c381TjJv77kwvJVyukgG2z9zK/fz7NxURzaQozhhP9zW+DeD3Nw9Ppnv93lBr38M9zX8MznMHVWxoZZQ7fOf/3GWp9hEX7zuLMfe/gnxbNo5h6WF59gmKlxL8tvYgrd86jYfAyiiXY2ZJojSJPzVjImX1b+MVnCsyefwvzu8+nresK/u/S/XxrYZEPtveQq6xl1eAe3rj7bEYKedp3r+f209ayp+VCLu69iIi93Nn2KNubd/HmXa9jfrGNV+07m84pO/n2/K/RHn9HobyAptHz2B4vo5ibwWn5T9KW/w6LCrczXG1jN6+iSmKkcgb9lVfy5PBNJBprv5sUgcTchr/n6XQFefrJxTZKlbMoVKcz2NhJNTcK1SCYCikPUYVUIFXmUmUm1VyFSuFxoAEimBV3Mdz/Rv5uwS/z1ftHSSQquWEu3dbCS3dVWTt3hPN6C1x5z+wj/ttSuzw8Qi418Kpu+OSDe9leWMa5vc20VGoh6qKeBp6aVp50+Pvbc77Lbaffz+yRKfzW+tdx6a4z2TBzJzef8WP2Ng9yVu98Xtm1gnP3LT7ifXkHdDUNUM118KXlQywa+hm2TWmkEI/yUFsf79h6Ff9p3xISiR/PHeCzK3fyf85/mluX7+HXnljIy7unTXiJN5G4a0EvnzpzJ3ta/OzyF4t9oyN0jwwfu6IMgFmRUiLt6SV33nLo3HmglL1NwzzaehaXdW0kBwy1PEZjaQFTRpZA9JGL2iWmyAGFHURqoUqwdk4bO6bOZyQftLAFYhFPTu+nv3k2a6f+FiMz5lPKNRGpRP+CdbRtLvPqrm/x+IobmFL6O/pmPsKW6X0s7n4ZDy/6Afct+R59eXhgHpy373207Qc4DQjKAefvG6S/7SmaKjCag74CDOchl6BQKZCrPkyl6f+1d+fRdVR3gse/v6p6+5OeVsuyFlu2bIMx3g0GDCTQgElISAYSSHqADkkzGeB0ku4skGU6Z5pOk6R7Tk9PFsJMQkI2IIF0SNgxBIMBG4M3eZdleZG1L+9Jb63lzh+vANlYtiFGDtb9nFPn1bt13616P5VKV3Xr3ns7GabRn/kqnXImroSozq9n2ApRba2lv/Z73FOyHDv3z5yZXM/cQRdbhN9PCYPRwwcL/8qroctYWbmA5QPbWdETpS7byX/WJfnY3smkAhl+O7mC2elONifq+PeZBSZnbW7b0sw9TdVcnbN5X69DzLU5GMny02nCkzWVdEUSTHHX0u9NQ0kDv6ofJuw2Esg1IIU69iRmsa5qDzPTnVzUO8hlPesYMmowgnEs0rxYUQ3ZARZ1CbZh4ikD11C8Ur6MGreJxszLxMwniHnQEZqBMbycjmiB1sQAs5Ml1BgRJqWEH656P082KfLuk5yb7KCQnsH2qgBnZi7ljIyDYxwkmo8QcUNsKe1DkUIxDJjMyszmhSaTluAWzu5OMH04wcdb4wiwpqabVbW9/O3WWXz75Xl89nybVZWDXNpVTtp06Apvpy9Sw0ggy+bYVuYnZ5IzCmQYwZMK/s+sHgyvke0xwRGPslwNtngMuibxUBcD0RpKCkKo4JDIBYBSOkthbcm1vFi1jr2BJj7VlgKqyIRGOGvnN3lqwU2Ehi5mKOBRba3ir9Lraew/nfl7LsRlgJFAnBvaTKJOkAenHeSMzCw6Ink6Q3Fq82nsvuu5t6GdtqZu0kaMiFcg5mYZkqmkZT87o/tZ3hNkKGDTGQpiR34NIwanDy+k3mvhgaokD9T/not6z2LO8BksyB9gXaSWyZkbObfnOhQWL5VBuQet3Ear80XisoP60B2UGw8T9cASIWXOo99dzoB3HjlVj0cIpQq0e/8FJTZ2YB8FyVFpPoKX+xjV+Qp64/dTCGaJeyPEvCAFbzoeEWyjBMebiXiVBArnoVCY0k9r4BLumLmfW1vagDrubxD+0GARc4f47K4oCwbCtMVdHpzag4qtJFgooTRfzp7SIXrCYTqDdYyYQSq9XpYPHeD6lvOZlGoi0/AL0u4cwkPL/Gfr4AezR/hgR4RLOot3wwuGojfsUmIblNpvNlH/rPll/lS7k69tWMH8gXpMz6AzkqQ5Vc03Nn7gbV37HHEJuwH+9PiNWH5FMWU5eDKLLy/5HZ8/+zfcvukyLj04h3P6Sjm7r4SVtUP8uLmLLy3Zw/yBGJcdLOec3lIqCgHyhse6ymF+Mb2HrWUZZqYi3NbSwN8vbXtjn0937H57F+gJzlUeP97WckLKWvHIgyeknIlAVwAnimQa0nmkrjjOmwLWltfwUO0VmMrj4r4duEaWZNkayvo+goVCAsXx81zJ4ZpZPKOAZw5iFJqZkhvmsckXs2C4E6Xq6YqU8Oqkc2jOQTJYRYU8QcB8mXxkHQUrS4tayOLd19N48FoMZdJZ0semuufpOu0ZegLgCSRskw92zCHmOkRdcMwUuyqHiORm05xMsyF5Bwcj+9kd6aNfLBynBJcYtvhNq/7fjxhdTONpGnmB+ekl9ERqCBauJJLupZD/FMrawytTv8xlHf9AW0kDUU84t2czZ/Ut5Y9nDDBcuZZMW4JlXc1sjzfxq9X9BJVB1hAqc21cNvgsPVYzO6JTyVtxAqL4bDvY4vBMtfBYrcGm8i5ML8Py3hg375rOrtIF7I/XUOpGiLgWFgZziuGlJwbZ0DRejUxhQ7nL0v59LBrYg8S242Hy/v1z8bw4EXeYzdUeayf30BkOkgoGCbq1GFzBTdv7qUmH6Jc69tYJFW6AiBHlqQabYWuQj+8sZ06PcMk+g8HAcmrcxwnGnqeaaSzuacTLT6ZuZBJNmQKmGmJJ6RmEZToDgV4y5iCnjTSwqXQvT1f38XR9Fx4HiNLFvPxuZqt95I1m/il+OXe+eCl3vLIQJ1aOhyLsmVzVMY/e2HME1eUUwgeoyixjb6iXj+5rZGXtbrzgKsLuDBrytaSCioo8ZALCQ005tscGWDZoc3VnI6vqBzm3J4Gh4PH6fhYP13DVwUt5qXSYpT1Btiby/HLOq3xu4wdZ2vIDphda2FoxRHM+gauaGYlvY0Pz3ZjJiylNxZiaLD5HuLe8j5m5pTxbsY7tzeX8a0uBj7Q3cyA4ma5JWeZ5mzg/tYU9zseZ21fF+/qKz5X2B11cCXLnhhIeqYuzo7yMqV6akvSn+VSuldWVq3mqeg0tFX+gUh2gxq4kmDuPvmCSQSuPkilMsgfoDhUomAdIBbdyUC4k6JYQs8+kTtZRaaxieuD7TOf7uCrIsGpknfePKMIUAgeoUPtpdDdRar1EeyhHOv8Zgs4s5ni/IlhYwAH7VoJ+03u5sY1JwR9yMNBEl3c+pYUEOanCKpzL+R1ZPrYvw+/qwvxkRnFQ9l7l8b2ZHewrGcEzCv7zhhcSVt2EVJaUeUaxMVf6qCn0YjqVPFB1Ns+c7fDv6xTT992IY6YBj+FoG7FsE59pjfO5pUNU5oRFgyH+WD/Cz2cMkwx6fGi/xed3RFld08oTDWv4H631zBiMUpItO+IlzUOxo7SLlybtoa20l1I7wuyhGmYMV1Nih3DEoycyTFckRcQJ0BsqI2MZnD4U4fzeBEKUH730SV6a1MYPT1vF/tggN7Seg6UMLuks531dCX7f0M+DU/v4ztxip7GwY1AwPDwDEgX48D6L63dPobpw6NznX1/79HFfmg+mj9HF/RQ3kMvxjVdeYF1v9wkp74vzl1ARCvPVtS8ccftn/vTECdnPqeC4hoERkRXA/wZM4P8ppe48bHsIuBdYDPQD1yil2v1ttwOfBlzg75RSTxytTBFpAu4DKoFXgeuUUoWjHZ8eBuZQnlLkHMjYioyjyNoKs6Wd+oeeZf1VF9H+wjZaEs10RBqIO/005jYToY+8NUJpIcC1O2fSH+2m1h6mLwSP1scwiCMkEFXG4t5uZowc4DeNVzEjdZClfdtYVR+gL7GJbdYyuow5nGd8goCRwgNMJXiimLvvCk47eDHKC3DP/LtYX9uC4cEZg9NY0XYOtcl5BLwwOcMlRBJXTL4wZybnDcW5pr2Hn86aRFTiNA13EnR7yRsZ+kIOMacfkxT9Je30l2/GtPqJOibRQpwPttxAy+Qt3F/9BRxJEJTtWNGvUpWOc/Nr/41fTWvktfIwTbl7GZZaZqZNTku7NA6HKHH93w1lUDxNbYqNXQFAsAV6Qia9IYPKfISGtEnKirIzPgtTRZiU72VKrngnQID2aAN74lOwBRxDWHLQIOQKL0yFlOURcBVVeYPZvYqaTDdm+SO4XimGF2FEYoSzdXxnoclA2AJMRAURQjgYeEpx4QGLiw5EyJsOTzXto8luJm0qOqPCwWCST2wpYcqwoq3CpnloBCO6AQnuLzYVKwuVOx2Vmw6YuIbiR3MLDMYqqc8GmVRQvFLWNOqs4wAAE4FJREFUw/7IAQYtj8HAQZRxEE8EUSYhL4DjTef8g9P45vpKskGDp+rzrJw8zGf2TsG0bSoyiopssZ+dYxiYSkiGhWxQOJCwSEUNhgxFzvPAFCa5wsMVLu1hxRW9wvIhi70hm/lDAbpCBdZUJKlxSjk9KSzo7OOxBpPna02W9UX5q45+YtLKa5ULGQqXEfDAJsuImSNLlrpUmBkpiwOxAjsmRfGsAD+v3YaLxVc3xbm4vwsx07TGQrxSHmNWymZ+agRX4MH6CI/WVGKGnmSy2s1Nm9/HlEyUm5f20xZzmD0yjeUDC4i7UTpC3bRHO6mwE8wcacTEoCs0SHW+HIDV5RvZUNoKEjykmVGhEGUSdCZRYVdTJRvxJEsbF+Fi4gTaiTjlROwmlGQZjjxKLthC5fCNWG4j1cbjNAXuAlx63BWkvWZsFSNpxsipOkJuAih2xmjObuHrG0P0RLL885LnyVvdZFQ5w2oRyi0Om2JZaxHpJycNgAFKIV6MZb1xtpZGSFsRPEkRYR9ldp5kMMz1uxPMGIbWyasZSaynoGJsseaxLTwDRZhbdsT46P4otnjsLk0xPWPQE1J8ZdEmrht4HptJZFQ9Q2aCbmMKSStO2lIsGTD4xF6LuPNmvIYCGR6v38pj9S3sLu17I70yF+OK/WdSP3Iad57ZwZK+ON9+bfohnTxe7zG9pmoPa6vbmZxJMG+wjsZ0OSE3QDKQ4bmabl6rGqI8H+L0ZAkLByqpzkXxRNEZGaIjluS+qSk2VCiUCOfUNPBS95uPqLxdS6vruOWMs5lTPukdl/GX7vWhVarDEVKFAl9asJQ7Xnv5zy73aMPAAMxOlLMjOXjc5d3zvhV86k+Pv+3j+On7V3BaWcVfyiDwRzyIY1YARcQEdgKXAAeAV4BPKKW2jspzMzBPKfVZEbkW+KhS6hoRmQP8GjgLmAI8DczyP3bEMkXkAeAhpdR9InIXsFEp9cOjHeN7rQKolMLxwPag4Ko3Xgsu2J7CdqHgQdavwGXs4pJ9vVLnL2nbJVNwydieX9mDnCNkvbc+D/ON1pdYmOrmunlXUulZlLgGOXHJiyKioNSBOakRPnxgH0HP45Gpp2OKQ84M40mxvLwokqaiItfN3+x/lpUVszi3f5Bt8QRfOX0BFjkiyqbaqcAlT97ch0kfJhlcM01BsvzjhkZmjQTpDdnsjTlMGwlRVTDIGcLqyhhPT4qxIRHg+v37+MTBQZQKsDExhfn9GYZi7cScAEEvSN4wsTwP85DxvhQj4W76Sjexp3I75SOzWNhxHo/MXMlAIIK4DYSdApFcDcs7JxN1FRkLEqO68NsidIaD9AQsSt0cecOgP1CF4SWYnM8wK7eLQbOOrDcXiL0x7h9AwOun0tmJKyZps4QyZ4CklWBfZDYN2TbKnD6GrCkMGw2EHIvSDGRCkHu9U6RSWI6iMtOLl3iWbCDP38w9kxv2OXy4awgx8uQNj/XlObaV5tkVt+mOKPqD+0ibURy1kLrhJr61romoY/CrmQPMKVQQ9UxSlgdKWNbmUloogCgKBAmqPIQOYIR2IYFuCqqS4fxSyrJRlCger/cwwxXsj+VojXmUeHEMBA/FiOGREcgaxXOjrACXdxss7nWpGC5gKnAELP9HZJvCcMgk5ChCjvKfN1WY/vauqM3vprXyaH2eghHh2u7ZVNlhtkY3E85DQ3YmDYUoEUeI2MUrmoeiLtlPyLXZVTmJ4bCJ5SU5c3ATyovRLwvIhgRlFP8JCdkQLwiuQCrkES8Ipiesrhnit01rSBjbCRcquWDvYj7QqcAawiAPGODF8ewKeso38y8zZ7AjnuDa7tdoMP/IuXuuoGB4/GzOanaXKDJSQv3IYmYMLyPmVOFIngORPawv3UlSLSRRaGBhdpBpuXI6Qv2sqtiAYb1KtDCTHplNNrgf10wiXgTcEoLOLBQxFA6u1U7EqcTEpRDYSTa0HoVNLHcBsdwFCMWONQBh2Y/FAFmm4qpipVPIMEleJOF0MqevkSvbpmKbiruX3E0uvhoRRUHF2csldKj3U50LMmg2EPRc6nKDDIQzJI0qIIpCEVBpSu08GStIzihB4eCZHXiSLA7r9MbvyOi/QULANTinz+KT7UFmjajiP1pehCONTjYYdFhXnWR7qcOjtQZRD67bY3FJp0ncPfRv24iVZzCYwRFhMGTyx/oBVtYWO2k8+vRcYu5br4+K4pBDIc/CEY/9sUE6I0lylk3QNanLlNM0Umw9yRk2OdMm4JlkTZuRQJ7J2VLCXoC+UIbnanq55EPncvX2x8hbx77B8g/zzuPfNh0653h5MMxgIcdFU6bzkWmnM7eihnggOEYJ7x0Zx6YznWZjfy/f3rAWgIZ4Cd86azmzyiqOOQ7g8ThWBXCscf3eDc2JMq5onM7i6hoaS0oJmyet0fUdVwDPAb6plLrMf387gFLqX0blecLP85KIWEAXUA3cNjrv6/n8j72lTOBOoBeYrJRyDt/3WMajArij3+Ebz2eKz9JRHEDWU2+OqOIp3kynWMk7ZNsb6W/mfSeCKkdIZQl7GcIqQ1hlCfmvxbQscaeSEnsZIgaiTK44sJIyO8WmsjPYVD7/jbIub99CyHWwPO+NylTGglVTk/THu8DoxzP68YwBlJHEw+GsfecwbaCemFfAEBcX4ZEpGe6bqnCJ4BGhULiYaqcEC6P4QDkgSvG1zXtpzOTZVmqSDbhU5m06IiFerEjwQmWC7KjZB5oLJpd17eOKgzvfqOQpBS4hnq6azH/MmEalDbOziml5m48cWEvQtQkoB8FB4RbHeFMGxTt2h1JKKEicvJThShyXEhxieEQPqdS9kR/ImVDmrafS3Y0tAfZEZ7M5cRrDARgOQsaEynyayw62UZ7Psqm8kkem1JKyFFHb5ZbdXTSme4uDDxPAw+Chxtks2x+jVp7FsrpB8oi45AkykjuHsF1BxIW2ONwzK8viZC8LUoM0ZtMc/mesIELOMHHFIG6buNnlpKwQMaeA4BBwi5UxD+gPB0mFTeygULAUEQfK3Haq1IuY5FDKLA7f41TjZRYDBmmrlN5SwZNiPELFwd7IWsVBnsNO8U93KgCrahQzhhxmD3lMGhEcw8AzTEy/p+iu8gwPNrWwsWwr01KN/O2OBZzZGyHoFn+/bFORM12eaTYISQlZo0BHuI8/1KymIruYqB3mW+tNqrMuJrCnLE1Dvg9D2Zi42BKkPbaIYCFMwFFE/EF9c6ZiICYMRBUBJYQcqExDSa4YQ0cUrgGDYbi3IculnVlMz+NPk8JsKQtyY9sIl3blsQWGggYra4LcMyPK7OFhvrVtGyWuS8oySJsm/3TaVDpDlQRVBBeFiIMiCLjUWndTFvwNifSHaBy4CUvFsMUma7jsCjusKfErnf4odgqFMoZwzX0oYxAvsN7v3GESsk8jnr0QTyzSwTZCJMGLYLhTML3yYoVQZfnBuhQxN4/phTA9i7B/gdpRYnF3cyX7YlEULgoHx8wwHNrFDPcFzpD/JKlmcNBdQVLNxFUh6tMW+xP7KJHNZM0QeVWOpyI4qgzPbSpWNkVAeQgOiMePXu0l5mYJeIqgEsKOhYmBI4qnJo/wYJ1JfTZEdc7ENhRDQZdUwKUyb3J+T5CzeyMEVfHnOBywyZgGrsBDjRmeqc3gGf1YpCgYioKhyFnFu80Rx+CqvVX8YkYPzz3x5vVvLCNWjqzpYCjBVIKSYgUx5gQJeUeekSZrFPjWvM1c1JXg3J6pb+QbtvIUTI+8Ifz9kj10Rt/amHWkQaOfveJGftm6kV+3biLtFDuYlASCRKwAH2uayw2zFx7ze5wsX3l5FTuGBnA8D1cpHOUV1z1F3nPfkv/5K68laBavZn+JFcDvLruQL7383Ns+jq8sWMrD7bvZNvTmYOkR0yIeCGAZBqeVVXDnsgvedrnv0DuuAF4NrFBKfcZ/fx1wtlLq1lF5Wvw8B/z3u4GzKVb2XlZK/cJP/zHwmP+xt5Q5Kn+zn94APKaUessAWiJyE3CT/3Y2sOPo318bpQroO2Yu7UTR8R4/OtbjS8d7fOl4j59TKdZ9SqkVhye+ZzuBKKXuBu4+2cfxXiQi65RSS072cUwUOt7jR8d6fOl4jy8d7/EzEWJ9PFPBdQANo97X+2lHzOM3AScodgYZ67NjpfcDZX4ZY+1L0zRN0zRN+zMcTwXwFWCmiDSJSBC4Fnj4sDwPAzf461cDz6hi2/LDwLUiEvJ7984E1o5Vpv+ZZ/0y8Mv8/Tv/epqmaZqmadrhjtkE7HfGuBV4guJYGD9RSm0Rkf8JrFNKPQz8GPi5iLQCAxQrdPj5HgC2Ag5wi1LKBThSmf4uvwLcJyJ3AOv9srUTSzedjy8d7/GjYz2+dLzHl473+DnlY31c4wBqmqZpmqZpp47jaQLWNE3TNE3TTiG6AqhpmqZpmjbB6ArgBCIiK0Rkh4i0ishtJ/t43mtEpF1ENovIBhFZ56dViMhTIrLLfy3300VE/sOP9SYRWTSqnBv8/LtE5IZR6Yv98lv9z/5FzCE0XkTkJyLS448r+nraux7fsfZxKhsj1t8UkQ7//N4gIh8Yte12P247ROSyUelHvKb4HfzW+On3+5398DsE3u+nrxGRaePzjU8uEWkQkWdFZKuIbBGRz/np+vw+wY4Sa31+H04ppZcJsFDsbLMbmA4EgY3AnJN9XO+lBWgHqg5L+w5wm79+G/Btf/0DFAc9F2AZsMZPrwDa/Ndyf73c37bWzyv+Zy8/2d95nON7AbAIaBnP+I61j1N5GSPW3wS+eIS8c/zrRQho8q8j5tGuKcADwLX++l3Af/fXbwbu8tevBe4/2bEYp3jXAov89RKKU6HO0ef3uMZan9+HLfoO4MRxFtCqlGpTShWA+4ArT/IxnQquBH7mr/8M+Mio9HtV0csUx7esBS4DnlJKDSilBoGngBX+tlKl1MuqePW4d1RZE4JSahXFUQRGG4/4jrWPU9YYsR7LlcB9Sqm8UmoP0ErxenLEa4p/5+ki4Lf+5w//ub0e698CF0+EO91KqU6l1Gv++jCwDahDn98n3FFiPZYJe37rCuDEUQfsH/X+AEf/pdDeSgFPisirUpyKEKBGKdXpr3cBNf76WPE+WvqBI6RPdOMR37H2MRHd6jc5/mRUU+HbjXUlMKSUcg5LP6Qsf3vSzz9h+M2CC4E16PP7XXVYrEGf34fQFUBNO37LlVKLgMuBW0TkkJm8/f+89bhK75LxiO8E/xn+EJgBLAA6gX87uYdz6hGROPAg8HmlVGr0Nn1+n1hHiLU+vw+jK4ATx/FM6acdhVKqw3/tAX5HsYmg229+wX/t8bO/3WkQO/z1w9MnuvGI71j7mFCUUt1KKVcp5QH/l+L5DSd2Ss+xpg095YlIgGKF5JdKqYf8ZH1+vwuOFGt9fr+VrgBOHMczpZ82BhGJiUjJ6+vApUALh06DOHrqwoeB6/3efMuApN8M8wRwqYiU+00QlwJP+NtSIrLMf2bkevQ0iDA+8R1rHxPK65UE30cpnt9wYqf0HGva0FOaf879GNimlPpfozbp8/sEGyvW+vw+gpPdC0Uv47dQ7Fm2k2LPpq+d7ON5Ly0Ue4Jt9Jctr8eP4vMdK4FdwNNAhZ8uwPf9WG8Glowq60aKDxq3Ap8alb6E4kVpN/A9/Jl6JsoC/Jpi04xN8bmaT49HfMfax6m8jBHrn/ux3ETxD1ntqPxf8+O2g1G908e6pvi/L2v9n8FvgJCfHvbft/rbp5/sWIxTvJdTbHrdBGzwlw/o83tcY63P78MWPRWcpmmapmnaBKObgDVN0zRN0yYYXQHUNE3TNE2bYHQFUNM0TdM0bYLRFUBN0zRN07QJRlcANU3TNE3TJhhdAdQ0TdM0TZtgdAVQ07QJSUT+TkS2icgv38Fn20WkaoxtZSJy83GUMU1EPnmc+VqOlU/TNO3t0BVATdMmqpuBS5RSf32Cyy3zyz6WacAxK4CapmnvBl0B1DRtwhGRuyiO5v+YiCRF5IujtrWIyDR//b+KyFoR2SAiPxIR8ziKvxOY4X/mu/50Xt/1y90sIteMyne+n+8L/p2+50XkNX8598R+a03TtDdZx86iaZp2alFKfVZEVgDvB249Uh4ROR24BjhPKWWLyA+AvwbuPUbxtwFzlVIL/HKuAhYA84Eq4BURWeXn+6JS6go/X5TiHcmciMykOF3bkj/zq2qaph2RrgBqmqYd2cXAYooVNoAI0PMOylkO/Fop5QLdIvIcsBRIHZYvAHxPRBYALjDrnR64pmnasegKoKZpE53DoY/DhP1XAX6mlLp9nI7jC0A3xTuFBpAbp/1qmjYB6WcANU2b6NqBRQAisgho8tNXAleLyCR/W4WITD2O8oaBklHvnweuERFTRKqBC4C1R8iXADqVUh5wHXA8zxtqmqa9I/oOoKZpE92DwPUisgVYA+wEUEptFZGvA0+KiAHYwC3A3qMVppTqF5HV/tAtjwFfBs4BNgIK+LJSqktE+gFXRDYCPwV+ADwoItcDjwPpE/9VNU3TikQpdbKPQdM0TdM0TRtHuglY0zRN0zRtgtFNwJqmae+AiFRSfE7wcBcrpfrH+3g0TdPeDt0ErGmapmmaNsHoJmBN0zRN07QJRlcANU3TNE3TJhhdAdQ0TdM0TZtgdAVQ0zRN0zRtgvn/t8laD4/1YukAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sclNqU37Eeyt"
      },
      "source": [
        "Right skewed plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "b0ChfQSWEeyu",
        "outputId": "2f0c714b-37dc-45cd-97d0-419bfdb42f64"
      },
      "source": [
        "for i in ['engine_rpm']:\n",
        "    \n",
        "    g = sns.FacetGrid(df_xirgo[['vin',i]], hue=\"vin\", height = 6, aspect = 1.5)\n",
        "    g = (g.map(sns.distplot, i , hist=False, rug=True))\n",
        "    plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ASZKkwhgAJUmSCmMAlCRJKowBUJIkqTAGQEmSpMIYACVJkgpjAJQkSSqMAVCSJKkwBkBJkqTCGAAlSZIKYwCUJEkqjAFQkiSpMAZASZKkwhgAJUmSCmMAlCRJKowBUJIkqTAGQEmSpMIYACVJkgpjAJQkSSqMAVCSJKkwBkBJkqTCGAAlSZIKYwCUJEkqjAFQkiSpMAZASZKkwswpAEbEzRHxcERsjIh3nWD7aER8stl+T0Rc1ZS/JiK+GRH3N8+vOrPNlyRJ0qk6aQCMiDbwfuD1wA3AbRFxw6xqbwP2Zea1wB8B723KdwP/JDNfCLwF+MiZargkSZJOz1xGAG8CNmbmY5nZAz4B3DKrzi3Ah5vlTwOvjojIzG9l5hNN+XpgYUSMnomGX4imhxX9YTXfzZAkSRe4uQTANcCWGetbm7IT1snMAXAAuGhWnZ8C7svM6dkfEBG3R8S6iFi3a9euubb9gjLRH/Jj/+0b/Ptvb5rvpkiSpAvcObkIJCJupD4s/Isn2p6Zd2Tm2sxcu3r16nPRpPPOjok6F3/m0SfnuSWSJOlCN5cAuA24csb6FU3ZCetERAdYDuxp1q8APgv888x89Jk2+EI11h/OdxMkSVIh5hIA7wWui4irI2IEuBW4c1adO6kv8gB4E/CVzMyIWAH8JfCuzPz6mWr0hWisP5jvJkiSpEKcNAA25/S9A7gLeAj4VGauj4j3RMQbmmofBC6KiI3AO4HDt4p5B3At8NsR8e3m8ZwzvhcXgHFHACVJ0jnSmUulzPw88PlZZb89Y3kK+OkTvO73gN97hm0sggFQkiSdK84Ecp6YGhoAJUnSuWEAPE/0Ztz/b1DlPLZEkiRd6AyA54nejNDXr07tZtBTwwFj/d6ZbpIkSbpAGQDPE9MzRgB7pzgbyM9/7XO86n/8+ZlukiRJukAZAM8TxwTAUzgEnJlsPLAPgGE6jZwkSTo5A+B5oneKI4Df2bOT//y9B5jsH51Zb/fkxFlpmyRJurAYAM8TM0cAn+4cwMwkM/mlv/ky//HB77B++/oj2z769d/l4IRTyUmSpKdnADxP9GaEvumnGAHMTMb/5OeY/sIfMmgO967fu+PI9i37NrBh29+c3YZKkqRnPQPgeaJ3zAjgic8BzH3bqJ74Hr27P3akbMf4waOvY4TJ3v6z10hJknRBMACeJ3rDnLH8FCOAE8eHu73TR88B3NBay+6x7We+cZIk6YJiADxPTA8rRloBHHs4eKacrEf7Zo4PHuz3m6UW+1uX8sAhf6SSJOnpmRbOE9NVxZKRemrmwWRFtbd/XJ2cPFTXjaNTOB/qTR1TZ6f3g5YkSSdhADxP9IYVS7p1sLv64weZ+tebyMGx5wLm1CH2L4AnFo8cKTvUHC4+XPPQ4Jw0V5IkPYsZAM8T08OKJd02ACsfrVNcbp0+ps5wfB/7F8HuRUcD4EQGOeOg8KFh+xy0VpIkPZsZAM8TvWHF0m7nmLJq/7HDef1D9QUeMw8BT2UbOHrO4L5cfPYaKUmSLggGwPNEr6pYMtKmO/P6jwPDY+uM7QRgutU9UjaIDlARNBeQ5Aj37V7P/9jy1bPdZEmS9CzVOXkVnQvTzTmAy3tHM3keOjYA9if3AtCLowGwog0crTegw9vv/m0Afvyyl7Ows+AstlqSJD0bOQJ4nug15wD+ygMXHSnLyWNvBzOYrq8C7lEHwJWjoyQtjv4Yk2pGpn98bOtZbbMkSXp2MgCeBwZVMkxY1Gnzyh0zzuGbPHYEcNifoBUdBlFfBLJqdAH1jzCO1MloHbkkePO4N4WWJEnHMwCeBw7P/LFkOiCOXviRU8eOAA6H07TbI/RbowCsbAJgHAmASR0G6x/r3ukDZ7nlkiTp2cgAeB6YbgLg9d/dTiz7KsQUGVCNzQqAVY92ZwH9dh0Al3e7zBz9O6o+DLy/d/AE2yRJUukMgOeBw1O/Pe97j9cFnd1U2WJ64+DIzaCzGlLlkFZ3MZOtRVAtZPtYdcy8cEcWs8OC9qgjgJIk6YQMgOeB3rCCTDq9Zvq31gSRLVq9iqkHmvMAp8aoAtojS/jWghuJXMpD+wZELoJjDgFDO0a5YtElBkBJknRCBsDzwMTuiqvH27QHzfl/MQm0iEz6m+sAOPjufQwD2lWHJ9qraTHJim5CjnA4+B2OgaOtpSzrLuFQf/yc74skSTr/GQDn2XAqGfszeMdjlwCQVQeiBwlB0ttWHx4e3PN35PAaxvcuZypG6eYES9vTQJs6+h09FtyNhSztLubQwAAoSZKOZwCcZ2Pfr0f4LurXz5lLIPq0RjYRMcVgb5KDAb3tW2jv/s/0d74TgJGcppvjQPuY8wAB2jnKspElHOwZACVJ0vEMgPNs+4Y6+LVzGoCqWkIERHcX0dlJdaAi9+xlgsvretXFACzIScjx5hYw7WMyYIsRlnYWc6g/di53RZIkPUsYAOfZYw/W5/11qikAsjp8I+iE1jgxVvHI5m28/4ofP/KaSFhYTTI9HG9qtjj2cuAOS0cWMzGcYlAdezNpSZIkA+A8mppO2lNwsAvtnKLKDpn1LB+ZCdEjcsBnNu2jk0uOvG7xoMPCnGQip5qSYwNgZptl3bq+F4JIkqTZDIDzaO/+ikUDGEbSzinIEVrZBLlcSAS0c5wnDwXLeyNHXrdk0GZh9pjk8OjezB9jMsw2SzuLADwMLEmSjmMAPMcysx7dA/buq1gwhG4G3ZwiqlHa2cz+kfVsHq2YpOotZM300VlBlg7ajFZ9es2NXyKPjgAmMMwOS0fqEcCDjgBKkqRZDIDnUE4kk782ztRvT5C95MCTFS1g2IJOTtf39GsN6xSX9Y+mxRQreiNcPl3RjzoELh50WFD1GR758bU5egg4GWbLQ8CSJOkpGQDPocHf9smdSbWpYnD3gB1P1Ff+TrWyvggkuxA9koCoyAxaTHLxZJel/RGeWFiHucXDNguqPkmbJKnvA3h0hLDKFsu69cUkBkBJkjSbAfAcGnxrQKxpERcHw28NmNxTh7Z9nQEd+lB1IfpAG1qTBC26sZuf27Kf7mAROxccBGDRoMOCqkdGF0iCNnD0at+KNkubAHjQcwAlSdIsBsBzJAdJtWFI+4Y27R9oU20cMjLWIklWH75hc3ag1aufYxqyRcSQxcOKpb0+YyP7GJIsGXRYOKxHAOuRvwAGNMeOqYgjAdARQEmSNJsB8BzJ7RX0oP38Nq1r2uSBZPVEiyHwon0P1pU6hyD6JB0ihkcCXj/aLBweJDv7Ge/0WTzosrDqNyN/FWSLFr2jn0WLBe1RRlpdA6AkSTpOZ74bUIpqS324t/XcFtmvyy6egP7oGMt7h5opfSeISDI7EIcP6VZU2WVkOEHkPsY6A5YMOrSqPnV+r4A2IznJdKxoXhNkViztLvYQsCRJOo4B8ByptlTQhrisRSRU3X1cNfk4MTVBRkUAES1g2ARA6mneAlok3ezxqs3P4fElPZYMugyGfepKCRksGxxgV/ey5tOC3mCSpd3FjgBKkqTjeAj4HKk2D2mtaRGdoNp8gPbo9yBbDKJFUJEJWQHtHbS7m6kP7da3dmllPV3cVQdXcuXYOIuHXSIPn/tXAS2ePzbB4XMAIRifPsSy7hIDoCRJOo4B8ByptlS0rmyRVTL10QcYxgjV+I10csAgAmhBe4Jo9YhWD2KSPHIvwPr8vqnuOGvGeyzpd+jTow6AdeB7RS5v7gRY3xz6yfH9zSFgA6AkSTqWAfAcyENJ7ktaV7apHtxNa8cEOX0V7dYE9XXAbYIgOnvIagE5WNncDqYWMSQz2LN4JwuHsKLX4vElAQTJkCB40ZUvO/xpADwxtt9DwJIk6YQMgOdAtbW+oCOubLH3S5sYZpdpVkH7AADtCjKm66A3XEYOl1APCk6QWd8UehBtDsVqAJb1+jy6YFUz1lcHvt6118ORm0LD1vEDLOsudi5gSZJ0HAPgOVBtrq8A3r14moUb9pHD1exc0SI6+xlrLYboEa1JshqFXAi0yAyifQiyQzKk1+pw8XSLyXawYDDF/riyfvNmerhtnfo+gNF85raJMZZ2l3CoP0GV1XFtkiRJ5TIAngPVlgqWBl/6+mO0gYm4iAOLEtoHGFZLidYYEUOoFhKtKaI9Vd8Mun2Iw/P8Bn1WD3YxNbqZ0Wo3reFqZk4Bt21iP0cvAoEdU/VVwBUVE4OpedlvSZJ0fjIAngPVliHDNcE1Dx4ks8v+hUvJOEBExYJepx79yzbkCAwX1OcB5kgdClt7iM4uFrUfIjq7WMVWFox8i//1+yubq4TrwLd17AAz5wPeM91j+cgSAPb3Ds7DXkuSpPOV9wE8y7JKqq0V33zBGGsPTjKoVrN/cbC0t5uKYKT7KBEVVX8lwQhZLSRa1PcCBFrtQ2SOwnA1WS3iry9ezY/v3czrd+1ky+KVfGfFy1g0XM6uB/exfMEUB7v7ADgw3eM5Cy4CYNfUXq5YfOlTtzETxoawpE1EPGU9SZJ0YTAAnmW5I6EHOyb30KFif2cV0+2Kaw/toNcZZ2FrO1mNUA2uoNXeD1EHsoh+fQEIAdViGC5nGEGvPUn2rmHPguRtm3bztZU7mIh9fGv1Em7sv5btoxv4/qIHmBjAJU0A3DG156nbNzVk+gOPUa0/SOu6JYz+yrXEgvY56h1JkjQfDIBnWbW5vgL4xoMTZLbZvXgFy/pbWBSbiNYUWY2Q1WIimrl9I6G9E2hBdpvbwSS093GwtYpVvQHDaHNoeAPLuZtX7VnPoHc9r9w5zv/zwie5jBdxce+5TLR386U7dnP5mpvZ/MkvMLn5vhO2L6evh+HF0HmSasOlTL7zC8TIo7NqHb7fYAuyVbeRYVPePLJLDi4juo9DtZgcXE6MbCT7V3Awr2FRNc3DFz/KC/as4cGlozx560v4yZ338d0HFvB7N36Hf/u1tdzxup38h1f9I/p/+TAfuvYhfvH6Nx5pwZ+u/x5vv/EHAOjf9Xm6r/sJBl/6Mzqv+QX+dP0jvP3GFxzT4j9dv4G333jdM/8BSpIueFPv/H0W/OGvM/3HH2P0HT8z3805JzwH8CwbbhjSaw25amqMXq4kWk9y/eRXIKapBsupquUwXEq0JsjsQmtvva1/OWSXiKznBW71GGGaGw61mWp3uHRykvb086A1zf+35lH6LXj7w9M8sugr7O8+STsXsH3hJVwz9jM8vOQXGO8sIIJjHgxXwnA10d1Ma/RR6GyHwaWQi2bVzea5IloDIoYzyqt6vTVFxKAub4835UlrZAvL2Um3dYAXju9iJKZ58cHkL77dZ3jXPdzw2E6ePHQ1zz+0jfu21wFv8PkN3PHIXxzTjx986OGjffqlL9TPf/3BZtuG4/r9gw/NDrGSJD29fGzrfDfhnDEAnmXj3+uzc/E+OgyY6Ha5ZvouoEX2n8OAZQQDoN2EqUmiPU5Wq5juvQjokBlk9Mnssij3s2wwySAGjOaAT6xZzUfXXMY/2jPOF567meeNL+WNm9tsWHIP9y//Mr+x4ZPsWvA+Bq1V/PmVr2LI0fP7Mltk7/kQE9Ct/8HHyGZgSPaunI+ukiRJ54gB8CzK6aSzOVnBPjIrlrf/rj6Y2r8EIugNL2lG+CYgxonOXvq5nKiW8NjykXpEMEeI1hiZHYKA9n6WDQ5ABnevCj5y+Q08uHgJP7X9II+uXM+tj6/myrFRkmAyBiwfPMah7sd4bPHl/N1FNx5tW+95kAuI0Y11G4CIAXSfhOHFZLVgnnpNkiSdbQbAs+jQt6dp55CluRu622gxRfYvIVp9GKxiUfsxyIpoj0FMsWH0cr6+8uUkwaLOk1SDi5vDwBWt9n6q4SqSAXR3krT5mScWsajq8bvXvZhDnREu7ffot8f5Vw+tITK4c80urpxYyCNLv8b1hzbzV6tfyv7OYnK4HAZroPME0T72FjHR3QZUZP+K+ek0SZJ01hkAz5KcmubRv9xFNbKd6D5Bqz1GDlY3N3xuEd1tREwRrQHDXEbv0M0s3fcydiyoODCymueO72DHyMVAu54hpHWAiGkYrqDVmiRHN/PCfXv4p7sv46r+In7n2h9mqt1ldGQjLz0wzk9uWcHHrhpw6XSLye6QH97/FTKC/3flTeTU9RATxMim49od0QtRH6IAAAy4SURBVIfODhg8p/5cSZJ0wfEq4DMoxyYYPrCR6v5HOPDIRtbsvp324q8T7QPkYFldJ3pEq1/f+y/bPDzygzxn7Aa+/rwBr9rc458+ElTdBbTawWW93WR7BKIi2j1i5FGyfxkMl9FqjbG4vYGfffxRhtGiRUXr8Ghhdxv/+rEW/3DfD/LxH7wDuJyHl2zi1k33c9W+xQyiTXfBd4k48RRx0d1GDi4l+1cQo15MIUnShWZOATAibgbeRz0v2Z9l5v89a/so8F+AlwJ7gDdn5qZm27uBt1HfN+R/z8y7zljr51kOh+TmJ6k2fp/hw5vIx7cx1j7AF567j0OL3s1bF36dVncHOVxA0qpH8JorZXvVxQz613DtFMDD/OMnOPLTqLJF1bukvpo220RnDwwD2uO0Rp6o5wmuRqFaTDtHaWcX6JJAv9WC1jgjHODl+wf8yN/ezvbOIkb6K7loegFPLgw+e/Wl/JOdB1k1XbGoatM6cnFIfUuXaE2SnSdhcBnZ3kt09s1D70qSpLPlpAEwItrA+4HXAFuBeyPizsx8cEa1twH7MvPaiLgVeC/w5oi4AbgVuBG4HPjriHhBZg7P9I6caVklVEOY6pETkzA+Se4/RO49yHD3bsa3b2HngcfYunA7jy87yMMrlrJ17Q/B2Bv51QcX8qLBl2mN7ierDvVVvj0iIKsuw95VRHUxvU6L+1eM88iycXYv6PHkwgk2Lp9ifzf5iSdezMv3Xc/yfpfuYIpVY3tZ0d9Eq/Nkfc5ga7qeM/hwe7NFZJsR2vW9+ggyKyImuHw4Ca39sGATl2XwLx9tARc3ddoMaZPZJamXq2jRbwcL2UZrsIyxWMaBTsW+zhSHuhP0W9P0Wz1GWn0WV8miYcVIFXS7O1mQ4yyqRhiJSUbJ5hyDBJxhRJKk80Vk5tNXiHg58DuZ+bpm/d0Amfl/zahzV1Pn7ojoAE8Cq4F3zaw7s95Tfd7atWtz3bp1z2in5qLatY/+Bz5FVhUMKxgM68A3bNZn9MvHr72X71y8hal2j2FABhwYvIL9U7/Crzy+mFds382i0b+lnou3vmdeJgSd+gbJ2aIarmIsns+GxVfy1Ut38Herv8/ehftgxmHYJAmmWT58khsPHWDV5BV0B89hpLqIA50VjHWCFxxcyes2t7h4+Dit9k5oTwH9+tzCGBxpw2xx5D/z49h/ZodvID1z/djnqn850GUYwc7RhEi+cGnw5887etpqiyROsK8kJMHqQdXUOfruf/L9HSyrTnzo+7xgTpakcy537ycuXnHk+WzrvvWNtC5/zln/nMYJ/88yl0PAa4AtM9a3Aj/yVHUycxARB4CLmvJvzHrtmuNaFnE7cHuzOhYRD8+uc/75FvDH/PXcKl8M7D6Vd//SqTfo2eSU++NM+cx8fOjczFufnKfsj+PZJ8eyP45nnxzr/O2P3/zFc/lpX8zMm2cXnhcXgWTmHcAd892OsyUi1mXm2vlux/nC/jiefXIs++N49smx7I/j2SfHsj+e3lxuA7MNmDk1xBVN2QnrNIeAl1NfDDKX10qSJOkcmksAvBe4LiKujogR6os67pxV507gLc3ym4CvZH1y4Z3ArRExGhFXA9cBf39mmi5JkqTTcdJDwM05fe8A7qK+DcyHMnN9RLwHWJeZdwIfBD4SERuBvdQhkabep4AHgQHwy8+GK4DPggv28PZpsj+OZ58cy/44nn1yLPvjePbJseyPp3HSq4AlSZJ0YXEqOEmSpMIYACVJkgpjADwNEfGhiNgZEQ/MKFsVEV+KiA3N88qmPCLiP0TExoj4bkS8ZMZr3tLU3xARbznRZz0bRMSVEfHViHgwItZHxP/RlJfcJwsi4u8j4jtNn/xuU351RNzT7PsnmwuraC6U+mRTfk9EXDXjvd7dlD8cEa+bnz06MyKiHRHfiojPNeul98emiLg/Ir4dEeuaspJ/b1ZExKcj4nsR8VBEvLzw/ri++bdx+HEwIn618D75V8136gMR8fHmu7bo75HTlpk+TvEB/CjwEuCBGWW/D7yrWX4X8N5m+SeAL1DfiftlwD1N+SrgseZ5ZbO8cr737TT74zLgJc3yUuAR4IbC+ySAJc1yF7in2ddPAbc25X8C/Mtm+ZeAP2mWbwU+2SzfAHwHGAWuBh4F2vO9f8+gX94JfAz4XLNeen9sAi6eVVby782HgV9olkeAFSX3x6y+aVPPsvW8UvuEeiKJx4GFzfqngLeW/j1y2v053w14tj6Aqzg2AD4MXNYsXwY83Cx/ALhtdj3gNuADM8qPqfdsfgB/QT13tH1S78ci4D7qGXR2A52m/OXAXc3yXcDLm+VOUy+AdwPvnvFeR+o92x7U9wH9MvAq4HPN/hXbH037N3F8ACzy94b6/rGP01ycWHp/nKB/Xgt8veQ+4eisY6ua74XPAa8r/XvkdB8eAj5zLsnM7c3yk8AlzfKJptJb8zTlz2rNEPsPUY94Fd0nzeHObwM7qWf3exTYn5mDpsrM/TtmOkVg5nSKF0qf/Hvg16knrIZ6/0ruD6gn7v6riPhm1FNiQrm/N1cDu4D/1Jwm8GcRsZhy+2O2W4GPN8tF9klmbgP+ANgMbKf+Xvgmfo+cFgPgWZD1nxTF3V8nIpYA/w341cw8OHNbiX2SmcPMfDH1yNdNwA/Mc5PmTUT8JLAzM7853205z7wyM18CvB745Yj40ZkbC/u96VCfWvMfM/OHgHHqw5tHFNYfRzTntL0B+K+zt5XUJ825jrdQ/7FwObAYOG6OW82NAfDM2RERlwE0zzub8qeaDu+CmiYvIrrU4e/PM/MzTXHRfXJYZu4Hvkp9aGJF1NMlwrH7d6FPp/gK4A0RsQn4BPVh4PdRbn8AR0Y0yMydwGep/1Ao9fdmK7A1M+9p1j9NHQhL7Y+ZXg/cl5k7mvVS++THgcczc1dm9oHPUH+3FP09croMgGfOzOnw3kJ9Htzh8n/eXJ31MuBAM3R/F/DaiFjZ/FXz2qbsWScigno2mIcy8w9nbCq5T1ZHxIpmeSH1OZEPUQfBNzXVZvfJBTudYma+OzOvyMyrqA9lfSUzf5ZC+wMgIhZHxNLDy9T/3h+g0N+bzHwS2BIR1zdFr6aeRarI/pjlNo4e/oVy+2Qz8LKIWNT8f+fwv5Fiv0eekfk+CfHZ+KD+RdwO9Kn/an0b9XkFXwY2AH8NrGrqBvB+6vO/7gfWznifnwc2No9/Md/79Qz645XUhyC+C3y7efxE4X3yIuBbTZ88APx2U34N9RfNRurDOaNN+YJmfWOz/ZoZ7/WbTV89DLx+vvftDPTNj3H0KuBi+6PZ9+80j/XAbzblJf/evBhY1/ze/HfqK1aL7Y9mXxZTj1otn1FWbJ8Avwt8r/le/Qj1lbzFfo88k4dTwUmSJBXGQ8CSJEmFMQBKkiQVxgAoSZJUGAOgJElSYQyAkiRJhTEASpIkFcYAKEmNiLg8Ij493+2QpLPN+wBK0jkSEe3MHM53OyTJEUBJF4SI+LmI+PuI+HZEfCAi2hExFhH/LiK+ExHfiIhLmrrPb9bvj4jfi4ixpvyqiHigWX5rRHwmIr4YERsi4vdnfNZrI+LuiLgvIv5rRCx5mnZtioj3RsR9wE9HxNci4n1NOx+IiJuaer8TER+OiL+NiO9HxP8SEb/ftPGLzXzbknRGGAAlPetFxD8A3gy8IjNfDAyBn6WeRusbmfk/AX8DvL15yfuA92XmC6mnc3wqL27e94XAmyPiyoi4GPgt4Mcz8yXUU5e98yRN3JOZL8nMTzTri5p2/hLwoRn1ng+8CngD8FHgq00bJ4F/fLJ+kKS56sx3AyTpDHg18FLg3nqOeBYCO4Ee8LmmzjeB1zTLLwfe2Cx/DPiDp3jfL2fmAYCIeBB4HrACuAH4evNZI8DdJ2nfJ2etfxwgM/8mIpZFxIqm/AuZ2Y+I+4E28MWm/H7gqpN8hiTNmQFQ0oUggA9n5ruPKYz4tTx6ovOQU//Om56xfPj1AXwpM287hfcZn7U+++Trw+vTAJlZRUR/Rtsr/L6WdAZ5CFjSheDLwJsi4jkAEbEqIp73NPW/AfxUs3zrKX7WN4BXRMS1zWctjogXnOJ7vLl57SuBA4dHGSXpXPEvSknPepn5YET8FvBXEdEC+sAvP81LfhX4aET8JvVh1jkHsMzcFRFvBT4eEaNN8W8Bj5xCk6ci4ltAF/j5U3idJJ0R3gZGUnEiYhEwmZkZEbcCt2XmLefos78G/FpmrjsXnydJJ+IIoKQSvRT446iv4tiPo3CSCuMIoCSdARHxWeDqWcW/kZl3zUd7JOnpGAAlSZIK41XAkiRJhTEASpIkFcYAKEmSVBgDoCRJUmH+f48Zp+mfJAxSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PME9X2BeEeyv"
      },
      "source": [
        "Right skewed plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "NWUZamt7Eeyw",
        "outputId": "9aa71c94-161d-4111-fae5-94134e467e34"
      },
      "source": [
        "for i in ['coolant_tmp']:\n",
        "    \n",
        "    g = sns.FacetGrid(df_xirgo[['vin',i]], hue=\"vin\", height = 6, aspect = 1.5)\n",
        "    g = (g.map(sns.distplot, i , hist=False, rug=True))\n",
        "    plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MrdX6IkREeyw"
      },
      "source": [
        "Left skewed plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLUmDwsWEeyx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "0db3ad2f-247f-4fae-e492-a5c4bdca89db"
      },
      "source": [
        "for i in ['oil_tmpl']:\n",
        "    \n",
        "    g = sns.FacetGrid(df_xirgo[['vin',i]], hue=\"vin\", height = 6, aspect = 1.5)\n",
        "    g = (g.map(sns.distplot, i , hist=False, rug=True))\n",
        "    plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etigjhV1Eeyy"
      },
      "source": [
        "Left skewed plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ylCZG8vDEeyy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "677d3b11-8b30-44e8-8261-fb36d2d74304"
      },
      "source": [
        "for i in ['throt_pos']:\n",
        "    \n",
        "    g = sns.FacetGrid(df_xirgo[['vin',i]], hue=\"vin\", height = 6, aspect = 1.5)\n",
        "    g = (g.map(sns.distplot, i , hist=False, rug=True))\n",
        "    plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5JuhvL8Eeyz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "5e384ddf-b5ed-4551-e83d-98f0019132c5"
      },
      "source": [
        "for i in ['torque']:\n",
        "    \n",
        "    g = sns.FacetGrid(df_xirgo[['vin',i]], hue=\"vin\", height = 6, aspect = 1.5)\n",
        "    g = (g.map(sns.distplot, i , hist=False, rug=True))\n",
        "    plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01BmjQSsEey0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "d554138f-74f8-45f6-99a2-765d0541b8bb"
      },
      "source": [
        "for i in ['oil_psi']:\n",
        "    \n",
        "    g = sns.FacetGrid(df_xirgo[['vin',i]], hue=\"vin\", height = 6, aspect = 1.5)\n",
        "    g = (g.map(sns.distplot, i , hist=False, rug=True))\n",
        "    plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XRQoA2nEEey1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "c6eb9c74-3812-4080-80fd-47739e0a971f"
      },
      "source": [
        "for i in ['manifold_tmp']:\n",
        "    \n",
        "    g = sns.FacetGrid(df_xirgo[['vin',i]], hue=\"vin\", height = 6, aspect = 1.5)\n",
        "    g = (g.map(sns.distplot, i , hist=False, rug=True))\n",
        "    plt.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5pSCciwHEey2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "5f2f8caa-952c-4f90-cb33-d860a055effc"
      },
      "source": [
        "for i in ['vehiclespeed']:\n",
        "    \n",
        "    g = sns.FacetGrid(df_xirgo[['vin',i]], hue=\"vin\", height = 6, aspect = 1.5)\n",
        "    g = (g.map(sns.distplot, i , hist=False, rug=True))\n",
        "    plt.show()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F8VmuSUlEey3"
      },
      "source": [
        "Left skewed plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KKF0FVAtEey3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "85b01219-5c52-4088-ccf7-b88532785d40"
      },
      "source": [
        "for i in ['odometerinmeters']:\n",
        "    \n",
        "    g = sns.FacetGrid(df_xirgo[['vin',i]], hue=\"vin\", height = 6, aspect = 1.5)\n",
        "    g = (g.map(sns.distplot, i , hist=False, rug=True))\n",
        "    plt.show()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "owcEc4D0Eey4"
      },
      "source": [
        "Right skewed plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txSmwGTUEey5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "outputId": "c4613f28-3163-48fd-e66f-7d3a6dd32ca3"
      },
      "source": [
        "corrmat = df_xirgo.corr()\n",
        "f, ax = plt.subplots(figsize=(12, 9))\n",
        "sns.heatmap(corrmat,vmax=1, square=True, annot = True)\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9W3i82QEey6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "0e7c878d-fa9e-4322-8027-b5d8bd9e3ac9"
      },
      "source": [
        "corrmat"
      ],
      "execution_count": 41,
      "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>fuel_total</th>\n",
              "      <th>engine_rpm</th>\n",
              "      <th>coolant_tmp</th>\n",
              "      <th>oil_tmpl</th>\n",
              "      <th>throt_pos</th>\n",
              "      <th>torque</th>\n",
              "      <th>oil_psi</th>\n",
              "      <th>manifold_tmp</th>\n",
              "      <th>vehiclespeed</th>\n",
              "      <th>odometerinmeters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fuel_total</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.059196</td>\n",
              "      <td>0.123325</td>\n",
              "      <td>0.089682</td>\n",
              "      <td>0.009028</td>\n",
              "      <td>0.075888</td>\n",
              "      <td>0.045446</td>\n",
              "      <td>0.124530</td>\n",
              "      <td>0.145805</td>\n",
              "      <td>0.945902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>engine_rpm</th>\n",
              "      <td>0.059196</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.151791</td>\n",
              "      <td>0.098110</td>\n",
              "      <td>0.202009</td>\n",
              "      <td>0.021925</td>\n",
              "      <td>0.513158</td>\n",
              "      <td>0.124394</td>\n",
              "      <td>0.141833</td>\n",
              "      <td>-0.009122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>coolant_tmp</th>\n",
              "      <td>0.123325</td>\n",
              "      <td>0.151791</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.831342</td>\n",
              "      <td>0.120744</td>\n",
              "      <td>0.240141</td>\n",
              "      <td>-0.424923</td>\n",
              "      <td>0.440825</td>\n",
              "      <td>0.354447</td>\n",
              "      <td>0.097230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oil_tmpl</th>\n",
              "      <td>0.089682</td>\n",
              "      <td>0.098110</td>\n",
              "      <td>0.831342</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.075773</td>\n",
              "      <td>0.140496</td>\n",
              "      <td>-0.452596</td>\n",
              "      <td>0.478477</td>\n",
              "      <td>0.325415</td>\n",
              "      <td>0.078920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>throt_pos</th>\n",
              "      <td>0.009028</td>\n",
              "      <td>0.202009</td>\n",
              "      <td>0.120744</td>\n",
              "      <td>0.075773</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.301602</td>\n",
              "      <td>0.138822</td>\n",
              "      <td>0.109816</td>\n",
              "      <td>0.090133</td>\n",
              "      <td>-0.018057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>torque</th>\n",
              "      <td>0.075888</td>\n",
              "      <td>0.021925</td>\n",
              "      <td>0.240141</td>\n",
              "      <td>0.140496</td>\n",
              "      <td>0.301602</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.187919</td>\n",
              "      <td>0.248835</td>\n",
              "      <td>0.233338</td>\n",
              "      <td>0.047378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oil_psi</th>\n",
              "      <td>0.045446</td>\n",
              "      <td>0.513158</td>\n",
              "      <td>-0.424923</td>\n",
              "      <td>-0.452596</td>\n",
              "      <td>0.138822</td>\n",
              "      <td>-0.187919</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.091435</td>\n",
              "      <td>-0.180792</td>\n",
              "      <td>-0.010419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manifold_tmp</th>\n",
              "      <td>0.124530</td>\n",
              "      <td>0.124394</td>\n",
              "      <td>0.440825</td>\n",
              "      <td>0.478477</td>\n",
              "      <td>0.109816</td>\n",
              "      <td>0.248835</td>\n",
              "      <td>-0.091435</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.094540</td>\n",
              "      <td>0.118339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vehiclespeed</th>\n",
              "      <td>0.145805</td>\n",
              "      <td>0.141833</td>\n",
              "      <td>0.354447</td>\n",
              "      <td>0.325415</td>\n",
              "      <td>0.090133</td>\n",
              "      <td>0.233338</td>\n",
              "      <td>-0.180792</td>\n",
              "      <td>0.094540</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.139108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>odometerinmeters</th>\n",
              "      <td>0.945902</td>\n",
              "      <td>-0.009122</td>\n",
              "      <td>0.097230</td>\n",
              "      <td>0.078920</td>\n",
              "      <td>-0.018057</td>\n",
              "      <td>0.047378</td>\n",
              "      <td>-0.010419</td>\n",
              "      <td>0.118339</td>\n",
              "      <td>0.139108</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  fuel_total  engine_rpm  coolant_tmp  oil_tmpl  throt_pos  \\\n",
              "fuel_total          1.000000    0.059196     0.123325  0.089682   0.009028   \n",
              "engine_rpm          0.059196    1.000000     0.151791  0.098110   0.202009   \n",
              "coolant_tmp         0.123325    0.151791     1.000000  0.831342   0.120744   \n",
              "oil_tmpl            0.089682    0.098110     0.831342  1.000000   0.075773   \n",
              "throt_pos           0.009028    0.202009     0.120744  0.075773   1.000000   \n",
              "torque              0.075888    0.021925     0.240141  0.140496   0.301602   \n",
              "oil_psi             0.045446    0.513158    -0.424923 -0.452596   0.138822   \n",
              "manifold_tmp        0.124530    0.124394     0.440825  0.478477   0.109816   \n",
              "vehiclespeed        0.145805    0.141833     0.354447  0.325415   0.090133   \n",
              "odometerinmeters    0.945902   -0.009122     0.097230  0.078920  -0.018057   \n",
              "\n",
              "                    torque   oil_psi  manifold_tmp  vehiclespeed  \\\n",
              "fuel_total        0.075888  0.045446      0.124530      0.145805   \n",
              "engine_rpm        0.021925  0.513158      0.124394      0.141833   \n",
              "coolant_tmp       0.240141 -0.424923      0.440825      0.354447   \n",
              "oil_tmpl          0.140496 -0.452596      0.478477      0.325415   \n",
              "throt_pos         0.301602  0.138822      0.109816      0.090133   \n",
              "torque            1.000000 -0.187919      0.248835      0.233338   \n",
              "oil_psi          -0.187919  1.000000     -0.091435     -0.180792   \n",
              "manifold_tmp      0.248835 -0.091435      1.000000      0.094540   \n",
              "vehiclespeed      0.233338 -0.180792      0.094540      1.000000   \n",
              "odometerinmeters  0.047378 -0.010419      0.118339      0.139108   \n",
              "\n",
              "                  odometerinmeters  \n",
              "fuel_total                0.945902  \n",
              "engine_rpm               -0.009122  \n",
              "coolant_tmp               0.097230  \n",
              "oil_tmpl                  0.078920  \n",
              "throt_pos                -0.018057  \n",
              "torque                    0.047378  \n",
              "oil_psi                  -0.010419  \n",
              "manifold_tmp              0.118339  \n",
              "vehiclespeed              0.139108  \n",
              "odometerinmeters          1.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sn107hykEey6"
      },
      "source": [
        "There is correlation between variables. To remove these correlation we need to do PCA."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNlt659wEey7"
      },
      "source": [
        "# Dimensionality Reduction using PCA on multivariate timeseries data\n",
        "\n",
        "https://medium.com/@ansjin/dimensionality-reduction-using-pca-on-multivariate-timeseries-data-b5cc07238dc4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fn04lTZ-Eey7"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler # for standardizing the Data\n",
        "from sklearn.decomposition import PCA # for PCA calculation"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rc05c58lEey8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "outputId": "d3e08594-bcb6-4d3b-d1c2-3a2231a6fdc6"
      },
      "source": [
        "df_xirgo = df_xirgo.sort_values('msg_ts')\n",
        "df_pca = df_xirgo\n",
        "df_pca = df_pca.set_index(['msg_ts'])\n",
        "df_pca"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th>vin</th>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>2020-05-31 20:02:05</th>\n",
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              "    <tr>\n",
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              "      <td>3HSDZTZR0LN384388</td>\n",
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              "      <td>956.0</td>\n",
              "      <td>186262080.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>215269 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   vin  fuel_total  engine_rpm  coolant_tmp  \\\n",
              "msg_ts                                                                        \n",
              "2020-04-01 00:00:19  3HSDZTZR5LN134290     83536.0      1489.0         84.0   \n",
              "2020-04-01 00:01:06  3HSDWTZR5LN844783     19870.0      1376.0         81.0   \n",
              "2020-04-01 00:02:21  3HSDZTZR5LN134290     83536.0      1462.0         82.0   \n",
              "2020-04-01 00:02:44  3HSDZTZR1LN125327     82402.0      1160.0         82.0   \n",
              "2020-04-01 00:03:33  3HSDWTZR5LN845092    119822.0      1387.0         84.0   \n",
              "...                                ...         ...         ...          ...   \n",
              "2020-05-31 18:02:50  3HSDZTZR2LN075067     73502.0      1087.0         79.0   \n",
              "2020-05-31 18:16:54  3HSDZTZR8KN398425     24661.0      1603.0         83.0   \n",
              "2020-05-31 18:31:11  3HSDWTZR9LN810071     61931.0      1001.0         82.0   \n",
              "2020-05-31 20:02:05  3HSDZTZR0LN384388     53694.0      1121.0         90.0   \n",
              "2020-05-31 21:11:51  3HSDZTZR0LN384388     53701.0      1085.0         81.0   \n",
              "\n",
              "                     oil_tmpl  throt_pos  torque  oil_psi  manifold_tmp  \\\n",
              "msg_ts                                                                    \n",
              "2020-04-01 00:00:19      94.0        0.0     0.0    452.0          43.0   \n",
              "2020-04-01 00:01:06      83.0      100.0    16.0    388.0          32.0   \n",
              "2020-04-01 00:02:21      91.0        0.0     0.0    380.0          39.0   \n",
              "2020-04-01 00:02:44      86.0        0.0    31.0    312.0          37.0   \n",
              "2020-04-01 00:03:33      89.0      100.0    49.0    380.0          40.0   \n",
              "...                       ...        ...     ...      ...           ...   \n",
              "2020-05-31 18:02:50      88.0       32.0    22.0    304.0          53.0   \n",
              "2020-05-31 18:16:54      92.0      100.0    38.0    472.0          47.0   \n",
              "2020-05-31 18:31:11      86.0       37.0    33.0    280.0          37.0   \n",
              "2020-05-31 20:02:05      92.0       18.0     8.0    320.0          62.0   \n",
              "2020-05-31 21:11:51      89.0       48.0    38.0    292.0          41.0   \n",
              "\n",
              "                     vehiclespeed  odometerinmeters  \n",
              "msg_ts                                               \n",
              "2020-04-01 00:00:19        1106.0       265633380.0  \n",
              "2020-04-01 00:01:06        1094.0       284931580.0  \n",
              "2020-04-01 00:02:21        1038.0       265636940.0  \n",
              "2020-04-01 00:02:44        1098.0       240438540.0  \n",
              "2020-04-01 00:03:33        1110.0       357404480.0  \n",
              "...                           ...               ...  \n",
              "2020-05-31 18:02:50         103.0       277824300.0  \n",
              "2020-05-31 18:16:54        1196.0        63787140.0  \n",
              "2020-05-31 18:31:11         672.0       196080560.0  \n",
              "2020-05-31 20:02:05         116.0       186249040.0  \n",
              "2020-05-31 21:11:51         956.0       186262080.0  \n",
              "\n",
              "[215269 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57BkKHv4bHWT"
      },
      "source": [
        "# list(df_xirgo['vin'].iloc[:1000])"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BhW1qtOEey9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "87b17b18-f931-498b-a8ea-bfc8a97b948e"
      },
      "source": [
        "df_pca = df_pca.drop(columns = ['vin'], axis=1)\n",
        "df_pca"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>2020-04-01 00:01:06</th>\n",
              "      <td>19870.0</td>\n",
              "      <td>1376.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>388.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1094.0</td>\n",
              "      <td>284931580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01 00:02:21</th>\n",
              "      <td>83536.0</td>\n",
              "      <td>1462.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>380.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1038.0</td>\n",
              "      <td>265636940.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01 00:02:44</th>\n",
              "      <td>82402.0</td>\n",
              "      <td>1160.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>312.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1098.0</td>\n",
              "      <td>240438540.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01 00:03:33</th>\n",
              "      <td>119822.0</td>\n",
              "      <td>1387.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>380.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1110.0</td>\n",
              "      <td>357404480.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-31 18:02:50</th>\n",
              "      <td>73502.0</td>\n",
              "      <td>1087.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>304.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>277824300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-31 18:16:54</th>\n",
              "      <td>24661.0</td>\n",
              "      <td>1603.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1196.0</td>\n",
              "      <td>63787140.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-31 18:31:11</th>\n",
              "      <td>61931.0</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>672.0</td>\n",
              "      <td>196080560.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-31 20:02:05</th>\n",
              "      <td>53694.0</td>\n",
              "      <td>1121.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>186249040.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-31 21:11:51</th>\n",
              "      <td>53701.0</td>\n",
              "      <td>1085.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>292.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>956.0</td>\n",
              "      <td>186262080.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>215269 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     fuel_total  engine_rpm  coolant_tmp  oil_tmpl  throt_pos  \\\n",
              "msg_ts                                                                          \n",
              "2020-04-01 00:00:19     83536.0      1489.0         84.0      94.0        0.0   \n",
              "2020-04-01 00:01:06     19870.0      1376.0         81.0      83.0      100.0   \n",
              "2020-04-01 00:02:21     83536.0      1462.0         82.0      91.0        0.0   \n",
              "2020-04-01 00:02:44     82402.0      1160.0         82.0      86.0        0.0   \n",
              "2020-04-01 00:03:33    119822.0      1387.0         84.0      89.0      100.0   \n",
              "...                         ...         ...          ...       ...        ...   \n",
              "2020-05-31 18:02:50     73502.0      1087.0         79.0      88.0       32.0   \n",
              "2020-05-31 18:16:54     24661.0      1603.0         83.0      92.0      100.0   \n",
              "2020-05-31 18:31:11     61931.0      1001.0         82.0      86.0       37.0   \n",
              "2020-05-31 20:02:05     53694.0      1121.0         90.0      92.0       18.0   \n",
              "2020-05-31 21:11:51     53701.0      1085.0         81.0      89.0       48.0   \n",
              "\n",
              "                     torque  oil_psi  manifold_tmp  vehiclespeed  \\\n",
              "msg_ts                                                             \n",
              "2020-04-01 00:00:19     0.0    452.0          43.0        1106.0   \n",
              "2020-04-01 00:01:06    16.0    388.0          32.0        1094.0   \n",
              "2020-04-01 00:02:21     0.0    380.0          39.0        1038.0   \n",
              "2020-04-01 00:02:44    31.0    312.0          37.0        1098.0   \n",
              "2020-04-01 00:03:33    49.0    380.0          40.0        1110.0   \n",
              "...                     ...      ...           ...           ...   \n",
              "2020-05-31 18:02:50    22.0    304.0          53.0         103.0   \n",
              "2020-05-31 18:16:54    38.0    472.0          47.0        1196.0   \n",
              "2020-05-31 18:31:11    33.0    280.0          37.0         672.0   \n",
              "2020-05-31 20:02:05     8.0    320.0          62.0         116.0   \n",
              "2020-05-31 21:11:51    38.0    292.0          41.0         956.0   \n",
              "\n",
              "                     odometerinmeters  \n",
              "msg_ts                                 \n",
              "2020-04-01 00:00:19       265633380.0  \n",
              "2020-04-01 00:01:06       284931580.0  \n",
              "2020-04-01 00:02:21       265636940.0  \n",
              "2020-04-01 00:02:44       240438540.0  \n",
              "2020-04-01 00:03:33       357404480.0  \n",
              "...                               ...  \n",
              "2020-05-31 18:02:50       277824300.0  \n",
              "2020-05-31 18:16:54        63787140.0  \n",
              "2020-05-31 18:31:11       196080560.0  \n",
              "2020-05-31 20:02:05       186249040.0  \n",
              "2020-05-31 21:11:51       186262080.0  \n",
              "\n",
              "[215269 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yY9tOjQyEey-"
      },
      "source": [
        "X = df_pca.values              # getting all values as a matrix of dataframe \n",
        "sc = StandardScaler()          # creating a StandardScaler object\n",
        "X_std = sc.fit_transform(X)    # standardizing the data"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9m2kPCvEey-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35eb0e42-ef12-4d01-fd7a-83cc2977766d"
      },
      "source": [
        "X"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[8.3536000e+04, 1.4890000e+03, 8.4000000e+01, ..., 4.3000000e+01,\n",
              "        1.1060000e+03, 2.6563338e+08],\n",
              "       [1.9870000e+04, 1.3760000e+03, 8.1000000e+01, ..., 3.2000000e+01,\n",
              "        1.0940000e+03, 2.8493158e+08],\n",
              "       [8.3536000e+04, 1.4620000e+03, 8.2000000e+01, ..., 3.9000000e+01,\n",
              "        1.0380000e+03, 2.6563694e+08],\n",
              "       ...,\n",
              "       [6.1931000e+04, 1.0010000e+03, 8.2000000e+01, ..., 3.7000000e+01,\n",
              "        6.7200000e+02, 1.9608056e+08],\n",
              "       [5.3694000e+04, 1.1210000e+03, 9.0000000e+01, ..., 6.2000000e+01,\n",
              "        1.1600000e+02, 1.8624904e+08],\n",
              "       [5.3701000e+04, 1.0850000e+03, 8.1000000e+01, ..., 4.1000000e+01,\n",
              "        9.5600000e+02, 1.8626208e+08]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbV7JGtPYSoW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62452d18-72c6-4e9e-ef81-58f5bce6472e"
      },
      "source": [
        "X_std"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.0675104 ,  1.52452616,  0.63854912, ...,  0.61104633,\n",
              "         0.59138057,  0.0734456 ],\n",
              "       [-1.31526287,  0.81280511, -0.11343303, ..., -0.57032667,\n",
              "         0.5416341 ,  0.21624347],\n",
              "       [ 0.0675104 ,  1.35446892,  0.13722769, ...,  0.18145615,\n",
              "         0.30948393,  0.07347195],\n",
              "       ...,\n",
              "       [-0.40173247, -1.54910101,  0.13722769, ..., -0.03333894,\n",
              "        -1.20778329, -0.44121351],\n",
              "       [-0.58063335, -0.79329105,  2.14251341, ...,  2.65159969,\n",
              "        -3.51270289, -0.51396227],\n",
              "       [-0.58048131, -1.02003404, -0.11343303, ...,  0.39625124,\n",
              "        -0.03045026, -0.51386578]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JlGIFMyiEey_"
      },
      "source": [
        "pca = PCA()\n",
        "X_pca = pca.fit(X_std)"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AeIZEggGEezA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "2d519707-8204-4572-fb9b-122bcfe4c990"
      },
      "source": [
        "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
        "plt.xlabel('number of components')\n",
        "plt.ylabel('cumulative explained variance');"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Fl2sWmNlogl/1zavbyN1Lw/ZvEMxVNMbd55jZ7cBkdx8PXAc8YmY/I+g4vsjdv3EXs0iizdtK+d+XZ/PStGUc3Ksdfzxrfzq2bBJ3WCJ1llV33DWzA4F5QGvgDoJRPr9190+SH9435ebm+uTJk+N4a0kBn60s5IqxU1m0djNXH9WHK4/sQ0YDVQ4TqY6ZTXH33J29FuWMoMzdNxHcT3BxjUYmEpG7M25SHreNn0PLrEaM/clwDundPu6wROqFKIng92bWGXgB+Lu7z05yTCI72LStlJtfmsX4Gcs5rE977j1jfzq0aBx3WCL1RpT7CI4IE8EZwF/NrCVBQvi/pEcnaW/O8gJGPzONJes2c/2xfbl8xN400KUgkRoVaYiFu6909z8BlwLTgVuTGpWkPXfnqU+W8IM//5ctxaU8e8lBjD6yj5KASBJUe0YQDus8k6Aq2Trg7wSjfUSSorCohJtemsVrM1dweN8O3HvGYNo116UgkWSJ0kcwBhgHHOfumgtIkmpWfgGjn51K/oat3DCyHz/9bi+dBYgkWZQ+goNrIxBJb+7Okx8v4c7X5tGueSZ/H3UQuT3axh2WSFqIckYgklQFW0u44YWZvD5nJUf268jvfziYNs0y4w5LJG0oEUisZuR9xehnp7LiqyJ+cUJ/fvydnroUJFLLlAgkNhNmreCacdPp0KIxz116MENz2sQdkkha2mUiMLNXCeb/2Sl3PykpEUlaeOrjxdw6fg5Dc9rw6AW5uhQkEqOqzgh+F/49FegMPB0unw2sSmZQUn+5O/e99Tl/emchR/XryAPnDCUrMyPusETS2i4Tgbu/D2Bmv680UdGrZqZZ32S3lZaV87+vzObZiXmckduNX/9gP00bLZICovQRNDOzXgm1h3sSlJUUiayopIwrn53GW3NXccURvbn+2H0wU6ewSCqIkgh+BrwXlqc0oDvw06RGJfVKwZYSfvLkJCYv2cBt3x/ARYf2jDskEUkQ5Yay182sD9AvXPWZu29LblhSX6wsKOLCMRNZtHYT9589hBMHdYk7JBGpJMpcQ02Ba4Hu7n5JWMx+H3f/Z/LDk7ps4epNXDhmIgVbS/jbxcM4dG/VDxBJRVF66h4HioGKqSaWAZqCWqo0bekGfvjQf9lWWsa4UQcpCYiksCiJoLe73wOUALj7FoK+ApGdenf+as555FNaZjXixcsOYWDXVnGHJCJViJIIis0si/DmMjPrDUTqIzCzkWY238wWmtmNO3n9PjObHj4+N7Ovdit6STkvTsnnJ09MpnfHZrxw6SF0b6cBZiKpLsqooV8CrwPZZjYWOBS4qLqNzCwDeBA4BsgHJpnZeHefW9HG3X+W0P5KYMhuRS8pw915+INF/OZfn3Ho3u346/m5NG+sGUxE6oIoo4beMrOpwEEEl4Sudve1EfY9DFiYcP/BOOBkYO4u2p9NkHSkjikvd+6cMI/HPvqSEwftxe/PGEzjhrpbWKSuiPqTrQmwIWw/wMxw9w+q2aYrkJewnA8M31lDM+sO9ATe2cXro4BRADk5ORFDltpQXFrOz1+YwSvTl3PRIT249cQBmj1UpI6JMnz0boJSlXOA8nC1A9Ulgt1xFvCCu5ft7EV3fxh4GCA3N3eXE+FJ7dq0rZTLnp7ChwvW8j8j9+Gyw3vrbmGROijKGcEpwD57cBPZMiA7YblbuG5nzgKu2M39S4zWbdrGxX+bxJzlhdxz2iDOODC7+o1EJCVFSQSLgEZEHCmUYBLQJ5ybaBnBwf6cyo3MrB/QBvh4N/cvMclbv4ULxkxk+Vdb+et5B3D0gE5xhyQi30KURLAFmG5mb5OQDNz9qqo2cvdSMxsNvAFkAGPcfY6Z3Q5MdvfxYdOzgHHurks+dcDc5YVc+PhEikvLeeaS4RzQXXWFReq6KIlgfPjYbe4+AZhQad2tlZZv25N9S+37+It1jHpyMs2bNOSZSw+mT6cWcYckIjUgyvDRJ2ojEElt/5q1gqvHTad7u6Y88aNhdGmdFXdIIlJDqipV+Zy7n2Fms9hJyUp3H5TUyCRlPPXJEm59ZTZDc9rw2IW5tG6qspIi9UlVZwRXh39PrI1AJPW4O/f9ewF/enuBykqK1GNVlapcEf5dUnvhSKoIykrO4dmJS1VWUqSeq/ZftpkdZGaTzGyTmRWbWZmZFdZGcBKPopIyLh87lWcnLuWKI3pz92mDlARE6rEoo4YeIBji+TyQC1wA9E1mUBKfgq0lXPLEZCYtWa+ykiJpItLPPHdfCGS4e5m7Pw6MTG5YEodVhUWc+dePmZa3gfvPHqIkIJImIt1QZmaZBDeV3QOsIGICkbrjizWbuOAxlZUUSUdRDujnE9wZPBrYTDB/0GnJDEpq17SlGzj9LyorKZKuotxQVjFqaCvwq+SGI7Xt3fmrufzpqXRo0ZinfjxMFcVE0lBVN5Tt9EayCrqhrO57cUo+N7w4k306t+BvFw+jQ4vGcYckIjGo6oxAN5LVY2M++pLb/zmXQ/dux0PnHUCLJo3iDklEYlLVDWXbbyQzs84EpScdmOTuK2shNkmSDz5fwx2vzWXkvp3549n7q6ykSJqLckPZT4CJwKnA6cAnZvajZAcmybGiYCvX/H06+3RqwX1nKgmISLThoz8Hhrj7OgAzawf8FxiTzMCk5pWUlTP6mWlsKynjwXM1b5CIBKIkgnXAxoTljeE6qWPu/tdnTFmygQfOGULvDs3jDkdEUkSURLAQ+NTMXiHoIzgZmGlm1wK4+71JjE9qyOuzV/LoR19y4cHdOXFQl7jDEZEUEiURfBE+KrwS/lV5qjpiybrN/Pz5GQzu1oqbv9c/7nBEJMVESQR3u3tR4goza+/ua6vb0MxGAn8kuDP5UXe/aydtzgBuIzjbmOHu3yhwL3uuYibRBg2MB88dqs5hEfmGKFNMTDSzgyoWzOw0gs7iKplZBvAgcDwwADjbzAZUatMHuAk41N33Ba7Zjdglgl+9Opc5ywu578zBdGvTNO5wRCQFRTkjOBcYY2bvAV2AdsCREbYbBix090UAZjaOoH9hbkKbS4AH3X0DgLuvjh66VOcf0/J5duJSLhvRmyP7dYo7HBFJUVHmGpplZncCTxGMGPquu+dH2HdXIC9hOR8YXqlNXwAz+w/B5aPb3P31KIFL1T5ftZGbX5rN8J5tue4YlY8QkV2rNhGY2WNAb2AQwYH7n2Z2v7s/WEPv3wcYAXQDPjCz/dz9q0oxjAJGAeTk5NTA29Zvm7eVcvnYqTRr3JD7zx6i6mIiUqUoR4hZwBHu/qW7v0Hwq35ohO2WEUxZXaFbuC5RPjDe3Uvc/Uvgc4LEsAN3f9jdc909t0OHDhHeOn25Ozf/YxaL1mziT2fvT8eWTeIOSURSXLWJwN3/AOSY2dHhqmKidepOAvqYWc+wsM1ZwPhKbV4mOBvAzNoTnHEsiha67MzYT5fyyvTlXHtMXw7prboCIlK9KHMNXQK8APw1XNWN4ABeJXcvJShm8wYwD3jO3eeY2e1mdlLY7A1gnZnNBd4Ffl4xlYXsvln5Bdz+6lxG7NOBy0fsHXc4IlJHmPsuSw4EDcymE4wA+tTdh4TrZrn7frUQ3zfk5ub65MmT43jrlFawpYQTH/iQsjLntasOo02zzLhDEpEUYmZT3D13Z69F6SPY5u7FCTtrSBUFa6T2uTvXvzCDlQVFPHDuUCUBEdktURLB+2Z2M5BlZscAzwOvJjcs2R2PfLiIt+au4qbj+zM0p03c4YhIHRMlEdwIrCEYPfRTYAJwSzKDkugmLV7P3a/P5/iBnbn40B5xhyMidVCUG8rKgUfCh6SQtZu2MfqZqWS3yeLu0wdhZnGHJCJ1kO40qqPKyp1rxk3nqy0l/PncA2ipmsMisoeizDUkKehPby/go4Vrufu0/RjQpWXc4YhIHRb5jMDMNHVlivjg8zX86Z0FnDa0G2fkZle/gYhIFaLcUHZIeMPXZ+HyYDP7c9Ijk52qKD7ft2ML/u+UgeoXEJFvLcoZwX3AcYR1it19BvDdZAYlO5dYfP7P56n4vIjUjEiXhtw9r9KqsiTEItW45/Wg+Pxdpw1S8XkRqTFROovzzOwQwM2sEXA1wdxBUovemLOSRz78kgsO7s73B6v4vIjUnChnBJcCVxAUmlkG7B8uSy1Zum4L14fF53+h4vMiUsOinBGYu5+b9Ehkp4pKyrhs7BQamPHAOSo+LyI1L8oZwX/M7E0z+7GZtU56RLKD2/8ZFJ+/94zBZLfVCF4RqXlRCtP0JZhbaF9gqpn908zOS3pkwsvTlvHMp0u59PDeHNVfxedFJDmijhqa6O7XEtQlWA88kdSohAWrNnLTS7MY1rMt1x+r4vMikjxRbihraWYXmtm/gP8CKwgSgiTJ5m2lXDZ2Ks0aZ/CAis+LSJJF6SyeQVCa8nZ3/zjJ8aQ9d+cXYfH5p388XMXnRSTpoiSCXl5dPUupMc9MXMrL05dz3TF9OWRvFZ8XkeTb5TUHM/tD+HS8mX3jEWXnZjbSzOab2UIzu3Enr19kZmvMbHr4+Mkefo56YfayAn41fi6H9+3AFUeo+LyI1I6qzgieCv/+bk92bGYZwIPAMUA+MMnMxrv73EpN/+7uo/fkPeqTgq0lXDZ2Cu2aZ3LfmfvToIEmkxOR2rHLMwJ3nxI+3d/d3098ENxdXJ1hwEJ3X+TuxcA44ORvH3L94+78/PkZrPiqiAfOGUpbFZ8XkVoUZTjKhTtZd1GE7boCiZPV5YfrKjvNzGaa2QtmttPJ9c1slJlNNrPJa9asifDWdcujH37Jm3NXcdMJ/Tmgu4rPi0jtqqqP4GwzexXoWal/4F2CewlqwqtAD3cfBLzFLu5PcPeH3T3X3XM7dOhQQ2+dGiYvXs9dr3/G8QM78yMVnxeRGFTVR1Bxz0B74PcJ6zcCMyPsexmQ+Au/W7huO3dfl7D4KHBPhP3WG+s2bWP0M9NUfF5EYrXLRODuS4AlwMF7uO9JQB8z60mQAM4CzklsYGZ7ufuKcPEk0mh667Jy55q/T2f9lmL+cfkhKj4vIrGJcmfxQWY2ycw2mVmxmZWZWWF127l7KTAaeIPgAP+cu88xs9vN7KSw2VVmNsfMZgBXEa3voV64/50FfLhgLbeftC/7dmkVdzgiksai3FD2AMGv+eeBXOACINLkN+4+AZhQad2tCc9vAm6KGmx98eGCNfzx7QWcOrQrZx6o4vMiEq+ok84tBDLcvczdHwdGJjes+mtlQRHXjJtOn47NVXxeRFJClDOCLWaWCUw3s3sIOpA1C9oecHeue346W0vK+PO5B9A0M8rXLyKSXFEO6OcDGQTX+zcTjAQ6LZlB1VfPTc7jPwvX8Yvv9Wfvjio+LyKpodqfpOHoIYCtwK+SG079tbqwiP97bR7Derbl7ANz4g5HRGS7XSYCM5sF7HLW0fAmMIno1lfmUFxazl2n7qd5hEQkpVR1RnBirUVRz/1r1gpen7OSG0b2o1cHXRISkdRS3Q1l8i0VbCnhf1+Zw75dWnLJYT3jDkdE5Buq7SMws418fYkoE2gEbHb3lskMrL74v9fmsmFLMX+7+ECVnBSRlBSls7hFxXMLBr2fDByUzKDqi48WrOX5KflcNqI3A7vq7mERSU279RPVAy8DxyUpnnpjS3EpN/1jJr3aN+Pqo/rEHY6IyC5FuTR0asJiA4JpJoqSFlE98fs3Pydv/Vb+PuogmjTKiDscEZFdinJr6/cTnpcCi1GlsSpNz/uKx//zJecOz2F4r3ZxhyMiUqUofQQX10Yg9UVxaTk3vDCTTi2bcOPx/eIOR0SkWlEuDfUErgR6JLZ395N2tU06+8t7XzB/1UYeuzCXFqoxICJ1QJRLQy8DjxGUlSxPbjh124JVG3ng3QWcNLgLR/XvFHc4IiKRREkERe7+p6RHUseVlTv/8+JMmjduyC+/PyDucEREIouSCP5oZr8E3gS2Vax096lJi6oOevLjxUxb+hX3nTmYds0bxx2OiEhkURLBfgRTUR/J15eGPFwWIG/9Fn77xnxG7NOBU/bvGnc4IiK7JUoi+CHQy92Lkx1MXeTu3PyPWRhw5w/2U8UxEalzotxZPBtovSc7N7ORZjbfzBaa2Y1VtDvNzNzMcvrENAEAAAzHSURBVPfkfeL00tRlfLhgLTcc34+urbPiDkdEZLdFOSNoDXxmZpPYsY+gyuGjZpYBPAgcA+QDk8xsvLvPrdSuBXA18Oluxh67NRu3ccdrc8nt3obzhnePOxwRkT0SJRH8cg/3PQxY6O6LAMxsHMEdyXMrtbsDuBv4+R6+T2xue3UOW7aVcddpg1RsRkTqrCh3Fr+/h/vuCuQlLOcDwxMbmNlQINvdXzOzXSYCMxsFjALIyUmNMo9vzlnJazNXcP2xfVV/WETqtGr7CMxso5kVho8iMyszs8Jv+8Zm1gC4F7iuurbu/rC757p7bocOHb7tW39rhUUl/O8rs+nXuQU/Pbx33OGIiHwryaxHsAzITljuFq6r0AIYCLwXjrTpDIw3s5PcfXKE/cfmNxM+Y83GbTxyQS6NVGxGROq4ZNYjmAT0MbOeZpYJnAWMT9hXgbu3d/ce7t4D+ARI+STw8RfreHbiUn5yWC8GddujwVQiIiklafUI3L3UzEYDbwAZwBh3n2NmtwOT3X181XtIPUUlZdz00ky6t2vKz47uG3c4IiI1Iqn1CNx9AjCh0rpbd9F2RJR9xum+f3/O4nVbeOaS4WRlqtiMiNQPqkcQ0az8Ah798EvOOjCbQ3q3jzscEZEaE2XU0BNm1jphuY2ZjUluWKmlpKyc/3lxJu2aZXLTCf3jDkdEpEZF6Swe5O5fVSy4+wZgSPJCSj0Pf7CIeSsKueOUgbTKUrEZEalfoiSCBmbWpmLBzNoSrW+hXvhizSb++PYCTtivM8ft2znucEREalyUA/rvgY/N7Plw+YfAnckLKXWUlzs3vjiTrEYZ3HbSvnGHIyKSFFE6i580s8l8XX/g1MoTx9VXYycuZdLiDfz29EF0bNEk7nBERJIi0iWe8MCfFgf/Csu/2spdE+ZxWJ/2nH5At7jDERFJGs2PsBPuzi0vz6bc4dcqNiMi9ZwSwU6Mn7Gcdz5bzfXH7UN226ZxhyMiklRKBJWs31zMr16dy/7ZrbnokB5xhyMiknRKBJXc/uocNhaVcM/pg8hQsRkRSQNKBAne+WwVL09fzuUj9qZvpxbVbyAiUg8oEYQ2bSvlln/Mpm+n5lx+hIrNiEj6SJs7hKtzz+ufsaKwiBfPPYTGDTWzqIikD50RAJMWr+fJj5dw8SE9GZrTpvoNRETqkbRPBEUlZdzw4ky6tcni+uNUbEZE0k/aXxp64J2FLFqzmSd/NIymmWn/dYhIGkrrM4K5ywt56P0vOP2Abny3b4e4wxERiUVSE4GZjTSz+Wa20Mxu3Mnrl5rZLDObbmYfmdmAZMaTqLSsnBtenEnrppnc8j0VmxGR9JW0RGBmGcCDwPHAAODsnRzon3H3/dx9f+Ae4N5kxVPZYx99yaxlBdx+8r60bppZW28rIpJyknlGMAxY6O6L3L0YGEelovfuXpiw2AzwJMaz3eK1m7n3rc85dkAnjh+oYjMikt6S2TvaFchLWM4HhlduZGZXANcCmXxd86Bym1HAKICcnJxvFZS7c+NLM8ls2IA7ThmomUVFJO3F3lns7g+6e2/gBuCWXbR52N1z3T23Q4dv16k7blIenyxazy9O6E+nlio2IyKSzESwDMhOWO4WrtuVccApSYyHVYVF/HrCPA7u1Y4zD8yufgMRkTSQzEQwCehjZj3NLBM4Cxif2MDM+iQsfg9YkKxgKorNFJeW85tTVWxGRKRC0voI3L3UzEYDbwAZwBh3n2NmtwOT3X08MNrMjgZKgA3AhcmKZ8Kslbw1dxU3n9CPHu2bJettRETqnKTeSuvuE4AJldbdmvD86mS+f6LmTRpyzIBO/OjQnrX1liIidULazKlweN8OHK67h0VEviH2UUMiIhIvJQIRkTSnRCAikuaUCERE0pwSgYhImlMiEBFJc0oEIiJpTolARCTNmXutlACoMWa2Bliyh5u3B9bWYDh1nb6PHen7+Jq+ix3Vh++ju7vv9K7aOpcIvg0zm+zuuXHHkSr0fexI38fX9F3sqL5/H7o0JCKS5pQIRETSXLolgofjDiDF6PvYkb6Pr+m72FG9/j7Sqo9ARES+Kd3OCEREpBIlAhGRNJc2icDMRprZfDNbaGY3xh1PXMws28zeNbO5ZjbHzGqtSlwqM7MMM5tmZv+MO5a4mVlrM3vBzD4zs3lmdnDcMcXFzH4W/juZbWbPmlmTuGNKhrRIBGaWATwIHA8MAM42swHxRhWbUuA6dx8AHARckcbfRaKrgXlxB5Ei/gi87u79gMGk6fdiZl2Bq4Bcdx9IUHv9rHijSo60SATAMGChuy9y92JgHHByzDHFwt1XuPvU8PlGgn/kXeONKl5m1g34HvBo3LHEzcxaAd8FHgNw92J3/yreqGLVEMgys4ZAU2B5zPEkRbokgq5AXsJyPml+8AMwsx7AEODTeCOJ3R+A/wHK4w4kBfQE1gCPh5fKHjWzZnEHFQd3Xwb8DlgKrAAK3P3NeKNKjnRJBFKJmTUHXgSucffCuOOJi5mdCKx29ylxx5IiGgJDgb+4+xBgM5CWfWpm1obgykFPoAvQzMzOizeq5EiXRLAMyE5Y7hauS0tm1oggCYx195fijidmhwInmdligkuGR5rZ0/GGFKt8IN/dK84SXyBIDOnoaOBLd1/j7iXAS8AhMceUFOmSCCYBfcysp5llEnT4jI85pliYmRFc/53n7vfGHU/c3P0md+/m7j0I/r94x93r5a++KNx9JZBnZvuEq44C5sYYUpyWAgeZWdPw381R1NOO84ZxB1Ab3L3UzEYDbxD0/I9x9zkxhxWXQ4HzgVlmNj1cd7O7T4gxJkktVwJjwx9Ni4CLY44nFu7+qZm9AEwlGG03jXo61YSmmBARSXPpcmlIRER2QYlARCTNKRGIiKQ5JQIRkTSnRCAikuaUCKROM7P3zCzpRcXN7KpwJs6xyX6vOIUzj14edxxSu5QIJG2FE4lFdTlwjLufm6x4UkRrgs8qaUSJQJLOzHqEv6YfCed2f9PMssLXtv+iN7P24VQPmNlFZvaymb1lZovNbLSZXRtOhPaJmbVNeIvzzWx6OGf8sHD7ZmY2xswmhtucnLDf8Wb2DvD2TmK9NtzPbDO7Jlz3ENAL+JeZ/axS+wwz+13YfqaZXRmuPyp831lhHI3D9YvN7DdhvJPNbKiZvWFmX5jZpWGbEWb2gZm9FtbQeMjMGoSvnR3uc7aZ3Z0QxyYzu9PMZoTfT6dwfQcze9HMJoWPQ8P1t4VxvWdmi8zsqnBXdwG9w/h+a2Z7hbFUfL+H7fH/CJK63F0PPZL6AHoQ3Jm5f7j8HHBe+Pw9gvneAdoDi8PnFwELgRZAB6AAuDR87T6CyfIqtn8kfP5dYHb4/NcJ79Ea+BxoFu43H2i7kzgPAGaF7ZoDc4Ah4WuLgfY72eYygvl4GobLbYEmBLPd9g3XPZkQ72LgsoTPMTPhM64K148AigiSTwbwFnA6wcRnS8O2DYF3gFPCbRz4fvj8HuCW8PkzwHfC5zkEU4sA3Ab8F2gcfu/rgEbhf6vZCZ/vOuAX4fMMoEXc/z/pUfOPtJhiQlLCl+5eMaXFFIIDTnXe9aBmwkYzKwBeDdfPAgYltHsWwN0/MLOWZtYaOJZgMrnrwzZNCA6EAG+5+/qdvN93gH+4+2YAM3sJOIxgaoFdORp4yN1LwxjWm9ng8PN+HrZ5AriCYLpr+Hqeq1lA84TPuC2MHWCiuy8K43g2jK0EeM/d14TrxxIkv5eBYqCiutoU4JiE+AYEU+UA0DKceRbgNXffBmwzs9VAp518vknAmHCiwpcT/htKPaJEILVlW8LzMiArfF7K15coK5cBTNymPGG5nB3/3608T4oDBpzm7vMTXzCz4QRTK8cp8XNU/owVn2tnn6kqJe5e0aYsYT8NgIPcvSixcZgYKv83+cbxIEyu3yUo3PM3M7vX3Z+sJhapY9RHIHFbTHBJBoLLH3viTAAz+w5B8ZACggkGrwxnjcTMhkTYz4fAKeFsk82AH4TrqvIW8NOKjuew72I+0MPM9g7bnA+8v5ufaZgFs+U2IPh8HwETgcPDvpQM4OwI+32TYBI5wvj2r6b9RoJLVRXtuxNcsnqEoIJbuk5JXa8pEUjcfgdcZmbTCK5V74micPuHgB+H6+4guOY908zmhMtV8qCE598IDrifAo+6e1WXhSA4OC4N32cGcE746/ti4Hkzm0XwS/+h3fxMk4AHCKY9/pLgktUKgiIx7wIzgCnu/ko1+7kKyA07sucCl1bV2N3XAf8JO4Z/S9BfMSP8fs8kqGcs9YxmHxVJMWY2Arje3U+MOxZJDzojEBFJczojEBFJczojEBFJc0oEIiJpTolARCTNKRGIiKQ5JQIRkTT3/yKOZTFozqmUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BND2_IPgEezB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4123b9b8-4a1d-4e56-8f29-40d1359ef973"
      },
      "source": [
        "pca = PCA(n_components = 0.95)   # takes the variables required to explain 95 percent variance\n",
        "X_pca = pca.fit_transform(X_std) # this will fit and reduce dimensions\n",
        "print(pca.n_components_)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rwMZpIIREezC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "b933bcc9-62bc-4ef4-f616-0d67f487234a"
      },
      "source": [
        "pd.DataFrame(pca.components_, columns = df_pca.columns)"
      ],
      "execution_count": 52,
      "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>fuel_total</th>\n",
              "      <th>engine_rpm</th>\n",
              "      <th>coolant_tmp</th>\n",
              "      <th>oil_tmpl</th>\n",
              "      <th>throt_pos</th>\n",
              "      <th>torque</th>\n",
              "      <th>oil_psi</th>\n",
              "      <th>manifold_tmp</th>\n",
              "      <th>vehiclespeed</th>\n",
              "      <th>odometerinmeters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.226406</td>\n",
              "      <td>-0.059395</td>\n",
              "      <td>-0.511264</td>\n",
              "      <td>-0.498764</td>\n",
              "      <td>-0.117049</td>\n",
              "      <td>-0.257392</td>\n",
              "      <td>0.286289</td>\n",
              "      <td>-0.358357</td>\n",
              "      <td>-0.311316</td>\n",
              "      <td>-0.218648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.639661</td>\n",
              "      <td>0.165688</td>\n",
              "      <td>-0.168183</td>\n",
              "      <td>-0.196110</td>\n",
              "      <td>0.020867</td>\n",
              "      <td>-0.049190</td>\n",
              "      <td>0.312988</td>\n",
              "      <td>-0.034286</td>\n",
              "      <td>0.012579</td>\n",
              "      <td>0.628082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.170772</td>\n",
              "      <td>0.628706</td>\n",
              "      <td>0.041631</td>\n",
              "      <td>-0.007687</td>\n",
              "      <td>0.457120</td>\n",
              "      <td>0.165055</td>\n",
              "      <td>0.503220</td>\n",
              "      <td>0.159748</td>\n",
              "      <td>0.079632</td>\n",
              "      <td>-0.229087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.024111</td>\n",
              "      <td>-0.341819</td>\n",
              "      <td>-0.205496</td>\n",
              "      <td>-0.286325</td>\n",
              "      <td>0.494949</td>\n",
              "      <td>0.669399</td>\n",
              "      <td>-0.173004</td>\n",
              "      <td>-0.155271</td>\n",
              "      <td>0.100882</td>\n",
              "      <td>0.032886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.034995</td>\n",
              "      <td>0.174632</td>\n",
              "      <td>0.018682</td>\n",
              "      <td>-0.030225</td>\n",
              "      <td>-0.128985</td>\n",
              "      <td>-0.082653</td>\n",
              "      <td>-0.046227</td>\n",
              "      <td>-0.577890</td>\n",
              "      <td>0.778307</td>\n",
              "      <td>-0.041148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-0.056413</td>\n",
              "      <td>0.097016</td>\n",
              "      <td>-0.178463</td>\n",
              "      <td>-0.210359</td>\n",
              "      <td>-0.668766</td>\n",
              "      <td>0.497012</td>\n",
              "      <td>0.128093</td>\n",
              "      <td>0.390929</td>\n",
              "      <td>0.208081</td>\n",
              "      <td>-0.067125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.071688</td>\n",
              "      <td>0.378728</td>\n",
              "      <td>0.267395</td>\n",
              "      <td>0.102344</td>\n",
              "      <td>-0.241928</td>\n",
              "      <td>0.410024</td>\n",
              "      <td>-0.129340</td>\n",
              "      <td>-0.537528</td>\n",
              "      <td>-0.486716</td>\n",
              "      <td>0.015646</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fuel_total  engine_rpm  coolant_tmp  oil_tmpl  throt_pos    torque  \\\n",
              "0   -0.226406   -0.059395    -0.511264 -0.498764  -0.117049 -0.257392   \n",
              "1    0.639661    0.165688    -0.168183 -0.196110   0.020867 -0.049190   \n",
              "2   -0.170772    0.628706     0.041631 -0.007687   0.457120  0.165055   \n",
              "3    0.024111   -0.341819    -0.205496 -0.286325   0.494949  0.669399   \n",
              "4   -0.034995    0.174632     0.018682 -0.030225  -0.128985 -0.082653   \n",
              "5   -0.056413    0.097016    -0.178463 -0.210359  -0.668766  0.497012   \n",
              "6    0.071688    0.378728     0.267395  0.102344  -0.241928  0.410024   \n",
              "\n",
              "    oil_psi  manifold_tmp  vehiclespeed  odometerinmeters  \n",
              "0  0.286289     -0.358357     -0.311316         -0.218648  \n",
              "1  0.312988     -0.034286      0.012579          0.628082  \n",
              "2  0.503220      0.159748      0.079632         -0.229087  \n",
              "3 -0.173004     -0.155271      0.100882          0.032886  \n",
              "4 -0.046227     -0.577890      0.778307         -0.041148  \n",
              "5  0.128093      0.390929      0.208081         -0.067125  \n",
              "6 -0.129340     -0.537528     -0.486716          0.015646  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52pf-_PGEezD"
      },
      "source": [
        "n_pcs= pca.n_components_ # get number of component\n",
        "\n",
        "# get the index of the most important feature on EACH component\n",
        "most_important_variables = [np.abs(pca.components_[i]).argmax() for i in range(n_pcs)]\n",
        "\n",
        "initial_variable_names = df_pca.columns\n",
        "\n",
        "# get the most important feature names\n",
        "most_important_variables = [initial_variable_names[most_important_variables[i]] for i in range(n_pcs)]"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6j_6QjKREezD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7e6b5d7-560b-4a16-8c62-a6915596e214"
      },
      "source": [
        "most_important_variables"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['coolant_tmp',\n",
              " 'fuel_total',\n",
              " 'engine_rpm',\n",
              " 'torque',\n",
              " 'vehiclespeed',\n",
              " 'throt_pos',\n",
              " 'manifold_tmp']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJbB1t-PEezE"
      },
      "source": [
        "# K means Clustering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KxgNAWJKkRU3"
      },
      "source": [
        "https://andrewm4894.com/2020/09/03/time-series-clustering-with-tslearn/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnJmTjbs4ucF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "outputId": "24e5b62b-6f1d-4462-c649-e619a3d1c0b4"
      },
      "source": [
        "df_xirgo"
      ],
      "execution_count": 55,
      "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>vin</th>\n",
              "      <th>msg_ts</th>\n",
              "      <th>fuel_total</th>\n",
              "      <th>engine_rpm</th>\n",
              "      <th>coolant_tmp</th>\n",
              "      <th>oil_tmpl</th>\n",
              "      <th>throt_pos</th>\n",
              "      <th>torque</th>\n",
              "      <th>oil_psi</th>\n",
              "      <th>manifold_tmp</th>\n",
              "      <th>vehiclespeed</th>\n",
              "      <th>odometerinmeters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>156451</th>\n",
              "      <td>3HSDZTZR5LN134290</td>\n",
              "      <td>2020-04-01 00:00:19</td>\n",
              "      <td>83536.0</td>\n",
              "      <td>1489.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>452.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>265633380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37260</th>\n",
              "      <td>3HSDWTZR5LN844783</td>\n",
              "      <td>2020-04-01 00:01:06</td>\n",
              "      <td>19870.0</td>\n",
              "      <td>1376.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>388.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1094.0</td>\n",
              "      <td>284931580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156452</th>\n",
              "      <td>3HSDZTZR5LN134290</td>\n",
              "      <td>2020-04-01 00:02:21</td>\n",
              "      <td>83536.0</td>\n",
              "      <td>1462.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>380.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1038.0</td>\n",
              "      <td>265636940.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113667</th>\n",
              "      <td>3HSDZTZR1LN125327</td>\n",
              "      <td>2020-04-01 00:02:44</td>\n",
              "      <td>82402.0</td>\n",
              "      <td>1160.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>312.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1098.0</td>\n",
              "      <td>240438540.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39929</th>\n",
              "      <td>3HSDWTZR5LN845092</td>\n",
              "      <td>2020-04-01 00:03:33</td>\n",
              "      <td>119822.0</td>\n",
              "      <td>1387.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>380.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1110.0</td>\n",
              "      <td>357404480.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120994</th>\n",
              "      <td>3HSDZTZR2LN075067</td>\n",
              "      <td>2020-05-31 18:02:50</td>\n",
              "      <td>73502.0</td>\n",
              "      <td>1087.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>304.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>277824300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194877</th>\n",
              "      <td>3HSDZTZR8KN398425</td>\n",
              "      <td>2020-05-31 18:16:54</td>\n",
              "      <td>24661.0</td>\n",
              "      <td>1603.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1196.0</td>\n",
              "      <td>63787140.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69600</th>\n",
              "      <td>3HSDWTZR9LN810071</td>\n",
              "      <td>2020-05-31 18:31:11</td>\n",
              "      <td>61931.0</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>672.0</td>\n",
              "      <td>196080560.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99767</th>\n",
              "      <td>3HSDZTZR0LN384388</td>\n",
              "      <td>2020-05-31 20:02:05</td>\n",
              "      <td>53694.0</td>\n",
              "      <td>1121.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>186249040.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99768</th>\n",
              "      <td>3HSDZTZR0LN384388</td>\n",
              "      <td>2020-05-31 21:11:51</td>\n",
              "      <td>53701.0</td>\n",
              "      <td>1085.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>292.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>956.0</td>\n",
              "      <td>186262080.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>215269 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      vin              msg_ts  fuel_total  engine_rpm  \\\n",
              "156451  3HSDZTZR5LN134290 2020-04-01 00:00:19     83536.0      1489.0   \n",
              "37260   3HSDWTZR5LN844783 2020-04-01 00:01:06     19870.0      1376.0   \n",
              "156452  3HSDZTZR5LN134290 2020-04-01 00:02:21     83536.0      1462.0   \n",
              "113667  3HSDZTZR1LN125327 2020-04-01 00:02:44     82402.0      1160.0   \n",
              "39929   3HSDWTZR5LN845092 2020-04-01 00:03:33    119822.0      1387.0   \n",
              "...                   ...                 ...         ...         ...   \n",
              "120994  3HSDZTZR2LN075067 2020-05-31 18:02:50     73502.0      1087.0   \n",
              "194877  3HSDZTZR8KN398425 2020-05-31 18:16:54     24661.0      1603.0   \n",
              "69600   3HSDWTZR9LN810071 2020-05-31 18:31:11     61931.0      1001.0   \n",
              "99767   3HSDZTZR0LN384388 2020-05-31 20:02:05     53694.0      1121.0   \n",
              "99768   3HSDZTZR0LN384388 2020-05-31 21:11:51     53701.0      1085.0   \n",
              "\n",
              "        coolant_tmp  oil_tmpl  throt_pos  torque  oil_psi  manifold_tmp  \\\n",
              "156451         84.0      94.0        0.0     0.0    452.0          43.0   \n",
              "37260          81.0      83.0      100.0    16.0    388.0          32.0   \n",
              "156452         82.0      91.0        0.0     0.0    380.0          39.0   \n",
              "113667         82.0      86.0        0.0    31.0    312.0          37.0   \n",
              "39929          84.0      89.0      100.0    49.0    380.0          40.0   \n",
              "...             ...       ...        ...     ...      ...           ...   \n",
              "120994         79.0      88.0       32.0    22.0    304.0          53.0   \n",
              "194877         83.0      92.0      100.0    38.0    472.0          47.0   \n",
              "69600          82.0      86.0       37.0    33.0    280.0          37.0   \n",
              "99767          90.0      92.0       18.0     8.0    320.0          62.0   \n",
              "99768          81.0      89.0       48.0    38.0    292.0          41.0   \n",
              "\n",
              "        vehiclespeed  odometerinmeters  \n",
              "156451        1106.0       265633380.0  \n",
              "37260         1094.0       284931580.0  \n",
              "156452        1038.0       265636940.0  \n",
              "113667        1098.0       240438540.0  \n",
              "39929         1110.0       357404480.0  \n",
              "...              ...               ...  \n",
              "120994         103.0       277824300.0  \n",
              "194877        1196.0        63787140.0  \n",
              "69600          672.0       196080560.0  \n",
              "99767          116.0       186249040.0  \n",
              "99768          956.0       186262080.0  \n",
              "\n",
              "[215269 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TtqECZWe1JT_"
      },
      "source": [
        "# df_xirgo['vin'].value_counts()"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_jwukK0J0TS-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "209ff265-355c-4747-eab8-ebc4f3b8ad63"
      },
      "source": [
        "!pip install tslearn"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tslearn\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/95/98/9a67e2869a8b1416eb6e6fd5e69c56f86869980403b18f30bdb5783ade9d/tslearn-0.5.0.5-cp37-cp37m-manylinux2010_x86_64.whl (790kB)\n",
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            "\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from tslearn) (0.22.2.post1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from tslearn) (1.4.1)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from tslearn) (1.0.1)\n",
            "Requirement already satisfied: Cython in /usr/local/lib/python3.7/dist-packages (from tslearn) (0.29.22)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from tslearn) (1.19.5)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from tslearn) (0.51.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba->tslearn) (56.0.0)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba->tslearn) (0.34.0)\n",
            "Installing collected packages: tslearn\n",
            "Successfully installed tslearn-0.5.0.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Af8WjtFYgfZ"
      },
      "source": [
        "# Function for Clustering the columns for each VIN\n",
        "\n",
        "def df_clustering(df = df_xirgo, \n",
        "                  columns_list = ['msg_ts','coolant_tmp', 'fuel_total', 'engine_rpm', 'torque', 'vehiclespeed', 'throt_pos', 'manifold_tmp'],\n",
        "                  time_col = 'msg_ts', \n",
        "                  start_date = \"2020-04-01\", \n",
        "                  end_date = \"2020-05-30\",\n",
        "                  n_clusters = 5):\n",
        "  \n",
        "  from tslearn.clustering import TimeSeriesKMeans\n",
        "  from tslearn.preprocessing import TimeSeriesScalerMeanVariance\n",
        "  \n",
        "  df_out = pd.DataFrame({\"vin\": [], \"variable\" : [], \"cluster\": []})\n",
        "  \n",
        "  counter2 = 0\n",
        "\n",
        "  for vin in df['vin'].unique():\n",
        "\n",
        "    df_vin = df[df['vin'] == vin]                        #dataframe for single vin\n",
        "    df_vin.drop(['vin'], axis = 1, inplace = True)\n",
        "\n",
        "    df_vin_imp = pd.DataFrame(df_vin, columns = columns_list)   #dataframe with important columns\n",
        "\n",
        "    df_vin_imp.set_index(pd.to_datetime(df_vin[time_col], unit='s'), drop = True, inplace = True)\n",
        "    df_vin_imp.sort_index(ascending= True, inplace = True)\n",
        "    df_vin_imp.drop(columns = [time_col],axis=1, inplace = True)\n",
        "\n",
        "    after_start_date = df_vin_imp.index >= start_date\n",
        "    before_end_date = df_vin_imp.index  < end_date\n",
        "    between_two_dates = after_start_date & before_end_date         #the start and end date\n",
        "\n",
        "    df_vin_imp_dates = df_vin_imp.loc[between_two_dates]           #dataframe for the selected period of dates\n",
        "\n",
        "    if df_vin_imp_dates.shape[0] < 100:                           #if datapoints less than 100, increase counter by 1\n",
        "      # print(\"{} has only {} datapoints in this time period\".format(vin,df_vin_imp_dates.shape[0]))\n",
        "      counter2 += 1\n",
        "\n",
        "    df_vin_imp_dates = (df_vin_imp_dates-df_vin_imp_dates.min())/(df_vin_imp_dates.max()-df_vin_imp_dates.min())   #Min Max scaling\n",
        "\n",
        "    X = df_vin_imp_dates.transpose().values\n",
        "    \n",
        "    counter = 1\n",
        "\n",
        "    while True:\n",
        "      try: \n",
        "        X = TimeSeriesScalerMeanVariance().fit_transform(X)       \n",
        "        break\n",
        "      except:\n",
        "        counter = 0\n",
        "        break\n",
        "\n",
        "    if counter == 1:\n",
        "      while True:\n",
        "        try:\n",
        "          df_kmeans = pd.DataFrame(X.reshape(df_vin_imp_dates.shape), columns=df_vin_imp_dates.columns, index=df_vin_imp_dates.index)\n",
        "          break\n",
        "        \n",
        "        except:\n",
        "          counter = 0\n",
        "          break\n",
        "      \n",
        "    if counter == 1:  \n",
        "      try:\n",
        "        model = TimeSeriesKMeans(n_clusters=n_clusters, metric=\"dtw\", max_iter=100, n_init=2).fit(X)\n",
        "\n",
        "        df_cluster = pd.DataFrame(list(zip(df_kmeans.columns, model.labels_)), columns=['variable', 'cluster'])\n",
        "\n",
        "        cluster_metrics_dict = df_cluster.groupby(['cluster'])['variable'].apply(lambda x: [x for x in x]).to_dict() #groupby and create dictionary \n",
        "        \n",
        "        cluster_len_dict = df_cluster['cluster'].value_counts().to_dict()\n",
        "         #if only one value in dictionary, ie key alone means no cluster assigned, drop value\n",
        "        clusters_dropped = [cluster for cluster in cluster_len_dict if cluster_len_dict[cluster]==1] \n",
        "        clusters_final = [cluster for cluster in cluster_len_dict if cluster_len_dict[cluster]>1]\n",
        "        clusters_final.sort()\n",
        "\n",
        "        df_cluster['vin'] = vin\n",
        "\n",
        "        df_out = df_out.append(df_cluster, ignore_index= True)\n",
        "\n",
        "      except:\n",
        "        None                                                                                                  \n",
        "\n",
        "    else:\n",
        "      None\n",
        "      # print(\"{} VIN have no recorded values from {} to {}\".format(vin,start_date, end_date))\n",
        "    \n",
        "\n",
        "  print(\"Total No. of VINS present : {}\".format(df_out['vin'].nunique()))\n",
        "  print(\"No. of VINS with datapoints less than 100 : {} \".format(counter2))\n",
        "  print()\n",
        "\n",
        "  dups = df_out[df_out.duplicated(['vin','cluster'], keep = False)].sort_values(['vin', 'cluster'])  #only selecting clusterswhich are duplicated for a vin\n",
        "\n",
        "  return df_out , dups\n"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SHAPh5tabs5"
      },
      "source": [
        "# Function for giving number of Vins with selected variables in same cluster\n",
        "\n",
        "def num_vins(var1 , var2, df):\n",
        "  df_final = pd.DataFrame({\"vin\": [], \"variable\" : [], \"cluster\": []})\n",
        "\n",
        "  for vin in df['vin'].unique():\n",
        "    df_final = df_final.append(df[(df['vin'] == vin) & (df['variable'].isin ([var1, var2]))])  #condition, vin, var1 & var2 as var1 & var2 passed\n",
        "\n",
        "\n",
        "  df_final = df_final[df_final.duplicated(['vin','cluster'], keep = False)].sort_values(['vin', 'cluster'])  #keeping only duplicated\n",
        "\n",
        "  return df_final['vin'].nunique()\n",
        "\n",
        "\n",
        "#Looping the above function for all variables\n",
        "\n",
        "def vins_with_var_in_same_cluster(df, time_period):\n",
        "  df_vins_number = pd.DataFrame({\"Variable1\": [], \"Variable2\" : [], time_period: []})\n",
        "  for i in range(len(df['variable'].unique())):\n",
        "      for j in range(len(df['variable'].unique())):\n",
        "          out2 = num_vins(df['variable'].unique()[i],df['variable'].unique()[j], df)\n",
        "          df_vins_number = df_vins_number.append(pd.DataFrame({\"Variable1\": [df['variable'].unique()[i]], \n",
        "                                                               \"Variable2\" : [df['variable'].unique()[j]], \n",
        "                                                               time_period: [out2]}), ignore_index= True )\n",
        "  \n",
        "  df_vins_number = df_vins_number[df_vins_number['Variable1'] != df_vins_number['Variable2']]  #dropping if var1 = var2\n",
        "\n",
        "  df_vins_number.sort_values(['Variable1', 'Variable2'], ascending = [True, True], inplace = True)\n",
        "  \n",
        "  df_vins_number.reset_index(inplace = True, drop = True)\n",
        "\n",
        "  return df_vins_number\n"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQJHiJvfxsd3",
        "outputId": "00ea744f-49b5-40cf-ffcf-1ec95057f91a"
      },
      "source": [
        "time_period = 'Week1'\n",
        "start_date = \"2020-04-01\"\n",
        "end_date = \"2020-04-08\"\n",
        "\n",
        "df_out, dups = df_clustering(start_date = start_date , end_date = end_date)\n",
        "\n",
        "df_vins_number = vins_with_var_in_same_cluster(dups,time_period)\n",
        "\n",
        "df_weekly = df_vins_number"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total No. of VINS present : 83\n",
            "No. of VINS with datapoints less than 100 : 19 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UvCKY3j2XPsA",
        "outputId": "665744a7-cd75-4d1c-9f5e-5db25ef6e82e"
      },
      "source": [
        "time_period = 'Week2'\n",
        "start_date = \"2020-04-08\"\n",
        "end_date = \"2020-04-15\"\n",
        "\n",
        "df_out, dups = df_clustering(start_date = start_date , end_date = end_date)\n",
        "\n",
        "df_vins_number = vins_with_var_in_same_cluster(dups,time_period)\n",
        "\n",
        "df_weekly[time_period] = list(df_vins_number[time_period])"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total No. of VINS present : 85\n",
            "No. of VINS with datapoints less than 100 : 21 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GDeI_y1JlU7t",
        "outputId": "037fa7d0-f1f7-4016-8392-aaabc70688b4"
      },
      "source": [
        "time_period = 'Week3'\n",
        "start_date = \"2020-04-15\"\n",
        "end_date = \"2020-04-22\"\n",
        "\n",
        "df_out, dups = df_clustering(start_date = start_date , end_date = end_date)\n",
        "\n",
        "df_vins_number = vins_with_var_in_same_cluster(dups,time_period)\n",
        "\n",
        "df_weekly[time_period] = list(df_vins_number[time_period])"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total No. of VINS present : 83\n",
            "No. of VINS with datapoints less than 100 : 19 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRBCsV3Zxq2G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec5e3b72-cab7-4db2-ddbf-2afb1a4efb0b"
      },
      "source": [
        "time_period = 'Week4'\n",
        "start_date = \"2020-04-22\"\n",
        "end_date = \"2020-04-29\"\n",
        "\n",
        "df_out, dups = df_clustering(start_date = start_date , end_date = end_date)\n",
        "\n",
        "df_vins_number = vins_with_var_in_same_cluster(dups,time_period)\n",
        "\n",
        "df_weekly[time_period] = list(df_vins_number[time_period])"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total No. of VINS present : 88\n",
            "No. of VINS with datapoints less than 100 : 26 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cP37kUxexwMs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d989fa9e-dd24-4b1d-a7e1-bf6984dec379"
      },
      "source": [
        "time_period = 'Week5'\n",
        "start_date = \"2020-04-29\"\n",
        "end_date = \"2020-05-06\"\n",
        "\n",
        "df_out, dups = df_clustering(start_date = start_date , end_date = end_date)\n",
        "\n",
        "df_vins_number = vins_with_var_in_same_cluster(dups,time_period)\n",
        "\n",
        "df_weekly[time_period] = list(df_vins_number[time_period])"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total No. of VINS present : 81\n",
            "No. of VINS with datapoints less than 100 : 23 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "laorz4Egx5s1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a46b2307-7e18-4d3a-b6e5-b82d05e1ee20"
      },
      "source": [
        "time_period = 'Week6'\n",
        "start_date = \"2020-05-06\"\n",
        "end_date = \"2020-05-13\"\n",
        "\n",
        "df_out, dups = df_clustering(start_date = start_date , end_date = end_date)\n",
        "\n",
        "df_vins_number = vins_with_var_in_same_cluster(dups,time_period)\n",
        "\n",
        "df_weekly[time_period] = list(df_vins_number[time_period])"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total No. of VINS present : 85\n",
            "No. of VINS with datapoints less than 100 : 35 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2ko-je5yADR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfb611c5-dc07-432a-c11d-e98aac4b752d"
      },
      "source": [
        "time_period = 'Week7'\n",
        "start_date = \"2020-05-13\"\n",
        "end_date = \"2020-05-20\"\n",
        "\n",
        "df_out, dups = df_clustering(start_date = start_date , end_date = end_date)\n",
        "\n",
        "df_vins_number = vins_with_var_in_same_cluster(dups,time_period)\n",
        "\n",
        "df_weekly[time_period] = list(df_vins_number[time_period])"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total No. of VINS present : 89\n",
            "No. of VINS with datapoints less than 100 : 75 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oz-ojv7CyFQC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02897d43-42a1-4593-9777-7e4c194501fc"
      },
      "source": [
        "time_period = 'Week8'\n",
        "start_date = \"2020-05-20\"\n",
        "end_date = \"2020-05-27\"\n",
        "\n",
        "df_out, dups = df_clustering(start_date = start_date , end_date = end_date)\n",
        "\n",
        "df_vins_number = vins_with_var_in_same_cluster(dups,time_period)\n",
        "\n",
        "df_weekly[time_period] = list(df_vins_number[time_period]) "
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total No. of VINS present : 85\n",
            "No. of VINS with datapoints less than 100 : 52 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hY0igfEH0mI3"
      },
      "source": [
        "**No. of VINS with variable1 and variable2 in same cluster**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sbAV4p6HyJD4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2a0fdd22-169e-4b2e-e210-2d47531dbfdc"
      },
      "source": [
        "df_weekly  "
      ],
      "execution_count": 68,
      "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>Variable1</th>\n",
              "      <th>Variable2</th>\n",
              "      <th>Week1</th>\n",
              "      <th>Week2</th>\n",
              "      <th>Week3</th>\n",
              "      <th>Week4</th>\n",
              "      <th>Week5</th>\n",
              "      <th>Week6</th>\n",
              "      <th>Week7</th>\n",
              "      <th>Week8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>coolant_tmp</td>\n",
              "      <td>engine_rpm</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>coolant_tmp</td>\n",
              "      <td>fuel_total</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>coolant_tmp</td>\n",
              "      <td>manifold_tmp</td>\n",
              "      <td>20.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>coolant_tmp</td>\n",
              "      <td>throt_pos</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>coolant_tmp</td>\n",
              "      <td>torque</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>coolant_tmp</td>\n",
              "      <td>vehiclespeed</td>\n",
              "      <td>13.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>engine_rpm</td>\n",
              "      <td>coolant_tmp</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>engine_rpm</td>\n",
              "      <td>fuel_total</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>engine_rpm</td>\n",
              "      <td>manifold_tmp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>engine_rpm</td>\n",
              "      <td>throt_pos</td>\n",
              "      <td>28.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>engine_rpm</td>\n",
              "      <td>torque</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>engine_rpm</td>\n",
              "      <td>vehiclespeed</td>\n",
              "      <td>22.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>fuel_total</td>\n",
              "      <td>coolant_tmp</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>fuel_total</td>\n",
              "      <td>engine_rpm</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>fuel_total</td>\n",
              "      <td>manifold_tmp</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>fuel_total</td>\n",
              "      <td>throt_pos</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>fuel_total</td>\n",
              "      <td>torque</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>fuel_total</td>\n",
              "      <td>vehiclespeed</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>manifold_tmp</td>\n",
              "      <td>coolant_tmp</td>\n",
              "      <td>20.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>manifold_tmp</td>\n",
              "      <td>engine_rpm</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>manifold_tmp</td>\n",
              "      <td>fuel_total</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>manifold_tmp</td>\n",
              "      <td>throt_pos</td>\n",
              "      <td>7.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>manifold_tmp</td>\n",
              "      <td>torque</td>\n",
              "      <td>20.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>manifold_tmp</td>\n",
              "      <td>vehiclespeed</td>\n",
              "      <td>22.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>throt_pos</td>\n",
              "      <td>coolant_tmp</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>throt_pos</td>\n",
              "      <td>engine_rpm</td>\n",
              "      <td>28.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>throt_pos</td>\n",
              "      <td>fuel_total</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>throt_pos</td>\n",
              "      <td>manifold_tmp</td>\n",
              "      <td>7.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>throt_pos</td>\n",
              "      <td>torque</td>\n",
              "      <td>38.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>throt_pos</td>\n",
              "      <td>vehiclespeed</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>torque</td>\n",
              "      <td>coolant_tmp</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>torque</td>\n",
              "      <td>engine_rpm</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>torque</td>\n",
              "      <td>fuel_total</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>torque</td>\n",
              "      <td>manifold_tmp</td>\n",
              "      <td>20.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>torque</td>\n",
              "      <td>throt_pos</td>\n",
              "      <td>38.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>torque</td>\n",
              "      <td>vehiclespeed</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>vehiclespeed</td>\n",
              "      <td>coolant_tmp</td>\n",
              "      <td>13.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>vehiclespeed</td>\n",
              "      <td>engine_rpm</td>\n",
              "      <td>22.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>vehiclespeed</td>\n",
              "      <td>fuel_total</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>vehiclespeed</td>\n",
              "      <td>manifold_tmp</td>\n",
              "      <td>22.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>vehiclespeed</td>\n",
              "      <td>throt_pos</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>vehiclespeed</td>\n",
              "      <td>torque</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Variable1     Variable2  Week1  Week2  Week3  Week4  Week5  Week6  \\\n",
              "0    coolant_tmp    engine_rpm    2.0    1.0    2.0    9.0    0.0    5.0   \n",
              "1    coolant_tmp    fuel_total    2.0    1.0    0.0    3.0    0.0    1.0   \n",
              "2    coolant_tmp  manifold_tmp   20.0   18.0   18.0   30.0   22.0   26.0   \n",
              "3    coolant_tmp     throt_pos    2.0    0.0    1.0    3.0    1.0    4.0   \n",
              "4    coolant_tmp        torque    1.0    0.0    1.0    5.0    1.0    1.0   \n",
              "5    coolant_tmp  vehiclespeed   13.0   11.0   12.0   15.0    6.0   14.0   \n",
              "6     engine_rpm   coolant_tmp    2.0    1.0    2.0    9.0    0.0    5.0   \n",
              "7     engine_rpm    fuel_total    1.0    1.0    0.0    6.0    0.0    1.0   \n",
              "8     engine_rpm  manifold_tmp    5.0   10.0   14.0   13.0    4.0    9.0   \n",
              "9     engine_rpm     throt_pos   28.0   27.0   24.0   25.0   24.0   23.0   \n",
              "10    engine_rpm        torque    6.0    6.0    6.0    4.0    4.0    5.0   \n",
              "11    engine_rpm  vehiclespeed   22.0   24.0   12.0   23.0   18.0   19.0   \n",
              "12    fuel_total   coolant_tmp    2.0    1.0    0.0    3.0    0.0    1.0   \n",
              "13    fuel_total    engine_rpm    1.0    1.0    0.0    6.0    0.0    1.0   \n",
              "14    fuel_total  manifold_tmp    2.0    3.0    3.0    5.0    6.0    2.0   \n",
              "15    fuel_total     throt_pos    1.0    1.0    1.0    4.0    0.0    2.0   \n",
              "16    fuel_total        torque    1.0    1.0    0.0    4.0    0.0    1.0   \n",
              "17    fuel_total  vehiclespeed    1.0    1.0    1.0    4.0    0.0    0.0   \n",
              "18  manifold_tmp   coolant_tmp   20.0   18.0   18.0   30.0   22.0   26.0   \n",
              "19  manifold_tmp    engine_rpm    5.0   10.0   14.0   13.0    4.0    9.0   \n",
              "20  manifold_tmp    fuel_total    2.0    3.0    3.0    5.0    6.0    2.0   \n",
              "21  manifold_tmp     throt_pos    7.0   12.0   18.0   12.0   14.0   14.0   \n",
              "22  manifold_tmp        torque   20.0   19.0   19.0   25.0   10.0   20.0   \n",
              "23  manifold_tmp  vehiclespeed   22.0   20.0   18.0   20.0   19.0   12.0   \n",
              "24     throt_pos   coolant_tmp    2.0    0.0    1.0    3.0    1.0    4.0   \n",
              "25     throt_pos    engine_rpm   28.0   27.0   24.0   25.0   24.0   23.0   \n",
              "26     throt_pos    fuel_total    1.0    1.0    1.0    4.0    0.0    2.0   \n",
              "27     throt_pos  manifold_tmp    7.0   12.0   18.0   12.0   14.0   14.0   \n",
              "28     throt_pos        torque   38.0   39.0   37.0   37.0   41.0   36.0   \n",
              "29     throt_pos  vehiclespeed   10.0   10.0    4.0   14.0    9.0    5.0   \n",
              "30        torque   coolant_tmp    1.0    0.0    1.0    5.0    1.0    1.0   \n",
              "31        torque    engine_rpm    6.0    6.0    6.0    4.0    4.0    5.0   \n",
              "32        torque    fuel_total    1.0    1.0    0.0    4.0    0.0    1.0   \n",
              "33        torque  manifold_tmp   20.0   19.0   19.0   25.0   10.0   20.0   \n",
              "34        torque     throt_pos   38.0   39.0   37.0   37.0   41.0   36.0   \n",
              "35        torque  vehiclespeed    6.0    4.0    1.0    4.0    3.0    6.0   \n",
              "36  vehiclespeed   coolant_tmp   13.0   11.0   12.0   15.0    6.0   14.0   \n",
              "37  vehiclespeed    engine_rpm   22.0   24.0   12.0   23.0   18.0   19.0   \n",
              "38  vehiclespeed    fuel_total    1.0    1.0    1.0    4.0    0.0    0.0   \n",
              "39  vehiclespeed  manifold_tmp   22.0   20.0   18.0   20.0   19.0   12.0   \n",
              "40  vehiclespeed     throt_pos   10.0   10.0    4.0   14.0    9.0    5.0   \n",
              "41  vehiclespeed        torque    6.0    4.0    1.0    4.0    3.0    6.0   \n",
              "\n",
              "    Week7  Week8  \n",
              "0     8.0   15.0  \n",
              "1     2.0    2.0  \n",
              "2    27.0   29.0  \n",
              "3     2.0    9.0  \n",
              "4     3.0    4.0  \n",
              "5    20.0   16.0  \n",
              "6     8.0   15.0  \n",
              "7     0.0    4.0  \n",
              "8     9.0    8.0  \n",
              "9    22.0   25.0  \n",
              "10    5.0    2.0  \n",
              "11   18.0   18.0  \n",
              "12    2.0    2.0  \n",
              "13    0.0    4.0  \n",
              "14    5.0    3.0  \n",
              "15    1.0    2.0  \n",
              "16    0.0    2.0  \n",
              "17    0.0    4.0  \n",
              "18   27.0   29.0  \n",
              "19    9.0    8.0  \n",
              "20    5.0    3.0  \n",
              "21   13.0   12.0  \n",
              "22   21.0   18.0  \n",
              "23    9.0    9.0  \n",
              "24    2.0    9.0  \n",
              "25   22.0   25.0  \n",
              "26    1.0    2.0  \n",
              "27   13.0   12.0  \n",
              "28   35.0   33.0  \n",
              "29    6.0   10.0  \n",
              "30    3.0    4.0  \n",
              "31    5.0    2.0  \n",
              "32    0.0    2.0  \n",
              "33   21.0   18.0  \n",
              "34   35.0   33.0  \n",
              "35    5.0    8.0  \n",
              "36   20.0   16.0  \n",
              "37   18.0   18.0  \n",
              "38    0.0    4.0  \n",
              "39    9.0    9.0  \n",
              "40    6.0   10.0  \n",
              "41    5.0    8.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dlyP1y2o0vcD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}