EV-Market_Segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9520b12b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2378c831",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: opendatasets in c:\\users\\asus\\appdata\\roaming\\python\\python39\\site-packages (0.1.22)\n",
      "Requirement already satisfied: click in c:\\users\\asus\\appdata\\roaming\\python\\python39\\site-packages (from opendatasets) (8.0.3)\n",
      "Requirement already satisfied: kaggle in c:\\users\\asus\\appdata\\roaming\\python\\python39\\site-packages (from opendatasets) (1.5.13)\n",
      "Requirement already satisfied: tqdm in c:\\users\\asus\\appdata\\roaming\\python\\python39\\site-packages (from opendatasets) (4.62.3)\n",
      "Requirement already satisfied: colorama in c:\\users\\asus\\appdata\\roaming\\python\\python39\\site-packages (from click->opendatasets) (0.4.4)\n",
      "Requirement already satisfied: six>=1.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from kaggle->opendatasets) (1.16.0)\n",
      "Requirement already satisfied: certifi in c:\\users\\asus\\appdata\\roaming\\python\\python39\\site-packages (from kaggle->opendatasets) (2021.10.8)\n",
      "Requirement already satisfied: urllib3 in c:\\users\\asus\\appdata\\roaming\\python\\python39\\site-packages (from kaggle->opendatasets) (1.26.7)\n",
      "Requirement already satisfied: python-slugify in c:\\programdata\\anaconda3\\lib\\site-packages (from kaggle->opendatasets) (5.0.2)\n",
      "Requirement already satisfied: requests in c:\\users\\asus\\appdata\\roaming\\python\\python39\\site-packages (from kaggle->opendatasets) (2.26.0)\n",
      "Requirement already satisfied: python-dateutil in c:\\programdata\\anaconda3\\lib\\site-packages (from kaggle->opendatasets) (2.8.2)\n",
      "Requirement already satisfied: text-unidecode>=1.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-slugify->kaggle->opendatasets) (1.3)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\asus\\appdata\\roaming\\python\\python39\\site-packages (from requests->kaggle->opendatasets) (3.3)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\asus\\appdata\\roaming\\python\\python39\\site-packages (from requests->kaggle->opendatasets) (2.0.9)\n"
     ]
    }
   ],
   "source": [
    "!pip install opendatasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "04190a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import opendatasets as od"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8ceb0498",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping, found downloaded files in \".\\electric-vehicles-india\" (use force=True to force download)\n"
     ]
    }
   ],
   "source": [
    "od.download(\"https://www.kaggle.com/datasets/kkhandekar/electric-vehicles-india\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a1552cf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\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>Car</th>\n",
       "      <th>Style</th>\n",
       "      <th>Range</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>VehicleType</th>\n",
       "      <th>PriceRange</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>BootSpace</th>\n",
       "      <th>BaseModel</th>\n",
       "      <th>TopModel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tata Nexon EV</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>312 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 13.99 - 17.4 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>350 L</td>\n",
       "      <td>XM</td>\n",
       "      <td>Dark XZ Plus LUX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tata Tigor EV</td>\n",
       "      <td>Subcompact Sedan</td>\n",
       "      <td>306 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 12.49 - 13.64 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>316 L</td>\n",
       "      <td>XE</td>\n",
       "      <td>XZ Plus Dual Tone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tata Nexon EV Max</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>437 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 17.74 - 19.24 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>350 L</td>\n",
       "      <td>XZ Plus 3.3 kW</td>\n",
       "      <td>XZ Plus Lux 7.2 kW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MG ZS EV</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>419 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 21.99 - 25.88 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>448 L</td>\n",
       "      <td>Excite</td>\n",
       "      <td>Exclusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hyundai Kona Electric</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>452 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 23.79 - 23.98 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>na</td>\n",
       "      <td>Premium Dual Tone</td>\n",
       "      <td>HSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jaguar I-Pace</td>\n",
       "      <td>Premium Midsize Sedan</td>\n",
       "      <td>470 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 1.06 - 1.12 Cr</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>656 L</td>\n",
       "      <td>S</td>\n",
       "      <td>Sportback 55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Audi E-Tron GT</td>\n",
       "      <td>Premium Coupe</td>\n",
       "      <td>388 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 1.8 Cr</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>405 L</td>\n",
       "      <td>Quattro</td>\n",
       "      <td>na</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BYD E6</td>\n",
       "      <td>Subcompact MPV</td>\n",
       "      <td>415 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 29.15 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>580 L</td>\n",
       "      <td>STD</td>\n",
       "      <td>na</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mercedes-Benz EQC</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>471 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 1 Cr</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BMW iX</td>\n",
       "      <td>Premium Fullsize SUV</td>\n",
       "      <td>425 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 1.16 Cr</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Porsche Taycan</td>\n",
       "      <td>Premium Sports Sedan</td>\n",
       "      <td>na</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 1.5 Cr</td>\n",
       "      <td>4 Seater</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Audi E-Tron</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>400 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 1.01 - 1.19 Cr</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>660 L</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Car                  Style               Range  \\\n",
       "0           Tata Nexon EV            Compact SUV  312 Km/Full Charge   \n",
       "1           Tata Tigor EV       Subcompact Sedan  306 Km/Full Charge   \n",
       "2       Tata Nexon EV Max            Compact SUV  437 Km/Full Charge   \n",
       "3                MG ZS EV            Compact SUV  419 Km/Full Charge   \n",
       "4   Hyundai Kona Electric            Compact SUV  452 Km/Full Charge   \n",
       "5           Jaguar I-Pace  Premium Midsize Sedan  470 Km/Full Charge   \n",
       "6          Audi E-Tron GT          Premium Coupe  388 Km/Full Charge   \n",
       "7                  BYD E6         Subcompact MPV  415 Km/Full Charge   \n",
       "8       Mercedes-Benz EQC            Compact SUV  471 Km/Full Charge   \n",
       "9                  BMW iX   Premium Fullsize SUV  425 Km/Full Charge   \n",
       "10         Porsche Taycan   Premium Sports Sedan                  na   \n",
       "11            Audi E-Tron            Compact SUV  400 Km/Full Charge   \n",
       "\n",
       "   Transmission VehicleType         PriceRange   Capacity BootSpace  \\\n",
       "0     Automatic    Electric   ₹ 13.99 - 17.4 L  5 Seater      350 L   \n",
       "1     Automatic    Electric  ₹ 12.49 - 13.64 L  5 Seater      316 L   \n",
       "2     Automatic    Electric  ₹ 17.74 - 19.24 L  5 Seater      350 L   \n",
       "3     Automatic    Electric  ₹ 21.99 - 25.88 L  5 Seater      448 L   \n",
       "4     Automatic    Electric  ₹ 23.79 - 23.98 L  5 Seater         na   \n",
       "5     Automatic    Electric   ₹ 1.06 - 1.12 Cr  5 Seater      656 L   \n",
       "6     Automatic    Electric           ₹ 1.8 Cr  5 Seater      405 L   \n",
       "7     Automatic    Electric          ₹ 29.15 L  5 Seater      580 L   \n",
       "8     Automatic    Electric             ₹ 1 Cr  5 Seater         na   \n",
       "9     Automatic    Electric          ₹ 1.16 Cr  5 Seater         na   \n",
       "10    Automatic    Electric           ₹ 1.5 Cr  4 Seater         na   \n",
       "11    Automatic    Electric   ₹ 1.01 - 1.19 Cr  5 Seater      660 L   \n",
       "\n",
       "             BaseModel             TopModel  \n",
       "0                  XM     Dark XZ Plus LUX   \n",
       "1                  XE    XZ Plus Dual Tone   \n",
       "2      XZ Plus 3.3 kW   XZ Plus Lux 7.2 kW   \n",
       "3              Excite            Exclusive   \n",
       "4   Premium Dual Tone                  HSE   \n",
       "5                   S         Sportback 55   \n",
       "6             Quattro                    na  \n",
       "7                 STD                    na  \n",
       "8                   na                   na  \n",
       "9                   na                   na  \n",
       "10                  na                   na  \n",
       "11                  na                   na  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"electric-vehicles-india/EVIndia.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f10dadd0",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d16c52c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Preprocess1(df,old_col):\n",
    "#     old_col=old_col[0]\n",
    "    df[old_col]=df[old_col].replace(\"na\",\"0 L\")\n",
    "    bootspace=df[old_col].str.split(\"L\",n=1,expand=True)\n",
    "    df[old_col+\"_new\"]=bootspace[0]\n",
    "    df[old_col+\"_new\"]=df[old_col+\"_new\"].astype(int)\n",
    "    df[old_col+\"_new\"].mean()\n",
    "    df[old_col+\"_new\"]=df[old_col+\"_new\"].replace(0,df[\"BootSpace_new\"].mean())\n",
    "    df=pd.DataFrame(df)\n",
    "    return df     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a5bf362f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car</th>\n",
       "      <th>Style</th>\n",
       "      <th>Range</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>VehicleType</th>\n",
       "      <th>PriceRange</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>BootSpace</th>\n",
       "      <th>BaseModel</th>\n",
       "      <th>TopModel</th>\n",
       "      <th>BootSpace_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tata Nexon EV</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>312 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 13.99 - 17.4 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>350 L</td>\n",
       "      <td>XM</td>\n",
       "      <td>Dark XZ Plus LUX</td>\n",
       "      <td>350.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tata Tigor EV</td>\n",
       "      <td>Subcompact Sedan</td>\n",
       "      <td>306 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 12.49 - 13.64 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>316 L</td>\n",
       "      <td>XE</td>\n",
       "      <td>XZ Plus Dual Tone</td>\n",
       "      <td>316.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tata Nexon EV Max</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>437 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 17.74 - 19.24 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>350 L</td>\n",
       "      <td>XZ Plus 3.3 kW</td>\n",
       "      <td>XZ Plus Lux 7.2 kW</td>\n",
       "      <td>350.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MG ZS EV</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>419 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 21.99 - 25.88 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>448 L</td>\n",
       "      <td>Excite</td>\n",
       "      <td>Exclusive</td>\n",
       "      <td>448.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hyundai Kona Electric</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>452 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 23.79 - 23.98 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>0 L</td>\n",
       "      <td>Premium Dual Tone</td>\n",
       "      <td>HSE</td>\n",
       "      <td>313.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jaguar I-Pace</td>\n",
       "      <td>Premium Midsize Sedan</td>\n",
       "      <td>470 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 1.06 - 1.12 Cr</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>656 L</td>\n",
       "      <td>S</td>\n",
       "      <td>Sportback 55</td>\n",
       "      <td>656.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Audi E-Tron GT</td>\n",
       "      <td>Premium Coupe</td>\n",
       "      <td>388 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 1.8 Cr</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>405 L</td>\n",
       "      <td>Quattro</td>\n",
       "      <td>na</td>\n",
       "      <td>405.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BYD E6</td>\n",
       "      <td>Subcompact MPV</td>\n",
       "      <td>415 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 29.15 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>580 L</td>\n",
       "      <td>STD</td>\n",
       "      <td>na</td>\n",
       "      <td>580.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mercedes-Benz EQC</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>471 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 1 Cr</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>0 L</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>313.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BMW iX</td>\n",
       "      <td>Premium Fullsize SUV</td>\n",
       "      <td>425 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 1.16 Cr</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>0 L</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>313.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Porsche Taycan</td>\n",
       "      <td>Premium Sports Sedan</td>\n",
       "      <td>na</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 1.5 Cr</td>\n",
       "      <td>4 Seater</td>\n",
       "      <td>0 L</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>313.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Audi E-Tron</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>400 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 1.01 - 1.19 Cr</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>660 L</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>660.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Car                  Style               Range  \\\n",
       "0           Tata Nexon EV            Compact SUV  312 Km/Full Charge   \n",
       "1           Tata Tigor EV       Subcompact Sedan  306 Km/Full Charge   \n",
       "2       Tata Nexon EV Max            Compact SUV  437 Km/Full Charge   \n",
       "3                MG ZS EV            Compact SUV  419 Km/Full Charge   \n",
       "4   Hyundai Kona Electric            Compact SUV  452 Km/Full Charge   \n",
       "5           Jaguar I-Pace  Premium Midsize Sedan  470 Km/Full Charge   \n",
       "6          Audi E-Tron GT          Premium Coupe  388 Km/Full Charge   \n",
       "7                  BYD E6         Subcompact MPV  415 Km/Full Charge   \n",
       "8       Mercedes-Benz EQC            Compact SUV  471 Km/Full Charge   \n",
       "9                  BMW iX   Premium Fullsize SUV  425 Km/Full Charge   \n",
       "10         Porsche Taycan   Premium Sports Sedan                  na   \n",
       "11            Audi E-Tron            Compact SUV  400 Km/Full Charge   \n",
       "\n",
       "   Transmission VehicleType         PriceRange   Capacity BootSpace  \\\n",
       "0     Automatic    Electric   ₹ 13.99 - 17.4 L  5 Seater      350 L   \n",
       "1     Automatic    Electric  ₹ 12.49 - 13.64 L  5 Seater      316 L   \n",
       "2     Automatic    Electric  ₹ 17.74 - 19.24 L  5 Seater      350 L   \n",
       "3     Automatic    Electric  ₹ 21.99 - 25.88 L  5 Seater      448 L   \n",
       "4     Automatic    Electric  ₹ 23.79 - 23.98 L  5 Seater        0 L   \n",
       "5     Automatic    Electric   ₹ 1.06 - 1.12 Cr  5 Seater      656 L   \n",
       "6     Automatic    Electric           ₹ 1.8 Cr  5 Seater      405 L   \n",
       "7     Automatic    Electric          ₹ 29.15 L  5 Seater      580 L   \n",
       "8     Automatic    Electric             ₹ 1 Cr  5 Seater        0 L   \n",
       "9     Automatic    Electric          ₹ 1.16 Cr  5 Seater        0 L   \n",
       "10    Automatic    Electric           ₹ 1.5 Cr  4 Seater        0 L   \n",
       "11    Automatic    Electric   ₹ 1.01 - 1.19 Cr  5 Seater      660 L   \n",
       "\n",
       "             BaseModel             TopModel  BootSpace_new  \n",
       "0                  XM     Dark XZ Plus LUX          350.00  \n",
       "1                  XE    XZ Plus Dual Tone          316.00  \n",
       "2      XZ Plus 3.3 kW   XZ Plus Lux 7.2 kW          350.00  \n",
       "3              Excite            Exclusive          448.00  \n",
       "4   Premium Dual Tone                  HSE          313.75  \n",
       "5                   S         Sportback 55          656.00  \n",
       "6             Quattro                    na         405.00  \n",
       "7                 STD                    na         580.00  \n",
       "8                   na                   na         313.75  \n",
       "9                   na                   na         313.75  \n",
       "10                  na                   na         313.75  \n",
       "11                  na                   na         660.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=Preprocess1(df,\"BootSpace\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "05a1c140",
   "metadata": {},
   "outputs": [
    {
     "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>Car</th>\n",
       "      <th>Style</th>\n",
       "      <th>Range</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>VehicleType</th>\n",
       "      <th>PriceRange</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>BootSpace</th>\n",
       "      <th>BaseModel</th>\n",
       "      <th>TopModel</th>\n",
       "      <th>BootSpace_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tata Nexon EV</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>312 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 13.99 - 17.4 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>350 L</td>\n",
       "      <td>XM</td>\n",
       "      <td>Dark XZ Plus LUX</td>\n",
       "      <td>350.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tata Tigor EV</td>\n",
       "      <td>Subcompact Sedan</td>\n",
       "      <td>306 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 12.49 - 13.64 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>316 L</td>\n",
       "      <td>XE</td>\n",
       "      <td>XZ Plus Dual Tone</td>\n",
       "      <td>316.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tata Nexon EV Max</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>437 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 17.74 - 19.24 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>350 L</td>\n",
       "      <td>XZ Plus 3.3 kW</td>\n",
       "      <td>XZ Plus Lux 7.2 kW</td>\n",
       "      <td>350.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MG ZS EV</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>419 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 21.99 - 25.88 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>448 L</td>\n",
       "      <td>Excite</td>\n",
       "      <td>Exclusive</td>\n",
       "      <td>448.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hyundai Kona Electric</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>452 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 23.79 - 23.98 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>0 L</td>\n",
       "      <td>Premium Dual Tone</td>\n",
       "      <td>HSE</td>\n",
       "      <td>313.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jaguar I-Pace</td>\n",
       "      <td>Premium Midsize Sedan</td>\n",
       "      <td>470 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 106 - 112 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>656 L</td>\n",
       "      <td>S</td>\n",
       "      <td>Sportback 55</td>\n",
       "      <td>656.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Audi E-Tron GT</td>\n",
       "      <td>Premium Coupe</td>\n",
       "      <td>388 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 180 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>405 L</td>\n",
       "      <td>Quattro</td>\n",
       "      <td>na</td>\n",
       "      <td>405.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BYD E6</td>\n",
       "      <td>Subcompact MPV</td>\n",
       "      <td>415 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 29.15 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>580 L</td>\n",
       "      <td>STD</td>\n",
       "      <td>na</td>\n",
       "      <td>580.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mercedes-Benz EQC</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>471 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 100 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>0 L</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>313.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BMW iX</td>\n",
       "      <td>Premium Fullsize SUV</td>\n",
       "      <td>425 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 116 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>0 L</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>313.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Porsche Taycan</td>\n",
       "      <td>Premium Sports Sedan</td>\n",
       "      <td>na</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 150 L</td>\n",
       "      <td>4 Seater</td>\n",
       "      <td>0 L</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>313.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Audi E-Tron</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>400 Km/Full Charge</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 101 - 119 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>660 L</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>660.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Car                  Style               Range  \\\n",
       "0           Tata Nexon EV            Compact SUV  312 Km/Full Charge   \n",
       "1           Tata Tigor EV       Subcompact Sedan  306 Km/Full Charge   \n",
       "2       Tata Nexon EV Max            Compact SUV  437 Km/Full Charge   \n",
       "3                MG ZS EV            Compact SUV  419 Km/Full Charge   \n",
       "4   Hyundai Kona Electric            Compact SUV  452 Km/Full Charge   \n",
       "5           Jaguar I-Pace  Premium Midsize Sedan  470 Km/Full Charge   \n",
       "6          Audi E-Tron GT          Premium Coupe  388 Km/Full Charge   \n",
       "7                  BYD E6         Subcompact MPV  415 Km/Full Charge   \n",
       "8       Mercedes-Benz EQC            Compact SUV  471 Km/Full Charge   \n",
       "9                  BMW iX   Premium Fullsize SUV  425 Km/Full Charge   \n",
       "10         Porsche Taycan   Premium Sports Sedan                  na   \n",
       "11            Audi E-Tron            Compact SUV  400 Km/Full Charge   \n",
       "\n",
       "   Transmission VehicleType         PriceRange   Capacity BootSpace  \\\n",
       "0     Automatic    Electric   ₹ 13.99 - 17.4 L  5 Seater      350 L   \n",
       "1     Automatic    Electric  ₹ 12.49 - 13.64 L  5 Seater      316 L   \n",
       "2     Automatic    Electric  ₹ 17.74 - 19.24 L  5 Seater      350 L   \n",
       "3     Automatic    Electric  ₹ 21.99 - 25.88 L  5 Seater      448 L   \n",
       "4     Automatic    Electric  ₹ 23.79 - 23.98 L  5 Seater        0 L   \n",
       "5     Automatic    Electric      ₹ 106 - 112 L  5 Seater      656 L   \n",
       "6     Automatic    Electric            ₹ 180 L  5 Seater      405 L   \n",
       "7     Automatic    Electric          ₹ 29.15 L  5 Seater      580 L   \n",
       "8     Automatic    Electric            ₹ 100 L  5 Seater        0 L   \n",
       "9     Automatic    Electric            ₹ 116 L  5 Seater        0 L   \n",
       "10    Automatic    Electric            ₹ 150 L  4 Seater        0 L   \n",
       "11    Automatic    Electric      ₹ 101 - 119 L  5 Seater      660 L   \n",
       "\n",
       "             BaseModel             TopModel  BootSpace_new  \n",
       "0                  XM     Dark XZ Plus LUX          350.00  \n",
       "1                  XE    XZ Plus Dual Tone          316.00  \n",
       "2      XZ Plus 3.3 kW   XZ Plus Lux 7.2 kW          350.00  \n",
       "3              Excite            Exclusive          448.00  \n",
       "4   Premium Dual Tone                  HSE          313.75  \n",
       "5                   S         Sportback 55          656.00  \n",
       "6             Quattro                    na         405.00  \n",
       "7                 STD                    na         580.00  \n",
       "8                   na                   na         313.75  \n",
       "9                   na                   na         313.75  \n",
       "10                  na                   na         313.75  \n",
       "11                  na                   na         660.00  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PriceRange']=df['PriceRange'].replace(['₹ 1.8 Cr','₹ 1.16 Cr','₹ 1.06 - 1.12 Cr','₹ 1 Cr','₹ 1.5 Cr','₹ 1.01 - 1.19 Cr'],['₹ 180 L','₹ 116 L','₹ 106 - 112 L','₹ 100 L','₹ 150 L','₹ 101 - 119 L'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c76e394a",
   "metadata": {},
   "outputs": [],
   "source": [
    "price=df['PriceRange'].str.split('₹',n=1,expand=True)\n",
    "price=price[1].str.split('-',n=1,expand=True)\n",
    "st_price,fi_price=price[0],price[1]\n",
    "st_price=st_price.str.split('L',n=1,expand=True)\n",
    "df['StartingPrice(in Lakh)']=st_price[0].str.split('L',n=1,expand=True)\n",
    "fi_price=fi_price.str.split('L',n=1,expand=True)\n",
    "df['FinalPrice(in Lakh)']=fi_price[0].str.split('L',n=1,expand=True)\n",
    "df['StartingPrice(in Lakh)']=df['StartingPrice(in Lakh)'].astype(float)\n",
    "df['FinalPrice(in Lakh)']=df['FinalPrice(in Lakh)'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ced8f992",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Range']=df['Range'].replace('na','0 Km/Full Charge')\n",
    "df[['Range','Unit']]=df['Range'].str.split(' ',n=1,expand=True)\n",
    "df=df.drop(['Unit'],axis=1)\n",
    "# df['Range']=df['Range'].replace(\"na\",0)\n",
    "df['Range']=df['Range'].astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "77f7f69d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=['Car', 'Style', 'Range(per Km)', 'Transmission', 'VehicleType', 'PriceRange',\n",
    "       'Capacity', 'BootSpace', 'BaseModel', 'TopModel', 'BootSpace_new',\n",
    "       'StartingPrice(in Lakh)', 'FinalPrice(in Lakh)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ced4db40",
   "metadata": {},
   "outputs": [
    {
     "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>Car</th>\n",
       "      <th>Style</th>\n",
       "      <th>Range(per Km)</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>VehicleType</th>\n",
       "      <th>PriceRange</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>BootSpace</th>\n",
       "      <th>BaseModel</th>\n",
       "      <th>TopModel</th>\n",
       "      <th>BootSpace_new</th>\n",
       "      <th>StartingPrice(in Lakh)</th>\n",
       "      <th>FinalPrice(in Lakh)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tata Nexon EV</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>312</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 13.99 - 17.4 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>350 L</td>\n",
       "      <td>XM</td>\n",
       "      <td>Dark XZ Plus LUX</td>\n",
       "      <td>350.00</td>\n",
       "      <td>13.99</td>\n",
       "      <td>17.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tata Tigor EV</td>\n",
       "      <td>Subcompact Sedan</td>\n",
       "      <td>306</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 12.49 - 13.64 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>316 L</td>\n",
       "      <td>XE</td>\n",
       "      <td>XZ Plus Dual Tone</td>\n",
       "      <td>316.00</td>\n",
       "      <td>12.49</td>\n",
       "      <td>13.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tata Nexon EV Max</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>437</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 17.74 - 19.24 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>350 L</td>\n",
       "      <td>XZ Plus 3.3 kW</td>\n",
       "      <td>XZ Plus Lux 7.2 kW</td>\n",
       "      <td>350.00</td>\n",
       "      <td>17.74</td>\n",
       "      <td>19.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MG ZS EV</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>419</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 21.99 - 25.88 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>448 L</td>\n",
       "      <td>Excite</td>\n",
       "      <td>Exclusive</td>\n",
       "      <td>448.00</td>\n",
       "      <td>21.99</td>\n",
       "      <td>25.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hyundai Kona Electric</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>452</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 23.79 - 23.98 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>0 L</td>\n",
       "      <td>Premium Dual Tone</td>\n",
       "      <td>HSE</td>\n",
       "      <td>313.75</td>\n",
       "      <td>23.79</td>\n",
       "      <td>23.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jaguar I-Pace</td>\n",
       "      <td>Premium Midsize Sedan</td>\n",
       "      <td>470</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 106 - 112 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>656 L</td>\n",
       "      <td>S</td>\n",
       "      <td>Sportback 55</td>\n",
       "      <td>656.00</td>\n",
       "      <td>106.00</td>\n",
       "      <td>112.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Audi E-Tron GT</td>\n",
       "      <td>Premium Coupe</td>\n",
       "      <td>388</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 180 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>405 L</td>\n",
       "      <td>Quattro</td>\n",
       "      <td>na</td>\n",
       "      <td>405.00</td>\n",
       "      <td>180.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BYD E6</td>\n",
       "      <td>Subcompact MPV</td>\n",
       "      <td>415</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 29.15 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>580 L</td>\n",
       "      <td>STD</td>\n",
       "      <td>na</td>\n",
       "      <td>580.00</td>\n",
       "      <td>29.15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mercedes-Benz EQC</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>471</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 100 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>0 L</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>313.75</td>\n",
       "      <td>100.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BMW iX</td>\n",
       "      <td>Premium Fullsize SUV</td>\n",
       "      <td>425</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 116 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>0 L</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>313.75</td>\n",
       "      <td>116.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Porsche Taycan</td>\n",
       "      <td>Premium Sports Sedan</td>\n",
       "      <td>0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 150 L</td>\n",
       "      <td>4 Seater</td>\n",
       "      <td>0 L</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>313.75</td>\n",
       "      <td>150.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Audi E-Tron</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>400</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>₹ 101 - 119 L</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>660 L</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>660.00</td>\n",
       "      <td>101.00</td>\n",
       "      <td>119.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Car                  Style  Range(per Km) Transmission  \\\n",
       "0           Tata Nexon EV            Compact SUV            312    Automatic   \n",
       "1           Tata Tigor EV       Subcompact Sedan            306    Automatic   \n",
       "2       Tata Nexon EV Max            Compact SUV            437    Automatic   \n",
       "3                MG ZS EV            Compact SUV            419    Automatic   \n",
       "4   Hyundai Kona Electric            Compact SUV            452    Automatic   \n",
       "5           Jaguar I-Pace  Premium Midsize Sedan            470    Automatic   \n",
       "6          Audi E-Tron GT          Premium Coupe            388    Automatic   \n",
       "7                  BYD E6         Subcompact MPV            415    Automatic   \n",
       "8       Mercedes-Benz EQC            Compact SUV            471    Automatic   \n",
       "9                  BMW iX   Premium Fullsize SUV            425    Automatic   \n",
       "10         Porsche Taycan   Premium Sports Sedan              0    Automatic   \n",
       "11            Audi E-Tron            Compact SUV            400    Automatic   \n",
       "\n",
       "   VehicleType         PriceRange   Capacity BootSpace           BaseModel  \\\n",
       "0     Electric   ₹ 13.99 - 17.4 L  5 Seater      350 L                 XM    \n",
       "1     Electric  ₹ 12.49 - 13.64 L  5 Seater      316 L                 XE    \n",
       "2     Electric  ₹ 17.74 - 19.24 L  5 Seater      350 L     XZ Plus 3.3 kW    \n",
       "3     Electric  ₹ 21.99 - 25.88 L  5 Seater      448 L             Excite    \n",
       "4     Electric  ₹ 23.79 - 23.98 L  5 Seater        0 L  Premium Dual Tone    \n",
       "5     Electric      ₹ 106 - 112 L  5 Seater      656 L                  S    \n",
       "6     Electric            ₹ 180 L  5 Seater      405 L            Quattro    \n",
       "7     Electric          ₹ 29.15 L  5 Seater      580 L                STD    \n",
       "8     Electric            ₹ 100 L  5 Seater        0 L                  na   \n",
       "9     Electric            ₹ 116 L  5 Seater        0 L                  na   \n",
       "10    Electric            ₹ 150 L  4 Seater        0 L                  na   \n",
       "11    Electric      ₹ 101 - 119 L  5 Seater      660 L                  na   \n",
       "\n",
       "               TopModel  BootSpace_new  StartingPrice(in Lakh)  \\\n",
       "0     Dark XZ Plus LUX          350.00                   13.99   \n",
       "1    XZ Plus Dual Tone          316.00                   12.49   \n",
       "2   XZ Plus Lux 7.2 kW          350.00                   17.74   \n",
       "3            Exclusive          448.00                   21.99   \n",
       "4                  HSE          313.75                   23.79   \n",
       "5         Sportback 55          656.00                  106.00   \n",
       "6                    na         405.00                  180.00   \n",
       "7                    na         580.00                   29.15   \n",
       "8                    na         313.75                  100.00   \n",
       "9                    na         313.75                  116.00   \n",
       "10                   na         313.75                  150.00   \n",
       "11                   na         660.00                  101.00   \n",
       "\n",
       "    FinalPrice(in Lakh)  \n",
       "0                 17.40  \n",
       "1                 13.64  \n",
       "2                 19.24  \n",
       "3                 25.88  \n",
       "4                 23.98  \n",
       "5                112.00  \n",
       "6                   NaN  \n",
       "7                   NaN  \n",
       "8                   NaN  \n",
       "9                   NaN  \n",
       "10                  NaN  \n",
       "11               119.00  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5d346ec9",
   "metadata": {},
   "outputs": [
    {
     "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>Car</th>\n",
       "      <th>Style</th>\n",
       "      <th>Range(per Km)</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>VehicleType</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>BaseModel</th>\n",
       "      <th>TopModel</th>\n",
       "      <th>BootSpace_new</th>\n",
       "      <th>StartingPrice(in Lakh)</th>\n",
       "      <th>FinalPrice(in Lakh)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tata Nexon EV</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>312</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>XM</td>\n",
       "      <td>Dark XZ Plus LUX</td>\n",
       "      <td>350.00</td>\n",
       "      <td>13.99</td>\n",
       "      <td>17.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tata Tigor EV</td>\n",
       "      <td>Subcompact Sedan</td>\n",
       "      <td>306</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>XE</td>\n",
       "      <td>XZ Plus Dual Tone</td>\n",
       "      <td>316.00</td>\n",
       "      <td>12.49</td>\n",
       "      <td>13.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tata Nexon EV Max</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>437</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>XZ Plus 3.3 kW</td>\n",
       "      <td>XZ Plus Lux 7.2 kW</td>\n",
       "      <td>350.00</td>\n",
       "      <td>17.74</td>\n",
       "      <td>19.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MG ZS EV</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>419</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>Excite</td>\n",
       "      <td>Exclusive</td>\n",
       "      <td>448.00</td>\n",
       "      <td>21.99</td>\n",
       "      <td>25.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hyundai Kona Electric</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>452</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>Premium Dual Tone</td>\n",
       "      <td>HSE</td>\n",
       "      <td>313.75</td>\n",
       "      <td>23.79</td>\n",
       "      <td>23.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jaguar I-Pace</td>\n",
       "      <td>Premium Midsize Sedan</td>\n",
       "      <td>470</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>S</td>\n",
       "      <td>Sportback 55</td>\n",
       "      <td>656.00</td>\n",
       "      <td>106.00</td>\n",
       "      <td>112.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Audi E-Tron GT</td>\n",
       "      <td>Premium Coupe</td>\n",
       "      <td>388</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>Quattro</td>\n",
       "      <td>na</td>\n",
       "      <td>405.00</td>\n",
       "      <td>180.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BYD E6</td>\n",
       "      <td>Subcompact MPV</td>\n",
       "      <td>415</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>STD</td>\n",
       "      <td>na</td>\n",
       "      <td>580.00</td>\n",
       "      <td>29.15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mercedes-Benz EQC</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>471</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>313.75</td>\n",
       "      <td>100.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BMW iX</td>\n",
       "      <td>Premium Fullsize SUV</td>\n",
       "      <td>425</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>313.75</td>\n",
       "      <td>116.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Porsche Taycan</td>\n",
       "      <td>Premium Sports Sedan</td>\n",
       "      <td>0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>4 Seater</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>313.75</td>\n",
       "      <td>150.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Audi E-Tron</td>\n",
       "      <td>Compact SUV</td>\n",
       "      <td>400</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Electric</td>\n",
       "      <td>5 Seater</td>\n",
       "      <td>na</td>\n",
       "      <td>na</td>\n",
       "      <td>660.00</td>\n",
       "      <td>101.00</td>\n",
       "      <td>119.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Car                  Style  Range(per Km) Transmission  \\\n",
       "0           Tata Nexon EV            Compact SUV            312    Automatic   \n",
       "1           Tata Tigor EV       Subcompact Sedan            306    Automatic   \n",
       "2       Tata Nexon EV Max            Compact SUV            437    Automatic   \n",
       "3                MG ZS EV            Compact SUV            419    Automatic   \n",
       "4   Hyundai Kona Electric            Compact SUV            452    Automatic   \n",
       "5           Jaguar I-Pace  Premium Midsize Sedan            470    Automatic   \n",
       "6          Audi E-Tron GT          Premium Coupe            388    Automatic   \n",
       "7                  BYD E6         Subcompact MPV            415    Automatic   \n",
       "8       Mercedes-Benz EQC            Compact SUV            471    Automatic   \n",
       "9                  BMW iX   Premium Fullsize SUV            425    Automatic   \n",
       "10         Porsche Taycan   Premium Sports Sedan              0    Automatic   \n",
       "11            Audi E-Tron            Compact SUV            400    Automatic   \n",
       "\n",
       "   VehicleType   Capacity           BaseModel             TopModel  \\\n",
       "0     Electric  5 Seater                  XM     Dark XZ Plus LUX    \n",
       "1     Electric  5 Seater                  XE    XZ Plus Dual Tone    \n",
       "2     Electric  5 Seater      XZ Plus 3.3 kW   XZ Plus Lux 7.2 kW    \n",
       "3     Electric  5 Seater              Excite            Exclusive    \n",
       "4     Electric  5 Seater   Premium Dual Tone                  HSE    \n",
       "5     Electric  5 Seater                   S         Sportback 55    \n",
       "6     Electric  5 Seater             Quattro                    na   \n",
       "7     Electric  5 Seater                 STD                    na   \n",
       "8     Electric  5 Seater                   na                   na   \n",
       "9     Electric  5 Seater                   na                   na   \n",
       "10    Electric  4 Seater                   na                   na   \n",
       "11    Electric  5 Seater                   na                   na   \n",
       "\n",
       "    BootSpace_new  StartingPrice(in Lakh)  FinalPrice(in Lakh)  \n",
       "0          350.00                   13.99                17.40  \n",
       "1          316.00                   12.49                13.64  \n",
       "2          350.00                   17.74                19.24  \n",
       "3          448.00                   21.99                25.88  \n",
       "4          313.75                   23.79                23.98  \n",
       "5          656.00                  106.00               112.00  \n",
       "6          405.00                  180.00                  NaN  \n",
       "7          580.00                   29.15                  NaN  \n",
       "8          313.75                  100.00                  NaN  \n",
       "9          313.75                  116.00                  NaN  \n",
       "10         313.75                  150.00                  NaN  \n",
       "11         660.00                  101.00               119.00  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['PriceRange','BootSpace'],axis=1,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7952edec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['FinalPrice(in Lakh)']=df['FinalPrice(in Lakh)'].fillna(0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "db529fad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['StartingPrice(in Lakh)'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89098784",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "963d2217",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Compact SUV              6\n",
       "Subcompact Sedan         1\n",
       "Premium Midsize Sedan    1\n",
       "Premium Coupe            1\n",
       "Subcompact MPV           1\n",
       "Premium Fullsize SUV     1\n",
       "Premium Sports Sedan     1\n",
       "Name: Style, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "style=df['Style'].value_counts()\n",
    "style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1a9de83a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# con=style.index\n",
    "# count_plot('Style',df,con,\"Style Distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "62e856d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def count_plot(col,df,con,title):\n",
    "    plt.figure(figsize=(10,6))\n",
    "    sb.countplot(y='Style',data=df,order=con,palette='coolwarm_r')\n",
    "    plt.title(title,{'fontweight':'bold','fontsize':'larger'})\n",
    "    plt.show()\n",
    "con=style.index\n",
    "count_plot('Style',df,con,\"Style Distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eea985dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "range_style=df.groupby('Style')['Range(per Km)'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bfb114c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Range(per Km)')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,8))\n",
    "# sb.barplot(x=range_style.index,y=range_style.values)\n",
    "# plt.barh(y=range_style.index,width=range_style.values,color='#CD5C5C')\n",
    "plt.bar(x=range_style.index,height=range_style.values,color='#CD5C5C')\n",
    "plt.title('Range(per Km) based on style of the vehicles',{'fontweight':'bold','fontsize':'larger'})\n",
    "plt.xlabel(\"Style\",{'fontweight':'bold','fontsize':'larger'})\n",
    "plt.ylabel(\"Range(per Km)\",{'fontweight':'bold','fontsize':'larger'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "79310dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Style                  Car                  \n",
       "Compact SUV            Audi E-Tron              101.00\n",
       "                       Hyundai Kona Electric     23.79\n",
       "                       MG ZS EV                  21.99\n",
       "                       Mercedes-Benz EQC        100.00\n",
       "                       Tata Nexon EV             13.99\n",
       "                       Tata Nexon EV Max         17.74\n",
       "Premium Coupe          Audi E-Tron GT           180.00\n",
       "Premium Fullsize SUV   BMW iX                   116.00\n",
       "Premium Midsize Sedan  Jaguar I-Pace            106.00\n",
       "Premium Sports Sedan   Porsche Taycan           150.00\n",
       "Subcompact MPV         BYD E6                    29.15\n",
       "Subcompact Sedan       Tata Tigor EV             12.49\n",
       "Name: StartingPrice(in Lakh), dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Style','Car'])['StartingPrice(in Lakh)'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cc5dc4d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_price=df.groupby('Style')['StartingPrice(in Lakh)'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bb7fb04e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Starting Price(in Lakh)')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,8))\n",
    "# sb.barplot(x=range_style.index,y=range_style.values)\n",
    "# plt.barh(y=range_style.index,width=range_style.values,color='#CD5C5C')\n",
    "plt.bar(x=start_price.index,height=start_price.values,color='#EE8912')\n",
    "plt.title('Starting Price(in Lakh) based on style',{'fontweight':'bold','fontsize':'larger'})\n",
    "plt.xlabel(\"Style\",{'fontweight':'bold','fontsize':'larger'})\n",
    "plt.ylabel(\"Starting Price(in Lakh)\",{'fontweight':'bold','fontsize':'larger'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0cfa48bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = list(final_price.index)\n",
    "\n",
    "start = list(start_price.values)\n",
    "final = list(final_price.values)\n",
    "  \n",
    "X_axis = np.arange(len(X))\n",
    "plt.figure(figsize=(20,8)) \n",
    "plt.bar(X_axis - 0.2, start, 0.4, label = 'Median Starting Price(in Lakh)')\n",
    "plt.bar(X_axis + 0.2, final, 0.4, label = 'Median Final Price(in Lakh)')\n",
    "  \n",
    "plt.xticks(X_axis, X)\n",
    "plt.xlabel(\"Style\",{'fontweight':'bold','fontsize':'larger'})\n",
    "plt.ylabel(\"Price(in Lakh)\",{'fontweight':'bold','fontsize':'larger'})\n",
    "plt.title(\"Style's median Starting and final price\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e6b060",
   "metadata": {},
   "source": [
    "## Dataset 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fc923b6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('alternative-fuel-stations-in-new-york.csv')\n",
    "# data.head(10)\n",
    "data_new=data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dda3f9e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "col=['Fuel Type Code', 'Station Name', 'Street Address',\n",
    "       'Intersection Directions', 'City', 'State', 'ZIP',\n",
    "       'Station Phone', 'Status Code',\n",
    "       'Groups With Access Code', 'Access Days Time', 'Cards Accepted',\n",
    "       'EV Level2 EVSE Num', 'EV DC Fast Count', 'EV Network',\n",
    "       'EV Network Web', 'Geocode Status', 'Latitude', 'Longitude',\n",
    "       'Date Last Confirmed', 'ID', 'Updated At', 'Owner Type Code',\n",
    "       'Open Date',\n",
    "       'E85 Blender Pump', 'EV Connector Types', 'Country',\n",
    "       'Groups With Access Code (French)',\n",
    "       'Access Code', 'Access Detail Code',\n",
    "       'Facility Type', 'EV Pricing', 'Georeference',\n",
    "       'NYS Municipal Boundaries 2', 'New York Zip Codes 2', 'Counties 2']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ffc8d2a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_new=data_new[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ccedf8ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2697 entries, 0 to 2696\n",
      "Data columns (total 36 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   Fuel Type Code                    2697 non-null   object \n",
      " 1   Station Name                      2697 non-null   object \n",
      " 2   Street Address                    2697 non-null   object \n",
      " 3   Intersection Directions           134 non-null    object \n",
      " 4   City                              2697 non-null   object \n",
      " 5   State                             2697 non-null   object \n",
      " 6   ZIP                               2697 non-null   int64  \n",
      " 7   Station Phone                     2673 non-null   object \n",
      " 8   Status Code                       2697 non-null   object \n",
      " 9   Groups With Access Code           2697 non-null   object \n",
      " 10  Access Days Time                  2695 non-null   object \n",
      " 11  Cards Accepted                    242 non-null    object \n",
      " 12  EV Level2 EVSE Num                2414 non-null   float64\n",
      " 13  EV DC Fast Count                  158 non-null    float64\n",
      " 14  EV Network                        2558 non-null   object \n",
      " 15  EV Network Web                    2388 non-null   object \n",
      " 16  Geocode Status                    2697 non-null   object \n",
      " 17  Latitude                          2697 non-null   float64\n",
      " 18  Longitude                         2697 non-null   float64\n",
      " 19  Date Last Confirmed               2697 non-null   object \n",
      " 20  ID                                2697 non-null   int64  \n",
      " 21  Updated At                        2697 non-null   object \n",
      " 22  Owner Type Code                   1146 non-null   object \n",
      " 23  Open Date                         2679 non-null   object \n",
      " 24  E85 Blender Pump                  73 non-null     object \n",
      " 25  EV Connector Types                2558 non-null   object \n",
      " 26  Country                           2697 non-null   object \n",
      " 27  Groups With Access Code (French)  2697 non-null   object \n",
      " 28  Access Code                       2697 non-null   object \n",
      " 29  Access Detail Code                199 non-null    object \n",
      " 30  Facility Type                     998 non-null    object \n",
      " 31  EV Pricing                        814 non-null    object \n",
      " 32  Georeference                      2697 non-null   object \n",
      " 33  NYS Municipal Boundaries 2        2695 non-null   float64\n",
      " 34  New York Zip Codes 2              2663 non-null   float64\n",
      " 35  Counties 2                        2685 non-null   float64\n",
      "dtypes: float64(7), int64(2), object(27)\n",
      "memory usage: 758.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "66149758",
   "metadata": {},
   "outputs": [
    {
     "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 Type Code</th>\n",
       "      <th>Station Name</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>Intersection Directions</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Station Phone</th>\n",
       "      <th>Status Code</th>\n",
       "      <th>Groups With Access Code</th>\n",
       "      <th>...</th>\n",
       "      <th>Country</th>\n",
       "      <th>Groups With Access Code (French)</th>\n",
       "      <th>Access Code</th>\n",
       "      <th>Access Detail Code</th>\n",
       "      <th>Facility Type</th>\n",
       "      <th>EV Pricing</th>\n",
       "      <th>Georeference</th>\n",
       "      <th>NYS Municipal Boundaries 2</th>\n",
       "      <th>New York Zip Codes 2</th>\n",
       "      <th>Counties 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ELEC</td>\n",
       "      <td>Village of Sodus</td>\n",
       "      <td>14-16 Mill St</td>\n",
       "      <td>Enter municipal lot from Main or Gaylord Streets</td>\n",
       "      <td>Sodus</td>\n",
       "      <td>NY</td>\n",
       "      <td>14551</td>\n",
       "      <td>315-483-9821</td>\n",
       "      <td>E</td>\n",
       "      <td>Public</td>\n",
       "      <td>...</td>\n",
       "      <td>US</td>\n",
       "      <td>Public</td>\n",
       "      <td>public</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MUNI_GOV</td>\n",
       "      <td>Free</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-77.061621, ...</td>\n",
       "      <td>628.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>631.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ELEC</td>\n",
       "      <td>DeNooyer Chevrolet</td>\n",
       "      <td>127 Wolf Rd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albany</td>\n",
       "      <td>NY</td>\n",
       "      <td>12205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>Public - Call ahead</td>\n",
       "      <td>...</td>\n",
       "      <td>US</td>\n",
       "      <td>Public - Appeler à l'avance</td>\n",
       "      <td>public</td>\n",
       "      <td>CALL</td>\n",
       "      <td>CAR_DEALER</td>\n",
       "      <td>Free</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-73.806317, ...</td>\n",
       "      <td>834.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2030.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ELEC</td>\n",
       "      <td>BRUNSWICK HD BRUNSWICK HD2</td>\n",
       "      <td>1130 Hoosick Rd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Troy</td>\n",
       "      <td>NY</td>\n",
       "      <td>12180</td>\n",
       "      <td>888-758-4389</td>\n",
       "      <td>E</td>\n",
       "      <td>Public</td>\n",
       "      <td>...</td>\n",
       "      <td>US</td>\n",
       "      <td>Public</td>\n",
       "      <td>public</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-73.60228, 4...</td>\n",
       "      <td>130.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ELEC</td>\n",
       "      <td>AL SIGL WOLK</td>\n",
       "      <td>1600 South Ave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rochester</td>\n",
       "      <td>NY</td>\n",
       "      <td>14620</td>\n",
       "      <td>888-758-4389</td>\n",
       "      <td>E</td>\n",
       "      <td>Public</td>\n",
       "      <td>...</td>\n",
       "      <td>US</td>\n",
       "      <td>Public</td>\n",
       "      <td>public</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-77.61388, 4...</td>\n",
       "      <td>537.0</td>\n",
       "      <td>1679.0</td>\n",
       "      <td>2093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ELEC</td>\n",
       "      <td>LaGrangeville Tops Market</td>\n",
       "      <td>16 John J Wagner Way</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LaGrangeville</td>\n",
       "      <td>NY</td>\n",
       "      <td>12540</td>\n",
       "      <td>855-900-7584</td>\n",
       "      <td>E</td>\n",
       "      <td>Public</td>\n",
       "      <td>...</td>\n",
       "      <td>US</td>\n",
       "      <td>Public</td>\n",
       "      <td>public</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-73.795294, ...</td>\n",
       "      <td>594.0</td>\n",
       "      <td>979.0</td>\n",
       "      <td>2040.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Fuel Type Code                Station Name        Street Address  \\\n",
       "0           ELEC            Village of Sodus         14-16 Mill St   \n",
       "1           ELEC          DeNooyer Chevrolet           127 Wolf Rd   \n",
       "2           ELEC  BRUNSWICK HD BRUNSWICK HD2       1130 Hoosick Rd   \n",
       "3           ELEC                AL SIGL WOLK        1600 South Ave   \n",
       "4           ELEC   LaGrangeville Tops Market  16 John J Wagner Way   \n",
       "\n",
       "                            Intersection Directions           City State  \\\n",
       "0  Enter municipal lot from Main or Gaylord Streets          Sodus    NY   \n",
       "1                                               NaN         Albany    NY   \n",
       "2                                               NaN           Troy    NY   \n",
       "3                                               NaN      Rochester    NY   \n",
       "4                                               NaN  LaGrangeville    NY   \n",
       "\n",
       "     ZIP Station Phone Status Code Groups With Access Code  ... Country  \\\n",
       "0  14551  315-483-9821           E                  Public  ...      US   \n",
       "1  12205           NaN           E     Public - Call ahead  ...      US   \n",
       "2  12180  888-758-4389           E                  Public  ...      US   \n",
       "3  14620  888-758-4389           E                  Public  ...      US   \n",
       "4  12540  855-900-7584           E                  Public  ...      US   \n",
       "\n",
       "  Groups With Access Code (French)  Access Code  Access Detail Code  \\\n",
       "0                           Public       public                 NaN   \n",
       "1      Public - Appeler à l'avance       public                CALL   \n",
       "2                           Public       public                 NaN   \n",
       "3                           Public       public                 NaN   \n",
       "4                           Public       public                 NaN   \n",
       "\n",
       "  Facility Type EV Pricing                                       Georeference  \\\n",
       "0      MUNI_GOV       Free  {'type': 'Point', 'coordinates': [-77.061621, ...   \n",
       "1    CAR_DEALER       Free  {'type': 'Point', 'coordinates': [-73.806317, ...   \n",
       "2           NaN        NaN  {'type': 'Point', 'coordinates': [-73.60228, 4...   \n",
       "3           NaN        NaN  {'type': 'Point', 'coordinates': [-77.61388, 4...   \n",
       "4           NaN        NaN  {'type': 'Point', 'coordinates': [-73.795294, ...   \n",
       "\n",
       "   NYS Municipal Boundaries 2  New York Zip Codes 2 Counties 2  \n",
       "0                       628.0                 408.0      631.0  \n",
       "1                       834.0                  42.0     2030.0  \n",
       "2                       130.0                  11.0     2138.0  \n",
       "3                       537.0                1679.0     2093.0  \n",
       "4                       594.0                 979.0     2040.0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "72089ac4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       43.235555\n",
       "1       42.717867\n",
       "2       42.758880\n",
       "3       43.123642\n",
       "4       41.667907\n",
       "          ...    \n",
       "2692    42.909700\n",
       "2693    42.831453\n",
       "2694    43.081539\n",
       "2695    42.901523\n",
       "2696    43.156137\n",
       "Name: Latitude, Length: 2697, dtype: float64"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new['Latitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "26de933e",
   "metadata": {},
   "outputs": [],
   "source": [
    "fuel_type_code=data_new['Fuel Type Code'].value_counts().index\n",
    "idx=data_new['Fuel Type Code'].value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "2c197abb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(x=idx,labels=fuel_type_code,explode=[0,0.3,0.4,0.5,1.3],colors=['gold','wheat','yellowgreen','beige','orange'],wedgeprops={'linewidth':1,'edgecolor':'k'},textprops={'fontweight':'bold'},autopct='%1.1f%%')\n",
    "plt.title(\"Distribution of types of cars based on Fuel Types\",{'fontweight':'bold'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "3b8926a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ocA7wlp79nwu8GTivPdxQkiRpmWRIIEmLx0pJbuz5eSvwcuCgJJcBV9E9HHDIicAr2uuQ0dbv5yPAKnRDFGYnOWVowSjPJHh9u8t/EN04/n8HqKqrgG8AvwB+CLyhqu5v+zo2ydBDCD/cvubwcuDpzOs98BjgkiRXA/8BvHKUeq+Q5Hy6i/ah7d8EzGjPOvgF8K9jnPuDWu+GPYHPJnlyKz4HeDzzQoJL6HpH/Pyhe5AkSVp2xBsmkqThksysqpkDOO4cuq9tvHVxH3sibLPuinXqv2wy6GoMxAbvuWLQVZAkSQsgycVV9ZBvm7IngSSpn1mDroAkSZIWPx9cKEl6iKqaNaDjTh/EcSVJktSxJ4EkSZIkSQIMCSRJkiRJUmNIIEmSJEmSAEMCSZIkSZLUGBJIkiRJkiTAkECSJEmSJDWGBJIkSZIkCTAkkCRJkiRJjSGBJEmSJEkCDAkkSZIkSVJjSCBJkiRJkgCYNugKSJI0VTx8na3Y4D0XDboakiRJC82eBJIkSZIkCTAkkCRJkiRJjSGBJEmSJEkCDAkkSZIkSVJjSCBJkiRJkgBDAkmSJEmS1BgSSJIkSZIkAKYNugKSJE0V1/zxGnb59C6DrsaozjnknEFXQZIkLcHsSSBJkiRJkgBDAkmSJEmS1BgSSJIkSZIkwJBAkiRJkiQ1hgSSJEmSJAkwJJAkSZIkSY0hgSRJkiRJAgwJJEmSJElSY0ggSZIkSZIAQwJJkiRJktQYEkiSJEmSJMCQQJIkSZIkNYYEkiRJkiQJmOSQIMn0JAcOK1shyYlJrktyfpLpfbZbKcn3k1yT5KokH+xZtkGSM5JcmuTyJM+bzHNox5yZ5NBhZdsmmd0zv1+Su5Ms3+afmOTyNj0nyZoLcdwJb7+2/J+T/KIt+58FrddkSXJski0XctvpSV42gXW5a6L2Nc7j9X1PkhyQ5Jft54ARtp2VZMawsjcn+WTP/NFJftwzf0iST03CeYz6WU8yc9h8knyqfZ4vT/KkEbZ7Y1unevefZK+23ewkFyXZdcJOZhEleW+SZy3ktqsnef0E1uUtSVaawP0dl2TvBVh/epIrJ+r4bZ+rJzmp/Z67OslTW/mjk5ze/s2cnuRRfbbdPcmpw8qS5Nah9ZOs0z5vu/asc0uSNSbyPCRJkpY0kxYSJHkdcBrwvnYRs3ZbdBDwl6raBPgE8KERdvHRqtoc2B7YJclzW/m7gG9U1fbAvsDnJqHu08ax2hXAhklWbfM7A9e0+g7Nn7MIdZiU9kuyKfBOYJeq2gp4y8LWcZS6j6f9HqKqXlNVv1jIw04HJiwkWJxGek+SPBr4T+DJwE7Af/a74BnBz+k+g0O2A1ZLslybX6TP54JK8qIWqr0uyTlJntgWPRfYtP0cDBw1wi7OAZ4F/GZY+U+AbatqO+DVwLETXPVF+Ty/p6p+PPaafa0OTFhIQPeZWuiQoOdzsyQ5Evhh+z23LXB1K38H8JOq2pTu8/GO8eysqgo4H3hqK9oZuLS9kmQz4Naq+tOEnYEkSdISaFJCgnbhfDiwP/Bu4EBgblu8F3B8mz4JeGaS9G5fVXdX1Rlt+m/AJcB6Q4uBR7bp1YCb+hx/eru7dHy7y3jS0F20JDskOTPJxUlOS7JOK5+V5IgkZwJvHuXcXpvkf4EVgAvpLuAAdgA+y7wLs53pLtSGHJLkkiRXJNm87WtGkodc1Exy+70W+GxV/aUt/+Nkt1+6nhjHJ/lRu9P84iQfbm3xw8zrffHgHfEkdyV5f5LLkpyX5LGtfL47mJl3x/+DwD+0O8r/lmS5JB9JcmE7h38Zfp5t+++0c7kqycHDln2svWc/SbJWK9uu1efyJCcneVSSLZJcMKz9hnqR9G2vYUZ6T54NnF5Vf27LTgee0+882rEe1tr5v+gubp6QZMUkqwF3A7OBoYvz4Z/PoX0cle6O/FVJDu8pn5Pk8D6f4TXa+3ppkqOBDN9n8zngn+lCgBcDQ+e4F/Dl6pwHrN6vjarq0qqa06f8rnZxB7Ay3e+Hfm1z1wjv58btM3hxkrN7zuu4JB9PcgbDgrgkB7bPzfeS/DpdL4e3tjY4L124M99ndZT2m6+XUpIr0/UO+iCwcfs8f6Qte3vP5/lw+uj3/iV5E/A44Ix2PsO3eWar+xVJvpRkhZ46vyfJz4B9+hzuWa3N/i/J89s2Y/67S/KIJP/djndpkqe38h8k2aZNX5rkPW36fUleM2wfjwSeBnwRut9zVXVbW9z7O/J44IX92qpnXzu24z2eLozq/R3+ceYPDR7yb0aSJGmqmayeBA8AD6ddzFfVnKq6sy1bF7ihld8H3A6M2H0zyerAnnR3hABmAq9IciPwA+CQETbdDDimqrYB7gBen+5i9NPA3lW1A/Al4P0926xeVbtV1cdGqMsbW11eWFX30O7WJlm5nfMs5v8Ds/dO7a1V9SS6i6RD2/lfVFXz/fHbTGb7PYHu4vGcdkEz0kXnRLffxsA/0f0BfwJwRlU9EbinlQ+3MnBeVW0LnEV3IT2adwBnV9V2VfUJuh4Xt1fVjsCOwGuTbNRnu1e3c5kBvCnzuhKvDFzS3rMz6e7oA3wZ+I/WLlcA/1lVVwMPbxcZAC8FvjGO9hoy0nvy4Hvd3NjK+pkGfBX4v6p6V/tszG7n/hS6O6Tn0X1eHwekqm7os5/DqmoGsA2w29BFW/OQz3Brl5+1nj2nABuMUL/7gMcCVNUfquoPC3GOfaXrpXAN8H263gT9jPR+HgMc0t6fQ5m/Z9ITgGdV1dv67G9rup4rO9G9p3e3NjiXLtzrp1/7jeQdwK/a5/ntSfag622xE12vkB2SPK3Pdg95/6rqU3Rh6tOr6um9Kyd5BHAc8NL273Ea8LqeVf5aVbtW1df7HGs6sBvdv9/Pt32N59/dGwDa8fYDjm/bnkUX9D2S7vOyS1t/V+DsYft4PHAL8N/tAv/Y9nsY4LFVdXM7xs3AY/rUfej8dwY+D+xVVdczfw+cnYDvAOu3+cXa+0aSJGlQJiUkqKq5dH8oH0HXXf6jmTcett+dxpHu/k0DvgZ8qv0BB90flcdV1XrA84CvJOl3HjdU1dAfdCfQ/aG5Gd0f96en6/r8LubdYQc4cZTTeiVd1+iXVNW9rWzortNOwIVV9Stgk3aXcpWeOgN8u71eTPfH9Ygmuf2m0V1s7E7Xlse2IGG4iW6//62qv9NdWC8H/LCVX0H/9vgbMDRmeMw262MPYP9Wz/PpgpRN+6z3piSX0V1Ar9+zzgPMO58TgF3T3ZFfvarObOXH093NBPgG3Z1y6EKCExm7vYaM9J6M+70GjgaurKreEGLo87kz3cXruW16F0a+I/rPSS6h64mwFdD7jIh+n+Gn0bUPVfV94C8j7Hdfut4xb0hyTOY9V2BBzrGvqjq5dTl/IfC+EVbr936uQtce32zvz9FAby+Gb1bV/SPs74yqurOqbqEL6r7Xykf6PMMC/A7oY4/2cyldz6DN6f95Hu3962cz4NdV9X9tvvczDaP/m/5GVT1QVb8Erm91Gs+/u12BrwBU1TV0Q0ieQBcEPK0t/z6wSvu9N72qrh22j2nAk4CjWjgzl3EOK+ixBV1ItGdV/baVXQBs3wKH5avqLuD6JJswSk+CJAe3HhwX/f2uvy9gNSRJkpYsCzXWdjyq6pR0Xa73pLtL+za6P+BvpLsYu7FdxK4G/HmE3RwD/LKqPtlTdhCty3VVndvuQK3JvO7LD1ahz3yAq6rqqfQ3d4RygCvp7uCtB/y6lZ1Hd7dsV7oLMOjOb18e+sfkULBwP+No90lsvxvp7tD/Hfh1kmvp/oi/cHgV+swvSvvd287rgSR/7+ki/gD926N3nd42u48WbiUJXY+LfkJ3h/i0kSqUZHe6ce5Praq7k8wCHjHC6mNduJ5Id7H5bbrhzb9MN+5+tPYaMtJ7ciNdcDBkPbreKv38HHh6ko9V1V97yv6lndNn6e68btleH3JHtN3xPRTYsar+kuQ45m+PkT7DY17Ut8DpGUk+1Lb/EN2/5aHPc+85PmQI0XhU1Vnphg+sWVW3jrU63efotuqeZ9DPmJ/n5oGe+ZE+z73b9P08NyN9/gJ8oKqOHqlC43j/RtrvaEZrg5F+Rzzk313mf8DqSMe8kO533fV0Q2vWpOtBdHGfdW8Ebqyq89v8ScwLCf6QZJ2qujnd0JWHDKlqbqZrn+1pn7n2e+A6uh4pl7T1zqMLpB8DDA8raNsdQ/f7llU2WGWBQi5JkqQlzWQ9k2CVJBu22TvpHig19IC/U4Chp7TvDfy052Kwdx//RXcB/JZhi34LPLOtswXdH3m39KnGBmlPu6a7O/szuj/w1sq8p2Avn2SrcZ7WpXQXXKe07tq0IQA30D0zYCgkOLfVecyxq0l2SvLlPuWT2X7fAYbGAK9Jdwfveh5qottvosyhe/4DdEMXlm/TdzKvjaB76OPrMu95B0/o6Y48ZDW6h0DenW6M+FN6lj2Mrn2h61b+s6q6HfhLkn9o5a+k67pO60VyP90zJIbuvo63vb5D//fkNGCPdM89eBTdXdqRQo8v0g2/+WbmPWjv5+2c1qqqP7bPyS107dbv8/lIuovC29M9A+K5fdYZ7izg5a3uzwX6PlgxydZt8h7gcub/PO+fzlPouqrfPI7jDu13kxYWke6bER4O9HuwXL/38w66UGaftn2SbDveY0+QOXR3xIfqP9Q1v9/n+dWt9wNJ1k0yvBv9aO/f8P0NuQaY3u6UQ89nehz2SfccjI3puv9fy/j+3fV+Zp5AN0Tl2uqen3IDXY+c8+h6FhzKQ4caUFW/B25I9zBB6P5PGHroae/vyAOA745Q/9vohkoc0QLDIefQ/d7s/Z3+ZrogzwBAkiRNeZPVk2B5uq67a9J1N/0t8548/0W6IQLX0d0B33dooySzq2q7JOsBh9H9AXtJuwb4TFUdS3dH/QtJ/o3uztWBI/zhdjVwQLqHqf2Srlvq39I9SOxTrev4NOCTwFXjOamq+lm6h4x9P8k/truV59CNZx0aV30u3TCB8TzgagO6i6bhJrP9hi48f0F3Ufv26v+07glvvwnyBeC76R4U+BPm3em8HLgv3dCB4+iefD6d7vxDd3H8wmH7+iHwr63HxrV0FyZD5gJbJbmYrjv5S1v5AXTjr1eiu5B/Vc82JwIfoV3oLUB7jfieJHkf83p5vLeqRuo1QlV9vB3nK0le3u4m3zLseOfSDTe4rM/2lyW5tK1/PeMbf3048LV0XdzPpPus9vNfLQDZCPgd854d8AO6u7TX0T1c8cH2TPID4DVVdVO6h+/9O7A2cHmSH1T3PI+X0IUMf6f7t/TSEX4fjPR+vhw4Ksm76P7dfZ0+bTOJvsW87vkXAv8HUFV/SveMiivphuq8vYWi57Z/z3cBr6DnLvkY798xwP8mubn3uQRV9dckr2JeuHQh3Rj98biW7j1/LPCvbV/HMva/u8/R/Ru6gq4nxYE9Q7jOBp7Zgruz6XqWPCQkaA4Bvprk4cz/b/GDdM8EOYju89jvoYtD5/+HJHvStc2rW8+Ec+hCgaGQYOjBrxP+zRmSJElLokzmjZF0XUx3r6rjJu0gIx/31Kraeqx1ByndU8u/UlWXj7B8OrafppAkM6tq5gCOe1dVrbK4j6tlzyobrFLbvn1xd0hZMOcc4vMXJUkSJLm4uodez2fSnknQ3Eb3hHX1UVVvH2OV27D9NLXMGnQFJEmSJI1sUkOC6r63evZkHmOE486he6r8Us3201RTVbMGdFx7EUiSJEnjMCkPLpQkSZIkSUsfQwJJkiRJkgQYEkiSJEmSpMaQQJIkSZIkAYYEkiRJkiSpMSSQJEmSJEmAIYEkSZIkSWoMCSRJkiRJEmBIIEmSJEmSGkMCSZIkSZIEwLRBV0CSpKli88dszjmHnDPoakiSJC00exJIkiRJkiTAkECSJEmSJDWGBJIkSZIkCTAkkCRJkiRJjSGBJEmSJEkCDAkkSZIkSVJjSCBJkiRJkgBDAkmSJEmS1EwbdAUkSZoq7rz2Ws582m5jrrfbWWcuhtpIkiQtOHsSSJIkSZIkwJBAkiRJkiQ1hgSSJEmSJAkwJJAkSZIkSY0hgSRJkiRJAgwJJEmSJElSY0ggSZIkSZIAQwJJkiRJktQYEkiSJEmSJMCQQJIkSZIkNYYEkiRJkiQJMCSQJEmSJEmNIYEkaUpJcn+S2T0/05PsnuT2JJcmuSbJR3vWPzDJLcO22bJtd8+w8v0HeW6SJEmTbdqgKyBJ0gS7p6q26y1IMh04u6qen2RF4NIkJ1fVOW2VE6vqjX22+dXwfUmSJE1l9iSQJC1TquoeYDaw7oCrIkmStMSxJ4EkaapZMcnsNv3rqnpR78IkjwI2Bc7qKX5pkl175p/aXjfu2RfAIVV19gTXV5IkaYlhSCBJmmoeMtyg+YcklwObAR+sqt/3LOs33ADGMdwgycHAwQCPXWGFRai2JEnS4DncQJK0rDi7qrYBngi8Lsl2E7HTqjqmqmZU1YzVll9+InYpSZI0MIYEkqRlSlX9H/AB4D8GXRdJkqQljSGBJGlZ9HngaUk2avMvHfZVhzu38o2Hlb9pQPWVJElaLHwmgSRpSqmqVfqUzQJm9czfw7xvN/g1cNwIu1txYmsnSZK0ZLMngSRJkiRJAgwJJEmSJElSY0ggSZIkSZIAQwJJkiRJktQYEkiSJEmSJMCQQJIkSZIkNYYEkiRJkiQJMCSQJEmSJEmNIYEkSZIkSQIMCSRJkiRJUmNIIEmSJEmSAEMCSZIkSZLUGBJIkiRJkiQApg26ApIkTRWrbrYZu5115qCrIUmStNDsSSBJkiRJkgBDAkmSJEmS1BgSSJIkSZIkwJBAkiRJkiQ1hgSSJEmSJAkwJJAkSZIkSY0hgSRJkiRJAmDaoCsgSdJU8ccbb+czb/veQ8rf+LE9B1AbSZKkBWdPAkmSJEmSBBgSSJIkSZKkxpBAkiRJkiQBhgSSJEmSJKkxJJAkSZIkSYAhgSRJkiRJagwJJEmSJEkSYEggSZIkSZIaQwJJkiRJkgQYEkiSJEmSpMaQQJIkSZIkAYYEkiRJkiSpMSSQJEmSJEmAIYEkaZgk05McOKxshSQnJrkuyflJpo+w7fuT3JDkroXZfiIlmZnk0GFl2yaZ3TO/X5K7kyzf5p+Y5PI2PSfJmpNdT0mSpCWJIYEk6UFJXgecBrwvyawka7dFBwF/qapNgE8AHxphF98DdupTPt7tF1qSaeNY7QpgwySrtvmdgWuA7Xvmz5noukmSJC0tDAkkSQC0C+fDgf2BdwMHAnPb4r2A49v0ScAzk2T4PqrqvKq6uc/ux9y+9WC4JsnxSS5PclKSldqyHZKcmeTiJKclWaeVz0pyRJIzgTePcm6vTfK/wArAhcCT26IdgM/ShQO015/3bHpIkkuSXJFk85H2L0mSNFUYEkiShjwAPBx4JEBVzamqO9uydYEbWvl9wO3AGguw7/FuvxlwTFVtA9wBvL4NBfg0sHdV7QB8CXh/zzarV9VuVfWxfgdO8kZgT+CFVXUPXQiwc5KV2znPYv6QoLcnwa1V9STgKGC+oQuSJElT0Xi6ZkqSlgFVNTfJ/sARwNpJtgbeU1V3Aw/pNQDUAux+vNvfUFVDF+knAG8CfghsDZzeOh8sB/T2VjhxlOO+EriRLiD4eys7B3gbcDZwYVX9KskmSdYCVqmq63u2/3Z7vRh4cd8TSw4GDgZ41KprjVIVSZKkJZ89CSRJD6qqU4B9gA8Da9FdTEN3ob0+PDj2fzXgzwuw6/FuPzw4KLqA4aqq2q79PLGq9uhZZy4juxKYDqzXU3YesCOwK3BuT/32Zf6hBgD3ttf7GSFYr6pjqmpGVc1YZaXVRqmKJEnSks+QQJIEQJJVkmzYZu8ErgaGHvB3CnBAm94b+GlVLUhPgvFuv0GSp7bp/YCfAdcCaw2VJ1k+yVbjPO6lwL8ApyR5HEAbQnED3TMXhkKCc4G38NCQQJIkaZliSCBJGrI8cDTdgwX/E3gucGRb9kVgjSTXAW8F3jG00bCvFPxwkhuBlZLcmGTmWNsPczVwQPsawkcDR1XV3+iChQ8luQyYzbxnCIypqn5G9zyB7/d8peE5wApVdUObPxd4PIYEkiRpGZfx3AhK8qU+xbcBP66qH0x0pSRJg5NkOrB7VR03gOOeWlVbL87jTqQN1t60/v3lH39I+Rs/tucAaiNJkjSyJBdX1Yzh5eN9cOGBzBsXSs/0m5O8oao+PyG1lCQtCW6ju1svSZKkZcx4hxt8lK4r5h7As9v0x4HT6Z48LUmaIqrqtqqaPYDjzlmaexFIkiRNBePtSbAP8NGq+jFAkk2B/wBeC3xncqomSZIkSZIWp/GGBAUckWSnNr8X8CdgReCuyaiYJEmSJElavMY73OA1wN3AK9vP3a2sgPdOTtUkSZIkSdLiNK6eBFX10/bd2Zu3omvaV1JJkiRJkqQpYlwhQZJHAC8BpgPLtbKqqvdNXtUkSZIkSdLiNN5nEnwXeBbzvgIRuqEGhgSSJEmSJE0R4w0JngycBhwP3Dd51ZEkSZIkSYMy3pDg28Afq+rEyayMJEmSJEkanPGGBLsCGyd5BfDnVlZVte3kVEuSJEmSJC1u4w0JNmmvj2s/kiRJkiRpihnvVyA+bLIrIknS0u4x663GGz+256CrIUmStNBGDQmSvBg4D3hKn8VVVSdPSq0kSZIkSdJiN1ZPgm8C+wFfp/vKwyFp88tNUr0kSZIkSdJiNlZI8F7gqvZaY6wrSZIkSZKWYqOGBFV1eJu8ajHURZIkSZIkDdC4HkiYZFaSj/fMfyLJGZNXLUmSJEmStLiN91sLdgKu6Jm/HHjyxFdHkiRJkiQNynhDgj8CL06yUpKVgb1bmSRJkiRJmiLGenDhkK8B/wHcQfcAw4cBH5ysSkmSJEmSpMVvvCHBe4B7gD3pQoLvYUggSdJ8bv71r3j/K/Z+SPlhJ5w0gNpIkiQtuHGFBFX1d7qvQXzv5FZHkiRJkiQNyqjPJEhyR5IXtdfhP7cvrkpKkiRJkqTJN1ZPgj8Bfwf+TDfMQJIkSZIkTVGjhgRVtVGShwEvAG6pqpsXT7UkSZIkSdLiNuZXIFbVA8CZwHMnvzqSJEmSJGlQxgwJmv8Bnp9klcmsjCRJkiRJGpzxfgXiAcCKwO1J5rayqqrVJqdakiRJkiRpcRtvSHDLpNZCkiRJkiQN3KghQZJHAR8G7gR+C/y/qrp8cVRMkiRJkiQtXmP1JPgc8NI2vTXwxCSbVNXfJ7dakiRJkiRpcRvrwYV7AN8CtgQOA9Zr05IkSZIkaYoZKyR4FHBSVV0DfAFIK5MkSZIkSVPMeL4C8e1JTgG+AhRwRJJTknx3cqsmSVrckkxPcuCwsqcluSTJfUn2HmXbWUmuTTK7/Tymla+Q5MQk1yU5P8n0yT0LSDIzyaGTfRxJkqSpZjzfbvCk9jPkKe21Jr46kqRBSfI64C3ASi0o2Leqfk/34NoDgfFcdL+8qi4aVnYQ8Jeq2iTJvsCHmPe8mwmRZFpV3TeR+xzkcSRJkgZlrJ4EG43y8/jJrZokaXFJsipwOLA/8G66UGAuQFXNad9s88BC7n4v4Pg2fRLwzCQZdvzpSa5JcnySy5OclGSltmyHJGcmuTjJaUnWaeWzkhyR5EzgzX2Ou2Vb5/okb+o51luTXNl+3tJz/Ct71jk0ycxxHkeSJGnKGLUnQVX9ZnFVRJI0UA8ADwceCV0wsJD7+e8k99M99Pa/qqqAdYEb2n7vS3I7sAZw67BtNwMOqqpzknwJeH2SI4FPA3tV1S1JXgq8H3h122b1qtpthLpsDjwdWBW4NslRwDbAq4An0z1n5/x28f+XMc5rtONIkiRNGeMZbiBJmuKqam6S/YEjgLWTbA28p6ruXoDdvLyqftd6JXwLeCXwZbqL8Yccsk/ZDVV1Tps+AXgT8EO6r+A9vXU+WA64uWebE0epz/er6l7g3iR/BB4L7AqcXFVzAZJ8G/gH4JQxzm3E4yQ5GDgYYLWVVhxjN5IkSUu28Ty4UJK0DKiqU4B9gA8DawFvW8Dtf9de7wT+B9ipLboRWB+6Mf3AasCf++2iz3yAq6pqu/bzxKrao2eduaNU6d6e6fvpgvF+gQXAfcz/f+Ijhi0f8ThVdUxVzaiqGSs/YoVRqiNJkrTkMySQJJFklSQbttk7gavpuumPd/tpSdZs08sDzweGxvifAhzQpvcGftqGIQy3QZKntun9gJ8B1wJrDZUnWT7JVuM/s4c4C3hhkpWSrAy8CDgb+APwmCRrJFmh1V+SJGmZ43ADSRLA8sDRwJp0zwv4LfAygCQ7AicDjwL2THJ4VW3Vls2uqu2AFYDTWkCwHPBj4Att318EvpLkOroeBPuOUIergQOSHA38Ejiqqv7WvnbxU0lWo/t/65PAVQtzklV1SZLjgAta0bFVdWk7l/cC5wO/Bq5ZmP1LkiQt7dL/Zo4kaVmUZDqwe1UdN4DjnlpVWy/O4060ddd4VL3+uc98SPlhJ5w0gNpIkiSNLMnFVTVjeLnDDSRJvW4DZg+4DpIkSRoQhxtIkh5UVbcxgJCgfeXiUt2LQJIkaSqwJ4EkSZIkSQIMCSRJkiRJUmNIIEmSJEmSAEMCSZIkSZLUGBJIkiRJkiTAkECSJEmSJDWGBJIkSZIkCTAkkCRJkiRJjSGBJEmSJEkCDAkkSZIkSVIzbdAVkCRpqlhno4057ISTBl0NSZKkhWZPAkmSJEmSBBgSSJIkSZKkxpBAkiRJkiQBhgSSJEmSJKkxJJAkSZIkSYAhgSRJkiRJagwJJEmSJEkSYEggSZIkSZKaaYOugCRJU8Vfb76Tq9//0/nKtjjsGQOqjSRJ0oKzJ4EkSZIkSQIMCSRJkiRJUmNIIEmSJEmSAEMCSZIkSZLUGBJIkiRJkiTAkECSJEmSJDWGBJIkSZIkCTAkkCRJkiRJjSGBJEmSJEkCDAkkSZIkSVJjSCBJkiRJkgBDAkmSJEmS1BgSSJIkSZIkwJBAktQjyfQkBw4rWyHJiUmuS3J+kukjbPv+JDckuWtY+QZJzkhyaZLLkzxv8s7gwWPOTHLoZB9HkiRpqjEkkCQBkOR1wGnA+5LMSrJ2W3QQ8Jeq2gT4BPChEXbxPWCnPuXvAr5RVdsD+wKfm9iaQ5JpE73PQR5HkiRpUAwJJEkkWRU4HNgfeDdwIDC3Ld4LOL5NnwQ8M0mG76Oqzquqm/vsvoBHtunVgJv6HH96kmuSHN96G5yUZKW2bIckZya5OMlpSdZp5bOSHJHkTODNfY67ZVvn+iRv6jnWW5Nc2X7e0nP8K3vWOTTJzHEeR5IkacrwjogkCeAB4OG0i/mqmtOzbF3ghlZ+X5LbgTWAW8e575nAj5IcAqwMPGuE9TYDDqqqc5J8CXh9kiOBTwN7VdUtSV4KvB94ddtm9arabYT9bQ48HVgVuDbJUcA2wKuAJwMBzm8X/38Z4xxGO44kSdKUYU8CSRJVNZeuF8ERdMMNPjp0J5/uYvohmyzA7vcDjquq9YDnAV9J0u//nxuq6pw2fQKwK11wsDVwepLZdEMX1uvZ5sRRjvv9qrq3qm4F/gg8tu3z5KqaW1V3Ad8G/mEc5zDicZIcnOSiJBf9ee5t49iVJEnSksuQQJIEQFWdAuwDfBhYC3hbW3QjsD48OCZ/NeDPC7Drg4BvtGOcCzwCWLNfFfrMB7iqqrZrP0+sqj161pnLyO7tmb6frvdcv8AD4D7m/z/xEcOWj3icqjqmqmZU1YxHr7z6KNWRJEla8hkSSJJIskqSDdvsncDVdN30AU4BDmjTewM/raoF6UnwW+CZ7Thb0F2A39JnvQ2SPLVN7wf8DLgWWGuoPMnySbZagGMPdxbwwiQrJVkZeBFwNvAH4DFJ1kiyAvD8RTiGJEnSUstnEkiSAJYHjqa7w78G3YX9y9qyL9INEbiOrgfBvkMbJZldVdu16Q+3bVZKciNwbFXNpOuR8IUk/0bXO+DAEUKGq4EDkhwN/BI4qqr+lmRv4FNJVqP7f+uTwFULc5JVdUmS44ALWtGxVXVpq/97gfOBXwPXLMz+JUmSlnZZsJtBkqSpLMl0YPeqOm4Axz21qrZenMedaFuvu1l98/VHzVe2xWHPGFBtJEmSRpbk4qqaMbzc4QaSpF63AbMHXAdJkiQNiMMNJEkPqqrbGEBI0L5ycanuRSBJkjQV2JNAkiRJkiQBhgSSJEmSJKkxJJAkSZIkSYAhgSRJkiRJagwJJEmSJEkSYEggSZIkSZIaQwJJkiRJkgQYEkiSJEmSpMaQQJIkSZIkAYYEkiRJkiSpmTboCkiSNFU8Yp1V2eKwZwy6GpIkSQvNngSSJEmSJAkwJJAkSZIkSY0hgSRJkiRJAgwJJEmSJElSY0ggSZIkSZIAQwJJkiRJktQYEkiSJEmSJMCQQJIkSZIkNdMGXQFJkqaKm266iZkzZz443zstSZK0NLAngSRJkiRJAgwJJEmSJElSY0ggSZIkSZIAQwJJkiRJktQYEkiSJEmSJMCQQJIkSZIkNYYEkiRJkiQJMCSQJEmSJEmNIYEkSZIkSQIMCSRJkiRJUmNIIEmSJEmSAEMCSZIkSZLUGBJI0jIuyfQkB46wbO8klWTGCMt/mOSyJFcl+XyS5Vr5hkl+kuTyJLOSrDeJpzApWrtcOeh6SJIkLU6GBJK0DEvyOuA04H3tYn7tnmWrAm8Czh9lF/9cVdsCWwNrAfu08o8CX66qbYD3Ah+YhLpPm+h9SpIkLesMCSRpGdVCgMOB/YF3AwcCc3tWeR/wYeCvI+2jqu5ok9OAhwPV5rcEftKmzwD26nP86UmuSXJ863FwUpKV2rIdkpyZ5OIkpyVZp5XPSnJEkjOBNw/b3xVJVk/nT0n2b+VfSfKsJMsl+UiSC9vx/qVn27f3lB/ep66PT3Jpkh1HagtJkqSpwJBAkpZdD9Bd2D8SoKrmVNWdAEm2B9avqlPH2kmS04A/AncCJ7Xiy4CXtOkXAasmWaPP5psBx7QeB3cAr0+yPPBpYO+q2gH4EvD+nm1Wr6rdqupjw/Z1DrALsBVwPfAPrfwpwHnAQcDtVbUjsCPw2iQbJdkD2BTYCdgO2CHJ03rObzPgW8CrqurCsdpDkiRpaWZXTUlaRlXV3Ha3/Qhg7SRbA++h6znwCbqeBePZz7OTPAL4KvAM4HTgUOAz7VkHZwG/A+7rs/kNVXVOmz6BbnjDD+mGL5yeBGA54OaebU4coSpnA08DfgMcBRycZF3gz1V1VwsDtkmyd1t/NbpwYI/2c2krX6WV/5ZuCMV3gZdU1VX9DprkYOBggNVWW22EqkmSJC0dDAkkaRlWVackuRzYE5gBvA34FN1F+qx2kb42cEqSF1TVRSPs569JTqEbVnB6Vd0EvBggySp0F9m399u0z3yAq6rqqSNUe+4I5WcBbwA2AA6j68GwN114QNvvIVV1Wu9GSZ4NfKCqjh5WPh24HbiBrodC35Cgqo4BjgF43OMeN/x8JEmSlioON5CkZVSSVZJs2GbvBK4GVq2q26tqzaqaXlXT6brqPyQgaNsPPStgGvA84Jo2v2aSof9j3kk3ZKCfDZIMhQH7AT8DrgXWGipPsnySrcY6n6q6AVgT2LSqrm/7OpR5IcFpwOvacAaSPCHJyq381S3MIMm6SR7Ttvkb8EJg/yQvG6sOkiRJSzt7EkjSsmt54Gi6C+s16LrXj3khnGR2VW0HrEzXw2AFuiEBPwU+31bbHfhAkmLeHf5+rgYOSHI08EvgqKr6WxsS8Kkkq9H9X/VJRriTP8z5rS7QhQMfoAsLAI4FpgOXpOsicQvwwqr6UZItgHNbz4m7gFcA98ODwzKeTzf8YW5VfXcc9ZAkSVoqpcqekZK0LGvd6nevquMGcNxTq2rrxXncyfS4xz2uDj744AfnZ86cObjKSJIkjSLJxVU1Y3i5ww0kSbcBswdcB0mSJC0BHG4gScu4qrqNAYQEVTWH7gGJkiRJWkLYk0CSJEmSJAGGBJIkSZIkqTEkkCRJkiRJgCGBJEmSJElqDAkkSZIkSRJgSCBJkiRJkhpDAkmSJEmSBBgSSJIkSZKkxpBAkiRJkiQBhgSSJEmSJKlJVQ26DpIkTQkzZsyoiy66aNDVkCRJGlOSi6tqxvByexJIkiRJkiTAkECSJEmSJDWGBJIkSZIkCTAkkCRJkiRJjSGBJEmSJEkCDAkkSZIkSVJjSCBJkiRJkgBDAkmSJEmS1BgSSJI0Qf7yl6v5xjd34hvf3GnQVZEkSVoohgSSJEmSJAkwJJAkSZIkSY0hgSRJkiRJAgwJJEmSJElSY0ggSZIkSZIAQwJJkiRJktQYEkiSJEmSJMCQQJIkSZIkNYYEkiRJkiQJMCSQJEmSJEmNIYEkSZIkSQIMCSRJkiRJUmNIIEmSJEmSAEMCSdIwSaYnOXBY2QpJTkxyXZLzk0zvs91KSb6f5JokVyX5YM+yA5PckmR2+3nNYjiPmUkOHVa2bZLZPfP7Jbk7yfJt/olJLm/Tc5KsOdn1lCRJWpIYEkiSHpTkdcBpwPuSzEqydlt0EPCXqtoE+ATwoRF28dGq2hzYHtglyXN7lp1YVdu1n2Mnoe7TxrHaFcCGSVZt8zsD19DVd2j+nImumyRJ0tLCkECSBEC7cD4c2B94N3AgMLct3gs4vk2fBDwzSXq3r6q7q+qMNv034BJgvQU4/vTWC+H4JJcnOSnJSm3ZDknOTHJxktOSrNPKZyU5IsmZwJtH2fdrk/wvsAJwIfDktmgH4LN04QDt9ec9mx6S5JIkVyTZfLznIkmStLQyJJAkDXkAeDjwSICqmlNVd7Zl6wI3tPL7gNuBNUbaUZLVgT2Bn/QUv6Tn4n/9ETbdDDimqrYB7gBe34YCfBrYu6p2AL4EvL9nm9Wrareq+tgIdXljq8sLq+oeuhBg5yQrt3OexfwhQW9Pglur6knAUcB8QxckSZKmIkMCSRIAVTWXrhfBEXTDDT46dCcfSL9N+u2ndfv/GvCpqrq+FX8PmN4u/n/MvF4Jw91QVUMX6ScAu9IFB1sDp7fnCbyL+XsonDjKab0SeC7wkqq6t5WdQxcG7ARcWFW/AjZJshawSk+dAb7dXi8Gpo9wvgcnuSjJRXfccd8oVZEkSVryGRJIkh5UVacA+wAfBtYC3tYW3QisDw+GAKsBfx5hN8cAv6yqT/bs9089F+lfoOvm37cKfeYDXNXzPIMnVtUePevMZWRX0l3c94YK5wE70gUQ57ayG4F9mX+oAcBQne8H+j7zoKqOqaoZVTXjkY8cz2MRJEmSllyGBJIkAJKskmTDNnsncDUw9IC/U4AD2vTewE+r6iE9CZL8F12A8JZh5ev0zL6g7bufDZI8tU3vB/wMuBZYa6g8yfJJthrnaV0K/AtwSpLHAbQhFDfQPXNhKCQ4t9V5eEggSZK0TDEkkCQNWR44mu7BhP9J103/yLbsi8AaSa4D3gq8Y2ijoa8UTLIecBiwJXDJsK86fFP7WsTLgDfRXaD3czVwQPsawkcDR7WHIO4NfKhtP5t5zxAYU1X9jO55At/v+UrDc4AVquqGNn8u8HgMCSRJ0jIufW4ESZKWYUmmA7tX1XEDOO6pVbX14jzuRNp445XrAx/sOjn88z4XDLg2kiRJI0tycVXNGF5uTwJJ0nC30d2tlyRJ0jLGJyxJkuZTVbcxgJCgqubQfYuBJEmSBsSeBJIkSZIkCTAkkCRJkiRJjSGBJEmSJEkCDAkkSZIkSVJjSCBJkiRJkgBDAkmSJEmS1BgSSJIkSZIkwJBAkiRJkiQ1hgSSJEmSJAkwJJAkSZIkSc20QVdAkqSp4lGP2oJ/3ueCQVdDkiRpodmTQJIkSZIkAYYEkiRJkiSpMSSQJEmSJEmAIYEkSZIkSWoMCSRJkiRJEmBIIEmSJEmSGkMCSZIkSZIEGBJIkiRJkqTGkECSpAnyi7/cMegqSJIkLRJDAkmSJEmSBBgSSJIkSZKkxpBAkiRJkiQBhgSSJEmSJKkxJJAkSZIkSYAhgSRJkiRJagwJJEmSJEkSYEggSZIkSZIaQwJJkiRJkgQYEkiSJEmSpMaQQJIkSZIkAYYEkiRJkiSpMSSQJCDJ/Ulm9/xMX4zHvmsS9nlgksf1zB+bZMuJPs6yIsnMJIcOuh6SJEmTbdqgKyBJS4h7qmq7fguSBEhVPbB4q7RIDgSuBG4CqKrXDLQ2i0GS5arq/kHXQ5IkaWlmTwJJ6iPJ9CRXJ/kccAmwfpK3J7kwyeVJDu9Z9xVJLmg9EI5OslwrPzbJjD773ijJuW1f7+spT5KPJLkyyRVJXtrKd08yK8lJSa5J8tUWXJDkPW0/VyY5pu1jb2AG8NVWpxXb9jPaNvu1/V+Z5EM9x78ryfuTXJbkvCSPbeX7tHUvS3LWCO31kLbpacMvJLkqyY+SrNiWbZLkx22flyTZeIzzP7XnWJ9JcmCbntPa4GfAPkle2+pxWZJvJVmprbdxO6cLk7y3t/fGKO/rYUmuTfJjYLNxfGwkSZKWeoYEktRZsWeowcmtbDPgy1W1fZveFNgJ2A7YIcnTkmwBvBTYpfVEuB94OXR376vqoj7HOhI4qqp2BH7fU/7itu9tgWcBH0myTlu2PfAWYEvg8cAurfwzVbVjVW0NrAg8v6pOAi4CXl5V21XVPUMHaEMQPgQ8ox1rxyQvbItXBs6rqm2Bs4DXtvL3AM9u5S8YfjJJ9ujXNm3xpsBnq2or4DbgJa38q618W2Bn4OYxzn80f62qXavq68C3W3tsC1wNHNTWORI4srX5TWPVPckOwL507f5iYMdx1EOSJGmpZ0ggSZ172gX1dlX1olb2m6o6r03v0X4upetZsDndxeUzgR2AC5PMbvOPH+NYuwBfa9Nf6SnfFfhaVd1fVX8AzmTexekFVXVjG/IwG5jeyp+e5PwkV9Bd+G81xrF3BGZV1S1VdR/dxfrQBf3fgKE79hf3HOMc4LgkrwWW67PPkdoG4NdVNbt3n0lWBdatqpMBquqvVXX3GOc/mhN7prdOcnZrj5czrz2eCnyzTf/POOr+D8DJVXV3Vd0BnDLSwZMcnOSiJBfdd8ft46iuJEnSkstnEkjSyOb2TAf4QFUd3btCkkOA46vqnQu47+pTllHWv7dn+n5gWpJHAJ8DZlTVDUlmAo8Y47ijHePvVTVUr/tp/0dU1b8meTLwT8DsJNtV1Z+G7bNf20zvU+8VR6nDSOX3MX+oPfwce9+n44AXVtVlbUjC7iPss/eY/er+Fvq/Rw9RVccAxwCstPETxrWNJEnSksqeBJI0PqcBr06yCkCSdZM8BvgJsHebJsmjk2zYpr+cZKc++zqHris7tKEJzVnAS5Msl2Qtujv8F4xSp6GL5VtbvfbuWXYnsGqfbc4HdkuyZrpnJ+xHd8d+REk2rqrzq+o9wK3A+sNWGalt+mp35m8cGuaQZIX27ICRzv83wJZtvdXoemuMZFXg5iTLM3/bnse8oQ779pSPVPezgBele57DqsCeoxxTkiRpyrAngSSNQ1X9qD1/4Nx0zwy8C3hFVf0iybuAHyV5GPB34A10F7bb0I21H+7NwP8keTPwrZ7yk+m6xV9Gdxf736vq90k2H6FOtyX5AnAFMAe4sGfxccDnk9zT9jm0zc1J3gmcQXcX/QdV9d0xTv8jSTZt6/+k1W/MtqHrOTCSVwJHJ3kvXZvtM9L5AyT5BnA58Eu6oQEjeTddEPIbunYZCkreApyQ5G3A94HbR6t7VV2S5ES6oR2/Ac4e5ZiSJElTRub1LJUkTZQkjwS+WFX7DLougtZT4Z6qqiT7AvtV1V4TfZyVNn5C3f2r/5vo3UqSJE24JBdX1UO+icueBJI0CVqXegOCJccOwGfSdRe4DXj1YKsjSZK0ZDIkkCRNeVV1Nt1XK0qSJGkUPrhQkiRJkiQBhgSSJEmSJKkxJJAkSZIkSYAhgSRJkiRJagwJJEmSJEkSYEggSZIkSZIaQwJJkiRJkgQYEkiSJEmSpMaQQJIkSZIkAYYEkiRJkiSpMSSQJGmCbPmoRw66CpIkSYvEkECSJEmSJAGGBJIkSZIkqTEkkCRJkiRJgCGBJEmSJElqUlWDroMkSVNCkjuBawddjylmTeDWQVdiCrE9J55tOvFs04lnm068qdCmG1bVWsMLpw2iJpIkTVHXVtWMQVdiKklykW06cWzPiWebTjzbdOLZphNvKrepww0kSZIkSRJgSCBJkiRJkhpDAkmSJs4xg67AFGSbTizbc+LZphPPNp14tunEm7Jt6oMLJUmSJEkSYE8CSZIkSZLUGBJIkrSIkjwnybVJrkvyjkHXZ2mR5EtJ/pjkyp6yRyc5Pckv2+ujepa9s7XxtUmePZhaL9mSrJ/kjCRXJ7kqyZtbue26kJI8IskFSS5rbXp4K7dNF0GS5ZJcmuTUNm97LoIkc5JckWR2kotamW26CJKsnuSkJNe036lPXVba1JBAkqRFkGQ54LPAc4Etgf2SbDnYWi01jgOeM6zsHcBPqmpT4Cdtntam+wJbtW0+19pe87sPeFtVbQE8BXhDazvbdeHdCzyjqrYFtgOek+Qp2KaL6s3A1T3ztueie3pVbdfztXy26aI5EvhhVW0ObEv3eV0m2tSQQJKkRbMTcF1VXV9VfwO+Duw14DotFarqLODPw4r3Ao5v08cDL+wp/3pV3VtVvwauo2t79aiqm6vqkjZ9J90ftetiuy606tzVZpdvP4VtutCSrAf8E3BsT7HtOfFs04WU5JHA04AvAlTV36rqNpaRNjUkkCRp0awL3NAzf2Mr08J5bFXdDN0FL/CYVm47L6Ak04HtgfOxXRdJ6xo/G/gjcHpV2aaL5pPAvwMP9JTZnoumgB8luTjJwa3MNl14jwduAf67DYs5NsnKLCNtakggSdKiSZ8yvzpo4tnOCyDJKsC3gLdU1R2jrdqnzHYdpqrur6rtgPWAnZJsPcrqtukokjwf+GNVXTzeTfqU2Z4PtUtVPYlu6NsbkjxtlHVt07FNA54EHFVV2wNzaUMLRjCl2tSQQJKkRXMjsH7P/HrATQOqy1TwhyTrALTXP7Zy23mckixPFxB8taq+3Ypt1wnQuhvPohtzbJsunF2AFySZQzc86xlJTsD2XCRVdVN7/SNwMl1Xd9t04d0I3Nh6DQGcRBcaLBNtakggSdKiuRDYNMlGSR5O9+CiUwZcp6XZKcABbfoA4Ls95fsmWSHJRsCmwAUDqN8SLUnoxtBeXVUf71lkuy6kJGslWb1Nrwg8C7gG23ShVNU7q2q9qppO9/vyp1X1CmzPhZZk5SSrDk0DewBXYpsutKr6PXBDks1a0TOBX7CMtOm0QVdAkqSlWVXdl+SNwGnAcsCXquqqAVdrqZDka8DuwJpJbgT+E/gg8I0kBwG/BfYBqKqrknyD7o+0+4A3VNX9A6n4km0X4JXAFW0MPcD/w3ZdFOsAx7cnlT8M+EZVnZrkXGzTieRndOE9Fji5ywiZBvxPVf0wyYXYpoviEOCr7QbA9cCraL8DpnqbpmqpHSohSZIkSZImkMMNJEmSJEkSYEggSZIkSZIaQwJJkiRJkgQYEkiSJEmSpMaQQJIkSZIkAYYEkiRJ0qRLskqSTya5Mclfk/xfkn+dpGO9LMnMJKtPxv4lTW1+BaIkSZI0idJ9gf0ZwG7A6cA3genA2lV10CQc71Tgn4CNqmrORO9f0tRmTwJJkiRpcj2DLiD4BfCcqvpCVR0GvDbJVkl+kuTOJL9J8u50piepdsFPkkPb/IFtfk6SuUk+lOTPSS5MsnaSmXQBAcCvk8xZ7GcraalmSCBJkiRNrh3a6+lV9UBP+XLAKcCTgcOAy4H3Aq8a535XAh4DfB+YAbwWOAm4tC1/E3DIItVc0jLHkECSJElaPIaP890MeDzw3ar6FPDWVv7cce7vAeB1wGfb/PSquhK4qc1/r6q+twj1lbQMMiSQJEmSJtdF7fUfk/T+/T003e8hYfe312ntdfU+69xTVX8F7mvzy42yP0kal2ljryJJkiRpEZwBzAJ2B36Q5CRgfWAD4FfAXkkOAZ7V1v8B8Afgr8AOSf4ZOGABjveX9npAkjOratainoCkZYc9CSRJkqRJVN3Xie0JfAp4IvA54BXABcBewIXAEcD2wHuA46rqb8A7gOVb2TkLcMijgd8CM4F3TchJSFpm+BWIkiRJkiQJsCeBJEmSJElqDAkkSZIkSRJgSCBJkiRJkhpDAkmSJEmSBBgSSJIkSZKkxpBAkiRJkiQBhgSSJEmSJKkxJJAkSZIkSQD8fxeDvIoifDkbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conds=data_new['EV Pricing'].value_counts().head(10).index\n",
    "plt.figure(figsize=(10,8))\n",
    "sb.countplot(y='EV Pricing',data=data_new,order=conds)\n",
    "plt.ylabel('Pricing',{'fontweight':'bold'})\n",
    "plt.xlabel('Count',{'fontweight':'bold'})\n",
    "plt.title('Range of EV pricing',{'fontweight':'bold'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "23ced51c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sb.countplot(y='EV Network',data=data_new)\n",
    "plt.ylabel('EV Network',{'fontweight':'bold'})\n",
    "plt.xlabel('No of Cities',{'fontweight':'bold'})\n",
    "plt.title('Distribution of EV Network',{'fontweight':'bold'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "3d7598f6",
   "metadata": {},
   "outputs": [
    {
     "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>ZIP</th>\n",
       "      <th>EV Level2 EVSE Num</th>\n",
       "      <th>EV DC Fast Count</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>ID</th>\n",
       "      <th>NYS Municipal Boundaries 2</th>\n",
       "      <th>New York Zip Codes 2</th>\n",
       "      <th>Counties 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14551</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.235555</td>\n",
       "      <td>-77.061621</td>\n",
       "      <td>92116</td>\n",
       "      <td>628.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>631.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.717867</td>\n",
       "      <td>-73.806317</td>\n",
       "      <td>117741</td>\n",
       "      <td>834.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2030.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12180</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.758880</td>\n",
       "      <td>-73.602280</td>\n",
       "      <td>150341</td>\n",
       "      <td>130.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14620</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.123642</td>\n",
       "      <td>-77.613880</td>\n",
       "      <td>163584</td>\n",
       "      <td>537.0</td>\n",
       "      <td>1679.0</td>\n",
       "      <td>2093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41.667907</td>\n",
       "      <td>-73.795294</td>\n",
       "      <td>164367</td>\n",
       "      <td>594.0</td>\n",
       "      <td>979.0</td>\n",
       "      <td>2040.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2692</th>\n",
       "      <td>12027</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.909700</td>\n",
       "      <td>-73.890620</td>\n",
       "      <td>191537</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>1153.0</td>\n",
       "      <td>2141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2693</th>\n",
       "      <td>12309</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.831453</td>\n",
       "      <td>-73.880977</td>\n",
       "      <td>148041</td>\n",
       "      <td>107.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2694</th>\n",
       "      <td>14303</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.081539</td>\n",
       "      <td>-79.070427</td>\n",
       "      <td>162242</td>\n",
       "      <td>159.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>2131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2695</th>\n",
       "      <td>14203</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.901523</td>\n",
       "      <td>-78.863471</td>\n",
       "      <td>169908</td>\n",
       "      <td>300.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2041.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2696</th>\n",
       "      <td>14614</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.156137</td>\n",
       "      <td>-77.614096</td>\n",
       "      <td>145450</td>\n",
       "      <td>537.0</td>\n",
       "      <td>1673.0</td>\n",
       "      <td>2093.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2697 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ZIP  EV Level2 EVSE Num  EV DC Fast Count   Latitude  Longitude  \\\n",
       "0     14551                 2.0               NaN  43.235555 -77.061621   \n",
       "1     12205                 NaN               1.0  42.717867 -73.806317   \n",
       "2     12180                 2.0               NaN  42.758880 -73.602280   \n",
       "3     14620                 2.0               NaN  43.123642 -77.613880   \n",
       "4     12540                 NaN               4.0  41.667907 -73.795294   \n",
       "...     ...                 ...               ...        ...        ...   \n",
       "2692  12027                 2.0               NaN  42.909700 -73.890620   \n",
       "2693  12309                10.0               NaN  42.831453 -73.880977   \n",
       "2694  14303                 2.0               NaN  43.081539 -79.070427   \n",
       "2695  14203                 2.0               NaN  42.901523 -78.863471   \n",
       "2696  14614                 2.0               NaN  43.156137 -77.614096   \n",
       "\n",
       "          ID  NYS Municipal Boundaries 2  New York Zip Codes 2  Counties 2  \n",
       "0      92116                       628.0                 408.0       631.0  \n",
       "1     117741                       834.0                  42.0      2030.0  \n",
       "2     150341                       130.0                  11.0      2138.0  \n",
       "3     163584                       537.0                1679.0      2093.0  \n",
       "4     164367                       594.0                 979.0      2040.0  \n",
       "...      ...                         ...                   ...         ...  \n",
       "2692  191537                      1011.0                1153.0      2141.0  \n",
       "2693  148041                       107.0                  78.0      2142.0  \n",
       "2694  162242                       159.0                 167.0      2131.0  \n",
       "2695  169908                       300.0                  34.0      2041.0  \n",
       "2696  145450                       537.0                1673.0      2093.0  \n",
       "\n",
       "[2697 rows x 9 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.select_dtypes(exclude='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "03574e26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(14,10))\n",
    "corr=data_new.corr('pearson')\n",
    "sb.heatmap(corr,cmap='RdBu_r',annot=True)\n",
    "plt.title('heatmap',{'fontweight':'bold'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "be2a5d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "owner_type=data_new['Owner Type Code'].value_counts().index\n",
    "indx=data_new['Owner Type Code'].value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "5c30dcf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1001,   97,   27,   10,    9,    2], dtype=int64)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "81d2638e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Type')"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,8))\n",
    "sb.barplot(x=indx,y=owner_type)\n",
    "plt.title('Distribution of Owner Type',{'fontweight':'bold','fontsize':'larger'})\n",
    "plt.xlabel(\"No of Owners\",{'fontweight':'bold','fontsize':'larger'})\n",
    "plt.ylabel(\"Type\",{'fontweight':'bold','fontsize':'larger'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "699284c3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}