ELECTRIC VEHICLES DATASET - DATA  ANALYSIS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9a066a8f",
   "metadata": {},
   "source": [
    "# ELECTRIC VEHICLES DATASET - DATA  ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bea8c40",
   "metadata": {},
   "source": [
    "Perform Data Cleaning and Data Analysis of the Electric Vehicles Dataset to find the relationship between the columns in the dataset and visualize the same using matplotlib and seaborn.\n",
    "\n",
    "DATASET SOURCE: https://www.kaggle.com/datasets/geoffnel/evs-one-electric-vehicle-dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9452670",
   "metadata": {},
   "source": [
    "The data contains information on Electric vehicles and its resepctive specifications"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "a0b6d8e9",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5a1b978d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "199926be",
   "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>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>AccelSec</th>\n",
       "      <th>TopSpeed_KmH</th>\n",
       "      <th>Range_Km</th>\n",
       "      <th>Efficiency_WhKm</th>\n",
       "      <th>FastCharge_KmH</th>\n",
       "      <th>RapidCharge</th>\n",
       "      <th>PowerTrain</th>\n",
       "      <th>PlugType</th>\n",
       "      <th>BodyStyle</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Seats</th>\n",
       "      <th>PriceEuro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tesla</td>\n",
       "      <td>Model 3 Long Range Dual Motor</td>\n",
       "      <td>4.6</td>\n",
       "      <td>233</td>\n",
       "      <td>450</td>\n",
       "      <td>161</td>\n",
       "      <td>940</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>55480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>ID.3 Pure</td>\n",
       "      <td>10.0</td>\n",
       "      <td>160</td>\n",
       "      <td>270</td>\n",
       "      <td>167</td>\n",
       "      <td>250</td>\n",
       "      <td>Yes</td>\n",
       "      <td>RWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Polestar</td>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>210</td>\n",
       "      <td>400</td>\n",
       "      <td>181</td>\n",
       "      <td>620</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Liftback</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>56440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>iX3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>180</td>\n",
       "      <td>360</td>\n",
       "      <td>206</td>\n",
       "      <td>560</td>\n",
       "      <td>Yes</td>\n",
       "      <td>RWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>SUV</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>68040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Honda</td>\n",
       "      <td>e</td>\n",
       "      <td>9.5</td>\n",
       "      <td>145</td>\n",
       "      <td>170</td>\n",
       "      <td>168</td>\n",
       "      <td>190</td>\n",
       "      <td>Yes</td>\n",
       "      <td>RWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>32997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lucid</td>\n",
       "      <td>Air</td>\n",
       "      <td>2.8</td>\n",
       "      <td>250</td>\n",
       "      <td>610</td>\n",
       "      <td>180</td>\n",
       "      <td>620</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>e-Golf</td>\n",
       "      <td>9.6</td>\n",
       "      <td>150</td>\n",
       "      <td>190</td>\n",
       "      <td>168</td>\n",
       "      <td>220</td>\n",
       "      <td>Yes</td>\n",
       "      <td>FWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>31900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Brand                          Model  AccelSec  TopSpeed_KmH  \\\n",
       "0       Tesla   Model 3 Long Range Dual Motor       4.6           233   \n",
       "1  Volkswagen                       ID.3 Pure      10.0           160   \n",
       "2    Polestar                               2       4.7           210   \n",
       "3         BMW                            iX3        6.8           180   \n",
       "4       Honda                              e        9.5           145   \n",
       "5       Lucid                            Air        2.8           250   \n",
       "6  Volkswagen                         e-Golf        9.6           150   \n",
       "\n",
       "   Range_Km  Efficiency_WhKm FastCharge_KmH RapidCharge PowerTrain  \\\n",
       "0       450              161            940         Yes        AWD   \n",
       "1       270              167            250         Yes        RWD   \n",
       "2       400              181            620         Yes        AWD   \n",
       "3       360              206            560         Yes        RWD   \n",
       "4       170              168            190         Yes        RWD   \n",
       "5       610              180            620         Yes        AWD   \n",
       "6       190              168            220         Yes        FWD   \n",
       "\n",
       "     PlugType  BodyStyle Segment  Seats  PriceEuro  \n",
       "0  Type 2 CCS      Sedan       D      5      55480  \n",
       "1  Type 2 CCS  Hatchback       C      5      30000  \n",
       "2  Type 2 CCS   Liftback       D      5      56440  \n",
       "3  Type 2 CCS        SUV       D      5      68040  \n",
       "4  Type 2 CCS  Hatchback       B      4      32997  \n",
       "5  Type 2 CCS      Sedan       F      5     105000  \n",
       "6  Type 2 CCS  Hatchback       C      5      31900  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import dataset\n",
    "df = pd.read_csv('ElectricCarData_Clean.csv')\n",
    "#display dataset\n",
    "df.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "06e1b8fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the total number of columns and the column names found in the dataset\n",
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4ce49738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Brand', 'Model', 'AccelSec', 'TopSpeed_KmH', 'Range_Km',\n",
       "       'Efficiency_WhKm', 'FastCharge_KmH', 'RapidCharge', 'PowerTrain',\n",
       "       'PlugType', 'BodyStyle', 'Segment', 'Seats', 'PriceEuro'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f4b8f5bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display total number of records in the dataset\n",
    "records = len(df)\n",
    "records"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50a81854",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fcaaa039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand              0\n",
       "Model              0\n",
       "AccelSec           0\n",
       "TopSpeed_KmH       0\n",
       "Range_Km           0\n",
       "Efficiency_WhKm    0\n",
       "FastCharge_KmH     0\n",
       "RapidCharge        0\n",
       "PowerTrain         0\n",
       "PlugType           0\n",
       "BodyStyle          0\n",
       "Segment            0\n",
       "Seats              0\n",
       "PriceEuro          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count number of NaN and Inf values in each column\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b057eb26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand              0\n",
       "Model              0\n",
       "AccelSec           0\n",
       "TopSpeed_KmH       0\n",
       "Range_Km           0\n",
       "Efficiency_WhKm    0\n",
       "FastCharge_KmH     0\n",
       "RapidCharge        0\n",
       "PowerTrain         0\n",
       "PlugType           0\n",
       "BodyStyle          0\n",
       "Segment            0\n",
       "Seats              0\n",
       "PriceEuro          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df == np.inf].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "dc9ff0b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>AccelSec</th>\n",
       "      <th>TopSpeed_KmH</th>\n",
       "      <th>Range_Km</th>\n",
       "      <th>Efficiency_WhKm</th>\n",
       "      <th>FastCharge_KmH</th>\n",
       "      <th>RapidCharge</th>\n",
       "      <th>PowerTrain</th>\n",
       "      <th>PlugType</th>\n",
       "      <th>BodyStyle</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Seats</th>\n",
       "      <th>PriceEuro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tesla</td>\n",
       "      <td>Model 3 Long Range Dual Motor</td>\n",
       "      <td>4.6</td>\n",
       "      <td>233</td>\n",
       "      <td>450</td>\n",
       "      <td>161</td>\n",
       "      <td>940</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>55480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>ID.3 Pure</td>\n",
       "      <td>10.0</td>\n",
       "      <td>160</td>\n",
       "      <td>270</td>\n",
       "      <td>167</td>\n",
       "      <td>250</td>\n",
       "      <td>Yes</td>\n",
       "      <td>RWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Polestar</td>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>210</td>\n",
       "      <td>400</td>\n",
       "      <td>181</td>\n",
       "      <td>620</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Liftback</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>56440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>iX3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>180</td>\n",
       "      <td>360</td>\n",
       "      <td>206</td>\n",
       "      <td>560</td>\n",
       "      <td>Yes</td>\n",
       "      <td>RWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>SUV</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>68040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Honda</td>\n",
       "      <td>e</td>\n",
       "      <td>9.5</td>\n",
       "      <td>145</td>\n",
       "      <td>170</td>\n",
       "      <td>168</td>\n",
       "      <td>190</td>\n",
       "      <td>Yes</td>\n",
       "      <td>RWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>32997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Ariya 63kWh</td>\n",
       "      <td>7.5</td>\n",
       "      <td>160</td>\n",
       "      <td>330</td>\n",
       "      <td>191</td>\n",
       "      <td>440</td>\n",
       "      <td>Yes</td>\n",
       "      <td>FWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Audi</td>\n",
       "      <td>e-tron S Sportback 55 quattro</td>\n",
       "      <td>4.5</td>\n",
       "      <td>210</td>\n",
       "      <td>335</td>\n",
       "      <td>258</td>\n",
       "      <td>540</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>SUV</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>96050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Ariya e-4ORCE 63kWh</td>\n",
       "      <td>5.9</td>\n",
       "      <td>200</td>\n",
       "      <td>325</td>\n",
       "      <td>194</td>\n",
       "      <td>440</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Ariya e-4ORCE 87kWh Performance</td>\n",
       "      <td>5.1</td>\n",
       "      <td>200</td>\n",
       "      <td>375</td>\n",
       "      <td>232</td>\n",
       "      <td>450</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Byton</td>\n",
       "      <td>M-Byte 95 kWh 2WD</td>\n",
       "      <td>7.5</td>\n",
       "      <td>190</td>\n",
       "      <td>400</td>\n",
       "      <td>238</td>\n",
       "      <td>480</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>SUV</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Brand                            Model  AccelSec  TopSpeed_KmH  \\\n",
       "0         Tesla     Model 3 Long Range Dual Motor       4.6           233   \n",
       "1    Volkswagen                         ID.3 Pure      10.0           160   \n",
       "2      Polestar                                 2       4.7           210   \n",
       "3           BMW                              iX3        6.8           180   \n",
       "4         Honda                                e        9.5           145   \n",
       "..           ...                              ...       ...           ...   \n",
       "98       Nissan                       Ariya 63kWh       7.5           160   \n",
       "99         Audi     e-tron S Sportback 55 quattro       4.5           210   \n",
       "100      Nissan               Ariya e-4ORCE 63kWh       5.9           200   \n",
       "101      Nissan   Ariya e-4ORCE 87kWh Performance       5.1           200   \n",
       "102       Byton                 M-Byte 95 kWh 2WD       7.5           190   \n",
       "\n",
       "     Range_Km  Efficiency_WhKm FastCharge_KmH RapidCharge PowerTrain  \\\n",
       "0         450              161            940         Yes        AWD   \n",
       "1         270              167            250         Yes        RWD   \n",
       "2         400              181            620         Yes        AWD   \n",
       "3         360              206            560         Yes        RWD   \n",
       "4         170              168            190         Yes        RWD   \n",
       "..        ...              ...            ...         ...        ...   \n",
       "98        330              191            440         Yes        FWD   \n",
       "99        335              258            540         Yes        AWD   \n",
       "100       325              194            440         Yes        AWD   \n",
       "101       375              232            450         Yes        AWD   \n",
       "102       400              238            480         Yes        AWD   \n",
       "\n",
       "       PlugType  BodyStyle Segment  Seats  PriceEuro  \n",
       "0    Type 2 CCS      Sedan       D      5      55480  \n",
       "1    Type 2 CCS  Hatchback       C      5      30000  \n",
       "2    Type 2 CCS   Liftback       D      5      56440  \n",
       "3    Type 2 CCS        SUV       D      5      68040  \n",
       "4    Type 2 CCS  Hatchback       B      4      32997  \n",
       "..          ...        ...     ...    ...        ...  \n",
       "98   Type 2 CCS  Hatchback       C      5      45000  \n",
       "99   Type 2 CCS        SUV       E      5      96050  \n",
       "100  Type 2 CCS  Hatchback       C      5      50000  \n",
       "101  Type 2 CCS  Hatchback       C      5      65000  \n",
       "102  Type 2 CCS        SUV       E      5      62000  \n",
       "\n",
       "[98 rows x 14 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove blank values from the FastCharge_KmH column\n",
    "df = df[df.FastCharge_KmH !='-']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "672b144f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8be4df95",
   "metadata": {},
   "source": [
    "### Display total number of vehicles avaiable for each Body Style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "35a05868",
   "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>BodyStyle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SUV</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hatchback</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sedan</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liftback</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pickup</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPV</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabrio</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Station</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPV</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           BodyStyle\n",
       "SUV               45\n",
       "Hatchback         29\n",
       "Sedan             10\n",
       "Liftback           5\n",
       "Pickup             3\n",
       "SPV                2\n",
       "Cabrio             2\n",
       "Station            1\n",
       "MPV                1"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(df['BodyStyle'].value_counts())\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a46f54e",
   "metadata": {},
   "source": [
    "Majority of the vehicles have SUV Body Style while the Body Style with least number of vehicles are MPV and Station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08142aa7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fc4d5327",
   "metadata": {},
   "source": [
    "### Display a pie chart to find the powertrain type used the most by electric car companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "073b83fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AWD    41\n",
      "FWD    36\n",
      "RWD    21\n",
      "Name: PowerTrain, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create figure for graph\n",
    "fig1 = plt.figure(figsize = (10, 10))\n",
    "#count number of occurences for each label\n",
    "vals = df['PowerTrain'].value_counts()\n",
    "print(vals)\n",
    "#create a list of labels to display on pie chart\n",
    "label = vals.index\n",
    "#create pie chart\n",
    "plt.pie(vals,labels=label, autopct='%.3f%%',\n",
    "        shadow=False, startangle=90)\n",
    "plt.legend(title = \"POWERTRAIN TYPES:\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25a9ee16",
   "metadata": {},
   "source": [
    "The Powertrain type used by vehicle companies the most is AWD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70c0bcc0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ab66b6a8",
   "metadata": {},
   "source": [
    "### Find out whether Range_Km and PriceEuro are correlated using scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "519d9c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create figure for plotting\n",
    "fig2 = plt.figure(figsize = (10, 7))\n",
    "\n",
    "x2 = df['Range_Km']\n",
    "y2 = df['PriceEuro']\n",
    "\n",
    "#create scatter plot\n",
    "plt.scatter(x2,y2,color = 'green')\n",
    "#display labels for the graph\n",
    "plt.xlabel(\"Range_Km\")\n",
    "plt.ylabel(\"PriceEuro\")\n",
    "plt.title(\"Range_Km vs PriceEuro\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393a8bab",
   "metadata": {},
   "source": [
    "The above plot shows that there is a positive correaltion between the two column values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f589e0ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7fc3c407",
   "metadata": {},
   "source": [
    "### Display brand and model of vehicles without Rapid Charge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bd2265e6",
   "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>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>RapidCharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Brand, Model, RapidCharge]\n",
       "Index: []"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#run a query to display the result\n",
    "df3 = pd.DataFrame(df.query('RapidCharge == \"No\"')[['Brand','Model','RapidCharge']])\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3660fa14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    98\n",
       "Name: RapidCharge, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for verification\n",
    "df['RapidCharge'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb4998b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "13fc9185",
   "metadata": {},
   "source": [
    "### Display brand and model of the vehicle with highest value in FastCharge_KmH column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "64812417",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4=df[df.FastCharge_KmH == df.FastCharge_KmH.max()][['Brand','Model','FastCharge_KmH']]\n",
    "df5=df[df.FastCharge_KmH == df.FastCharge_KmH.min()][['Brand','Model','FastCharge_KmH']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "516f026b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vehicle with Highest Fast Charge KmH:\n"
     ]
    },
    {
     "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>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>FastCharge_KmH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tesla</td>\n",
       "      <td>Model 3 Long Range Dual Motor</td>\n",
       "      <td>940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Brand                          Model FastCharge_KmH\n",
       "0  Tesla   Model 3 Long Range Dual Motor            940"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Vehicle with Highest Fast Charge KmH:\")\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e1e1f149",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vehicle with Lowest Fast Charge KmH:\n"
     ]
    },
    {
     "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>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>FastCharge_KmH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>e-Up!</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Skoda</td>\n",
       "      <td>CITIGOe iV</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>SEAT</td>\n",
       "      <td>Mii Electric</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Brand          Model FastCharge_KmH\n",
       "17  Volkswagen          e-Up!             170\n",
       "43       Skoda     CITIGOe iV             170\n",
       "44        SEAT   Mii Electric             170"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Vehicle with Lowest Fast Charge KmH:\")\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98535851",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5bb25e21",
   "metadata": {},
   "source": [
    "### Display number of models released by each vehicle brand and find the brand with most number of models using bar graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e32ee974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 32 artists>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig6 = plt.figure(figsize=(30,10))\n",
    "x6 = df['Brand'].value_counts()\n",
    "#display the bar graph\n",
    "plt.bar(x6.index,x6,color='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e4fe196",
   "metadata": {},
   "source": [
    "Telsa has the most number of models according to the above graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "354be6f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4a50a355",
   "metadata": {},
   "source": [
    "### Visualize the dataset columns Range_Km and Brand to find the brand with highest and lowest Range_Km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7ed944d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 98 artists>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig7 = plt.figure(figsize=(10,10))\n",
    "x=df['Range_Km']\n",
    "y=df['Brand']\n",
    "#plot a horizontal bar graph\n",
    "plt.barh(y,x,color='purple')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9233ed21",
   "metadata": {},
   "source": [
    "Telsa has the highest Range_Km value while Honda has the lowest Range_Km value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6b66363",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e8909af0",
   "metadata": {},
   "source": [
    "### Analyse the columns PlugType and PriceEuro using Swarmplot from Seaborn Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "69188a27",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aishwarya Menon\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Aishwarya Menon\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PriceEuro vs PlugType')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig8 = plt.figure(figsize=(9,7))\n",
    "x=df['PriceEuro']\n",
    "y=df['PlugType']\n",
    "#plot a swarmplot\n",
    "sns.swarmplot(x,y,data=df,size = 9)\n",
    "plt.title('PriceEuro vs PlugType')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d068a8",
   "metadata": {},
   "source": [
    "The above swarmplot shows that majority of the vehicles have the PlugType called Type 2 CCS. \n",
    "The vehicles with Type 2 CCS Plug are found to have its Price Range over a large scale compared to the vehciles with other plugtypes being restricted to a smaller price range.\n",
    "Type 2 CCs PlugType vehicles have vehicles with the least price and the highest price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e876ee8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f340a4d6",
   "metadata": {},
   "source": [
    "### Find out the average number of seats offered by all vehicle brands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "511e4f71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig8 = plt.figure(figsize=(10,10))\n",
    "#plot a violin plot\n",
    "plt1 = sns.violinplot(x=df[\"Seats\"],color='limegreen')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b318c5d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3a31077f",
   "metadata": {},
   "source": [
    "### Find the vehicle Segment with the highest Efficiency_WhKm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "14e8a867",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Efficiency_WhKm', ylabel='Segment'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig9 = plt.figure(figsize=(10,10))\n",
    "#plot a barplot using seaborn\n",
    "sns.barplot(data=df, x=df[\"Efficiency_WhKm\"], y=df[\"Segment\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495600cf",
   "metadata": {},
   "source": [
    "The vehicles belonging to the Segment N have the best/highest Efficiency_WhKm value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95fdd8fa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}