sample-project/introduction-to-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       BMW\n",
       "1    Toyota\n",
       "2     Honda\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2 main datatypes\n",
    "series = pd.Series([\"BMW\", \"Toyota\", \"Honda\"])\n",
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Red\n",
       "1     Blue\n",
       "2    White\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# series = 1-dimenional data (1 column)\n",
    "colours = pd.Series([\"Red\", \"Blue\", \"White\"])\n",
    "colours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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 make</th>\n",
       "      <th>Colour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car make Colour\n",
       "0      BMW    Red\n",
       "1   Toyota   Blue\n",
       "2    Honda  White"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame = 2-dimenional data (Table)\n",
    "car_data = pd.DataFrame({ \"Car make\": series, \"Colour\": colours })\n",
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import data\n",
    "car_sales = pd.read_csv(\"data/car-sales.csv\")\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](anatomy-of-a-dataframe.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exporting a dataframe\n",
    "car_sales.to_csv(\"data/exported-car-sales.csv\", index=False)\n",
    "export_car_sales = pd.read_csv(\"data/exported-car-sales.csv\")\n",
    "export_car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# conda install openpyxl xlrd cannot work -> ModuleNotFoundError\n",
    "# pip3 install openpyxl xlrd work\n",
    "car_sales.to_excel(\"data/exported-car-sales.xlsx\", index=False)\n",
    "export_car_sales_excel = pd.read_excel(\"data/exported-car-sales.xlsx\")\n",
    "export_car_sales_excel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Describing Data with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             object\n",
       "Colour           object\n",
       "Odometer (KM)     int64\n",
       "Doors             int64\n",
       "Price            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Attribute\n",
    "car_sales.dtypes\n",
    "\n",
    "# Function\n",
    "# car_sales.to_csv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Make', 'Colour', 'Odometer (KM)', 'Doors', 'Price'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_columns = car_sales.columns\n",
    "car_sales_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=10, step=1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_index = car_sales.index\n",
    "car_sales_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>78601.400000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>61983.471735</td>\n",
       "      <td>0.471405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11179.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35836.250000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>57369.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>96384.500000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>213095.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Odometer (KM)      Doors\n",
       "count      10.000000  10.000000\n",
       "mean    78601.400000   4.000000\n",
       "std     61983.471735   0.471405\n",
       "min     11179.000000   3.000000\n",
       "25%     35836.250000   4.000000\n",
       "50%     57369.000000   4.000000\n",
       "75%     96384.500000   4.000000\n",
       "max    213095.000000   5.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 5 columns):\n",
      "Make             10 non-null object\n",
      "Colour           10 non-null object\n",
      "Odometer (KM)    10 non-null int64\n",
      "Doors            10 non-null int64\n",
      "Price            10 non-null object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 528.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "car_sales.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Odometer (KM)    78601.4\n",
       "Doors                4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38583.333333333336"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_prices = pd.Series([3000, 1500, 111250])\n",
    "car_prices.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             ToyotaHondaToyotaBMWNissanToyotaHondaHondaToyo...\n",
       "Colour               WhiteRedBlueBlackWhiteGreenBlueBlueWhiteWhite\n",
       "Odometer (KM)                                               786014\n",
       "Doors                                                           40\n",
       "Price            $4,000.00$5,000.00$7,000.00$22,000.00$3,500.00...\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Doors\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(car_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting and Viewing Data with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors      Price\n",
       "5  Toyota  Green          99213      4  $4,500.00\n",
       "6   Honda   Blue          45698      4  $7,500.00\n",
       "7   Honda   Blue          54738      4  $7,000.00\n",
       "8  Toyota  White          60000      4  $6,250.00\n",
       "9  Nissan  White          31600      4  $9,700.00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      cat\n",
       "3      dog\n",
       "9     bird\n",
       "8    panda\n",
       "3    snake\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals = pd.Series([\"cat\", \"dog\", \"bird\", \"panda\", \"snake\"], \n",
    "                    index=[0, 3, 9, 8, 3])\n",
    "animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      dog\n",
       "3    snake\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loc refers to index\n",
    "animals.loc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'panda'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iloc refers to position\n",
    "animals.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                    BMW\n",
       "Colour                Black\n",
       "Odometer (KM)         11179\n",
       "Doors                     5\n",
       "Price            $22,000.00\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# car_sales item has same position and index\n",
    "# loc refers to index\n",
    "car_sales.loc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                    BMW\n",
       "Colour                Black\n",
       "Odometer (KM)         11179\n",
       "Doors                     5\n",
       "Price            $22,000.00\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iloc refers to position\n",
    "car_sales.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      cat\n",
       "3      dog\n",
       "9     bird\n",
       "8    panda\n",
       "3    snake\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     cat\n",
       "3     dog\n",
       "9    bird\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1st to 3rd positions, 4th is excluded\n",
    "animals.iloc[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index 0 to 3 (included)\n",
    "car_sales.loc[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
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       "  <tbody>\n",
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       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Toyota\n",
       "1     Honda\n",
       "2    Toyota\n",
       "3       BMW\n",
       "4    Nissan\n",
       "5    Toyota\n",
       "6     Honda\n",
       "7     Honda\n",
       "8    Toyota\n",
       "9    Nissan\n",
       "Name: Make, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Make\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Toyota\n",
       "1     Honda\n",
       "2    Toyota\n",
       "3       BMW\n",
       "4    Nissan\n",
       "5    Toyota\n",
       "6     Honda\n",
       "7     Honda\n",
       "8    Toyota\n",
       "9    Nissan\n",
       "Name: Make, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.Make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors      Price\n",
       "0  Toyota  White         150043      4  $4,000.00\n",
       "2  Toyota   Blue          32549      3  $7,000.00\n",
       "5  Toyota  Green          99213      4  $4,500.00\n",
       "8  Toyota  White          60000      4  $6,250.00"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[car_sales[\"Make\"] == \"Toyota\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors      Price\n",
       "0  Toyota  White         150043      4  $4,000.00\n",
       "4  Nissan  White         213095      4  $3,500.00"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[car_sales[\"Odometer (KM)\"] > 100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Doors</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honda</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nissan</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toyota</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Doors   3  4  5\n",
       "Make           \n",
       "BMW     0  0  1\n",
       "Honda   0  3  0\n",
       "Nissan  0  2  0\n",
       "Toyota  1  3  0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(car_sales[\"Make\"], car_sales[\"Doors\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>11179.000000</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honda</th>\n",
       "      <td>62778.333333</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nissan</th>\n",
       "      <td>122347.500000</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toyota</th>\n",
       "      <td>85451.250000</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Odometer (KM)  Doors\n",
       "Make                        \n",
       "BMW      11179.000000   5.00\n",
       "Honda    62778.333333   4.00\n",
       "Nissan  122347.500000   4.00\n",
       "Toyota   85451.250000   3.75"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby\n",
    "car_sales.groupby([\"Make\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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></th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <th>Black</th>\n",
       "      <td>11179.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Honda</th>\n",
       "      <th>Blue</th>\n",
       "      <td>50218.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red</th>\n",
       "      <td>87899.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nissan</th>\n",
       "      <th>White</th>\n",
       "      <td>122347.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Toyota</th>\n",
       "      <th>Blue</th>\n",
       "      <td>32549.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Green</th>\n",
       "      <td>99213.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>105021.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Odometer (KM)  Doors\n",
       "Make   Colour                      \n",
       "BMW    Black         11179.0    5.0\n",
       "Honda  Blue          50218.0    4.0\n",
       "       Red           87899.0    4.0\n",
       "Nissan White        122347.5    4.0\n",
       "Toyota Blue          32549.0    3.0\n",
       "       Green         99213.0    4.0\n",
       "       White        105021.5    4.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.groupby([\"Make\", \"Colour\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118f583d0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_sales[\"Odometer (KM)\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b050f10>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_sales[\"Odometer (KM)\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Price\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales[\"Price\"] = car_sales[\"Price\"].str.replace('[\\$\\,\\.]','').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price\n",
       "0  Toyota  White         150043      4   400000\n",
       "1   Honda    Red          87899      4   500000\n",
       "2  Toyota   Blue          32549      3   700000\n",
       "3     BMW  Black          11179      5  2200000\n",
       "4  Nissan  White         213095      4   350000\n",
       "5  Toyota  Green          99213      4   450000\n",
       "6   Honda   Blue          45698      4   750000\n",
       "7   Honda   Blue          54738      4   700000\n",
       "8  Toyota  White          60000      4   625000\n",
       "9  Nissan  White          31600      4   970000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b259950>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_sales[\"Price\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    toyota\n",
       "1     honda\n",
       "2    toyota\n",
       "3       bmw\n",
       "4    nissan\n",
       "5    toyota\n",
       "6     honda\n",
       "7     honda\n",
       "8    toyota\n",
       "9    nissan\n",
       "Name: Make, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Make\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price\n",
       "0  Toyota  White         150043      4   400000\n",
       "1   Honda    Red          87899      4   500000\n",
       "2  Toyota   Blue          32549      3   700000\n",
       "3     BMW  Black          11179      5  2200000\n",
       "4  Nissan  White         213095      4   350000\n",
       "5  Toyota  Green          99213      4   450000\n",
       "6   Honda   Blue          45698      4   750000\n",
       "7   Honda   Blue          54738      4   700000\n",
       "8  Toyota  White          60000      4   625000\n",
       "9  Nissan  White          31600      4   970000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price\n",
       "0  toyota  White         150043      4   400000\n",
       "1   honda    Red          87899      4   500000\n",
       "2  toyota   Blue          32549      3   700000\n",
       "3     bmw  Black          11179      5  2200000\n",
       "4  nissan  White         213095      4   350000\n",
       "5  toyota  Green          99213      4   450000\n",
       "6   honda   Blue          45698      4   750000\n",
       "7   honda   Blue          54738      4   700000\n",
       "8  toyota  White          60000      4   625000\n",
       "9  nissan  White          31600      4   970000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Make\"] = car_sales[\"Make\"].str.lower()\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>Doors</th>\n",
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       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$7,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>31600.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer  Doors    Price\n",
       "0  Toyota  White  150043.0    4.0   $4,000\n",
       "1   Honda    Red   87899.0    4.0   $5,000\n",
       "2  Toyota   Blue       NaN    3.0   $7,000\n",
       "3     BMW  Black   11179.0    5.0  $22,000\n",
       "4  Nissan  White  213095.0    4.0   $3,500\n",
       "5  Toyota  Green       NaN    4.0   $4,500\n",
       "6   Honda    NaN       NaN    4.0   $7,500\n",
       "7   Honda   Blue       NaN    4.0      NaN\n",
       "8  Toyota  White   60000.0    NaN      NaN\n",
       "9     NaN  White   31600.0    4.0   $9,700"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing = pd.read_csv(\"data/car-sales-missing-data.csv\")\n",
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92302.66666666667"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing[\"Odometer\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    150043.000000\n",
       "1     87899.000000\n",
       "2     92302.666667\n",
       "3     11179.000000\n",
       "4    213095.000000\n",
       "5     92302.666667\n",
       "6     92302.666667\n",
       "7     92302.666667\n",
       "8     60000.000000\n",
       "9     31600.000000\n",
       "Name: Odometer, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing[\"Odometer\"].fillna(car_sales_missing[\"Odometer\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>$4,000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
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       "      <td>87899.0</td>\n",
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       "      <td>Toyota</td>\n",
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       "      <td>$22,000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>31600.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer  Doors    Price\n",
       "0  Toyota  White  150043.0    4.0   $4,000\n",
       "1   Honda    Red   87899.0    4.0   $5,000\n",
       "2  Toyota   Blue       NaN    3.0   $7,000\n",
       "3     BMW  Black   11179.0    5.0  $22,000\n",
       "4  Nissan  White  213095.0    4.0   $3,500\n",
       "5  Toyota  Green       NaN    4.0   $4,500\n",
       "6   Honda    NaN       NaN    4.0   $7,500\n",
       "7   Honda   Blue       NaN    4.0      NaN\n",
       "8  Toyota  White   60000.0    NaN      NaN\n",
       "9     NaN  White   31600.0    4.0   $9,700"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing[\"Odometer\"].fillna(car_sales_missing[\"Odometer\"].mean(), \n",
    "                                     inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$7,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>31600.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour       Odometer  Doors    Price\n",
       "0  Toyota  White  150043.000000    4.0   $4,000\n",
       "1   Honda    Red   87899.000000    4.0   $5,000\n",
       "2  Toyota   Blue   92302.666667    3.0   $7,000\n",
       "3     BMW  Black   11179.000000    5.0  $22,000\n",
       "4  Nissan  White  213095.000000    4.0   $3,500\n",
       "5  Toyota  Green   92302.666667    4.0   $4,500\n",
       "6   Honda    NaN   92302.666667    4.0   $7,500\n",
       "7   Honda   Blue   92302.666667    4.0      NaN\n",
       "8  Toyota  White   60000.000000    NaN      NaN\n",
       "9     NaN  White   31600.000000    4.0   $9,700"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Make</th>\n",
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       "      <td>Toyota</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$7,000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>31600.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour       Odometer  Doors    Price\n",
       "0  Toyota  White  150043.000000    4.0   $4,000\n",
       "1   Honda    Red   87899.000000    4.0   $5,000\n",
       "2  Toyota   Blue   92302.666667    3.0   $7,000\n",
       "3     BMW  Black   11179.000000    5.0  $22,000\n",
       "4  Nissan  White  213095.000000    4.0   $3,500\n",
       "5  Toyota  Green   92302.666667    4.0   $4,500\n",
       "6   Honda    NaN   92302.666667    4.0   $7,500\n",
       "7   Honda   Blue   92302.666667    4.0      NaN\n",
       "8  Toyota  White   60000.000000    NaN      NaN\n",
       "9     NaN  White   31600.000000    4.0   $9,700"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing = pd.read_csv(\"data/car-sales-missing-data.csv\")\n",
    "car_sales_missing[\"Odometer\"] = car_sales_missing[\"Odometer\"].fillna(car_sales_missing[\"Odometer\"].mean())\n",
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer  Doors    Price\n",
       "0  Toyota  White  150043.0    4.0   $4,000\n",
       "1   Honda    Red   87899.0    4.0   $5,000\n",
       "3     BMW  Black   11179.0    5.0  $22,000\n",
       "4  Nissan  White  213095.0    4.0   $3,500"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing = pd.read_csv(\"data/car-sales-missing-data.csv\")\n",
    "car_sales_missing.dropna(inplace=True)\n",
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Toyota</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer  Doors    Price\n",
       "0  Toyota  White  150043.0    4.0   $4,000\n",
       "1   Honda    Red   87899.0    4.0   $5,000\n",
       "3     BMW  Black   11179.0    5.0  $22,000\n",
       "4  Nissan  White  213095.0    4.0   $3,500"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing = pd.read_csv(\"data/car-sales-missing-data.csv\")\n",
    "car_sales_missing_dropped = car_sales_missing.dropna()\n",
    "car_sales_missing_dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing_dropped.to_csv(\"data/car-sales-missing-dropped.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price\n",
       "0  toyota  White         150043      4   400000\n",
       "1   honda    Red          87899      4   500000\n",
       "2  toyota   Blue          32549      3   700000\n",
       "3     bmw  Black          11179      5  2200000\n",
       "4  nissan  White         213095      4   350000\n",
       "5  toyota  Green          99213      4   450000\n",
       "6   honda   Blue          45698      4   750000\n",
       "7   honda   Blue          54738      4   700000\n",
       "8  toyota  White          60000      4   625000\n",
       "9  nissan  White          31600      4   970000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Doors</th>\n",
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       "      <td>400000</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>500000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats\n",
       "0  toyota  White         150043      4   400000    5.0\n",
       "1   honda    Red          87899      4   500000    5.0\n",
       "2  toyota   Blue          32549      3   700000    5.0\n",
       "3     bmw  Black          11179      5  2200000    5.0\n",
       "4  nissan  White         213095      4   350000    5.0\n",
       "5  toyota  Green          99213      4   450000    NaN\n",
       "6   honda   Blue          45698      4   750000    NaN\n",
       "7   honda   Blue          54738      4   700000    NaN\n",
       "8  toyota  White          60000      4   625000    NaN\n",
       "9  nissan  White          31600      4   970000    NaN"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Column from series\n",
    "seats_column = pd.Series([5, 5, 5, 5, 5])\n",
    "\n",
    "# New column called seats\n",
    "car_sales[\"Seats\"] = seats_column\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
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       "    <tr>\n",
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       "      <td>honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
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       "      <td>500000</td>\n",
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       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>nissan</td>\n",
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       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
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       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats\n",
       "0  toyota  White         150043      4   400000    5.0\n",
       "1   honda    Red          87899      4   500000    5.0\n",
       "2  toyota   Blue          32549      3   700000    5.0\n",
       "3     bmw  Black          11179      5  2200000    5.0\n",
       "4  nissan  White         213095      4   350000    5.0\n",
       "5  toyota  Green          99213      4   450000    5.0\n",
       "6   honda   Blue          45698      4   750000    5.0\n",
       "7   honda   Blue          54738      4   700000    5.0\n",
       "8  toyota  White          60000      4   625000    5.0\n",
       "9  nissan  White          31600      4   970000    5.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Seats\"].fillna(5, inplace=True)\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>bmw</td>\n",
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       "      <td>toyota</td>\n",
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       "      <td>4</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
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       "      <th>7</th>\n",
       "      <td>honda</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
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       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM\n",
       "0  toyota  White         150043      4   400000    5.0             7.5\n",
       "1   honda    Red          87899      4   500000    5.0             9.2\n",
       "2  toyota   Blue          32549      3   700000    5.0             5.0\n",
       "3     bmw  Black          11179      5  2200000    5.0             9.6\n",
       "4  nissan  White         213095      4   350000    5.0             8.7\n",
       "5  toyota  Green          99213      4   450000    5.0             4.7\n",
       "6   honda   Blue          45698      4   750000    5.0             7.6\n",
       "7   honda   Blue          54738      4   700000    5.0             8.7\n",
       "8  toyota  White          60000      4   625000    5.0             3.0\n",
       "9  nissan  White          31600      4   970000    5.0             4.5"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Column from Python list - same length as exsiting data frame\n",
    "fuel_economy = [7.5, 9.2, 5.0, 9.6, 8.7, 4.7, 7.6, 8.7, 3.0, 4.5]\n",
    "car_sales[\"Fuel per 100KM\"] = fuel_economy\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
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       "      <td>400000</td>\n",
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       "      <td>honda</td>\n",
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       "      <td>toyota</td>\n",
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       "      <td>700000</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>1627.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
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       "      <td>2200000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1073.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18539.265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4663.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3473.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4762.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1422.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "0  toyota  White         150043      4   400000    5.0             7.5   \n",
       "1   honda    Red          87899      4   500000    5.0             9.2   \n",
       "2  toyota   Blue          32549      3   700000    5.0             5.0   \n",
       "3     bmw  Black          11179      5  2200000    5.0             9.6   \n",
       "4  nissan  White         213095      4   350000    5.0             8.7   \n",
       "5  toyota  Green          99213      4   450000    5.0             4.7   \n",
       "6   honda   Blue          45698      4   750000    5.0             7.6   \n",
       "7   honda   Blue          54738      4   700000    5.0             8.7   \n",
       "8  toyota  White          60000      4   625000    5.0             3.0   \n",
       "9  nissan  White          31600      4   970000    5.0             4.5   \n",
       "\n",
       "   Total fuel used (L)  \n",
       "0            11253.225  \n",
       "1             8086.708  \n",
       "2             1627.450  \n",
       "3             1073.184  \n",
       "4            18539.265  \n",
       "5             4663.011  \n",
       "6             3473.048  \n",
       "7             4762.206  \n",
       "8             1800.000  \n",
       "9             1422.000  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Total fuel used (L)\"] = car_sales[\"Odometer (KM)\"] / 100 * car_sales[\"Fuel per 100KM\"]\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>toyota</td>\n",
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       "      <td>honda</td>\n",
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       "      <td>toyota</td>\n",
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       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1627.450</td>\n",
       "      <td>1627.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1073.184</td>\n",
       "      <td>1073.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18539.265</td>\n",
       "      <td>18539.265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4663.011</td>\n",
       "      <td>4663.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3473.048</td>\n",
       "      <td>3473.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4762.206</td>\n",
       "      <td>4762.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.000</td>\n",
       "      <td>1800.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1422.000</td>\n",
       "      <td>1422.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "0  toyota  White         150043      4   400000    5.0             7.5   \n",
       "1   honda    Red          87899      4   500000    5.0             9.2   \n",
       "2  toyota   Blue          32549      3   700000    5.0             5.0   \n",
       "3     bmw  Black          11179      5  2200000    5.0             9.6   \n",
       "4  nissan  White         213095      4   350000    5.0             8.7   \n",
       "5  toyota  Green          99213      4   450000    5.0             4.7   \n",
       "6   honda   Blue          45698      4   750000    5.0             7.6   \n",
       "7   honda   Blue          54738      4   700000    5.0             8.7   \n",
       "8  toyota  White          60000      4   625000    5.0             3.0   \n",
       "9  nissan  White          31600      4   970000    5.0             4.5   \n",
       "\n",
       "   Total fuel used (L)  Total fuel used  \n",
       "0            11253.225        11253.225  \n",
       "1             8086.708         8086.708  \n",
       "2             1627.450         1627.450  \n",
       "3             1073.184         1073.184  \n",
       "4            18539.265        18539.265  \n",
       "5             4663.011         4663.011  \n",
       "6             3473.048         3473.048  \n",
       "7             4762.206         4762.206  \n",
       "8             1800.000         1800.000  \n",
       "9             1422.000         1422.000  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Total fuel used\"] = car_sales[\"Odometer (KM)\"] / 100 * car_sales[\"Fuel per 100KM\"]\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
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       "      <th>Total fuel used</th>\n",
       "      <th>Number of wheels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>400000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11253.225</td>\n",
       "      <td>11253.225</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>500000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8086.708</td>\n",
       "      <td>8086.708</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1627.450</td>\n",
       "      <td>1627.450</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1073.184</td>\n",
       "      <td>1073.184</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18539.265</td>\n",
       "      <td>18539.265</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4663.011</td>\n",
       "      <td>4663.011</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3473.048</td>\n",
       "      <td>3473.048</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4762.206</td>\n",
       "      <td>4762.206</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.000</td>\n",
       "      <td>1800.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1422.000</td>\n",
       "      <td>1422.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "0  toyota  White         150043      4   400000    5.0             7.5   \n",
       "1   honda    Red          87899      4   500000    5.0             9.2   \n",
       "2  toyota   Blue          32549      3   700000    5.0             5.0   \n",
       "3     bmw  Black          11179      5  2200000    5.0             9.6   \n",
       "4  nissan  White         213095      4   350000    5.0             8.7   \n",
       "5  toyota  Green          99213      4   450000    5.0             4.7   \n",
       "6   honda   Blue          45698      4   750000    5.0             7.6   \n",
       "7   honda   Blue          54738      4   700000    5.0             8.7   \n",
       "8  toyota  White          60000      4   625000    5.0             3.0   \n",
       "9  nissan  White          31600      4   970000    5.0             4.5   \n",
       "\n",
       "   Total fuel used (L)  Total fuel used  Number of wheels  \n",
       "0            11253.225        11253.225                 4  \n",
       "1             8086.708         8086.708                 4  \n",
       "2             1627.450         1627.450                 4  \n",
       "3             1073.184         1073.184                 4  \n",
       "4            18539.265        18539.265                 4  \n",
       "5             4663.011         4663.011                 4  \n",
       "6             3473.048         3473.048                 4  \n",
       "7             4762.206         4762.206                 4  \n",
       "8             1800.000         1800.000                 4  \n",
       "9             1422.000         1422.000                 4  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a column from a single value\n",
    "car_sales[\"Number of wheels\"] = 4\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Fuel per 100KM</th>\n",
       "      <th>Total fuel used (L)</th>\n",
       "      <th>Total fuel used</th>\n",
       "      <th>Number of wheels</th>\n",
       "      <th>Passed road safety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>400000</td>\n",
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       "      <td>11253.225</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>honda</td>\n",
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       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>500000</td>\n",
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       "      <td>9.2</td>\n",
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       "      <td>8086.708</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1627.450</td>\n",
       "      <td>1627.450</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1073.184</td>\n",
       "      <td>1073.184</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18539.265</td>\n",
       "      <td>18539.265</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4663.011</td>\n",
       "      <td>4663.011</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3473.048</td>\n",
       "      <td>3473.048</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4762.206</td>\n",
       "      <td>4762.206</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.000</td>\n",
       "      <td>1800.000</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1422.000</td>\n",
       "      <td>1422.000</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "0  toyota  White         150043      4   400000    5.0             7.5   \n",
       "1   honda    Red          87899      4   500000    5.0             9.2   \n",
       "2  toyota   Blue          32549      3   700000    5.0             5.0   \n",
       "3     bmw  Black          11179      5  2200000    5.0             9.6   \n",
       "4  nissan  White         213095      4   350000    5.0             8.7   \n",
       "5  toyota  Green          99213      4   450000    5.0             4.7   \n",
       "6   honda   Blue          45698      4   750000    5.0             7.6   \n",
       "7   honda   Blue          54738      4   700000    5.0             8.7   \n",
       "8  toyota  White          60000      4   625000    5.0             3.0   \n",
       "9  nissan  White          31600      4   970000    5.0             4.5   \n",
       "\n",
       "   Total fuel used (L)  Total fuel used  Number of wheels  Passed road safety  \n",
       "0            11253.225        11253.225                 4                True  \n",
       "1             8086.708         8086.708                 4                True  \n",
       "2             1627.450         1627.450                 4                True  \n",
       "3             1073.184         1073.184                 4                True  \n",
       "4            18539.265        18539.265                 4                True  \n",
       "5             4663.011         4663.011                 4                True  \n",
       "6             3473.048         3473.048                 4                True  \n",
       "7             4762.206         4762.206                 4                True  \n",
       "8             1800.000         1800.000                 4                True  \n",
       "9             1422.000         1422.000                 4                True  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Passed road safety\"] = True\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                    object\n",
       "Colour                  object\n",
       "Odometer (KM)            int64\n",
       "Doors                    int64\n",
       "Price                    int64\n",
       "Seats                  float64\n",
       "Fuel per 100KM         float64\n",
       "Total fuel used (L)    float64\n",
       "Total fuel used        float64\n",
       "Number of wheels         int64\n",
       "Passed road safety        bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       "      <td>toyota</td>\n",
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       "      <td>500000</td>\n",
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       "      <td>toyota</td>\n",
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       "      <td>32549</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4663.011</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
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       "      <td>True</td>\n",
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       "      <th>9</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
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       "      <td>1422.000</td>\n",
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       "      <td>True</td>\n",
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      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "0  toyota  White         150043      4   400000    5.0             7.5   \n",
       "1   honda    Red          87899      4   500000    5.0             9.2   \n",
       "2  toyota   Blue          32549      3   700000    5.0             5.0   \n",
       "3     bmw  Black          11179      5  2200000    5.0             9.6   \n",
       "4  nissan  White         213095      4   350000    5.0             8.7   \n",
       "5  toyota  Green          99213      4   450000    5.0             4.7   \n",
       "6   honda   Blue          45698      4   750000    5.0             7.6   \n",
       "7   honda   Blue          54738      4   700000    5.0             8.7   \n",
       "8  toyota  White          60000      4   625000    5.0             3.0   \n",
       "9  nissan  White          31600      4   970000    5.0             4.5   \n",
       "\n",
       "   Total fuel used (L)  Total fuel used  Number of wheels  Passed road safety  \n",
       "0            11253.225        11253.225                 4                True  \n",
       "1             8086.708         8086.708                 4                True  \n",
       "2             1627.450         1627.450                 4                True  \n",
       "3             1073.184         1073.184                 4                True  \n",
       "4            18539.265        18539.265                 4                True  \n",
       "5             4663.011         4663.011                 4                True  \n",
       "6             3473.048         3473.048                 4                True  \n",
       "7             4762.206         4762.206                 4                True  \n",
       "8             1800.000         1800.000                 4                True  \n",
       "9             1422.000         1422.000                 4                True  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
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    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>bmw</td>\n",
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       "      <td>2200000</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
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       "      <td>4663.011</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honda</td>\n",
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       "      <td>54738</td>\n",
       "      <td>4</td>\n",
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       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>1800.000</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1422.000</td>\n",
       "      <td>1422.000</td>\n",
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       "      <td>True</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "0  toyota  White         150043      4   400000    5.0             7.5   \n",
       "1   honda    Red          87899      4   500000    5.0             9.2   \n",
       "2  toyota   Blue          32549      3   700000    5.0             5.0   \n",
       "3     bmw  Black          11179      5  2200000    5.0             9.6   \n",
       "4  nissan  White         213095      4   350000    5.0             8.7   \n",
       "5  toyota  Green          99213      4   450000    5.0             4.7   \n",
       "6   honda   Blue          45698      4   750000    5.0             7.6   \n",
       "7   honda   Blue          54738      4   700000    5.0             8.7   \n",
       "8  toyota  White          60000      4   625000    5.0             3.0   \n",
       "9  nissan  White          31600      4   970000    5.0             4.5   \n",
       "\n",
       "   Total fuel used (L)  Total fuel used  Number of wheels  Passed road safety  \n",
       "0            11253.225        11253.225                 4                True  \n",
       "1             8086.708         8086.708                 4                True  \n",
       "2             1627.450         1627.450                 4                True  \n",
       "3             1073.184         1073.184                 4                True  \n",
       "4            18539.265        18539.265                 4                True  \n",
       "5             4663.011         4663.011                 4                True  \n",
       "6             3473.048         3473.048                 4                True  \n",
       "7             4762.206         4762.206                 4                True  \n",
       "8             1800.000         1800.000                 4                True  \n",
       "9             1422.000         1422.000                 4                True  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# axis=1 - refer to column\n",
    "car_sales.drop(\"Total fuel used\", axis=1)\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18539.265</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4663.011</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3473.048</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4762.206</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.000</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1422.000</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "0  toyota  White         150043      4   400000    5.0             7.5   \n",
       "1   honda    Red          87899      4   500000    5.0             9.2   \n",
       "2  toyota   Blue          32549      3   700000    5.0             5.0   \n",
       "3     bmw  Black          11179      5  2200000    5.0             9.6   \n",
       "4  nissan  White         213095      4   350000    5.0             8.7   \n",
       "5  toyota  Green          99213      4   450000    5.0             4.7   \n",
       "6   honda   Blue          45698      4   750000    5.0             7.6   \n",
       "7   honda   Blue          54738      4   700000    5.0             8.7   \n",
       "8  toyota  White          60000      4   625000    5.0             3.0   \n",
       "9  nissan  White          31600      4   970000    5.0             4.5   \n",
       "\n",
       "   Total fuel used (L)  Number of wheels  Passed road safety  \n",
       "0            11253.225                 4                True  \n",
       "1             8086.708                 4                True  \n",
       "2             1627.450                 4                True  \n",
       "3             1073.184                 4                True  \n",
       "4            18539.265                 4                True  \n",
       "5             4663.011                 4                True  \n",
       "6             3473.048                 4                True  \n",
       "7             4762.206                 4                True  \n",
       "8             1800.000                 4                True  \n",
       "9             1422.000                 4                True  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.drop(\"Total fuel used\", axis=1, inplace=True)\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Fuel per 100KM</th>\n",
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       "      <th>Number of wheels</th>\n",
       "      <th>Passed road safety</th>\n",
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       "      <td>nissan</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>toyota</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
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       "      <td>500000</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3473.048</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4663.011</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18539.265</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4762.206</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.000</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1073.184</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>400000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11253.225</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "9  nissan  White          31600      4   970000    5.0             4.5   \n",
       "2  toyota   Blue          32549      3   700000    5.0             5.0   \n",
       "1   honda    Red          87899      4   500000    5.0             9.2   \n",
       "6   honda   Blue          45698      4   750000    5.0             7.6   \n",
       "5  toyota  Green          99213      4   450000    5.0             4.7   \n",
       "4  nissan  White         213095      4   350000    5.0             8.7   \n",
       "7   honda   Blue          54738      4   700000    5.0             8.7   \n",
       "8  toyota  White          60000      4   625000    5.0             3.0   \n",
       "3     bmw  Black          11179      5  2200000    5.0             9.6   \n",
       "0  toyota  White         150043      4   400000    5.0             7.5   \n",
       "\n",
       "   Total fuel used (L)  Number of wheels  Passed road safety  \n",
       "9             1422.000                 4                True  \n",
       "2             1627.450                 4                True  \n",
       "1             8086.708                 4                True  \n",
       "6             3473.048                 4                True  \n",
       "5             4663.011                 4                True  \n",
       "4            18539.265                 4                True  \n",
       "7             4762.206                 4                True  \n",
       "8             1800.000                 4                True  \n",
       "3             1073.184                 4                True  \n",
       "0            11253.225                 4                True  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_shuffled = car_sales.sample(frac=1)\n",
    "car_sales_shuffled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Fuel per 100KM</th>\n",
       "      <th>Total fuel used (L)</th>\n",
       "      <th>Number of wheels</th>\n",
       "      <th>Passed road safety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1073.184</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1627.450</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "3     bmw  Black          11179      5  2200000    5.0             9.6   \n",
       "2  toyota   Blue          32549      3   700000    5.0             5.0   \n",
       "\n",
       "   Total fuel used (L)  Number of wheels  Passed road safety  \n",
       "3             1073.184                 4                True  \n",
       "2             1627.450                 4                True  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only select 20% of data for experiment\n",
    "car_sales_shuffled.sample(frac=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
       "      <td>toyota</td>\n",
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       "      <th>1</th>\n",
       "      <td>honda</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3473.048</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4663.011</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18539.265</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>4762.206</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.000</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
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       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>400000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11253.225</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "9  nissan  White          31600      4   970000    5.0             4.5   \n",
       "2  toyota   Blue          32549      3   700000    5.0             5.0   \n",
       "1   honda    Red          87899      4   500000    5.0             9.2   \n",
       "6   honda   Blue          45698      4   750000    5.0             7.6   \n",
       "5  toyota  Green          99213      4   450000    5.0             4.7   \n",
       "4  nissan  White         213095      4   350000    5.0             8.7   \n",
       "7   honda   Blue          54738      4   700000    5.0             8.7   \n",
       "8  toyota  White          60000      4   625000    5.0             3.0   \n",
       "3     bmw  Black          11179      5  2200000    5.0             9.6   \n",
       "0  toyota  White         150043      4   400000    5.0             7.5   \n",
       "\n",
       "   Total fuel used (L)  Number of wheels  Passed road safety  \n",
       "9             1422.000                 4                True  \n",
       "2             1627.450                 4                True  \n",
       "1             8086.708                 4                True  \n",
       "6             3473.048                 4                True  \n",
       "5             4663.011                 4                True  \n",
       "4            18539.265                 4                True  \n",
       "7             4762.206                 4                True  \n",
       "8             1800.000                 4                True  \n",
       "3             1073.184                 4                True  \n",
       "0            11253.225                 4                True  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_shuffled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>1422.000</td>\n",
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       "      <th>1</th>\n",
       "      <td>toyota</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>500000</td>\n",
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       "      <th>3</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3473.048</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4663.011</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18539.265</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4762.206</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.000</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1073.184</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>400000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11253.225</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "0  nissan  White          31600      4   970000    5.0             4.5   \n",
       "1  toyota   Blue          32549      3   700000    5.0             5.0   \n",
       "2   honda    Red          87899      4   500000    5.0             9.2   \n",
       "3   honda   Blue          45698      4   750000    5.0             7.6   \n",
       "4  toyota  Green          99213      4   450000    5.0             4.7   \n",
       "5  nissan  White         213095      4   350000    5.0             8.7   \n",
       "6   honda   Blue          54738      4   700000    5.0             8.7   \n",
       "7  toyota  White          60000      4   625000    5.0             3.0   \n",
       "8     bmw  Black          11179      5  2200000    5.0             9.6   \n",
       "9  toyota  White         150043      4   400000    5.0             7.5   \n",
       "\n",
       "   Total fuel used (L)  Number of wheels  Passed road safety  \n",
       "0             1422.000                 4                True  \n",
       "1             1627.450                 4                True  \n",
       "2             8086.708                 4                True  \n",
       "3             3473.048                 4                True  \n",
       "4             4663.011                 4                True  \n",
       "5            18539.265                 4                True  \n",
       "6             4762.206                 4                True  \n",
       "7             1800.000                 4                True  \n",
       "8             1073.184                 4                True  \n",
       "9            11253.225                 4                True  "
      ]
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     "execution_count": 77,
     "metadata": {},
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   "source": [
    "car_sales_shuffled.reset_index(drop=True, inplace=True)\n",
    "car_sales_shuffled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>toyota</td>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>20343.125</td>\n",
       "      <td>3</td>\n",
       "      <td>700000</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>6986.875</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>133184.375</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>62008.125</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4663.011</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>28561.250</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3473.048</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>34211.250</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4762.206</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>37500.000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.000</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>19750.000</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1422.000</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "0  toyota  White      93776.875      4   400000    5.0             7.5   \n",
       "1   honda    Red      54936.875      4   500000    5.0             9.2   \n",
       "2  toyota   Blue      20343.125      3   700000    5.0             5.0   \n",
       "3     bmw  Black       6986.875      5  2200000    5.0             9.6   \n",
       "4  nissan  White     133184.375      4   350000    5.0             8.7   \n",
       "5  toyota  Green      62008.125      4   450000    5.0             4.7   \n",
       "6   honda   Blue      28561.250      4   750000    5.0             7.6   \n",
       "7   honda   Blue      34211.250      4   700000    5.0             8.7   \n",
       "8  toyota  White      37500.000      4   625000    5.0             3.0   \n",
       "9  nissan  White      19750.000      4   970000    5.0             4.5   \n",
       "\n",
       "   Total fuel used (L)  Number of wheels  Passed road safety  \n",
       "0            11253.225                 4                True  \n",
       "1             8086.708                 4                True  \n",
       "2             1627.450                 4                True  \n",
       "3             1073.184                 4                True  \n",
       "4            18539.265                 4                True  \n",
       "5             4663.011                 4                True  \n",
       "6             3473.048                 4                True  \n",
       "7             4762.206                 4                True  \n",
       "8             1800.000                 4                True  \n",
       "9             1422.000                 4                True  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Odometer (KM)\"] = car_sales[\"Odometer (KM)\"].apply(lambda x: x / 1.6)\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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