intro-to-pd.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2 main datatypes\n",
    "series=pd.Series([\"BMW\",\"Toyota\",\"Honda\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       BMW\n",
       "1    Toyota\n",
       "2     Honda\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# series = 1-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Red\n",
       "1     Blue\n",
       "2    White\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colours = pd.Series([\"Red\",\"Blue\",\"White\"])\n",
    "colours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car 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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame = 2-dimensional\n",
    "car_data = pd.DataFrame({\"Car make\": series, \"Colour\": colours})\n",
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Data\n",
    "car_sales = pd.read_csv(\"car-sales.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
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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",
       "    <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": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporting a dataframe\n",
    "car_sales.to_csv(\"exported-car-sales.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_car_sales = pd.read_csv(\"exported-car-sales.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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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",
       "    <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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_car_sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Describe data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Attribute\n",
    "car_sales.dtypes\n",
    "\n",
    "# Function\n",
    "# car_sales.to_csv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Make', 'Colour', 'Odometer (KM)', 'Doors', 'Price'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Make', 'Colour', 'Odometer (KM)', 'Doors', 'Price'], dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_columns = car_sales.columns\n",
    "car_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=10, step=1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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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",
       "    <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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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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>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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.describe()\n",
    "# 只对数字有效"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Odometer (KM)    78601.4\n",
       "Doors                4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2166.6666666666665"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_prices = pd.Series([3000, 2000, 1500])\n",
    "car_prices.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Doors\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(car_sales)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing and selecting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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",
       "  </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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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",
       "  </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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# .loc & .iloc\n",
    "animals = pd.Series([\"cat\", \"dog\", \"bird\", \"panda\", \"snake\"],\n",
    "                   index=[0, 3, 9, 8, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      cat\n",
       "3      dog\n",
       "9     bird\n",
       "8    panda\n",
       "3    snake\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      dog\n",
       "3    snake\n",
       "dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loc refers to index\n",
    "animals.loc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bird'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.loc[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.loc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      cat\n",
       "3      dog\n",
       "9     bird\n",
       "8    panda\n",
       "3    snake\n",
       "dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'panda'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .iloc refers to position\n",
    "animals.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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",
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       "      <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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      cat\n",
       "3      dog\n",
       "9     bird\n",
       "8    panda\n",
       "3    snake\n",
       "dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     cat\n",
       "3     dog\n",
       "9    bird\n",
       "dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.iloc[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
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       "      <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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.loc[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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",
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       "      <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",
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       "      <td>Toyota</td>\n",
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       "      <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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Make\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.Make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    White\n",
       "1      Red\n",
       "2     Blue\n",
       "3    Black\n",
       "4    White\n",
       "5    Green\n",
       "6     Blue\n",
       "7     Blue\n",
       "8    White\n",
       "9    White\n",
       "Name: Colour, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Colour\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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",
       "        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>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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[car_sales[\"Make\"] == \"Toyota\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[car_sales[\"Odometer (KM)\"] > 100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(car_sales[\"Make\"], car_sales[\"Doors\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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",
       "    .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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.groupby([\"Make\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xaa3eec8>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q7dU89++Nbqdj2ol/D42hIXilAdaiMh1LRBUNgPOHZDJ2UHce/eca2wgnQnjKvHRLjCMjqZPbqdTLWlSmI4m4oiEi3HvxUOpUue9tj9vpmHbgKfcyOCvZtT00gmEtKtNRRFzRAMhNi+eGc/J5b8U2Plpd4XY6pg0dqq1j7fa9DO2Z4nYqjZpgLSrTQURk0QD4yRl96JuRwD2zi22XvzBWUrGXQ0fqQnYQ3C+vWwJDeybzrhUNE+Iitmh0ionmdxOHsblyP3/9qMTtdEwb8Q+Ch+J022ONH57FV9aiMiEumE2YZopIhYisCIj9SURWicgyEXlTRLo68TwROSAiS5zH3wLOOdHZuKlERGaI02AWkTQRmS8ia52vqU5cnONKnM85obV/+NP6d+OSgp787ZP1rN+xt7Xf3oQAT5mXzrFR9AmBPTSaYi0q0xEEc6XxHDDumNh8YJiqjgDWALcHvLZOVQucx38HxJ8EpuLbAjY/4D1vAz5Q1XzgA77eoe/CgGOnOue3ujsmDKZTbBR3zy62Xf7CkKe8ikE9komOCt1BcD9rUZmOoMmioaqfApXHxP7hbOEKsADIaew9nD3Fk1X1c2ef7+eBS5yXJwKznOezjok/rz4LgK7H7E3eKrondeZXFwzkXyU7eWeZ/WMNJ6qKJ4T20AiGv0W11VpUJkS1xpjGj4H3Ar7vIyJficgnInKGE8sGSgOOKXViAJmqWg7gfO0ecM6WBs75BhGZKiJFIlK0Y8eO4/4Bvn9Kb4Znp3D/Ox7b5S+MbN1zAO/B2pAfBA/kb1G9Z1cbJkS1qGiIyG+AWuAFJ1QO9FLVUcAtwIsikgzU1xtoqhcU9Dmq+pSqFqpqYUZGRnDJB4iO8i1ouGNvDY/MX3Pc55vQVFzWcQbB/axFZUJds4uGiFwDXAR832k5oao1qrrLeb4IWAcMwHeVENjCygH8mwhs97ednK/+GydKgdwGzml1I3O78v1TejHrPxtZsdV2+QsHnjIvUQKDQmgPjWBMGGEtKhO6mlU0RGQc8GvgYlXdHxDPEJFo53lffIPY6522U7WIjHZmTV0NzHZOmwNc4zy/5pj41c4sqtFAlb+N1VZ+df4g0hLiuNN2+QsLnnIvfbol0CUu2u1Ujou1qEwoC2bK7UvA58BAESkVkWuBPwNJwPxjptaeCSwTkaXAa8B/q6p/EP064BmgBN8ViH8c5EHgPBFZC5znfA8wF1jvHP80cH2LftIg+Hf5W7JlDy8vtF3+OjpPmZchIX4neH16p1uLyoSuJrcxU9Up9YSfbeDY14HXG3itCBhWT3wXMLaeuALTmsqvtV06KptXFm7hD++v4oKhmaQnhuYid6ZxVfsPs3XPAa46tbfbqTTLhBFZ/PH91Wzdc4Dsrl3cTseYoyL2jvCG+Hf521dTy+/fW+V2OqaZist941IdaRA8kLWoTKiyolGP/MwkfnpmX15bVMqXG2yXv47I48ycGtxBi0bv9ASGZSfbvUMm5FjRaMAN5/Qnu2sX7nrLdvnriDzlXrondQrZPTSCMX54Fku22CwqE1qsaDQgPi6Ge51d/mb+a4Pb6Zjj5BsE75hXGX7WojKhyIpGI84bksm5g7vz2D/X2sqjHUhN7RFKKvZ2qOVD6mMtKhOKrGg04Z7vDkVRfvt2sdupmCCt3b6X2jplSFbHm257LH+LqnT3/qYPNqYdWNFoQm5aPD8fm8+84u18uGq72+mYIPgHwTt6ewoCW1TbXM7EGB8rGkH4yZi+9O+eyD1zijlwyHb5C3Weci/xcdH0Tot3O5UW87eo7EY/EyqsaAQhLiaK+ycOY0vlAf76se3yF+o8ZV4GZyUT1QH20AjGhOE9rUVlQoYVjSCd2i+dS0dl87dP1rHOdvkLWXV1iqfc22Fv6quPtahMKLGicRzuGD+YzrHR3D17he3yF6JKdx9gb01th585FahXery1qEzIsKJxHDKSOnHrBQP5d8ku5ixts1XaTQt4/MuHhFHRAGtRmdBhReM4fe+U3ozISeF3767Ea7v8hZziMi/RUcKAzCS3U2lV1qIyocKKxnHy7/K3c28Nj/zDdvkLNZ4yL/0yEugc27H20GhKr/R4hmen8I61qIzLrGg0w4icrlw1ujfPf267/IWacBsEDzR+eBZLrUVlXGZFo5l+cf5A0hI68Zu3VnDEdvkLCZX7DlFedTDsxjP8rEVlQkFQRUNEZopIhYisCIilich8EVnrfE114iIiM0SkRESWicgJAedc4xy/1tlj3B8/UUSWO+fMcLaEbfAzQkFKl1junDCYpVv28NKXm91OxwAry313gg/tgLv1BcNaVCYUBHul8Rww7pjYbcAHqpoPfOB8D3Ahvr3B84GpwJPgKwDAPcApwMnAPQFF4EnnWP9545r4jJAwsaAnp/ZN54/vr2Ln3hq304l4HX0PjWD4W1RbKq1FZdwRVNFQ1U+BY3cjmgjMcp7PAi4JiD+vPguAriKSBVwAzFfVSlXdDcwHxjmvJavq584Wr88f8171fUZIEBHuv2QoBw4f4X/mrnQ7nYhXXFZFVkpn0hLi3E6lzRxtUa2wqw3jjpaMaWSqajmA87W7E88GtgQcV+rEGouX1hNv7DO+QUSmikiRiBTt2LGjBT/S8evfPYmfntGXNxZvZcH6Xe362eabwnkQ3M/fonq1qJRDtbY5mGl/bTEQXt+CP9qMeNBU9SlVLVTVwoyMjOM5tVXccE4+Oam+Xf7sH7I7Dh4+wrod+8J2EDzQjWPzKanYy8PzV7udiolALSka253WEs7XCideCuQGHJcDlDURz6kn3thnhJQucdH89uKhrK3Yy8x/2y5/blizvZojdRpWy4c05NwhmXz/lF489el6/lOy0+10TIRpSdGYA/hnQF0DzA6IX+3MohoNVDmtpXnA+SKS6gyAnw/Mc16rFpHRzqypq495r/o+I+SMHZzJeUMyefyfa20evQuO7qERBhsvBePOCUPo0y2BW15dyp79h9xOx0SQYKfcvgR8DgwUkVIRuRZ4EDhPRNYC5znfA8wF1gMlwNPA9QCqWgncDyx0Hvc5MYDrgGecc9YB7znxhj4jJN3z3SEA3Pe2x+VMIk9xmZekTjHkpHZxO5V20SUumhmTR7FrXw13vLncFtA07SYmmINUdUoDL42t51gFpjXwPjOBmfXEi4Bh9cR31fcZoSon1bfL3x/eX8UHK7czdnCm2ylFDE95eO2hEYxh2Sn84vyBPPjeKl5bVMqkwtymTzKmheyO8FZ27Zg+5Nsuf+2qrk5ZWe6NiEHwY009oy+n9k3n3jnFbNy5z+10TASwotHK4mKiuP+SYZTuPsCfP1rrdjoRYVPlfvYfOhL2023rExUlPHzFSKKjhJteWcLhIzZ7z7QtKxptYHTfdC47IZunPl1PSYXt8tfWjg6CR+CVBkDPrl34/WUjWLJlD098YL+omLZlRaON3DF+MF1io7nrLdvlr615yquIiRLyMxPdTsU1E0ZkcfmJOfz5oxIWbjx28QZjWo8VjTbSLbETt44bxOfrdzF7ie3y15aKy7z0755Ip5jw2kPjeN178VByUuO5+ZUltkGYaTNWNNrQlJN7MTInhd+962H3PptL31Y8ZZE5CH6sxE4xPDa5gPKqg9wzu9jtdEyYsqLRhqKjhN9fNoKqA4e5c/aKpk8wx21HdQ0V1TUROQhenxN6pfLzc/J586utzF6y1e10TBiyotHGhvRM5qZzB/DusnLmLLU2VWsL9z00mmPa2f04sXcqd765wlYnMK3OikY7+NmZfRnVqyt3vbWCbVUH3U4nrHjK/cuH2JWGX0x0FI9dWYACt7yy1HaWNK3KikY7iImO4pErCjhUW8etry+z2VStyFPmJbtrF1LiY91OJaTkpsVz/yVD+XJjJX/7ZJ3b6ZgwYkWjnfTplsDt4wfx6ZodvPCFbQ/bWorLqmwQvAGXFGTz3ZE9eXT+GpZs2eN2OiZMWNFoRz84pTdn5HfjgXdX2pIPrWD/oVrW79xnrakGiAi/u2QYmcmduenlr9hXU+t2SiYMWNFoR1FRwh8vH0FMtPCLv1uvuaVWb6tGNXLvBA9GSpdYHrliJJsq99vqy6ZVWNFoZ1kpXbh/4jAWbdrNU5+udzudDs1zdOaUFY3GnNI3nevP6scrRVt43/YWNy1kRcMFEwt6cuGwHjwyf/XRKaPm+HnKvCR3jiG7a2TsodESN507gBE5Kdz2xnKbwWdapNlFQ0QGisiSgIdXRG4SkXtFZGtAfHzAObeLSImIrBaRCwLi45xYiYjcFhDvIyJfiMhaEXlFROKa/6OGDn+vOaVLHDe/soSaWltCvTk8znLovg0fTWNinWm4NYfr+MXfl1BnrVHTTM0uGqq6WlULVLUAOBHYD7zpvPyo/zVVnQsgIkOAycBQYBzwVxGJFpFo4C/AhcAQYIpzLMAfnPfKB3YD1zY331CTntiJBy8bzqpt1Tz+T1uZ9HgdqVNWlVdHzPauraFvRiL3fHcI/y7ZxbP/sr3sTfO0VntqLLBOVTc1csxE4GVVrVHVDfi2dj3ZeZSo6npVPQS8DEx09gs/B3jNOX8WcEkr5RsSzh2SyZWFufztk3Us2mQrkx6PDTv3ceDwERsEP05XnpTLBUMz+dO81RSXVbmdjumAWqtoTAZeCvh+uogsE5GZIpLqxLKBLQHHlDqxhuLpwB5VrT0m/i0iMlVEikSkaMeOHS3/adrRnRcNJiulC7e8upT9h2xKZLBsELx5RIQHLxtB1/hYbnx5ie0uaY5bi4uGM85wMfB3J/Qk0A8oAMqBh/2H1nO6NiP+7aDqU6paqKqFGRkZx5G9+5I6x/LwFSPZXLmf389d5XY6HYanzEtcdBT9MiJ3D43mSk2I4+ErRlJSsZffv7fS7XRMB9MaVxoXAotVdTuAqm5X1SOqWgc8ja/9BL4rhdyA83KAskbiO4GuIhJzTDzsjO6bzrWn9+H/FmzikzUd60rJLZ5yL/mZicTF2ATA5jgjP4OfjOnD859v4oOV291Ox3QgrfEvbgoBrSkRyQp47VLAvyb4HGCyiHQSkT5APvAlsBDId2ZKxeFrdc1R3wJNHwGXO+dfA8xuhXxD0i8vGEj/7onc+tpSqvbbBjpN8ZR57U7wFvrVuIEM6pHEra8tY0d1jdvpmA6iRUVDROKB84A3AsJ/FJHlIrIMOBu4GUBVi4FXAQ/wPjDNuSKpBaYD84CVwKvOsQC/Bm4RkRJ8YxzPtiTfUNY5NppHryhg195D3D3H9t5oTIX3IDv31tggeAt1iolmxpRR7K2p5dbXltpCmiYoMU0f0jBV3Y/vf+aBsasaOf4B4IF64nOBufXE1/N1eyvsDc9J4YZz8nn0n2s4f0gPJozIavqkCFRsy6G3mgGZSdwxfjD3zCnm/xZs4upT89xOyYQ4awiHmOvP7sfInBTufGs5FV67c7c+njJf0RhsVxqt4upTe3P2wAweeHcla7ZXu52OCXFWNEJMbHQUD19RwP5DR7jtjeXWMqiHp9xLr7R4kjvbHhqtQUT44+UjSewUw89f+spWKDCNsqIRgvp3T+TX4wbx4aoKXlm4pekTIsxKGwRvdRlJnfjTpBGs2lbNQ/NWu52OCWFWNELUD0/L49S+6dz/joctlbbPs9++mlo27Npng+Bt4JxBmVw1ujdPf7aBf63d6XY6JkRZ0QhRUVHCQ1eMJEqEX7xqe2/4rdrm9e2hYVcabeKO8YPp3z2RW15dwu59h9xOx4QgKxohLLtrF+7+7hC+3FjJTFtgDvh6EHxothWNttAlLprHJxewe/8hbnvD9rM332ZFI8RdfmIO5w3xLTBnM1t8g+Cp8bH0SO7sdipha2jPFG69YBDzirfbmJr5FisaIU5E+P1lw0nqHMPNryzhUG2d2ym5ylNme2i0h2vH9OH0/un89m0P63fsdTsdE0KsaHQA3RI78cClwyku8/LnDyN3743aI3Ws2lZt4xntICpKeHhSAXExUdz0yhIOH4nsX1ZaourAYZaXVvHOsjL+8lEJd89eweLNu91Oq9ladEe4aT/jhvXgshOy+cvH6zh7UHdG9Upt+qQws37nPmpq62zmVDvpkdKZBy8bznUvLOaxf67hVxcMcqquCjgAABVkSURBVDulkKSq7Nx7iE279rFp137f18r9bNy1n8279rH7mLXkOsVE8eIXm/nVBQP56Rl9iYrqWFfNVjQ6kHu+O5QF63bxi1eX8u7Pz6BLXLTbKbUr/yC47dbXfi4cnsWVhbn89eN1nJmfwSl905s+KQwdqVPKqw44RWE/myr3sWnnfjZV+orE/oB9SaIEenbtQl56AhcOz6J3Wjy90xPonR5Pr7R4auuUX7+2jN+/t4ovNlTy8KSRpCZ0nJ2sJdxmRxQWFmpRUZHbabSZf5fs5PvPfMEPT8vj3ouHup1Ou/qfuSt57j8b8fz2AmKirbPaXvbV1DJhxmccPqLMvfEMUrqE5534NbVHKN19gM279rPxmKuG0soDHApo0cVFR5Gb1uVoMeidFk/vbgn0TosnJzW+ySX7VZXnP9/EA++uJD0xjiemjKIwL62tf8RGicgiVS1s6ji70uhgTu/fjR+elsdz/9nIeUMyOb1/N7dTajeeMi+DeiRZwWhnCZ1ieGzyKP7ryf9w51srmDG5oMNORNhXU8umXfvZXLmPjf6rBqdAlFcdIPB2qIS4aHqnJzAwM4nzh/T4RnHokdyZ6Ba0lUSEa07L44ReqUx7cTFXPrWAX54/kJ+dGfrtKisaHdCvxw3i0zU7+OXfl/L+TWeG7W9+gVQVT7mX84dkup1KRCrI7crN5+bz0D/WcM6gDC4dleN2So3aV1PL4s27WbJ5Dxt27XOuHvazc+839w1JS4ijV1o8J+Wl0is9h7z0eF9xSE8gPSGuzYvj8JwU3vn5GG5/fTl/eH8VX2zYxcOTRpKe2KlNP7clrGh0QF3ionnkygL+68n/8Nu3i3nkigK3U2pz2701VO47ZIPgLrrurP58smYHd71VTGHvNHLT4t1O6aide2so2ljJlxt2U7SpkuIy79FVFLJSOtMrLZ6xg7rTKz2ePP/4QnpoLHqZ3DmWP39vFKO/SOf+tz1MmPEvZkwZxcl93G1XNcSKRgdVkNuVaWf1Y8aHJZw/pAfjhvVwO6U2VVxWBdjyIW6KjhIevbKACx/7jJteWcIrU0e70ipUVUp3H+DLDZUs3FjJlxsrWb9jH+CbmVSQ25XrvtOPk/qkcUKvriSFQGFoiohw1ejejMrtyvQXFzPl6QXcct4ArvtOv5BrV7W4aIjIRqAaOALUqmqhiKQBrwB5wEbgClXdLb5rvceB8cB+4Iequth5n2uAO523/Z2qznLiJwLPAV3wbdR0o4bb6H0zTT8nnw9WVfCbN5dTmJdKtxC+pG0pT5kXERhkRcNVOanx/O7SYdz48hL++vE6fj42v80/s65OWVNRzcINlXy5cTcLN1SyzdlrJrlzDIV5aUw6MZeT+6QyLDuFTjEdd1bhsOwU3r5hDLe/sZw/zVvNgvW7ePTKgpD6t91aVxpnq2rgspi3AR+o6oMicpvz/a+BC/HtDZ4PnAI8CZziFJl7gEJAgUUiMkdVdzvHTAUW4Csa44D3WinvDi0uJopHryzgoif+xe1vLOepq07ssAOUTfGUe8lLTyCxk10cu21iQTYfrarg8Q/WMia/Gye08j1Dh2rrWL51D19u2M3CjZUUbazEe7AWgMzkTpyUl8bJfdI4KS+NgZlJIfebeEsldY7liSmjOLWf74788Y9/xowpoxgdItOd2+pf4ETgLOf5LOBjfEVjIvC8c6WwQES6ikiWc+x8Va0EEJH5wDgR+RhIVtXPnfjzwCVY0ThqQGYSvzp/IA/MXclri0qZVJjrdkptwlPuZVhPuz8jVNx3yTAWbtzNTS8vYe6NZ7SomO+tqWXxJl+B+HJDJUu27KHGWS6nb0YC44dnHS0UOaldwvYXo0AiwvdP6c2o3FSmv7iY7z29gJvPHcD1Z/dv0ayt1tAaRUOBf4iIAv+rqk8BmapaDqCq5SLS3Tk2GwhcAa3UiTUWL60n/g0iMhXf1Qi9evVqhR+pY/nxmD7MX7md377t4dR+6eSkhs4AZWuoPniYTbv2c0WYFsSOKLlzLI9NLuDK//2ce+cU89CkkUGfGzhovXBjJcVlVdSp76a4oT1T+P4pvTm5TyqFeWkh1ZZxw5Ceycy5YQy/eXM5D89fwxcbKnn0ygIyktz7c2mNonG6qpY5hWG+iKxq5Nj6SqQ2I/7NgK9QPQW+m/uaTjm8REcJD08aybjHPuVXf1/GCz85Jawu2Vdt863ua4PgoeWkvDSmnd2fJz4s4eyB3ZkwIutbx6gqWyoP8OXGShY6A9frd349aD2qV1emn92fk/qkMapXqrUf65HYKYbHrizg1L7p3DOnmPEzPuPxyQWc1s+de7Ra/F9IVcucrxUi8iZwMrBdRLKcq4wsoMI5vBQI/HUxByhz4mcdE//YiefUc7w5Rm5aPHddNITb3ljOc//ZyI/H9HE7pVZTvNWZOWXTbUPOz8fm8+nandz+xjJG9epKj+TOrN5efbTVtHBjJdu9vnsjUrrEUtg7lStOyuWkvDSGZ6c0eee08RERJp/ci5G5XZn24mJ+8MwX3Dh2ANPPaf92VYuKhogkAFGqWu08Px+4D5gDXAM86Hyd7ZwyB5guIi/jGwivcgrLPOB/RMQ/onY+cLuqVopItYiMBr4ArgaeaEnO4ezKk3L5h2c7f3h/FWcO6Eb/7klup9QqPOVeuiXG0d3FS3JTv9joKB6/soDxMz7j8if/w96a2qOD1lkpnTmlTzon9Unj5Lw08rsnhtUVsBsGZyXz9vQx3PXWCh795xq+2LCLxyYX0D2p/faXadHaUyLSF3jT+TYGeFFVHxCRdOBVoBewGZjkFAAB/oxvBtR+4EeqWuS814+BO5z3ekBV/58TL+TrKbfvATc0NuU23NeeakpF9UEuePRTctPief2604gNgyU3LnriM1Lj4/i/a09xOxXTgDlLy3jy43UU5KZwUp5vZlOkDFq7QVX5+6JS7p69gsROsTw+uaDFSwoFu/aULVgYht5dVs60Fxdz87kDuPHctp9H35YOH6lj6N3z+NGYPG6/cLDb6RgTUtZsr+b6FxazbsdebjgnnxvH5je7XRVs0ej4v4aab5kwIouJBT154sO1LCvd43Y6LbJux14OHamzQXBj6jEgM4k500/nv07IYcYHa/n+MwuocG58bCtWNMLUfRcPIz0xjlteXcrBw0eaPiFEFW/17aEx1AbBjalXfFwMD00ayUOTRrJ0SxXjZ3zGZ2t3tNnnWdEIUynxsfzx8pGUVOzlT/NWu51Os3nKvXSOjaJPt0S3UzEmpF1+Yg5zpp9OWkIcV8/8kofmraa2DbbptaIRxr4zIIMfjO7FzH9v4PN1u9xOp1l8e2gku34XrDEdQX5mErOnjWHSiTn8+aMSvvfMF2yrat12lRWNMHfH+MH0Sovnl39fSvXBw02fEEL8e2jY/RnGBK9LXDR/vHwkj145khVbfe2qT9a0XrvKikaYi4+L4ZErRlJedYD73/G4nc5xKas6SNWBwzYIbkwzXDoqhznTx9A9qRPXzPySP7y/qlXaVVY0IsCJvdP42Xf68WpRKf/0bHc7naB5ynyD4HalYUzz9O+eyFvTTmfKybk8+fE6pjy9gPKqAy16TysaEeKmc/MZ1COJ295Yxq5jtrwMVcVlVb49NHqEx53txrihc2w0v79sBI9PLsBT5mX845/x0aqKpk9sgBWNCNEpJppHryyg6sBhfvPmCjrCTZ2eMi99uyUQH2eL2BnTUhMLsnn7hjH0SOnCj55byO/fW8nhZrSrrGhEkMFZydxy3kDeL97GW0u2up1Ok3yD4LaHhjGtpW9GIm9efxrfP6UX//vJeiY/tYCte46vXWVFI8JMPbMvJ/ZO5e7ZxXy4anvIXnFUHThM6e4DNghuTCvrHBvNA5cO54kpo1i9rZoJMz7jg5XBj3Va0Ygw0VHCo1f49hz+8XNFXPXsl6ws97qd1rf4c7JBcGPaxndH9uTtG8aQ3bUL184Kfr0+KxoRqFd6PPNuOpN7vjuE5VurmDDjM257fRkV1W27Zs3xODpzyq40jGkzfbol8Pp1p3H1qb2DPseKRoSKi4niR6f34ZNfncWPTu/Da4tKOftPH/PnD9eGxFpVxWVeMpI6ubqtpTGRoHNsNPdNHBb08VY0IlzX+DjuumgI82/5DmPyu/HQP9ZwzkMf89ZXW6mrc2+8w1PutUUKjQlBzS4aIpIrIh+JyEoRKRaRG534vSKyVUSWOI/xAefcLiIlIrJaRC4IiI9zYiUicltAvI+IfCEia0XkFRGJa26+pnF9uiXwv1cV8tJPR5OaEMdNryzh0r/+m4UbK9s9l0O1dZRUVFtrypgQ1JIrjVrgF6o6GBgNTBORIc5rj6pqgfOYC+C8NhkYim/nvr+KSLSIRAN/AS4EhgBTAt7nD8575QO7gWtbkK8Jwqn90nl7+hgemjSSbd6DTPrb51z/wiI279rfbjmsrajm8BG1QXBjQlCzi4aqlqvqYud5NbASyG7klInAy6pao6obgBLgZOdRoqrrVfUQ8DIw0dka9hzgNef8WcAlzc3XBC8qSrj8xBw++uVZ3HzuAD5atYNzH/mE/5m7kqoDbb/ooQ2CGxO6WmVMQ0TygFHAF05ouogsE5GZIpLqxLKBLQGnlTqxhuLpwB5VrT0mXt/nTxWRIhEp2rGj7TYfiTTxcTHceG4+H//qLCYW9OTpz9Zz1p8+4vnPNzbrTtJgecq9xMdFk5ee0GafYYxpnhYXDRFJBF4HblJVL/Ak0A8oAMqBh/2H1nO6NiP+7aDqU6paqKqFGRkZx/kTmKZkJnfmT5NG8vb0MQzskcTds4sZ99inbXZzYHGZl8FZyUTZHhrGhJwWFQ0RicVXMF5Q1TcAVHW7qh5R1TrgaXztJ/BdKeQGnJ4DlDUS3wl0FZGYY+LGJcOyU3jpp6N5+upC6pQ2uTlQVVlZ5rXWlDEhqiWzpwR4Flipqo8ExLMCDrsUWOE8nwNMFpFOItIHyAe+BBYC+c5MqTh8g+Vz1Pcr7EfA5c751wCzm5uvaR0iwnlDMr9xc+D4Vrw5sHT3Aapram0Q3JgQ1ZLlQ08HrgKWi8gSJ3YHvtlPBfhaSRuBnwGoarGIvAp48M28mqaqRwBEZDowD4gGZqpqsfN+vwZeFpHfAV/hK1ImBPhvDrx0VDZPfFjC859vZM7SMq4/qx8/OaMvnWOjm/W+xTYIbkxIk1BdsK65CgsLtago+HVUTOvYsHMfD763knnF2+mZ0plbxw3i4pE9j3tc4pH5a/jzh2vx3Deu2YXHGHP8RGSRqhY2dZzdEW5ahf/mwJenjiYtsfk3B3rKquiXkWgFw5gQZUXDtKrRfdOZM20MD08ayXZvzXHfHOgps+VDjAlltiWaaXVRUcJ/nZjDhcN78PSnG/jbJ+v4p6eCH56ex7Sz+5PSJbbe83bvO0RZ1UEbBDcmhNmVhmkzx3tz4NE9NLJstz5jQpUVDdPmAm8OHNQjucGbAz1O0RicleRWqsaYJljRMO1mWHYKL/70lAZvDvSUeemR3Jn0RNtDw5hQZWMapl35bw78zoAMXvhiE49/sJbxMz7jihNzWbx5tw2CGxPirGgYV/hvDrxsVA5PfLiWWZ9v5PAR5bsje7qdmjGmEVY0jKtS4mO586Ih/GB0b/5vwSYmnZjb9EnGGNdY0TAhIa9bAnddNKTpA40xrrKBcGOMMUGzomGMMSZoVjSMMcYEzYqGMcaYoFnRMMYYEzQrGsYYY4JmRcMYY0zQrGgYY4wJWtht9yoi1cBqt/M4Rjdgp9tJHCMUc4LQzMtyCo7lFLxQzGugqja5xHQ43hG+Oph9btuTiBRZTsEJxbwsp+BYTsELxbxEpCiY46w9ZYwxJmhWNIwxxgQtHIvGU24nUA/LKXihmJflFBzLKXihmFdQOYXdQLgxxpi2E45XGsYYY9qIFQ1jjDFBC6uiISLjRGS1iJSIyG0hkM9MEakQkRVu5+InIrki8pGIrBSRYhG5MQRy6iwiX4rIUien37qdk5+IRIvIVyLyjtu5+InIRhFZLiJLgp0m2dZEpKuIvCYiq5y/W6e6nM9A58/H//CKyE1u5uTkdbPzd3yFiLwkIp1DIKcbnXyKg/kzCpsxDRGJBtYA5wGlwEJgiqp6XMzpTGAv8LyqDnMrj0AikgVkqepiEUkCFgGXuPznJECCqu4VkVjgX8CNqrrArZz8ROQWoBBIVtWL3M4HfEUDKFTVkLk5TERmAZ+p6jMiEgfEq+oet/OCo/9v2AqcoqqbXMwjG9/f7SGqekBEXgXmqupzLuY0DHgZOBk4BLwPXKeqaxs6J5yuNE4GSlR1vaoewvcHMdHNhFT1U6DSzRyOparlqrrYeV4NrASyXc5JVXWv822s83D9txkRyQEmAM+4nUsoE5Fk4EzgWQBVPRQqBcMxFljnZsEIEAN0EZEYIB4oczmfwcACVd2vqrXAJ8CljZ0QTkUjG9gS8H0pLv/PMNSJSB4wCvjC3UyOtoGWABXAfFV1PSfgMeBWoM7tRI6hwD9EZJGITHU7GaAvsAP4f04r7xkRSXA7qQCTgZfcTkJVtwIPAZuBcqBKVf/hblasAM4UkXQRiQfGA7mNnRBORUPqibn+22qoEpFE4HXgJlX1up2Pqh5R1QIgBzjZuWx2jYhcBFSo6iI382jA6ap6AnAhMM1pg7opBjgBeFJVRwH7ANfHFAGcVtnFwN9DIJdUfN2PPkBPIEFEfuBmTqq6EvgDMB9fa2opUNvYOeFUNEr5ZoXMwf1Lv5DkjBu8Drygqm+4nU8gp63xMTDO5VROBy52xg9eBs4Rkf/P3ZR8VLXM+VoBvImvNeumUqA04OrwNXxFJBRcCCxW1e1uJwKcC2xQ1R2qehh4AzjN5ZxQ1WdV9QRVPRNfO73B8QwIr6KxEMgXkT7ObxeTgTku5xRynEHnZ4GVqvqI2/kAiEiGiHR1nnfB949rlZs5qertqpqjqnn4/i59qKqu/lYIICIJzgQGnBbQ+fhaDK5R1W3AFhEZ6ITGAq5NrDjGFEKgNeXYDIwWkXjn3+FYfGOKrhKR7s7XXsBlNPHnFTar3KpqrYhMB+YB0cBMVS12MycReQk4C+gmIqXAPar6rJs54fsN+ipguTOGAHCHqs51MacsYJYzyyUKeFVVQ2aKa4jJBN70/T+HGOBFVX3f3ZQAuAF4wfmFbT3wI5fzwenRnwf8zO1cAFT1CxF5DViMrwX0FaGxnMjrIpIOHAamqeruxg4Omym3xhhj2l44taeMMca0MSsaxhhjgmZFwxhjTNCsaBhjjAmaFQ1jjDFBs6JhjDEmaFY0jDHGBO3/Byjb0lwh0W9BAAAAAElFTkSuQmCC\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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb260148>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k1VMn0L8PnBQRJ0TEYcB6YHNLn83ARdX0ucA3s9u1HElSX3W95JKZ+yLiEuAu4BDgxsx8NCI+AWzJzM3ADcCXImKWxpn5+kEWPSAjeSkI6+rFKNYE1tWLUawJRreuA3T9UFSS9PvBvxSVpEIY6JJUisws5kHjTpt7ge3Ao8CHqvaPA08CD1aPs5rWuRyYpXFP/J83ta+r2maBjU3tJwD3Az8GbgUOq1nbDmBbtf8tVdvLgburbd0NHFO1B/CZat8PA6c2beeiqv+PgYua2tdW25+t1o0aNb2maUweBJ4HPrwU4wXcCDwNPNLUNvDx6bSPeWr6F+CH1X6/Bhxdta8G9jaN2fUL3fd8z2+eugb+mgGHV/Oz1fLVNeq6tammHcCDwxwvOmfCkh5bA8vAQe9gmA9g5f4XADgK+BGNryv4OPCRNv1PBh6qDtQTgMdpfPB7SDV9InBY1efkap0vA+ur6euBD9SsbQewoqXtn/f/IAEbgaur6bOAb1QH1xnA/U0HyBPVv8dU0/sPxO8Br6/W+QZwZo9jdwjw38AfLcV4AW8CTuXAMBj4+HTaxzw1vR1YVk1f3VTT6uZ+Lc+tp313en5d6hr4awb8DVXw0rjx4dZudbUsvwa4cpjjRedMWNJja1CPJQ/hgT45+DrwtnkO9suBy5vm76pemNcDd7X2q16wZ3jxB/qAfl1q2cHBgf4YsLLpwHusmv4ccF5rP+A84HNN7Z+r2lYCP2xqP6BfzfreDvxnNb0k40XLD/kwxqfTPjrV1LLsXcDN8/VbyL47Pb8uYzXw12z/utX0sqpfzFdXU3sAPwFOWorxapMJS35sDeJR7DX06hsfX0fjrSHAJRHxcETcGBHHVG2raBxk++2s2jq1vwL4eWbua2mvI4H/iIit1VcgAIxn5i6A6t8/XGBdq6rp1vZerAduaZpf6vGC4YxPp33U8R4aZ2T7nRAR/xUR34qINzbV2uu+Oz2Pbgb9mv1unWr5nqp/HW8Edmfmj5vahjpeLZkw6sfWghQZ6BExBtwGfDgznwc+C/wx8FpgF423ftD5Kwt6ba/jDZl5Ko1vrfxgRLxpnr7DrIvqD8bOBv6tahqF8ZrPktcREVcA+4Cbq6ZdwKsy83XA3wP/GhEvW+C+F7LOMF6zxYzjeRx4wjDU8WqTCb1ua9jH+IIUF+gRcSiNF+7mzPwqQGbuzszfZOZvgc/T+AZJ6Py1Bp3anwGOrr7eoLm9q8x8qvr3aRofpp0G7I6IlVXdK2l8oLSQunZW063tdZ0JPJCZu6sal3y8KsMYn0776CgiLgLeCZyf1fvpzHwhM5+tprfSuD79Jwvcd52v2zjAkF6zBX3FR9X3L2l8QLq/3qGNV7tMWMC2hnJsLVZRgV59Ze8NwPbM/GRT+8qmbu8CHqmmNwPrq/+g4wTgJBofcLT9uoPqh/deGl9vAI1Pvb9eo64jI+Ko/dM0rlc/woFfmdC8rc3AhdFwBrCnest2F/D2iDimekv9dhrXN3cBv4iIM6oxuLBOXU0OOHta6vFqMozx6bSPtiJiHXAZcHZm/qqp/djq/w4gIk6sxuaJBe670/Obr65hvGYL/YqPt9K4zvy7SxPDGq9OmbCAbQ382OqLQV+kH+YD+DMab3cepun2LeBLNG4rerga5JVN61xB4+zgMZruDKnW+1G17Iqm9hNp/EDM0rhEcXiNuk6kcRfBQzRunbqian8FcA+N25ruAV6eL36AdF21723ARNO23lPtexZ4d1P7BI0f4seBa6lx22K13h8AzwLLm9qGPl40fqHsAv6XxlnPe4cxPp32MU9NszSupR5wux3wV9Vr+xDwAPAXC933fM9vnroG/poBL63mZ6vlJ3arq2r/AvD+lr5DGS86Z8KSHluDevin/5JUiKIuuUjS/2cGuiQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSrE/wGfavZ6EuJbUgAAAABJRU5ErkJggg==\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": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Price\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "no numeric data to plot",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m----------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-78-83bf2f29b5a6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcar_sales\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Price\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Desktop\\sample_project\\env\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    792\u001b[0m                     \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlabel_name\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    793\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 794\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    795\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    796\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\sample_project\\env\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m     60\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ax\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"left_ax\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 62\u001b[1;33m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     63\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\sample_project\\env\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    277\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    278\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 279\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    280\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\sample_project\\env\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    412\u001b[0m         \u001b[1;31m# no non-numeric frames or series allowed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    413\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 414\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"no numeric data to plot\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    415\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    416\u001b[0m         \u001b[1;31m# GH25587: cast ExtensionArray of pandas (IntegerArray, etc.) to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: no numeric data to plot"
     ]
    }
   ],
   "source": [
    "car_sales[\"Price\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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   $9,700.00"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales[\"Price\"] = car_sales[\"Price\"].str.replace(\"[\\$\\,\\.]\", \"\").astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa6904c8>"
      ]
     },
     "execution_count": 97,
     "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": 98,
   "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": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Make\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales[\"Make\"] = car_sales[\"Make\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Odometer (KM)</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>toyota</td>\n",
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       "      <td>400000</td>\n",
       "    </tr>\n",
       "    <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",
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       "      <th>2</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
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       "      <td>700000</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",
       "    </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": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing = pd.read_csv(\"car-sales-missing-data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Make</th>\n",
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       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
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       "      <td>$7,000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.0</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",
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       "      <td>4.0</td>\n",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92302.66666666667"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing[\"Odometer\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing[\"Odometer\"].fillna(car_sales_missing[\"Odometer\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Toyota</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <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": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing[\"Odometer\"].fillna(car_sales_missing[\"Odometer\"].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <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": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Toyota</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>5.0</td>\n",
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       "      <td>Nissan</td>\n",
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       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
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       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</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"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
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       "      <td>$22,000</td>\n",
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       "    <tr>\n",
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       "      <td>Nissan</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>Toyota</td>\n",
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       "      <td>$4,500</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
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       "      <td>$7,500</td>\n",
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       "    <tr>\n",
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       "      <td>Honda</td>\n",
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       "      <td>4.0</td>\n",
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       "    <tr>\n",
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       "      <td>Toyota</td>\n",
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       "      <td>60000.0</td>\n",
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       "      <th>9</th>\n",
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       "      <td>$9,700</td>\n",
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       "</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": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing = pd.read_csv(\"car-sales-missing-data.csv\")\n",
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <td>$3,500</td>\n",
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      "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": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing_dropped = car_sales_missing.dropna()\n",
    "car_sales_missing_dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing_dropped.to_csv(\"car-sales-missing-dropped.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>honda</td>\n",
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       "      <td>500000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>toyota</td>\n",
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       "      <td>bmw</td>\n",
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       "      <td>nissan</td>\n",
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       "      <td>350000</td>\n",
       "      <td>5.0</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>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": 127,
     "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": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales[\"Seats\"].fillna(5, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>toyota</td>\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",
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       "      <td>11179</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",
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       "      <td>213095</td>\n",
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       "      <td>350000</td>\n",
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       "      <td>99213</td>\n",
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       "      <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": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Column from Python list\n",
    "fuel_economy = [7.5, 9.2, 5.0, 9.6, 7.8, 5.5, 9.1, 3.1, 4.5, 9.8]\n",
    "car_sales[\"Fuel per 100KM\"] = fuel_economy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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",
       "      <th>Seats</th>\n",
       "      <th>Fuel per 100KM</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",
       "    </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",
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       "    <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",
       "    </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",
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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>7.8</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>5.5</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>9.1</td>\n",
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       "    <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>3.1</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>4.5</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>9.8</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             7.8\n",
       "5  toyota  Green          99213      4   450000    5.0             5.5\n",
       "6   honda   Blue          45698      4   750000    5.0             9.1\n",
       "7   honda   Blue          54738      4   700000    5.0             3.1\n",
       "8  toyota  White          60000      4   625000    5.0             4.5\n",
       "9  nissan  White          31600      4   970000    5.0             9.8"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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</th>\n",
       "      <th>Total fuel used (L)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <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>11253.225</td>\n",
       "      <td>11253.225</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>honda</td>\n",
       "      <td>Red</td>\n",
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       "      <td>4</td>\n",
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       "      <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",
       "    </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>7.8</td>\n",
       "      <td>16621.410</td>\n",
       "      <td>16621.410</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>5.5</td>\n",
       "      <td>5456.715</td>\n",
       "      <td>5456.715</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>9.1</td>\n",
       "      <td>4158.518</td>\n",
       "      <td>4158.518</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>3.1</td>\n",
       "      <td>1696.878</td>\n",
       "      <td>1696.878</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>4.5</td>\n",
       "      <td>2700.000</td>\n",
       "      <td>2700.000</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>9.8</td>\n",
       "      <td>3096.800</td>\n",
       "      <td>3096.800</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             7.8   \n",
       "5  toyota  Green          99213      4   450000    5.0             5.5   \n",
       "6   honda   Blue          45698      4   750000    5.0             9.1   \n",
       "7   honda   Blue          54738      4   700000    5.0             3.1   \n",
       "8  toyota  White          60000      4   625000    5.0             4.5   \n",
       "9  nissan  White          31600      4   970000    5.0             9.8   \n",
       "\n",
       "   Total fuel used  Total fuel used (L)  \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        16621.410            16621.410  \n",
       "5         5456.715             5456.715  \n",
       "6         4158.518             4158.518  \n",
       "7         1696.878             1696.878  \n",
       "8         2700.000             2700.000  \n",
       "9         3096.800             3096.800  "
      ]
     },
     "execution_count": 137,
     "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": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a column from a single value\n",
    "car_sales[\"Number of wheels\"] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Total fuel used (L)</th>\n",
       "      <th>Number of wheels</th>\n",
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       "      <td>toyota</td>\n",
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       "      <th>1</th>\n",
       "      <td>honda</td>\n",
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       "      <td>8086.708</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
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       "      <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",
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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",
       "      <td>9.6</td>\n",
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       "      <td>1073.184</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nissan</td>\n",
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       "      <td>350000</td>\n",
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       "      <th>5</th>\n",
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       "      <td>5.5</td>\n",
       "      <td>5456.715</td>\n",
       "      <td>5456.715</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>9.1</td>\n",
       "      <td>4158.518</td>\n",
       "      <td>4158.518</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>3.1</td>\n",
       "      <td>1696.878</td>\n",
       "      <td>1696.878</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>4.5</td>\n",
       "      <td>2700.000</td>\n",
       "      <td>2700.000</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>9.8</td>\n",
       "      <td>3096.800</td>\n",
       "      <td>3096.800</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             7.8   \n",
       "5  toyota  Green          99213      4   450000    5.0             5.5   \n",
       "6   honda   Blue          45698      4   750000    5.0             9.1   \n",
       "7   honda   Blue          54738      4   700000    5.0             3.1   \n",
       "8  toyota  White          60000      4   625000    5.0             4.5   \n",
       "9  nissan  White          31600      4   970000    5.0             9.8   \n",
       "\n",
       "   Total fuel used  Total fuel used (L)  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        16621.410            16621.410                 4  \n",
       "5         5456.715             5456.715                 4  \n",
       "6         4158.518             4158.518                 4  \n",
       "7         1696.878             1696.878                 4  \n",
       "8         2700.000             2700.000                 4  \n",
       "9         3096.800             3096.800                 4  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                    object\n",
       "Colour                  object\n",
       "Odometer (KM)            int64\n",
       "Doors                    int64\n",
       "Price                    int32\n",
       "Seats                  float64\n",
       "Fuel per 100KM         float64\n",
       "Total fuel used        float64\n",
       "Total fuel used (L)    float64\n",
       "Number of wheels         int64\n",
       "Passed road safety        bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Passed road safety\"] = True\n",
    "car_sales.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales = car_sales.drop(\"Total fuel used\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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",
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       "      <td>toyota</td>\n",
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       "      <td>True</td>\n",
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       "    <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",
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       "      <td>5.0</td>\n",
       "      <td>1627.450</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>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>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>7.8</td>\n",
       "      <td>16621.410</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>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5456.715</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>9.1</td>\n",
       "      <td>4158.518</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",
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       "      <td>1696.878</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
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       "      <td>2700.000</td>\n",
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       "      <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>9.8</td>\n",
       "      <td>3096.800</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             7.8   \n",
       "5  toyota  Green          99213      4   450000    5.0             5.5   \n",
       "6   honda   Blue          45698      4   750000    5.0             9.1   \n",
       "7   honda   Blue          54738      4   700000    5.0             3.1   \n",
       "8  toyota  White          60000      4   625000    5.0             4.5   \n",
       "9  nissan  White          31600      4   970000    5.0             9.8   \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            16621.410                 4                True  \n",
       "5             5456.715                 4                True  \n",
       "6             4158.518                 4                True  \n",
       "7             1696.878                 4                True  \n",
       "8             2700.000                 4                True  \n",
       "9             3096.800                 4                True  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
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   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "car_sales_shuffled = car_sales.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <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",
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       "      <td>87899</td>\n",
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       "      <td>toyota</td>\n",
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       "      <td>11253.225</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
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      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "3     bmw  Black          11179      5  2200000    5.0             9.6   \n",
       "6   honda   Blue          45698      4   750000    5.0             9.1   \n",
       "8  toyota  White          60000      4   625000    5.0             4.5   \n",
       "7   honda   Blue          54738      4   700000    5.0             3.1   \n",
       "4  nissan  White         213095      4   350000    5.0             7.8   \n",
       "5  toyota  Green          99213      4   450000    5.0             5.5   \n",
       "2  toyota   Blue          32549      3   700000    5.0             5.0   \n",
       "9  nissan  White          31600      4   970000    5.0             9.8   \n",
       "1   honda    Red          87899      4   500000    5.0             9.2   \n",
       "0  toyota  White         150043      4   400000    5.0             7.5   \n",
       "\n",
       "   Total fuel used (L)  Number of wheels  Passed road safety  \n",
       "3             1073.184                 4                True  \n",
       "6             4158.518                 4                True  \n",
       "8             2700.000                 4                True  \n",
       "7             1696.878                 4                True  \n",
       "4            16621.410                 4                True  \n",
       "5             5456.715                 4                True  \n",
       "2             1627.450                 4                True  \n",
       "9             3096.800                 4                True  \n",
       "1             8086.708                 4                True  \n",
       "0            11253.225                 4                True  "
      ]
     },
     "execution_count": 161,
     "metadata": {},
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   "source": [
    "car_sales_shuffled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "1  honda    Red          87899      4   500000    5.0             9.2   \n",
       "3    bmw  Black          11179      5  2200000    5.0             9.6   \n",
       "\n",
       "   Total fuel used (L)  Number of wheels  Passed road safety  \n",
       "1             8086.708                 4                True  \n",
       "3             1073.184                 4                True  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only select 20% of data\n",
    "car_sales_shuffled.sample(frac=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_shuffled.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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",
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       "      <th>0</th>\n",
       "      <td>bmw</td>\n",
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       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>honda</td>\n",
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       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
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       "      <td>4158.518</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2</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>4.5</td>\n",
       "      <td>2700.000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>3.1</td>\n",
       "      <td>1696.878</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>7.8</td>\n",
       "      <td>16621.410</td>\n",
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       "      <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>5.5</td>\n",
       "      <td>5456.715</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</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",
       "    <tr>\n",
       "      <th>7</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>9.8</td>\n",
       "      <td>3096.800</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</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>4</td>\n",
       "      <td>True</td>\n",
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       "    <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     bmw  Black          11179      5  2200000    5.0             9.6   \n",
       "1   honda   Blue          45698      4   750000    5.0             9.1   \n",
       "2  toyota  White          60000      4   625000    5.0             4.5   \n",
       "3   honda   Blue          54738      4   700000    5.0             3.1   \n",
       "4  nissan  White         213095      4   350000    5.0             7.8   \n",
       "5  toyota  Green          99213      4   450000    5.0             5.5   \n",
       "6  toyota   Blue          32549      3   700000    5.0             5.0   \n",
       "7  nissan  White          31600      4   970000    5.0             9.8   \n",
       "8   honda    Red          87899      4   500000    5.0             9.2   \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             1073.184                 4                True  \n",
       "1             4158.518                 4                True  \n",
       "2             2700.000                 4                True  \n",
       "3             1696.878                 4                True  \n",
       "4            16621.410                 4                True  \n",
       "5             5456.715                 4                True  \n",
       "6             1627.450                 4                True  \n",
       "7             3096.800                 4                True  \n",
       "8             8086.708                 4                True  \n",
       "9            11253.225                 4                True  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_shuffled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>toyota</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>honda</td>\n",
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       "      <td>4</td>\n",
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       "      <th>2</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>12714.453125</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>4366.796875</td>\n",
       "      <td>5</td>\n",
       "      <td>2200000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
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       "      <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>83240.234375</td>\n",
       "      <td>4</td>\n",
       "      <td>350000</td>\n",
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       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>38755.078125</td>\n",
       "      <td>4</td>\n",
       "      <td>450000</td>\n",
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       "      <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>17850.781250</td>\n",
       "      <td>4</td>\n",
       "      <td>750000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4158.518</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>21382.031250</td>\n",
       "      <td>4</td>\n",
       "      <td>700000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1696.878</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>23437.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>625000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2700.000</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>12343.750000</td>\n",
       "      <td>4</td>\n",
       "      <td>970000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>3096.800</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   58610.546875      4   400000    5.0             7.5   \n",
       "1   honda    Red   34335.546875      4   500000    5.0             9.2   \n",
       "2  toyota   Blue   12714.453125      3   700000    5.0             5.0   \n",
       "3     bmw  Black    4366.796875      5  2200000    5.0             9.6   \n",
       "4  nissan  White   83240.234375      4   350000    5.0             7.8   \n",
       "5  toyota  Green   38755.078125      4   450000    5.0             5.5   \n",
       "6   honda   Blue   17850.781250      4   750000    5.0             9.1   \n",
       "7   honda   Blue   21382.031250      4   700000    5.0             3.1   \n",
       "8  toyota  White   23437.500000      4   625000    5.0             4.5   \n",
       "9  nissan  White   12343.750000      4   970000    5.0             9.8   \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            16621.410                 4                True  \n",
       "5             5456.715                 4                True  \n",
       "6             4158.518                 4                True  \n",
       "7             1696.878                 4                True  \n",
       "8             2700.000                 4                True  \n",
       "9             3096.800                 4                True  "
      ]
     },
     "execution_count": 166,
     "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": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try it, run your code\n",
    "# Search for it\n",
    "# Try again\n",
    "# Ask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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