00_introduction_to_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e4b14ec-b13e-4a9e-90fa-45bed2b2296d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d79bc814-86bc-4422-a330-acb6d50b8290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       BMW\n",
       "1    Toyota\n",
       "2     Honda\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2 main datatype\n",
    "series = pd.Series([\"BMW\", \"Toyota\", \"Honda\"])\n",
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca530b0b-0f50-4df3-b4a5-2257a0bb8f98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Red\n",
       "1     Blue\n",
       "2    White\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colours = pd.Series([\"Red\", \"Blue\", \"White\"])\n",
    "colours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5af544b9-b12b-4d5f-a041-967090273246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data = pd.DataFrame({\"Car make\": series, \"Colour\": colours})\n",
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "52f8cd22-8ed9-43e8-922f-ec2e986a3df2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import data\n",
    "car_sales = pd.read_csv(\"./data/car-sales.csv\")\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9c17fc1f-3982-4158-a89e-d10e99f26d60",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exporting a dataframe\n",
    "car_sales.to_csv(\"exported-car-sales.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de6c212d-1dfd-4aeb-a43e-0fd52a6b84d7",
   "metadata": {},
   "source": [
    "## Describe Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "11f68916-8f2b-46b9-a82a-2474b0d5985a",
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Attribute\n",
    "car_sales.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a8a83423-4480-45d6-82aa-cb82415df99e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Make', 'Colour', 'Odometer (KM)', 'Doors', 'Price'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "69f86ece-9072-483b-863e-618bdc5cb609",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=10, step=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3f8721a4-cf94-454d-8953-9d3e4570c56c",
   "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>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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bf46e207-b7a4-473b-9225-04946570a90c",
   "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",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Make           10 non-null     object\n",
      " 1   Colour         10 non-null     object\n",
      " 2   Odometer (KM)  10 non-null     int64 \n",
      " 3   Doors          10 non-null     int64 \n",
      " 4   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": 16,
   "id": "fe33fb37-bac8-4fcc-86a8-82557e26a290",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\htrav\\AppData\\Local\\Temp\\ipykernel_5576\\4073448239.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  car_sales.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Odometer (KM)    78601.4\n",
       "Doors                4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e522ed23-f26d-4858-9672-c94417722de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Doors\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5f01ff16-e641-421f-8dec-ca4a15c7fc65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# .loc & B.iloc\n",
    "# .loc refers to index\n",
    "# .iloc refers to position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6cf35695-38bc-4f5c-8410-5a8d46db8d1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      cat\n",
       "3      dog\n",
       "9     bird\n",
       "8    panda\n",
       "3    snake\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals = pd.Series([\"cat\", \"dog\", \"bird\", \"panda\", \"snake\"], index=[0,3,9,8,3])\n",
    "animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "76861492-206d-431d-ade9-33bf89f300e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      dog\n",
       "3    snake\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.loc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c5dc47de-42e0-462d-8270-e6cf009e78b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'panda'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "959a036d-e8eb-481b-be30-c8f82034bf8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     cat\n",
       "3     dog\n",
       "9    bird\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.iloc[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c8179182-3345-48d9-bdc8-f56d7b230dcd",
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Make\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0611add1-21dc-41fd-9d20-3975cf6be287",
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.Make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0de59322-de82-44f2-a64d-771f74ef5d8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors      Price\n",
       "0  Toyota  White         150043      4  $4,000.00\n",
       "2  Toyota   Blue          32549      3  $7,000.00\n",
       "5  Toyota  Green          99213      4  $4,500.00\n",
       "8  Toyota  White          60000      4  $6,250.00"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[car_sales[\"Make\"] == \"Toyota\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8be73568-a5dd-4b38-b811-1dfabd3126a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors      Price\n",
       "0  Toyota  White         150043      4  $4,000.00\n",
       "4  Nissan  White         213095      4  $3,500.00"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[car_sales[\"Odometer (KM)\"] > 100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "de85aeb2-7e25-4bec-8247-f3a7b286dad2",
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(car_sales[\"Make\"], car_sales[\"Doors\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "78ad7aa2-ecfc-4e0f-a12e-818a543e128a",
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.groupby([\"Make\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0727ce5d-41ee-456b-8529-21f74e71d869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hYUhJScHJkydRVFQEtVoNAFi7di3mzZuHt956C97e3ti0aRMaGhrw+eefQ6FQIDw8HGfPnsW6deuwdOlSSCR3f+I1EXWM6WiNztxR+nqeChcM69MdGQUa/Hz+Mvr06GO11yIi+2bVHqGysjJs27YN8+fPv+GxTZs2wc/PD0OHDsXy5ctRXV1tfiwtLQ1KpdIcggAgKioKSqUSBw8eNI8JDw83hyAAmDp1KvR6PTIzM81jJkyYAIVCYTGmuLgYBQUFbdas1+uh0+ksbkTUeTr7sNWb4XEbRNQeVg1CX3zxBby8vPDoo49a3D979mx8/fXX2LdvH1577TVs3rzZYkxpaSkCAgJueL6AgACUlpaaxwQGBlo87uPjA7lcfssxpj+bxlxv9erV5r4kpVKJoKCgDr5rIrqV/MstjdL9rByETH1CB89dhtHIvkAialuHL411xD/+8Q/Mnj0bbm5uFvcnJiaa/394eDgGDBiAESNG4OjRoxg+fDgAtHnZShAEi/vvZIypUfpml8VWrlyJpUuXmv+s0+kYhog6UZ7p1Hl/66wYM7k3qDs85TJo6ppwqlSHoWqlVV+PiOyT1WaEfvrpJ5w5cwbPPffcbccOHz4crq6uyM3NBdDSZ1RWVnbDuIqKCvOMjkqlumFWR6PRoKmp6ZZjysvLAeCGmSIThUIBb29vixsRdQ6DUcCFyjoA1p8RcpVJMbqfaZdpXh4jorZZLQh9+umniIyMxL333nvbsSdOnEBTUxN69uwJAIiOjoZWq8Xhw4fNYw4dOgStVosxY8aYx+Tk5FisUktJSYFCoUBkZKR5zIEDByyW1KekpECtVqNv376d8TaJqAOKq+rRaDBCLpNC3d399l9wl672CXFjRSJqW4eDUE1NDbKzs5GdnQ0AyM/PR3Z2NgoLC81jdDod/v3vf7c5G3T+/Hm88cYbOHLkCAoKCrB9+3Y8/vjjGDZsGGJiYgAAgwcPxrRp05CYmIj09HSkp6cjMTERM2bMQFhYGAAgNjYWQ4YMQUJCArKysrB7924sX74ciYmJ5lmcWbNmQaFQYN68ecjJycGWLVuwatUqrhgjEompUTq4hwdkUut/D8a0njuWkX8F+maD1V+PiOxPh4PQkSNHMGzYMAwbNgwAsHTpUgwbNgx/+MMfzGOSkpIgCAKefvrpG75eLpdj9+7dmDp1KsLCwrB48WLExsZi165dkMmu7imyadMmREREIDY2FrGxsbjnnnuwceNG8+MymQzbtm2Dm5sbYmJi8MQTT2DmzJlYs2aNeYxSqcTOnTtx8eJFjBgxAi+99BKWLl1q0QNERF3n6o7S1r0sZhIW6AW/bnLUNxmQVVjVJa9JRPZFInCb5VvS6XRQKpXQarXsFyK6S3/8PgdfpF3AixNC8er0QV3ymi8nZeH77GIseqA/lsWGdclrEpH42vv5zbPGiKjLmC6NWbtR+loxrafRs2GaiNrCIEREXebq0vkuDEIDWoLQLxe10DU0ddnrEpF9YBAioi7R0GRAsbYeQNf1CAFAr+7uCPHzhMEo4FDelS57XSKyDwxCRNQlLlTWQRAALzcX9PCUd+lrjwnlfkJE1DYGISLqEuajNfy7dfn2FabjNhiEiOh6DEJE1CXEaJQ2iQ7tAYkEyC2vQZmuoctfn4hsF4MQEXUJc6O0CEGou4ccEb1azhrjrBARXYtBiIi6RP5l8YIQAIwxL6PncRtEdBWDEBF1CbGD0LV9QtxHlohMGISIyOqq6hpxpbbl8GOxgtCIvj6Qu0hRqmvA+dbLdEREDEJEZHWm2SCVtxs8FS6i1ODmKsOIYB8AwMHz7BMiohYMQkRkdWJfFjOJab08lprLIERELRiEiMjqxDhaoy2mPqG0vEo0G4yi1kJEtoFBiIisLl/EPYSuFd5LCW83F1Q3NCOnWCdqLURkGxiEiMjq8mzk0phMKkE0j9sgomswCBGRVRmNAgpsJAgBVy+PsU+IiAAGISKysrLqBtQ3GeAilSDI10PscjCmNQhlXtCgvtEgcjVEJDYGISKyqvzWRuk+vh5wlYn/I6efnyd6Kt3QaDDiyIUrYpdDRCIT/6cSETm08zZ0WQwAJBLJ1WX07BMicnoMQkRkVfkiHrZ6MzH9WxqmD/LcMSKnxyBERFaVf7kGgPh7CF0rpvUA1pxiLTStR38QkXNiECIiq7KVXaWvFeDthoGB3SAILZsrEpHzYhAiIqtpbDaiSFMPAAj17yZyNZbGhF49jZ6InBeDEBFZTeGVOhiMAjzkMgR4KcQux4JpPyEGISLnxiBERFZz7WUxiUQicjWWRvfzhUwqQUFlHS5q6sQuh4hEwiBERFZjbpS2of4gEy83V9zbWwmAq8eInBmDEBFZja0ctnozY7mfEJHTYxAiIqvJM+0hZENL569l2ljx4PnLEARB5GqISAwMQkRkNVdnhGxrxZjJsD4+cHeV4XJNI86UVYtdDhGJgEGIiKyiuqEJ5dV6AEBfG700JneRYlSILwCeRk/krDochA4cOID4+Hio1WpIJBJ89913Fo/PmzcPEonE4hYVFWUxRq/XY9GiRfDz84OnpyceeughXLx40WKMRqNBQkIClEollEolEhISUFVVZTGmsLAQ8fHx8PT0hJ+fHxYvXozGRstdYo8fP44JEybA3d0dvXr1whtvvMEpcKIuUHC5ZSWWXzc5lO6uIldzc1xGT+TcOhyEamtrce+992LDhg03HTNt2jSUlJSYb9u3b7d4fMmSJdiyZQuSkpKQmpqKmpoazJgxAwaDwTxm1qxZyM7ORnJyMpKTk5GdnY2EhATz4waDAXFxcaitrUVqaiqSkpKwefNmLFu2zDxGp9NhypQpUKvVyMjIwPr167FmzRqsW7euo2+biDooz4ZXjF1rTOu5Y4fyr6DJYBS5GiLqai4d/YLp06dj+vTptxyjUCigUqnafEyr1eLTTz/Fxo0bMXnyZADAl19+iaCgIOzatQtTp07FqVOnkJycjPT0dIwePRoA8PHHHyM6OhpnzpxBWFgYUlJScPLkSRQVFUGtVgMA1q5di3nz5uGtt96Ct7c3Nm3ahIaGBnz++edQKBQIDw/H2bNnsW7dOixdutTm9jUhciS2eLRGWwarvOHrKceV2kZkF1VhZF9fsUsioi5klR6hffv2ISAgAAMHDkRiYiLKy8vNj2VmZqKpqQmxsbHm+9RqNcLDw3Hw4EEAQFpaGpRKpTkEAUBUVBSUSqXFmPDwcHMIAoCpU6dCr9cjMzPTPGbChAlQKBQWY4qLi1FQUNBm7Xq9HjqdzuJmDQfPXcaTH6bhEM85Igd1NQjZZqO0iVQqwZjQllkh9gkROZ9OD0LTp0/Hpk2bsGfPHqxduxYZGRl44IEHoNe3NE2WlpZCLpfDx8fH4usCAwNRWlpqHhMQEHDDcwcEBFiMCQwMtHjcx8cHcrn8lmNMfzaNud7q1avNfUlKpRJBQUEd/Stol63HS3Ao/wo27D1nlecnEpt5xZiNLp2/FvuEiJxXpwehJ598EnFxcQgPD0d8fDx27NiBs2fPYtu2bbf8OkEQLC5VtXXZqjPGmBqlb3ZZbOXKldBqteZbUVHRLeu+U7+ZEAqZVIKfci8ju6jKKq9BJBZBEMx7CNnqZorXMu0nlF1UhRp9s8jVEFFXsvry+Z49eyI4OBi5ubkAAJVKhcbGRmg0Gotx5eXl5tkalUqFsrKyG56roqLCYsz1szoajQZNTU23HGO6THf9TJGJQqGAt7e3xc0agnw9MPO+XgCADXs4K0SOpaJGjxp9MyQSoE8PD7HLua0gXw/08fVAs1HA4XxeriZyJlYPQpWVlSgqKkLPnj0BAJGRkXB1dcXOnTvNY0pKSpCTk4MxY8YAAKKjo6HVanH48GHzmEOHDkGr1VqMycnJQUlJiXlMSkoKFAoFIiMjzWMOHDhgsaQ+JSUFarUaffv2tdp7bq+X7g+FRALsOlWGk8XW6UUiEkN+62xQbx93KFxkIlfTPqZZodRcBiEiZ9LhIFRTU4Ps7GxkZ2cDAPLz85GdnY3CwkLU1NRg+fLlSEtLQ0FBAfbt24f4+Hj4+fnhkUceAQAolUrMnz8fy5Ytw+7du5GVlYU5c+YgIiLCvIps8ODBmDZtGhITE5Geno709HQkJiZixowZCAsLAwDExsZiyJAhSEhIQFZWFnbv3o3ly5cjMTHRPIsza9YsKBQKzJs3Dzk5OdiyZQtWrVplMyvGQv27IS6iJSD+fR9nhchx2Euj9LViWpfRHzzPPiEipyJ00N69ewUAN9zmzp0r1NXVCbGxsYK/v7/g6uoq9OnTR5g7d65QWFho8Rz19fXCwoULBV9fX8Hd3V2YMWPGDWMqKyuF2bNnC15eXoKXl5cwe/ZsQaPRWIy5cOGCEBcXJ7i7uwu+vr7CwoULhYaGBosxx44dE8aNGycoFApBpVIJr7/+umA0Gtv9frVarQBA0Gq1HfuLaqeTxVoheMVWoe+rW4XcsmqrvAZRV1u17aQQvGKr8Mfvc8Qupd0qa/RC8IqtQvCKrUKZrl7scojoLrX381siCNxm+VZ0Oh2USiW0Wq3V+oUS/3kEO0+W4dHhvbDuifus8hpEXcn03/QbDw/FM9F9xS6n3eL+9hNOFOvw16fuw8OtPXxEZJ/a+/nNs8ZswML7+wMAvs8uRmFlncjVEN29vAr72FX6ejFcRk/kdBiEbMC9Qd0xfqA/DEYB7+8/L3Y5RHel2WBE4ZWWQG+vQSg19zLPJCRyEgxCNmLRAy2zQv/JLEKJtl7kaoju3KWqejQZBMhdpFAr3cUup0NG9vWBXCZFsbYBBZydJXIKDEI2YmRfX4wO8UWTQcCH+/PELofojuWZVoz18IRUKv7qzI7wkLtgWJ/uAHh5jMhZMAjZkEUPDAAAfH24EBXVepGrIbozpj2E7OFojbbwuA0i58IgZENi+vfAfUHdoW824pNUzgqRfbKXU+dvJmZASxA6eL4SBiP7hIgcHYOQDZFIJOZeoS/TLkBT23ibryCyPXmX7XPFmMk9vZTwUrhAW9/EHd+JnACDkI15YFAABvf0Rm2jAZ8dLBC7HKIOs/dLYy4yKUb3a9llOpWXx4gcHoOQjbl2Vujzn/Oha2gSuSKi9qtvNKBY2wDAvo7XuN7Y1uM22CdE5PgYhGzQtKEq9A/oBl1DMzamXRC7HKJ2K6hsmQ1SurvCx8NV5GrunGk/oYyCK2hoMohcDRFZE4OQDZJKJVhwfygA4NPUfNQ1NotcEVH7mBql+/l72sTBxneqf0A3BHgpoG824ugFjdjlEJEVMQjZqPh71Ojj64ErtY346lCh2OUQtYu9Hq1xPYlEYl5Gzz4hIsfGIGSjXGRSvDSxZVboowN5nJ4nu2DaTLGfnQchABhj2k/ofKXIlRCRNTEI2bBHh/dGT6Ubyqv1+HfmRbHLIbqtq3sI2W+jtElMa8P08YtV0NZx0QKRo2IQsmFyFylenNAyK/TBvvNoMhhFrojo1ux9M8Vr9VS6I9TfE0YBSMvjrBCRo2IQsnFPjgyCXzcFLlXVY0vWJbHLIbopTW0jqlpnTvr6eYhcTecwrR47eJ59QkSOikHIxrm5yvD8+BAAwHt7z3HLf7JZpv4gtdINHnIXkavpHDFsmCZyeAxCdmD26GB093BFQWUdth4rFrscojaZV4zZ6Y7SbYnq1wNSCZBXUYviqnqxyyEiK2AQsgOeChfMj2mZFfr73nMwclaIbJAj9QeZKN1dcU/v7gC4yzSRo2IQshPPjOkLL4ULzpbVIOVkmdjlEN3AkVaMXcu0euwgl9ETOSQGITuhdHfF3DF9AQAb9uZCEDgrRLYl34H2ELrWtX1C/L4jcjwMQnbk2bEh8JDLkHNJh31nK8Quh8jMaBQc8tIYAAzv4wM3VykqqvXILa8Ruxwi6mQMQnbE11OOOVHBAID1uzkrRLajRNcAfbMRrjIJevu4i11Op3JzlWFkX18A7BMickQMQnbmuXEhkLtIcbSwCmnsWSAbYVox1sfXAy4yx/uxYro8xiBE5Hgc7yeWgwvwcsPTI4MAAOv3nBO5GqIWjtoobWI6gDU97wqaucM7kUNhELJDz08IhatMgrS8ShwpuCJ2OUTIq2htlHagPYSuNaSnN7p7uKJG34xfLmrFLoeIOhGDkB3q1d0djw3vDQDYsJezQiQ+R22UNpFKJRgT2rKMnpfHiBwLg5Cd+s3EUEglwL4zFTjO31BJZI66dP5aPG6DyDExCNmp4B6eePi+XgBa9hUiEou+2YCLmjoAjnW8xvViQluCUFahBnWNzSJXQ0SdhUHIjr00MRQSCfDjiTKcKa0WuxxyUoWVdTAKQDeFC/y7KcQux2qCe3igV3d3NBkEHM5nbx6Ro2AQsmMDAr0wPVwFoOUMMiIx5F3THySRSESuxnokEol59Rj7hIgcR4eD0IEDBxAfHw+1Wg2JRILvvvvO/FhTUxNWrFiBiIgIeHp6Qq1W45lnnkFxseWJ6RMnToREIrG4PfXUUxZjNBoNEhISoFQqoVQqkZCQgKqqKosxhYWFiI+Ph6enJ/z8/LB48WI0NjZajDl+/DgmTJgAd3d39OrVC2+88YZDbUS44P7+AICtx4rNe7kQdSVHb5S+1pj+poZp7uFF5Cg6HIRqa2tx7733YsOGDTc8VldXh6NHj+K1117D0aNH8e233+Ls2bN46KGHbhibmJiIkpIS8+3DDz+0eHzWrFnIzs5GcnIykpOTkZ2djYSEBPPjBoMBcXFxqK2tRWpqKpKSkrB582YsW7bMPEan02HKlClQq9XIyMjA+vXrsWbNGqxbt66jb9tmDVUrMWlQAIwC8P6+82KXQ04ov8KJglBrn9DJEh0qa/QiV0NEncGlo18wffp0TJ8+vc3HlEoldu7caXHf+vXrMWrUKBQWFqJPnz7m+z08PKBSqdp8nlOnTiE5ORnp6ekYPXo0AODjjz9GdHQ0zpw5g7CwMKSkpODkyZMoKiqCWq0GAKxduxbz5s3DW2+9BW9vb2zatAkNDQ34/PPPoVAoEB4ejrNnz2LdunVYunSpw0zjL3igP3afLseWrEtYPGkAgnw9xC6JnIh5xZgDN0qb+HspMEjlhdOl1Th4vhLx96rFLomI7pLVe4S0Wi0kEgm6d+9ucf+mTZvg5+eHoUOHYvny5aiuvtrsm5aWBqVSaQ5BABAVFQWlUomDBw+ax4SHh5tDEABMnToVer0emZmZ5jETJkyAQqGwGFNcXIyCgoI269Xr9dDpdBY3Wze8jw/G9vdDs1HAhwc4K0RdK8+8dN4xd5W+nmkZ/cHz7BMicgRWDUINDQ149dVXMWvWLHh7e5vvnz17Nr7++mvs27cPr732GjZv3oxHH33U/HhpaSkCAgJueL6AgACUlpaaxwQGBlo87uPjA7lcfssxpj+bxlxv9erV5r4kpVKJoKCgO3jnXW/hAy29Qv/KuIgyXYPI1ZCz0DU04XLrJaK+fs4xEzmW+wkRORSrBaGmpiY89dRTMBqNeO+99yweS0xMxOTJkxEeHo6nnnoK//nPf7Br1y4cPXrUPKaty1aCIFjcfydjTI3SN7sstnLlSmi1WvOtqKioHe9WfKNDfDGyrw8aDUZ8dCBP7HLISZj6g/y9FPBycxW5mq4xKsQXLlIJiq7Uo7CyTuxyiOguWSUINTU14YknnkB+fj527txpMRvUluHDh8PV1RW5uS0bA6pUKpSVld0wrqKiwjyjo1KpbpjV0Wg0aGpquuWY8vJyALhhpshEoVDA29vb4mYPJBIJFj4wAACw6dAFNnJSl3CmFWMmngoXDOvTHQDwMy+PEdm9Tg9CphCUm5uLXbt2oUePHrf9mhMnTqCpqQk9e/YEAERHR0Or1eLw4cPmMYcOHYJWq8WYMWPMY3JyclBSUmIek5KSAoVCgcjISPOYAwcOWCypT0lJgVqtRt++fTvj7dqU8QP8cE9vJRqajPg0NV/scsgJ5DnB0Rpt4XEbRI6jw0GopqYG2dnZyM7OBgDk5+cjOzsbhYWFaG5uxq9+9SscOXIEmzZtgsFgQGlpKUpLS81h5Pz583jjjTdw5MgRFBQUYPv27Xj88ccxbNgwxMTEAAAGDx6MadOmITExEenp6UhPT0diYiJmzJiBsLAwAEBsbCyGDBmChIQEZGVlYffu3Vi+fDkSExPNszizZs2CQqHAvHnzkJOTgy1btmDVqlUOtWLsWhKJBAtb9xX6Z9oFaOuaRK6IHJ0zrRi7lqlP6OC5yzAaHWdfMiKnJHTQ3r17BQA33ObOnSvk5+e3+RgAYe/evYIgCEJhYaEwfvx4wdfXV5DL5UJoaKiwePFiobKy0uJ1KisrhdmzZwteXl6Cl5eXMHv2bEGj0ViMuXDhghAXFye4u7sLvr6+wsKFC4WGhgaLMceOHRPGjRsnKBQKQaVSCa+//rpgNBrb/X61Wq0AQNBqtR39qxKFwWAUpr67XwhesVX4y86zYpdDDu7Bvx4QgldsFVJOlIpdSpdqbDYIQ17bIQSv2CrkXKoSuxwiakN7P78lguBA2yxbgU6ng1KphFartZt+of/+UoxFX2dB6e6Kn199AN0UHd4uiui2BEHA0D/+iLpGA3YtnYD+Ac6xfN7k2c8zsOd0Of7nwUF4fnyo2OUQ0XXa+/nNs8Yc0IMRPdHPzxPa+iZ8mX5B7HLIQZVX61HXaIBUAvRxwk08r/YJ8bgNInvGIOSAZFIJXmrtFfrkpzzUNxpErogcUV7r0vkgXw/IXZzvR0lM67ljGflXoG/m9xiRvXK+n15O4uH71Ojt447LNY1IyigUuxxyQM64dP5aYYFe8OsmR32TAVmFVWKXQ0R3iEHIQbnKpPjNxJa+hQ/35/E3Vup0+ZdrADjP0RrXk0gk5stjP3MZPZHdYhByYL+K7I1AbwVKdQ3YnHlJ7HLIwZgujYU42dL5a3E/ISL7xyDkwBQuMrzQuprlvX3n0GQwilwROZJ8J91M8VqmIHTsoha6Bu7bRWSPGIQc3NOj+qCHpxwXNfX4IbtY7HLIQTQZjCi80nLOlrP2CAFAr+7uCPHzhMEo4FDeFbHLIaI7wCDk4NzlMjw3rh8A4O/7zsHAXXCpE1zU1KPZKMDNVQqVt5vY5YjKtHqMfUJE9olByAnMieoDpbsr8ipqsSOn5PZfQHQbpkbpvj08IZU63nE1HRETyoZpInvGIOQEvNxc8euYvgCADXvO8WwkumumRulQf+dcMXat6NAekEiA3PIalOkaxC6HiDqIQchJzBvTF90ULjhdWo3dp8vFLofsXJ6T7yF0re4eckT0UgLgrBCRPWIQchLdPeRIiA4GAGzYkwseMUd3I7+CQehaY8yXx3jcBpG9YRByIvPHhsDNVYpfLmpxIJe/udKdM+8q7cR7CF1r7DUbK/KXDCL7wiDkRPy6KTBrVMus0PrdnBWiO1Orb0Zpay+MM+8hdK0RfX0gd5GiVNeA862zZURkHxiEnMzz4/tBLpPiyAUNDuVz3xPquILKlg96Hw9XdPeQi1yNbXBzlWFEsA8A4OB5zrYS2RMGISejUrrhiZG9AbSsICPqKPOO0lwxZsF83AYvOxPZFQYhJ/TC+FC4SCVIPXcZRws1YpdDdiaPjdJtMvUJpeVVopnH2RDZDQYhJxTk64FHhvUCAPyds0LUQflcOt+m8F5KeLu5oLqhGTnFOrHLIaJ2YhByUr+ZGAqpBNh9uhw5l7Ril0N2JI+HrbZJJpUgOpTHbRDZGwYhJ9XPvxtm3KMG0HIyPVF7CIKA/IqW4zW4dP5GY9knRGR3GISc2IL7+wMAduSUIresWuRqyB5cqW2ErqEZEknLOWNkaUxrEMq8oEF9o0HkaoioPRiEnFiYygtThwZCEID39p0XuxyyA6b+ILXSHW6uMpGrsT39/DzRU+mGRoMRRy5wewoie8Ag5OQW3j8AAPB99iVcqORGcHRrphVj/XhZrE0SieTqMnr2CRHZBQYhJxfRW4mJYf4wCsD7nBWi2+Bhq7cX07+lYfogzx0jsgsMQoRFD7T0Cm0+ehGXqupFroZsWf7l1kZpBqGbimk9gDWnWAtNbaPI1RDR7TAIESKDfRHdrweaDAI+2s9ZIbo57iF0ewHebhgY2A2C0LK5IhHZNgYhAnB1VujrjCKUVzeIXA3ZIoNRQEFlHQAglMdr3NKY0Kun0RORbWMQIgBAdGgPDO/THY3NRnzyU77Y5ZANKq6qR2OzEXKZFOru7mKXY9NM+wkxCBHZPgYhAtCy2mXRAy0ryL5Mv4Ar7G2g65gapYN7eEAmlYhcjW0b3c8XMqkEBZV1KLpSJ3Y5RHQLDEJkNjHMH0PV3qhrNOCznzkrRJbMO0qzP+i2vNxccV9QdwDAwfOcFSKyZR0OQgcOHEB8fDzUajUkEgm+++47i8cFQcDrr78OtVoNd3d3TJw4ESdOnLAYo9frsWjRIvj5+cHT0xMPPfQQLl68aDFGo9EgISEBSqUSSqUSCQkJqKqqshhTWFiI+Ph4eHp6ws/PD4sXL0Zjo+VMxvHjxzFhwgS4u7ujV69eeOONNyAIQkfftlNomRVq6RX6/OcCaOubRK6IbIm5UZp7CLVLjPncMTZME9myDgeh2tpa3HvvvdiwYUObj7/99ttYt24dNmzYgIyMDKhUKkyZMgXV1VePcFiyZAm2bNmCpKQkpKamoqamBjNmzIDBcHVL+lmzZiE7OxvJyclITk5GdnY2EhISzI8bDAbExcWhtrYWqampSEpKwubNm7Fs2TLzGJ1OhylTpkCtViMjIwPr16/HmjVrsG7duo6+bacRO0SFAQHdUK1vxsa0ArHLIRvCw1Y7JuaaPiGjkb98Edks4S4AELZs2WL+s9FoFFQqlfCnP/3JfF9DQ4OgVCqFDz74QBAEQaiqqhJcXV2FpKQk85hLly4JUqlUSE5OFgRBEE6ePCkAENLT081j0tLSBADC6dOnBUEQhO3btwtSqVS4dOmSeczXX38tKBQKQavVCoIgCO+9956gVCqFhoYG85jVq1cLarVaMBqN7XqPWq1WAGB+TmfwXdZFIXjFVuG+//tRqGloErscshExf9otBK/YKhzOrxS7FLugbzIIg36/QwhesVU4Wew8Pz+IbEV7P787tUcoPz8fpaWliI2NNd+nUCgwYcIEHDx4EACQmZmJpqYmizFqtRrh4eHmMWlpaVAqlRg9erR5TFRUFJRKpcWY8PBwqNVq85ipU6dCr9cjMzPTPGbChAlQKBQWY4qLi1FQUNCZb92hxEX0RN8eHtDUNeGrQ4Vil0M2oKHJYN5skz1C7SN3kWJUiC8Arh4jsmWdGoRKS0sBAIGBgRb3BwYGmh8rLS2FXC6Hj4/PLccEBATc8PwBAQEWY65/HR8fH8jl8luOMf3ZNOZ6er0eOp3O4uZsXGRSvDSxpVfoo5/y0NDEU7Sd3YXKOggC4OXmgh6ecrHLsRtcRk9k+6yyakwisVxaKwjCDfdd7/oxbY3vjDFCa6P0zepZvXq1uUFbqVQiKCjolnU7qpnDeqFXd3dUVOvxryNFYpdDIjMdrdHPz/O238t0lalP6FD+FdTqm0Wuhoja0qlBSKVSAbhxtqW8vNw8E6NSqdDY2AiNRnPLMWVlZTc8f0VFhcWY619Ho9GgqanplmPKy8sB3DhrZbJy5UpotVrzrajIOUOA3EWKFyf0AwB8sO88GpuNIldEYuJhq3dmkMoLwT08UNdowIc8vobIJnVqEAoJCYFKpcLOnTvN9zU2NmL//v0YM2YMACAyMhKurq4WY0pKSpCTk2MeEx0dDa1Wi8OHD5vHHDp0CFqt1mJMTk4OSkpKzGNSUlKgUCgQGRlpHnPgwAGLJfUpKSlQq9Xo27dvm+9BoVDA29vb4uasHh8RBH8vBYq1DdiSdfH2X0AOK7/CFIR4tEZHSKUSvDptEICWy8zFPNSYyOZ0OAjV1NQgOzsb2dnZAFoapLOzs1FYWAiJRIIlS5Zg1apV2LJlC3JycjBv3jx4eHhg1qxZAAClUon58+dj2bJl2L17N7KysjBnzhxERERg8uTJAIDBgwdj2rRpSExMRHp6OtLT05GYmIgZM2YgLCwMABAbG4shQ4YgISEBWVlZ2L17N5YvX47ExERzeJk1axYUCgXmzZuHnJwcbNmyBatWrcLSpUs5vd8Obq4yvDC+ZVbovX3n0WzgrJCzMu0h1I97CHXYtHAVRvb1QUOTEWt+PCN2OUR0vY4uR9u7d68A4Ibb3LlzBUFoWUL/xz/+UVCpVIJCoRDGjx8vHD9+3OI56uvrhYULFwq+vr6Cu7u7MGPGDKGwsNBiTGVlpTB79mzBy8tL8PLyEmbPni1oNBqLMRcuXBDi4uIEd3d3wdfXV1i4cKHFUnlBEIRjx44J48aNExQKhaBSqYTXX3+93UvnBcE5l89fq1bfJNz3fz8KwSu2CluOXhS7HBLJsDdShOAVW4XjF6vELsUuZRdqhOAVW4XgFVuFX4o0YpdD5BTa+/ktEQRus3wrOp0OSqUSWq3WaS+T/X3vObzz4xn0D+iGlCXjIeU5U06lqq4R973Rcin7xP9NhafCReSK7NNvv8nGlqxLGNXXF9+8EMVZaSIra+/nN88ao9tKiA6Gl5sLzpXX4McTbW87QI7LdFks0FvBEHQXXpkaBoWLFIcLrvD7iMiGMAjRbXm7ueLXY/oCANbvOcez2pxMPleMdQp1d3c839pzt3rHaeibuT8XkS1gEKJ2+XVMCDzkMpws0WHP6XKxy6EudDUIccXY3XpxQij8vRS4UFmHjWkXxC6HiMAgRO3k4ylHQlQwAM4KORvTHkKhXDF21zwVLlgeOxAA8NfdubhS23ibryAia2MQonabPy4EChcpsouq8PO5SrHLoS6SV8FLY53pV5FBGKTyQnVDM/62O1fscoicHoMQtVuAlxueHtUHALB+D3+AOwOjUUABe4Q6lUwqwe/jhgAANqZfwLnyGpErInJuDELUIc+P7wdXmQSH8q/gcP4VscshKyurbkB9kwEyqQRBvh5il+Mwxg7ww6RBATAYBfxpxymxyyFyagxC1CHq7u74VWRvAMCGvedEroaszXS0Rh9fD7jK+OOiM618cDBkUgl2nSrn6fREIuJPNuqw30zoD5lUggNnK/BLUZXY5ZAVmRql+/GyWKfrH9ANc0a3XGp+c9spGIxcgEAkBgYh6rA+PTzw8H1qAJwVcnTcQ8i6Xp48EF5uLjhVosPmTB5sTCQGBiG6Iy9N7A+JBNh5sgynSnRil0NWklfR0sgbwqXzVuHrKcfLkwYAAN5JOYNafbPIFRE5HwYhuiP9A7rhwYieAFrOIiPHxBkh60uIDkZwDw9UVOvx4f7zYpdD5HQYhOiOLby/PwBg2/ESnK/gEmBH09hsRJGmHgDQj7tKW43CRYaV0wcBAD76KQ/FVfUiV0TkXBiE6I4N7umNyYMDIQjAem4M53CKNHUwGAV4yGUI9FaIXY5DmzpUhVF9fdHQZMSaH8+IXQ6RU2EQorti6m/4/pdinCjWilwNdab8a3aUlkgkIlfj2CQSCX4/YzAA4NusSzh2sUrcgoicCIMQ3ZWI3krE36uGIAB/2nFa7HKoE+Vdbm2UZn9Ql7ind3c8OqwXAODNrad4nh9RF2EQorv2SmwYXGUS/JR7GT/lVohdDnWSfO4h1OWWTw2Dm6sUhwuu4McTpWKXQ+QUGITorvXp4YHZo1tOpv/TjtMwcmM4h2A+bJVL57uMurs7nh/XDwCwesdp6JsNIldE5PgYhKhTLHqgP7wULjhRrMMPvxSLXQ51gqtL57lirCu9MCEU/l4KXKisw8a0C2KXQ+TwGISoU/TopsCLE0MBAGtSzvA3WTtXo29GebUeAHuEupqnwgWvxIYBAP66OxdXahtFrojIsTEIUad5NiYEgd4KXNTU8zdZO1fQOhvk100OpburyNU4n8cie2NwT29UNzTjb9yagsiqGISo07jLZfjt5IEAWs4g09Y3iVwR3SnTBpmcDRKHTCrB7+NaltNvTL+Ac+XcsJTIWhiEqFP9KrI3BgR0Q1VdE97fx+MC7BWP1hBfTH8/TB4cAINRwJ92nBK7HCKHxSBEncpFJsWKaS3HBXz2cz6PC7BTbJS2DSsfHAwXqQS7TpXj53OXxS6HyCExCFGnmzQ4AKP6+kLfbMS7O8+KXQ7dAc4I2YZQ/26YE9WyNcWb207BwK0piDodgxB1OolEglcfbJkV+s/RizhdqhO5IuoIQRDMx2v04x5Cont50gB4u7ngVIkOmzMvil0OkcNhECKrGN7HBw9GqCAIwJ959IZduVzTiGp9MyQSILiHh9jlOD0fTzkWt57p907KGdTqm0WuiMixMAiR1bwydRBcpBLsPVOBtPOVYpdD7ZTXumKst487FC4ykashAEiIDkZwDw9UVOvx4X4uQiDqTAxCZDUhfp54elQfAMDqHad49IadYKO07VG4yLByesvl5o9+yuMiBKJOxCBEVrV40gB4ymU4dlGLbcdLxC6H2oGHrdqmqUNVGNXXFw1NRqz58YzY5RA5DAYhsip/LwWeH99y9MY7P55BY7NR5IrodvK4YswmSSQS/H5GyyaL32ZdwrGLVeIWROQgOj0I9e3bFxKJ5IbbggULAADz5s274bGoqCiL59Dr9Vi0aBH8/Pzg6emJhx56CBcvWq6W0Gg0SEhIgFKphFKpREJCAqqqqizGFBYWIj4+Hp6envDz88PixYvR2Mhze7rac+NC4NdNgcIrdfjqEI/esHVcOm+77undHY8O6wUAeHPrKQgCLzcT3a1OD0IZGRkoKSkx33bu3AkAePzxx81jpk2bZjFm+/btFs+xZMkSbNmyBUlJSUhNTUVNTQ1mzJgBg+HqQZ6zZs1CdnY2kpOTkZycjOzsbCQkJJgfNxgMiIuLQ21tLVJTU5GUlITNmzdj2bJlnf2W6TY8FS5YMrll1cvf9pxDdQOP3rBVBqOAC5VcOm/Llk8Ng5urFIcLriA5p1TscojsXqcHIX9/f6hUKvNt69atCA0NxYQJE8xjFAqFxRhfX1/zY1qtFp9++inWrl2LyZMnY9iwYfjyyy9x/Phx7Nq1CwBw6tQpJCcn45NPPkF0dDSio6Px8ccfY+vWrThzpuXaeUpKCk6ePIkvv/wSw4YNw+TJk7F27Vp8/PHH0Om4r01Xe3JkEPr5eeJKbSM+3J8ndjl0Exc1dWgyCJC7SKFWuotdDrVB3d0dz4/rBwBYveM09M2G23wFEd2KVXuEGhsb8eWXX+LZZ5+FRCIx379v3z4EBARg4MCBSExMRHl5ufmxzMxMNDU1ITY21nyfWq1GeHg4Dh48CABIS0uDUqnE6NGjzWOioqKgVCotxoSHh0OtVpvHTJ06FXq9HpmZmTetWa/XQ6fTWdzo7rnKpPhd69Ebn6TmoUzXIHJF1BZzf1APT0ilktuMJrG8MCEU/l4tl5v/eZCXm4nuhlWD0HfffYeqqirMmzfPfN/06dOxadMm7NmzB2vXrkVGRgYeeOAB6PV6AEBpaSnkcjl8fHwsniswMBClpaXmMQEBATe8XkBAgMWYwMBAi8d9fHwgl8vNY9qyevVqc9+RUqlEUFDQHb13utHUoYGIDPZBQ5MRf9nFozdskWlHafYH2TZPhQteiQ0DAPxtTy6u1LL3kehOWTUIffrpp5g+fbrFrMyTTz6JuLg4hIeHIz4+Hjt27MDZs2exbdu2Wz6XIAgWs0rX/v+7GXO9lStXQqvVmm9FRUW3rIvaTyKRmPdC+SajCOfKq0WuiK5nbpRmf5DNeyyyNwb39EZ1QzP+yl8siO6Y1YLQhQsXsGvXLjz33HO3HNezZ08EBwcjNzcXAKBSqdDY2AiNRmMxrry83DzDo1KpUFZWdsNzVVRUWIy5fuZHo9GgqanphpmiaykUCnh7e1vcqPOM6OuLKUMCYRSAP+3gXii2hivG7IdMKsHv41qW0395qBDnymtErojIPlktCH322WcICAhAXFzcLcdVVlaiqKgIPXv2BABERkbC1dXVvNoMAEpKSpCTk4MxY8YAAKKjo6HVanH48GHzmEOHDkGr1VqMycnJQUnJ1U38UlJSoFAoEBkZ2WnvkzpuxbRBkEkl2HWqDBkFV8Quh65hCkKhnBGyCzH9/TB5cAAMRgGrt58Suxwiu2SVIGQ0GvHZZ59h7ty5cHFxMd9fU1OD5cuXIy0tDQUFBdi3bx/i4+Ph5+eHRx55BACgVCoxf/58LFu2DLt370ZWVhbmzJmDiIgITJ48GQAwePBgTJs2DYmJiUhPT0d6ejoSExMxY8YMhIW1XDePjY3FkCFDkJCQgKysLOzevRvLly9HYmIiZ3lE1j+gG54Y0dJ7tWo790KxFfWNBlxqPbqBx2vYj5UPDoaLVILdp8uRmntZ7HKI7I5VgtCuXbtQWFiIZ5991uJ+mUyG48eP4+GHH8bAgQMxd+5cDBw4EGlpafDy8jKPe/fddzFz5kw88cQTiImJgYeHB/773/9CJrt6AOSmTZsQERGB2NhYxMbG4p577sHGjRstXmvbtm1wc3NDTEwMnnjiCcycORNr1qyxxlumDvrt5AFwd5Uhq7AKP57gXii2oKB1/yCluyt8PFxFrobaK9S/G+ZEBQMA3tx2Egae6UfUIRKBv47fkk6ng1KphFar5UxSJ1ubcgbr95xDiJ8nUn47Hq4ynvgipu3HS/DSpqO4L6g7vlsQI3Y51AGa2kZMeGcvdA3N+PNjEXhyZB+xSyISXXs/v/nJQ6J5fnw/9PCUI/9yLZIyuDpPbDxs1X75eMqxeFLL7u1rUs6iRt8sckVE9oNBiETj5eZq/uH91125qOUPb1HlVfBoDXuWEB2M4B4eqKjW48P958Uuh8huMAiRqJ4e1QfBPTxwuUaPj3/i0RtiyrvcsvyajdL2SeEiw8rpLcvpPzqQh+LWxnciujUGIRKV3EWKV6a2rPT76EAeyqt59IZYuIeQ/Zs6NBCjQnyhbzbinR+5TxdRezAIkejiInri3t5K1DUa8LfduWKX45Q0tY2oqmsCAPT18xC5GrpTEokEr8UNAQBsybqEX4qqxC2IyA4wCJHoJBIJVj7YMqX/9eEi5FVwh9yuZjpstafSDR5yl9uMJlsW0VuJR4f3AtCynJ4Lg4lujUGIbEJUvx54YFDLDrmc0u96vCzmWF6ZGgY3VykyCjRIzuE+XUS3wiBENmPFtEGQSoAdOaXIvKC5/RdQp8lvbZTmijHH0FPpjufHhwIAVu84DX2zQeSKiGwXgxDZjDCVF34V2RsA8KcdPHqjK5mWznPFmON4YXw/BHgpUHilDv88eEHscohsFoMQ2ZTfThkIhUvLlP6uU+Vil+M0uJmi4/FUuGB564rMv+3JxZXaRpErIrJNDEJkU3oq3fHs2BAAwJ+TT6PZYBS5IsdnNArsEXJQjw3vjSE9vVHd0Iy/7jordjlENolBiGzObyaGwsfDFefKa/DvzItil+PwSnQN0Dcb4SKVoLePu9jlUCeSSSX4fVzLiswvDxXiXDlXZBJdj0GIbI63mysWPtBy9Ma7O8+irpFHb1hTfmt/UJ8eHnDhwbcOZ0x/P0weHAiDUcDq7afELofI5vCnHtmkOVF90NvHHeXVevwjNV/schyaecUYG6Ud1soHB8FFKsHu0+VIzb0sdjlENoVBiGySwkVmPnrjg/15qKzRi1yR4zrPw1YdXqh/N8yJCgbQssmiwcgVmUQmDEJks+LvUSO8lzdq9M1Yv+ec2OU4LDZKO4eXJw2At5sLTpdW4z+ZRWKXQ2QzGITIZkmlEvNp2psOXcCFylqRK3JMDELOwcdTjsWTWnrv1qScRY2evXdEAIMQ2biY/n4YP9AfTQYevWEN+mYDLmrqAHAPIWfwTHRf9O3hgYpqPT7cf17scqgDmg1GnCuvwbZjJVi38yxe2HgEL2w8gtyyarFLs3s8XZFs3qvTBuGn3ApsPVaCxHFVuDeou9glOYyiK3UwCoCnXAZ/L4XY5ZCVyV2keHX6YLz4ZSY+OpCHp0f1gbo7t0ywJYIgoEynx+lSHc6UVrfcyqqRW16DxuYb91U7nH8F/3x2NCJ6K0Wo1jEwCJHNG6L2xiPDeuHbo5ewescpfJ0YBYlEInZZDiHP3CjdjX+nTmLq0ECMCvHF4fwreOfHM3j3yfvELslpaeubcLas+mrgaQ092vqmNse7u8owUOWFsMBuCFN544dfivFLURWe/jgd/5g3EqNCfLv4HTgGBiGyC8tiw7D1WAnS865g35kK3D8oQOySHEIe+4OcjkQiwWtxQxC/IRVbsi5h3pi+nGW1Mn2zAefLa3GmTIfTpdU42xp6irUNbY6XSSUI8fNEmMoLgwK9MFDlhUEqLwT5eEAqvfoLy5Mjg/DcFxlIz7uCZ/5xCB/MicTEMP5s7CgGIbILvbq7Y96YvvjoQB7+tOM0xg/0h0zKGYy7lV/BIOSMInor8ejwllnWN7edxL9eiOaMYCcwGgUUaerMszunW2d78i/X3nTLArXSDWGqq2EnLNAboQGeULjIbvt63RQu+PzXo/DSpqPYc7ocif88gr8+NQwPRvTs7Lfm0BiEyG68NDEUSYcLcaasGt8evYjHRwSJXZLdMx+2yj2EnM4rU8Ow/XgJMgo0SM4pxXR+eHbI5Rp9S9hpneE5XVaN3LJq1DUa2hzv7eaCQSpvhKm8zLeBgV5QurveVR1urjJ8mBCJpf/6Bf/9pRgLvzqKPz92D38+dgCDENmN7h5yLHygP1ZtP411O88i/l413Fxv/1sT3RwvjTmvnkp3PD8+FH/bnYvVO07jgcEB7ZqFcDZ1jc04W1aDM6Utl7XOlFbjbFk1Ltc0tjle7iLFgIBuCAu8GngGqbwR6K2w2qybq0yKvzx5HzzlMiRlFOGV/xxDjb4Zv44JscrrORoGIbIrz0T3xRcHL+BSVT0++7kAv5kYKnZJdkvX0ITLrTt2Mwg5pxfG90PS4UIUXqnDPw9eQOL4fmKXJJpmgxH5l2txpvVylin0FGnqILRxVUsiAYJ9PVrDjrc5+PQV6cw+mVSC1Y9GoJvCBZ+k5uP//nsStfpmLLi/Py973gaDENkVN1cZlk4ZiGX//gXv7TuHp0YGwcdTLnZZdqmgdTbI30sBL7e7m54n++SpcMHyqWH43X+O4W97cvFYZG/4Ovj3kyAIKNE2XL2sVdbyv+fLa9BouHF5OtDyPTKo9VJWWGsvT/+AbvCQ29ZHqEQiwf/GDYaXmyve3XUWa1LOorqhGa9OH8QwdAu29a9I1A4zh/XCxz/l4XRpNf6+9xx+P2OI2CXZpTw2ShOAx4b3xuc/F+BkiQ5/3XUW//dwuNgldTp9swFp5yux+1Q59pwux6Wq+jbHecpl5qblq6HH267CoUQiwcuTB6Cbmwv+39aT+PBAHqr1zfh/D4dzgclNMAiR3ZFJJVj54GDM/cdh/DPtAuaO6YsgXw+xy7I7pv4g7ijt3GRSCX4fNxizPjmELw8VIiG6L/oHdBO7rLt2uUaPPafLsftUGX7KvWzRxOwilSDUv9s1K7VaQk+v7u4Wy9Pt2fyxIeimkOHVb4/jq0OFqNU3Y83j98JVhMt2to5BiOzS+AF+iOnfAz+fq8TalDP4y1PDxC7J7vCMMTIZ098PkwcHYtepMqzefgqfzhspdkkdJggCTpdWY8/pcuw6VYbsoiqL3p5AbwUeGBSIyYMDMCbUD+5yx28Mf3JkH3gqXLAkKRvfZxejVt+MDbOGc5HJdRiEyC5JJBK8Om0w4jek4rvsYjw3rh/Ce3GL+Y7Iv1wDgEGIWqx8cBD2nSnH7tPlSM29jLED/MQu6bb0zQak513B7lNl2H3qxkteEb2UmDQ4AJMGBSK8l7dT9snMuEcNT7kLXvwyE7tOlePZzzPw8TMj4Kngx78J/ybIbkX0VuKhe9X44Zdi/Dn5NDbOHy12SXZDEATzZorcQ4gAINS/G+ZEBePzgwV4c9tJbFs8ziZ7SkyXvPacKsdPuRWoveaSl8JFirH9/TBpcCAeGBQAldJNxEptx/2DAvDFs6Mw//MMHDxfiTmfHsLn80ZB6cFFEoAVTp9//fXXIZFILG4qlcr8uCAIeP3116FWq+Hu7o6JEyfixIkTFs+h1+uxaNEi+Pn5wdPTEw899BAuXrxoMUaj0SAhIQFKpRJKpRIJCQmoqqqyGFNYWIj4+Hh4enrCz88PixcvRmNj23s/kH16ZWoYXGUS/JR7GQfOVohdjt0or9ajttEAqQTo48sgRC1enjQA3m4uOF1ajX8fKRK7HACmS146/H3vOTzy3s8Y+dYu/O4/x5B8ohS1jQYEeCnw9KggfPLMCGT/IRafzhuJWaP7MARdJ6pfD3yVGIXuHq7IKqzCkx+loaJaL3ZZNsEqXVNDhw5FSUmJ+Xb8+HHzY2+//TbWrVuHDRs2ICMjAyqVClOmTEF1dbV5zJIlS7BlyxYkJSUhNTUVNTU1mDFjBgyGq8l/1qxZyM7ORnJyMpKTk5GdnY2EhATz4waDAXFxcaitrUVqaiqSkpKwefNmLFu2zBpvmUQS5OuBhKi+AIA/7TgN4022sSdLphVjQb4ekLuweZJa+HjKsXjSAADAmpSzqNE3i1KHvtmA/Wcr8IfvczD2z3sx7S8/4Z0fzyCrsKXvJ7yXN16eNAD/XTgW6SsnYfWj92DykECn6Pu5G/cGdcc3z0fD30uB06XVePLDtJuuoHMmEkFoa6uoO/f666/ju+++Q3Z29g2PCYIAtVqNJUuWYMWKFQBaZn8CAwPx5z//GS+88AK0Wi38/f2xceNGPPnkkwCA4uJiBAUFYfv27Zg6dSpOnTqFIUOGID09HaNHt1wOSU9PR3R0NE6fPo2wsDDs2LEDM2bMQFFREdRqNQAgKSkJ8+bNQ3l5Oby9vdv1fnQ6HZRKJbRabbu/hrrWldpGTHh7L6r1zXj3yXvxyLDeYpdk8746VIj/2XIcE8P88fmvR4ldDtmQxmYjYt/dj4LKOiy8vz+WTw3rkte9XKPH3tPl2H2TS14x/f3M/T6c7bk7BZdrMfuTQ7hUVQ+10g2bEqMcslewvZ/fVvlVMDc3F2q1GiEhIXjqqaeQl5cHAMjPz0dpaSliY2PNYxUKBSZMmICDBw8CADIzM9HU1GQxRq1WIzw83DwmLS0NSqXSHIIAICoqCkql0mJMeHi4OQQBwNSpU6HX65GZmXnT2vV6PXQ6ncWNbJuvpxwvtu4wvebHs2hoavusH7qKjdJ0M3IXKV6dPhgA8PFPeVabMbj2ktejrZe8XrnFJa9/zBuJ2aODGYI6QV8/T/znN9Ho5++JYm0DHv8gDadKnPezrtObpUePHo1//vOfGDhwIMrKyvDmm29izJgxOHHiBEpLSwEAgYGBFl8TGBiICxcuAABKS0shl8vh4+NzwxjT15eWliIgIOCG1w4ICLAYc/3r+Pj4QC6Xm8e0ZfXq1fi///u/Dr5rEtuzMSHYmNZy9MbGNOc+KqA98rmHEN3C1KGBGB3ii0P5V/BO8ulO255C32zAIdMqr9PluKixDFlD1d6YNLhliXu4Wukwe/rYop5Kd/zrhWg88+lhnCzR4amP0vH5r0diWB+f23+xg+n0IDR9+nTz/4+IiEB0dDRCQ0PxxRdfICoqCgBuWMIoCMJtlzVeP6at8Xcy5norV67E0qVLzX/W6XQICuIpvrbOXd5y9MbvNh/Dhr3n8MSIIK6IuAXzZor+9r9xHnU+iUSC12YMMW9PMS8mBPcFdb+j56o0b2x44yUvuYsUMaE9MGlwICYNDkBPpXsnvQNqD79uCnz9fBSe/TwDmRc0mP3JIXwydwTGhNr+1gmdyepdkp6enoiIiEBubq559dj1MzLl5eXm2RuVSoXGxkZoNJpbjikrK7vhtSoqKizGXP86Go0GTU1NN8wUXUuhUMDb29viRvbhscjeGBjYDdr6Jry3/5zY5disJoMRhZV1AHhpjG4uvJcSj7b227259STa204qCALOtB5/8+h7P2PEdZe8/L0UeGpkED5+ZgSy/zAFn/16FOZEBTMEiUTp7oqN80dhbH8/1DUaMO+zDOw6eePnqyOzehDS6/U4deoUevbsiZCQEKhUKuzcudP8eGNjI/bv348xY8YAACIjI+Hq6moxpqSkBDk5OeYx0dHR0Gq1OHz4sHnMoUOHoNVqLcbk5OSgpKTEPCYlJQUKhQKRkZFWfc8kDplUghXTBgEAPvu5gKshbuKiph7NRgFurlKovNlvQTf3ytQwuLlKceSCBjtybt5SoG824MDZCrz+wwmMe3svpv7lAN758QyOtq7yGtLTG4sf6I/vF8Tg0MpJ+NNj92DKkECbO7TUWXnIXfDJ3BGIHRKIxmYjXvwyEz/8Uix2WV2m0/8rXL58OeLj49GnTx+Ul5fjzTffhE6nw9y5cyGRSLBkyRKsWrUKAwYMwIABA7Bq1Sp4eHhg1qxZAAClUon58+dj2bJl6NGjB3x9fbF8+XJERERg8uTJAIDBgwdj2rRpSExMxIcffggAeP755zFjxgyEhbWscIiNjcWQIUOQkJCAd955B1euXMHy5cuRmJjIWR4H9sCgAHNvw7qUs1j7xL1il2RzTI3SfXt4sgeDbkmldMML40Px1925WL3jFCYNDoDCpWWJemWNHnvPVGD3qTIcOHvzS14PDAqAujtne2ydm6sM780ejlf+cwxbsi7h5aQs1Oqb8fSoPmKXZnWdHoQuXryIp59+GpcvX4a/vz+ioqKQnp6O4OBgAMDvfvc71NfX46WXXoJGo8Ho0aORkpICLy8v83O8++67cHFxwRNPPIH6+npMmjQJn3/+OWSyq3tEbNq0CYsXLzavLnvooYewYcMG8+MymQzbtm3DSy+9hJiYGLi7u2PWrFlYs2ZNZ79lsiESScuBrDP//jO+zbqI58aFYHBPBt9r5XFHaeqAFyb0w9eHC1F0pR5rU85C6e6KPafLcbRQY3GWl183BSYNCsCkwQEYO8CPsz12yEUmxdrH74WnQoYv0wux8tvjqGlodvjFJ52+j5Cj4T5C9mnBpqPYdryE++S04X+3HMemQ4VYcH8oXpk6SOxyyA78+0gRXvnPsRvuH9LTG5MHB2DS4EBE9OIqL0chCAL+nHwGH+w/DwBYPGkAfjt5gN2d1dbez29GdnJIr0wNw48nSrHvTAUOnruMMf2daxXErVxdOs8VY9Q+jw3vjc1HL+JoYRXGmFZ58ZKXw5JIJHh1+iB4ubngnR/P4G+7c1Hd0ITX4oY4ZNhlECKH1NfPE7NH98EXaRewesdpfL8gxiG/ge+E6dJYCC+NUTtJpRJ8nRgFg1GAi4xHsjiLBff3h5ebC/7w/Ql89nMBavXNWP3oPTZ5GO/d4H/R5LAWTRoAT7kMxy9psfV4ye2/wAnU6ptRqmsAwM0UqWMkEglDkBN6Jrov1j5+L6QS4F9HLmLx11lobDaKXVan4n/V5LD8uinwwgTT0RtnHO6b904UVLbMBvl4uKK7h1zkaojIHjwW2RvvzR4OV5kE246X4PmNR1Df6DhHGTEIkUN7blwI/L0UKLxSh02HLohdjuhM/UHcSJGIOmJaeE98Onck3Fyl2HemAnM/O4zqhiaxy+oUDELk0DzkLvjt5IEAgL/tzoXOQb5x71S+qT+IjdJE1EHjB/pj4/zR8FK44HD+Fcz+5BA0tY1il3XXGITI4T0xojdC/T2hqWvCh63LQZ2VecUYG6WJ6A6M7OuLr5+Pgq+nHMcuavHkR2kob+07tFcMQuTwXGRS/K716I1PU/NRqrXvb9q7cZ6nzhPRXQrvpcS/XohCoLcCZ8tq8PiHaSi6Uid2WXeMQYicQuyQQIwI9kFDkxF/2XVW7HJEIQgC8itajtfg0nkiuhv9A7zwnxfHoI+vBy5U1uHxD9JwrrxG7LLuCIMQOYWWozdaZoX+daQIuWXVIlfU9a7UNkLX0Ayg5ZwxIqK7EeTrgX+/GI0BAd1QqmvAEx+mIeeSVuyyOoxBiJxGZLAvpg4NhFEA/px8WuxyupypP6hXd3e4ucpuM5qI6PYCvd3wzQvRiOilxJXaRjz9cTqOFFwRu6wOYRAip/K7aYMgk0qw61Q5Dufb1zfr3crj0nkisgJfTzm+ShyNUX19Ud3QjIRPD+On3Aqxy2o3BiFyKqH+3fDUyCAAwKrtp+BMZw7z1HkishYvN1d88ewoTBjoj/omA+Z/fgTJOaVil9UuDELkdF6ePAAechmyi6qww06+UTtD/uXWRmnOCBGRFbjLZfj4mRF4MEKFRoMRC746im+PXhS7rNtiECKnE+DlhufG9QMAvPPjGTQZnOPoDe4qTUTWJneR4m9PDcOvInvDYBSw9F+/YGNagdhl3RKDEDml58f3g183OfIv1yLpcKHY5VidwSigoLJln49+3FWaiKzIRSbF24/dg3lj+gIAXvv+BN7bd07com6BQYicUjeFC16eNAAA8NfduajRN4tckXUVV9WjsdkIV5kEvXzcxS6HiBycVCrBH+OHYPED/QEAbyefwZ+TT9tkXyaDEDmtp0b1QYifJy7XNOKjA3lil2NVpstiwT08IZNKRK6GiJyBRCLB0tgwrJzesofb+/vO4w/fn4DRaFthiEGInJarTIpXpoYBAD75KQ/l1Y579EZe647SPFqDiLraCxNC8dYj4ZBIgI3pF7D837+g2YZ6MxmEyKlND1fhvqDuqGs04K+7csUux2rMjdJcOk9EIpg9Ohh/efI+yKQSfJt1CQu+Ogp9s0HssgAwCJGTk0gk5mnbpIwinK+wz7NybiePh60Skcgevq8XPpgTCbmLFD+eKMNzXxxBXaP4/ZkMQuT0RvfrgcmDA2AwClj0VZZ59sSRXF06zxVjRCSeKUMC8dm8kfCQy/BT7mUkfHoY2vomUWtiECICsPLBwVC6u+JkiQ5xf/sJ32QU2uTqhjvR0GTApap6ANxDiIjEF9PfD18+Nxrebi7IvKDB0x+l43KNXrR6GISI0HL0RvKScYju1wN1jQas2HwcL36ZCU1to9il3bXCK3UQBMDLzQV+3eRil0NEhOF9fJD0fDT8uslxskSHN/57UrRaGISIWvVUumPTc6OxcvoguMok+PFEGab99QBScy+LXdpduXbFmETCpfNEZBuGqL3xrxeiMWGgP15/aKhodTAIEV1DKpXghQmh2PJSDPr5e6JMp8ecTw/hrW0nbWaFQ0fx1HkislX9/Lvhi2dHwddTvNlqBiGiNoT3UmLbonGYE9UHAPDxT/mY+feDOFtWLXJlHZdfwUZpIqKbYRAiugl3uQxvzozAJ8+MgK+nHKdKdIhfn4p/phXYVSM19xAiIro5BiGi25g8JBDJS8ZhwkB/6JuN+MP3J/Ds5xmoqBZvlUNH5HMPISKim2IQImqHAC83fP7rkXg9fgjkLlLsPVOBaX85gD2ny8Qu7Za0dU2obF35xh4hIqIbdXoQWr16NUaOHAkvLy8EBARg5syZOHPmjMWYefPmQSKRWNyioqIsxuj1eixatAh+fn7w9PTEQw89hIsXL1qM0Wg0SEhIgFKphFKpREJCAqqqqizGFBYWIj4+Hp6envDz88PixYvR2Gj/S6Kp60kkEsyLCcF/F47FIJUXKmsb8eznR/CH73PQ0GSbjdR5l1tWjAV6K+CpcBG5GiIi29PpQWj//v1YsGAB0tPTsXPnTjQ3NyM2Nha1tZa79U6bNg0lJSXm2/bt2y0eX7JkCbZs2YKkpCSkpqaipqYGM2bMgMFw9QNn1qxZyM7ORnJyMpKTk5GdnY2EhATz4waDAXFxcaitrUVqaiqSkpKwefNmLFu2rLPfNjmRMJUXvlsQg/ljQwAA/0y7gBnrU3GiWCtyZTfK54oxIqJbE6ysvLxcACDs37/ffN/cuXOFhx9++KZfU1VVJbi6ugpJSUnm+y5duiRIpVIhOTlZEARBOHnypABASE9PN49JS0sTAAinT58WBEEQtm/fLkilUuHSpUvmMV9//bWgUCgErVbbrvq1Wq0AoN3jybnsP1MujHxzpxC8YqvQ/3+2CR/uPycYDEaxyzJb8+NpIXjFVuHVzcfELoWIqEu19/Pb6j1CWm3Lb8m+vr4W9+/btw8BAQEYOHAgEhMTUV5ebn4sMzMTTU1NiI2NNd+nVqsRHh6OgwcPAgDS0tKgVCoxevRo85ioqCgolUqLMeHh4VCr1eYxU6dOhV6vR2ZmZpv16vV66HQ6ixvRzYwf6I/kJeMxZUggmgwCVm0/jYR/HEKptkHs0gDwsFUiotuxahASBAFLly7F2LFjER4ebr5/+vTp2LRpE/bs2YO1a9ciIyMDDzzwAPT6llU4paWlkMvl8PHxsXi+wMBAlJaWmscEBATc8JoBAQEWYwIDAy0e9/HxgVwuN4+53urVq809R0qlEkFBQXf+F0BOwddTjo8SIrH60Qi4u8rw87lKTPvrAew4XiJ2adfsIcQgRETUFqsGoYULF+LYsWP4+uuvLe5/8sknERcXh/DwcMTHx2PHjh04e/Ystm3bdsvnEwTB4oiAto4LuJMx11q5ciW0Wq35VlRUdMuaiICW/86eHtUH2xaPRUQvJarqmvCbTUex4j/HUKtvFqUmQRCuLp3nHkJERG2yWhBatGgRfvjhB+zduxe9e/e+5diePXsiODgYubm5AACVSoXGxkZoNBqLceXl5eYZHpVKhbKyG5cuV1RUWIy5fuZHo9GgqanphpkiE4VCAW9vb4sbUXv18++Gzb8Zg5cmhkIiAb45UoS4v/2E7KKqLq+lVNeA+iYDZFIJgnw9uvz1iYjsQacHIUEQsHDhQnz77bfYs2cPQkJCbvs1lZWVKCoqQs+ePQEAkZGRcHV1xc6dO81jSkpKkJOTgzFjxgAAoqOjodVqcfjwYfOYQ4cOQavVWozJyclBScnVSxQpKSlQKBSIjIzslPdLdD25ixS/mzYIXydGQa10Q0FlHR57/yA27MmFwdh1O1KbLov18fWAq4xbhhERtaXTfzouWLAAX375Jb766it4eXmhtLQUpaWlqK+vBwDU1NRg+fLlSEtLQ0FBAfbt24f4+Hj4+fnhkUceAQAolUrMnz8fy5Ytw+7du5GVlYU5c+YgIiICkydPBgAMHjwY06ZNQ2JiItLT05Geno7ExETMmDEDYWFhAIDY2FgMGTIECQkJyMrKwu7du7F8+XIkJiZypoesLqpfD+x4eTxm3NMTBqOANSln8dRHabioqeuS1+dhq0REt9fpQej999+HVqvFxIkT0bNnT/Ptm2++AQDIZDIcP34cDz/8MAYOHIi5c+di4MCBSEtLg5eXl/l53n33XcycORNPPPEEYmJi4OHhgf/+97+QyWTmMZs2bUJERARiY2MRGxuLe+65Bxs3bjQ/LpPJsG3bNri5uSEmJgZPPPEEZs6ciTVr1nT22yZqk9LDFeufHoZ1T9yLbgoXZBRoMP0vP+H77EtWf23uIUREdHsSQbCj0yNFoNPpoFQqodVqOYtEd6XoSh2WfJONzAstvW8P36fG/5sZDm83V6u83rOfZ2DP6XK8OTMcc6KCrfIaRES2qr2f32wcIOoiQb4e+Ob5KPx28kDIpBJ8n12M6X/5CRkFV6zyenkVLcdrcMUYEdHNMQgRdSEXmRQvTx6Af78YjT6+HrhUVY8nP0zD2pQzaDIYO+11GpuNKNK09OX18+vWac9LRORoGISIRDC8jw+2vzwOv4rsDaMArN9zDr/6IM3c13O3ijR1MBgFuLvKEOit6JTnJCJyRAxCRCLppnDBmsfvxd9nDYe3mwt+KapC3N9+wr8yinC3rXvX7ih9s81DiYiIQYhIdHH39ETykvGI6ueLukYDfrf5GF7adBSa2sY7fk7zijH2BxER3RKDEJENUHd3x6bnovDq9EFwlUmwI6cU0/56AD+fu3xHz8fDVomI2odBiMhGyKQSvDghFFteikE/f0+U6fSY/ckhrNp+CvpmQ4eeiyvGiIjah0GIyMaE91Ji26JxmD26DwDgowN5eOTvB3GuvLrdz3F1M0WuGCMiuhUGISIb5C6X4a1HIvDxMyPg6ynHyRId4v6Wio1pBbdtpK7RN6O8Wg8ACOnBGSEiolthECKyYVOGBCJ5yTiMH+gPfbMRr31/AvO/OILLNfqbfk1B62xQD085lB7W2bWaiMhRMAgR2bgALzd8Pm8k/hg/BHIXKfacLse0vxzA3tPlbY7nYatERO3HIERkB6RSCX4dE4IfFsZgkMoLl2sa8evPM/DH73PQ0GTZSH3tHkJERHRrDEJEdmSQyhvfLYjBszEhAIAv0i4gfn0qThbrzGPyLptWjLFRmojodhiEiOyMm6sMf4gfgi+eHQV/LwVyy2sw8+8/45Of8mA0CtesGOOMEBHR7TAIEdmpCQP9kfzyOEwZEohGgxFvbjuFZ/5xGHmtl8a4hxAR0e0xCBHZsR7dFPgoIRKrHomAm6sUqecuo0bfDIkE6OPrIXZ5REQ2j0GIyM5JJBLMGt0HWxeNQ3gvbwAt+we5ucpEroyIyPa5iF0AEXWO/gHd8O1vYrD56EUMVXuLXQ4RkV1gECJyIHIXKZ4e1UfsMoiI7AYvjREREZHTYhAiIiIip8UgRERERE6LQYiIiIicFoMQEREROS0GISIiInJaDEJERETktBiEiIiIyGkxCBEREZHTYhAiIiIip8UgRERERE6LQYiIiIicFoMQEREROS2ePn8bgiAAAHQ6nciVEBERUXuZPrdNn+M3wyB0G9XV1QCAoKAgkSshIiKijqquroZSqbzp4xLhdlHJyRmNRhQXF8PLywsSiaRTn1un0yEoKAhFRUXw9vbu1OemjuO/h23hv4dt4b+HbeG/x+0JgoDq6mqo1WpIpTfvBOKM0G1IpVL07t3bqq/h7e3N/5BtCP89bAv/PWwL/z1sC/89bu1WM0EmbJYmIiIip8UgRERERE6LQUhECoUCf/zjH6FQKMQuhcB/D1vDfw/bwn8P28J/j87DZmkiIiJyWpwRIiIiIqfFIEREREROi0GIiIiInBaDEBERETktBiGRvPfeewgJCYGbmxsiIyPx008/iV2SU1q9ejVGjhwJLy8vBAQEYObMmThz5ozYZVGr1atXQyKRYMmSJWKX4tQuXbqEOXPmoEePHvDw8MB9992HzMxMsctySs3Nzfj973+PkJAQuLu7o1+/fnjjjTdgNBrFLs1uMQiJ4JtvvsGSJUvwv//7v8jKysK4ceMwffp0FBYWil2a09m/fz8WLFiA9PR07Ny5E83NzYiNjUVtba3YpTm9jIwMfPTRR7jnnnvELsWpaTQaxMTEwNXVFTt27MDJkyexdu1adO/eXezSnNKf//xnfPDBB9iwYQNOnTqFt99+G++88w7Wr18vdml2i8vnRTB69GgMHz4c77//vvm+wYMHY+bMmVi9erWIlVFFRQUCAgKwf/9+jB8/XuxynFZNTQ2GDx+O9957D2+++Sbuu+8+/OUvfxG7LKf06quv4ueff+astY2YMWMGAgMD8emnn5rve+yxx+Dh4YGNGzeKWJn94oxQF2tsbERmZiZiY2Mt7o+NjcXBgwdFqopMtFotAMDX11fkSpzbggULEBcXh8mTJ4tditP74YcfMGLECDz++OMICAjAsGHD8PHHH4tdltMaO3Ysdu/ejbNnzwIAfvnlF6SmpuLBBx8UuTL7xUNXu9jly5dhMBgQGBhocX9gYCBKS0tFqoqAlpOKly5dirFjxyI8PFzscpxWUlISMjMzceTIEbFLIQB5eXl4//33sXTpUvzP//wPDh8+jMWLF0OhUOCZZ54Ruzyns2LFCmi1WgwaNAgymQwGgwFvvfUWnn76abFLs1sMQiKRSCQWfxYE4Yb7qGstXLgQx44dQ2pqqtilOK2ioiK8/PLLSElJgZubm9jlEACj0YgRI0Zg1apVAIBhw4bhxIkTeP/99xmERPDNN9/gyy+/xFdffYWhQ4ciOzsbS5YsgVqtxty5c8Uuzy4xCHUxPz8/yGSyG2Z/ysvLb5gloq6zaNEi/PDDDzhw4AB69+4tdjlOKzMzE+Xl5YiMjDTfZzAYcODAAWzYsAF6vR4ymUzECp1Pz549MWTIEIv7Bg8ejM2bN4tUkXN75ZVX8Oqrr+Kpp54CAERERODChQtYvXo1g9AdYo9QF5PL5YiMjMTOnTst7t+5cyfGjBkjUlXOSxAELFy4EN9++y327NmDkJAQsUtyapMmTcLx48eRnZ1tvo0YMQKzZ89GdnY2Q5AIYmJibthS4uzZswgODhapIudWV1cHqdTyo1smk3H5/F3gjJAIli5dioSEBIwYMQLR0dH46KOPUFhYiBdffFHs0pzOggUL8NVXX+H777+Hl5eXeaZOqVTC3d1d5Oqcj5eX1w39WZ6enujRowf7tkTy29/+FmPGjMGqVavwxBNP4PDhw/joo4/w0UcfiV2aU4qPj8dbb72FPn36YOjQocjKysK6devw7LPPil2a3eLyeZG89957ePvtt1FSUoLw8HC8++67XK4tgpv1ZX322WeYN29e1xZDbZo4cSKXz4ts69atWLlyJXJzcxESEoKlS5ciMTFR7LKcUnV1NV577TVs2bIF5eXlUKvVePrpp/GHP/wBcrlc7PLsEoMQEREROS32CBEREZHTYhAiIiIip8UgRERERE6LQYiIiIicFoMQEREROS0GISIiInJaDEJERETktBiEiIiIyGkxCBEREZHTYhAiIiIip8UgRERERE6LQYiIiIic1v8H/IEUdK8M5z8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_sales[\"Odometer (KM)\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bd027ad0-1dfa-42f9-9351-d050b03e1968",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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33Xefrly5orvuuks/+tGPtHDhwgH7+nw++Xy+wHJ3d7ckye/3y+/3j/pYx0J/HdHU45lgYj2cmPMkmKDfTjXQ/Ixk/pyA+pwrnmuTqM9phluHyxgT9dnC5XJp7969WrZs2YB9mpubdeTIEeXl5cnn8+mNN95QZWWlamtrNW/evLDblJWVqby8PKS9urpaU6ZMiXa4AABgDPX09GjFihXq6uoKulTjRqMeRsJZsmSJXC6X9u3bF3Z9uGdGsrKydO7cuUGLcRK/3y+v16tFixbJ7XZHtG1u2dujNKrY8SQYvZjfp+caEuTrc9keTtTeLysK2z6S+XMC6nOueK5Noj6n6e7u1vTp04cMI2P2Ms315syZo927dw+43uPxyOPxhLS73e64mJzrRVOT75pzTu6+PpejxnujoeYmHo/J61Gfc8VzbRL1OcVwa7DyOSONjY3KyMiwsWsAADDORPzMyCeffKLf//73geWWlhadPHlSU6dO1axZs1RaWqqzZ8/q9ddflyRVVFTo1ltvVU5Ojnp7e7V7927V1NSopqYmdlUAAADHijiMNDQ0BL0TpqSkRJK0evVq7dq1S+3t7WptbQ2s7+3t1caNG3X27FklJiYqJydH+/fv1+LFi2MwfAAA4HQRh5EFCxZosGted+3aFbS8adMmbdq0KeKBAQCAzwa+mwYAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGBVxGHkyJEjWrJkiTIzM+VyufTmm28Ouc3hw4eVl5enyZMn67bbblNlZWU0YwUAAHEo4jBy+fJl3XvvvfrHf/zHYfVvaWnR4sWLNXfuXDU2Nmrz5s3asGGDampqIh4sAACIPxMj3aC4uFjFxcXD7l9ZWalZs2apoqJCkjR79mw1NDRo69atWr58eaS7BwAAcSbiMBKp+vp6FRYWBrUVFRWpqqpKfr9fbrc7ZBufzyefzxdY7u7uliT5/X75/f7RHfAY6a8jmno8E0yshxNzngQT9NupBpqfkcyfE1Cfc8VzbRL1Oc1w63AZY6I+W7hcLu3du1fLli0bsM+dd96pNWvWaPPmzYG2uro6felLX1JbW5syMjJCtikrK1N5eXlIe3V1taZMmRLtcAEAwBjq6enRihUr1NXVpeTk5AH7jfozI9KnoeV6/fnnxvZ+paWlKikpCSx3d3crKytLhYWFgxYTjdyyt2N6e8PlSTB6Mb9PzzUkyNcX/u/gZNTnbGNd3/tlRaO+j+v5/X55vV4tWrQo7LOzw2HrsWMog83dWP+dR0Ms5m60jeTYsPXYMlrHRv8rG0MZ9TCSnp6ujo6OoLbOzk5NnDhR06ZNC7uNx+ORx+MJaXe73TE/+HzX7J5IfH0u62MYTdTnbGNVn62TykgeU8b7vIebu/F68o7GaJwPYiUWx8ZYP7aM1t9yuLc76p8zUlBQIK/XG9R28OBB5efnj9sDCQAAjJ2Iw8gnn3yikydP6uTJk5I+fevuyZMn1draKunTl1hWrVoV6L927VqdOXNGJSUlOnXqlHbs2KGqqipt3LgxNhUAAABHi/hlmoaGBi1cuDCw3H9tx+rVq7Vr1y61t7cHgokkZWdn68CBA3rmmWf06quvKjMzU6+88gpv6wUAAJKiCCMLFizQYG/A2bVrV0jb/PnzdeLEiUh3BQAAPgP4bhoAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWRRVGtm3bpuzsbE2ePFl5eXk6evTogH1ra2vlcrlCfk6fPh31oAEAQPyIOIzs2bNH3/ve9/Tss8+qsbFRc+fOVXFxsVpbWwfdrrm5We3t7YGfO+64I+pBAwCA+BFxGHn55Zf1xBNP6Mknn9Ts2bNVUVGhrKwsbd++fdDtUlNTlZ6eHviZMGFC1IMGAADxI6Iw0tvbq+PHj6uwsDCovbCwUHV1dYNue9999ykjI0MPP/ywDh06FPlIAQBAXJoYSedz587p2rVrSktLC2pPS0tTR0dH2G0yMjL085//XHl5efL5fHrjjTf08MMPq7a2VvPmzQu7jc/nk8/nCyx3d3dLkvx+v/x+fyRDHpJngonp7Q17vwkm6He8oT5nG+v6Yn2/Hu7+RrJfW48dQxls7sb67zwaYjF3o20kx4atx5bR+nsO93ZdxphhV9zW1qYZM2aorq5OBQUFgfYf//jHeuONN4Z9UeqSJUvkcrm0b9++sOvLyspUXl4e0l5dXa0pU6YMd7gAAMCinp4erVixQl1dXUpOTh6wX0TPjEyfPl0TJkwIeRaks7Mz5NmSwcyZM0e7d+8ecH1paalKSkoCy93d3crKylJhYeGgxUQjt+ztmN7ecHkSjF7M79NzDQny9bmsjGE0UZ+zjXV975cVjfo+ruf3++X1erVo0SK53e6obsPWY8dQBpu7sf47j4ZYzN1oG8mxYeuxZbSOjf5XNoYSURiZNGmS8vLy5PV69bWvfS3Q7vV6tXTp0mHfTmNjozIyMgZc7/F45PF4QtrdbnfMDz7fNbsnEl+fy/oYRhP1OdtY1WfrpDKSx5TxPu/h5m68nryjMRrng1iJxbEx1o8to/W3HO7tRhRGJKmkpESPP/648vPzVVBQoJ///OdqbW3V2rVrJX36rMbZs2f1+uuvS5IqKip06623KicnR729vdq9e7dqampUU1MT6a4BAEAcijiMPProozp//rxeeOEFtbe3Kzc3VwcOHNAtt9wiSWpvbw/6zJHe3l5t3LhRZ8+eVWJionJycrR//34tXrw4dlUAAADHijiMSNJ3vvMdfec73wm7bteuXUHLmzZt0qZNm6LZDQAA+Azgu2kAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWRRVGtm3bpuzsbE2ePFl5eXk6evTooP0PHz6svLw8TZ48WbfddpsqKyujGiwAAIg/EYeRPXv26Hvf+56effZZNTY2au7cuSouLlZra2vY/i0tLVq8eLHmzp2rxsZGbd68WRs2bFBNTc2IBw8AAJwv4jDy8ssv64knntCTTz6p2bNnq6KiQllZWdq+fXvY/pWVlZo1a5YqKio0e/ZsPfnkk/r2t7+trVu3jnjwAADA+SZG0rm3t1fHjx/XD3/4w6D2wsJC1dXVhd2mvr5ehYWFQW1FRUWqqqqS3++X2+0O2cbn88nn8wWWu7q6JEl/+tOf5Pf7IxnykCZevRzT2xv2fvuMenr6NNGfoGt9LitjGE3U52xjXd/58+dHfR/X8/v96unp0fnz58M+Bg2HrceOoQw2d2P9dx4NsZi70TaSY8PWY8toHRuXLl2SJBljBu0XURg5d+6crl27prS0tKD2tLQ0dXR0hN2mo6MjbP+rV6/q3LlzysjICNlmy5YtKi8vD2nPzs6OZLjj3grbAxhl1OdsY1nf9J+O4c4+AwaaO/7OzmDjsWW0j41Lly4pJSVlwPURhZF+LldwWjPGhLQN1T9ce7/S0lKVlJQElvv6+vSnP/1J06ZNG3Q/TtLd3a2srCz98Y9/VHJysu3hxBz1ORv1OVc81yZRn9MYY3Tp0iVlZmYO2i+iMDJ9+nRNmDAh5FmQzs7OkGc/+qWnp4ftP3HiRE2bNi3sNh6PRx6PJ6jt5ptvjmSojpGcnBwXB9xAqM/ZqM+54rk2ifqcZLBnRPpFdAHrpEmTlJeXJ6/XG9Tu9Xr1xS9+Mew2BQUFIf0PHjyo/Pz8cft6HwAAGDsRv5umpKRE//zP/6wdO3bo1KlTeuaZZ9Ta2qq1a9dK+vQlllWrVgX6r127VmfOnFFJSYlOnTqlHTt2qKqqShs3boxdFQAAwLEivmbk0Ucf1fnz5/XCCy+ovb1dubm5OnDggG655RZJUnt7e9BnjmRnZ+vAgQN65pln9OqrryozM1OvvPKKli9fHrsqHMjj8ej5558PeTkqXlCfs1Gfc8VzbRL1xSuXGer9NgAAAKOI76YBAABWEUYAAIBVhBEAAGAVYQQAAFhFGBmmLVu26P7771dSUpJSU1O1bNkyNTc3B/VZs2aNXC5X0M+cOXOC+vh8Pq1fv17Tp0/XTTfdpK9+9av63//936A+Fy5c0OOPP66UlBSlpKTo8ccf18WLF4P6tLa2asmSJbrppps0ffp0bdiwQb29vVHXV1ZWFjL29PT0wHpjjMrKypSZmanExEQtWLBAH3zwgSNqk6Rbb701pD6Xy6V169ZJct7cHTlyREuWLFFmZqZcLpfefPPNoPXjbb6ampo0f/58JSYmasaMGXrhhRcG/a6Kwerz+/36wQ9+oLvvvls33XSTMjMztWrVKrW1tQXdxoIFC0Lm9LHHHrNe31BzN96OxVjOnaSw90OXy6W///u/D/QZr3M3nPOA0+971hgMS1FRkdm5c6d5//33zcmTJ81XvvIVM2vWLPPJJ58E+qxevdp8+ctfNu3t7YGf8+fPB93O2rVrzYwZM4zX6zUnTpwwCxcuNPfee6+5evVqoM+Xv/xlk5uba+rq6kxdXZ3Jzc01jzzySGD91atXTW5urlm4cKE5ceKE8Xq9JjMz0zz99NNR1/f888+bnJycoLF3dnYG1r/00ksmKSnJ1NTUmKamJvPoo4+ajIwM093dPe5rM8aYzs7OoNq8Xq+RZA4dOmSMcd7cHThwwDz77LOmpqbGSDJ79+4NWj+e5qurq8ukpaWZxx57zDQ1NZmamhqTlJRktm7dGlV9Fy9eNH/7t39r9uzZY06fPm3q6+vNAw88YPLy8oJuY/78+eapp54KmtOLFy8G9bFR31BzN56OxVjPnTEmqK729nazY8cO43K5zP/8z/8E+ozXuRvOecDp9z1bCCNR6uzsNJLM4cOHA22rV682S5cuHXCbixcvGrfbbX71q18F2s6ePWsSEhLMW2+9ZYwx5r/+67+MJHPs2LFAn/r6eiPJnD592hjz6Z09ISHBnD17NtDnl7/8pfF4PKarqyuqep5//nlz7733hl3X19dn0tPTzUsvvRRou3LliklJSTGVlZXjvrZwvvvd75rbb7/d9PX1GWOcPXc3PuCPt/natm2bSUlJMVeuXAn02bJli8nMzAz8/SOpL5z//M//NJLMmTNnAm3z58833/3udwfcZjzUN1AYGS/H4ljM3dKlS81DDz0U1OaEuTMm9DwQb/e9scTLNFHq6uqSJE2dOjWovba2Vqmpqbrzzjv11FNPqbOzM7Du+PHj8vv9KiwsDLRlZmYqNzdXdXV1kqT6+nqlpKTogQceCPSZM2eOUlJSgvrk5uYGffFQUVGRfD6fjh8/HnVNH374oTIzM5Wdna3HHntMf/jDHyRJLS0t6ujoCBq3x+PR/PnzA2Ma77Vdr7e3V7t379a3v/3toC9edPLcXW+8zVd9fb3mz58f9CFORUVFamtr00cffRSTmru6uuRyuUK+w+oXv/iFpk+frpycHG3cuDHwdebjvb7xciyO9tx9/PHH2r9/v5544omQdU6YuxvPA5/F+16sEEaiYIxRSUmJHnzwQeXm5gbai4uL9Ytf/EK//e1v9dOf/lTvvfeeHnroIfl8PklSR0eHJk2apM997nNBt5eWlhb4MsGOjg6lpqaG7DM1NTWoz41fTPi5z31OkyZNCvlSwuF64IEH9Prrr+vtt9/WP/3TP6mjo0Nf/OIXdf78+cBt3rjPG8c9Xmu70ZtvvqmLFy9qzZo1gTYnz92Nxtt8hevTvxyLmq9cuaIf/vCHWrFiRdAXi61cuVK//OUvVVtbq+eee041NTX6+te/Hlg/XusbT8fiaM/da6+9pqSkpKB5kZwxd+HOA5+1+14sRfxx8JCefvpp/e53v9O7774b1P7oo48G/p2bm6v8/Hzdcsst2r9/f8id7XrGmKD/oV//75H0iURxcXHg33fffbcKCgp0++2367XXXgtcPHfjbQ9nf+OhthtVVVWpuLg46H8UTp67gYyn+Qo3loG2jYTf79djjz2mvr4+bdu2LWjdU089Ffh3bm6u7rjjDuXn5+vEiRP6whe+EPXYh9NnJPWNt2NxtOZOknbs2KGVK1dq8uTJQe1OmLuBzgMD3Wa83fdijWdGIrR+/Xrt27dPhw4d0syZMwftm5GRoVtuuUUffvihJCk9PV29vb26cOFCUL/Ozs5AWk1PT9fHH38cclv/93//F9TnxlR74cIF+f3+kBQcrZtuukl33323Pvzww8C7am7c543jdkJtZ86c0TvvvKMnn3xy0H5OnrvxNl/h+vS/7DCSmv1+v77xjW+opaVFXq93yK9b/8IXviC32x00p+O5vn42j8XRrO3o0aNqbm4e8r4ojb+5G+g88Fm5742K0b8sJT709fWZdevWmczMTPPf//3fw9rm3LlzxuPxmNdee80Y8/8vXNqzZ0+gT1tbW9gLl/7jP/4j0OfYsWNhL1xqa2sL9PnVr34V04s8r1y5YmbMmGHKy8sDF2X95Cc/Caz3+XxhL8oa77U9//zzJj093fj9/kH7OWnuNMAFrONlvrZt22Zuvvlm4/P5An1eeumlEV0E2dvba5YtW2ZycnKC3vU1mKampqCLDcdDfeFqu5HNY3E05q7f6tWrQ94BNZDxMndDnQfi7b43lggjw/R3f/d3JiUlxdTW1ga93aynp8cYY8ylS5fM97//fVNXV2daWlrMoUOHTEFBgZkxY0bIW7pmzpxp3nnnHXPixAnz0EMPhX1L1z333GPq6+tNfX29ufvuu8O+pevhhx82J06cMO+8846ZOXPmiN7++v3vf9/U1taaP/zhD+bYsWPmkUceMUlJSeajjz4yxnx6AKekpJhf//rXpqmpyXzzm98M+3a18Vhbv2vXrplZs2aZH/zgB0HtTpy7S5cumcbGRtPY2GgkmZdfftk0NjYG3k0ynubr4sWLJi0tzXzzm980TU1N5te//rVJTk4e9O2Fg9Xn9/vNV7/6VTNz5kxz8uTJoPtj/4Pu73//e1NeXm7ee+8909LSYvbv32/++q//2tx3333W6xustvF2LMZ67vp1dXWZKVOmmO3bt4dsP57nbqjzgDHOv+/ZQhgZJklhf3bu3GmMMaanp8cUFhaaz3/+88btdptZs2aZ1atXm9bW1qDb+fOf/2yefvppM3XqVJOYmGgeeeSRkD7nz583K1euNElJSSYpKcmsXLnSXLhwIajPmTNnzFe+8hWTmJhopk6dap5++umgt29Fqv+98G6322RmZpqvf/3r5oMPPgis7+vrCzyr4PF4zLx580xTU5Mjauv39ttvG0mmubk5qN2Jc3fo0KGwx+Pq1auNMeNvvn73u9+ZuXPnGo/HY9LT001ZWdmg/zMbrL6WlpYB74/9nxvT2tpq5s2bZ6ZOnWomTZpkbr/9drNhw4aQz+uwUd9gtY3HYzGWc9fvZz/7mUlMTAz57BBjxvfcDXUeMMb59z1bXMaMx49iAwAAnxVcwAoAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALDq/wEt/2QnOSS/NwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_sales[\"Odometer (KM)\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f144b405-eeb2-45de-9cea-2df81e47750e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     $4,000.00\n",
       "1     $5,000.00\n",
       "2     $7,000.00\n",
       "3    $22,000.00\n",
       "4     $3,500.00\n",
       "5     $4,500.00\n",
       "6     $7,500.00\n",
       "7     $7,000.00\n",
       "8     $6,250.00\n",
       "9     $9,700.00\n",
       "Name: Price, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "41b13474-4ef9-4db7-8ace-bc6ca7618678",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\htrav\\AppData\\Local\\Temp\\ipykernel_5576\\2993200403.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  car_sales[\"Price\"] = car_sales[\"Price\"].str.replace('[\\$\\,\\.]','').astype('int64')/100\n"
     ]
    }
   ],
   "source": [
    "car_sales[\"Price\"] = car_sales[\"Price\"].str.replace('[\\$\\,\\.]','').astype('int64')/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d95bed5a-cd54-493a-882b-799faa66d600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_sales[\"Price\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7159fb5e-b80c-42d8-99bb-77c4e459c4a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales = pd.read_csv(\"data/car-sales-missing-data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "906f7092-bc79-4229-8ed8-1efc42068693",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
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       "      <td>87899.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>Toyota</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <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",
       "      <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",
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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",
       "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"
      ]
     },
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    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7e99fd3e-a341-494b-b289-ecd2684b8fee",
   "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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Odometer\"].fillna(car_sales[\"Odometer\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8881056c-3a45-43ad-90f1-1444f72114bf",
   "metadata": {},
   "outputs": [
    {
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       "      <th>7</th>\n",
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       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
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      "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": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Odometer\"].fillna(car_sales[\"Odometer\"].mean(), inplace=True)\n",
    "car_sales"
   ]
  },
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   "cell_type": "code",
   "execution_count": 73,
   "id": "12840acd-ca2e-43d2-80c1-2fbc1b827478",
   "metadata": {},
   "outputs": [
    {
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       "     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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "95830b33-7b4c-4410-bc13-fa7df28e82a5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Toyota</td>\n",
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       "      <td>4</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500.0</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>7000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700.0</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   4000.0    5.0\n",
       "1   Honda    Red          87899      4   5000.0    5.0\n",
       "2  Toyota   Blue          32549      3   7000.0    5.0\n",
       "3     BMW  Black          11179      5  22000.0    5.0\n",
       "4  Nissan  White         213095      4   3500.0    5.0\n",
       "5  Toyota  Green          99213      4   4500.0    NaN\n",
       "6   Honda   Blue          45698      4   7500.0    NaN\n",
       "7   Honda   Blue          54738      4   7000.0    NaN\n",
       "8  Toyota  White          60000      4   6250.0    NaN\n",
       "9  Nissan  White          31600      4   9700.0    NaN"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Column from PD Series\n",
    "seats_column = pd.Series([5, 5, 5, 5,5])\n",
    "car_sales[\"Seats\"] = seats_column\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f0e54067-6a56-4026-92c9-69684975001f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000.0</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>5000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.2</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>7000.0</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>22000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.6</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>3500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</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>4500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.7</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>7500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.6</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>7000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.7</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>6250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM\n",
       "0  Toyota  White         150043      4   4000.0    5.0             7.5\n",
       "1   Honda    Red          87899      4   5000.0    5.0             9.2\n",
       "2  Toyota   Blue          32549      3   7000.0    5.0             5.0\n",
       "3     BMW  Black          11179      5  22000.0    5.0             9.6\n",
       "4  Nissan  White         213095      4   3500.0    5.0             8.7\n",
       "5  Toyota  Green          99213      4   4500.0    NaN             4.7\n",
       "6   Honda   Blue          45698      4   7500.0    NaN             7.6\n",
       "7   Honda   Blue          54738      4   7000.0    NaN             8.7\n",
       "8  Toyota  White          60000      4   6250.0    NaN             3.0\n",
       "9  Nissan  White          31600      4   9700.0    NaN             4.5"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Column from Python list\n",
    "fuel_economy = [7.5, 9.2, 5.0, 9.6, 8.7, 4.7, 7.6, 8.7, 3.0, 4.5]\n",
    "car_sales[\"Fuel per 100KM\"] = fuel_economy\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "32766377-a3d0-4d28-a338-180dc9eeb364",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Number of wheels</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",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11253.225</td>\n",
       "      <td>4</td>\n",
       "      <td>11253.225</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>5000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8086.708</td>\n",
       "      <td>4</td>\n",
       "      <td>8086.708</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>7000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1627.450</td>\n",
       "      <td>4</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>22000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1073.184</td>\n",
       "      <td>4</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>3500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18539.265</td>\n",
       "      <td>4</td>\n",
       "      <td>18539.265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4663.011</td>\n",
       "      <td>4</td>\n",
       "      <td>4663.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3473.048</td>\n",
       "      <td>4</td>\n",
       "      <td>3473.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4762.206</td>\n",
       "      <td>4</td>\n",
       "      <td>4762.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.000</td>\n",
       "      <td>4</td>\n",
       "      <td>1800.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1422.000</td>\n",
       "      <td>4</td>\n",
       "      <td>1422.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "0  Toyota  White         150043      4   4000.0    5.0             7.5   \n",
       "1   Honda    Red          87899      4   5000.0    5.0             9.2   \n",
       "2  Toyota   Blue          32549      3   7000.0    5.0             5.0   \n",
       "3     BMW  Black          11179      5  22000.0    5.0             9.6   \n",
       "4  Nissan  White         213095      4   3500.0    5.0             8.7   \n",
       "5  Toyota  Green          99213      4   4500.0    NaN             4.7   \n",
       "6   Honda   Blue          45698      4   7500.0    NaN             7.6   \n",
       "7   Honda   Blue          54738      4   7000.0    NaN             8.7   \n",
       "8  Toyota  White          60000      4   6250.0    NaN             3.0   \n",
       "9  Nissan  White          31600      4   9700.0    NaN             4.5   \n",
       "\n",
       "   Total fuel used  Number of wheels  Total fuel used (L)  \n",
       "0        11253.225                 4            11253.225  \n",
       "1         8086.708                 4             8086.708  \n",
       "2         1627.450                 4             1627.450  \n",
       "3         1073.184                 4             1073.184  \n",
       "4        18539.265                 4            18539.265  \n",
       "5         4663.011                 4             4663.011  \n",
       "6         3473.048                 4             3473.048  \n",
       "7         4762.206                 4             4762.206  \n",
       "8         1800.000                 4             1800.000  \n",
       "9         1422.000                 4             1422.000  "
      ]
     },
     "execution_count": 84,
     "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": 85,
   "id": "ca10effc-0ea5-43b0-ae70-1ba7435d56bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Fuel per 100KM</th>\n",
       "      <th>Total fuel used</th>\n",
       "      <th>Number of wheels</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",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000.0</td>\n",
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       "      <td>11253.225</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>5000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8086.708</td>\n",
       "      <td>4</td>\n",
       "      <td>8086.708</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>7000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1627.450</td>\n",
       "      <td>4</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>22000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1073.184</td>\n",
       "      <td>4</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>3500.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
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       "      <td>4</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4663.011</td>\n",
       "      <td>4</td>\n",
       "      <td>4663.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3473.048</td>\n",
       "      <td>4</td>\n",
       "      <td>3473.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4762.206</td>\n",
       "      <td>4</td>\n",
       "      <td>4762.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.000</td>\n",
       "      <td>4</td>\n",
       "      <td>1800.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1422.000</td>\n",
       "      <td>4</td>\n",
       "      <td>1422.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "0  Toyota  White         150043      4   4000.0    5.0             7.5   \n",
       "1   Honda    Red          87899      4   5000.0    5.0             9.2   \n",
       "2  Toyota   Blue          32549      3   7000.0    5.0             5.0   \n",
       "3     BMW  Black          11179      5  22000.0    5.0             9.6   \n",
       "4  Nissan  White         213095      4   3500.0    5.0             8.7   \n",
       "5  Toyota  Green          99213      4   4500.0    NaN             4.7   \n",
       "6   Honda   Blue          45698      4   7500.0    NaN             7.6   \n",
       "7   Honda   Blue          54738      4   7000.0    NaN             8.7   \n",
       "8  Toyota  White          60000      4   6250.0    NaN             3.0   \n",
       "9  Nissan  White          31600      4   9700.0    NaN             4.5   \n",
       "\n",
       "   Total fuel used  Number of wheels  Total fuel used (L)  \n",
       "0        11253.225                 4            11253.225  \n",
       "1         8086.708                 4             8086.708  \n",
       "2         1627.450                 4             1627.450  \n",
       "3         1073.184                 4             1073.184  \n",
       "4        18539.265                 4            18539.265  \n",
       "5         4663.011                 4             4663.011  \n",
       "6         3473.048                 4             3473.048  \n",
       "7         4762.206                 4             4762.206  \n",
       "8         1800.000                 4             1800.000  \n",
       "9         1422.000                 4             1422.000  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a column for a single value\n",
    "car_sales[\"Number of wheels\"] = 4\n",
    "car_sales\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "beb8a5dc-995d-4ead-90da-3b5d9cfeb332",
   "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",
       "      <th>Seats</th>\n",
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       "      <th>Number of wheels</th>\n",
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       "  </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>4000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>4</td>\n",
       "      <td>11253.225</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>5000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>4</td>\n",
       "      <td>8086.708</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>7000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</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>22000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>4</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>3500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>18539.265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4</td>\n",
       "      <td>4663.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4</td>\n",
       "      <td>3473.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>4762.206</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1800.000</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4</td>\n",
       "      <td>1422.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "0  Toyota  White         150043      4   4000.0    5.0             7.5   \n",
       "1   Honda    Red          87899      4   5000.0    5.0             9.2   \n",
       "2  Toyota   Blue          32549      3   7000.0    5.0             5.0   \n",
       "3     BMW  Black          11179      5  22000.0    5.0             9.6   \n",
       "4  Nissan  White         213095      4   3500.0    5.0             8.7   \n",
       "5  Toyota  Green          99213      4   4500.0    NaN             4.7   \n",
       "6   Honda   Blue          45698      4   7500.0    NaN             7.6   \n",
       "7   Honda   Blue          54738      4   7000.0    NaN             8.7   \n",
       "8  Toyota  White          60000      4   6250.0    NaN             3.0   \n",
       "9  Nissan  White          31600      4   9700.0    NaN             4.5   \n",
       "\n",
       "   Number of wheels  Total fuel used (L)  \n",
       "0                 4            11253.225  \n",
       "1                 4             8086.708  \n",
       "2                 4             1627.450  \n",
       "3                 4             1073.184  \n",
       "4                 4            18539.265  \n",
       "5                 4             4663.011  \n",
       "6                 4             3473.048  \n",
       "7                 4             4762.206  \n",
       "8                 4             1800.000  \n",
       "9                 4             1422.000  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales = car_sales.drop(\"Total fuel used\", axis =1)\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "991b7e7d-3a0a-4cfe-903a-d07ce9538861",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
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       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
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       "      <td>Toyota</td>\n",
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       "      <td>3</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
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       "      <td>8086.708</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>3500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</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>4500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4</td>\n",
       "      <td>4663.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4</td>\n",
       "      <td>3473.048</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>22000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>4</td>\n",
       "      <td>1073.184</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>9700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4</td>\n",
       "      <td>1422.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>4</td>\n",
       "      <td>11253.225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "2  Toyota   Blue          32549      3   7000.0    5.0             5.0   \n",
       "7   Honda   Blue          54738      4   7000.0    NaN             8.7   \n",
       "8  Toyota  White          60000      4   6250.0    NaN             3.0   \n",
       "1   Honda    Red          87899      4   5000.0    5.0             9.2   \n",
       "4  Nissan  White         213095      4   3500.0    5.0             8.7   \n",
       "5  Toyota  Green          99213      4   4500.0    NaN             4.7   \n",
       "6   Honda   Blue          45698      4   7500.0    NaN             7.6   \n",
       "3     BMW  Black          11179      5  22000.0    5.0             9.6   \n",
       "9  Nissan  White          31600      4   9700.0    NaN             4.5   \n",
       "0  Toyota  White         150043      4   4000.0    5.0             7.5   \n",
       "\n",
       "   Number of wheels  Total fuel used (L)  \n",
       "2                 4             1627.450  \n",
       "7                 4             4762.206  \n",
       "8                 4             1800.000  \n",
       "1                 4             8086.708  \n",
       "4                 4            18539.265  \n",
       "5                 4             4663.011  \n",
       "6                 4             3473.048  \n",
       "3                 4             1073.184  \n",
       "9                 4             1422.000  \n",
       "0                 4            11253.225  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales = car_sales.sample(frac=1)\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "9af8cb21-368e-428d-b51b-11ccb994dadc",
   "metadata": {},
   "outputs": [
    {
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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>3500.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
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       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4</td>\n",
       "      <td>3473.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nissan</td>\n",
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       "      <td>4</td>\n",
       "      <td>9700.0</td>\n",
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       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>4</td>\n",
       "      <td>11253.225</td>\n",
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      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "0  Toyota   Blue          32549      3   7000.0    5.0             5.0   \n",
       "1   Honda   Blue          54738      4   7000.0    NaN             8.7   \n",
       "2  Toyota  White          60000      4   6250.0    NaN             3.0   \n",
       "3   Honda    Red          87899      4   5000.0    5.0             9.2   \n",
       "4  Nissan  White         213095      4   3500.0    5.0             8.7   \n",
       "5  Toyota  Green          99213      4   4500.0    NaN             4.7   \n",
       "6   Honda   Blue          45698      4   7500.0    NaN             7.6   \n",
       "7     BMW  Black          11179      5  22000.0    5.0             9.6   \n",
       "8  Nissan  White          31600      4   9700.0    NaN             4.5   \n",
       "9  Toyota  White         150043      4   4000.0    5.0             7.5   \n",
       "\n",
       "   Number of wheels  Total fuel used (L)  \n",
       "0                 4             1627.450  \n",
       "1                 4             4762.206  \n",
       "2                 4             1800.000  \n",
       "3                 4             8086.708  \n",
       "4                 4            18539.265  \n",
       "5                 4             4663.011  \n",
       "6                 4             3473.048  \n",
       "7                 4             1073.184  \n",
       "8                 4             1422.000  \n",
       "9                 4            11253.225  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales = car_sales.reset_index(drop = True)\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "280e9ecf-6433-4628-b336-174f5f29a952",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Fuel per 100KM</th>\n",
       "      <th>Number of wheels</th>\n",
       "      <th>Total fuel used (L)</th>\n",
       "      <th>Odometer (M)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1627.450</td>\n",
       "      <td>20343.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>4762.206</td>\n",
       "      <td>34211.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1800.000</td>\n",
       "      <td>37500.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>4</td>\n",
       "      <td>8086.708</td>\n",
       "      <td>54936.875</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>3500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>18539.265</td>\n",
       "      <td>133184.375</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>4500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4</td>\n",
       "      <td>4663.011</td>\n",
       "      <td>62008.125</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>7500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4</td>\n",
       "      <td>3473.048</td>\n",
       "      <td>28561.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>4</td>\n",
       "      <td>1073.184</td>\n",
       "      <td>6986.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4</td>\n",
       "      <td>1422.000</td>\n",
       "      <td>19750.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>4</td>\n",
       "      <td>11253.225</td>\n",
       "      <td>93776.875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100KM  \\\n",
       "0  Toyota   Blue          32549      3   7000.0    5.0             5.0   \n",
       "1   Honda   Blue          54738      4   7000.0    NaN             8.7   \n",
       "2  Toyota  White          60000      4   6250.0    NaN             3.0   \n",
       "3   Honda    Red          87899      4   5000.0    5.0             9.2   \n",
       "4  Nissan  White         213095      4   3500.0    5.0             8.7   \n",
       "5  Toyota  Green          99213      4   4500.0    NaN             4.7   \n",
       "6   Honda   Blue          45698      4   7500.0    NaN             7.6   \n",
       "7     BMW  Black          11179      5  22000.0    5.0             9.6   \n",
       "8  Nissan  White          31600      4   9700.0    NaN             4.5   \n",
       "9  Toyota  White         150043      4   4000.0    5.0             7.5   \n",
       "\n",
       "   Number of wheels  Total fuel used (L)  Odometer (M)  \n",
       "0                 4             1627.450     20343.125  \n",
       "1                 4             4762.206     34211.250  \n",
       "2                 4             1800.000     37500.000  \n",
       "3                 4             8086.708     54936.875  \n",
       "4                 4            18539.265    133184.375  \n",
       "5                 4             4663.011     62008.125  \n",
       "6                 4             3473.048     28561.250  \n",
       "7                 4             1073.184      6986.875  \n",
       "8                 4             1422.000     19750.000  \n",
       "9                 4            11253.225     93776.875  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Odometer (M)\"] = car_sales[\"Odometer (KM)\"].apply(lambda x: x/1.6)\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f734c04-4cdd-41d7-b232-d5da65c94578",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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