introduction-to-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b199585d-0431-4170-94a4-91eeeeece806",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "acac2134-8abf-4bc2-9a30-06696dbe80e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#There are 2 main data types in Pandas::\n",
    "series=pd.Series([\"BMW\",\"Toyota\",\"Jaguar\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2cbc1eb0-f98d-47d4-a0fd-407cc860ebfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       BMW\n",
       "1    Toyota\n",
       "2    Jaguar\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5960f4b7-6f46-4492-a3dc-bacbd61442c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Series is 1-dimentional data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "70d76f0d-34ee-47f7-95e3-adf2207478a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "colours=pd.Series([\"Red\",\"White\",\"Blue\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "682f9b51-d6ad-4af9-9959-96fba6d7c5c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Red\n",
       "1    White\n",
       "2     Blue\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8fc9984-4fa3-41e6-9782-3357cdcb6db6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DataFrame = 2-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "92574baa-1295-4808-aa93-4b6d5d2fa100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car make</th>\n",
       "      <th>Colours</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>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jaguar</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car make Colours\n",
       "0      BMW     Red\n",
       "1   Toyota   White\n",
       "2   Jaguar    Blue"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car=pd.DataFrame({\"Car make\":series ,\"Colours\":colours})\n",
    "car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3be938a2-7e68-4f19-b05a-3b0db70c85b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Data\n",
    "car_data=pd.read_csv(\"car-sales.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "991371cf-d20f-4c61-b35f-b6714def0d2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>0</td>\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>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\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>6</td>\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>7</td>\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>8</td>\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>9</td>\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": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors       Price\n",
       "0           0  Toyota  White         150043      4   $4,000.00\n",
       "1           1   Honda    Red          87899      4   $5,000.00\n",
       "2           2  Toyota   Blue          32549      3   $7,000.00\n",
       "3           3     BMW  Black          11179      5  $22,000.00\n",
       "4           4  Nissan  White         213095      4   $3,500.00\n",
       "5           5  Toyota  Green          99213      4   $4,500.00\n",
       "6           6   Honda   Blue          45698      4   $7,500.00\n",
       "7           7   Honda   Blue          54738      4   $7,000.00\n",
       "8           8  Toyota  White          60000      4   $6,250.00\n",
       "9           9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a8ccd2aa-d900-463e-acac-a710521bc295",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exporting a DataFrame\n",
    "car_data.to_csv(\"car-sales-import.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "966f4a95-73a3-46d8-8ed5-e7cd061ebfa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing data from URL::\n",
    "heart_disease = pd.read_csv(\"https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/heart-disease.csv\")\n",
    "heart_disease"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d58ad91-ec7c-4491-8e64-504b549711ad",
   "metadata": {},
   "source": [
    "##Describe Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "396e87b9-2278-49eb-9e0c-d21cc731dce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0        int64\n",
       "Make             object\n",
       "Colour           object\n",
       "Odometer (KM)     int64\n",
       "Doors             int64\n",
       "Price            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Attribute::(attribute doesn't have the bracket)\n",
    "car_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1de9a00a-7e2d-4a11-8b63-3e336aab14e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Make', 'Colour', 'Odometer (KM)', 'Doors', 'Price'], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9f41ab91-3e65-40f3-bdef-fc1b3044cee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Make', 'Colour', 'Odometer (KM)', 'Doors', 'Price'], dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_columns=car_data.columns\n",
    "car_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ba54a4e0-10cd-4d1a-9359-d049f9686591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=10, step=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d5997869-8a55-4505-bc4f-ecf7723deb4d",
   "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>Unnamed: 0</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>0</td>\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>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\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>6</td>\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>7</td>\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>8</td>\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>9</td>\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": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors       Price\n",
       "0           0  Toyota  White         150043      4   $4,000.00\n",
       "1           1   Honda    Red          87899      4   $5,000.00\n",
       "2           2  Toyota   Blue          32549      3   $7,000.00\n",
       "3           3     BMW  Black          11179      5  $22,000.00\n",
       "4           4  Nissan  White         213095      4   $3,500.00\n",
       "5           5  Toyota  Green          99213      4   $4,500.00\n",
       "6           6   Honda   Blue          45698      4   $7,500.00\n",
       "7           7   Honda   Blue          54738      4   $7,000.00\n",
       "8           8  Toyota  White          60000      4   $6,250.00\n",
       "9           9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8db25a9-c443-4d16-a611-16f2349f5f5b",
   "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>Unnamed: 0</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.50000</td>\n",
       "      <td>78601.400000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.02765</td>\n",
       "      <td>61983.471735</td>\n",
       "      <td>0.471405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>11179.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.25000</td>\n",
       "      <td>35836.250000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.50000</td>\n",
       "      <td>57369.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.75000</td>\n",
       "      <td>96384.500000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.00000</td>\n",
       "      <td>213095.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  Odometer (KM)      Doors\n",
       "count    10.00000      10.000000  10.000000\n",
       "mean      4.50000   78601.400000   4.000000\n",
       "std       3.02765   61983.471735   0.471405\n",
       "min       0.00000   11179.000000   3.000000\n",
       "25%       2.25000   35836.250000   4.000000\n",
       "50%       4.50000   57369.000000   4.000000\n",
       "75%       6.75000   96384.500000   4.000000\n",
       "max       9.00000  213095.000000   5.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.describe() #STATISTICAL INFO ABOUT THE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6fdbbf5b-3674-4406-90ab-81ac3fe367ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Unnamed: 0     10 non-null     int64 \n",
      " 1   Make           10 non-null     object\n",
      " 2   Colour         10 non-null     object\n",
      " 3   Odometer (KM)  10 non-null     int64 \n",
      " 4   Doors          10 non-null     int64 \n",
      " 5   Price          10 non-null     object\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 608.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "car_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0a40c268-55ab-405b-ab1e-115abfab4869",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_columns = car_data.select_dtypes(include=[int, float])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fa92cddb-9b0c-4e78-9530-cd379be47844",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_data_mean = numeric_columns.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a8315210-a600-4af2-8fa3-cdd4b7fc9f0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0           4.5\n",
      "Odometer (KM)    78601.4\n",
      "Doors                4.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(car_data_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6f1449b2-9acf-479f-8e75-6423753fd552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "866666.6666666666"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_prices=pd.Series([1200000,500000,900000])\n",
    "car_prices.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a4607178-a648-4a0a-ac2f-ecf6805f79d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                                                      45\n",
       "Make             ToyotaHondaToyotaBMWNissanToyotaHondaHondaToyo...\n",
       "Colour               WhiteRedBlueBlackWhiteGreenBlueBlueWhiteWhite\n",
       "Odometer (KM)                                               786014\n",
       "Doors                                                           40\n",
       "Price            $4,000.00$5,000.00$7,000.00$22,000.00$3,500.00...\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "92121ef6-e9b8-4dd3-8b76-f52e032b6c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[\"Doors\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1a173bb0-ab5f-482d-b44d-9563d7279f5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(car_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "24c82801-ebf3-4c63-82de-cc7baa7908cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    4\n",
       "2    3\n",
       "3    5\n",
       "4    4\n",
       "5    4\n",
       "6    4\n",
       "7    4\n",
       "8    4\n",
       "9    4\n",
       "Name: Doors, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[\"Doors\"].abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "abb9fd9a-fa9a-44c0-bc49-981002fe0f86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_data.boxplot() #Just exploring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0c39e625-a645-4c1d-b436-fadca9b4e794",
   "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>Unnamed: 0</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>0</td>\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>1</td>\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>2</td>\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>3</td>\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>4</td>\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": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors       Price\n",
       "0           0  Toyota  White         150043      4   $4,000.00\n",
       "1           1   Honda    Red          87899      4   $5,000.00\n",
       "2           2  Toyota   Blue          32549      3   $7,000.00\n",
       "3           3     BMW  Black          11179      5  $22,000.00\n",
       "4           4  Nissan  White         213095      4   $3,500.00"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#.head() and .tail() :: displays top 5 and bottom 5 rows respectiely.\n",
    "car_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "81536b37-1bef-4047-8917-f149f8c934b0",
   "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>Unnamed: 0</th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\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>6</td>\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>7</td>\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>8</td>\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>9</td>\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": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors      Price\n",
       "5           5  Toyota  Green          99213      4  $4,500.00\n",
       "6           6   Honda   Blue          45698      4  $7,500.00\n",
       "7           7   Honda   Blue          54738      4  $7,000.00\n",
       "8           8  Toyota  White          60000      4  $6,250.00\n",
       "9           9  Nissan  White          31600      4  $9,700.00"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "96bde988-590d-44df-a6cf-98df71c64d54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
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       "      <td>Toyota</td>\n",
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       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\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>2</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>BMW</td>\n",
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       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
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       "      <th>5</th>\n",
       "      <td>5</td>\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>6</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors       Price\n",
       "0           0  Toyota  White         150043      4   $4,000.00\n",
       "1           1   Honda    Red          87899      4   $5,000.00\n",
       "2           2  Toyota   Blue          32549      3   $7,000.00\n",
       "3           3     BMW  Black          11179      5  $22,000.00\n",
       "4           4  Nissan  White         213095      4   $3,500.00\n",
       "5           5  Toyota  Green          99213      4   $4,500.00\n",
       "6           6   Honda   Blue          45698      4   $7,500.00"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.head(7) #displays top-7 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d88c8d06-ea48-4600-b7f9-116e5b9220f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# .loc and .iloc :: .loc prefers the index whereas .iloc prefers position(starts from 0)\n",
    "animals=pd.Series([\"cat\",\"dog\",\"panda\",\"camel\",\"bear\"],index=[1,6,8,9,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7ea45d51-3a33-4e89-8752-a411ac3bce57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      cat\n",
       "6      dog\n",
       "8    panda\n",
       "9    camel\n",
       "6     bear\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "97efa4c1-a8eb-460a-8d82-8729884a104b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'camel'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.loc[9] #it is kind of key and value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "438f8191-95cc-4dd1-8a80-7146ceaf71fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6     dog\n",
       "6    bear\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.loc[6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1c95a4ce-a0bf-4756-97b9-e39275e77ed2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dog'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.iloc[1] #It returns element at 1st position starting from 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dd7a3cfa-c2f4-4f66-90ef-92bd02a24bb5",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "single positional indexer is out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[37], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43manimals\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m]\u001b[49m   \u001b[38;5;66;03m#error because no 10 elements\u001b[39;00m\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\indexing.py:1191\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1189\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m   1190\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_deprecated_callable_usage(key, maybe_callable)\n\u001b[1;32m-> 1191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\indexing.py:1752\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1749\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot index by location index with a non-integer key\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1751\u001b[0m \u001b[38;5;66;03m# validate the location\u001b[39;00m\n\u001b[1;32m-> 1752\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_integer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1754\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_ixs(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\indexing.py:1685\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_integer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1683\u001b[0m len_axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis))\n\u001b[0;32m   1684\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m len_axis \u001b[38;5;129;01mor\u001b[39;00m key \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39mlen_axis:\n\u001b[1;32m-> 1685\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle positional indexer is out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
     ]
    }
   ],
   "source": [
    "animals.iloc[10]   #error because no 10 elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f63aad84-df7f-4e0a-8b44-030c83a8bf74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bear'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.iloc[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "41a525a7-f401-4775-a5f2-95d650109dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
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       "      <th>9</th>\n",
       "      <td>9</td>\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": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors       Price\n",
       "0           0  Toyota  White         150043      4   $4,000.00\n",
       "1           1   Honda    Red          87899      4   $5,000.00\n",
       "2           2  Toyota   Blue          32549      3   $7,000.00\n",
       "3           3     BMW  Black          11179      5  $22,000.00\n",
       "4           4  Nissan  White         213095      4   $3,500.00\n",
       "5           5  Toyota  Green          99213      4   $4,500.00\n",
       "6           6   Honda   Blue          45698      4   $7,500.00\n",
       "7           7   Honda   Blue          54738      4   $7,000.00\n",
       "8           8  Toyota  White          60000      4   $6,250.00\n",
       "9           9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0d83a839-a337-4b59-947b-6d78d72118d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0               4\n",
       "Make                Nissan\n",
       "Colour               White\n",
       "Odometer (KM)       213095\n",
       "Doors                    4\n",
       "Price            $3,500.00\n",
       "Name: 4, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.iloc[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4a271159-99de-4ac0-ba57-2b347f3c1a35",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\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>2</td>\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>3</td>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors       Price\n",
       "0           0  Toyota  White         150043      4   $4,000.00\n",
       "1           1   Honda    Red          87899      4   $5,000.00\n",
       "2           2  Toyota   Blue          32549      3   $7,000.00\n",
       "3           3     BMW  Black          11179      5  $22,000.00"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.loc[:3] #that is equivalent to .head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c73a1ae9-c776-47e1-be8e-2c5b132f406f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To Select Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f71e10ba-0e24-46ae-9176-50fc4ac044dc",
   "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>Unnamed: 0</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>0</td>\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>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\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>6</td>\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>7</td>\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>8</td>\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>9</td>\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": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors       Price\n",
       "0           0  Toyota  White         150043      4   $4,000.00\n",
       "1           1   Honda    Red          87899      4   $5,000.00\n",
       "2           2  Toyota   Blue          32549      3   $7,000.00\n",
       "3           3     BMW  Black          11179      5  $22,000.00\n",
       "4           4  Nissan  White         213095      4   $3,500.00\n",
       "5           5  Toyota  Green          99213      4   $4,500.00\n",
       "6           6   Honda   Blue          45698      4   $7,500.00\n",
       "7           7   Honda   Blue          54738      4   $7,000.00\n",
       "8           8  Toyota  White          60000      4   $6,250.00\n",
       "9           9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "eda88162-f2bc-4ca0-a8a4-4d97f98adb9d",
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[\"Make\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "994cfe44-862c-4cdd-a7ab-df1fe59e20e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    White\n",
       "1      Red\n",
       "2     Blue\n",
       "3    Black\n",
       "4    White\n",
       "5    Green\n",
       "6     Blue\n",
       "7     Blue\n",
       "8    White\n",
       "9    White\n",
       "Name: Colour, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[\"Colour\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "eaef3b34-b992-4241-a4bb-fe5549255dea",
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f68c4a19-4865-497a-8335-49e7a52046f1",
   "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>Unnamed: 0</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>0</td>\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>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\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>6</td>\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>7</td>\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>8</td>\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>9</td>\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": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors       Price\n",
       "0           0  Toyota  White         150043      4   $4,000.00\n",
       "1           1   Honda    Red          87899      4   $5,000.00\n",
       "2           2  Toyota   Blue          32549      3   $7,000.00\n",
       "3           3     BMW  Black          11179      5  $22,000.00\n",
       "4           4  Nissan  White         213095      4   $3,500.00\n",
       "5           5  Toyota  Green          99213      4   $4,500.00\n",
       "6           6   Honda   Blue          45698      4   $7,500.00\n",
       "7           7   Honda   Blue          54738      4   $7,000.00\n",
       "8           8  Toyota  White          60000      4   $6,250.00\n",
       "9           9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6fba5c3-b7d3-4252-96ac-c10ad777bcef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#car_data[\"Colour\"] and car_data.Colour both are same only diff comes when column names contains space in that case . can't be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4af7faac-396e-4066-8bd3-f1e80f21484d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    White\n",
       "1      Red\n",
       "2     Blue\n",
       "3    Black\n",
       "4    White\n",
       "5    Green\n",
       "6     Blue\n",
       "7     Blue\n",
       "8    White\n",
       "9    White\n",
       "Name: Colour, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.Colour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d6675383-fba3-4beb-bdc1-0f84f43a964f",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'Odometer'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3952\\3914362614.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcar_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOdometer\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mKM\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#this is the case where . not works\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   6295\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6296\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6297\u001b[0m         ):\n\u001b[0;32m   6298\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6299\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'Odometer'"
     ]
    }
   ],
   "source": [
    "car_data.Odometer (KM) #this is the case where . not works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bf0614e6-9ccd-467f-bbaa-08684b32ae83",
   "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>Unnamed: 0</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>0</td>\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>2</td>\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>5</td>\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>8</td>\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": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors      Price\n",
       "0           0  Toyota  White         150043      4  $4,000.00\n",
       "2           2  Toyota   Blue          32549      3  $7,000.00\n",
       "5           5  Toyota  Green          99213      4  $4,500.00\n",
       "8           8  Toyota  White          60000      4  $6,250.00"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[car_data[\"Make\"]==\"Toyota\"] #Returns row with Make=Toyota"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1fdbfcf0-8297-4868-a7fa-50faa5b3eb2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
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       "      <td>$4,000.00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Honda</td>\n",
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       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
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       "      <th>5</th>\n",
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       "      <td>Toyota</td>\n",
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       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors      Price\n",
       "0           0  Toyota  White         150043      4  $4,000.00\n",
       "1           1   Honda    Red          87899      4  $5,000.00\n",
       "4           4  Nissan  White         213095      4  $3,500.00\n",
       "5           5  Toyota  Green          99213      4  $4,500.00"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[car_data[\"Odometer (KM)\"]>80000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e140ef54-051f-4be3-b012-9fff7dfd37cc",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
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       "      <th>0</th>\n",
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       "      <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>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\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>6</td>\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>7</td>\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>8</td>\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>9</td>\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": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors       Price\n",
       "0           0  Toyota  White         150043      4   $4,000.00\n",
       "1           1   Honda    Red          87899      4   $5,000.00\n",
       "2           2  Toyota   Blue          32549      3   $7,000.00\n",
       "3           3     BMW  Black          11179      5  $22,000.00\n",
       "4           4  Nissan  White         213095      4   $3,500.00\n",
       "5           5  Toyota  Green          99213      4   $4,500.00\n",
       "6           6   Honda   Blue          45698      4   $7,500.00\n",
       "7           7   Honda   Blue          54738      4   $7,000.00\n",
       "8           8  Toyota  White          60000      4   $6,250.00\n",
       "9           9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6f348eb3-9e04-42dd-b845-4b722df1632b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(car_data[\"Make\"],car_data[\"Doors\"]) #cross tab will make the group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0fdab1e5-bc17-4240-9a5b-6977bcb9f7ba",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "agg function failed [how->mean,dtype->object]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1942\u001b[0m, in \u001b[0;36mGroupBy._agg_py_fallback\u001b[1;34m(self, how, values, ndim, alt)\u001b[0m\n\u001b[0;32m   1941\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1942\u001b[0m     res_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_grouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_series\u001b[49m\u001b[43m(\u001b[49m\u001b[43mser\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreserve_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m   1943\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\groupby\\ops.py:864\u001b[0m, in \u001b[0;36mBaseGrouper.agg_series\u001b[1;34m(self, obj, func, preserve_dtype)\u001b[0m\n\u001b[0;32m    862\u001b[0m     preserve_dtype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 864\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_aggregate_series_pure_python\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    866\u001b[0m npvalues \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mmaybe_convert_objects(result, try_float\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\groupby\\ops.py:885\u001b[0m, in \u001b[0;36mBaseGrouper._aggregate_series_pure_python\u001b[1;34m(self, obj, func)\u001b[0m\n\u001b[0;32m    884\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(splitter):\n\u001b[1;32m--> 885\u001b[0m     res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    886\u001b[0m     res \u001b[38;5;241m=\u001b[39m extract_result(res)\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py:2454\u001b[0m, in \u001b[0;36mGroupBy.mean.<locals>.<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   2451\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   2452\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_agg_general(\n\u001b[0;32m   2453\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m-> 2454\u001b[0m         alt\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[43mSeries\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[0;32m   2455\u001b[0m         numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only,\n\u001b[0;32m   2456\u001b[0m     )\n\u001b[0;32m   2457\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\series.py:6549\u001b[0m, in \u001b[0;36mSeries.mean\u001b[1;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m   6541\u001b[0m \u001b[38;5;129m@doc\u001b[39m(make_doc(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m   6542\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean\u001b[39m(\n\u001b[0;32m   6543\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   6547\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   6548\u001b[0m ):\n\u001b[1;32m-> 6549\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m NDFrame\u001b[38;5;241m.\u001b[39mmean(\u001b[38;5;28mself\u001b[39m, axis, skipna, numeric_only, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\generic.py:12420\u001b[0m, in \u001b[0;36mNDFrame.mean\u001b[1;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  12413\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean\u001b[39m(\n\u001b[0;32m  12414\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m  12415\u001b[0m     axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  12418\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m  12419\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[1;32m> 12420\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stat_function(\n\u001b[0;32m  12421\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m, nanops\u001b[38;5;241m.\u001b[39mnanmean, axis, skipna, numeric_only, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m  12422\u001b[0m     )\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\generic.py:12377\u001b[0m, in \u001b[0;36mNDFrame._stat_function\u001b[1;34m(self, name, func, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  12375\u001b[0m validate_bool_kwarg(skipna, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskipna\u001b[39m\u001b[38;5;124m\"\u001b[39m, none_allowed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m> 12377\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m  12378\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\n\u001b[0;32m  12379\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\series.py:6457\u001b[0m, in \u001b[0;36mSeries._reduce\u001b[1;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[0;32m   6453\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m   6454\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSeries.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not allow \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkwd_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnumeric_only\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   6455\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwith non-numeric dtypes.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   6456\u001b[0m     )\n\u001b[1;32m-> 6457\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op(delegate, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__.<locals>.f\u001b[1;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[0;32m    146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 147\u001b[0m     result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat.<locals>.new_func\u001b[1;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[0;32m    402\u001b[0m     mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[1;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m func(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, mask\u001b[38;5;241m=\u001b[39mmask, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\nanops.py:720\u001b[0m, in \u001b[0;36mnanmean\u001b[1;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[0;32m    719\u001b[0m the_sum \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39msum(axis, dtype\u001b[38;5;241m=\u001b[39mdtype_sum)\n\u001b[1;32m--> 720\u001b[0m the_sum \u001b[38;5;241m=\u001b[39m \u001b[43m_ensure_numeric\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthe_sum\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    722\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(the_sum, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\nanops.py:1701\u001b[0m, in \u001b[0;36m_ensure_numeric\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1699\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m   1700\u001b[0m     \u001b[38;5;66;03m# GH#44008, GH#36703 avoid casting e.g. strings to numeric\u001b[39;00m\n\u001b[1;32m-> 1701\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not convert string \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to numeric\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1702\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "\u001b[1;31mTypeError\u001b[0m: Could not convert string 'Black' to numeric",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[54], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mcar_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mMake\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py:2452\u001b[0m, in \u001b[0;36mGroupBy.mean\u001b[1;34m(self, numeric_only, engine, engine_kwargs)\u001b[0m\n\u001b[0;32m   2445\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_numba_agg_general(\n\u001b[0;32m   2446\u001b[0m         grouped_mean,\n\u001b[0;32m   2447\u001b[0m         executor\u001b[38;5;241m.\u001b[39mfloat_dtype_mapping,\n\u001b[0;32m   2448\u001b[0m         engine_kwargs,\n\u001b[0;32m   2449\u001b[0m         min_periods\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[0;32m   2450\u001b[0m     )\n\u001b[0;32m   2451\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 2452\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cython_agg_general\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2453\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2454\u001b[0m \u001b[43m        \u001b[49m\u001b[43malt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mSeries\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2455\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2456\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2457\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1998\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general\u001b[1;34m(self, how, alt, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[0;32m   1995\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_agg_py_fallback(how, values, ndim\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim, alt\u001b[38;5;241m=\u001b[39malt)\n\u001b[0;32m   1996\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m-> 1998\u001b[0m new_mgr \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouped_reduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray_func\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1999\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_agged_manager(new_mgr)\n\u001b[0;32m   2000\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m how \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midxmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midxmax\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\internals\\managers.py:1469\u001b[0m, in \u001b[0;36mBlockManager.grouped_reduce\u001b[1;34m(self, func)\u001b[0m\n\u001b[0;32m   1465\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m blk\u001b[38;5;241m.\u001b[39mis_object:\n\u001b[0;32m   1466\u001b[0m     \u001b[38;5;66;03m# split on object-dtype blocks bc some columns may raise\u001b[39;00m\n\u001b[0;32m   1467\u001b[0m     \u001b[38;5;66;03m#  while others do not.\u001b[39;00m\n\u001b[0;32m   1468\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m sb \u001b[38;5;129;01min\u001b[39;00m blk\u001b[38;5;241m.\u001b[39m_split():\n\u001b[1;32m-> 1469\u001b[0m         applied \u001b[38;5;241m=\u001b[39m \u001b[43msb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1470\u001b[0m         result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[0;32m   1471\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:393\u001b[0m, in \u001b[0;36mBlock.apply\u001b[1;34m(self, func, **kwargs)\u001b[0m\n\u001b[0;32m    387\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m    388\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Block]:\n\u001b[0;32m    389\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    390\u001b[0m \u001b[38;5;124;03m    apply the function to my values; return a block if we are not\u001b[39;00m\n\u001b[0;32m    391\u001b[0m \u001b[38;5;124;03m    one\u001b[39;00m\n\u001b[0;32m    392\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 393\u001b[0m     result \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    395\u001b[0m     result \u001b[38;5;241m=\u001b[39m maybe_coerce_values(result)\n\u001b[0;32m    396\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_split_op_result(result)\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1995\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general.<locals>.array_func\u001b[1;34m(values)\u001b[0m\n\u001b[0;32m   1992\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m   1994\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m alt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1995\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_agg_py_fallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mndim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1996\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1946\u001b[0m, in \u001b[0;36mGroupBy._agg_py_fallback\u001b[1;34m(self, how, values, ndim, alt)\u001b[0m\n\u001b[0;32m   1944\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124magg function failed [how->\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,dtype->\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mser\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1945\u001b[0m     \u001b[38;5;66;03m# preserve the kind of exception that raised\u001b[39;00m\n\u001b[1;32m-> 1946\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(err)(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   1948\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ser\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[0;32m   1949\u001b[0m     res_values \u001b[38;5;241m=\u001b[39m res_values\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "\u001b[1;31mTypeError\u001b[0m: agg function failed [how->mean,dtype->object]"
     ]
    }
   ],
   "source": [
    "car_data.groupby([\"Make\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ac0d9371-05c1-42cd-9f31-c5479e251060",
   "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>Unnamed: 0</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>11179.000000</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honda</th>\n",
       "      <td>4.666667</td>\n",
       "      <td>62778.333333</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nissan</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>122347.500000</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toyota</th>\n",
       "      <td>3.750000</td>\n",
       "      <td>85451.250000</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  Odometer (KM)  Doors\n",
       "Make                                    \n",
       "BMW       3.000000   11179.000000   5.00\n",
       "Honda     4.666667   62778.333333   4.00\n",
       "Nissan    6.500000  122347.500000   4.00\n",
       "Toyota    3.750000   85451.250000   3.75"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.groupby([\"Make\"]).mean(numeric_only=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "65b7655e-c2e5-49ed-a7df-ce3ed7c610a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[\"Odometer (KM)\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ff6316ba-9742-449c-8cbd-04d1163a0c74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[\"Doors\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5cfb10a6-7d90-4853-8e60-db5aee444f82",
   "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>Unnamed: 0</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>0</td>\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>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\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>6</td>\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>7</td>\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>8</td>\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>9</td>\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": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors       Price\n",
       "0           0  Toyota  White         150043      4   $4,000.00\n",
       "1           1   Honda    Red          87899      4   $5,000.00\n",
       "2           2  Toyota   Blue          32549      3   $7,000.00\n",
       "3           3     BMW  Black          11179      5  $22,000.00\n",
       "4           4  Nissan  White         213095      4   $3,500.00\n",
       "5           5  Toyota  Green          99213      4   $4,500.00\n",
       "6           6   Honda   Blue          45698      4   $7,500.00\n",
       "7           7   Honda   Blue          54738      4   $7,000.00\n",
       "8           8  Toyota  White          60000      4   $6,250.00\n",
       "9           9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "18997850-c21e-457d-ab7a-c974ef28fbed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: unrecognized arguments: #magic command used in Jupyter notebooks to display matplotlib plots directly within the notebook. When this command is used, the plots appear inline, meaning within the notebook itself, directly below the code cell that produces them.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline #magic command used in Jupyter notebooks to display matplotlib plots directly within the notebook. When this command is used, the plots appear inline, meaning within the notebook itself, directly below the code cell that produces them.\n",
    "import matplotlib.pyplot as plt\n",
    "car_data[\"Doors\"].plot()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "39c4a595-1b93-4c8b-9611-b2a94bf2321b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "car_data[\"Doors\"].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "eb9d6ef5-5432-4345-a324-e7d04b01cc93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[\"Odometer (KM)\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "64568c80-6e0c-499f-80a6-ff7ac188d8a7",
   "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>Unnamed: 0</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>0</td>\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>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\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>6</td>\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>7</td>\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>8</td>\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>9</td>\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": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors       Price\n",
       "0           0  Toyota  White         150043      4   $4,000.00\n",
       "1           1   Honda    Red          87899      4   $5,000.00\n",
       "2           2  Toyota   Blue          32549      3   $7,000.00\n",
       "3           3     BMW  Black          11179      5  $22,000.00\n",
       "4           4  Nissan  White         213095      4   $3,500.00\n",
       "5           5  Toyota  Green          99213      4   $4,500.00\n",
       "6           6   Honda   Blue          45698      4   $7,500.00\n",
       "7           7   Honda   Blue          54738      4   $7,000.00\n",
       "8           8  Toyota  White          60000      4   $6,250.00\n",
       "9           9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8ff9a99f-3747-40da-8fc4-3f496c731350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[\"Price\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4dcceacb-72d5-48ba-8c9d-eb0d5c4c29f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_data[\"Price\"] = car_data[\"Price\"].str.replace('[\\$\\,]|\\.\\d*', '', regex=True).astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0f3a246f-a783-4f0e-9f0b-22083ffb0405",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[\"Price\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2cbbe8e-8945-4b85-9694-853d585d9862",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Manipulating Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "97291967-c528-4c81-85bc-394bb9e337c3",
   "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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[\"Make\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "75afbe2f-f32b-43e0-91e6-58e8a5c9b28d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
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       "      <th>0</th>\n",
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       "      <td>toyota</td>\n",
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       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>7000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>nissan</td>\n",
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       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price\n",
       "0           0  toyota  White         150043      4   4000\n",
       "1           1   honda    Red          87899      4   5000\n",
       "2           2  toyota   Blue          32549      3   7000\n",
       "3           3     bmw  Black          11179      5  22000\n",
       "4           4  nissan  White         213095      4   3500\n",
       "5           5  toyota  Green          99213      4   4500\n",
       "6           6   honda   Blue          45698      4   7500\n",
       "7           7   honda   Blue          54738      4   7000\n",
       "8           8  toyota  White          60000      4   6250\n",
       "9           9  nissan  White          31600      4   9700"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "74d6880a-6aed-47df-bd45-63fee3926649",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note: The lowercase change is not  reflecting so in pandas we need to assign the updated value.\n",
    "car_data[\"Make\"]=car_data[\"Make\"].str.lower()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6b4fa1a1-5070-42b3-8712-5d3eca1dbc11",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>honda</td>\n",
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       "      <td>87899</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>toyota</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price\n",
       "0           0  toyota  White         150043      4   4000\n",
       "1           1   honda    Red          87899      4   5000\n",
       "2           2  toyota   Blue          32549      3   7000\n",
       "3           3     bmw  Black          11179      5  22000\n",
       "4           4  nissan  White         213095      4   3500\n",
       "5           5  toyota  Green          99213      4   4500\n",
       "6           6   honda   Blue          45698      4   7500\n",
       "7           7   honda   Blue          54738      4   7000\n",
       "8           8  toyota  White          60000      4   6250\n",
       "9           9  nissan  White          31600      4   9700"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e80c0bff-a308-4edc-8095-0c074df875ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing=pd.read_csv(\"car-sales-missing-data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1f8f43d7-3ace-47f7-8efb-3d768846c672",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\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>2</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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>4</td>\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>5</td>\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>6</td>\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>7</td>\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>8</td>\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>9</td>\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": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors       Price\n",
       "0           0  Toyota  White         150043      4   $4,000.00\n",
       "1           1   Honda    Red          87899      4   $5,000.00\n",
       "2           2  Toyota   Blue          32549      3   $7,000.00\n",
       "3           3     BMW  Black          11179      5  $22,000.00\n",
       "4           4  Nissan  White         213095      4   $3,500.00\n",
       "5           5  Toyota  Green          99213      4   $4,500.00\n",
       "6           6   Honda   Blue          45698      4   $7,500.00\n",
       "7           7   Honda   Blue          54738      4   $7,000.00\n",
       "8           8  Toyota  White          60000      4   $6,250.00\n",
       "9           9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f45d5eea-b615-4f79-8455-64525e75864d",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing[\"Doors\"]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "7616d432-6312-4b55-9d13-3297e78e7930",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing.loc[2, \"Doors\"] = None\n",
    "car_sales_missing.loc[4, \"Doors\"] = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "09350094-fe76-4f8b-8cba-c6b1205a92ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Make</th>\n",
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       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Nissan</td>\n",
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       "      <td>213095</td>\n",
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       "      <td>$3,500.00</td>\n",
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       "      <td>5</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500.00</td>\n",
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       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors       Price\n",
       "0           0  Toyota  White         150043    4.0   $4,000.00\n",
       "1           1   Honda    Red          87899    4.0   $5,000.00\n",
       "2           2  Toyota   Blue          32549    NaN   $7,000.00\n",
       "3           3     BMW  Black          11179    5.0  $22,000.00\n",
       "4           4  Nissan  White         213095    NaN   $3,500.00\n",
       "5           5  Toyota  Green          99213    4.0   $4,500.00\n",
       "6           6   Honda   Blue          45698    4.0   $7,500.00\n",
       "7           7   Honda   Blue          54738    4.0   $7,000.00\n",
       "8           8  Toyota  White          60000    4.0   $6,250.00\n",
       "9           9  Nissan  White          31600    4.0   $9,700.00"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f68c2fb7-38cb-4fb9-9936-4102557b9416",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing.loc[3, \"Odometer (KM)\"] = None\n",
    "car_sales_missing.loc[5, \"Odometer (KM)\"] = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7aafc4fe-5d6d-461a-8258-32374ebc56e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000.00</td>\n",
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       "      <th>4</th>\n",
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       "      <td>Nissan</td>\n",
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       "      <td>213095</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>5</td>\n",
       "      <td>Toyota</td>\n",
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       "      <td>99213</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
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       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,000.00</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors       Price\n",
       "0           0  Toyota  White         150043    4.0   $4,000.00\n",
       "1           1   Honda    Red          87899    4.0   $5,000.00\n",
       "2           2  Toyota   Blue          32549    NaN   $7,000.00\n",
       "3           3     BMW  Black          11179    5.0  $22,000.00\n",
       "4           4  Nissan  White         213095    NaN   $3,500.00\n",
       "5           5  Toyota  Green          99213    4.0   $4,500.00\n",
       "6           6   Honda   Blue          45698    4.0   $7,500.00\n",
       "7           7   Honda   Blue          54738    4.0   $7,000.00\n",
       "8           8  Toyota  White          60000    4.0   $6,250.00\n",
       "9           9  Nissan  White          31600    4.0   $9,700.00"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "dce987bc-3597-4d43-9e85-d4e5c49a8703",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing.rename(columns={\"Odometer (KM)\": \"Odometer\"}, inplace=True) #Renaming\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "3a04e52a-6aa2-4894-8918-c01a513776de",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <td>BMW</td>\n",
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       "      <td>11179</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Nissan</td>\n",
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       "      <td>213095</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$3,500.00</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,000.00</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer  Doors       Price\n",
       "0           0  Toyota  White    150043    4.0   $4,000.00\n",
       "1           1   Honda    Red     87899    4.0   $5,000.00\n",
       "2           2  Toyota   Blue     32549    NaN   $7,000.00\n",
       "3           3     BMW  Black     11179    5.0  $22,000.00\n",
       "4           4  Nissan  White    213095    NaN   $3,500.00\n",
       "5           5  Toyota  Green     99213    4.0   $4,500.00\n",
       "6           6   Honda   Blue     45698    4.0   $7,500.00\n",
       "7           7   Honda   Blue     54738    4.0   $7,000.00\n",
       "8           8  Toyota  White     60000    4.0   $6,250.00\n",
       "9           9  Nissan  White     31600    4.0   $9,700.00"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5e9741ae-eb1e-420f-a64a-a09f4c6c3aa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78601.4"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing[\"Odometer\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "5906b967-3600-4e38-9c32-b8d5ad380749",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing[\"Odometer\"]=car_sales_missing[\"Odometer\"].fillna(car_sales_missing[\"Odometer\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "dd6acbc1-a596-4a40-956c-ff287a372010",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
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       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Nissan</td>\n",
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       "      <td>213095</td>\n",
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       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500.00</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer  Doors       Price\n",
       "0           0  Toyota  White    150043    4.0   $4,000.00\n",
       "1           1   Honda    Red     87899    4.0   $5,000.00\n",
       "2           2  Toyota   Blue     32549    NaN   $7,000.00\n",
       "3           3     BMW  Black     11179    5.0  $22,000.00\n",
       "4           4  Nissan  White    213095    NaN   $3,500.00\n",
       "5           5  Toyota  Green     99213    4.0   $4,500.00\n",
       "6           6   Honda   Blue     45698    4.0   $7,500.00\n",
       "7           7   Honda   Blue     54738    4.0   $7,000.00\n",
       "8           8  Toyota  White     60000    4.0   $6,250.00\n",
       "9           9  Nissan  White     31600    4.0   $9,700.00"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing"
   ]
  },
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   "cell_type": "code",
   "execution_count": 86,
   "id": "d5418fce-5de2-48ee-9989-54e4649a18dc",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Toyota</td>\n",
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       "      <td>4.0</td>\n",
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      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer  Doors       Price\n",
       "0           0  Toyota  White    150043    4.0   $4,000.00\n",
       "1           1   Honda    Red     87899    4.0   $5,000.00\n",
       "3           3     BMW  Black     11179    5.0  $22,000.00\n",
       "5           5  Toyota  Green     99213    4.0   $4,500.00\n",
       "6           6   Honda   Blue     45698    4.0   $7,500.00\n",
       "7           7   Honda   Blue     54738    4.0   $7,000.00\n",
       "8           8  Toyota  White     60000    4.0   $6,250.00\n",
       "9           9  Nissan  White     31600    4.0   $9,700.00"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing.dropna() #Drop the missing data row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "dfc085c1-59bf-4cb1-8889-c79d38e6959f",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price\n",
       "0           0  toyota  White         150043      4   4000\n",
       "1           1   honda    Red          87899      4   5000\n",
       "2           2  toyota   Blue          32549      3   7000\n",
       "3           3     bmw  Black          11179      5  22000\n",
       "4           4  nissan  White         213095      4   3500\n",
       "5           5  toyota  Green          99213      4   4500\n",
       "6           6   honda   Blue          45698      4   7500\n",
       "7           7   honda   Blue          54738      4   7000\n",
       "8           8  toyota  White          60000      4   6250\n",
       "9           9  nissan  White          31600      4   9700"
      ]
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     "execution_count": 89,
     "metadata": {},
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    }
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   "source": [
    "\n",
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2075f0ef-8d72-42dd-b5c0-f990d6dff0e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Insert new Column::\n",
    "seats_column=pd.Series([\"5\",\"5\",\"5\",\"5\"])\n",
    "car_data[\"Seats\"]=seats_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9be70ee7-7dc5-4643-a50c-e318de3ad101",
   "metadata": {},
   "outputs": [
    {
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       "      <td>toyota</td>\n",
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       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
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       "    <tr>\n",
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       "      <td>9</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price Seats\n",
       "0           0  toyota  White         150043      4   4000     5\n",
       "1           1   honda    Red          87899      4   5000     5\n",
       "2           2  toyota   Blue          32549      3   7000     5\n",
       "3           3     bmw  Black          11179      5  22000     5\n",
       "4           4  nissan  White         213095      4   3500   NaN\n",
       "5           5  toyota  Green          99213      4   4500   NaN\n",
       "6           6   honda   Blue          45698      4   7500   NaN\n",
       "7           7   honda   Blue          54738      4   7000   NaN\n",
       "8           8  toyota  White          60000      4   6250   NaN\n",
       "9           9  nissan  White          31600      4   9700   NaN"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "5121c704-3d7b-433f-abcc-fa18d142ac84",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_data[\"Seats\"].fillna(5,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a3358dc6-7d17-4273-abd8-11152a92bc47",
   "metadata": {},
   "outputs": [
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       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>7000</td>\n",
       "      <td>5</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>3500</td>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price Seats Logo\n",
       "0           0  toyota  White         150043      4   4000     5    L\n",
       "1           1   honda    Red          87899      4   5000     5    A\n",
       "2           2  toyota   Blue          32549      3   7000     5    K\n",
       "3           3     bmw  Black          11179      5  22000     5    N\n",
       "4           4  nissan  White         213095      4   3500     5    M\n",
       "5           5  toyota  Green          99213      4   4500     5  NaN\n",
       "6           6   honda   Blue          45698      4   7500     5  NaN\n",
       "7           7   honda   Blue          54738      4   7000     5  NaN\n",
       "8           8  toyota  White          60000      4   6250     5  NaN\n",
       "9           9  nissan  White          31600      4   9700     5  NaN"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_logo=pd.Series([\"L\",\"A\",\"K\",\"N\",\"M\"])\n",
    "car_data[\"Logo\"]=car_logo\n",
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "6877f7a4-f208-4fee-af6d-4fd1e7bb6bcb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "003b21ee-c011-4efe-8006-d5e544d3eb24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Make</th>\n",
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       "    <tr>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>nissan</td>\n",
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       "      <td>213095</td>\n",
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       "      <td>3500</td>\n",
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       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>honda</td>\n",
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       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price Seats Logo\n",
       "0           0  toyota  White         150043      4   4000     5    L\n",
       "1           1   honda    Red          87899      4   5000     5    A\n",
       "2           2  toyota   Blue          32549      3   7000     5    K\n",
       "3           3     bmw  Black          11179      5  22000     5    N\n",
       "4           4  nissan  White         213095      4   3500     5    M\n",
       "5           5  toyota  Green          99213      4   4500     5  NaN\n",
       "6           6   honda   Blue          45698      4   7500     5  NaN\n",
       "7           7   honda   Blue          54738      4   7000     5  NaN\n",
       "8           8  toyota  White          60000      4   6250     5  NaN\n",
       "9           9  nissan  White          31600      4   9700     5  NaN"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d6fcf3af-e979-4b43-acf2-f7fca7fa4355",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Make</th>\n",
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       "      <th>3</th>\n",
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       "      <td>11179</td>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
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       "      <td>Green</td>\n",
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       "      <th>6</th>\n",
       "      <td>6</td>\n",
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       "      <td>H</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price Seats Logo\n",
       "0           0  toyota  White         150043      4   4000     5    L\n",
       "1           1   honda    Red          87899      4   5000     5    A\n",
       "2           2  toyota   Blue          32549      3   7000     5    K\n",
       "3           3     bmw  Black          11179      5  22000     5    N\n",
       "4           4  nissan  White         213095      4   3500     5    M\n",
       "5           5  toyota  Green          99213      4   4500     5    H\n",
       "6           6   honda   Blue          45698      4   7500     5    H\n",
       "7           7   honda   Blue          54738      4   7000     5    H\n",
       "8           8  toyota  White          60000      4   6250     5    H\n",
       "9           9  nissan  White          31600      4   9700     5    H"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[\"Logo\"].fillna(\"H\",inplace=True)\n",
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "ea1ce258-acbd-4c3d-9bc5-bd3f96d52d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "      <th>5</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>7500</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price Seats Logo  \\\n",
       "0           0  toyota  White         150043      4   4000     5    L   \n",
       "1           1   honda    Red          87899      4   5000     5    A   \n",
       "2           2  toyota   Blue          32549      3   7000     5    K   \n",
       "3           3     bmw  Black          11179      5  22000     5    N   \n",
       "4           4  nissan  White         213095      4   3500     5    M   \n",
       "5           5  toyota  Green          99213      4   4500     5    H   \n",
       "6           6   honda   Blue          45698      4   7500     5    H   \n",
       "7           7   honda   Blue          54738      4   7000     5    H   \n",
       "8           8  toyota  White          60000      4   6250     5    H   \n",
       "9           9  nissan  White          31600      4   9700     5    H   \n",
       "\n",
       "   Fuel per 100 KM  \n",
       "0              3.3  \n",
       "1              6.7  \n",
       "2              8.9  \n",
       "3              7.0  \n",
       "4              9.8  \n",
       "5              5.7  \n",
       "6              8.1  \n",
       "7              6.5  \n",
       "8              8.0  \n",
       "9              6.6  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Column from python list--(For this length of list should be same of the dataframe)\n",
    "fuel_economy=[3.3,6.7,8.9,7.0,9.8,5.7,8.1,6.5,8.0,6.6]\n",
    "car_data[\"Fuel per 100 KM\"]=fuel_economy\n",
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "c1b2455f-867a-424d-8380-d9bc47ca6da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Unnamed: 0</th>\n",
       "      <th>Make</th>\n",
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       "      <th>Seats</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
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       "      <td>4000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>honda</td>\n",
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       "      <td>87899</td>\n",
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       "      <td>A</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "      <td>7.0</td>\n",
       "      <td>782.530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>3500</td>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>9.8</td>\n",
       "      <td>20883.310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5655.141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3701.538</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3557.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2085.600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price Seats Logo  \\\n",
       "0           0  toyota  White         150043      4   4000     5    L   \n",
       "1           1   honda    Red          87899      4   5000     5    A   \n",
       "2           2  toyota   Blue          32549      3   7000     5    K   \n",
       "3           3     bmw  Black          11179      5  22000     5    N   \n",
       "4           4  nissan  White         213095      4   3500     5    M   \n",
       "5           5  toyota  Green          99213      4   4500     5    H   \n",
       "6           6   honda   Blue          45698      4   7500     5    H   \n",
       "7           7   honda   Blue          54738      4   7000     5    H   \n",
       "8           8  toyota  White          60000      4   6250     5    H   \n",
       "9           9  nissan  White          31600      4   9700     5    H   \n",
       "\n",
       "   Fuel per 100 KM  Total Fuels used  \n",
       "0              3.3          4951.419  \n",
       "1              6.7          5889.233  \n",
       "2              8.9          2896.861  \n",
       "3              7.0           782.530  \n",
       "4              9.8         20883.310  \n",
       "5              5.7          5655.141  \n",
       "6              8.1          3701.538  \n",
       "7              6.5          3557.970  \n",
       "8              8.0          4800.000  \n",
       "9              6.6          2085.600  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#column with other columns\n",
    "car_data[\"Total Fuels used\"]=(car_data[\"Odometer (KM)\"]/100)* car_data[\"Fuel per 100 KM\"]\n",
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "524ecbd5-c687-4c1f-b54a-9bc6b0155765",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>4500</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
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       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
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       "      <td>9</td>\n",
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       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2085.600</td>\n",
       "      <td>4</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price Seats Logo  \\\n",
       "0           0  toyota  White         150043      4   4000     5    L   \n",
       "1           1   honda    Red          87899      4   5000     5    A   \n",
       "2           2  toyota   Blue          32549      3   7000     5    K   \n",
       "3           3     bmw  Black          11179      5  22000     5    N   \n",
       "4           4  nissan  White         213095      4   3500     5    M   \n",
       "5           5  toyota  Green          99213      4   4500     5    H   \n",
       "6           6   honda   Blue          45698      4   7500     5    H   \n",
       "7           7   honda   Blue          54738      4   7000     5    H   \n",
       "8           8  toyota  White          60000      4   6250     5    H   \n",
       "9           9  nissan  White          31600      4   9700     5    H   \n",
       "\n",
       "   Fuel per 100 KM  Total Fuels used  Wheels  \n",
       "0              3.3          4951.419       4  \n",
       "1              6.7          5889.233       4  \n",
       "2              8.9          2896.861       4  \n",
       "3              7.0           782.530       4  \n",
       "4              9.8         20883.310       4  \n",
       "5              5.7          5655.141       4  \n",
       "6              8.1          3701.538       4  \n",
       "7              6.5          3557.970       4  \n",
       "8              8.0          4800.000       4  \n",
       "9              6.6          2085.600       4  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data[\"Wheels\"]=4\n",
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b2c19dd3-01f8-4912-843c-35b99a8af84c",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_data[\"Launched\"]=\"Yes\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "aaf6e2a2-dfd6-4d48-9fa0-bf4201488fee",
   "metadata": {},
   "outputs": [
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       "      <td>5000</td>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>6.7</td>\n",
       "      <td>5889.233</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>7000</td>\n",
       "      <td>5</td>\n",
       "      <td>K</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2896.861</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "      <td>7.0</td>\n",
       "      <td>782.530</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>3500</td>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>9.8</td>\n",
       "      <td>20883.310</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5655.141</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3701.538</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3557.970</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4800.000</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2085.600</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price Seats Logo  \\\n",
       "0           0  toyota  White         150043      4   4000     5    L   \n",
       "1           1   honda    Red          87899      4   5000     5    A   \n",
       "2           2  toyota   Blue          32549      3   7000     5    K   \n",
       "3           3     bmw  Black          11179      5  22000     5    N   \n",
       "4           4  nissan  White         213095      4   3500     5    M   \n",
       "5           5  toyota  Green          99213      4   4500     5    H   \n",
       "6           6   honda   Blue          45698      4   7500     5    H   \n",
       "7           7   honda   Blue          54738      4   7000     5    H   \n",
       "8           8  toyota  White          60000      4   6250     5    H   \n",
       "9           9  nissan  White          31600      4   9700     5    H   \n",
       "\n",
       "   Fuel per 100 KM  Total Fuels used  Wheels Launched  \n",
       "0              3.3          4951.419       4      Yes  \n",
       "1              6.7          5889.233       4      Yes  \n",
       "2              8.9          2896.861       4      Yes  \n",
       "3              7.0           782.530       4      Yes  \n",
       "4              9.8         20883.310       4      Yes  \n",
       "5              5.7          5655.141       4      Yes  \n",
       "6              8.1          3701.538       4      Yes  \n",
       "7              6.5          3557.970       4      Yes  \n",
       "8              8.0          4800.000       4      Yes  \n",
       "9              6.6          2085.600       4      Yes  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data"
   ]
  },
  {
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   "execution_count": 115,
   "id": "5d9f9c89-d10b-4665-b568-9d83f8fe3eee",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price Seats Logo  \\\n",
       "1           1   honda    Red          87899      4   5000     5    A   \n",
       "4           4  nissan  White         213095      4   3500     5    M   \n",
       "3           3     bmw  Black          11179      5  22000     5    N   \n",
       "8           8  toyota  White          60000      4   6250     5    H   \n",
       "5           5  toyota  Green          99213      4   4500     5    H   \n",
       "2           2  toyota   Blue          32549      3   7000     5    K   \n",
       "0           0  toyota  White         150043      4   4000     5    L   \n",
       "9           9  nissan  White          31600      4   9700     5    H   \n",
       "6           6   honda   Blue          45698      4   7500     5    H   \n",
       "7           7   honda   Blue          54738      4   7000     5    H   \n",
       "\n",
       "   Fuel per 100 KM  Total Fuels used  Wheels Launched  \n",
       "1              6.7          5889.233       4      Yes  \n",
       "4              9.8         20883.310       4      Yes  \n",
       "3              7.0           782.530       4      Yes  \n",
       "8              8.0          4800.000       4      Yes  \n",
       "5              5.7          5655.141       4      Yes  \n",
       "2              8.9          2896.861       4      Yes  \n",
       "0              3.3          4951.419       4      Yes  \n",
       "9              6.6          2085.600       4      Yes  \n",
       "6              8.1          3701.538       4      Yes  \n",
       "7              6.5          3557.970       4      Yes  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Randomizing the data: Needed for ML algorithms to get the better results--\n",
    "car_data.sample(frac=1)  #frac 1 means applied on 100% data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "fd37368d-9236-4a5a-a1fb-ca0e57f3b003",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price Seats Logo  \\\n",
       "9           9  nissan  White          31600      4   9700     5    H   \n",
       "3           3     bmw  Black          11179      5  22000     5    N   \n",
       "7           7   honda   Blue          54738      4   7000     5    H   \n",
       "6           6   honda   Blue          45698      4   7500     5    H   \n",
       "5           5  toyota  Green          99213      4   4500     5    H   \n",
       "\n",
       "   Fuel per 100 KM  Total Fuels used  Wheels Launched  \n",
       "9              6.6          2085.600       4      Yes  \n",
       "3              7.0           782.530       4      Yes  \n",
       "7              6.5          3557.970       4      Yes  \n",
       "6              8.1          3701.538       4      Yes  \n",
       "5              5.7          5655.141       4      Yes  "
      ]
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     "execution_count": 116,
     "metadata": {},
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   "source": [
    "car_data.sample(frac=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "f80cd432-85c7-48b6-adc0-29c467dbe469",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_data_shuffled=car_data.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "0d7a6678-1b18-4cac-b706-c9ccf7214451",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>Logo</th>\n",
       "      <th>Fuel per 100 KM</th>\n",
       "      <th>Total Fuels used</th>\n",
       "      <th>Wheels</th>\n",
       "      <th>Launched</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3557.970</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>5000</td>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>6.7</td>\n",
       "      <td>5889.233</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000</td>\n",
       "      <td>5</td>\n",
       "      <td>L</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4951.419</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4800.000</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2085.600</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3701.538</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5655.141</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "      <td>7.0</td>\n",
       "      <td>782.530</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>3500</td>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>9.8</td>\n",
       "      <td>20883.310</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>7000</td>\n",
       "      <td>5</td>\n",
       "      <td>K</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2896.861</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price Seats Logo  \\\n",
       "7           7   honda   Blue          54738      4   7000     5    H   \n",
       "1           1   honda    Red          87899      4   5000     5    A   \n",
       "0           0  toyota  White         150043      4   4000     5    L   \n",
       "8           8  toyota  White          60000      4   6250     5    H   \n",
       "9           9  nissan  White          31600      4   9700     5    H   \n",
       "6           6   honda   Blue          45698      4   7500     5    H   \n",
       "5           5  toyota  Green          99213      4   4500     5    H   \n",
       "3           3     bmw  Black          11179      5  22000     5    N   \n",
       "4           4  nissan  White         213095      4   3500     5    M   \n",
       "2           2  toyota   Blue          32549      3   7000     5    K   \n",
       "\n",
       "   Fuel per 100 KM  Total Fuels used  Wheels Launched  \n",
       "7              6.5          3557.970       4      Yes  \n",
       "1              6.7          5889.233       4      Yes  \n",
       "0              3.3          4951.419       4      Yes  \n",
       "8              8.0          4800.000       4      Yes  \n",
       "9              6.6          2085.600       4      Yes  \n",
       "6              8.1          3701.538       4      Yes  \n",
       "5              5.7          5655.141       4      Yes  \n",
       "3              7.0           782.530       4      Yes  \n",
       "4              9.8         20883.310       4      Yes  \n",
       "2              8.9          2896.861       4      Yes  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data_shuffled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "cef2454f-bfa9-4822-99d8-af53fd6d86e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Unnamed: 0</th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
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       "  <tbody>\n",
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       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
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       "      <td>Yes</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price Seats Logo  \\\n",
       "7           7   honda   Blue          54738      4   7000     5    H   \n",
       "0           0  toyota  White         150043      4   4000     5    L   \n",
       "\n",
       "   Fuel per 100 KM  Total Fuels used  Wheels Launched  \n",
       "7              6.5          3557.970       4      Yes  \n",
       "0              3.3          4951.419       4      Yes  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#In real life we need to deal with millions of rows--\n",
    "#Selecting 20% data::\n",
    "car_data_shuffled.sample(frac=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "4930bdf3-36b3-4497-ad6f-fad045b018c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_data_shuffled[\"Odometer (KM)\"]=car_data_shuffled[\"Odometer (KM)\"].apply(lambda x: x/1.6)  #KM/1.6=miles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "424ffe14-a6dd-4020-bdd5-889fcc7d0cd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
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       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>honda</td>\n",
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       "      <td>Yes</td>\n",
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       "      <th>0</th>\n",
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       "      <td>4</td>\n",
       "      <td>4000</td>\n",
       "      <td>5</td>\n",
       "      <td>L</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4951.419</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>37500.000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "      <td>5</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>4800.000</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>19750.000</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
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       "      <td>2085.600</td>\n",
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       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>28561.250</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3701.538</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>62008.125</td>\n",
       "      <td>4</td>\n",
       "      <td>4500</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5655.141</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>6986.875</td>\n",
       "      <td>5</td>\n",
       "      <td>22000</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
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       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>133184.375</td>\n",
       "      <td>4</td>\n",
       "      <td>3500</td>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
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       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>20343.125</td>\n",
       "      <td>3</td>\n",
       "      <td>7000</td>\n",
       "      <td>5</td>\n",
       "      <td>K</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2896.861</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Make Colour  Odometer (KM)  Doors  Price Seats Logo  \\\n",
       "7           7   honda   Blue      34211.250      4   7000     5    H   \n",
       "1           1   honda    Red      54936.875      4   5000     5    A   \n",
       "0           0  toyota  White      93776.875      4   4000     5    L   \n",
       "8           8  toyota  White      37500.000      4   6250     5    H   \n",
       "9           9  nissan  White      19750.000      4   9700     5    H   \n",
       "6           6   honda   Blue      28561.250      4   7500     5    H   \n",
       "5           5  toyota  Green      62008.125      4   4500     5    H   \n",
       "3           3     bmw  Black       6986.875      5  22000     5    N   \n",
       "4           4  nissan  White     133184.375      4   3500     5    M   \n",
       "2           2  toyota   Blue      20343.125      3   7000     5    K   \n",
       "\n",
       "   Fuel per 100 KM  Total Fuels used  Wheels Launched  \n",
       "7              6.5          3557.970       4      Yes  \n",
       "1              6.7          5889.233       4      Yes  \n",
       "0              3.3          4951.419       4      Yes  \n",
       "8              8.0          4800.000       4      Yes  \n",
       "9              6.6          2085.600       4      Yes  \n",
       "6              8.1          3701.538       4      Yes  \n",
       "5              5.7          5655.141       4      Yes  \n",
       "3              7.0           782.530       4      Yes  \n",
       "4              9.8         20883.310       4      Yes  \n",
       "2              8.9          2896.861       4      Yes  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data_shuffled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "888c9e82-1f14-4a49-bea8-8de4b48a77d2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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