Case study 01 .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a2376323",
   "metadata": {},
   "source": [
    "Do the following operations on ‘cars_data’ dataset:\n",
    "1. Read the dataset to the python environment.\n",
    "2. Check for the null values present in the dataset.\n",
    "3. Plot a bar graph of male vs female buyers participated in the sales.\n",
    "4. Find the top 5 cars based on their sales price.\n",
    "5. Find the least 5 cars based on their Resell price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dffe3c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read the dataset to the python environment.\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "car_df=pd.read_csv('cars_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9b3b7a98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Nickname</th>\n",
       "      <th>Car Gender</th>\n",
       "      <th>Buyer Gender</th>\n",
       "      <th>Buyer Age</th>\n",
       "      <th>Buzzword</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Dealer Latitude</th>\n",
       "      <th>Dealer Longitude</th>\n",
       "      <th>Color</th>\n",
       "      <th>New Car</th>\n",
       "      <th>Purchase Date</th>\n",
       "      <th>Sale Price</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Resell Price</th>\n",
       "      <th>5-yr Depreciation</th>\n",
       "      <th>Top Speed</th>\n",
       "      <th>0-60 Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Suzuki</td>\n",
       "      <td>Vitara</td>\n",
       "      <td>Philippine</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>51</td>\n",
       "      <td>User-friendly</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>51.247239</td>\n",
       "      <td>4.440346</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>False</td>\n",
       "      <td>19/07/2009</td>\n",
       "      <td>54806.14</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>33858.32</td>\n",
       "      <td>0.13</td>\n",
       "      <td>200.9</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>S2000</td>\n",
       "      <td>Henrik</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>analyzing</td>\n",
       "      <td>China</td>\n",
       "      <td>Jinhu</td>\n",
       "      <td>33.025433</td>\n",
       "      <td>119.020584</td>\n",
       "      <td>Crimson</td>\n",
       "      <td>False</td>\n",
       "      <td>26/10/2015</td>\n",
       "      <td>51826.30</td>\n",
       "      <td>0.3147</td>\n",
       "      <td>2989.28</td>\n",
       "      <td>0.02</td>\n",
       "      <td>158.5</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Z4</td>\n",
       "      <td>Lebbie</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>54</td>\n",
       "      <td>software</td>\n",
       "      <td>China</td>\n",
       "      <td>Gemo</td>\n",
       "      <td>30.916207</td>\n",
       "      <td>121.677640</td>\n",
       "      <td>Khaki</td>\n",
       "      <td>False</td>\n",
       "      <td>28/03/2010</td>\n",
       "      <td>82929.14</td>\n",
       "      <td>0.5414</td>\n",
       "      <td>35049.16</td>\n",
       "      <td>0.24</td>\n",
       "      <td>149.5</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Tacoma</td>\n",
       "      <td>Jefferey</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>68</td>\n",
       "      <td>monitoring</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Queluz de Baixo</td>\n",
       "      <td>38.745168</td>\n",
       "      <td>-9.267087</td>\n",
       "      <td>Puce</td>\n",
       "      <td>False</td>\n",
       "      <td>07/05/2008</td>\n",
       "      <td>56928.66</td>\n",
       "      <td>0.0850</td>\n",
       "      <td>8236.15</td>\n",
       "      <td>0.20</td>\n",
       "      <td>153.3</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Festiva</td>\n",
       "      <td>Glendon</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>70</td>\n",
       "      <td>Sharable</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>Jaunpils</td>\n",
       "      <td>56.729980</td>\n",
       "      <td>23.016588</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>False</td>\n",
       "      <td>01/06/2008</td>\n",
       "      <td>77201.26</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>32765.76</td>\n",
       "      <td>0.18</td>\n",
       "      <td>122.0</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make    Model    Nickname Car Gender Buyer Gender  Buyer Age  \\\n",
       "0  Suzuki   Vitara  Philippine     Female       Female         51   \n",
       "1   Honda    S2000      Henrik       Male         Male         30   \n",
       "2     BMW       Z4      Lebbie       Male       Female         54   \n",
       "3  Toyota   Tacoma    Jefferey     Female         Male         68   \n",
       "4    Ford  Festiva     Glendon     Female         Male         70   \n",
       "\n",
       "        Buzzword   Country             City  Dealer Latitude  \\\n",
       "0  User-friendly   Belgium        Antwerpen        51.247239   \n",
       "1      analyzing     China            Jinhu        33.025433   \n",
       "2       software     China             Gemo        30.916207   \n",
       "3     monitoring  Portugal  Queluz de Baixo        38.745168   \n",
       "4       Sharable    Latvia         Jaunpils        56.729980   \n",
       "\n",
       "   Dealer Longitude    Color  New Car Purchase Date  Sale Price  Discount  \\\n",
       "0          4.440346   Yellow    False    19/07/2009    54806.14    0.2467   \n",
       "1        119.020584  Crimson    False    26/10/2015    51826.30    0.3147   \n",
       "2        121.677640    Khaki    False    28/03/2010    82929.14    0.5414   \n",
       "3         -9.267087     Puce    False    07/05/2008    56928.66    0.0850   \n",
       "4         23.016588   Yellow    False    01/06/2008    77201.26    0.1642   \n",
       "\n",
       "   Resell Price  5-yr Depreciation  Top Speed  0-60 Time  \n",
       "0      33858.32               0.13      200.9       11.5  \n",
       "1       2989.28               0.02      158.5        8.8  \n",
       "2      35049.16               0.24      149.5       14.4  \n",
       "3       8236.15               0.20      153.3       11.5  \n",
       "4      32765.76               0.18      122.0       14.9  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the details of  car data set\n",
    "car_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07fc0425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 20 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Make               10000 non-null  object \n",
      " 1   Model              10000 non-null  object \n",
      " 2   Nickname           10000 non-null  object \n",
      " 3   Car Gender         10000 non-null  object \n",
      " 4   Buyer Gender       10000 non-null  object \n",
      " 5   Buyer Age          10000 non-null  int64  \n",
      " 6   Buzzword           10000 non-null  object \n",
      " 7   Country            10000 non-null  object \n",
      " 8   City               10000 non-null  object \n",
      " 9   Dealer Latitude    10000 non-null  float64\n",
      " 10  Dealer Longitude   10000 non-null  float64\n",
      " 11  Color              10000 non-null  object \n",
      " 12  New Car            10000 non-null  bool   \n",
      " 13  Purchase Date      10000 non-null  object \n",
      " 14  Sale Price         10000 non-null  float64\n",
      " 15  Discount           10000 non-null  float64\n",
      " 16  Resell Price       10000 non-null  float64\n",
      " 17  5-yr Depreciation  10000 non-null  float64\n",
      " 18  Top Speed          10000 non-null  float64\n",
      " 19  0-60 Time          10000 non-null  float64\n",
      "dtypes: bool(1), float64(8), int64(1), object(10)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "car_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "69715f64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                 0\n",
       "Model                0\n",
       "Nickname             0\n",
       "Car Gender           0\n",
       "Buyer Gender         0\n",
       "Buyer Age            0\n",
       "Buzzword             0\n",
       "Country              0\n",
       "City                 0\n",
       "Dealer Latitude      0\n",
       "Dealer Longitude     0\n",
       "Color                0\n",
       "New Car              0\n",
       "Purchase Date        0\n",
       "Sale Price           0\n",
       "Discount             0\n",
       "Resell Price         0\n",
       "5-yr Depreciation    0\n",
       "Top Speed            0\n",
       "0-60 Time            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Check for the null values present in the dataset.\n",
    "car_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "89f98050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([5052.,    0.,    0., 4948.]),\n",
       " array([0.  , 0.25, 0.5 , 0.75, 1.  ]),\n",
       " <BarContainer object of 4 artists>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bk/x8274M+A7gyVHO/3IO/ZnCdJi/8Qz7T7bl1wfWX9pemOQG4C3AG6vqf5I8xPQf1sxz7Kqq7fM1aEkaYi/we8ANwOCMw3uBT1bVO9pfDA8N6RvgR6vqnH6I8uU8vTPTYWAiyRsBklyS5No59H8N8B8t8L8HWD+kzX7gnUkWt3NcmWToL9VJ0nm4F/iNqjo4o/4a/u/B7rvP0PdjwE+nTUckuW4uJ37FhH77/f13Au9L8jngMeD753CIjzJ9x/8403+bfnrIOZ4AfgX4eGu3D1hynkOXpP+nqo5W1R8N2fU7wG8n+Semp7CHeS/T0z6PJ/l82x6ZP8MgSR15xdzpS5LOn6EvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOvK/GVPUUQTj11kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a bar graph of male vs female buyers participated in the sales.\n",
    "plt.hist(car_df['Buyer Gender'],bins=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "463e4751",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    5052\n",
       "Male      4948\n",
       "Name: Buyer Gender, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_df['Buyer Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "230c096f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can deduce from the graph above that there isn't much of a difference in buyer gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "415fc32e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Nickname</th>\n",
       "      <th>Car Gender</th>\n",
       "      <th>Buyer Gender</th>\n",
       "      <th>Buyer Age</th>\n",
       "      <th>Buzzword</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Dealer Latitude</th>\n",
       "      <th>Dealer Longitude</th>\n",
       "      <th>Color</th>\n",
       "      <th>New Car</th>\n",
       "      <th>Purchase Date</th>\n",
       "      <th>Sale Price</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Resell Price</th>\n",
       "      <th>5-yr Depreciation</th>\n",
       "      <th>Top Speed</th>\n",
       "      <th>0-60 Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3513</th>\n",
       "      <td>Geo</td>\n",
       "      <td>Tracker</td>\n",
       "      <td>Nowell</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>64</td>\n",
       "      <td>impactful</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Hagi</td>\n",
       "      <td>32.428624</td>\n",
       "      <td>130.649340</td>\n",
       "      <td>Orange</td>\n",
       "      <td>True</td>\n",
       "      <td>28/01/2016</td>\n",
       "      <td>10003.76</td>\n",
       "      <td>0.6230</td>\n",
       "      <td>41226.94</td>\n",
       "      <td>0.09</td>\n",
       "      <td>219.9</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7234</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Excursion</td>\n",
       "      <td>Patric</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>Decentralized</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>Zaqatala</td>\n",
       "      <td>41.603085</td>\n",
       "      <td>46.638308</td>\n",
       "      <td>Green</td>\n",
       "      <td>True</td>\n",
       "      <td>31/07/2004</td>\n",
       "      <td>10019.95</td>\n",
       "      <td>0.3103</td>\n",
       "      <td>22336.96</td>\n",
       "      <td>0.20</td>\n",
       "      <td>197.7</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5956</th>\n",
       "      <td>Mazda</td>\n",
       "      <td>Mazda6</td>\n",
       "      <td>Milzie</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>motivating</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Shezë</td>\n",
       "      <td>41.021678</td>\n",
       "      <td>19.822316</td>\n",
       "      <td>Turquoise</td>\n",
       "      <td>False</td>\n",
       "      <td>07/01/2004</td>\n",
       "      <td>10023.85</td>\n",
       "      <td>0.3271</td>\n",
       "      <td>34125.80</td>\n",
       "      <td>0.20</td>\n",
       "      <td>246.3</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6270</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>240SX</td>\n",
       "      <td>Paloma</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>48</td>\n",
       "      <td>Self-enabling</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>Lanas</td>\n",
       "      <td>18.168683</td>\n",
       "      <td>120.705003</td>\n",
       "      <td>Pink</td>\n",
       "      <td>True</td>\n",
       "      <td>10/12/2017</td>\n",
       "      <td>10024.86</td>\n",
       "      <td>0.5319</td>\n",
       "      <td>12695.97</td>\n",
       "      <td>0.04</td>\n",
       "      <td>138.7</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6241</th>\n",
       "      <td>Lincoln</td>\n",
       "      <td>Continental Mark VII</td>\n",
       "      <td>Malissa</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>57</td>\n",
       "      <td>budgetary management</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Safakulevo</td>\n",
       "      <td>54.989789</td>\n",
       "      <td>62.547128</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>False</td>\n",
       "      <td>14/10/2009</td>\n",
       "      <td>10039.82</td>\n",
       "      <td>0.1612</td>\n",
       "      <td>10343.75</td>\n",
       "      <td>0.18</td>\n",
       "      <td>182.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Make                 Model Nickname Car Gender Buyer Gender  \\\n",
       "3513      Geo               Tracker   Nowell       Male         Male   \n",
       "7234     Ford             Excursion   Patric       Male         Male   \n",
       "5956    Mazda                Mazda6   Milzie       Male       Female   \n",
       "6270   Nissan                 240SX   Paloma       Male       Female   \n",
       "6241  Lincoln  Continental Mark VII  Malissa     Female       Female   \n",
       "\n",
       "      Buyer Age              Buzzword      Country        City  \\\n",
       "3513         64             impactful        Japan        Hagi   \n",
       "7234         30         Decentralized   Azerbaijan    Zaqatala   \n",
       "5956         39            motivating      Albania       Shezë   \n",
       "6270         48         Self-enabling  Philippines       Lanas   \n",
       "6241         57  budgetary management       Russia  Safakulevo   \n",
       "\n",
       "      Dealer Latitude  Dealer Longitude      Color  New Car Purchase Date  \\\n",
       "3513        32.428624        130.649340     Orange     True    28/01/2016   \n",
       "7234        41.603085         46.638308      Green     True    31/07/2004   \n",
       "5956        41.021678         19.822316  Turquoise    False    07/01/2004   \n",
       "6270        18.168683        120.705003       Pink     True    10/12/2017   \n",
       "6241        54.989789         62.547128     Yellow    False    14/10/2009   \n",
       "\n",
       "      Sale Price  Discount  Resell Price  5-yr Depreciation  Top Speed  \\\n",
       "3513    10003.76    0.6230      41226.94               0.09      219.9   \n",
       "7234    10019.95    0.3103      22336.96               0.20      197.7   \n",
       "5956    10023.85    0.3271      34125.80               0.20      246.3   \n",
       "6270    10024.86    0.5319      12695.97               0.04      138.7   \n",
       "6241    10039.82    0.1612      10343.75               0.18      182.0   \n",
       "\n",
       "      0-60 Time  \n",
       "3513       12.3  \n",
       "7234        3.5  \n",
       "5956       13.1  \n",
       "6270        2.6  \n",
       "6241        3.5  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the top 5 cars based on their sales price.\n",
    "car_df.sort_values(by=['Sale Price'],na_position='first').head(5)\n",
    "#df.sort_values(by=['Population'], na_position='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0bec0cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_df.drop_duplicates(subset =\"Sale Price\",keep = False, inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1536c91d",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Nickname</th>\n",
       "      <th>Car Gender</th>\n",
       "      <th>Buyer Gender</th>\n",
       "      <th>Buyer Age</th>\n",
       "      <th>Buzzword</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Dealer Latitude</th>\n",
       "      <th>Dealer Longitude</th>\n",
       "      <th>Color</th>\n",
       "      <th>New Car</th>\n",
       "      <th>Purchase Date</th>\n",
       "      <th>Sale Price</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Resell Price</th>\n",
       "      <th>5-yr Depreciation</th>\n",
       "      <th>Top Speed</th>\n",
       "      <th>0-60 Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Suzuki</td>\n",
       "      <td>Vitara</td>\n",
       "      <td>Philippine</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>51</td>\n",
       "      <td>User-friendly</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>51.247239</td>\n",
       "      <td>4.440346</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>False</td>\n",
       "      <td>19/07/2009</td>\n",
       "      <td>54806.14</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>33858.32</td>\n",
       "      <td>0.13</td>\n",
       "      <td>200.9</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>S2000</td>\n",
       "      <td>Henrik</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>analyzing</td>\n",
       "      <td>China</td>\n",
       "      <td>Jinhu</td>\n",
       "      <td>33.025433</td>\n",
       "      <td>119.020584</td>\n",
       "      <td>Crimson</td>\n",
       "      <td>False</td>\n",
       "      <td>26/10/2015</td>\n",
       "      <td>51826.30</td>\n",
       "      <td>0.3147</td>\n",
       "      <td>2989.28</td>\n",
       "      <td>0.02</td>\n",
       "      <td>158.5</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Z4</td>\n",
       "      <td>Lebbie</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>54</td>\n",
       "      <td>software</td>\n",
       "      <td>China</td>\n",
       "      <td>Gemo</td>\n",
       "      <td>30.916207</td>\n",
       "      <td>121.677640</td>\n",
       "      <td>Khaki</td>\n",
       "      <td>False</td>\n",
       "      <td>28/03/2010</td>\n",
       "      <td>82929.14</td>\n",
       "      <td>0.5414</td>\n",
       "      <td>35049.16</td>\n",
       "      <td>0.24</td>\n",
       "      <td>149.5</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Tacoma</td>\n",
       "      <td>Jefferey</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>68</td>\n",
       "      <td>monitoring</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Queluz de Baixo</td>\n",
       "      <td>38.745168</td>\n",
       "      <td>-9.267087</td>\n",
       "      <td>Puce</td>\n",
       "      <td>False</td>\n",
       "      <td>07/05/2008</td>\n",
       "      <td>56928.66</td>\n",
       "      <td>0.0850</td>\n",
       "      <td>8236.15</td>\n",
       "      <td>0.20</td>\n",
       "      <td>153.3</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Festiva</td>\n",
       "      <td>Glendon</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>70</td>\n",
       "      <td>Sharable</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>Jaunpils</td>\n",
       "      <td>56.729980</td>\n",
       "      <td>23.016588</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>False</td>\n",
       "      <td>01/06/2008</td>\n",
       "      <td>77201.26</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>32765.76</td>\n",
       "      <td>0.18</td>\n",
       "      <td>122.0</td>\n",
       "      <td>14.9</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>Mazda</td>\n",
       "      <td>Tribute</td>\n",
       "      <td>Cooper</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>hardware</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Chishtiān Mandi</td>\n",
       "      <td>29.919386</td>\n",
       "      <td>73.074787</td>\n",
       "      <td>Pink</td>\n",
       "      <td>False</td>\n",
       "      <td>05/01/2020</td>\n",
       "      <td>58580.65</td>\n",
       "      <td>0.1611</td>\n",
       "      <td>42640.82</td>\n",
       "      <td>0.14</td>\n",
       "      <td>245.8</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>GMC</td>\n",
       "      <td>Sierra 2500</td>\n",
       "      <td>Dud</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>Enhanced</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>Ángeles</td>\n",
       "      <td>10.075000</td>\n",
       "      <td>-84.241523</td>\n",
       "      <td>Puce</td>\n",
       "      <td>False</td>\n",
       "      <td>28/12/2005</td>\n",
       "      <td>75229.74</td>\n",
       "      <td>0.2691</td>\n",
       "      <td>21115.58</td>\n",
       "      <td>0.11</td>\n",
       "      <td>147.4</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>Mercury</td>\n",
       "      <td>Mariner</td>\n",
       "      <td>Chariot</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>monitoring</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Dąbrowice</td>\n",
       "      <td>52.311923</td>\n",
       "      <td>19.084965</td>\n",
       "      <td>Blue</td>\n",
       "      <td>False</td>\n",
       "      <td>08/03/2013</td>\n",
       "      <td>34755.44</td>\n",
       "      <td>0.2493</td>\n",
       "      <td>2731.25</td>\n",
       "      <td>0.12</td>\n",
       "      <td>198.1</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>Daewoo</td>\n",
       "      <td>Leganza</td>\n",
       "      <td>Dynah</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>cohesive</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>Villasis</td>\n",
       "      <td>15.901640</td>\n",
       "      <td>120.587828</td>\n",
       "      <td>Teal</td>\n",
       "      <td>True</td>\n",
       "      <td>21/02/2008</td>\n",
       "      <td>98725.42</td>\n",
       "      <td>0.4654</td>\n",
       "      <td>18718.58</td>\n",
       "      <td>0.04</td>\n",
       "      <td>206.7</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Sequoia</td>\n",
       "      <td>Zedekiah</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>analyzing</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>Basud</td>\n",
       "      <td>14.048167</td>\n",
       "      <td>122.955697</td>\n",
       "      <td>Pink</td>\n",
       "      <td>False</td>\n",
       "      <td>16/12/2017</td>\n",
       "      <td>96769.78</td>\n",
       "      <td>0.5446</td>\n",
       "      <td>4545.95</td>\n",
       "      <td>0.07</td>\n",
       "      <td>242.1</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Make        Model    Nickname Car Gender Buyer Gender  Buyer Age  \\\n",
       "0      Suzuki       Vitara  Philippine     Female       Female         51   \n",
       "1       Honda        S2000      Henrik       Male         Male         30   \n",
       "2         BMW           Z4      Lebbie       Male       Female         54   \n",
       "3      Toyota       Tacoma    Jefferey     Female         Male         68   \n",
       "4        Ford      Festiva     Glendon     Female         Male         70   \n",
       "...       ...          ...         ...        ...          ...        ...   \n",
       "9995    Mazda      Tribute      Cooper       Male         Male         44   \n",
       "9996      GMC  Sierra 2500         Dud       Male         Male         40   \n",
       "9997  Mercury      Mariner     Chariot       Male         Male         37   \n",
       "9998   Daewoo      Leganza       Dynah     Female       Female         21   \n",
       "9999   Toyota      Sequoia    Zedekiah     Female         Male         21   \n",
       "\n",
       "           Buzzword      Country             City  Dealer Latitude  \\\n",
       "0     User-friendly      Belgium        Antwerpen        51.247239   \n",
       "1         analyzing        China            Jinhu        33.025433   \n",
       "2          software        China             Gemo        30.916207   \n",
       "3        monitoring     Portugal  Queluz de Baixo        38.745168   \n",
       "4          Sharable       Latvia         Jaunpils        56.729980   \n",
       "...             ...          ...              ...              ...   \n",
       "9995       hardware     Pakistan  Chishtiān Mandi        29.919386   \n",
       "9996       Enhanced   Costa Rica          Ángeles        10.075000   \n",
       "9997     monitoring       Poland        Dąbrowice        52.311923   \n",
       "9998       cohesive  Philippines         Villasis        15.901640   \n",
       "9999      analyzing  Philippines            Basud        14.048167   \n",
       "\n",
       "      Dealer Longitude    Color  New Car Purchase Date  Sale Price  Discount  \\\n",
       "0             4.440346   Yellow    False    19/07/2009    54806.14    0.2467   \n",
       "1           119.020584  Crimson    False    26/10/2015    51826.30    0.3147   \n",
       "2           121.677640    Khaki    False    28/03/2010    82929.14    0.5414   \n",
       "3            -9.267087     Puce    False    07/05/2008    56928.66    0.0850   \n",
       "4            23.016588   Yellow    False    01/06/2008    77201.26    0.1642   \n",
       "...                ...      ...      ...           ...         ...       ...   \n",
       "9995         73.074787     Pink    False    05/01/2020    58580.65    0.1611   \n",
       "9996        -84.241523     Puce    False    28/12/2005    75229.74    0.2691   \n",
       "9997         19.084965     Blue    False    08/03/2013    34755.44    0.2493   \n",
       "9998        120.587828     Teal     True    21/02/2008    98725.42    0.4654   \n",
       "9999        122.955697     Pink    False    16/12/2017    96769.78    0.5446   \n",
       "\n",
       "      Resell Price  5-yr Depreciation  Top Speed  0-60 Time  \n",
       "0         33858.32               0.13      200.9       11.5  \n",
       "1          2989.28               0.02      158.5        8.8  \n",
       "2         35049.16               0.24      149.5       14.4  \n",
       "3          8236.15               0.20      153.3       11.5  \n",
       "4         32765.76               0.18      122.0       14.9  \n",
       "...            ...                ...        ...        ...  \n",
       "9995      42640.82               0.14      245.8        8.0  \n",
       "9996      21115.58               0.11      147.4       12.3  \n",
       "9997       2731.25               0.12      198.1       11.8  \n",
       "9998      18718.58               0.04      206.7       12.2  \n",
       "9999       4545.95               0.07      242.1        3.2  \n",
       "\n",
       "[10000 rows x 20 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "693d0743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Nickname</th>\n",
       "      <th>Car Gender</th>\n",
       "      <th>Buyer Gender</th>\n",
       "      <th>Buyer Age</th>\n",
       "      <th>Buzzword</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Dealer Latitude</th>\n",
       "      <th>Dealer Longitude</th>\n",
       "      <th>Color</th>\n",
       "      <th>New Car</th>\n",
       "      <th>Purchase Date</th>\n",
       "      <th>Sale Price</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Resell Price</th>\n",
       "      <th>5-yr Depreciation</th>\n",
       "      <th>Top Speed</th>\n",
       "      <th>0-60 Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2446</th>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>1500</td>\n",
       "      <td>Corenda</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>25</td>\n",
       "      <td>collaboration</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>Tubigan</td>\n",
       "      <td>8.490877</td>\n",
       "      <td>124.345771</td>\n",
       "      <td>Khaki</td>\n",
       "      <td>False</td>\n",
       "      <td>20/03/2002</td>\n",
       "      <td>49909.44</td>\n",
       "      <td>0.4480</td>\n",
       "      <td>2000.84</td>\n",
       "      <td>0.25</td>\n",
       "      <td>239.6</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>Jaguar</td>\n",
       "      <td>XF</td>\n",
       "      <td>Payton</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>64</td>\n",
       "      <td>Visionary</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Ikot-Ekpene</td>\n",
       "      <td>5.174447</td>\n",
       "      <td>7.714515</td>\n",
       "      <td>Red</td>\n",
       "      <td>True</td>\n",
       "      <td>26/01/2016</td>\n",
       "      <td>24124.99</td>\n",
       "      <td>0.5328</td>\n",
       "      <td>2014.09</td>\n",
       "      <td>0.19</td>\n",
       "      <td>249.3</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2538</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>3000GT</td>\n",
       "      <td>Thia</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>25</td>\n",
       "      <td>well-modulated</td>\n",
       "      <td>China</td>\n",
       "      <td>Yanweigang</td>\n",
       "      <td>34.509325</td>\n",
       "      <td>119.751715</td>\n",
       "      <td>Goldenrod</td>\n",
       "      <td>True</td>\n",
       "      <td>13/02/2011</td>\n",
       "      <td>88195.95</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>2017.24</td>\n",
       "      <td>0.14</td>\n",
       "      <td>222.0</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5869</th>\n",
       "      <td>Infiniti</td>\n",
       "      <td>G37</td>\n",
       "      <td>Sonny</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>optimizing</td>\n",
       "      <td>France</td>\n",
       "      <td>Clermont-Ferrand</td>\n",
       "      <td>45.786764</td>\n",
       "      <td>3.160264</td>\n",
       "      <td>Indigo</td>\n",
       "      <td>False</td>\n",
       "      <td>24/11/2002</td>\n",
       "      <td>77791.65</td>\n",
       "      <td>0.5691</td>\n",
       "      <td>2017.83</td>\n",
       "      <td>0.04</td>\n",
       "      <td>131.6</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6396</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Challenger</td>\n",
       "      <td>Mabelle</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>70</td>\n",
       "      <td>Stand-alone</td>\n",
       "      <td>China</td>\n",
       "      <td>Xiushi</td>\n",
       "      <td>27.999411</td>\n",
       "      <td>115.932899</td>\n",
       "      <td>Crimson</td>\n",
       "      <td>True</td>\n",
       "      <td>11/12/2020</td>\n",
       "      <td>76241.47</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>2020.89</td>\n",
       "      <td>0.03</td>\n",
       "      <td>171.6</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Make       Model Nickname Car Gender Buyer Gender  Buyer Age  \\\n",
       "2446   Chevrolet        1500  Corenda       Male       Female         25   \n",
       "566       Jaguar          XF   Payton     Female         Male         64   \n",
       "2538  Mitsubishi      3000GT     Thia     Female       Female         25   \n",
       "5869    Infiniti         G37    Sonny       Male         Male         20   \n",
       "6396  Mitsubishi  Challenger  Mabelle       Male       Female         70   \n",
       "\n",
       "            Buzzword      Country              City  Dealer Latitude  \\\n",
       "2446   collaboration  Philippines           Tubigan         8.490877   \n",
       "566        Visionary      Nigeria       Ikot-Ekpene         5.174447   \n",
       "2538  well-modulated        China        Yanweigang        34.509325   \n",
       "5869      optimizing       France  Clermont-Ferrand        45.786764   \n",
       "6396     Stand-alone        China            Xiushi        27.999411   \n",
       "\n",
       "      Dealer Longitude      Color  New Car Purchase Date  Sale Price  \\\n",
       "2446        124.345771      Khaki    False    20/03/2002    49909.44   \n",
       "566           7.714515        Red     True    26/01/2016    24124.99   \n",
       "2538        119.751715  Goldenrod     True    13/02/2011    88195.95   \n",
       "5869          3.160264     Indigo    False    24/11/2002    77791.65   \n",
       "6396        115.932899    Crimson     True    11/12/2020    76241.47   \n",
       "\n",
       "      Discount  Resell Price  5-yr Depreciation  Top Speed  0-60 Time  \n",
       "2446    0.4480       2000.84               0.25      239.6       11.2  \n",
       "566     0.5328       2014.09               0.19      249.3        6.9  \n",
       "2538    0.0391       2017.24               0.14      222.0       10.3  \n",
       "5869    0.5691       2017.83               0.04      131.6       12.3  \n",
       "6396    0.0139       2020.89               0.03      171.6        2.6  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the least 5 cars based on their Resell price\n",
    "car_df.sort_values(by=['Resell Price'],na_position='first').head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bb9d8c2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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