3_1_Lecture_Intro_to_Pandas.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"https://github.com/ReDI-School/nrw-data-analytics/blob/main/3_1_Lecture_Intro_to_Pandas_%5BFilter_%26_Groupby%5D.ipynb","timestamp":1663061213702}],"collapsed_sections":["GsASEyGHa-Ky","3JWh05FobUX0","FCMbI98-bY9_","FCBp8ttbXOMR"]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zfY7LFjFlcZv"},"source":["# Pandas Exploring Data by Filtering and Grouping\n","\n","## Let's remember cars dataset:\n","The dataset contains the information of 10.000 cars. There are 9 different columns:\n","- Make (Car brand, example: Ford)\n","- Model (The Model of the Car, example: Focus)\n","- Year (The Year in which the car was build, example: 2012)\n","- Variant (The car model version showing the PS, example: 1.6 Trendline)\n","- Kms (The kilometers the cars has been driven, example: 90000)\n","- Price (The offered price for the car, example: 10000)\n","- Doors (How many doors the car has, example: 4)\n","- Kind (Type of car, example: Pick-Up)\n","- Location (Where the car is located, example: Buenos Aires)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"R2rbeR95mSqz"},"source":["## Recall Dataset"]},{"cell_type":"code","metadata":{"id":"jYijDuuObu_5","executionInfo":{"status":"ok","timestamp":1663515466397,"user_tz":-120,"elapsed":365,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["import pandas as pd"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"mrtbg_VhjZcy","executionInfo":{"status":"ok","timestamp":1663515468162,"user_tz":-120,"elapsed":309,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["#reading data\n","cars = pd.read_csv(\"https://raw.githubusercontent.com/juliandnl/redi_ss20/master/cars.csv\")"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"b71aiXbEjbrh","outputId":"2c423bc7-6e7b-41d9-9455-8a577ee7c7e4","executionInfo":{"status":"ok","timestamp":1663515469183,"user_tz":-120,"elapsed":5,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars.head()"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         Make   Model  Year                           Variant     Kms   Price  \\\n","0  Volkswagen   Vento  2012                  2.5 Luxury 170cv   99950  360000   \n","1        Ford  Ranger  2012                2.3 Cd Xl Plus 4x2  140000  320000   \n","2  Volkswagen     Fox  2011                     1.6 Trendline  132000  209980   \n","3        Ford  Ranger  2017  3.2 Cd Xls Tdci 200cv Automática   13000  798000   \n","4  Volkswagen     Gol  2013                1.4 Power 83cv 3 p  107000  146000   \n","\n","   Doors       Kind           Location  \n","0    4.0      Sedán            Córdoba  \n","1    2.0    Pick-Up         Entre Ríos  \n","2    5.0  Hatchback  Bs.as. G.b.a. Sur  \n","3    4.0    Pick-Up            Neuquén  \n","4    3.0  Hatchback            Córdoba  "],"text/html":["\n","  <div id=\"df-423a4f5d-a39b-4217-bfbd-2f2126bbb109\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Volkswagen</td>\n","      <td>Vento</td>\n","      <td>2012</td>\n","      <td>2.5 Luxury 170cv</td>\n","      <td>99950</td>\n","      <td>360000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Córdoba</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2012</td>\n","      <td>2.3 Cd Xl Plus 4x2</td>\n","      <td>140000</td>\n","      <td>320000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Entre Ríos</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Volkswagen</td>\n","      <td>Fox</td>\n","      <td>2011</td>\n","      <td>1.6 Trendline</td>\n","      <td>132000</td>\n","      <td>209980</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2017</td>\n","      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n","      <td>13000</td>\n","      <td>798000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Volkswagen</td>\n","      <td>Gol</td>\n","      <td>2013</td>\n","      <td>1.4 Power 83cv 3 p</td>\n","      <td>107000</td>\n","      <td>146000</td>\n","      <td>3.0</td>\n","      <td>Hatchback</td>\n","      <td>Córdoba</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-423a4f5d-a39b-4217-bfbd-2f2126bbb109')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-423a4f5d-a39b-4217-bfbd-2f2126bbb109 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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The columns with text are by default marked as Object datatype**.💡"],"metadata":{"id":"Jze65A3HehZZ"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MZcerQfepxWW","outputId":"6107fc4d-1074-4422-bf16-e47c3561089a","executionInfo":{"status":"ok","timestamp":1663515520149,"user_tz":-120,"elapsed":310,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars.nunique(0)\n","# number of unique values per column"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Make           5\n","Model         62\n","Year           8\n","Variant      592\n","Kms         1201\n","Price        948\n","Doors          4\n","Kind          10\n","Location      28\n","dtype: int64"]},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"Dn_hISggYHKu"},"source":["\n","## Task\n","\n","**Familiarize yourself with the dataset**: When you first start to work with a dataset, it's crucial to explore and understand data.\n","\n","1. Checkars is particularly interested in cars of t*he make Ford  produced in or after 2016.* How many Ford cars from 2016 are in the dataset?\n","2. We would like to analyze cars based on *price and km by brands*. How expensive are the car makes in general? Which car make is the cheapest one? Which car make has on average the most kms?\n","3. What are the *average prices by brands and by the number of doors* that cars have?\n","4. How many *unique models* does each brand have?\n","5. Which kind of cars is the *most expensive and most popular*?\n","\n","Bonus:\n","6. What are the *min, max and average production years* for Ford cars?\n"]},{"cell_type":"markdown","metadata":{"id":"-EZa41HuLaWz"},"source":["## 1.How many Fords from 2016 are in the dataset?\n","\n","During this exploration, you might want to check a subset of data. For example, you would like to have a subset for just 4 columns namely, Make, Model, Year, Location.\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"Jnz7WtgJkuUr","outputId":"1603574d-0566-4186-a74d-0e862e2b7fc7","executionInfo":{"status":"ok","timestamp":1663515523089,"user_tz":-120,"elapsed":323,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars[['Make', 'Model', 'Year', 'Location']].head(5)"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         Make   Model  Year           Location\n","0  Volkswagen   Vento  2012            Córdoba\n","1        Ford  Ranger  2012         Entre Ríos\n","2  Volkswagen     Fox  2011  Bs.as. G.b.a. Sur\n","3        Ford  Ranger  2017            Neuquén\n","4  Volkswagen     Gol  2013            Córdoba"],"text/html":["\n","  <div id=\"df-157d00ef-e798-48db-94eb-2f81cbba5aac\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Volkswagen</td>\n","      <td>Vento</td>\n","      <td>2012</td>\n","      <td>Córdoba</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2012</td>\n","      <td>Entre Ríos</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Volkswagen</td>\n","      <td>Fox</td>\n","      <td>2011</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2017</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Volkswagen</td>\n","      <td>Gol</td>\n","      <td>2013</td>\n","      <td>Córdoba</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-157d00ef-e798-48db-94eb-2f81cbba5aac')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-157d00ef-e798-48db-94eb-2f81cbba5aac button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Logical Operators\n"]},{"cell_type":"code","source":["# 1. Get all cars made in 2016 or later\n","# returns TRUE for all rows, where Year >= 2016\n","cars['Year'] >= 2016"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iMvMtb5rlXce","executionInfo":{"status":"ok","timestamp":1663504345220,"user_tz":-120,"elapsed":270,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}},"outputId":"c6f77c96-63ac-4b1f-a705-95151a208bee"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0       False\n","1       False\n","2       False\n","3        True\n","4       False\n","        ...  \n","9995     True\n","9996    False\n","9997    False\n","9998    False\n","9999    False\n","Name: Year, Length: 10000, dtype: bool"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"V7Mm7ByMcNXr","outputId":"6bc487e8-28a0-4204-8f82-e47095fb4ef8","executionInfo":{"status":"ok","timestamp":1663504404066,"user_tz":-120,"elapsed":244,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["# returns rows across all columns in the dataframe cars, where the condition cars['Year'] >= 2016 is TRUE\n","cars[cars['Year'] >= 2016]"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Make      Model  Year                             Variant    Kms  \\\n","3           Ford     Ranger  2017    3.2 Cd Xls Tdci 200cv Automática  13000   \n","8           Ford  Focus III  2017                               1.6 S  17500   \n","29    Volkswagen    Saveiro  2017  1.6 Gp Ce 101cv Safety + Pack High  20000   \n","35          Ford  Focus III  2016                2.0 Sedan Se Plus Mt  15022   \n","49          Ford  Focus III  2016                               1.6 S  39000   \n","...          ...        ...   ...                                 ...    ...   \n","9985  Volkswagen  Gol Trend  2016                 1.6 Trendline 101cv  54100   \n","9990        Ford  Focus III  2016                      2.0 Se Plus Mt  48000   \n","9991        Ford     Ranger  2016        3.2 Cd 4x2 Xlt At Tdci 200cv  59000   \n","9994        Ford  Focus III  2016                              2.0 Se  18000   \n","9995        Ford  Focus III  2016                              2.0 Se  67000   \n","\n","       Price  Doors       Kind               Location  \n","3     798000    4.0    Pick-Up                Neuquén  \n","8     367000    5.0  Hatchback  Buenos Aires Interior  \n","29    370000    2.0    Pick-Up                Córdoba  \n","35    550000    4.0      Sedán      Bs.as. G.b.a. Sur  \n","49    454800    5.0  Hatchback      Bs.as. G.b.a. Sur  \n","...      ...    ...        ...                    ...  \n","9985  270000    5.0  Hatchback  Buenos Aires Interior  \n","9990  450000    5.0  Hatchback      Bs.as. G.b.a. Sur  \n","9991  790000    4.0    Pick-Up    Bs.as. G.b.a. Oeste  \n","9994  370000    5.0  Hatchback        Capital Federal  \n","9995  399000    5.0  Hatchback    Bs.as. G.b.a. Oeste  \n","\n","[1414 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-b4121d4d-315d-4ba1-9f15-8ab919063a56\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2017</td>\n","      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n","      <td>13000</td>\n","      <td>798000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2017</td>\n","      <td>1.6 S</td>\n","      <td>17500</td>\n","      <td>367000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>Volkswagen</td>\n","      <td>Saveiro</td>\n","      <td>2017</td>\n","      <td>1.6 Gp Ce 101cv Safety + Pack High</td>\n","      <td>20000</td>\n","      <td>370000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Córdoba</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Sedan Se Plus Mt</td>\n","      <td>15022</td>\n","      <td>550000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>1.6 S</td>\n","      <td>39000</td>\n","      <td>454800</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</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","    </tr>\n","    <tr>\n","      <th>9985</th>\n","      <td>Volkswagen</td>\n","      <td>Gol Trend</td>\n","      <td>2016</td>\n","      <td>1.6 Trendline 101cv</td>\n","      <td>54100</td>\n","      <td>270000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>9990</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se Plus Mt</td>\n","      <td>48000</td>\n","      <td>450000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>9991</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2016</td>\n","      <td>3.2 Cd 4x2 Xlt At Tdci 200cv</td>\n","      <td>59000</td>\n","      <td>790000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","    <tr>\n","      <th>9994</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>18000</td>\n","      <td>370000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Capital Federal</td>\n","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>67000</td>\n","      <td>399000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1414 rows × 9 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4121d4d-315d-4ba1-9f15-8ab919063a56')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-b4121d4d-315d-4ba1-9f15-8ab919063a56 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Get only Ford cars\n","cars[cars['Make'] == 'Ford']"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Make                  Model  Year                           Variant  \\\n","1     Ford                 Ranger  2012                2.3 Cd Xl Plus 4x2   \n","3     Ford                 Ranger  2017  3.2 Cd Xls Tdci 200cv Automática   \n","6     Ford                     Ka  2012                1.0 Fly Viral 63cv   \n","7     Ford                     Ka  2012                1.6 Fly Viral 95cv   \n","8     Ford              Focus III  2017                             1.6 S   \n","...    ...                    ...   ...                               ...   \n","9994  Ford              Focus III  2016                            2.0 Se   \n","9995  Ford              Focus III  2016                            2.0 Se   \n","9997  Ford  Fiesta Kinetic Design  2012         1.6 Design 120cv Titanium   \n","9998  Ford  Fiesta Kinetic Design  2013         1.6 Design 120cv Titanium   \n","9999  Ford              Focus III  2014                  2.0 Titanium At6   \n","\n","         Kms   Price  Doors       Kind               Location  \n","1     140000  320000    2.0    Pick-Up             Entre Ríos  \n","3      13000  798000    4.0    Pick-Up                Neuquén  \n","6      95243  142000    3.0  Hatchback      Bs.as. G.b.a. Sur  \n","7     110000  148000    3.0  Hatchback  Buenos Aires Interior  \n","8      17500  367000    5.0  Hatchback  Buenos Aires Interior  \n","...      ...     ...    ...        ...                    ...  \n","9994   18000  370000    5.0  Hatchback        Capital Federal  \n","9995   67000  399000    5.0  Hatchback    Bs.as. G.b.a. Oeste  \n","9997   89000  250000    5.0  Hatchback                Tucumán  \n","9998   76000  295000    5.0  Hatchback  Buenos Aires Interior  \n","9999   32000  380000    5.0  Hatchback      Bs.as. G.b.a. Sur  \n","\n","[4025 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-8177f892-b8c8-49b8-9d96-c2787f7588a4\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2012</td>\n","      <td>2.3 Cd Xl Plus 4x2</td>\n","      <td>140000</td>\n","      <td>320000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Entre Ríos</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2017</td>\n","      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n","      <td>13000</td>\n","      <td>798000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Ford</td>\n","      <td>Ka</td>\n","      <td>2012</td>\n","      <td>1.0 Fly Viral 63cv</td>\n","      <td>95243</td>\n","      <td>142000</td>\n","      <td>3.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Ford</td>\n","      <td>Ka</td>\n","      <td>2012</td>\n","      <td>1.6 Fly Viral 95cv</td>\n","      <td>110000</td>\n","      <td>148000</td>\n","      <td>3.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2017</td>\n","      <td>1.6 S</td>\n","      <td>17500</td>\n","      <td>367000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</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","    </tr>\n","    <tr>\n","      <th>9994</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>18000</td>\n","      <td>370000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Capital Federal</td>\n","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>67000</td>\n","      <td>399000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. 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Sur</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>4025 rows × 9 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8177f892-b8c8-49b8-9d96-c2787f7588a4')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-8177f892-b8c8-49b8-9d96-c2787f7588a4 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":17}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"1TGVAZTqe0sY","outputId":"63b55ef4-0cfb-4e08-cbe0-750da6ea6ea8","executionInfo":{"status":"ok","timestamp":1663504601766,"user_tz":-120,"elapsed":272,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["# Combine Ford & >= 2016\n","cars[(cars['Make'] == 'Ford') & (cars['Year'] >= 2016)]"],"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Make                  Model  Year                           Variant  \\\n","1     Ford                 Ranger  2012                2.3 Cd Xl Plus 4x2   \n","3     Ford                 Ranger  2017  3.2 Cd Xls Tdci 200cv Automática   \n","6     Ford                     Ka  2012                1.0 Fly Viral 63cv   \n","7     Ford                     Ka  2012                1.6 Fly Viral 95cv   \n","8     Ford              Focus III  2017                             1.6 S   \n","...    ...                    ...   ...                               ...   \n","9994  Ford              Focus III  2016                            2.0 Se   \n","9995  Ford              Focus III  2016                            2.0 Se   \n","9997  Ford  Fiesta Kinetic Design  2012         1.6 Design 120cv Titanium   \n","9998  Ford  Fiesta Kinetic Design  2013         1.6 Design 120cv Titanium   \n","9999  Ford              Focus III  2014                  2.0 Titanium At6   \n","\n","         Kms   Price  Doors       Kind               Location  \n","1     140000  320000    2.0    Pick-Up             Entre Ríos  \n","3      13000  798000    4.0    Pick-Up                Neuquén  \n","6      95243  142000    3.0  Hatchback      Bs.as. G.b.a. Sur  \n","7     110000  148000    3.0  Hatchback  Buenos Aires Interior  \n","8      17500  367000    5.0  Hatchback  Buenos Aires Interior  \n","...      ...     ...    ...        ...                    ...  \n","9994   18000  370000    5.0  Hatchback        Capital Federal  \n","9995   67000  399000    5.0  Hatchback    Bs.as. G.b.a. Oeste  \n","9997   89000  250000    5.0  Hatchback                Tucumán  \n","9998   76000  295000    5.0  Hatchback  Buenos Aires Interior  \n","9999   32000  380000    5.0  Hatchback      Bs.as. G.b.a. Sur  \n","\n","[4808 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-eabb3490-9bd7-4af4-aeb1-a9ad1bc94454\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2012</td>\n","      <td>2.3 Cd Xl Plus 4x2</td>\n","      <td>140000</td>\n","      <td>320000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Entre Ríos</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2017</td>\n","      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n","      <td>13000</td>\n","      <td>798000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Ford</td>\n","      <td>Ka</td>\n","      <td>2012</td>\n","      <td>1.0 Fly Viral 63cv</td>\n","      <td>95243</td>\n","      <td>142000</td>\n","      <td>3.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Ford</td>\n","      <td>Ka</td>\n","      <td>2012</td>\n","      <td>1.6 Fly Viral 95cv</td>\n","      <td>110000</td>\n","      <td>148000</td>\n","      <td>3.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2017</td>\n","      <td>1.6 S</td>\n","      <td>17500</td>\n","      <td>367000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</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","    </tr>\n","    <tr>\n","      <th>9994</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>18000</td>\n","      <td>370000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Capital Federal</td>\n","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>67000</td>\n","      <td>399000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","    <tr>\n","      <th>9997</th>\n","      <td>Ford</td>\n","      <td>Fiesta Kinetic Design</td>\n","      <td>2012</td>\n","      <td>1.6 Design 120cv Titanium</td>\n","      <td>89000</td>\n","      <td>250000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Tucumán</td>\n","    </tr>\n","    <tr>\n","      <th>9998</th>\n","      <td>Ford</td>\n","      <td>Fiesta Kinetic Design</td>\n","      <td>2013</td>\n","      <td>1.6 Design 120cv Titanium</td>\n","      <td>76000</td>\n","      <td>295000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>9999</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2014</td>\n","      <td>2.0 Titanium At6</td>\n","      <td>32000</td>\n","      <td>380000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>4808 rows × 9 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eabb3490-9bd7-4af4-aeb1-a9ad1bc94454')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-eabb3490-9bd7-4af4-aeb1-a9ad1bc94454 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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G.b.a. Sur  \n","3     798000    4.0      Pick-Up                Neuquén  \n","4     146000    3.0    Hatchback                Córdoba  \n","5     790000    4.0      Pick-Up  Buenos Aires Interior  \n","...      ...    ...          ...                    ...  \n","9992  190000    5.0  Monovolumen    Bs.as. G.b.a. Norte  \n","9993  490000    5.0          SUV                Córdoba  \n","9994  370000    5.0    Hatchback        Capital Federal  \n","9995  399000    5.0    Hatchback    Bs.as. G.b.a. Oeste  \n","9996  240000    4.0        Sedán  Buenos Aires Interior  \n","\n","[6526 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-160f3dab-fb34-4a06-bdb4-b905b89071ce\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Volkswagen</td>\n","      <td>Vento</td>\n","      <td>2012</td>\n","      <td>2.5 Luxury 170cv</td>\n","      <td>99950</td>\n","      <td>360000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Córdoba</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Volkswagen</td>\n","      <td>Fox</td>\n","      <td>2011</td>\n","      <td>1.6 Trendline</td>\n","      <td>132000</td>\n","      <td>209980</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2017</td>\n","      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n","      <td>13000</td>\n","      <td>798000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Volkswagen</td>\n","      <td>Gol</td>\n","      <td>2013</td>\n","      <td>1.4 Power 83cv 3 p</td>\n","      <td>107000</td>\n","      <td>146000</td>\n","      <td>3.0</td>\n","      <td>Hatchback</td>\n","      <td>Córdoba</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Volkswagen</td>\n","      <td>Amarok</td>\n","      <td>2014</td>\n","      <td>2.0 Cd Tdi 180cv 4x4 Highline C34</td>\n","      <td>115000</td>\n","      <td>790000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Buenos Aires Interior</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","    </tr>\n","    <tr>\n","      <th>9992</th>\n","      <td>Volkswagen</td>\n","      <td>Suran</td>\n","      <td>2011</td>\n","      <td>1.6 Imotion Trendline 11b</td>\n","      <td>80000</td>\n","      <td>190000</td>\n","      <td>5.0</td>\n","      <td>Monovolumen</td>\n","      <td>Bs.as. G.b.a. Norte</td>\n","    </tr>\n","    <tr>\n","      <th>9993</th>\n","      <td>Ford</td>\n","      <td>Ecosport</td>\n","      <td>2015</td>\n","      <td>2.0 Titanium 143cv 4x2</td>\n","      <td>50000</td>\n","      <td>490000</td>\n","      <td>5.0</td>\n","      <td>SUV</td>\n","      <td>Córdoba</td>\n","    </tr>\n","    <tr>\n","      <th>9994</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>18000</td>\n","      <td>370000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Capital Federal</td>\n","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>67000</td>\n","      <td>399000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","    <tr>\n","      <th>9996</th>\n","      <td>Volkswagen</td>\n","      <td>Bora</td>\n","      <td>2012</td>\n","      <td>1.9 Trendline I 100cv</td>\n","      <td>120000</td>\n","      <td>240000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>6526 rows × 9 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-160f3dab-fb34-4a06-bdb4-b905b89071ce')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-160f3dab-fb34-4a06-bdb4-b905b89071ce button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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G.b.a. Sur  \n","7     110000  148000    3.0  Hatchback  Buenos Aires Interior  \n","8      17500  367000    5.0  Hatchback  Buenos Aires Interior  \n","...      ...     ...    ...        ...                    ...  \n","9994   18000  370000    5.0  Hatchback        Capital Federal  \n","9995   67000  399000    5.0  Hatchback    Bs.as. G.b.a. Oeste  \n","9997   89000  250000    5.0  Hatchback                Tucumán  \n","9998   76000  295000    5.0  Hatchback  Buenos Aires Interior  \n","9999   32000  380000    5.0  Hatchback      Bs.as. G.b.a. Sur  \n","\n","[4025 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-9e5dd5ce-0452-4636-bf1c-12e810fbbe8c\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2012</td>\n","      <td>2.3 Cd Xl Plus 4x2</td>\n","      <td>140000</td>\n","      <td>320000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Entre Ríos</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2017</td>\n","      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n","      <td>13000</td>\n","      <td>798000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Ford</td>\n","      <td>Ka</td>\n","      <td>2012</td>\n","      <td>1.0 Fly Viral 63cv</td>\n","      <td>95243</td>\n","      <td>142000</td>\n","      <td>3.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Ford</td>\n","      <td>Ka</td>\n","      <td>2012</td>\n","      <td>1.6 Fly Viral 95cv</td>\n","      <td>110000</td>\n","      <td>148000</td>\n","      <td>3.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2017</td>\n","      <td>1.6 S</td>\n","      <td>17500</td>\n","      <td>367000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</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","    </tr>\n","    <tr>\n","      <th>9994</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>18000</td>\n","      <td>370000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Capital Federal</td>\n","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>67000</td>\n","      <td>399000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","    <tr>\n","      <th>9997</th>\n","      <td>Ford</td>\n","      <td>Fiesta Kinetic Design</td>\n","      <td>2012</td>\n","      <td>1.6 Design 120cv Titanium</td>\n","      <td>89000</td>\n","      <td>250000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Tucumán</td>\n","    </tr>\n","    <tr>\n","      <th>9998</th>\n","      <td>Ford</td>\n","      <td>Fiesta Kinetic Design</td>\n","      <td>2013</td>\n","      <td>1.6 Design 120cv Titanium</td>\n","      <td>76000</td>\n","      <td>295000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>9999</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2014</td>\n","      <td>2.0 Titanium At6</td>\n","      <td>32000</td>\n","      <td>380000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. 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G.b.a. Sur  \n","49      5.0  Hatchback      Bs.as. G.b.a. Sur  \n","57      4.0      Sedán             Entre Ríos  \n","...     ...        ...                    ...  \n","9949    5.0  Hatchback    Bs.as. G.b.a. Oeste  \n","9990    5.0  Hatchback      Bs.as. G.b.a. Sur  \n","9991    4.0    Pick-Up    Bs.as. G.b.a. Oeste  \n","9994    5.0  Hatchback        Capital Federal  \n","9995    5.0  Hatchback    Bs.as. G.b.a. Oeste  \n","\n","[631 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-e228739b-b95d-4305-ae50-fb2e266918eb\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2017</td>\n","      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n","      <td>13000</td>\n","      <td>798000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2017</td>\n","      <td>1.6 S</td>\n","      <td>17500</td>\n","      <td>367000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Sedan Se Plus Mt</td>\n","      <td>15022</td>\n","      <td>550000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>1.6 S</td>\n","      <td>39000</td>\n","      <td>454800</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>57</th>\n","      <td>Ford</td>\n","      <td>Mondeo</td>\n","      <td>2018</td>\n","      <td>2.0 Se Ecoboost At 240cv</td>\n","      <td>15000</td>\n","      <td>1200000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Entre Ríos</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","    </tr>\n","    <tr>\n","      <th>9949</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2017</td>\n","      <td>1.6 S</td>\n","      <td>10000</td>\n","      <td>530000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","    <tr>\n","      <th>9990</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se Plus Mt</td>\n","      <td>48000</td>\n","      <td>450000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>9991</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2016</td>\n","      <td>3.2 Cd 4x2 Xlt At Tdci 200cv</td>\n","      <td>59000</td>\n","      <td>790000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","    <tr>\n","      <th>9994</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>18000</td>\n","      <td>370000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Capital Federal</td>\n","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>67000</td>\n","      <td>399000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>631 rows × 9 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e228739b-b95d-4305-ae50-fb2e266918eb')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-e228739b-b95d-4305-ae50-fb2e266918eb button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":33}]},{"cell_type":"markdown","metadata":{"id":"FCMbI98-bY9_"},"source":["## 3. Indexing with `.loc()` \n","https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html\n","\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"UUEUVhgCbl96","outputId":"2a5373ec-e00f-4991-b361-d1aa3ecb46be","executionInfo":{"status":"ok","timestamp":1663506197192,"user_tz":-120,"elapsed":273,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars.loc[cars['Year'] >= 2016]\n"],"execution_count":34,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Make      Model  Year                             Variant    Kms  \\\n","3           Ford     Ranger  2017    3.2 Cd Xls Tdci 200cv Automática  13000   \n","8           Ford  Focus III  2017                               1.6 S  17500   \n","29    Volkswagen    Saveiro  2017  1.6 Gp Ce 101cv Safety + Pack High  20000   \n","35          Ford  Focus III  2016                2.0 Sedan Se Plus Mt  15022   \n","49          Ford  Focus III  2016                               1.6 S  39000   \n","...          ...        ...   ...                                 ...    ...   \n","9985  Volkswagen  Gol Trend  2016                 1.6 Trendline 101cv  54100   \n","9990        Ford  Focus III  2016                      2.0 Se Plus Mt  48000   \n","9991        Ford     Ranger  2016        3.2 Cd 4x2 Xlt At Tdci 200cv  59000   \n","9994        Ford  Focus III  2016                              2.0 Se  18000   \n","9995        Ford  Focus III  2016                              2.0 Se  67000   \n","\n","       Price  Doors       Kind               Location  \n","3     798000    4.0    Pick-Up                Neuquén  \n","8     367000    5.0  Hatchback  Buenos Aires Interior  \n","29    370000    2.0    Pick-Up                Córdoba  \n","35    550000    4.0      Sedán      Bs.as. G.b.a. Sur  \n","49    454800    5.0  Hatchback      Bs.as. G.b.a. Sur  \n","...      ...    ...        ...                    ...  \n","9985  270000    5.0  Hatchback  Buenos Aires Interior  \n","9990  450000    5.0  Hatchback      Bs.as. G.b.a. Sur  \n","9991  790000    4.0    Pick-Up    Bs.as. G.b.a. Oeste  \n","9994  370000    5.0  Hatchback        Capital Federal  \n","9995  399000    5.0  Hatchback    Bs.as. G.b.a. Oeste  \n","\n","[1414 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-5da7d0d7-37e3-4fd5-b9a2-b6736b4e0b4d\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2017</td>\n","      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n","      <td>13000</td>\n","      <td>798000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2017</td>\n","      <td>1.6 S</td>\n","      <td>17500</td>\n","      <td>367000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>Volkswagen</td>\n","      <td>Saveiro</td>\n","      <td>2017</td>\n","      <td>1.6 Gp Ce 101cv Safety + Pack High</td>\n","      <td>20000</td>\n","      <td>370000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Córdoba</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Sedan Se Plus Mt</td>\n","      <td>15022</td>\n","      <td>550000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>1.6 S</td>\n","      <td>39000</td>\n","      <td>454800</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</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","    </tr>\n","    <tr>\n","      <th>9985</th>\n","      <td>Volkswagen</td>\n","      <td>Gol Trend</td>\n","      <td>2016</td>\n","      <td>1.6 Trendline 101cv</td>\n","      <td>54100</td>\n","      <td>270000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>9990</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se Plus Mt</td>\n","      <td>48000</td>\n","      <td>450000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>9991</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2016</td>\n","      <td>3.2 Cd 4x2 Xlt At Tdci 200cv</td>\n","      <td>59000</td>\n","      <td>790000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","    <tr>\n","      <th>9994</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>18000</td>\n","      <td>370000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Capital Federal</td>\n","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>67000</td>\n","      <td>399000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1414 rows × 9 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5da7d0d7-37e3-4fd5-b9a2-b6736b4e0b4d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-5da7d0d7-37e3-4fd5-b9a2-b6736b4e0b4d button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":34}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"zX2m6a-wgeIg","outputId":"db64f2b9-8c32-409a-c0ef-fb0a9f3d6ab2","executionInfo":{"status":"ok","timestamp":1663515896286,"user_tz":-120,"elapsed":361,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["# To answer our question:\n","cars.loc[(cars['Make'] == 'Ford') & (cars['Year'] >=2016)]"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Make      Model  Year                           Variant    Kms    Price  \\\n","3     Ford     Ranger  2017  3.2 Cd Xls Tdci 200cv Automática  13000   798000   \n","8     Ford  Focus III  2017                             1.6 S  17500   367000   \n","35    Ford  Focus III  2016              2.0 Sedan Se Plus Mt  15022   550000   \n","49    Ford  Focus III  2016                             1.6 S  39000   454800   \n","57    Ford     Mondeo  2018          2.0 Se Ecoboost At 240cv  15000  1200000   \n","...    ...        ...   ...                               ...    ...      ...   \n","9949  Ford  Focus III  2017                             1.6 S  10000   530000   \n","9990  Ford  Focus III  2016                    2.0 Se Plus Mt  48000   450000   \n","9991  Ford     Ranger  2016      3.2 Cd 4x2 Xlt At Tdci 200cv  59000   790000   \n","9994  Ford  Focus III  2016                            2.0 Se  18000   370000   \n","9995  Ford  Focus III  2016                            2.0 Se  67000   399000   \n","\n","      Doors       Kind               Location  \n","3       4.0    Pick-Up                Neuquén  \n","8       5.0  Hatchback  Buenos Aires Interior  \n","35      4.0      Sedán      Bs.as. G.b.a. Sur  \n","49      5.0  Hatchback      Bs.as. G.b.a. Sur  \n","57      4.0      Sedán             Entre Ríos  \n","...     ...        ...                    ...  \n","9949    5.0  Hatchback    Bs.as. G.b.a. Oeste  \n","9990    5.0  Hatchback      Bs.as. G.b.a. Sur  \n","9991    4.0    Pick-Up    Bs.as. G.b.a. Oeste  \n","9994    5.0  Hatchback        Capital Federal  \n","9995    5.0  Hatchback    Bs.as. G.b.a. Oeste  \n","\n","[631 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-172aa7fc-e728-45c6-801b-fd1d7982ae95\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2017</td>\n","      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n","      <td>13000</td>\n","      <td>798000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2017</td>\n","      <td>1.6 S</td>\n","      <td>17500</td>\n","      <td>367000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Sedan Se Plus Mt</td>\n","      <td>15022</td>\n","      <td>550000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>1.6 S</td>\n","      <td>39000</td>\n","      <td>454800</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>57</th>\n","      <td>Ford</td>\n","      <td>Mondeo</td>\n","      <td>2018</td>\n","      <td>2.0 Se Ecoboost At 240cv</td>\n","      <td>15000</td>\n","      <td>1200000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Entre Ríos</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","    </tr>\n","    <tr>\n","      <th>9949</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2017</td>\n","      <td>1.6 S</td>\n","      <td>10000</td>\n","      <td>530000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","    <tr>\n","      <th>9990</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se Plus Mt</td>\n","      <td>48000</td>\n","      <td>450000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>9991</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2016</td>\n","      <td>3.2 Cd 4x2 Xlt At Tdci 200cv</td>\n","      <td>59000</td>\n","      <td>790000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","    <tr>\n","      <th>9994</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>18000</td>\n","      <td>370000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Capital Federal</td>\n","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>67000</td>\n","      <td>399000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>631 rows × 9 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-172aa7fc-e728-45c6-801b-fd1d7982ae95')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-172aa7fc-e728-45c6-801b-fd1d7982ae95 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":15}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"Csx4rFNRVgI8","outputId":"25651d1a-64b2-4bb1-f8f4-9c59649f05c2","executionInfo":{"status":"ok","timestamp":1663515899358,"user_tz":-120,"elapsed":316,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars.loc[(cars['Year'] <=2018) & (cars['Year'] >=2016)]"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Make      Model  Year                             Variant    Kms  \\\n","3           Ford     Ranger  2017    3.2 Cd Xls Tdci 200cv Automática  13000   \n","8           Ford  Focus III  2017                               1.6 S  17500   \n","29    Volkswagen    Saveiro  2017  1.6 Gp Ce 101cv Safety + Pack High  20000   \n","35          Ford  Focus III  2016                2.0 Sedan Se Plus Mt  15022   \n","49          Ford  Focus III  2016                               1.6 S  39000   \n","...          ...        ...   ...                                 ...    ...   \n","9985  Volkswagen  Gol Trend  2016                 1.6 Trendline 101cv  54100   \n","9990        Ford  Focus III  2016                      2.0 Se Plus Mt  48000   \n","9991        Ford     Ranger  2016        3.2 Cd 4x2 Xlt At Tdci 200cv  59000   \n","9994        Ford  Focus III  2016                              2.0 Se  18000   \n","9995        Ford  Focus III  2016                              2.0 Se  67000   \n","\n","       Price  Doors       Kind               Location  \n","3     798000    4.0    Pick-Up                Neuquén  \n","8     367000    5.0  Hatchback  Buenos Aires Interior  \n","29    370000    2.0    Pick-Up                Córdoba  \n","35    550000    4.0      Sedán      Bs.as. 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Oeste  \n","\n","[1414 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-dab4c732-1a8f-4401-9af2-1b715eb6c7e9\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2017</td>\n","      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n","      <td>13000</td>\n","      <td>798000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2017</td>\n","      <td>1.6 S</td>\n","      <td>17500</td>\n","      <td>367000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>Volkswagen</td>\n","      <td>Saveiro</td>\n","      <td>2017</td>\n","      <td>1.6 Gp Ce 101cv Safety + Pack High</td>\n","      <td>20000</td>\n","      <td>370000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Córdoba</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Sedan Se Plus Mt</td>\n","      <td>15022</td>\n","      <td>550000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>1.6 S</td>\n","      <td>39000</td>\n","      <td>454800</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</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","    </tr>\n","    <tr>\n","      <th>9985</th>\n","      <td>Volkswagen</td>\n","      <td>Gol Trend</td>\n","      <td>2016</td>\n","      <td>1.6 Trendline 101cv</td>\n","      <td>54100</td>\n","      <td>270000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>9990</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se Plus Mt</td>\n","      <td>48000</td>\n","      <td>450000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. 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Oeste</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1414 rows × 9 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dab4c732-1a8f-4401-9af2-1b715eb6c7e9')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-dab4c732-1a8f-4401-9af2-1b715eb6c7e9 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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G.b.a. Sur  \n","3                   Neuquén  \n","4                   Córdoba  \n","...                     ...  \n","9995    Bs.as. G.b.a. Oeste  \n","9996  Buenos Aires Interior  \n","9997                Tucumán  \n","9998  Buenos Aires Interior  \n","9999      Bs.as. G.b.a. Sur  \n","\n","[10000 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-efc0876a-4232-49f1-b5e1-21140dd56722\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Volkswagen</td>\n","      <td>Vento</td>\n","      <td>2012</td>\n","      <td>2.5 Luxury 170cv</td>\n","      <td>99950</td>\n","      <td>360000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Córdoba</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2012</td>\n","      <td>2.3 Cd Xl Plus 4x2</td>\n","      <td>140000</td>\n","      <td>320000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Entre Ríos</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Volkswagen</td>\n","      <td>Fox</td>\n","      <td>2011</td>\n","      <td>1.6 Trendline</td>\n","      <td>132000</td>\n","      <td>209980</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2017</td>\n","      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n","      <td>13000</td>\n","      <td>798000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Volkswagen</td>\n","      <td>Gol</td>\n","      <td>2013</td>\n","      <td>1.4 Power 83cv 3 p</td>\n","      <td>107000</td>\n","      <td>146000</td>\n","      <td>3.0</td>\n","      <td>Hatchback</td>\n","      <td>Córdoba</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","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>67000</td>\n","      <td>399000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","    <tr>\n","      <th>9996</th>\n","      <td>Volkswagen</td>\n","      <td>Bora</td>\n","      <td>2012</td>\n","      <td>1.9 Trendline I 100cv</td>\n","      <td>120000</td>\n","      <td>240000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>9997</th>\n","      <td>Ford</td>\n","      <td>Fiesta Kinetic Design</td>\n","      <td>2012</td>\n","      <td>1.6 Design 120cv Titanium</td>\n","      <td>89000</td>\n","      <td>250000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Tucumán</td>\n","    </tr>\n","    <tr>\n","      <th>9998</th>\n","      <td>Ford</td>\n","      <td>Fiesta Kinetic Design</td>\n","      <td>2013</td>\n","      <td>1.6 Design 120cv Titanium</td>\n","      <td>76000</td>\n","      <td>295000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>9999</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2014</td>\n","      <td>2.0 Titanium At6</td>\n","      <td>32000</td>\n","      <td>380000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>10000 rows × 9 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-efc0876a-4232-49f1-b5e1-21140dd56722')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-efc0876a-4232-49f1-b5e1-21140dd56722 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Which car make is the cheapest one? \n","3. Which car make has on average the most Kms?"]},{"cell_type":"markdown","metadata":{"id":"K5zxF9yIts_Z"},"source":["The `groupby` function is one of the most used function of the pandas library. In addition to filtering, grouping data during the exploration is quite useful.\n","https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html"]},{"cell_type":"code","source":["# Mean price over all cars & brands:\n","cars.Price.mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oKQXpgA0SLE4","executionInfo":{"status":"ok","timestamp":1663516152993,"user_tz":-120,"elapsed":11,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}},"outputId":"76aa0f5b-41ef-4235-a03d-3c91abbb5345"},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["349545.4545"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8HszbsoYuNA7","outputId":"16eb1f3b-495b-4631-8c73-8a35f294330c","executionInfo":{"status":"ok","timestamp":1663516243572,"user_tz":-120,"elapsed":452,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["# Mean price per car brand:\n","print(cars.groupby('Make')[['Price']].mean())\n","print(cars.groupby('Make')[['Price']].mean().round(1).sort_values('Price', ascending= False))"],"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["                       Price\n","Make                        \n","Chrysler       366845.833333\n","Ford           353041.877764\n","Honda          381520.603448\n","Mercedes Benz  512749.515625\n","Volkswagen     335441.534119\n","                  Price\n","Make                   \n","Mercedes Benz  512749.5\n","Honda          381520.6\n","Chrysler       366845.8\n","Ford           353041.9\n","Volkswagen     335441.5\n"]}]},{"cell_type":"markdown","metadata":{"id":"6wWvj2NhvSmt"},"source":["We can also add another column to the groups. For example, what is the average kms by each make?"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":237},"id":"FT6Q2cXUvmr4","outputId":"be28986b-a281-4572-b2ca-56ef38fbe77e","executionInfo":{"status":"ok","timestamp":1663516413565,"user_tz":-120,"elapsed":366,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars.groupby('Make')[['Price','Kms']].mean().round(1)"],"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                  Price      Kms\n","Make                            \n","Chrysler       366845.8  79594.0\n","Ford           353041.9  69897.5\n","Honda          381520.6  81619.7\n","Mercedes Benz  512749.5  67358.7\n","Volkswagen     335441.5  77660.2"],"text/html":["\n","  <div id=\"df-df2a8a55-ca42-42bf-8fa4-0a36b650cf30\">\n","    <div class=\"colab-df-container\">\n","      <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>Price</th>\n","      <th>Kms</th>\n","    </tr>\n","    <tr>\n","      <th>Make</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Chrysler</th>\n","      <td>366845.8</td>\n","      <td>79594.0</td>\n","    </tr>\n","    <tr>\n","      <th>Ford</th>\n","      <td>353041.9</td>\n","      <td>69897.5</td>\n","    </tr>\n","    <tr>\n","      <th>Honda</th>\n","      <td>381520.6</td>\n","      <td>81619.7</td>\n","    </tr>\n","    <tr>\n","      <th>Mercedes Benz</th>\n","      <td>512749.5</td>\n","      <td>67358.7</td>\n","    </tr>\n","    <tr>\n","      <th>Volkswagen</th>\n","      <td>335441.5</td>\n","      <td>77660.2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-df2a8a55-ca42-42bf-8fa4-0a36b650cf30')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-df2a8a55-ca42-42bf-8fa4-0a36b650cf30 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":23}]},{"cell_type":"markdown","metadata":{"id":"JxV1W6X6nsT1"},"source":["### Conclusion:\n","- The most expensive car make is Mercedes Benz\n","- Volkswagen is the cheapest car make.\n","- The most used cars in Honda make (The highest km)"]},{"cell_type":"markdown","metadata":{"id":"crHX5Hiyv3H7"},"source":["## 3.What are the average prices by brands and by the number of doors that cars have?"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":551},"id":"Nrxqcx5_v_lN","outputId":"5a54d99a-9b08-402f-cea7-a2db6d911fdf","executionInfo":{"status":"ok","timestamp":1663516442681,"user_tz":-120,"elapsed":363,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars.groupby(['Make','Doors'])[['Price']].mean().round(2)"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                         Price\n","Make          Doors           \n","Chrysler      5.0    366845.83\n","Ford          2.0    481503.91\n","              3.0    158703.58\n","              4.0    455984.90\n","              5.0    331894.57\n","Honda         3.0     26000.00\n","              4.0    323781.20\n","              5.0    431716.49\n","Mercedes Benz 2.0    574315.37\n","              4.0    497913.05\n","              5.0    476930.08\n","Volkswagen    2.0    379124.71\n","              3.0    226298.65\n","              4.0    422831.05\n","              5.0    271274.80"],"text/html":["\n","  <div id=\"df-12d0eafe-e852-453c-a93a-1aa58fdc3996\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th></th>\n","      <th>Price</th>\n","    </tr>\n","    <tr>\n","      <th>Make</th>\n","      <th>Doors</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Chrysler</th>\n","      <th>5.0</th>\n","      <td>366845.83</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"4\" valign=\"top\">Ford</th>\n","      <th>2.0</th>\n","      <td>481503.91</td>\n","    </tr>\n","    <tr>\n","      <th>3.0</th>\n","      <td>158703.58</td>\n","    </tr>\n","    <tr>\n","      <th>4.0</th>\n","      <td>455984.90</td>\n","    </tr>\n","    <tr>\n","      <th>5.0</th>\n","      <td>331894.57</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">Honda</th>\n","      <th>3.0</th>\n","      <td>26000.00</td>\n","    </tr>\n","    <tr>\n","      <th>4.0</th>\n","      <td>323781.20</td>\n","    </tr>\n","    <tr>\n","      <th>5.0</th>\n","      <td>431716.49</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">Mercedes Benz</th>\n","      <th>2.0</th>\n","      <td>574315.37</td>\n","    </tr>\n","    <tr>\n","      <th>4.0</th>\n","      <td>497913.05</td>\n","    </tr>\n","    <tr>\n","      <th>5.0</th>\n","      <td>476930.08</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"4\" valign=\"top\">Volkswagen</th>\n","      <th>2.0</th>\n","      <td>379124.71</td>\n","    </tr>\n","    <tr>\n","      <th>3.0</th>\n","      <td>226298.65</td>\n","    </tr>\n","    <tr>\n","      <th>4.0</th>\n","      <td>422831.05</td>\n","    </tr>\n","    <tr>\n","      <th>5.0</th>\n","      <td>271274.80</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-12d0eafe-e852-453c-a93a-1aa58fdc3996')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-727cd6e3-9aa6-4b78-a481-59726460b76a');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"CFy67BqSqG2D"},"source":["### Conclusion\n","- There are 28 different models in Volkswagen.\n","- The least variety is in Chrysler: only 2 models."]},{"cell_type":"markdown","metadata":{"id":"pTQ52dzjhAyl"},"source":["## 5.Which kind of cars is the most expensive and most popular?"]},{"cell_type":"markdown","metadata":{"id":"SJRkCwVA_2wp"},"source":["The `agg()` method allows you to perform specific aggregation(s) on specific columns.https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.agg.html"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"46TO8dsJ9gM2","outputId":"26a05445-85b4-4eeb-fce2-a449244213f2","executionInfo":{"status":"ok","timestamp":1663516785538,"user_tz":-120,"elapsed":346,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["print(cars.groupby('Kind')[['Price']].agg( ['mean','count']).round(1))\n","print(cars.groupby('Kind')[['Price']].mean().round(1))"],"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["                Price      \n","                 mean count\n","Kind                       \n","Cabriolet     79272.6    11\n","Coupé        543092.2    90\n","Furgón       702054.7    53\n","Hatchback    276554.8  4056\n","Minivan      650000.0     1\n","Monovolumen  270500.2   705\n","Pick-Up      545194.6  1547\n","Rural        330247.1    59\n","SUV          380708.0  1305\n","Sedán        338569.6  2173\n","                Price\n","Kind                 \n","Cabriolet     79272.6\n","Coupé        543092.2\n","Furgón       702054.7\n","Hatchback    276554.8\n","Minivan      650000.0\n","Monovolumen  270500.2\n","Pick-Up      545194.6\n","Rural        330247.1\n","SUV          380708.0\n","Sedán        338569.6\n"]}]},{"cell_type":"markdown","metadata":{"id":"wZPJMiAtrA7I"},"source":["### Conclusion\n","- The most popular kind is Hatchback and then Sedan.\n","- The most expensive kind is Furgon, while the cheapest one is Cabriolet."]},{"cell_type":"markdown","metadata":{"id":"MJdkMmVDBj_D"},"source":["## BONUS: What are the min, max and average production year for Ford cars?"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"94Zw8ER-D2BH","outputId":"4b27ffda-a49e-49e9-f0d6-f83215afa163","executionInfo":{"status":"ok","timestamp":1663516828467,"user_tz":-120,"elapsed":373,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["ford_cars = cars[cars['Make'] == 'Ford']\n","ford_cars['Year'].agg(['mean','min','max']).round(1)"],"execution_count":30,"outputs":[{"output_type":"execute_result","data":{"text/plain":["mean    2013.5\n","min     2011.0\n","max     2018.0\n","Name: Year, dtype: float64"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","metadata":{"id":"aq1jGBxxjGnh"},"source":["#cars[cars['Make'] == 'Ford']['Year'].agg(['mean','min','max']).round(1)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FCBp8ttbXOMR"},"source":["## Further Materials"]},{"cell_type":"markdown","metadata":{"id":"akGU0y1A_VsW"},"source":["Useful aggregation functions:\n","- `mean()`\n","- `median()`\n","- `var()`\n","- `std()`\n","- `min()`\n","- `max()`\n","- `count()`\n","- `sum()`\n","- `nunique()`"]},{"cell_type":"markdown","metadata":{"id":"OZY8d3lrmip3"},"source":["### Sorting Data"]},{"cell_type":"markdown","metadata":{"id":"na6fCqDImupY"},"source":["Let's say, we would like to see the least used cars (in terms of kms).\n","\n","We can use the `sort_values()` for this purpose. (`sort_values()` sorts ascending by deafult.)\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"YO-omnj_nuxz","outputId":"515eb33a-7e60-450b-f291-6d42f2e54ea6","executionInfo":{"status":"ok","timestamp":1663516831297,"user_tz":-120,"elapsed":314,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars.sort_values('Kms').head(5)"],"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Make      Model  Year                 Variant   Kms   Price  \\\n","5665  Volkswagen    Saveiro  2017  1.6 Gp Cs 101cv Safety  5094  320000   \n","8673        Ford   Ecosport  2017         1.6 S 110cv 4x2  5100  390000   \n","8828  Volkswagen  Gol Trend  2017  1.6 Trendline 101cv 3p  5500  330000   \n","2461        Ford  Focus III  2017                  2.0 Se  5500  480000   \n","7910  Volkswagen    Saveiro  2017  1.6 Gp Cs 101cv Safety  5600  360000   \n","\n","      Doors       Kind                Location  \n","5665    2.0    Pick-Up       Bs.as. G.b.a. Sur  \n","8673    5.0        SUV       Bs.as. G.b.a. Sur  \n","8828    3.0  Hatchback       Bs.as. G.b.a. Sur  \n","2461    5.0  Hatchback         Capital Federal  \n","7910    2.0    Pick-Up  Bs.as. Costa Atlántica  "],"text/html":["\n","  <div id=\"df-40471652-404d-4319-9c56-49e14f4d47d8\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>5665</th>\n","      <td>Volkswagen</td>\n","      <td>Saveiro</td>\n","      <td>2017</td>\n","      <td>1.6 Gp Cs 101cv Safety</td>\n","      <td>5094</td>\n","      <td>320000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>8673</th>\n","      <td>Ford</td>\n","      <td>Ecosport</td>\n","      <td>2017</td>\n","      <td>1.6 S 110cv 4x2</td>\n","      <td>5100</td>\n","      <td>390000</td>\n","      <td>5.0</td>\n","      <td>SUV</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>8828</th>\n","      <td>Volkswagen</td>\n","      <td>Gol Trend</td>\n","      <td>2017</td>\n","      <td>1.6 Trendline 101cv 3p</td>\n","      <td>5500</td>\n","      <td>330000</td>\n","      <td>3.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>2461</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2017</td>\n","      <td>2.0 Se</td>\n","      <td>5500</td>\n","      <td>480000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Capital Federal</td>\n","    </tr>\n","    <tr>\n","      <th>7910</th>\n","      <td>Volkswagen</td>\n","      <td>Saveiro</td>\n","      <td>2017</td>\n","      <td>1.6 Gp Cs 101cv Safety</td>\n","      <td>5600</td>\n","      <td>360000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Bs.as. Costa Atlántica</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40471652-404d-4319-9c56-49e14f4d47d8')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-40471652-404d-4319-9c56-49e14f4d47d8 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":31}]},{"cell_type":"markdown","metadata":{"id":"wVfFJAOVqD5o"},"source":["We can modify the order of sort_values if we would like to see the most used cars."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"VpiAjHyvqQBb","outputId":"5196c406-c98f-4afb-9ee6-a735e711b323","executionInfo":{"status":"ok","timestamp":1663516831633,"user_tz":-120,"elapsed":11,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars.sort_values('Kms', ascending=False).head(5)"],"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Make   Model  Year                            Variant     Kms  \\\n","5204        Ford  Ranger  2011                      3.0 Cd Xl 4x2  218000   \n","5793        Ford  Ranger  2011               3.0 Cd Superduty 4x4  215000   \n","7369        Ford  Ranger  2011                 3.0 Cd Xl Plus 4x2  215000   \n","4029  Volkswagen  Amarok  2011  2.0 Cd Tdi 4x2 Trendline Ll17 1t8  214000   \n","2049  Volkswagen  Amarok  2011   2.0 Cd Tdi 4x4 Highline Pack 1hp  209898   \n","\n","       Price  Doors     Kind             Location  \n","5204  300000    2.0  Pick-Up    Bs.as. G.b.a. Sur  \n","5793  385000    2.0  Pick-Up    Bs.as. G.b.a. Sur  \n","7369  310000    2.0  Pick-Up              Córdoba  \n","4029  400000    4.0  Pick-Up               Chubut  \n","2049  550000    4.0  Pick-Up  Bs.as. G.b.a. Norte  "],"text/html":["\n","  <div id=\"df-f7873eaa-3cfd-45ba-af4b-3f49800ee1b4\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>5204</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2011</td>\n","      <td>3.0 Cd Xl 4x2</td>\n","      <td>218000</td>\n","      <td>300000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>5793</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2011</td>\n","      <td>3.0 Cd Superduty 4x4</td>\n","      <td>215000</td>\n","      <td>385000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>7369</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2011</td>\n","      <td>3.0 Cd Xl Plus 4x2</td>\n","      <td>215000</td>\n","      <td>310000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Córdoba</td>\n","    </tr>\n","    <tr>\n","      <th>4029</th>\n","      <td>Volkswagen</td>\n","      <td>Amarok</td>\n","      <td>2011</td>\n","      <td>2.0 Cd Tdi 4x2 Trendline Ll17 1t8</td>\n","      <td>214000</td>\n","      <td>400000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Chubut</td>\n","    </tr>\n","    <tr>\n","      <th>2049</th>\n","      <td>Volkswagen</td>\n","      <td>Amarok</td>\n","      <td>2011</td>\n","      <td>2.0 Cd Tdi 4x4 Highline Pack 1hp</td>\n","      <td>209898</td>\n","      <td>550000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Bs.as. G.b.a. Norte</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7873eaa-3cfd-45ba-af4b-3f49800ee1b4')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-f7873eaa-3cfd-45ba-af4b-3f49800ee1b4 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":32}]},{"cell_type":"markdown","metadata":{"id":"-ik-w00jtXt2"},"source":["**NOTE**: Using the `sort_values()` method does not overwrite the object. It remains unaltered after applying it the `sort_values()` function\n","\n","If you want to overwrite the object with the new values ordered there are 2 ways to do it:\n"]},{"cell_type":"markdown","metadata":{"id":"Aeop68pLFrlU"},"source":["1.   Assign to `cars` the value `cars.sort_values()`\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"24dRYsD3F24B","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1663516831633,"user_tz":-120,"elapsed":10,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}},"outputId":"ae90ba72-c720-4fe0-8d05-affff65a464e"},"source":["sorted_cars = cars.sort_values('Kms')\n","sorted_cars.head(5)"],"execution_count":33,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Make      Model  Year                 Variant   Kms   Price  \\\n","5665  Volkswagen    Saveiro  2017  1.6 Gp Cs 101cv Safety  5094  320000   \n","8673        Ford   Ecosport  2017         1.6 S 110cv 4x2  5100  390000   \n","8828  Volkswagen  Gol Trend  2017  1.6 Trendline 101cv 3p  5500  330000   \n","2461        Ford  Focus III  2017                  2.0 Se  5500  480000   \n","7910  Volkswagen    Saveiro  2017  1.6 Gp Cs 101cv Safety  5600  360000   \n","\n","      Doors       Kind                Location  \n","5665    2.0    Pick-Up       Bs.as. G.b.a. Sur  \n","8673    5.0        SUV       Bs.as. G.b.a. Sur  \n","8828    3.0  Hatchback       Bs.as. G.b.a. Sur  \n","2461    5.0  Hatchback         Capital Federal  \n","7910    2.0    Pick-Up  Bs.as. Costa Atlántica  "],"text/html":["\n","  <div id=\"df-265ca073-93cb-4d76-b2fb-d78043a7b4df\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>5665</th>\n","      <td>Volkswagen</td>\n","      <td>Saveiro</td>\n","      <td>2017</td>\n","      <td>1.6 Gp Cs 101cv Safety</td>\n","      <td>5094</td>\n","      <td>320000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Bs.as. 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Use the `inplace` `parameter` of `.sort_values()`"]},{"cell_type":"code","metadata":{"id":"Aa2OhdtdFqH5","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1663516831634,"user_tz":-120,"elapsed":10,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}},"outputId":"41d733f5-e9cc-49d3-b0de-9515bc4d31e1"},"source":["cars.sort_values('Kms', ascending=False, inplace = True)\n","cars.head(5)"],"execution_count":34,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Make   Model  Year                            Variant     Kms  \\\n","5204        Ford  Ranger  2011                      3.0 Cd Xl 4x2  218000   \n","5793        Ford  Ranger  2011               3.0 Cd Superduty 4x4  215000   \n","7369        Ford  Ranger  2011                 3.0 Cd Xl Plus 4x2  215000   \n","4029  Volkswagen  Amarok  2011  2.0 Cd Tdi 4x2 Trendline Ll17 1t8  214000   \n","2049  Volkswagen  Amarok  2011   2.0 Cd Tdi 4x4 Highline Pack 1hp  209898   \n","\n","       Price  Doors     Kind             Location  \n","5204  300000    2.0  Pick-Up    Bs.as. G.b.a. Sur  \n","5793  385000    2.0  Pick-Up    Bs.as. G.b.a. Sur  \n","7369  310000    2.0  Pick-Up              Córdoba  \n","4029  400000    4.0  Pick-Up               Chubut  \n","2049  550000    4.0  Pick-Up  Bs.as. G.b.a. 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"]},{"cell_type":"code","metadata":{"id":"UGgE7n9hI18N","colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"status":"ok","timestamp":1663516832375,"user_tz":-120,"elapsed":749,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}},"outputId":"079e9f6f-3649-48c3-f4e3-15b32f735e08"},"source":["cars.sort_index()"],"execution_count":35,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Make                  Model  Year  \\\n","0     Volkswagen                  Vento  2012   \n","1           Ford                 Ranger  2012   \n","2     Volkswagen                    Fox  2011   \n","3           Ford                 Ranger  2017   \n","4     Volkswagen                    Gol  2013   \n","...          ...                    ...   ...   \n","9995        Ford              Focus III  2016   \n","9996  Volkswagen                   Bora  2012   \n","9997        Ford  Fiesta Kinetic Design  2012   \n","9998        Ford  Fiesta Kinetic Design  2013   \n","9999        Ford              Focus III  2014   \n","\n","                               Variant     Kms   Price  Doors       Kind  \\\n","0                     2.5 Luxury 170cv   99950  360000    4.0      Sedán   \n","1                   2.3 Cd Xl Plus 4x2  140000  320000    2.0    Pick-Up   \n","2                        1.6 Trendline  132000  209980    5.0  Hatchback   \n","3     3.2 Cd Xls Tdci 200cv Automática   13000  798000    4.0    Pick-Up   \n","4                   1.4 Power 83cv 3 p  107000  146000    3.0  Hatchback   \n","...                                ...     ...     ...    ...        ...   \n","9995                            2.0 Se   67000  399000    5.0  Hatchback   \n","9996             1.9 Trendline I 100cv  120000  240000    4.0      Sedán   \n","9997         1.6 Design 120cv Titanium   89000  250000    5.0  Hatchback   \n","9998         1.6 Design 120cv Titanium   76000  295000    5.0  Hatchback   \n","9999                  2.0 Titanium At6   32000  380000    5.0  Hatchback   \n","\n","                   Location  \n","0                   Córdoba  \n","1                Entre Ríos  \n","2         Bs.as. G.b.a. Sur  \n","3                   Neuquén  \n","4                   Córdoba  \n","...                     ...  \n","9995    Bs.as. G.b.a. Oeste  \n","9996  Buenos Aires Interior  \n","9997                Tucumán  \n","9998  Buenos Aires Interior  \n","9999      Bs.as. G.b.a. Sur  \n","\n","[10000 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-6fe1a79c-15c4-416b-816f-da3ec1b2e8c3\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Volkswagen</td>\n","      <td>Vento</td>\n","      <td>2012</td>\n","      <td>2.5 Luxury 170cv</td>\n","      <td>99950</td>\n","      <td>360000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Córdoba</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2012</td>\n","      <td>2.3 Cd Xl Plus 4x2</td>\n","      <td>140000</td>\n","      <td>320000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Entre Ríos</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Volkswagen</td>\n","      <td>Fox</td>\n","      <td>2011</td>\n","      <td>1.6 Trendline</td>\n","      <td>132000</td>\n","      <td>209980</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2017</td>\n","      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n","      <td>13000</td>\n","      <td>798000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Volkswagen</td>\n","      <td>Gol</td>\n","      <td>2013</td>\n","      <td>1.4 Power 83cv 3 p</td>\n","      <td>107000</td>\n","      <td>146000</td>\n","      <td>3.0</td>\n","      <td>Hatchback</td>\n","      <td>Córdoba</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","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>67000</td>\n","      <td>399000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","    <tr>\n","      <th>9996</th>\n","      <td>Volkswagen</td>\n","      <td>Bora</td>\n","      <td>2012</td>\n","      <td>1.9 Trendline I 100cv</td>\n","      <td>120000</td>\n","      <td>240000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>9997</th>\n","      <td>Ford</td>\n","      <td>Fiesta Kinetic Design</td>\n","      <td>2012</td>\n","      <td>1.6 Design 120cv Titanium</td>\n","      <td>89000</td>\n","      <td>250000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Tucumán</td>\n","    </tr>\n","    <tr>\n","      <th>9998</th>\n","      <td>Ford</td>\n","      <td>Fiesta Kinetic Design</td>\n","      <td>2013</td>\n","      <td>1.6 Design 120cv Titanium</td>\n","      <td>76000</td>\n","      <td>295000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>9999</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2014</td>\n","      <td>2.0 Titanium At6</td>\n","      <td>32000</td>\n","      <td>380000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. 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G.b.a. Sur  \n","3                   Neuquén  \n","4                   Córdoba  \n","...                     ...  \n","9995    Bs.as. G.b.a. Oeste  \n","9996  Buenos Aires Interior  \n","9997                Tucumán  \n","9998  Buenos Aires Interior  \n","9999      Bs.as. G.b.a. Sur  \n","\n","[10000 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-b3a0c636-ac90-404c-82e5-25d81a5d648e\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Volkswagen</td>\n","      <td>Vento</td>\n","      <td>2012</td>\n","      <td>2.5 Luxury 170cv</td>\n","      <td>99950</td>\n","      <td>360000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Córdoba</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2012</td>\n","      <td>2.3 Cd Xl Plus 4x2</td>\n","      <td>140000</td>\n","      <td>320000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Entre Ríos</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Volkswagen</td>\n","      <td>Fox</td>\n","      <td>2011</td>\n","      <td>1.6 Trendline</td>\n","      <td>132000</td>\n","      <td>209980</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2017</td>\n","      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n","      <td>13000</td>\n","      <td>798000</td>\n","      <td>4.0</td>\n","      <td>Pick-Up</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Volkswagen</td>\n","      <td>Gol</td>\n","      <td>2013</td>\n","      <td>1.4 Power 83cv 3 p</td>\n","      <td>107000</td>\n","      <td>146000</td>\n","      <td>3.0</td>\n","      <td>Hatchback</td>\n","      <td>Córdoba</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","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2016</td>\n","      <td>2.0 Se</td>\n","      <td>67000</td>\n","      <td>399000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","    <tr>\n","      <th>9996</th>\n","      <td>Volkswagen</td>\n","      <td>Bora</td>\n","      <td>2012</td>\n","      <td>1.9 Trendline I 100cv</td>\n","      <td>120000</td>\n","      <td>240000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>9997</th>\n","      <td>Ford</td>\n","      <td>Fiesta Kinetic Design</td>\n","      <td>2012</td>\n","      <td>1.6 Design 120cv Titanium</td>\n","      <td>89000</td>\n","      <td>250000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Tucumán</td>\n","    </tr>\n","    <tr>\n","      <th>9998</th>\n","      <td>Ford</td>\n","      <td>Fiesta Kinetic Design</td>\n","      <td>2013</td>\n","      <td>1.6 Design 120cv Titanium</td>\n","      <td>76000</td>\n","      <td>295000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Buenos Aires Interior</td>\n","    </tr>\n","    <tr>\n","      <th>9999</th>\n","      <td>Ford</td>\n","      <td>Focus III</td>\n","      <td>2014</td>\n","      <td>2.0 Titanium At6</td>\n","      <td>32000</td>\n","      <td>380000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. 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Please use `sort_values()` and `head()`.\n","While finding the top ten expensive cars overwrite the dataset and then return back to original sorting by using `sort_index()`."]},{"cell_type":"code","metadata":{"id":"rq09jJfLK1lB","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1663516832377,"user_tz":-120,"elapsed":13,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}},"outputId":"184ba5bf-deec-4c1b-9b68-34227ee9310b"},"source":["cars.head()"],"execution_count":37,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         Make   Model  Year                           Variant     Kms   Price  \\\n","0  Volkswagen   Vento  2012                  2.5 Luxury 170cv   99950  360000   \n","1        Ford  Ranger  2012                2.3 Cd Xl Plus 4x2  140000  320000   \n","2  Volkswagen     Fox  2011                     1.6 Trendline  132000  209980   \n","3        Ford  Ranger  2017  3.2 Cd Xls Tdci 200cv Automática   13000  798000   \n","4  Volkswagen     Gol  2013                1.4 Power 83cv 3 p  107000  146000   \n","\n","   Doors       Kind           Location  \n","0    4.0      Sedán            Córdoba  \n","1    2.0    Pick-Up         Entre Ríos  \n","2    5.0  Hatchback  Bs.as. G.b.a. Sur  \n","3    4.0    Pick-Up            Neuquén  \n","4    3.0  Hatchback            Córdoba  "],"text/html":["\n","  <div id=\"df-d5b7878a-707f-43cd-9dfe-859ac7d6e534\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Volkswagen</td>\n","      <td>Vento</td>\n","      <td>2012</td>\n","      <td>2.5 Luxury 170cv</td>\n","      <td>99950</td>\n","      <td>360000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Córdoba</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Ford</td>\n","      <td>Ranger</td>\n","      <td>2012</td>\n","      <td>2.3 Cd Xl Plus 4x2</td>\n","      <td>140000</td>\n","      <td>320000</td>\n","      <td>2.0</td>\n","      <td>Pick-Up</td>\n","      <td>Entre Ríos</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Volkswagen</td>\n","      <td>Fox</td>\n","      <td>2011</td>\n","      <td>1.6 Trendline</td>\n","      <td>132000</td>\n","      <td>209980</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. 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G.b.a. Sur  \n","2632  Bs.as. G.b.a. Norte  \n","9520              Córdoba  \n","5404      Capital Federal  \n","2032      Capital Federal  \n","7897  Bs.as. G.b.a. Oeste  \n","5831              Neuquén  \n","57             Entre Ríos  \n","6434  Bs.as. G.b.a. Norte  \n","9187  Bs.as. G.b.a. Oeste  "],"text/html":["\n","  <div id=\"df-16ef9062-4032-4bc6-aead-3578b20d9f48\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>8007</th>\n","      <td>Mercedes Benz</td>\n","      <td>Clase GLC</td>\n","      <td>2017</td>\n","      <td>2.0 Glc250 300 4matic Atomático</td>\n","      <td>18000</td>\n","      <td>2800000</td>\n","      <td>5.0</td>\n","      <td>SUV</td>\n","      <td>Bs.as. 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G.b.a. Oeste</td>\n","    </tr>\n","    <tr>\n","      <th>5831</th>\n","      <td>Mercedes Benz</td>\n","      <td>ML</td>\n","      <td>2011</td>\n","      <td>3.5 Ml350 4matic Sport Facelift</td>\n","      <td>31700</td>\n","      <td>1200000</td>\n","      <td>5.0</td>\n","      <td>SUV</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>57</th>\n","      <td>Ford</td>\n","      <td>Mondeo</td>\n","      <td>2018</td>\n","      <td>2.0 Se Ecoboost At 240cv</td>\n","      <td>15000</td>\n","      <td>1200000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Entre Ríos</td>\n","    </tr>\n","    <tr>\n","      <th>6434</th>\n","      <td>Mercedes Benz</td>\n","      <td>Clase A</td>\n","      <td>2014</td>\n","      <td>2.0 A 250 At Sport B.efficiency</td>\n","      <td>48255</td>\n","      <td>1170000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Norte</td>\n","    </tr>\n","    <tr>\n","      <th>9187</th>\n","      <td>Mercedes Benz</td>\n","      <td>Clase A</td>\n","      <td>2014</td>\n","      <td>2.0 A 250 At Sport B.efficiency</td>\n","      <td>48455</td>\n","      <td>1170000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. 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G.b.a. Sur  \n","2632  Bs.as. G.b.a. Norte  \n","9520              Córdoba  \n","5404      Capital Federal  \n","2032      Capital Federal  \n","7897  Bs.as. G.b.a. Oeste  \n","5831              Neuquén  \n","57             Entre Ríos  \n","6434  Bs.as. G.b.a. Norte  \n","9187  Bs.as. G.b.a. Oeste  "],"text/html":["\n","  <div id=\"df-0072bbdb-f252-44fc-a312-5dfc675f2014\">\n","    <div class=\"colab-df-container\">\n","      <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>Year</th>\n","      <th>Variant</th>\n","      <th>Kms</th>\n","      <th>Price</th>\n","      <th>Doors</th>\n","      <th>Kind</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>8007</th>\n","      <td>Mercedes Benz</td>\n","      <td>Clase GLC</td>\n","      <td>2017</td>\n","      <td>2.0 Glc250 300 4matic Atomático</td>\n","      <td>18000</td>\n","      <td>2800000</td>\n","      <td>5.0</td>\n","      <td>SUV</td>\n","      <td>Bs.as. G.b.a. Sur</td>\n","    </tr>\n","    <tr>\n","      <th>2632</th>\n","      <td>Ford</td>\n","      <td>Mustang</td>\n","      <td>2018</td>\n","      <td>5.0 Gt 421cv</td>\n","      <td>7000</td>\n","      <td>2350000</td>\n","      <td>2.0</td>\n","      <td>Coupé</td>\n","      <td>Bs.as. G.b.a. Norte</td>\n","    </tr>\n","    <tr>\n","      <th>9520</th>\n","      <td>Mercedes Benz</td>\n","      <td>Clase E</td>\n","      <td>2014</td>\n","      <td>3.5 E350 Avantgarde Sport B.eff At</td>\n","      <td>34000</td>\n","      <td>1725000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Córdoba</td>\n","    </tr>\n","    <tr>\n","      <th>5404</th>\n","      <td>Mercedes Benz</td>\n","      <td>ML</td>\n","      <td>2013</td>\n","      <td>3.5 Ml350 4matic Sport B.efficiency</td>\n","      <td>72000</td>\n","      <td>1499990</td>\n","      <td>5.0</td>\n","      <td>SUV</td>\n","      <td>Capital Federal</td>\n","    </tr>\n","    <tr>\n","      <th>2032</th>\n","      <td>Mercedes Benz</td>\n","      <td>Clase E</td>\n","      <td>2014</td>\n","      <td>3.5 E350 Avantgarde Sport B.eff At</td>\n","      <td>59000</td>\n","      <td>1390000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Capital Federal</td>\n","    </tr>\n","    <tr>\n","      <th>7897</th>\n","      <td>Volkswagen</td>\n","      <td>Tiguan Allspace</td>\n","      <td>2018</td>\n","      <td>1.4 Tsi Trendline 150cv Dsg</td>\n","      <td>10000</td>\n","      <td>1310000</td>\n","      <td>5.0</td>\n","      <td>SUV</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","    <tr>\n","      <th>5831</th>\n","      <td>Mercedes Benz</td>\n","      <td>ML</td>\n","      <td>2011</td>\n","      <td>3.5 Ml350 4matic Sport Facelift</td>\n","      <td>31700</td>\n","      <td>1200000</td>\n","      <td>5.0</td>\n","      <td>SUV</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>57</th>\n","      <td>Ford</td>\n","      <td>Mondeo</td>\n","      <td>2018</td>\n","      <td>2.0 Se Ecoboost At 240cv</td>\n","      <td>15000</td>\n","      <td>1200000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Entre Ríos</td>\n","    </tr>\n","    <tr>\n","      <th>6434</th>\n","      <td>Mercedes Benz</td>\n","      <td>Clase A</td>\n","      <td>2014</td>\n","      <td>2.0 A 250 At Sport B.efficiency</td>\n","      <td>48255</td>\n","      <td>1170000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Norte</td>\n","    </tr>\n","    <tr>\n","      <th>9187</th>\n","      <td>Mercedes Benz</td>\n","      <td>Clase A</td>\n","      <td>2014</td>\n","      <td>2.0 A 250 At Sport B.efficiency</td>\n","      <td>48455</td>\n","      <td>1170000</td>\n","      <td>5.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Oeste</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0072bbdb-f252-44fc-a312-5dfc675f2014')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-0072bbdb-f252-44fc-a312-5dfc675f2014 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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