3_2_Lecture_Pandas_Transformations.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"https://github.com/ReDI-School/nrw-data-analytics/blob/main/3_2_Pandas_Transformations_Lecture.ipynb","timestamp":1663496442568}],"collapsed_sections":["u9odIUO-aKZG","Fpcu6VJWxlQn","ZnbTmYxAeI6y","T9RKqoaFf3o5","F_QqjWW57Dx7","OklqZD6gH9vv"]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"He_F9QFclfQf"},"source":["![data_tasks.jpg](https://thumbor.forbes.com/thumbor/960x0/https%3A%2F%2Fblogs-images.forbes.com%2Fgilpress%2Ffiles%2F2016%2F03%2FTime-1200x511.jpg)"]},{"cell_type":"markdown","metadata":{"id":"O8DbXHW2ii5L"},"source":["# Transformations with Pandas\n","\n","Sometimes if we have a question about our dataset, we may not be able to answer it directly, even if we have all of the data needed. It may happen that we will need a few additional steps in between where we will transform our data to a representation that will be easier for later data analysis. \n","\n","Today, we will learn how to modify our dataframe - transform the information we already have, add new columns, make some calculations... . We will also cover KPIs topic."]},{"cell_type":"markdown","metadata":{"id":"3A1LrEnbZYYK"},"source":["## 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"]},{"cell_type":"markdown","metadata":{"id":"u9odIUO-aKZG"},"source":["## Prepare the dataset"]},{"cell_type":"code","metadata":{"id":"1Vc_H9eMkvtF","executionInfo":{"status":"ok","timestamp":1663517421693,"user_tz":-120,"elapsed":470,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["# Imports\n","import pandas as pd\n","import plotly"],"execution_count":1,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yQ07jVRuA8Fx","outputId":"6222504c-5ea8-4422-f675-45cf3677c33e","executionInfo":{"status":"ok","timestamp":1663517443508,"user_tz":-120,"elapsed":4174,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["# install plotly\n","# https://plotly.com/python/pandas-backend/\n","# !pip install plotly==4.14.3\n","!pip install plotly\n","pd.options.plotting.backend = \"plotly\""],"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (5.5.0)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly) (8.0.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plotly) (1.15.0)\n"]}]},{"cell_type":"code","metadata":{"id":"PtnPQ3erkwRj","executionInfo":{"status":"ok","timestamp":1663517447820,"user_tz":-120,"elapsed":421,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["# read in the data\n","cars = pd.read_csv(\"https://raw.githubusercontent.com/juliandnl/redi_ss20/master/cars.csv\")\n"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"h4DqFv2sk0w2","colab":{"base_uri":"https://localhost:8080/","height":206},"outputId":"da6c1b9e-0670-4899-8ebd-9088f0483d5e","executionInfo":{"status":"ok","timestamp":1663517449970,"user_tz":-120,"elapsed":344,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars.sample(5)"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Make      Model  Year               Variant     Kms   Price  \\\n","8367        Ford         Ka  2013    1.0 Fly Viral 63cv   61000  150000   \n","1057       Honda       HR-V  2017      1.8 Ex-l 2wd Cvt   10500  825000   \n","5013  Volkswagen  Gol Trend  2014     1.6 Pack Ii 101cv   48000  210000   \n","9014       Honda       City  2011  1.5 Ex-l At 120cv Br  120000  215000   \n","9475  Volkswagen      Vento  2012      2.5 Luxury 170cv   80000  330000   \n","\n","      Doors       Kind             Location  \n","8367    3.0  Hatchback  Bs.as. G.b.a. Norte  \n","1057    5.0        SUV      Capital Federal  \n","5013    5.0  Hatchback  Bs.as. G.b.a. Oeste  \n","9014    4.0      Sedán              Neuquén  \n","9475    4.0      Sedán              Neuquén  "],"text/html":["\n","  <div id=\"df-7b6397b7-e432-4583-a95c-3521deaba103\">\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>8367</th>\n","      <td>Ford</td>\n","      <td>Ka</td>\n","      <td>2013</td>\n","      <td>1.0 Fly Viral 63cv</td>\n","      <td>61000</td>\n","      <td>150000</td>\n","      <td>3.0</td>\n","      <td>Hatchback</td>\n","      <td>Bs.as. G.b.a. Norte</td>\n","    </tr>\n","    <tr>\n","      <th>1057</th>\n","      <td>Honda</td>\n","      <td>HR-V</td>\n","      <td>2017</td>\n","      <td>1.8 Ex-l 2wd Cvt</td>\n","      <td>10500</td>\n","      <td>825000</td>\n","      <td>5.0</td>\n","      <td>SUV</td>\n","      <td>Capital Federal</td>\n","    </tr>\n","    <tr>\n","      <th>5013</th>\n","      <td>Volkswagen</td>\n","      <td>Gol Trend</td>\n","      <td>2014</td>\n","      <td>1.6 Pack Ii 101cv</td>\n","      <td>48000</td>\n","      <td>210000</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>9014</th>\n","      <td>Honda</td>\n","      <td>City</td>\n","      <td>2011</td>\n","      <td>1.5 Ex-l At 120cv Br</td>\n","      <td>120000</td>\n","      <td>215000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Neuquén</td>\n","    </tr>\n","    <tr>\n","      <th>9475</th>\n","      <td>Volkswagen</td>\n","      <td>Vento</td>\n","      <td>2012</td>\n","      <td>2.5 Luxury 170cv</td>\n","      <td>80000</td>\n","      <td>330000</td>\n","      <td>4.0</td>\n","      <td>Sedán</td>\n","      <td>Neuquén</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7b6397b7-e432-4583-a95c-3521deaba103')\"\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-7b6397b7-e432-4583-a95c-3521deaba103 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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What is the distribution of horsepower?"]},{"cell_type":"markdown","metadata":{"id":"Fpcu6VJWxlQn"},"source":["### 1. How old (in years) are the cars in our dataset?\n","\n","We don't have a column providing us a direct information to answer this question. However, we have `Year` column which we can use to extract the information from.\n","\n","Let's do it step by step!\n","\n","What date do we have right now?\n","\n","We can use a hardcoded value for that like this:"]},{"cell_type":"code","metadata":{"id":"FobW82Ufxpna","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663518586428,"user_tz":-120,"elapsed":312,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}},"outputId":"f13ee0d8-db13-4bf3-ecdd-abd465d8f75d"},"source":["# create hard-coded date\n","today_hardcoded = 2022\n","print(today_hardcoded)\n","\n","# is configured as integer\n","print(type(today_hardcoded))"],"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["2022\n","<class 'int'>\n"]}]},{"cell_type":"markdown","metadata":{"id":"Id1iGzUIzW4s"},"source":["But we can also get this value programatically, so we can run the code whenever we want and we don't need to care about checking if all the used values are up to date. "]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SpxN1qMGxplF","outputId":"2246b216-66f8-45a2-b051-273a20085d19","executionInfo":{"status":"ok","timestamp":1663518604562,"user_tz":-120,"elapsed":7,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["from datetime import date\n","\n","today = date.today()\n","print(today)\n","\n","# automatically configured as date-time stamp\n","print(type(today))"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["2022-09-18\n","<class 'datetime.date'>\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wbL1nBYQ1FdB","outputId":"ab2c07ec-e7e7-420b-9c55-1323e5c82255","executionInfo":{"status":"ok","timestamp":1663518889081,"user_tz":-120,"elapsed":417,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["print('Year: ' + str(today.year))\n","print('Month: ' + str(today.month))\n","print('Day: ' + str(today.day))"],"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["Year: 2022\n","Month: 9\n","Day: 18\n"]}]},{"cell_type":"markdown","metadata":{"id":"0nzK3eA_0T9W"},"source":["Now, when we already have the information about the current day, let's try to calculate the age of the first car in our dataset:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Editew-Axpiy","outputId":"d02676e2-994e-48af-dd9f-973404d99b1e","executionInfo":{"status":"ok","timestamp":1663518945422,"user_tz":-120,"elapsed":308,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["first_car = cars.loc[0]\n","\n","# You can also use iloc function, like this:\n","# first_car = cars.iloc[0]\n","# Here, it doesn't matter which function you will use, however there are differences between those two. \n","# You can read more about this here: https://www.analyticsvidhya.com/blog/2020/02/loc-iloc-pandas/\n","\n","first_car"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Make              Volkswagen\n","Model                  Vento\n","Year                    2012\n","Variant     2.5 Luxury 170cv\n","Kms                    99950\n","Price                 360000\n","Doors                    4.0\n","Kind                   Sedán\n","Location             Córdoba\n","Name: 0, dtype: object"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"00Qlw850xpaw","outputId":"bbe8ece3-b679-4bd0-9155-61e6fde83179","executionInfo":{"status":"ok","timestamp":1663518954055,"user_tz":-120,"elapsed":342,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["car_age_in_years = today.year - first_car.Year\n","car_age_in_years"],"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["10"]},"metadata":{},"execution_count":23}]},{"cell_type":"markdown","source":["Alright! Right now we know how to calculate a single age difference. Let's see how to automate this process and calculate this information for all of the cars at once."],"metadata":{"id":"QW4I5lwPdyRS"}},{"cell_type":"markdown","source":["### Functions\n","\n","A function is a block of code which only runs when it is called. Data can be passed into a function in form of paramenters. A function returns data as a result.\n","\n","* `def` creates a function and assigns it a name,\n","* `return` sends a result back to the caller,\n","* arguments are passed by assignment,\n","* arguments and return types are not declared,\n","* to execute a function it must be called.\n","\n","``` python\n","def <name>(param1, param2, ..., paramN):\n","    <statements> \n","    return <value> \n","\n","<name>(arg1, arg2, ..., argN)\n","```"],"metadata":{"id":"ZnbTmYxAeI6y"}},{"cell_type":"code","metadata":{"id":"kOMsIYZEoIuZ","executionInfo":{"status":"ok","timestamp":1663519542971,"user_tz":-120,"elapsed":440,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["today = date.today()\n","\n","# A function calculating the age of a car.\n","def calculate_age_of_a_car(year):\n","    return today.year - year\n","\n","cars['Age'] = cars.Year.apply(calculate_age_of_a_car)"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":195},"id":"2ED4Pz27n7Hc","outputId":"17adbc61-19c2-4785-d740-5e0b942e61f1"},"source":["cars.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Make</th>\n","      <th>Model</th>\n","      <th>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","      <th>Age</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","      <td>9</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","      <td>9</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","      <td>10</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","      <td>4</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","      <td>8</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         Make   Model  Year  ...       Kind           Location  Age\n","0  Volkswagen   Vento  2012  ...      Sedán            Córdoba    9\n","1        Ford  Ranger  2012  ...    Pick-Up         Entre Ríos    9\n","2  Volkswagen     Fox  2011  ...  Hatchback  Bs.as. G.b.a. Sur   10\n","3        Ford  Ranger  2017  ...    Pick-Up            Neuquén    4\n","4  Volkswagen     Gol  2013  ...  Hatchback            Córdoba    8\n","\n","[5 rows x 10 columns]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"markdown","metadata":{"id":"VdC7NApoxwa3"},"source":["Let's answer our question: How old (in years) are the cars in our dataset?"]},{"cell_type":"code","metadata":{"id":"x5Z360uMxvkk","colab":{"base_uri":"https://localhost:8080/","height":542},"outputId":"69867aed-b407-4770-cd90-3d06a301ede7","executionInfo":{"status":"ok","timestamp":1663519558967,"user_tz":-120,"elapsed":2766,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["# displays the ages on a bar plot\n","ages = cars.Age.value_counts()\n","ages.plot.bar()"],"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/html":["<html>\n","<head><meta charset=\"utf-8\" /></head>\n","<body>\n","    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if 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this:"],"metadata":{"id":"1WH558WLhQaJ"}},{"cell_type":"code","metadata":{"id":"qe9LAxRFomjc","executionInfo":{"status":"ok","timestamp":1663520069999,"user_tz":-120,"elapsed":4,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["today = date.today()\n","\n","cars['Age'] = cars.Year.apply(lambda x: today.year - x)"],"execution_count":29,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"u-PWlq8zRVve","outputId":"8d702a10-5ca1-407a-e494-51bfe272fa26","executionInfo":{"status":"ok","timestamp":1663520073162,"user_tz":-120,"elapsed":12,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars.head()"],"execution_count":30,"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  Age  \n","0    4.0      Sedán            Córdoba   10  \n","1    2.0    Pick-Up         Entre Ríos   10  \n","2    5.0  Hatchback  Bs.as. G.b.a. Sur   11  \n","3    4.0    Pick-Up            Neuquén    5  \n","4    3.0  Hatchback            Córdoba    9  "],"text/html":["\n","  <div id=\"df-ba0c1ad9-f3cf-4901-98f9-ef7eb6fcf6ad\">\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","      <th>Age</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","      <td>10</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","      <td>10</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. 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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":30}]},{"cell_type":"markdown","metadata":{"id":"F_QqjWW57Dx7"},"source":["### 2. What is the min and max kilometers per year that the cars in the dataset traveled?\n","\n","The procedure here will be very similar. We will create a new column `KM_per_year` to answer this question."]},{"cell_type":"code","metadata":{"id":"aqiYWZzF7IrR","executionInfo":{"status":"ok","timestamp":1663523417953,"user_tz":-120,"elapsed":312,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["# A function calculating kilometers per year.\n","def calculate_km_per_year(row):\n","    return row.Kms / row.Age\n","\n","cars['KM_per_year'] = cars.apply(calculate_km_per_year, axis=1)"],"execution_count":35,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"oi5Vrg3AEtXr"},"source":["Notice, that we have used an additional argument with the apply function: `axis`. We need that here since we will use more than one column to calculate a new value. In order to do that we will pass the whole row. And the `axis` parameter is just telling us in which direction we want to read our dataframe (Pandas Dataframe is a 2D structure which could be read as rows or as columns). "]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"u3bLqOf4C4uh","outputId":"76692e8e-02d2-46d1-c39e-95efea6d3eeb","executionInfo":{"status":"ok","timestamp":1663523421696,"user_tz":-120,"elapsed":324,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars.head()"],"execution_count":36,"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  Age   KM_per_year  \n","0    4.0      Sedán            Córdoba   10   9995.000000  \n","1    2.0    Pick-Up         Entre Ríos   10  14000.000000  \n","2    5.0  Hatchback  Bs.as. G.b.a. Sur   11  12000.000000  \n","3    4.0    Pick-Up            Neuquén    5   2600.000000  \n","4    3.0  Hatchback            Córdoba    9  11888.888889  "],"text/html":["\n","  <div id=\"df-ce337d62-20c5-45ec-88df-994e7e24472f\">\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","      <th>Age</th>\n","      <th>KM_per_year</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","      <td>10</td>\n","      <td>9995.000000</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","      <td>10</td>\n","      <td>14000.000000</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","      <td>11</td>\n","      <td>12000.000000</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","      <td>5</td>\n","      <td>2600.000000</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","      <td>9</td>\n","      <td>11888.888889</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce337d62-20c5-45ec-88df-994e7e24472f')\"\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-ce337d62-20c5-45ec-88df-994e7e24472f 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":36}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2fYAfkw9Ea0R","outputId":"e83b7d7e-020a-4e9b-9dbf-b22baf2806cb","executionInfo":{"status":"ok","timestamp":1663523424741,"user_tz":-120,"elapsed":319,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["round(cars.KM_per_year.max(), 1)"],"execution_count":37,"outputs":[{"output_type":"execute_result","data":{"text/plain":["20000.0"]},"metadata":{},"execution_count":37}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tj_Nq-nsEeBd","outputId":"c0c3f625-914d-4c3c-ebec-8fcd6de92680","executionInfo":{"status":"ok","timestamp":1663523431516,"user_tz":-120,"elapsed":287,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars.KM_per_year.min()"],"execution_count":38,"outputs":[{"output_type":"execute_result","data":{"text/plain":["618.1818181818181"]},"metadata":{},"execution_count":38}]},{"cell_type":"markdown","metadata":{"id":"tm-kgNtJ7H6_"},"source":["The maximum `kilometer per year` is 20.000 km/year and the minimum is 618 km/year."]},{"cell_type":"markdown","metadata":{"id":"OklqZD6gH9vv"},"source":["### 3. What is the distribution of horsepower?\n","\n","How to find horsepower? If you take a look on `Variant` column you will see that it contains a few information there. But the format is always the same - it starts with a number representing the horsepower (e.g. 2.5, 2.3...). And we can extract this information from there."]},{"cell_type":"code","metadata":{"id":"00zXGSCcJMoT","executionInfo":{"status":"ok","timestamp":1663523627310,"user_tz":-120,"elapsed":321,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["# A function extracting horsepower from `Variant` column\n","def get_horsepower(variant):\n","    # Extract the first element from our string. (More on strings next week!)\n","    return variant.split(' ')[0]  \n","\n","cars['Horsepower'] = cars.Variant.apply(lambda x: get_horsepower(x))"],"execution_count":40,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"HSkcyI9zKCUT","outputId":"ba7bc292-abf5-487b-b241-52a70784b1c6","executionInfo":{"status":"ok","timestamp":1663523676121,"user_tz":-120,"elapsed":337,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars.head()"],"execution_count":42,"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  Age   KM_per_year Horsepower  \n","0    4.0      Sedán            Córdoba   10   9995.000000        2.5  \n","1    2.0    Pick-Up         Entre Ríos   10  14000.000000        2.3  \n","2    5.0  Hatchback  Bs.as. G.b.a. Sur   11  12000.000000        1.6  \n","3    4.0    Pick-Up            Neuquén    5   2600.000000        3.2  \n","4    3.0  Hatchback            Córdoba    9  11888.888889        1.4  "],"text/html":["\n","  <div id=\"df-11b3a5e2-342a-4a75-80f1-53ca425f76c9\">\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","      <th>Age</th>\n","      <th>KM_per_year</th>\n","      <th>Horsepower</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","      <td>10</td>\n","      <td>9995.000000</td>\n","      <td>2.5</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","      <td>10</td>\n","      <td>14000.000000</td>\n","      <td>2.3</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","      <td>11</td>\n","      <td>12000.000000</td>\n","      <td>1.6</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","      <td>5</td>\n","      <td>2600.000000</td>\n","      <td>3.2</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","      <td>9</td>\n","      <td>11888.888889</td>\n","      <td>1.4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11b3a5e2-342a-4a75-80f1-53ca425f76c9')\"\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-11b3a5e2-342a-4a75-80f1-53ca425f76c9 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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distribution of horsepower:\n","cars.Horsepower.value_counts().plot.bar()"],"execution_count":41,"outputs":[{"output_type":"display_data","data":{"text/html":["<html>\n","<head><meta charset=\"utf-8\" /></head>\n","<body>\n","    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n","        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"ef3d99d3-8f32-4b95-aded-b5a5111f3663\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if 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Only very few cars have a horsepower of 3.0 are higher."],"metadata":{"id":"U33-Uz6tvZrR"}},{"cell_type":"markdown","metadata":{"id":"we8ulwhPiBuQ"},"source":["### Add a new column with random values to a dataframe:\n","\n","Let's imagine that our dataset has an additional column `Sold_date` (we will add some random data here on our own).\n","\n","1. How many cars per month are sold?\n","\n","Let's create `Sold_date` column! We already know how to add a new column to our dataframe. Here, we will assign each row a random date from 2019. To imitate real case scenario, where not all of the products are sold, we will leave some of the dates as NULL values."]},{"cell_type":"code","metadata":{"id":"whHpwfI0luxI","executionInfo":{"status":"ok","timestamp":1663524875951,"user_tz":-120,"elapsed":287,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["from random import randrange\n","import datetime \n","import numpy as np\n","\n","start_date = datetime.datetime(2019, 1, 1, 12, 0)\n","\n","def create_new_date(start=start_date):\n","    # Creates a random date within a year distance from `start` date.\n","    \n","    # 30% of cars are not sold - they won't have a sold date.\n","    if randrange(10) > 2:\n","        # Generate random date from 2019.\n","        return start_date + datetime.timedelta(days=randrange(365))\n","    return np.nan\n","\n","# Add a new column `Sold_date` to our dataframe.\n","cars['Sold_date'] = cars.Year.apply(lambda x: create_new_date())"],"execution_count":46,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"LDv8F8z0KpoZ","outputId":"044b6867-8355-40de-e0d4-b55057e8dbfd","executionInfo":{"status":"ok","timestamp":1663524877157,"user_tz":-120,"elapsed":4,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars.head()\n","# NaT is missing date-time value: https://pandas.pydata.org/docs/user_guide/missing_data.html"],"execution_count":47,"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  Age   KM_per_year Horsepower  \\\n","0    4.0      Sedán            Córdoba   10   9995.000000        2.5   \n","1    2.0    Pick-Up         Entre Ríos   10  14000.000000        2.3   \n","2    5.0  Hatchback  Bs.as. G.b.a. Sur   11  12000.000000        1.6   \n","3    4.0    Pick-Up            Neuquén    5   2600.000000        3.2   \n","4    3.0  Hatchback            Córdoba    9  11888.888889        1.4   \n","\n","            Sold_date  \n","0                 NaT  \n","1 2019-12-15 12:00:00  \n","2 2019-08-19 12:00:00  \n","3 2019-05-01 12:00:00  \n","4 2019-04-12 12:00:00  "],"text/html":["\n","  <div id=\"df-bd69c1b2-e1d5-47d5-97b3-f8c45d8320b7\">\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","      <th>Age</th>\n","      <th>KM_per_year</th>\n","      <th>Horsepower</th>\n","      <th>Sold_date</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","      <td>10</td>\n","      <td>9995.000000</td>\n","      <td>2.5</td>\n","      <td>NaT</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","      <td>10</td>\n","      <td>14000.000000</td>\n","      <td>2.3</td>\n","      <td>2019-12-15 12:00:00</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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But we can transform it to datetime again very easily!"]},{"cell_type":"code","metadata":{"id":"eOdWXZ9ntdiG","executionInfo":{"status":"ok","timestamp":1663524981318,"user_tz":-120,"elapsed":302,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars_from_file['Sold_date'] = pd.to_datetime(cars_from_file['Sold_date'])"],"execution_count":52,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UZEiL_KmLn75","outputId":"554e3c19-0581-447a-9ac9-e5bb2889bab9","executionInfo":{"status":"ok","timestamp":1663524982772,"user_tz":-120,"elapsed":2,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["cars_from_file.info()"],"execution_count":53,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 10000 entries, 0 to 9999\n","Data columns (total 14 columns):\n"," #   Column       Non-Null Count  Dtype         \n","---  ------       --------------  -----         \n"," 0   Unnamed: 0   10000 non-null  int64         \n"," 1   Make         10000 non-null  object        \n"," 2   Model        10000 non-null  object        \n"," 3   Year         10000 non-null  int64         \n"," 4   Variant      10000 non-null  object        \n"," 5   Kms          10000 non-null  int64         \n"," 6   Price        10000 non-null  int64         \n"," 7   Doors        10000 non-null  float64       \n"," 8   Kind         10000 non-null  object        \n"," 9   Location     10000 non-null  object        \n"," 10  Age          10000 non-null  int64         \n"," 11  KM_per_year  10000 non-null  float64       \n"," 12  Horsepower   10000 non-null  float64       \n"," 13  Sold_date    7021 non-null   datetime64[ns]\n","dtypes: datetime64[ns](1), float64(3), int64(5), object(5)\n","memory usage: 1.1+ MB\n"]}]},{"cell_type":"markdown","metadata":{"id":"rlDLBuTaLt0M"},"source":["We will group our dataset by month (since we have the data only from one year) and calculate how many of them we have for each group:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"foXlcJdUMd_Y","outputId":"723d43ce-64e5-4c7b-ff0a-f426026b67fc","executionInfo":{"status":"ok","timestamp":1663524988489,"user_tz":-120,"elapsed":306,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["grouped = cars_from_file.groupby(by=cars_from_file.Sold_date.dt.month)[['Year']].size()\n","grouped.index.rename('Sold_date_month', inplace=True)\n","grouped"],"execution_count":54,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Sold_date_month\n","1.0     608\n","2.0     534\n","3.0     618\n","4.0     563\n","5.0     592\n","6.0     602\n","7.0     584\n","8.0     588\n","9.0     574\n","10.0    557\n","11.0    596\n","12.0    605\n","dtype: int64"]},"metadata":{},"execution_count":54}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"TEKUKyo9OioE","outputId":"78c6fe83-6944-4815-b123-a498d142acc1","executionInfo":{"status":"ok","timestamp":1663524990593,"user_tz":-120,"elapsed":310,"user":{"displayName":"Ka Ba","userId":"08277395367003334094"}}},"source":["grouped.plot.bar()"],"execution_count":55,"outputs":[{"output_type":"display_data","data":{"text/html":["<html>\n","<head><meta charset=\"utf-8\" /></head>\n","<body>\n","    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n","        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div 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Now, we can communicate how number of sold cars was changing in time :)"]},{"cell_type":"markdown","metadata":{"id":"P5OYfbYv1uIm"},"source":["Extra materials:\n","- [Difference between loc and iloc functions](https://www.analyticsvidhya.com/blog/2020/02/loc-iloc-pandas/)\n","- [Apply, Map and ApplyMap](https://towardsdatascience.com/introduction-to-pandas-apply-applymap-and-map-5d3e044e93ff)\n","- [Transform function](https://towardsdatascience.com/when-to-use-pandas-transform-function-df8861aa0dcf)\n","- [Numpy library documentation](https://numpy.org/)\n"]}]}