03 Combinación de datasets.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"03 Combinación de datasets.ipynb","provenance":[{"file_id":"1eqXeb_ldsOeHgzW2wxGh6Hs-p_huhHcq","timestamp":1650495349321}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zO4bRoxr2Apy"},"source":["**Diplomatura en Ciencia de Datos, Aprendizaje Automático y sus Aplicaciones**\n","\n","**Exploración y Curación de Datos**\n","\n","*Edición 2022*\n","\n","----\n","\n","# Combinación de conjuntos de datos\n","\n","En esta notebook, vamos a cargar el conjunto de datos de [la compentencia Kaggle](https://www.kaggle.com/dansbecker/melbourne-housing-snapshot) sobre estimación de precios de ventas de propiedades en Melbourne, Australia.\n","\n","Utilizaremos el conjunto de datos reducido producido por [DanB](https://www.kaggle.com/dansbecker). Hemos subido una copia a un servidor de la Universidad Nacional de Córdoba para facilitar su acceso remoto."]},{"cell_type":"code","metadata":{"id":"4udjxjk1EtVU"},"source":["import matplotlib.pyplot as plt\n","import numpy\n","import pandas\n","\n","import seaborn\n","seaborn.set_context('talk')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"3XdIsB8hISis","executionInfo":{"status":"ok","timestamp":1622578029868,"user_tz":180,"elapsed":815,"user":{"displayName":"Milagro Teruel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5Cl9j0p5Ont2qFoH4hE2P4Pws7ek_gATkrdVJ=s64","userId":"13159704290090351697"}},"outputId":"816cab63-a7b0-4a55-a4ca-453327331392"},"source":["import plotly\n","plotly.__version__\n","# Make sure it's 4.14.3"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'4.4.1'"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"i2nqY0GGIVn2"},"source":["# To update plotly, uncomment and run the following line:\n","# !pip install plotly --upgrade"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":227},"id":"_qeFN3GnEvMk","executionInfo":{"status":"ok","timestamp":1622578033832,"user_tz":180,"elapsed":3336,"user":{"displayName":"Milagro Teruel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5Cl9j0p5Ont2qFoH4hE2P4Pws7ek_gATkrdVJ=s64","userId":"13159704290090351697"}},"outputId":"eb228c7b-f421-4f5d-924d-c706df7f92d2"},"source":["melb_df = pandas.read_csv(\n","    'https://cs.famaf.unc.edu.ar/~mteruel/datasets/diplodatos/melb_data.csv')\n","melb_df[:3]"],"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>Suburb</th>\n","      <th>Address</th>\n","      <th>Rooms</th>\n","      <th>Type</th>\n","      <th>Price</th>\n","      <th>Method</th>\n","      <th>SellerG</th>\n","      <th>Date</th>\n","      <th>Distance</th>\n","      <th>Postcode</th>\n","      <th>Bedroom2</th>\n","      <th>Bathroom</th>\n","      <th>Car</th>\n","      <th>Landsize</th>\n","      <th>BuildingArea</th>\n","      <th>YearBuilt</th>\n","      <th>CouncilArea</th>\n","      <th>Lattitude</th>\n","      <th>Longtitude</th>\n","      <th>Regionname</th>\n","      <th>Propertycount</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Abbotsford</td>\n","      <td>85 Turner St</td>\n","      <td>2</td>\n","      <td>h</td>\n","      <td>1480000.0</td>\n","      <td>S</td>\n","      <td>Biggin</td>\n","      <td>3/12/2016</td>\n","      <td>2.5</td>\n","      <td>3067.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>202.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Yarra</td>\n","      <td>-37.7996</td>\n","      <td>144.9984</td>\n","      <td>Northern Metropolitan</td>\n","      <td>4019.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Abbotsford</td>\n","      <td>25 Bloomburg St</td>\n","      <td>2</td>\n","      <td>h</td>\n","      <td>1035000.0</td>\n","      <td>S</td>\n","      <td>Biggin</td>\n","      <td>4/02/2016</td>\n","      <td>2.5</td>\n","      <td>3067.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>156.0</td>\n","      <td>79.0</td>\n","      <td>1900.0</td>\n","      <td>Yarra</td>\n","      <td>-37.8079</td>\n","      <td>144.9934</td>\n","      <td>Northern Metropolitan</td>\n","      <td>4019.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Abbotsford</td>\n","      <td>5 Charles St</td>\n","      <td>3</td>\n","      <td>h</td>\n","      <td>1465000.0</td>\n","      <td>SP</td>\n","      <td>Biggin</td>\n","      <td>4/03/2017</td>\n","      <td>2.5</td>\n","      <td>3067.0</td>\n","      <td>3.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>134.0</td>\n","      <td>150.0</td>\n","      <td>1900.0</td>\n","      <td>Yarra</td>\n","      <td>-37.8093</td>\n","      <td>144.9944</td>\n","      <td>Northern Metropolitan</td>\n","      <td>4019.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       Suburb          Address  ...             Regionname Propertycount\n","0  Abbotsford     85 Turner St  ...  Northern Metropolitan        4019.0\n","1  Abbotsford  25 Bloomburg St  ...  Northern Metropolitan        4019.0\n","2  Abbotsford     5 Charles St  ...  Northern Metropolitan        4019.0\n","\n","[3 rows x 21 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"s-mixICN22kA"},"source":["## Datos adicionales\n","\n","Vamos a aumentar los datos presentes en el conjunto dado con un dataset similar: las publicaciones de la plataforma AirBnB en Melbourne en el año 2018. El objetivo es estimar con mayor presición el valor del vecidanrio de cada propiedad.\n","\n","Para ello, utilizaremos [un conjunto de datos](https://www.kaggle.com/tylerx/melbourne-airbnb-open-data?select=cleansed_listings_dec18.csv) de *scrapings* del sitio realizado por [Tyler Xie](https://www.kaggle.com/tylerx), también disponible en una competencia de Kaggle."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k3Z2vxNHJfJK","executionInfo":{"status":"ok","timestamp":1622578044773,"user_tz":180,"elapsed":10955,"user":{"displayName":"Milagro Teruel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5Cl9j0p5Ont2qFoH4hE2P4Pws7ek_gATkrdVJ=s64","userId":"13159704290090351697"}},"outputId":"6c37a5ec-37d7-4d4a-839f-44fb93b1524e"},"source":["# data source:\n","# https://www.kaggle.com/tylerx/melbourne-airbnb-open-data?select=cleansed_listings_dec18.csv\n","interesting_cols = [\n","  'description', 'neighborhood_overview',\n","  'street', 'neighborhood', 'city', 'suburb', 'state', 'zipcode',\n","  'price', 'weekly_price', 'monthly_price',\n","  'latitude', 'longitude',\n","]\n","airbnb_df = pandas.read_csv(\n","    'https://cs.famaf.unc.edu.ar/~mteruel/datasets/diplodatos/cleansed_listings_dec18.csv',\n","    usecols=interesting_cols)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning:\n","\n","Columns (35) have mixed types.Specify dtype option on import or set low_memory=False.\n","\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":313},"id":"bc55TFlhF6tP","executionInfo":{"status":"ok","timestamp":1622578044774,"user_tz":180,"elapsed":42,"user":{"displayName":"Milagro Teruel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5Cl9j0p5Ont2qFoH4hE2P4Pws7ek_gATkrdVJ=s64","userId":"13159704290090351697"}},"outputId":"be34ed33-9b24-4944-e907-640e443aa2ab"},"source":["airbnb_df[:3]"],"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>description</th>\n","      <th>neighborhood_overview</th>\n","      <th>street</th>\n","      <th>neighborhood</th>\n","      <th>city</th>\n","      <th>suburb</th>\n","      <th>state</th>\n","      <th>zipcode</th>\n","      <th>latitude</th>\n","      <th>longitude</th>\n","      <th>price</th>\n","      <th>weekly_price</th>\n","      <th>monthly_price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>House: Clean, New, Modern, Quite, Safe. 10Km f...</td>\n","      <td>Very safe! Family oriented. Older age group.</td>\n","      <td>Bulleen, VIC, Australia</td>\n","      <td>Balwyn North</td>\n","      <td>Manningham</td>\n","      <td>Bulleen</td>\n","      <td>VIC</td>\n","      <td>3105</td>\n","      <td>-37.772684</td>\n","      <td>145.092133</td>\n","      <td>60</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>A large air conditioned room with queen spring...</td>\n","      <td>This hip area is a crossroads between two grea...</td>\n","      <td>Brunswick East, VIC, Australia</td>\n","      <td>Brunswick</td>\n","      <td>Moreland</td>\n","      <td>Brunswick East</td>\n","      <td>VIC</td>\n","      <td>3057</td>\n","      <td>-37.766505</td>\n","      <td>144.980736</td>\n","      <td>35</td>\n","      <td>200.0</td>\n","      <td>803.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>RIGHT IN THE HEART OF ST KILDA! It doesn't get...</td>\n","      <td>A stay at our apartment means you can enjoy so...</td>\n","      <td>St Kilda, VIC, Australia</td>\n","      <td>St Kilda</td>\n","      <td>Port Phillip</td>\n","      <td>St Kilda</td>\n","      <td>VIC</td>\n","      <td>3182</td>\n","      <td>-37.859755</td>\n","      <td>144.977369</td>\n","      <td>159</td>\n","      <td>1253.0</td>\n","      <td>4452.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                         description  ... monthly_price\n","0  House: Clean, New, Modern, Quite, Safe. 10Km f...  ...           NaN\n","1  A large air conditioned room with queen spring...  ...         803.0\n","2  RIGHT IN THE HEART OF ST KILDA! It doesn't get...  ...        4452.0\n","\n","[3 rows x 13 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kdqexZvsJTcT","executionInfo":{"status":"ok","timestamp":1622578044775,"user_tz":180,"elapsed":32,"user":{"displayName":"Milagro Teruel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5Cl9j0p5Ont2qFoH4hE2P4Pws7ek_gATkrdVJ=s64","userId":"13159704290090351697"}},"outputId":"28bb4e22-db39-49a6-e03c-d148aa37c390"},"source":["len(airbnb_df)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["22895"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"ismngxPcfoWb"},"source":["Para poder realizar esta combinación de datos, tenemos que encontrar una columna que contenga información en común. Utilizaremos el zipcode.\n","\n","* ¿Qué desventajas tiene utilizar el zipcode para agregar información sobre la vecindad de cada propiedad en venta? ¿Se corresponde con lo que entendemos intuitivamente como vecindad?\n","* ¿Qué otros métodos podrían utilizarse?"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dTss9P-PgGOi","executionInfo":{"status":"ok","timestamp":1621442479258,"user_tz":180,"elapsed":1643,"user":{"displayName":"Milagro Teruel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5Cl9j0p5Ont2qFoH4hE2P4Pws7ek_gATkrdVJ=s64","userId":"13159704290090351697"}},"outputId":"eaafb89c-ce4a-4209-cae7-14b10a79daca"},"source":["airbnb_df.zipcode.value_counts()[:5]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["3000.0    2491\n","3000       876\n","3006.0     860\n","3182.0     579\n","3182       556\n","Name: zipcode, dtype: int64"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"kAMLtgh6gNQN"},"source":[" Sin embargo, este zipcode tiene tipos diferentes, que tendremos que estandarizar antes de continuar con la combinación de los datos."]},{"cell_type":"code","metadata":{"id":"xbDm0-VwQd97"},"source":["airbnb_df['zipcode'] = pandas.to_numeric(airbnb_df.zipcode, errors='coerce')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"JOahRB4qggBA"},"source":["### Valores en común\n","\n","Antes de unir los conjuntos de datos, tenemos que asegurarnos de que tengamos suficientes registros en común como para agregar información relevantes."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2Gxz99YJJSCk","executionInfo":{"status":"ok","timestamp":1622578044777,"user_tz":180,"elapsed":20,"user":{"displayName":"Milagro Teruel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5Cl9j0p5Ont2qFoH4hE2P4Pws7ek_gATkrdVJ=s64","userId":"13159704290090351697"}},"outputId":"e1f4b72f-46a7-4776-8fe4-31313d30386b"},"source":["intersection = numpy.intersect1d(\n","    airbnb_df.zipcode.values, melb_df.Postcode.values, assume_unique=False)\n","print(\"Airbnb unique zipcodes\", len(airbnb_df.zipcode.unique()))\n","print(\"Sales unique zipcodes\", len(melb_df.Postcode.unique()))\n","print(\"Common zipcodes\", len(intersection))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Airbnb unique zipcodes 248\n","Sales unique zipcodes 198\n","Common zipcodes 191\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AB41oWdhLKJU","executionInfo":{"status":"ok","timestamp":1622578044778,"user_tz":180,"elapsed":17,"user":{"displayName":"Milagro Teruel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5Cl9j0p5Ont2qFoH4hE2P4Pws7ek_gATkrdVJ=s64","userId":"13159704290090351697"}},"outputId":"cf329630-8d4a-4e73-e5f6-d8c9eda8d7fc"},"source":["print('Records in Sales df with corresponding zipcode form Airbnb df',\n","      melb_df.Postcode.isin(intersection).sum() / len(melb_df))\n","print('Records in Airbnb df with corresponding zipcode form Sales df',\n","      airbnb_df.zipcode.isin(intersection).sum() / len(airbnb_df))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Records in Sales df with corresponding zipcode form Airbnb df 0.9985272459499264\n","Records in Airbnb df with corresponding zipcode form Sales df 0.9302904564315353\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"bwvm0M48OdF6"},"source":["### Exploración visual\n","\n","Para terminar de asegurarnos de que las áreas representadas por ambos datasets son consitentes, podemos representar las coordenadas en un mapa usando Plotly.\n","\n","Aunque estas visualizaciones no son completas en cuanto a la información geográfica que muestran, aproximadamente podemos ver que se tratan de zonas similares."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"J5P1Tu8UE5Zm","executionInfo":{"status":"ok","timestamp":1621210373330,"user_tz":180,"elapsed":1100,"user":{"displayName":"Milagro Teruel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5Cl9j0p5Ont2qFoH4hE2P4Pws7ek_gATkrdVJ=s64","userId":"13159704290090351697"}},"outputId":"20d3d491-41fa-4fcf-c160-e4940a95cd56"},"source":["import plotly.express as px\n","data = melb_df.sample(300)\n","fig = px.scatter_geo(\n","    data, lat=data.Lattitude, lon=data.Longtitude, color=data.YearBuilt\n","    )\n","fig.update_geos(fitbounds=\"locations\")\n","fig.show()"],"execution_count":null,"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-latest.min.js\"></script>                <div id=\"1a99a893-a235-4a63-b3db-99375bf7e062\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"1a99a893-a235-4a63-b3db-99375bf7e062\")) {                    Plotly.newPlot(                        \"1a99a893-a235-4a63-b3db-99375bf7e062\",                        [{\"geo\": \"geo\", \"hovertemplate\": 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)\n","fig.update_geos(fitbounds=\"locations\")\n","fig.show()"],"execution_count":null,"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-latest.min.js\"></script>                <div id=\"7cfdfabf-72f1-44b7-955c-ffae5409cc7b\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if 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</div>\n","</body>\n","</html>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"rWJHY4f5RT_b"},"source":["## Operación merge\n","\n","Es hora de combinar ambos conjuntos de datos. Una opción frecuentemente tentadora es aplicar la operación merge sin analizar demasiado la situación. Sin embargo, ¡esta operación puede ocasionar una **explosión exponencial** de nuestro conjunto de datos!\n","\n","¿Por qué la siguiente operación genera un DataFrame con 2 millones de filas? "]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"X6NqwTaShgTD","executionInfo":{"status":"ok","timestamp":1621442972318,"user_tz":180,"elapsed":3139,"user":{"displayName":"Milagro Teruel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5Cl9j0p5Ont2qFoH4hE2P4Pws7ek_gATkrdVJ=s64","userId":"13159704290090351697"}},"outputId":"2435a779-e736-48f9-9f97-49c7096a697e"},"source":["## Incorrecto!\n","len(melb_df.merge(airbnb_df, how='left',\n","                  left_on='Postcode', right_on='zipcode'))"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2139684"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"markdown","metadata":{"id":"ZjlzIlBGhfKA"},"source":["Recordemos que nuestro objetivo es agregar información sobre el valor del vecindario de la propiedad. ¿Qué información del conjunto de airbnb nos sirve para representar esto?\n","\n","* Promedio de `price` por zipcode\n","* Promedio de `weekly_price` por zipcode\n","* Promedio de `monthly_price` por zipcode\n","\n","Queda a interpretación de ustedes decidir cuál de todos ellos utilizar."]},{"cell_type":"code","metadata":{"id":"satTM4vKlqtS"},"source":["relevant_cols = ['price', 'weekly_price', 'monthly_price']"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Y2ORIRhGlY1z"},"source":["Cuando queremos agregar un único dato luego de agrupar un DataFrame, la operación es bastante sencilla. Sólo tienen que tener en cuenta que el nombre de la columnas será el mismo, aunque su valor ahora esté agregado."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":198},"id":"I41gc3aflTrw","executionInfo":{"status":"ok","timestamp":1622578209933,"user_tz":180,"elapsed":8,"user":{"displayName":"Milagro Teruel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5Cl9j0p5Ont2qFoH4hE2P4Pws7ek_gATkrdVJ=s64","userId":"13159704290090351697"}},"outputId":"2af34a50-0c12-40f7-de98-e022ac7e98aa"},"source":["# Option 1: inefficient form.\n","# The aggregation is calculated over all columns, and after is calculated\n","# we filter only the columns we want.\n","# airbnb_df.groupby('zipcode').mean()[relevant_cols].reset_index()[:5]\n","\n","# Option 2: efficient form\n","# First we filter out the columns we want, and then we only aggregate\n","# those. Be careful to include the grouping column as well.\n","airbnb_df[relevant_cols + ['zipcode']]\\\n","  .groupby('zipcode').mean().reset_index()[:5]"],"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>zipcode</th>\n","      <th>price</th>\n","      <th>weekly_price</th>\n","      <th>monthly_price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2010.0</td>\n","      <td>40.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2134.0</td>\n","      <td>50.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2582.0</td>\n","      <td>104.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3000.0</td>\n","      <td>150.504307</td>\n","      <td>918.738956</td>\n","      <td>3407.204651</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>3001.0</td>\n","      <td>132.500000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   zipcode       price  weekly_price  monthly_price\n","0   2010.0   40.000000           NaN            NaN\n","1   2134.0   50.000000           NaN            NaN\n","2   2582.0  104.000000           NaN            NaN\n","3   3000.0  150.504307    918.738956    3407.204651\n","4   3001.0  132.500000           NaN            NaN"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"markdown","metadata":{"id":"8kR2TEpni9i5"},"source":["Agrupar y agregar utilizando más de un tipo de operación es un poco más complicado. Pandas nos provee de muchas formas de escribir la misma operación. Ninguna de ellas es muy intuitiva, y usualmente requiere renombrar las columnas para darles nombres más significativos."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":138},"id":"K72zWkuJRl0M","executionInfo":{"status":"ok","timestamp":1622578212955,"user_tz":180,"elapsed":15,"user":{"displayName":"Milagro Teruel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5Cl9j0p5Ont2qFoH4hE2P4Pws7ek_gATkrdVJ=s64","userId":"13159704290090351697"}},"outputId":"732d1ab3-1573-4d27-fe26-722fb9a6deec"},"source":["relevant_cols = ['price', 'weekly_price', 'monthly_price', 'zipcode']\n","## Option 1\n","# Pass a dictionary where the keys are the original columns to aggregate and\n","# the values are the operations (or list of operations).\n","airbnb_price_by_zipcode = airbnb_df[relevant_cols].groupby('zipcode')\\\n","  .agg({'price': ['mean', 'count'], 'weekly_price': 'mean',\n","        'monthly_price': 'mean'})\\\n","  .reset_index()\n","# Flatten the two level columns\n","airbnb_price_by_zipcode.columns = [\n","  ' '.join(col).strip()\n","  for col in airbnb_price_by_zipcode.columns.values]\n","# Rename columns\n","airbnb_price_by_zipcode = airbnb_price_by_zipcode.rename(\n","    columns={'price mean': 'airbnb_price_mean',\n","             'price count': 'airbnb_record_count',\n","             'weekly_price mean': 'airbnb_weekly_price_mean',\n","             'monthly_price mean': 'airbnb_monthly_price_mean'}\n",")\n","\n","## Option 2\n","# Pass as argument name the new name of the column, and as value a tuple where\n","# the first value is the original column and the second value is the operation.\n","# airbnb_price_by_zipcode = airbnb_df[relevant_cols].groupby('zipcode')\\\n","#   .agg(airbnb_record_count=('price', 'count'),\n","#        airbnb_price_mean=('price', 'mean'),\n","#        airbnb_weekly_price_mean=('weekly_price', 'mean'),\n","#        airbnb_monthly_price_mean=('monthly_price', 'mean'))\\\n","#   .reset_index()\n","\n","airbnb_price_by_zipcode[:3]"],"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>zipcode</th>\n","      <th>airbnb_price_mean</th>\n","      <th>airbnb_record_count</th>\n","      <th>airbnb_weekly_price_mean</th>\n","      <th>airbnb_monthly_price_mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2010.0</td>\n","      <td>40.0</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2134.0</td>\n","      <td>50.0</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2582.0</td>\n","      <td>104.0</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   zipcode  ...  airbnb_monthly_price_mean\n","0   2010.0  ...                        NaN\n","1   2134.0  ...                        NaN\n","2   2582.0  ...                        NaN\n","\n","[3 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"markdown","source":["Guardamos el dataset construido ya que nos sera util para el entregable"],"metadata":{"id":"5e22EeNigvjw"}},{"cell_type":"code","source":["airbnb_price_by_zipcode.to_csv(\"airbnb_price_by_zipcode.csv\", index=None)"],"metadata":{"id":"tXI0Sz2DgqXx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from google.colab import files\n","files.download('airbnb_price_by_zipcode.csv') "],"metadata":{"id":"lnbszPGAgjBk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["Finalmente hacemos la combinacion de ambos datasets creados"],"metadata":{"id":"7efbKs7Vg2Z_"},"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":321},"id":"RfUAyonwMOrx","executionInfo":{"status":"ok","timestamp":1622578216571,"user_tz":180,"elapsed":400,"user":{"displayName":"Milagro Teruel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5Cl9j0p5Ont2qFoH4hE2P4Pws7ek_gATkrdVJ=s64","userId":"13159704290090351697"}},"outputId":"7fdc48da-bffa-41cd-e72a-f7b9f91e6b73"},"source":["merged_sales_df = melb_df.merge(\n","    airbnb_price_by_zipcode, how='left',\n","    left_on='Postcode', right_on='zipcode'\n",")\n","merged_sales_df.sample(5)"],"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>Suburb</th>\n","      <th>Address</th>\n","      <th>Rooms</th>\n","      <th>Type</th>\n","      <th>Price</th>\n","      <th>Method</th>\n","      <th>SellerG</th>\n","      <th>Date</th>\n","      <th>Distance</th>\n","      <th>Postcode</th>\n","      <th>Bedroom2</th>\n","      <th>Bathroom</th>\n","      <th>Car</th>\n","      <th>Landsize</th>\n","      <th>BuildingArea</th>\n","      <th>YearBuilt</th>\n","      <th>CouncilArea</th>\n","      <th>Lattitude</th>\n","      <th>Longtitude</th>\n","      <th>Regionname</th>\n","      <th>Propertycount</th>\n","      <th>zipcode</th>\n","      <th>airbnb_price_mean</th>\n","      <th>airbnb_record_count</th>\n","      <th>airbnb_weekly_price_mean</th>\n","      <th>airbnb_monthly_price_mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>5307</th>\n","      <td>Richmond</td>\n","      <td>508/18 Tanner St</td>\n","      <td>1</td>\n","      <td>u</td>\n","      <td>681000.0</td>\n","      <td>S</td>\n","      <td>hockingstuart</td>\n","      <td>10/09/2016</td>\n","      <td>2.6</td>\n","      <td>3121.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>50.0</td>\n","      <td>2004.0</td>\n","      <td>Yarra</td>\n","      <td>-37.82310</td>\n","      <td>144.99060</td>\n","      <td>Northern Metropolitan</td>\n","      <td>14949.0</td>\n","      <td>3121.0</td>\n","      <td>162.262739</td>\n","      <td>628.0</td>\n","      <td>1089.068182</td>\n","      <td>3771.90625</td>\n","    </tr>\n","    <tr>\n","      <th>8319</th>\n","      <td>Richmond</td>\n","      <td>7 Berry St</td>\n","      <td>2</td>\n","      <td>h</td>\n","      <td>950000.0</td>\n","      <td>VB</td>\n","      <td>hockingstuart</td>\n","      <td>6/05/2017</td>\n","      <td>2.6</td>\n","      <td>3121.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>150.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Yarra</td>\n","      <td>-37.81910</td>\n","      <td>144.99770</td>\n","      <td>Northern Metropolitan</td>\n","      <td>14949.0</td>\n","      <td>3121.0</td>\n","      <td>162.262739</td>\n","      <td>628.0</td>\n","      <td>1089.068182</td>\n","      <td>3771.90625</td>\n","    </tr>\n","    <tr>\n","      <th>11663</th>\n","      <td>Epping</td>\n","      <td>111 Duffy St</td>\n","      <td>3</td>\n","      <td>h</td>\n","      <td>410000.0</td>\n","      <td>SP</td>\n","      <td>hockingstuart</td>\n","      <td>22/07/2017</td>\n","      <td>19.6</td>\n","      <td>3076.0</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>216.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Whittlesea</td>\n","      <td>-37.64151</td>\n","      <td>145.02265</td>\n","      <td>Northern Metropolitan</td>\n","      <td>10926.0</td>\n","      <td>3076.0</td>\n","      <td>51.529412</td>\n","      <td>17.0</td>\n","      <td>395.000000</td>\n","      <td>1440.00000</td>\n","    </tr>\n","    <tr>\n","      <th>5056</th>\n","      <td>Reservoir</td>\n","      <td>1/40 Godley St</td>\n","      <td>1</td>\n","      <td>h</td>\n","      <td>260000.0</td>\n","      <td>PI</td>\n","      <td>hockingstuart</td>\n","      <td>7/05/2016</td>\n","      <td>11.2</td>\n","      <td>3073.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>141.0</td>\n","      <td>80.0</td>\n","      <td>2000.0</td>\n","      <td>Darebin</td>\n","      <td>-37.70250</td>\n","      <td>144.98870</td>\n","      <td>Northern Metropolitan</td>\n","      <td>21650.0</td>\n","      <td>3073.0</td>\n","      <td>273.926471</td>\n","      <td>68.0</td>\n","      <td>399.888889</td>\n","      <td>1459.62500</td>\n","    </tr>\n","    <tr>\n","      <th>9611</th>\n","      <td>Kingsville</td>\n","      <td>62A Williamstown Rd</td>\n","      <td>2</td>\n","      <td>h</td>\n","      <td>735000.0</td>\n","      <td>PI</td>\n","      <td>McGrath</td>\n","      <td>17/06/2017</td>\n","      <td>6.4</td>\n","      <td>3012.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>170.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Maribyrnong</td>\n","      <td>-37.80635</td>\n","      <td>144.88450</td>\n","      <td>Western Metropolitan</td>\n","      <td>1808.0</td>\n","      <td>3012.0</td>\n","      <td>112.631579</td>\n","      <td>76.0</td>\n","      <td>518.000000</td>\n","      <td>2335.00000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           Suburb  ... airbnb_monthly_price_mean\n","5307     Richmond  ...                3771.90625\n","8319     Richmond  ...                3771.90625\n","11663      Epping  ...                1440.00000\n","5056    Reservoir  ...                1459.62500\n","9611   Kingsville  ...                2335.00000\n","\n","[5 rows x 26 columns]"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"G4ClSr_JapCw"},"source":["merged_sales_df.to_csv(\"melb_data_extended.csv\", index=None)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"9YD0kPzAM1he","executionInfo":{"status":"ok","timestamp":1622578307673,"user_tz":180,"elapsed":376,"user":{"displayName":"Milagro Teruel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5Cl9j0p5Ont2qFoH4hE2P4Pws7ek_gATkrdVJ=s64","userId":"13159704290090351697"}},"outputId":"86928e54-678d-4822-d08a-5b6c035d3543"},"source":["from google.colab import files\n","files.download('melb_data_extended.csv') "],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/javascript":["\n","    async function download(id, filename, size) {\n","      if (!google.colab.kernel.accessAllowed) {\n","        return;\n","      }\n","      const div = document.createElement('div');\n","      const label = document.createElement('label');\n","      label.textContent = `Downloading \"${filename}\": `;\n","      div.appendChild(label);\n","      const progress = document.createElement('progress');\n","      progress.max = size;\n","      div.appendChild(progress);\n","      document.body.appendChild(div);\n","\n","      const buffers = [];\n","      let downloaded = 0;\n","\n","      const channel = await google.colab.kernel.comms.open(id);\n","      // Send a message to notify the kernel that we're ready.\n","      channel.send({})\n","\n","      for await (const message of channel.messages) {\n","        // Send a message to notify the kernel that we're ready.\n","        channel.send({})\n","        if (message.buffers) {\n","          for (const buffer of message.buffers) {\n","            buffers.push(buffer);\n","            downloaded += buffer.byteLength;\n","            progress.value = downloaded;\n","          }\n","        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