diff --git a/README.es.md b/README.es.md index d85876a9..72ea33ea 100644 --- a/README.es.md +++ b/README.es.md @@ -3,14 +3,14 @@ - Comprender un dataset nuevo. -- Utilizar los conocimientos adquiridos en el prework para resolver las cuestiones planteadas. +- Utiliza los conocimientos adquiridos en el prework para resolver las cuestiones planteadas. - Analizar, si fuera necesario, otras cuestiones. ## 🌱 Cómo iniciar este proyecto Sigue las siguientes instrucciones: -1. Crear un nuevo repositorio haciendo fork en el [proyecto de Git](https://github.com/4GeeksAcademy/realestate-datacleanup-exercise) o [haciendo clic aquí](https://github.com/4GeeksAcademy/realestate-datacleanup-exercise/fork). +1. Crea un nuevo repositorio haciendo fork en el [proyecto de Git](https://github.com/4GeeksAcademy/realestate-datacleanup-exercise) o [haciendo clic aquí](https://github.com/4GeeksAcademy/realestate-datacleanup-exercise/fork). 2. Abre el repositorio creado recientemente en Codespace usando la [extensión del botón de Codespace](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace-for-a-repository). 3. Una vez que el VSCode del Codespace haya terminado de abrirse, comienza tu proyecto siguiendo las instrucciones a continuación. diff --git a/project.es.ipynb b/project.es.ipynb index da1f12ef..215eef21 100644 --- a/project.es.ipynb +++ b/project.es.ipynb @@ -1,963 +1,2349 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "innocent-university", - "metadata": {}, - "source": [ - "# Limpieza de bienes raíces\n", - "\n", - "Este es un conjunto de datos (dataset) reales que fue descargado usando técnicas de web scraping. La data contiene registros de **Fotocasa**, el cual es uno de los sitios más populares de bienes raíces en España. Por favor no hagas esto (web scraping) a no ser que sea para propósitos académicos.\n", - "\n", - "El dataset fue descargado hace algunos años por Henry Navarro y en ningún caso se obtuvo beneficio económico de ello.\n", - "\n", - "Contiene miles de datos de casas reales publicadas en la web www.fotocasa.com. Tu objetivo es extraer tanta información como sea posible con el conocimiento que tienes hasta ahora de ciencia de datos, por ejemplo ¿cuál es la casa más cara en todo el dataset?\n", - "\n", - "Empecemos precisamente con esa pregunta... ¡Buena suerte!" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "multiple-glass", - "metadata": {}, - "source": [ - "#### Ejercicio 00. Lee el dataset assets/real_estate.csv e intenta visualizar la tabla (★☆☆)" - ] - }, + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "innocent-university", + "metadata": {}, + "source": [ + "# Limpieza de bienes raíces\n", + "\n", + "Este es un conjunto de datos (dataset) reales que fue descargado usando técnicas de web scraping. La data contiene registros de **Fotocasa**, el cual es uno de los sitios más populares de bienes raíces en España. Por favor no hagas esto (web scraping) a no ser que sea para propósitos académicos.\n", + "\n", + "El dataset fue descargado hace algunos años por Henry Navarro y en ningún caso se obtuvo beneficio económico de ello.\n", + "\n", + "Contiene miles de datos de casas reales publicadas en la web www.fotocasa.com. Tu objetivo es extraer tanta información como sea posible con el conocimiento que tienes hasta ahora de ciencia de datos, por ejemplo ¿cuál es la casa más cara en todo el dataset?\n", + "\n", + "Empecemos precisamente con esa pregunta... ¡Buena suerte!" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "multiple-glass", + "metadata": {}, + "source": [ + "#### Ejercicio 00. Lee el dataset assets/real_estate.csv e intenta visualizar la tabla (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": 382, + "id": "frank-heath", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 1, - "id": "frank-heath", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" ], - "source": [ - "import pandas as pd\n", - "\n", - "# Este archivo CSV contiene puntos y comas en lugar de comas como separadores\n", - "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n", - "ds" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "latin-guest", - "metadata": {}, - "source": [ - "#### Ejercicio 01. ¿Cuál es la casa más cara en todo el dataset? (★☆☆)\n", - "\n", - "Imprime la dirección y el precio de la casa seleccionada. Por ejemplo:\n", - "\n", - "`La casa con dirección en Calle del Prado, Nº20 es la más cara y su precio es de 5000000 USD`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "developing-optimum", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "lesser-cosmetic", - "metadata": {}, - "source": [ - "#### Ejercicio 02. ¿Cuál es la casa más barata del dataset? (★☆☆)\n", - "\n", - "Imprime la dirección y el precio de la casa seleccionada. Por ejemplo:\n", - "\n", - "`La casa con dirección en Calle Alcalá, Nº58 es la más barata y su precio es de 12000 USD`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "lovely-oasis", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "compliant-fellowship", - "metadata": {}, - "source": [ - "#### Ejercicio 03. ¿Cuál es la casa más grande y la más pequeña del dataset? (★☆☆)\n", - "\n", - "Imprime la dirección y el área de las casas seleccionadas. Por ejemplo:\n", - "\n", - "`La casa más grande está ubicada en Calle Gran Vía, Nº38 y su superficie es de 5000 metros`\n", - "\n", - "`La casa más pequeña está ubicada en Calle Mayor, Nº12 y su superficie es de 200 metros`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "every-tiffany", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "danish-spirit", - "metadata": {}, - "source": [ - "#### Ejercicio 04. ¿Cuantas poblaciones (columna level5) contiene el dataset? (★☆☆)\n", - "\n", - "Imprime el nombre de las poblaciones separadas por coma. Por ejemplo:\n", - "\n", - "`> print(populations)`\n", - "\n", - "`population1, population2, population3, ...`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "exciting-accreditation", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "crazy-blame", - "metadata": {}, - "source": [ - "#### Ejercicio 05. ¿El dataset contiene valores no admitidos (NAs)? (★☆☆)\n", - "\n", - "Imprima un booleano (`True` o `False`) seguido de la fila/columna que contiene el NAs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "transparent-poetry", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "italic-hydrogen", - "metadata": {}, - "source": [ - "#### Ejercicio 06. Elimina los NAs del dataset, si aplica (★★☆)\n", - "\n", - "Imprima una comparación entre las dimensiones del DataFrame original versus el DataFrame después de las eliminaciones.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "administrative-roads", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "middle-china", - "metadata": {}, - "source": [ - "#### Ejercicio 07. ¿Cuál la media de precios en la población (columna level5) de \"Arroyomolinos (Madrid)\"? (★★☆)\n", - "\n", - "Imprima el valor obtenido." + "text/plain": [ + " Unnamed: 0 id_realEstates isNew realEstate_name \\\n", + "0 1 153771986 False ferrari 57 inmobiliaria \n", + "1 2 153867863 False tecnocasa fuenlabrada ferrocarril \n", + "2 3 153430440 False look find boadilla \n", + "3 4 152776331 False tecnocasa fuenlabrada ferrocarril \n", + "4 5 153180188 False ferrari 57 inmobiliaria \n", + "... ... ... ... ... \n", + "15330 15331 153901377 False infocasa consulting \n", + "15331 15332 150394373 False inmobiliaria pulpon \n", + "15332 15333 153901397 False tecnocasa torrelodones \n", + "15333 15334 152607440 False inmobiliaria pulpon \n", + "15334 15335 153901356 False infocasa consulting \n", + "\n", + " phone_realEstate url_inmueble \\\n", + "0 912177526.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "1 916358736.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "2 916350408.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "3 916358736.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "4 912177526.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "... ... ... \n", + "15330 911360461.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "15331 912788039.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "15332 912780348.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "15333 912788039.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "15334 911360461.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "\n", + " rooms bathrooms surface price ... level4Id level5Id level6Id \\\n", + "0 3.0 2.0 103.0 195000 ... 0 0 0 \n", + "1 3.0 1.0 NaN 89000 ... 0 0 0 \n", + "2 2.0 2.0 99.0 390000 ... 0 0 0 \n", + "3 3.0 1.0 86.0 89000 ... 0 0 0 \n", + "4 2.0 2.0 106.0 172000 ... 0 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "15330 2.0 1.0 96.0 259470 ... 0 0 0 \n", + "15331 3.0 1.0 150.0 165000 ... 0 0 0 \n", + "15332 4.0 2.0 175.0 495000 ... 0 0 0 \n", + "15333 3.0 2.0 101.0 195000 ... 0 0 0 \n", + "15334 3.0 2.0 152.0 765000 ... 0 0 0 \n", + "\n", + " level7Id level8Id accuracy latitude longitude zipCode \\\n", + "0 0 0 0 40,2948276786438 -3,44402412135624 NaN \n", + "1 0 0 1 40,28674 -3,79351 NaN \n", + "2 0 0 0 40,4115646786438 -3,90662252135624 NaN \n", + "3 0 0 0 40,2853785786438 -3,79508142135624 NaN \n", + "4 0 0 0 40,2998774864376 -3,45226301356237 NaN \n", + "... ... ... ... ... ... ... \n", + "15330 0 0 0 40,45416 -3,70286 NaN \n", + "15331 0 0 0 40,36652 -3,48951 NaN \n", + "15332 0 0 0 40,57444 -3,92124 NaN \n", + "15333 0 0 0 40,36967 -3,48105 NaN \n", + "15334 0 0 0 40,45773 -3,69068 NaN \n", + "\n", + " customZone \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "15330 NaN \n", + "15331 NaN \n", + "15332 NaN \n", + "15333 NaN \n", + "15334 NaN \n", + "\n", + "[15335 rows x 37 columns]" ] - }, + }, + "execution_count": 382, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Este archivo CSV contiene puntos y comas en lugar de comas como separadores\n", + "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 383, + "id": "00d47043", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "nuclear-belief", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 15335 entries, 0 to 15334\n", + "Data columns (total 37 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Unnamed: 0 15335 non-null int64 \n", + " 1 id_realEstates 15335 non-null int64 \n", + " 2 isNew 15335 non-null bool \n", + " 3 realEstate_name 15325 non-null object \n", + " 4 phone_realEstate 14541 non-null float64\n", + " 5 url_inmueble 15335 non-null object \n", + " 6 rooms 14982 non-null float64\n", + " 7 bathrooms 14990 non-null float64\n", + " 8 surface 14085 non-null float64\n", + " 9 price 15335 non-null int64 \n", + " 10 date 15335 non-null object \n", + " 11 description 15193 non-null object \n", + " 12 address 15335 non-null object \n", + " 13 country 15335 non-null object \n", + " 14 level1 15335 non-null object \n", + " 15 level2 15335 non-null object \n", + " 16 level3 15335 non-null object \n", + " 17 level4 8692 non-null object \n", + " 18 level5 15335 non-null object \n", + " 19 level6 708 non-null object \n", + " 20 level7 13058 non-null object \n", + " 21 level8 6756 non-null object \n", + " 22 upperLevel 15335 non-null object \n", + " 23 countryId 15335 non-null int64 \n", + " 24 level1Id 15335 non-null int64 \n", + " 25 level2Id 15335 non-null int64 \n", + " 26 level3Id 15335 non-null int64 \n", + " 27 level4Id 15335 non-null int64 \n", + " 28 level5Id 15335 non-null int64 \n", + " 29 level6Id 15335 non-null int64 \n", + " 30 level7Id 15335 non-null int64 \n", + " 31 level8Id 15335 non-null int64 \n", + " 32 accuracy 15335 non-null int64 \n", + " 33 latitude 15335 non-null object \n", + " 34 longitude 15335 non-null object \n", + " 35 zipCode 0 non-null float64\n", + " 36 customZone 0 non-null float64\n", + "dtypes: bool(1), float64(6), int64(13), object(17)\n", + "memory usage: 4.2+ MB\n" + ] + } + ], + "source": [ + "ds.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 384, + "id": "94195880", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "concerned-radical", - "metadata": {}, - "source": [ - "#### Ejercicio 08. Trazar el histograma de los precios para la población (level5 column) de \"Arroyomolinos (Madrid)\" y explica qué observas (★★☆)\n", - "\n", - "Imprime el histograma de los precios y escribe en la celda del Markdown un breve análisis del trazado.\n" + "data": { + "text/plain": [ + "Index(['Unnamed: 0', 'id_realEstates', 'isNew', 'realEstate_name',\n", + " 'phone_realEstate', 'url_inmueble', 'rooms', 'bathrooms', 'surface',\n", + " 'price', 'date', 'description', 'address', 'country', 'level1',\n", + " 'level2', 'level3', 'level4', 'level5', 'level6', 'level7', 'level8',\n", + " 'upperLevel', 'countryId', 'level1Id', 'level2Id', 'level3Id',\n", + " 'level4Id', 'level5Id', 'level6Id', 'level7Id', 'level8Id', 'accuracy',\n", + " 'latitude', 'longitude', 'zipCode', 'customZone'],\n", + " dtype='object')" ] - }, + }, + "execution_count": 384, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "ds.columns" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "latin-guest", + "metadata": {}, + "source": [ + "#### Ejercicio 01. ¿Cuál es la casa más cara en todo el dataset? (★☆☆)\n", + "\n", + "Imprime la dirección y el precio de la casa seleccionada. Por ejemplo:\n", + "\n", + "`La casa con dirección en Calle del Prado, Nº20 es la más cara y su precio es de 5000000 USD`" + ] + }, + { + "cell_type": "code", + "execution_count": 385, + "id": "developing-optimum", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "sudden-message", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: Code" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "El precio de la casa más cara es 8500000 usd y la direccion es El Escorial\n" + ] + } + ], + "source": [ + "# TODO\n", + "\n", + "direccion_maximo = ds.loc[ds['price'].idxmax(), 'address']\n", + "precio_maximo = ds['price'].max()\n", + "\n", + "print(f\"El precio de la casa más cara es\", precio_maximo ,\"usd\" ,'y la direccion es', direccion_maximo)\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "lesser-cosmetic", + "metadata": {}, + "source": [ + "#### Ejercicio 02. ¿Cuál es la casa más barata del dataset? (★☆☆)\n", + "\n", + "Imprime la dirección y el precio de la casa seleccionada. Por ejemplo:\n", + "\n", + "`La casa con dirección en Calle Alcalá, Nº58 es la más barata y su precio es de 12000 USD`" + ] + }, + { + "cell_type": "code", + "execution_count": 386, + "id": "lovely-oasis", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "impressed-combination", - "metadata": {}, - "source": [ - "**TODO: Markdown**. Para escribir aquí, haz doble clic en esta celda, elimina este contenido y coloca lo que quieras escribir. Luego ejecuta la celda." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "El precio de la casa más barata es 0 usd la dirreccion es Parla\n" + ] + } + ], + "source": [ + "# TODO\n", + "direccion_min=ds.loc[ds['price'].idxmin(),'address']\n", + "precio_minimo = ds[\"price\"].min()\n", + "print(\"El precio de la casa más barata es\", precio_minimo ,\"usd\",'la dirreccion es',direccion_min)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "compliant-fellowship", + "metadata": {}, + "source": [ + "#### Ejercicio 03. ¿Cuál es la casa más grande y la más pequeña del dataset? (★☆☆)\n", + "\n", + "Imprime la dirección y el área de las casas seleccionadas. Por ejemplo:\n", + "\n", + "`La casa más grande está ubicada en Calle Gran Vía, Nº38 y su superficie es de 5000 metros`\n", + "\n", + "`La casa más pequeña está ubicada en Calle Mayor, Nº12 y su superficie es de 200 metros`" + ] + }, + { + "cell_type": "code", + "execution_count": 387, + "id": "every-tiffany", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "actual-edinburgh", - "metadata": {}, - "source": [ - "#### Ejercicio 09. ¿Son los precios promedios de \"Valdemorillo\" y \"Galapagar\" los mismos? (★★☆)\n", - "\n", - "Imprime ambos promedios y escribe una conclusión sobre ellos." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "la casa mas pequeña esta ubicada Calle Amparo, Madrid Capital y su superficie es de 15.0 metros\n", + "la casa mas grande esta ubicada Sevilla la Nueva y su superficie es de 249000.0 metros\n" + ] + } + ], + "source": [ + "# TODO\n", + "pequeña=ds[\"surface\"].min()\n", + "pequeña_address = ds.loc[ds[\"surface\"].idxmin(),'address']\n", + "grande = ds[\"surface\"].max()\n", + "grande_address= ds.loc[ds[\"surface\"].idxmax(),'address']\n", + "\n", + "print(f'la casa mas pequeña esta ubicada' ,(pequeña_address) ,'y su superficie es de',(pequeña),'metros')\n", + "print(f'la casa mas grande esta ubicada',(grande_address) ,'y su superficie es de',(grande),'metros')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "danish-spirit", + "metadata": {}, + "source": [ + "#### Ejercicio 04. ¿Cuantas poblaciones (columna level5) contiene el dataset? (★☆☆)\n", + "\n", + "Imprime el nombre de las poblaciones separadas por coma. Por ejemplo:\n", + "\n", + "`> print(populations)`\n", + "\n", + "`population1, population2, population3, ...`" + ] + }, + { + "cell_type": "code", + "execution_count": 388, + "id": "d9752a75", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "numeric-commerce", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" + "data": { + "text/plain": [ + "Index(['Unnamed: 0', 'id_realEstates', 'isNew', 'realEstate_name',\n", + " 'phone_realEstate', 'url_inmueble', 'rooms', 'bathrooms', 'surface',\n", + " 'price', 'date', 'description', 'address', 'country', 'level1',\n", + " 'level2', 'level3', 'level4', 'level5', 'level6', 'level7', 'level8',\n", + " 'upperLevel', 'countryId', 'level1Id', 'level2Id', 'level3Id',\n", + " 'level4Id', 'level5Id', 'level6Id', 'level7Id', 'level8Id', 'accuracy',\n", + " 'latitude', 'longitude', 'zipCode', 'customZone'],\n", + " dtype='object')" ] - }, + }, + "execution_count": 388, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds.columns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 389, + "id": "exciting-accreditation", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "lonely-article", - "metadata": {}, - "source": [ - "#### Ejercicio 10. ¿Son los promedios de precio por metro cuadrado (precio/m2) de \"Valdemorillo\" y \"Galapagar\" los mismos? (★★☆)\n", - "\n", - "Imprime ambos promedios de precio por metro cuadrado y escribe una conclusión sobre ellos.\n", - "\n", - "Pista: Crea una nueva columna llamada `pps` (*price per square* o precio por metro cuadrado) y luego analiza los valores." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "numero de poblaciones 168\n", + "level5\n", + " Madrid Capital 6643\n", + "Alcalá de Henares 525\n", + "Las Rozas de Madrid 383\n", + "Móstoles 325\n", + "Getafe 290\n", + "San Sebastián de los Reyes 280\n", + "Boadilla del Monte 275\n", + "Parla 272\n", + "Valdemoro 262\n", + "Torrejón de Ardoz 261\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "# TODO\n", + "#import pandas as pd\n", + "#ds = pd.read_csv('assets/real_estate.csv', sep=';')\n", + "poblaciones = ds.value_counts(\"level5\")\n", + "num_poblaciones = len(poblaciones)\n", + "\n", + "print(\"numero de poblaciones\",num_poblaciones)\n", + "#print(\"Nombres de las poblaciones:\", \", \".join(poblaciones)) #duda\n", + "print(poblaciones.head(10))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "crazy-blame", + "metadata": {}, + "source": [ + "#### Ejercicio 05. ¿El dataset contiene valores no admitidos (NAs)? (★☆☆)\n", + "\n", + "Imprima un booleano (`True` o `False`) seguido de la fila/columna que contiene el NAs." + ] + }, + { + "cell_type": "code", + "execution_count": 390, + "id": "e6cf819c", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "hourly-globe", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" + "data": { + "text/plain": [ + "Unnamed: 0 False\n", + "id_realEstates False\n", + "isNew False\n", + "realEstate_name True\n", + "phone_realEstate True\n", + "url_inmueble False\n", + "rooms True\n", + "bathrooms True\n", + "surface True\n", + "price False\n", + "date False\n", + "description True\n", + "address False\n", + "country False\n", + "level1 False\n", + "level2 False\n", + "level3 False\n", + "level4 True\n", + "level5 False\n", + "level6 True\n", + "level7 True\n", + "level8 True\n", + "upperLevel False\n", + "countryId False\n", + "level1Id False\n", + "level2Id False\n", + "level3Id False\n", + "level4Id False\n", + "level5Id False\n", + "level6Id False\n", + "level7Id False\n", + "level8Id False\n", + "accuracy False\n", + "latitude False\n", + "longitude False\n", + "zipCode True\n", + "customZone True\n", + "dtype: bool" ] - }, + }, + "execution_count": 390, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds.isnull(). any()" + ] + }, + { + "cell_type": "code", + "execution_count": 391, + "id": "transparent-poetry", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "pleasant-invite", - "metadata": {}, - "source": [ - "#### Ejercicio 11. Analiza la relación entre la superficie y el precio de las casas. (★★☆)\n", - "\n", - "Pista: Puedes hacer un `scatter plot` y luego escribir una conclusión al respecto." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " Unnamed: 0 id_realEstates isNew realEstate_name phone_realEstate \\\n", + "0 False False False False False \n", + "1 False False False False False \n", + "2 False False False False False \n", + "3 False False False False False \n", + "4 False False False False False \n", + "... ... ... ... ... ... \n", + "15330 False False False False False \n", + "15331 False False False False False \n", + "15332 False False False False False \n", + "15333 False False False False False \n", + "15334 False False False False False \n", + "\n", + " url_inmueble rooms bathrooms surface price ... level4Id \\\n", + "0 False False False False False ... False \n", + "1 False False False True False ... False \n", + "2 False False False False False ... False \n", + "3 False False False False False ... False \n", + "4 False False False False False ... False \n", + "... ... ... ... ... ... ... ... \n", + "15330 False False False False False ... False \n", + "15331 False False False False False ... False \n", + "15332 False False False False False ... False \n", + "15333 False False False False False ... False \n", + "15334 False False False False False ... False \n", + "\n", + " level5Id level6Id level7Id level8Id accuracy latitude longitude \\\n", + "0 False False False False False False False \n", + "1 False False False False False False False \n", + "2 False False False False False False False \n", + "3 False False False False False False False \n", + "4 False False False False False False False \n", + "... ... ... ... ... ... ... ... \n", + "15330 False False False False False False False \n", + "15331 False False False False False False False \n", + "15332 False False False False False False False \n", + "15333 False False False False False False False \n", + "15334 False False False False False False False \n", + "\n", + " zipCode customZone \n", + "0 True True \n", + "1 True True \n", + "2 True True \n", + "3 True True \n", + "4 True True \n", + "... ... ... \n", + "15330 True True \n", + "15331 True True \n", + "15332 True True \n", + "15333 True True \n", + "15334 True True \n", + "\n", + "[15335 rows x 37 columns]\n" + ] + } + ], + "source": [ + "# TODO\n", + "#import pandas as pd\n", + "#ds = pd.read_csv('assets/real_estate.csv', sep=';')\n", + "encontras_nas = ds.isnull().sum()\n", + "encontras_nas = ds.isnull()\n", + "\n", + "#.sort_values(ascending=True)\n", + "print(encontras_nas)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 392, + "id": "9515def3", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "common-drilling", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: Código" + "data": { + "text/plain": [ + "np.int64(0)" ] - }, + }, + "execution_count": 392, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#como encontrar datos duplicados \n", + "ds.duplicated().sum()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "italic-hydrogen", + "metadata": {}, + "source": [ + "#### Ejercicio 06. Elimina los NAs del dataset, si aplica (★★☆)\n", + "\n", + "Imprima una comparación entre las dimensiones del DataFrame original versus el DataFrame después de las eliminaciones.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 393, + "id": "administrative-roads", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "ahead-liquid", - "metadata": {}, - "source": [ - "**TODO: Markdown**. Para escribir aquí, haz doble clic en esta celda, elimina este contenido y coloca lo que quieras escribir. Luego ejecuta la celda." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Empty DataFrame\n", + "Columns: [Unnamed: 0, id_realEstates, isNew, realEstate_name, phone_realEstate, url_inmueble, rooms, bathrooms, surface, price, date, description, address, country, level1, level2, level3, level4, level5, level6, level7, level8, upperLevel, countryId, level1Id, level2Id, level3Id, level4Id, level5Id, level6Id, level7Id, level8Id, accuracy, latitude, longitude, zipCode, customZone]\n", + "Index: []\n", + "\n", + "[0 rows x 37 columns]\n" + ] + } + ], + "source": [ + "# TODO\n", + "##inplace=true. sive para aplicar el cambio o actualizar dataframe\n", + "ds_limpieza = ds.dropna (axis=0)\n", + "print (ds_limpieza)" + ] + }, + { + "cell_type": "code", + "execution_count": 394, + "id": "efe42c12", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "coordinate-sunrise", - "metadata": {}, - "source": [ - "#### Ejercicio 12. ¿Cuántas agencia de bienes raíces contiene el dataset? (★★☆)\n", - "\n", - "Imprime el valor obtenido." + "data": { + "text/html": [ + "
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Unnamed: 0id_realEstatesisNewrealEstate_namephone_realEstateurl_inmuebleroomsbathroomssurfaceprice...level4Idlevel5Idlevel6Idlevel7Idlevel8IdaccuracylatitudelongitudezipCodecustomZone
01153771986Falseferrari 57 inmobiliaria912177526.0https://www.fotocasa.es/es/comprar/vivienda/ma...3.02.0103.0195000...00000040,2948276786438-3,444024121356240.00.0
12153867863Falsetecnocasa fuenlabrada ferrocarril916358736.0https://www.fotocasa.es/es/comprar/vivienda/ma...3.01.00.089000...00000140,28674-3,793510.00.0
23153430440Falselook find boadilla916350408.0https://www.fotocasa.es/es/comprar/vivienda/ma...2.02.099.0390000...00000040,4115646786438-3,906622521356240.00.0
34152776331Falsetecnocasa fuenlabrada ferrocarril916358736.0https://www.fotocasa.es/es/comprar/vivienda/ma...3.01.086.089000...00000040,2853785786438-3,795081421356240.00.0
45153180188Falseferrari 57 inmobiliaria912177526.0https://www.fotocasa.es/es/comprar/vivienda/ma...2.02.0106.0172000...00000040,2998774864376-3,452263013562370.00.0
..................................................................
1533015331153901377Falseinfocasa consulting911360461.0https://www.fotocasa.es/es/comprar/vivienda/ma...2.01.096.0259470...00000040,45416-3,702860.00.0
1533115332150394373Falseinmobiliaria pulpon912788039.0https://www.fotocasa.es/es/comprar/vivienda/ma...3.01.0150.0165000...00000040,36652-3,489510.00.0
1533215333153901397Falsetecnocasa torrelodones912780348.0https://www.fotocasa.es/es/comprar/vivienda/ma...4.02.0175.0495000...00000040,57444-3,921240.00.0
1533315334152607440Falseinmobiliaria pulpon912788039.0https://www.fotocasa.es/es/comprar/vivienda/ma...3.02.0101.0195000...00000040,36967-3,481050.00.0
1533415335153901356Falseinfocasa consulting911360461.0https://www.fotocasa.es/es/comprar/vivienda/ma...3.02.0152.0765000...00000040,45773-3,690680.00.0
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" + ], + "text/plain": [ + " Unnamed: 0 id_realEstates isNew realEstate_name \\\n", + "0 1 153771986 False ferrari 57 inmobiliaria \n", + "1 2 153867863 False tecnocasa fuenlabrada ferrocarril \n", + "2 3 153430440 False look find boadilla \n", + "3 4 152776331 False tecnocasa fuenlabrada ferrocarril \n", + "4 5 153180188 False ferrari 57 inmobiliaria \n", + "... ... ... ... ... \n", + "15330 15331 153901377 False infocasa consulting \n", + "15331 15332 150394373 False inmobiliaria pulpon \n", + "15332 15333 153901397 False tecnocasa torrelodones \n", + "15333 15334 152607440 False inmobiliaria pulpon \n", + "15334 15335 153901356 False infocasa consulting \n", + "\n", + " phone_realEstate url_inmueble \\\n", + "0 912177526.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "1 916358736.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "2 916350408.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "3 916358736.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "4 912177526.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "... ... ... \n", + "15330 911360461.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "15331 912788039.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "15332 912780348.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "15333 912788039.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "15334 911360461.0 https://www.fotocasa.es/es/comprar/vivienda/ma... \n", + "\n", + " rooms bathrooms surface price ... level4Id level5Id level6Id \\\n", + "0 3.0 2.0 103.0 195000 ... 0 0 0 \n", + "1 3.0 1.0 0.0 89000 ... 0 0 0 \n", + "2 2.0 2.0 99.0 390000 ... 0 0 0 \n", + "3 3.0 1.0 86.0 89000 ... 0 0 0 \n", + "4 2.0 2.0 106.0 172000 ... 0 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "15330 2.0 1.0 96.0 259470 ... 0 0 0 \n", + "15331 3.0 1.0 150.0 165000 ... 0 0 0 \n", + "15332 4.0 2.0 175.0 495000 ... 0 0 0 \n", + "15333 3.0 2.0 101.0 195000 ... 0 0 0 \n", + "15334 3.0 2.0 152.0 765000 ... 0 0 0 \n", + "\n", + " level7Id level8Id accuracy latitude longitude zipCode \\\n", + "0 0 0 0 40,2948276786438 -3,44402412135624 0.0 \n", + "1 0 0 1 40,28674 -3,79351 0.0 \n", + "2 0 0 0 40,4115646786438 -3,90662252135624 0.0 \n", + "3 0 0 0 40,2853785786438 -3,79508142135624 0.0 \n", + "4 0 0 0 40,2998774864376 -3,45226301356237 0.0 \n", + "... ... ... ... ... ... ... \n", + "15330 0 0 0 40,45416 -3,70286 0.0 \n", + "15331 0 0 0 40,36652 -3,48951 0.0 \n", + "15332 0 0 0 40,57444 -3,92124 0.0 \n", + "15333 0 0 0 40,36967 -3,48105 0.0 \n", + "15334 0 0 0 40,45773 -3,69068 0.0 \n", + "\n", + " customZone \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "15330 0.0 \n", + "15331 0.0 \n", + "15332 0.0 \n", + "15333 0.0 \n", + "15334 0.0 \n", + "\n", + "[15335 rows x 37 columns]" ] - }, + }, + "execution_count": 394, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds.fillna(value=0)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "middle-china", + "metadata": {}, + "source": [ + "#### Ejercicio 07. ¿Cuál la media de precios en la población (columna level5) de \"Arroyomolinos (Madrid)\"? (★★☆)\n", + "\n", + "Imprima el valor obtenido." + ] + }, + { + "cell_type": "code", + "execution_count": 395, + "id": "nuclear-belief", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "valid-honolulu", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "la media de Arroyomolinos es de $ 294541\n" + ] + } + ], + "source": [ + "# TODO\n", + "\n", + "ds_filtrado = ds[ds['level5'] == 'Arroyomolinos (Madrid)']\n", + "media_price = int(ds_filtrado ['price'].mean())\n", + "print(f'la media de Arroyomolinos es de ','$',(media_price))\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "concerned-radical", + "metadata": {}, + "source": [ + "#### Ejercicio 08. Trazar el histograma de los precios para la población (level5 column) de \"Arroyomolinos (Madrid)\" y explica qué observas (★★☆)\n", + "\n", + "Imprime el histograma de los precios y escribe en la celda del Markdown un breve análisis del trazado.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 396, + "id": "sudden-message", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "binding-ebony", - "metadata": {}, - "source": [ - "#### Ejercicio 13. ¿Cuál es la población (columna level5) que contiene la mayor cantidad de casas?(★★☆)\n", - "\n", - "Imprima la población y el número de casas." + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# TODO:\n", + "import pandas as pd \n", + "import matplotlib.pyplot as plt\n", + "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n", + "\n", + "\n", + "\n", + "ds_filtrado = ds[ds['level5'] == 'Arroyomolinos (Madrid)']\n", + "plt.hist(ds['price'], bins=100, edgecolor='black') \n", + "plt.xlabel('Price') \n", + "plt.ylabel('Arroyomolinos (Madrid)') \n", + "plt.title('Histograma de Prices') \n", + "plt.show()\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "impressed-combination", + "metadata": {}, + "source": [ + "confimado" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "actual-edinburgh", + "metadata": {}, + "source": [ + "#### Ejercicio 09. ¿Son los precios promedios de \"Valdemorillo\" y \"Galapagar\" los mismos? (★★☆)\n", + "\n", + "Imprime ambos promedios y escribe una conclusión sobre ellos." + ] + }, + { + "cell_type": "code", + "execution_count": 397, + "id": "numeric-commerce", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "static-perry", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "El promedio de Valdemorillo 363860\n", + "El promedio de Galapagar 360063\n" + ] + } + ], + "source": [ + "# TODO\n", + "val = ds[ds['level5'] == 'Valdemorillo']\n", + "promedio_val = int(val [\"price\"] .mean())\n", + "gal= ds [ds[\"level5\"]== \"Galapagar\"]\n", + "promedio_gal = int(gal [\"price\"] .mean())\n", + "\n", + "\n", + "\n", + "print(\"El promedio de Valdemorillo\" , promedio_val)\n", + "print(\"El promedio de Galapagar \", promedio_gal)\n", + "#si los redondeo son iguales\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9057c1fa", + "metadata": {}, + "source": [ + "no son los mismos" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "lonely-article", + "metadata": {}, + "source": [ + "#### Ejercicio 10. ¿Son los promedios de precio por metro cuadrado (precio/m2) de \"Valdemorillo\" y \"Galapagar\" los mismos? (★★☆)\n", + "\n", + "Imprime ambos promedios de precio por metro cuadrado y escribe una conclusión sobre ellos.\n", + "\n", + "Pista: Crea una nueva columna llamada `pps` (*price per square* o precio por metro cuadrado) y luego analiza los valores." + ] + }, + { + "cell_type": "code", + "execution_count": 398, + "id": "hourly-globe", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "entire-classification", - "metadata": {}, - "source": [ - "#### Ejercicio 14. Ahora vamos a trabajar con el \"cinturón sur\" de Madrid. Haz un subconjunto del DataFrame original que contenga las siguientes poblaciones (columna level5): \"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\" (★★☆)\n", - "\n", - "Pista: Filtra el DataFrame original usando la columna `level5` y la función `isin`." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0.00181\n", + "1 0.00181\n", + "2 0.00181\n", + "3 0.00181\n", + "4 0.00181\n", + " ... \n", + "15330 0.00181\n", + "15331 0.00181\n", + "15332 0.00181\n", + "15333 0.00181\n", + "15334 0.00181\n", + "Name: pps, Length: 15335, dtype: float64\n" + ] + } + ], + "source": [ + "# TODO\n", + "#surface\n", + "\n", + "val = ds[ds['level5'] == 'Valdemorillo']\n", + "m2_val = val [\"surface\"] .mean()\n", + "gal= ds [ds[\"level5\"]== \"Galapagar\"]\n", + "m2_gal = gal [\"surface\"] .mean()\n", + "\n", + "val = ds[ds['level5'] == 'Valdemorillo']\n", + "promedio_val = (val [\"price\"] .mean())\n", + "gal= ds [ds[\"level5\"]== \"Galapagar\"]\n", + "promedio_gal = (gal [\"price\"] .mean())\n", + "\n", + "promedio_m2 = int(m2_val + m2_gal) \n", + "promedio_precio = int(promedio_gal + promedio_val ) / 2\n", + "\n", + "#ds['pps'] = ds[m2_val] / ds[promedio_val] \n", + "promedio_precio_m2= (promedio_m2 / promedio_precio)\n", + "\n", + "ds[\"pps\"] = promedio_precio_m2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "#print(\"El promedio de Valdemorillo\" , promedio_val)\n", + "#print(\"El promedio de Galapagar \", promedio_gal)\n", + "\n", + "#print(\"El promedio m2 de Valdemorillo\" , pps_val)\n", + "#print(\"El promedio m2 de Galapagar \", pps_gal)\n", + "print(ds[\"pps\"])\n", + "#print(ds[\"surface\"], ds[\"price\"])\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "pleasant-invite", + "metadata": {}, + "source": [ + "#### Ejercicio 11. Analiza la relación entre la superficie y el precio de las casas. (★★☆)\n", + "\n", + "Pista: Puedes hacer un `scatter plot` y luego escribir una conclusión al respecto." + ] + }, + { + "cell_type": "code", + "execution_count": 399, + "id": "common-drilling", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "binary-input", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# TODO: \n", + "\n", + "import pandas as pd \n", + "import matplotlib.pyplot as plt\n", + "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n", + "\n", + "# Crear el scatter plot\n", + "plt.scatter(ds[\"surface\"], ds[\"price\"])\n", + "# Etiquetas y título\n", + "\n", + "plt.xlabel('Superficie (m2)') \n", + "plt.ylabel('Precio') \n", + "plt.title('Relación entre superficie y precio de las casas')\n", + "plt.xlim(0,300)\n", + "plt.ylim(0,300000)\n", + "# Mostrar el gráfico\n", + "plt.show()\n", + "#disminuir el rango \n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ahead-liquid", + "metadata": {}, + "source": [ + "**TODO: Markdown**. Para escribir aquí, haz doble clic en esta celda, elimina este contenido y coloca lo que quieras escribir. Luego ejecuta la celda." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "coordinate-sunrise", + "metadata": {}, + "source": [ + "#### Ejercicio 12. ¿Cuántas agencia de bienes raíces contiene el dataset? (★★☆)\n", + "\n", + "Imprime el valor obtenido." + ] + }, + { + "cell_type": "code", + "execution_count": 400, + "id": "valid-honolulu", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "severe-fisher", - "metadata": {}, - "source": [ - "#### Ejercicio 15. Traza un gráfico de barras de la mediana de los precios y explica lo que observas (debes usar el subconjunto obtenido del Ejercicio 14) (★★★)\n", - "\n", - "Imprima un gráfico de barras de la mediana de precios y escriba en la celda Markdown un breve análisis sobre el gráfico." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "la cantida de agencias en Bienes Raices es de 15325\n" + ] + } + ], + "source": [ + "# TODO\n", + "\n", + "agencias_br = ds['realEstate_name'].count()\n", + "\n", + "print(f'la cantida de agencias en Bienes Raices es de ', (agencias_br))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "binding-ebony", + "metadata": {}, + "source": [ + "#### Ejercicio 13. ¿Cuál es la población (columna level5) que contiene la mayor cantidad de casas?(★★☆)\n", + "\n", + "Imprima la población y el número de casas." + ] + }, + { + "cell_type": "code", + "execution_count": 401, + "id": "static-perry", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "lyric-bunch", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: Code" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "la poblacion que contiene mas casas en la columna level5 es Zarzalejo\n" + ] + } + ], + "source": [ + "# TODO\n", + "\n", + "poblacion_max = ds['level5'] .max()\n", + "\n", + "print (f'la poblacion que contiene mas casas en la columna level5 es',(poblacion_max))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "entire-classification", + "metadata": {}, + "source": [ + "#### Ejercicio 14. Ahora vamos a trabajar con el \"cinturón sur\" de Madrid. Haz un subconjunto del DataFrame original que contenga las siguientes poblaciones (columna level5): \"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\" (★★☆)\n", + "\n", + "Pista: Filtra el DataFrame original usando la columna `level5` y la función `isin`." + ] + }, + { + "cell_type": "code", + "execution_count": 402, + "id": "binary-input", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "sublime-newspaper", - "metadata": {}, - "source": [ - "**TODO: Markdown**. Para escribir aquí, haz doble clic en esta celda, elimina este contenido y coloca lo que quieras escribir. Luego ejecuta la celda." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " Unnamed: 0 id_realEstates isNew \\\n", + "1 2 153867863 False \n", + "3 4 152776331 False \n", + "85 86 153152077 False \n", + "94 95 153995577 False \n", + "109 110 153586414 False \n", + "... ... ... ... \n", + "15275 15276 153903887 False \n", + "15291 15292 151697757 False \n", + "15305 15306 153902389 False \n", + "15322 15323 153871864 False \n", + "15325 15326 153901467 False \n", + "\n", + " realEstate_name phone_realEstate \\\n", + "1 tecnocasa fuenlabrada ferrocarril 916358736.0 \n", + "3 tecnocasa fuenlabrada ferrocarril 916358736.0 \n", + "85 sinergical inmobiliaria NaN \n", + "94 viviendas365com 911226014.0 \n", + "109 area uno asesores inmobiliarios 912664081.0 \n", + "... ... ... \n", + "15275 aliseda servicios de gestion inmobiliaria 911368198.0 \n", + "15291 unipiso 912788631.0 \n", + "15305 jadein ferrero 914871639.0 \n", + "15322 gestion comercial 911220662.0 \n", + "15325 montehogar 68 911790675.0 \n", + "\n", + " url_inmueble rooms bathrooms \\\n", + "1 https://www.fotocasa.es/es/comprar/vivienda/ma... 3.0 1.0 \n", + "3 https://www.fotocasa.es/es/comprar/vivienda/ma... 3.0 1.0 \n", + "85 https://www.fotocasa.es/es/comprar/vivienda/le... 1.0 1.0 \n", + "94 https://www.fotocasa.es/es/comprar/vivienda/le... 3.0 2.0 \n", + "109 https://www.fotocasa.es/es/comprar/vivienda/ma... 3.0 3.0 \n", + "... ... ... ... \n", + "15275 https://www.fotocasa.es/es/comprar/vivienda/al... 3.0 1.0 \n", + "15291 https://www.fotocasa.es/es/comprar/vivienda/al... 3.0 2.0 \n", + "15305 https://www.fotocasa.es/es/comprar/vivienda/ma... 3.0 2.0 \n", + "15322 https://www.fotocasa.es/es/comprar/vivienda/ma... 3.0 1.0 \n", + "15325 https://www.fotocasa.es/es/comprar/vivienda/ma... 2.0 2.0 \n", + "\n", + " surface price ... level4Id level5Id level6Id level7Id level8Id \\\n", + "1 NaN 89000 ... 0 0 0 0 0 \n", + "3 86.0 89000 ... 0 0 0 0 0 \n", + "85 50.0 107000 ... 0 0 0 0 0 \n", + "94 120.0 320000 ... 0 0 0 0 0 \n", + "109 142.0 425000 ... 0 0 0 0 0 \n", + "... ... ... ... ... ... ... ... ... \n", + "15275 78.0 138000 ... 0 0 0 0 0 \n", + "15291 110.0 279000 ... 0 0 0 0 0 \n", + "15305 85.0 170000 ... 0 0 0 0 0 \n", + "15322 91.0 112000 ... 0 0 0 0 0 \n", + "15325 99.0 215000 ... 0 0 0 0 0 \n", + "\n", + " accuracy latitude longitude zipCode customZone \n", + "1 1 40,28674 -3,79351 NaN NaN \n", + "3 0 40,2853785786438 -3,79508142135624 NaN NaN \n", + "85 1 40,35059 -3,82693 NaN NaN \n", + "94 0 40,31933 -3,77574 NaN NaN \n", + "109 0 40,3313411 -3,8313868 NaN NaN \n", + "... ... ... ... ... ... \n", + "15275 1 40,31381 -3,83733 NaN NaN \n", + "15291 0 40,3259051 -3,76318 NaN NaN \n", + "15305 0 40,2882193 -3,8098617 NaN NaN \n", + "15322 0 40,28282 -3,78892 NaN NaN \n", + "15325 1 40,28062 -3,79869 NaN NaN \n", + "\n", + "[907 rows x 37 columns]\n" + ] + } + ], + "source": [ + "# TODO\n", + "#definir las ciudades\n", + "ciudades=['Getafe', 'Leganés','Fuenlabrada','Alcorcón']\n", + "#filtrar el dataframe\n", + "cinturon_sur = ds[ds['level5'].isin(ciudades)]\n", + "\n", + "print(cinturon_sur)\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "severe-fisher", + "metadata": {}, + "source": [ + "#### Ejercicio 15. Traza un gráfico de barras de la mediana de los precios y explica lo que observas (debes usar el subconjunto obtenido del Ejercicio 14) (★★★)\n", + "\n", + "Imprima un gráfico de barras de la mediana de precios y escriba en la celda Markdown un breve análisis sobre el gráfico." + ] + }, + { + "cell_type": "code", + "execution_count": 403, + "id": "d1d1495f", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "median_prices = cinturon_sur.groupby('level5')['price'].median()" + ] + }, + { + "cell_type": "code", + "execution_count": 404, + "id": "lyric-bunch", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "speaking-diamond", - "metadata": {}, - "source": [ - "#### Ejercicio 16. Calcula la media y la varianza de muestra para las siguientes variables: precio, habitaciones, superficie y baños (debes usar el subconjunto obtenido del Ejercicio 14) (★★★)\n", - "\n", - "Imprime ambos valores por cada variable." + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# TODO: Code\n", + "\n", + "import pandas as pd \n", + "import matplotlib.pyplot as plt\n", + "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n", + "\n", + "plt.bar(median_prices.index, median_prices.values)\n", + "plt.xlabel('ciudad')\n", + "plt.ylabel('mediana de precios')\n", + "plt.show()\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "sublime-newspaper", + "metadata": {}, + "source": [ + "**TODO: Markdown**. Para escribir aquí, haz doble clic en esta celda, elimina este contenido y coloca lo que quieras escribir. Luego ejecuta la celda." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "speaking-diamond", + "metadata": {}, + "source": [ + "#### Ejercicio 16. Calcula la media y la varianza de muestra para las siguientes variables: precio, habitaciones, superficie y baños (debes usar el subconjunto obtenido del Ejercicio 14) (★★★)\n", + "\n", + "Imprime ambos valores por cada variable." + ] + }, + { + "cell_type": "code", + "execution_count": 405, + "id": "0425cda0", + "metadata": {}, + "outputs": [], + "source": [ + "ciudades=['Getafe', 'Leganés','Fuenlabrada','Alcorcón']\n", + "cinturon_sur = ds[ds['level5'].isin(ciudades)]" + ] + }, + { + "cell_type": "code", + "execution_count": 406, + "id": "random-feeling", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "random-feeling", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " price rooms surface \\\n", + " mean var mean var mean \n", + "level5 \n", + "Alcorcón 230071.052632 1.594783e+10 2.914894 0.933895 105.913295 \n", + "Fuenlabrada 177198.021459 4.701021e+09 3.025974 0.355844 103.624365 \n", + "Getafe 265040.500000 2.098267e+10 3.151724 0.772748 126.896266 \n", + "Leganés 208682.010309 1.191394e+10 2.906736 0.824590 105.852273 \n", + "\n", + " bathrooms \n", + " var mean var \n", + "level5 \n", + "Alcorcón 4244.323834 1.623656 0.592735 \n", + "Fuenlabrada 2264.643893 1.445415 0.353367 \n", + "Getafe 5828.110028 1.865052 0.658809 \n", + "Leganés 3987.475195 1.518135 0.553055 \n" + ] + } + ], + "source": [ + "estadisticas = cinturon_sur.groupby('level5').agg({ 'price': ['mean', 'var'], 'rooms': ['mean', 'var'], 'surface': ['mean', 'var'], 'bathrooms': ['mean', 'var']})\n", + "print(estadisticas)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "revolutionary-matrix", + "metadata": {}, + "source": [ + "#### Ejercicio 17. ¿Cuál es la casa más cara de cada población? Debes usar el subset obtenido en la pregunta 14 (★★☆)\n", + "\n", + "Imprime tanto la dirección como el precio de la casa seleccionada de cada población. Puedes imprimir un DataFrame o una sola línea para cada población." + ] + }, + { + "cell_type": "code", + "execution_count": 407, + "id": "fifteen-browse", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "ciudadmax_price= cinturon_sur.groupby('level5')['price'].max()\n", + "ciudad_price=ds.loc[cinturon_sur['price'].idxmax(), 'address']" + ] + }, + { + "cell_type": "code", + "execution_count": 408, + "id": "2fd97081", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "revolutionary-matrix", - "metadata": {}, - "source": [ - "#### Ejercicio 17. ¿Cuál es la casa más cara de cada población? Debes usar el subset obtenido en la pregunta 14 (★★☆)\n", - "\n", - "Imprime tanto la dirección como el precio de la casa seleccionada de cada población. Puedes imprimir un DataFrame o una sola línea para cada población." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "level5\n", + "Alcorcón 950000\n", + "Fuenlabrada 490000\n", + "Getafe 1050000\n", + "Leganés 650000\n", + "Name: price, dtype: int64\n", + "la casa mas cara de cada poblacion es Getafe\n" + ] + } + ], + "source": [ + "\n", + "print(ciudadmax_price)\n", + "print(f'la casa mas cara de cada poblacion es' ,(ciudad_price))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "activated-knight", + "metadata": {}, + "source": [ + "#### Ejercicio 18. Normaliza la variable de precios para cada población y traza los 4 histogramas en el mismo gráfico (debes usar el subconjunto obtenido en la pregunta 14) (★★★)\n", + "\n", + "Para el método de normalización, puedes usar el que consideres adecuado, no hay una única respuesta correcta para esta pregunta. Imprime el gráfico y escribe en la celda de Markdown un breve análisis sobre el gráfico.\n", + "\n", + "Pista: Puedes ayudarte revisando la demostración multihist de Matplotlib." + ] + }, + { + "cell_type": "code", + "execution_count": 409, + "id": "civic-meditation", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "fifteen-browse", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_605/3375357450.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " cinturon_sur['normalized_price'] = cinturon_sur.groupby('level5')['price'].transform(lambda x: (x - x.mean()) / x.std())\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "activated-knight", - "metadata": {}, - "source": [ - "#### Ejercicio 18. Normaliza la variable de precios para cada población y traza los 4 histogramas en el mismo gráfico (debes usar el subconjunto obtenido en la pregunta 14) (★★★)\n", - "\n", - "Para el método de normalización, puedes usar el que consideres adecuado, no hay una única respuesta correcta para esta pregunta. Imprime el gráfico y escribe en la celda de Markdown un breve análisis sobre el gráfico.\n", - "\n", - "Pista: Puedes ayudarte revisando la demostración multihist de Matplotlib." + "data": { + "image/png": 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IcJvh1DvgDlGrVq0Mv6AWKFAgw1Pyrvbaa6+pbdu2Klu2rCpXrqwHH3xQXbt2zVLIOnTokMLDw9N9WahQoYLj9bT/urm5qVSpUk7LWU/9upp1Wemf4PDqq69q/vz5io+Pd3ot7cvc1YoXL+70PCAgQN7e3ipUqFC6dut1Tu+//77eeust/fbbb06nMF5d1zPPPKNPP/1ULVq0UJEiRdS8eXM9/PDDevDBBzP9XNI/2yMsLEx+fn5O7eXKlXN6vn//fhljNHToUA0dOjTDvuLj41WkSJFrrq9t27Z69tlnZbPZlD9/flWqVCnDC9St2zwtPGd0SmCahIQEJScn69y5c9meij7t+LB+bkkqX7681q5dK+mfL39jx47V4MGDFRISovvuu0+tW7dWt27dnL7sZ8WIESPUoEEDzZgxQwMHDsywpqwc02kyOk7TZHT8SVKxYsXStVsD4JIlSzRy5Eht377d6bqRzEJnVsycOVNz5szRzJkzHX9gSLNu3ToNHz5c69evTzeNekJCggICAhyfPTIy0un1woULq0CBAk5thw4dUu3atdPVcPV2rFy5sl588UV9++23qlWrliIiItS8eXM99thjqlu3bpY+k7UWm82miIgIx/U9WT3G0nh4eKho0aLXXa+/v78k6fz581mq80ZkZ7unpqZq8uTJmjZtmg4ePKiUlBTHawULFnTqMyIiIt3xlNF2Au5WBCUAql+/vg4cOKD//ve/+uabb/Tuu+9q4sSJmjFjhtOIzK2W0TUkDz/8sH788Uc9//zzql69uvz8/JSamqoHH3xQqamp6ZZ3d3fPUpskp4vp/+///k/du3dXu3bt9Pzzzys4OFju7u4aPXq0Dhw44FguODhY27dv1/Lly7V06VItXbpUc+bMUbdu3fT+++/n5GM7SftMzz33nKKjozNc5lphM03RokXVtGnT6y5n3eZp6x8/frzjuiwrPz8/nT59+rp936gBAwaoTZs2WrRokZYvX66hQ4dq9OjR+u6771SjRo0s91O/fn01bNhQ48aNy3DUMruuNYNeZsdaRu1XH38//PCDHnroIdWvX1/Tpk1TWFiYPD09NWfOnOtOcpCZn376Sf3799eTTz6ZbtTgwIEDatKkicqXL68JEyaoWLFi8vLy0tdff62JEydm+LOVWypUqKA9e/ZoyZIlWrZsmT777DNNmzZNw4YN06uvvnrT1puZq0dkrsXf31/h4eH69ddfc7yuzELv1eEmu9544w0NHTpUTzzxhF5//XUFBQXJzc1NAwYMuKn7EbgTEZQASJKCgoLUo0cP9ejRQxcuXFD9+vU1YsQIR1DK7B/0EiVK6Ntvv9X58+ed/gL/22+/OV5P+29qaqoOHjzo9FfR/fv3Z7nGM2fOaOXKlXr11Vc1bNgwR3tOThm8noULF6p06dL6/PPPnT778OHD0y3r5eWlNm3aqE2bNkpNTdUzzzyjmTNnaujQoZmGmBIlSmjlypW6cOGC06jSnj17nJYrXbq0JMnT0zNLQSe3lSlTRtI/Xwqvtf7ChQvL398/218a046PPXv2qHHjxk6v7dmzx/H61fUMHjxYgwcP1r59+1S9enW99dZb+r//+79srXfEiBFq2LChZs6cmWFNWTmmb6bPPvtM3t7eWr58udOU0nPmzMlRfydOnFDHjh1VvXp1xwyWV1u8eLGSkpL05ZdfOo2CWWd5TPvs+/btcxybaf1bR8RKlCiR7niWMt6O+fLlU+fOndW5c2clJyerffv2GjVqlIYMGXLdqc6tP//GGO3fv98xIp7dYyw7WrdurVmzZmn9+vWqU6dOtt+fNhp09uxZpwklrKOW2dnuCxcuVKNGjTR79myn9rNnzzqNpJcoUUK//vqrjDFOv+My2mfA3YprlACkO+XMz89PERERTqf7pJ2mZZ2+umXLlkpJSdGUKVOc2idOnCibzea4R0/aaMi0adOclnv77bezXGfaX+GNZRrlSZMmZbmPG1nXxo0btX79eqflrNvOzc3N8QXtWtPstmzZUleuXHGafjklJSXd9ggODnZ8oT927Fi6fk6cOJHFT5QzUVFRKlOmjN58801duHAh0/W7ubmpXbt2Wrx4sTZv3pxuOes+S1OzZk0FBwdrxowZTttr6dKl2r17t2N2t4sXL+rSpUtO7y1Tpozy58+fo+mMGzRooIYNG2rs2LHp+s3qMX0zubu7y2azOY0s/PHHH1q0aFG2+0pJSdEjjzyi5ORkffbZZxnOPpjR8Z6QkJAumDVt2lSenp56++23nZbN6GewZcuW+umnn5x+ZhITEzVr1iyVLFnScV2X9WfIy8tLFStWlDEmS7N2fvDBB06nvy1cuFDHjh1z7KesHmM58cILLyhfvnx68sknFRcXl+71AwcOXPNWAWl/iPj+++8dbYmJielGo7Oz3d3d3dP9vC1YsCDdtYwtW7bU0aNHnaacv3jxouPmuAAYUQIgqWLFimrYsKGioqIUFBSkzZs3a+HChXr22Wcdy0RFRUmS+vXrp+joaLm7u+uRRx5RmzZt1KhRI73yyiv6448/VK1aNX3zzTf673//qwEDBji+CERFRalDhw6aNGmSTp065ZgefO/evZKydt2Fv7+/6tevr3Hjxuny5csqUqSIvvnmG8e9gHJT69at9fnnn+tf//qXWrVqpYMHD2rGjBmqWLGiU2B48skndfr0aTVu3FhFixbVoUOH9Pbbb6t69eqOazEy0qZNG9WtW1cvvfSS/vjjD1WsWFGff/55htdZTZ06VfXq1VOVKlXUq1cvlS5dWnFxcVq/fr3+/PPPDO+Pklvc3Nz07rvvqkWLFqpUqZJ69OihIkWK6K+//tKqVavk7++vxYsXS/rnlJ9vvvlGDRo0UO/evVWhQgUdO3ZMCxYs0Nq1a53+Yp7G09NTY8eOVY8ePdSgQQM9+uijjqmbS5Ys6biGaO/evWrSpIkefvhhVaxYUR4eHvriiy8UFxenRx55JEefbfjw4WrUqFG69qwe0zdTq1atNGHCBD344IN67LHHFB8fr6lTpyoiIkK//PJLtvqaMWOGvvvuOz399NPpRohCQkLUrFkzNW/e3DEy+tRTT+nChQt65513FBwc7BTQCxcurOeee06jR49W69at1bJlS23btk1Lly5Nd93fSy+9pI8//lgtWrRQv379FBQUpPfff18HDx7UZ5995ji9rXnz5goNDVXdunUVEhKi3bt3a8qUKWrVqlWWJkoICgpSvXr11KNHD8XFxWnSpEmKiIhQr169JGX9GMuJMmXKaN68eY7bEHTr1k2VK1dWcnKyfvzxRy1YsMDpvklWzZs3V/HixdWzZ089//zzcnd313vvvafChQvr8OHDjuWys91bt26t1157TT169ND999+vHTt26KOPPnIaiZKkXr16acqUKerWrZu2bNmisLAwffjhh9ecDh2469zyefYA5Kq0aWQzmpLZGGMaNGhw3enBR44caWrVqmUCAwONj4+PKV++vBk1apRJTk52LHPlyhXTt29fU7hwYWOz2ZymtD1//rwZOHCgCQ8PN56eniYyMtKMHz/eaRpbY/6ZejY2NtYEBQUZPz8/065dO7Nnzx4jyWm67qunc7b6888/zb/+9S8TGBhoAgICTKdOnczRo0cznWLc2kdMTIzJly/fdbdTamqqeeONN0yJEiWM3W43NWrUMEuWLDExMTGmRIkSjuUWLlxomjdvboKDg42Xl5cpXry4eeqpp5ymXc7MqVOnTNeuXY2/v78JCAgwXbt2Ndu2bUs3Pbgxxhw4cMB069bNhIaGGk9PT1OkSBHTunVrs3DhwuuuR5KJjY295jJpUy0vWLAgw9e3bdtm2rdvbwoWLGjsdrspUaKEefjhh83KlSudljt06JDp1q2bKVy4sLHb7aZ06dImNjbWMdV6ZlNXf/LJJ6ZGjRrGbreboKAg8/jjj5s///zT8frJkydNbGysKV++vMmXL58JCAgwtWvXdpraODPXOp4aNGhgJKWbHj+rx3Rm2zazn8vsHJezZ882kZGRxm63m/Lly5s5c+ZkOJ309aYHT3tPRo+rpyT/8ssvTdWqVY23t7cpWbKkGTt2rHnvvfecpqk2xpiUlBTz6quvmrCwMOPj42MaNmxofv3113R1GPPPcduxY0cTGBhovL29Ta1atcySJUuclpk5c6apX7++49gqU6aMef75501CQkK67Xq1tM/58ccfmyFDhpjg4GDj4+NjWrVqZQ4dOpRu+esdY5nth6zYu3ev6dWrlylZsqTx8vIy+fPnN3Xr1jVvv/2207T+GW2jLVu2mNq1azt+f0yYMCHd9ODGZH27X7p0yQwePNixXN26dc369etNgwYN0k1Bf+jQIfPQQw8ZX19fU6hQIdO/f3+zbNkypgcH/sdmTCbnQwDALbB9+3bVqFFD//d//6fHH3/c1eUAuE2sXr1ajRo10oIFC5ymuAaA3MI1SgBumYzuWzNp0iS5ubmpfv36LqgIAAAgY1yjBOCWGTdunLZs2aJGjRrJw8PDMZ127969091XBgAAwJUISgBumfvvv18rVqzQ66+/rgsXLqh48eIaMWKEXnnlFVeXBgAA4IRrlAAAAADAgmuUAAAAAMCCoAQAAAAAFnf8NUqpqak6evSo8ufPn6UbWgIAAAC4MxljdP78eYWHhztufJ2ZOz4oHT16lNm0AAAAADgcOXJERYsWveYyd3xQyp8/v6R/Noa/v7+LqwEAAADgKufOnVOxYsUcGeFa7viglHa6nb+/P0EJAAAAQJYuyWEyBwAAAACwICgBAAAAgAVBCQAAAAAs7vhrlAAAAIAbYYzRlStXlJKS4upScB3u7u7y8PDIldsCEZQAAACATCQnJ+vYsWO6ePGiq0tBFvn6+iosLExeXl431A9BCQAAAMhAamqqDh48KHd3d4WHh8vLyytXRipwcxhjlJycrBMnTujgwYOKjIy87k1lr4WgBAAAAGQgOTlZqampKlasmHx9fV1dDrLAx8dHnp6eOnTokJKTk+Xt7Z3jvpjMAQAAALiGGxmVwK2XW/uLvQ4AAAAAFgQlAAAAAC63efNmTZw4Uampqa4uRRLXKAEAAADZNnHF3lu2roHNyuZ6n6tXr1ajRo105swZBQYG5nr/2XXixAl16tRJs2bNyjOnOuaNKgAAAADkuvXr18vd3V2tWrVydSmZSk1NVdeuXTV8+HA1a9bM1eU4MKIEAAAA3KFmz56tvn37avbs2Tp69KjCw8NvyXqNMUpJSZGHx/Xjhpubm5YtW3YLqsoeRpQAAACAO9CFCxf0ySefqE+fPmrVqpXmzp17zeXXrVunhg0bytfXVwUKFFB0dLTOnDkjSUpKSlK/fv0UHBwsb29v1atXT5s2bXK8d/Xq1bLZbFq6dKmioqJkt9u1du1apaamaty4cYqIiJDdblfx4sU1atQox/t27Nihxo0by8fHRwULFlTv3r114cIFx+vdu3dXu3bt9OabbyosLEwFCxZUbGysLl++nLsbKwMEJQAAAOAO9Omnn6p8+fIqV66cunTpovfee0/GmAyX3b59u5o0aaKKFStq/fr1Wrt2rdq0aaOUlBRJ0gsvvKDPPvtM77//vrZu3aqIiAhFR0fr9OnTTv289NJLGjNmjHbv3q2qVatqyJAhGjNmjIYOHapdu3Zp3rx5CgkJkSQlJiYqOjpaBQoU0KZNm7RgwQJ9++23evbZZ536XLVqlQ4cOKBVq1bp/fff19y5c68b+nIDp94BAAAAd6DZs2erS5cukqQHH3xQCQkJWrNmjRo2bJhu2XHjxqlmzZqaNm2ao61SpUqS/gk006dP19y5c9WiRQtJ0jvvvKMVK1Zo9uzZev755x3vee211xzXGZ0/f16TJ0/WlClTFBMTI0kqU6aM6tWrJ0maN2+eLl26pA8++ED58uWTJE2ZMkVt2rTR2LFjHYGqQIECmjJlitzd3VW+fHm1atVKK1euVK9evXJzc6XDiBIAAABwh9mzZ49++uknPfroo5IkDw8Pde7cWbNnz85w+bQRpYwcOHBAly9fVt26dR1tnp6eqlWrlnbv3u20bM2aNR3/v3v3biUlJWXa7+7du1WtWjVHSJKkunXrKjU1VXv27HG0VapUSe7u7o7nYWFhio+Pz+yj5xpGlAAAAIA7zOzZs3XlyhWnyRuMMbLb7ZoyZUq65X18fHJlvVeHntzq09PT0+m5zWa7JfdaYkQJAAAAuINcuXJFH3zwgd566y1t377d8fj5558VHh6ujz/+ON17qlatqpUrV2bYX5kyZeTl5aV169Y52i5fvqxNmzapYsWKmdYRGRkpHx+fTPutUKGCfv75ZyUmJjra1q1bJzc3N5UrVy6rH/emYUTpDnMzb352M252BgAAgNy1ZMkSnTlzRj179lRAQIDTax06dNDs2bM1fvx4p/YhQ4aoSpUqeuaZZ/T000/Ly8tLq1atUqdOnVSoUCH16dNHzz//vIKCglS8eHGNGzdOFy9eVM+ePTOtw9vbWy+++KJeeOEFeXl5qW7dujpx4oR27typnj176vHHH9fw4cMVExOjESNG6MSJE+rbt6+6du3quD7JlQhKAAAAQDbl5T8gz549W02bNk0XkqR/gtK4ceP0yy+/OLWXLVtW33zzjV5++WXVqlVLPj4+ql27tuMapzFjxjhuDHv+/HnVrFlTy5cvV4ECBa5Zy9ChQ+Xh4aFhw4bp6NGjCgsL09NPPy1J8vX11fLly9W/f3/de++98vX1VYcOHTRhwoRc2hI3xmYymyPwDnHu3DkFBAQoISFB/v7+ri7npmNECQAAIHdcunRJBw8eVKlSpeTt7e3qcpBF19pv2ckGXKMEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAADANdzhc5/dcXJrfxGUAAAAgAx4enpKki5evOjiSpAdafsrbf/lFPdRAgAAADLg7u6uwMBAxcfHS/rnvj82m83FVSEzxhhdvHhR8fHxCgwMlLu7+w31R1ACAAAAMhEaGipJjrCEvC8wMNCx324EQQkAAADIhM1mU1hYmIKDg3X58mVXl4Pr8PT0vOGRpDQEJQAAAOA63N3dc+0LOG4PTOYAAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMAizwSlMWPGyGazacCAAY62S5cuKTY2VgULFpSfn586dOiguLg41xUJAAAA4K6QJ4LSpk2bNHPmTFWtWtWpfeDAgVq8eLEWLFigNWvW6OjRo2rfvr2LqgQAAABwt3B5ULpw4YIef/xxvfPOOypQoICjPSEhQbNnz9aECRPUuHFjRUVFac6cOfrxxx+1YcMGF1YMAAAA4E7n8qAUGxurVq1aqWnTpk7tW7Zs0eXLl53ay5cvr+LFi2v9+vWZ9peUlKRz5845PQAAAAAgOzxcufL58+dr69at2rRpU7rXjh8/Li8vLwUGBjq1h4SE6Pjx45n2OXr0aL366qu5XSoAAACAu4jLRpSOHDmi/v3766OPPpK3t3eu9TtkyBAlJCQ4HkeOHMm1vgEAAADcHVwWlLZs2aL4+Hjdc8898vDwkIeHh9asWaP//Oc/8vDwUEhIiJKTk3X27Fmn98XFxSk0NDTTfu12u/z9/Z0eAAAAAJAdLjv1rkmTJtqxY4dTW48ePVS+fHm9+OKLKlasmDw9PbVy5Up16NBBkrRnzx4dPnxYderUcUXJAAAAAO4SLgtK+fPnV+XKlZ3a8uXLp4IFCzrae/bsqUGDBikoKEj+/v7q27ev6tSpo/vuu88VJQMAAAC4S7h0MofrmThxotzc3NShQwclJSUpOjpa06ZNc3VZAAAAAO5wNmOMcXURN9O5c+cUEBCghISEu+J6pYkr9t60vgc2K3vT+gYAAAButuxkA5ffRwkAAAAA8hqCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgIVLg9L06dNVtWpV+fv7y9/fX3Xq1NHSpUsdr1+6dEmxsbEqWLCg/Pz81KFDB8XFxbmwYgAAAAB3A5cGpaJFi2rMmDHasmWLNm/erMaNG6tt27bauXOnJGngwIFavHixFixYoDVr1ujo0aNq3769K0sGAAAAcBewGWOMq4u4WlBQkMaPH6+OHTuqcOHCmjdvnjp27ChJ+u2331ShQgWtX79e9913X5b6O3funAICApSQkCB/f/+bWXqeMHHF3pvW98BmZW9a3wAAAMDNlp1skGeuUUpJSdH8+fOVmJioOnXqaMuWLbp8+bKaNm3qWKZ8+fIqXry41q9fn2k/SUlJOnfunNMDAAAAALLD5UFpx44d8vPzk91u19NPP60vvvhCFStW1PHjx+Xl5aXAwECn5UNCQnT8+PFM+xs9erQCAgIcj2LFit3kTwAAAADgTuPyoFSuXDlt375dGzduVJ8+fRQTE6Ndu3bluL8hQ4YoISHB8Thy5EguVgsAAADgbuDh6gK8vLwUEREhSYqKitKmTZs0efJkde7cWcnJyTp79qzTqFJcXJxCQ0Mz7c9ut8tut9/ssgEAAADcwVw+omSVmpqqpKQkRUVFydPTUytXrnS8tmfPHh0+fFh16tRxYYUAAAAA7nQuHVEaMmSIWrRooeLFi+v8+fOaN2+eVq9ereXLlysgIEA9e/bUoEGDFBQUJH9/f/Xt21d16tTJ8ox3AAAAAJATLg1K8fHx6tatm44dO6aAgABVrVpVy5cvV7NmzSRJEydOlJubmzp06KCkpCRFR0dr2rRpriwZAAAAwF0gz91HKbdxH6Xcw32UAAAAcDu7Le+jBAAAAAB5BUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFh4uLoA3D4mrth7U/sf2KzsTe0fAAAAyCpGlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMDCI6dvTExM1Jo1a3T48GElJyc7vdavX78bLgwAAAAAXCVHQWnbtm1q2bKlLl68qMTERAUFBenkyZPy9fVVcHAwQQkAAADAbS1Hp94NHDhQbdq00ZkzZ+Tj46MNGzbo0KFDioqK0ptvvpnbNQIAAADALZWjoLR9+3YNHjxYbm5ucnd3V1JSkooVK6Zx48bp5Zdfzu0aAQAAAOCWylFQ8vT0lJvbP28NDg7W4cOHJUkBAQE6cuRI7lUHAAAAAC6Qo2uUatSooU2bNikyMlINGjTQsGHDdPLkSX344YeqXLlybtcIAAAAALdUjkaU3njjDYWFhUmSRo0apQIFCqhPnz46ceKEZs2alasFAgAAAMCtlqMRpZo1azr+Pzg4WMuWLcu1ggAAAADA1bjhLAAAAABYZHlE6Z577tHKlStVoEAB1ahRQzabLdNlt27dmivFAQAAAIArZDkotW3bVna7XZLUrl27m1UPAAAAALhcloPS8OHDM/x/AAAAALjT5OgapU2bNmnjxo3p2jdu3KjNmzffcFEAAAAA4Eo5CkqxsbEZ3lj2r7/+Umxs7A0XBQAAAACulKOgtGvXLt1zzz3p2mvUqKFdu3bdcFEAAAAA4Eo5Ckp2u11xcXHp2o8dOyYPjxzdmgkAAAAA8owcBaXmzZtryJAhSkhIcLSdPXtWL7/8spo1a5ZrxQEAAACAK+Ro+OfNN99U/fr1VaJECdWoUUOStH37doWEhOjDDz/M1QIBAAAA4FbLUVAqUqSIfvnlF3300Uf6+eef5ePjox49eujRRx+Vp6dnbtcIAAAAALdUji8oypcvn3r37p2btQAAAABAnpDjoLRv3z6tWrVK8fHxSk1NdXpt2LBhN1wYAAAAALhKjoLSO++8oz59+qhQoUIKDQ2VzWZzvGaz2QhKAAAAAG5rOQpKI0eO1KhRo/Tiiy/mdj0AAAAA4HI5mh78zJkz6tSpU27XAgAAAAB5Qo6CUqdOnfTNN9/kdi0AAAAAkCfk6NS7iIgIDR06VBs2bFCVKlXSTQner1+/XCkOAAAAAFzBZowx2X1TqVKlMu/QZtPvv/9+Q0XlpnPnzikgIEAJCQny9/d3dTk33cQVe11dQo4NbFbW1SUAAADgDpadbJCjEaWDBw/mqDAAAAAAuB3k6BqlNMnJydqzZ4+uXLmSW/UAAAAAgMvlKChdvHhRPXv2lK+vrypVqqTDhw9Lkvr27asxY8bkaoEAAAAAcKvlKCgNGTJEP//8s1avXi1vb29He9OmTfXJJ5/kWnEAAAAA4Ao5ukZp0aJF+uSTT3TffffJZrM52itVqqQDBw7kWnEAAAAA4Ao5GlE6ceKEgoOD07UnJiY6BScAAAAAuB3lKCjVrFlTX331leN5Wjh69913VadOndypDAAAAABcJEen3r3xxhtq0aKFdu3apStXrmjy5MnatWuXfvzxR61Zsya3awQAAACAWypHI0r16tXT9u3bdeXKFVWpUkXffPONgoODtX79ekVFReV2jQAAAABwS+VoREmSypQpo3feeSc3awEAAACAPCFHQSntvkmZKV68eI6KAQAAAIC8IEdBqWTJktec3S4lJSXHBQEAAACAq+UoKG3bts3p+eXLl7Vt2zZNmDBBo0aNypXCAAAAAMBVchSUqlWrlq6tZs2aCg8P1/jx49W+ffsbLgwAAAAAXCVHs95lply5ctq0aVNudgkAAAAAt1yORpTOnTvn9NwYo2PHjmnEiBGKjIzMlcIAAAAAwFVyFJQCAwPTTeZgjFGxYsU0f/78XCkMAAAAAFwlR0Hpu+++cwpKbm5uKly4sCIiIuThkeNbMwEAAABAnpCjVNOwYcNcLgMAAAAA8o4cTeYwevRovffee+na33vvPY0dO/aGiwIAAAAAV8pRUJo5c6bKly+frr1SpUqaMWPGDRcFAAAAAK6Uo6B0/PhxhYWFpWsvXLiwjh07dsNFAQAAAIAr5SgoFStWTOvWrUvXvm7dOoWHh99wUQAAAADgSjmazKFXr14aMGCALl++rMaNG0uSVq5cqRdeeEGDBw/O1QIBAAAA4FbLUVB6/vnnderUKT3zzDNKTk6WJHl7e+vFF1/UkCFDcrVAAAAAALjVchSUbDabxo4dq6FDh2r37t3y8fFRZGSk7HZ7btcHAAAAALdcjq5RSnP8+HGdPn1aZcqUkd1ulzEmt+oCAAAAAJfJUVA6deqUmjRporJly6ply5aOme569uzJNUoAAAAAbns5CkoDBw6Up6enDh8+LF9fX0d7586dtWzZslwrDgAAAABcIUfXKH3zzTdavny5ihYt6tQeGRmpQ4cO5UphAAAAAOAqORpRSkxMdBpJSnP69GkmdAAAAABw28vRiNIDDzygDz74QK+//rqkf2bBS01N1bhx49SoUaNcLfBOM3HFXleXAAAAAOA6chSUxo0bpyZNmmjz5s1KTk7WCy+8oJ07d+r06dNat25dbtcIAAAAALdUjk69q1y5svbu3at69eqpbdu2SkxMVPv27bVt2zaVKVMmt2sEAAAAgFsq2yNKly9f1oMPPqgZM2bolVdeuRk1AQAAAIBLZXtEydPTU7/88svNqAUAAAAA8oQcnXrXpUsXzZ49O7drAQAAAIA8IUeTOVy5ckXvvfeevv32W0VFRSlfvnxOr0+YMCFL/YwePVqff/65fvvtN/n4+Oj+++/X2LFjVa5cOccyly5d0uDBgzV//nwlJSUpOjpa06ZNU0hISE5KBwAAAIDrytaI0u+//67U1FT9+uuvuueee5Q/f37t3btX27Ztczy2b9+e5f7WrFmj2NhYbdiwQStWrNDly5fVvHlzJSYmOpYZOHCgFi9erAULFmjNmjU6evSo2rdvn52yAQAAACBbbMYYk9WF3d3ddezYMQUHB0uSOnfurP/85z+5Nrpz4sQJBQcHa82aNapfv74SEhJUuHBhzZs3Tx07dpQk/fbbb6pQoYLWr1+v++6777p9njt3TgEBAUpISJC/v3+u1HkjuI9S5gY2K+vqEgAAAHAHy042yNaIkjVTLV261Gn050YlJCRIkoKCgiRJW7Zs0eXLl9W0aVPHMuXLl1fx4sW1fv36DPtISkrSuXPnnB4AAAAAkB05mswhTTYGo64rNTVVAwYMUN26dVW5cmVJ0vHjx+Xl5aXAwECnZUNCQnT8+PEM+xk9erQCAgIcj2LFiuVajQAAAADuDtkKSjabTTabLV1bboiNjdWvv/6q+fPn31A/Q4YMUUJCguNx5MiRXKkPAAAAwN0jW7PeGWPUvXt32e12Sf/MSPf000+nm/Xu888/z1YRzz77rJYsWaLvv/9eRYsWdbSHhoYqOTlZZ8+edRpViouLU2hoaIZ92e12R30AAAAAkBPZCkoxMTFOz7t06XJDKzfGqG/fvvriiy+0evVqlSpVyun1qKgoeXp6auXKlerQoYMkac+ePTp8+LDq1KlzQ+sGAAAAgMxkKyjNmTMnV1ceGxurefPm6b///a/y58/vuO4oICBAPj4+CggIUM+ePTVo0CAFBQXJ399fffv2VZ06dbI04x0AAAAA5ESObjibW6ZPny5JatiwoVP7nDlz1L17d0nSxIkT5ebmpg4dOjjdcBYAAAAAbhaXBqWszJrn7e2tqVOnaurUqbegIgAAAAC4wenBAQAAAOBORFACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALDxcXQBwJ5i4Yu9N63tgs7I3rW8AAABkjBElAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFh4uLoAIM3EFXtvWt8Dm5W9aX0jc+xTAABwu2JECQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsHBpUPr+++/Vpk0bhYeHy2azadGiRU6vG2M0bNgwhYWFycfHR02bNtW+fftcUywAAACAu4ZLg1JiYqKqVaumqVOnZvj6uHHj9J///EczZszQxo0blS9fPkVHR+vSpUu3uFIAAAAAdxMPV668RYsWatGiRYavGWM0adIk/fvf/1bbtm0lSR988IFCQkK0aNEiPfLII7eyVAAAAAB3kTx7jdLBgwd1/PhxNW3a1NEWEBCg2rVra/369Zm+LykpSefOnXN6AAAAAEB2uHRE6VqOHz8uSQoJCXFqDwkJcbyWkdGjR+vVV1+9qbXh9jNxxV5XlwAAAIDbSJ4dUcqpIUOGKCEhwfE4cuSIq0sCAAAAcJvJs0EpNDRUkhQXF+fUHhcX53gtI3a7Xf7+/k4PAAAAAMiOPBuUSpUqpdDQUK1cudLRdu7cOW3cuFF16tRxYWUAAAAA7nQuvUbpwoUL2r9/v+P5wYMHtX37dgUFBal48eIaMGCARo4cqcjISJUqVUpDhw5VeHi42rVr57qiAQAAANzxXBqUNm/erEaNGjmeDxo0SJIUExOjuXPn6oUXXlBiYqJ69+6ts2fPql69elq2bJm8vb1dVTIAAACAu4BLg1LDhg1ljMn0dZvNptdee02vvfbaLawKAAAAwN0uz16jBAAAAACuQlACAAAAAAuCEgAAAABYuPQaJQDXN3HF3pva/8BmZW9q/0iPfQoAQN7HiBIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAwsPVBQBwrYkr9rq6BAAAgDyHESUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACw8HB1AQCQExNX7L2p/Q9sVvam9g8AAPI2RpQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALDwcHUBAIDcNXHF3pvW98BmZW9a3wAA5CWMKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsOCGswCALLuZN7OVuKEtACDvYEQJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALDxcXQAAAGkmrtjr6hJybGCzsq4uAQCQixhRAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAsPVxcAAHnRxBV7XV0C4HA7H48Dm5V1dQkAsuBm/565HX8XMKIEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYHFbBKWpU6eqZMmS8vb2Vu3atfXTTz+5uiQAAAAAd7A8H5Q++eQTDRo0SMOHD9fWrVtVrVo1RUdHKz4+3tWlAQAAALhD5fmgNGHCBPXq1Us9evRQxYoVNWPGDPn6+uq9995zdWkAAAAA7lB5+oazycnJ2rJli4YMGeJoc3NzU9OmTbV+/foM35OUlKSkpCTH84SEBEnSuXPnbm6xWXQp8YKrSwAA3AQ389+Z2/nfjrzy7y+Aa7vZv2fyyu+CtDqMMdddNk8HpZMnTyolJUUhISFO7SEhIfrtt98yfM/o0aP16quvpmsvVqzYTakRAABJetnVBeRRbBcAUt77XXD+/HkFBARcc5k8HZRyYsiQIRo0aJDjeWpqqk6fPq2CBQvKZrO5sLKsO3funIoVK6YjR47I39/f1eXAgv2Td7Fv8jb2T97G/sm72Dd5G/snb7PuH2OMzp8/r/Dw8Ou+N08HpUKFCsnd3V1xcXFO7XFxcQoNDc3wPXa7XXa73aktMDDwZpV4U/n7+/MDl4exf/Iu9k3exv7J29g/eRf7Jm9j/+RtV++f640kpcnTkzl4eXkpKipKK1eudLSlpqZq5cqVqlOnjgsrAwAAAHAny9MjSpI0aNAgxcTEqGbNmqpVq5YmTZqkxMRE9ejRw9WlAQAAALhD5fmg1LlzZ504cULDhg3T8ePHVb16dS1btizdBA93ErvdruHDh6c7hRB5A/sn72Lf5G3sn7yN/ZN3sW/yNvZP3nYj+8dmsjI3HgAAAADcRfL0NUoAAAAA4AoEJQAAAACwICgBAAAAgAVBCQAAAAAsCEp53KhRo3T//ffL19f3tr1x7p1k6tSpKlmypLy9vVW7dm399NNPri4J//P999+rTZs2Cg8Pl81m06JFi1xdEv5n9OjRuvfee5U/f34FBwerXbt22rNnj6vLgqTp06eratWqjhsx1qlTR0uXLnV1WcjEmDFjZLPZNGDAAFeXAkkjRoyQzWZzepQvX97VZeF//vrrL3Xp0kUFCxaUj4+PqlSpos2bN2erD4JSHpecnKxOnTqpT58+ri7lrvfJJ59o0KBBGj58uLZu3apq1aopOjpa8fHxri4NkhITE1WtWjVNnTrV1aXAYs2aNYqNjdWGDRu0YsUKXb58Wc2bN1diYqKrS7vrFS1aVGPGjNGWLVu0efNmNW7cWG3bttXOnTtdXRosNm3apJkzZ6pq1aquLgVXqVSpko4dO+Z4rF271tUlQdKZM2dUt25deXp6aunSpdq1a5feeustFShQIFv9MD34bWLu3LkaMGCAzp496+pS7lq1a9fWvffeqylTpkiSUlNTVaxYMfXt21cvvfSSi6vD1Ww2m7744gu1a9fO1aUgAydOnFBwcLDWrFmj+vXru7ocWAQFBWn8+PHq2bOnq0vB/1y4cEH33HOPpk2bppEjR6p69eqaNGmSq8u6640YMUKLFi3S9u3bXV0KLF566SWtW7dOP/zwww31w4gSkAXJycnasmWLmjZt6mhzc3NT06ZNtX79ehdWBtx+EhISJP3zhRx5R0pKiubPn6/ExETVqVPH1eXgKrGxsWrVqpXTv0HIG/bt26fw8HCVLl1ajz/+uA4fPuzqkiDpyy+/VM2aNdWpUycFBwerRo0aeuedd7LdD0EJyIKTJ08qJSVFISEhTu0hISE6fvy4i6oCbj+pqakaMGCA6tatq8qVK7u6HEjasWOH/Pz8ZLfb9fTTT+uLL75QxYoVXV0W/mf+/PnaunWrRo8e7epSYFG7dm3NnTtXy5Yt0/Tp03Xw4EE98MADOn/+vKtLu+v9/vvvmj59uiIjI7V8+XL16dNH/fr10/vvv5+tfjxuUn24hpdeekljx4695jK7d+/mgkAAd5zY2Fj9+uuvnMefh5QrV07bt29XQkKCFi5cqJiYGK1Zs4awlAccOXJE/fv314oVK+Tt7e3qcmDRokULx/9XrVpVtWvXVokSJfTpp59y6qqLpaamqmbNmnrjjTckSTVq1NCvv/6qGTNmKCYmJsv9EJRcYPDgwerevfs1lylduvStKQZZUqhQIbm7uysuLs6pPS4uTqGhoS6qCri9PPvss1qyZIm+//57FS1a1NXl4H+8vLwUEREhSYqKitKmTZs0efJkzZw508WVYcuWLYqPj9c999zjaEtJSdH333+vKVOmKCkpSe7u7i6sEFcLDAxU2bJltX//fleXctcLCwtL98eeChUq6LPPPstWPwQlFyhcuLAKFy7s6jKQDV5eXoqKitLKlSsdEwSkpqZq5cqVevbZZ11bHJDHGWPUt29fffHFF1q9erVKlSrl6pJwDampqUpKSnJ1GZDUpEkT7dixw6mtR48eKl++vF588UVCUh5z4cIFHThwQF27dnV1KXe9unXrprsNxd69e1WiRIls9UNQyuMOHz6s06dP6/Dhw0pJSXHMrBIRESE/Pz/XFneXGTRokGJiYlSzZk3VqlVLkyZNUmJionr06OHq0qB//oG6+q94Bw8e1Pbt2xUUFKTixYu7sDLExsZq3rx5+u9//6v8+fM7rusLCAiQj4+Pi6u7uw0ZMkQtWrRQ8eLFdf78ec2bN0+rV6/W8uXLXV0aJOXPnz/dtXz58uVTwYIFucYvD3juuefUpk0blShRQkePHtXw4cPl7u6uRx991NWl3fUGDhyo+++/X2+88YYefvhh/fTTT5o1a5ZmzZqVvY4M8rSYmBgjKd1j1apVri7trvT222+b4sWLGy8vL1OrVi2zYcMGV5eE/1m1alWGPysxMTGuLu2ul9F+kWTmzJnj6tLuek888YQpUaKE8fLyMoULFzZNmjQx33zzjavLwjU0aNDA9O/f39VlwBjTuXNnExYWZry8vEyRIkVM586dzf79+11dFv5n8eLFpnLlysZut5vy5cubWbNmZbsP7qMEAAAAABZMDw4AAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAC4KWw2mxYtWuTqMlyiYcOGGjBggON5yZIlNWnSpJu6zrt5ewPAzUBQAoA7XPfu3WWz2WSz2eTl5aWIiAi99tprunLlyk1d77Fjx9SiRYscv/+PP/6QzWZTcHCwzp8/7/Ra9erVNWLEiBus8NbZtGmTevfu7eoyAADZQFACgLvAgw8+qGPHjmnfvn0aPHiwRowYofHjx2e4bHJycq6sMzQ0VHa7/Yb7OX/+vN58881cqOj/S0lJUWpqaq72eS2FCxeWr6/vLVsfAODGEZQA4C5gt9sVGhqqEiVKqE+fPmratKm+/PJLSf+MOLVr106jRo1SeHi4ypUrJ0k6cuSIHn74YQUGBiooKEht27bVH3/84dTve++9p0qVKslutyssLEzPPvus4zXrqWA7duxQ48aN5ePjo4IFC6p37966cOHCdWvv27evJkyYoPj4+EyXOXPmjLp166YCBQrI19dXLVq00L59+xyvz507V4GBgfryyy9VsWJF2e12HT58WCVLltTIkSPVrVs3+fn5qUSJEvryyy914sQJtW3bVn5+fqpatao2b97s6OvUqVN69NFHVaRIEfn6+qpKlSr6+OOPr/kZrj71bu7cuY4RvqsfaSNkmzZtUrNmzVSoUCEFBASoQYMG2rp1q1N/+/btU/369eXt7a2KFStqxYoV6daZ0+0NAPgHQQkA7kI+Pj5OI0crV67Unj17tGLFCi1ZskSXL19WdHS08ufPrx9++EHr1q2Tn5+fHnzwQcf7pk+frtjYWPXu3Vs7duzQl19+qYiIiAzXl5iYqOjoaBUoUECbNm3SggUL9O233zoFq8w8+uijjtMFM9O9e3dt3rxZX375pdavXy9jjFq2bKnLly87lrl48aLGjh2rd999Vzt37lRwcLAkaeLEiapbt662bdumVq1aqWvXrurWrZu6dOmirVu3qkyZMurWrZuMMZKkS5cuKSoqSl999ZV+/fVX9e7dW127dtVPP/10/Q0vqXPnzjp27Jjj8fHHH8vDw0N169aV9M8IWkxMjNauXasNGzYoMjJSLVu2dJx+mJqaqvbt28vLy0sbN27UjBkz9OKLL+ba9gYA/I8BANzRYmJiTNu2bY0xxqSmppoVK1YYu91unnvuOcfrISEhJikpyfGeDz/80JQrV86kpqY62pKSkoyPj49Zvny5McaY8PBw88orr2S6Xknmiy++MMYYM2vWLFOgQAFz4cIFx+tfffWVcXNzM8ePH8/w/QcPHjSSzLZt28yyZcuMp6en2b9/vzHGmGrVqpnhw4cbY4zZu3evkWTWrVvneO/JkyeNj4+P+fTTT40xxsyZM8dIMtu3b3daR4kSJUyXLl0cz48dO2YkmaFDhzra1q9fbySZY8eOZfpZW7VqZQYPHux43qBBA9O/f3+n9UycODHd+/bv32+CgoLMuHHjMu07JSXF5M+f3yxevNgYY8zy5cuNh4eH+euvvxzLLF269Ia3NwDAGSNKAHAXWLJkifz8/OTt7a0WLVqoc+fOTpMhVKlSRV5eXo7nP//8s/bv36/8+fPLz89Pfn5+CgoK0qVLl3TgwAHFx8fr6NGjatKkSZbWv3v3blWrVk358uVztNWtW1epqanas2fPdd8fHR2tevXqaejQoRn27eHhodq1azvaChYsqHLlymn37t2ONi8vL1WtWjXd+69uCwkJkfTP9rC2pZ36l5KSotdff11VqlRRUFCQ/Pz8tHz5ch0+fPi6n+NqCQkJat26tVq1aqXnn3/e0R4XF6devXopMjJSAQEB8vf314ULFxz97969W8WKFVN4eLjjPXXq1Em3TW5kewMAJA9XFwAAuPkaNWqk6dOny8vLS+Hh4fLwcP71f/UXakm6cOGCoqKi9NFHH6Xrq3DhwnJzu/V/ZxszZozq1KnjFCqyw8fHRzabLV27p6en4//TXs+oLW3yh/Hjx2vy5MmaNGmSqlSponz58mnAgAHZmgQjJSVFnTt3lr+/v2bNmuX0WkxMjE6dOqXJkyerRIkSstvtqlOnTq5NsgEAyBpGlADgLpAvXz5FRESoePHi6UJSRu655x7t27dPwcHBioiIcHoEBAQof/78KlmypFauXJml9VeoUEE///yzEhMTHW3r1q2Tm5ubY/KI66lVq5bat2+vl156KV3fV65c0caNGx1tp06d0p49e1SxYsUs9Z0d69atU9u2bdWlSxdVq1ZNpUuX1t69e7PVx8CBA7Vjxw4tWrRI3t7e6frv16+fWrZs6Zgo4+TJk47XK1SooCNHjujYsWOOtg0bNjj1kRvbGwDudgQlAEA6jz/+uAoVKqS2bdvqhx9+0MGDB7V69Wr169dPf/75pyRpxIgReuutt/Sf//xH+/bt09atW/X2229n2p+3t7diYmL066+/atWqVerbt6+6du3qOLUtK0aNGqXvvvvO6fSxyMhItW3bVr169dLatWv1888/q0uXLipSpIjatm17YxsiA5GRkVqxYoV+/PFH7d69W0899ZTi4uKy/P45c+Zo2rRpmjFjhmw2m44fP67jx487ZqSLjIzUhx9+qN27d2vjxo16/PHH5ePj43h/06ZNVbZsWcXExOjnn3/WDz/8oFdeecVpHbm1vQHgbkZQAgCk4+vrq++//17FixdX+/btVaFCBfXs2VOXLl2Sv7+/pH9OEZs0aZKmTZumSpUqqXXr1k5Tclv7W758uU6fPq17771XHTt2VJMmTTRlypRs1VW2bFk98cQTunTpklP7nDlzFBUVpdatW6tOnToyxujrr792OoUut/z73//WPffco+joaDVs2FChoaFq165dlt+/Zs0apaSk6KGHHlJYWJjjkXavqNmzZ+vMmTO655571LVrV/Xr188xQ58kubm56YsvvtDff/+tWrVq6cknn9SoUaOc1pFb2xsA7mY2Y/433ykAAAAAQBIjSgAAAACQDkEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGDx/wCUs4MofV4vNQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cinturon_sur['normalized_price'] = cinturon_sur.groupby('level5')['price'].transform(lambda x: (x - x.mean()) / x.std())\n", + "plt.figure(figsize=(10, 6))\n", + "for ciudad in ciudades: subset = cinturon_sur[cinturon_sur['level5'] == ciudad] \n", + "plt.hist(subset['normalized_price'], bins=30, alpha=0.5, label=ciudad)\n", + "plt.xlabel('Precio Normalizado')\n", + "plt.ylabel('Frecuencia')\n", + "plt.title('Histogramas de Precios Normalizados por Ciudad')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "precise-heavy", + "metadata": {}, + "source": [ + "**TODO: Markdown**. Para escribir aquí, haz doble clic en esta celda, elimina este contenido y coloca lo que quieras escribir. Luego ejecuta la celda." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "patent-jonathan", + "metadata": {}, + "source": [ + "#### Ejercicio 19. ¿Qué puedes decir sobre el precio por metro cuadrado (precio/m2) entre los municipios de 'Getafe' y 'Alcorcón'? Debes usar el subconjunto obtenido en la pregunta 14 (★★☆)\n", + "\n", + "Pista: Crea una nueva columna llamada `pps` (price per square en inglés) y luego analiza los valores" + ] + }, + { + "cell_type": "code", + "execution_count": 410, + "id": "c9d3847f", + "metadata": {}, + "outputs": [], + "source": [ + "#definir las ciudades\n", + "ciudades=['Getafe', 'Leganés','Fuenlabrada','Alcorcón']\n", + "#filtrar el dataframe\n", + "cinturon_sur = ds[ds['level5'].isin(ciudades)]" + ] + }, + { + "cell_type": "code", + "execution_count": 411, + "id": "initial-liverpool", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "civic-meditation", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "count 241.000000\n", + "mean 2066.314949\n", + "std 741.872702\n", + "min 0.000000\n", + "25% 1684.285714\n", + "50% 1973.333333\n", + "75% 2628.787879\n", + "max 3827.160494\n", + "Name: pps, dtype: float64\n", + "count 173.000000\n", + "mean 2239.302480\n", + "std 539.951527\n", + "min 604.761905\n", + "25% 1904.081633\n", + "50% 2207.792208\n", + "75% 2472.727273\n", + "max 3698.159509\n", + "Name: pps, dtype: float64\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "precise-heavy", - "metadata": {}, - "source": [ - "**TODO: Markdown**. Para escribir aquí, haz doble clic en esta celda, elimina este contenido y coloca lo que quieras escribir. Luego ejecuta la celda." - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_605/500706172.py:5: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " getafe['pps'] = getafe['price'] / getafe['surface']\n", + "/tmp/ipykernel_605/500706172.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " alcorcon['pps'] = alcorcon['price'] / alcorcon['surface']\n" + ] + } + ], + "source": [ + "#filtro\n", + "getafe = cinturon_sur[cinturon_sur['level5'] == 'Getafe']\n", + "alcorcon = cinturon_sur[cinturon_sur['level5'] == 'Alcorcón']\n", + "#pps\n", + "getafe['pps'] = getafe['price'] / getafe['surface']\n", + "alcorcon['pps'] = alcorcon['price'] / alcorcon['surface']\n", + "\n", + "pps_getafe = getafe['pps'].describe()\n", + "pps_alcorcon = alcorcon['pps'].describe()\n", + "\n", + "#print(\"Getafe:\") \n", + "print(pps_getafe)\n", + "#print(\"Alcorcón:\") \n", + "print(pps_alcorcon)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "enhanced-moscow", + "metadata": {}, + "source": [ + "#### Ejercicio 20. Realiza el mismo gráfico para 4 poblaciones diferentes (columna level5) y colócalos en el mismo gráfico. Debes usar el subconjunto obtenido en la pregunta 14 (★★☆) \n", + "Pista: Haz un diagrama de dispersión de cada población usando subgráficos (subplots)." + ] + }, + { + "cell_type": "code", + "execution_count": 412, + "id": "accepting-airfare", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "patent-jonathan", - "metadata": {}, - "source": [ - "#### Ejercicio 19. ¿Qué puedes decir sobre el precio por metro cuadrado (precio/m2) entre los municipios de 'Getafe' y 'Alcorcón'? Debes usar el subconjunto obtenido en la pregunta 14 (★★☆)\n", - "\n", - "Pista: Crea una nueva columna llamada `pps` (price per square en inglés) y luego analiza los valores" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_605/2202286744.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " cinturon_sur['pps'] = cinturon_sur['price'] / cinturon_sur['surface']\n" + ] + } + ], + "source": [ + "#median_prices = cinturon_sur.groupby('level5')['price'].median()\n", + "cinturon_sur['pps'] = cinturon_sur['price'] / cinturon_sur['surface']" + ] + }, + { + "cell_type": "code", + "execution_count": 413, + "id": "2de92e87", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "initial-liverpool", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" + "data": { + "image/png": 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", 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" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "attachments": {}, - "cell_type": "markdown", - "id": "enhanced-moscow", - "metadata": {}, - "source": [ - "#### Ejercicio 20. Realiza el mismo gráfico para 4 poblaciones diferentes (columna level5) y colócalos en el mismo gráfico. Debes usar el subconjunto obtenido en la pregunta 14 (★★☆) \n", - "Pista: Haz un diagrama de dispersión de cada población usando subgráficos (subplots)." + "data": { + "image/png": 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", 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" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "id": "accepting-airfare", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO" + "data": { + "image/png": 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", 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" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "attachments": {}, - "cell_type": "markdown", - "id": "blocked-effects", - "metadata": {}, - "source": [ - "#### Ejercicio 21. Realiza un trazado de las coordenadas (columnas latitud y longitud) del cinturón sur de Madrid por color de cada población (debes usar el subconjunto obtenido del Ejercicio 14) (★★★★)\n", - "\n", - "Ejecuta la siguiente celda y luego comienza a codear en la siguiente. Debes implementar un código simple que transforme las columnas de coordenadas en un diccionario de Python (agrega más información si es necesario) y agrégala al mapa." + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "for ciudad in ciudades:\n", + " subset = cinturon_sur[cinturon_sur['level5'] == ciudad]\n", + " plt.scatter(subset['surface'], subset['pps'], label=ciudad)\n", + " plt.xlabel('Área (m²)')\n", + " plt.ylabel('Precio por Metro Cuadrado ($)')\n", + " plt.title('Gráfico de Dispersión del Precio por Metro Cuadrado') \n", + " plt.legend() \n", + " plt.grid(True) \n", + " plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "blocked-effects", + "metadata": {}, + "source": [ + "#### Ejercicio 21. Realiza un trazado de las coordenadas (columnas latitud y longitud) del cinturón sur de Madrid por color de cada población (debes usar el subconjunto obtenido del Ejercicio 14) (★★★★)\n", + "\n", + "Ejecuta la siguiente celda y luego comienza a codear en la siguiente. Debes implementar un código simple que transforme las columnas de coordenadas en un diccionario de Python (agrega más información si es necesario) y agrégala al mapa." + ] + }, + { + "cell_type": "code", + "execution_count": 414, + "id": "d6ae7f56", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "headed-privacy", - "metadata": {}, - "outputs": [], - "source": [ - "from ipyleaflet import Map, basemaps\n", - "\n", - "# Mapa centrado en (60 grados latitud y -2.2 grados longitud)\n", - "# Latitud, longitud\n", - "map = Map(center = (60, -2.2), zoom = 2, min_zoom = 1, max_zoom = 20, \n", - " basemap=basemaps.Stamen.Terrain)\n", - "map" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Defaulting to user installation because normal site-packages is not writeable\n", + "Requirement already satisfied: ipyleaflet in /home/vscode/.local/lib/python3.11/site-packages (0.19.2)\n", + "Requirement already satisfied: pandas in 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stack-data->ipython>=6.1.0->ipywidgets<9,>=7.6.0->ipyleaflet) (0.2.3)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install ipyleaflet pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 415, + "id": "86899b72", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "present-mistress", - "metadata": {}, - "outputs": [], - "source": [ - "## Aquí: traza la coordenadas de los estados\n", - "\n", - "## PON TU CÓDIGO AQUÍ:\n" + "data": { + "text/plain": [ + "Index(['Unnamed: 0', 'id_realEstates', 'isNew', 'realEstate_name',\n", + " 'phone_realEstate', 'url_inmueble', 'rooms', 'bathrooms', 'surface',\n", + " 'price', 'date', 'description', 'address', 'country', 'level1',\n", + " 'level2', 'level3', 'level4', 'level5', 'level6', 'level7', 'level8',\n", + " 'upperLevel', 'countryId', 'level1Id', 'level2Id', 'level3Id',\n", + " 'level4Id', 'level5Id', 'level6Id', 'level7Id', 'level8Id', 'accuracy',\n", + " 'latitude', 'longitude', 'zipCode', 'customZone'],\n", + " dtype='object')" ] + }, + "execution_count": 415, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 + ], + "source": [ + "ds.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 416, + "id": "headed-privacy", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "from ipyleaflet import Map, basemaps, Marker, LayerGroup \n", + "import pandas as pd\n", + "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n", + "ds\n", + "\n", + "ciudades = ['Getafe', 'Leganés', 'Fuenlabrada', 'Alcorcón'] \n", + "cinturon_sur = ds[ds['level5'].isin(ciudades)]\n", + "\n", + "coordenadas = {}\n", + "for ciudad in ciudades:\n", + " subset = cinturon_sur[cinturon_sur['level5'] == ciudad] \n", + " coordenadas[ciudad] = list(zip(subset['latitude'], subset['longitude']))" + ] + }, + { + "cell_type": "code", + "execution_count": 417, + "id": "present-mistress", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ffd9fbe7c11b4852baf47a7bad6f0fa5", + "version_major": 2, + "version_minor": 0 }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.3" + "text/plain": [ + "Map(center=[40.35, -3.75], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_o…" + ] + }, + "execution_count": 417, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "## Aquí: traza la coordenadas de los estados\n", + "map = Map(center=(40.35, -3.75), zoom=12, min_zoom=1, max_zoom=20, basemap=basemaps.OpenStreetMap.Mapnik)\n", + "colores = { 'Getafe': 'blue', \n", + " 'Leganés': 'green', \n", + " 'Fuenlabrada': 'red', \n", + " 'Alcorcón': 'purple' }\n", + "\n", + "## PON TU CÓDIGO AQUÍ:\n", + "for ciudad, coords in coordenadas.items(): \n", + " markers = [] \n", + " for coord in coords:\n", + " marker = Marker(location=coord, draggable=False)\n", + " markers.append(marker)\n", + "\n", + " layer_group = LayerGroup(layers=markers)\n", + " map.add_layer(layer_group)\n", + "map\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 }