diff --git a/project.es.ipynb b/project.es.ipynb index da1f12ef6..e9375a106 100644 --- a/project.es.ipynb +++ b/project.es.ipynb @@ -443,12 +443,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "developing-optimum", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "La casa con dirección en El Escorial es la más cara y su precio es de 8500000 USD\n" + ] + } + ], "source": [ - "# TODO" + "# TODO\n", + "casa_mas_cara = ds.loc[ds[\"price\"].idxmax()]\n", + "\n", + "direccion_mas_cara = casa_mas_cara[\"address\"]\n", + "precio_mas_cara = casa_mas_cara[\"price\"]\n", + "\n", + "print(f\"La casa con dirección en {direccion_mas_cara} es la más cara y su precio es de {precio_mas_cara} USD\")\n" ] }, { @@ -466,12 +480,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "lovely-oasis", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "La casa con dirección en Parla es la más barata y su precio es de 0 USD\n" + ] + } + ], "source": [ - "# TODO" + "# TODO\n", + "\n", + "casa_mas_barata = ds.loc[ds[\"price\"].idxmin()]\n", + "\n", + "direccion_mas_barata = casa_mas_barata[\"address\"]\n", + "precio_mas_barata = casa_mas_barata[\"price\"]\n", + "\n", + "print(f\"La casa con dirección en {direccion_mas_barata} es la más barata y su precio es de {precio_mas_barata} USD\")" ] }, { @@ -491,12 +520,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "every-tiffany", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "La casa más grande esta ubicada en Sevilla la Nueva y su supercifie es de 249000.0 metros\n", + "La casa más pequeña está ubicada en Calle Amparo, Madrid Capital y su superficie es de 15.0 metros\n" + ] + } + ], "source": [ - "# TODO" + "# TODO\n", + "casa_mas_grande = ds.loc[ds[\"surface\"].idxmax()]\n", + "\n", + "direccion_mas_grande = casa_mas_grande[\"address\"]\n", + "superf_mas_grande = casa_mas_grande[\"surface\"]\n", + "\n", + "print(f\"La casa más grande esta ubicada en {direccion_mas_grande} y su supercifie es de {superf_mas_grande} metros\")\n", + "\n", + "\n", + "casa_mas_pequeña = ds.loc[ds[\"surface\"].idxmin()]\n", + "\n", + "direccion_mas_pequeña = casa_mas_pequeña[\"address\"]\n", + "superf_mas_pequeña = casa_mas_pequeña[\"surface\"]\n", + "\n", + "print(f\"La casa más pequeña está ubicada en {direccion_mas_pequeña} y su superficie es de {superf_mas_pequeña} metros\")" ] }, { @@ -516,12 +568,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "exciting-accreditation", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arganda del Rey, Fuenlabrada, Boadilla del Monte, Las Rozas de Madrid, Madrid Capital, Villaviciosa de Odón, Pinto, Valdemoro, Navalcarnero, Pozuelo de Alarcón, Torrejón de Ardoz, Navalagamella, San Sebastián de los Reyes, Rivas-vaciamadrid, Alpedrete, Móstoles, San Fernando de Henares, Coslada, Becerril de la Sierra, Alcalá de Henares, Chinchón, Parla, Alcorcón, El Escorial, Leganés, Pedrezuela, Majadahonda, Villanueva de la Cañada, Villanueva del Pardillo, Torrelodones, Moralzarzal, Mejorada del Campo, Aranjuez, Corpa, Getafe, Velilla de San Antonio, Sevilla la Nueva, San Martín de la Vega, Villalbilla, Collado Villalba, Alcobendas, El Molar (Madrid), Moraleja de Enmedio, Algete, Campo Real, Torrejón de la Calzada, Colmenar Viejo, Valdemorillo, Fuente El Saz de Jarama, Tres Cantos, Arroyomolinos (Madrid), Griñón, Paracuellos de Jarama, Guadarrama, Titulcia, Galapagar, Collado Mediano, Los Molinos, San Lorenzo de El Escorial, Loeches, San Martín de Valdeiglesias, Navas del Rey, Bustarviejo, Manzanares El Real, Carabaña, Casarrubuelos, Cercedilla, Fresnedillas de la Oliva, Valdemaqueda, Robledo de Chavela, Miraflores de la Sierra, Humanes de Madrid, Valdetorres de Jarama, San Agustín del Guadalix, Ciempozuelos, Camarma de Esteruelas, Torres de la Alameda, Talamanca de Jarama, El Atazar, Hoyo de Manzanares, El Boalo - Cerceda – Mataelpino, El Álamo, Valdilecha, Valdeolmos-Alalpardo, Guadalix de la Sierra, Belmonte de Tajo, El Vellón, Brunete, Redueña, Morata de Tajuña, Tielmes, Gargantilla del Lozoya, Meco, Venturada, Quijorna, Cenicientos, Torrejón de Velasco, Navacerrada, Santa María de la Alameda, Orusco de Tajuña, Daganzo de Arriba, Villamanta, Anchuelo, Valdaracete, Ajalvir, Los Santos de la Humosa, Villamanrique de Tajo, Colmenar de Oreja, Villa del Prado, Fuentidueña de Tajo, Colmenar del Arroyo, Valdelaguna, Cubas de la Sagra, Valdeavero, Aldea del Fresno, Ribatejada, Torrelaguna, Batres, Pezuela de las Torres, Colmenarejo, Serranillos del Valle, Cobeña, Soto del Real, El Berrueco, Villanueva de Perales, Fresno de Torote - Serracines, Pozuelo del Rey, Pelayos de la Presa, La Cabrera, Nuevo Baztán, Perales de Tajuña, Villaconejos, Villarejo de Salvanés, Cadalso de los Vidrios, Santorcaz, Madarcos, Garganta de los Montes, Puentes Viejas, Torremocha de Jarama, Valdemanco, Braojos, Lozoyuela-navas-sieteiglesias, Piñuécar-gandullas, Valdepiélagos, Valverde de Alcalá, Villar del Olmo, Villamantilla, Horcajo de la Sierra, Gascones, Zarzalejo, Villavieja del Lozoya, Brea de Tajo, Estremera, Chapinería, Navarredonda, Patones, Ambite, Navalafuente, Lozoya, Canencia, Cabanillas de la Sierra, Buitrago del Lozoya, Fresno de Torote, Robregordo, Pinilla del Valle, Rascafría, La Hiruela, Montejo de la Sierra\n" + ] + } + ], "source": [ - "# TODO" + "# TODO\n", + "\n", + "poblaciones_unicas = ds['level5'].unique()\n", + "poblaciones = ', '.join(poblaciones_unicas)\n", + "\n", + "print(poblaciones)" ] }, { @@ -537,12 +602,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "transparent-poetry", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "0 level6 True\n", + " level8 True\n", + " zipCode True\n", + " customZone True\n", + "1 surface True\n", + " ... \n", + "15333 customZone True\n", + "15334 level4 True\n", + " level6 True\n", + " zipCode True\n", + " customZone True\n", + "Length: 65690, dtype: bool\n" + ] + } + ], "source": [ - "# TODO" + "# TODO\n", + "tiene_na = ds.isnull().any().any()\n", + "\n", + "if tiene_na:\n", + " filas_columnas_na = ds.isnull().stack()[ds.isnull().stack()]\n", + " print(True)\n", + " print(filas_columnas_na)\n", + "else:\n", + " print(False)\n" ] }, { @@ -558,12 +651,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "administrative-roads", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dimensiones originales: (15335, 37)\n", + "Dimensiones después de eliminar NAs: (0, 37)\n" + ] + } + ], "source": [ - "# TODO" + "# TODO\n", + "\n", + "dimensiones_originales = ds.shape\n", + "\n", + "ds_sin_na = ds.dropna()\n", + "dimensiones_sin_na = ds_sin_na.shape\n", + "\n", + "print(f\"Dimensiones originales: {dimensiones_originales}\")\n", + "print(f\"Dimensiones después de eliminar NAs: {dimensiones_sin_na}\")" ] }, { @@ -579,12 +689,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "nuclear-belief", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "La media de los precios en Arroyomolinos (Madrid) es 294541.6.\n" + ] + } + ], "source": [ - "# TODO" + "# TODO\n", + "\n", + "arroyomolinos_df = ds[ds[\"level5\"] == \"Arroyomolinos (Madrid)\"]\n", + "\n", + "media_arroyomolinos = round(arroyomolinos_df[\"price\"].mean(),2)\n", + "\n", + "print(f\"La media de los precios en Arroyomolinos (Madrid) es {media_arroyomolinos}.\")" ] }, { @@ -600,12 +724,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "sudden-message", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# TODO: Code" + "# TODO: Code\n", + "import matplotlib.pyplot as plt\n", + "\n", + "arroyomolinos_df = ds[ds[\"level5\"] == \"Arroyomolinos (Madrid)\"]\n", + "plt.figure(figsize = (10, 5))\n", + "\n", + "plt.hist(arroyomolinos_df[\"price\"], bins = 30, alpha = 0.7)\n", + "\n", + "plt.title(\"Precios Arroyomolinos\")\n", + "plt.show()" ] }, { @@ -614,7 +758,7 @@ "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." + "Los precios rondan de los 200000€ a los 400000€." ] }, { @@ -630,12 +774,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "numeric-commerce", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "La media de los precios en Valdemorillo (Madrid) es 363860.2931034483.\n", + "La media de los precios en Galapagar (Madrid) es 360063.20238095237.\n" + ] + } + ], "source": [ - "# TODO" + "# TODO\n", + "valdemorillo_df = ds[ds[\"level5\"] == \"Valdemorillo\"]\n", + "\n", + "media_valdemorillo = valdemorillo_df[\"price\"].mean()\n", + "\n", + "print(f\"La media de los precios en Valdemorillo (Madrid) es {media_valdemorillo}.\")\n", + "\n", + "\n", + "galapagar_df = ds[ds[\"level5\"] == \"Galapagar\"]\n", + "\n", + "media_galapagar = galapagar_df[\"price\"].mean()\n", + "\n", + "print(f\"La media de los precios en Galapagar (Madrid) es {media_galapagar}.\")\n", + "\n", + "#La vivienda es más cara en Valdemorillo." ] }, { @@ -653,12 +820,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "hourly-globe", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Promedio de precio por metro cuadrado en Valdemorillo: 1317.9502109024986.\n", + "Promedio de precio por metro cuadrado en Galapagar: 1606.3240303094024.\n", + "Los promedios de precio por metro cuadrado son diferentes.\n" + ] + } + ], "source": [ - "# TODO" + "# TODO\n", + "import numpy as np\n", + "\n", + "ds[\"pps\"] = ds[\"price\"] / ds[\"surface\"]\n", + "\n", + "valdemorillo = ds[ds[\"level5\"] == \"Valdemorillo\"]\n", + "galapagar = ds[ds[\"level5\"] == \"Galapagar\"]\n", + "\n", + "promedio_valdemorillo = valdemorillo[\"pps\"].mean()\n", + "promedio_galapagar = galapagar[\"pps\"].mean()\n", + "\n", + "print(f\"Promedio de precio por metro cuadrado en Valdemorillo: {promedio_valdemorillo}.\")\n", + "print(f\"Promedio de precio por metro cuadrado en Galapagar: {promedio_galapagar}.\")\n", + "\n", + "if promedio_valdemorillo == promedio_galapagar:\n", + " print(\"Los promedios de precios por metro cuadrado son iguales.\")\n", + "else:\n", + " print(\"Los promedios de precio por metro cuadrado son diferentes.\")" ] }, { @@ -674,12 +868,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "common-drilling", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# TODO: Código" + "# TODO: Código\n", + "import matplotlib.pyplot as plt\n", + "\n", + "ds_especifico = ds[ds[\"level5\"].isin([\"Valdemorillo\", \"Galapagar\"])]\n", + "\n", + "valdemorillo = ds_especifico[ds_especifico[\"level5\"] == \"Valdemorillo\"]\n", + "galapagar = ds_especifico[ds_especifico[\"level5\"] == \"Galapagar\"]\n", + "\n", + "plt.figure(figsize = (10, 6))\n", + "plt.scatter(valdemorillo[\"surface\"], valdemorillo[\"price\"], alpha=0.5, label = \"Valdemorillo\")\n", + "plt.scatter(galapagar[\"surface\"], galapagar[\"price\"], alpha=0.5, label = \"Galapagar\")\n", + "\n", + "plt.title(\"Relación Superficie / Precio en Valdemorillo y Galapagar\")\n", + "plt.xlabel(\"Superficie m^2\")\n", + "plt.ylabel(\"Precio €\")\n", + "\n", + "plt.legend()\n", + "plt.show()\n" ] }, { @@ -688,7 +910,7 @@ "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." + "En Valdemorillo, los precios aumentan con el incremento de superficie. Lo mismo ocurre en el caso de Galapagar." ] }, { @@ -704,12 +926,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "valid-honolulu", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "El número de agencias es de 168 agencias de bienes raíces.\n" + ] + } + ], "source": [ - "# TODO" + "agencias = ds['level5'].nunique()\n", + "\n", + "print(f\"El número de agencias es de {agencias} agencias de bienes raíces.\")" ] }, { @@ -725,12 +957,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "static-perry", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "La población con más casas es Madrid Capital con 6643.\n" + ] + } + ], "source": [ - "# TODO" + "# TODO\n", + "\n", + "poblacion_casas = ds[\"level5\"].value_counts()\n", + "\n", + "poblacion_mas = poblacion_casas.idxmax()\n", + "total_casas = poblacion_casas.max()\n", + "\n", + "print(f\"La población con más casas es {poblacion_mas} con {total_casas}.\")" ] }, { @@ -746,12 +993,403 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "binary-input", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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8586153152077Falsesinergical inmobiliariaNaNhttps://www.fotocasa.es/es/comprar/vivienda/le...1.01.050.0107000...0000140,35059-3,82693NaNNaN2140.000000
9495153995577Falseviviendas365com911226014.0https://www.fotocasa.es/es/comprar/vivienda/le...3.02.0120.0320000...0000040,31933-3,77574NaNNaN2666.666667
109110153586414Falsearea uno asesores inmobiliarios912664081.0https://www.fotocasa.es/es/comprar/vivienda/ma...3.03.0142.0425000...0000040,3313411-3,8313868NaNNaN2992.957746
..................................................................
1527515276153903887Falsealiseda servicios de gestion inmobiliaria911368198.0https://www.fotocasa.es/es/comprar/vivienda/al...3.01.078.0138000...0000140,31381-3,83733NaNNaN1769.230769
1529115292151697757Falseunipiso912788631.0https://www.fotocasa.es/es/comprar/vivienda/al...3.02.0110.0279000...0000040,3259051-3,76318NaNNaN2536.363636
1530515306153902389Falsejadein ferrero914871639.0https://www.fotocasa.es/es/comprar/vivienda/ma...3.02.085.0170000...0000040,2882193-3,8098617NaNNaN2000.000000
1532215323153871864Falsegestion comercial911220662.0https://www.fotocasa.es/es/comprar/vivienda/ma...3.01.091.0112000...0000040,28282-3,78892NaNNaN1230.769231
1532515326153901467Falsemontehogar 68911790675.0https://www.fotocasa.es/es/comprar/vivienda/ma...2.02.099.0215000...0000140,28062-3,79869NaNNaN2171.717172
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907 rows × 38 columns

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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 ... level5Id level6Id level7Id level8Id accuracy \\\n", + "1 NaN 89000 ... 0 0 0 0 1 \n", + "3 86.0 89000 ... 0 0 0 0 0 \n", + "85 50.0 107000 ... 0 0 0 0 1 \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 1 \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 1 \n", + "\n", + " latitude longitude zipCode customZone pps \n", + "1 40,28674 -3,79351 NaN NaN NaN \n", + "3 40,2853785786438 -3,79508142135624 NaN NaN 1034.883721 \n", + "85 40,35059 -3,82693 NaN NaN 2140.000000 \n", + "94 40,31933 -3,77574 NaN NaN 2666.666667 \n", + "109 40,3313411 -3,8313868 NaN NaN 2992.957746 \n", + "... ... ... ... ... ... \n", + "15275 40,31381 -3,83733 NaN NaN 1769.230769 \n", + "15291 40,3259051 -3,76318 NaN NaN 2536.363636 \n", + "15305 40,2882193 -3,8098617 NaN NaN 2000.000000 \n", + "15322 40,28282 -3,78892 NaN NaN 1230.769231 \n", + "15325 40,28062 -3,79869 NaN NaN 2171.717172 \n", + "\n", + "[907 rows x 38 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# TODO" + "# TODO\n", + "\n", + "sur = ds[ds[\"level5\"].isin([\"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\"])]\n", + "sur\n" ] }, { @@ -767,12 +1405,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "lyric-bunch", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# TODO: Code" + "# TODO: Code\n", + "\n", + "fuenla = np.median(sur[sur[\"level5\"] == \"Fuenlabrada\"][\"price\"])\n", + "lega = np.median(sur[sur[\"level5\"] == \"Leganés\"][\"price\"])\n", + "geta = np.median(sur[sur[\"level5\"] == \"Getafe\"][\"price\"])\n", + "alcor = np.median(sur[sur[\"level5\"] == \"Alcorcón\"][\"price\"])\n", + "\n", + "labels = [\"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\"]\n", + "values = [fuenla, lega, geta, alcor]\n", + "\n", + "plt.figure(figsize = (10, 5))\n", + "\n", + "plt.bar(labels, values)\n", + "plt.xlabel(\"Población\")\n", + "plt.ylabel(\"Mediana de Precio (€)\")\n", + "\n", + "plt.title(\"Mediana de precios en el cinturón sur de Madrid\")\n", + "plt.show()" ] }, { @@ -781,7 +1447,7 @@ "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." + "Se puede observar que la población más cara del cinturón sur de Madrid es Getafe, mientras que el resto de poblaciones están más a la par." ] }, { @@ -797,12 +1463,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "random-feeling", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variables: price\n", + "Media: 1.633221850613155\n", + "Varianza: 0.5717968625577321\n", + "Variables: rooms\n", + "Media: 1.633221850613155\n", + "Varianza: 0.5717968625577321\n", + "Variables: surface\n", + "Media: 1.633221850613155\n", + "Varianza: 0.5717968625577321\n", + "Variables: bathrooms\n", + "Media: 1.633221850613155\n", + "Varianza: 0.5717968625577321\n" + ] + } + ], "source": [ - "# TODO" + "# TODO\n", + "\n", + "variables = [\"price\", \"rooms\", \"surface\", \"bathrooms\"]\n", + "\n", + "for var in variables:\n", + " media = sur[var].mean()\n", + " varianza = sur[var].var()\n", + " estadísticas = {\"Media\": media, \"Varianza\": varianza}\n", + "\n", + "for var in variables:\n", + " print(f\"Variables: {var}\")\n", + " print(f\"Media: {media}\")\n", + " print(f\"Varianza: {varianza}\")" ] }, { @@ -818,12 +1515,199 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "fifteen-browse", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Propiedad con el precio más alto en Fuenlabrada:\n", + "Unnamed: 0 11562\n", + "id_realEstates 153660921\n", + "isNew False\n", + "realEstate_name unna madrid\n", + "phone_realEstate 912780846.0\n", + "url_inmueble https://www.fotocasa.es/es/comprar/vivienda/va...\n", + "rooms 4.0\n", + "bathrooms 3.0\n", + "surface 274.0\n", + "price 490000\n", + "date 2019-12-20T10:31:00Z\n", + "description INTERHABITAT vende impresionante chalet unifam...\n", + "address Calle de Paulo Freire, 5, Fuenlabrada\n", + "country España\n", + "level1 Madrid\n", + "level2 Madrid\n", + "level3 Zona Sur de Madrid\n", + "level4 Fuenlabrada, Zona de\n", + "level5 Fuenlabrada\n", + "level6 NaN\n", + "level7 Universidad - Hospital en Fuenlabrada\n", + "level8 NaN\n", + "upperLevel Universidad - Hospital en Fuenlabrada\n", + "countryId 0\n", + "level1Id 0\n", + "level2Id 0\n", + "level3Id 0\n", + "level4Id 0\n", + "level5Id 0\n", + "level6Id 0\n", + "level7Id 0\n", + "level8Id 0\n", + "accuracy 1\n", + "latitude 40,28286\n", + "longitude -3,81475\n", + "zipCode NaN\n", + "customZone NaN\n", + "pps 1788.321168\n", + "Name: 11561, dtype: object\n", + "\n", + "Propiedad con el precio más alto en Leganés:\n", + "Unnamed: 0 10413\n", + "id_realEstates 152251506\n", + "isNew False\n", + "realEstate_name bafre gestion y servicios inmobiliarios\n", + "phone_realEstate 910752245.0\n", + "url_inmueble https://www.fotocasa.es/es/comprar/vivienda/co...\n", + "rooms 6.0\n", + "bathrooms 5.0\n", + "surface 360.0\n", + "price 650000\n", + "date 2019-12-21T16:00:00Z\n", + "description Estupendo chalet pareado de lujo en venta en L...\n", + "address Avenida Reina Sofía, Leganés\n", + "country España\n", + "level1 Madrid\n", + "level2 Madrid\n", + "level3 Zona Sur de Madrid\n", + "level4 Leganés, Zona de\n", + "level5 Leganés\n", + "level6 NaN\n", + "level7 Leganés Norte\n", + "level8 NaN\n", + "upperLevel Leganés Norte\n", + "countryId 0\n", + "level1Id 0\n", + "level2Id 0\n", + "level3Id 0\n", + "level4Id 0\n", + "level5Id 0\n", + "level6Id 0\n", + "level7Id 0\n", + "level8Id 0\n", + "accuracy 0\n", + "latitude 40,3423636291504\n", + "longitude -3,74814414978027\n", + "zipCode NaN\n", + "customZone NaN\n", + "pps 1805.555556\n", + "Name: 10412, dtype: object\n", + "\n", + "Propiedad con el precio más alto en Getafe:\n", + "Unnamed: 0 2882\n", + "id_realEstates 151105544\n", + "isNew False\n", + "realEstate_name unna grupo inmobiliario\n", + "phone_realEstate 912665694.0\n", + "url_inmueble https://www.fotocasa.es/es/comprar/vivienda/ma...\n", + "rooms 6.0\n", + "bathrooms 4.0\n", + "surface 600.0\n", + "price 1050000\n", + "date 2019-12-27T15:59:00Z\n", + "description Edificio residencial, único en toda la zona su...\n", + "address Getafe\n", + "country España\n", + "level1 Madrid\n", + "level2 Madrid\n", + "level3 Zona Sur de Madrid\n", + "level4 Getafe, Zona de\n", + "level5 Getafe\n", + "level6 NaN\n", + "level7 San Isidro\n", + "level8 NaN\n", + "upperLevel San Isidro\n", + "countryId 0\n", + "level1Id 0\n", + "level2Id 0\n", + "level3Id 0\n", + "level4Id 0\n", + "level5Id 0\n", + "level6Id 0\n", + "level7Id 0\n", + "level8Id 0\n", + "accuracy 0\n", + "latitude 40,302820239187\n", + "longitude -3,7281704612835\n", + "zipCode NaN\n", + "customZone NaN\n", + "pps 1750.0\n", + "Name: 2881, dtype: object\n", + "\n", + "Propiedad con el precio más alto en Alcorcón:\n", + "Unnamed: 0 5586\n", + "id_realEstates 153275915\n", + "isNew False\n", + "realEstate_name 100 home red inmobiliaria\n", + "phone_realEstate 912669623.0\n", + "url_inmueble https://www.fotocasa.es/es/comprar/vivienda/ca...\n", + "rooms 6.0\n", + "bathrooms 6.0\n", + "surface 722.0\n", + "price 950000\n", + "date 2019-12-26T11:01:00Z\n", + "description 100% HOME RED INMOBILIARIA DE MOSTOLES VENDE M...\n", + "address Alcorcón\n", + "country España\n", + "level1 Madrid\n", + "level2 Madrid\n", + "level3 Zona Suroeste\n", + "level4 Alcorcón, Zona de\n", + "level5 Alcorcón\n", + "level6 NaN\n", + "level7 Campodón - Ventorro del Cano\n", + "level8 NaN\n", + "upperLevel Campodón - Ventorro del Cano\n", + "countryId 0\n", + "level1Id 0\n", + "level2Id 0\n", + "level3Id 0\n", + "level4Id 0\n", + "level5Id 0\n", + "level6Id 0\n", + "level7Id 0\n", + "level8Id 0\n", + "accuracy 0\n", + "latitude 40,3535169\n", + "longitude -3,8664683\n", + "zipCode NaN\n", + "customZone NaN\n", + "pps 1315.789474\n", + "Name: 5585, dtype: object\n" + ] + } + ], "source": [ - "# TODO" + "# TODO\n", + "\n", + "fuenla = sur[sur[\"level5\"] == \"Fuenlabrada\"][\"price\"].idxmax()\n", + "lega = sur[sur[\"level5\"] == \"Leganés\"][\"price\"].idxmax()\n", + "geta = sur[sur[\"level5\"] == \"Getafe\"][\"price\"].idxmax()\n", + "alcor = sur[sur[\"level5\"] == \"Alcorcón\"][\"price\"].idxmax()\n", + "\n", + "print(\"Propiedad con el precio más alto en Fuenlabrada:\")\n", + "print(sur.loc[fuenla])\n", + "\n", + "print(\"\\nPropiedad con el precio más alto en Leganés:\")\n", + "print(sur.loc[lega])\n", + "\n", + "print(\"\\nPropiedad con el precio más alto en Getafe:\")\n", + "print(sur.loc[geta])\n", + "\n", + "print(\"\\nPropiedad con el precio más alto en Alcorcón:\")\n", + "print(sur.loc[alcor])" ] }, { @@ -841,12 +1725,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "civic-meditation", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_10078/2568559061.py:3: 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", + " sur['precio_normalizado'] = sur.groupby(\"level5\")[\"price\"].transform(lambda x: (x - x.min()) / (x.max() - x.min()))\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# TODO" + "# TODO\n", + "\n", + "sur['precio_normalizado'] = sur.groupby(\"level5\")[\"price\"].transform(lambda x: (x - x.min()) / (x.max() - x.min()))\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "\n", + "for poblacion in sur[\"level5\"].unique():\n", + " subset = sur[sur['level5'] == poblacion]\n", + " plt.hist(subset[\"precio_normalizado\"], bins=10, alpha=0.5, label=poblacion)\n", + "\n", + "plt.xlabel('Precio Normalizado')\n", + "plt.ylabel('Frecuencia')\n", + "plt.title('Histogramas de Precios Normalizados por Población')\n", + "plt.legend(loc='upper right')\n", + "plt.grid(True)\n", + "plt.show()" ] }, { @@ -855,7 +1777,7 @@ "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." + "En Fuenlabrada y Getafe, los precios están alrededor de la media. En Leganés y Alcorcón, se muestra que hay un mayor variabilidad. " ] }, { @@ -871,12 +1793,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "initial-liverpool", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Promedio de precio por metro cuadrado en Getafe: 2066.314949251463.\n", + "Promedio de precio por metro cuadrado en Alcorcón: 2239.302480199618.\n", + "Los promedios de precio por metro cuadrado son diferentes.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_10078/1839169179.py:3: 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", + " sur[\"pps\"] = sur[\"price\"] / sur[\"surface\"]\n" + ] + } + ], "source": [ - "# TODO" + "# TODO\n", + "\n", + "sur[\"pps\"] = sur[\"price\"] / sur[\"surface\"]\n", + "\n", + "getafe = sur[sur[\"level5\"] == \"Getafe\"]\n", + "alcorcon = sur[sur[\"level5\"] == \"Alcorcón\"]\n", + "\n", + "promedio_getafe = getafe[\"pps\"].mean()\n", + "promedio_alcorcon = alcorcon[\"pps\"].mean()\n", + "\n", + "print(f\"Promedio de precio por metro cuadrado en Getafe: {promedio_getafe}.\")\n", + "print(f\"Promedio de precio por metro cuadrado en Alcorcón: {promedio_alcorcon}.\")\n", + "\n", + "if promedio_getafe == promedio_alcorcon:\n", + " print(\"Los promedios de precios por metro cuadrado son iguales.\")\n", + "else:\n", + " print(\"Los promedios de precio por metro cuadrado son diferentes.\")" ] }, { @@ -891,12 +1851,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "accepting-airfare", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# TODO" + "# TODO\n", + "\n", + "sur = ds[ds[\"level5\"].isin([\"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\"])]\n", + "\n", + "fig, axs = plt.subplots(2, 2, figsize=(15, 10))\n", + "\n", + "poblacion_subplot = {\n", + " \"Fuenlabrada\": (0, 0),\n", + " \"Leganés\": (0, 1),\n", + " \"Getafe\": (1, 0),\n", + " \"Alcorcón\": (1, 1)\n", + "}\n", + "\n", + "colores = {\n", + " \"Fuenlabrada\": \"red\",\n", + " \"Leganés\": \"blue\",\n", + " \"Getafe\": \"green\",\n", + " \"Alcorcón\": \"orange\"\n", + "}\n", + "\n", + "for poblacion, (row, col) in poblacion_subplot.items():\n", + " data_poblacion = sur[sur['level5'] == poblacion]\n", + " axs[row, col].scatter(data_poblacion['surface'], data_poblacion['price'], color=colores[poblacion])\n", + " axs[row, col].set_title(poblacion)\n", + " axs[row, col].set_xlabel('surface')\n", + " axs[row, col].set_ylabel('price')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -912,30 +1911,159 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "headed-privacy", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cad1b99070f74cd89374638ebcecc0db", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[40.3142, -3.4854], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zo…" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "from ipyleaflet import Map, basemaps\n", + "from ipyleaflet import Map, basemaps, Marker, LayerGroup, CircleMarker\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", + "\n", + "map = Map(center = (40.3142, -3.4854), zoom = 10, min_zoom = 1, max_zoom = 20, \n", + " basemaps=basemaps.OpenStreetMap.Mapnik)\n", + "\n", "map" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "present-mistress", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cb63430d7af84c5f8cd87c40c736b87d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[40.3142, -3.4854], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zo…" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ + "\n", "## Aquí: traza la coordenadas de los estados\n", "\n", - "## PON TU CÓDIGO AQUÍ:\n" + "from ipyleaflet import Map, basemaps, Marker, CircleMarker\n", + "\n", + "map = Map(center = (40.3142, -3.4854), zoom = 10, min_zoom = 1, max_zoom = 20, \n", + " basemaps=basemaps.OpenStreetMap.Mapnik)\n", + "\n", + "coordenadas = {\n", + " \"Fuenlabrada\": {\"latitude\": [], \"longitude\": []},\n", + " \"Leganés\": {\"latitude\": [], \"longitude\": []},\n", + " \"Getafe\": {\"latitude\": [], \"longitude\": []},\n", + " \"Alcorcón\": {\"latitude\": [], \"longitude\": []}\n", + "}\n", + "\n", + "for poblacion in coordenadas.keys():\n", + " df_poblacion = sur[sur['address'] == poblacion]\n", + " coordenadas[poblacion][\"latitude\"] = df_poblacion[\"latitude\"].tolist()\n", + " coordenadas[poblacion][\"longitude\"] = df_poblacion[\"longitude\"].tolist()\n", + "\n", + "\n", + "colores = {\n", + " \"Fuenlabrada\": 'red',\n", + " \"Leganés\": 'blue',\n", + " \"Getafe\": 'green',\n", + " \"Alcorcón\": 'pink'\n", + "}\n", + "\n", + "for poblacion, coords in coordenadas.items(): \n", + " latitudes = coords[\"latitude\"] \n", + " longitudes = coords[\"longitude\"] \n", + " color = colores[poblacion] \n", + "\n", + "for lat, lon in zip(latitudes, longitudes): \n", + " marker = CircleMarker(location=(lat, lon), color=color, fill_color=color, radius=5, fill_opacity=0.8) \n", + " map.add_layer(marker) \n", + "\n", + "for poblacion, coords in coordenadas.items():\n", + " for coord in coords:\n", + " marker = Marker(location=coord, title=poblacion)\n", + " map.add_layer(marker)\n", + "\n", + "map\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "36f3335d", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "de5d418c5a874a279e8c723ec350d508", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[40.3142, -3.4854], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zo…" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from ipyleaflet import Map, CircleMarker\n", + "\n", + "map = Map(center=(40.3142, -3.4854), zoom=10, min_zoom=1, max_zoom=20)\n", + "\n", + "coordenadas = {\n", + " \"Fuenlabrada\": {\"latitude\": [40.2833], \"longitude\": [-3.7944]},\n", + " \"Leganés\": {\"latitude\": [40.3272], \"longitude\": [-3.7635]},\n", + " \"Getafe\": {\"latitude\": [40.3083], \"longitude\": [-3.7328]},\n", + " \"Alcorcón\": {\"latitude\": [40.3462], \"longitude\": [-3.8278]}\n", + "}\n", + "\n", + "colores = {\n", + " \"Fuenlabrada\": 'red',\n", + " \"Leganés\": 'blue',\n", + " \"Getafe\": 'green',\n", + " \"Alcorcón\": 'pink'\n", + "}\n", + "\n", + "for poblacion, coords in coordenadas.items():\n", + " latitudes = coords[\"latitude\"]\n", + " longitudes = coords[\"longitude\"]\n", + " color = colores[poblacion]\n", + " \n", + " for lat, lon in zip(latitudes, longitudes):\n", + " \n", + " marker = CircleMarker(location=(lat, lon), color=color, fill_color=color, \n", + " radius=5, fill_opacity=0.8)\n", + " map.add_layer(marker)\n", + "\n", + "map" ] } ], @@ -955,7 +2083,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.11.4" } }, "nbformat": 4,