precio_medio_dietas_especiales.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b5bf3def-7f4d-46bf-9ac5-0e17c5b25f07",
   "metadata": {},
   "source": [
    "### Precio Medio por dieta especial Madrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b57fab09-3dac-47cb-8fcd-b3f509de1e35",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dad444a9-0bd3-423d-82fa-28b7dc3ba739",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('datos_tripadvisor_madrid_limpio.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "48f462c3-e0aa-4940-afa4-55178c649cab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "dietas_especiales = ['Halal', 'Kosher', 'Opciones sin gluten', 'Opciones veganas', 'Opciones vegetarianas']\n",
    "precios_medios_dieta = {}\n",
    "\n",
    "for dieta in dietas_especiales:\n",
    "    seleccion = df[(df[dieta] == True) & (df['precio promedio'].notnull())]\n",
    "    precios_medios_dieta[dieta] = seleccion['precio promedio'].mean()\n",
    "\n",
    "#gráfica de barras con estos precios medios.\n",
    "nombres_dieta = list(precios_medios_dieta.keys())\n",
    "valores_precio = list(precios_medios_dieta.values())\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3f4f5fc2-1d28-431f-81ff-b61e88375a25",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4],\n",
       " [Text(0, 0, 'Halal'),\n",
       "  Text(1, 0, 'Kosher'),\n",
       "  Text(2, 0, 'Opciones sin gluten'),\n",
       "  Text(3, 0, 'Opciones veganas'),\n",
       "  Text(4, 0, 'Opciones vegetarianas')])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5)) \n",
    "plt.bar(nombres_dieta, valores_precio, color='skyblue') \n",
    "plt.xlabel('Tipo de Dieta') \n",
    "plt.ylabel('Precio Promedio') \n",
    "plt.title('Precio Promedio por Tipo de Dieta Especial') \n",
    "plt.xticks(rotation=45) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f9321acd-fdd6-477e-b5c7-a35823685624",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1487.000000\n",
      "mean       21.236046\n",
      "std        20.462225\n",
      "min         2.000000\n",
      "25%        11.000000\n",
      "50%        17.000000\n",
      "75%        25.000000\n",
      "max       509.000000\n",
      "Name: precio promedio, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Obtener estadísticas descriptivas de la columna 'Precio Promedio'\n",
    "estadisticas_descriptivas = df['precio promedio'].describe()\n",
    "\n",
    "print(estadisticas_descriptivas)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e699c52c-e841-476b-b232-b9eb1649fbf9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d2e098ed-b461-42b0-9556-18fc26fe4b45",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estadísticas para la dieta Halal:\n",
      "count    0.0\n",
      "mean     NaN\n",
      "std      NaN\n",
      "min      NaN\n",
      "25%      NaN\n",
      "50%      NaN\n",
      "75%      NaN\n",
      "max      NaN\n",
      "Name: precio promedio, dtype: float64\n",
      "\n",
      "\n",
      "Estadísticas para la dieta Kosher:\n",
      "count    0.0\n",
      "mean     NaN\n",
      "std      NaN\n",
      "min      NaN\n",
      "25%      NaN\n",
      "50%      NaN\n",
      "75%      NaN\n",
      "max      NaN\n",
      "Name: precio promedio, dtype: float64\n",
      "\n",
      "\n",
      "Estadísticas para la dieta Opciones sin gluten:\n",
      "count    366.000000\n",
      "mean      26.702186\n",
      "std       17.625211\n",
      "min        3.000000\n",
      "25%       15.000000\n",
      "50%       22.000000\n",
      "75%       31.000000\n",
      "max      130.000000\n",
      "Name: precio promedio, dtype: float64\n",
      "\n",
      "\n",
      "Estadísticas para la dieta Opciones veganas:\n",
      "count    254.000000\n",
      "mean      21.000000\n",
      "std       14.683485\n",
      "min        3.000000\n",
      "25%       12.000000\n",
      "50%       18.000000\n",
      "75%       25.000000\n",
      "max      105.000000\n",
      "Name: precio promedio, dtype: float64\n",
      "\n",
      "\n",
      "Estadísticas para la dieta Opciones vegetarianas:\n",
      "count    431.000000\n",
      "mean      22.111369\n",
      "std       15.390019\n",
      "min        3.000000\n",
      "25%       12.000000\n",
      "50%       19.000000\n",
      "75%       27.000000\n",
      "max      105.000000\n",
      "Name: precio promedio, dtype: float64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Obtener estadísticas descriptivas de la columna 'Precio promedio' para cada categoría de dieta especial\n",
    "for dieta in dietas_especiales:\n",
    "    estadisticas = df[df[dieta] == True]['precio promedio'].describe()\n",
    "    print(f\"Estadísticas para la dieta {dieta}:\")\n",
    "    print(estadisticas)\n",
    "    print(\"\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3da849d-82d9-46aa-a6a9-708d861abcea",
   "metadata": {},
   "source": [
    "Los precios promedio para las dietas sin gluten, veganas y vegetarianas varían de 21 a 27, con medias que indican que las opciones sin gluten tienden a ser ligeramente más caras.\n",
    "Los datos generales muestran un precio promedio ligeramente más bajo de aproximadamente 21, con una mediana de 17, lo que sugiere que hay muchas opciones económicas disponibles."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9acff2f6-c918-4155-b0a6-b50afc061c06",
   "metadata": {},
   "source": [
    "## Precio Medio por dieta especial Galicia\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "19965e2b-855b-4dd8-a5ae-1c622a10d22e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('datos_tripadvisor_galicia_limpio.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4a884248-6bbb-4563-8813-9b54261ff845",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dietas_especiales = ['Halal', 'Kosher', 'Opciones sin gluten', 'Opciones veganas', 'Opciones vegetarianas']\n",
    "precios_medios_dieta = {}\n",
    "\n",
    "for dieta in dietas_especiales:\n",
    "    seleccion = df[(df[dieta] == True) & (df['precio promedio'].notnull())]\n",
    "    precios_medios_dieta[dieta] = seleccion['precio promedio'].mean()\n",
    "\n",
    "# gráfica de barras con estos precios medios.\n",
    "nombres_dieta = list(precios_medios_dieta.keys())\n",
    "valores_precio = list(precios_medios_dieta.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "62f5baaf-50df-4aa9-b0a1-253b08855b79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4],\n",
       " [Text(0, 0, 'Halal'),\n",
       "  Text(1, 0, 'Kosher'),\n",
       "  Text(2, 0, 'Opciones sin gluten'),\n",
       "  Text(3, 0, 'Opciones veganas'),\n",
       "  Text(4, 0, 'Opciones vegetarianas')])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5)) \n",
    "plt.bar(nombres_dieta, valores_precio, color='skyblue') \n",
    "plt.xlabel('Tipo de Dieta') \n",
    "plt.ylabel('Precio Promedio') \n",
    "plt.title('Precio Promedio por Tipo de Dieta Especial') \n",
    "plt.xticks(rotation=45) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4401070f-ac4a-4daa-b4d4-9e770686a2d5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1487.000000\n",
      "mean       21.236046\n",
      "std        20.462225\n",
      "min         2.000000\n",
      "25%        11.000000\n",
      "50%        17.000000\n",
      "75%        25.000000\n",
      "max       509.000000\n",
      "Name: precio promedio, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Obtener estadísticas descriptivas de la columna 'Precio promedio'\n",
    "estadisticas_descriptivas = df['precio promedio'].describe()\n",
    "\n",
    "print(estadisticas_descriptivas)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7198d2a4-2943-4d01-8a6f-096b7bb5dbe9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estadísticas para la dieta Halal:\n",
      "count    0.0\n",
      "mean     NaN\n",
      "std      NaN\n",
      "min      NaN\n",
      "25%      NaN\n",
      "50%      NaN\n",
      "75%      NaN\n",
      "max      NaN\n",
      "Name: precio promedio, dtype: float64\n",
      "\n",
      "\n",
      "Estadísticas para la dieta Kosher:\n",
      "count    0.0\n",
      "mean     NaN\n",
      "std      NaN\n",
      "min      NaN\n",
      "25%      NaN\n",
      "50%      NaN\n",
      "75%      NaN\n",
      "max      NaN\n",
      "Name: precio promedio, dtype: float64\n",
      "\n",
      "\n",
      "Estadísticas para la dieta Opciones sin gluten:\n",
      "count    366.000000\n",
      "mean      26.702186\n",
      "std       17.625211\n",
      "min        3.000000\n",
      "25%       15.000000\n",
      "50%       22.000000\n",
      "75%       31.000000\n",
      "max      130.000000\n",
      "Name: precio promedio, dtype: float64\n",
      "\n",
      "\n",
      "Estadísticas para la dieta Opciones veganas:\n",
      "count    254.000000\n",
      "mean      21.000000\n",
      "std       14.683485\n",
      "min        3.000000\n",
      "25%       12.000000\n",
      "50%       18.000000\n",
      "75%       25.000000\n",
      "max      105.000000\n",
      "Name: precio promedio, dtype: float64\n",
      "\n",
      "\n",
      "Estadísticas para la dieta Opciones vegetarianas:\n",
      "count    431.000000\n",
      "mean      22.111369\n",
      "std       15.390019\n",
      "min        3.000000\n",
      "25%       12.000000\n",
      "50%       19.000000\n",
      "75%       27.000000\n",
      "max      105.000000\n",
      "Name: precio promedio, dtype: float64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Obtener estadísticas descriptivas de la columna 'Precio promedio' para cada categoría de dieta especial\n",
    "for dieta in dietas_especiales:\n",
    "    estadisticas = df[df[dieta] == True]['precio promedio'].describe()\n",
    "    print(f\"Estadísticas para la dieta {dieta}:\")\n",
    "    print(estadisticas)\n",
    "    print(\"\\n\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a29f7f72-35de-4f96-a40c-18c67f1f4407",
   "metadata": {},
   "source": [
    "Las opciones de alimentos sin gluten tienden a ser ligeramente más caras, mientras que las opciones veganas y vegetarianas están más en línea con el promedio general."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04b916d5-76db-4268-89b0-f7a8036f10b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5c42f8f0-989d-44f4-ad17-e1d9a9616f9c",
   "metadata": {},
   "source": [
    "Conclusión:\n",
    "Las opciones de restaurantes en Madrid tienden a ser más costosas en promedio y presentan una mayor variabilidad en los precios para las dietas especializadas comparadas con Galicia. Esto podría deberse a una mayor demanda y disponibilidad de opciones especializadas en Madrid."
   ]
  }
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