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fn-registry agent ea833af54c chore: initial sync
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Turismo Extranjero en Ciudades de Espana\n",
"\n",
"Analisis de la Encuesta de Ocupacion Hotelera (INE) — tabla 2078.\n",
"\n",
"**Objetivo:** Identificar que ciudades de Espana tienen mayor afluencia de turismo extranjero y como ha evolucionado."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Vault: /home/lucas/fn_registry/vaults/turismo_spain\n",
"Archivos: [PosixPath('/home/lucas/fn_registry/vaults/turismo_spain/raw/turismo-espana-2025'), PosixPath('/home/lucas/fn_registry/vaults/turismo_spain/raw/turismo-espana-2025.zip'), PosixPath('/home/lucas/fn_registry/vaults/turismo_spain/raw/ine_2074_viajeros_provincias.csv'), PosixPath('/home/lucas/fn_registry/vaults/turismo_spain/raw/ine_2078_viajeros_puntos_turisticos.csv'), PosixPath('/home/lucas/fn_registry/vaults/turismo_spain/raw/turismo-espana-2025.zip:Zone.Identifier')]\n"
]
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"from pathlib import Path\n",
"import os\n",
"\n",
"VAULT = Path(os.environ.get('FN_REGISTRY_ROOT', '../..')) / 'vaults' / 'turismo_spain'\n",
"RAW = VAULT / 'raw'\n",
"print(f'Vault: {VAULT}')\n",
"print(f'Archivos: {list(RAW.glob(\"*\"))}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Carga de datos INE — Tabla 2078 (Viajeros por puntos turisticos)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape: (140208, 5)\n",
"Columnas: ['Puntos turísticos', 'Viajeros y pernoctaciones', 'Residencia', 'Periodo', 'Total']\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Puntos turísticos</th>\n",
" <th>Viajeros y pernoctaciones</th>\n",
" <th>Residencia</th>\n",
" <th>Periodo</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en España</td>\n",
" <td>2026M02</td>\n",
" <td>18.573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en España</td>\n",
" <td>2026M01</td>\n",
" <td>18.376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en España</td>\n",
" <td>2025M12</td>\n",
" <td>23.737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en España</td>\n",
" <td>2025M11</td>\n",
" <td>21.544</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en España</td>\n",
" <td>2025M10</td>\n",
" <td>24.269</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Puntos turísticos Viajeros y pernoctaciones Residencia \\\n",
"0 01059 Vitoria-Gasteiz Viajero Residentes en España \n",
"1 01059 Vitoria-Gasteiz Viajero Residentes en España \n",
"2 01059 Vitoria-Gasteiz Viajero Residentes en España \n",
"3 01059 Vitoria-Gasteiz Viajero Residentes en España \n",
"4 01059 Vitoria-Gasteiz Viajero Residentes en España \n",
"\n",
" Periodo Total \n",
"0 2026M02 18.573 \n",
"1 2026M01 18.376 \n",
"2 2025M12 23.737 \n",
"3 2025M11 21.544 \n",
"4 2025M10 24.269 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\n",
" RAW / 'ine_2078_viajeros_puntos_turisticos.csv',\n",
" sep=';',\n",
" encoding='utf-8-sig'\n",
")\n",
"print(f'Shape: {df.shape}')\n",
"print(f'Columnas: {list(df.columns)}')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ciudades unicas: 138\n",
"Rango temporal: 2005-01-01 00:00:00 — 2026-02-01 00:00:00\n",
"Valores nulos en Total: 44788\n"
]
},
{
"data": {
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Puntos turísticos</th>\n",
" <th>Viajeros y pernoctaciones</th>\n",
" <th>Residencia</th>\n",
" <th>Periodo</th>\n",
" <th>Total</th>\n",
" <th>cod_municipio</th>\n",
" <th>ciudad</th>\n",
" <th>fecha</th>\n",
" <th>anio</th>\n",
" <th>mes</th>\n",
" <th>valor</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en España</td>\n",
" <td>2026M02</td>\n",
" <td>18.573</td>\n",
" <td>01059</td>\n",
" <td>Vitoria-Gasteiz</td>\n",
" <td>2026-02-01</td>\n",
" <td>2026</td>\n",
" <td>2</td>\n",
" <td>18573.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en España</td>\n",
" <td>2026M01</td>\n",
" <td>18.376</td>\n",
" <td>01059</td>\n",
" <td>Vitoria-Gasteiz</td>\n",
" <td>2026-01-01</td>\n",
" <td>2026</td>\n",
" <td>1</td>\n",
" <td>18376.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en España</td>\n",
" <td>2025M12</td>\n",
" <td>23.737</td>\n",
" <td>01059</td>\n",
" <td>Vitoria-Gasteiz</td>\n",
" <td>2025-12-01</td>\n",
" <td>2025</td>\n",
" <td>12</td>\n",
" <td>23737.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en España</td>\n",
" <td>2025M11</td>\n",
" <td>21.544</td>\n",
" <td>01059</td>\n",
" <td>Vitoria-Gasteiz</td>\n",
" <td>2025-11-01</td>\n",
" <td>2025</td>\n",
" <td>11</td>\n",
" <td>21544.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en España</td>\n",
" <td>2025M10</td>\n",
" <td>24.269</td>\n",
" <td>01059</td>\n",
" <td>Vitoria-Gasteiz</td>\n",
" <td>2025-10-01</td>\n",
" <td>2025</td>\n",
" <td>10</td>\n",
" <td>24269.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Puntos turísticos Viajeros y pernoctaciones Residencia \\\n",
"0 01059 Vitoria-Gasteiz Viajero Residentes en España \n",
"1 01059 Vitoria-Gasteiz Viajero Residentes en España \n",
"2 01059 Vitoria-Gasteiz Viajero Residentes en España \n",
"3 01059 Vitoria-Gasteiz Viajero Residentes en España \n",
"4 01059 Vitoria-Gasteiz Viajero Residentes en España \n",
"\n",
" Periodo Total cod_municipio ciudad fecha anio mes \\\n",
"0 2026M02 18.573 01059 Vitoria-Gasteiz 2026-02-01 2026 2 \n",
"1 2026M01 18.376 01059 Vitoria-Gasteiz 2026-01-01 2026 1 \n",
"2 2025M12 23.737 01059 Vitoria-Gasteiz 2025-12-01 2025 12 \n",
"3 2025M11 21.544 01059 Vitoria-Gasteiz 2025-11-01 2025 11 \n",
"4 2025M10 24.269 01059 Vitoria-Gasteiz 2025-10-01 2025 10 \n",
"\n",
" valor \n",
"0 18573.0 \n",
"1 18376.0 \n",
"2 23737.0 \n",
"3 21544.0 \n",
"4 24269.0 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Limpiar datos\n",
"# Separar codigo municipal y nombre de ciudad\n",
"df[['cod_municipio', 'ciudad']] = df['Puntos turísticos'].str.extract(r'^(\\d+)\\s+(.*)')\n",
"\n",
"# Parsear periodo a fecha\n",
"df['fecha'] = pd.to_datetime(df['Periodo'].str.replace('M', '-'), format='%Y-%m')\n",
"df['anio'] = df['fecha'].dt.year\n",
"df['mes'] = df['fecha'].dt.month\n",
"\n",
"# Limpiar columna Total (quitar puntos de miles, convertir a numerico)\n",
"df['valor'] = pd.to_numeric(df['Total'].str.replace('.', '', regex=False), errors='coerce')\n",
"\n",
"print(f'Ciudades unicas: {df[\"ciudad\"].nunique()}')\n",
"print(f'Rango temporal: {df[\"fecha\"].min()} — {df[\"fecha\"].max()}')\n",
"print(f'Valores nulos en Total: {df[\"valor\"].isna().sum()}')\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Filtrar turismo extranjero — solo viajeros (no pernoctaciones)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Registros de viajeros extranjeros: 35052\n",
"Ciudades: 138\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Puntos turísticos</th>\n",
" <th>Viajeros y pernoctaciones</th>\n",
" <th>Residencia</th>\n",
" <th>Periodo</th>\n",
" <th>Total</th>\n",
" <th>cod_municipio</th>\n",
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" <th>fecha</th>\n",
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" <th>mes</th>\n",
" <th>valor</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>254</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en el Extranjero</td>\n",
" <td>2026M02</td>\n",
" <td>5.059</td>\n",
" <td>01059</td>\n",
" <td>Vitoria-Gasteiz</td>\n",
" <td>2026-02-01</td>\n",
" <td>2026</td>\n",
" <td>2</td>\n",
" <td>5059.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>255</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en el Extranjero</td>\n",
" <td>2026M01</td>\n",
" <td>5.931</td>\n",
" <td>01059</td>\n",
" <td>Vitoria-Gasteiz</td>\n",
" <td>2026-01-01</td>\n",
" <td>2026</td>\n",
" <td>1</td>\n",
" <td>5931.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>256</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en el Extranjero</td>\n",
" <td>2025M12</td>\n",
" <td>6.265</td>\n",
" <td>01059</td>\n",
" <td>Vitoria-Gasteiz</td>\n",
" <td>2025-12-01</td>\n",
" <td>2025</td>\n",
" <td>12</td>\n",
" <td>6265.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>257</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en el Extranjero</td>\n",
" <td>2025M11</td>\n",
" <td>6.114</td>\n",
" <td>01059</td>\n",
" <td>Vitoria-Gasteiz</td>\n",
" <td>2025-11-01</td>\n",
" <td>2025</td>\n",
" <td>11</td>\n",
" <td>6114.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>258</th>\n",
" <td>01059 Vitoria-Gasteiz</td>\n",
" <td>Viajero</td>\n",
" <td>Residentes en el Extranjero</td>\n",
" <td>2025M10</td>\n",
" <td>10.308</td>\n",
" <td>01059</td>\n",
" <td>Vitoria-Gasteiz</td>\n",
" <td>2025-10-01</td>\n",
" <td>2025</td>\n",
" <td>10</td>\n",
" <td>10308.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Puntos turísticos Viajeros y pernoctaciones \\\n",
"254 01059 Vitoria-Gasteiz Viajero \n",
"255 01059 Vitoria-Gasteiz Viajero \n",
"256 01059 Vitoria-Gasteiz Viajero \n",
"257 01059 Vitoria-Gasteiz Viajero \n",
"258 01059 Vitoria-Gasteiz Viajero \n",
"\n",
" Residencia Periodo Total cod_municipio \\\n",
"254 Residentes en el Extranjero 2026M02 5.059 01059 \n",
"255 Residentes en el Extranjero 2026M01 5.931 01059 \n",
"256 Residentes en el Extranjero 2025M12 6.265 01059 \n",
"257 Residentes en el Extranjero 2025M11 6.114 01059 \n",
"258 Residentes en el Extranjero 2025M10 10.308 01059 \n",
"\n",
" ciudad fecha anio mes valor \n",
"254 Vitoria-Gasteiz 2026-02-01 2026 2 5059.0 \n",
"255 Vitoria-Gasteiz 2026-01-01 2026 1 5931.0 \n",
"256 Vitoria-Gasteiz 2025-12-01 2025 12 6265.0 \n",
"257 Vitoria-Gasteiz 2025-11-01 2025 11 6114.0 \n",
"258 Vitoria-Gasteiz 2025-10-01 2025 10 10308.0 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Filtrar: solo viajeros extranjeros\n",
"extranjeros = df[\n",
" (df['Residencia'] == 'Residentes en el Extranjero') &\n",
" (df['Viajeros y pernoctaciones'] == 'Viajero')\n",
"].copy()\n",
"\n",
"print(f'Registros de viajeros extranjeros: {len(extranjeros)}')\n",
"print(f'Ciudades: {extranjeros[\"ciudad\"].nunique()}')\n",
"extranjeros.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Top 20 ciudades con mas turismo extranjero (ultimo anio completo)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Analizando anio: 2025\n",
"\n",
"Top 20 ciudades — Viajeros extranjeros 2025:\n",
" 1. Barcelona 7,449,646\n",
" 2. Madrid 6,107,041\n",
" 3. Palma 2,324,397\n",
" 4. Sevilla 1,958,368\n",
" 5. Calvià 1,647,583\n",
" 6. San Bartolomé de Tirajana 1,487,692\n",
" 7. Adeje 1,436,865\n",
" 8. Benidorm 1,423,238\n",
" 9. València 1,195,690\n",
" 10. Málaga 1,165,168\n",
" 11. Granada 1,030,915\n",
" 12. Lloret de Mar 890,699\n",
" 13. Pájara 853,842\n",
" 14. Arona 823,909\n",
" 15. Torremolinos 782,406\n",
" 16. Salou 750,134\n",
" 17. Donostia/San Sebastián 643,984\n",
" 18. Bilbao 637,721\n",
" 19. Yaiza 617,982\n",
" 20. Mogán 607,679\n"
]
}
],
"source": [
"# Ultimo anio completo\n",
"ultimo_anio = extranjeros[extranjeros['anio'] == extranjeros['anio'].max() - 1]['anio'].iloc[0] if len(extranjeros) > 0 else 2025\n",
"print(f'Analizando anio: {ultimo_anio}')\n",
"\n",
"# Agregar por ciudad, anio completo\n",
"top_ciudades = (\n",
" extranjeros[extranjeros['anio'] == ultimo_anio]\n",
" .groupby('ciudad')['valor']\n",
" .sum()\n",
" .sort_values(ascending=False)\n",
" .head(20)\n",
")\n",
"\n",
"print(f'\\nTop 20 ciudades — Viajeros extranjeros {ultimo_anio}:')\n",
"for i, (ciudad, total) in enumerate(top_ciudades.items(), 1):\n",
" print(f' {i:2d}. {ciudad:30s} {total:>12,.0f}')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "Viajeros extranjeros=%{x}<br>Ciudad=%{y}<br>color=%{marker.color}<extra></extra>",
"legendgroup": "",
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"coloraxis": "coloraxis",
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"showlegend": false,
"textposition": "auto",
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"dtype": "f8"
},
"xaxis": "x",
"y": [
"Barcelona",
"Madrid",
"Palma",
"Sevilla",
"Calvià",
"San Bartolomé de Tirajana",
"Adeje",
"Benidorm",
"València",
"Málaga",
"Granada",
"Lloret de Mar",
"Pájara",
"Arona",
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},
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"source": [
"# Grafico de barras horizontal — Top 20\n",
"fig = px.bar(\n",
" x=top_ciudades.values,\n",
" y=top_ciudades.index,\n",
" orientation='h',\n",
" title=f'Top 20 ciudades de Espana por turismo extranjero ({ultimo_anio})',\n",
" labels={'x': 'Viajeros extranjeros', 'y': 'Ciudad'},\n",
" color=top_ciudades.values,\n",
" color_continuous_scale='Viridis'\n",
")\n",
"fig.update_layout(\n",
" yaxis={'categoryorder': 'total ascending'},\n",
" height=600,\n",
" showlegend=False,\n",
" coloraxis_showscale=False\n",
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Evolucion mensual — Top 10 ciudades"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
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"source": [
"# Top 10 ciudades por volumen total reciente\n",
"top10_nombres = top_ciudades.head(10).index.tolist()\n",
"\n",
"# Filtrar datos recientes (ultimos 3 anios)\n",
"reciente = extranjeros[\n",
" (extranjeros['ciudad'].isin(top10_nombres)) &\n",
" (extranjeros['anio'] >= ultimo_anio - 2)\n",
"].copy()\n",
"\n",
"fig = px.line(\n",
" reciente.sort_values('fecha'),\n",
" x='fecha',\n",
" y='valor',\n",
" color='ciudad',\n",
" title=f'Evolucion mensual de turismo extranjero — Top 10 ciudades ({ultimo_anio-2}-{ultimo_anio})',\n",
" labels={'valor': 'Viajeros extranjeros', 'fecha': 'Mes', 'ciudad': 'Ciudad'},\n",
")\n",
"fig.update_layout(height=500)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Ratio extranjeros vs nacionales por ciudad"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
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"name": "stdout",
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"text": [
"Ciudades con mayor % de turismo extranjero (2025):\n",
" 1. Viveiro 94.7% ( 30,737 ext / 32,449 total)\n",
" 2. Vall de Boí, La 94.1% ( 45,275 ext / 48,133 total)\n",
" 3. Cazorla 92.6% ( 37,728 ext / 40,744 total)\n",
" 4. Sallent de Gállego 91.1% ( 76,995 ext / 84,552 total)\n",
" 5. Carboneras 90.9% ( 25,962 ext / 28,556 total)\n",
" 6. Soria 90.8% ( 125,315 ext / 138,061 total)\n",
" 7. Sigüenza 90.1% ( 33,895 ext / 37,602 total)\n",
" 8. Benasque 88.5% ( 70,763 ext / 79,960 total)\n",
" 9. Plasencia 88.2% ( 76,453 ext / 86,652 total)\n",
" 10. Palencia 88.0% ( 90,303 ext / 102,610 total)\n",
" 11. Albarracín 87.6% ( 44,121 ext / 50,377 total)\n",
" 12. Benavente 87.5% ( 23,035 ext / 26,335 total)\n",
" 13. Cuenca 86.3% ( 155,294 ext / 179,884 total)\n",
" 14. Jaca 86.1% ( 150,658 ext / 175,036 total)\n",
" 15. Ciudad Real 85.9% ( 144,047 ext / 167,656 total)\n",
" 16. Zamora 85.6% ( 99,415 ext / 116,159 total)\n",
" 17. Albacete 85.4% ( 190,983 ext / 223,756 total)\n",
" 18. Grove, O 85.4% ( 91,753 ext / 107,471 total)\n",
" 19. Úbeda 85.3% ( 69,094 ext / 81,030 total)\n",
" 20. Puerto de Santa María, El 85.2% ( 174,458 ext / 204,761 total)\n"
]
}
],
"source": [
"# Comparar viajeros nacionales vs extranjeros por ciudad\n",
"viajeros = df[\n",
" (df['Viajeros y pernoctaciones'] == 'Viajero') &\n",
" (df['anio'] == ultimo_anio)\n",
"].copy()\n",
"\n",
"pivot = viajeros.pivot_table(\n",
" index='ciudad',\n",
" columns='Residencia',\n",
" values='valor',\n",
" aggfunc='sum'\n",
").fillna(0)\n",
"\n",
"pivot.columns = ['extranjeros', 'nacionales']\n",
"pivot['total'] = pivot['extranjeros'] + pivot['nacionales']\n",
"pivot['pct_extranjeros'] = (pivot['extranjeros'] / pivot['total'] * 100).round(1)\n",
"pivot = pivot.sort_values('pct_extranjeros', ascending=False)\n",
"\n",
"print(f'Ciudades con mayor % de turismo extranjero ({ultimo_anio}):')\n",
"for i, (ciudad, row) in enumerate(pivot.head(20).iterrows(), 1):\n",
" print(f' {i:2d}. {ciudad:30s} {row[\"pct_extranjeros\"]:5.1f}% ({row[\"extranjeros\"]:>10,.0f} ext / {row[\"total\"]:>10,.0f} total)')"
]
},
{
"cell_type": "code",
"execution_count": 9,
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0,
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"pivot_top = pivot[pivot['total'] > 50000].copy() # filtrar ciudades con volumen minimo\n",
"\n",
"fig = px.scatter(\n",
" pivot_top.reset_index(),\n",
" x='total',\n",
" y='pct_extranjeros',\n",
" text='ciudad',\n",
" size='extranjeros',\n",
" title=f'Volumen total vs % turismo extranjero por ciudad ({ultimo_anio})',\n",
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"fig.update_layout(height=600)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Crecimiento interanual — quien crece mas rapido"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 15 ciudades con mayor crecimiento de turismo extranjero (2024 -> 2025):\n",
" 1. Algeciras + 34.6% ( 94,487 -> 127,134)\n",
" 2. Badajoz + 30.1% ( 47,990 -> 62,419)\n",
" 3. Ciudad Rodrigo + 29.8% ( 12,616 -> 16,377)\n",
" 4. Monachil + 27.7% ( 32,057 -> 40,924)\n",
" 5. Trujillo + 25.3% ( 10,051 -> 12,596)\n",
" 6. Mérida + 24.7% ( 53,678 -> 66,923)\n",
" 7. Murcia + 24.0% ( 107,906 -> 133,812)\n",
" 8. Guadalajara + 23.9% ( 19,523 -> 24,193)\n",
" 9. Altea + 23.7% ( 45,731 -> 56,554)\n",
" 10. Albacete + 20.2% ( 27,258 -> 32,773)\n",
" 11. Pontevedra + 19.2% ( 67,532 -> 80,478)\n",
" 12. Coruña, A + 18.5% ( 105,402 -> 124,924)\n",
" 13. Cangas de Onís + 18.1% ( 24,027 -> 28,378)\n",
" 14. Ciudad Real + 17.7% ( 20,052 -> 23,609)\n",
" 15. Málaga + 17.1% ( 995,289 -> 1,165,168)\n",
"\n",
"Top 10 ciudades con mayor caida:\n",
" 1. Huelva -100.0% ( 26,630 -> 0)\n",
" 2. Jaén -100.0% ( 16,452 -> 0)\n",
" 3. Villaviciosa -100.0% ( 13,655 -> 0)\n",
" 4. Ejido, El -100.0% ( 19,820 -> 0)\n",
" 5. Arcos de la Frontera -87.1% ( 16,866 -> 2,177)\n",
" 6. Huesca -49.4% ( 21,476 -> 10,864)\n",
" 7. Úbeda -22.8% ( 15,467 -> 11,936)\n",
" 8. Sóller -18.3% ( 139,480 -> 114,008)\n",
" 9. Palencia -17.8% ( 14,964 -> 12,307)\n",
" 10. Cartagena -17.3% ( 85,415 -> 70,612)\n"
]
}
],
"source": [
"# Crecimiento interanual\n",
"anual = (\n",
" extranjeros\n",
" .groupby(['ciudad', 'anio'])['valor']\n",
" .sum()\n",
" .reset_index()\n",
")\n",
"\n",
"# Pivotear para calcular crecimiento\n",
"anual_pivot = anual.pivot(index='ciudad', columns='anio', values='valor')\n",
"\n",
"# Crecimiento ultimo anio vs anterior\n",
"anio_prev = ultimo_anio - 1\n",
"if anio_prev in anual_pivot.columns and ultimo_anio in anual_pivot.columns:\n",
" crecimiento = pd.DataFrame({\n",
" 'anterior': anual_pivot[anio_prev],\n",
" 'actual': anual_pivot[ultimo_anio],\n",
" }).dropna()\n",
" crecimiento['cambio_pct'] = ((crecimiento['actual'] / crecimiento['anterior']) - 1) * 100\n",
" crecimiento['cambio_abs'] = crecimiento['actual'] - crecimiento['anterior']\n",
" \n",
" # Top crecimiento (ciudades con volumen minimo)\n",
" crece_filtrado = crecimiento[crecimiento['anterior'] > 10000].sort_values('cambio_pct', ascending=False)\n",
" \n",
" print(f'Top 15 ciudades con mayor crecimiento de turismo extranjero ({anio_prev} -> {ultimo_anio}):')\n",
" for i, (ciudad, row) in enumerate(crece_filtrado.head(15).iterrows(), 1):\n",
" signo = '+' if row['cambio_pct'] > 0 else ''\n",
" print(f' {i:2d}. {ciudad:30s} {signo}{row[\"cambio_pct\"]:5.1f}% ({row[\"anterior\"]:>10,.0f} -> {row[\"actual\"]:>10,.0f})')\n",
" \n",
" print(f'\\nTop 10 ciudades con mayor caida:')\n",
" for i, (ciudad, row) in enumerate(crece_filtrado.tail(10).sort_values('cambio_pct').iterrows(), 1):\n",
" signo = '+' if row['cambio_pct'] > 0 else ''\n",
" print(f' {i:2d}. {ciudad:30s} {signo}{row[\"cambio_pct\"]:5.1f}% ({row[\"anterior\"]:>10,.0f} -> {row[\"actual\"]:>10,.0f})')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Estacionalidad — patrones mensuales por ciudad"
]
},
{
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"execution_count": 11,
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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Heatmap de estacionalidad para top ciudades\n",
"top5 = top_ciudades.head(5).index.tolist()\n",
"\n",
"estacional = (\n",
" extranjeros[\n",
" (extranjeros['ciudad'].isin(top5)) &\n",
" (extranjeros['anio'] == ultimo_anio)\n",
" ]\n",
" .pivot_table(index='ciudad', columns='mes', values='valor', aggfunc='sum')\n",
" .fillna(0)\n",
")\n",
"\n",
"meses = ['Ene','Feb','Mar','Abr','May','Jun','Jul','Ago','Sep','Oct','Nov','Dic']\n",
"estacional.columns = [meses[m-1] for m in estacional.columns]\n",
"\n",
"fig = px.imshow(\n",
" estacional,\n",
" title=f'Estacionalidad del turismo extranjero — Top 5 ciudades ({ultimo_anio})',\n",
" labels={'color': 'Viajeros'},\n",
" color_continuous_scale='YlOrRd',\n",
" aspect='auto'\n",
")\n",
"fig.update_layout(height=350)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Resumen ejecutivo"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== RESUMEN 2025 ===\n",
"Total viajeros extranjeros en hoteles: 49,697,349\n",
"Total viajeros nacionales en hoteles: 32,599,738\n",
"Ratio ext/nac: 1.52\n",
"\n",
"Top 5 ciudades por volumen extranjero:\n",
" 1. Barcelona: 7,449,646 (15.0% del total)\n",
" 2. Madrid: 6,107,041 (12.3% del total)\n",
" 3. Palma: 2,324,397 (4.7% del total)\n",
" 4. Sevilla: 1,958,368 (3.9% del total)\n",
" 5. Calvià: 1,647,583 (3.3% del total)\n",
"\n",
"Top 5 ciudades por % de turismo extranjero:\n",
" 1. Viveiro: 94.7% extranjeros\n",
" 2. Vall de Boí, La: 94.1% extranjeros\n",
" 3. Cazorla: 92.6% extranjeros\n",
" 4. Sallent de Gállego: 91.1% extranjeros\n",
" 5. Carboneras: 90.9% extranjeros\n"
]
}
],
"source": [
"# Resumen\n",
"total_ext = extranjeros[extranjeros['anio'] == ultimo_anio]['valor'].sum()\n",
"total_nac = viajeros[viajeros['Residencia'] == 'Residentes en España']['valor'].sum()\n",
"\n",
"print(f'=== RESUMEN {ultimo_anio} ===')\n",
"print(f'Total viajeros extranjeros en hoteles: {total_ext:,.0f}')\n",
"print(f'Total viajeros nacionales en hoteles: {total_nac:,.0f}')\n",
"print(f'Ratio ext/nac: {total_ext/total_nac:.2f}')\n",
"print()\n",
"print(f'Top 5 ciudades por volumen extranjero:')\n",
"for i, (c, v) in enumerate(top_ciudades.head(5).items(), 1):\n",
" pct = v / total_ext * 100\n",
" print(f' {i}. {c}: {v:,.0f} ({pct:.1f}% del total)')\n",
"print()\n",
"print(f'Top 5 ciudades por % de turismo extranjero:')\n",
"for i, (c, row) in enumerate(pivot.head(5).iterrows(), 1):\n",
" print(f' {i}. {c}: {row[\"pct_extranjeros\"]:.1f}% extranjeros')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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