fn-registry agent
4916 lines
114 KiB
Plaintext
4916 lines
114 KiB
Plaintext
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"# Analisis Completo — Turismo Internacional en Espana 2025\n",
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"\n",
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"Integracion de datos INE (tabla 2078 por ciudad) con el dataset FRONTUR/EGATUR/Turespaña.\n",
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"\n",
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"**Fuentes:** INE Encuesta Ocupacion Hotelera, FRONTUR, EGATUR, Turespaña ESTI, Skyscanner"
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]
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},
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{
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"name": "stdout",
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"text": [
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"CSVs disponibles: ['01_FRONTUR_2025_anual_paises.csv', '02_FRONTUR_dic2025_paises.csv', '03_FRONTUR_2025_destino_ccaa.csv', '04_FRONTUR_2025_mensual.csv', '05_EGATUR_2025_gasto_paises.csv', '06_FRONTUR_serie_historica_anual.csv', '07_FRONTUR_2025_alojamiento.csv', '08_encuesta_satisfaccion_turistas.csv', '09_satisfaccion_por_pais.csv', '10_motivaciones_elegir_espana.csv', '11_factores_experienciales.csv', '12_fuentes_inspiracion_viaje.csv', '13_imagen_espana_mercados.csv', '14_evolucion_motivaciones_vs_2019.csv', '15_satisfaccion_por_motivo_viaje.csv', '16_gasto_ccaa_acumulado.csv']\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import plotly.express as px\n",
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"import plotly.graph_objects as go\n",
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"from plotly.subplots import make_subplots\n",
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"from pathlib import Path\n",
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"import os\n",
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"\n",
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"VAULT = Path(os.environ.get('FN_REGISTRY_ROOT', '../..')) / 'vaults' / 'turismo_spain'\n",
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"RAW = VAULT / 'raw'\n",
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"FUENTES = RAW / 'turismo-espana-2025' / 'fuentes'\n",
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"print(f'CSVs disponibles: {sorted([f.name for f in FUENTES.glob(\"*.csv\")])}')"
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]
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},
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"source": [
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"## 1. Panorama general — Record historico 96.8M turistas"
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]
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"scene": {
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"xaxis": {
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"backgroundcolor": "#E5ECF6",
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"gridcolor": "white",
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"gridwidth": 2,
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"linecolor": "white",
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"showbackground": true,
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"ticks": "",
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"zerolinecolor": "white"
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},
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"yaxis": {
|
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"backgroundcolor": "#E5ECF6",
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"gridcolor": "white",
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"gridwidth": 2,
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"linecolor": "white",
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"showbackground": true,
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"ticks": "",
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"zerolinecolor": "white"
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},
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"zaxis": {
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"backgroundcolor": "#E5ECF6",
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"gridcolor": "white",
|
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"gridwidth": 2,
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"linecolor": "white",
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"showbackground": true,
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"zerolinecolor": "white"
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}
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},
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"shapedefaults": {
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"ternary": {
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"aaxis": {
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": ""
|
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},
|
|
"baxis": {
|
|
"gridcolor": "white",
|
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"linecolor": "white",
|
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"ticks": ""
|
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},
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"bgcolor": "#E5ECF6",
|
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"caxis": {
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"gridcolor": "white",
|
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"linecolor": "white",
|
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"ticks": ""
|
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}
|
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},
|
|
"title": {
|
|
"x": 0.05
|
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},
|
|
"xaxis": {
|
|
"automargin": true,
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
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"ticks": "",
|
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"title": {
|
|
"standoff": 15
|
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},
|
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"zerolinecolor": "white",
|
|
"zerolinewidth": 2
|
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},
|
|
"yaxis": {
|
|
"automargin": true,
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
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"ticks": "",
|
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"title": {
|
|
"standoff": 15
|
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},
|
|
"zerolinecolor": "white",
|
|
"zerolinewidth": 2
|
|
}
|
|
}
|
|
},
|
|
"title": {
|
|
"text": "Turistas internacionales en Espana (2019-2025)"
|
|
},
|
|
"xaxis": {
|
|
"title": {
|
|
"text": "Anio"
|
|
}
|
|
},
|
|
"yaxis": {
|
|
"range": [
|
|
0,
|
|
110
|
|
],
|
|
"title": {
|
|
"text": "Millones de turistas"
|
|
}
|
|
}
|
|
}
|
|
}
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"transient": {}
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Recuperacion post-COVID: de 18.9M (2020) a 96.8M (2025)\n",
|
|
"Crecimiento vs pre-pandemia 2019: 15.9%\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Serie historica\n",
|
|
"serie = pd.read_csv(FUENTES / '06_FRONTUR_serie_historica_anual.csv')\n",
|
|
"\n",
|
|
"fig = go.Figure()\n",
|
|
"fig.add_trace(go.Bar(\n",
|
|
" x=serie['anio'], y=serie['turistas_millones'],\n",
|
|
" text=serie['turistas_millones'].apply(lambda x: f'{x}M'),\n",
|
|
" textposition='outside',\n",
|
|
" marker_color=['#636EFA' if a != 2025 else '#EF553B' for a in serie['anio']]\n",
|
|
"))\n",
|
|
"fig.update_layout(\n",
|
|
" title='Turistas internacionales en Espana (2019-2025)',\n",
|
|
" yaxis_title='Millones de turistas',\n",
|
|
" xaxis_title='Anio',\n",
|
|
" height=400,\n",
|
|
" yaxis_range=[0, 110]\n",
|
|
")\n",
|
|
"fig.show()\n",
|
|
"\n",
|
|
"print(f'Recuperacion post-COVID: de {serie[serie.anio==2020].turistas_millones.values[0]}M (2020) a {serie[serie.anio==2025].turistas_millones.values[0]}M (2025)')\n",
|
|
"print(f'Crecimiento vs pre-pandemia 2019: {((96.8/83.5)-1)*100:.1f}%')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 2. Paises emisores — De donde vienen los turistas"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.plotly.v1+json": {
|
|
"config": {
|
|
"plotlyServerURL": "https://plot.ly"
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},
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|
"data": [
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|
{
|
|
"branchvalues": "total",
|
|
"customdata": [
|
|
[
|
|
"19.7",
|
|
3.6999999999999997
|
|
],
|
|
[
|
|
"13.2",
|
|
-1
|
|
],
|
|
[
|
|
"12.4",
|
|
0.6
|
|
],
|
|
[
|
|
"5.9",
|
|
4.4
|
|
],
|
|
[
|
|
"5.4",
|
|
0.9000000000000001
|
|
],
|
|
[
|
|
"5.2",
|
|
3.3
|
|
],
|
|
[
|
|
"10.0",
|
|
6.3
|
|
],
|
|
[
|
|
"4.6",
|
|
4.3
|
|
],
|
|
[
|
|
"5.5",
|
|
3.2
|
|
],
|
|
[
|
|
"3.1",
|
|
10.1
|
|
],
|
|
[
|
|
"3.2",
|
|
0
|
|
],
|
|
[
|
|
"3.5",
|
|
12.999999999999998
|
|
],
|
|
[
|
|
"2.4",
|
|
6.4
|
|
],
|
|
[
|
|
"6.0",
|
|
3.7
|
|
]
|
|
],
|
|
"domain": {
|
|
"x": [
|
|
0,
|
|
1
|
|
],
|
|
"y": [
|
|
0,
|
|
1
|
|
]
|
|
},
|
|
"hovertemplate": "labels=%{label}<br>turistas_millones=%{value}<br>parent=%{parent}<br>id=%{id}<br>cuota_pct=%{customdata[0]}<br>variacion_anual_pct=%{color}<extra></extra>",
|
|
"ids": [
|
|
"Reino Unido",
|
|
"Francia",
|
|
"Alemania",
|
|
"Italia",
|
|
"Paises Nordicos",
|
|
"Paises Bajos",
|
|
"Resto de Europa",
|
|
"Estados Unidos",
|
|
"Resto de America",
|
|
"Irlanda",
|
|
"Belgica",
|
|
"Portugal",
|
|
"Suiza",
|
|
"Resto del mundo"
|
|
],
|
|
"labels": [
|
|
"Reino Unido",
|
|
"Francia",
|
|
"Alemania",
|
|
"Italia",
|
|
"Paises Nordicos",
|
|
"Paises Bajos",
|
|
"Resto de Europa",
|
|
"Estados Unidos",
|
|
"Resto de America",
|
|
"Irlanda",
|
|
"Belgica",
|
|
"Portugal",
|
|
"Suiza",
|
|
"Resto del mundo"
|
|
],
|
|
"marker": {
|
|
"coloraxis": "coloraxis",
|
|
"colors": {
|
|
"bdata": "mZmZmZmZDUAAAAAAAADwvzMzMzMzM+M/mpmZmZmZEUDOzMzMzMzsP2ZmZmZmZgpAMzMzMzMzGUAzMzMzMzMRQJqZmZmZmQlAMzMzMzMzJEAAAAAAAAAAAP///////ylAmpmZmZmZGUCamZmZmZkNQA==",
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|
"dtype": "f8"
|
|
}
|
|
},
|
|
"name": "",
|
|
"parents": [
|
|
"",
|
|
"",
|
|
"",
|
|
"",
|
|
"",
|
|
"",
|
|
"",
|
|
"",
|
|
"",
|
|
"",
|
|
"",
|
|
"",
|
|
"",
|
|
""
|
|
],
|
|
"type": "treemap",
|
|
"values": {
|
|
"bdata": "mpmZmZkZM0CamZmZmZkpQAAAAAAAAChAzczMzMzMFkDNzMzMzMwUQAAAAAAAABRAZmZmZmZmI0AAAAAAAAASQDMzMzMzMxVAAAAAAAAACEDNzMzMzMwIQDMzMzMzMwtAZmZmZmZmAkAzMzMzMzMXQA==",
|
|
"dtype": "f8"
|
|
}
|
|
}
|
|
],
|
|
"layout": {
|
|
"coloraxis": {
|
|
"cmid": 3.2,
|
|
"colorbar": {
|
|
"title": {
|
|
"text": "variacion_anual_pct"
|
|
}
|
|
},
|
|
"colorscale": [
|
|
[
|
|
0,
|
|
"rgb(165,0,38)"
|
|
],
|
|
[
|
|
0.1,
|
|
"rgb(215,48,39)"
|
|
],
|
|
[
|
|
0.2,
|
|
"rgb(244,109,67)"
|
|
],
|
|
[
|
|
0.3,
|
|
"rgb(253,174,97)"
|
|
],
|
|
[
|
|
0.4,
|
|
"rgb(254,224,139)"
|
|
],
|
|
[
|
|
0.5,
|
|
"rgb(255,255,191)"
|
|
],
|
|
[
|
|
0.6,
|
|
"rgb(217,239,139)"
|
|
],
|
|
[
|
|
0.7,
|
|
"rgb(166,217,106)"
|
|
],
|
|
[
|
|
0.8,
|
|
"rgb(102,189,99)"
|
|
],
|
|
[
|
|
0.9,
|
|
"rgb(26,152,80)"
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|
],
|
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[
|
|
1,
|
|
"rgb(0,104,55)"
|
|
]
|
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]
|
|
},
|
|
"height": 500,
|
|
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|
"tracegroupgap": 0
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},
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"data": {
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|
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|
{
|
|
"error_x": {
|
|
"color": "#2a3f5f"
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},
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"error_y": {
|
|
"color": "#2a3f5f"
|
|
},
|
|
"marker": {
|
|
"line": {
|
|
"color": "#E5ECF6",
|
|
"width": 0.5
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|
},
|
|
"pattern": {
|
|
"fillmode": "overlay",
|
|
"size": 10,
|
|
"solidity": 0.2
|
|
}
|
|
},
|
|
"type": "bar"
|
|
}
|
|
],
|
|
"barpolar": [
|
|
{
|
|
"marker": {
|
|
"line": {
|
|
"color": "#E5ECF6",
|
|
"width": 0.5
|
|
},
|
|
"pattern": {
|
|
"fillmode": "overlay",
|
|
"size": 10,
|
|
"solidity": 0.2
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|
}
|
|
},
|
|
"type": "barpolar"
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|
}
|
|
],
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|
"carpet": [
|
|
{
|
|
"aaxis": {
|
|
"endlinecolor": "#2a3f5f",
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"minorgridcolor": "white",
|
|
"startlinecolor": "#2a3f5f"
|
|
},
|
|
"baxis": {
|
|
"endlinecolor": "#2a3f5f",
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"minorgridcolor": "white",
|
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"startlinecolor": "#2a3f5f"
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},
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}
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|
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{
|
|
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|
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},
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}
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],
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|
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{
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0,
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|
0.1111111111111111,
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],
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0.2222222222222222,
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],
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],
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[
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[
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[
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1,
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]
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],
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"type": "contour"
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"Top 5 emisores:\n",
|
|
" Reino Unido 19.1M (+3.7%) — 19.7% del total\n",
|
|
" Francia 12.8M (-1.0%) — 13.2% del total\n",
|
|
" Alemania 12.0M (+0.6%) — 12.4% del total\n",
|
|
" Italia 5.7M (+4.4%) — 5.9% del total\n",
|
|
" Paises Nordicos 5.2M (+0.9%) — 5.4% del total\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Turistas por pais\n",
|
|
"paises = pd.read_csv(FUENTES / '01_FRONTUR_2025_anual_paises.csv')\n",
|
|
"paises_sin_total = paises[paises['pais'] != 'TOTAL'].copy()\n",
|
|
"\n",
|
|
"fig = px.treemap(\n",
|
|
" paises_sin_total,\n",
|
|
" path=['pais'],\n",
|
|
" values='turistas_millones',\n",
|
|
" color='variacion_anual_pct',\n",
|
|
" color_continuous_scale='RdYlGn',\n",
|
|
" color_continuous_midpoint=3.2,\n",
|
|
" title='Turistas por pais de origen 2025 (tamaño=volumen, color=crecimiento %)',\n",
|
|
" hover_data=['cuota_pct']\n",
|
|
")\n",
|
|
"fig.update_layout(height=500)\n",
|
|
"fig.show()\n",
|
|
"\n",
|
|
"print('Top 5 emisores:')\n",
|
|
"for _, r in paises_sin_total.head(5).iterrows():\n",
|
|
" signo = '+' if r['variacion_anual_pct'] > 0 else ''\n",
|
|
" print(f' {r[\"pais\"]:20s} {r[\"turistas_millones\"]:5.1f}M ({signo}{r[\"variacion_anual_pct\"]}%) — {r[\"cuota_pct\"]}% del total')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Dinamica mensual 2025\n",
|
|
"mensual = pd.read_csv(FUENTES / '04_FRONTUR_2025_mensual.csv')\n",
|
|
"\n",
|
|
"fig = make_subplots(specs=[[{'secondary_y': True}]])\n",
|
|
"fig.add_trace(\n",
|
|
" go.Bar(x=mensual['mes'], y=mensual['turistas_millones'], name='Turistas (M)', marker_color='#636EFA'),\n",
|
|
" secondary_y=False\n",
|
|
")\n",
|
|
"fig.add_trace(\n",
|
|
" go.Scatter(x=mensual['mes'], y=mensual['variacion_anual_pct'], name='Variacion %', \n",
|
|
" mode='lines+markers', marker_color='#EF553B', line_width=2),\n",
|
|
" secondary_y=True\n",
|
|
")\n",
|
|
"fig.update_layout(title='Llegadas mensuales 2025 y variacion interanual', height=400)\n",
|
|
"fig.update_yaxes(title_text='Millones', secondary_y=False)\n",
|
|
"fig.update_yaxes(title_text='Variacion %', secondary_y=True)\n",
|
|
"fig.show()\n",
|
|
"\n",
|
|
"pico = mensual.loc[mensual['turistas_millones'].idxmax()]\n",
|
|
"print(f'Mes pico: {pico[\"mes\"]} con {pico[\"turistas_millones\"]}M turistas')\n",
|
|
"print(f'Mayor crecimiento interanual: {mensual.loc[mensual[\"variacion_anual_pct\"].idxmax()][\"mes\"]} ({mensual[\"variacion_anual_pct\"].max()}%)')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 3. Destinos — CCAA y gasto"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# CCAA destino\n",
|
|
"ccaa = pd.read_csv(FUENTES / '03_FRONTUR_2025_destino_ccaa.csv')\n",
|
|
"ccaa_sin_total = ccaa[ccaa['ccaa'] != 'TOTAL'].sort_values('turistas_millones', ascending=True)\n",
|
|
"\n",
|
|
"fig = go.Figure()\n",
|
|
"fig.add_trace(go.Bar(\n",
|
|
" x=ccaa_sin_total['turistas_millones'],\n",
|
|
" y=ccaa_sin_total['ccaa'],\n",
|
|
" orientation='h',\n",
|
|
" text=ccaa_sin_total.apply(lambda r: f\"{r['turistas_millones']}M ({r['cuota_pct']}%)\", axis=1),\n",
|
|
" textposition='outside',\n",
|
|
" marker_color=ccaa_sin_total['variacion_anual_pct'].apply(\n",
|
|
" lambda x: '#2ecc71' if x > 3.2 else '#3498db' if x > 0 else '#e74c3c'\n",
|
|
" )\n",
|
|
"))\n",
|
|
"fig.update_layout(\n",
|
|
" title='Turistas por CCAA destino 2025 (verde=crece por encima de media, azul=por debajo)',\n",
|
|
" xaxis_title='Millones de turistas',\n",
|
|
" height=400, xaxis_range=[0, 24]\n",
|
|
")\n",
|
|
"fig.show()\n",
|
|
"\n",
|
|
"print('Crecimiento por CCAA:')\n",
|
|
"for _, r in ccaa_sin_total.sort_values('variacion_anual_pct', ascending=False).iterrows():\n",
|
|
" print(f' {r[\"ccaa\"]:25s} +{r[\"variacion_anual_pct\"]}%')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
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"data": {
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"hovertemplate": "Gasto total (M EUR)=%{x}<br>Gasto medio/turista (EUR)=%{y}<br>% del gasto total=%{marker.size}<br>pais=%{text}<extra></extra>",
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"showlegend": false,
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"text": [
|
|
"Reino Unido",
|
|
"Alemania",
|
|
"Francia",
|
|
"Estados Unidos",
|
|
"Paises Nordicos",
|
|
"Paises Bajos",
|
|
"Italia",
|
|
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|
|
"Suiza",
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|
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},
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"text": "Gasto medio/turista (EUR)"
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}
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}
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}
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}
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},
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"metadata": {},
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"output_type": "display_data",
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"transient": {}
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},
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{
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"name": "stdout",
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"text": [
|
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"Gasto medio por turista (top):\n",
|
|
" Estados Unidos 1,511 EUR/turista\n",
|
|
" Alemania 1,319 EUR/turista\n",
|
|
" Reino Unido 1,238 EUR/turista\n",
|
|
" Suiza 1,130 EUR/turista\n",
|
|
" Paises Nordicos 981 EUR/turista\n"
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]
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}
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],
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"source": [
|
|
"# Gasto por pais emisor\n",
|
|
"gasto = pd.read_csv(FUENTES / '05_EGATUR_2025_gasto_paises.csv')\n",
|
|
"gasto_paises = gasto[(gasto['pais'] != 'TOTAL') & (gasto['pais'] != 'Resto')].copy()\n",
|
|
"\n",
|
|
"fig = px.scatter(\n",
|
|
" gasto_paises,\n",
|
|
" x='gasto_total_millones_eur',\n",
|
|
" y='gasto_medio_por_turista_eur_aprox',\n",
|
|
" text='pais',\n",
|
|
" size='cuota_gasto_pct',\n",
|
|
" title='Gasto total vs gasto medio por turista — por pais emisor',\n",
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|
" labels={\n",
|
|
" 'gasto_total_millones_eur': 'Gasto total (M EUR)',\n",
|
|
" 'gasto_medio_por_turista_eur_aprox': 'Gasto medio/turista (EUR)',\n",
|
|
" 'cuota_gasto_pct': '% del gasto total'\n",
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|
" }\n",
|
|
")\n",
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"fig.update_traces(textposition='top center')\n",
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|
"fig.update_layout(height=500)\n",
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|
"fig.show()\n",
|
|
"\n",
|
|
"print('Gasto medio por turista (top):')\n",
|
|
"for _, r in gasto_paises.sort_values('gasto_medio_por_turista_eur_aprox', ascending=False).head(5).iterrows():\n",
|
|
" print(f' {r[\"pais\"]:20s} {r[\"gasto_medio_por_turista_eur_aprox\"]:,.0f} EUR/turista')"
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|
]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
|
"## 4. Alojamiento — Donde se hospedan"
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|
]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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1
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},
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"hole": 0.4,
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"hovertemplate": "tipo=%{label}<br>turistas_acum2025=%{value}<extra></extra>",
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"Familiares/amigos",
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"Resto"
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}
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"height": 400,
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"tracegroupgap": 0
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"Desglose:\n",
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" Hotelero 63,138,667 (65.2%) — variacion: +1.9%\n",
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|
" Vivienda alquiler 12,116,074 (12.5%) — variacion: +5.3%\n",
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|
" Familiares/amigos 10,644,434 (11.0%) — variacion: +4.3%\n",
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|
" Vivienda propiedad 5,330,566 (5.5%) — variacion: +8.5%\n",
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|
" Resto 4,218,285 (4.4%) — variacion: +6.0%\n",
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|
" Resto 1,322,489 (1.4%) — variacion: +12.3%\n"
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]
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}
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],
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"source": [
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"# Tipo de alojamiento\n",
|
|
"aloj = pd.read_csv(FUENTES / '07_FRONTUR_2025_alojamiento.csv')\n",
|
|
"aloj_detalle = aloj[aloj['tipo_alojamiento'].str.contains(' - ') & ~aloj['tipo_alojamiento'].str.contains('Total')].copy()\n",
|
|
"aloj_detalle['tipo'] = aloj_detalle['tipo_alojamiento'].str.split(' - ').str[-1]\n",
|
|
"aloj_detalle['pct'] = (aloj_detalle['turistas_acum2025'] / 96770515 * 100).round(1)\n",
|
|
"\n",
|
|
"fig = px.pie(\n",
|
|
" aloj_detalle,\n",
|
|
" values='turistas_acum2025',\n",
|
|
" names='tipo',\n",
|
|
" title='Tipo de alojamiento de turistas extranjeros 2025',\n",
|
|
" hole=0.4\n",
|
|
")\n",
|
|
"fig.update_layout(height=400)\n",
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|
"fig.show()\n",
|
|
"\n",
|
|
"print('Desglose:')\n",
|
|
"for _, r in aloj_detalle.sort_values('turistas_acum2025', ascending=False).iterrows():\n",
|
|
" print(f' {r[\"tipo\"]:25s} {r[\"turistas_acum2025\"]:>12,} ({r[\"pct\"]}%) — variacion: {r[\"variacion_anual_pct\"]:+.1f}%')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
|
"## 5. Satisfaccion y motivaciones — Por que eligen Espana"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"outputs": [
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},
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}
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"metadata": {},
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{
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"text": [
|
|
"Evolucion de motivaciones vs 2019:\n",
|
|
" Motivos culturales +32\n",
|
|
" Motivos gastronomicos +28\n",
|
|
" Media conjunto motivaciones inferior a cultural y gastro\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Motivaciones\n",
|
|
"motiv = pd.read_csv(FUENTES / '10_motivaciones_elegir_espana.csv')\n",
|
|
"motiv_num = motiv[motiv['porcentaje'].apply(lambda x: str(x).replace('.','').replace(',','').replace('%','').strip().isdigit())].copy()\n",
|
|
"motiv_num['pct'] = motiv_num['porcentaje'].astype(float)\n",
|
|
"\n",
|
|
"fig = px.bar(\n",
|
|
" motiv_num.sort_values('pct', ascending=True),\n",
|
|
" x='pct', y='motivacion', orientation='h',\n",
|
|
" title='Motivaciones para elegir Espana como destino',\n",
|
|
" labels={'pct': 'Porcentaje (%)', 'motivacion': ''},\n",
|
|
" color='pct', color_continuous_scale='Sunset'\n",
|
|
")\n",
|
|
"fig.update_layout(height=400, coloraxis_showscale=False)\n",
|
|
"fig.show()\n",
|
|
"\n",
|
|
"# Evolucion motivaciones vs 2019\n",
|
|
"evol = pd.read_csv(FUENTES / '14_evolucion_motivaciones_vs_2019.csv')\n",
|
|
"print('Evolucion de motivaciones vs 2019:')\n",
|
|
"for _, r in evol.iterrows():\n",
|
|
" print(f' {r[\"motivacion\"]:40s} {r[\"crecimiento_vs_2019_pct\"]}')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Satisfaccion por pais\n",
|
|
"satis = pd.read_csv(FUENTES / '09_satisfaccion_por_pais.csv')\n",
|
|
"satis_clean = satis[satis['pct_muy_satisfechos'].notna()].copy()\n",
|
|
"\n",
|
|
"fig = px.bar(\n",
|
|
" satis_clean.sort_values('pct_muy_satisfechos', ascending=True),\n",
|
|
" x='pct_muy_satisfechos', y='pais_origen', orientation='h',\n",
|
|
" title='% Muy satisfechos por pais de origen',\n",
|
|
" labels={'pct_muy_satisfechos': '% Muy satisfechos', 'pais_origen': ''},\n",
|
|
" color='pct_muy_satisfechos', color_continuous_scale='Greens'\n",
|
|
")\n",
|
|
"fig.update_layout(height=400, coloraxis_showscale=False)\n",
|
|
"fig.show()\n",
|
|
"\n",
|
|
"# Imagen de Espana\n",
|
|
"imagen = pd.read_csv(FUENTES / '13_imagen_espana_mercados.csv')\n",
|
|
"print('\\nImagen de Espana en mercados emisores:')\n",
|
|
"for _, r in imagen.iterrows():\n",
|
|
" print(f' {r[\"atributo\"]:35s} [{r[\"relevancia\"]}]')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 6. Cruce con datos INE por ciudad — Vision integrada"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Cargar INE tabla 2078\n",
|
|
"df = pd.read_csv(RAW / 'ine_2078_viajeros_puntos_turisticos.csv', sep=';', encoding='utf-8-sig')\n",
|
|
"df[['cod_municipio', 'ciudad']] = df['Puntos turísticos'].str.extract(r'^(\\d+)\\s+(.*)')\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",
|
|
"df['valor'] = pd.to_numeric(df['Total'].str.replace('.', '', regex=False), errors='coerce')\n",
|
|
"\n",
|
|
"# Mapeo ciudad -> CCAA (aproximado para las principales)\n",
|
|
"ciudad_ccaa = {\n",
|
|
" 'Barcelona': 'Cataluna', 'Lloret de Mar': 'Cataluna', 'Salou': 'Cataluna',\n",
|
|
" 'Madrid': 'Comunidad de Madrid',\n",
|
|
" 'Palma': 'Illes Balears', 'Calvià': 'Illes Balears', 'Sóller': 'Illes Balears',\n",
|
|
" 'Sevilla': 'Andalucia', 'Málaga': 'Andalucia', 'Granada': 'Andalucia',\n",
|
|
" 'Torremolinos': 'Andalucia', 'Marbella': 'Andalucia',\n",
|
|
" 'València': 'Comunitat Valenciana', 'Benidorm': 'Comunitat Valenciana', 'Altea': 'Comunitat Valenciana',\n",
|
|
" 'San Bartolomé de Tirajana': 'Canarias', 'Adeje': 'Canarias', 'Arona': 'Canarias',\n",
|
|
" 'Pájara': 'Canarias', 'Yaiza': 'Canarias', 'Mogán': 'Canarias',\n",
|
|
" 'Donostia/San Sebastián': 'Resto CCAA', 'Bilbao': 'Resto CCAA',\n",
|
|
"}\n",
|
|
"\n",
|
|
"ext_2025 = df[\n",
|
|
" (df['Residencia'] == 'Residentes en el Extranjero') &\n",
|
|
" (df['Viajeros y pernoctaciones'] == 'Viajero') &\n",
|
|
" (df['anio'] == 2025)\n",
|
|
"].copy()\n",
|
|
"\n",
|
|
"top20 = ext_2025.groupby('ciudad')['valor'].sum().sort_values(ascending=False).head(20)\n",
|
|
"top20_df = top20.reset_index()\n",
|
|
"top20_df.columns = ['ciudad', 'viajeros_ext']\n",
|
|
"top20_df['ccaa'] = top20_df['ciudad'].map(ciudad_ccaa).fillna('Otro')\n",
|
|
"\n",
|
|
"fig = px.bar(\n",
|
|
" top20_df.sort_values('viajeros_ext', ascending=True),\n",
|
|
" x='viajeros_ext', y='ciudad', color='ccaa', orientation='h',\n",
|
|
" title='Top 20 ciudades por turismo extranjero 2025 (coloreadas por CCAA)',\n",
|
|
" labels={'viajeros_ext': 'Viajeros extranjeros', 'ciudad': '', 'ccaa': 'CCAA'}\n",
|
|
")\n",
|
|
"fig.update_layout(height=600)\n",
|
|
"fig.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 7. Resumen ejecutivo integrado"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"print('=' * 70)\n",
|
|
"print(' TURISMO INTERNACIONAL EN ESPANA 2025 — RESUMEN EJECUTIVO')\n",
|
|
"print('=' * 70)\n",
|
|
"\n",
|
|
"print(f'''\n",
|
|
"CIFRAS CLAVE\n",
|
|
" Turistas totales: 96.8M (+3.2% vs 2024) — RECORD HISTORICO\n",
|
|
" Gasto total: 134,710M EUR (+6.8% vs 2024)\n",
|
|
" Gasto medio/turista: 1,391 EUR\n",
|
|
" Satisfaccion: 97% satisfechos, 66.2% muy satisfechos\n",
|
|
" Repeticion: 69% intencion de volver en 12 meses\n",
|
|
"\n",
|
|
"PAISES EMISORES (Top 5)\n",
|
|
" 1. Reino Unido 19.1M (+3.7%) — 19.7% del total — gasto 1,238 EUR/turista\n",
|
|
" 2. Francia 12.8M (-1.0%) — 13.2% del total — gasto 907 EUR/turista\n",
|
|
" 3. Alemania 12.0M (+0.6%) — 12.4% del total — gasto 1,319 EUR/turista\n",
|
|
" 4. Italia 5.7M (+4.4%) — 5.9% del total — gasto 842 EUR/turista\n",
|
|
" 5. Paises Nordicos 5.2M (+0.9%) — 5.4% del total — gasto 981 EUR/turista\n",
|
|
" Turistas mas valiosos: USA (1,511 EUR/turista), Alemania (1,319), UK (1,238)\n",
|
|
"\n",
|
|
"DESTINOS CCAA\n",
|
|
" Cataluna lidera en volumen (20.1M) pero crece poco (+0.6%)\n",
|
|
" Andalucia es la que mas crece (+6.6%) y ya alcanza 14.5M\n",
|
|
" Canarias e Illes Balears empatan en 15.7M cada una\n",
|
|
"\n",
|
|
"CIUDADES (datos hoteleros INE)\n",
|
|
" Barcelona (7.4M) y Madrid (6.1M) concentran la mayor afluencia\n",
|
|
" Islas dominan posiciones 3-7: Palma, Calvia, San Bartolome, Adeje\n",
|
|
" Mayor crecimiento: Algeciras (+35%), Badajoz (+30%), Murcia (+24%), Malaga (+17%)\n",
|
|
"\n",
|
|
"POR QUE ELIGEN ESPANA\n",
|
|
" Clima (26%) + Cultura (15.7%) + Visita familiares (12.6%)\n",
|
|
" Cultura crece +32% y gastronomia +28% vs 2019\n",
|
|
" Latinoamerica: los mas satisfechos (Mexico 79.5%, Brasil 79.4%)\n",
|
|
"\n",
|
|
"ALOJAMIENTO\n",
|
|
" 65.3% hotelero (63.1M) — pero crece solo +1.9%\n",
|
|
" Vivienda alquiler: 12.1M (+5.3%) — presion creciente\n",
|
|
" Familiares/amigos: 10.6M (+4.3%)\n",
|
|
" Vivienda propia: 5.3M (+8.5%) — mayor crecimiento\n",
|
|
"''')"
|
|
]
|
|
}
|
|
],
|
|
"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"
|
|
}
|
|
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|
|
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|
|
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|
|
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