opencode
404fd30ac3
El script 02_build_dataset.py hacia SUM de hut_registros/hut_plazas por seccion censal, multiplicando los valores de Eivissa (76 secciones) y Formentera (6 secciones) por el numero de secciones del municipio. Creado scripts/04_regenerate_web_json.py que: - Lee el CSV raw de HUT Eivissa y agrega por municipio (total real: 2.364 registros / 18.184 plazas) - Lee el JSON raw de IBESTAT Formentera (1.375 / 14.935) - Para Mallorca/Menorca mantiene SUM por seccion del parquet (correcto, granularidad seccion censal) Bug secundario corregido: Mallorca y Menorca tenian los mismos totales porque se agregaba el dict combinado. Ahora se agrega por isla individual. Nuevo scripts/05_export_airbnb_geojson.py genera el GeoJSON de listings individuales para los clusters del mapa (18.391 puntos, 2.97MB).
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
"""
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Genera web/public/data/airbnb_listings.geojson a partir de los CSVs raw
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de Inside Airbnb Mallorca y Menorca.
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Output: FeatureCollection con ~18K puntos. Solo geometria + 2 props
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(isla, room_type) para mantener el fichero ligero (~700KB).
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"""
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from pathlib import Path
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import json
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import pandas as pd
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ROOT = Path(__file__).resolve().parent.parent
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RAW = ROOT / "data" / "raw"
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OUT = ROOT / "web" / "public" / "data"
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OUT.mkdir(parents=True, exist_ok=True)
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def load_isla(name: str) -> pd.DataFrame:
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df = pd.read_csv(RAW / f"airbnb_{name.lower()}_listings.csv.gz", low_memory=False)
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df = df[(df["availability_365"] > 0) | (df["room_type"] == "Entire home/apt")]
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df = df.dropna(subset=["latitude", "longitude"])
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df["isla"] = name
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return df[["id", "latitude", "longitude", "room_type", "isla"]]
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def main() -> None:
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mall = load_isla("mallorca")
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men = load_isla("menorca")
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df = pd.concat([mall, men], ignore_index=True)
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print(f"Total puntos: {len(df):,}")
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features = []
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for _, row in df.iterrows():
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features.append({
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"type": "Feature",
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"geometry": {
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"type": "Point",
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"coordinates": [float(row["longitude"]), float(row["latitude"])],
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},
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"properties": {
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"id": str(row["id"]),
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"isla": row["isla"],
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"room_type": row["room_type"],
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},
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})
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out = {"type": "FeatureCollection", "features": features}
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out_path = OUT / "airbnb_listings.geojson"
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with open(out_path, "w", encoding="utf-8") as f:
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json.dump(out, f, ensure_ascii=False, separators=(",", ":"))
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size_mb = out_path.stat().st_size / (1024 * 1024)
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print(f"Guardado {out_path} ({size_mb:.2f} MB)")
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if __name__ == "__main__":
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main()
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