perfect-postcode/pipeline/transform/school_proximity.py
2026-02-08 18:40:17 +00:00

73 lines
2.1 KiB
Python

"""Compute good-rated school proximity counts per postcode."""
import argparse
from pathlib import Path
import polars as pl
from pipeline.utils.poi_counts import count_pois_per_postcode
SCHOOL_GROUPS = {
"good_primary": ["good_primary"],
"good_secondary": ["good_secondary"],
}
def main():
parser = argparse.ArgumentParser(
description="Count good+ primary/secondary schools within 2km per postcode"
)
parser.add_argument(
"--ofsted", type=Path, required=True, help="Ofsted inspection parquet"
)
parser.add_argument(
"--arcgis", type=Path, required=True, help="ArcGIS postcode parquet"
)
parser.add_argument(
"--output", type=Path, required=True, help="Output parquet path"
)
args = parser.parse_args()
# Load Ofsted data: filter to good+ (1, 2) primary/secondary schools
ofsted = pl.read_parquet(args.ofsted).filter(
pl.col("Ofsted phase").is_in(["Primary", "Secondary"])
& pl.col("Overall effectiveness").is_in(["1", "2"])
)
print(f"Good+ schools: {len(ofsted):,}")
# Assign category based on phase
ofsted = ofsted.with_columns(
pl.when(pl.col("Ofsted phase") == "Primary")
.then(pl.lit("good_primary"))
.otherwise(pl.lit("good_secondary"))
.alias("category")
).select(
pl.col("Postcode").alias("postcode"),
"category",
)
# Join with arcgis to get lat/lng for each school's postcode
arcgis = pl.read_parquet(args.arcgis).select(
pl.col("pcds").alias("postcode"),
"lat",
pl.col("long").alias("lng"),
)
schools = ofsted.join(arcgis, on="postcode", how="inner")
print(f"Schools with coordinates: {len(schools):,}")
# Load all postcodes for proximity counting
postcodes = arcgis.rename({"lng": "lon"})
result = count_pois_per_postcode(
postcodes, schools, radius_km=5, groups=SCHOOL_GROUPS
)
result.write_parquet(args.output)
size_mb = args.output.stat().st_size / (1024 * 1024)
print(f"Wrote {args.output} ({size_mb:.1f} MB)")
if __name__ == "__main__":
main()