112 lines
3.9 KiB
Python
112 lines
3.9 KiB
Python
import argparse
|
|
from pathlib import Path
|
|
|
|
import httpx
|
|
import polars as pl
|
|
|
|
pl.Config.set_tbl_cols(-1)
|
|
|
|
|
|
URL = "https://www.ethnicity-facts-figures.service.gov.uk/uk-population-by-ethnicity/national-and-regional-populations/regional-ethnic-diversity/latest/downloads/population-by-ethnicity-and-local-authority-2021.csv"
|
|
|
|
|
|
def download_and_convert(output_path: Path) -> None:
|
|
print("Downloading ethnicity data...")
|
|
response = httpx.get(URL, follow_redirects=True, timeout=60)
|
|
response.raise_for_status()
|
|
|
|
df = pl.read_csv(response.content)
|
|
print(f"Raw shape: {df.head(100)}")
|
|
|
|
# Use the detailed 19+1 breakdown to get sub-categories for Asian ethnicity,
|
|
# then aggregate back to the broad groups plus South Asian / East Asian split.
|
|
detailed = df.filter(
|
|
(pl.col("Ethnicity_type") == "ONS 2021 19+1") & (pl.col("Ethnicity") != "All")
|
|
)
|
|
|
|
# Map detailed categories to our output groups
|
|
group_map = {
|
|
# White
|
|
"White British": "White",
|
|
"White Irish": "White",
|
|
"Gypsy Or Irish Traveller": "White",
|
|
"Roma": "White",
|
|
"Any Other White Background": "White",
|
|
# South Asian
|
|
"Indian": "South Asian",
|
|
"Pakistani": "South Asian",
|
|
"Bangladeshi": "South Asian",
|
|
"Any Other Asian Background": "South Asian",
|
|
# East Asian
|
|
"Chinese": "East Asian",
|
|
# Black
|
|
"Black African": "Black",
|
|
"Black Caribbean": "Black",
|
|
"Any Other Black Background": "Black",
|
|
# Mixed
|
|
"Mixed White And Asian": "Mixed",
|
|
"Mixed White And Black African": "Mixed",
|
|
"Mixed White And Black Caribbean": "Mixed",
|
|
"Any Other Mixed/Multiple Ethnic Background": "Mixed",
|
|
# Other
|
|
"Arab": "Other",
|
|
"Any Other Ethnic Background": "Other",
|
|
}
|
|
|
|
detailed = detailed.with_columns(
|
|
pl.col("Ethnicity").replace_strict(group_map).alias("group"),
|
|
)
|
|
|
|
# Sum percentages within each group per local authority (keep full precision)
|
|
grouped = detailed.group_by("Geography_code", "group").agg(pl.col("Value1").sum())
|
|
wide = grouped.pivot(on="group", index="Geography_code", values="Value1")
|
|
|
|
# Normalize so each row sums to exactly 100%, then round using largest-remainder
|
|
# method to preserve the sum. Independent rounding of 6 values can drift ±0.3.
|
|
group_cols = [c for c in wide.columns if c != "Geography_code"]
|
|
row_total = sum(pl.col(c) for c in group_cols)
|
|
# Scale each group so they sum to exactly 100
|
|
wide = wide.with_columns(
|
|
[(pl.col(c) / row_total * 100.0).alias(c) for c in group_cols]
|
|
)
|
|
# Round to 1 decimal, then adjust the largest group to absorb residual
|
|
rounded_cols = [pl.col(c).round(1).alias(c) for c in group_cols]
|
|
wide = wide.with_columns(rounded_cols)
|
|
rounded_sum = sum(pl.col(c) for c in group_cols)
|
|
residual = (100.0 - rounded_sum).round(1)
|
|
# Find which group is largest per row and add the residual there
|
|
largest_col = pl.concat_list(group_cols).list.arg_max()
|
|
wide = wide.with_columns(
|
|
[
|
|
pl.when(largest_col == i)
|
|
.then(pl.col(c) + residual)
|
|
.otherwise(pl.col(c))
|
|
.alias(c)
|
|
for i, c in enumerate(group_cols)
|
|
]
|
|
)
|
|
|
|
# Rename columns to be descriptive
|
|
rename_map = {col: f"% {col}" for col in wide.columns if col != "Geography_code"}
|
|
wide = wide.rename(rename_map)
|
|
|
|
print(f"Output shape: {wide.shape}")
|
|
print(f"Columns: {wide.columns}")
|
|
|
|
wide.write_parquet(output_path, compression="zstd")
|
|
print(f"Saved to {output_path}")
|
|
|
|
|
|
def main() -> None:
|
|
parser = argparse.ArgumentParser(
|
|
description="Download and convert ethnicity by local authority data"
|
|
)
|
|
parser.add_argument(
|
|
"--output", type=Path, required=True, help="Output parquet file path"
|
|
)
|
|
args = parser.parse_args()
|
|
download_and_convert(args.output)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|