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#!/usr/bin/env python3
"""
extract_countries.py — Extract unique country names from a CSV.
Assumptions:
- Preferred source is a column named 'country' if present.
- Otherwise, parses a 'location' column formatted roughly as 'city, country'.
- Trims whitespace and canonicalizes common country synonyms (via csv_repair).
Output formats:
- text (default): one country per line to stdout or file
- json: JSON array of countries
- csv: single-column CSV with header 'country'
Usage examples:
python extract_countries.py -i data.csv
python extract_countries.py -i data.csv --format json > countries.json
python extract_countries.py -i data.csv --format csv -o countries.csv
python extract_countries.py -i data.csv --column location --sep ';' --encoding latin-1
"""
from __future__ import annotations
import argparse
import json
import sys
from typing import List, Optional
import numpy as np
import pandas as pd
try:
# Reuse robust CSV reading and parsing helpers from csv_repair
from csv_repair import try_read_csv, parse_location, canonical_country
except Exception as e: # pragma: no cover
print("This script expects csv_repair.py in the same directory.", file=sys.stderr)
raise
def extract_countries(
df: pd.DataFrame,
column: str = "location",
prefer_country_col: bool = True,
dropna: bool = True,
) -> List[str]:
if prefer_country_col and "country" in df.columns:
s = df["country"].astype(str).str.strip().replace({"": np.nan})
elif column in df.columns:
# Parse from location
_, country = parse_location(df[column])
s = country
else:
raise ValueError(f"Column '{column}' not found and no 'country' column present")
# Canonicalize and clean
s = s.apply(canonical_country)
if dropna:
s = s.dropna()
# Unique sorted list (case-insensitive sort, stable)
uniq = pd.Series(s.unique(), dtype=object).dropna().astype(str)
countries = sorted(uniq.tolist(), key=lambda x: x.casefold())
return countries
def main(argv: Optional[List[str]] = None) -> int:
p = argparse.ArgumentParser(description="Extract unique country names from a CSV")
p.add_argument("-i", "--input", required=True, help="Path to input CSV")
p.add_argument("--encoding", default=None, help="Optional file encoding (auto if omitted)")
p.add_argument("--sep", default=None, help="CSV delimiter; if omitted, inferred by pandas")
p.add_argument("--column", default="location", help="Column to parse when 'country' not present")
p.add_argument(
"--format", choices=["text", "json", "csv"], default="text", help="Output format"
)
p.add_argument("-o", "--output", default=None, help="Optional output file; default stdout")
p.add_argument("--keep-nulls", action="store_true", help="Include empty/NA countries in output")
args = p.parse_args(argv)
# Read CSV robustly
df = try_read_csv(args.input, encoding=args.encoding, sep=args.sep)
try:
countries = extract_countries(df, column=args.column, dropna=not args.keep_nulls)
except Exception as e:
print(f"Error: {e}", file=sys.stderr)
return 2
out = None
if args.format == "text":
out = "\n".join(countries) + ("\n" if countries else "")
elif args.format == "json":
out = json.dumps(countries, ensure_ascii=False, indent=2)
elif args.format == "csv":
# Build a simple CSV; avoid pandas roundtrip to keep deps minimal here
lines = ["country"] + [c.replace("\"", "\"") for c in countries]
out = "\n".join(lines) + "\n"
if args.output:
with open(args.output, "w", encoding="utf-8") as f:
f.write(out)
else:
sys.stdout.write(out)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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