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"""
Fetch ISO country code mappings from GeoNames.
This script fetches comprehensive country data from GeoNames countryInfo.txt
and saves it as a CSV file for use in data preprocessing pipelines.
"""
import io
from pathlib import Path
import httpx
import pandas as pd
def fetch_country_mappings(save_raw=True):
"""
Fetch country code mappings from GeoNames.
Args:
save_raw: Whether to save raw data file to data/input
Returns:
pd.DataFrame: DataFrame with country information from GeoNames
"""
# Fetch countryInfo.txt from GeoNames
geonames_url = "https://download.geonames.org/export/dump/countryInfo.txt"
with httpx.Client() as client:
response = client.get(geonames_url)
response.raise_for_status()
content = response.text
# Save raw file to data/input for reference
if save_raw:
input_dir = Path("../data/input")
input_dir.mkdir(parents=True, exist_ok=True)
raw_path = input_dir / "geonames_countryInfo.txt"
with open(raw_path, "w", encoding="utf-8") as f:
f.write(content)
# Extract column names from the last comment line
lines = content.split("\n")
header_line = [line for line in lines if line.startswith("#")][-1]
column_names = header_line[1:].split("\t") # Remove # and split by tab
# Parse the tab-separated file
# keep_default_na=False to prevent "NA" (Namibia) from becoming NaN
df = pd.read_csv(
io.StringIO(content),
sep="\t",
comment="#",
header=None, # No header row in the data
keep_default_na=False, # Don't interpret "NA" as NaN (needed for Namibia)
na_values=[""], # Only treat empty strings as NaN
names=column_names, # Use the column names from the comment
)
# Rename columns to our standard format
df = df.rename(
columns={"ISO": "iso_alpha_2", "ISO3": "iso_alpha_3", "Country": "country_name"}
)
return df
def create_country_dataframe(geonames_df):
"""
Create a cleaned DataFrame with country codes and names.
Args:
geonames_df: DataFrame from GeoNames with all country information
Returns:
pd.DataFrame: DataFrame with columns [iso_alpha_2, iso_alpha_3, country_name]
"""
# Select only the columns we need
df = geonames_df[["iso_alpha_2", "iso_alpha_3", "country_name"]].copy()
# Sort by country name for consistency
df = df.sort_values("country_name").reset_index(drop=True)
return df
def save_country_codes(output_path="../data/intermediate/iso_country_codes.csv"):
"""
Fetch country codes from GeoNames and save to CSV.
Args:
output_path: Path to save the CSV file
"""
# Fetch full GeoNames data
geonames_df = fetch_country_mappings()
# Create cleaned DataFrame with just the columns we need
df = create_country_dataframe(geonames_df)
# Ensure output directory exists
output_file = Path(output_path)
output_file.parent.mkdir(parents=True, exist_ok=True)
# Save to CSV
df.to_csv(output_file, index=False)
return df
if __name__ == "__main__":
# Fetch and save country codes
df = save_country_codes()
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