| import io |
| import warnings |
| import zipfile |
|
|
| import httpx |
| import polars as pl |
|
|
| from datadex import materialize |
|
|
|
|
| def fetch_bytes( |
| url: str, |
| *, |
| timeout: float | httpx.Timeout = 120.0, |
| follow_redirects: bool = True, |
| ) -> bytes: |
| """Download the content at ``url`` and return the raw bytes. |
| |
| The request is attempted with standard TLS verification. If that fails due to |
| certificate validation errors (common behind corporate proxies), a single |
| retry is performed with verification disabled while emitting a warning. |
| """ |
|
|
| headers = {"User-Agent": "datadex/0.1"} |
|
|
| try: |
| response = httpx.get( |
| url, |
| follow_redirects=follow_redirects, |
| timeout=timeout, |
| headers=headers, |
| ) |
| except httpx.HTTPError as exc: |
| if "CERTIFICATE_VERIFY_FAILED" not in repr(exc): |
| raise |
|
|
| warnings.warn( |
| f"Falling back to insecure TLS download for {url}", |
| RuntimeWarning, |
| stacklevel=2, |
| ) |
| else: |
| response.raise_for_status() |
| return response.content |
|
|
| response = httpx.get( |
| url, |
| follow_redirects=follow_redirects, |
| timeout=timeout, |
| headers=headers, |
| verify=False, |
| ) |
| response.raise_for_status() |
| return response.content |
|
|
|
|
| def world_development_indicators() -> pl.DataFrame: |
| """ |
| World Development Indicators (WDI) is the World Bank's premier compilation of cross-country comparable data on development. |
| |
| Bulk data download is available at https://datatopics.worldbank.org/world-development-indicators/ |
| """ |
|
|
| url = "https://databank.worldbank.org/data/download/WDI_CSV.zip" |
|
|
| archive_bytes = fetch_bytes(url, timeout=300.0) |
|
|
| with zipfile.ZipFile(io.BytesIO(archive_bytes)) as archive: |
| with archive.open("WDICSV.csv") as csv_file: |
| df = pl.read_csv(csv_file) |
|
|
| |
| df = df.unpivot( |
| index=["Country Name", "Country Code", "Indicator Name", "Indicator Code"], |
| value_name="Indicator Value", |
| variable_name="Year", |
| ) |
|
|
| df = df.with_columns(pl.col("Year").cast(pl.Int32)) |
|
|
| df = df.rename( |
| { |
| "Country Name": "country_name", |
| "Country Code": "country_code", |
| "Indicator Name": "indicator_name", |
| "Indicator Code": "indicator_code", |
| "Year": "year", |
| "Indicator Value": "indicator_value", |
| } |
| ) |
|
|
| df = df.drop_nulls(subset=["indicator_value"]) |
|
|
| return df.sort(["country_code", "year", "indicator_code"]) |
|
|
|
|
| def main() -> None: |
| materialize(world_development_indicators) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
