Add post code dataset

download_arcgis_data.py

@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+"""Download ArcGIS data and convert to Parquet."""
+
+# Run it with:
+#   uv run download_arcgis_data.py
+
+import time
+import zipfile
+import httpx
+import polars as pl
+from pathlib import Path
+from tqdm import tqdm
+
+URL = "https://www.arcgis.com/sharing/rest/content/items/077631e063eb4e1ab43575d01381ec33/data"
+
+BASE_DATA_PATH = Path("./data_sources")
+BASE_DATA_PATH.mkdir(exist_ok=True)
+DOWNLOAD_PATH = BASE_DATA_PATH / "arcgis_data.zip"
+EXTRACT_PATH = BASE_DATA_PATH / "arcgis_extracted"
+PARQUET_PATH = BASE_DATA_PATH / "arcgis_data.parquet"
+
+MAX_RETRIES = 3
+
+
+def download_with_progress(url: str, output_path: Path) -> None:
+    """Download a file with progress bar and retry logic."""
+    for attempt in range(1, MAX_RETRIES + 1):
+        try:
+            with httpx.stream(
+                "GET",
+                url,
+                follow_redirects=True,
+                timeout=httpx.Timeout(30.0, read=None),
+            ) as response:
+                response.raise_for_status()  # pyright: ignore[reportUnusedCallResult]
+                total = int(response.headers.get("content-length", 0))
+
+                with open(output_path, "wb") as f, tqdm(
+                    total=total,
+                    unit="B",
+                    unit_scale=True,
+                    unit_divisor=1024,
+                    desc="Downloading",
+                ) as pbar:
+                    for chunk in response.iter_bytes(chunk_size=8192):
+                        f.write(chunk)
+                        pbar.update(len(chunk))
+                return  # Success
+        except (httpx.ConnectError, httpx.ReadTimeout) as e:
+            if attempt < MAX_RETRIES:
+                wait = 2**attempt
+                print(f"Attempt {attempt} failed: {e}. Retrying in {wait}s...")
+                time.sleep(wait)
+            else:
+                raise
+
+
+def extract_zip(zip_path: Path, extract_path: Path) -> list[Path]:
+    """Extract ZIP file and return list of extracted files."""
+    print("Extracting ZIP file...")
+    extract_path.mkdir(exist_ok=True)
+
+    with zipfile.ZipFile(zip_path, "r") as zf:
+        zf.extractall(extract_path)
+        return [extract_path / name for name in zf.namelist()]
+
+
+def find_data_file(extract_path: Path) -> Path:
+    """Find the main data file (CSV, XLSX, or similar) in extracted files."""
+    # Look for common data file extensions
+    for ext in ["*.csv", "*.xlsx", "*.xls", "*.json", "*.geojson"]:
+        files = list(extract_path.rglob(ext))
+        if files:
+            # Return the largest file if multiple found
+            return max(files, key=lambda f: f.stat().st_size)
+
+    raise FileNotFoundError(f"No data file found in {extract_path}")
+
+
+def convert_to_parquet(data_path: Path, parquet_path: Path) -> None:
+    """Convert data file to Parquet using Polars."""
+    print(f"Converting {data_path.name} to Parquet...")
+
+    suffix = data_path.suffix.lower()
+
+    if suffix == ".csv":
+        df = pl.read_csv(data_path, try_parse_dates=True)
+    elif suffix in [".xlsx", ".xls"]:
+        df = pl.read_excel(data_path)
+    elif suffix in [".json", ".geojson"]:
+        df = pl.read_json(data_path)
+    else:
+        raise ValueError(f"Unsupported file format: {suffix}")
+
+    df.write_parquet(parquet_path, compression="zstd")
+    print(f"Saved to {parquet_path}")
+    print(f"Rows: {df.height:,}")
+    print(f"Columns: {df.columns}")
+    print(f"Original size: {data_path.stat().st_size / 1024**2:.1f} MB")
+    print(f"Parquet size: {parquet_path.stat().st_size / 1024**2:.1f} MB")
+
+
+def main() -> None:
+    if not DOWNLOAD_PATH.exists():
+        download_with_progress(URL, DOWNLOAD_PATH)
+    else:
+        print(f"File already exists at {DOWNLOAD_PATH}, skipping download")
+
+    # Check if it's a ZIP file
+    if zipfile.is_zipfile(DOWNLOAD_PATH):
+        extracted_files = extract_zip(DOWNLOAD_PATH, EXTRACT_PATH)
+        print(f"Extracted {len(extracted_files)} files")
+        data_file = find_data_file(EXTRACT_PATH)
+    else:
+        # Not a ZIP, treat as direct data file
+        data_file = DOWNLOAD_PATH
+
+    convert_to_parquet(data_file, PARQUET_PATH)
+
+
+if __name__ == "__main__":
+    main()

download_land_registry.py

@@ -2,7 +2,7 @@
 """Download Land Registry price paid data and convert to Parquet."""
 
 # Run it with:
-#   uv run --with httpx --with polars --with tqdm python download_land_registry.py
+#   uv run download_land_registry.py
 
 # The download failed in this environment due to network restrictions, but the script will work on your local machine. The ~5GB CSV should compress to roughly ~1GB in Parquet format with ZSTD compression.