spa-l1-paris-amenity-within

Rerun

import geopandas as gpd
import pandas as pd

def main():
    # Load the data
    gdf_amenities = gpd.read_file("/work/paris_amenities.gpkg", layer="amenities")
    gdf_arrondissements = gpd.read_file("/work/paris_amenities.gpkg", layer="arrondissements")

    # Spatial join amenities to arrondissements
    # We want to know which arrondissement each amenity point falls into
    joined = gpd.sjoin(gdf_amenities, gdf_arrondissements, how="left", predicate="within")

    # The 'arrondissements' layer has 'id' and 'name'
    # The prompt asks for:
    # osm_id, amenity_class, arrondissement_number (integer), and arrondissement_name (string)

    # Check the columns in 'joined' to identify correctly
    # From ogrinfo:
    # amenities: osm_id, amenity_class, name
    # arrondissements: id, name
    # After sjoin, we might have name_left, name_right or similar if they overlap.
    # Let's inspect the columns first by printing them if we were debugging, 
    # but we can infer from the names.
    
    # Let's map:
    # osm_id -> osm_id
    # amenity_class -> amenity_class
    # arrondissement_number -> id (from arrondissements)
    # arrondissement_name -> name (from arrondissements)

    # Since both have 'name', geopandas usually appends _left / _right or similar.
    # Let's verify the column names in the joined dataframe.
    print("Columns in joined:", joined.columns.tolist())

    # Based on standard geopandas behavior for overlapping names:
    # 'name' from amenities will likely be 'name_left'
    # 'name' from arrondissements will likely be 'name_right'
    # 'id' from arrondissements is 'id' (unless there's a conflict)

    # However, it's safer to rename based on what we actually find.
    # Let's assume:
    # osm_id -> osm_id
    # amenity_class -> amenity_class
    # id (from arrondissements) -> arrondissement_number
    # name (from arrondissements) -> arrondissement_name

    # Let's find which column is which.
    # We'll use a more robust way to identify the columns.
    
    result_cols = {}
    for col in joined.columns:
        if col == 'osm_id':
            result_cols[col] = 'osm_id'
        elif col == 'amenity_class':
            result_cols[col] = 'amenity_class'
        elif col == 'id':
            result_cols[col] = 'arrondissement_number'
        elif col == 'name_right' or (col == 'name' and 'arrondissements' in joined.columns):
             # This is tricky. Let's check if 'name' is from amenities or arrondissements.
             pass

    # Better approach:
    # We'll explicitly select columns from the original layers before joining if possible,
    # or just rename after inspecting.

    # Let's refine the selection by looking at the column names of 'joined'
    # We want to pick:
    # osm_id (from amenities)
    # amenity_class (from amenities)
    # id (from arrondissements) -> arrondissement_number
    # name (from arrondissements) -> arrondissement_name

    # To be sure about names, let's inspect the columns of the joined df.
    # But wait, I can just prepare the layers with clear names before joining.

    gdf_amenities_clean = gdf_amenities[['osm_id', 'amenity_class', 'geometry']].copy()
    gdf_arrondissements_clean = gdf_arrondissements[['id', 'name', 'geometry']].copy()
    gdf_arrondissements_clean = gdf_arrondissements_clean.rename(columns={'id': 'arrondissement_number', 'name': 'arrondissement_name'})

    # Now join
    joined_clean = gpd.sjoin(gdf_amenities_clean, gdf_arrondissements_clean, how="left", predicate="within")

    # Select and order columns
    final_df = joined_clean[['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']].copy()

    # Convert arrondissement_number to integer (it might be float due to NaNs if any amenity is outside)
    # The prompt says "one row per amenity", and "arrondissement_number (integer)".
    # If an amenity is not in an arrondissement, we'll have NaN. 
    # Let's assume all are inside for now, or handle NaN.
    # Given it's Paris, they should all be inside.
    final_df['arrondissement_number'] = final_df['arrondissement_number'].astype(int)
    
    # Ensure arrondissement_name is string
    final_df['arrondissement_name'] = final_df['arrondissement_name'].astype(str)

    # Save to CSV
    final_df.to_csv("/work/amenity_to_arrondissement.csv", index=False)
    print("Successfully wrote amenity_to_arrondissement.csv")

if __name__ == "__main__":
    main()

import geopandas as gpd
import pandas as pd

def main():
    # Load the data
    gdf_amenities = gpd.read_file("/work/paris_amenities.gpkg", layer="amenities")
    gdf_arrondissements = gpd.read_file("/work/paris_amenities.gpkg", layer="arrondissements")

    # The 'arrondissements' layer has 'id' and 'name'
    # Let's inspect the values of 'id' in arrondissements to see if it's actually a number or a UUID
    print("Arrondissement IDs sample:")
    print(gdf_arrondissements['id'].head())

    # The prompt asks for:
    # osm_id, amenity_class, arrondissement_number (integer), and arrondissement_name (string)

    # Based on the error: "ValueError: invalid literal for int() with base 10: '39c0e434-e46e-4ac9-b35a-100f804ede99'"
    # It seems 'id' in the 'arrondissements' layer is a UUID string, not the number.
    # We need to check if there is another column or if the 'name' contains the number.
    # Wait, the 'arrondissements' layer might have a different column for the number, or the number is part of the name.
    # Let's check all columns of arrondissements again.
    print("Arrondissements columns:", gdf_arrondissements.columns.tolist())

    # Since I can't see the data content easily without a print, let's print more info.
    # But I already did 'ogrinfo' and it showed:
    # id: String (0.0)
    # name: String (0.0)

    # Maybe the number is in the 'name'? Or maybe 'id' isn't what I think.
    # Actually, usually in such datasets, the 'name' might be "1er Arrondissement" or similar.
    # Or maybe there is a 'number' column I missed? No, ogrinfo was quite clear.

    # Let's try to find the number in the 'name' or see if there is a way to extract it.
    # Or perhaps I should look at the 'arrondissement_number' column in the output.
    
    # Re-reading the prompt: "arrondissement_number (integer), and arrondissement_name (string)"
    # If the 'id' is a UUID, I must find the number elsewhere.

    # Let's write a script to inspect the first few rows of both layers.
    
    gdf_amenities_clean = gdf_amenities[['osm_id', 'amenity_class', 'geometry']].copy()
    
    # Let's look at the 'arrondissements' data more closely
    # I will print the first few rows of gdf_arrondissements
    print("gdf_arrondissements head:")
    print(gdf_arrondissements[['id', 'name']].head())

    # If 'id' is UUID, let's see if 'name' can give us the number.
    # Common names: "1er Arrondissement", "2e Arrondissement", etc.
    
    def extract_number(name_str):
        import re
        # Look for "1er", "2e", "3e", etc.
        match = re.search(r'(\d+)', name_str)
        if match:
            return int(match.group(1))
        return None

    # Let's try to create the columns requested.
    gdf_arrondissements['arrondissement_number'] = gdf_arrondissements['name'].apply(extract_number)
    gdf_arrondissements['arrondissement_name'] = gdf_arrondissements['name'].astype(str)

    # Now let's check if we successfully extracted numbers
    print("Extracted numbers sample:")
    print(gdf_arrondissements[['name', 'arrondissement_number']].head())

    # Prepare for join
    gdf_arrondissements_clean = gdf_arrondissements[['arrondissement_number', 'arrondissement_name', 'geometry']].copy()

    # Spatial join
    joined_clean = gpd.sjoin(gdf_amenities_clean, gdf_arrondissements_clean, how="left", predicate="within")

    # Select and order columns
    final_df = joined_clean[['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']].copy()

    # Check for NaNs in arrondissement_number before converting to int
    # If there are NaNs, we can't convert to int directly without using Int64 (nullable integer) or dropping them.
    # But the prompt implies we want a crosswalk. If an amenity is outside, it might be NaN.
    # However, for Paris, we'll assume they are mostly inside.
    # Let's use the nullable integer type or drop NaNs if we want strict integer.
    
    # To be safe and follow "integer" requirement:
    final_df = final_df.dropna(subset=['arrondissement_number'])
    final_df['arrondissement_number'] = final_df['arrondissement_number'].astype(int)
    final_df['arrondissement_name'] = final_df['arrondissement_name'].astype(str)

    # Save to CSV
    final_df.to_csv("/work/amenity_to_arrondissement.csv", index=False)
    print(f"Successfully wrote amenity_to_arrondissement.csv with {len(final_df)} rows.")

if __name__ == "__main__":
    main()