spa-l1-paris-amenity-within

Rerun

import geopandas as gpd

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")

    # Perform a spatial join
    # We want to know which arrondissement each amenity falls into
    # op='within' or just default (intersects) for point in polygon
    joined = gpd.sjoin(gdf_amenities, gdf_arrondissements, how="left", predicate="within")

    # Prepare the output dataframe
    # Columns required: osm_id, amenity_class, arrondissement_number, arrondissement_name
    # Based on ogrinfo:
    # amenities: osm_id, amenity_class, name
    # arrondissements: id, name
    
    # Note: The 'id' in arrondissements seems to be the number (String). 
    # Let's check the 'id' content later or just convert it.
    
    output_df = joined[['osm_id', 'amenity_class', 'id', 'name']].copy()
    output_df.columns = ['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']

    # Convert arrondissement_number to integer
    # It might be "1", "2", etc. or "01", "02".
    # We should handle potential non-integer values or NaNs if any amenity is outside.
    output_df['arrondissement_number'] = output_df['arrondissement_number'].astype(float).astype(int)

    # Write to CSV
    output_df.to_csv("/work/amenity_to_arrondissement.csv", index=False)
    print("Successfully created amenity_to_arrondissement.csv")

if __name__ == "__main__":
    main()

import geopandas as gpd

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")

    # Print columns to debug
    print("Amenities columns:", gdf_amenities.columns.tolist())
    print("Arrondissements columns:", gdf_arrondissements.columns.tolist())

    # Perform a spatial join
    joined = gpd.sjoin(gdf_amenities, gdf_arrondissements, how="left", predicate="within")

    # Columns expected from amenities: osm_id, amenity_class, name
    # Columns expected from arrondissements: id, name
    # After sjoin, they will have suffixes if they overlap.
    # Let's check the joined columns.
    print("Joined columns:", joined.columns.tolist())

    # The 'name' column in amenities and 'name' in arrondissements will likely be 'name_left' and 'name_right'
    # or 'name_x' and 'name_y' depending on geopandas version.
    # However, we only need arrondissement_name and arrondissement_number.
    
    # Identify the columns for arrondissement_number and arrondissement_name
    # Since 'id' and 'name' are in arrondissements, after sjoin (left),
    # they should be 'id' and 'name_right' (if 'name' was also in amenities) 
    # or just 'id' and 'name' if 'name' was only in one.
    # Wait, both have 'name'.
    
    # Let's look at the print output from the previous error to see what happened.
    # It said 'name' was not in index.
    
    # Let's find the columns that correspond to arrondissement info.
    # We want 'id' from arrondissements (as arrondissement_number)
    # and 'name' from arrondissements (as arrondissement_name)
    
    # After sjoin, columns from the right gdf (arrondissements) are added.
    # If 'name' is in both, the right one becomes 'name_right'.
    
    # Let's find column names dynamically or assume standard suffixing.
    
    # We want to select columns: 
    # osm_id (from amenities)
    # amenity_class (from amenities)
    # id (from arrondissements)
    # name_right (or name) (from arrondissements)

    # Actually, let's check what joined actually has.
    # If name is in both, it's name_left (amenities) and name_right (arrondissements).
    
    # Based on the error: "['name'] not in index"
    # This happened when I tried: output_df = joined[['osm_id', 'amenity_class', 'id', 'name']]
    # It implies 'name' is not in 'joined'.
    
    # Let's assume 'name' from arrondissements became 'name_right'
    
    # I will rewrite the selection to be safer.
    
    # We will identify columns by looking for suffixes.
    
    # Columns from amenities: osm_id, amenity_class, name
    # Columns from arrondissements: id, name
    
    # Expected columns in joined:
    # osm_id, amenity_class, name_left, id, name_right, geometry, index_right
    
    # Let's just find the right columns.
    
    target_osm_id = 'osm_id'
    target_amenity_class = 'amenity_class'
    target_arr_num = 'id'
    
    # For arrondissement name, it's either 'name' or 'name_right'
    if 'name_right' in joined.columns:
        target_arr_name = 'name_right'
    elif 'name' in joined.columns:
        # This would mean 'name' was only in one of them or didn't clash
        # But if 'name' is in both, geopandas suffixes them.
        # If only in arrondissements, it's 'name'.
        target_arr_name = 'name'
    else:
        # Fallback/Error
        raise KeyError("Could not find arrondissement name column")

    output_df = joined[[target_osm_id, target_amenity_class, target_arr_num, target_arr_name]].copy()
    output_df.columns = ['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']

    # Convert arrondissement_number to integer
    # Remove NaNs if any (amenities outside)
    output_df = output_df.dropna(subset=['arrondissement_number'])
    output_df['arrondissement_number'] = output_df['arrondissement_number'].astype(float).astype(int)

    # Write to CSV
    output_df.to_csv("/work/amenity_to_arrondissement.csv", index=False)
    print("Successfully created 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")

    # Perform a spatial join
    # We want to know which arrondissement each amenity falls into
    joined = gpd.sjoin(gdf_amenities, gdf_arrondissements, how="left", predicate="within")

    # Based on the previous error:
    # 'id' column in arrondissements contains UUIDs like '39c0e434-e46e-4ac9-b35a-100f804ede99'
    # BUT the prompt says we need 'arrondissement_number' (integer)
    # Looking at 'ogrinfo' for arrondissements:
    # id: String (0.0)
    # name: String (0.0)
    # However, the 'name' column of arrondissements probably contains the name like "1er Arrondissement" or just "1er".
    # Wait, if 'id' is a UUID, where is the number?
    # Maybe the number is part of the 'name'? Or maybe I should look for another column?
    # The ogrinfo only showed 'id' and 'name'.
    
    # Let's check the content of 'name' and 'id' in arrondissements.
    print("Arrondissement Sample Data:")
    print(gdf_arrondissements[['id', 'name']].head())
    
    # If 'id' is a UUID, let's see if 'name' contains the number.
    # Common pattern in Paris: "1er Arrondissement", "2e Arrondissement", etc.
    # Or just "1er", "2e"...
    
    # Let's try to extract the number from the 'name' column if possible.
    # But first, let's just see what's in there.
    
    # If 'name' is something like "1er Arrondissement", we can extract the digit.
    
    # Let's try to find a column that actually has the integer number.
    # Since it's not 'id', maybe 'name' is the key.

    # Let's refine the selection.
    # We need: osm_id, amenity_class, arrondissement_number, arrondissement_name
    
    # According to the error:
    # joined columns: ['osm_id', 'amenity_class', 'name_left', 'geometry', 'index_right', 'id', 'name_right']
    
    # We'll take:
    # osm_id = 'osm_id'
    # amenity_class = 'amenity_class'
    # arrondissement_name = 'name_right'
    
    # For arrondissement_number, we'll try to extract it from 'name_right'.
    
    # Let's write a helper to extract number.
    import re
    def extract_number(s):
        if pd.isna(s):
            return None
        match = re.search(r'(\d+)', str(s))
        if match:
            return int(match.group(1))
        return None

    output_df = pd.DataFrame()
    output_df['osm_id'] = joined['osm_id']
    output_df['amenity_class'] = joined['amenity_class']
    output_df['arrondissement_name'] = joined['name_right']
    output_df['arrondissement_number'] = joined['name_right'].apply(extract_number)

    # Drop amenities that didn't fall into an arrondissement
    output_df = output_df.dropna(subset=['arrondissement_number'])
    output_df['arrondissement_number'] = output_df['arrondissement_number'].astype(int)

    # Write to CSV
    output_df.to_csv("/work/amenity_to_arrondissement.csv", index=False)
    print("Successfully created amenity_to_arrondissement.csv")

if __name__ == "__main__":
    main()

import geopandas as gpd
import pandas as pd
import re

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")

    # Perform a spatial join
    # 'within' ensures the amenity point is inside the arrondissement polygon
    joined = gpd.sjoin(gdf_amenities, gdf_arrondissements, how="left", predicate="within")

    # Based on previous debugging:
    # amenities columns: ['osm_id', 'amenity_class', 'name', 'geometry']
    # arrondissements columns: ['id', 'name', 'geometry']
    # After sjoin, 'name' from arrondissements becomes 'name_right'
    
    def extract_number(s):
        if pd.isna(s):
            return None
        # Find the first sequence of digits in the string
        match = re.search(r'(\d+)', str(s))
        if match:
            return int(match.group(1))
        return None

    # Create the output DataFrame
    output_df = pd.DataFrame()
    output_df['osm_id'] = joined['osm_id']
    output_df['amenity_class'] = joined['amenity_class']
    output_df['arrondissement_name'] = joined['name_right']
    output_df['arrondissement_number'] = joined['name_right'].apply(extract_number)

    # Drop rows where no arrondissement was found (if any)
    # The user asked for one row per amenity, and presumably those that fall in an arrondissement.
    # If an amenity is outside, it won't have an arrondissement number.
    output_df = output_df.dropna(subset=['arrondissement_number'])
    
    # Ensure types are correct
    output_df['arrondissement_number'] = output_df['arrondissement_number'].astype(int)
    output_df['osm_id'] = output_df['osm_id'].astype(int)
    output_df['amenity_class'] = output_df['amenity_class'].astype(str)
    output_df['arrondissement_name'] = output_df['arrondissement_name'].astype(str)

    # Reorder columns to match the user's request exactly:
    # osm_id, amenity_class, arrondissement_number, arrondissement_name
    output_df = output_df[['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']]

    # Write to CSV
    output_df.to_csv("/work/amenity_to_arrondissement.csv", index=False)
    print("Successfully created amenity_to_arrondissement.csv")

if __name__ == "__main__":
    main()