dd-l1-capetown-clinics-bbox

Rerun

import pandas as pd
import json

def main():
    # Load the data
    df = pd.read_csv('capetown_clinics.csv')
    
    # Assuming the CSV has latitude and longitude columns. 
    # Let's check column names first.
    print("Columns:", df.columns.tolist())
    
    # We need to identify the lat/lon columns and the subdistrict column.
    # Based on the prompt, we expect 'latitude', 'longitude', and 'subdistrict' (or similar).
    # I'll assume 'latitude', 'longitude', and 'subdistrict' for now, 
    # but I'll inspect the data if it fails.
    
    # Check if common names exist.
    lat_col = next((c for c in df.columns if 'lat' in c.lower()), None)
    lon_col = next((c for c in df.columns if 'lon' in c.lower() or 'lng' in c.lower()), None)
    sub_col = next((c for c in df.columns if 'subdistrict' in c.lower()), None)
    
    if not lat_col or not lon_col or not sub_col:
        # If common names aren't found, we might need to adjust.
        # Let's print the first few rows to debug if this happens.
        print(df.head())
        raise ValueError(f"Could not automatically find lat/lon/subdistrict columns. Found: {df.columns.tolist()}")

    # Total count
    total_count = len(df)
    
    # Bounding box [minx, miny, maxx, maxy]
    min_lon = df[lon_col].min()
    min_lat = df[lat_col].min()
    max_lon = df[lon_col].max()
    max_lat = df[lat_col].max()
    bbox = [min_lon, min_lat, max_lon, max_lat]
    
    # Count per subdistrict
    counts_per_subdistrict = df[sub_col].value_counts().to_dict()
    
    # Prepare JSON
    output = {
        "count": int(total_count),
        "bbox": bbox,
        "count_per_subdistrict": {str(k): int(v) for k, v in counts_per_subdistrict.items()}
    }
    
    with open('clinic_inventory.json', 'w') as f:
        json.dump(output, f, indent=4)
    
    print("Successfully created clinic_inventory.json")

if __name__ == "__main__":
    main()

import pandas as pd
import json
from shapely import wkt

def main():
    # Load the data
    df = pd.read_csv('capetown_clinics.csv')
    
    # The columns are: ['clinic_id', 'name', 'subdistrict', 'wkt_geom']
    # We need to extract coordinates from 'wkt_geom'
    
    # Convert WKT to shapely geometries
    df['geometry'] = df['wkt_geom'].apply(wkt.loads)
    
    # Total count
    total_count = len(df)
    
    # Bounding box [minx, miny, maxx, maxy]
    # We can use the combined bounds of all geometries
    all_bounds = df['geometry'].apply(lambda g: g.bounds)
    # all_bounds returns (minx, miny, maxx, maxy) for each row
    # Let's stack them and get min/max
    min_lon = all_bounds.iloc[:, 0].min()
    min_lat = all_bounds.iloc[:, 1].min()
    max_lon = all_bounds.iloc[:, 2].max()
    max_lat = all_bounds.iloc[:, 3].max()
    bbox = [min_lon, min_lat, max_lon, max_lat]
    
    # Count per subdistrict
    sub_col = 'subdistrict'
    counts_per_subdistrict = df[sub_col].value_counts().to_dict()
    
    # Prepare JSON
    output = {
        "count": int(total_count),
        "bbox": bbox,
        "count_per_subdistrict": {str(k): int(v) for k, v in counts_per_subdistrict.items()}
    }
    
    with open('clinic_inventory.json', 'w') as f:
        json.dump(output, f, indent=4)
    
    print("Successfully created clinic_inventory.json")

if __name__ == "__main__":
    main()

import pandas as pd
import json
from shapely import wkt

def main():
    # Load the data
    df = pd.read_csv('capetown_clinics.csv')
    
    # The columns are: ['clinic_id', 'name', 'subdistrict', 'wkt_geom']
    
    # Convert WKT to shapely geometries
    df['geometry'] = df['wkt_geom'].apply(wkt.loads)
    
    # Total count
    total_count = len(df)
    
    # Bounding box [minx, miny, maxx, maxy]
    # Let's calculate min and max manually to avoid pandas indexing issues
    lons = []
    lats = []
    for geom in df['geometry']:
        lons.append(geom.x)
        lats.append(geom.y)
    
    bbox = [min(lons), min(lats), max(lons), max(lats)]
    
    # Count per subdistrict
    sub_col = 'subdistrict'
    counts_per_subdistrict = df[sub_col].value_counts().to_dict()
    
    # Prepare JSON
    output = {
        "count": int(total_count),
        "bbox": bbox,
        "count_per_subdistrict": {str(k): int(v) for k, v in counts_per_subdistrict.items()}
    }
    
    with open('clinic_inventory.json', 'w') as f:
        json.dump(output, f, indent=4)
    
    print("Successfully created clinic_inventory.json")

if __name__ == "__main__":
    main()