geopandas双层循环中如何求和大面内小面统计值并存入大面新列

你原有写法的问题主要集中在两点：

1. 没有提前给large_polygon初始化smth_sum字段，直接赋值会触发索引对齐异常
2. 计算小面点指标时，单个小面对应多个点的场景下，直接取df_idx['someAttribute']得到的是Series类型，直接赋值会导致smth列不是纯数值类型，无法正常求和

完整修改后代码 #

import geopandas as gpd

small_polygon = gpd.read_file(r'R:\...\small.shp')
large_polygon = gpd.read_file(r'R:\...\large.shp')
points = gpd.read_file(r'R:\...\points.shp')

# 提前初始化求和字段，默认值设为0避免空值问题
large_polygon['smth_sum'] = 0

SmallJoin =gpd.sjoin(small_polygon, large_polygon)[['FID', 'ID', 'someValue','geometry']]

for i in large_polygon.index:
    df_i = SmallJoin[SmallJoin['FID'] == i]
    # 跳过没有关联小面的大面，减少无效计算
    if df_i.empty:
        continue

    # 此处执行其他处理，例如计算小面面积
    df_res = gpd.overlay(large_polygon, df_i, how='intersection')
    df_res['area'] = round((df_res.apply(lambda row: row.geometry.area, axis=1)), 4)

    # 已得到落在当前大面内的所有小面数据
    df_res_2 = df_res[df_res['FID_1'] == i]

    # 统计小面内的点数据指标
    PointsJoin =gpd.sjoin(points, df_res)[['ID','someAttribute', 'someAttribute2','geometry']]

    for idx, val in df_res_2['ID'].items():
        df_idx = PointsJoin[PointsJoin['ID'] == val]
        # 单个小面内多个点时先对属性求和，保证得到单个数值
        number_of_somethin = df_idx['someAttribute'].sum() + 121 + df_idx['someAttribute2'].sum()
        df_res_2.loc[idx, 'smth'] = number_of_somethin
    
    # 对当前大面对应的所有小面smth值求和，写入大面字段
    large_polygon.loc[i, 'smth_sum'] = df_res_2['smth'].sum()

# 处理完成后可以保存结果
# large_polygon.to_file(r'R:\...\large_with_sum.shp', encoding='utf-8')

原错误写法原因说明 #

• large_polygon.loc[i, 'smth_sum'] = df_res_2['smth']：试图把一整列Series赋值给单个单元格，类型不匹配
• large_polygon['smth_sum'] = large_polygon[large_polygon['FID'] == df_res_2['FID_1'].sum()]：逻辑完全错误，将大面FID和小面FID的求和结果做匹配，和需求完全不符
• 你写的large_polygon.loc[i, 'smth_sum'] = df_res_2['smth'].sum()本身逻辑接近正确，只是因为前面没有初始化字段、且smth列数值计算错误才无法得到正确结果

内容的提问来源于stack exchange，提问作者g123456k