Python3.7+Pandas1.1.3下如何将DataFrame转换为指定结构的嵌套字典？（去除冗余键并实现人口排序）

Let's fix your code to get the exact nested dictionary structure you want, while also handling the population-based sorting requirement for duplicate city names.

Corrected Code #

import pandas as pd

# Your original DataFrame setup
location = {'city_id': [22000,25000,27000,35000], 
            'population': [3971883,2720546,667137,1323], 
            'region_name': ['California','Illinois','Massachusetts','Georgia'], 
            'city_name': ['Los Angeles','Chicago','Boston','Boston']}
df = pd.DataFrame(location, columns = ['city_id', 'population','region_name', 'city_name'])

# Generate the target nested dictionary
result = df.sort_values(['city_name', 'population'], ascending=[True, False]) \
           .groupby('city_name')[['region_name', 'city_id']] \
           .apply(lambda x: x.set_index('region_name')['city_id'].to_dict()) \
           .to_dict()

print(result)

Output #

{
 'Boston': {'Massachusetts': 27000, 'Georgia': 35000},
 'Chicago': {'Illinois': 25000},
 'Los Angeles': {'California': 22000}
}

Key Fixes & Explanations #

1. Remove Redundant "city_id" Key
   Your original code used x.set_index('region_name').to_dict() which converts the entire grouped sub-DataFrame (with the city_id column) into a dictionary. By adding ['city_id'] before .to_dict(), we directly convert only the city_id values into a dictionary indexed by region_name, eliminating the extra nested city_id key.

2. Sort by Population for Duplicate Cities
   The sort_values(['city_name', 'population'], ascending=[True, False]) step ensures:

   • Groups are ordered by city name (optional, but keeps results clean)
   • Within each city group, rows are sorted descending by population. Since Python 3.7+ preserves dictionary insertion order, this ensures the region with the larger population appears first in the nested dictionary for duplicate cities like Boston.

内容的提问来源于stack exchange，提问作者che kiria