Master Python Dictionary Merging: From Simple Loops to Pandas Grouping

1. Introduction

Hi, I’m PiPi. In a Python enthusiasts group a member asked how to process a list of dictionaries that contain time, content and a nested speaker list. The original data looks like this:

a = [
    {'time': '8:30-9:30', 'content': '开场致词', 'speaker': [{'name': '李明', 'hs': '重庆附属永川'}]},
    {'time': '8:30-9:30', 'content': '开场致词', 'speaker': [{'name': '主席:李伟', 'hs': '苏州附属院'}]},
    {'time': '8:30-9:30', 'content': '开场致词', 'speaker': [{'name': '王斌', 'hs': '佛山市院'}]}
]

The desired output is a single dictionary that merges the speaker entries while keeping the common time and content fields.

2. Basic Implementation

The first solution builds a new dictionary by iterating over the list and appending each speaker entry:

a = [
    {'time': '8:30-9:30', 'content': '开场致词', 'speaker': [{'name': '李明', 'hs': '重庆附属永川'}]},
    {'time': '8:30-9:30', 'content': '开场致词', 'speaker': [{'name': '主席:李伟', 'hs': '苏州附属院'}]},
    {'time': '8:30-9:30', 'content': '开场致词', 'speaker': [{'name': '王斌', 'hs': '佛山市院'}]}
]
new_dict = {}
new_lst = []
for item in a:
    new_dict.setdefault('speaker', []).append(item['speaker'])
front_dict = {'time': '8:30-9:30', 'content': '开场致词'}
final_dict = {**front_dict, **new_dict}
print(final_dict)

This produces the expected merged structure (see image).

3. Optimized Solutions

Using itertools.groupby and operator.itemgetter the same result can be obtained in a single comprehension:

# This version
from itertools import groupby
from operator import itemgetter

[dict(zip(('time', 'content', 'speaker'),
          (*key, sum([i['speaker'] for i in value], []))))
 for key, value in groupby(a, itemgetter('time', 'content'))]

When Pandas is available, the operation becomes even more concise:

import pandas as pd
pd.DataFrame(a).groupby(['time', 'content']).speaker.sum().reset_index().to_dict(orient='records')

4. Summary

The article demonstrates several ways to aggregate nested dictionary data in Python, from straightforward loops to advanced grouping with itertools and pandas, giving readers practical code snippets that can be adapted to similar data‑processing tasks.