How to Reshape Complex Python Dictionaries into Structured Data with Simple Code

1. Introduction

Hello, I am PiPi. In a Python community a member asked how to process a list of dictionaries that contain time, content and a nested speaker list. The original data is shown below.

The expected result is a merged dictionary where speakers from the same time and content are combined, as illustrated in the following image.

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'}]
    }
]

2. Basic Implementation

The first solution, shared by a community member, uses a simple loop to collect speakers under the same key.

My own implementation follows the same idea but adds explicit merging of the static part of the dictionary.

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)

The result is correct, although the code contains some redundancy.

3. Using Pandas

A more concise solution leverages Pandas to group and aggregate the speakers.

4. Optimized Solutions

Another community member refined the code with itertools.groupby and operator.itemgetter to produce the merged structure in a single expression.

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'))]

The Pandas approach can also be written more compactly:

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

5. Conclusion

This article presented a practical Python dictionary processing problem, explored multiple implementations—including basic loops, itertools grouping, and Pandas aggregation—and provided complete, runnable code snippets that achieve the desired merged output.