如何从含字典数组的df['comments']列生成text&score完整表格？

问题背景 #

我的DataFrame的comments列包含88107行数据，每行都是由字典组成的数组，示例结构如下：

[{"text": "It will be curious to see where this heads in the long run.  CBS is on a tear but will it fit their image, will they try and establish control, overall agenda.  I've enjoyed last.fm for many years supporting through paypal donations each time I expire...it'll be interesting.","score": 0}, {"text": "Does this mean that there's now a big-name company who will fight for the repeal of the recent streaming-music royalty hike?","score": 1}, {"text": 'Also on BBC News:  http://news.bbc.co.uk/1/low/technology/6701863.stm .Nice to see a London-based co. hit the headlines.','score': 2}, {"text": "I don't understand what they do that is worth $70M a year. ","score": 3}, {"text": 'sold out too cheaply. given their leadership position, they should have ask for at least $500m','score': 4}]

我需要生成包含text和score列的完整表格，但之前写的循环代码只能处理单个数组，无法覆盖全部88107行：

l=pd.DataFrame()
for i in range(len(df['comments'][0])+1):
    n1=pd.DataFrame(data=df['comments'][i])
    l=pd.concat([l, n1], axis=0)

可行解决方案 #

方法1：explode + json_normalize（高效简洁，推荐） #

用Pandas内置的explode把每行的数组拆成单独行，再用json_normalize将字典展开为列：

import pandas as pd

# 拆分comments列的数组为单行
exploded_df = df.explode('comments', ignore_index=True)
# 展开字典为text和score列
result = pd.json_normalize(exploded_df['comments'])

这种方法无需手动循环，处理大规模数据时性能远优于循环拼接。

方法2：列表推导式扁平化字典数组 #

先把所有行的字典收集到一个列表，再一次性转为DataFrame：

# 遍历所有行，收集所有字典到一个列表
all_comments = [item for sublist in df['comments'] for item in sublist]
# 转换为目标DataFrame
result = pd.DataFrame(all_comments)

这种方式也能避免循环拼接的低效问题，适合处理88107行的大规模数据。

原代码问题说明 #

你之前的循环只遍历了第一行数组的元素个数对应的范围，相当于只处理了前N行数据（N是第一行数组的长度），并没有遍历全部88107行的comments列内容，所以无法生成完整表格。

内容的提问来源于stack exchange，提问作者doriam