Python如何将嵌套字典列表按series拆分生成对应列的DataFrame

你可以通过字典推导式把每个内层列表转换为字段映射字典，再直接传入pandas的DataFrame构造函数即可，代码如下：

import pandas as pd

# 原始嵌套列表数据
raw_data = [[{'contributionScore': 0.841473400592804, 'variable': 'series_2'},
  {'contributionScore': 0.6113986968994141, 'variable': 'series_3'},
  {'contributionScore': 0.5985525250434875, 'variable': 'series_1'},
  {'contributionScore': 0.5641148686408997, 'variable': 'series_4'},
  {'contributionScore': 0.138543963432312, 'variable': 'series_0'}],
 [{'contributionScore': 1.1316605806350708, 'variable': 'series_1'},
  {'contributionScore': 0.5188271403312683, 'variable': 'series_4'},
  {'contributionScore': 0.38711458444595337, 'variable': 'series_3'},
  {'contributionScore': 0.35055238008499146, 'variable': 'series_0'},
  {'contributionScore': 0.06044715642929077, 'variable': 'series_2'}]]

# 逐行处理为符合DataFrame输入要求的字典结构
row_list = [{item['variable']: item['contributionScore'] for item in row} for row in raw_data]

# 生成目标DataFrame
df = pd.DataFrame(row_list)

输出结果 #

最终得到的DataFrame结构如下：

内容的提问来源于stack exchange，提问作者luca canonico