Pandas读取字典生成DataFrame时文本拆分多列的解决方法

先导入必要的库，再按以下流程处理：

1. 将原嵌套字典转为DataFrame

import pandas as pd
import numpy as np

original_dict = {
    0: {0: "It's chic", 1: 'Samsung and Panasonic were overpriced.', 2: 'Others that compare are much more expensive.', 3: "Can't beat it at the price.", 4: 'I bought the more expensive case  for my 8.9 but it made my kindle very heavy.'},
    1: {0: " looks expensive but it's affordable", 1: np.nan, 2: np.nan, 3: np.nan, 4: np.nan},
    2: {0: ' what more can you want.', 1: np.nan, 2: np.nan, 3: np.nan, 4: np.nan}
}

df = pd.DataFrame(original_dict)

2. 合并每行的非空文本
   原字典转成DataFrame后会生成3列（对应外层键0、1、2），我们需要把每行里的非空字符串拼接起来：

df['combined'] = df.apply(lambda row: ''.join(str(val) for val in row if pd.notna(val)), axis=1)

3. 转换为目标格式的字典
   把合并后的单列数据转成你需要的嵌套字典结构：

dict_2 = {0: df['combined'].to_dict()}

运行以上代码后，dict_2就会和你指定的格式完全一致。

内容的提问来源于stack exchange，提问作者Timskouten