如何基于列行拆分数据框

本文介绍了如何基于列行拆分数据框的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我有一个Excel文件，数据框有20行.在几行之后，又有一个列名行，我想根据列名行划分数据框.

I have one excel file , dataframe have 20 rows . after few rows there is again column names row, i want to divide dataframe based on column names row.

这是示例:

x 0 1 2 3 4 x 23 34 5 6

预期输出为:

df1 x 0 1 2 3 4 df2 x 23 34 5 6

推荐答案

考虑到列名是col，您可以首先使用col所在的col上j1 ...... 0 .... 1..gws-wiz.ailbZ6jJV_Y& ved = 0ahUKEwjli8P4jNrkAhUd4nMBHdvUBCcQ4dUDCAs& uact = 5"rel =" nofollow noreferrer> cumsum 值等于x乘df['col'].eq('x').cumsum()，然后为每个组创建一个数据帧，方法是使用df.iloc[]将该组第二行和列的值作为该组的第一个值，并将它们保存在字典中: /p>

Considering your column name is col , you can first group the dataframe taking a cumsum on the col where the value equals x by df['col'].eq('x').cumsum() , then for each group create a dataframe by taking the values from the 2nd row of that group and the columns as the first value of that group using df.iloc[] and save them in a dictionary:

d={f'df{i}':pd.DataFrame(g.iloc[1:].values,columns=g.iloc[0].values) for i,g in df.groupby(df['col'].eq('x').cumsum())}

print(d['df1']) x 0 0 1 1 2 2 3 3 4 4 print(d['df2']) x 0 23 1 34 2 5 3 6