如何将特定列转换为 pandas 中的行关联其他列值

本文介绍了如何将特定列转换为 pandas 中的行关联其他列值的处理方法，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

我正在尝试在熊猫中进行转置操作，但条件是一列的值应与转置的行相关联. 下面给出的示例将说明更好的方法: 数据如下:

Hi I am trying to do transpose operation in pandas, but the condition is the value of one column should be associated with the transposed rows. The example given below will explain the better way: the data is looks like:

A 1 2 3 4 51 52 53 54 B 11 22 23 24 71 72 73 74

我要这样做的结果:

A 1 51 A 2 52 A 3 53 A 4 54 B 11 71 B 22 72 B 23 73 B 24 74

在第一行中，数据在单行中，我想在另一列中将数据从1转换为4，并将值"A"转置.谁能建议我该怎么做?

In first row, the data is in single row, I want to transpose data from 1 to 4 with the value 'A' in other column. Can anyone suggest how can I do this??

推荐答案

似乎您需要 melt 或 stack :

It seems you need melt or stack:

print (df) 0 1 2 3 4 0 A 1 2 3 4 1 B 11 22 23 24 df1 = pd.melt(df, id_vars=0).drop('variable', axis=1).sort_values(0) df1.columns = list('ab') print (df1) a b 0 A 1 2 A 2 4 A 3 6 A 4 1 B 11 3 B 22 5 B 23 7 B 24 df2 = df.set_index(0).stack().reset_index(level=1, drop=True).reset_index(name='a') df2.columns = list('ab') print (df2) a b 0 A 1 1 A 2 2 A 3 3 A 4 4 B 11 5 B 22 6 B 23 7 B 24

通过评论

#set index with first column df = df.set_index(0) #create MultiIndex cols = np.arange(len(df.columns)) df.columns = [ cols // 4, cols % 4] print (df) 0 1 0 1 2 3 0 1 2 3 0 A 1 2 3 4 51 52 53 54 B 11 22 23 24 71 72 73 74 #stack, reset index names, remove level and reset index df1 = df.stack().rename_axis((None, None)).reset_index(level=1, drop=True).reset_index() #set new columns names df1.columns = ['a','b','c'] print (df1) a b c 0 A 1 51 1 A 2 52 2 A 3 53 3 A 4 54 4 B 11 71 5 B 22 72 6 B 23 73 7 B 24 74