如何在没有"pandas.merge"的情况下将某些列上的几个pandas数据帧合并在一起? [英] How can I merge together several pandas dataframes on a certain column without 'pandas.merge'?

问题描述

我经常发现自己有几种以下形式的熊猫数据框:

I often find myself with several pandas dataframes in the following form:

import pandas as pd
df1 = pd.read_table('filename1.dat')
df2 = pd.read_table('filename2.dat')
df3 = pd.read_table('filename3.dat')

print(df1)
columnA   first_values
name1     342
name2     822
name3     121
name4     3434

print(df2)
columnA   second_values
name1     8
name2     1
name3     1
name4     2

print(df3)
columnA   third_values
name1     910
name2     301
name3     132
name4     299

我想将'columnA'上的每个数据框合并在一起，给出

I would like to merge together each of these dataframes on 'columnA', giving

columnA   first_values    second_values    third_values
name1     342             8                910
name2     822             1                301
name3     121             1                132
name4     3434            2                299

我通常会采取这种手段:

I normally resort to this hack:

merged1 = df1.merge(df2, on='columnA')

然后

merged2 = df3.merge(merged1, on='columnA')

但是，这并不适合许多数据帧.正确的方法是什么?

But this doesn't scale for many dataframes. What is the correct way to do this?

推荐答案

您可以将columnA设置为索引和concat(末尾重置索引):

You can set columnA as the index and concat (reset index at the end):

dfs = [df1, df2, df3]

pd.concat([df.set_index('columnA') for df in dfs], axis=1).reset_index()
Out: 
  columnA  first_values  second_values  third_values
0   name1           342              8           910
1   name2           822              1           301
2   name3           121              1           132
3   name4          3434              2           299

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