使用 pandas 合并具有不同尺寸的多个数据框 [英] Merge multiple data frames with different dimensions using Pandas

问题描述

我有以下数据帧(实际上，它们超过3个).

I have the following data frames (in reality they are more than 3).

import pandas as pd
df1 = pd.DataFrame({'head1': ['foo', 'bix', 'bar'],'val': [11, 22, 32]})
df2 = pd.DataFrame({'head2': ['foo', 'xoo', 'bar','qux'],'val': [1, 2, 3,10]})
df3 = pd.DataFrame({'head3': ['xoo', 'bar',],'val': [20, 100]})
# Note that the value in column 'head' is always unique

我要做的是基于head列合并它们.每当一个数据帧中不存在head的值时，我们都将其分配为NA.

What I want to do is to merge them based on head column. And whenever the value of a head does not exist in one data frame we would assign it with NA.

最后看起来像这样:

         head1   head2  head3
 -------------------------------
 foo     11      1       NA
 bix     22      NA      NA
 bar     32      3      100
 xoo     NA      2      20
 qux     NA      10     NA

如何使用Pandas实现这一目标?

How can I achieve that using Pandas?

推荐答案

您可以使用 pandas.concat 选择axis=1来连接多个DataFrame.

You can use pandas.concat selecting the axis=1 to concatenate your multiple DataFrames.

但是请注意，我首先将df1, df2, df3的索引设置为使用变量(foo，bar等)而不是默认整数.

Note however that I've first set the index of the df1, df2, df3 to use the variables (foo, bar, etc) rather than the default integers.

import pandas as pd

df1 = pd.DataFrame({'head1': ['foo', 'bix', 'bar'],'val': [11, 22, 32]})
df2 = pd.DataFrame({'head2': ['foo', 'xoo', 'bar','qux'],'val': [1, 2, 3,10]})
df3 = pd.DataFrame({'head3': ['xoo', 'bar',],'val': [20, 100]})

df1 = df1.set_index('head1')
df2 = df2.set_index('head2')
df3 = df3.set_index('head3')


df = pd.concat([df1, df2, df3], axis = 1)

columns = ['head1', 'head2', 'head3']
df.columns = columns

print(df)

     head1  head2  head3
bar     32      3    100
bix     22    NaN    NaN
foo     11      1    NaN
qux    NaN     10    NaN
xoo    NaN      2     20

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