如何从另一个数据框中删除 pandas 数据框 [英] How to remove a pandas dataframe from another dataframe

问题描述

如何从另一个数据框中删除熊猫数据框，就像设置的减法一样:

How to remove a pandas dataframe from another dataframe, just like the set subtraction:

a=[1,2,3,4,5]
b=[1,5]
a-b=[2,3,4]

现在我们有两个熊猫数据框，如何从df1中删除df2:

And now we have two pandas dataframe, how to remove df2 from df1:

In [5]: df1=pd.DataFrame([[1,2],[3,4],[5,6]],columns=['a','b'])
In [6]: df1
Out[6]:
   a  b
0  1  2
1  3  4
2  5  6


In [9]: df2=pd.DataFrame([[1,2],[5,6]],columns=['a','b'])
In [10]: df2
Out[10]:
   a  b
0  1  2
1  5  6

那么我们期望df1-df2的结果将是:

Then we expect df1-df2 result will be:

In [14]: df
Out[14]:
   a  b
0  3  4

该怎么做?

谢谢.

推荐答案

解决方案

使用pd.concat，然后使用drop_duplicates(keep=False)

pd.concat([df1, df2, df2]).drop_duplicates(keep=False)

看起来像

   a  b
1  3  4

说明

pd.concat将两个DataFrame加在一起，方法是将一个紧接在另一个后面.如果有任何重叠，则将通过drop_duplicates方法捕获它.但是，默认情况下drop_duplicates会保留第一个观察值，并删除所有其他观察值.在这种情况下，我们希望删除所有重复项.因此，keep=False参数可以做到这一点.

Explanation

pd.concat adds the two DataFrames together by appending one right after the other. if there is any overlap, it will be captured by the drop_duplicates method. However, drop_duplicates by default leaves the first observation and removes every other observation. In this case, we want every duplicate removed. Hence, the keep=False parameter which does exactly that.

对重复的df2的特殊说明.只有一个df2，df2中的任何行都不会被视为重复，而是会保留.仅当df2是df1的子集时，只有一个df2的此解决方案才有效.但是，如果我们两次连接df2，则可以保证它是重复的，随后将被删除.

A special note to the repeated df2. With only one df2 any row in df2 not in df1 won't be considered a duplicate and will remain. This solution with only one df2 only works when df2 is a subset of df1. However, if we concat df2 twice, it is guaranteed to be a duplicate and will subsequently be removed.

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