pandas 获取不在其他数据框中的行 [英] pandas get rows which are NOT in other dataframe

问题描述

我有两个大熊猫数据框，它们有一些共同点.

I've two pandas data frames which have some rows in common.

假设dataframe2是dataframe1的子集.

Suppose dataframe2 is a subset of dataframe1.

如何获取dataframe1中不在dataframe2中的行?

df1 = pandas.DataFrame(data = {'col1' : [1, 2, 3, 4, 5], 'col2' : [10, 11, 12, 13, 14]}) 
df2 = pandas.DataFrame(data = {'col1' : [1, 2, 3], 'col2' : [10, 11, 12]})

推荐答案

一种方法是存储两个dfs的内部合并结果，然后我们可以简单地在一个列的值不在此通用范围内时选择行:

One method would be to store the result of an inner merge form both dfs, then we can simply select the rows when one column's values are not in this common:

In [119]:

common = df1.merge(df2,on=['col1','col2'])
print(common)
df1[(~df1.col1.isin(common.col1))&(~df1.col2.isin(common.col2))]
   col1  col2
0     1    10
1     2    11
2     3    12
Out[119]:
   col1  col2
3     4    13
4     5    14

编辑

您发现的另一种方法是使用isin，它会产生NaN行，您可以将其删除:

Another method as you've found is to use isin which will produce NaN rows which you can drop:

In [138]:

df1[~df1.isin(df2)].dropna()
Out[138]:
   col1  col2
3     4    13
4     5    14

但是，如果df2不能以相同的方式开始行，那么它将行不通:

However if df2 does not start rows in the same manner then this won't work:

df2 = pd.DataFrame(data = {'col1' : [2, 3,4], 'col2' : [11, 12,13]})

将产生整个df:

In [140]:

df1[~df1.isin(df2)].dropna()
Out[140]:
   col1  col2
0     1    10
1     2    11
2     3    12
3     4    13
4     5    14

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