问题描述
我想做的操作类似于合并.例如，通过 inner 合并，我们得到一个数据框，其中包含出现在第一个和第二个数据框中的行.通过 outer 合并，我们得到一个数据帧，它出现在第二个数据帧的第一个 OR 中.
The operation that I want to do is similar to merger. For example, with the inner merger we get a data frame that contains rows that are present in the first AND second data frame. With the outer merger we get a data frame that are present EITHER in the first OR in the second data frame.
我需要的是一个数据框，其中包含出现在第一个数据框中但不出现在第二个数据框中的行?有没有一种快速而优雅的方法来做到这一点?
What I need is a data frame that contains rows that are present in the first data frame AND NOT present in the second one? Is there a fast and elegant way to do it?
推荐答案
像下面这样的怎么样?
print df1
Team Year foo
0 Hawks 2001 5
1 Hawks 2004 4
2 Nets 1987 3
3 Nets 1988 6
4 Nets 2001 8
5 Nets 2000 10
6 Heat 2004 6
7 Pacers 2003 12
print df2
Team Year foo
0 Pacers 2003 12
1 Heat 2004 6
2 Nets 1988 6
只要有一个非键常用的列，就可以让后缀来完成工作(如果没有非键常用的列，可以创建一个临时使用... df1['common'] = 1 和 df2['common'] = 1):
As long as there is a non-key commonly named column, you can let the added on sufffexes do the work (if there is no non-key common column then you could create one to use temporarily ... df1['common'] = 1 and df2['common'] = 1):
new = df1.merge(df2,on=['Team','Year'],how='left')
print new[new.foo_y.isnull()]
Team Year foo_x foo_y
0 Hawks 2001 5 NaN
1 Hawks 2004 4 NaN
2 Nets 1987 3 NaN
4 Nets 2001 8 NaN
5 Nets 2000 10 NaN
或者您可以使用 isin 但您必须创建一个键:
Or you can use isin but you would have to create a single key:
df1['key'] = df1['Team'] + df1['Year'].astype(str)
df2['key'] = df1['Team'] + df2['Year'].astype(str)
print df1[~df1.key.isin(df2.key)]
Team Year foo key
0 Hawks 2001 5 Hawks2001
2 Nets 1987 3 Nets1987
4 Nets 2001 8 Nets2001
5 Nets 2000 10 Nets2000
6 Heat 2004 6 Heat2004
7 Pacers 2003 12 Pacers2003
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