问题描述
我有数据框:
df = pd.DataFrame({'ID':[1,1,2,2,3,3],
'YEAR' : [2011,2012,2012,2013,2013,2014],
'V': [0,1,1,0,1,0],
'C':[00,11,22,33,44,55]})
我想按 ID 分组，并在每个组中选择 V = 0 的行.
I would like to group by ID, and select the row with V = 0 within each group.
这似乎不起作用:
print(df.groupby(['ID']).filter(lambda x: x['V'] == 0))
出现错误:
TypeError: filter 函数返回了一个 Series，但预期的是一个标量 bool
TypeError: filter function returned a Series, but expected a scalar bool
如何使用过滤器来实现目标?谢谢.
How can I use filter to achieve the goal? Thank you.
编辑:V 上的条件可能因每个组而异，例如，对于 ID 1，它可能是 V==0，对于 ID 2，它可能是 V==1，并且可以通过另一个 DF 获得此信息:
EDIT: The condition on V may vary for each group, e.g., it could be V==0 for ID 1, V==1 for ID 2, and this info can be available through another DF:
df = pd.DataFrame({'ID':[1,2,3],
'V': [0,1,0])
那么如何在每个组内进行行过滤呢?
So how to do row filtering within each group?
推荐答案
我觉得groupby没必要，用boolean indexing 仅在需要 V 为 0<的所有行时/代码>:
I think groupby is not necessary, use boolean indexing only if need all rows where V is 0:
print (df[df.V == 0])
C ID V YEAR
0 0 1 0 2011
3 33 2 0 2013
5 55 3 0 2014
但如果需要返回列 V 的至少一个值等于 0 的所有组，请添加 any，因为 filter 需要 True 或 False 用于过滤组中的所有行:
But if need return all groups where is at least one value of column V equal 0 add any, because filter need True or False for filtering all rows in group:
print(df.groupby(['ID']).filter(lambda x: (x['V'] == 0).any()))
C ID V YEAR
0 0 1 0 2011
1 11 1 1 2012
2 22 2 1 2012
3 33 2 0 2013
4 44 3 1 2013
5 55 3 0 2014
更好的测试是更改 groupby 的列 - 2012 的行被过滤掉，因为没有 V==0:
Better for testing is change column for groupby - row with 2012 is filter out because no V==0:
print(df.groupby(['YEAR']).filter(lambda x: (x['V'] == 0).any()))
C ID V YEAR
0 0 1 0 2011
3 33 2 0 2013
4 44 3 1 2013
5 55 3 0 2014
如果性能很重要，请使用 GroupBy.transform 与 布尔索引:
If performance is important use GroupBy.transform with boolean indexing:
print(df[(df['V'] == 0).groupby(df['YEAR']).transform('any')])
ID YEAR V C
0 1 2011 0 0
3 2 2013 0 33
4 3 2013 1 44
5 3 2014 0 55
详情:
print((df['V'] == 0).groupby(df['YEAR']).transform('any'))
0 True
1 False
2 False
3 True
4 True
5 True
Name: V, dtype: bool
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