pandas 熊猫分组和过滤
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Pandas groupby and filter
提问by iwbabn
I have dataframe:
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]})
I would like to group by ID, and select the row with V = 0 within each group.
我想按 ID 分组，并在每组中选择 V = 0 的行。
This doesn't seem to work:
这似乎不起作用：
print(df.groupby(['ID']).filter(lambda x: x['V'] == 0))
Got an error:
出现错误：
TypeError: filter function returned a Series, but expected a scalar bool
类型错误：过滤器函数返回一个系列，但需要一个标量布尔值
How can I use filter to achieve the goal? Thank you.
如何使用过滤器来实现目标？谢谢你。
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:
编辑：V 上的条件可能因每个组而异，例如，ID 1 可能是 V==0，ID 2 可能是 V==1，并且可以通过另一个 DF 获得此信息：
df = pd.DataFrame({'ID':[1,2,3],
'V': [0,1,0])
So how to do row filtering within each group?
那么如何在每个组内进行行过滤呢？
回答by jezrael
I think groupbyis not necessary, use boolean indexingonly if need all rows where Vis 0:
我认为groupby是没有必要的，使用boolean indexing仅需要其中的所有行V是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
But if need return all groups where is at least one value of column Vequal 0add any, because filterneed Trueor Falsefor filtering all rows in group:
但是如果需要返回所有组，其中列的至少一个值V等于0add any，因为过滤器需要True或False过滤组中的所有行：
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
Better for testing is change column for groupby- row with 2012is filter out because no V==0:
更好的测试是更改列groupby- 行2012被过滤掉，因为没有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
If performance is important use GroupBy.transformwith boolean indexing:
如果性能是重要的用途GroupBy.transform有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
Detail:
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
