Pandas groupby 对象过滤
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Pandas groupby object filtering
提问by chattrat423
i have a pandas dataframe
我有一个Pandas数据框
df.columns
Index([u'car_id',u'color',u'make',u'year')]
I would like to create a new FILTERABLE object that has the count of each group (color,make,year);
我想创建一个新的 FILTERABLE 对象，该对象具有每个组的计数（颜色、品牌、年份）；
grp = df[[‘color','make','year']].groupby([‘color','make','year']).size()
which will return something like this
这将返回这样的东西
color make year count
black honda 2011 416
I would like to be able to filter it, however when I try this:
我希望能够过滤它，但是当我尝试这样做时：
grp.filter(lambda x: x[‘color']==‘black')
I receive this error
我收到此错误
TypeError: 'function' object is not iterable
类型错误：“函数”对象不可迭代
How do I leverage a 'groupby' object in order to filter the rows out?
如何利用“groupby”对象来过滤行？
回答by jezrael
I think you need add reset_indexand then output is DataFrame. Last use boolean indexing:
我认为你需要添加reset_index然后输出是DataFrame. 最后使用boolean indexing：
df = df[['color','make','year']].groupby(['color','make','year'])
.size()
.reset_index(name='count')
df1 = df[df.color == 'black']
回答by piRSquared
Option 1
Filter ahead of time
选项 1
提前过滤
cols = ['color','make','year']
df[df.color == 'black', cols].grouby(cols).size()
Option 2Use xsfor index cross sections
选项2使用xs索引截面
cols = ['color','make','year']
grp = df[cols].groupby(cols).size()
df.xs('black', level='color', drop_level=False)
or
或者
df.xs('honda', level='make', drop_level=False)
or
或者
df.xs(2011, level='year', drop_level=False)
