Setup
d1 = pd.DataFrame(dict(
year=np.random.choice((2014, 2015, 2016), 100),
cntry=['United States' for _ in range(100)],
State=np.random.choice(states, 100),
Col1=np.random.randint(0, 20, 100),
Col2=np.random.randint(0, 20, 100),
Col3=np.random.randint(0, 20, 100),
))
df = d1.groupby(['year', 'cntry', 'State']).agg(['size', 'sum'])
df
Answer
最简单的方法就是只跑size
after groupby
d1.groupby(['year', 'cntry', 'State']).size()
year cntry State
2014 United States California 10
Florida 9
Massachusetts 8
Minnesota 5
2015 United States California 9
Florida 7
Massachusetts 4
Minnesota 11
2016 United States California 8
Florida 8
Massachusetts 11
Minnesota 10
dtype: int64
要使用计算出的df
df.xs('size', axis=1, level=1)
如果size
每列都不同。但因为size
列是相同的['Col1', 'Col2', 'Col3']
,我们可以这样做
df[('Col1', 'size')]
year cntry State
2014 United States California 10
Florida 9
Massachusetts 8
Minnesota 5
2015 United States California 9
Florida 7
Massachusetts 4
Minnesota 11
2016 United States California 8
Florida 8
Massachusetts 11
Minnesota 10
Name: (Col1, size), dtype: int64
组合视图1
pd.concat([df[('Col1', 'size')].rename('size'),
df.xs('sum', axis=1, level=1)], axis=1)
组合视图2
pd.concat([df[('Col1', 'size')].rename(('', 'size')),
df.xs('sum', axis=1, level=1, drop_level=False)], axis=1)