我想從任何分數列中獲取平均值、stp、偏度，同時我將我的資料集分組為其他 2 個列（組、塊）。我為此使用了此代碼-
scores_list = ['A','B','C']
descriptive_agg = df.groupby(['group','block'])[scores_list].agg(['mean', 'std','skew'])
并得到了這個資料框：
A A A B B B C C C
mean std skew mean std skew mean std skew
組塊
0負26.76470588 54.79291496 6.069163775 3.098039216 1.170553749 0.114238196 1.738755233 0.611860454 1.063953504 0 neu的29.92 70.9644464 6.275474539 3.6 1.245399698 -0.039619494 1.906404475 0.568964543 0.561075178 1 NEG 16.42391304 18.0702133 2.968326848 2.891304348 1.253185144 0.209586627 1.684455875 0.598785419 0.872917578 1 neu的16.92391304 18.49159815 2.951129818 3.5 1.172018077 -0.313988331 1.893045967 0.646930842 1.11778034
但我想在左邊有一個“分數”列，我的預期輸出是：
評分組塊平均STD SKEW A 0 NEG 26.764796 0 NEU 29.92 70.9644464 6.27547758 6.275477764 6.27547496 1 NEU 16.42391304 18.0702133 2.9683263911899139139118891339111881118
B 0 NEG 3.098039216 1.170553749 0.114238196 0 NEU 3.6 1.245399698 -0.039619694 1 NEC 2.891304348 1.25318514341 1.193186271 0.253185144 0.2095866271 1.172018077 1NE.253185144 0.2095866271.03186627710.31139883311
提前致謝！
uj5u.com熱心網友回復：
添加DataFrame.stack和：DataFrame.reorder_levels_DataFrame.sort_index
df = df.stack(0).reorder_levels([2,0,1]).sort_index()
print (df)
mean skew std
A 0 neg 26.764706 6.069164 54.792915
neu 29.920000 6.275475 70.964446
1 neg 16.423913 2.968327 18.070213
neu 16.923913 2.951130 18.491598
B 0 neg 3.098039 0.114238 1.170554
neu 3.600000 -0.039619 1.245400
1 neg 2.891304 0.209587 1.253185
neu 3.500000 -0.313988 1.172018
C 0 neg 1.738755 1.063954 0.611860
neu 1.906404 0.561075 0.568965
1 neg 1.684456 0.872918 0.598785
neu 1.893046 1.117780 0.646931
編輯：如果需要將重復值替換為空字串：
#original index
print (df.index)
MultiIndex([('A', 0, 'neg'),
('A', 0, 'neu'),
('A', 1, 'neg'),
('A', 1, 'neu'),
('B', 0, 'neg'),
('B', 0, 'neu'),
('B', 1, 'neg'),
('B', 1, 'neu'),
('C', 0, 'neg'),
('C', 0, 'neu'),
('C', 1, 'neg'),
('C', 1, 'neu')],
)
df1 = df.index.to_frame(index=False)
df1.columns = [0,1,2]
m1 = df1[0].duplicated()
m2 = df1.duplicated(subset=[0,1])
df1[0] = df1[0].mask(m1, '')
df1[1] = df1[1].mask(m2, '')
print (df1)
0 1 2
0 A 0 neg
1 neu
2 1 neg
3 neu
4 B 0 neg
5 neu
6 1 neg
7 neu
8 C 0 neg
9 neu
10 1 neg
11 neu
df.index = pd.MultiIndex.from_frame(df1)
df = df.rename_axis([None, None, None])
print (df)
mean skew std
A 0 neg 26.764706 6.069164 54.792915
neu 29.920000 6.275475 70.964446
1 neg 16.423913 2.968327 18.070213
neu 16.923913 2.951130 18.491598
B 0 neg 3.098039 0.114238 1.170554
neu 3.600000 -0.039619 1.245400
1 neg 2.891304 0.209587 1.253185
neu 3.500000 -0.313988 1.172018
C 0 neg 1.738755 1.063954 0.611860
neu 1.906404 0.561075 0.568965
1 neg 1.684456 0.872918 0.598785
neu 1.893046 1.117780 0.646931
print (df.index)
MultiIndex([('A', 0, 'neg'),
( '', '', 'neu'),
( '', 1, 'neg'),
( '', '', 'neu'),
('B', 0, 'neg'),
( '', '', 'neu'),
( '', 1, 'neg'),
( '', '', 'neu'),
('C', 0, 'neg'),
( '', '', 'neu'),
( '', 1, 'neg'),
( '', '', 'neu')],
)
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