为了更容易reshaping，重新命名索引层

import pandas as pd
# 读取college数据集，分组后，统计本科生的SAT数学成绩信息
college = pd.read_csv('data/college.csv')
cg = college.groupby(['STABBR', 'RELAFFIL'])['UGDS', 'SATMTMID'] \
.agg(['count', 'min', 'max']).head(6)
cg

		UGDS			SATMTMID
		count	min	max	count	min	max
STABBR	RELAFFIL
AK	0	7	109.0	12865.0	0	NaN	NaN
	1	3	27.0	275.0	1	503.0	503.0
AL	0	71	12.0	29851.0	13	420.0	590.0
	1	18	13.0	3033.0	8	400.0	560.0
AR	0	68	18.0	21405.0	9	427.0	565.0
	1	14	20.0	4485.0	7	495.0	600.0

# 行索引的两级都有名字，而列索引没有名字。用rename_axis给列索引的两级命名
cg = cg.rename_axis(['AGG_COLS', 'AGG_FUNCS'], axis='columns')
cg

	AGG_COLS	UGDS			SATMTMID
	AGG_FUNCS	count	min	max	count	min	max
STABBR	RELAFFIL
AK	0	7	109.0	12865.0	0	NaN	NaN
	1	3	27.0	275.0	1	503.0	503.0
AL	0	71	12.0	29851.0	13	420.0	590.0
	1	18	13.0	3033.0	8	400.0	560.0
AR	0	68	18.0	21405.0	9	427.0	565.0
	1	14	20.0	4485.0	7	495.0	600.0

# 将AGG_FUNCS列移到行索引
cg.stack('AGG_FUNCS').head()

		AGG_COLS	UGDS	SATMTMID
STABBR	RELAFFIL	AGG_FUNCS
AK	0	count	7.0	0.0
		min	109.0	NaN
		max	12865.0	NaN
	1	count	3.0	1.0
		min	27.0	503.0

# stack默认是将列放到行索引的最内层，可以使用swaplevel改变层级
cg.stack('AGG_FUNCS').swaplevel('AGG_FUNCS', 'STABBR', axis='index').head()

		AGG_COLS	UGDS	SATMTMID
AGG_FUNCS	RELAFFIL	STABBR
count	0	AK	7.0	0.0
min	0	AK	109.0	NaN
max	0	AK	12865.0	NaN
count	1	AK	3.0	1.0
min	1	AK	27.0	503.0

# 在此前的基础上再做sort_index #.swaplevel 重排层次化索引分级顺序
cg.stack('AGG_FUNCS').swaplevel('AGG_FUNCS', 'STABBR', axis='index') \
          .sort_index(level='RELAFFIL', axis='index') \
          .sort_index(level='AGG_COLS', axis='columns').head(6)

		AGG_COLS	SATMTMID	UGDS
AGG_FUNCS	RELAFFIL	STABBR
count	0	AK	0.0	7.0
		AL	13.0	71.0
		AR	9.0	68.0
max	0	AK	NaN	12865.0
		AL	590.0	29851.0
		AR	565.0	21405.0

# 对一些列做stack，对其它列做unstack
cg.stack('AGG_FUNCS').unstack(['RELAFFIL', 'STABBR'])

AGG_COLS	UGDS						SATMTMID
RELAFFIL	0	1	0	1	0	1	0	1	0	1	0	1
STABBR	AK	AK	AL	AL	AR	AR	AK	AK	AL	AL	AR	AR
AGG_FUNCS
count	7.0	3.0	71.0	18.0	68.0	14.0	0.0	1.0	13.0	8.0	9.0	7.0
min	109.0	27.0	12.0	13.0	18.0	20.0	NaN	503.0	420.0	400.0	427.0	495.0
max	12865.0	275.0	29851.0	3033.0	21405.0	4485.0	NaN	503.0	590.0	560.0	565.0	600.0

# 对所有列做stack，会返回一个Series
cg.stack(['AGG_FUNCS', 'AGG_COLS']).head(12)
'''
STABBR  RELAFFIL  AGG_FUNCS  AGG_COLS
AK      0         count      UGDS            7.0
                             SATMTMID        0.0
                  min        UGDS          109.0
                  max        UGDS        12865.0
        1         count      UGDS            3.0
                             SATMTMID        1.0
                  min        UGDS           27.0
                             SATMTMID      503.0
                  max        UGDS          275.0
                             SATMTMID      503.0
AL      0         count      UGDS           71.0
                             SATMTMID       13.0
dtype: float64

'''

# 删除行和列索引所有层级的名称
cg.rename_axis([None, None], axis='index').rename_axis([None, None],axis='columns')

		UGDS			SATMTMID
		count	min	max	count	min	max
AK	0	7	109.0	12865.0	0	NaN	NaN
	1	3	27.0	275.0	1	503.0	503.0
AL	0	71	12.0	29851.0	13	420.0	590.0
	1	18	13.0	3033.0	8	400.0	560.0
AR	0	68	18.0	21405.0	9	427.0	565.0
	1	14	20.0	4485.0	7	495.0	600.0