我有一个像这样的pandas数据框：
symbol ABHD17C ADAM19 ADAMTS1 ADAMTS1 ADM ADM
Control 15 8.118767 7.418533 10.352104 11.489224 10.868479 11.214037
15 8.035596 7.623869 10.482607 11.618165 10.569137 11.186141
15 8.066988 7.480469 10.274919 11.554862 10.744955 11.225492
15 8.047028 7.547017 10.318972 11.499156 10.831416 11.203028
15 8.153588 7.507617 10.372795 11.526810 10.844520 11.125373
15 7.888492 7.494270 10.295306 11.542063 10.750984 11.157360
30 8.021081 7.417150 10.266634 11.481706 10.685487 11.060077
30 8.111274 7.430963 10.209418 11.426212 10.662505 11.097215
30 8.052534 7.359658 10.115304 11.432636 10.597524 11.004115
30 8.010348 7.391775 10.266453 11.546372 10.572780 11.011062
30 8.012789 7.420152 10.303099 11.472436 10.708051 11.084203
30 8.074147 7.331148 10.273618 11.430159 10.649350 11.144293
60 7.989894 7.419103 10.248334 11.345593 10.541806 10.926970
60 8.077109 7.417790 10.090361 11.289033 10.458097 10.958197
60 7.983865 7.492862 10.129944 11.418077 10.522366 10.873455
60 8.053574 7.441354 10.138749 11.297438 10.547018 10.942314
60 8.044626 7.389152 10.128528 11.328647 10.503515 10.932906
60 7.966093 7.487729 10.141745 11.406319 10.533793 10.982887
90 8.164200 7.610294 9.828901 11.023355 10.427788 10.902193
90 8.130045 7.425974 9.972879 11.163064 10.339801 10.840847
90 8.208286 7.422047 9.754889 11.120437 10.411414 10.935003
90 7.935916 7.324434 9.808280 10.977562 10.251636 10.694670
90 8.226764 7.399737 9.760450 11.015849 10.545208 10.911852
90 8.211148 7.627093 9.841167 11.172223 10.446194 10.801106
第二个索引列是时间（15,30,60和90分钟），具有相同索引的行是单独的重复。我需要给这个数据帧的索引另一个级别来指示重复，但无法弄清楚如何做到这一点。有人可以给我一些建议吗？
这就是我希望输出的样子：
symbol ABHD17C ADAM19 ADAMTS1 ADAMTS1 ADM ADM \
Control 15 1 8.118767 7.418533 10.352104 11.489224 10.868479 11.214037
15 2 8.035596 7.623869 10.482607 11.618165 10.569137 11.186141
15 3 8.066988 7.480469 10.274919 11.554862 10.744955 11.225492
15 4 8.047028 7.547017 10.318972 11.499156 10.831416 11.203028
15 5 8.153588 7.507617 10.372795 11.526810 10.844520 11.125373
15 6 7.888492 7.494270 10.295306 11.542063 10.750984 11.157360
30 1 8.021081 7.417150 10.266634 11.481706 10.685487 11.060077
30 2 8.111274 7.430963 10.209418 11.426212 10.662505 11.097215
30 3 8.052534 7.359658 10.115304 11.432636 10.597524 11.004115
30 4 8.010348 7.391775 10.266453 11.546372 10.572780 11.011062
30 5 8.012789 7.420152 10.303099 11.472436 10.708051 11.084203
30 6 8.074147 7.331148 10.273618 11.430159 10.649350 11.144293
60 1 7.989894 7.419103 10.248334 11.345593 10.541806 10.926970
60 2 8.077109 7.417790 10.090361 11.289033 10.458097 10.958197
60 3 7.983865 7.492862 10.129944 11.418077 10.522366 10.873455
60 4 8.053574 7.441354 10.138749 11.297438 10.547018 10.942314
60 5 8.044626 7.389152 10.128528 11.328647 10.503515 10.932906
60 6 7.966093 7.487729 10.141745 11.406319 10.533793 10.982887
90 1 8.164200 7.610294 9.828901 11.023355 10.427788 10.902193
90 2 8.130045 7.425974 9.972879 11.163064 10.339801 10.840847
90 3 8.208286 7.422047 9.754889 11.120437 10.411414 10.935003
90 4 7.935916 7.324434 9.808280 10.977562 10.251636 10.694670
90 5 8.226764 7.399737 9.760450 11.015849 10.545208 10.911852
90 6 8.211148 7.627093 9.841167 11.172223 10.446194 10.801106
答案 0 :(得分：3)
df['rep'] = df.groupby(level=[0,1]).cumcount() + 1
df = df.set_index('rep', append=True)
print (df)
ABHD17C ADAM19 ADAMTS1 ADAMTS1 ADM ADM
symbol rep
Control 15 1 8.118767 7.418533 10.352104 11.489224 10.868479 11.214037
2 8.035596 7.623869 10.482607 11.618165 10.569137 11.186141
3 8.066988 7.480469 10.274919 11.554862 10.744955 11.225492
4 8.047028 7.547017 10.318972 11.499156 10.831416 11.203028
5 8.153588 7.507617 10.372795 11.526810 10.844520 11.125373
6 7.888492 7.494270 10.295306 11.542063 10.750984 11.157360
30 1 8.021081 7.417150 10.266634 11.481706 10.685487 11.060077
2 8.111274 7.430963 10.209418 11.426212 10.662505 11.097215
3 8.052534 7.359658 10.115304 11.432636 10.597524 11.004115
4 8.010348 7.391775 10.266453 11.546372 10.572780 11.011062
5 8.012789 7.420152 10.303099 11.472436 10.708051 11.084203
6 8.074147 7.331148 10.273618 11.430159 10.649350 11.144293
60 1 7.989894 7.419103 10.248334 11.345593 10.541806 10.926970
2 8.077109 7.417790 10.090361 11.289033 10.458097 10.958197
3 7.983865 7.492862 10.129944 11.418077 10.522366 10.873455
4 8.053574 7.441354 10.138749 11.297438 10.547018 10.942314
5 8.044626 7.389152 10.128528 11.328647 10.503515 10.932906
6 7.966093 7.487729 10.141745 11.406319 10.533793 10.982887
90 1 8.164200 7.610294 9.828901 11.023355 10.427788 10.902193
2 8.130045 7.425974 9.972879 11.163064 10.339801 10.840847
3 8.208286 7.422047 9.754889 11.120437 10.411414 10.935003
4 7.935916 7.324434 9.808280 10.977562 10.251636 10.694670
5 8.226764 7.399737 9.760450 11.015849 10.545208 10.911852
6 8.211148 7.627093 9.841167 11.172223 10.446194 10.801106
但是如果从来没有遗漏某些行，如果前两个级别都是长度为6的组合，则可以使用MultiIndex.from_product：
a = df.index.levels[0]
b = df.index.levels[1]
c = [1,2,3,4,5,6]
df.index = pd.MultiIndex.from_product([a,b,c], names=('symbol','a','b'))
print (df)
ABHD17C ADAM19 ADAMTS1 ADAMTS1 ADM ADM
symbol a b
Control 15 1 8.118767 7.418533 10.352104 11.489224 10.868479 11.214037
2 8.035596 7.623869 10.482607 11.618165 10.569137 11.186141
3 8.066988 7.480469 10.274919 11.554862 10.744955 11.225492
4 8.047028 7.547017 10.318972 11.499156 10.831416 11.203028
5 8.153588 7.507617 10.372795 11.526810 10.844520 11.125373
6 7.888492 7.494270 10.295306 11.542063 10.750984 11.157360
30 1 8.021081 7.417150 10.266634 11.481706 10.685487 11.060077
2 8.111274 7.430963 10.209418 11.426212 10.662505 11.097215
3 8.052534 7.359658 10.115304 11.432636 10.597524 11.004115
4 8.010348 7.391775 10.266453 11.546372 10.572780 11.011062
5 8.012789 7.420152 10.303099 11.472436 10.708051 11.084203
6 8.074147 7.331148 10.273618 11.430159 10.649350 11.144293
60 1 7.989894 7.419103 10.248334 11.345593 10.541806 10.926970
2 8.077109 7.417790 10.090361 11.289033 10.458097 10.958197
3 7.983865 7.492862 10.129944 11.418077 10.522366 10.873455
4 8.053574 7.441354 10.138749 11.297438 10.547018 10.942314
5 8.044626 7.389152 10.128528 11.328647 10.503515 10.932906
6 7.966093 7.487729 10.141745 11.406319 10.533793 10.982887
90 1 8.164200 7.610294 9.828901 11.023355 10.427788 10.902193
2 8.130045 7.425974 9.972879 11.163064 10.339801 10.840847
3 8.208286 7.422047 9.754889 11.120437 10.411414 10.935003
4 7.935916 7.324434 9.808280 10.977562 10.251636 10.694670
5 8.226764 7.399737 9.760450 11.015849 10.545208 10.911852
6 8.211148 7.627093 9.841167 11.172223 10.446194 10.801106