我都是，
我在Pandas中有两个数据帧：
一个：
In [96]: a
Out[96]:
count mean std min max 25% 50% 75%
10m 604656 4.19 2.43 0 25.92 2.43 3.71 5.5
In [98]: a.to_dict()
Out[98]:
{'25%': {'10m': 2.429999828338623},
'50%': {'10m': 3.7100000381469727},
'75%': {'10m': 5.5},
'count': {'10m': 604656.0},
'max': {'10m': 25.920000076293945},
'mean': {'10m': 4.1893915969076261},
'min': {'10m': 0.0},
'std': {'10m': 2.4321994530033586}}
和 b ：
In [97]: b
Out[97]:
count mean std min max 25% 50% 75%
0.0_90.0 119842 3.34 1.72 0 14.37 2.08 3.06 4.37
180.0_270.0 234074 5.11 2.82 0 25.92 2.99 4.64 6.82
270.0_360.0 126376 3.79 2.19 0 19.55 2.12 3.40 5.13
90.0_180.0 124364 3.67 1.83 0 14.93 2.37 3.40 4.70
In [99]: b.to_dict()
Out[99]:
{'25%': {'0.0_90.0': 2.0799999237060547,
'180.0_270.0': 2.9900000095367432,
'270.0_360.0': 2.119999885559082,
'90.0_180.0': 2.3681280016899109},
'50%': {'0.0_90.0': 3.0579087734222412,
'180.0_270.0': 4.6399998664855957,
'270.0_360.0': 3.4000000953674316,
'90.0_180.0': 3.4006340503692627},
'75%': {'0.0_90.0': 4.369999885559082,
'180.0_270.0': 6.8199996948242188,
'270.0_360.0': 5.130000114440918,
'90.0_180.0': 4.6960808038711548},
'count': {'0.0_90.0': 119842.0,
'180.0_270.0': 234074.0,
'270.0_360.0': 126376.0,
'90.0_180.0': 124364.0},
'max': {'0.0_90.0': 14.369999885559082,
'180.0_270.0': 25.920000076293945,
'270.0_360.0': 19.549999237060547,
'90.0_180.0': 14.930000305175781},
'mean': {'0.0_90.0': 3.3417930869221379,
'180.0_270.0': 5.1125810579269269,
'270.0_360.0': 3.7938859684522601,
'90.0_180.0': 3.670476718299061},
'min': {'0.0_90.0': 0.0,
'180.0_270.0': 0.0,
'270.0_360.0': 0.0,
'90.0_180.0': 0.0},
'std': {'0.0_90.0': 1.7153268584149644,
'180.0_270.0': 2.8194581011555386,
'270.0_360.0': 2.1909571297061241,
'90.0_180.0': 1.8334834361369423}}
我想将新数据框中的两个数据框合并为多索引，如：
new_df ：
count mean std min max 25% 50% 75%
10m all 604656 4.19 2.43 0 25.92 2.43 3.71 5.5
0.0_90.0 119842 3.34 1.72 0 14.37 2.08 3.06 4.37
180.0_270.0 234074 5.11 2.82 0 25.92 2.99 4.64 6.82
270.0_360.0 126376 3.79 2.19 0 19.55 2.12 3.40 5.13
90.0_180.0 124364 3.67 1.83 0 14.93 2.37 3.40 4.70
具有多索引（'10m'，'all'）的元素是 a ，接下来的行是 b 。
有人知道如何在熊猫中实现这个目标吗？
非常感谢，
格雷格
修改
大家好，
我向前移动并向高处扩展。我现在有一个问题，因为高度和扇区不是从低到高的高度，从低扇区到高扇区。
这就是我得到的：
In [141]: df_stats_windSpeed
Out[142]:
count mean std min max 25% 50% 75%
Height Sector
10m All 604656 4.19 2.43 0 25.92 2.43 3.71 5.50
[0.0, 90.0[ 119842 3.34 1.72 0 14.37 2.08 3.06 4.37
[180.0, 270.0[ 234074 5.11 2.82 0 25.92 2.99 4.64 6.82
[270.0, 360.0] 126376 3.79 2.19 0 19.55 2.12 3.40 5.13
[90.0, 180.0[ 124364 3.67 1.83 0 14.93 2.37 3.40 4.70
140m All 604656 7.85 3.63 0 35.20 5.19 7.70 10.20
[0.0, 90.0[ 116374 6.69 2.89 0 22.86 4.49 6.67 8.80
[180.0, 270.0[ 243590 8.95 3.83 0 35.20 6.29 8.86 11.37
[270.0, 360.0] 135292 7.22 3.40 0 29.81 4.84 6.98 9.23
[90.0, 180.0[ 109400 7.39 3.46 0 20.91 4.62 7.31 10.10
200m All 604656 8.47 4.08 0 34.88 5.38 8.21 11.20
[0.0, 90.0[ 113475 7.07 3.25 0 24.56 4.57 6.92 9.45
[180.0, 270.0[ 242157 9.80 4.30 0 34.88 6.65 9.74 12.71
[270.0, 360.0] 143254 7.75 3.74 0 33.73 5.08 7.48 10.00
[90.0, 180.0[ 105770 7.93 3.96 0 21.54 4.75 7.61 10.86
20m All 604656 4.82 2.60 0 27.69 2.99 4.33 6.18
[0.0, 90.0[ 116748 3.91 1.81 0 15.59 2.64 3.65 4.95
[180.0, 270.0[ 235304 5.83 2.99 0 27.69 3.67 5.32 7.61
[270.0, 360.0] 126961 4.35 2.34 0 21.65 2.61 3.93 5.71
[90.0, 180.0[ 125643 4.22 1.96 0 15.98 2.86 3.98 5.29
40m All 604656 5.68 2.77 0 29.39 3.81 5.29 7.10
[0.0, 90.0[ 120426 4.80 1.99 0 17.69 3.46 4.69 5.97
[180.0, 270.0[ 238381 6.65 3.13 0 29.39 4.48 6.16 8.45
[270.0, 360.0] 128104 5.36 2.63 0 25.19 3.55 4.98 6.81
[90.0, 180.0[ 117745 4.96 2.11 0 16.79 3.49 4.90 6.22
80m All 604656 6.84 3.12 0 32.28 4.69 6.66 8.65
[0.0, 90.0[ 119330 5.91 2.44 0 20.74 4.16 5.95 7.58
[180.0, 270.0[ 239146 7.80 3.38 0 32.28 5.54 7.54 9.74
[270.0, 360.0] 133220 6.42 3.02 0 27.94 4.37 6.15 8.11
[90.0, 180.0[ 112960 6.29 2.71 0 19.53 4.24 6.38 8.28
我想对多索引进行排序，以便高度顺序为：10,20,40,80,140和200m;和行业：'全'，'[0.0,90.0 ['，'[90.0,180.0 ['，'[180.0,270.0 ['，'[270.0,360.0]'。 我尝试像这样重新索引，但它不起作用：
In [255]: df_stats.reindex(index=['10m','20m','40m','80m','200m','140m'],level=0)
In [256]: df_stats.reindex(index=['All','[0.0, 90.0[','[90.0, 180.0[','[180.0, 270.0[','[270.0, 360.0]'],level=1)
这是df dict：
In [257]: df_stats_windSpeed.to_dict()
Out[257]:
{'25%': {('10m', 'All'): 2.429999828338623,
('10m', '[0.0, 90.0['): 2.0799999237060547,
('10m', '[180.0, 270.0['): 2.9900000095367432,
('10m', '[270.0, 360.0]'): 2.119999885559082,
('10m', '[90.0, 180.0['): 2.3681280016899109,
('140m', 'All'): 5.1884875297546387,
('140m', '[0.0, 90.0['): 4.4935483932495117,
('140m', '[180.0, 270.0['): 6.2855626344680786,
('140m', '[270.0, 360.0]'): 4.8426017761230469,
('140m', '[90.0, 180.0['): 4.6205065250396729,
('200m', 'All'): 5.3844937086105347,
('200m', '[0.0, 90.0['): 4.572603702545166,
('200m', '[180.0, 270.0['): 6.6515130996704102,
('200m', '[270.0, 360.0]'): 5.0821070671081543,
('200m', '[90.0, 180.0['): 4.749258279800415,
('20m', 'All'): 2.9900000095367432,
('20m', '[0.0, 90.0['): 2.6400001049041748,
('20m', '[180.0, 270.0['): 3.6700000762939453,
('20m', '[270.0, 360.0]'): 2.6099998950958252,
('20m', '[90.0, 180.0['): 2.8554879426956177,
('40m', 'All'): 3.8135370016098022,
('40m', '[0.0, 90.0['): 3.4552559852600098,
('40m', '[180.0, 270.0['): 4.4779624938964844,
('40m', '[270.0, 360.0]'): 3.5464469790458679,
('40m', '[90.0, 180.0['): 3.4928045272827148,
('80m', 'All'): 4.6858876943588257,
('80m', '[0.0, 90.0['): 4.1649158000946045,
('80m', '[180.0, 270.0['): 5.5375603437423706,
('80m', '[270.0, 360.0]'): 4.3738168478012085,
('80m', '[90.0, 180.0['): 4.2378913164138794},
'50%': {('10m', 'All'): 3.7100000381469727,
('10m', '[0.0, 90.0['): 3.0579087734222412,
('10m', '[180.0, 270.0['): 4.6399998664855957,
('10m', '[270.0, 360.0]'): 3.4000000953674316,
('10m', '[90.0, 180.0['): 3.4006340503692627,
('140m', 'All'): 7.701094388961792,
('140m', '[0.0, 90.0['): 6.6736810207366943,
('140m', '[180.0, 270.0['): 8.8593416213989258,
('140m', '[270.0, 360.0]'): 6.9792094230651855,
('140m', '[90.0, 180.0['): 7.3094825744628906,
('200m', 'All'): 8.2149920463562012,
('200m', '[0.0, 90.0['): 6.9200782775878906,
('200m', '[180.0, 270.0['): 9.7363834381103516,
('200m', '[270.0, 360.0]'): 7.4800474643707275,
('200m', '[90.0, 180.0['): 7.6083860397338867,
('20m', 'All'): 4.3299999237060547,
('20m', '[0.0, 90.0['): 3.6500000953674316,
('20m', '[180.0, 270.0['): 5.3199996948242187,
('20m', '[270.0, 360.0]'): 3.929999828338623,
('20m', '[90.0, 180.0['): 3.9796528816223145,
('40m', 'All'): 5.291872501373291,
('40m', '[0.0, 90.0['): 4.692425012588501,
('40m', '[180.0, 270.0['): 6.1558408737182617,
('40m', '[270.0, 360.0]'): 4.9811406135559082,
('40m', '[90.0, 180.0['): 4.8983759880065918,
('80m', 'All'): 6.6626186370849609,
('80m', '[0.0, 90.0['): 5.9457294940948486,
('80m', '[180.0, 270.0['): 7.544825553894043,
('80m', '[270.0, 360.0]'): 6.1506271362304687,
('80m', '[90.0, 180.0['): 6.3810868263244629},
'75%': {('10m', 'All'): 5.5,
('10m', '[0.0, 90.0['): 4.369999885559082,
('10m', '[180.0, 270.0['): 6.8199996948242188,
('10m', '[270.0, 360.0]'): 5.130000114440918,
('10m', '[90.0, 180.0['): 4.6960808038711548,
('140m', 'All'): 10.203519582748413,
('140m', '[0.0, 90.0['): 8.7971394062042236,
('140m', '[180.0, 270.0['): 11.370761156082153,
('140m', '[270.0, 360.0]'): 9.2274019718170166,
('140m', '[90.0, 180.0['): 10.097956657409668,
('200m', 'All'): 11.203938484191895,
('200m', '[0.0, 90.0['): 9.4468526840209961,
('200m', '[180.0, 270.0['): 12.706465721130371,
('200m', '[270.0, 360.0]'): 10.000725984573364,
('200m', '[90.0, 180.0['): 10.862814903259277,
('20m', 'All'): 6.179999828338623,
('20m', '[0.0, 90.0['): 4.9499998092651367,
('20m', '[180.0, 270.0['): 7.6100001335144043,
('20m', '[270.0, 360.0]'): 5.7100000381469727,
('20m', '[90.0, 180.0['): 5.2929890155792236,
('40m', 'All'): 7.0959796905517578,
('40m', '[0.0, 90.0['): 5.9702688455581665,
('40m', '[180.0, 270.0['): 8.4523344039916992,
('40m', '[270.0, 360.0]'): 6.8096575736999512,
('40m', '[90.0, 180.0['): 6.2155957221984863,
('80m', 'All'): 8.6509017944335938,
('80m', '[0.0, 90.0['): 7.5837295055389404,
('80m', '[180.0, 270.0['): 9.7384757995605469,
('80m', '[270.0, 360.0]'): 8.1105818748474121,
('80m', '[90.0, 180.0['): 8.2832918167114258},
'count': {('10m', 'All'): 604656.0,
('10m', '[0.0, 90.0['): 119842.0,
('10m', '[180.0, 270.0['): 234074.0,
('10m', '[270.0, 360.0]'): 126376.0,
('10m', '[90.0, 180.0['): 124364.0,
('140m', 'All'): 604656.0,
('140m', '[0.0, 90.0['): 116374.0,
('140m', '[180.0, 270.0['): 243590.0,
('140m', '[270.0, 360.0]'): 135292.0,
('140m', '[90.0, 180.0['): 109400.0,
('200m', 'All'): 604656.0,
('200m', '[0.0, 90.0['): 113475.0,
('200m', '[180.0, 270.0['): 242157.0,
('200m', '[270.0, 360.0]'): 143254.0,
('200m', '[90.0, 180.0['): 105770.0,
('20m', 'All'): 604656.0,
('20m', '[0.0, 90.0['): 116748.0,
('20m', '[180.0, 270.0['): 235304.0,
('20m', '[270.0, 360.0]'): 126961.0,
('20m', '[90.0, 180.0['): 125643.0,
('40m', 'All'): 604656.0,
('40m', '[0.0, 90.0['): 120426.0,
('40m', '[180.0, 270.0['): 238381.0,
('40m', '[270.0, 360.0]'): 128104.0,
('40m', '[90.0, 180.0['): 117745.0,
('80m', 'All'): 604656.0,
('80m', '[0.0, 90.0['): 119330.0,
('80m', '[180.0, 270.0['): 239146.0,
('80m', '[270.0, 360.0]'): 133220.0,
('80m', '[90.0, 180.0['): 112960.0},
'max': {('10m', 'All'): 25.920000076293945,
('10m', '[0.0, 90.0['): 14.369999885559082,
('10m', '[180.0, 270.0['): 25.920000076293945,
('10m', '[270.0, 360.0]'): 19.549999237060547,
('10m', '[90.0, 180.0['): 14.930000305175781,
('140m', 'All'): 35.195941925048828,
('140m', '[0.0, 90.0['): 22.86467170715332,
('140m', '[180.0, 270.0['): 35.195941925048828,
('140m', '[270.0, 360.0]'): 29.814235687255859,
('140m', '[90.0, 180.0['): 20.905771255493164,
('200m', 'All'): 34.877243041992188,
('200m', '[0.0, 90.0['): 24.561836242675781,
('200m', '[180.0, 270.0['): 34.877243041992188,
('200m', '[270.0, 360.0]'): 33.732143402099609,
('200m', '[90.0, 180.0['): 21.536584854125977,
('20m', 'All'): 27.689998626708984,
('20m', '[0.0, 90.0['): 15.589999198913574,
('20m', '[180.0, 270.0['): 27.689998626708984,
('20m', '[270.0, 360.0]'): 21.649999618530273,
('20m', '[90.0, 180.0['): 15.979999542236328,
('40m', 'All'): 29.387109756469727,
('40m', '[0.0, 90.0['): 17.693622589111328,
('40m', '[180.0, 270.0['): 29.387109756469727,
('40m', '[270.0, 360.0]'): 25.192754745483398,
('40m', '[90.0, 180.0['): 16.793560028076172,
('80m', 'All'): 32.280239105224609,
('80m', '[0.0, 90.0['): 20.743719100952148,
('80m', '[180.0, 270.0['): 32.280239105224609,
('80m', '[270.0, 360.0]'): 27.942413330078125,
('80m', '[90.0, 180.0['): 19.532955169677734},
'mean': {('10m', 'All'): 4.1893915969076261,
('10m', '[0.0, 90.0['): 3.3417930869221379,
('10m', '[180.0, 270.0['): 5.1125810579269269,
('10m', '[270.0, 360.0]'): 3.7938859684522601,
('10m', '[90.0, 180.0['): 3.670476718299061,
('140m', 'All'): 7.8465228797278623,
('140m', '[0.0, 90.0['): 6.6866495964827086,
('140m', '[180.0, 270.0['): 8.9531109376609503,
('140m', '[270.0, 360.0]'): 7.2187838345351443,
('140m', '[90.0, 180.0['): 7.3927146469551381,
('200m', 'All'): 8.4738967491657924,
('200m', '[0.0, 90.0['): 7.0707511105622967,
('200m', '[180.0, 270.0['): 9.7955929565714968,
('200m', '[270.0, 360.0]'): 7.7549135952858128,
('200m', '[90.0, 180.0['): 7.9270609315399172,
('20m', 'All'): 4.8153452911920738,
('20m', '[0.0, 90.0['): 3.9108075360827947,
('20m', '[180.0, 270.0['): 5.8345712321566516,
('20m', '[270.0, 360.0]'): 4.3517044317594324,
('20m', '[90.0, 180.0['): 4.2155453833197427,
('40m', 'All'): 5.6803902578298446,
('40m', '[0.0, 90.0['): 4.8012498160193742,
('40m', '[180.0, 270.0['): 6.6519476987395914,
('40m', '[270.0, 360.0]'): 5.3629782195278182,
('40m', '[90.0, 180.0['): 4.9579161339061493,
('80m', 'All'): 6.8429105603612399,
('80m', '[0.0, 90.0['): 5.9149683147210084,
('80m', '[180.0, 270.0['): 7.8021544717360065,
('80m', '[270.0, 360.0]'): 6.424779540308954,
('80m', '[90.0, 180.0['): 6.2855045603079853},
'min': {('10m', 'All'): 0.0,
('10m', '[0.0, 90.0['): 0.0,
('10m', '[180.0, 270.0['): 0.0,
('10m', '[270.0, 360.0]'): 0.0,
('10m', '[90.0, 180.0['): 0.0,
('140m', 'All'): 0.0,
('140m', '[0.0, 90.0['): 0.0,
('140m', '[180.0, 270.0['): 0.0,
('140m', '[270.0, 360.0]'): 0.0,
('140m', '[90.0, 180.0['): 0.0,
('200m', 'All'): 0.0,
('200m', '[0.0, 90.0['): 0.0,
('200m', '[180.0, 270.0['): 0.0,
('200m', '[270.0, 360.0]'): 0.0,
('200m', '[90.0, 180.0['): 0.0,
('20m', 'All'): 0.0,
('20m', '[0.0, 90.0['): 0.0,
('20m', '[180.0, 270.0['): 0.0,
('20m', '[270.0, 360.0]'): 0.0,
('20m', '[90.0, 180.0['): 0.0,
('40m', 'All'): 0.0,
('40m', '[0.0, 90.0['): 0.0,
('40m', '[180.0, 270.0['): 0.0,
('40m', '[270.0, 360.0]'): 0.0,
('40m', '[90.0, 180.0['): 0.0,
('80m', 'All'): 0.0,
('80m', '[0.0, 90.0['): 0.0,
('80m', '[180.0, 270.0['): 0.0,
('80m', '[270.0, 360.0]'): 0.0,
('80m', '[90.0, 180.0['): 0.0},
'std': {('10m', 'All'): 2.4321994530033586,
('10m', '[0.0, 90.0['): 1.7153268584149644,
('10m', '[180.0, 270.0['): 2.8194581011555386,
('10m', '[270.0, 360.0]'): 2.1909571297061241,
('10m', '[90.0, 180.0['): 1.8334834361369423,
('140m', 'All'): 3.6272652696793761,
('140m', '[0.0, 90.0['): 2.8894363480141649,
('140m', '[180.0, 270.0['): 3.8302160204846252,
('140m', '[270.0, 360.0]'): 3.4038884427629861,
('140m', '[90.0, 180.0['): 3.463171121328295,
('200m', 'All'): 4.0834920171291111,
('200m', '[0.0, 90.0['): 3.246180377116834,
('200m', '[180.0, 270.0['): 4.2979603238677564,
('200m', '[270.0, 360.0]'): 3.7366849435738714,
('200m', '[90.0, 180.0['): 3.9631501181722597,
('20m', 'All'): 2.5956531035815531,
('20m', '[0.0, 90.0['): 1.8115698416394523,
('20m', '[180.0, 270.0['): 2.9884465540389979,
('20m', '[270.0, 360.0]'): 2.342034699432777,
('20m', '[90.0, 180.0['): 1.9553532925384289,
('40m', 'All'): 2.7650269372360587,
('40m', '[0.0, 90.0['): 1.9926422334110316,
('40m', '[180.0, 270.0['): 3.1345356834325013,
('40m', '[270.0, 360.0]'): 2.6327655213933481,
('40m', '[90.0, 180.0['): 2.1057487187047053,
('80m', 'All'): 3.1164449954856375,
('80m', '[0.0, 90.0['): 2.4419473697940042,
('80m', '[180.0, 270.0['): 3.3838903052504601,
('80m', '[270.0, 360.0]'): 3.017648294312663,
('80m', '[90.0, 180.0['): 2.707882324438323}}
是否有人知道如何重新索引此数据框以使索引级别按排序顺序？
由于
答案 0 :(得分：2)
@unutbu是正确的，这里没有手工构建索引。
df = a.append(b)
df.index = MultiIndex.from_arrays([a.index.tolist()*(len(b) + 1),
["all"] + b.index.tolist() ] )
df
25% 50% 75% count max mean min std
10m all 2.43 3.71 5.5 6.047e+05 25.92 4.189 0 2.432
0.0_90.0 2.08 3.058 4.37 1.198e+05 14.37 3.342 0 1.715
180.0_270.0 2.99 4.64 6.82 2.341e+05 25.92 5.113 0 2.819
270.0_360.0 2.12 3.4 5.13 1.264e+05 19.55 3.794 0 2.191
90.0_180.0 2.368 3.401 4.696 1.244e+05 14.93 3.67 0 1.833
答案 1 :(得分：1)
import pandas as PD
a = PD.DataFrame({'25%': {'10m': 2.429999828338623},
'50%': {'10m': 3.7100000381469727},
'75%': {'10m': 5.5},
'count': {'10m': 604656.0},
'max': {'10m': 25.920000076293945},
'mean': {'10m': 4.1893915969076261},
'min': {'10m': 0.0},
'std': {'10m': 2.4321994530033586}})
index = PD.MultiIndex.from_arrays([['10m'],['all']], names = ['dist', 'angle'])
a.index = index
b = PD.DataFrame({'25%': {'0.0_90.0': 2.0799999237060547,
'180.0_270.0': 2.9900000095367432,
'270.0_360.0': 2.119999885559082,
'90.0_180.0': 2.3681280016899109},
'50%': {'0.0_90.0': 3.0579087734222412,
'180.0_270.0': 4.6399998664855957,
'270.0_360.0': 3.4000000953674316,
'90.0_180.0': 3.4006340503692627},
'75%': {'0.0_90.0': 4.369999885559082,
'180.0_270.0': 6.8199996948242188,
'270.0_360.0': 5.130000114440918,
'90.0_180.0': 4.6960808038711548},
'count': {'0.0_90.0': 119842.0,
'180.0_270.0': 234074.0,
'270.0_360.0': 126376.0,
'90.0_180.0': 124364.0},
'max': {'0.0_90.0': 14.369999885559082,
'180.0_270.0': 25.920000076293945,
'270.0_360.0': 19.549999237060547,
'90.0_180.0': 14.930000305175781},
'mean': {'0.0_90.0': 3.3417930869221379,
'180.0_270.0': 5.1125810579269269,
'270.0_360.0': 3.7938859684522601,
'90.0_180.0': 3.670476718299061},
'min': {'0.0_90.0': 0.0,
'180.0_270.0': 0.0,
'270.0_360.0': 0.0,
'90.0_180.0': 0.0},
'std': {'0.0_90.0': 1.7153268584149644,
'180.0_270.0': 2.8194581011555386,
'270.0_360.0': 2.1909571297061241,
'90.0_180.0': 1.8334834361369423}})
index = PD.MultiIndex.from_arrays(
[['']*4,
['0.0_90.0','180.0_270.0','270.0_360.0','90.0_180.0']],
names = ['dist', 'angle'])
b.index = index
PD.set_printoptions(precision = 3)
new_df = PD.concat([a,b])
print(new_df[['count','mean','std','min','max','25%','50%','75%']])
产量
count mean std min max 25% 50% 75%
dist angle
10m all 604656 4.19 2.43 0 25.92 2.43 3.71 5.50
0.0_90.0 119842 3.34 1.72 0 14.37 2.08 3.06 4.37
180.0_270.0 234074 5.11 2.82 0 25.92 2.99 4.64 6.82
270.0_360.0 126376 3.79 2.19 0 19.55 2.12 3.40 5.13
90.0_180.0 124364 3.67 1.83 0 14.93 2.37 3.40 4.70
请注意，b的多索引上方为第一个索引使用了一个空字符串：例如('','0.0_90.0')。您可以通过定义
('10m', '0.0_90.0')
index = PD.MultiIndex.from_arrays(
[['10m']*4,
['0.0_90.0','180.0_270.0','270.0_360.0','90.0_180.0']],
names = ['dist', 'angle'])
b.index = index
默认情况下打印方式相同，或者调用
PD.set_printoptions(multi_sparse = True)
答案 2 :(得分：1)
其他有趣的选择：
In [19]: result = pd.concat([a, b]).rename({'10m': 'all'})
In [20]: result.set_index(np.array(['10m'] * 5), append=True).swaplevel(0,1)
Out[20]:
count mean std min max 25% 50% 75%
10m all 604656 4.19 2.43 0 25.92 2.43 3.71 5.50
0.0_90.0 119842 3.34 1.72 0 14.37 2.08 3.06 4.37
180.0_270.0 234074 5.11 2.82 0 25.92 2.99 4.64 6.82
270.0_360.0 126376 3.79 2.19 0 19.55 2.12 3.40 5.13
90.0_180.0 124364 3.67 1.83 0 14.93 2.37 3.40 4.70