为什么这个pandas groupby 对象有所有这些额外的组？
[英]Why does this pandas groupby object have all these extra groups?
问题描述
这是我正在使用的数据的链接。
我尝试根据我想要的列（ cuepos 、 targetpos和soa ）创建一个组，但是当我列出这些组时，它似乎也在与其他一些列一起创建组...
groups = t.groupby(['cuepos', 'targetpos', 'soa'])
for name, _ in groups:
print name
输出：
(-89, -89, -89.41139261318807)
(-88, -88, -88.44728835230345)
(-88, -88, -88.20648583606493)
(-87, -87, -87.77339640061896)
(-86, -86, -86.8637199297012)
(-85, -85, -85.50514526170076)
(-83, -83, -83.87935779179833)
(-83, -83, -83.86953491773222)
(-81, -81, -81.43570407709822)
(-80, -80, -80.70639872201482)
(-80, -80, -80.38454772926528)
(-79, -79, -79.81516051155803)
(-75, -75, -75.83933409447087)
(-74, -74, -74.53528962061156)
(-73, -73, -73.10397238440302)
(-70, -70, -70.33208101764106)
(-64, -64, -64.18024404177129)
(-61, -61, -61.969216551968344)
(-61, -61, -61.89154280549519)
(-61, -61, -61.81223645812457)
(-61, -61, -61.80055105439692)
(-59, -59, -59.81551441456813)
(-57, -57, -57.67934478380107)
(-53, -53, -53.91038834185852)
(-51, -51, -51.35605559139145)
(-48, -48, -48.63443042074468)
(-48, -48, -48.026567177299825)
(-44, -44, -44.84750981999042)
(-44, -44, -44.20816797871376)
(-43, -43, -43.97185684796753)
(-39, -39, -39.03132145644588)
(-37, -37, -37.09246448040565)
(-37, -37, -37.06406445785262)
(-36, -36, -36.89551705610748)
(-34, -34, -34.23312940622742)
(-33, -33, -33.771084303661524)
(-31, -31, -31.183030415916534)
(-29, -29, -29.062383175092265)
(-22, -22, -22.1763042325164)
(-17, -17, -17.51138905398824)
(-14, -14, -14.673170146200675)
(-9, -9, -9.389620131659427)
(-9, -9, -9.28109130634627)
(-8, -8, -8.871025817651997)
(-8, -8, -8.47526860623043)
(-7, -7, -7.484697635519495)
(-3, -3, -3.265563116265213)
(-2, -2, -2.842961251214575)
(1, 1, -0.1)
(1, 1, 0.1)
(1, 1, 0.4)
(1, 2, -0.1)
(1, 2, 0.4)
(2, 1, -0.1)
(2, 1, 0.4)
(2, 2, -0.1)
(2, 2, 0.1)
(2, 2, 0.4)
(6, 6, 6.928400268960042)
(8, 8, 8.476818809273727)
(11, 11, 11.225720357570507)
(13, 13, 13.949059199458294)
(17, 17, 17.272663104264836)
(18, 18, 18.548979295124248)
(21, 21, 21.075945669054835)
(22, 22, 22.101344720547228)
(22, 22, 22.36405009971824)
(24, 24, 24.658480906080996)
(27, 27, 27.977154868918745)
(33, 33, 33.75660016684323)
(49, 49, 49.59296862775889)
(51, 51, 51.09435632596291)
(52, 52, 52.107845391762766)
(54, 54, 54.22026217046835)
(54, 54, 54.55461208382168)
(56, 56, 56.92397800238861)
(57, 57, 57.15634257840432)
(57, 57, 57.490226928649264)
(57, 57, 57.82030543311612)
(58, 58, 58.20496727209113)
(58, 58, 58.44217165553367)
(58, 58, 58.591804845872765)
(58, 58, 58.84514017314996)
(60, 60, 60.15474896731822)
(60, 60, 60.49526399943247)
(60, 60, 60.621239605283456)
(61, 61, 61.73542327989246)
(61, 61, 61.882729155824705)
(63, 63, 63.15716022529575)
(65, 65, 65.62684954629724)
(67, 67, 67.32622273875754)
(68, 68, 68.72997170017184)
(71, 71, 71.64012395084114)
(71, 71, 71.87357582509455)
(71, 71, 71.91237771102328)
(72, 72, 72.87756472051248)
(73, 73, 73.23547239962096)
(75, 75, 75.20111322246554)
(76, 76, 76.37312687962122)
(78, 78, 78.39727821292199)
(79, 79, 79.27674426299386)
(80, 80, 80.22644745900354)
(82, 82, 82.38562004739285)
(82, 82, 82.75922122217577)
(85, 85, 85.19181215842043)
(85, 85, 85.6896980533089)
(85, 85, 85.84141277449113)
(87, 87, 87.21598891172931)
(87, 87, 87.60810304014197)
(87, 87, 87.80910737578778)
所需的组位于中间（看起来像(1, 1, -0.1) ）。 这又是什么东西？ 我在这里做错了什么？
1 个解决方案
解决方案1
0 已采纳 2012-11-28 22:20:31
好吧，数据并没有对观察到的 3 元组说谎：
In [9]: df[['cuepos', 'targetpos', 'soa']].drop_duplicates()
Out[9]:
cuepos targetpos soa
0 2 2 0.400000
1 2 1 0.400000
2 1 1 -0.100000
3 1 1 0.400000
4 1 2 -0.100000
5 1 1 0.100000
8 2 2 -0.100000
12 1 2 0.400000
18 2 2 0.100000
24 2 1 -0.100000
52 85 85 85.689698
77 -3 -3 -3.265563
117 -83 -83 -83.869535
133 11 11 11.225720
26 -88 -88 -88.206486
31 -48 -48 -48.634430
34 63 63 63.157160
55 85 85 85.841413
80 -61 -61 -61.812236
86 -61 -61 -61.891543
89 87 87 87.215989
92 80 80 80.226447
94 58 58 58.204967
126 71 71 71.912378
128 60 60 60.154749
132 8 8 8.476819
139 65 65 65.626850
141 54 54 54.554612
11 -61 -61 -61.800551
39 -33 -33 -33.771084
46 76 76 76.373127
52 -37 -37 -37.064064
55 -44 -44 -44.847510
60 -70 -70 -70.332081
62 61 61 61.735423
63 75 75 75.201113
69 58 58 58.845140
94 -79 -79 -79.815161
109 -29 -29 -29.062383
111 -51 -51 -51.356056
117 -83 -83 -83.879358
123 21 21 21.075946
135 -31 -31 -31.183030
143 6 6 6.928400
4 -17 -17 -17.511389
11 57 57 57.490227
18 -88 -88 -88.447288
36 78 78 78.397278
39 -14 -14 -14.673170
42 52 52 52.107845
49 -87 -87 -87.773396
50 60 60 60.495264
71 33 33 33.756600
74 -61 -61 -61.969217
84 18 18 18.548979
85 -8 -8 -8.475269
98 -59 -59 -59.815514
101 -80 -80 -80.384548
114 -39 -39 -39.031321
119 71 71 71.873576
121 -86 -86 -86.863720
128 68 68 68.729972
130 -34 -34 -34.233129
140 82 82 82.759221
0 -75 -75 -75.839334
15 67 67 67.326223
34 -57 -57 -57.679345
35 -74 -74 -74.535290
42 -48 -48 -48.026567
67 85 85 85.191812
75 72 72 72.877565
80 -7 -7 -7.484698
99 -9 -9 -9.389620
118 -44 -44 -44.208168
130 73 73 73.235472
143 58 58 58.442172
56 22 22 22.364050
67 -85 -85 -85.505145
95 60 60 60.621240
109 54 54 54.220262
111 87 87 87.809107
112 -81 -81 -81.435704
114 71 71 71.640124
119 -22 -22 -22.176304
120 27 27 27.977155
121 56 56 56.923978
128 57 57 57.820305
133 22 22 22.101345
11 61 61 61.882729
13 58 58 58.591805
28 57 57 57.156343
78 -80 -80 -80.706399
80 49 49 49.592969
81 -37 -37 -37.092464
101 -36 -36 -36.895517
124 17 17 17.272663
128 51 51 51.094356
137 -89 -89 -89.411393
140 -64 -64 -64.180244
36 -8 -8 -8.871026
44 -73 -73 -73.103972
47 -9 -9 -9.281091
49 -2 -2 -2.842961
51 87 87 87.608103
85 24 24 24.658481
90 -53 -53 -53.910388
98 82 82 82.385620
120 79 79 79.276744
127 -43 -43 -43.971857
130 13 13 13.949059
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