scikit-learnのデータセットを使います。
In [4]:
import pandas as pd
from sklearn import datasets
diabetes = datasets.load_diabetes()
X = diabetes.data
y = diabetes.target
print(X)
print(y)
[[ 0.03807591 0.05068012 0.06169621 ..., -0.00259226 0.01990842
-0.01764613]
[-0.00188202 -0.04464164 -0.05147406 ..., -0.03949338 -0.06832974
-0.09220405]
[ 0.08529891 0.05068012 0.04445121 ..., -0.00259226 0.00286377
-0.02593034]
...,
[ 0.04170844 0.05068012 -0.01590626 ..., -0.01107952 -0.04687948
0.01549073]
[-0.04547248 -0.04464164 0.03906215 ..., 0.02655962 0.04452837
-0.02593034]
[-0.04547248 -0.04464164 -0.0730303 ..., -0.03949338 -0.00421986
0.00306441]]
[ 151. 75. 141. 206. 135. 97. 138. 63. 110. 310. 101. 69.
179. 185. 118. 171. 166. 144. 97. 168. 68. 49. 68. 245.
184. 202. 137. 85. 131. 283. 129. 59. 341. 87. 65. 102.
265. 276. 252. 90. 100. 55. 61. 92. 259. 53. 190. 142.
75. 142. 155. 225. 59. 104. 182. 128. 52. 37. 170. 170.
61. 144. 52. 128. 71. 163. 150. 97. 160. 178. 48. 270.
202. 111. 85. 42. 170. 200. 252. 113. 143. 51. 52. 210.
65. 141. 55. 134. 42. 111. 98. 164. 48. 96. 90. 162.
150. 279. 92. 83. 128. 102. 302. 198. 95. 53. 134. 144.
232. 81. 104. 59. 246. 297. 258. 229. 275. 281. 179. 200.
200. 173. 180. 84. 121. 161. 99. 109. 115. 268. 274. 158.
107. 83. 103. 272. 85. 280. 336. 281. 118. 317. 235. 60.
174. 259. 178. 128. 96. 126. 288. 88. 292. 71. 197. 186.
25. 84. 96. 195. 53. 217. 172. 131. 214. 59. 70. 220.
268. 152. 47. 74. 295. 101. 151. 127. 237. 225. 81. 151.
107. 64. 138. 185. 265. 101. 137. 143. 141. 79. 292. 178.
91. 116. 86. 122. 72. 129. 142. 90. 158. 39. 196. 222.
277. 99. 196. 202. 155. 77. 191. 70. 73. 49. 65. 263.
248. 296. 214. 185. 78. 93. 252. 150. 77. 208. 77. 108.
160. 53. 220. 154. 259. 90. 246. 124. 67. 72. 257. 262.
275. 177. 71. 47. 187. 125. 78. 51. 258. 215. 303. 243.
91. 150. 310. 153. 346. 63. 89. 50. 39. 103. 308. 116.
145. 74. 45. 115. 264. 87. 202. 127. 182. 241. 66. 94.
283. 64. 102. 200. 265. 94. 230. 181. 156. 233. 60. 219.
80. 68. 332. 248. 84. 200. 55. 85. 89. 31. 129. 83.
275. 65. 198. 236. 253. 124. 44. 172. 114. 142. 109. 180.
144. 163. 147. 97. 220. 190. 109. 191. 122. 230. 242. 248.
249. 192. 131. 237. 78. 135. 244. 199. 270. 164. 72. 96.
306. 91. 214. 95. 216. 263. 178. 113. 200. 139. 139. 88.
148. 88. 243. 71. 77. 109. 272. 60. 54. 221. 90. 311.
281. 182. 321. 58. 262. 206. 233. 242. 123. 167. 63. 197.
71. 168. 140. 217. 121. 235. 245. 40. 52. 104. 132. 88.
69. 219. 72. 201. 110. 51. 277. 63. 118. 69. 273. 258.
43. 198. 242. 232. 175. 93. 168. 275. 293. 281. 72. 140.
189. 181. 209. 136. 261. 113. 131. 174. 257. 55. 84. 42.
146. 212. 233. 91. 111. 152. 120. 67. 310. 94. 183. 66.
173. 72. 49. 64. 48. 178. 104. 132. 220. 57.]
In [46]:
df_X = pd.DataFrame(X)
df_y = pd.DataFrame(y)
In [47]:
df_X
Out[47]:
0
1
2
3
4
5
6
7
8
9
0
0.038076
0.050680
0.061696
0.021872
-0.044223
-0.034821
-0.043401
-0.002592
0.019908
-0.017646
1
-0.001882
-0.044642
-0.051474
-0.026328
-0.008449
-0.019163
0.074412
-0.039493
-0.068330
-0.092204
2
0.085299
0.050680
0.044451
-0.005671
-0.045599
-0.034194
-0.032356
-0.002592
0.002864
-0.025930
3
-0.089063
-0.044642
-0.011595
-0.036656
0.012191
0.024991
-0.036038
0.034309
0.022692
-0.009362
4
0.005383
-0.044642
-0.036385
0.021872
0.003935
0.015596
0.008142
-0.002592
-0.031991
-0.046641
5
-0.092695
-0.044642
-0.040696
-0.019442
-0.068991
-0.079288
0.041277
-0.076395
-0.041180
-0.096346
6
-0.045472
0.050680
-0.047163
-0.015999
-0.040096
-0.024800
0.000779
-0.039493
-0.062913
-0.038357
7
0.063504
0.050680
-0.001895
0.066630
0.090620
0.108914
0.022869
0.017703
-0.035817
0.003064
8
0.041708
0.050680
0.061696
-0.040099
-0.013953
0.006202
-0.028674
-0.002592
-0.014956
0.011349
9
-0.070900
-0.044642
0.039062
-0.033214
-0.012577
-0.034508
-0.024993
-0.002592
0.067736
-0.013504
10
-0.096328
-0.044642
-0.083808
0.008101
-0.103389
-0.090561
-0.013948
-0.076395
-0.062913
-0.034215
11
0.027178
0.050680
0.017506
-0.033214
-0.007073
0.045972
-0.065491
0.071210
-0.096433
-0.059067
12
0.016281
-0.044642
-0.028840
-0.009113
-0.004321
-0.009769
0.044958
-0.039493
-0.030751
-0.042499
13
0.005383
0.050680
-0.001895
0.008101
-0.004321
-0.015719
-0.002903
-0.002592
0.038393
-0.013504
14
0.045341
-0.044642
-0.025607
-0.012556
0.017694
-0.000061
0.081775
-0.039493
-0.031991
-0.075636
15
-0.052738
0.050680
-0.018062
0.080401
0.089244
0.107662
-0.039719
0.108111
0.036056
-0.042499
16
-0.005515
-0.044642
0.042296
0.049415
0.024574
-0.023861
0.074412
-0.039493
0.052280
0.027917
17
0.070769
0.050680
0.012117
0.056301
0.034206
0.049416
-0.039719
0.034309
0.027368
-0.001078
18
-0.038207
-0.044642
-0.010517
-0.036656
-0.037344
-0.019476
-0.028674
-0.002592
-0.018118
-0.017646
19
-0.027310
-0.044642
-0.018062
-0.040099
-0.002945
-0.011335
0.037595
-0.039493
-0.008944
-0.054925
20
-0.049105
-0.044642
-0.056863
-0.043542
-0.045599
-0.043276
0.000779
-0.039493
-0.011901
0.015491
21
-0.085430
0.050680
-0.022373
0.001215
-0.037344
-0.026366
0.015505
-0.039493
-0.072128
-0.017646
22
-0.085430
-0.044642
-0.004050
-0.009113
-0.002945
0.007767
0.022869
-0.039493
-0.061177
-0.013504
23
0.045341
0.050680
0.060618
0.031053
0.028702
-0.047347
-0.054446
0.071210
0.133599
0.135612
24
-0.063635
-0.044642
0.035829
-0.022885
-0.030464
-0.018850
-0.006584
-0.002592
-0.025952
-0.054925
25
-0.067268
0.050680
-0.012673
-0.040099
-0.015328
0.004636
-0.058127
0.034309
0.019199
-0.034215
26
-0.107226
-0.044642
-0.077342
-0.026328
-0.089630
-0.096198
0.026550
-0.076395
-0.042572
-0.005220
27
-0.023677
-0.044642
0.059541
-0.040099
-0.042848
-0.043589
0.011824
-0.039493
-0.015998
0.040343
28
0.052606
-0.044642
-0.021295
-0.074528
-0.040096
-0.037639
-0.006584
-0.039493
-0.000609
-0.054925
29
0.067136
0.050680
-0.006206
0.063187
-0.042848
-0.095885
0.052322
-0.076395
0.059424
0.052770
...
...
...
...
...
...
...
...
...
...
...
412
0.074401
-0.044642
0.085408
0.063187
0.014942
0.013091
0.015505
-0.002592
0.006209
0.085907
413
-0.052738
-0.044642
-0.000817
-0.026328
0.010815
0.007141
0.048640
-0.039493
-0.035817
0.019633
414
0.081666
0.050680
0.006728
-0.004523
0.109883
0.117056
-0.032356
0.091875
0.054724
0.007207
415
-0.005515
-0.044642
0.008883
-0.050428
0.025950
0.047224
-0.043401
0.071210
0.014823
0.003064
416
-0.027310
-0.044642
0.080019
0.098763
-0.002945
0.018101
-0.017629
0.003312
-0.029528
0.036201
417
-0.052738
-0.044642
0.071397
-0.074528
-0.015328
-0.001314
0.004460
-0.021412
-0.046879
0.003064
418
0.009016
-0.044642
-0.024529
-0.026328
0.098876
0.094196
0.070730
-0.002592
-0.021394
0.007207
419
-0.020045
-0.044642
-0.054707
-0.053871
-0.066239
-0.057367
0.011824
-0.039493
-0.074089
-0.005220
420
0.023546
-0.044642
-0.036385
0.000068
0.001183
0.034698
-0.043401
0.034309
-0.033249
0.061054
421
0.038076
0.050680
0.016428
0.021872
0.039710
0.045032
-0.043401
0.071210
0.049769
0.015491
422
-0.078165
0.050680
0.077863
0.052858
0.078236
0.064447
0.026550
-0.002592
0.040672
-0.009362
423
0.009016
0.050680
-0.039618
0.028758
0.038334
0.073529
-0.072854
0.108111
0.015567
-0.046641
424
0.001751
0.050680
0.011039
-0.019442
-0.016704
-0.003819
-0.047082
0.034309
0.024053
0.023775
425
-0.078165
-0.044642
-0.040696
-0.081414
-0.100638
-0.112795
0.022869
-0.076395
-0.020289
-0.050783
426
0.030811
0.050680
-0.034229
0.043677
0.057597
0.068831
-0.032356
0.057557
0.035462
0.085907
427
-0.034575
0.050680
0.005650
-0.005671
-0.073119
-0.062691
-0.006584
-0.039493
-0.045421
0.032059
428
0.048974
0.050680
0.088642
0.087287
0.035582
0.021546
-0.024993
0.034309
0.066048
0.131470
429
-0.041840
-0.044642
-0.033151
-0.022885
0.046589
0.041587
0.056003
-0.024733
-0.025952
-0.038357
430
-0.009147
-0.044642
-0.056863
-0.050428
0.021822
0.045345
-0.028674
0.034309
-0.009919
-0.017646
431
0.070769
0.050680
-0.030996
0.021872
-0.037344
-0.047034
0.033914
-0.039493
-0.014956
-0.001078
432
0.009016
-0.044642
0.055229
-0.005671
0.057597
0.044719
-0.002903
0.023239
0.055684
0.106617
433
-0.027310
-0.044642
-0.060097
-0.029771
0.046589
0.019980
0.122273
-0.039493
-0.051401
-0.009362
434
0.016281
-0.044642
0.001339
0.008101
0.005311
0.010899
0.030232
-0.039493
-0.045421
0.032059
435
-0.012780
-0.044642
-0.023451
-0.040099
-0.016704
0.004636
-0.017629
-0.002592
-0.038459
-0.038357
436
-0.056370
-0.044642
-0.074108
-0.050428
-0.024960
-0.047034
0.092820
-0.076395
-0.061177
-0.046641
437
0.041708
0.050680
0.019662
0.059744
-0.005697
-0.002566
-0.028674
-0.002592
0.031193
0.007207
438
-0.005515
0.050680
-0.015906
-0.067642
0.049341
0.079165
-0.028674
0.034309
-0.018118
0.044485
439
0.041708
0.050680
-0.015906
0.017282
-0.037344
-0.013840
-0.024993
-0.011080
-0.046879
0.015491
440
-0.045472
-0.044642
0.039062
0.001215
0.016318
0.015283
-0.028674
0.026560
0.044528
-0.025930
441
-0.045472
-0.044642
-0.073030
-0.081414
0.083740
0.027809
0.173816
-0.039493
-0.004220
0.003064
442 rows × 10 columns
In [48]:
df_y.head(5)
Out[48]:
0
0
151.0
1
75.0
2
141.0
3
206.0
4
135.0
In [49]:
type(df_y)
Out[49]:
pandas.core.frame.DataFrame
In [50]:
characters = [chr(i) for i in range(97, 97+26)]
print(characters)
['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
In [51]:
df_X.columns = characters[:len(df_X.ix[0])]
df_X.head(5)
Out[51]:
a
b
c
d
e
f
g
h
i
j
0
0.038076
0.050680
0.061696
0.021872
-0.044223
-0.034821
-0.043401
-0.002592
0.019908
-0.017646
1
-0.001882
-0.044642
-0.051474
-0.026328
-0.008449
-0.019163
0.074412
-0.039493
-0.068330
-0.092204
2
0.085299
0.050680
0.044451
-0.005671
-0.045599
-0.034194
-0.032356
-0.002592
0.002864
-0.025930
3
-0.089063
-0.044642
-0.011595
-0.036656
0.012191
0.024991
-0.036038
0.034309
0.022692
-0.009362
4
0.005383
-0.044642
-0.036385
0.021872
0.003935
0.015596
0.008142
-0.002592
-0.031991
-0.046641
maskの復習
In [52]:
df_X['a'] = df_X['a'].mask(df_X['a']>0,'T')
In [53]:
df_X['a'] = df_X['a'].mask(df_X['a']!='T','F')
In [54]:
df_X.head(10)
Out[54]:
a
b
c
d
e
f
g
h
i
j
0
T
0.050680
0.061696
0.021872
-0.044223
-0.034821
-0.043401
-0.002592
0.019908
-0.017646
1
F
-0.044642
-0.051474
-0.026328
-0.008449
-0.019163
0.074412
-0.039493
-0.068330
-0.092204
2
T
0.050680
0.044451
-0.005671
-0.045599
-0.034194
-0.032356
-0.002592
0.002864
-0.025930
3
F
-0.044642
-0.011595
-0.036656
0.012191
0.024991
-0.036038
0.034309
0.022692
-0.009362
4
T
-0.044642
-0.036385
0.021872
0.003935
0.015596
0.008142
-0.002592
-0.031991
-0.046641
5
F
-0.044642
-0.040696
-0.019442
-0.068991
-0.079288
0.041277
-0.076395
-0.041180
-0.096346
6
F
0.050680
-0.047163
-0.015999
-0.040096
-0.024800
0.000779
-0.039493
-0.062913
-0.038357
7
T
0.050680
-0.001895
0.066630
0.090620
0.108914
0.022869
0.017703
-0.035817
0.003064
8
T
0.050680
0.061696
-0.040099
-0.013953
0.006202
-0.028674
-0.002592
-0.014956
0.011349
9
F
-0.044642
0.039062
-0.033214
-0.012577
-0.034508
-0.024993
-0.002592
0.067736
-0.013504
In [55]:
k=[df_X.ix[i,'a']+str(df_X.index[i]) for i in df_X.index]#リスト内包表記は別途詳しく説明
k
Out[55]:
['T0',
'F1',
'T2',
'F3',
'T4',
'F5',
'F6',
'T7',
'T8',
'F9',
'F10',
'T11',
'T12',
'T13',
'T14',
'F15',
'F16',
'T17',
'F18',
'F19',
'F20',
'F21',
'F22',
'T23',
'F24',
'F25',
'F26',
'F27',
'T28',
'T29',
'F30',
'F31',
'T32',
'T33',
'T34',
'T35',
'T36',
'F37',
'F38',
'F39',
'T40',
'F41',
'F42',
'T43',
'T44',
'T45',
'F46',
'F47',
'T48',
'F49',
'T50',
'T51',
'F52',
'F53',
'F54',
'F55',
'F56',
'F57',
'T58',
'T59',
'F60',
'F61',
'F62',
'F63',
'T64',
'F65',
'F66',
'T67',
'T68',
'T69',
'F70',
'F71',
'T72',
'T73',
'T74',
'F75',
'F76',
'F77',
'T78',
'F79',
'T80',
'T81',
'F82',
'F83',
'T84',
'T85',
'F86',
'T87',
'F88',
'F89',
'T90',
'T91',
'F92',
'F93',
'F94',
'F95',
'T96',
'F97',
'T98',
'F99',
'T100',
'T101',
'F102',
'T103',
'F104',
'T105',
'F106',
'T107',
'T108',
'T109',
'T110',
'T111',
'F112',
'T113',
'T114',
'F115',
'T116',
'T117',
'F118',
'T119',
'F120',
'T121',
'T122',
'T123',
'F124',
'F125',
'F126',
'T127',
'F128',
'T129',
'F130',
'F131',
'T132',
'F133',
'F134',
'F135',
'F136',
'T137',
'T138',
'T139',
'T140',
'F141',
'T142',
'F143',
'T144',
'F145',
'F146',
'F147',
'F148',
'F149',
'T150',
'T151',
'F152',
'T153',
'T154',
'F155',
'F156',
'F157',
'F158',
'F159',
'F160',
'F161',
'F162',
'T163',
'T164',
'F165',
'F166',
'T167',
'T168',
'F169',
'T170',
'F171',
'T172',
'F173',
'T174',
'T175',
'T176',
'T177',
'T178',
'F179',
'F180',
'T181',
'T182',
'T183',
'T184',
'F185',
'F186',
'F187',
'T188',
'F189',
'T190',
'F191',
'T192',
'T193',
'F194',
'T195',
'F196',
'T197',
'F198',
'T199',
'T200',
'F201',
'T202',
'F203',
'T204',
'F205',
'T206',
'T207',
'T208',
'T209',
'T210',
'T211',
'T212',
'T213',
'T214',
'T215',
'T216',
'T217',
'F218',
'F219',
'T220',
'F221',
'F222',
'F223',
'F224',
'T225',
'F226',
'T227',
'F228',
'F229',
'F230',
'T231',
'T232',
'T233',
'T234',
'T235',
'T236',
'T237',
'T238',
'T239',
'T240',
'T241',
'F242',
'T243',
'F244',
'F245',
'T246',
'F247',
'F248',
'F249',
'T250',
'F251',
'T252',
'T253',
'T254',
'T255',
'F256',
'F257',
'T258',
'T259',
'T260',
'T261',
'F262',
'F263',
'T264',
'F265',
'F266',
'T267',
'T268',
'T269',
'T270',
'T271',
'T272',
'T273',
'T274',
'F275',
'T276',
'F277',
'T278',
'T279',
'T280',
'F281',
'T282',
'F283',
'T284',
'T285',
'F286',
'T287',
'T288',
'F289',
'T290',
'T291',
'T292',
'F293',
'T294',
'F295',
'T296',
'T297',
'T298',
'T299',
'T300',
'F301',
'T302',
'T303',
'T304',
'F305',
'T306',
'T307',
'T308',
'F309',
'F310',
'T311',
'F312',
'T313',
'F314',
'T315',
'T316',
'T317',
'T318',
'T319',
'F320',
'T321',
'T322',
'T323',
'T324',
'F325',
'T326',
'T327',
'F328',
'F329',
'T330',
'T331',
'T332',
'T333',
'F334',
'T335',
'F336',
'T337',
'F338',
'T339',
'F340',
'T341',
'T342',
'F343',
'F344',
'T345',
'T346',
'T347',
'T348',
'T349',
'F350',
'F351',
'T352',
'F353',
'F354',
'F355',
'F356',
'F357',
'F358',
'T359',
'T360',
'T361',
'T362',
'F363',
'T364',
'T365',
'F366',
'F367',
'F368',
'F369',
'T370',
'T371',
'F372',
'F373',
'F374',
'T375',
'F376',
'T377',
'T378',
'F379',
'T380',
'F381',
'T382',
'T383',
'T384',
'T385',
'T386',
'F387',
'T388',
'F389',
'T390',
'F391',
'F392',
'F393',
'T394',
'F395',
'F396',
'T397',
'T398',
'T399',
'F400',
'T401',
'T402',
'F403',
'F404',
'T405',
'F406',
'T407',
'T408',
'F409',
'F410',
'T411',
'T412',
'F413',
'T414',
'F415',
'F416',
'F417',
'T418',
'F419',
'T420',
'T421',
'F422',
'T423',
'T424',
'F425',
'T426',
'F427',
'T428',
'F429',
'F430',
'T431',
'T432',
'F433',
'T434',
'F435',
'F436',
'T437',
'F438',
'T439',
'F440',
'F441']
新たに列を追加
In [56]:
df_X['k']=k
In [57]:
df_X.head(5)
Out[57]:
a
b
c
d
e
f
g
h
i
j
k
0
T
0.050680
0.061696
0.021872
-0.044223
-0.034821
-0.043401
-0.002592
0.019908
-0.017646
T0
1
F
-0.044642
-0.051474
-0.026328
-0.008449
-0.019163
0.074412
-0.039493
-0.068330
-0.092204
F1
2
T
0.050680
0.044451
-0.005671
-0.045599
-0.034194
-0.032356
-0.002592
0.002864
-0.025930
T2
3
F
-0.044642
-0.011595
-0.036656
0.012191
0.024991
-0.036038
0.034309
0.022692
-0.009362
F3
4
T
-0.044642
-0.036385
0.021872
0.003935
0.015596
0.008142
-0.002592
-0.031991
-0.046641
T4
インデックスをデータの中に追加
In [58]:
df_X['index']=df_X.index
df_X.head(5)
Out[58]:
a
b
c
d
e
f
g
h
i
j
k
index
0
T
0.050680
0.061696
0.021872
-0.044223
-0.034821
-0.043401
-0.002592
0.019908
-0.017646
T0
0
1
F
-0.044642
-0.051474
-0.026328
-0.008449
-0.019163
0.074412
-0.039493
-0.068330
-0.092204
F1
1
2
T
0.050680
0.044451
-0.005671
-0.045599
-0.034194
-0.032356
-0.002592
0.002864
-0.025930
T2
2
3
F
-0.044642
-0.011595
-0.036656
0.012191
0.024991
-0.036038
0.034309
0.022692
-0.009362
F3
3
4
T
-0.044642
-0.036385
0.021872
0.003935
0.015596
0.008142
-0.002592
-0.031991
-0.046641
T4
4
In [59]:
ran = list(range(0,450))
ran.reverse()
ran
Out[59]:
[449,
448,
447,
446,
445,
444,
443,
442,
441,
440,
439,
438,
437,
436,
435,
434,
433,
432,
431,
430,
429,
428,
427,
426,
425,
424,
423,
422,
421,
420,
419,
418,
417,
416,
415,
414,
413,
412,
411,
410,
409,
408,
407,
406,
405,
404,
403,
402,
401,
400,
399,
398,
397,
396,
395,
394,
393,
392,
391,
390,
389,
388,
387,
386,
385,
384,
383,
382,
381,
380,
379,
378,
377,
376,
375,
374,
373,
372,
371,
370,
369,
368,
367,
366,
365,
364,
363,
362,
361,
360,
359,
358,
357,
356,
355,
354,
353,
352,
351,
350,
349,
348,
347,
346,
345,
344,
343,
342,
341,
340,
339,
338,
337,
336,
335,
334,
333,
332,
331,
330,
329,
328,
327,
326,
325,
324,
323,
322,
321,
320,
319,
318,
317,
316,
315,
314,
313,
312,
311,
310,
309,
308,
307,
306,
305,
304,
303,
302,
301,
300,
299,
298,
297,
296,
295,
294,
293,
292,
291,
290,
289,
288,
287,
286,
285,
284,
283,
282,
281,
280,
279,
278,
277,
276,
275,
274,
273,
272,
271,
270,
269,
268,
267,
266,
265,
264,
263,
262,
261,
260,
259,
258,
257,
256,
255,
254,
253,
252,
251,
250,
249,
248,
247,
246,
245,
244,
243,
242,
241,
240,
239,
238,
237,
236,
235,
234,
233,
232,
231,
230,
229,
228,
227,
226,
225,
224,
223,
222,
221,
220,
219,
218,
217,
216,
215,
214,
213,
212,
211,
210,
209,
208,
207,
206,
205,
204,
203,
202,
201,
200,
199,
198,
197,
196,
195,
194,
193,
192,
191,
190,
189,
188,
187,
186,
185,
184,
183,
182,
181,
180,
179,
178,
177,
176,
175,
174,
173,
172,
171,
170,
169,
168,
167,
166,
165,
164,
163,
162,
161,
160,
159,
158,
157,
156,
155,
154,
153,
152,
151,
150,
149,
148,
147,
146,
145,
144,
143,
142,
141,
140,
139,
138,
137,
136,
135,
134,
133,
132,
131,
130,
129,
128,
127,
126,
125,
124,
123,
122,
121,
120,
119,
118,
117,
116,
115,
114,
113,
112,
111,
110,
109,
108,
107,
106,
105,
104,
103,
102,
101,
100,
99,
98,
97,
96,
95,
94,
93,
92,
91,
90,
89,
88,
87,
86,
85,
84,
83,
82,
81,
80,
79,
78,
77,
76,
75,
74,
73,
72,
71,
70,
69,
68,
67,
66,
65,
64,
63,
62,
61,
60,
59,
58,
57,
56,
55,
54,
53,
52,
51,
50,
49,
48,
47,
46,
45,
44,
43,
42,
41,
40,
39,
38,
37,
36,
35,
34,
33,
32,
31,
30,
29,
28,
27,
26,
25,
24,
23,
22,
21,
20,
19,
18,
17,
16,
15,
14,
13,
12,
11,
10,
9,
8,
7,
6,
5,
4,
3,
2,
1,
0]
In [60]:
df_X.reindex(ran)
Out[60]:
a
b
c
d
e
f
g
h
i
j
k
index
449
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
448
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
447
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
446
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
445
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
444
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
443
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
442
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
441
F
-0.044642
-0.073030
-0.081414
0.083740
0.027809
0.173816
-0.039493
-0.004220
0.003064
F441
441.0
440
F
-0.044642
0.039062
0.001215
0.016318
0.015283
-0.028674
0.026560
0.044528
-0.025930
F440
440.0
439
T
0.050680
-0.015906
0.017282
-0.037344
-0.013840
-0.024993
-0.011080
-0.046879
0.015491
T439
439.0
438
F
0.050680
-0.015906
-0.067642
0.049341
0.079165
-0.028674
0.034309
-0.018118
0.044485
F438
438.0
437
T
0.050680
0.019662
0.059744
-0.005697
-0.002566
-0.028674
-0.002592
0.031193
0.007207
T437
437.0
436
F
-0.044642
-0.074108
-0.050428
-0.024960
-0.047034
0.092820
-0.076395
-0.061177
-0.046641
F436
436.0
435
F
-0.044642
-0.023451
-0.040099
-0.016704
0.004636
-0.017629
-0.002592
-0.038459
-0.038357
F435
435.0
434
T
-0.044642
0.001339
0.008101
0.005311
0.010899
0.030232
-0.039493
-0.045421
0.032059
T434
434.0
433
F
-0.044642
-0.060097
-0.029771
0.046589
0.019980
0.122273
-0.039493
-0.051401
-0.009362
F433
433.0
432
T
-0.044642
0.055229
-0.005671
0.057597
0.044719
-0.002903
0.023239
0.055684
0.106617
T432
432.0
431
T
0.050680
-0.030996
0.021872
-0.037344
-0.047034
0.033914
-0.039493
-0.014956
-0.001078
T431
431.0
430
F
-0.044642
-0.056863
-0.050428
0.021822
0.045345
-0.028674
0.034309
-0.009919
-0.017646
F430
430.0
429
F
-0.044642
-0.033151
-0.022885
0.046589
0.041587
0.056003
-0.024733
-0.025952
-0.038357
F429
429.0
428
T
0.050680
0.088642
0.087287
0.035582
0.021546
-0.024993
0.034309
0.066048
0.131470
T428
428.0
427
F
0.050680
0.005650
-0.005671
-0.073119
-0.062691
-0.006584
-0.039493
-0.045421
0.032059
F427
427.0
426
T
0.050680
-0.034229
0.043677
0.057597
0.068831
-0.032356
0.057557
0.035462
0.085907
T426
426.0
425
F
-0.044642
-0.040696
-0.081414
-0.100638
-0.112795
0.022869
-0.076395
-0.020289
-0.050783
F425
425.0
424
T
0.050680
0.011039
-0.019442
-0.016704
-0.003819
-0.047082
0.034309
0.024053
0.023775
T424
424.0
423
T
0.050680
-0.039618
0.028758
0.038334
0.073529
-0.072854
0.108111
0.015567
-0.046641
T423
423.0
422
F
0.050680
0.077863
0.052858
0.078236
0.064447
0.026550
-0.002592
0.040672
-0.009362
F422
422.0
421
T
0.050680
0.016428
0.021872
0.039710
0.045032
-0.043401
0.071210
0.049769
0.015491
T421
421.0
420
T
-0.044642
-0.036385
0.000068
0.001183
0.034698
-0.043401
0.034309
-0.033249
0.061054
T420
420.0
...
...
...
...
...
...
...
...
...
...
...
...
...
29
T
0.050680
-0.006206
0.063187
-0.042848
-0.095885
0.052322
-0.076395
0.059424
0.052770
T29
29.0
28
T
-0.044642
-0.021295
-0.074528
-0.040096
-0.037639
-0.006584
-0.039493
-0.000609
-0.054925
T28
28.0
27
F
-0.044642
0.059541
-0.040099
-0.042848
-0.043589
0.011824
-0.039493
-0.015998
0.040343
F27
27.0
26
F
-0.044642
-0.077342
-0.026328
-0.089630
-0.096198
0.026550
-0.076395
-0.042572
-0.005220
F26
26.0
25
F
0.050680
-0.012673
-0.040099
-0.015328
0.004636
-0.058127
0.034309
0.019199
-0.034215
F25
25.0
24
F
-0.044642
0.035829
-0.022885
-0.030464
-0.018850
-0.006584
-0.002592
-0.025952
-0.054925
F24
24.0
23
T
0.050680
0.060618
0.031053
0.028702
-0.047347
-0.054446
0.071210
0.133599
0.135612
T23
23.0
22
F
-0.044642
-0.004050
-0.009113
-0.002945
0.007767
0.022869
-0.039493
-0.061177
-0.013504
F22
22.0
21
F
0.050680
-0.022373
0.001215
-0.037344
-0.026366
0.015505
-0.039493
-0.072128
-0.017646
F21
21.0
20
F
-0.044642
-0.056863
-0.043542
-0.045599
-0.043276
0.000779
-0.039493
-0.011901
0.015491
F20
20.0
19
F
-0.044642
-0.018062
-0.040099
-0.002945
-0.011335
0.037595
-0.039493
-0.008944
-0.054925
F19
19.0
18
F
-0.044642
-0.010517
-0.036656
-0.037344
-0.019476
-0.028674
-0.002592
-0.018118
-0.017646
F18
18.0
17
T
0.050680
0.012117
0.056301
0.034206
0.049416
-0.039719
0.034309
0.027368
-0.001078
T17
17.0
16
F
-0.044642
0.042296
0.049415
0.024574
-0.023861
0.074412
-0.039493
0.052280
0.027917
F16
16.0
15
F
0.050680
-0.018062
0.080401
0.089244
0.107662
-0.039719
0.108111
0.036056
-0.042499
F15
15.0
14
T
-0.044642
-0.025607
-0.012556
0.017694
-0.000061
0.081775
-0.039493
-0.031991
-0.075636
T14
14.0
13
T
0.050680
-0.001895
0.008101
-0.004321
-0.015719
-0.002903
-0.002592
0.038393
-0.013504
T13
13.0
12
T
-0.044642
-0.028840
-0.009113
-0.004321
-0.009769
0.044958
-0.039493
-0.030751
-0.042499
T12
12.0
11
T
0.050680
0.017506
-0.033214
-0.007073
0.045972
-0.065491
0.071210
-0.096433
-0.059067
T11
11.0
10
F
-0.044642
-0.083808
0.008101
-0.103389
-0.090561
-0.013948
-0.076395
-0.062913
-0.034215
F10
10.0
9
F
-0.044642
0.039062
-0.033214
-0.012577
-0.034508
-0.024993
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F9
9.0
8
T
0.050680
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0.006202
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T8
8.0
7
T
0.050680
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0.066630
0.090620
0.108914
0.022869
0.017703
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T7
7.0
6
F
0.050680
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F6
6.0
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F
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F5
5.0
4
T
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T4
4.0
3
F
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F3
3.0
2
T
0.050680
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T2
2.0
1
F
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F1
1.0
0
T
0.050680
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0.019908
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T0
0.0
450 rows × 12 columns
In [45]:
Out[45]:
a
b
c
d
e
f
g
h
i
j
k
index
449
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
448
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
447
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
446
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
445
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
444
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
443
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
442
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
441
F
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441.0
440
F
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F440
440.0
439
T
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T439
439.0
438
F
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F438
438.0
437
T
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T437
437.0
436
F
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436.0
435
F
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F435
435.0
434
T
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T434
434.0
433
F
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F433
433.0
432
T
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T432
432.0
431
T
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T431
431.0
430
F
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430.0
429
F
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F429
429.0
428
T
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T428
428.0
427
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F427
427.0
426
T
0.050680
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T426
426.0
425
F
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F425
425.0
424
T
0.050680
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0.034309
0.024053
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T424
424.0
423
T
0.050680
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T423
423.0
422
F
0.050680
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F422
422.0
421
T
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T421
421.0
420
T
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T420
420.0
...
...
...
...
...
...
...
...
...
...
...
...
...
29
T
0.050680
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T29
29.0
28
T
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T28
28.0
27
F
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F27
27.0
26
F
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F26
26.0
25
F
0.050680
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0.019199
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F25
25.0
24
F
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F24
24.0
23
T
0.050680
0.060618
0.031053
0.028702
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0.071210
0.133599
0.135612
T23
23.0
22
F
-0.044642
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0.007767
0.022869
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F22
22.0
21
F
0.050680
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0.001215
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0.015505
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F21
21.0
20
F
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0.000779
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0.015491
F20
20.0
19
F
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0.037595
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F19
19.0
18
F
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F18
18.0
17
T
0.050680
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0.034206
0.049416
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0.034309
0.027368
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T17
17.0
16
F
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0.049415
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0.074412
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0.052280
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F16
16.0
15
F
0.050680
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0.080401
0.089244
0.107662
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0.108111
0.036056
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F15
15.0
14
T
-0.044642
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0.017694
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0.081775
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T14
14.0
13
T
0.050680
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0.008101
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0.038393
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T13
13.0
12
T
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0.044958
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T12
12.0
11
T
0.050680
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0.071210
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T11
11.0
10
F
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0.008101
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F10
10.0
9
F
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0.039062
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0.067736
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F9
9.0
8
T
0.050680
0.061696
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0.006202
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0.011349
T8
8.0
7
T
0.050680
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0.066630
0.090620
0.108914
0.022869
0.017703
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0.003064
T7
7.0
6
F
0.050680
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0.000779
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F6
6.0
5
F
-0.044642
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0.041277
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F5
5.0
4
T
-0.044642
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0.021872
0.003935
0.015596
0.008142
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T4
4.0
3
F
-0.044642
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0.012191
0.024991
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0.034309
0.022692
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F3
3.0
2
T
0.050680
0.044451
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0.002864
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T2
2.0
1
F
-0.044642
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0.074412
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F1
1.0
0
T
0.050680
0.061696
0.021872
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-0.034821
-0.043401
-0.002592
0.019908
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T0
0.0
450 rows × 12 columns
In [61]:
df_X=df_X.set_index('k')
Out[61]:
a
b
c
d
e
f
g
h
i
j
index
k
T0
T
0.050680
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0
F1
F
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1
T2
T
0.050680
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2
F3
F
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3
T4
T
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4
F5
F
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5
F6
F
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6
T7
T
0.050680
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0.066630
0.090620
0.108914
0.022869
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0.003064
7
T8
T
0.050680
0.061696
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0.006202
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0.011349
8
F9
F
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0.067736
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9
F10
F
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0.008101
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10
T11
T
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0.045972
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11
T12
T
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12
T13
T
0.050680
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13
T14
T
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14
F15
F
0.050680
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0.089244
0.107662
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15
F16
F
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0.042296
0.049415
0.024574
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0.052280
0.027917
16
T17
T
0.050680
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0.056301
0.034206
0.049416
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0.034309
0.027368
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17
F18
F
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18
F19
F
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19
F20
F
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20
F21
F
0.050680
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21
F22
F
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22
T23
T
0.050680
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0.031053
0.028702
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0.071210
0.133599
0.135612
23
F24
F
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24
F25
F
0.050680
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0.004636
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0.019199
-0.034215
25
F26
F
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0.026550
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26
F27
F
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0.040343
27
T28
T
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-0.040096
-0.037639
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28
T29
T
0.050680
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0.063187
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0.052322
-0.076395
0.059424
0.052770
29
...
...
...
...
...
...
...
...
...
...
...
...
T412
T
-0.044642
0.085408
0.063187
0.014942
0.013091
0.015505
-0.002592
0.006209
0.085907
412
F413
F
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-0.000817
-0.026328
0.010815
0.007141
0.048640
-0.039493
-0.035817
0.019633
413
T414
T
0.050680
0.006728
-0.004523
0.109883
0.117056
-0.032356
0.091875
0.054724
0.007207
414
F415
F
-0.044642
0.008883
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0.025950
0.047224
-0.043401
0.071210
0.014823
0.003064
415
F416
F
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0.003312
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0.036201
416
F417
F
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-0.015328
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0.004460
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0.003064
417
T418
T
-0.044642
-0.024529
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0.098876
0.094196
0.070730
-0.002592
-0.021394
0.007207
418
F419
F
-0.044642
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-0.057367
0.011824
-0.039493
-0.074089
-0.005220
419
T420
T
-0.044642
-0.036385
0.000068
0.001183
0.034698
-0.043401
0.034309
-0.033249
0.061054
420
T421
T
0.050680
0.016428
0.021872
0.039710
0.045032
-0.043401
0.071210
0.049769
0.015491
421
F422
F
0.050680
0.077863
0.052858
0.078236
0.064447
0.026550
-0.002592
0.040672
-0.009362
422
T423
T
0.050680
-0.039618
0.028758
0.038334
0.073529
-0.072854
0.108111
0.015567
-0.046641
423
T424
T
0.050680
0.011039
-0.019442
-0.016704
-0.003819
-0.047082
0.034309
0.024053
0.023775
424
F425
F
-0.044642
-0.040696
-0.081414
-0.100638
-0.112795
0.022869
-0.076395
-0.020289
-0.050783
425
T426
T
0.050680
-0.034229
0.043677
0.057597
0.068831
-0.032356
0.057557
0.035462
0.085907
426
F427
F
0.050680
0.005650
-0.005671
-0.073119
-0.062691
-0.006584
-0.039493
-0.045421
0.032059
427
T428
T
0.050680
0.088642
0.087287
0.035582
0.021546
-0.024993
0.034309
0.066048
0.131470
428
F429
F
-0.044642
-0.033151
-0.022885
0.046589
0.041587
0.056003
-0.024733
-0.025952
-0.038357
429
F430
F
-0.044642
-0.056863
-0.050428
0.021822
0.045345
-0.028674
0.034309
-0.009919
-0.017646
430
T431
T
0.050680
-0.030996
0.021872
-0.037344
-0.047034
0.033914
-0.039493
-0.014956
-0.001078
431
T432
T
-0.044642
0.055229
-0.005671
0.057597
0.044719
-0.002903
0.023239
0.055684
0.106617
432
F433
F
-0.044642
-0.060097
-0.029771
0.046589
0.019980
0.122273
-0.039493
-0.051401
-0.009362
433
T434
T
-0.044642
0.001339
0.008101
0.005311
0.010899
0.030232
-0.039493
-0.045421
0.032059
434
F435
F
-0.044642
-0.023451
-0.040099
-0.016704
0.004636
-0.017629
-0.002592
-0.038459
-0.038357
435
F436
F
-0.044642
-0.074108
-0.050428
-0.024960
-0.047034
0.092820
-0.076395
-0.061177
-0.046641
436
T437
T
0.050680
0.019662
0.059744
-0.005697
-0.002566
-0.028674
-0.002592
0.031193
0.007207
437
F438
F
0.050680
-0.015906
-0.067642
0.049341
0.079165
-0.028674
0.034309
-0.018118
0.044485
438
T439
T
0.050680
-0.015906
0.017282
-0.037344
-0.013840
-0.024993
-0.011080
-0.046879
0.015491
439
F440
F
-0.044642
0.039062
0.001215
0.016318
0.015283
-0.028674
0.026560
0.044528
-0.025930
440
F441
F
-0.044642
-0.073030
-0.081414
0.083740
0.027809
0.173816
-0.039493
-0.004220
0.003064
441
442 rows × 11 columns
In [62]:
df_X.head(5)
Out[62]:
a
b
c
d
e
f
g
h
i
j
k
index
0
T
0.050680
0.061696
0.021872
-0.044223
-0.034821
-0.043401
-0.002592
0.019908
-0.017646
T0
0
1
F
-0.044642
-0.051474
-0.026328
-0.008449
-0.019163
0.074412
-0.039493
-0.068330
-0.092204
F1
1
2
T
0.050680
0.044451
-0.005671
-0.045599
-0.034194
-0.032356
-0.002592
0.002864
-0.025930
T2
2
3
F
-0.044642
-0.011595
-0.036656
0.012191
0.024991
-0.036038
0.034309
0.022692
-0.009362
F3
3
4
T
-0.044642
-0.036385
0.021872
0.003935
0.015596
0.008142
-0.002592
-0.031991
-0.046641
T4
4
In [63]:
df_X=df_X.set_index('a')
df_X.head(10)
Out[63]:
b
c
d
e
f
g
h
i
j
k
index
a
T
0.050680
0.061696
0.021872
-0.044223
-0.034821
-0.043401
-0.002592
0.019908
-0.017646
T0
0
F
-0.044642
-0.051474
-0.026328
-0.008449
-0.019163
0.074412
-0.039493
-0.068330
-0.092204
F1
1
T
0.050680
0.044451
-0.005671
-0.045599
-0.034194
-0.032356
-0.002592
0.002864
-0.025930
T2
2
F
-0.044642
-0.011595
-0.036656
0.012191
0.024991
-0.036038
0.034309
0.022692
-0.009362
F3
3
T
-0.044642
-0.036385
0.021872
0.003935
0.015596
0.008142
-0.002592
-0.031991
-0.046641
T4
4
F
-0.044642
-0.040696
-0.019442
-0.068991
-0.079288
0.041277
-0.076395
-0.041180
-0.096346
F5
5
F
0.050680
-0.047163
-0.015999
-0.040096
-0.024800
0.000779
-0.039493
-0.062913
-0.038357
F6
6
T
0.050680
-0.001895
0.066630
0.090620
0.108914
0.022869
0.017703
-0.035817
0.003064
T7
7
T
0.050680
0.061696
-0.040099
-0.013953
0.006202
-0.028674
-0.002592
-0.014956
0.011349
T8
8
F
-0.044642
0.039062
-0.033214
-0.012577
-0.034508
-0.024993
-0.002592
0.067736
-0.013504
F9
9
インデックスが入れ替わる。
In [ ]: