In [1]:
import pandas as pd
import numpy as np
In [683]:
df = pd.DataFrame(np.random.randn(1000,10), columns= ['topic 1', 'topic 2', 'topic 3', 'topic 4', 'topic 5', 'topic 6', 'topic 7', 'topic 8', 'topic 9', 'topic 10'])
In [684]:
df.shape
Out[684]:
(1000, 10)
In [685]:
# make a distribution in every row
def apply_distribution(row):
s = np.random.exponential(scale=1.0, size=10)
return pd.Series(s/s.sum())
In [686]:
df = df.apply(apply_distribution, axis=1) # apply the distribution
In [687]:
df['date_sent'] = pd.Series() # add a column for date the email is sent
In [688]:
df
Out[688]:
0
1
2
3
4
5
6
7
8
9
date_sent
0
0.093013
0.022149
0.152614
0.122789
0.171756
0.008851
0.257211
0.011800
0.041068
0.118750
NaN
1
0.085686
0.042914
0.193660
0.052411
0.377262
0.054326
0.103518
0.013129
0.029596
0.047497
NaN
2
0.052673
0.013017
0.187891
0.027889
0.134614
0.109124
0.062208
0.023950
0.020901
0.367733
NaN
3
0.080543
0.241699
0.013637
0.036184
0.010589
0.312160
0.143825
0.005105
0.141908
0.014350
NaN
4
0.075367
0.102129
0.345652
0.095193
0.014923
0.130467
0.039766
0.037884
0.027764
0.130855
NaN
5
0.029140
0.122982
0.183885
0.051679
0.087894
0.198557
0.029177
0.068652
0.194724
0.033311
NaN
6
0.120828
0.080681
0.134731
0.087057
0.078469
0.151554
0.077353
0.145072
0.074153
0.050101
NaN
7
0.234988
0.009897
0.029432
0.028404
0.054993
0.048191
0.349070
0.097725
0.056284
0.091017
NaN
8
0.000614
0.034910
0.002672
0.010680
0.136185
0.043953
0.060662
0.047986
0.317369
0.344967
NaN
9
0.014547
0.058735
0.153404
0.077710
0.002322
0.137340
0.038911
0.014986
0.289575
0.212471
NaN
10
0.008695
0.026700
0.054163
0.387308
0.022092
0.041383
0.221447
0.016484
0.214351
0.007377
NaN
11
0.041390
0.159062
0.054547
0.153180
0.088022
0.080946
0.234152
0.046570
0.125441
0.016689
NaN
12
0.051968
0.035934
0.032615
0.059201
0.166128
0.229201
0.313454
0.046930
0.006893
0.057677
NaN
13
0.051099
0.020306
0.159952
0.059786
0.112925
0.179584
0.040922
0.229889
0.069481
0.076057
NaN
14
0.078620
0.038959
0.055026
0.031491
0.203583
0.132747
0.236623
0.107255
0.082339
0.033358
NaN
15
0.181892
0.228793
0.063566
0.097666
0.067363
0.111606
0.012283
0.070138
0.035407
0.131286
NaN
16
0.085797
0.337739
0.214189
0.113755
0.142805
0.007106
0.006445
0.051139
0.013605
0.027420
NaN
17
0.054453
0.023030
0.018715
0.141864
0.416313
0.000064
0.223425
0.050595
0.009697
0.061843
NaN
18
0.144780
0.072253
0.090191
0.087188
0.098689
0.000098
0.038569
0.321993
0.084521
0.061717
NaN
19
0.118873
0.199206
0.006197
0.070047
0.038159
0.063785
0.168631
0.059917
0.018397
0.256788
NaN
20
0.149439
0.184593
0.157709
0.110559
0.221373
0.068101
0.004920
0.013690
0.047693
0.041923
NaN
21
0.036040
0.054603
0.000063
0.053995
0.116011
0.070401
0.269245
0.158960
0.206584
0.034098
NaN
22
0.044565
0.245600
0.113162
0.021003
0.071046
0.362133
0.020708
0.031813
0.016154
0.073817
NaN
23
0.119449
0.069428
0.003672
0.094421
0.018252
0.234732
0.358718
0.039515
0.012741
0.049072
NaN
24
0.069022
0.028948
0.113062
0.114830
0.104455
0.066402
0.089271
0.061627
0.134000
0.218384
NaN
25
0.013061
0.001281
0.058749
0.263196
0.099328
0.225964
0.021755
0.033043
0.110618
0.173005
NaN
26
0.008506
0.019529
0.101543
0.098604
0.239346
0.003608
0.445534
0.072408
0.003736
0.007185
NaN
27
0.013911
0.191937
0.343870
0.015008
0.039092
0.059851
0.211369
0.074437
0.009818
0.040706
NaN
28
0.105587
0.101039
0.027008
0.128465
0.266995
0.059113
0.025297
0.061519
0.000380
0.224596
NaN
29
0.012471
0.014510
0.068683
0.008481
0.256034
0.045586
0.055363
0.394376
0.045543
0.098953
NaN
...
...
...
...
...
...
...
...
...
...
...
...
970
0.104878
0.189771
0.170740
0.165510
0.007500
0.124253
0.102664
0.033310
0.071928
0.029446
NaN
971
0.010716
0.096125
0.065954
0.068900
0.097438
0.045346
0.094105
0.146061
0.156118
0.219237
NaN
972
0.059508
0.225429
0.160102
0.034499
0.071608
0.003279
0.306367
0.015398
0.070192
0.053618
NaN
973
0.236618
0.088829
0.261178
0.013578
0.288976
0.020972
0.067341
0.005094
0.014801
0.002615
NaN
974
0.092546
0.027781
0.137550
0.050823
0.198944
0.036406
0.040805
0.000247
0.205088
0.209810
NaN
975
0.020758
0.098846
0.183029
0.310018
0.124005
0.007286
0.006040
0.104336
0.140854
0.004829
NaN
976
0.002662
0.178143
0.127200
0.185732
0.153589
0.097458
0.065183
0.051932
0.001569
0.136531
NaN
977
0.101562
0.160400
0.168354
0.008245
0.212808
0.027732
0.178496
0.045559
0.083988
0.012857
NaN
978
0.021030
0.002906
0.024274
0.136584
0.310754
0.159391
0.168610
0.011742
0.156293
0.008417
NaN
979
0.022348
0.172398
0.006543
0.128303
0.062145
0.096896
0.007330
0.205899
0.100726
0.197413
NaN
980
0.018218
0.023606
0.250958
0.001435
0.021875
0.011041
0.079913
0.022351
0.170612
0.399991
NaN
981
0.107563
0.118399
0.192274
0.075835
0.044716
0.083989
0.051816
0.110655
0.200965
0.013788
NaN
982
0.323178
0.012526
0.179570
0.044297
0.066666
0.003718
0.191621
0.017643
0.082578
0.078201
NaN
983
0.013611
0.199819
0.011796
0.158551
0.169127
0.062300
0.010697
0.087363
0.013686
0.273049
NaN
984
0.060021
0.006483
0.090845
0.064929
0.049884
0.011103
0.028937
0.517896
0.073503
0.096398
NaN
985
0.263112
0.017212
0.095081
0.132283
0.037569
0.077197
0.012491
0.133828
0.195391
0.035837
NaN
986
0.083490
0.097345
0.093668
0.056783
0.121588
0.134282
0.139747
0.087321
0.080412
0.105364
NaN
987
0.015644
0.030562
0.097094
0.260417
0.012915
0.026136
0.054619
0.019881
0.041496
0.441235
NaN
988
0.141781
0.087416
0.286679
0.088753
0.008396
0.063025
0.250268
0.062960
0.005094
0.005629
NaN
989
0.377045
0.119088
0.072912
0.034323
0.003338
0.064164
0.114463
0.059107
0.023050
0.132509
NaN
990
0.051661
0.047893
0.057490
0.200066
0.081752
0.154186
0.009546
0.032937
0.176595
0.187873
NaN
991
0.012132
0.227984
0.045219
0.048532
0.072076
0.117802
0.263475
0.144284
0.028966
0.039529
NaN
992
0.353518
0.009456
0.200745
0.039707
0.035467
0.100722
0.008388
0.086243
0.139374
0.026378
NaN
993
0.137577
0.124454
0.009536
0.232614
0.101888
0.075090
0.059695
0.124679
0.004009
0.130457
NaN
994
0.092338
0.029579
0.039457
0.402105
0.106955
0.018004
0.037641
0.009545
0.234853
0.029522
NaN
995
0.147465
0.249481
0.182494
0.029455
0.050113
0.017217
0.049526
0.157240
0.049318
0.067690
NaN
996
0.091999
0.318989
0.088607
0.059343
0.048333
0.287783
0.007916
0.004893
0.066485
0.025651
NaN
997
0.024519
0.273072
0.079934
0.130409
0.004359
0.179298
0.144581
0.024430
0.026700
0.112698
NaN
998
0.052035
0.004493
0.045288
0.118129
0.113495
0.215003
0.267225
0.027923
0.114969
0.041440
NaN
999
0.080285
0.077427
0.028365
0.103227
0.100557
0.146552
0.109205
0.129704
0.153433
0.071244
NaN
1000 rows × 11 columns
In [690]:
df.iloc[0].sum() # sum per row should be 1
Out[690]:
1.0
In [691]:
import datetime
import radar
In [692]:
# random between two dates
def random_date(row):
return radar.random_date(start = datetime.datetime(year=2000, month=1, day=1),stop = datetime.datetime(year=2002, month=1, day=30))
In [694]:
df['date_sent'] = df.apply(random_date, axis=1) # assign random dates drawn from an intervals
In [695]:
df
Out[695]:
0
1
2
3
4
5
6
7
8
9
date_sent
0
0.093013
0.022149
0.152614
0.122789
0.171756
0.008851
0.257211
0.011800
0.041068
0.118750
2001-02-08 02:11:19
1
0.085686
0.042914
0.193660
0.052411
0.377262
0.054326
0.103518
0.013129
0.029596
0.047497
2000-04-24 03:13:02
2
0.052673
0.013017
0.187891
0.027889
0.134614
0.109124
0.062208
0.023950
0.020901
0.367733
2001-04-05 18:06:51
3
0.080543
0.241699
0.013637
0.036184
0.010589
0.312160
0.143825
0.005105
0.141908
0.014350
2001-12-07 18:49:41
4
0.075367
0.102129
0.345652
0.095193
0.014923
0.130467
0.039766
0.037884
0.027764
0.130855
2001-05-21 16:55:44
5
0.029140
0.122982
0.183885
0.051679
0.087894
0.198557
0.029177
0.068652
0.194724
0.033311
2001-09-04 01:35:07
6
0.120828
0.080681
0.134731
0.087057
0.078469
0.151554
0.077353
0.145072
0.074153
0.050101
2001-01-05 04:02:09
7
0.234988
0.009897
0.029432
0.028404
0.054993
0.048191
0.349070
0.097725
0.056284
0.091017
2001-07-23 08:16:57
8
0.000614
0.034910
0.002672
0.010680
0.136185
0.043953
0.060662
0.047986
0.317369
0.344967
2001-04-05 04:41:15
9
0.014547
0.058735
0.153404
0.077710
0.002322
0.137340
0.038911
0.014986
0.289575
0.212471
2001-07-28 05:39:16
10
0.008695
0.026700
0.054163
0.387308
0.022092
0.041383
0.221447
0.016484
0.214351
0.007377
2000-04-25 09:32:47
11
0.041390
0.159062
0.054547
0.153180
0.088022
0.080946
0.234152
0.046570
0.125441
0.016689
2001-08-15 06:49:17
12
0.051968
0.035934
0.032615
0.059201
0.166128
0.229201
0.313454
0.046930
0.006893
0.057677
2001-09-17 12:16:59
13
0.051099
0.020306
0.159952
0.059786
0.112925
0.179584
0.040922
0.229889
0.069481
0.076057
2000-08-03 18:15:42
14
0.078620
0.038959
0.055026
0.031491
0.203583
0.132747
0.236623
0.107255
0.082339
0.033358
2000-02-13 04:09:58
15
0.181892
0.228793
0.063566
0.097666
0.067363
0.111606
0.012283
0.070138
0.035407
0.131286
2000-04-15 18:31:42
16
0.085797
0.337739
0.214189
0.113755
0.142805
0.007106
0.006445
0.051139
0.013605
0.027420
2001-06-29 11:58:24
17
0.054453
0.023030
0.018715
0.141864
0.416313
0.000064
0.223425
0.050595
0.009697
0.061843
2000-05-08 07:41:02
18
0.144780
0.072253
0.090191
0.087188
0.098689
0.000098
0.038569
0.321993
0.084521
0.061717
2001-05-22 20:07:18
19
0.118873
0.199206
0.006197
0.070047
0.038159
0.063785
0.168631
0.059917
0.018397
0.256788
2000-10-01 08:45:06
20
0.149439
0.184593
0.157709
0.110559
0.221373
0.068101
0.004920
0.013690
0.047693
0.041923
2001-12-23 19:10:42
21
0.036040
0.054603
0.000063
0.053995
0.116011
0.070401
0.269245
0.158960
0.206584
0.034098
2001-08-23 11:25:09
22
0.044565
0.245600
0.113162
0.021003
0.071046
0.362133
0.020708
0.031813
0.016154
0.073817
2001-06-08 15:48:47
23
0.119449
0.069428
0.003672
0.094421
0.018252
0.234732
0.358718
0.039515
0.012741
0.049072
2000-11-02 15:24:09
24
0.069022
0.028948
0.113062
0.114830
0.104455
0.066402
0.089271
0.061627
0.134000
0.218384
2001-06-28 03:59:06
25
0.013061
0.001281
0.058749
0.263196
0.099328
0.225964
0.021755
0.033043
0.110618
0.173005
2001-05-29 03:36:11
26
0.008506
0.019529
0.101543
0.098604
0.239346
0.003608
0.445534
0.072408
0.003736
0.007185
2001-07-30 20:29:29
27
0.013911
0.191937
0.343870
0.015008
0.039092
0.059851
0.211369
0.074437
0.009818
0.040706
2002-01-28 06:53:10
28
0.105587
0.101039
0.027008
0.128465
0.266995
0.059113
0.025297
0.061519
0.000380
0.224596
2001-01-31 00:29:07
29
0.012471
0.014510
0.068683
0.008481
0.256034
0.045586
0.055363
0.394376
0.045543
0.098953
2000-12-23 05:24:25
...
...
...
...
...
...
...
...
...
...
...
...
970
0.104878
0.189771
0.170740
0.165510
0.007500
0.124253
0.102664
0.033310
0.071928
0.029446
2000-09-13 18:08:15
971
0.010716
0.096125
0.065954
0.068900
0.097438
0.045346
0.094105
0.146061
0.156118
0.219237
2001-02-10 10:56:47
972
0.059508
0.225429
0.160102
0.034499
0.071608
0.003279
0.306367
0.015398
0.070192
0.053618
2000-07-21 18:31:22
973
0.236618
0.088829
0.261178
0.013578
0.288976
0.020972
0.067341
0.005094
0.014801
0.002615
2000-01-04 00:21:20
974
0.092546
0.027781
0.137550
0.050823
0.198944
0.036406
0.040805
0.000247
0.205088
0.209810
2000-06-11 00:25:58
975
0.020758
0.098846
0.183029
0.310018
0.124005
0.007286
0.006040
0.104336
0.140854
0.004829
2001-06-16 14:58:11
976
0.002662
0.178143
0.127200
0.185732
0.153589
0.097458
0.065183
0.051932
0.001569
0.136531
2000-04-11 09:47:53
977
0.101562
0.160400
0.168354
0.008245
0.212808
0.027732
0.178496
0.045559
0.083988
0.012857
2000-04-18 05:52:08
978
0.021030
0.002906
0.024274
0.136584
0.310754
0.159391
0.168610
0.011742
0.156293
0.008417
2000-10-25 02:14:54
979
0.022348
0.172398
0.006543
0.128303
0.062145
0.096896
0.007330
0.205899
0.100726
0.197413
2000-05-27 22:22:48
980
0.018218
0.023606
0.250958
0.001435
0.021875
0.011041
0.079913
0.022351
0.170612
0.399991
2000-08-19 13:40:20
981
0.107563
0.118399
0.192274
0.075835
0.044716
0.083989
0.051816
0.110655
0.200965
0.013788
2002-01-01 01:56:51
982
0.323178
0.012526
0.179570
0.044297
0.066666
0.003718
0.191621
0.017643
0.082578
0.078201
2000-06-24 09:42:36
983
0.013611
0.199819
0.011796
0.158551
0.169127
0.062300
0.010697
0.087363
0.013686
0.273049
2000-04-07 00:59:29
984
0.060021
0.006483
0.090845
0.064929
0.049884
0.011103
0.028937
0.517896
0.073503
0.096398
2001-08-09 18:29:44
985
0.263112
0.017212
0.095081
0.132283
0.037569
0.077197
0.012491
0.133828
0.195391
0.035837
2000-05-26 03:11:17
986
0.083490
0.097345
0.093668
0.056783
0.121588
0.134282
0.139747
0.087321
0.080412
0.105364
2001-01-30 20:10:11
987
0.015644
0.030562
0.097094
0.260417
0.012915
0.026136
0.054619
0.019881
0.041496
0.441235
2001-12-16 18:13:26
988
0.141781
0.087416
0.286679
0.088753
0.008396
0.063025
0.250268
0.062960
0.005094
0.005629
2001-01-03 20:23:44
989
0.377045
0.119088
0.072912
0.034323
0.003338
0.064164
0.114463
0.059107
0.023050
0.132509
2001-12-19 19:03:49
990
0.051661
0.047893
0.057490
0.200066
0.081752
0.154186
0.009546
0.032937
0.176595
0.187873
2001-02-14 21:29:58
991
0.012132
0.227984
0.045219
0.048532
0.072076
0.117802
0.263475
0.144284
0.028966
0.039529
2000-10-22 19:25:50
992
0.353518
0.009456
0.200745
0.039707
0.035467
0.100722
0.008388
0.086243
0.139374
0.026378
2000-08-24 19:19:32
993
0.137577
0.124454
0.009536
0.232614
0.101888
0.075090
0.059695
0.124679
0.004009
0.130457
2001-06-07 21:10:03
994
0.092338
0.029579
0.039457
0.402105
0.106955
0.018004
0.037641
0.009545
0.234853
0.029522
2000-06-03 03:57:04
995
0.147465
0.249481
0.182494
0.029455
0.050113
0.017217
0.049526
0.157240
0.049318
0.067690
2000-02-07 15:04:23
996
0.091999
0.318989
0.088607
0.059343
0.048333
0.287783
0.007916
0.004893
0.066485
0.025651
2000-01-14 23:45:04
997
0.024519
0.273072
0.079934
0.130409
0.004359
0.179298
0.144581
0.024430
0.026700
0.112698
2000-07-15 09:03:26
998
0.052035
0.004493
0.045288
0.118129
0.113495
0.215003
0.267225
0.027923
0.114969
0.041440
2000-03-23 15:47:57
999
0.080285
0.077427
0.028365
0.103227
0.100557
0.146552
0.109205
0.129704
0.153433
0.071244
2001-07-30 08:22:48
1000 rows × 11 columns
In [696]:
df.iloc[10,0:10].sum() # ok
Out[696]:
1.0
In [26]:
import time
import datetime
In [697]:
s = df.iloc[10,10]
In [698]:
s
Out[698]:
Timestamp('2000-04-25 09:32:47')
In [35]:
type(s)
Out[35]:
pandas.tslib.Timestamp
In [699]:
type(s.to_datetime())
Out[699]:
datetime.datetime
In [700]:
s_datetime = s.to_pydatetime()
In [701]:
s_datetime
Out[701]:
datetime.datetime(2000, 4, 25, 9, 32, 47)
In [703]:
unixtime = time.mktime(s_datetime.timetuple())
In [705]:
int(time.mktime(datetime.datetime.strptime(str(s_datetime), "%Y-%m-%d %H:%M:%S").timetuple()))
Out[705]:
956647967
In [73]:
df.date_sent.min()
Out[73]:
Timestamp('2000-01-01 15:40:23')
In [74]:
df.date_sent.max()
Out[74]:
Timestamp('2002-01-29 01:37:55')
In [709]:
df.columns = ['topic 1', 'topic 2', 'topic 3', 'topic 4', 'topic 5', 'topic 6', 'topic 7', 'topic 8', 'topic 9', 'topic 10', 'date_sent']
In [710]:
df
Out[710]:
topic 1
topic 2
topic 3
topic 4
topic 5
topic 6
topic 7
topic 8
topic 9
topic 10
date_sent
0
0.093013
0.022149
0.152614
0.122789
0.171756
0.008851
0.257211
0.011800
0.041068
0.118750
2001-02-08 02:11:19
1
0.085686
0.042914
0.193660
0.052411
0.377262
0.054326
0.103518
0.013129
0.029596
0.047497
2000-04-24 03:13:02
2
0.052673
0.013017
0.187891
0.027889
0.134614
0.109124
0.062208
0.023950
0.020901
0.367733
2001-04-05 18:06:51
3
0.080543
0.241699
0.013637
0.036184
0.010589
0.312160
0.143825
0.005105
0.141908
0.014350
2001-12-07 18:49:41
4
0.075367
0.102129
0.345652
0.095193
0.014923
0.130467
0.039766
0.037884
0.027764
0.130855
2001-05-21 16:55:44
5
0.029140
0.122982
0.183885
0.051679
0.087894
0.198557
0.029177
0.068652
0.194724
0.033311
2001-09-04 01:35:07
6
0.120828
0.080681
0.134731
0.087057
0.078469
0.151554
0.077353
0.145072
0.074153
0.050101
2001-01-05 04:02:09
7
0.234988
0.009897
0.029432
0.028404
0.054993
0.048191
0.349070
0.097725
0.056284
0.091017
2001-07-23 08:16:57
8
0.000614
0.034910
0.002672
0.010680
0.136185
0.043953
0.060662
0.047986
0.317369
0.344967
2001-04-05 04:41:15
9
0.014547
0.058735
0.153404
0.077710
0.002322
0.137340
0.038911
0.014986
0.289575
0.212471
2001-07-28 05:39:16
10
0.008695
0.026700
0.054163
0.387308
0.022092
0.041383
0.221447
0.016484
0.214351
0.007377
2000-04-25 09:32:47
11
0.041390
0.159062
0.054547
0.153180
0.088022
0.080946
0.234152
0.046570
0.125441
0.016689
2001-08-15 06:49:17
12
0.051968
0.035934
0.032615
0.059201
0.166128
0.229201
0.313454
0.046930
0.006893
0.057677
2001-09-17 12:16:59
13
0.051099
0.020306
0.159952
0.059786
0.112925
0.179584
0.040922
0.229889
0.069481
0.076057
2000-08-03 18:15:42
14
0.078620
0.038959
0.055026
0.031491
0.203583
0.132747
0.236623
0.107255
0.082339
0.033358
2000-02-13 04:09:58
15
0.181892
0.228793
0.063566
0.097666
0.067363
0.111606
0.012283
0.070138
0.035407
0.131286
2000-04-15 18:31:42
16
0.085797
0.337739
0.214189
0.113755
0.142805
0.007106
0.006445
0.051139
0.013605
0.027420
2001-06-29 11:58:24
17
0.054453
0.023030
0.018715
0.141864
0.416313
0.000064
0.223425
0.050595
0.009697
0.061843
2000-05-08 07:41:02
18
0.144780
0.072253
0.090191
0.087188
0.098689
0.000098
0.038569
0.321993
0.084521
0.061717
2001-05-22 20:07:18
19
0.118873
0.199206
0.006197
0.070047
0.038159
0.063785
0.168631
0.059917
0.018397
0.256788
2000-10-01 08:45:06
20
0.149439
0.184593
0.157709
0.110559
0.221373
0.068101
0.004920
0.013690
0.047693
0.041923
2001-12-23 19:10:42
21
0.036040
0.054603
0.000063
0.053995
0.116011
0.070401
0.269245
0.158960
0.206584
0.034098
2001-08-23 11:25:09
22
0.044565
0.245600
0.113162
0.021003
0.071046
0.362133
0.020708
0.031813
0.016154
0.073817
2001-06-08 15:48:47
23
0.119449
0.069428
0.003672
0.094421
0.018252
0.234732
0.358718
0.039515
0.012741
0.049072
2000-11-02 15:24:09
24
0.069022
0.028948
0.113062
0.114830
0.104455
0.066402
0.089271
0.061627
0.134000
0.218384
2001-06-28 03:59:06
25
0.013061
0.001281
0.058749
0.263196
0.099328
0.225964
0.021755
0.033043
0.110618
0.173005
2001-05-29 03:36:11
26
0.008506
0.019529
0.101543
0.098604
0.239346
0.003608
0.445534
0.072408
0.003736
0.007185
2001-07-30 20:29:29
27
0.013911
0.191937
0.343870
0.015008
0.039092
0.059851
0.211369
0.074437
0.009818
0.040706
2002-01-28 06:53:10
28
0.105587
0.101039
0.027008
0.128465
0.266995
0.059113
0.025297
0.061519
0.000380
0.224596
2001-01-31 00:29:07
29
0.012471
0.014510
0.068683
0.008481
0.256034
0.045586
0.055363
0.394376
0.045543
0.098953
2000-12-23 05:24:25
...
...
...
...
...
...
...
...
...
...
...
...
970
0.104878
0.189771
0.170740
0.165510
0.007500
0.124253
0.102664
0.033310
0.071928
0.029446
2000-09-13 18:08:15
971
0.010716
0.096125
0.065954
0.068900
0.097438
0.045346
0.094105
0.146061
0.156118
0.219237
2001-02-10 10:56:47
972
0.059508
0.225429
0.160102
0.034499
0.071608
0.003279
0.306367
0.015398
0.070192
0.053618
2000-07-21 18:31:22
973
0.236618
0.088829
0.261178
0.013578
0.288976
0.020972
0.067341
0.005094
0.014801
0.002615
2000-01-04 00:21:20
974
0.092546
0.027781
0.137550
0.050823
0.198944
0.036406
0.040805
0.000247
0.205088
0.209810
2000-06-11 00:25:58
975
0.020758
0.098846
0.183029
0.310018
0.124005
0.007286
0.006040
0.104336
0.140854
0.004829
2001-06-16 14:58:11
976
0.002662
0.178143
0.127200
0.185732
0.153589
0.097458
0.065183
0.051932
0.001569
0.136531
2000-04-11 09:47:53
977
0.101562
0.160400
0.168354
0.008245
0.212808
0.027732
0.178496
0.045559
0.083988
0.012857
2000-04-18 05:52:08
978
0.021030
0.002906
0.024274
0.136584
0.310754
0.159391
0.168610
0.011742
0.156293
0.008417
2000-10-25 02:14:54
979
0.022348
0.172398
0.006543
0.128303
0.062145
0.096896
0.007330
0.205899
0.100726
0.197413
2000-05-27 22:22:48
980
0.018218
0.023606
0.250958
0.001435
0.021875
0.011041
0.079913
0.022351
0.170612
0.399991
2000-08-19 13:40:20
981
0.107563
0.118399
0.192274
0.075835
0.044716
0.083989
0.051816
0.110655
0.200965
0.013788
2002-01-01 01:56:51
982
0.323178
0.012526
0.179570
0.044297
0.066666
0.003718
0.191621
0.017643
0.082578
0.078201
2000-06-24 09:42:36
983
0.013611
0.199819
0.011796
0.158551
0.169127
0.062300
0.010697
0.087363
0.013686
0.273049
2000-04-07 00:59:29
984
0.060021
0.006483
0.090845
0.064929
0.049884
0.011103
0.028937
0.517896
0.073503
0.096398
2001-08-09 18:29:44
985
0.263112
0.017212
0.095081
0.132283
0.037569
0.077197
0.012491
0.133828
0.195391
0.035837
2000-05-26 03:11:17
986
0.083490
0.097345
0.093668
0.056783
0.121588
0.134282
0.139747
0.087321
0.080412
0.105364
2001-01-30 20:10:11
987
0.015644
0.030562
0.097094
0.260417
0.012915
0.026136
0.054619
0.019881
0.041496
0.441235
2001-12-16 18:13:26
988
0.141781
0.087416
0.286679
0.088753
0.008396
0.063025
0.250268
0.062960
0.005094
0.005629
2001-01-03 20:23:44
989
0.377045
0.119088
0.072912
0.034323
0.003338
0.064164
0.114463
0.059107
0.023050
0.132509
2001-12-19 19:03:49
990
0.051661
0.047893
0.057490
0.200066
0.081752
0.154186
0.009546
0.032937
0.176595
0.187873
2001-02-14 21:29:58
991
0.012132
0.227984
0.045219
0.048532
0.072076
0.117802
0.263475
0.144284
0.028966
0.039529
2000-10-22 19:25:50
992
0.353518
0.009456
0.200745
0.039707
0.035467
0.100722
0.008388
0.086243
0.139374
0.026378
2000-08-24 19:19:32
993
0.137577
0.124454
0.009536
0.232614
0.101888
0.075090
0.059695
0.124679
0.004009
0.130457
2001-06-07 21:10:03
994
0.092338
0.029579
0.039457
0.402105
0.106955
0.018004
0.037641
0.009545
0.234853
0.029522
2000-06-03 03:57:04
995
0.147465
0.249481
0.182494
0.029455
0.050113
0.017217
0.049526
0.157240
0.049318
0.067690
2000-02-07 15:04:23
996
0.091999
0.318989
0.088607
0.059343
0.048333
0.287783
0.007916
0.004893
0.066485
0.025651
2000-01-14 23:45:04
997
0.024519
0.273072
0.079934
0.130409
0.004359
0.179298
0.144581
0.024430
0.026700
0.112698
2000-07-15 09:03:26
998
0.052035
0.004493
0.045288
0.118129
0.113495
0.215003
0.267225
0.027923
0.114969
0.041440
2000-03-23 15:47:57
999
0.080285
0.077427
0.028365
0.103227
0.100557
0.146552
0.109205
0.129704
0.153433
0.071244
2001-07-30 08:22:48
1000 rows × 11 columns
In [711]:
df_transposed = df.transpose()
In [712]:
df_transposed
Out[712]:
0
1
2
3
4
5
6
7
8
9
...
990
991
992
993
994
995
996
997
998
999
topic 1
0.0930134
0.0856861
0.0526728
0.0805431
0.0753671
0.0291399
0.120828
0.234988
0.00061385
0.014547
...
0.051661
0.0121318
0.353518
0.137577
0.0923384
0.147465
0.0919987
0.0245186
0.0520352
0.0802854
topic 2
0.0221486
0.0429138
0.0130168
0.241699
0.102129
0.122982
0.0806812
0.00989652
0.0349104
0.0587349
...
0.0478931
0.227984
0.00945635
0.124454
0.0295791
0.249481
0.318989
0.273072
0.00449325
0.0774268
topic 3
0.152614
0.19366
0.187891
0.013637
0.345652
0.183885
0.134731
0.0294317
0.0026724
0.153404
...
0.0574905
0.0452194
0.200745
0.00953647
0.0394566
0.182494
0.0886073
0.0799344
0.0452882
0.0283653
topic 4
0.122789
0.0524111
0.0278894
0.0361837
0.095193
0.051679
0.087057
0.0284038
0.0106801
0.0777098
...
0.200066
0.0485323
0.0397071
0.232614
0.402105
0.0294553
0.0593434
0.130409
0.118129
0.103227
topic 5
0.171756
0.377262
0.134614
0.0105889
0.0149226
0.0878941
0.0784691
0.0549926
0.136185
0.00232202
...
0.0817525
0.0720759
0.0354671
0.101888
0.106955
0.0501135
0.0483328
0.00435915
0.113495
0.100557
topic 6
0.00885137
0.0543256
0.109124
0.31216
0.130467
0.198557
0.151554
0.0481912
0.0439529
0.13734
...
0.154186
0.117802
0.100722
0.0750903
0.0180042
0.0172174
0.287783
0.179298
0.215003
0.146552
topic 7
0.257211
0.103518
0.0622078
0.143825
0.0397664
0.0291767
0.0773528
0.34907
0.0606622
0.0389112
...
0.00954551
0.263475
0.00838837
0.0596946
0.0376408
0.049526
0.00791628
0.144581
0.267225
0.109205
topic 8
0.0117995
0.0131293
0.0239497
0.00510541
0.0378838
0.0686517
0.145072
0.0977247
0.0479862
0.0149862
...
0.0329374
0.144284
0.0862433
0.124679
0.00954525
0.15724
0.00489273
0.0244297
0.0279229
0.129704
topic 9
0.0410675
0.0295965
0.0209008
0.141908
0.0277643
0.194724
0.0741531
0.0562845
0.317369
0.289575
...
0.176595
0.0289658
0.139374
0.00400948
0.234853
0.0493184
0.0664848
0.0267002
0.114969
0.153433
topic 10
0.11875
0.0474974
0.367733
0.0143504
0.130855
0.0333113
0.0501012
0.0910169
0.344967
0.212471
...
0.187873
0.0395291
0.0263781
0.130457
0.0295222
0.0676899
0.0256515
0.112698
0.0414397
0.0712442
date_sent
2001-02-08 02:11:19
2000-04-24 03:13:02
2001-04-05 18:06:51
2001-12-07 18:49:41
2001-05-21 16:55:44
2001-09-04 01:35:07
2001-01-05 04:02:09
2001-07-23 08:16:57
2001-04-05 04:41:15
2001-07-28 05:39:16
...
2001-02-14 21:29:58
2000-10-22 19:25:50
2000-08-24 19:19:32
2001-06-07 21:10:03
2000-06-03 03:57:04
2000-02-07 15:04:23
2000-01-14 23:45:04
2000-07-15 09:03:26
2000-03-23 15:47:57
2001-07-30 08:22:48
11 rows × 1000 columns
In [713]:
df_sorted = df.sort('date_sent')
/home/daniela/anaconda/envs/spark-lda/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)
if __name__ == '__main__':
In [714]:
df_sorted
Out[714]:
topic 1
topic 2
topic 3
topic 4
topic 5
topic 6
topic 7
topic 8
topic 9
topic 10
date_sent
581
0.295507
0.145733
0.053776
0.127954
0.013860
0.052633
0.046821
0.008934
0.208308
0.046475
2000-01-02 17:31:04
973
0.236618
0.088829
0.261178
0.013578
0.288976
0.020972
0.067341
0.005094
0.014801
0.002615
2000-01-04 00:21:20
645
0.084831
0.019443
0.055865
0.478758
0.057965
0.149225
0.007808
0.024633
0.013256
0.108216
2000-01-04 19:33:03
375
0.107813
0.071776
0.203054
0.155733
0.012676
0.201241
0.090976
0.094853
0.019180
0.042697
2000-01-05 08:17:26
693
0.026330
0.021470
0.007319
0.140718
0.008898
0.030695
0.149755
0.418972
0.165031
0.030811
2000-01-05 15:34:53
249
0.037885
0.230147
0.243607
0.041750
0.044757
0.085000
0.014769
0.068876
0.122314
0.110894
2000-01-07 19:29:33
856
0.218569
0.108124
0.155278
0.047027
0.000258
0.179628
0.097584
0.046384
0.081707
0.065441
2000-01-09 11:15:21
369
0.049964
0.041053
0.238971
0.025215
0.014680
0.090712
0.039412
0.115293
0.219682
0.165017
2000-01-12 21:42:07
136
0.065566
0.015938
0.212923
0.049847
0.210171
0.225969
0.022989
0.156832
0.010195
0.029569
2000-01-13 03:24:28
952
0.142812
0.038038
0.050256
0.041003
0.016806
0.127495
0.362249
0.076115
0.081099
0.064128
2000-01-13 08:29:35
243
0.041462
0.000775
0.278813
0.092175
0.101982
0.003340
0.008886
0.120068
0.009842
0.342656
2000-01-13 11:06:08
996
0.091999
0.318989
0.088607
0.059343
0.048333
0.287783
0.007916
0.004893
0.066485
0.025651
2000-01-14 23:45:04
735
0.081630
0.259295
0.012869
0.092532
0.137904
0.016525
0.047223
0.084235
0.222095
0.045693
2000-01-15 13:17:34
317
0.065678
0.425565
0.029173
0.028390
0.143771
0.009974
0.015340
0.183663
0.095894
0.002554
2000-01-17 02:47:47
696
0.019383
0.139534
0.020148
0.124191
0.033727
0.004149
0.229452
0.275995
0.049924
0.103498
2000-01-17 03:09:38
754
0.381909
0.089532
0.023149
0.096199
0.039376
0.016216
0.144339
0.024773
0.089756
0.094750
2000-01-18 11:50:35
130
0.131184
0.015120
0.010257
0.022295
0.368155
0.247395
0.100258
0.056490
0.023369
0.025477
2000-01-18 11:58:28
835
0.047104
0.060768
0.441872
0.005807
0.157505
0.096719
0.085034
0.070779
0.007283
0.027130
2000-01-19 04:02:57
526
0.004985
0.145901
0.058732
0.366175
0.157370
0.095307
0.022548
0.019888
0.121144
0.007949
2000-01-20 04:47:47
244
0.113741
0.035646
0.035689
0.017731
0.054782
0.195462
0.126084
0.192876
0.058650
0.169339
2000-01-22 03:43:43
477
0.019514
0.009611
0.042144
0.245132
0.074417
0.007386
0.135803
0.218575
0.246487
0.000931
2000-01-24 03:01:43
570
0.184911
0.014117
0.284819
0.069457
0.019148
0.123333
0.262974
0.019656
0.016401
0.005185
2000-01-25 19:49:19
800
0.039403
0.010842
0.270326
0.013437
0.025608
0.045082
0.005486
0.027049
0.486764
0.076003
2000-01-26 02:44:47
515
0.070215
0.148913
0.006539
0.014833
0.064827
0.409218
0.038896
0.125216
0.013844
0.107499
2000-01-26 04:06:14
42
0.272102
0.011961
0.079270
0.285127
0.068953
0.154831
0.006997
0.091408
0.026234
0.003117
2000-01-26 22:09:44
300
0.184049
0.004759
0.058452
0.037811
0.025051
0.553627
0.015274
0.003822
0.035220
0.081934
2000-01-28 00:24:16
687
0.339530
0.174255
0.057523
0.017691
0.093041
0.024877
0.037956
0.096565
0.138142
0.020420
2000-01-28 06:34:30
392
0.049062
0.195580
0.082824
0.309190
0.111692
0.059761
0.049459
0.116540
0.010004
0.015888
2000-01-28 11:56:19
851
0.002121
0.027337
0.100050
0.394699
0.112648
0.095919
0.244262
0.012520
0.007283
0.003160
2000-01-30 07:14:27
307
0.079660
0.161457
0.064052
0.146606
0.026922
0.076124
0.111235
0.123168
0.036777
0.173999
2000-02-03 21:59:37
...
...
...
...
...
...
...
...
...
...
...
...
82
0.071735
0.021860
0.160378
0.185358
0.009312
0.040617
0.157661
0.311180
0.005601
0.036298
2002-01-06 14:25:02
956
0.064726
0.152685
0.188650
0.018602
0.129237
0.075963
0.022317
0.021138
0.209570
0.117112
2002-01-08 12:36:01
949
0.071648
0.028338
0.238363
0.029513
0.079119
0.149625
0.023512
0.065848
0.134221
0.179814
2002-01-08 13:42:25
758
0.015657
0.060679
0.099374
0.139240
0.098926
0.063583
0.055317
0.238904
0.178717
0.049602
2002-01-09 19:33:31
590
0.093205
0.460975
0.032448
0.025523
0.001965
0.130665
0.031558
0.114500
0.090612
0.018550
2002-01-09 20:36:00
467
0.156644
0.199625
0.003367
0.001315
0.089877
0.143798
0.005428
0.095168
0.097631
0.207147
2002-01-10 10:40:06
233
0.043264
0.119681
0.060697
0.128899
0.036163
0.042360
0.056641
0.000603
0.467818
0.043874
2002-01-11 14:56:32
612
0.075110
0.072387
0.046349
0.137404
0.083118
0.037604
0.035507
0.030352
0.018395
0.463774
2002-01-14 00:22:35
738
0.202896
0.114105
0.060333
0.021988
0.086536
0.092099
0.169198
0.041070
0.097610
0.114165
2002-01-14 15:50:07
528
0.173248
0.117832
0.035754
0.173387
0.006274
0.294593
0.003139
0.034721
0.090612
0.070440
2002-01-14 17:25:03
840
0.095381
0.031664
0.018289
0.154094
0.094605
0.034408
0.132710
0.053548
0.073285
0.312014
2002-01-14 22:24:19
178
0.132631
0.144264
0.161660
0.010232
0.217883
0.140049
0.114788
0.035421
0.005166
0.037907
2002-01-15 21:11:28
663
0.065938
0.196917
0.090966
0.010165
0.016798
0.046848
0.328187
0.029974
0.139907
0.074302
2002-01-15 21:58:40
496
0.119269
0.049479
0.092995
0.034295
0.345417
0.070855
0.045676
0.095302
0.003077
0.143636
2002-01-17 07:23:52
579
0.106536
0.020095
0.027605
0.167471
0.136147
0.002179
0.312278
0.108939
0.102902
0.015848
2002-01-18 02:49:39
411
0.043196
0.020536
0.326856
0.013626
0.098761
0.189175
0.021365
0.047730
0.127414
0.111340
2002-01-18 20:11:54
737
0.196192
0.119975
0.169150
0.036279
0.196575
0.034753
0.031298
0.086331
0.080996
0.048452
2002-01-19 00:00:40
573
0.039824
0.081854
0.001252
0.015216
0.098921
0.041452
0.321443
0.049956
0.129689
0.220393
2002-01-19 06:46:56
31
0.241266
0.030317
0.193640
0.066061
0.042074
0.040608
0.092595
0.072156
0.087654
0.133630
2002-01-20 19:21:57
445
0.112893
0.058848
0.103363
0.056181
0.002882
0.100528
0.052107
0.077873
0.351376
0.083950
2002-01-23 02:50:00
959
0.019550
0.111985
0.132963
0.043651
0.183033
0.213980
0.086179
0.034559
0.062056
0.112045
2002-01-23 18:35:15
434
0.030324
0.074279
0.205430
0.022739
0.002250
0.063655
0.079286
0.200747
0.233848
0.087442
2002-01-24 02:57:03
537
0.192026
0.003181
0.151608
0.093771
0.000745
0.111472
0.092321
0.085358
0.179258
0.090261
2002-01-24 10:09:57
226
0.110069
0.291211
0.053493
0.144194
0.023110
0.013772
0.011617
0.271902
0.035154
0.045478
2002-01-25 01:36:38
630
0.124617
0.128002
0.127085
0.124267
0.131347
0.039676
0.015123
0.033080
0.245513
0.031291
2002-01-25 07:48:25
720
0.074761
0.021177
0.299361
0.254209
0.051425
0.055237
0.105428
0.095883
0.016022
0.026496
2002-01-25 12:59:09
864
0.112015
0.266132
0.323055
0.068769
0.009773
0.084479
0.059088
0.015410
0.022656
0.038622
2002-01-25 20:03:45
910
0.105642
0.078913
0.197672
0.147098
0.087234
0.020433
0.036837
0.084054
0.175224
0.066893
2002-01-27 08:07:46
27
0.013911
0.191937
0.343870
0.015008
0.039092
0.059851
0.211369
0.074437
0.009818
0.040706
2002-01-28 06:53:10
853
0.025118
0.324607
0.095469
0.074470
0.029985
0.046520
0.165440
0.210486
0.006159
0.021745
2002-01-28 22:40:49
1000 rows × 11 columns
In [715]:
type(df.iloc[0]['date_sent'])
Out[715]:
pandas.tslib.Timestamp
In [716]:
int(time.mktime(datetime.datetime.strptime(str(df_sorted.iloc[0]['date_sent'].to_datetime()), "%Y-%m-%d %H:%M:%S").timetuple()))
Out[716]:
946830664
In [717]:
# convert date to timestamp
def date_to_timestamp(col):
return int(time.mktime(datetime.datetime.strptime(str(col.to_datetime()), "%Y-%m-%d %H:%M:%S").timetuple())*1000)
In [718]:
df_sorted['timestamp'] = df_sorted.date_sent.apply(date_to_timestamp)
In [719]:
df_sorted_transposed = df_sorted.transpose()
In [720]:
df_sorted_transposed
Out[720]:
581
973
645
375
693
249
856
369
136
952
...
959
434
537
226
630
720
864
910
27
853
topic 1
0.295507
0.236618
0.084831
0.107813
0.0263304
0.0378846
0.218569
0.0499639
0.065566
0.142812
...
0.0195496
0.0303236
0.192026
0.110069
0.124617
0.0747611
0.112015
0.105642
0.0139111
0.0251184
topic 2
0.145733
0.0888287
0.0194426
0.0717761
0.0214702
0.230147
0.108124
0.0410534
0.0159378
0.0380379
...
0.111985
0.0742786
0.00318074
0.291211
0.128002
0.021177
0.266132
0.0789129
0.191937
0.324607
topic 3
0.0537758
0.261178
0.0558645
0.203054
0.00731908
0.243607
0.155278
0.238971
0.212923
0.0502558
...
0.132963
0.20543
0.151608
0.0534928
0.127085
0.299361
0.323055
0.197672
0.34387
0.0954687
topic 4
0.127954
0.0135775
0.478758
0.155733
0.140718
0.0417502
0.0470271
0.0252149
0.0498472
0.0410025
...
0.0436509
0.0227393
0.0937707
0.144194
0.124267
0.254209
0.0687687
0.147098
0.0150084
0.0744703
topic 5
0.0138603
0.288976
0.057965
0.0126758
0.00889843
0.0447575
0.000257812
0.01468
0.210171
0.016806
...
0.183033
0.00225048
0.000744507
0.0231104
0.131347
0.0514248
0.00977319
0.0872341
0.0390924
0.0299853
topic 6
0.0526328
0.0209717
0.149225
0.201241
0.0306953
0.0850002
0.179628
0.090712
0.225969
0.127495
...
0.21398
0.0636547
0.111472
0.0137718
0.0396759
0.0552367
0.0844794
0.0204332
0.059851
0.0465197
topic 7
0.0468208
0.0673406
0.00780847
0.0909765
0.149755
0.014769
0.0975838
0.0394123
0.0229889
0.362249
...
0.0861787
0.0792857
0.0923209
0.0116169
0.0151229
0.105428
0.0590881
0.0368366
0.211369
0.16544
topic 8
0.00893422
0.0050943
0.0246328
0.0948531
0.418972
0.0688765
0.0463843
0.115293
0.156832
0.0761151
...
0.0345586
0.200747
0.0853583
0.271902
0.03308
0.095883
0.0154095
0.0840537
0.0744368
0.210486
topic 9
0.208308
0.0148006
0.013256
0.01918
0.165031
0.122314
0.0817072
0.219682
0.0101951
0.0810989
...
0.0620559
0.233848
0.179258
0.035154
0.245513
0.0160223
0.0226559
0.175224
0.00981819
0.00615851
topic 10
0.046475
0.00261485
0.108216
0.0426973
0.0308107
0.110894
0.0654407
0.165017
0.0295693
0.0641284
...
0.112045
0.0874419
0.0902607
0.045478
0.031291
0.0264962
0.0386222
0.0668927
0.0407056
0.0217455
date_sent
2000-01-02 17:31:04
2000-01-04 00:21:20
2000-01-04 19:33:03
2000-01-05 08:17:26
2000-01-05 15:34:53
2000-01-07 19:29:33
2000-01-09 11:15:21
2000-01-12 21:42:07
2000-01-13 03:24:28
2000-01-13 08:29:35
...
2002-01-23 18:35:15
2002-01-24 02:57:03
2002-01-24 10:09:57
2002-01-25 01:36:38
2002-01-25 07:48:25
2002-01-25 12:59:09
2002-01-25 20:03:45
2002-01-27 08:07:46
2002-01-28 06:53:10
2002-01-28 22:40:49
timestamp
946830664000
946941680000
947010783000
947056646000
947082893000
947269773000
947412921000
947709727000
947730268000
947748575000
...
1011807315000
1011837423000
1011863397000
1011918998000
1011941305000
1011959949000
1011985425000
1012115266000
1012197190000
1012254049000
12 rows × 1000 columns
In [721]:
df_sorted
Out[721]:
topic 1
topic 2
topic 3
topic 4
topic 5
topic 6
topic 7
topic 8
topic 9
topic 10
date_sent
timestamp
581
0.295507
0.145733
0.053776
0.127954
0.013860
0.052633
0.046821
0.008934
0.208308
0.046475
2000-01-02 17:31:04
946830664000
973
0.236618
0.088829
0.261178
0.013578
0.288976
0.020972
0.067341
0.005094
0.014801
0.002615
2000-01-04 00:21:20
946941680000
645
0.084831
0.019443
0.055865
0.478758
0.057965
0.149225
0.007808
0.024633
0.013256
0.108216
2000-01-04 19:33:03
947010783000
375
0.107813
0.071776
0.203054
0.155733
0.012676
0.201241
0.090976
0.094853
0.019180
0.042697
2000-01-05 08:17:26
947056646000
693
0.026330
0.021470
0.007319
0.140718
0.008898
0.030695
0.149755
0.418972
0.165031
0.030811
2000-01-05 15:34:53
947082893000
249
0.037885
0.230147
0.243607
0.041750
0.044757
0.085000
0.014769
0.068876
0.122314
0.110894
2000-01-07 19:29:33
947269773000
856
0.218569
0.108124
0.155278
0.047027
0.000258
0.179628
0.097584
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2000-01-09 11:15:21
947412921000
369
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2000-01-12 21:42:07
947709727000
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2000-01-13 03:24:28
947730268000
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2000-01-13 08:29:35
947748575000
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2000-01-13 11:06:08
947757968000
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2000-01-14 23:45:04
947889904000
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2000-01-15 13:17:34
947938654000
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2000-01-17 02:47:47
948073667000
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2000-01-17 03:09:38
948074978000
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2000-01-18 11:50:35
948192635000
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2000-01-18 11:58:28
948193108000
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2000-01-19 04:02:57
948250977000
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2000-01-20 04:47:47
948340067000
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2000-01-22 03:43:43
948509023000
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2000-01-24 03:01:43
948679303000
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2000-01-25 19:49:19
948826159000
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2000-01-26 02:44:47
948851087000
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2000-01-26 04:06:14
948855974000
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2000-01-26 22:09:44
948920984000
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2000-01-28 00:24:16
949015456000
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2000-01-28 06:34:30
949037670000
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2000-01-28 11:56:19
949056979000
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2000-01-30 07:14:27
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2000-02-03 21:59:37
949611577000
...
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...
...
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2002-01-06 14:25:02
1010323502000
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2002-01-08 12:36:01
1010489761000
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2002-01-08 13:42:25
1010493745000
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2002-01-09 19:33:31
1010601211000
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2002-01-09 20:36:00
1010604960000
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2002-01-10 10:40:06
1010655606000
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2002-01-11 14:56:32
1010757392000
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2002-01-14 00:22:35
1010964155000
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2002-01-14 15:50:07
1011019807000
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2002-01-14 17:25:03
1011025503000
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2002-01-14 22:24:19
1011043459000
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2002-01-15 21:11:28
1011125488000
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2002-01-15 21:58:40
1011128320000
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2002-01-17 07:23:52
1011248632000
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2002-01-18 02:49:39
1011318579000
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2002-01-18 20:11:54
1011381114000
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2002-01-19 00:00:40
1011394840000
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2002-01-19 06:46:56
1011419216000
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2002-01-20 19:21:57
1011550917000
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2002-01-23 02:50:00
1011750600000
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2002-01-23 18:35:15
1011807315000
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2002-01-24 02:57:03
1011837423000
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2002-01-24 10:09:57
1011863397000
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2002-01-25 01:36:38
1011918998000
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2002-01-25 07:48:25
1011941305000
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2002-01-25 12:59:09
1011959949000
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2002-01-25 20:03:45
1011985425000
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2002-01-27 08:07:46
1012115266000
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2002-01-28 06:53:10
1012197190000
853
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2002-01-28 22:40:49
1012254049000
1000 rows × 12 columns
In [722]:
df_sorted['timestamp'].max()
Out[722]:
1012254049000
In [723]:
df_sorted['timestamp'].min()
Out[723]:
946830664000
In [724]:
# binning
df_sorted['bins'] = pd.cut(x=df_sorted['timestamp'], bins=80) # 80 bins
In [725]:
df_result = df_sorted.groupby('bins').sum()
In [726]:
df_result.shape # YES, 80 bins
Out[726]:
(80, 11)
In [745]:
df_result
Out[745]:
topic 1
topic 2
topic 3
topic 4
topic 5
topic 6
topic 7
topic 8
topic 9
topic 10
timestamp
bins
(946765240615, 947648456312.5]
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12143181834000
80 rows × 11 columns
In [315]:
test = [
{
"key" : "Consumer Discretionary" ,
"values" : [ [ 946737623000 , 27.38478809681] , [ 946750546000 , 27.371377218208] , [ 948420439000 , 26.309915460827] , [ 1009790138000, 26.425199957521] , [ 1012264675000 , 50.823411519395] , [ 1151640000000 , 23.850443591584] , [ 1154318400000 , 23.158355444054] , [ 1156996800000 , 22.998689393694] , [ 1159588800000 , 27.977128511299] , [ 1162270800000 , 29.073672469721] , [ 1164862800000 , 28.587640408904] , [ 1167541200000 , 22.788453687638] , [ 1170219600000 , 22.429199073597] , [ 1172638800000 , 22.324103271051] , [ 1175313600000 , 17.558388444186] , [ 1177905600000 , 16.769518096208] , [ 1180584000000 , 16.214738201302] , [ 1183176000000 , 18.729632971228] , [ 1185854400000 , 18.814523318848] , [ 1188532800000 , 19.789986451358] , [ 1191124800000 , 17.070049054933] , [ 1193803200000 , 16.121349575715] , [ 1196398800000 , 15.141659430091] , [ 1199077200000 , 17.175388025298] , [ 1201755600000 , 17.286592443521] , [ 1204261200000 , 16.323141626569] , [ 1206936000000 , 19.231263773952] , [ 1209528000000 , 18.446256391094] , [ 1212206400000 , 17.822632399764] , [ 1214798400000 , 15.539366475979] , [ 1217476800000 , 15.255131790216] , [ 1220155200000 , 15.660963922593] , [ 1222747200000 , 13.254482273697] , [ 1225425600000 , 11.920796202299] , [ 1228021200000 , 12.122809090925] , [ 1230699600000 , 15.691026271393] , [ 1233378000000 , 14.720881635107] , [ 1235797200000 , 15.387939360044] , [ 1238472000000 , 13.765436672229] , [ 1241064000000 , 14.6314458648] , [ 1243742400000 , 14.292446536221] , [ 1246334400000 , 16.170071367016] , [ 1249012800000 , 15.948135554337] , [ 1251691200000 , 16.612872685134] , [ 1254283200000 , 18.778338719091] , [ 1256961600000 , 16.75602606542] , [ 1259557200000 , 19.385804443147] , [ 1262235600000 , 22.950590240168] , [ 1264914000000 , 23.61159018141] , [ 1267333200000 , 25.708586989581] , [ 1270008000000 , 26.883915999885] , [ 1272600000000 , 25.893486687065] , [ 1275278400000 , 24.678914263176] , [ 1277870400000 , 25.937275793023] , [ 1280548800000 , 29.46138169384] , [ 1283227200000 , 27.357322961862] , [ 1285819200000 , 29.057235285673] , [ 1288497600000 , 28.549434189386] , [ 1291093200000 , 28.506352379723] , [ 1293771600000 , 29.449241421597] , [ 1296450000000 , 25.796838168807] , [ 1298869200000 , 28.740145449189] , [ 1301544000000 , 22.091744141872] , [ 1304136000000 , 25.079662545409] , [ 1306814400000 , 23.674906973064] , [ 1309406400000 , 23.41800274293] , [ 1312084800000 , 23.243644138871] , [ 1314763200000 , 31.591854066817] , [ 1317355200000 , 31.497112374114] , [ 1320033600000 , 26.672380820431] , [ 1322629200000 , 27.297080015495] , [ 1325307600000 , 20.174315530051] , [ 1327986000000 , 19.631084213899] , [ 1330491600000 , 20.366462219462] , [ 1333166400000 , 17.429019937289] , [ 1335758400000 , 16.75543633539] , [ 1338436800000 , 16.182906906042]]
} ,
{
"key" : "Consumer Staples" ,
"values" : [ [ 1138683600000 , 7.2800122043237] , [ 1141102800000 , 7.1187787503354] , [ 1143781200000 , 8.351887016482] , [ 1146369600000 , 8.4156698763993] , [ 1149048000000 , 8.1673298604231] , [ 1151640000000 , 5.5132447126042] , [ 1154318400000 , 6.1152537710599] , [ 1156996800000 , 6.076765091942] , [ 1159588800000 , 4.6304473798646] , [ 1162270800000 , 4.6301068469402] , [ 1164862800000 , 4.3466656309389] , [ 1167541200000 , 6.830104897003] , [ 1170219600000 , 7.241633040029] , [ 1172638800000 , 7.1432372054153] , [ 1175313600000 , 10.608942063374] , [ 1177905600000 , 10.914964549494] , [ 1180584000000 , 10.933223880565] , [ 1183176000000 , 8.3457524851265] , [ 1185854400000 , 8.1078413081882] , [ 1188532800000 , 8.2697185922474] , [ 1191124800000 , 8.4742436475968] , [ 1193803200000 , 8.4994601179319] , [ 1196398800000 , 8.7387319683243] , [ 1199077200000 , 6.8829183612895] , [ 1201755600000 , 6.984133637885] , [ 1204261200000 , 7.0860136043287] , [ 1206936000000 , 4.3961787956053] , [ 1209528000000 , 3.8699674365231] , [ 1212206400000 , 3.6928925238305] , [ 1214798400000 , 6.7571718894253] , [ 1217476800000 , 6.4367313362344] , [ 1220155200000 , 6.4048441521454] , [ 1222747200000 , 5.4643833239669] , [ 1225425600000 , 5.3150786833374] , [ 1228021200000 , 5.3011272612576] , [ 1230699600000 , 4.1203601430809] , [ 1233378000000 , 4.0881783200525] , [ 1235797200000 , 4.1928665957189] , [ 1238472000000 , 7.0249415663205] , [ 1241064000000 , 7.006530880769] , [ 1243742400000 , 6.994835633224] , [ 1246334400000 , 6.1220222336254] , [ 1249012800000 , 6.1177436137653] , [ 1251691200000 , 6.1413396231981] , [ 1254283200000 , 4.8046006145874] , [ 1256961600000 , 4.6647600660544] , [ 1259557200000 , 4.544865006255] , [ 1262235600000 , 6.0488249316539] , [ 1264914000000 , 6.3188669540206] , [ 1267333200000 , 6.5873958262306] , [ 1270008000000 , 6.2281189839578] , [ 1272600000000 , 5.8948915746059] , [ 1275278400000 , 5.5967320482214] , [ 1277870400000 , 0.99784432084837] , [ 1280548800000 , 1.0950794175359] , [ 1283227200000 , 0.94479734407491] , [ 1285819200000 , 1.222093988688] , [ 1288497600000 , 1.335093106856] , [ 1291093200000 , 1.3302565104985] , [ 1293771600000 , 1.340824670897] , [ 1296450000000 , 0] , [ 1298869200000 , 0] , [ 1301544000000 , 0] , [ 1304136000000 , 0] , [ 1306814400000 , 0] , [ 1309406400000 , 0] , [ 1312084800000 , 0] , [ 1314763200000 , 0] , [ 1317355200000 , 4.4583692315] , [ 1320033600000 , 3.6493043348059] , [ 1322629200000 , 3.8610064091761] , [ 1325307600000 , 5.5144800685202] , [ 1327986000000 , 5.1750695220792] , [ 1330491600000 , 5.6710066952691] , [ 1333166400000 , 8.5658461590953] , [ 1335758400000 , 8.6135447714243] , [ 1338436800000 , 8.0231460925212]]
}
]
In [316]:
pd.DataFrame(test)
Out[316]:
key
values
0
Consumer Discretionary
[[946737623000, 27.3847880968], [946750546000,...
1
Consumer Staples
[[1138683600000, 7.28001220432], [114110280000...
In [761]:
df_transposed = df_result.transpose()
In [762]:
df_transposed
Out[762]:
bins
(946765240615, 947648456312.5]
(947648456312.5, 948466248625]
(948466248625, 949284040937.5]
(949284040937.5, 950101833250]
(950101833250, 950919625562.5]
(950919625562.5, 951737417875]
(951737417875, 952555210187.5]
(952555210187.5, 953373002500]
(953373002500, 954190794812.5]
(954190794812.5, 955008587125]
...
(1004076125875, 1004893918187.5]
(1004893918187.5, 1005711710500]
(1005711710500, 1006529502812.5]
(1006529502812.5, 1007347295125]
(1007347295125, 1008165087437.5]
(1008165087437.5, 1008982879750]
(1008982879750, 1009800672062.5]
(1009800672062.5, 1010618464375]
(1010618464375, 1011436256687.5]
(1011436256687.5, 1012254049000]
topic 1
1.007552e+00
1.123675e+00
1.274648e+00
1.596416e+00
8.981042e-01
1.220334e+00
1.674430e+00
9.247754e-01
1.277759e+00
5.930853e-01
...
2.277811e+00
1.364079e+00
8.827850e-01
2.733801e+00
1.233819e+00
1.130610e+00
2.969147e+00
1.246296e+00
1.450128e+00
1.162192e+00
topic 2
6.855218e-01
1.550507e+00
6.330213e-01
1.842282e+00
1.433256e+00
9.487168e-01
1.086312e+00
1.571181e+00
1.929480e+00
6.882746e-01
...
1.605148e+00
2.359265e+00
6.087159e-01
1.412488e+00
1.208151e+00
9.143736e-01
1.762991e+00
1.182903e+00
1.288415e+00
1.580589e+00
topic 3
9.800757e-01
1.465771e+00
1.017636e+00
7.554436e-01
1.498241e+00
7.563559e-01
2.586929e+00
1.345554e+00
2.587259e+00
7.792470e-01
...
1.539709e+00
9.274865e-01
6.572272e-01
1.596065e+00
9.491888e-01
4.617965e-01
2.002811e+00
1.934072e+00
1.095273e+00
2.227009e+00
topic 4
1.005518e+00
1.003171e+00
1.405109e+00
1.557636e+00
1.416790e+00
1.266777e+00
1.600311e+00
1.572265e+00
1.680129e+00
1.120279e+00
...
2.018681e+00
1.420245e+00
8.713426e-01
1.943555e+00
1.597651e+00
1.473279e+00
1.748394e+00
9.810101e-01
9.043678e-01
1.110418e+00
topic 5
4.273911e-01
1.429781e+00
6.501658e-01
8.350258e-01
1.228560e+00
1.095931e+00
2.071624e+00
1.539072e+00
1.566446e+00
1.007076e+00
...
1.936518e+00
1.367862e+00
1.022222e+00
1.663032e+00
8.568001e-01
7.299047e-01
2.266416e+00
6.699625e-01
1.507076e+00
6.029518e-01
topic 6
7.193949e-01
1.221584e+00
1.669496e+00
7.356299e-01
1.273009e+00
1.117851e+00
1.781700e+00
1.286522e+00
2.025569e+00
6.212633e-01
...
2.268702e+00
8.841315e-01
1.278891e+00
1.275319e+00
1.154128e+00
1.538429e+00
1.515704e+00
1.827940e+00
1.170172e+00
8.502097e-01
topic 7
4.750539e-01
1.085647e+00
9.231901e-01
1.121967e+00
1.991276e+00
1.485811e+00
1.277053e+00
1.088337e+00
2.314296e+00
7.736294e-01
...
1.586163e+00
1.590219e+00
1.218833e+00
1.764865e+00
1.087854e+00
1.564066e+00
1.358508e+00
1.140974e+00
1.577658e+00
1.007390e+00
topic 8
6.677472e-01
1.189025e+00
9.042269e-01
1.354338e+00
1.332969e+00
9.557812e-01
1.321565e+00
1.363976e+00
1.330716e+00
1.363644e+00
...
1.451735e+00
1.111860e+00
8.454229e-01
1.305410e+00
9.378932e-01
8.757110e-01
1.747357e+00
1.645983e+00
7.091152e-01
1.255943e+00
topic 9
6.245971e-01
9.967665e-01
1.039030e+00
8.070582e-01
1.844143e+00
1.031973e+00
1.467317e+00
1.345093e+00
2.313009e+00
1.211784e+00
...
2.215973e+00
1.361097e+00
7.430493e-01
2.050944e+00
1.195240e+00
1.168716e+00
1.495434e+00
1.416513e+00
1.434502e+00
1.424739e+00
topic 10
4.071485e-01
9.340721e-01
4.834767e-01
1.394204e+00
1.083653e+00
1.120469e+00
1.132759e+00
9.632244e-01
1.975336e+00
8.417165e-01
...
2.099558e+00
1.613754e+00
8.715114e-01
1.254521e+00
7.792765e-01
1.143114e+00
1.133238e+00
9.543461e-01
1.863294e+00
7.785587e-01
timestamp
6.629605e+12
1.137590e+13
9.488966e+12
1.139865e+13
1.330682e+13
1.046442e+13
1.523402e+13
1.238836e+13
1.812209e+13
8.591610e+12
...
1.908547e+13
1.407400e+13
9.056020e+12
1.711937e+13
1.108591e+13
1.109491e+13
1.816921e+13
1.313295e+13
1.314448e+13
1.214318e+13
11 rows × 80 columns
In [763]:
type(df_transposed.columns.values)
Out[763]:
pandas.core.categorical.Categorical
In [764]:
df_transposed.columns = df_transposed.columns.astype(str) # YAAY, convert cathegorical column type to string
In [765]:
df_transposed['key'] = df_transposed.index # add "key" as a new column
In [766]:
df_transposed
Out[766]:
bins
(946765240615, 947648456312.5]
(947648456312.5, 948466248625]
(948466248625, 949284040937.5]
(949284040937.5, 950101833250]
(950101833250, 950919625562.5]
(950919625562.5, 951737417875]
(951737417875, 952555210187.5]
(952555210187.5, 953373002500]
(953373002500, 954190794812.5]
(954190794812.5, 955008587125]
...
(1004893918187.5, 1005711710500]
(1005711710500, 1006529502812.5]
(1006529502812.5, 1007347295125]
(1007347295125, 1008165087437.5]
(1008165087437.5, 1008982879750]
(1008982879750, 1009800672062.5]
(1009800672062.5, 1010618464375]
(1010618464375, 1011436256687.5]
(1011436256687.5, 1012254049000]
key
topic 1
1.007552e+00
1.123675e+00
1.274648e+00
1.596416e+00
8.981042e-01
1.220334e+00
1.674430e+00
9.247754e-01
1.277759e+00
5.930853e-01
...
1.364079e+00
8.827850e-01
2.733801e+00
1.233819e+00
1.130610e+00
2.969147e+00
1.246296e+00
1.450128e+00
1.162192e+00
topic 1
topic 2
6.855218e-01
1.550507e+00
6.330213e-01
1.842282e+00
1.433256e+00
9.487168e-01
1.086312e+00
1.571181e+00
1.929480e+00
6.882746e-01
...
2.359265e+00
6.087159e-01
1.412488e+00
1.208151e+00
9.143736e-01
1.762991e+00
1.182903e+00
1.288415e+00
1.580589e+00
topic 2
topic 3
9.800757e-01
1.465771e+00
1.017636e+00
7.554436e-01
1.498241e+00
7.563559e-01
2.586929e+00
1.345554e+00
2.587259e+00
7.792470e-01
...
9.274865e-01
6.572272e-01
1.596065e+00
9.491888e-01
4.617965e-01
2.002811e+00
1.934072e+00
1.095273e+00
2.227009e+00
topic 3
topic 4
1.005518e+00
1.003171e+00
1.405109e+00
1.557636e+00
1.416790e+00
1.266777e+00
1.600311e+00
1.572265e+00
1.680129e+00
1.120279e+00
...
1.420245e+00
8.713426e-01
1.943555e+00
1.597651e+00
1.473279e+00
1.748394e+00
9.810101e-01
9.043678e-01
1.110418e+00
topic 4
topic 5
4.273911e-01
1.429781e+00
6.501658e-01
8.350258e-01
1.228560e+00
1.095931e+00
2.071624e+00
1.539072e+00
1.566446e+00
1.007076e+00
...
1.367862e+00
1.022222e+00
1.663032e+00
8.568001e-01
7.299047e-01
2.266416e+00
6.699625e-01
1.507076e+00
6.029518e-01
topic 5
topic 6
7.193949e-01
1.221584e+00
1.669496e+00
7.356299e-01
1.273009e+00
1.117851e+00
1.781700e+00
1.286522e+00
2.025569e+00
6.212633e-01
...
8.841315e-01
1.278891e+00
1.275319e+00
1.154128e+00
1.538429e+00
1.515704e+00
1.827940e+00
1.170172e+00
8.502097e-01
topic 6
topic 7
4.750539e-01
1.085647e+00
9.231901e-01
1.121967e+00
1.991276e+00
1.485811e+00
1.277053e+00
1.088337e+00
2.314296e+00
7.736294e-01
...
1.590219e+00
1.218833e+00
1.764865e+00
1.087854e+00
1.564066e+00
1.358508e+00
1.140974e+00
1.577658e+00
1.007390e+00
topic 7
topic 8
6.677472e-01
1.189025e+00
9.042269e-01
1.354338e+00
1.332969e+00
9.557812e-01
1.321565e+00
1.363976e+00
1.330716e+00
1.363644e+00
...
1.111860e+00
8.454229e-01
1.305410e+00
9.378932e-01
8.757110e-01
1.747357e+00
1.645983e+00
7.091152e-01
1.255943e+00
topic 8
topic 9
6.245971e-01
9.967665e-01
1.039030e+00
8.070582e-01
1.844143e+00
1.031973e+00
1.467317e+00
1.345093e+00
2.313009e+00
1.211784e+00
...
1.361097e+00
7.430493e-01
2.050944e+00
1.195240e+00
1.168716e+00
1.495434e+00
1.416513e+00
1.434502e+00
1.424739e+00
topic 9
topic 10
4.071485e-01
9.340721e-01
4.834767e-01
1.394204e+00
1.083653e+00
1.120469e+00
1.132759e+00
9.632244e-01
1.975336e+00
8.417165e-01
...
1.613754e+00
8.715114e-01
1.254521e+00
7.792765e-01
1.143114e+00
1.133238e+00
9.543461e-01
1.863294e+00
7.785587e-01
topic 10
timestamp
6.629605e+12
1.137590e+13
9.488966e+12
1.139865e+13
1.330682e+13
1.046442e+13
1.523402e+13
1.238836e+13
1.812209e+13
8.591610e+12
...
1.407400e+13
9.056020e+12
1.711937e+13
1.108591e+13
1.109491e+13
1.816921e+13
1.313295e+13
1.314448e+13
1.214318e+13
timestamp
11 rows × 81 columns
In [767]:
df_transposed = df_transposed[:10] # drop meaningless timestamp row (it's sum)
In [768]:
df_transposed
Out[768]:
bins
(946765240615, 947648456312.5]
(947648456312.5, 948466248625]
(948466248625, 949284040937.5]
(949284040937.5, 950101833250]
(950101833250, 950919625562.5]
(950919625562.5, 951737417875]
(951737417875, 952555210187.5]
(952555210187.5, 953373002500]
(953373002500, 954190794812.5]
(954190794812.5, 955008587125]
...
(1004893918187.5, 1005711710500]
(1005711710500, 1006529502812.5]
(1006529502812.5, 1007347295125]
(1007347295125, 1008165087437.5]
(1008165087437.5, 1008982879750]
(1008982879750, 1009800672062.5]
(1009800672062.5, 1010618464375]
(1010618464375, 1011436256687.5]
(1011436256687.5, 1012254049000]
key
topic 1
1.007552
1.123675
1.274648
1.596416
0.898104
1.220334
1.674430
0.924775
1.277759
0.593085
...
1.364079
0.882785
2.733801
1.233819
1.130610
2.969147
1.246296
1.450128
1.162192
topic 1
topic 2
0.685522
1.550507
0.633021
1.842282
1.433256
0.948717
1.086312
1.571181
1.929480
0.688275
...
2.359265
0.608716
1.412488
1.208151
0.914374
1.762991
1.182903
1.288415
1.580589
topic 2
topic 3
0.980076
1.465771
1.017636
0.755444
1.498241
0.756356
2.586929
1.345554
2.587259
0.779247
...
0.927486
0.657227
1.596065
0.949189
0.461797
2.002811
1.934072
1.095273
2.227009
topic 3
topic 4
1.005518
1.003171
1.405109
1.557636
1.416790
1.266777
1.600311
1.572265
1.680129
1.120279
...
1.420245
0.871343
1.943555
1.597651
1.473279
1.748394
0.981010
0.904368
1.110418
topic 4
topic 5
0.427391
1.429781
0.650166
0.835026
1.228560
1.095931
2.071624
1.539072
1.566446
1.007076
...
1.367862
1.022222
1.663032
0.856800
0.729905
2.266416
0.669962
1.507076
0.602952
topic 5
topic 6
0.719395
1.221584
1.669496
0.735630
1.273009
1.117851
1.781700
1.286522
2.025569
0.621263
...
0.884132
1.278891
1.275319
1.154128
1.538429
1.515704
1.827940
1.170172
0.850210
topic 6
topic 7
0.475054
1.085647
0.923190
1.121967
1.991276
1.485811
1.277053
1.088337
2.314296
0.773629
...
1.590219
1.218833
1.764865
1.087854
1.564066
1.358508
1.140974
1.577658
1.007390
topic 7
topic 8
0.667747
1.189025
0.904227
1.354338
1.332969
0.955781
1.321565
1.363976
1.330716
1.363644
...
1.111860
0.845423
1.305410
0.937893
0.875711
1.747357
1.645983
0.709115
1.255943
topic 8
topic 9
0.624597
0.996766
1.039030
0.807058
1.844143
1.031973
1.467317
1.345093
2.313009
1.211784
...
1.361097
0.743049
2.050944
1.195240
1.168716
1.495434
1.416513
1.434502
1.424739
topic 9
topic 10
0.407149
0.934072
0.483477
1.394204
1.083653
1.120469
1.132759
0.963224
1.975336
0.841716
...
1.613754
0.871511
1.254521
0.779277
1.143114
1.133238
0.954346
1.863294
0.778559
topic 10
10 rows × 81 columns
In [769]:
# just unify the open/closed interval for easier eval, and then take the left interval
df_transposed.columns = [int(eval(col_name.replace("]",")"))[0]) if not col_name=='key' else col_name for col_name in df_transposed.columns ]
In [770]:
df_transposed
Out[770]:
946765240615
947648456312
948466248625
949284040937
950101833250
950919625562
951737417875
952555210187
953373002500
954190794812
...
1004893918187
1005711710500
1006529502812
1007347295125
1008165087437
1008982879750
1009800672062
1010618464375
1011436256687
key
topic 1
1.007552
1.123675
1.274648
1.596416
0.898104
1.220334
1.674430
0.924775
1.277759
0.593085
...
1.364079
0.882785
2.733801
1.233819
1.130610
2.969147
1.246296
1.450128
1.162192
topic 1
topic 2
0.685522
1.550507
0.633021
1.842282
1.433256
0.948717
1.086312
1.571181
1.929480
0.688275
...
2.359265
0.608716
1.412488
1.208151
0.914374
1.762991
1.182903
1.288415
1.580589
topic 2
topic 3
0.980076
1.465771
1.017636
0.755444
1.498241
0.756356
2.586929
1.345554
2.587259
0.779247
...
0.927486
0.657227
1.596065
0.949189
0.461797
2.002811
1.934072
1.095273
2.227009
topic 3
topic 4
1.005518
1.003171
1.405109
1.557636
1.416790
1.266777
1.600311
1.572265
1.680129
1.120279
...
1.420245
0.871343
1.943555
1.597651
1.473279
1.748394
0.981010
0.904368
1.110418
topic 4
topic 5
0.427391
1.429781
0.650166
0.835026
1.228560
1.095931
2.071624
1.539072
1.566446
1.007076
...
1.367862
1.022222
1.663032
0.856800
0.729905
2.266416
0.669962
1.507076
0.602952
topic 5
topic 6
0.719395
1.221584
1.669496
0.735630
1.273009
1.117851
1.781700
1.286522
2.025569
0.621263
...
0.884132
1.278891
1.275319
1.154128
1.538429
1.515704
1.827940
1.170172
0.850210
topic 6
topic 7
0.475054
1.085647
0.923190
1.121967
1.991276
1.485811
1.277053
1.088337
2.314296
0.773629
...
1.590219
1.218833
1.764865
1.087854
1.564066
1.358508
1.140974
1.577658
1.007390
topic 7
topic 8
0.667747
1.189025
0.904227
1.354338
1.332969
0.955781
1.321565
1.363976
1.330716
1.363644
...
1.111860
0.845423
1.305410
0.937893
0.875711
1.747357
1.645983
0.709115
1.255943
topic 8
topic 9
0.624597
0.996766
1.039030
0.807058
1.844143
1.031973
1.467317
1.345093
2.313009
1.211784
...
1.361097
0.743049
2.050944
1.195240
1.168716
1.495434
1.416513
1.434502
1.424739
topic 9
topic 10
0.407149
0.934072
0.483477
1.394204
1.083653
1.120469
1.132759
0.963224
1.975336
0.841716
...
1.613754
0.871511
1.254521
0.779277
1.143114
1.133238
0.954346
1.863294
0.778559
topic 10
10 rows × 81 columns
In [771]:
def myfunction(row):
myarray = []
for colname in df_transposed.columns.values[:-1]:
myarray.append([ colname, row[colname]])
return myarray
df_transposed['values'] = df_transposed.apply(myfunction, axis=1)
/home/daniela/anaconda/envs/spark-lda/lib/python2.7/site-packages/ipykernel/__main__.py:7: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
In [679]:
pd.DataFrame(test) # how it should look like
Out[679]:
key
values
0
Consumer Discretionary
[[946737623000, 27.3847880968], [946750546000,...
1
Consumer Staples
[[1138683600000, 7.28001220432], [114110280000...
In [772]:
df_transposed
Out[772]:
946765240615
947648456312
948466248625
949284040937
950101833250
950919625562
951737417875
952555210187
953373002500
954190794812
...
1005711710500
1006529502812
1007347295125
1008165087437
1008982879750
1009800672062
1010618464375
1011436256687
key
values
topic 1
1.007552
1.123675
1.274648
1.596416
0.898104
1.220334
1.674430
0.924775
1.277759
0.593085
...
0.882785
2.733801
1.233819
1.130610
2.969147
1.246296
1.450128
1.162192
topic 1
[[946765240615, 1.00755188807], [947648456312,...
topic 2
0.685522
1.550507
0.633021
1.842282
1.433256
0.948717
1.086312
1.571181
1.929480
0.688275
...
0.608716
1.412488
1.208151
0.914374
1.762991
1.182903
1.288415
1.580589
topic 2
[[946765240615, 0.685521798709], [947648456312...
topic 3
0.980076
1.465771
1.017636
0.755444
1.498241
0.756356
2.586929
1.345554
2.587259
0.779247
...
0.657227
1.596065
0.949189
0.461797
2.002811
1.934072
1.095273
2.227009
topic 3
[[946765240615, 0.980075687334], [947648456312...
topic 4
1.005518
1.003171
1.405109
1.557636
1.416790
1.266777
1.600311
1.572265
1.680129
1.120279
...
0.871343
1.943555
1.597651
1.473279
1.748394
0.981010
0.904368
1.110418
topic 4
[[946765240615, 1.00551785658], [947648456312,...
topic 5
0.427391
1.429781
0.650166
0.835026
1.228560
1.095931
2.071624
1.539072
1.566446
1.007076
...
1.022222
1.663032
0.856800
0.729905
2.266416
0.669962
1.507076
0.602952
topic 5
[[946765240615, 0.427391105468], [947648456312...
topic 6
0.719395
1.221584
1.669496
0.735630
1.273009
1.117851
1.781700
1.286522
2.025569
0.621263
...
1.278891
1.275319
1.154128
1.538429
1.515704
1.827940
1.170172
0.850210
topic 6
[[946765240615, 0.719394904485], [947648456312...
topic 7
0.475054
1.085647
0.923190
1.121967
1.991276
1.485811
1.277053
1.088337
2.314296
0.773629
...
1.218833
1.764865
1.087854
1.564066
1.358508
1.140974
1.577658
1.007390
topic 7
[[946765240615, 0.475053920076], [947648456312...
topic 8
0.667747
1.189025
0.904227
1.354338
1.332969
0.955781
1.321565
1.363976
1.330716
1.363644
...
0.845423
1.305410
0.937893
0.875711
1.747357
1.645983
0.709115
1.255943
topic 8
[[946765240615, 0.667747195668], [947648456312...
topic 9
0.624597
0.996766
1.039030
0.807058
1.844143
1.031973
1.467317
1.345093
2.313009
1.211784
...
0.743049
2.050944
1.195240
1.168716
1.495434
1.416513
1.434502
1.424739
topic 9
[[946765240615, 0.624597137077], [947648456312...
topic 10
0.407149
0.934072
0.483477
1.394204
1.083653
1.120469
1.132759
0.963224
1.975336
0.841716
...
0.871511
1.254521
0.779277
1.143114
1.133238
0.954346
1.863294
0.778559
topic 10
[[946765240615, 0.40714850653], [947648456312,...
10 rows × 82 columns
In [786]:
df_transposed[['key','values']].transpose()
Out[786]:
topic 1
topic 2
topic 3
topic 4
topic 5
topic 6
topic 7
topic 8
topic 9
topic 10
key
topic 1
topic 2
topic 3
topic 4
topic 5
topic 6
topic 7
topic 8
topic 9
topic 10
values
[[946765240615, 1.00755188807], [947648456312,...
[[946765240615, 0.685521798709], [947648456312...
[[946765240615, 0.980075687334], [947648456312...
[[946765240615, 1.00551785658], [947648456312,...
[[946765240615, 0.427391105468], [947648456312...
[[946765240615, 0.719394904485], [947648456312...
[[946765240615, 0.475053920076], [947648456312...
[[946765240615, 0.667747195668], [947648456312...
[[946765240615, 0.624597137077], [947648456312...
[[946765240615, 0.40714850653], [947648456312,...
In [804]:
df_dict = df_transposed[['key','values']].transpose().to_dict("dict")
In [813]:
df_dict.values()
Out[813]:
[{'key': 'topic 8',
'values': [[946765240615, 0.6677471956680243],
[947648456312, 1.189024701020503],
[948466248625, 0.9042269302846203],
[949284040937, 1.3543375803842652],
[950101833250, 1.3329688088483491],
[950919625562, 0.9557812047062483],
[951737417875, 1.3215651864744191],
[952555210187, 1.3639758536507816],
[953373002500, 1.3307161882794916],
[954190794812, 1.3636443677391412],
[955008587125, 1.0880887567825195],
[955826379437, 1.0581912122151949],
[956644171750, 1.6999105620636243],
[957461964062, 1.029512421230252],
[958279756375, 0.9111799733451189],
[959097548687, 1.5817987700120943],
[959915341000, 1.4375279241711918],
[960733133312, 0.476193231470948],
[961550925625, 0.9650315486847262],
[962368717937, 1.70512820428428],
[963186510250, 1.7142694979974198],
[964004302562, 0.7722504833649471],
[964822094875, 1.1304949834320812],
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In [ ]:
Content source: nlesc-sherlock/analyzing-corpora
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