forked from pandas-dev/pandas
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathcode_checks.sh
executable file
·1277 lines (1256 loc) · 65.9 KB
/
code_checks.sh
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#!/bin/bash
#
# Run checks related to code quality.
#
# This script is intended for both the CI and to check locally that code standards are
# respected. We run doctests here (currently some files only), and we
# validate formatting error in docstrings.
#
# Usage:
# $ ./ci/code_checks.sh # run all checks
# $ ./ci/code_checks.sh code # checks on imported code
# $ ./ci/code_checks.sh doctests # run doctests
# $ ./ci/code_checks.sh docstrings # validate docstring errors
# $ ./ci/code_checks.sh single-docs # check single-page docs build warning-free
# $ ./ci/code_checks.sh notebooks # check execution of documentation notebooks
set -uo pipefail
if [[ -v 1 ]]; then
CHECK=$1
else
# script will fail if it uses an unset variable (i.e. $1 is not provided)
CHECK=""
fi
[[ -z "$CHECK" || "$CHECK" == "code" || "$CHECK" == "doctests" || "$CHECK" == "docstrings" || "$CHECK" == "single-docs" || "$CHECK" == "notebooks" ]] || \
{ echo "Unknown command $1. Usage: $0 [code|doctests|docstrings|single-docs|notebooks]"; exit 9999; }
BASE_DIR="$(dirname $0)/.."
RET=0
### CODE ###
if [[ -z "$CHECK" || "$CHECK" == "code" ]]; then
MSG='Check import. No warnings, and blocklist some optional dependencies' ; echo $MSG
python -W error -c "
import sys
import pandas
blocklist = {'bs4', 'gcsfs', 'html5lib', 'http', 'ipython', 'jinja2', 'hypothesis',
'lxml', 'matplotlib', 'openpyxl', 'py', 'pytest', 's3fs', 'scipy',
'tables', 'urllib.request', 'xlrd', 'xlsxwriter'}
# GH#28227 for some of these check for top-level modules, while others are
# more specific (e.g. urllib.request)
import_mods = set(m.split('.')[0] for m in sys.modules) | set(sys.modules)
mods = blocklist & import_mods
if mods:
sys.stderr.write('err: pandas should not import: {}\n'.format(', '.join(mods)))
sys.exit(len(mods))
"
RET=$(($RET + $?)) ; echo $MSG "DONE"
fi
### DOCTESTS ###
if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
MSG='Python and Cython Doctests' ; echo $MSG
python -c 'import pandas as pd; pd.test(run_doctests=True)'
RET=$(($RET + $?)) ; echo $MSG "DONE"
fi
### DOCSTRINGS ###
if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
MSG='Validate Docstrings' ; echo $MSG
$BASE_DIR/scripts/validate_docstrings.py \
--format=actions \
-i ES01 `# For now it is ok if docstrings are missing the extended summary` \
-i "pandas.Series.dt PR01" `# Accessors are implemented as classes, but we do not document the Parameters section` \
-i "pandas.Categorical.__array__ SA01" \
-i "pandas.Categorical.codes SA01" \
-i "pandas.Categorical.dtype SA01" \
-i "pandas.Categorical.from_codes SA01" \
-i "pandas.Categorical.ordered SA01" \
-i "pandas.CategoricalDtype.categories SA01" \
-i "pandas.CategoricalDtype.ordered SA01" \
-i "pandas.CategoricalIndex.codes SA01" \
-i "pandas.CategoricalIndex.ordered SA01" \
-i "pandas.DataFrame.__dataframe__ SA01" \
-i "pandas.DataFrame.__iter__ SA01" \
-i "pandas.DataFrame.assign SA01" \
-i "pandas.DataFrame.at_time PR01" \
-i "pandas.DataFrame.axes SA01" \
-i "pandas.DataFrame.backfill PR01,SA01" \
-i "pandas.DataFrame.bfill SA01" \
-i "pandas.DataFrame.columns SA01" \
-i "pandas.DataFrame.copy SA01" \
-i "pandas.DataFrame.droplevel SA01" \
-i "pandas.DataFrame.dtypes SA01" \
-i "pandas.DataFrame.ffill SA01" \
-i "pandas.DataFrame.first_valid_index SA01" \
-i "pandas.DataFrame.get SA01" \
-i "pandas.DataFrame.hist RT03" \
-i "pandas.DataFrame.infer_objects RT03" \
-i "pandas.DataFrame.keys SA01" \
-i "pandas.DataFrame.kurt RT03,SA01" \
-i "pandas.DataFrame.kurtosis RT03,SA01" \
-i "pandas.DataFrame.last_valid_index SA01" \
-i "pandas.DataFrame.mask RT03" \
-i "pandas.DataFrame.max RT03" \
-i "pandas.DataFrame.mean RT03,SA01" \
-i "pandas.DataFrame.median RT03,SA01" \
-i "pandas.DataFrame.min RT03" \
-i "pandas.DataFrame.pad PR01,SA01" \
-i "pandas.DataFrame.plot PR02,SA01" \
-i "pandas.DataFrame.pop SA01" \
-i "pandas.DataFrame.prod RT03" \
-i "pandas.DataFrame.product RT03" \
-i "pandas.DataFrame.reorder_levels SA01" \
-i "pandas.DataFrame.sem PR01,RT03,SA01" \
-i "pandas.DataFrame.skew RT03,SA01" \
-i "pandas.DataFrame.sparse PR01,SA01" \
-i "pandas.DataFrame.sparse.density SA01" \
-i "pandas.DataFrame.sparse.from_spmatrix SA01" \
-i "pandas.DataFrame.sparse.to_coo SA01" \
-i "pandas.DataFrame.sparse.to_dense SA01" \
-i "pandas.DataFrame.std PR01,RT03,SA01" \
-i "pandas.DataFrame.sum RT03" \
-i "pandas.DataFrame.swapaxes PR01,SA01" \
-i "pandas.DataFrame.swaplevel SA01" \
-i "pandas.DataFrame.to_feather SA01" \
-i "pandas.DataFrame.to_markdown SA01" \
-i "pandas.DataFrame.to_parquet RT03" \
-i "pandas.DataFrame.to_period SA01" \
-i "pandas.DataFrame.to_timestamp SA01" \
-i "pandas.DataFrame.tz_convert SA01" \
-i "pandas.DataFrame.tz_localize SA01" \
-i "pandas.DataFrame.unstack RT03" \
-i "pandas.DataFrame.value_counts RT03" \
-i "pandas.DataFrame.var PR01,RT03,SA01" \
-i "pandas.DataFrame.where RT03" \
-i "pandas.DatetimeIndex.ceil SA01" \
-i "pandas.DatetimeIndex.date SA01" \
-i "pandas.DatetimeIndex.day SA01" \
-i "pandas.DatetimeIndex.day_name SA01" \
-i "pandas.DatetimeIndex.day_of_year SA01" \
-i "pandas.DatetimeIndex.dayofyear SA01" \
-i "pandas.DatetimeIndex.floor SA01" \
-i "pandas.DatetimeIndex.freqstr SA01" \
-i "pandas.DatetimeIndex.hour SA01" \
-i "pandas.DatetimeIndex.indexer_at_time PR01,RT03" \
-i "pandas.DatetimeIndex.indexer_between_time RT03" \
-i "pandas.DatetimeIndex.inferred_freq SA01" \
-i "pandas.DatetimeIndex.is_leap_year SA01" \
-i "pandas.DatetimeIndex.microsecond SA01" \
-i "pandas.DatetimeIndex.minute SA01" \
-i "pandas.DatetimeIndex.month SA01" \
-i "pandas.DatetimeIndex.month_name SA01" \
-i "pandas.DatetimeIndex.nanosecond SA01" \
-i "pandas.DatetimeIndex.quarter SA01" \
-i "pandas.DatetimeIndex.round SA01" \
-i "pandas.DatetimeIndex.second SA01" \
-i "pandas.DatetimeIndex.snap PR01,RT03,SA01" \
-i "pandas.DatetimeIndex.std PR01,RT03" \
-i "pandas.DatetimeIndex.time SA01" \
-i "pandas.DatetimeIndex.timetz SA01" \
-i "pandas.DatetimeIndex.to_period RT03" \
-i "pandas.DatetimeIndex.to_pydatetime RT03,SA01" \
-i "pandas.DatetimeIndex.tz SA01" \
-i "pandas.DatetimeIndex.tz_convert RT03" \
-i "pandas.DatetimeIndex.year SA01" \
-i "pandas.DatetimeTZDtype SA01" \
-i "pandas.DatetimeTZDtype.tz SA01" \
-i "pandas.DatetimeTZDtype.unit SA01" \
-i "pandas.ExcelFile PR01,SA01" \
-i "pandas.ExcelFile.parse PR01,SA01" \
-i "pandas.ExcelWriter SA01" \
-i "pandas.Float32Dtype SA01" \
-i "pandas.Float64Dtype SA01" \
-i "pandas.Grouper PR02,SA01" \
-i "pandas.HDFStore.append PR01,SA01" \
-i "pandas.HDFStore.get SA01" \
-i "pandas.HDFStore.groups SA01" \
-i "pandas.HDFStore.info RT03,SA01" \
-i "pandas.HDFStore.keys SA01" \
-i "pandas.HDFStore.put PR01,SA01" \
-i "pandas.HDFStore.select SA01" \
-i "pandas.HDFStore.walk SA01" \
-i "pandas.Index PR07" \
-i "pandas.Index.T SA01" \
-i "pandas.Index.append PR07,RT03,SA01" \
-i "pandas.Index.astype SA01" \
-i "pandas.Index.copy PR07,SA01" \
-i "pandas.Index.difference PR07,RT03,SA01" \
-i "pandas.Index.drop PR07,SA01" \
-i "pandas.Index.drop_duplicates RT03" \
-i "pandas.Index.droplevel RT03,SA01" \
-i "pandas.Index.dropna RT03,SA01" \
-i "pandas.Index.dtype SA01" \
-i "pandas.Index.duplicated RT03" \
-i "pandas.Index.empty GL08" \
-i "pandas.Index.equals SA01" \
-i "pandas.Index.fillna RT03" \
-i "pandas.Index.get_indexer PR07,SA01" \
-i "pandas.Index.get_indexer_for PR01,SA01" \
-i "pandas.Index.get_indexer_non_unique PR07,SA01" \
-i "pandas.Index.get_loc PR07,RT03,SA01" \
-i "pandas.Index.get_slice_bound PR07" \
-i "pandas.Index.hasnans SA01" \
-i "pandas.Index.identical PR01,SA01" \
-i "pandas.Index.inferred_type SA01" \
-i "pandas.Index.insert PR07,RT03,SA01" \
-i "pandas.Index.intersection PR07,RT03,SA01" \
-i "pandas.Index.item SA01" \
-i "pandas.Index.join PR07,RT03,SA01" \
-i "pandas.Index.map SA01" \
-i "pandas.Index.memory_usage RT03" \
-i "pandas.Index.name SA01" \
-i "pandas.Index.names GL08" \
-i "pandas.Index.nbytes SA01" \
-i "pandas.Index.ndim SA01" \
-i "pandas.Index.nunique RT03" \
-i "pandas.Index.putmask PR01,RT03" \
-i "pandas.Index.ravel PR01,RT03" \
-i "pandas.Index.reindex PR07" \
-i "pandas.Index.shape SA01" \
-i "pandas.Index.size SA01" \
-i "pandas.Index.slice_indexer PR07,RT03,SA01" \
-i "pandas.Index.slice_locs RT03" \
-i "pandas.Index.str PR01,SA01" \
-i "pandas.Index.symmetric_difference PR07,RT03,SA01" \
-i "pandas.Index.take PR01,PR07" \
-i "pandas.Index.to_list RT03" \
-i "pandas.Index.union PR07,RT03,SA01" \
-i "pandas.Index.unique RT03" \
-i "pandas.Index.value_counts RT03" \
-i "pandas.Index.view GL08" \
-i "pandas.Int16Dtype SA01" \
-i "pandas.Int32Dtype SA01" \
-i "pandas.Int64Dtype SA01" \
-i "pandas.Int8Dtype SA01" \
-i "pandas.Interval PR02" \
-i "pandas.Interval.closed SA01" \
-i "pandas.Interval.left SA01" \
-i "pandas.Interval.mid SA01" \
-i "pandas.Interval.right SA01" \
-i "pandas.IntervalDtype PR01,SA01" \
-i "pandas.IntervalDtype.subtype SA01" \
-i "pandas.IntervalIndex.closed SA01" \
-i "pandas.IntervalIndex.contains RT03" \
-i "pandas.IntervalIndex.get_indexer PR07,SA01" \
-i "pandas.IntervalIndex.get_loc PR07,RT03,SA01" \
-i "pandas.IntervalIndex.is_non_overlapping_monotonic SA01" \
-i "pandas.IntervalIndex.left GL08" \
-i "pandas.IntervalIndex.length GL08" \
-i "pandas.IntervalIndex.mid GL08" \
-i "pandas.IntervalIndex.right GL08" \
-i "pandas.IntervalIndex.set_closed RT03,SA01" \
-i "pandas.IntervalIndex.to_tuples RT03,SA01" \
-i "pandas.MultiIndex PR01" \
-i "pandas.MultiIndex.append PR07,SA01" \
-i "pandas.MultiIndex.copy PR07,RT03,SA01" \
-i "pandas.MultiIndex.drop PR07,RT03,SA01" \
-i "pandas.MultiIndex.droplevel RT03,SA01" \
-i "pandas.MultiIndex.dtypes SA01" \
-i "pandas.MultiIndex.get_indexer PR07,SA01" \
-i "pandas.MultiIndex.get_level_values SA01" \
-i "pandas.MultiIndex.get_loc PR07" \
-i "pandas.MultiIndex.get_loc_level PR07" \
-i "pandas.MultiIndex.levels SA01" \
-i "pandas.MultiIndex.levshape SA01" \
-i "pandas.MultiIndex.names SA01" \
-i "pandas.MultiIndex.nlevels SA01" \
-i "pandas.MultiIndex.remove_unused_levels RT03,SA01" \
-i "pandas.MultiIndex.reorder_levels RT03,SA01" \
-i "pandas.MultiIndex.set_codes SA01" \
-i "pandas.MultiIndex.set_levels RT03,SA01" \
-i "pandas.MultiIndex.sortlevel PR07,SA01" \
-i "pandas.MultiIndex.to_frame RT03" \
-i "pandas.MultiIndex.truncate SA01" \
-i "pandas.NA SA01" \
-i "pandas.NaT SA01" \
-i "pandas.NamedAgg SA01" \
-i "pandas.Period SA01" \
-i "pandas.Period.asfreq SA01" \
-i "pandas.Period.freq GL08" \
-i "pandas.Period.freqstr SA01" \
-i "pandas.Period.is_leap_year SA01" \
-i "pandas.Period.month SA01" \
-i "pandas.Period.now SA01" \
-i "pandas.Period.ordinal GL08" \
-i "pandas.Period.quarter SA01" \
-i "pandas.Period.strftime PR01,SA01" \
-i "pandas.Period.to_timestamp SA01" \
-i "pandas.Period.year SA01" \
-i "pandas.PeriodDtype SA01" \
-i "pandas.PeriodDtype.freq SA01" \
-i "pandas.PeriodIndex.day SA01" \
-i "pandas.PeriodIndex.day_of_week SA01" \
-i "pandas.PeriodIndex.day_of_year SA01" \
-i "pandas.PeriodIndex.dayofweek SA01" \
-i "pandas.PeriodIndex.dayofyear SA01" \
-i "pandas.PeriodIndex.days_in_month SA01" \
-i "pandas.PeriodIndex.daysinmonth SA01" \
-i "pandas.PeriodIndex.freqstr SA01" \
-i "pandas.PeriodIndex.from_fields PR07,SA01" \
-i "pandas.PeriodIndex.from_ordinals SA01" \
-i "pandas.PeriodIndex.hour SA01" \
-i "pandas.PeriodIndex.is_leap_year SA01" \
-i "pandas.PeriodIndex.minute SA01" \
-i "pandas.PeriodIndex.month SA01" \
-i "pandas.PeriodIndex.quarter SA01" \
-i "pandas.PeriodIndex.qyear GL08" \
-i "pandas.PeriodIndex.second SA01" \
-i "pandas.PeriodIndex.to_timestamp RT03,SA01" \
-i "pandas.PeriodIndex.week SA01" \
-i "pandas.PeriodIndex.weekday SA01" \
-i "pandas.PeriodIndex.weekofyear SA01" \
-i "pandas.PeriodIndex.year SA01" \
-i "pandas.RangeIndex PR07" \
-i "pandas.RangeIndex.from_range PR01,SA01" \
-i "pandas.RangeIndex.start SA01" \
-i "pandas.RangeIndex.step SA01" \
-i "pandas.RangeIndex.stop SA01" \
-i "pandas.Series SA01" \
-i "pandas.Series.T SA01" \
-i "pandas.Series.__iter__ RT03,SA01" \
-i "pandas.Series.add PR07" \
-i "pandas.Series.at_time PR01" \
-i "pandas.Series.backfill PR01,SA01" \
-i "pandas.Series.bfill SA01" \
-i "pandas.Series.case_when RT03" \
-i "pandas.Series.cat PR07,SA01" \
-i "pandas.Series.cat.add_categories PR01,PR02" \
-i "pandas.Series.cat.as_ordered PR01" \
-i "pandas.Series.cat.as_unordered PR01" \
-i "pandas.Series.cat.codes SA01" \
-i "pandas.Series.cat.ordered SA01" \
-i "pandas.Series.cat.remove_categories PR01,PR02" \
-i "pandas.Series.cat.remove_unused_categories PR01" \
-i "pandas.Series.cat.rename_categories PR01,PR02" \
-i "pandas.Series.cat.reorder_categories PR01,PR02" \
-i "pandas.Series.cat.set_categories PR01,PR02" \
-i "pandas.Series.copy SA01" \
-i "pandas.Series.div PR07" \
-i "pandas.Series.droplevel SA01" \
-i "pandas.Series.dt.as_unit PR01,PR02" \
-i "pandas.Series.dt.ceil PR01,PR02,SA01" \
-i "pandas.Series.dt.components SA01" \
-i "pandas.Series.dt.date SA01" \
-i "pandas.Series.dt.day SA01" \
-i "pandas.Series.dt.day_name PR01,PR02,SA01" \
-i "pandas.Series.dt.day_of_year SA01" \
-i "pandas.Series.dt.dayofyear SA01" \
-i "pandas.Series.dt.days SA01" \
-i "pandas.Series.dt.days_in_month SA01" \
-i "pandas.Series.dt.daysinmonth SA01" \
-i "pandas.Series.dt.floor PR01,PR02,SA01" \
-i "pandas.Series.dt.freq GL08" \
-i "pandas.Series.dt.hour SA01" \
-i "pandas.Series.dt.is_leap_year SA01" \
-i "pandas.Series.dt.microsecond SA01" \
-i "pandas.Series.dt.microseconds SA01" \
-i "pandas.Series.dt.minute SA01" \
-i "pandas.Series.dt.month SA01" \
-i "pandas.Series.dt.month_name PR01,PR02,SA01" \
-i "pandas.Series.dt.nanosecond SA01" \
-i "pandas.Series.dt.nanoseconds SA01" \
-i "pandas.Series.dt.normalize PR01" \
-i "pandas.Series.dt.quarter SA01" \
-i "pandas.Series.dt.qyear GL08" \
-i "pandas.Series.dt.round PR01,PR02,SA01" \
-i "pandas.Series.dt.second SA01" \
-i "pandas.Series.dt.seconds SA01" \
-i "pandas.Series.dt.strftime PR01,PR02" \
-i "pandas.Series.dt.time SA01" \
-i "pandas.Series.dt.timetz SA01" \
-i "pandas.Series.dt.to_period PR01,PR02,RT03" \
-i "pandas.Series.dt.total_seconds PR01" \
-i "pandas.Series.dt.tz SA01" \
-i "pandas.Series.dt.tz_convert PR01,PR02,RT03" \
-i "pandas.Series.dt.tz_localize PR01,PR02" \
-i "pandas.Series.dt.unit GL08" \
-i "pandas.Series.dt.year SA01" \
-i "pandas.Series.dtype SA01" \
-i "pandas.Series.dtypes SA01" \
-i "pandas.Series.empty GL08" \
-i "pandas.Series.eq PR07,SA01" \
-i "pandas.Series.ffill SA01" \
-i "pandas.Series.first_valid_index SA01" \
-i "pandas.Series.floordiv PR07" \
-i "pandas.Series.ge PR07,SA01" \
-i "pandas.Series.get SA01" \
-i "pandas.Series.gt PR07,SA01" \
-i "pandas.Series.hasnans SA01" \
-i "pandas.Series.infer_objects RT03" \
-i "pandas.Series.is_monotonic_decreasing SA01" \
-i "pandas.Series.is_monotonic_increasing SA01" \
-i "pandas.Series.is_unique SA01" \
-i "pandas.Series.item SA01" \
-i "pandas.Series.keys SA01" \
-i "pandas.Series.kurt RT03,SA01" \
-i "pandas.Series.kurtosis RT03,SA01" \
-i "pandas.Series.last_valid_index SA01" \
-i "pandas.Series.le PR07,SA01" \
-i "pandas.Series.list.__getitem__ SA01" \
-i "pandas.Series.list.flatten SA01" \
-i "pandas.Series.list.len SA01" \
-i "pandas.Series.lt PR07,SA01" \
-i "pandas.Series.mask RT03" \
-i "pandas.Series.max RT03" \
-i "pandas.Series.mean RT03,SA01" \
-i "pandas.Series.median RT03,SA01" \
-i "pandas.Series.min RT03" \
-i "pandas.Series.mod PR07" \
-i "pandas.Series.mode SA01" \
-i "pandas.Series.mul PR07" \
-i "pandas.Series.nbytes SA01" \
-i "pandas.Series.ndim SA01" \
-i "pandas.Series.ne PR07,SA01" \
-i "pandas.Series.nunique RT03" \
-i "pandas.Series.pad PR01,SA01" \
-i "pandas.Series.plot PR02,SA01" \
-i "pandas.Series.pop RT03,SA01" \
-i "pandas.Series.pow PR07" \
-i "pandas.Series.prod RT03" \
-i "pandas.Series.product RT03" \
-i "pandas.Series.radd PR07" \
-i "pandas.Series.rdiv PR07" \
-i "pandas.Series.reorder_levels RT03,SA01" \
-i "pandas.Series.rfloordiv PR07" \
-i "pandas.Series.rmod PR07" \
-i "pandas.Series.rmul PR07" \
-i "pandas.Series.rpow PR07" \
-i "pandas.Series.rsub PR07" \
-i "pandas.Series.rtruediv PR07" \
-i "pandas.Series.sem PR01,RT03,SA01" \
-i "pandas.Series.shape SA01" \
-i "pandas.Series.size SA01" \
-i "pandas.Series.skew RT03,SA01" \
-i "pandas.Series.sparse PR01,SA01" \
-i "pandas.Series.sparse.density SA01" \
-i "pandas.Series.sparse.fill_value SA01" \
-i "pandas.Series.sparse.from_coo PR07,SA01" \
-i "pandas.Series.sparse.npoints SA01" \
-i "pandas.Series.sparse.sp_values SA01" \
-i "pandas.Series.sparse.to_coo PR07,RT03,SA01" \
-i "pandas.Series.std PR01,RT03,SA01" \
-i "pandas.Series.str PR01,SA01" \
-i "pandas.Series.str.capitalize RT03" \
-i "pandas.Series.str.casefold RT03" \
-i "pandas.Series.str.center RT03,SA01" \
-i "pandas.Series.str.decode PR07,RT03,SA01" \
-i "pandas.Series.str.encode PR07,RT03,SA01" \
-i "pandas.Series.str.find RT03" \
-i "pandas.Series.str.fullmatch RT03" \
-i "pandas.Series.str.get RT03,SA01" \
-i "pandas.Series.str.index RT03" \
-i "pandas.Series.str.ljust RT03,SA01" \
-i "pandas.Series.str.lower RT03" \
-i "pandas.Series.str.lstrip RT03" \
-i "pandas.Series.str.match RT03" \
-i "pandas.Series.str.normalize RT03,SA01" \
-i "pandas.Series.str.partition RT03" \
-i "pandas.Series.str.repeat SA01" \
-i "pandas.Series.str.replace SA01" \
-i "pandas.Series.str.rfind RT03" \
-i "pandas.Series.str.rindex RT03" \
-i "pandas.Series.str.rjust RT03,SA01" \
-i "pandas.Series.str.rpartition RT03" \
-i "pandas.Series.str.rstrip RT03" \
-i "pandas.Series.str.strip RT03" \
-i "pandas.Series.str.swapcase RT03" \
-i "pandas.Series.str.title RT03" \
-i "pandas.Series.str.translate RT03,SA01" \
-i "pandas.Series.str.upper RT03" \
-i "pandas.Series.str.wrap RT03,SA01" \
-i "pandas.Series.str.zfill RT03" \
-i "pandas.Series.struct.dtypes SA01" \
-i "pandas.Series.sub PR07" \
-i "pandas.Series.sum RT03" \
-i "pandas.Series.swaplevel SA01" \
-i "pandas.Series.to_dict SA01" \
-i "pandas.Series.to_frame SA01" \
-i "pandas.Series.to_list RT03" \
-i "pandas.Series.to_markdown SA01" \
-i "pandas.Series.to_period SA01" \
-i "pandas.Series.to_string SA01" \
-i "pandas.Series.to_timestamp RT03,SA01" \
-i "pandas.Series.truediv PR07" \
-i "pandas.Series.tz_convert SA01" \
-i "pandas.Series.tz_localize SA01" \
-i "pandas.Series.unstack SA01" \
-i "pandas.Series.update PR07,SA01" \
-i "pandas.Series.value_counts RT03" \
-i "pandas.Series.var PR01,RT03,SA01" \
-i "pandas.Series.where RT03" \
-i "pandas.SparseDtype SA01" \
-i "pandas.Timedelta PR07,SA01" \
-i "pandas.Timedelta.as_unit SA01" \
-i "pandas.Timedelta.asm8 SA01" \
-i "pandas.Timedelta.ceil SA01" \
-i "pandas.Timedelta.components SA01" \
-i "pandas.Timedelta.days SA01" \
-i "pandas.Timedelta.floor SA01" \
-i "pandas.Timedelta.max PR02,PR07,SA01" \
-i "pandas.Timedelta.min PR02,PR07,SA01" \
-i "pandas.Timedelta.resolution PR02,PR07,SA01" \
-i "pandas.Timedelta.round SA01" \
-i "pandas.Timedelta.to_numpy PR01" \
-i "pandas.Timedelta.to_timedelta64 SA01" \
-i "pandas.Timedelta.total_seconds SA01" \
-i "pandas.Timedelta.view SA01" \
-i "pandas.TimedeltaIndex PR01" \
-i "pandas.TimedeltaIndex.as_unit RT03,SA01" \
-i "pandas.TimedeltaIndex.ceil SA01" \
-i "pandas.TimedeltaIndex.components SA01" \
-i "pandas.TimedeltaIndex.days SA01" \
-i "pandas.TimedeltaIndex.floor SA01" \
-i "pandas.TimedeltaIndex.inferred_freq SA01" \
-i "pandas.TimedeltaIndex.microseconds SA01" \
-i "pandas.TimedeltaIndex.nanoseconds SA01" \
-i "pandas.TimedeltaIndex.round SA01" \
-i "pandas.TimedeltaIndex.seconds SA01" \
-i "pandas.TimedeltaIndex.to_pytimedelta RT03,SA01" \
-i "pandas.Timestamp PR07,SA01" \
-i "pandas.Timestamp.as_unit SA01" \
-i "pandas.Timestamp.asm8 SA01" \
-i "pandas.Timestamp.astimezone SA01" \
-i "pandas.Timestamp.ceil SA01" \
-i "pandas.Timestamp.combine PR01,SA01" \
-i "pandas.Timestamp.ctime SA01" \
-i "pandas.Timestamp.date SA01" \
-i "pandas.Timestamp.day GL08" \
-i "pandas.Timestamp.day_name SA01" \
-i "pandas.Timestamp.day_of_week SA01" \
-i "pandas.Timestamp.day_of_year SA01" \
-i "pandas.Timestamp.dayofweek SA01" \
-i "pandas.Timestamp.dayofyear SA01" \
-i "pandas.Timestamp.days_in_month SA01" \
-i "pandas.Timestamp.daysinmonth SA01" \
-i "pandas.Timestamp.dst SA01" \
-i "pandas.Timestamp.floor SA01" \
-i "pandas.Timestamp.fold GL08" \
-i "pandas.Timestamp.fromordinal SA01" \
-i "pandas.Timestamp.fromtimestamp PR01,SA01" \
-i "pandas.Timestamp.hour GL08" \
-i "pandas.Timestamp.is_leap_year SA01" \
-i "pandas.Timestamp.isocalendar SA01" \
-i "pandas.Timestamp.isoformat SA01" \
-i "pandas.Timestamp.isoweekday SA01" \
-i "pandas.Timestamp.max PR02,PR07,SA01" \
-i "pandas.Timestamp.microsecond GL08" \
-i "pandas.Timestamp.min PR02,PR07,SA01" \
-i "pandas.Timestamp.minute GL08" \
-i "pandas.Timestamp.month GL08" \
-i "pandas.Timestamp.month_name SA01" \
-i "pandas.Timestamp.nanosecond GL08" \
-i "pandas.Timestamp.normalize SA01" \
-i "pandas.Timestamp.now SA01" \
-i "pandas.Timestamp.quarter SA01" \
-i "pandas.Timestamp.replace PR07,SA01" \
-i "pandas.Timestamp.resolution PR02,PR07,SA01" \
-i "pandas.Timestamp.round SA01" \
-i "pandas.Timestamp.second GL08" \
-i "pandas.Timestamp.strftime SA01" \
-i "pandas.Timestamp.strptime PR01,SA01" \
-i "pandas.Timestamp.time SA01" \
-i "pandas.Timestamp.timestamp SA01" \
-i "pandas.Timestamp.timetuple SA01" \
-i "pandas.Timestamp.timetz SA01" \
-i "pandas.Timestamp.to_datetime64 SA01" \
-i "pandas.Timestamp.to_julian_date SA01" \
-i "pandas.Timestamp.to_numpy PR01" \
-i "pandas.Timestamp.to_period PR01,SA01" \
-i "pandas.Timestamp.to_pydatetime PR01,SA01" \
-i "pandas.Timestamp.today SA01" \
-i "pandas.Timestamp.toordinal SA01" \
-i "pandas.Timestamp.tz SA01" \
-i "pandas.Timestamp.tz_convert SA01" \
-i "pandas.Timestamp.tz_localize SA01" \
-i "pandas.Timestamp.tzinfo GL08" \
-i "pandas.Timestamp.tzname SA01" \
-i "pandas.Timestamp.unit SA01" \
-i "pandas.Timestamp.utcfromtimestamp PR01,SA01" \
-i "pandas.Timestamp.utcnow SA01" \
-i "pandas.Timestamp.utcoffset SA01" \
-i "pandas.Timestamp.utctimetuple SA01" \
-i "pandas.Timestamp.value GL08" \
-i "pandas.Timestamp.week SA01" \
-i "pandas.Timestamp.weekday SA01" \
-i "pandas.Timestamp.weekofyear SA01" \
-i "pandas.Timestamp.year GL08" \
-i "pandas.UInt16Dtype SA01" \
-i "pandas.UInt32Dtype SA01" \
-i "pandas.UInt64Dtype SA01" \
-i "pandas.UInt8Dtype SA01" \
-i "pandas.api.extensions.ExtensionArray SA01" \
-i "pandas.api.extensions.ExtensionArray._accumulate RT03,SA01" \
-i "pandas.api.extensions.ExtensionArray._concat_same_type PR07,SA01" \
-i "pandas.api.extensions.ExtensionArray._formatter SA01" \
-i "pandas.api.extensions.ExtensionArray._from_sequence SA01" \
-i "pandas.api.extensions.ExtensionArray._from_sequence_of_strings SA01" \
-i "pandas.api.extensions.ExtensionArray._hash_pandas_object RT03,SA01" \
-i "pandas.api.extensions.ExtensionArray._pad_or_backfill PR01,RT03,SA01" \
-i "pandas.api.extensions.ExtensionArray._reduce RT03,SA01" \
-i "pandas.api.extensions.ExtensionArray._values_for_factorize SA01" \
-i "pandas.api.extensions.ExtensionArray.astype SA01" \
-i "pandas.api.extensions.ExtensionArray.copy RT03,SA01" \
-i "pandas.api.extensions.ExtensionArray.dropna RT03,SA01" \
-i "pandas.api.extensions.ExtensionArray.dtype SA01" \
-i "pandas.api.extensions.ExtensionArray.duplicated RT03,SA01" \
-i "pandas.api.extensions.ExtensionArray.equals SA01" \
-i "pandas.api.extensions.ExtensionArray.fillna SA01" \
-i "pandas.api.extensions.ExtensionArray.insert PR07,RT03,SA01" \
-i "pandas.api.extensions.ExtensionArray.interpolate PR01,SA01" \
-i "pandas.api.extensions.ExtensionArray.isin PR07,RT03,SA01" \
-i "pandas.api.extensions.ExtensionArray.isna SA01" \
-i "pandas.api.extensions.ExtensionArray.nbytes SA01" \
-i "pandas.api.extensions.ExtensionArray.ndim SA01" \
-i "pandas.api.extensions.ExtensionArray.ravel RT03,SA01" \
-i "pandas.api.extensions.ExtensionArray.shape SA01" \
-i "pandas.api.extensions.ExtensionArray.shift SA01" \
-i "pandas.api.extensions.ExtensionArray.take RT03" \
-i "pandas.api.extensions.ExtensionArray.tolist RT03,SA01" \
-i "pandas.api.extensions.ExtensionArray.unique RT03,SA01" \
-i "pandas.api.extensions.ExtensionArray.view SA01" \
-i "pandas.api.extensions.register_extension_dtype SA01" \
-i "pandas.api.indexers.BaseIndexer PR01,SA01" \
-i "pandas.api.indexers.FixedForwardWindowIndexer PR01,SA01" \
-i "pandas.api.indexers.VariableOffsetWindowIndexer PR01,SA01" \
-i "pandas.api.interchange.from_dataframe RT03,SA01" \
-i "pandas.api.types.infer_dtype PR07,SA01" \
-i "pandas.api.types.is_any_real_numeric_dtype SA01" \
-i "pandas.api.types.is_bool PR01,SA01" \
-i "pandas.api.types.is_bool_dtype SA01" \
-i "pandas.api.types.is_categorical_dtype SA01" \
-i "pandas.api.types.is_complex PR01,SA01" \
-i "pandas.api.types.is_complex_dtype SA01" \
-i "pandas.api.types.is_datetime64_any_dtype SA01" \
-i "pandas.api.types.is_datetime64_dtype SA01" \
-i "pandas.api.types.is_datetime64_ns_dtype SA01" \
-i "pandas.api.types.is_datetime64tz_dtype SA01" \
-i "pandas.api.types.is_dict_like PR07,SA01" \
-i "pandas.api.types.is_extension_array_dtype SA01" \
-i "pandas.api.types.is_file_like PR07,SA01" \
-i "pandas.api.types.is_float PR01,SA01" \
-i "pandas.api.types.is_float_dtype SA01" \
-i "pandas.api.types.is_hashable PR01,RT03,SA01" \
-i "pandas.api.types.is_int64_dtype SA01" \
-i "pandas.api.types.is_integer PR01,SA01" \
-i "pandas.api.types.is_integer_dtype SA01" \
-i "pandas.api.types.is_interval_dtype SA01" \
-i "pandas.api.types.is_iterator PR07,SA01" \
-i "pandas.api.types.is_list_like SA01" \
-i "pandas.api.types.is_named_tuple PR07,SA01" \
-i "pandas.api.types.is_numeric_dtype SA01" \
-i "pandas.api.types.is_object_dtype SA01" \
-i "pandas.api.types.is_period_dtype SA01" \
-i "pandas.api.types.is_re PR07,SA01" \
-i "pandas.api.types.is_re_compilable PR07,SA01" \
-i "pandas.api.types.is_scalar SA01" \
-i "pandas.api.types.is_signed_integer_dtype SA01" \
-i "pandas.api.types.is_sparse SA01" \
-i "pandas.api.types.is_string_dtype SA01" \
-i "pandas.api.types.is_timedelta64_dtype SA01" \
-i "pandas.api.types.is_timedelta64_ns_dtype SA01" \
-i "pandas.api.types.is_unsigned_integer_dtype SA01" \
-i "pandas.api.types.pandas_dtype PR07,RT03,SA01" \
-i "pandas.api.types.union_categoricals RT03,SA01" \
-i "pandas.arrays.ArrowExtensionArray PR07,SA01" \
-i "pandas.arrays.BooleanArray SA01" \
-i "pandas.arrays.DatetimeArray SA01" \
-i "pandas.arrays.FloatingArray SA01" \
-i "pandas.arrays.IntegerArray SA01" \
-i "pandas.arrays.IntervalArray.closed SA01" \
-i "pandas.arrays.IntervalArray.contains RT03" \
-i "pandas.arrays.IntervalArray.is_non_overlapping_monotonic SA01" \
-i "pandas.arrays.IntervalArray.left SA01" \
-i "pandas.arrays.IntervalArray.length SA01" \
-i "pandas.arrays.IntervalArray.mid SA01" \
-i "pandas.arrays.IntervalArray.right SA01" \
-i "pandas.arrays.IntervalArray.set_closed RT03,SA01" \
-i "pandas.arrays.IntervalArray.to_tuples RT03,SA01" \
-i "pandas.arrays.NumpyExtensionArray SA01" \
-i "pandas.arrays.SparseArray PR07,SA01" \
-i "pandas.arrays.TimedeltaArray PR07,SA01" \
-i "pandas.bdate_range RT03,SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.__iter__ RT03,SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.agg RT03" \
-i "pandas.core.groupby.DataFrameGroupBy.aggregate RT03" \
-i "pandas.core.groupby.DataFrameGroupBy.boxplot PR07,RT03,SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.filter SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.get_group RT03,SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.groups SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.hist RT03" \
-i "pandas.core.groupby.DataFrameGroupBy.indices SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.max SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.median SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.min SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.nth PR02" \
-i "pandas.core.groupby.DataFrameGroupBy.nunique SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.ohlc SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.plot PR02,SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.prod SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.sem SA01" \
-i "pandas.core.groupby.DataFrameGroupBy.sum SA01" \
-i "pandas.core.groupby.SeriesGroupBy.__iter__ RT03,SA01" \
-i "pandas.core.groupby.SeriesGroupBy.agg RT03" \
-i "pandas.core.groupby.SeriesGroupBy.aggregate RT03" \
-i "pandas.core.groupby.SeriesGroupBy.filter PR01,SA01" \
-i "pandas.core.groupby.SeriesGroupBy.get_group RT03,SA01" \
-i "pandas.core.groupby.SeriesGroupBy.groups SA01" \
-i "pandas.core.groupby.SeriesGroupBy.indices SA01" \
-i "pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing SA01" \
-i "pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing SA01" \
-i "pandas.core.groupby.SeriesGroupBy.max SA01" \
-i "pandas.core.groupby.SeriesGroupBy.median SA01" \
-i "pandas.core.groupby.SeriesGroupBy.min SA01" \
-i "pandas.core.groupby.SeriesGroupBy.nth PR02" \
-i "pandas.core.groupby.SeriesGroupBy.ohlc SA01" \
-i "pandas.core.groupby.SeriesGroupBy.plot PR02,SA01" \
-i "pandas.core.groupby.SeriesGroupBy.prod SA01" \
-i "pandas.core.groupby.SeriesGroupBy.sem SA01" \
-i "pandas.core.groupby.SeriesGroupBy.sum SA01" \
-i "pandas.core.resample.Resampler.__iter__ RT03,SA01" \
-i "pandas.core.resample.Resampler.ffill RT03" \
-i "pandas.core.resample.Resampler.get_group RT03,SA01" \
-i "pandas.core.resample.Resampler.groups SA01" \
-i "pandas.core.resample.Resampler.indices SA01" \
-i "pandas.core.resample.Resampler.max PR01,RT03,SA01" \
-i "pandas.core.resample.Resampler.mean SA01" \
-i "pandas.core.resample.Resampler.median SA01" \
-i "pandas.core.resample.Resampler.min PR01,RT03,SA01" \
-i "pandas.core.resample.Resampler.ohlc SA01" \
-i "pandas.core.resample.Resampler.prod SA01" \
-i "pandas.core.resample.Resampler.quantile PR01,PR07" \
-i "pandas.core.resample.Resampler.sem SA01" \
-i "pandas.core.resample.Resampler.std SA01" \
-i "pandas.core.resample.Resampler.sum SA01" \
-i "pandas.core.resample.Resampler.transform PR01,RT03,SA01" \
-i "pandas.core.resample.Resampler.var SA01" \
-i "pandas.core.window.expanding.Expanding.corr PR01" \
-i "pandas.core.window.expanding.Expanding.count PR01" \
-i "pandas.core.window.rolling.Rolling.max PR01" \
-i "pandas.core.window.rolling.Window.std PR01" \
-i "pandas.core.window.rolling.Window.var PR01" \
-i "pandas.date_range RT03" \
-i "pandas.describe_option SA01" \
-i "pandas.errors.AbstractMethodError PR01,SA01" \
-i "pandas.errors.AttributeConflictWarning SA01" \
-i "pandas.errors.CSSWarning SA01" \
-i "pandas.errors.CategoricalConversionWarning SA01" \
-i "pandas.errors.ChainedAssignmentError SA01" \
-i "pandas.errors.ClosedFileError SA01" \
-i "pandas.errors.DataError SA01" \
-i "pandas.errors.DuplicateLabelError SA01" \
-i "pandas.errors.EmptyDataError SA01" \
-i "pandas.errors.IntCastingNaNError SA01" \
-i "pandas.errors.InvalidIndexError SA01" \
-i "pandas.errors.InvalidVersion SA01" \
-i "pandas.errors.MergeError SA01" \
-i "pandas.errors.NullFrequencyError SA01" \
-i "pandas.errors.NumExprClobberingError SA01" \
-i "pandas.errors.NumbaUtilError SA01" \
-i "pandas.errors.OptionError SA01" \
-i "pandas.errors.OutOfBoundsDatetime SA01" \
-i "pandas.errors.OutOfBoundsTimedelta SA01" \
-i "pandas.errors.PerformanceWarning SA01" \
-i "pandas.errors.PossibleDataLossError SA01" \
-i "pandas.errors.PossiblePrecisionLoss SA01" \
-i "pandas.errors.SpecificationError SA01" \
-i "pandas.errors.UndefinedVariableError PR01,SA01" \
-i "pandas.errors.UnsortedIndexError SA01" \
-i "pandas.errors.UnsupportedFunctionCall SA01" \
-i "pandas.errors.ValueLabelTypeMismatch SA01" \
-i "pandas.get_option SA01" \
-i "pandas.infer_freq SA01" \
-i "pandas.interval_range RT03" \
-i "pandas.io.formats.style.Styler.apply RT03" \
-i "pandas.io.formats.style.Styler.apply_index RT03" \
-i "pandas.io.formats.style.Styler.background_gradient RT03" \
-i "pandas.io.formats.style.Styler.bar RT03,SA01" \
-i "pandas.io.formats.style.Styler.clear SA01" \
-i "pandas.io.formats.style.Styler.concat RT03,SA01" \
-i "pandas.io.formats.style.Styler.export RT03" \
-i "pandas.io.formats.style.Styler.format RT03" \
-i "pandas.io.formats.style.Styler.format_index RT03" \
-i "pandas.io.formats.style.Styler.from_custom_template SA01" \
-i "pandas.io.formats.style.Styler.hide RT03,SA01" \
-i "pandas.io.formats.style.Styler.highlight_between RT03" \
-i "pandas.io.formats.style.Styler.highlight_max RT03" \
-i "pandas.io.formats.style.Styler.highlight_min RT03" \
-i "pandas.io.formats.style.Styler.highlight_null RT03" \
-i "pandas.io.formats.style.Styler.highlight_quantile RT03" \
-i "pandas.io.formats.style.Styler.map RT03" \
-i "pandas.io.formats.style.Styler.map_index RT03" \
-i "pandas.io.formats.style.Styler.relabel_index RT03" \
-i "pandas.io.formats.style.Styler.set_caption RT03,SA01" \
-i "pandas.io.formats.style.Styler.set_properties RT03,SA01" \
-i "pandas.io.formats.style.Styler.set_sticky RT03,SA01" \
-i "pandas.io.formats.style.Styler.set_table_attributes PR07,RT03" \
-i "pandas.io.formats.style.Styler.set_table_styles RT03" \
-i "pandas.io.formats.style.Styler.set_td_classes RT03" \
-i "pandas.io.formats.style.Styler.set_tooltips RT03,SA01" \
-i "pandas.io.formats.style.Styler.set_uuid PR07,RT03,SA01" \
-i "pandas.io.formats.style.Styler.text_gradient RT03" \
-i "pandas.io.formats.style.Styler.to_excel PR01" \
-i "pandas.io.formats.style.Styler.to_string SA01" \
-i "pandas.io.formats.style.Styler.use RT03" \
-i "pandas.io.json.build_table_schema PR07,RT03,SA01" \
-i "pandas.io.stata.StataReader.data_label SA01" \
-i "pandas.io.stata.StataReader.value_labels RT03,SA01" \
-i "pandas.io.stata.StataReader.variable_labels RT03,SA01" \
-i "pandas.io.stata.StataWriter.write_file SA01" \
-i "pandas.json_normalize RT03,SA01" \
-i "pandas.merge PR07" \
-i "pandas.merge_asof PR07,RT03" \
-i "pandas.merge_ordered PR07" \
-i "pandas.option_context SA01" \
-i "pandas.period_range RT03,SA01" \
-i "pandas.pivot PR07" \
-i "pandas.pivot_table PR07" \
-i "pandas.plotting.andrews_curves RT03,SA01" \
-i "pandas.plotting.autocorrelation_plot RT03,SA01" \
-i "pandas.plotting.lag_plot RT03,SA01" \
-i "pandas.plotting.parallel_coordinates PR07,RT03,SA01" \
-i "pandas.plotting.plot_params SA01" \
-i "pandas.plotting.scatter_matrix PR07,SA01" \
-i "pandas.plotting.table PR07,RT03,SA01" \
-i "pandas.qcut PR07,SA01" \
-i "pandas.read_feather SA01" \
-i "pandas.read_orc SA01" \
-i "pandas.read_sas SA01" \
-i "pandas.read_spss SA01" \
-i "pandas.reset_option SA01" \
-i "pandas.set_eng_float_format RT03,SA01" \
-i "pandas.set_option SA01" \
-i "pandas.show_versions SA01" \
-i "pandas.test SA01" \
-i "pandas.testing.assert_extension_array_equal SA01" \
-i "pandas.testing.assert_index_equal PR07,SA01" \
-i "pandas.testing.assert_series_equal PR07,SA01" \
-i "pandas.timedelta_range SA01" \
-i "pandas.tseries.api.guess_datetime_format SA01" \
-i "pandas.tseries.offsets.BDay PR02,SA01" \
-i "pandas.tseries.offsets.BMonthBegin PR02" \
-i "pandas.tseries.offsets.BMonthEnd PR02" \
-i "pandas.tseries.offsets.BQuarterBegin PR02" \
-i "pandas.tseries.offsets.BQuarterBegin.copy SA01" \
-i "pandas.tseries.offsets.BQuarterBegin.freqstr SA01" \
-i "pandas.tseries.offsets.BQuarterBegin.is_on_offset GL08" \
-i "pandas.tseries.offsets.BQuarterBegin.kwds SA01" \
-i "pandas.tseries.offsets.BQuarterBegin.n GL08" \
-i "pandas.tseries.offsets.BQuarterBegin.name SA01" \
-i "pandas.tseries.offsets.BQuarterBegin.nanos GL08" \
-i "pandas.tseries.offsets.BQuarterBegin.normalize GL08" \
-i "pandas.tseries.offsets.BQuarterBegin.rule_code GL08" \
-i "pandas.tseries.offsets.BQuarterBegin.startingMonth GL08" \
-i "pandas.tseries.offsets.BQuarterEnd PR02" \
-i "pandas.tseries.offsets.BQuarterEnd.copy SA01" \
-i "pandas.tseries.offsets.BQuarterEnd.freqstr SA01" \
-i "pandas.tseries.offsets.BQuarterEnd.is_on_offset GL08" \
-i "pandas.tseries.offsets.BQuarterEnd.kwds SA01" \
-i "pandas.tseries.offsets.BQuarterEnd.n GL08" \
-i "pandas.tseries.offsets.BQuarterEnd.name SA01" \
-i "pandas.tseries.offsets.BQuarterEnd.nanos GL08" \
-i "pandas.tseries.offsets.BQuarterEnd.normalize GL08" \
-i "pandas.tseries.offsets.BQuarterEnd.rule_code GL08" \
-i "pandas.tseries.offsets.BQuarterEnd.startingMonth GL08" \
-i "pandas.tseries.offsets.BYearBegin PR02" \
-i "pandas.tseries.offsets.BYearBegin.copy SA01" \
-i "pandas.tseries.offsets.BYearBegin.freqstr SA01" \
-i "pandas.tseries.offsets.BYearBegin.is_on_offset GL08" \
-i "pandas.tseries.offsets.BYearBegin.kwds SA01" \
-i "pandas.tseries.offsets.BYearBegin.month GL08" \
-i "pandas.tseries.offsets.BYearBegin.n GL08" \
-i "pandas.tseries.offsets.BYearBegin.name SA01" \
-i "pandas.tseries.offsets.BYearBegin.nanos GL08" \
-i "pandas.tseries.offsets.BYearBegin.normalize GL08" \
-i "pandas.tseries.offsets.BYearBegin.rule_code GL08" \
-i "pandas.tseries.offsets.BYearEnd PR02" \
-i "pandas.tseries.offsets.BYearEnd.copy SA01" \
-i "pandas.tseries.offsets.BYearEnd.freqstr SA01" \
-i "pandas.tseries.offsets.BYearEnd.is_on_offset GL08" \
-i "pandas.tseries.offsets.BYearEnd.kwds SA01" \
-i "pandas.tseries.offsets.BYearEnd.month GL08" \
-i "pandas.tseries.offsets.BYearEnd.n GL08" \
-i "pandas.tseries.offsets.BYearEnd.name SA01" \
-i "pandas.tseries.offsets.BYearEnd.nanos GL08" \
-i "pandas.tseries.offsets.BYearEnd.normalize GL08" \
-i "pandas.tseries.offsets.BYearEnd.rule_code GL08" \
-i "pandas.tseries.offsets.BusinessDay PR02,SA01" \
-i "pandas.tseries.offsets.BusinessDay.calendar GL08" \
-i "pandas.tseries.offsets.BusinessDay.copy SA01" \
-i "pandas.tseries.offsets.BusinessDay.freqstr SA01" \
-i "pandas.tseries.offsets.BusinessDay.holidays GL08" \
-i "pandas.tseries.offsets.BusinessDay.is_on_offset GL08" \
-i "pandas.tseries.offsets.BusinessDay.kwds SA01" \
-i "pandas.tseries.offsets.BusinessDay.n GL08" \
-i "pandas.tseries.offsets.BusinessDay.name SA01" \
-i "pandas.tseries.offsets.BusinessDay.nanos GL08" \
-i "pandas.tseries.offsets.BusinessDay.normalize GL08" \
-i "pandas.tseries.offsets.BusinessDay.rule_code GL08" \
-i "pandas.tseries.offsets.BusinessDay.weekmask GL08" \
-i "pandas.tseries.offsets.BusinessHour PR02,SA01" \
-i "pandas.tseries.offsets.BusinessHour.calendar GL08" \
-i "pandas.tseries.offsets.BusinessHour.copy SA01" \
-i "pandas.tseries.offsets.BusinessHour.end GL08" \
-i "pandas.tseries.offsets.BusinessHour.freqstr SA01" \
-i "pandas.tseries.offsets.BusinessHour.holidays GL08" \
-i "pandas.tseries.offsets.BusinessHour.is_on_offset GL08" \
-i "pandas.tseries.offsets.BusinessHour.kwds SA01" \
-i "pandas.tseries.offsets.BusinessHour.n GL08" \
-i "pandas.tseries.offsets.BusinessHour.name SA01" \
-i "pandas.tseries.offsets.BusinessHour.nanos GL08" \
-i "pandas.tseries.offsets.BusinessHour.normalize GL08" \
-i "pandas.tseries.offsets.BusinessHour.rule_code GL08" \
-i "pandas.tseries.offsets.BusinessHour.start GL08" \
-i "pandas.tseries.offsets.BusinessHour.weekmask GL08" \
-i "pandas.tseries.offsets.BusinessMonthBegin PR02" \
-i "pandas.tseries.offsets.BusinessMonthBegin.copy SA01" \
-i "pandas.tseries.offsets.BusinessMonthBegin.freqstr SA01" \
-i "pandas.tseries.offsets.BusinessMonthBegin.is_on_offset GL08" \
-i "pandas.tseries.offsets.BusinessMonthBegin.kwds SA01" \
-i "pandas.tseries.offsets.BusinessMonthBegin.n GL08" \
-i "pandas.tseries.offsets.BusinessMonthBegin.name SA01" \
-i "pandas.tseries.offsets.BusinessMonthBegin.nanos GL08" \
-i "pandas.tseries.offsets.BusinessMonthBegin.normalize GL08" \
-i "pandas.tseries.offsets.BusinessMonthBegin.rule_code GL08" \
-i "pandas.tseries.offsets.BusinessMonthEnd PR02" \
-i "pandas.tseries.offsets.BusinessMonthEnd.copy SA01" \
-i "pandas.tseries.offsets.BusinessMonthEnd.freqstr SA01" \
-i "pandas.tseries.offsets.BusinessMonthEnd.is_on_offset GL08" \
-i "pandas.tseries.offsets.BusinessMonthEnd.kwds SA01" \
-i "pandas.tseries.offsets.BusinessMonthEnd.n GL08" \
-i "pandas.tseries.offsets.BusinessMonthEnd.name SA01" \
-i "pandas.tseries.offsets.BusinessMonthEnd.nanos GL08" \
-i "pandas.tseries.offsets.BusinessMonthEnd.normalize GL08" \
-i "pandas.tseries.offsets.BusinessMonthEnd.rule_code GL08" \
-i "pandas.tseries.offsets.CBMonthBegin PR02" \
-i "pandas.tseries.offsets.CBMonthEnd PR02" \
-i "pandas.tseries.offsets.CDay PR02,SA01" \
-i "pandas.tseries.offsets.CustomBusinessDay PR02,SA01" \
-i "pandas.tseries.offsets.CustomBusinessDay.calendar GL08" \
-i "pandas.tseries.offsets.CustomBusinessDay.copy SA01" \
-i "pandas.tseries.offsets.CustomBusinessDay.freqstr SA01" \
-i "pandas.tseries.offsets.CustomBusinessDay.holidays GL08" \
-i "pandas.tseries.offsets.CustomBusinessDay.is_on_offset GL08" \
-i "pandas.tseries.offsets.CustomBusinessDay.kwds SA01" \
-i "pandas.tseries.offsets.CustomBusinessDay.n GL08" \
-i "pandas.tseries.offsets.CustomBusinessDay.name SA01" \
-i "pandas.tseries.offsets.CustomBusinessDay.nanos GL08" \
-i "pandas.tseries.offsets.CustomBusinessDay.normalize GL08" \
-i "pandas.tseries.offsets.CustomBusinessDay.rule_code GL08" \
-i "pandas.tseries.offsets.CustomBusinessDay.weekmask GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour PR02,SA01" \
-i "pandas.tseries.offsets.CustomBusinessHour.calendar GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.copy SA01" \
-i "pandas.tseries.offsets.CustomBusinessHour.end GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.freqstr SA01" \
-i "pandas.tseries.offsets.CustomBusinessHour.holidays GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.is_on_offset GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.kwds SA01" \
-i "pandas.tseries.offsets.CustomBusinessHour.n GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.name SA01" \
-i "pandas.tseries.offsets.CustomBusinessHour.nanos GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.normalize GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.rule_code GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.start GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.weekmask GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin PR02" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.calendar GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.copy SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.freqstr SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.holidays GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.is_on_offset SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.kwds SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.m_offset GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.n GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.name SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.nanos GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.normalize GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.rule_code GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.weekmask GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd PR02" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.calendar GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.copy SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.freqstr SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.holidays GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.is_on_offset SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.kwds SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.m_offset GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.n GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.name SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.nanos GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.normalize GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.rule_code GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.weekmask GL08" \
-i "pandas.tseries.offsets.DateOffset PR02" \
-i "pandas.tseries.offsets.DateOffset.copy SA01" \
-i "pandas.tseries.offsets.DateOffset.freqstr SA01" \
-i "pandas.tseries.offsets.DateOffset.is_on_offset GL08" \
-i "pandas.tseries.offsets.DateOffset.kwds SA01" \
-i "pandas.tseries.offsets.DateOffset.n GL08" \
-i "pandas.tseries.offsets.DateOffset.name SA01" \
-i "pandas.tseries.offsets.DateOffset.nanos GL08" \