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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 785 new columns ({'pixel255', 'pixel670', 'pixel336', 'pixel104', 'pixel551', 'pixel454', 'pixel455', 'pixel43', 'pixel729', 'pixel241', 'pixel700', 'pixel625', 'pixel414', 'pixel664', 'pixel413', 'pixel126', 'pixel504', 'pixel358', 'pixel226', 'pixel718', 'pixel338', 'pixel314', 'pixel88', 'pixel342', 'pixel588', 'pixel587', 'pixel200', 'pixel3', 'pixel732', 'pixel315', 'pixel23', 'pixel320', 'pixel507', 'pixel523', 'pixel293', 'pixel38', 'pixel28', 'pixel198', 'pixel355', 'pixel441', 'pixel100', 'pixel756', 'pixel41', 'pixel346', 'pixel274', 'pixel296', 'pixel288', 'pixel300', 'pixel710', 'pixel40', 'pixel316', 'pixel142', 'pixel98', 'pixel686', 'pixel172', 'pixel760', 'pixel152', 'pixel186', 'pixel582', 'pixel71', 'pixel483', 'pixel459', 'pixel531', 'pixel408', 'pixel155', 'pixel623', 'pixel234', 'pixel227', 'pixel656', 'pixel722', 'pixel113', 'pixel417', 'pixel266', 'pixel324', 'pixel444', 'pixel323', 'pixel335', 'pixel606', 'pixel341', 'pixel468', 'pixel261', 'pixel371', 'pixel714', 'pixel765', 'pixel58', 'pixel13', 'pixel586', 'pixel299', 'pixel403', 'pixel753', 'pixel731', 'pixel135', 'pixel639', 'pixel422', 'pixel648', 'pixel734', 'pixel66', 'pixel616', 'pixel138', 'pixel619', 'pixel612', 'pixel650', 'pixel62', 'pixel232', 'pixel548', 'pixel93', 'pixel617', 'pixel759', 'pixel190', 'pixel554', 'pixel247', 'pixel529', 'pixel351', 'pixel501', 'pixel277', 'pixel724', 'pixel25', 'pixel36', 'pixel54', 'pixel771', 'pixel158', 'pixel253', 'pixel580', 'pixel111', 'pixel140', 'pixel430', 'pixel
...
'pixel597', 'pixel399', 'pixel72', 'pixel425', 'pixel18', 'pixel543', 'pixel290', 'pixel329', 'pixel486', 'pixel657', 'pixel516', 'pixel723', 'pixel4', 'pixel624', 'pixel44', 'pixel641', 'pixel618', 'pixel123', 'pixel112', 'pixel470', 'pixel746', 'pixel154', 'pixel545', 'pixel195', 'pixel194', 'pixel285', 'pixel676', 'pixel51', 'pixel220', 'pixel506', 'pixel536', 'pixel141', 'pixel763', 'pixel127', 'pixel63', 'pixel370', 'pixel432', 'pixel481', 'pixel95', 'pixel304', 'pixel350', 'pixel64', 'pixel46', 'pixel173', 'pixel228', 'pixel139', 'pixel742', 'pixel31', 'pixel171', 'pixel426', 'pixel429', 'pixel133', 'pixel663', 'pixel423', 'pixel144', 'pixel514', 'pixel177', 'pixel559', 'pixel433', 'pixel75', 'pixel33', 'pixel307', 'pixel517', 'pixel8', 'pixel163', 'pixel240', 'pixel243', 'pixel480', 'pixel672', 'pixel157', 'pixel706', 'pixel183', 'pixel393', 'pixel268', 'pixel21', 'pixel1', 'pixel108', 'pixel19', 'pixel610', 'pixel519', 'pixel521', 'pixel443', 'pixel547', 'pixel537', 'pixel608', 'pixel653', 'pixel627', 'pixel347', 'pixel79', 'pixel640', 'pixel675', 'pixel461', 'pixel583', 'pixel310', 'pixel549', 'pixel704', 'pixel339', 'pixel235', 'pixel9', 'pixel751', 'pixel464', 'pixel502', 'pixel301', 'pixel730', 'pixel276', 'pixel774', 'pixel87', 'pixel325', 'pixel387', 'pixel496', 'pixel690', 'pixel784', 'pixel511', 'pixel783', 'pixel741', 'pixel317', 'pixel471', 'pixel622', 'pixel105', 'pixel246', 'pixel174', 'pixel420', 'pixel737', 'pixel662', 'pixel180', 'pixel585', 'pixel638'}) and 33 missing columns ({'concave points_mean', 'fractal_dimension_se', 'fractal_dimension_worst', 'texture_worst', 'perimeter_worst', 'radius_mean', 'texture_mean', 'concavity_se', 'smoothness_se', 'smoothness_worst', 'perimeter_se', 'Unnamed: 32', 'compactness_mean', 'symmetry_mean', 'concavity_worst', 'perimeter_mean', 'fractal_dimension_mean', 'area_se', 'compactness_se', 'texture_se', 'symmetry_se', 'diagnosis', 'area_mean', 'concavity_mean', 'concave points_se', 'id', 'compactness_worst', 'concave points_worst', 'symmetry_worst', 'smoothness_mean', 'area_worst', 'radius_worst', 'radius_se'}).

This happened while the csv dataset builder was generating data using

zip://fashion-mnist_test.csv::/tmp/hf-datasets-cache/medium/datasets/21768584185532-config-parquet-and-info-tqxg2022-IDEM2211-02004538/hub/datasets--tqxg2022--IDEM2211/snapshots/664fd6ee60a21152fc30646242c4834f3f3d2cad/Fashion-MNIST.zip

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              label: int64
              pixel1: int64
              pixel2: int64
              pixel3: int64
              pixel4: int64
              pixel5: int64
              pixel6: int64
              pixel7: int64
              pixel8: int64
              pixel9: int64
              pixel10: int64
              pixel11: int64
              pixel12: int64
              pixel13: int64
              pixel14: int64
              pixel15: int64
              pixel16: int64
              pixel17: int64
              pixel18: int64
              pixel19: int64
              pixel20: int64
              pixel21: int64
              pixel22: int64
              pixel23: int64
              pixel24: int64
              pixel25: int64
              pixel26: int64
              pixel27: int64
              pixel28: int64
              pixel29: int64
              pixel30: int64
              pixel31: int64
              pixel32: int64
              pixel33: int64
              pixel34: int64
              pixel35: int64
              pixel36: int64
              pixel37: int64
              pixel38: int64
              pixel39: int64
              pixel40: int64
              pixel41: int64
              pixel42: int64
              pixel43: int64
              pixel44: int64
              pixel45: int64
              pixel46: int64
              pixel47: int64
              pixel48: int64
              pixel49: int64
              pixel50: int64
              pixel51: int64
              pixel52: int64
              pixel53: int64
              pixel54: int64
              pixel55: int64
              pixel56: int64
              pixel57: int64
              pixel58: int64
              pixel59: int64
              pixel60: int64
              pixel61: int64
              pixel62: int64
              pixel63: int64
              pixel64: int64
              pixel65: int64
              pixel66: int64
              pixel67: int64
              pixel68: int64
              pixel69: int64
              pixel70: int64
              pixel71: int64
              pixel72: int64
              pixel73: int64
              pixel74: int64
              pixel75: int64
              pixel76: int64
              pixel77: int64
              pixel78: int64
              pixel79: int64
              pixel80: int64
              pixel81: int64
              pixel82: int64
              pixel83: int64
              pixel84: int64
              pixel85: int64
              pixel86: int64
              pixel87: int64
              pixel88: int64
              pixel89: int64
              pixel90: int64
              pixel91: int64
              pixel92: int64
              pixel93: int64
              pixel94: int64
              pixel95: int64
              pixel96: int64
              pixel97: int64
              pixel98: int64
              pixel99: int64
              pixel100: i
              ...
              t64
              pixel698: int64
              pixel699: int64
              pixel700: int64
              pixel701: int64
              pixel702: int64
              pixel703: int64
              pixel704: int64
              pixel705: int64
              pixel706: int64
              pixel707: int64
              pixel708: int64
              pixel709: int64
              pixel710: int64
              pixel711: int64
              pixel712: int64
              pixel713: int64
              pixel714: int64
              pixel715: int64
              pixel716: int64
              pixel717: int64
              pixel718: int64
              pixel719: int64
              pixel720: int64
              pixel721: int64
              pixel722: int64
              pixel723: int64
              pixel724: int64
              pixel725: int64
              pixel726: int64
              pixel727: int64
              pixel728: int64
              pixel729: int64
              pixel730: int64
              pixel731: int64
              pixel732: int64
              pixel733: int64
              pixel734: int64
              pixel735: int64
              pixel736: int64
              pixel737: int64
              pixel738: int64
              pixel739: int64
              pixel740: int64
              pixel741: int64
              pixel742: int64
              pixel743: int64
              pixel744: int64
              pixel745: int64
              pixel746: int64
              pixel747: int64
              pixel748: int64
              pixel749: int64
              pixel750: int64
              pixel751: int64
              pixel752: int64
              pixel753: int64
              pixel754: int64
              pixel755: int64
              pixel756: int64
              pixel757: int64
              pixel758: int64
              pixel759: int64
              pixel760: int64
              pixel761: int64
              pixel762: int64
              pixel763: int64
              pixel764: int64
              pixel765: int64
              pixel766: int64
              pixel767: int64
              pixel768: int64
              pixel769: int64
              pixel770: int64
              pixel771: int64
              pixel772: int64
              pixel773: int64
              pixel774: int64
              pixel775: int64
              pixel776: int64
              pixel777: int64
              pixel778: int64
              pixel779: int64
              pixel780: int64
              pixel781: int64
              pixel782: int64
              pixel783: int64
              pixel784: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 88746
              to
              {'id': Value(dtype='int64', id=None), 'diagnosis': Value(dtype='string', id=None), 'radius_mean': Value(dtype='float64', id=None), 'texture_mean': Value(dtype='float64', id=None), 'perimeter_mean': Value(dtype='float64', id=None), 'area_mean': Value(dtype='float64', id=None), 'smoothness_mean': Value(dtype='float64', id=None), 'compactness_mean': Value(dtype='float64', id=None), 'concavity_mean': Value(dtype='float64', id=None), 'concave points_mean': Value(dtype='float64', id=None), 'symmetry_mean': Value(dtype='float64', id=None), 'fractal_dimension_mean': Value(dtype='float64', id=None), 'radius_se': Value(dtype='float64', id=None), 'texture_se': Value(dtype='float64', id=None), 'perimeter_se': Value(dtype='float64', id=None), 'area_se': Value(dtype='float64', id=None), 'smoothness_se': Value(dtype='float64', id=None), 'compactness_se': Value(dtype='float64', id=None), 'concavity_se': Value(dtype='float64', id=None), 'concave points_se': Value(dtype='float64', id=None), 'symmetry_se': Value(dtype='float64', id=None), 'fractal_dimension_se': Value(dtype='float64', id=None), 'radius_worst': Value(dtype='float64', id=None), 'texture_worst': Value(dtype='float64', id=None), 'perimeter_worst': Value(dtype='float64', id=None), 'area_worst': Value(dtype='float64', id=None), 'smoothness_worst': Value(dtype='float64', id=None), 'compactness_worst': Value(dtype='float64', id=None), 'concavity_worst': Value(dtype='float64', id=None), 'concave points_worst': Value(dtype='float64', id=None), 'symmetry_worst': Value(dtype='float64', id=None), 'fractal_dimension_worst': Value(dtype='float64', id=None), 'Unnamed: 32': Value(dtype='float64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1420, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1052, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1872, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 785 new columns ({'pixel255', 'pixel670', 'pixel336', 'pixel104', 'pixel551', 'pixel454', 'pixel455', 'pixel43', 'pixel729', 'pixel241', 'pixel700', 'pixel625', 'pixel414', 'pixel664', 'pixel413', 'pixel126', 'pixel504', 'pixel358', 'pixel226', 'pixel718', 'pixel338', 'pixel314', 'pixel88', 'pixel342', 'pixel588', 'pixel587', 'pixel200', 'pixel3', 'pixel732', 'pixel315', 'pixel23', 'pixel320', 'pixel507', 'pixel523', 'pixel293', 'pixel38', 'pixel28', 'pixel198', 'pixel355', 'pixel441', 'pixel100', 'pixel756', 'pixel41', 'pixel346', 'pixel274', 'pixel296', 'pixel288', 'pixel300', 'pixel710', 'pixel40', 'pixel316', 'pixel142', 'pixel98', 'pixel686', 'pixel172', 'pixel760', 'pixel152', 'pixel186', 'pixel582', 'pixel71', 'pixel483', 'pixel459', 'pixel531', 'pixel408', 'pixel155', 'pixel623', 'pixel234', 'pixel227', 'pixel656', 'pixel722', 'pixel113', 'pixel417', 'pixel266', 'pixel324', 'pixel444', 'pixel323', 'pixel335', 'pixel606', 'pixel341', 'pixel468', 'pixel261', 'pixel371', 'pixel714', 'pixel765', 'pixel58', 'pixel13', 'pixel586', 'pixel299', 'pixel403', 'pixel753', 'pixel731', 'pixel135', 'pixel639', 'pixel422', 'pixel648', 'pixel734', 'pixel66', 'pixel616', 'pixel138', 'pixel619', 'pixel612', 'pixel650', 'pixel62', 'pixel232', 'pixel548', 'pixel93', 'pixel617', 'pixel759', 'pixel190', 'pixel554', 'pixel247', 'pixel529', 'pixel351', 'pixel501', 'pixel277', 'pixel724', 'pixel25', 'pixel36', 'pixel54', 'pixel771', 'pixel158', 'pixel253', 'pixel580', 'pixel111', 'pixel140', 'pixel430', 'pixel
              ...
              'pixel597', 'pixel399', 'pixel72', 'pixel425', 'pixel18', 'pixel543', 'pixel290', 'pixel329', 'pixel486', 'pixel657', 'pixel516', 'pixel723', 'pixel4', 'pixel624', 'pixel44', 'pixel641', 'pixel618', 'pixel123', 'pixel112', 'pixel470', 'pixel746', 'pixel154', 'pixel545', 'pixel195', 'pixel194', 'pixel285', 'pixel676', 'pixel51', 'pixel220', 'pixel506', 'pixel536', 'pixel141', 'pixel763', 'pixel127', 'pixel63', 'pixel370', 'pixel432', 'pixel481', 'pixel95', 'pixel304', 'pixel350', 'pixel64', 'pixel46', 'pixel173', 'pixel228', 'pixel139', 'pixel742', 'pixel31', 'pixel171', 'pixel426', 'pixel429', 'pixel133', 'pixel663', 'pixel423', 'pixel144', 'pixel514', 'pixel177', 'pixel559', 'pixel433', 'pixel75', 'pixel33', 'pixel307', 'pixel517', 'pixel8', 'pixel163', 'pixel240', 'pixel243', 'pixel480', 'pixel672', 'pixel157', 'pixel706', 'pixel183', 'pixel393', 'pixel268', 'pixel21', 'pixel1', 'pixel108', 'pixel19', 'pixel610', 'pixel519', 'pixel521', 'pixel443', 'pixel547', 'pixel537', 'pixel608', 'pixel653', 'pixel627', 'pixel347', 'pixel79', 'pixel640', 'pixel675', 'pixel461', 'pixel583', 'pixel310', 'pixel549', 'pixel704', 'pixel339', 'pixel235', 'pixel9', 'pixel751', 'pixel464', 'pixel502', 'pixel301', 'pixel730', 'pixel276', 'pixel774', 'pixel87', 'pixel325', 'pixel387', 'pixel496', 'pixel690', 'pixel784', 'pixel511', 'pixel783', 'pixel741', 'pixel317', 'pixel471', 'pixel622', 'pixel105', 'pixel246', 'pixel174', 'pixel420', 'pixel737', 'pixel662', 'pixel180', 'pixel585', 'pixel638'}) and 33 missing columns ({'concave points_mean', 'fractal_dimension_se', 'fractal_dimension_worst', 'texture_worst', 'perimeter_worst', 'radius_mean', 'texture_mean', 'concavity_se', 'smoothness_se', 'smoothness_worst', 'perimeter_se', 'Unnamed: 32', 'compactness_mean', 'symmetry_mean', 'concavity_worst', 'perimeter_mean', 'fractal_dimension_mean', 'area_se', 'compactness_se', 'texture_se', 'symmetry_se', 'diagnosis', 'area_mean', 'concavity_mean', 'concave points_se', 'id', 'compactness_worst', 'concave points_worst', 'symmetry_worst', 'smoothness_mean', 'area_worst', 'radius_worst', 'radius_se'}).
              
              This happened while the csv dataset builder was generating data using
              
              zip://fashion-mnist_test.csv::/tmp/hf-datasets-cache/medium/datasets/21768584185532-config-parquet-and-info-tqxg2022-IDEM2211-02004538/hub/datasets--tqxg2022--IDEM2211/snapshots/664fd6ee60a21152fc30646242c4834f3f3d2cad/Fashion-MNIST.zip
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

id
int64
diagnosis
string
radius_mean
float64
texture_mean
float64
perimeter_mean
float64
area_mean
float64
smoothness_mean
float64
compactness_mean
float64
concavity_mean
float64
concave points_mean
float64
symmetry_mean
float64
fractal_dimension_mean
float64
radius_se
float64
texture_se
float64
perimeter_se
float64
area_se
float64
smoothness_se
float64
compactness_se
float64
concavity_se
float64
concave points_se
float64
symmetry_se
float64
fractal_dimension_se
float64
radius_worst
float64
texture_worst
float64
perimeter_worst
float64
area_worst
float64
smoothness_worst
float64
compactness_worst
float64
concavity_worst
float64
concave points_worst
float64
symmetry_worst
float64
fractal_dimension_worst
float64
Unnamed: 32
null
842,302
M
17.99
10.38
122.8
1,001
0.1184
0.2776
0.3001
0.1471
0.2419
0.07871
1.095
0.9053
8.589
153.4
0.006399
0.04904
0.05373
0.01587
0.03003
0.006193
25.38
17.33
184.6
2,019
0.1622
0.6656
0.7119
0.2654
0.4601
0.1189
null
842,517
M
20.57
17.77
132.9
1,326
0.08474
0.07864
0.0869
0.07017
0.1812
0.05667
0.5435
0.7339
3.398
74.08
0.005225
0.01308
0.0186
0.0134
0.01389
0.003532
24.99
23.41
158.8
1,956
0.1238
0.1866
0.2416
0.186
0.275
0.08902
null
84,300,903
M
19.69
21.25
130
1,203
0.1096
0.1599
0.1974
0.1279
0.2069
0.05999
0.7456
0.7869
4.585
94.03
0.00615
0.04006
0.03832
0.02058
0.0225
0.004571
23.57
25.53
152.5
1,709
0.1444
0.4245
0.4504
0.243
0.3613
0.08758
null
84,348,301
M
11.42
20.38
77.58
386.1
0.1425
0.2839
0.2414
0.1052
0.2597
0.09744
0.4956
1.156
3.445
27.23
0.00911
0.07458
0.05661
0.01867
0.05963
0.009208
14.91
26.5
98.87
567.7
0.2098
0.8663
0.6869
0.2575
0.6638
0.173
null
84,358,402
M
20.29
14.34
135.1
1,297
0.1003
0.1328
0.198
0.1043
0.1809
0.05883
0.7572
0.7813
5.438
94.44
0.01149
0.02461
0.05688
0.01885
0.01756
0.005115
22.54
16.67
152.2
1,575
0.1374
0.205
0.4
0.1625
0.2364
0.07678
null
843,786
M
12.45
15.7
82.57
477.1
0.1278
0.17
0.1578
0.08089
0.2087
0.07613
0.3345
0.8902
2.217
27.19
0.00751
0.03345
0.03672
0.01137
0.02165
0.005082
15.47
23.75
103.4
741.6
0.1791
0.5249
0.5355
0.1741
0.3985
0.1244
null
844,359
M
18.25
19.98
119.6
1,040
0.09463
0.109
0.1127
0.074
0.1794
0.05742
0.4467
0.7732
3.18
53.91
0.004314
0.01382
0.02254
0.01039
0.01369
0.002179
22.88
27.66
153.2
1,606
0.1442
0.2576
0.3784
0.1932
0.3063
0.08368
null
84,458,202
M
13.71
20.83
90.2
577.9
0.1189
0.1645
0.09366
0.05985
0.2196
0.07451
0.5835
1.377
3.856
50.96
0.008805
0.03029
0.02488
0.01448
0.01486
0.005412
17.06
28.14
110.6
897
0.1654
0.3682
0.2678
0.1556
0.3196
0.1151
null
844,981
M
13
21.82
87.5
519.8
0.1273
0.1932
0.1859
0.09353
0.235
0.07389
0.3063
1.002
2.406
24.32
0.005731
0.03502
0.03553
0.01226
0.02143
0.003749
15.49
30.73
106.2
739.3
0.1703
0.5401
0.539
0.206
0.4378
0.1072
null
84,501,001
M
12.46
24.04
83.97
475.9
0.1186
0.2396
0.2273
0.08543
0.203
0.08243
0.2976
1.599
2.039
23.94
0.007149
0.07217
0.07743
0.01432
0.01789
0.01008
15.09
40.68
97.65
711.4
0.1853
1.058
1.105
0.221
0.4366
0.2075
null
845,636
M
16.02
23.24
102.7
797.8
0.08206
0.06669
0.03299
0.03323
0.1528
0.05697
0.3795
1.187
2.466
40.51
0.004029
0.009269
0.01101
0.007591
0.0146
0.003042
19.19
33.88
123.8
1,150
0.1181
0.1551
0.1459
0.09975
0.2948
0.08452
null
84,610,002
M
15.78
17.89
103.6
781
0.0971
0.1292
0.09954
0.06606
0.1842
0.06082
0.5058
0.9849
3.564
54.16
0.005771
0.04061
0.02791
0.01282
0.02008
0.004144
20.42
27.28
136.5
1,299
0.1396
0.5609
0.3965
0.181
0.3792
0.1048
null
846,226
M
19.17
24.8
132.4
1,123
0.0974
0.2458
0.2065
0.1118
0.2397
0.078
0.9555
3.568
11.07
116.2
0.003139
0.08297
0.0889
0.0409
0.04484
0.01284
20.96
29.94
151.7
1,332
0.1037
0.3903
0.3639
0.1767
0.3176
0.1023
null
846,381
M
15.85
23.95
103.7
782.7
0.08401
0.1002
0.09938
0.05364
0.1847
0.05338
0.4033
1.078
2.903
36.58
0.009769
0.03126
0.05051
0.01992
0.02981
0.003002
16.84
27.66
112
876.5
0.1131
0.1924
0.2322
0.1119
0.2809
0.06287
null
84,667,401
M
13.73
22.61
93.6
578.3
0.1131
0.2293
0.2128
0.08025
0.2069
0.07682
0.2121
1.169
2.061
19.21
0.006429
0.05936
0.05501
0.01628
0.01961
0.008093
15.03
32.01
108.8
697.7
0.1651
0.7725
0.6943
0.2208
0.3596
0.1431
null
84,799,002
M
14.54
27.54
96.73
658.8
0.1139
0.1595
0.1639
0.07364
0.2303
0.07077
0.37
1.033
2.879
32.55
0.005607
0.0424
0.04741
0.0109
0.01857
0.005466
17.46
37.13
124.1
943.2
0.1678
0.6577
0.7026
0.1712
0.4218
0.1341
null
848,406
M
14.68
20.13
94.74
684.5
0.09867
0.072
0.07395
0.05259
0.1586
0.05922
0.4727
1.24
3.195
45.4
0.005718
0.01162
0.01998
0.01109
0.0141
0.002085
19.07
30.88
123.4
1,138
0.1464
0.1871
0.2914
0.1609
0.3029
0.08216
null
84,862,001
M
16.13
20.68
108.1
798.8
0.117
0.2022
0.1722
0.1028
0.2164
0.07356
0.5692
1.073
3.854
54.18
0.007026
0.02501
0.03188
0.01297
0.01689
0.004142
20.96
31.48
136.8
1,315
0.1789
0.4233
0.4784
0.2073
0.3706
0.1142
null
849,014
M
19.81
22.15
130
1,260
0.09831
0.1027
0.1479
0.09498
0.1582
0.05395
0.7582
1.017
5.865
112.4
0.006494
0.01893
0.03391
0.01521
0.01356
0.001997
27.32
30.88
186.8
2,398
0.1512
0.315
0.5372
0.2388
0.2768
0.07615
null
8,510,426
B
13.54
14.36
87.46
566.3
0.09779
0.08129
0.06664
0.04781
0.1885
0.05766
0.2699
0.7886
2.058
23.56
0.008462
0.0146
0.02387
0.01315
0.0198
0.0023
15.11
19.26
99.7
711.2
0.144
0.1773
0.239
0.1288
0.2977
0.07259
null
8,510,653
B
13.08
15.71
85.63
520
0.1075
0.127
0.04568
0.0311
0.1967
0.06811
0.1852
0.7477
1.383
14.67
0.004097
0.01898
0.01698
0.00649
0.01678
0.002425
14.5
20.49
96.09
630.5
0.1312
0.2776
0.189
0.07283
0.3184
0.08183
null
8,510,824
B
9.504
12.44
60.34
273.9
0.1024
0.06492
0.02956
0.02076
0.1815
0.06905
0.2773
0.9768
1.909
15.7
0.009606
0.01432
0.01985
0.01421
0.02027
0.002968
10.23
15.66
65.13
314.9
0.1324
0.1148
0.08867
0.06227
0.245
0.07773
null
8,511,133
M
15.34
14.26
102.5
704.4
0.1073
0.2135
0.2077
0.09756
0.2521
0.07032
0.4388
0.7096
3.384
44.91
0.006789
0.05328
0.06446
0.02252
0.03672
0.004394
18.07
19.08
125.1
980.9
0.139
0.5954
0.6305
0.2393
0.4667
0.09946
null
851,509
M
21.16
23.04
137.2
1,404
0.09428
0.1022
0.1097
0.08632
0.1769
0.05278
0.6917
1.127
4.303
93.99
0.004728
0.01259
0.01715
0.01038
0.01083
0.001987
29.17
35.59
188
2,615
0.1401
0.26
0.3155
0.2009
0.2822
0.07526
null
852,552
M
16.65
21.38
110
904.6
0.1121
0.1457
0.1525
0.0917
0.1995
0.0633
0.8068
0.9017
5.455
102.6
0.006048
0.01882
0.02741
0.0113
0.01468
0.002801
26.46
31.56
177
2,215
0.1805
0.3578
0.4695
0.2095
0.3613
0.09564
null
852,631
M
17.14
16.4
116
912.7
0.1186
0.2276
0.2229
0.1401
0.304
0.07413
1.046
0.976
7.276
111.4
0.008029
0.03799
0.03732
0.02397
0.02308
0.007444
22.25
21.4
152.4
1,461
0.1545
0.3949
0.3853
0.255
0.4066
0.1059
null
852,763
M
14.58
21.53
97.41
644.8
0.1054
0.1868
0.1425
0.08783
0.2252
0.06924
0.2545
0.9832
2.11
21.05
0.004452
0.03055
0.02681
0.01352
0.01454
0.003711
17.62
33.21
122.4
896.9
0.1525
0.6643
0.5539
0.2701
0.4264
0.1275
null
852,781
M
18.61
20.25
122.1
1,094
0.0944
0.1066
0.149
0.07731
0.1697
0.05699
0.8529
1.849
5.632
93.54
0.01075
0.02722
0.05081
0.01911
0.02293
0.004217
21.31
27.26
139.9
1,403
0.1338
0.2117
0.3446
0.149
0.2341
0.07421
null
852,973
M
15.3
25.27
102.4
732.4
0.1082
0.1697
0.1683
0.08751
0.1926
0.0654
0.439
1.012
3.498
43.5
0.005233
0.03057
0.03576
0.01083
0.01768
0.002967
20.27
36.71
149.3
1,269
0.1641
0.611
0.6335
0.2024
0.4027
0.09876
null
853,201
M
17.57
15.05
115
955.1
0.09847
0.1157
0.09875
0.07953
0.1739
0.06149
0.6003
0.8225
4.655
61.1
0.005627
0.03033
0.03407
0.01354
0.01925
0.003742
20.01
19.52
134.9
1,227
0.1255
0.2812
0.2489
0.1456
0.2756
0.07919
null
853,401
M
18.63
25.11
124.8
1,088
0.1064
0.1887
0.2319
0.1244
0.2183
0.06197
0.8307
1.466
5.574
105
0.006248
0.03374
0.05196
0.01158
0.02007
0.00456
23.15
34.01
160.5
1,670
0.1491
0.4257
0.6133
0.1848
0.3444
0.09782
null
853,612
M
11.84
18.7
77.93
440.6
0.1109
0.1516
0.1218
0.05182
0.2301
0.07799
0.4825
1.03
3.475
41
0.005551
0.03414
0.04205
0.01044
0.02273
0.005667
16.82
28.12
119.4
888.7
0.1637
0.5775
0.6956
0.1546
0.4761
0.1402
null
85,382,601
M
17.02
23.98
112.8
899.3
0.1197
0.1496
0.2417
0.1203
0.2248
0.06382
0.6009
1.398
3.999
67.78
0.008268
0.03082
0.05042
0.01112
0.02102
0.003854
20.88
32.09
136.1
1,344
0.1634
0.3559
0.5588
0.1847
0.353
0.08482
null
854,002
M
19.27
26.47
127.9
1,162
0.09401
0.1719
0.1657
0.07593
0.1853
0.06261
0.5558
0.6062
3.528
68.17
0.005015
0.03318
0.03497
0.009643
0.01543
0.003896
24.15
30.9
161.4
1,813
0.1509
0.659
0.6091
0.1785
0.3672
0.1123
null
854,039
M
16.13
17.88
107
807.2
0.104
0.1559
0.1354
0.07752
0.1998
0.06515
0.334
0.6857
2.183
35.03
0.004185
0.02868
0.02664
0.009067
0.01703
0.003817
20.21
27.26
132.7
1,261
0.1446
0.5804
0.5274
0.1864
0.427
0.1233
null
854,253
M
16.74
21.59
110.1
869.5
0.0961
0.1336
0.1348
0.06018
0.1896
0.05656
0.4615
0.9197
3.008
45.19
0.005776
0.02499
0.03695
0.01195
0.02789
0.002665
20.01
29.02
133.5
1,229
0.1563
0.3835
0.5409
0.1813
0.4863
0.08633
null
854,268
M
14.25
21.72
93.63
633
0.09823
0.1098
0.1319
0.05598
0.1885
0.06125
0.286
1.019
2.657
24.91
0.005878
0.02995
0.04815
0.01161
0.02028
0.004022
15.89
30.36
116.2
799.6
0.1446
0.4238
0.5186
0.1447
0.3591
0.1014
null
854,941
B
13.03
18.42
82.61
523.8
0.08983
0.03766
0.02562
0.02923
0.1467
0.05863
0.1839
2.342
1.17
14.16
0.004352
0.004899
0.01343
0.01164
0.02671
0.001777
13.3
22.81
84.46
545.9
0.09701
0.04619
0.04833
0.05013
0.1987
0.06169
null
855,133
M
14.99
25.2
95.54
698.8
0.09387
0.05131
0.02398
0.02899
0.1565
0.05504
1.214
2.188
8.077
106
0.006883
0.01094
0.01818
0.01917
0.007882
0.001754
14.99
25.2
95.54
698.8
0.09387
0.05131
0.02398
0.02899
0.1565
0.05504
null
855,138
M
13.48
20.82
88.4
559.2
0.1016
0.1255
0.1063
0.05439
0.172
0.06419
0.213
0.5914
1.545
18.52
0.005367
0.02239
0.03049
0.01262
0.01377
0.003187
15.53
26.02
107.3
740.4
0.161
0.4225
0.503
0.2258
0.2807
0.1071
null
855,167
M
13.44
21.58
86.18
563
0.08162
0.06031
0.0311
0.02031
0.1784
0.05587
0.2385
0.8265
1.572
20.53
0.00328
0.01102
0.0139
0.006881
0.0138
0.001286
15.93
30.25
102.5
787.9
0.1094
0.2043
0.2085
0.1112
0.2994
0.07146
null
855,563
M
10.95
21.35
71.9
371.1
0.1227
0.1218
0.1044
0.05669
0.1895
0.0687
0.2366
1.428
1.822
16.97
0.008064
0.01764
0.02595
0.01037
0.01357
0.00304
12.84
35.34
87.22
514
0.1909
0.2698
0.4023
0.1424
0.2964
0.09606
null
855,625
M
19.07
24.81
128.3
1,104
0.09081
0.219
0.2107
0.09961
0.231
0.06343
0.9811
1.666
8.83
104.9
0.006548
0.1006
0.09723
0.02638
0.05333
0.007646
24.09
33.17
177.4
1,651
0.1247
0.7444
0.7242
0.2493
0.467
0.1038
null
856,106
M
13.28
20.28
87.32
545.2
0.1041
0.1436
0.09847
0.06158
0.1974
0.06782
0.3704
0.8249
2.427
31.33
0.005072
0.02147
0.02185
0.00956
0.01719
0.003317
17.38
28
113.1
907.2
0.153
0.3724
0.3664
0.1492
0.3739
0.1027
null
85,638,502
M
13.17
21.81
85.42
531.5
0.09714
0.1047
0.08259
0.05252
0.1746
0.06177
0.1938
0.6123
1.334
14.49
0.00335
0.01384
0.01452
0.006853
0.01113
0.00172
16.23
29.89
105.5
740.7
0.1503
0.3904
0.3728
0.1607
0.3693
0.09618
null
857,010
M
18.65
17.6
123.7
1,076
0.1099
0.1686
0.1974
0.1009
0.1907
0.06049
0.6289
0.6633
4.293
71.56
0.006294
0.03994
0.05554
0.01695
0.02428
0.003535
22.82
21.32
150.6
1,567
0.1679
0.509
0.7345
0.2378
0.3799
0.09185
null
85,713,702
B
8.196
16.84
51.71
201.9
0.086
0.05943
0.01588
0.005917
0.1769
0.06503
0.1563
0.9567
1.094
8.205
0.008968
0.01646
0.01588
0.005917
0.02574
0.002582
8.964
21.96
57.26
242.2
0.1297
0.1357
0.0688
0.02564
0.3105
0.07409
null
85,715
M
13.17
18.66
85.98
534.6
0.1158
0.1231
0.1226
0.0734
0.2128
0.06777
0.2871
0.8937
1.897
24.25
0.006532
0.02336
0.02905
0.01215
0.01743
0.003643
15.67
27.95
102.8
759.4
0.1786
0.4166
0.5006
0.2088
0.39
0.1179
null
857,155
B
12.05
14.63
78.04
449.3
0.1031
0.09092
0.06592
0.02749
0.1675
0.06043
0.2636
0.7294
1.848
19.87
0.005488
0.01427
0.02322
0.00566
0.01428
0.002422
13.76
20.7
89.88
582.6
0.1494
0.2156
0.305
0.06548
0.2747
0.08301
null
857,156
B
13.49
22.3
86.91
561
0.08752
0.07698
0.04751
0.03384
0.1809
0.05718
0.2338
1.353
1.735
20.2
0.004455
0.01382
0.02095
0.01184
0.01641
0.001956
15.15
31.82
99
698.8
0.1162
0.1711
0.2282
0.1282
0.2871
0.06917
null
857,343
B
11.76
21.6
74.72
427.9
0.08637
0.04966
0.01657
0.01115
0.1495
0.05888
0.4062
1.21
2.635
28.47
0.005857
0.009758
0.01168
0.007445
0.02406
0.001769
12.98
25.72
82.98
516.5
0.1085
0.08615
0.05523
0.03715
0.2433
0.06563
null
857,373
B
13.64
16.34
87.21
571.8
0.07685
0.06059
0.01857
0.01723
0.1353
0.05953
0.1872
0.9234
1.449
14.55
0.004477
0.01177
0.01079
0.007956
0.01325
0.002551
14.67
23.19
96.08
656.7
0.1089
0.1582
0.105
0.08586
0.2346
0.08025
null
857,374
B
11.94
18.24
75.71
437.6
0.08261
0.04751
0.01972
0.01349
0.1868
0.0611
0.2273
0.6329
1.52
17.47
0.00721
0.00838
0.01311
0.008
0.01996
0.002635
13.1
21.33
83.67
527.2
0.1144
0.08906
0.09203
0.06296
0.2785
0.07408
null
857,392
M
18.22
18.7
120.3
1,033
0.1148
0.1485
0.1772
0.106
0.2092
0.0631
0.8337
1.593
4.877
98.81
0.003899
0.02961
0.02817
0.009222
0.02674
0.005126
20.6
24.13
135.1
1,321
0.128
0.2297
0.2623
0.1325
0.3021
0.07987
null
857,438
M
15.1
22.02
97.26
712.8
0.09056
0.07081
0.05253
0.03334
0.1616
0.05684
0.3105
0.8339
2.097
29.91
0.004675
0.0103
0.01603
0.009222
0.01095
0.001629
18.1
31.69
117.7
1,030
0.1389
0.2057
0.2712
0.153
0.2675
0.07873
null
85,759,902
B
11.52
18.75
73.34
409
0.09524
0.05473
0.03036
0.02278
0.192
0.05907
0.3249
0.9591
2.183
23.47
0.008328
0.008722
0.01349
0.00867
0.03218
0.002386
12.84
22.47
81.81
506.2
0.1249
0.0872
0.09076
0.06316
0.3306
0.07036
null
857,637
M
19.21
18.57
125.5
1,152
0.1053
0.1267
0.1323
0.08994
0.1917
0.05961
0.7275
1.193
4.837
102.5
0.006458
0.02306
0.02945
0.01538
0.01852
0.002608
26.14
28.14
170.1
2,145
0.1624
0.3511
0.3879
0.2091
0.3537
0.08294
null
857,793
M
14.71
21.59
95.55
656.9
0.1137
0.1365
0.1293
0.08123
0.2027
0.06758
0.4226
1.15
2.735
40.09
0.003659
0.02855
0.02572
0.01272
0.01817
0.004108
17.87
30.7
115.7
985.5
0.1368
0.429
0.3587
0.1834
0.3698
0.1094
null
857,810
B
13.05
19.31
82.61
527.2
0.0806
0.03789
0.000692
0.004167
0.1819
0.05501
0.404
1.214
2.595
32.96
0.007491
0.008593
0.000692
0.004167
0.0219
0.00299
14.23
22.25
90.24
624.1
0.1021
0.06191
0.001845
0.01111
0.2439
0.06289
null
858,477
B
8.618
11.79
54.34
224.5
0.09752
0.05272
0.02061
0.007799
0.1683
0.07187
0.1559
0.5796
1.046
8.322
0.01011
0.01055
0.01981
0.005742
0.0209
0.002788
9.507
15.4
59.9
274.9
0.1733
0.1239
0.1168
0.04419
0.322
0.09026
null
858,970
B
10.17
14.88
64.55
311.9
0.1134
0.08061
0.01084
0.0129
0.2743
0.0696
0.5158
1.441
3.312
34.62
0.007514
0.01099
0.007665
0.008193
0.04183
0.005953
11.02
17.45
69.86
368.6
0.1275
0.09866
0.02168
0.02579
0.3557
0.0802
null
858,981
B
8.598
20.98
54.66
221.8
0.1243
0.08963
0.03
0.009259
0.1828
0.06757
0.3582
2.067
2.493
18.39
0.01193
0.03162
0.03
0.009259
0.03357
0.003048
9.565
27.04
62.06
273.9
0.1639
0.1698
0.09001
0.02778
0.2972
0.07712
null
858,986
M
14.25
22.15
96.42
645.7
0.1049
0.2008
0.2135
0.08653
0.1949
0.07292
0.7036
1.268
5.373
60.78
0.009407
0.07056
0.06899
0.01848
0.017
0.006113
17.67
29.51
119.1
959.5
0.164
0.6247
0.6922
0.1785
0.2844
0.1132
null
859,196
B
9.173
13.86
59.2
260.9
0.07721
0.08751
0.05988
0.0218
0.2341
0.06963
0.4098
2.265
2.608
23.52
0.008738
0.03938
0.04312
0.0156
0.04192
0.005822
10.01
19.23
65.59
310.1
0.09836
0.1678
0.1397
0.05087
0.3282
0.0849
null
85,922,302
M
12.68
23.84
82.69
499
0.1122
0.1262
0.1128
0.06873
0.1905
0.0659
0.4255
1.178
2.927
36.46
0.007781
0.02648
0.02973
0.0129
0.01635
0.003601
17.09
33.47
111.8
888.3
0.1851
0.4061
0.4024
0.1716
0.3383
0.1031
null
859,283
M
14.78
23.94
97.4
668.3
0.1172
0.1479
0.1267
0.09029
0.1953
0.06654
0.3577
1.281
2.45
35.24
0.006703
0.0231
0.02315
0.01184
0.019
0.003224
17.31
33.39
114.6
925.1
0.1648
0.3416
0.3024
0.1614
0.3321
0.08911
null
859,464
B
9.465
21.01
60.11
269.4
0.1044
0.07773
0.02172
0.01504
0.1717
0.06899
0.2351
2.011
1.66
14.2
0.01052
0.01755
0.01714
0.009333
0.02279
0.004237
10.41
31.56
67.03
330.7
0.1548
0.1664
0.09412
0.06517
0.2878
0.09211
null
859,465
B
11.31
19.04
71.8
394.1
0.08139
0.04701
0.03709
0.0223
0.1516
0.05667
0.2727
0.9429
1.831
18.15
0.009282
0.009216
0.02063
0.008965
0.02183
0.002146
12.33
23.84
78
466.7
0.129
0.09148
0.1444
0.06961
0.24
0.06641
null
859,471
B
9.029
17.33
58.79
250.5
0.1066
0.1413
0.313
0.04375
0.2111
0.08046
0.3274
1.194
1.885
17.67
0.009549
0.08606
0.3038
0.03322
0.04197
0.009559
10.31
22.65
65.5
324.7
0.1482
0.4365
1.252
0.175
0.4228
0.1175
null
859,487
B
12.78
16.49
81.37
502.5
0.09831
0.05234
0.03653
0.02864
0.159
0.05653
0.2368
0.8732
1.471
18.33
0.007962
0.005612
0.01585
0.008662
0.02254
0.001906
13.46
19.76
85.67
554.9
0.1296
0.07061
0.1039
0.05882
0.2383
0.0641
null
859,575
M
18.94
21.31
123.6
1,130
0.09009
0.1029
0.108
0.07951
0.1582
0.05461
0.7888
0.7975
5.486
96.05
0.004444
0.01652
0.02269
0.0137
0.01386
0.001698
24.86
26.58
165.9
1,866
0.1193
0.2336
0.2687
0.1789
0.2551
0.06589
null
859,711
B
8.888
14.64
58.79
244
0.09783
0.1531
0.08606
0.02872
0.1902
0.0898
0.5262
0.8522
3.168
25.44
0.01721
0.09368
0.05671
0.01766
0.02541
0.02193
9.733
15.67
62.56
284.4
0.1207
0.2436
0.1434
0.04786
0.2254
0.1084
null
859,717
M
17.2
24.52
114.2
929.4
0.1071
0.183
0.1692
0.07944
0.1927
0.06487
0.5907
1.041
3.705
69.47
0.00582
0.05616
0.04252
0.01127
0.01527
0.006299
23.32
33.82
151.6
1,681
0.1585
0.7394
0.6566
0.1899
0.3313
0.1339
null
859,983
M
13.8
15.79
90.43
584.1
0.1007
0.128
0.07789
0.05069
0.1662
0.06566
0.2787
0.6205
1.957
23.35
0.004717
0.02065
0.01759
0.009206
0.0122
0.00313
16.57
20.86
110.3
812.4
0.1411
0.3542
0.2779
0.1383
0.2589
0.103
null
8,610,175
B
12.31
16.52
79.19
470.9
0.09172
0.06829
0.03372
0.02272
0.172
0.05914
0.2505
1.025
1.74
19.68
0.004854
0.01819
0.01826
0.007965
0.01386
0.002304
14.11
23.21
89.71
611.1
0.1176
0.1843
0.1703
0.0866
0.2618
0.07609
null
8,610,404
M
16.07
19.65
104.1
817.7
0.09168
0.08424
0.09769
0.06638
0.1798
0.05391
0.7474
1.016
5.029
79.25
0.01082
0.02203
0.035
0.01809
0.0155
0.001948
19.77
24.56
128.8
1,223
0.15
0.2045
0.2829
0.152
0.265
0.06387
null
8,610,629
B
13.53
10.94
87.91
559.2
0.1291
0.1047
0.06877
0.06556
0.2403
0.06641
0.4101
1.014
2.652
32.65
0.0134
0.02839
0.01162
0.008239
0.02572
0.006164
14.08
12.49
91.36
605.5
0.1451
0.1379
0.08539
0.07407
0.271
0.07191
null
8,610,637
M
18.05
16.15
120.2
1,006
0.1065
0.2146
0.1684
0.108
0.2152
0.06673
0.9806
0.5505
6.311
134.8
0.00794
0.05839
0.04658
0.0207
0.02591
0.007054
22.39
18.91
150.1
1,610
0.1478
0.5634
0.3786
0.2102
0.3751
0.1108
null
8,610,862
M
20.18
23.97
143.7
1,245
0.1286
0.3454
0.3754
0.1604
0.2906
0.08142
0.9317
1.885
8.649
116.4
0.01038
0.06835
0.1091
0.02593
0.07895
0.005987
23.37
31.72
170.3
1,623
0.1639
0.6164
0.7681
0.2508
0.544
0.09964
null
8,610,908
B
12.86
18
83.19
506.3
0.09934
0.09546
0.03889
0.02315
0.1718
0.05997
0.2655
1.095
1.778
20.35
0.005293
0.01661
0.02071
0.008179
0.01748
0.002848
14.24
24.82
91.88
622.1
0.1289
0.2141
0.1731
0.07926
0.2779
0.07918
null
861,103
B
11.45
20.97
73.81
401.5
0.1102
0.09362
0.04591
0.02233
0.1842
0.07005
0.3251
2.174
2.077
24.62
0.01037
0.01706
0.02586
0.007506
0.01816
0.003976
13.11
32.16
84.53
525.1
0.1557
0.1676
0.1755
0.06127
0.2762
0.08851
null
8,611,161
B
13.34
15.86
86.49
520
0.1078
0.1535
0.1169
0.06987
0.1942
0.06902
0.286
1.016
1.535
12.96
0.006794
0.03575
0.0398
0.01383
0.02134
0.004603
15.53
23.19
96.66
614.9
0.1536
0.4791
0.4858
0.1708
0.3527
0.1016
null
8,611,555
M
25.22
24.91
171.5
1,878
0.1063
0.2665
0.3339
0.1845
0.1829
0.06782
0.8973
1.474
7.382
120
0.008166
0.05693
0.0573
0.0203
0.01065
0.005893
30
33.62
211.7
2,562
0.1573
0.6076
0.6476
0.2867
0.2355
0.1051
null
8,611,792
M
19.1
26.29
129.1
1,132
0.1215
0.1791
0.1937
0.1469
0.1634
0.07224
0.519
2.91
5.801
67.1
0.007545
0.0605
0.02134
0.01843
0.03056
0.01039
20.33
32.72
141.3
1,298
0.1392
0.2817
0.2432
0.1841
0.2311
0.09203
null
8,612,080
B
12
15.65
76.95
443.3
0.09723
0.07165
0.04151
0.01863
0.2079
0.05968
0.2271
1.255
1.441
16.16
0.005969
0.01812
0.02007
0.007027
0.01972
0.002607
13.67
24.9
87.78
567.9
0.1377
0.2003
0.2267
0.07632
0.3379
0.07924
null
8,612,399
M
18.46
18.52
121.1
1,075
0.09874
0.1053
0.1335
0.08795
0.2132
0.06022
0.6997
1.475
4.782
80.6
0.006471
0.01649
0.02806
0.0142
0.0237
0.003755
22.93
27.68
152.2
1,603
0.1398
0.2089
0.3157
0.1642
0.3695
0.08579
null
86,135,501
M
14.48
21.46
94.25
648.2
0.09444
0.09947
0.1204
0.04938
0.2075
0.05636
0.4204
2.22
3.301
38.87
0.009369
0.02983
0.05371
0.01761
0.02418
0.003249
16.21
29.25
108.4
808.9
0.1306
0.1976
0.3349
0.1225
0.302
0.06846
null
86,135,502
M
19.02
24.59
122
1,076
0.09029
0.1206
0.1468
0.08271
0.1953
0.05629
0.5495
0.6636
3.055
57.65
0.003872
0.01842
0.0371
0.012
0.01964
0.003337
24.56
30.41
152.9
1,623
0.1249
0.3206
0.5755
0.1956
0.3956
0.09288
null
861,597
B
12.36
21.8
79.78
466.1
0.08772
0.09445
0.06015
0.03745
0.193
0.06404
0.2978
1.502
2.203
20.95
0.007112
0.02493
0.02703
0.01293
0.01958
0.004463
13.83
30.5
91.46
574.7
0.1304
0.2463
0.2434
0.1205
0.2972
0.09261
null
861,598
B
14.64
15.24
95.77
651.9
0.1132
0.1339
0.09966
0.07064
0.2116
0.06346
0.5115
0.7372
3.814
42.76
0.005508
0.04412
0.04436
0.01623
0.02427
0.004841
16.34
18.24
109.4
803.6
0.1277
0.3089
0.2604
0.1397
0.3151
0.08473
null
861,648
B
14.62
24.02
94.57
662.7
0.08974
0.08606
0.03102
0.02957
0.1685
0.05866
0.3721
1.111
2.279
33.76
0.004868
0.01818
0.01121
0.008606
0.02085
0.002893
16.11
29.11
102.9
803.7
0.1115
0.1766
0.09189
0.06946
0.2522
0.07246
null
861,799
M
15.37
22.76
100.2
728.2
0.092
0.1036
0.1122
0.07483
0.1717
0.06097
0.3129
0.8413
2.075
29.44
0.009882
0.02444
0.04531
0.01763
0.02471
0.002142
16.43
25.84
107.5
830.9
0.1257
0.1997
0.2846
0.1476
0.2556
0.06828
null
861,853
B
13.27
14.76
84.74
551.7
0.07355
0.05055
0.03261
0.02648
0.1386
0.05318
0.4057
1.153
2.701
36.35
0.004481
0.01038
0.01358
0.01082
0.01069
0.001435
16.36
22.35
104.5
830.6
0.1006
0.1238
0.135
0.1001
0.2027
0.06206
null
862,009
B
13.45
18.3
86.6
555.1
0.1022
0.08165
0.03974
0.0278
0.1638
0.0571
0.295
1.373
2.099
25.22
0.005884
0.01491
0.01872
0.009366
0.01884
0.001817
15.1
25.94
97.59
699.4
0.1339
0.1751
0.1381
0.07911
0.2678
0.06603
null
862,028
M
15.06
19.83
100.3
705.6
0.1039
0.1553
0.17
0.08815
0.1855
0.06284
0.4768
0.9644
3.706
47.14
0.00925
0.03715
0.04867
0.01851
0.01498
0.00352
18.23
24.23
123.5
1,025
0.1551
0.4203
0.5203
0.2115
0.2834
0.08234
null
86,208
M
20.26
23.03
132.4
1,264
0.09078
0.1313
0.1465
0.08683
0.2095
0.05649
0.7576
1.509
4.554
87.87
0.006016
0.03482
0.04232
0.01269
0.02657
0.004411
24.22
31.59
156.1
1,750
0.119
0.3539
0.4098
0.1573
0.3689
0.08368
null
86,211
B
12.18
17.84
77.79
451.1
0.1045
0.07057
0.0249
0.02941
0.19
0.06635
0.3661
1.511
2.41
24.44
0.005433
0.01179
0.01131
0.01519
0.0222
0.003408
12.83
20.92
82.14
495.2
0.114
0.09358
0.0498
0.05882
0.2227
0.07376
null
862,261
B
9.787
19.94
62.11
294.5
0.1024
0.05301
0.006829
0.007937
0.135
0.0689
0.335
2.043
2.132
20.05
0.01113
0.01463
0.005308
0.00525
0.01801
0.005667
10.92
26.29
68.81
366.1
0.1316
0.09473
0.02049
0.02381
0.1934
0.08988
null
862,485
B
11.6
12.84
74.34
412.6
0.08983
0.07525
0.04196
0.0335
0.162
0.06582
0.2315
0.5391
1.475
15.75
0.006153
0.0133
0.01693
0.006884
0.01651
0.002551
13.06
17.16
82.96
512.5
0.1431
0.1851
0.1922
0.08449
0.2772
0.08756
null
862,548
M
14.42
19.77
94.48
642.5
0.09752
0.1141
0.09388
0.05839
0.1879
0.0639
0.2895
1.851
2.376
26.85
0.008005
0.02895
0.03321
0.01424
0.01462
0.004452
16.33
30.86
109.5
826.4
0.1431
0.3026
0.3194
0.1565
0.2718
0.09353
null
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