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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 19 new columns ({'hour_std', 'total_carts', 'num_categories', 'total_events', 'num_products', 'purchase_rate', 'high_activity', 'has_carted', 'price_std', 'avg_hour', 'total_views', 'total_purchases', 'purchased', 'active_days', 'min_price', 'multi_category', 'avg_price', 'max_price', 'cart_rate'}) and 8 missing columns ({'category_code', 'product_id', 'brand', 'price', 'event_time', 'event_type', 'category_id', 'user_session'}).

This happened while the csv dataset builder was generating data using

hf://datasets/skpy/E_Commerce_Behavioral_Analysis/compressed_data.csv (at revision aea4e1aa7891f1f8810986830b197b16804fe262), [/tmp/hf-datasets-cache/medium/datasets/93203725679971-config-parquet-and-info-skpy-E_Commerce_Behaviora-f2c20ede/hub/datasets--skpy--E_Commerce_Behavioral_Analysis/snapshots/aea4e1aa7891f1f8810986830b197b16804fe262/2019-Oct.csv.gz (origin=hf://datasets/skpy/E_Commerce_Behavioral_Analysis@aea4e1aa7891f1f8810986830b197b16804fe262/2019-Oct.csv.gz), /tmp/hf-datasets-cache/medium/datasets/93203725679971-config-parquet-and-info-skpy-E_Commerce_Behaviora-f2c20ede/hub/datasets--skpy--E_Commerce_Behavioral_Analysis/snapshots/aea4e1aa7891f1f8810986830b197b16804fe262/compressed_data.csv (origin=hf://datasets/skpy/E_Commerce_Behavioral_Analysis@aea4e1aa7891f1f8810986830b197b16804fe262/compressed_data.csv), /tmp/hf-datasets-cache/medium/datasets/93203725679971-config-parquet-and-info-skpy-E_Commerce_Behaviora-f2c20ede/hub/datasets--skpy--E_Commerce_Behavioral_Analysis/snapshots/aea4e1aa7891f1f8810986830b197b16804fe262/event_feature_table_v3.csv (origin=hf://datasets/skpy/E_Commerce_Behavioral_Analysis@aea4e1aa7891f1f8810986830b197b16804fe262/event_feature_table_v3.csv)]

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 "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1800, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2281, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2227, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              user_id: int64
              total_views: int64
              total_carts: int64
              total_purchases: int64
              avg_price: double
              max_price: double
              purchased: int64
              total_events: int64
              cart_rate: double
              purchase_rate: double
              avg_hour: double
              hour_std: double
              active_days: int64
              num_categories: int64
              num_products: int64
              price_std: double
              min_price: double
              has_carted: int64
              high_activity: int64
              multi_category: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2661
              to
              {'event_time': Value('string'), 'event_type': Value('string'), 'product_id': Value('int64'), 'category_id': Value('int64'), 'category_code': Value('string'), 'brand': Value('string'), 'price': Value('float64'), 'user_id': Value('int64'), 'user_session': Value('string')}
              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 1347, 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 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1802, 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 19 new columns ({'hour_std', 'total_carts', 'num_categories', 'total_events', 'num_products', 'purchase_rate', 'high_activity', 'has_carted', 'price_std', 'avg_hour', 'total_views', 'total_purchases', 'purchased', 'active_days', 'min_price', 'multi_category', 'avg_price', 'max_price', 'cart_rate'}) and 8 missing columns ({'category_code', 'product_id', 'brand', 'price', 'event_time', 'event_type', 'category_id', 'user_session'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/skpy/E_Commerce_Behavioral_Analysis/compressed_data.csv (at revision aea4e1aa7891f1f8810986830b197b16804fe262), [/tmp/hf-datasets-cache/medium/datasets/93203725679971-config-parquet-and-info-skpy-E_Commerce_Behaviora-f2c20ede/hub/datasets--skpy--E_Commerce_Behavioral_Analysis/snapshots/aea4e1aa7891f1f8810986830b197b16804fe262/2019-Oct.csv.gz (origin=hf://datasets/skpy/E_Commerce_Behavioral_Analysis@aea4e1aa7891f1f8810986830b197b16804fe262/2019-Oct.csv.gz), /tmp/hf-datasets-cache/medium/datasets/93203725679971-config-parquet-and-info-skpy-E_Commerce_Behaviora-f2c20ede/hub/datasets--skpy--E_Commerce_Behavioral_Analysis/snapshots/aea4e1aa7891f1f8810986830b197b16804fe262/compressed_data.csv (origin=hf://datasets/skpy/E_Commerce_Behavioral_Analysis@aea4e1aa7891f1f8810986830b197b16804fe262/compressed_data.csv), /tmp/hf-datasets-cache/medium/datasets/93203725679971-config-parquet-and-info-skpy-E_Commerce_Behaviora-f2c20ede/hub/datasets--skpy--E_Commerce_Behavioral_Analysis/snapshots/aea4e1aa7891f1f8810986830b197b16804fe262/event_feature_table_v3.csv (origin=hf://datasets/skpy/E_Commerce_Behavioral_Analysis@aea4e1aa7891f1f8810986830b197b16804fe262/event_feature_table_v3.csv)]
              
              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.

event_time
string
event_type
string
product_id
int64
category_id
int64
category_code
string
brand
string
price
float64
user_id
int64
user_session
string
2019-10-01 00:00:00 UTC
view
44,600,062
2,103,807,459,595,387,600
null
shiseido
35.79
541,312,140
72d76fde-8bb3-4e00-8c23-a032dfed738c
2019-10-01 00:00:00 UTC
view
3,900,821
2,053,013,552,326,771,000
appliances.environment.water_heater
aqua
33.2
554,748,717
9333dfbd-b87a-4708-9857-6336556b0fcc
2019-10-01 00:00:01 UTC
view
17,200,506
2,053,013,559,792,632,600
furniture.living_room.sofa
null
543.1
519,107,250
566511c2-e2e3-422b-b695-cf8e6e792ca8
2019-10-01 00:00:01 UTC
view
1,307,067
2,053,013,558,920,217,000
computers.notebook
lenovo
251.74
550,050,854
7c90fc70-0e80-4590-96f3-13c02c18c713
2019-10-01 00:00:04 UTC
view
1,004,237
2,053,013,555,631,882,800
electronics.smartphone
apple
1,081.98
535,871,217
c6bd7419-2748-4c56-95b4-8cec9ff8b80d
2019-10-01 00:00:05 UTC
view
1,480,613
2,053,013,561,092,866,800
computers.desktop
pulser
908.62
512,742,880
0d0d91c2-c9c2-4e81-90a5-86594dec0db9
2019-10-01 00:00:08 UTC
view
17,300,353
2,053,013,553,853,497,600
null
creed
380.96
555,447,699
4fe811e9-91de-46da-90c3-bbd87ed3a65d
2019-10-01 00:00:08 UTC
view
31,500,053
2,053,013,558,031,024,600
null
luminarc
41.16
550,978,835
6280d577-25c8-4147-99a7-abc6048498d6
2019-10-01 00:00:10 UTC
view
28,719,074
2,053,013,565,480,109,000
apparel.shoes.keds
baden
102.71
520,571,932
ac1cd4e5-a3ce-4224-a2d7-ff660a105880
2019-10-01 00:00:11 UTC
view
1,004,545
2,053,013,555,631,882,800
electronics.smartphone
huawei
566.01
537,918,940
406c46ed-90a4-4787-a43b-59a410c1a5fb
2019-10-01 00:00:11 UTC
view
2,900,536
2,053,013,554,776,244,500
appliances.kitchen.microwave
elenberg
51.46
555,158,050
b5bdd0b3-4ca2-4c55-939e-9ce44bb50abd
2019-10-01 00:00:11 UTC
view
1,005,011
2,053,013,555,631,882,800
electronics.smartphone
samsung
900.64
530,282,093
50a293fb-5940-41b2-baf3-17af0e812101
2019-10-01 00:00:13 UTC
view
3,900,746
2,053,013,552,326,771,000
appliances.environment.water_heater
haier
102.38
555,444,559
98b88fa0-d8fa-4b9d-8a71-3dd403afab85
2019-10-01 00:00:15 UTC
view
44,600,062
2,103,807,459,595,387,600
null
shiseido
35.79
541,312,140
72d76fde-8bb3-4e00-8c23-a032dfed738c
2019-10-01 00:00:16 UTC
view
13,500,240
2,053,013,557,099,889,200
furniture.bedroom.bed
brw
93.18
555,446,365
7f0062d8-ead0-4e0a-96f6-43a0b79a2fc4
2019-10-01 00:00:17 UTC
view
23,100,006
2,053,013,561,638,126,300
null
null
357.79
513,642,368
17566c27-0a8f-4506-9f30-c6a2ccbf583b
2019-10-01 00:00:18 UTC
view
1,801,995
2,053,013,554,415,534,300
electronics.video.tv
haier
193.03
537,192,226
e3151795-c355-4efa-acf6-e1fe1bebeee5
2019-10-01 00:00:18 UTC
view
10,900,029
2,053,013,555,069,845,800
appliances.kitchen.mixer
bosch
58.95
519,528,062
901b9e3c-3f8f-4147-a442-c25d5c5ed332
2019-10-01 00:00:19 UTC
view
1,306,631
2,053,013,558,920,217,000
computers.notebook
hp
580.89
550,050,854
7c90fc70-0e80-4590-96f3-13c02c18c713
2019-10-01 00:00:19 UTC
view
1,005,135
2,053,013,555,631,882,800
electronics.smartphone
apple
1,747.79
535,871,217
c6bd7419-2748-4c56-95b4-8cec9ff8b80d
2019-10-01 00:00:20 UTC
view
1,003,306
2,053,013,555,631,882,800
electronics.smartphone
apple
588.77
555,446,831
6ec635da-ea15-4a5d-96b4-c8ca9d38f89f
2019-10-01 00:00:20 UTC
view
4,803,399
2,053,013,554,658,804,000
electronics.audio.headphone
jbl
33.21
555,428,858
8a6afed4-77f8-40c9-8e76-e062b28216ce
2019-10-01 00:00:22 UTC
view
1,480,714
2,053,013,561,092,866,800
computers.desktop
pulser
921.49
512,742,880
0d0d91c2-c9c2-4e81-90a5-86594dec0db9
2019-10-01 00:00:23 UTC
view
1,004,739
2,053,013,555,631,882,800
electronics.smartphone
xiaomi
197.55
519,530,528
9882d21f-2c5f-496b-90d4-a1503edb6562
2019-10-01 00:00:23 UTC
view
6,200,260
2,053,013,552,293,216,500
appliances.environment.air_heater
midea
47.62
538,645,907
7d9a8784-7b6c-426e-9924-9f688812fd71
2019-10-01 00:00:24 UTC
view
1,003,306
2,053,013,555,631,882,800
electronics.smartphone
apple
588.77
555,446,831
6ec635da-ea15-4a5d-96b4-c8ca9d38f89f
2019-10-01 00:00:24 UTC
view
34,700,031
2,061,717,937,420,501,800
null
null
151.87
539,512,263
f27a45f8-fb98-459a-96a6-45271f56a987
2019-10-01 00:00:25 UTC
view
3,900,990
2,053,013,552,326,771,000
appliances.environment.water_heater
ariston
122.18
554,748,717
5459fbe4-2aa5-42b9-9064-05f853218fe0
2019-10-01 00:00:25 UTC
view
27,500,014
2,053,013,554,692,358,400
null
redmond
37.98
555,217,733
74d40a28-41f9-4325-bbae-b179bd2c0a38
2019-10-01 00:00:25 UTC
view
19,001,139
2,053,013,557,225,718,300
null
gran-stone
67.58
525,734,504
83f584ed-c7f7-442e-8ae9-713cb27fdece
2019-10-01 00:00:26 UTC
view
28,719,071
2,053,013,565,480,109,000
apparel.shoes.keds
baden
102.71
520,571,932
ac1cd4e5-a3ce-4224-a2d7-ff660a105880
2019-10-01 00:00:26 UTC
view
13,500,046
2,053,013,557,099,889,200
furniture.bedroom.bed
null
60.75
555,446,365
7f0062d8-ead0-4e0a-96f6-43a0b79a2fc4
2019-10-01 00:00:27 UTC
view
31,501,072
2,053,013,558,031,024,600
null
null
165.64
550,978,835
6280d577-25c8-4147-99a7-abc6048498d6
2019-10-01 00:00:27 UTC
view
10,800,001
2,053,013,554,994,348,300
null
maxwell
32.92
539,194,858
5fe9d0a0-0de6-47de-a55a-eae9f89475cd
2019-10-01 00:00:28 UTC
view
28,600,026
2,053,013,558,282,683,000
null
null
399.73
555,447,224
889da81c-2cfc-4df6-a038-ed436c79ee80
2019-10-01 00:00:28 UTC
view
26,200,591
2,053,013,563,693,335,300
null
null
203.35
548,449,430
99617d1c-1b5a-42f8-99f1-42ad83a6155f
2019-10-01 00:00:28 UTC
view
28,714,755
2,053,013,565,228,450,800
apparel.shoes
respect
51.22
555,447,570
99877fbe-d5a8-475e-a662-66bc9d29b6f8
2019-10-01 00:00:30 UTC
view
3,701,388
2,053,013,565,983,425,500
appliances.environment.vacuum
dauscher
33.21
515,342,595
0e30e1c0-4d3e-4e1a-90e3-ab93b5f5c1a2
2019-10-01 00:00:31 UTC
view
3,900,746
2,053,013,552,326,771,000
appliances.environment.water_heater
haier
102.38
555,444,559
98b88fa0-d8fa-4b9d-8a71-3dd403afab85
2019-10-01 00:00:31 UTC
view
28,718,079
2,053,013,565,362,668,500
apparel.shoes.keds
respect
66.67
545,323,115
75fb5d0c-e907-4293-9c87-2419c2a7709d
2019-10-01 00:00:33 UTC
view
2,700,239
2,053,013,563,911,439,000
appliances.kitchen.refrigerators
atlant
283.12
521,242,564
f102dd9b-1cbc-4271-b1f8-a34548d02ec3
2019-10-01 00:00:33 UTC
view
28,717,908
2,053,013,565,782,099,000
apparel.shoes
burgerschuhe
102.45
513,798,668
2034798f-43f2-8bcb-b169-c5f04a7a5a4f
2019-10-01 00:00:34 UTC
view
26,200,591
2,053,013,563,693,335,300
null
null
203.35
555,447,748
b50d1ae8-1948-4517-8460-09b7601ceef6
2019-10-01 00:00:35 UTC
view
3,701,244
2,053,013,565,983,425,500
appliances.environment.vacuum
elenberg
33.44
515,535,834
d90a6f2f-0d0f-47cf-9fe9-62db93dffdb9
2019-10-01 00:00:35 UTC
view
4,300,070
2,053,013,552,385,491,200
null
timberk
38.59
544,648,245
bb8e28c8-d11f-428a-95e7-056e974fe835
2019-10-01 00:00:35 UTC
view
3,601,505
2,053,013,563,810,775,800
appliances.kitchen.washer
samsung
463.15
526,631,741
5ec9bd77-beef-443e-b987-62ffb55b8132
2019-10-01 00:00:36 UTC
view
12,712,064
2,053,013,553,559,896,300
null
triangle
30.89
515,454,339
828dbd8e-8683-409b-aef6-6a94ac983b45
2019-10-01 00:00:36 UTC
view
3,600,575
2,053,013,563,810,775,800
appliances.kitchen.washer
hotpoint-ariston
275.37
554,754,045
bd0302ef-c5ca-4b6a-b916-95cc2840c72c
2019-10-01 00:00:36 UTC
view
2,900,475
2,053,013,554,776,244,500
appliances.kitchen.microwave
gorenje
295.99
533,078,094
6eaaf55a-7bbe-4b3d-95a8-f3b3e31c39c8
2019-10-01 00:00:36 UTC
view
1,004,767
2,053,013,555,631,882,800
electronics.smartphone
samsung
254.82
512,558,158
9a206ba2-37c7-4354-9d31-37ff3bb297ed
2019-10-01 00:00:37 UTC
view
1,701,111
2,053,013,553,031,414,000
computers.peripherals.monitor
acer
514.79
547,028,884
3ea7c620-a8d7-45c5-9ced-2e9874e2f549
2019-10-01 00:00:41 UTC
view
1,003,141
2,053,013,555,631,882,800
electronics.smartphone
apple
382.97
551,377,651
ca11a570-47da-4630-898b-9a03127703da
2019-10-01 00:00:42 UTC
view
4,803,399
2,053,013,554,658,804,000
electronics.audio.headphone
jbl
33.21
555,428,858
8a6afed4-77f8-40c9-8e76-e062b28216ce
2019-10-01 00:00:42 UTC
view
26,400,291
2,053,013,563,651,392,300
null
lucente
188.94
551,331,813
19ded3e3-823b-4f51-ad00-ab84f364f5ef
2019-10-01 00:00:43 UTC
view
1,005,135
2,053,013,555,631,882,800
electronics.smartphone
apple
1,747.79
535,871,217
c6bd7419-2748-4c56-95b4-8cec9ff8b80d
2019-10-01 00:00:44 UTC
view
23,100,006
2,053,013,561,638,126,300
null
null
357.79
513,642,368
17566c27-0a8f-4506-9f30-c6a2ccbf583b
2019-10-01 00:00:44 UTC
view
31,501,163
2,053,013,558,031,024,600
null
luminarc
128.45
550,978,835
6280d577-25c8-4147-99a7-abc6048498d6
2019-10-01 00:00:44 UTC
view
1,002,544
2,053,013,555,631,882,800
electronics.smartphone
apple
464.13
532,085,144
77ae546a-542b-414c-a01b-c5ceca7e99cf
2019-10-01 00:00:44 UTC
view
26,500,313
2,053,013,563,550,729,000
null
lucente
267.19
525,856,698
f72ea16b-4ec3-44f8-8fcd-35d89984b744
2019-10-01 00:00:46 UTC
view
13,500,041
2,053,013,557,099,889,200
furniture.bedroom.bed
brw
80.31
555,446,365
7f0062d8-ead0-4e0a-96f6-43a0b79a2fc4
2019-10-01 00:00:46 UTC
view
4,100,126
2,053,013,561,218,696,000
null
sony
326.62
519,885,473
b70cb218-db90-4011-b582-0bd237109df1
2019-10-01 00:00:50 UTC
view
19,000,296
2,053,013,557,225,718,300
null
gran-stone
64.93
525,734,504
83f584ed-c7f7-442e-8ae9-713cb27fdece
2019-10-01 00:00:50 UTC
view
28,717,211
2,053,013,565,882,762,200
null
respect
76.96
555,447,577
4337a670-6520-4159-aff4-fd620d2599f9
2019-10-01 00:00:50 UTC
view
1,005,105
2,053,013,555,631,882,800
electronics.smartphone
apple
1,415.48
529,755,884
0b828fb6-99bd-4d26-beb3-3021f5d6102c
2019-10-01 00:00:50 UTC
view
1,307,135
2,053,013,558,920,217,000
computers.notebook
hp
320.35
542,378,517
244570b9-ebb4-4d4a-b63a-653225d975d5
2019-10-01 00:00:51 UTC
view
27,500,014
2,053,013,554,692,358,400
null
redmond
37.98
555,217,733
74d40a28-41f9-4325-bbae-b179bd2c0a38
2019-10-01 00:00:55 UTC
view
1,004,659
2,053,013,555,631,882,800
electronics.smartphone
samsung
787.18
512,558,158
9a206ba2-37c7-4354-9d31-37ff3bb297ed
2019-10-01 00:00:56 UTC
view
4,804,295
2,053,013,554,658,804,000
electronics.audio.headphone
xiaomi
23.13
541,366,014
6d8ce6fb-2953-4e95-96fe-b40f1ec50ba8
2019-10-01 00:00:57 UTC
view
1,004,873
2,053,013,555,631,882,800
electronics.smartphone
samsung
388.81
555,447,651
c3240a5e-6cb8-4d78-9732-a63c608444ef
2019-10-01 00:00:57 UTC
view
1,005,073
2,053,013,555,631,882,800
electronics.smartphone
samsung
1,207.71
543,427,258
4fc3e61d-5f94-45bb-82dc-ac77f59b5870
2019-10-01 00:00:58 UTC
view
4,802,639
2,053,013,554,658,804,000
electronics.audio.headphone
sony
218.77
514,808,401
1877639d-46a4-44f8-bae9-a14456952240
2019-10-01 00:01:00 UTC
view
3,900,930
2,053,013,552,326,771,000
appliances.environment.water_heater
teploross
90.32
555,444,559
98b88fa0-d8fa-4b9d-8a71-3dd403afab85
2019-10-01 00:01:00 UTC
view
3,701,062
2,053,013,565,983,425,500
appliances.environment.vacuum
gorenje
90.07
515,342,595
0e30e1c0-4d3e-4e1a-90e3-ab93b5f5c1a2
2019-10-01 00:01:00 UTC
view
4,300,262
2,053,013,552,385,491,200
null
vitek
72
523,239,174
464a96b0-03ee-420d-92fb-643469379b89
2019-10-01 00:01:02 UTC
view
27,500,015
2,053,013,554,692,358,400
null
kelli
36.6
555,217,733
74d40a28-41f9-4325-bbae-b179bd2c0a38
2019-10-01 00:01:02 UTC
view
10,900,026
2,053,013,555,069,845,800
appliances.kitchen.mixer
panasonic
40.8
514,080,443
b65b8d08-14af-496c-94cb-44886c6a96d5
2019-10-01 00:01:04 UTC
view
1,004,870
2,053,013,555,631,882,800
electronics.smartphone
samsung
286.86
516,489,361
7d6d03fb-39d6-4143-8694-981a550acaaa
2019-10-01 00:01:05 UTC
view
1,306,083
2,053,013,558,920,217,000
computers.notebook
hp
1,512.78
550,050,854
7c90fc70-0e80-4590-96f3-13c02c18c713
2019-10-01 00:01:06 UTC
view
1,307,004
2,053,013,558,920,217,000
computers.notebook
lenovo
290.61
542,378,517
244570b9-ebb4-4d4a-b63a-653225d975d5
2019-10-01 00:01:06 UTC
view
1,004,856
2,053,013,555,631,882,800
electronics.smartphone
samsung
130.76
555,447,788
94c1a98c-41a3-401e-ad99-439beac4495c
2019-10-01 00:01:07 UTC
view
27,700,113
2,053,013,560,086,233,900
construction.tools.pump
leo
48.9
515,630,204
f9cc0313-5572-4894-a4eb-45d855f064cc
2019-10-01 00:01:08 UTC
view
1,004,659
2,053,013,555,631,882,800
electronics.smartphone
samsung
787.18
512,558,158
9a206ba2-37c7-4354-9d31-37ff3bb297ed
2019-10-01 00:01:09 UTC
view
15,100,337
2,053,013,557,024,391,700
null
null
257.15
519,107,250
566511c2-e2e3-422b-b695-cf8e6e792ca8
2019-10-01 00:01:10 UTC
view
4,100,126
2,053,013,561,218,696,000
null
sony
326.62
519,885,473
b70cb218-db90-4011-b582-0bd237109df1
2019-10-01 00:01:11 UTC
view
1,004,836
2,053,013,555,631,882,800
electronics.smartphone
samsung
241.19
546,259,103
6e2984c8-502e-4fe7-bbba-34087f760175
2019-10-01 00:01:12 UTC
view
26,500,144
2,053,013,563,550,729,000
null
lucente
259.21
525,856,698
f72ea16b-4ec3-44f8-8fcd-35d89984b744
2019-10-01 00:01:14 UTC
view
1,004,659
2,053,013,555,631,882,800
electronics.smartphone
samsung
787.18
512,558,158
9a206ba2-37c7-4354-9d31-37ff3bb297ed
2019-10-01 00:01:14 UTC
view
32,601,078
2,053,013,566,587,404,300
null
okuma
32.18
513,696,314
1c073854-b838-4cf9-bfe9-b30b742b1751
2019-10-01 00:01:15 UTC
view
1,801,555
2,053,013,554,415,534,300
electronics.video.tv
lg
462.25
537,918,940
406c46ed-90a4-4787-a43b-59a410c1a5fb
2019-10-01 00:01:16 UTC
view
1,004,497
2,053,013,555,631,882,800
electronics.smartphone
nokia
159.33
550,859,983
4718c88d-1892-4a5f-931a-f61ad7a20459
2019-10-01 00:01:17 UTC
view
50,500,080
2,127,425,438,190,928,400
null
krause
77.69
513,642,368
17566c27-0a8f-4506-9f30-c6a2ccbf583b
2019-10-01 00:01:17 UTC
view
1,004,856
2,053,013,555,631,882,800
electronics.smartphone
samsung
130.76
515,757,896
4938043e-e50f-44ad-944d-958d04df62d6
2019-10-01 00:01:21 UTC
view
28,708,392
2,053,013,565,069,067,300
apparel.shoes.keds
strobbs
31.92
555,447,570
99877fbe-d5a8-475e-a662-66bc9d29b6f8
2019-10-01 00:01:23 UTC
view
10,900,026
2,053,013,555,069,845,800
appliances.kitchen.mixer
panasonic
40.8
514,080,443
b65b8d08-14af-496c-94cb-44886c6a96d5
2019-10-01 00:01:24 UTC
view
34,700,023
2,061,717,937,420,501,800
null
null
109.4
539,512,263
f27a45f8-fb98-459a-96a6-45271f56a987
2019-10-01 00:01:24 UTC
view
1,004,785
2,053,013,555,631,882,800
electronics.smartphone
huawei
278.55
514,336,739
cccc58c1-4986-4d08-b595-d837c0c7d514
2019-10-01 00:01:25 UTC
view
3,900,990
2,053,013,552,326,771,000
appliances.environment.water_heater
ariston
122.18
554,748,717
5459fbe4-2aa5-42b9-9064-05f853218fe0
2019-10-01 00:01:26 UTC
view
15,100,008
2,053,013,557,024,391,700
null
bts
488.82
519,107,250
566511c2-e2e3-422b-b695-cf8e6e792ca8
2019-10-01 00:01:27 UTC
view
10,800,076
2,053,013,554,994,348,300
null
redmond
54.03
539,194,858
5fe9d0a0-0de6-47de-a55a-eae9f89475cd
2019-10-01 00:01:28 UTC
view
4,300,070
2,053,013,552,385,491,200
null
timberk
38.59
544,648,245
bb8e28c8-d11f-428a-95e7-056e974fe835
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