The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
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 5 new columns ({'geolocation_city', 'geolocation_state', 'geolocation_zip_code_prefix', 'geolocation_lat', 'geolocation_lng'}) and 5 missing columns ({'customer_city', 'customer_state', 'customer_unique_id', 'customer_zip_code_prefix', 'customer_id'}).
This happened while the csv dataset builder was generating data using
hf://datasets/josaputra/Dataset_BukanDukun/olist_geolocation_dataset.csv (at revision 0566a5d0305a8696452dc1e755f7e4ea5fbd10cc)
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 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, 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 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
geolocation_zip_code_prefix: int64
geolocation_lat: double
geolocation_lng: double
geolocation_city: string
geolocation_state: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 942
to
{'customer_id': Value(dtype='string', id=None), 'customer_unique_id': Value(dtype='string', id=None), 'customer_zip_code_prefix': Value(dtype='int64', id=None), 'customer_city': Value(dtype='string', id=None), 'customer_state': Value(dtype='string', 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 1436, 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 1053, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, 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 1742, 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 1873, 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 5 new columns ({'geolocation_city', 'geolocation_state', 'geolocation_zip_code_prefix', 'geolocation_lat', 'geolocation_lng'}) and 5 missing columns ({'customer_city', 'customer_state', 'customer_unique_id', 'customer_zip_code_prefix', 'customer_id'}).
This happened while the csv dataset builder was generating data using
hf://datasets/josaputra/Dataset_BukanDukun/olist_geolocation_dataset.csv (at revision 0566a5d0305a8696452dc1e755f7e4ea5fbd10cc)
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.
customer_id
string | customer_unique_id
string | customer_zip_code_prefix
int64 | customer_city
string | customer_state
string |
|---|---|---|---|---|
06b8999e2fba1a1fbc88172c00ba8bc7
|
861eff4711a542e4b93843c6dd7febb0
| 14,409
|
franca
|
SP
|
18955e83d337fd6b2def6b18a428ac77
|
290c77bc529b7ac935b93aa66c333dc3
| 9,790
|
sao bernardo do campo
|
SP
|
4e7b3e00288586ebd08712fdd0374a03
|
060e732b5b29e8181a18229c7b0b2b5e
| 1,151
|
sao paulo
|
SP
|
b2b6027bc5c5109e529d4dc6358b12c3
|
259dac757896d24d7702b9acbbff3f3c
| 8,775
|
mogi das cruzes
|
SP
|
4f2d8ab171c80ec8364f7c12e35b23ad
|
345ecd01c38d18a9036ed96c73b8d066
| 13,056
|
campinas
|
SP
|
879864dab9bc3047522c92c82e1212b8
|
4c93744516667ad3b8f1fb645a3116a4
| 89,254
|
jaragua do sul
|
SC
|
fd826e7cf63160e536e0908c76c3f441
|
addec96d2e059c80c30fe6871d30d177
| 4,534
|
sao paulo
|
SP
|
5e274e7a0c3809e14aba7ad5aae0d407
|
57b2a98a409812fe9618067b6b8ebe4f
| 35,182
|
timoteo
|
MG
|
5adf08e34b2e993982a47070956c5c65
|
1175e95fb47ddff9de6b2b06188f7e0d
| 81,560
|
curitiba
|
PR
|
4b7139f34592b3a31687243a302fa75b
|
9afe194fb833f79e300e37e580171f22
| 30,575
|
belo horizonte
|
MG
|
9fb35e4ed6f0a14a4977cd9aea4042bb
|
2a7745e1ed516b289ed9b29c7d0539a5
| 39,400
|
montes claros
|
MG
|
5aa9e4fdd4dfd20959cad2d772509598
|
2a46fb94aef5cbeeb850418118cee090
| 20,231
|
rio de janeiro
|
RJ
|
b2d1536598b73a9abd18e0d75d92f0a3
|
918dc87cd72cd9f6ed4bd442ed785235
| 18,682
|
lencois paulista
|
SP
|
eabebad39a88bb6f5b52376faec28612
|
295c05e81917928d76245e842748184d
| 5,704
|
sao paulo
|
SP
|
1f1c7bf1c9b041b292af6c1c4470b753
|
3151a81801c8386361b62277d7fa5ecf
| 95,110
|
caxias do sul
|
RS
|
206f3129c0e4d7d0b9550426023f0a08
|
21f748a16f4e1688a9014eb3ee6fa325
| 13,412
|
piracicaba
|
SP
|
a7c125a0a07b75146167b7f04a7f8e98
|
5c2991dbd08bbf3cf410713c4de5a0b5
| 22,750
|
rio de janeiro
|
RJ
|
c5c61596a3b6bd0cee5766992c48a9a1
|
b6e99561fe6f34a55b0b7da92f8ed775
| 7,124
|
guarulhos
|
SP
|
9b8ce803689b3562defaad4613ef426f
|
7f3a72e8f988c6e735ba118d54f47458
| 5,416
|
sao paulo
|
SP
|
49d0ea0986edde72da777f15456a0ee0
|
3e6fd6b2f0d499456a6a6820a40f2d79
| 68,485
|
pacaja
|
PA
|
154c4ded6991bdfa3cd249d11abf4130
|
e607ede0e63436308660236f5a52da5e
| 88,034
|
florianopolis
|
SC
|
690172ab319622688d3b4df42f676898
|
a96d5cfa0d3181817e2b946f921ea021
| 74,914
|
aparecida de goiania
|
GO
|
2938121a40a20953c43caa8c98787fcb
|
482441ea6a06b1f72fe9784756c0ea75
| 5,713
|
sao paulo
|
SP
|
237098a64674ae89babdc426746260fc
|
4390ddbb6276a66ff1736a6710205dca
| 82,820
|
curitiba
|
PR
|
cb721d7b4f271fd87011c4c83462c076
|
a5844ba4bfc8d0cc61d13027c7e63bcc
| 8,225
|
sao paulo
|
SP
|
f681356046d9fde60e70c73a18d65ea2
|
5f102dd37243f152aec3607970aad100
| 9,121
|
santo andre
|
SP
|
167bd30a409e3e4127df5a9408ebd394
|
9c0096673baf55453a50073f12d1a37f
| 74,310
|
goiania
|
GO
|
6e359a57a91f84095cc64e1b351aef8c
|
2e6a42a9b5cbb0da62988694f18ee295
| 4,571
|
sao paulo
|
SP
|
e0eea8f69a457b3f1fa246e44c9ebefd
|
4d221875624017bc47b4d1ce7314a5b7
| 29,311
|
cachoeiro de itapemirim
|
ES
|
e3109970a3fe8021d5ff82c577ce5606
|
a8654e2af5da6bb72f52c22b164855e1
| 5,528
|
sao paulo
|
SP
|
261cb4f92498ca05d5bd1a327a261d9c
|
424aca6872c5bab80780a8dec03b7516
| 12,235
|
sao jose dos campos
|
SP
|
6f92779347724b67e44e3224f3b4cffd
|
bf4862777db128507e9efcc789215e9b
| 18,130
|
sao roque
|
SP
|
2d5831cb2dff7cdefba62e950ae3dc7b
|
e9dd12dca17352644a959d9dea133935
| 42,800
|
camacari
|
BA
|
b2bed119388167a954382cca36c4777f
|
e079b18794454de9d2be5c12b4392294
| 27,525
|
resende
|
RJ
|
469634941c27cd844170935a3cf60b95
|
ef07ba9aa5226f77264ffa5762b2280b
| 81,750
|
curitiba
|
PR
|
df0aa5b8586495e0ddf6b601122e43a1
|
85d234692f7bee8d6fea586e237334b6
| 13,175
|
sumare
|
SP
|
41c8f4b570869791379a925899a6af8a
|
fe3634ccefbcdb0537b45fd589e32e8e
| 7,170
|
guarulhos
|
SP
|
54f755c3fd2709231f9964a1430c5218
|
40febde16f4718a5def537786473b0be
| 93,415
|
novo hamburgo
|
RS
|
4c06b42fbf7b97ab10779cda5549cd1c
|
07d190f123147d9e89d4b922543d7948
| 65,075
|
sao luis
|
MA
|
b6368ca0f56d4632f44d58ca431487b2
|
dd992305cba295d997f263dbdf4e8c2e
| 88,104
|
sao jose
|
SC
|
4a0e66fd30684aa1409cd1b66fec77cc
|
86085586aaa8c5f47ed0b400da64c59d
| 7,176
|
guarulhos
|
SP
|
c168abb9077b7821adae01dc1f0886c5
|
5ad58a4e6a1a656b6bed070cadbaa003
| 35,960
|
santa barbara
|
MG
|
a3b0fda37bae14cf754877bed475e80c
|
c9158d089637ab443c78984d20da7fc0
| 5,727
|
sao paulo
|
SP
|
0ccd415657ae8a6cd1c71b00155a019e
|
66cc90195ca44cc7ac6a1cd0e1e1e7b2
| 7,053
|
guarulhos
|
SP
|
c532a74a3ebf1bacce2e2bcce3783317
|
91ec50a00ae74d0a229d2efdf4344e1e
| 14,026
|
ribeirao preto
|
SP
|
19cecb194f54e614b70d971306a9931b
|
d251c190ca75786e9ab937982d60d1d4
| 30,320
|
belo horizonte
|
MG
|
f34a6e874087ec1f0e3dab9fdf659c5d
|
233896de79986082f1f479f1f85281cb
| 38,300
|
ituiutaba
|
MG
|
c132855c926907970dcf6f2bf0b33a24
|
a8ae36a2bb6c2bbc3b5d62ede131c9ef
| 18,740
|
taquarituba
|
SP
|
df85b96ba2ce3e49bde101b1614f52ac
|
8d46223c91cbeb93e0930ca8bd8ffca2
| 83,085
|
sao jose dos pinhais
|
PR
|
4d27341acd30a36bca39008ee9bb9050
|
e021e698833bdeb89dfef3acb2e91f37
| 89,254
|
jaragua do sul
|
SC
|
d3b6830d18c7de943d1e707d1f061d40
|
27cf4b153010911a0957150255a6c6db
| 5,351
|
sao paulo
|
SP
|
79de53946db384e2d7a9bd131792ad17
|
7ce5b57a120a2da6a804afa58ffcbfb5
| 39,406
|
montes claros
|
MG
|
a562ab1e728449e3461829dfe2e36f73
|
d33eeadf54cb883e79be640f38c32cdc
| 14,860
|
barrinha
|
SP
|
b64ed91eab98972150bdaf77ca921934
|
3da7750bf3c1dbd724624a60a9f5942b
| 21,310
|
rio de janeiro
|
RJ
|
8247b5583327ab8be19f96e1fb82f77b
|
d85547cd859833520b311b4458a14c1c
| 23,970
|
parati
|
RJ
|
8fcaa9368903f3a9a28aeaff28c14638
|
3af0b2f7654f613ff1527b997a2ac57e
| 79,804
|
dourados
|
MS
|
a9b0d1c26105279e1b8edc63d06bd668
|
3d49f4455a3947c8dd7e972b3ad8cb76
| 5,017
|
sao paulo
|
SP
|
aa9f03ecd3728c9bd12e6d962c66c7cb
|
b03e9d9818ee170e9d6b983803c7d406
| 75,388
|
trindade
|
GO
|
230c0d740401730c7197d16376893525
|
a302a693d5722d95984e6472150b9391
| 85,808
|
cascavel
|
PR
|
a905baa530258422594f1b05615bd225
|
c80da60feddb7cf8325bd104032e314a
| 60,140
|
fortaleza
|
CE
|
4fa19f7da692e6bf9602aaad3c372eda
|
a2b8841410cf77619574d311cd06fd5e
| 72,270
|
brasilia
|
DF
|
03f846ad03437d864a8d2a22976dcafe
|
7677c213007e9a6ec9267ea50b5ce5bc
| 2,075
|
sao paulo
|
SP
|
de4e13fd7d6469c5ada77d0843c55e42
|
0c17f9ac28cbd7323f0f4043e9db5907
| 96,015
|
pelotas
|
RS
|
8276de07ef25225d412b8462d73f8664
|
332cf4e83e16004ba7dca932ce82475b
| 90,010
|
porto alegre
|
RS
|
cc32707d2e2f7c92ab449f9b28154809
|
0d516ca029d6a28d5cfddd80b27a26dc
| 22,440
|
rio de janeiro
|
RJ
|
a02f66c3af7b16eec19ddcd98b645fe3
|
b3548d0cec408ae13d143bb4eeebaa6c
| 13,323
|
salto
|
SP
|
26acee41e2f75689a5615892f06ea0bd
|
c3293e875ffb1116018edf76d24e52a2
| 30,190
|
belo horizonte
|
MG
|
f64cdee66599119324ce57a97e43700d
|
d89e05e2d23c3d8247aeecd07758004b
| 13,212
|
jundiai
|
SP
|
7ab7a537b678b6dd73d825ff6ee7be9d
|
dad5018ffc0de85eb72f72575b552784
| 29,307
|
cachoeiro de itapemirim
|
ES
|
7300450cedf7e4c35c243c4a03c1e8a6
|
95700615deef776ed32faa08f0be634e
| 12,280
|
cacapava
|
SP
|
4c7241af24b5344cb01fe687643de4fe
|
b157c176c3fe04914fde33f2dc8b878a
| 60,336
|
fortaleza
|
CE
|
97e126f19a6f04b3462619f36862bcd2
|
d4397835ae287e492b186d497099439a
| 11,310
|
sao vicente
|
SP
|
6d27a9361e591da38c87a5e70253f3f2
|
76b029c87118a29f2e3de420f5ec2fa2
| 38,408
|
uberlandia
|
MG
|
6810c3dc47f641181fcc7f73275c3d19
|
7eaa86786b5955ab188db287f4726d79
| 37,720
|
botelhos
|
MG
|
b514422efcf14bef34858a0829bef189
|
b436a108536c1dabbc1d3e808d782df9
| 24,431
|
sao goncalo
|
RJ
|
0aae2862f8eac77f10a34f44860720ac
|
cd076285a12f40041b32f5ad8c98699f
| 5,890
|
sao paulo
|
SP
|
6c9a5923526346cbc0bd7bbd92269c01
|
cf6d4152d758efc43910e0141ae5b912
| 3,733
|
sao paulo
|
SP
|
1b2cb35b19b40b61f953d32ea157b337
|
468d559ef2dcd2bea6d8db78959fb90f
| 83,709
|
araucaria
|
PR
|
12d1b4294fef21016c9614eb31e55e15
|
7556f182460418cf30957e6ce377c674
| 11,347
|
sao vicente
|
SP
|
f6529ffebe6b3440d45d89604a4239ac
|
e5dbefdfdf3eff75c8e6cd655f128279
| 26,272
|
nova iguacu
|
RJ
|
8264e3518163dd09211870b24a5d741d
|
67d21c8bea9d6017d1b124d3879dd815
| 5,415
|
sao paulo
|
SP
|
8392e3d4cfeec63f2a8bfea68bf1f91f
|
fd2d5fdb84e65fa6b54b98b0e2df5645
| 59,655
|
areia branca
|
RN
|
38d1cd89306128348ffdf4cc23f3a50a
|
d491a65a6ef3c04e145d37395996bad7
| 4,548
|
sao paulo
|
SP
|
91ec76836092bba85d11761078ed7bb5
|
6edd17d0a29e2d4057e694afee5eaa3b
| 28,010
|
campos dos goytacazes
|
RJ
|
f9dfa0a2934ffbb22e66924952548be8
|
bf6e263ffc1f89999827615522b0aa45
| 13,573
|
sao carlos
|
SP
|
5a3260cfde2a918b597dada7ddd247bb
|
6d3f61e35d0422fd8cae65b1798784be
| 2,175
|
sao paulo
|
SP
|
ee3a81b2771fec5f9e982cdb1b3a4804
|
a9a77b4e25980b7ca58cb71f878abb27
| 37,500
|
itajuba
|
MG
|
784c407781aa34749a388c9283782b56
|
eca666aec08df69fe31aa9a11d4a3302
| 90,670
|
porto alegre
|
RS
|
3f6ede29d4c69cd3316d2035b6cec1fb
|
7a380cb5434e6b6b5b37d45bb99dbe8a
| 9,890
|
sao bernardo do campo
|
SP
|
6bed27564bd99d78d09c1fac13da56fd
|
463093247faa080167d77f2e6d1b297d
| 13,321
|
salto
|
SP
|
670254dd2e886ffe621b3831afb47d7d
|
914b142462685e3161cf3a9f4152a028
| 44,380
|
cruz das almas
|
BA
|
f7cb015ff73be957ee6a30e2577742c5
|
e235442a956c524e6c141141171f5801
| 27,700
|
vassouras
|
RJ
|
ea2196dc456ba36fe4f6b81dca4867d4
|
4a4de987b37555970ffcc9608d858a72
| 44,033
|
feira de santana
|
BA
|
09241c552e9fe2420997a6c535e9d408
|
44e9a1246448bd68a2e3bf0f1966c57a
| 4,537
|
sao paulo
|
SP
|
e50a30de3c32f9406a7185f40ce6874d
|
b4d6e1b900d99b52e901860bc1f44e35
| 71,540
|
brasilia
|
DF
|
f89c1a6b9c966869e441e55bc14acddc
|
809353196a0456095716566dd226bb48
| 13,569
|
sao carlos
|
SP
|
23e96758fd640560e9b1fbcda90abfc4
|
9e1f719fe5b17b9c51905fee6d6385c1
| 5,565
|
sao paulo
|
SP
|
369708cabd9831ea6fde670a3b602a92
|
94b731a41867b47c3856e324840c4c99
| 3,636
|
sao paulo
|
SP
|
5f8b4882b5a4ec7bf6d2107e6cd0cf29
|
694cb45ff29b603ac2acd51016770097
| 24,120
|
niteroi
|
RJ
|
ad6891a1937cb8723a2c08ba1ae59873
|
9dbb05f5577e862337b93feb8f358839
| 65,058
|
sao luis
|
MA
|
End of preview.