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 7 new columns ({'datetime', 'stationname', 'stationcode', 'value', 'municipality_id', 'sensordescription', 'measureunit'}) and 7 missing columns ({'date_event', 'place_id', 'taxonomy_id', 'registered_by', 'elevation_m', 'code_record', 'common_name'}).
This happened while the csv dataset builder was generating data using
hf://datasets/juanpac96/urban_tree_census_data/climate.csv (at revision f87ba58bace16cbd9f4a48273f8a0728df6053a1)
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 623, 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
municipality_id: int64
stationcode: int64
stationname: string
datetime: string
latitude: double
longitude: double
sensordescription: string
measureunit: string
value: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1350
to
{'code_record': Value(dtype='int64', id=None), 'common_name': Value(dtype='string', id=None), 'latitude': Value(dtype='float64', id=None), 'longitude': Value(dtype='float64', id=None), 'elevation_m': Value(dtype='float64', id=None), 'registered_by': Value(dtype='string', id=None), 'date_event': Value(dtype='string', id=None), 'place_id': Value(dtype='int64', id=None), 'taxonomy_id': Value(dtype='int64', 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 1438, 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 1050, 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 7 new columns ({'datetime', 'stationname', 'stationcode', 'value', 'municipality_id', 'sensordescription', 'measureunit'}) and 7 missing columns ({'date_event', 'place_id', 'taxonomy_id', 'registered_by', 'elevation_m', 'code_record', 'common_name'}).
This happened while the csv dataset builder was generating data using
hf://datasets/juanpac96/urban_tree_census_data/climate.csv (at revision f87ba58bace16cbd9f4a48273f8a0728df6053a1)
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.
code_record
int64 | common_name
string | latitude
float64 | longitude
float64 | elevation_m
float64 | registered_by
string | date_event
string | place_id
int64 | taxonomy_id
int64 |
|---|---|---|---|---|---|---|---|---|
1
|
Gmelina melina
| 4.407358
| -75.143061
| 939
|
Cortolima
|
2017-09-30 08:58:15
| 495
| 197
|
2
|
Gmelina melina
| 4.407582
| -75.14304
| 939
|
Cortolima
|
2017-09-30 08:54:56
| 495
| 197
|
3
|
Gmelina melina
| 4.407822
| -75.142962
| 939
|
Cortolima
|
2017-09-30 08:51:43
| 495
| 197
|
4
|
Gmelina melina
| 4.407983
| -75.142962
| 937
|
Cortolima
|
2017-09-30 08:49:51
| 495
| 197
|
5
|
Gmelina melina
| 4.408368
| -75.142898
| 937
|
Cortolima
|
2017-09-30 08:48:01
| 495
| 197
|
6
|
Gmelina melina
| 4.408599
| -75.142869
| 937
|
Cortolima
|
2017-09-30 08:44:46
| 495
| 197
|
7
|
Gmelina melina
| 4.408738
| -75.142816
| 937
|
Cortolima
|
2017-09-30 08:41:28
| 495
| 197
|
8
|
Gmelina melina
| 4.408872
| -75.14284
| 937
|
Cortolima
|
2017-09-30 08:37:49
| 495
| 197
|
9
|
Gmelina melina
| 4.409477
| -75.142604
| 934
|
Cortolima
|
2017-09-30 08:34:29
| 495
| 197
|
10
|
Gmelina melina
| 4.409829
| -75.142551
| 934
|
Cortolima
|
2017-09-30 08:30:29
| 495
| 197
|
11
|
Gmelina melina
| 4.410075
| -75.142449
| 934
|
Cortolima
|
2017-09-30 08:27:46
| 235
| 197
|
12
|
Ocobo
| 4.40978
| -75.146175
| 942
|
Cortolima
|
2017-09-30 07:50:09
| 427
| 399
|
13
|
Ocobo
| 4.409618
| -75.146547
| 942
|
Cortolima
|
2017-09-30 07:44:29
| 427
| 399
|
14
|
Tulipan africano
| 4.409752
| -75.146766
| 942
|
Cortolima
|
2017-09-30 07:41:22
| 427
| 387
|
15
|
Ocobo
| 4.409678
| -75.146869
| 942
|
Cortolima
|
2017-09-30 07:37:38
| 427
| 399
|
16
|
Tulipan africano
| 4.409698
| -75.146806
| 942
|
Cortolima
|
2017-09-30 07:34:29
| 427
| 387
|
17
|
Ocobo
| 4.409741
| -75.146846
| 942
|
Cortolima
|
2017-09-30 07:33:30
| 21
| 399
|
18
|
Ocobo
| 4.409816
| -75.146937
| 942
|
Cortolima
|
2017-09-30 07:21:44
| 427
| 399
|
19
|
Ocobo
| 4.409771
| -75.146972
| 944
|
Cortolima
|
2017-09-30 07:18:31
| 427
| 399
|
20
|
Ocobo
| 4.409723
| -75.146991
| 944
|
Cortolima
|
2017-09-30 07:15:34
| 427
| 399
|
21
|
Caucho matapalo
| 4.409549
| -75.147259
| 944
|
Cortolima
|
2017-09-30 07:06:41
| 427
| 181
|
22
|
Matarraton
| 4.409442
| -75.147316
| 946
|
Cortolima
|
2017-09-30 07:01:23
| 427
| 196
|
23
|
Limon
| 4.407188
| -75.145363
| 945
|
Cortolima
|
2017-09-29 13:18:53
| 427
| 108
|
24
|
Tulipan africano
| 4.407031
| -75.145365
| 945
|
Cortolima
|
2017-09-29 13:16:25
| 427
| 387
|
25
|
Tulipan africano
| 4.406994
| -75.145446
| 945
|
Cortolima
|
2017-09-29 13:14:21
| 427
| 387
|
26
|
Almendro
| 4.407007
| -75.145545
| 945
|
Cortolima
|
2017-09-29 13:10:58
| 427
| 408
|
27
|
Tulipan africano
| 4.407058
| -75.145604
| 945
|
Cortolima
|
2017-09-29 13:08:34
| 427
| 387
|
28
|
Tulipan africano
| 4.407313
| -75.145532
| 945
|
Cortolima
|
2017-09-29 13:05:58
| 427
| 387
|
29
|
Millon croto
| 4.407419
| -75.145919
| 945
|
Cortolima
|
2017-09-29 13:03:06
| 427
| 333
|
30
|
Payande
| 4.408384
| -75.145836
| 942
|
Cortolima
|
2017-09-29 12:56:04
| 427
| 321
|
31
|
Palo cruz
| 4.408325
| -75.145873
| 942
|
Cortolima
|
2017-09-29 12:52:44
| 427
| 52
|
32
|
Carbonero
| 4.408285
| -75.1459
| 942
|
Cortolima
|
2017-09-29 12:50:06
| 427
| 72
|
33
|
Ocobo
| 4.408301
| -75.145927
| 942
|
Cortolima
|
2017-09-29 12:47:46
| 427
| 399
|
34
|
Habano laurel de judea
| 4.408241
| -75.146456
| 946
|
Cortolima
|
2017-09-29 12:41:37
| 427
| 286
|
35
|
Guanabano
| 4.40834
| -75.146499
| 946
|
Cortolima
|
2017-09-29 12:39:05
| 427
| 27
|
36
|
Limon
| 4.407994
| -75.146565
| 946
|
Cortolima
|
2017-09-29 11:33:54
| 427
| 108
|
37
|
Ocobo
| 4.408027
| -75.146479
| 946
|
Cortolima
|
2017-09-29 11:30:12
| 427
| 399
|
38
|
Ocobo
| 4.408137
| -75.146461
| 946
|
Cortolima
|
2017-09-29 11:26:23
| 427
| 399
|
39
|
Mirto
| 4.408015
| -75.14638
| 946
|
Cortolima
|
2017-09-29 11:20:22
| 427
| 278
|
40
|
Pera de malaca
| 4.40797
| -75.146362
| 946
|
Cortolima
|
2017-09-29 11:17:23
| 427
| 396
|
41
|
Cardo
| 4.407872
| -75.146336
| 946
|
Cortolima
|
2017-09-29 11:15:07
| 427
| 96
|
42
|
Nacedero
| 4.407765
| -75.146281
| 947
|
Cortolima
|
2017-09-29 11:12:07
| 427
| 418
|
43
|
Nevado
| 4.407752
| -75.146286
| 947
|
Cortolima
|
2017-09-29 11:09:31
| 427
| 217
|
44
|
Pino libro
| 4.407647
| -75.146236
| 947
|
Cortolima
|
2017-09-29 10:01:47
| 427
| 323
|
45
|
Pera de malaca
| 4.407688
| -75.146496
| 947
|
Cortolima
|
2017-09-29 09:56:04
| 427
| 396
|
46
|
Nevado
| 4.407729
| -75.146516
| 947
|
Cortolima
|
2017-09-29 09:53:02
| 427
| 217
|
47
|
Mirto
| 4.407751
| -75.146528
| 947
|
Cortolima
|
2017-09-29 09:49:21
| 427
| 278
|
48
|
Monaca
| 4.407789
| -75.146539
| 947
|
Cortolima
|
2017-09-29 09:45:46
| 427
| 55
|
49
|
Ebano arboreo costenno
| 4.407853
| -75.146571
| 947
|
Cortolima
|
2017-09-29 09:42:11
| 427
| 62
|
50
|
Arbol de la felicidad
| 4.407981
| -75.146618
| 946
|
Cortolima
|
2017-09-29 09:39:15
| 427
| 149
|
51
|
Araza
| 4.408025
| -75.146794
| 946
|
Cortolima
|
2017-09-29 09:34:29
| 427
| 171
|
52
|
Limon
| 4.407855
| -75.147262
| 948
|
Cortolima
|
2017-09-29 09:23:28
| 427
| 108
|
53
|
Acacio amarillo
| 4.407808
| -75.14722
| 948
|
Cortolima
|
2017-09-29 09:21:11
| 427
| 376
|
54
|
Casco de vaca pate buey
| 4.40783
| -75.147228
| 948
|
Cortolima
|
2017-09-29 09:18:27
| 427
| 44
|
55
|
Saman
| 4.40787
| -75.147281
| 948
|
Cortolima
|
2017-09-29 09:14:29
| 427
| 359
|
56
|
Noni
| 4.407819
| -75.1474
| 948
|
Cortolima
|
2017-09-29 09:11:40
| 427
| 273
|
57
|
Ocobo
| 4.407884
| -75.147416
| 948
|
Cortolima
|
2017-09-29 09:08:25
| 427
| 399
|
58
|
Ocobo
| 4.407956
| -75.147456
| 948
|
Cortolima
|
2017-09-29 09:05:49
| 427
| 399
|
59
|
Ocobo
| 4.407999
| -75.147477
| 948
|
Cortolima
|
2017-09-29 09:02:34
| 427
| 399
|
60
|
Noni
| 4.408017
| -75.147453
| 948
|
Cortolima
|
2017-09-29 08:59:39
| 427
| 273
|
61
|
Saman
| 4.408031
| -75.147496
| 948
|
Cortolima
|
2017-09-29 08:53:50
| 427
| 359
|
62
|
Limon
| 4.408128
| -75.147509
| 948
|
Cortolima
|
2017-09-29 08:40:49
| 427
| 108
|
63
|
Mango
| 4.408165
| -75.147531
| 948
|
Cortolima
|
2017-09-29 08:37:44
| 427
| 261
|
64
|
Almendro
| 4.408224
| -75.147574
| 948
|
Cortolima
|
2017-09-29 08:35:05
| 427
| 408
|
65
|
Saman
| 4.408299
| -75.147641
| 948
|
Cortolima
|
2017-09-29 08:30:44
| 427
| 359
|
66
|
Saman
| 4.408409
| -75.147732
| 948
|
Cortolima
|
2017-09-29 08:28:25
| 427
| 359
|
67
|
Gualanday
| 4.408259
| -75.147906
| 949
|
Cortolima
|
2017-09-29 08:24:25
| 427
| 227
|
68
|
Chirlobirlo
| 4.408155
| -75.147879
| 949
|
Cortolima
|
2017-09-29 08:21:41
| 427
| 405
|
69
|
Saman
| 4.408184
| -75.147984
| 949
|
Cortolima
|
2017-09-29 08:17:47
| 427
| 359
|
70
|
Acacio rojo
| 4.408012
| -75.14785
| 949
|
Cortolima
|
2017-09-29 08:12:12
| 427
| 146
|
71
|
Ocobo
| 4.408036
| -75.147905
| 949
|
Cortolima
|
2017-09-29 08:09:40
| 427
| 399
|
72
|
Palma areca
| 4.407845
| -75.147289
| 948
|
Cortolima
|
2017-09-29 08:04:44
| 427
| 153
|
73
|
Noni
| 4.408124
| -75.147205
| 948
|
Cortolima
|
2017-09-29 08:01:15
| 427
| 273
|
74
|
Pera de malaca
| 4.408183
| -75.147225
| 948
|
Cortolima
|
2017-09-29 07:58:27
| 427
| 396
|
75
|
Totumo
| 4.408358
| -75.147288
| 948
|
Cortolima
|
2017-09-29 07:55:52
| 427
| 136
|
76
|
Ocobo
| 4.408551
| -75.147219
| 948
|
Cortolima
|
2017-09-29 07:48:33
| 427
| 399
|
77
|
Ocobo
| 4.408567
| -75.147229
| 948
|
Cortolima
|
2017-09-29 07:41:55
| 427
| 399
|
78
|
Arbol de la felicidad
| 4.408503
| -75.14732
| 948
|
Cortolima
|
2017-09-29 07:34:40
| 427
| 149
|
79
|
Chirlobirlo
| 4.408503
| -75.147339
| 948
|
Cortolima
|
2017-09-29 07:31:31
| 427
| 405
|
80
|
Ocobo
| 4.408476
| -75.147379
| 948
|
Cortolima
|
2017-09-29 07:28:12
| 427
| 399
|
81
|
Papayuelo espinaco
| 4.408454
| -75.147449
| 948
|
Cortolima
|
2017-09-29 07:24:25
| 427
| 120
|
82
|
Palma areca
| 4.408435
| -75.147512
| 948
|
Cortolima
|
2017-09-29 07:20:20
| 427
| 153
|
83
|
Igua
| 4.408478
| -75.14757
| 948
|
Cortolima
|
2017-09-29 07:16:45
| 427
| 343
|
84
|
Payande
| 4.408665
| -75.147685
| 946
|
Cortolima
|
2017-09-29 07:08:22
| 427
| 321
|
85
|
Limon
| 4.406684
| -75.146053
| 945
|
Cortolima
|
2017-09-27 12:05:34
| 256
| 108
|
86
|
Aguacate
| 4.407421
| -75.147058
| 948
|
Cortolima
|
2017-09-27 11:55:24
| 256
| 306
|
87
|
Aguacate
| 4.407022
| -75.146159
| 949
|
Cortolima
|
2017-09-27 11:51:36
| 256
| 306
|
88
|
Cobalonga
| 4.406942
| -75.14599
| 945
|
Cortolima
|
2017-09-27 11:47:01
| 256
| 413
|
89
|
Almendro
| 4.407037
| -75.1472
| 951
|
Cortolima
|
2017-09-27 10:37:39
| 256
| 408
|
90
|
Oiti
| 4.407147
| -75.147299
| 948
|
Cortolima
|
2017-09-27 10:33:34
| 256
| 247
|
91
|
Oiti
| 4.407133
| -75.147267
| 948
|
Cortolima
|
2017-09-27 10:31:11
| 256
| 247
|
92
|
Tulipan africano
| 4.407332
| -75.147204
| 948
|
Cortolima
|
2017-09-27 10:27:20
| 256
| 387
|
93
|
Pera de malaca
| 4.407274
| -75.147207
| 948
|
Cortolima
|
2017-09-27 10:24:31
| 256
| 396
|
94
|
Marannon
| 4.407496
| -75.147741
| 948
|
Cortolima
|
2017-09-27 10:20:51
| 256
| 24
|
95
|
Oiti
| 4.407506
| -75.147782
| 951
|
Cortolima
|
2017-09-27 10:16:34
| 256
| 247
|
96
|
Munneco
| 4.407581
| -75.147965
| 951
|
Cortolima
|
2017-09-27 10:13:50
| 256
| 130
|
97
|
Pino libro
| 4.407626
| -75.148101
| 951
|
Cortolima
|
2017-09-27 10:10:57
| 256
| 323
|
98
|
Guanabano
| 4.408489
| -75.145839
| 942
|
Cortolima
|
2017-09-29 12:54:09
| 427
| 27
|
99
|
Pera de malaca
| 4.408879
| -75.146185
| 944
|
Cortolima
|
2017-09-29 12:49:46
| 427
| 396
|
100
|
Pera de malaca
| 4.408696
| -75.146106
| 941
|
Cortolima
|
2017-09-29 12:46:29
| 427
| 396
|
End of preview.