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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 1 new columns ({'World Bank Commodity Price Data (The Pink Sheet)'}) and 3 missing columns ({'Unnamed: 18', 'Weights Used in the World Bank Commodity Price Index (in Percent) 1/', 'Unnamed: 17'}).
This happened while the csv dataset builder was generating data using
hf://datasets/aaronmat1905/global-commodity-shocks-analysis-data/raw/commodity_prices/extracted/cmoMonthly_monthly_indices.csv (at revision d9d429d4420d08c99a9bb15cedfbca4fb71d1c4a)
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 1831, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
Unnamed: 0: int64
World Bank Commodity Price Data (The Pink Sheet): string
Unnamed: 1: string
Unnamed: 2: string
Unnamed: 3: string
Unnamed: 4: string
Unnamed: 5: string
Unnamed: 6: string
Unnamed: 7: string
Unnamed: 8: string
Unnamed: 9: string
Unnamed: 10: string
Unnamed: 11: string
Unnamed: 12: string
Unnamed: 13: string
Unnamed: 14: string
Unnamed: 15: string
Unnamed: 16: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2508
to
{'Unnamed: 0': Value('int64'), 'Weights Used in the World Bank Commodity Price Index (in Percent) 1/': Value('string'), 'Unnamed: 1': Value('string'), 'Unnamed: 2': Value('string'), 'Unnamed: 3': Value('string'), 'Unnamed: 4': Value('string'), 'Unnamed: 5': Value('string'), 'Unnamed: 6': Value('float64'), 'Unnamed: 7': Value('float64'), 'Unnamed: 8': Value('string'), 'Unnamed: 9': Value('float64'), 'Unnamed: 10': Value('float64'), 'Unnamed: 11': Value('string'), 'Unnamed: 12': Value('float64'), 'Unnamed: 13': Value('float64'), 'Unnamed: 14': Value('string'), 'Unnamed: 15': Value('float64'), 'Unnamed: 16': Value('float64'), 'Unnamed: 17': Value('float64'), 'Unnamed: 18': 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 1450, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 993, in stream_convert_to_parquet
builder._prepare_split(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, 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 1833, 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 1 new columns ({'World Bank Commodity Price Data (The Pink Sheet)'}) and 3 missing columns ({'Unnamed: 18', 'Weights Used in the World Bank Commodity Price Index (in Percent) 1/', 'Unnamed: 17'}).
This happened while the csv dataset builder was generating data using
hf://datasets/aaronmat1905/global-commodity-shocks-analysis-data/raw/commodity_prices/extracted/cmoMonthly_monthly_indices.csv (at revision d9d429d4420d08c99a9bb15cedfbca4fb71d1c4a)
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.
Unnamed: 0
int64 | Weights Used in the World Bank Commodity Price Index (in Percent) 1/
string | Unnamed: 1
null | Unnamed: 2
string | Unnamed: 3
string | Unnamed: 4
null | Unnamed: 5
null | Unnamed: 6
null | Unnamed: 7
null | Unnamed: 8
string | Unnamed: 9
null | Unnamed: 10
null | Unnamed: 11
string | Unnamed: 12
null | Unnamed: 13
null | Unnamed: 14
string | Unnamed: 15
null | Unnamed: 16
null | Unnamed: 17
null | Unnamed: 18
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0
|
based on 2002-04 developing countries' export values 2/ and 3/
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
1
| null | null |
Commodity Group
| null | null | null | null | null |
Share of
energy and non-energy indices
| null | null |
Share of
sub-group indices
| null | null |
Share of
food index
| null | null | null | null |
2
| null | null |
Total Index
| null | null | null | null | null |
100
| null | null | null | null | null | null | null | null | null |
World Bank Commodity Price Indexes: Groups and weights
|
3
| null | null | null |
Energy
| null | null | null | null |
67
| null | null | null | null | null | null | null | null | null | null |
4
| null | null | null |
Non-Energy
| null | null | null | null |
33
| null | null | null | null | null | null | null | null | null | null |
5
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
6
| null | null | null | null | null | null | null | null |
100
| null | null |
100
| null | null | null | null | null | null | null |
7
| null | null | null | null | null | null | null | null |
4.657418566293743
| null | null |
4.657418566293743
| null | null | null | null | null | null | null |
8
| null | null | null | null | null | null | null | null |
84.57082988045218
| null | null |
84.57082988045218
| null | null | null | null | null | null | null |
9
| null | null | null | null | null | null | null | null |
10.771751553254079
| null | null |
10.771751553254079
| null | null | null | null | null | null | null |
10
| null | null | null | null | null | null | null | null |
100.04584448798855
| null | null | null | null | null | null | null | null | null | null |
11
| null | null |
Agriculture
| null | null | null | null | null |
64.85646094819245
| null | null | null | null | null | null | null | null | null | null |
12
| null | null | null |
Food
| null | null | null | null |
40.030537993261014
| null | null | null | null | null |
100
| null | null | null | null |
13
| null | null | null | null | null | null | null | null |
11.282998404917572
| null | null |
100
| null | null |
28.185977432571658
| null | null | null | null |
14
| null | null | null | null | null | null | null | null |
3.3918635593970645
| null | null |
30.061721518269096
| null | null |
8.473190042981866
| null | null | null | null |
15
| null | null | null | null | null | null | null | null |
2.8381865342642207
| null | null |
25.154541660018566
| null | null |
7.0900534355596685
| null | null | null | null |
16
| null | null | null | null | null | null | null | null |
4.589332160012415
| null | null |
40.674756791707104
| null | null |
11.464577770063972
| null | null | null | null |
17
| null | null | null | null | null | null | null | null |
0.46361615124387273
| null | null |
4.108980030005239
| null | null |
1.1581561839661527
| null | null | null | null |
18
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
19
| null | null | null | null | null | null | null | null |
16.33278497355562
| null | null |
100
| null | null |
40.80081306003227
| null | null | null | null |
20
| null | null | null | null | null | null | null | null |
4.024177157695958
| null | null |
24.63864652728543
| null | null |
10.052768110119862
| null | null | null | null |
21
| null | null | null | null | null | null | null | null |
2.1299989425180166
| null | null |
13.041247686580665
| null | null |
5.32093508929756
| null | null | null | null |
22
| null | null | null | null | null | null | null | null |
4.287789107702601
| null | null |
26.25265142867522
| null | null |
10.711295232715667
| null | null | null | null |
23
| null | null | null | null | null | null | null | null |
4.932507478151657
| null | null |
30.200039283795572
| null | null |
12.32186157223774
| null | null | null | null |
24
| null | null | null | null | null | null | null | null |
0.5014055543166628
| null | null |
3.069932991394227
| null | null |
1.252557620887015
| null | null | null | null |
25
| null | null | null | null | null | null | null | null |
0.45690673317072256
| null | null |
2.7974820822688806
| null | null |
1.141395434774424
| null | null | null | null |
26
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
27
| null | null | null | null | null | null | null | null |
12.414754614787823
| null | null |
100
| null | null |
31.013209507396077
| null | null | null | null |
28
| null | null | null | null | null | null | null | null |
3.911378343466987
| null | null |
31.505885253728266
| null | null |
9.770986200898554
| null | null | null | null |
29
| null | null | null | null | null | null | null | null |
1.9453198071452942
| null | null |
15.669418103746716
| null | null |
4.859589465104818
| null | null | null | null |
30
| null | null | null | null | null | null | null | null |
2.734047287643245
| null | null |
22.02256405766238
| null | null |
6.829903930103341
| null | null | null | null |
31
| null | null | null | null | null | null | null | null |
2.3821596956467834
| null | null |
19.18813355206615
| null | null |
5.950856059061237
| null | null | null | null |
32
| null | null | null | null | null | null | null | null |
1.4418494808855147
| null | null |
11.613999032796483
| null | null |
3.6018738522281275
| null | null | null | null |
33
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
34
| null | null | null |
Beverages
| null | null | null | null |
8.368368171913593
| null | null |
100
| null | null | null | null | null | null | null |
35
| null | null | null | null | null | null | null | null |
3.8257358547804765
| null | null |
45.71662929005244
| null | null | null | null | null | null | null |
36
| null | null | null | null | null | null | null | null |
3.086380523179113
| null | null |
36.88150974926992
| null | null | null | null | null | null | null |
37
| null | null | null | null | null | null | null | null |
1.4562517939540034
| null | null |
17.40186096067762
| null | null | null | null | null | null | null |
38
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
39
| null | null | null |
Agricultural Raw Materials
| null | null | null | null |
16.457554783017844
| null | null | null | null | null | null | null | null | null | null |
40
| null | null | null | null | null | null | null | null |
8.596595075709784
| null | null |
100
| null | null | null | null | null | null | null |
41
| null | null | null | null | null | null | null | null |
1.8964976088833747
| null | null |
22.06103221311479
| null | null | null | null | null | null | null |
42
| null | null | null | null | null | null | null | null |
6.700097466826409
| null | null |
77.93896778688523
| null | null | null | null | null | null | null |
43
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
44
| null | null | null | null | null | null | null | null |
7.86095970730806
| null | null |
100
| null | null | null | null | null | null | null |
45
| null | null | null | null | null | null | null | null |
1.9405307342868943
| null | null |
24.685672062189177
| null | null | null | null | null | null | null |
46
| null | null | null | null | null | null | null | null |
3.668003474746443
| null | null |
46.66101355711603
| null | null | null | null | null | null | null |
47
| null | null | null | null | null | null | null | null |
2.2524254982747234
| null | null |
28.653314380694784
| null | null | null | null | null | null | null |
48
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
49
| null | null |
Metals and Minerals
| null | null | null | null | null |
31.60210106325134
| null | null |
100
| null | null | null | null | null | null | null |
50
| null | null | null | null | null | null | null | null |
8.431346761078801
| null | null |
26.679703176075325
| null | null | null | null | null | null | null |
51
| null | null | null | null | null | null | null | null |
12.144605442884556
| null | null |
38.429740537115656
| null | null | null | null | null | null | null |
52
| null | null | null | null | null | null | null | null |
5.960606450276898
| null | null |
18.861424556382484
| null | null | null | null | null | null | null |
53
| null | null | null | null | null | null | null | null |
0.5674225120345996
| null | null |
1.795521477824871
| null | null | null | null | null | null | null |
54
| null | null | null | null | null | null | null | null |
2.5480035264875704
| null | null |
8.06276621097997
| null | null | null | null | null | null | null |
55
| null | null | null | null | null | null | null | null |
0.6629095549834744
| null | null |
2.0976755743444606
| null | null | null | null | null | null | null |
56
| null | null | null | null | null | null | null | null |
1.2872068155054393
| null | null |
4.073168467277241
| null | null | null | null | null | null | null |
57
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
58
| null | null |
Fertilizers
| null | null | null | null | null |
3.5872824765447575
| null | null |
100
| null | null | null | null | null | null | null |
59
| null | null | null | null | null | null | null | null |
0.604711805888795
| null | null |
16.8571003215573
| null | null | null | null | null | null | null |
60
| null | null | null | null | null | null | null | null |
0.7773794468329338
| null | null |
21.670427459108254
| null | null | null | null | null | null | null |
61
| null | null | null | null | null | null | null | null |
0.7220096479838362
| null | null |
20.12692484365687
| null | null | null | null | null | null | null |
62
| null | null | null | null | null | null | null | null |
1.4831815758391922
| null | null |
41.34554737567757
| null | null | null | null | null | null | null |
63
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
64
| null | null | null | null | null | null | null | null | null | null | null |
100
| null | null | null | null | null | null | null |
65
| null | null | null |
Gold
| null | null | null | null | null | null | null |
77.78901550026363
| null | null | null | null | null | null | null |
66
| null | null | null |
Silver
| null | null | null | null | null | null | null |
18.93154923627381
| null | null | null | null | null | null | null |
67
| null | null | null |
Platinum
| null | null | null | null | null | null | null |
3.2794352634625588
| null | null | null | null | null | null | null |
68
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
69
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
70
|
Source: World Bank Prospects Group
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
71
|
Notes:
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
72
|
Differences in group totals and components are due to rounding.
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
73
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
74
|
1/
| null |
Laspeyres Index.
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
75
|
2/
| null |
Developing countries is represented by Low- and Middle-income Countries (LMIC) as defined by the World Bank Development Data Group Classification of Income Group as of June 20, 2006.
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
76
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
77
|
3/
| null |
Trade data sources are United Nations' Comtrade Database via World Bank WITS system, Food and Agriculture Organization FAOSTAT Database, International Energy Agency Database, BP Statistical Review of World Energy, World Metal Statistics, World Bureau of Metal Statistics and World Bank staff estimates.
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
78
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
79
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
80
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
81
|
4/
| null |
The maize weight includes sorghum.
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
82
|
5/
| null |
The groundnut oil weight includes groundnuts.
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
83
|
6/
| null |
The oranges weight includes orange juice.
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
84
|
7/
| null |
Rubber TSR20 replaces Rubber RSS3 in Other Raw Materials index from 2018
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
85
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
86
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
87
|
World Bank, Prospects Group.
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
88
|
May 2, 2023
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
0
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
1
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
2
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
3
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
4
| null | null |
Energy
|
Non-energy **
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
5
| null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Metals & Minerals
| null | null | null | null |
6
| null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
7
| null | null | null | null | null | null | null |
Grains
| null | null |
Timber
| null | null | null | null | null | null | null |
|
8
| null | null |
iENERGY
|
iNONFUEL
| null | null | null | null |
iGRAINS
| null | null |
iTIMBER
| null | null |
iMETMIN
| null | null | null | null |
9
| null | null |
2.13444915445322
|
18.81589520307
| null | null | null | null |
23.57750535562
| null | null |
16.23922227558
| null | null |
12.89589240086
| null | null | null | null |
10
| null | null |
2.13444915445322
|
18.68146974844
| null | null | null | null |
23.42698276517
| null | null |
16.23922227558
| null | null |
12.9291469329
| null | null | null | null |
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