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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 4 new columns ({'product', 'quantity', 'price', 'id'}) and 5 missing columns ({'Age', 'CustomerID', 'Spending Score (1-100)', 'Gender', 'Annual Income (k$)'}). This happened while the csv dataset builder was generating data using hf://datasets/nisanth074/iimiacpbda_pba_assignment1/product data.csv (at revision ac709c2485d2ba486c158e41461ab370de5a4ba7) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations) Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single writer.write_table(table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table pa_table = table_cast(pa_table, self._schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast id: int64 product: string price: double quantity: int64 -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 701 to {'CustomerID': Value(dtype='int64', id=None), 'Gender': Value(dtype='string', id=None), 'Age': Value(dtype='int64', id=None), 'Annual Income (k$)': Value(dtype='int64', id=None), 'Spending Score (1-100)': 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 1321, 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 935, in convert_to_parquet builder.download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare self._download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, 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 1882, 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 2013, 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 4 new columns ({'product', 'quantity', 'price', 'id'}) and 5 missing columns ({'Age', 'CustomerID', 'Spending Score (1-100)', 'Gender', 'Annual Income (k$)'}). This happened while the csv dataset builder was generating data using hf://datasets/nisanth074/iimiacpbda_pba_assignment1/product data.csv (at revision ac709c2485d2ba486c158e41461ab370de5a4ba7) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
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CustomerID
int64 | Gender
string | Age
int64 | Annual Income (k$)
int64 | Spending Score (1-100)
int64 |
---|---|---|---|---|
1 | Male | 19 | 15 | 39 |
2 | Male | 21 | 15 | 81 |
3 | Female | 20 | 16 | 6 |
4 | Female | 23 | 16 | 77 |
5 | Female | 31 | 17 | 40 |
6 | Female | 22 | 17 | 76 |
7 | Female | 35 | 18 | 6 |
8 | Female | 23 | 18 | 94 |
9 | Male | 64 | 19 | 3 |
10 | Female | 30 | 19 | 72 |
11 | Male | 67 | 19 | 14 |
12 | Female | 35 | 19 | 99 |
13 | Female | 58 | 20 | 15 |
14 | Female | 24 | 20 | 77 |
15 | Male | 37 | 20 | 13 |
16 | Male | 22 | 20 | 79 |
17 | Female | 35 | 21 | 35 |
18 | Male | 20 | 21 | 66 |
19 | Male | 52 | 23 | 29 |
20 | Female | 35 | 23 | 98 |
21 | Male | 35 | 24 | 35 |
22 | Male | 25 | 24 | 73 |
23 | Female | 46 | 25 | 5 |
24 | Male | 31 | 25 | 73 |
25 | Female | 54 | 28 | 14 |
26 | Male | 29 | 28 | 82 |
27 | Female | 45 | 28 | 32 |
28 | Male | 35 | 28 | 61 |
29 | Female | 40 | 29 | 31 |
30 | Female | 23 | 29 | 87 |
31 | Male | 60 | 30 | 4 |
32 | Female | 21 | 30 | 73 |
33 | Male | 53 | 33 | 4 |
34 | Male | 18 | 33 | 92 |
35 | Female | 49 | 33 | 14 |
36 | Female | 21 | 33 | 81 |
37 | Female | 42 | 34 | 17 |
38 | Female | 30 | 34 | 73 |
39 | Female | 36 | 37 | 26 |
40 | Female | 20 | 37 | 75 |
41 | Female | 65 | 38 | 35 |
42 | Male | 24 | 38 | 92 |
43 | Male | 48 | 39 | 36 |
44 | Female | 31 | 39 | 61 |
45 | Female | 49 | 39 | 28 |
46 | Female | 24 | 39 | 65 |
47 | Female | 50 | 40 | 55 |
48 | Female | 27 | 40 | 47 |
49 | Female | 29 | 40 | 42 |
50 | Female | 31 | 40 | 42 |
51 | Female | 49 | 42 | 52 |
52 | Male | 33 | 42 | 60 |
53 | Female | 31 | 43 | 54 |
54 | Male | 59 | 43 | 60 |
55 | Female | 50 | 43 | 45 |
56 | Male | 47 | 43 | 41 |
57 | Female | 51 | 44 | 50 |
58 | Male | 69 | 44 | 46 |
59 | Female | 27 | 46 | 51 |
60 | Male | 53 | 46 | 46 |
61 | Male | 70 | 46 | 56 |
62 | Male | 19 | 46 | 55 |
63 | Female | 67 | 47 | 52 |
64 | Female | 54 | 47 | 59 |
65 | Male | 63 | 48 | 51 |
66 | Male | 18 | 48 | 59 |
67 | Female | 43 | 48 | 50 |
68 | Female | 68 | 48 | 48 |
69 | Male | 19 | 48 | 59 |
70 | Female | 32 | 48 | 47 |
71 | Male | 70 | 49 | 55 |
72 | Female | 47 | 49 | 42 |
73 | Female | 60 | 50 | 49 |
74 | Female | 60 | 50 | 56 |
75 | Male | 59 | 54 | 47 |
76 | Male | 26 | 54 | 54 |
77 | Female | 45 | 54 | 53 |
78 | Male | 40 | 54 | 48 |
79 | Female | 23 | 54 | 52 |
80 | Female | 49 | 54 | 42 |
81 | Male | 57 | 54 | 51 |
82 | Male | 38 | 54 | 55 |
83 | Male | 67 | 54 | 41 |
84 | Female | 46 | 54 | 44 |
85 | Female | 21 | 54 | 57 |
86 | Male | 48 | 54 | 46 |
87 | Female | 55 | 57 | 58 |
88 | Female | 22 | 57 | 55 |
89 | Female | 34 | 58 | 60 |
90 | Female | 50 | 58 | 46 |
91 | Female | 68 | 59 | 55 |
92 | Male | 18 | 59 | 41 |
93 | Male | 48 | 60 | 49 |
94 | Female | 40 | 60 | 40 |
95 | Female | 32 | 60 | 42 |
96 | Male | 24 | 60 | 52 |
97 | Female | 47 | 60 | 47 |
98 | Female | 27 | 60 | 50 |
99 | Male | 48 | 61 | 42 |
100 | Male | 20 | 61 | 49 |
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