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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 3 new columns ({'Sales', 'Product', 'Profit'}) and 6 missing columns ({'Quantity', 'Unit Price', 'Reorder Status', 'Stock Value', 'Product Name', 'Product ID'}).

This happened while the csv dataset builder was generating data using

hf://datasets/DhineshKumaar/osworld_tasks_files/Rework3/quarterly_sales.csv (at revision 4cd6edc4f27c082da1ad699ed5458d3f37f736f3)

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
              Product: string
              Sales: double
              Profit: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 598
              to
              {'Product ID': Value('string'), 'Product Name': Value('string'), 'Quantity': Value('int64'), 'Unit Price': Value('float64'), 'Stock Value': Value('string'), 'Reorder Status': 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 1339, 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 972, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                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 3 new columns ({'Sales', 'Product', 'Profit'}) and 6 missing columns ({'Quantity', 'Unit Price', 'Reorder Status', 'Stock Value', 'Product Name', 'Product ID'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/DhineshKumaar/osworld_tasks_files/Rework3/quarterly_sales.csv (at revision 4cd6edc4f27c082da1ad699ed5458d3f37f736f3)
              
              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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Product ID
string
Product Name
string
Quantity
int64
Unit Price
float64
Stock Value
string
Reorder Status
string
PID_001
Product_1
63
91.08
?5,738.04
OK
PID_002
Product_2
150
75.51
?11,326.50
OK
PID_003
Product_3
108
75.97
?8,204.76
OK
PID_004
Product_4
122
65.91
?8,041.02
OK
PID_005
Product_5
55
14.41
?792.55
OK
PID_006
Product_6
199
98.51
?19,603.49
OK
PID_007
Product_7
164
82.73
?13,567.72
OK
PID_008
Product_8
105
22.27
?2,338.35
OK
PID_009
Product_9
88
88.36
?7,775.68
OK
PID_010
Product_10
131
41.28
?5,407.68
OK
PID_011
Product_11
51
71.86
?3,664.86
OK
PID_012
Product_12
7
69.68
?487.76
REORDER NOW
PID_013
Product_13
40
86.48
?3,459.20
OK
PID_014
Product_14
22
63.15
?1,389.30
OK
PID_015
Product_15
178
49.02
?8,725.56
OK
PID_016
Product_16
26
80.03
?2,080.78
OK
PID_017
Product_17
150
56.41
?8,461.50
OK
PID_018
Product_18
165
86.59
?14,287.35
OK
PID_019
Product_19
199
29.61
?5,892.39
OK
PID_020
Product_20
27
70.01
?1,890.27
OK
PID_021
Product_21
15
52.78
?791.70
REORDER NOW
PID_022
Product_22
171
81.32
?13,905.72
OK
PID_023
Product_23
117
51.59
?6,036.03
OK
PID_024
Product_24
181
19.65
?3,556.65
OK
PID_025
Product_25
74
72.96
?5,399.04
OK
PID_026
Product_26
92
80.17
?7,375.64
OK
PID_027
Product_27
163
91.68
?14,943.84
OK
PID_028
Product_28
188
8.68
?1,631.84
OK
PID_029
Product_29
31
97.1
?3,010.10
OK
PID_030
Product_30
194
53.06
?10,293.64
OK
PID_031
Product_31
39
30.67
?1,196.13
OK
PID_032
Product_32
104
13.41
?1,394.64
OK
PID_033
Product_33
142
55.38
?7,863.96
OK
PID_034
Product_34
168
87.21
?14,651.28
OK
PID_035
Product_35
76
56.41
?4,287.16
OK
PID_036
Product_36
111
84.47
?9,376.17
OK
PID_037
Product_37
158
99.51
?15,722.58
OK
PID_038
Product_38
61
73.49
?4,482.89
OK
PID_039
Product_39
147
77.35
?11,370.45
OK
PID_040
Product_40
71
31.27
?2,220.17
OK
PID_041
Product_41
191
68.9
?13,159.90
OK
PID_042
Product_42
169
90.02
?15,213.38
OK
PID_043
Product_43
158
38.48
?6,079.84
OK
PID_044
Product_44
34
89.98
?3,059.32
OK
PID_045
Product_45
84
38.76
?3,255.84
OK
PID_046
Product_46
195
28.48
?5,553.60
OK
PID_047
Product_47
146
41.25
?6,022.50
OK
PID_048
Product_48
19
91.79
?1,744.01
REORDER NOW
PID_049
Product_49
145
64.15
?9,301.75
OK
PID_050
Product_50
164
74.16
?12,162.24
OK
PID_051
Product_51
76
87.81
?6,673.56
OK
PID_052
Product_52
21
31.83
?668.43
OK
PID_053
Product_53
194
79.56
?15,434.64
OK
PID_054
Product_54
49
25.61
?1,254.89
OK
PID_055
Product_55
49
76.12
?3,729.88
OK
PID_056
Product_56
14
43.45
?608.30
REORDER NOW
PID_057
Product_57
9
17.03
?153.27
REORDER NOW
PID_058
Product_58
157
11.44
?1,796.08
OK
PID_059
Product_59
191
28.35
?5,414.85
OK
PID_060
Product_60
185
35.03
?6,480.55
OK
PID_061
Product_61
151
79.51
?12,006.01
OK
PID_062
Product_62
93
23.63
?2,197.59
OK
PID_063
Product_63
147
81.46
?11,974.62
OK
PID_064
Product_64
196
32.63
?6,395.48
OK
PID_065
Product_65
45
40.88
?1,839.60
OK
PID_066
Product_66
181
26.01
?4,707.81
OK
PID_067
Product_67
85
14.04
?1,193.40
OK
PID_068
Product_68
163
13.18
?2,148.34
OK
PID_069
Product_69
129
46.82
?6,039.78
OK
PID_070
Product_70
178
9.1
?1,619.80
OK
PID_071
Product_71
64
33.29
?2,130.56
OK
PID_072
Product_72
119
70.25
?8,359.75
OK
PID_073
Product_73
43
94.03
?4,043.29
OK
PID_074
Product_74
62
74.03
?4,589.86
OK
PID_075
Product_75
20
76.76
?1,535.20
OK
PID_076
Product_76
91
75.73
?6,891.43
OK
PID_077
Product_77
136
23.57
?3,205.52
OK
PID_078
Product_78
176
98.77
?17,383.52
OK
PID_079
Product_79
91
56.37
?5,129.67
OK
PID_080
Product_80
10
76.26
?762.60
REORDER NOW
PID_081
Product_81
170
12.94
?2,199.80
OK
PID_082
Product_82
24
3.06
?73.44
OK
PID_083
Product_83
28
78.12
?2,187.36
OK
PID_084
Product_84
2
7.4
?14.80
REORDER NOW
PID_085
Product_85
94
70.23
?6,601.62
OK
PID_086
Product_86
22
55.67
?1,224.74
OK
PID_087
Product_87
164
48.7
?7,986.80
OK
PID_088
Product_88
76
10.07
?765.32
OK
PID_089
Product_89
22
14.69
?323.18
OK
PID_090
Product_90
51
67.57
?3,446.07
OK
PID_091
Product_91
198
97.07
?19,219.86
OK
PID_092
Product_92
107
79.55
?8,511.85
OK
PID_093
Product_93
104
49.88
?5,187.52
OK
PID_094
Product_94
144
68.97
?9,931.68
OK
PID_095
Product_95
125
94.05
?11,756.25
OK
PID_096
Product_96
42
65.47
?2,749.74
OK
PID_097
Product_97
109
81.85
?8,921.65
OK
PID_098
Product_98
159
16.7
?2,655.30
OK
PID_099
Product_99
23
63.78
?1,466.94
OK
PID_100
Product_100
122
50.13
?6,115.86
OK
End of preview.

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