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merve
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student_scores

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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 8 new columns ({'lunch', 'writing score', 'reading score', 'parental level of education', 'math score', 'test preparation course', 'gender', 'race/ethnicity'}) and 12 missing columns ({'2', '11', '8', '4', '3', '7', '9', '10', '6', '5', '1', '0'}).

This happened while the csv dataset builder was generating data using

hf://datasets/merve/student_scores/dataset.csv (at revision 38f9e34cc1a66302e7dfd4e01dc228eafbf4dbc1)

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
              Unnamed: 0: int64
              gender: string
              race/ethnicity: string
              parental level of education: string
              lunch: string
              test preparation course: string
              math score: int64
              reading score: int64
              writing score: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1392
              to
              {'Unnamed: 0': Value(dtype='int64', id=None), '0': Value(dtype='string', id=None), '1': Value(dtype='string', id=None), '2': Value(dtype='string', id=None), '3': Value(dtype='string', id=None), '4': Value(dtype='string', id=None), '5': Value(dtype='float64', id=None), '6': Value(dtype='float64', id=None), '7': Value(dtype='float64', id=None), '8': Value(dtype='float64', id=None), '9': Value(dtype='float64', id=None), '10': Value(dtype='float64', id=None), '11': Value(dtype='float64', 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 8 new columns ({'lunch', 'writing score', 'reading score', 'parental level of education', 'math score', 'test preparation course', 'gender', 'race/ethnicity'}) and 12 missing columns ({'2', '11', '8', '4', '3', '7', '9', '10', '6', '5', '1', '0'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/merve/student_scores/dataset.csv (at revision 38f9e34cc1a66302e7dfd4e01dc228eafbf4dbc1)
              
              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
0
string
1
string
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string
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string
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string
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float64
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float64
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float64
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float64
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float64
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float64
0
female
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bachelor's degree
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master's degree
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93
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male
group A
associate's degree
free/reduced
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male
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some college
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78
75
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female
group B
associate's degree
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none
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female
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some college
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1
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male
group B
some college
free/reduced
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male
group D
high school
free/reduced
completed
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female
group B
high school
free/reduced
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associate's degree
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male
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associate's degree
standard
none
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52
43
0
1
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female
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high school
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none
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81
73
2
1
1
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male
group A
some college
standard
completed
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70
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female
group A
master's degree
standard
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female
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some high school
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none
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78
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male
group C
high school
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female
group B
some high school
free/reduced
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male
group C
master's degree
free/reduced
completed
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female
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associate's degree
free/reduced
none
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male
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high school
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female
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some college
free/reduced
completed
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male
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some college
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none
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female
group C
some high school
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none
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male
group D
bachelor's degree
free/reduced
completed
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male
group A
master's degree
free/reduced
none
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male
group B
some college
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female
group C
bachelor's degree
standard
none
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male
group C
high school
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female
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master's degree
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female
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some college
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female
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some college
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female
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master's degree
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male
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some college
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male
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some college
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male
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female
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associate's degree
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female
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some high school
free/reduced
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female
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associate's degree
free/reduced
completed
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male
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associate's degree
free/reduced
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male
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female
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female
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associate's degree
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none
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male
group B
some college
free/reduced
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female
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associate's degree
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male
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female
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female
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female
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male
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high school
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male
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some college
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male
group E
associate's degree
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male
group C
some college
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male
group D
high school
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female
group C
some high school
free/reduced
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female
group C
high school
free/reduced
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female
group E
associate's degree
standard
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male
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associate's degree
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male
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female
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some high school
free/reduced
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male
group E
bachelor's degree
free/reduced
completed
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male
group A
some high school
free/reduced
none
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5
1
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male
group A
associate's degree
free/reduced
none
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55
0
1
0
1
63
female
group C
associate's degree
standard
none
69
80
71
0
1
1
0
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female
group D
some high school
standard
none
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59
5
1
1
0
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male
group B
some high school
standard
none
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61
5
1
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male
group D
some high school
free/reduced
none
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5
1
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female
group C
some college
standard
none
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74
4
1
1
0
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male
group B
associate's degree
free/reduced
none
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58
56
0
1
0
1
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female
group C
associate's degree
standard
none
39
64
57
0
1
1
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female
group D
some college
free/reduced
completed
58
63
73
4
0
1
0
71
male
group D
some college
standard
completed
63
55
63
4
0
0
1
72
female
group A
associate's degree
free/reduced
none
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48
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1
1
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male
group C
some high school
free/reduced
none
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5
1
0
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male
group C
some high school
standard
none
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49
41
5
1
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male
group B
associate's degree
free/reduced
none
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41
38
0
1
0
1
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male
group E
some high school
standard
none
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26
22
5
1
0
1
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male
group A
bachelor's degree
standard
completed
80
78
81
1
0
0
1
78
female
group D
some high school
standard
completed
61
74
72
5
0
1
0
79
female
group E
master's degree
standard
none
62
68
68
3
1
1
0
80
female
group B
associate's degree
standard
none
47
49
50
0
1
1
0
81
male
group B
high school
free/reduced
none
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45
45
2
1
0
1
82
male
group A
some college
free/reduced
completed
50
47
54
4
0
0
1
83
male
group E
associate's degree
standard
none
72
64
63
0
1
0
1
84
male
group D
high school
free/reduced
none
42
39
34
2
1
0
1
85
female
group C
some college
standard
none
73
80
82
4
1
1
0
86
female
group C
some college
free/reduced
none
76
83
88
4
1
1
0
87
female
group D
associate's degree
standard
none
71
71
74
0
1
1
0
88
female
group A
some college
standard
none
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70
67
4
1
1
0
89
female
group D
some high school
standard
none
73
86
82
5
1
1
0
90
female
group C
bachelor's degree
standard
none
65
72
74
1
1
1
0
91
male
group C
high school
free/reduced
none
27
34
36
2
1
0
1
92
male
group C
high school
standard
none
71
79
71
2
1
0
1
93
male
group C
associate's degree
free/reduced
completed
43
45
50
0
0
0
1
94
female
group B
some college
standard
none
79
86
92
4
1
1
0
95
male
group C
associate's degree
free/reduced
completed
78
81
82
0
0
0
1
96
male
group B
some high school
standard
completed
65
66
62
5
0
0
1
97
female
group E
some college
standard
completed
63
72
70
4
0
1
0
98
female
group D
some college
free/reduced
none
58
67
62
4
1
1
0
99
female
group D
bachelor's degree
standard
none
65
67
62
1
1
1
0
End of preview.
YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

Student Scores Dataset

This dataset contains clean and original versions of Student Scores Dataset and the transformer used to transform it from original to clean, can be used for inferences.

Here's the plot of the transformer:

ColumnTransformer(remainder='passthrough',transformers=[('categorical_missing_value_imputer',SimpleImputer(fill_value='missing',strategy='constant'),[0, 1, 2, 3, 4]),('numerical_missing_value_imputer',SimpleImputer(strategy='median'), [5, 6, 7]),('school_encoder', OrdinalEncoder(), [2]),('status_encoder', OrdinalEncoder(), [4]),('gender_encoder', OneHotEncoder(), [0])])
Please rerun this cell to show the HTML repr or trust the notebook.
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