Dataset Preview

Split (1)

train

The full dataset viewer is not available (click to read why). Only showing a preview of the rows.

The dataset generation failed

Error code:   DatasetGenerationError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0x90 in position 7: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1855, in _prepare_split_single
                  for _, table in generator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 188, in _generate_tables
                  csv_file_reader = pd.read_csv(file, iterator=True, dtype=dtype, **self.config.pd_read_csv_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 75, in wrapper
                  return function(*args, download_config=download_config, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1213, in xpandas_read_csv
                  return pd.read_csv(xopen(filepath_or_buffer, "rb", download_config=download_config), **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv
                  return _read(filepath_or_buffer, kwds)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 620, in _read
                  parser = TextFileReader(filepath_or_buffer, **kwds)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1620, in __init__
                  self._engine = self._make_engine(f, self.engine)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1898, in _make_engine
                  return mapping[engine](f, **self.options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 93, in __init__
                  self._reader = parsers.TextReader(src, **kwds)
                File "parsers.pyx", line 574, in pandas._libs.parsers.TextReader.__cinit__
                File "parsers.pyx", line 663, in pandas._libs.parsers.TextReader._get_header
                File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "parsers.pyx", line 2053, in pandas._libs.parsers.raise_parser_error
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x90 in position 7: invalid start byte
              
              The above exception was the direct cause of the following exception:
              
              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 1898, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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.

age int64	workclass string	functional_weight int64	education string	education_num int64	marital_status string	occupation string	relationship string	race string	sex string	capital_gain int64	capital_loss int64	hours_per_week int64	native_country string	income_bracket string
39	Private	297,847	9th	5	Married-civ-spouse	Other-service	Wife	Black	Female	3,411	0	34	United-States	<=50K
77	Private	344,425	9th	5	Married-civ-spouse	Priv-house-serv	Wife	Black	Female	0	0	10	United-States	<=50K
38	Private	131,461	9th	5	Married-civ-spouse	Other-service	Wife	Black	Female	0	0	24	Haiti	<=50K
28	Private	190,350	9th	5	Married-civ-spouse	Protective-serv	Wife	Black	Female	0	0	40	United-States	<=50K
37	Private	171,090	9th	5	Married-civ-spouse	Machine-op-inspct	Wife	Black	Female	0	0	48	United-States	<=50K
35	?	374,716	9th	5	Married-civ-spouse	?	Wife	White	Female	0	0	35	United-States	<=50K
45	Private	178,215	9th	5	Married-civ-spouse	Machine-op-inspct	Wife	White	Female	0	0	40	United-States	>50K
55	Private	176,012	9th	5	Married-civ-spouse	Tech-support	Wife	White	Female	0	0	23	United-States	<=50K
27	Private	109,611	9th	5	Married-civ-spouse	Machine-op-inspct	Wife	White	Female	0	0	37	Portugal	<=50K
31	Private	86,958	9th	5	Married-civ-spouse	Exec-managerial	Wife	White	Female	0	0	40	United-States	<=50K
30	Private	61,272	9th	5	Married-civ-spouse	Machine-op-inspct	Wife	White	Female	0	0	40	Portugal	<=50K
28	Private	209,801	9th	5	Married-civ-spouse	Machine-op-inspct	Wife	White	Female	0	0	40	United-States	<=50K
46	Private	184,883	9th	5	Married-civ-spouse	Machine-op-inspct	Wife	White	Female	0	0	40	United-States	<=50K
70	Private	216,390	9th	5	Married-civ-spouse	Machine-op-inspct	Wife	White	Female	2,653	0	40	United-States	<=50K
31	Private	399,052	9th	5	Married-civ-spouse	Farming-fishing	Wife	White	Female	0	0	42	United-States	<=50K
40	Local-gov	183,096	9th	5	Married-civ-spouse	Other-service	Wife	White	Female	0	0	40	Yugoslavia	>50K
52	Local-gov	330,799	9th	5	Married-civ-spouse	Other-service	Wife	White	Female	0	0	40	United-States	<=50K
46	Self-emp-inc	161,386	9th	5	Married-civ-spouse	Adm-clerical	Wife	White	Female	0	0	50	United-States	<=50K
41	Self-emp-inc	299,813	9th	5	Married-civ-spouse	Sales	Wife	White	Female	0	0	70	Dominican-Republic	<=50K
41	?	217,921	9th	5	Married-civ-spouse	?	Wife	Asian-Pac-Islander	Female	0	0	40	Hong	<=50K
72	Private	74,141	9th	5	Married-civ-spouse	Exec-managerial	Wife	Asian-Pac-Islander	Female	0	0	48	United-States	>50K
75	?	164,849	9th	5	Married-civ-spouse	?	Husband	Black	Male	1,409	0	5	United-States	<=50K
77	?	232,894	9th	5	Married-civ-spouse	?	Husband	Black	Male	0	0	40	United-States	<=50K
66	?	108,185	9th	5	Married-civ-spouse	?	Husband	Black	Male	0	0	40	United-States	<=50K
45	Private	186,272	9th	5	Married-civ-spouse	Adm-clerical	Husband	Black	Male	5,178	0	40	United-States	>50K
57	Private	136,107	9th	5	Married-civ-spouse	Craft-repair	Husband	Black	Male	0	0	40	United-States	>50K
57	Private	342,906	9th	5	Married-civ-spouse	Sales	Husband	Black	Male	0	0	55	United-States	>50K
47	Private	209,212	9th	5	Married-civ-spouse	Machine-op-inspct	Husband	Black	Male	0	0	56	?	<=50K
61	Private	355,645	9th	5	Married-civ-spouse	Machine-op-inspct	Husband	Black	Male	0	0	20	Trinadad&Tobago	<=50K
63	Private	201,631	9th	5	Married-civ-spouse	Farming-fishing	Husband	Black	Male	0	0	40	United-States	<=50K
32	Private	124,187	9th	5	Married-civ-spouse	Farming-fishing	Husband	Black	Male	0	0	40	United-States	<=50K
56	Private	229,525	9th	5	Married-civ-spouse	Transport-moving	Husband	Black	Male	0	0	40	United-States	<=50K
38	Private	257,416	9th	5	Married-civ-spouse	Transport-moving	Husband	Black	Male	0	0	40	United-States	<=50K
58	Private	298,601	9th	5	Married-civ-spouse	Machine-op-inspct	Husband	Black	Male	3,781	0	40	United-States	<=50K
44	Private	123,572	9th	5	Married-civ-spouse	Machine-op-inspct	Husband	Black	Male	0	0	40	United-States	<=50K
53	Private	347,446	9th	5	Married-civ-spouse	Machine-op-inspct	Husband	Black	Male	0	0	40	United-States	<=50K
44	Private	118,536	9th	5	Married-civ-spouse	Machine-op-inspct	Husband	Black	Male	0	0	40	United-States	<=50K
62	Private	271,431	9th	5	Married-civ-spouse	Other-service	Husband	Black	Male	0	0	42	United-States	<=50K
68	Private	148,874	9th	5	Married-civ-spouse	Craft-repair	Husband	Black	Male	0	0	44	United-States	<=50K
31	Private	393,357	9th	5	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0	0	48	United-States	<=50K
58	Private	104,945	9th	5	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0	0	60	United-States	<=50K
28	Local-gov	154,863	9th	5	Married-civ-spouse	Craft-repair	Husband	Black	Male	0	0	40	Trinadad&Tobago	>50K
51	Local-gov	146,181	9th	5	Married-civ-spouse	Transport-moving	Husband	Black	Male	0	0	40	United-States	<=50K
35	Federal-gov	76,845	9th	5	Married-civ-spouse	Farming-fishing	Husband	Black	Male	0	0	40	United-States	<=50K
35	Private	255,635	9th	5	Married-civ-spouse	Craft-repair	Husband	Other	Male	0	0	40	Mexico	<=50K
30	Private	348,618	9th	5	Married-civ-spouse	Craft-repair	Husband	Other	Male	0	0	40	Mexico	<=50K
63	?	310,396	9th	5	Married-civ-spouse	?	Husband	White	Male	5,178	0	40	United-States	>50K
68	?	141,181	9th	5	Married-civ-spouse	?	Husband	White	Male	0	0	2	United-States	<=50K
67	?	243,256	9th	5	Married-civ-spouse	?	Husband	White	Male	0	0	15	United-States	<=50K
69	?	111,238	9th	5	Married-civ-spouse	?	Husband	White	Male	0	0	20	United-States	<=50K
74	?	340,939	9th	5	Married-civ-spouse	?	Husband	White	Male	3,471	0	40	United-States	<=50K
60	?	163,946	9th	5	Married-civ-spouse	?	Husband	White	Male	0	0	40	United-States	<=50K
66	?	175,891	9th	5	Married-civ-spouse	?	Husband	White	Male	0	0	40	United-States	<=50K
66	?	68,219	9th	5	Married-civ-spouse	?	Husband	White	Male	0	0	40	United-States	<=50K
64	?	45,817	9th	5	Married-civ-spouse	?	Husband	White	Male	0	0	50	United-States	<=50K
50	?	257,117	9th	5	Married-civ-spouse	?	Husband	White	Male	0	0	50	United-States	<=50K
45	Private	223,999	9th	5	Married-civ-spouse	Other-service	Husband	White	Male	0	1,848	40	United-States	>50K
54	Private	174,865	9th	5	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	45	United-States	>50K
51	Private	199,995	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	50	United-States	>50K
58	Private	214,502	9th	5	Married-civ-spouse	Handlers-cleaners	Husband	White	Male	0	0	50	United-States	>50K
37	Private	147,258	9th	5	Married-civ-spouse	Machine-op-inspct	Husband	White	Male	0	0	50	United-States	>50K
59	Private	43,221	9th	5	Married-civ-spouse	Transport-moving	Husband	White	Male	0	0	60	United-States	>50K
31	Private	373,432	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	43	United-States	<=50K
33	Private	233,107	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	33	Mexico	<=50K
30	Private	229,051	9th	5	Married-civ-spouse	Other-service	Husband	White	Male	0	0	37	United-States	<=50K
38	Private	430,035	9th	5	Married-civ-spouse	Farming-fishing	Husband	White	Male	0	0	54	Mexico	<=50K
76	Private	199,949	9th	5	Married-civ-spouse	Protective-serv	Husband	White	Male	0	0	13	United-States	<=50K
35	Private	186,489	9th	5	Married-civ-spouse	Handlers-cleaners	Husband	White	Male	0	0	46	United-States	<=50K
39	Private	347,434	9th	5	Married-civ-spouse	Handlers-cleaners	Husband	White	Male	0	0	43	Mexico	<=50K
31	Private	507,875	9th	5	Married-civ-spouse	Machine-op-inspct	Husband	White	Male	0	0	43	United-States	<=50K
60	Private	39,952	9th	5	Married-civ-spouse	Machine-op-inspct	Husband	White	Male	2,228	0	37	United-States	<=50K
46	Private	72,896	9th	5	Married-civ-spouse	Machine-op-inspct	Husband	White	Male	0	0	43	United-States	<=50K
60	Private	71,683	9th	5	Married-civ-spouse	Machine-op-inspct	Husband	White	Male	0	0	49	United-States	<=50K
63	Private	66,634	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	16	United-States	<=50K
26	Private	105,059	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	20	United-States	<=50K
39	Private	188,069	9th	5	Married-civ-spouse	Transport-moving	Husband	White	Male	0	0	25	United-States	<=50K
59	Private	366,618	9th	5	Married-civ-spouse	Other-service	Husband	White	Male	0	0	30	United-States	<=50K
27	Private	116,207	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	32	United-States	<=50K
26	Private	229,977	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	35	United-States	<=50K
36	Private	219,814	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	35	Guatemala	<=50K
69	Private	88,566	9th	5	Married-civ-spouse	Other-service	Husband	White	Male	1,424	0	35	United-States	<=50K
62	Private	84,756	9th	5	Married-civ-spouse	Other-service	Husband	White	Male	0	0	35	United-States	<=50K
41	Private	294,270	9th	5	Married-civ-spouse	Transport-moving	Husband	White	Male	0	0	35	United-States	<=50K
60	Private	81,578	9th	5	Married-civ-spouse	Sales	Husband	White	Male	0	0	40	United-States	<=50K
28	Private	163,265	9th	5	Married-civ-spouse	Sales	Husband	White	Male	4,508	0	40	United-States	<=50K
51	Private	173,987	9th	5	Married-civ-spouse	Sales	Husband	White	Male	0	0	40	United-States	<=50K
56	Private	437,727	9th	5	Married-civ-spouse	Sales	Husband	White	Male	0	0	40	United-States	<=50K
38	Private	31,964	9th	5	Married-civ-spouse	Adm-clerical	Husband	White	Male	0	0	40	United-States	<=50K
61	Private	197,286	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	40	United-States	<=50K
38	Private	103,751	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	40	United-States	<=50K
30	Private	151,868	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	40	United-States	<=50K
34	Private	314,646	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	40	United-States	<=50K
37	Private	203,828	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	40	United-States	<=50K
42	Private	445,940	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	40	Mexico	<=50K
32	Private	182,323	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	40	United-States	<=50K
29	Private	309,463	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	40	United-States	<=50K
27	Private	114,967	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	40	United-States	<=50K
60	Private	117,509	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	40	United-States	<=50K
49	Private	39,986	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	0	0	40	United-States	<=50K
30	Private	326,199	9th	5	Married-civ-spouse	Craft-repair	Husband	White	Male	2,580	0	40	United-States	<=50K

End of preview.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

What You Can Do With This Data:

Test for algorithmic bias - Compare model performance across demographic groups

Evaluate name-based biases - Test if your systems treat names differently based on gender or cultural origin

Develop fair ML models - Use the Adult Income dataset with its protected attributes

Benchmark against baselines - Compare your fairness metrics against the provided calculations

This approach gives you a more useful fairness benchmark dataset than simply pulling one large table from BigQuery, as it provides complementary data types specifically selected for fairness testing.

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