Datasets:

davidwisdom
/

fake_railroad_company

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 5 new columns ({'expected_departure', 'expected_arrival', 'route_id', 'src', 'dst'}) and 8 missing columns ({'passenger_id', 'membership_start', 'birthday', 'children', 'gender', 'age', 'name', 'is_married'}).

This happened while the csv dataset builder was generating data using

hf://datasets/davidwisdom/fake_railroad_company/v1/routes.csv (at revision a0f40bf036af3ed5a037137c356f60317be71a17)

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
              route_id: int64
              src: string
              dst: string
              expected_departure: string
              expected_arrival: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 853
              to
              {'name': Value(dtype='string', id=None), 'birthday': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'is_married': Value(dtype='bool', id=None), 'children': Value(dtype='int64', id=None), 'membership_start': Value(dtype='string', id=None), 'age': Value(dtype='int64', id=None), 'passenger_id': 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 5 new columns ({'expected_departure', 'expected_arrival', 'route_id', 'src', 'dst'}) and 8 missing columns ({'passenger_id', 'membership_start', 'birthday', 'children', 'gender', 'age', 'name', 'is_married'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/davidwisdom/fake_railroad_company/v1/routes.csv (at revision a0f40bf036af3ed5a037137c356f60317be71a17)
              
              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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name string	birthday string	gender string	is_married bool	children int64	membership_start string	age int64	passenger_id int64
Brett Johal	1975-07-06	male	false	2	2004-02-16	48	1
Jason Martinez	1995-12-17	male	true	1	null	28	2
Andrew Stewart	1988-11-22	male	true	3	null	35	3
Patrick Alexander	1970-08-14	male	false	2	null	53	4
Kevin Neerachapong	1994-04-28	male	false	1	2020-12-24	29	5
Gerald Chadwick	1956-09-02	male	true	2	null	67	6
Michelle Kennedy	1983-11-26	female	true	6	null	40	7
Susan Crowley	1980-09-16	female	true	2	null	43	8
Sara Stewart	1999-04-24	female	false	0	null	24	9
Nirvi Wood	1966-11-19	female	true	4	null	57	10
Steven Dyer	1982-04-15	male	true	1	1998-07-07	41	11
Jared Chaney	1943-03-01	male	true	3	1979-02-21	80	12
Howard Price	1980-07-01	male	false	3	null	43	13
Rebecca Dora	1978-07-30	female	true	1	null	45	14
Marcia Hansen	2010-01-01	female	false	0	null	14	15
Kyle Arya	2009-12-26	male	false	0	null	14	16
Nutkrita Farragher	1995-04-17	female	false	2	2018-11-18	28	17
Erin Mccarthy	1970-04-15	female	false	1	null	53	18
Laura Thomas	1929-08-25	female	false	1	null	94	19
Shawn Woods	1949-09-24	male	false	0	null	74	20
Bryan Roche	1978-05-15	male	true	5	null	45	21
Akarsh O'Freil	1983-03-14	female	true	2	null	40	22
Lily Walker	1992-02-14	female	true	3	null	31	23
Ashley Ward	1951-10-17	female	true	4	null	72	24
Yakshit Sparks	1975-03-11	male	true	4	2012-04-02	48	25
Pattamon Cobb	2007-12-16	female	false	0	null	16	26
Chanya Davidson	1948-05-23	female	true	1	null	75	27
Brittney McMonagle	1981-01-25	female	true	0	null	42	28
Angela Andra	1974-06-21	female	true	3	1991-04-17	49	29
Courtney Master	2007-09-14	female	false	0	null	16	30
Chaiwut Hayes	1957-07-16	female	false	2	null	66	31
Barbara Keller	1994-08-17	female	true	2	null	29	32
Desmond Blake	1980-09-30	male	true	3	2018-02-27	43	33
Tiya Turner	2015-09-19	male	false	0	null	8	34
Ashley Johnson	1956-11-09	female	false	2	null	67	35
Philip Williams	1992-10-29	male	false	0	null	31	36
James Long	1952-12-31	male	false	3	null	71	37
Lisa Austin	1960-06-21	female	true	1	2017-12-08	63	38
Eric Pitanuwat	2021-05-16	male	false	0	null	2	39
Caolan Polauaypon	2014-06-10	male	false	0	null	9	40
Riya Jones	1959-05-12	male	false	0	2006-01-30	64	41
George Rout	2010-02-12	male	false	0	null	13	42
Patcharaporn Wilkins	1999-09-26	male	false	0	null	24	43
William Hall	1973-08-19	male	true	3	1995-09-28	50	44
Jared Harrihy	1983-03-25	male	false	2	null	40	45
Orlaigh Levy	2015-10-04	female	false	0	null	8	46
Catherine Palmer	2009-07-01	female	false	0	null	14	47
Louise Kongsri	1972-06-01	female	true	2	null	51	48
Shannon Wade	1969-02-11	female	true	1	null	54	49
Nancy Keeney	1941-03-24	female	true	5	null	82	50
Louis Butala	1950-03-20	male	true	2	null	73	51
Sitiwat Williams	1967-07-06	female	true	2	null	56	52
Teetat Arnold	1967-04-19	male	true	4	null	56	53
Patcharaporn Raj	2005-01-16	female	false	1	2022-04-09	19	54
Cynthia Tapp	1986-11-28	female	true	3	null	37	55
Shalv Moore	1986-09-17	female	true	4	2006-08-05	37	56
Yakshit Davis	1966-06-20	female	false	1	null	57	57
Christie Choeychuen	1946-02-25	female	true	8	null	77	58
Chayapat Sahota	1994-01-15	male	false	0	null	30	59
Prisha Bhatt	1964-12-12	female	false	0	null	59	60
Karen Bell	1997-09-17	female	true	1	null	26	61
Carl Stafford	1957-11-12	male	true	4	null	66	62
Janice Garcia	1947-12-18	female	false	1	null	76	63
Rick Benton	1950-06-25	male	true	1	1995-02-10	73	64
Sherri Smith	2018-01-13	female	false	0	null	6	65
Donald Barker	1938-08-25	male	true	4	2002-04-16	85	66
Margaret Dillon	1964-01-12	female	true	3	2010-04-03	60	67
Rachael Weaver	1983-06-09	female	false	0	null	40	68
Karen Phillips	1954-01-15	female	false	1	null	70	69
Jaruwan Halder	1950-09-26	female	true	3	null	73	70
Puntira Bryant	1965-07-12	male	true	6	null	58	71
Leon Bell	1969-12-15	male	true	1	null	54	72
Gillian Franklin	2012-02-03	female	false	0	null	11	73
Scott Webster	1994-10-03	male	false	0	null	29	74
Kimberly Sireelert	1993-02-15	female	false	1	2021-06-15	30	75
Elizabeth Agrawal	1996-05-18	female	false	1	null	27	76
Jessica Youprasert	2012-09-25	female	false	0	null	11	77
Ryan West	2012-06-07	female	false	0	null	11	78
Joel Gray	1984-02-05	male	true	2	null	39	79
Katherine Alvarez	1977-04-17	female	false	0	2002-12-04	46	80
Michele Vinyuvanichkul	2010-06-01	female	false	0	null	13	81
Robert McGinley	2015-11-22	male	false	0	null	8	82
Stephanie Rees	1978-06-23	female	true	1	null	45	83
Prisha Mann	1998-01-28	female	false	1	null	25	84
Cody Dugar	1991-07-07	male	false	0	null	32	85
Liam McGuinn	1967-03-07	male	true	1	null	56	86
Lisa Morgan	1979-10-19	female	false	1	null	44	87
Barbara Curneen	1986-12-29	female	true	2	null	37	88
Dana Miller	2007-01-31	female	false	0	null	16	89
Cindy Browning	1956-02-20	female	false	2	2012-10-29	67	90
Fintan Burgess	2018-05-08	male	false	0	null	5	91
Ivan Cook	1997-01-14	female	false	0	2020-02-15	27	92
Helen Cruz	1978-06-22	female	true	3	null	45	93
Barbara Raju	2010-05-30	female	false	0	null	13	94
Andrew Cloonan	1943-05-16	male	false	1	null	80	95
Evan Uniacke	2018-05-14	male	false	0	null	5	96
Raghav Moore	2015-10-20	male	false	0	null	8	97
Patrick Weldon	1998-08-01	male	true	1	null	25	98
Raunak McDunphy	2004-04-29	female	false	0	null	19	99
Susan Baker	1950-07-08	female	true	1	null	73	100

End of preview.

fake_railroad_company

This is toy data I created about an imaginary railroad company.

V1

This is the first version of the data that I generated.

V2

I tweaked some of the weights I used to calculate the satisfaction score.

V3

Some customers are now power users who ride more often than other users.

V4

Customers with children are more likely to be members

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