Datasets:

EduDevCommons
/

Global-AIML-Hiring-Layoffs-Dataset

Tasks:

Tabular Classification

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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 12 new columns ({'experience_level', 'job_title', 'employee_residence', 'work_year', 'salary_currency', 'salary', 'company_size', 'Unnamed: 0', 'employment_type', 'company_location', 'salary_in_usd', 'remote_ratio'}) and 21 missing columns ({'job_count_exp_SE', 'job_count_exp_EN', 'country_code', 'country_name', 'job_count', 'min_salary_usd', 'job_count_emp_FL', 'avg_percentage_laid_off', 'year', 'total_funds_raised_m', 'most_common_stage', 'job_count_exp_EX', 'layoff_events', 'avg_salary_usd', 'job_count_emp_CT', 'job_count_emp_FT', 'max_salary_usd', 'job_count_exp_MI', 'total_layoffs', 'median_salary_usd', 'job_count_emp_PT'}).

This happened while the csv dataset builder was generating data using

zip://data-science-job-salaries/ds_salaries.csv::hf://datasets/EduDevCommons/Global-AIML-Hiring-Layoffs-Dataset@83b72808ae6e03a7dd8866506b514da03dbae1d1/Global AI.ML -  Hiring & Layoffs Dataset.zip, ['hf://datasets/EduDevCommons/Global-AIML-Hiring-Layoffs-Dataset@83b72808ae6e03a7dd8866506b514da03dbae1d1/Global AI.ML -  Hiring & Layoffs Dataset.zip']

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/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              Unnamed: 0: int64
              work_year: int64
              experience_level: string
              employment_type: string
              job_title: string
              salary: int64
              salary_currency: string
              salary_in_usd: int64
              employee_residence: string
              remote_ratio: int64
              company_location: string
              company_size: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1766
              to
              {'country_code': Value('string'), 'country_name': Value('string'), 'year': Value('int64'), 'total_layoffs': Value('int64'), 'layoff_events': Value('int64'), 'avg_percentage_laid_off': Value('float64'), 'total_funds_raised_m': Value('float64'), 'most_common_stage': Value('string'), 'job_count': Value('int64'), 'avg_salary_usd': Value('float64'), 'median_salary_usd': Value('float64'), 'min_salary_usd': Value('float64'), 'max_salary_usd': Value('float64'), 'job_count_exp_EN': Value('int64'), 'job_count_exp_EX': Value('int64'), 'job_count_exp_MI': Value('int64'), 'job_count_exp_SE': Value('int64'), 'job_count_emp_CT': Value('int64'), 'job_count_emp_FL': Value('int64'), 'job_count_emp_FT': Value('int64'), 'job_count_emp_PT': Value('int64')}
              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 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              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 12 new columns ({'experience_level', 'job_title', 'employee_residence', 'work_year', 'salary_currency', 'salary', 'company_size', 'Unnamed: 0', 'employment_type', 'company_location', 'salary_in_usd', 'remote_ratio'}) and 21 missing columns ({'job_count_exp_SE', 'job_count_exp_EN', 'country_code', 'country_name', 'job_count', 'min_salary_usd', 'job_count_emp_FL', 'avg_percentage_laid_off', 'year', 'total_funds_raised_m', 'most_common_stage', 'job_count_exp_EX', 'layoff_events', 'avg_salary_usd', 'job_count_emp_CT', 'job_count_emp_FT', 'max_salary_usd', 'job_count_exp_MI', 'total_layoffs', 'median_salary_usd', 'job_count_emp_PT'}).
              
              This happened while the csv dataset builder was generating data using
              
              zip://data-science-job-salaries/ds_salaries.csv::hf://datasets/EduDevCommons/Global-AIML-Hiring-Layoffs-Dataset@83b72808ae6e03a7dd8866506b514da03dbae1d1/Global AI.ML -  Hiring & Layoffs Dataset.zip, ['hf://datasets/EduDevCommons/Global-AIML-Hiring-Layoffs-Dataset@83b72808ae6e03a7dd8866506b514da03dbae1d1/Global AI.ML -  Hiring & Layoffs Dataset.zip']
              
              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.

country_code string	country_name string	year int64	total_layoffs int64	layoff_events int64	avg_percentage_laid_off float64	total_funds_raised_m float64	most_common_stage string	job_count int64	avg_salary_usd float64	median_salary_usd float64	min_salary_usd float64	max_salary_usd float64	job_count_exp_EN int64	job_count_exp_EX int64	job_count_exp_MI int64	job_count_exp_SE int64	job_count_emp_CT int64	job_count_emp_FL int64	job_count_emp_FT int64	job_count_emp_PT int64
AE	United Arab Emirates	2,020	0	0	1	0.00003	Series A	1	115,000	115,000	115,000	115,000	0	0	1	0	0	0	1	0
AT	Austria	2,020	0	0	0	0	0	2	82,684	82,684	74,130	91,237	0	0	1	1	0	0	2	0
CA	Canada	2,020	80	3	0.2	0.000201	Series B	1	117,104	117,104	117,104	117,104	0	0	0	1	0	0	1	0
CN	China	2,020	0	0	0	0	0	1	43,331	43,331	43,331	43,331	0	0	1	0	0	0	1	0
DE	Germany	2,020	0	0	0	0	0	7	67,157	59,303	15,966	148,261	4	0	2	1	0	0	6	1
DK	Denmark	2,020	0	0	0	0	0	1	45,896	45,896	45,896	45,896	1	0	0	0	0	0	1	0
ES	Spain	2,020	0	0	0	0	0	2	59,304	59,304	38,776	79,833	0	1	1	0	0	0	2	0
FR	France	2,020	0	0	0	0	0	5	50,066	46,759	39,916	70,139	2	0	3	0	0	0	5	0
GB	United Kingdom	2,020	0	0	0	0	0	4	103,225	110,948	76,958	114,047	0	0	2	2	0	0	4	0
GR	Greece	2,020	0	0	0	0	0	1	47,899	47,899	47,899	47,899	0	0	0	1	0	0	1	0
HN	Honduras	2,020	0	0	0	0	0	1	20,000	20,000	20,000	20,000	0	0	1	0	0	0	1	0
HR	Croatia	2,020	0	0	0	0	0	1	45,618	45,618	45,618	45,618	0	0	0	1	0	0	1	0
HU	Hungary	2,020	0	0	0	0	0	1	35,735	35,735	35,735	35,735	0	0	1	0	0	0	1	0
ID	Indonesia	2,020	0	0	1	0.000027	Series B	0	0	0	0	0	0	0	0	0	0	0	0	0
IL	Israel	2,020	120	1	0.1	0.000386	Series D	0	0	0	0	0	0	0	0	0	0	0	0	0
IN	India	2,020	850	3	0.276667	0.000493	Series D	3	17,542	6,072	6,072	40,481	1	0	2	0	0	0	3	0
IT	Italy	2,020	0	0	0	0	0	1	21,669	21,669	21,669	21,669	1	0	0	0	0	0	0	1
JP	Japan	2,020	0	0	0	0	0	2	150,844	150,844	41,689	260,000	1	0	0	1	0	0	2	0
LU	Luxembourg	2,020	0	0	0	0	0	1	62,726	62,726	62,726	62,726	0	0	1	0	0	0	1	0
MX	Mexico	2,020	0	0	0	0	0	1	33,511	33,511	33,511	33,511	0	0	0	1	0	0	1	0
NG	Nigeria	2,020	0	0	0	0	0	1	10,000	10,000	10,000	10,000	1	0	0	0	0	0	1	0
NZ	New Zealand	2,020	0	0	0	0	0	1	125,000	125,000	125,000	125,000	0	0	0	1	0	0	1	0
PK	Pakistan	2,020	0	0	0	0	0	1	8,000	8,000	8,000	8,000	0	0	1	0	0	0	1	0
PT	Portugal	2,020	0	0	0	0	0	1	50,180	50,180	50,180	50,180	0	0	1	0	0	0	1	0
SG	Singapore	2,020	144	2	0.085	0.000614	Series D	0	0	0	0	0	0	0	0	0	0	0	0	0
US	United States	2,020	8,076	46	0.359767	0.010541	Series C	26	124,205	105,500	45,760	412,000	7	0	12	7	1	1	24	0
AS	American Samoa	2,021	0	0	0	0	0	1	18,053	18,053	18,053	18,053	1	0	0	0	0	0	1	0
AT	Austria	2,021	0	0	0	0	0	1	61,467	61,467	61,467	61,467	0	0	1	0	0	0	1	0
BE	Belgium	2,021	0	0	0	0	0	2	85,699	85,699	82,744	88,654	0	0	1	1	0	0	2	0
BR	Brazil	2,021	0	0	0	0	0	3	18,603	18,907	12,901	24,000	0	0	1	2	0	0	3	0
CA	Canada	2,021	45	1	1	0.000061	Series B	7	106,493	87,738	54,238	225,000	0	0	2	5	0	0	7	0
CH	Switzerland	2,021	0	0	0	0	0	1	5,882	5,882	5,882	5,882	1	0	0	0	0	0	1	0
CL	Chile	2,021	0	0	0	0	0	1	40,038	40,038	40,038	40,038	0	0	1	0	0	0	1	0
CO	Colombia	2,021	0	0	0	0	0	1	21,844	21,844	21,844	21,844	1	0	0	0	0	0	1	0
DE	Germany	2,021	0	0	0	0	0	11	80,168	85,000	24,823	173,762	3	0	4	4	0	0	11	0
DK	Denmark	2,021	0	0	0	0	0	2	58,632	58,632	28,609	88,654	1	0	0	1	0	0	1	1
ES	Spain	2,021	0	0	0	0	0	5	39,454	46,809	10,354	55,000	1	0	3	1	0	0	4	1
FR	France	2,021	0	0	0	0	0	5	55,253	53,192	36,643	77,684	3	0	0	2	0	0	5	0
GB	United Kingdom	2,021	20	1	0	0.00015	Series C	13	81,318	76,833	50,000	116,914	1	0	6	6	0	0	13	0
GR	Greece	2,021	0	0	0	0	0	1	40,189	40,189	40,189	40,189	0	0	1	0	0	0	1	0
IL	Israel	2,021	0	0	0	0	0	1	119,059	119,059	119,059	119,059	0	0	1	0	0	0	1	0
IN	India	2,021	0	0	0	0	0	15	25,695	22,611	5,409	66,265	6	0	6	3	0	0	14	1
IQ	Iraq	2,021	0	0	0	0	0	1	100,000	100,000	100,000	100,000	1	0	0	0	0	0	1	0
IR	Iran, Islamic Republic of	2,021	0	0	0	0	0	1	4,000	4,000	4,000	4,000	0	0	1	0	0	0	1	0
IT	Italy	2,021	0	0	0	0	0	1	51,064	51,064	51,064	51,064	0	0	1	0	0	0	1	0
JP	Japan	2,021	0	0	0	0	0	3	71,692	74,000	63,711	77,364	0	0	3	0	0	0	3	0
KE	Kenya	2,021	0	0	0	0	0	1	9,272	9,272	9,272	9,272	1	0	0	0	0	0	1	0
LU	Luxembourg	2,021	0	0	0	0	0	1	59,102	59,102	59,102	59,102	1	0	0	0	0	0	1	0
MD	Moldova, Republic of	2,021	0	0	0	0	0	1	18,000	18,000	18,000	18,000	0	0	1	0	0	0	1	0
MT	Malta	2,021	0	0	0	0	0	1	28,369	28,369	28,369	28,369	0	0	1	0	0	0	1	0
MX	Mexico	2,021	0	0	0	0	0	1	2,859	2,859	2,859	2,859	0	0	1	0	0	0	1	0
NG	Nigeria	2,021	0	0	0	0	0	1	50,000	50,000	50,000	50,000	0	0	1	0	0	0	1	0
NL	Netherlands	2,021	0	0	0	0	0	2	57,566	57,566	45,391	69,741	0	0	2	0	0	0	1	1
PK	Pakistan	2,021	0	0	0	0	0	1	12,000	12,000	12,000	12,000	0	0	1	0	0	1	0	0
PL	Poland	2,021	0	0	0	0	0	2	37,536	37,536	28,476	46,597	0	0	2	0	0	0	2	0
RO	Romania	2,021	0	0	0	0	0	1	60,000	60,000	60,000	60,000	0	0	1	0	0	0	1	0
SG	Singapore	2,021	0	0	0	0	0	1	89,294	89,294	89,294	89,294	0	0	1	0	0	0	1	0
SI	Slovenia	2,021	0	0	0	0	0	1	24,823	24,823	24,823	24,823	0	0	1	0	0	0	1	0
TR	Türkiye	2,021	0	0	0	0	0	3	20,097	20,171	12,103	28,016	0	0	2	1	0	0	3	0
UA	Ukraine	2,021	0	0	0	0	0	1	13,400	13,400	13,400	13,400	1	0	0	0	0	0	1	0
US	United States	2,021	1,113	3	0.27	0.000926	Acquired	83	140,542	115,000	5,679	600,000	18	3	35	27	3	1	77	2
VN	Viet Nam	2,021	0	0	0	0	0	1	4,000	4,000	4,000	4,000	1	0	0	0	0	0	1	0
AE	United Arab Emirates	2,022	0	0	0	0	0	2	92,500	92,500	65,000	120,000	0	0	0	2	0	0	2	0
AU	Australia	2,022	0	0	0	0.000023	Post-IPO	3	108,043	87,425	86,703	150,000	2	0	1	0	0	0	3	0
BE	Belgium	2,022	0	0	0	0.000596	Series G	0	0	0	0	0	0	0	0	0	0	0	0	0
BR	Brazil	2,022	615	5	0.148	0.00155	Series E	0	0	0	0	0	0	0	0	0	0	0	0	0
CA	Canada	2,022	1,469	8	0.335	0.00051	Series A	15	80,334	75,000	52,000	130,000	3	1	6	5	0	0	15	0
CH	Switzerland	2,022	0	0	0	0	0	1	122,346	122,346	122,346	122,346	0	0	1	0	0	0	1	0
CZ	Czechia	2,022	0	0	0	0	0	1	31,875	31,875	31,875	31,875	1	0	0	0	1	0	0	0
DE	Germany	2,022	458	7	0.24	0.004548	Unknown	5	90,306	87,932	54,957	162,674	1	0	2	2	0	0	4	1
DZ	Algeria	2,022	0	0	0	0	0	1	100,000	100,000	100,000	100,000	1	0	0	0	0	0	0	1
ES	Spain	2,022	0	0	0	0	0	6	59,537	57,705	32,974	87,932	0	0	6	0	0	0	6	0
FI	Finland	2,022	250	1	0.17	0.000169	Series C	0	0	0	0	0	0	0	0	0	0	0	0	0
FR	France	2,022	93	1	0.13	0.001	Series E	1	68,147	68,147	68,147	68,147	0	0	1	0	0	0	1	0
GB	United Kingdom	2,022	104	3	0.325	0.00134	Unknown	29	78,684	78,526	37,300	183,228	3	0	22	4	0	0	29	0
GR	Greece	2,022	0	0	0	0	0	7	53,889	49,461	20,000	87,932	0	0	7	0	0	0	7	0
ID	Indonesia	2,022	494	3	0.203333	0.005332	Series B	0	0	0	0	0	0	0	0	0	0	0	0	0
IE	Ireland	2,022	0	0	0	0	0	1	71,444	71,444	71,444	71,444	0	0	0	1	0	0	1	0
IL	Israel	2,022	170	6	0.286667	0.000934	Series C	0	0	0	0	0	0	0	0	0	0	0	0	0
IN	India	2,022	2,064	10	0.46	0.01431	Series A	4	36,884	25,028	18,442	79,039	1	1	2	0	0	0	4	0
KE	Kenya	2,022	54	1	0.105	0.00012	Private Equity	0	0	0	0	0	0	0	0	0	0	0	0	0
LU	Luxembourg	2,022	0	0	0	0	0	1	10,000	10,000	10,000	10,000	1	0	0	0	0	0	1	0
MX	Mexico	2,022	0	0	0	0	0	1	60,000	60,000	60,000	60,000	0	0	0	1	0	0	1	0
MY	Malaysia	2,022	50	1	0.2	0.000026	Unknown	1	40,000	40,000	40,000	40,000	1	0	0	0	0	0	1	0
NG	Nigeria	2,022	900	1	0.2	0.001216	Post-IPO	0	0	0	0	0	0	0	0	0	0	0	0	0
NL	Netherlands	2,022	300	1	0.1	0	Acquired	1	62,651	62,651	62,651	62,651	0	0	0	1	0	0	1	0
PK	Pakistan	2,022	0	0	0	0	0	1	20,000	20,000	20,000	20,000	1	0	0	0	0	0	1	0
PL	Poland	2,022	0	0	0	0	0	1	35,590	35,590	35,590	35,590	0	0	1	0	0	0	1	0
PT	Portugal	2,022	0	0	0	0	0	2	40,119	40,119	21,983	58,255	1	0	1	0	0	0	2	0
SE	Sweden	2,022	270	2	0.1	0.000056	Post-IPO	0	0	0	0	0	0	0	0	0	0	0	0	0
SG	Singapore	2,022	290	3	0.1	0.002427	Series E	0	0	0	0	0	0	0	0	0	0	0	0	0
TH	Thailand	2,022	55	1	0.08	0.00012	Unknown	0	0	0	0	0	0	0	0	0	0	0	0	0
US	United States	2,022	14,507	34	0.185366	0.026819	Post-IPO	205	141,413	135,000	25,000	380,000	4	8	35	158	0	1	204	0
AU	Australia	2,023	354	6	0.156	0.000299	Unknown	0	0	0	0	0	0	0	0	0	0	0	0	0
BR	Brazil	2,023	125	2	0.1	0.000561	Series C	0	0	0	0	0	0	0	0	0	0	0	0	0
CA	Canada	2,023	2,784	5	0.146	0.002728	Post-IPO	0	0	0	0	0	0	0	0	0	0	0	0	0
CH	Switzerland	2,023	0	0	0.1	0.000084	Post-IPO	0	0	0	0	0	0	0	0	0	0	0	0	0
CN	China	2,023	0	0	0.07	0	Subsidiary	0	0	0	0	0	0	0	0	0	0	0	0	0
DE	Germany	2,023	325	3	0.39	0.001523	Post-IPO	0	0	0	0	0	0	0	0	0	0	0	0	0
FI	Finland	2,023	0	0	0.08	0.000245	Post-IPO	0	0	0	0	0	0	0	0	0	0	0	0	0

End of preview.

Global AI.ML - Hiring & Layoffs Dataset

This dataset was published on Kaggle by Samyakraj Bayar and mirrored here.

Download

The dataset is available as a ZIP archive: Global AI.ML - Hiring & Layoffs Dataset.zip

License

MIT

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