Dataset Preview
Duplicate
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
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 2 new columns ({'MONTHS_BALANCE', 'STATUS'}) and 17 missing columns ({'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'FLAG_PHONE', 'NAME_EDUCATION_TYPE', 'FLAG_MOBIL', 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'CODE_GENDER', 'OCCUPATION_TYPE', 'DAYS_EMPLOYED', 'DAYS_BIRTH', 'AMT_INCOME_TOTAL', 'NAME_INCOME_TYPE', 'FLAG_WORK_PHONE', 'FLAG_EMAIL', 'FLAG_OWN_REALTY', 'FLAG_OWN_CAR'}).

This happened while the csv dataset builder was generating data using

hf://datasets/mahdy12/Credit-card-fraud-detection/credit_record.csv (at revision 5097123c87d3c8e09b3170b3b8ce7682a273c1af), [/tmp/hf-datasets-cache/medium/datasets/11467238450901-config-parquet-and-info-mahdy12-Credit-card-fraud-4828f243/hub/datasets--mahdy12--Credit-card-fraud-detection/snapshots/5097123c87d3c8e09b3170b3b8ce7682a273c1af/application_record.csv (origin=hf://datasets/mahdy12/Credit-card-fraud-detection@5097123c87d3c8e09b3170b3b8ce7682a273c1af/application_record.csv), /tmp/hf-datasets-cache/medium/datasets/11467238450901-config-parquet-and-info-mahdy12-Credit-card-fraud-4828f243/hub/datasets--mahdy12--Credit-card-fraud-detection/snapshots/5097123c87d3c8e09b3170b3b8ce7682a273c1af/credit_record.csv (origin=hf://datasets/mahdy12/Credit-card-fraud-detection@5097123c87d3c8e09b3170b3b8ce7682a273c1af/credit_record.csv)]

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 1800, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/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/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              ID: int64
              MONTHS_BALANCE: int64
              STATUS: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 601
              to
              {'ID': Value('int64'), 'CODE_GENDER': Value('string'), 'FLAG_OWN_CAR': Value('string'), 'FLAG_OWN_REALTY': Value('string'), 'CNT_CHILDREN': Value('int64'), 'AMT_INCOME_TOTAL': Value('float64'), 'NAME_INCOME_TYPE': Value('string'), 'NAME_EDUCATION_TYPE': Value('string'), 'NAME_FAMILY_STATUS': Value('string'), 'NAME_HOUSING_TYPE': Value('string'), 'DAYS_BIRTH': Value('int64'), 'DAYS_EMPLOYED': Value('int64'), 'FLAG_MOBIL': Value('int64'), 'FLAG_WORK_PHONE': Value('int64'), 'FLAG_PHONE': Value('int64'), 'FLAG_EMAIL': Value('int64'), 'OCCUPATION_TYPE': Value('string'), 'CNT_FAM_MEMBERS': Value('float64')}
              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 1348, 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 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, 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 1802, 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 2 new columns ({'MONTHS_BALANCE', 'STATUS'}) and 17 missing columns ({'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'FLAG_PHONE', 'NAME_EDUCATION_TYPE', 'FLAG_MOBIL', 'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'CODE_GENDER', 'OCCUPATION_TYPE', 'DAYS_EMPLOYED', 'DAYS_BIRTH', 'AMT_INCOME_TOTAL', 'NAME_INCOME_TYPE', 'FLAG_WORK_PHONE', 'FLAG_EMAIL', 'FLAG_OWN_REALTY', 'FLAG_OWN_CAR'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/mahdy12/Credit-card-fraud-detection/credit_record.csv (at revision 5097123c87d3c8e09b3170b3b8ce7682a273c1af), [/tmp/hf-datasets-cache/medium/datasets/11467238450901-config-parquet-and-info-mahdy12-Credit-card-fraud-4828f243/hub/datasets--mahdy12--Credit-card-fraud-detection/snapshots/5097123c87d3c8e09b3170b3b8ce7682a273c1af/application_record.csv (origin=hf://datasets/mahdy12/Credit-card-fraud-detection@5097123c87d3c8e09b3170b3b8ce7682a273c1af/application_record.csv), /tmp/hf-datasets-cache/medium/datasets/11467238450901-config-parquet-and-info-mahdy12-Credit-card-fraud-4828f243/hub/datasets--mahdy12--Credit-card-fraud-detection/snapshots/5097123c87d3c8e09b3170b3b8ce7682a273c1af/credit_record.csv (origin=hf://datasets/mahdy12/Credit-card-fraud-detection@5097123c87d3c8e09b3170b3b8ce7682a273c1af/credit_record.csv)]
              
              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.

ID
int64
CODE_GENDER
string
FLAG_OWN_CAR
string
FLAG_OWN_REALTY
string
CNT_CHILDREN
int64
AMT_INCOME_TOTAL
float64
NAME_INCOME_TYPE
string
NAME_EDUCATION_TYPE
string
NAME_FAMILY_STATUS
string
NAME_HOUSING_TYPE
string
DAYS_BIRTH
int64
DAYS_EMPLOYED
int64
FLAG_MOBIL
int64
FLAG_WORK_PHONE
int64
FLAG_PHONE
int64
FLAG_EMAIL
int64
OCCUPATION_TYPE
string
CNT_FAM_MEMBERS
float64
5,008,804
M
Y
Y
0
427,500
Working
Higher education
Civil marriage
Rented apartment
-12,005
-4,542
1
1
0
0
null
2
5,008,805
M
Y
Y
0
427,500
Working
Higher education
Civil marriage
Rented apartment
-12,005
-4,542
1
1
0
0
null
2
5,008,806
M
Y
Y
0
112,500
Working
Secondary / secondary special
Married
House / apartment
-21,474
-1,134
1
0
0
0
Security staff
2
5,008,808
F
N
Y
0
270,000
Commercial associate
Secondary / secondary special
Single / not married
House / apartment
-19,110
-3,051
1
0
1
1
Sales staff
1
5,008,809
F
N
Y
0
270,000
Commercial associate
Secondary / secondary special
Single / not married
House / apartment
-19,110
-3,051
1
0
1
1
Sales staff
1
5,008,810
F
N
Y
0
270,000
Commercial associate
Secondary / secondary special
Single / not married
House / apartment
-19,110
-3,051
1
0
1
1
Sales staff
1
5,008,811
F
N
Y
0
270,000
Commercial associate
Secondary / secondary special
Single / not married
House / apartment
-19,110
-3,051
1
0
1
1
Sales staff
1
5,008,812
F
N
Y
0
283,500
Pensioner
Higher education
Separated
House / apartment
-22,464
365,243
1
0
0
0
null
1
5,008,813
F
N
Y
0
283,500
Pensioner
Higher education
Separated
House / apartment
-22,464
365,243
1
0
0
0
null
1
5,008,814
F
N
Y
0
283,500
Pensioner
Higher education
Separated
House / apartment
-22,464
365,243
1
0
0
0
null
1
5,008,815
M
Y
Y
0
270,000
Working
Higher education
Married
House / apartment
-16,872
-769
1
1
1
1
Accountants
2
5,112,956
M
Y
Y
0
270,000
Working
Higher education
Married
House / apartment
-16,872
-769
1
1
1
1
Accountants
2
6,153,651
M
Y
Y
0
270,000
Working
Higher education
Married
House / apartment
-16,872
-769
1
1
1
1
Accountants
2
5,008,819
M
Y
Y
0
135,000
Commercial associate
Secondary / secondary special
Married
House / apartment
-17,778
-1,194
1
0
0
0
Laborers
2
5,008,820
M
Y
Y
0
135,000
Commercial associate
Secondary / secondary special
Married
House / apartment
-17,778
-1,194
1
0
0
0
Laborers
2
5,008,821
M
Y
Y
0
135,000
Commercial associate
Secondary / secondary special
Married
House / apartment
-17,778
-1,194
1
0
0
0
Laborers
2
5,008,822
M
Y
Y
0
135,000
Commercial associate
Secondary / secondary special
Married
House / apartment
-17,778
-1,194
1
0
0
0
Laborers
2
5,008,823
M
Y
Y
0
135,000
Commercial associate
Secondary / secondary special
Married
House / apartment
-17,778
-1,194
1
0
0
0
Laborers
2
5,008,824
M
Y
Y
0
135,000
Commercial associate
Secondary / secondary special
Married
House / apartment
-17,778
-1,194
1
0
0
0
Laborers
2
5,008,825
F
Y
N
0
130,500
Working
Incomplete higher
Married
House / apartment
-10,669
-1,103
1
0
0
0
Accountants
2
5,008,826
F
Y
N
0
130,500
Working
Incomplete higher
Married
House / apartment
-10,669
-1,103
1
0
0
0
Accountants
2
5,008,830
F
N
Y
0
157,500
Working
Secondary / secondary special
Married
House / apartment
-10,031
-1,469
1
0
1
0
Laborers
2
5,008,831
F
N
Y
0
157,500
Working
Secondary / secondary special
Married
House / apartment
-10,031
-1,469
1
0
1
0
Laborers
2
5,008,832
F
N
Y
0
157,500
Working
Secondary / secondary special
Married
House / apartment
-10,031
-1,469
1
0
1
0
Laborers
2
5,008,834
F
N
Y
1
112,500
Working
Secondary / secondary special
Single / not married
House / apartment
-10,968
-1,620
1
0
0
0
null
2
5,008,835
F
N
Y
1
112,500
Working
Secondary / secondary special
Single / not married
House / apartment
-10,968
-1,620
1
0
0
0
null
2
6,153,712
F
N
Y
1
112,500
Working
Secondary / secondary special
Single / not married
House / apartment
-10,968
-1,620
1
0
0
0
null
2
5,008,836
M
Y
Y
3
270,000
Working
Secondary / secondary special
Married
House / apartment
-12,689
-1,163
1
0
0
0
Laborers
5
5,008,837
M
Y
Y
3
270,000
Working
Secondary / secondary special
Married
House / apartment
-12,689
-1,163
1
0
0
0
Laborers
5
5,008,838
M
N
Y
1
405,000
Commercial associate
Higher education
Married
House / apartment
-11,842
-2,016
1
0
0
0
Managers
3
5,008,839
M
N
Y
1
405,000
Commercial associate
Higher education
Married
House / apartment
-11,842
-2,016
1
0
0
0
Managers
3
5,008,840
M
N
Y
1
405,000
Commercial associate
Higher education
Married
House / apartment
-11,842
-2,016
1
0
0
0
Managers
3
5,008,841
M
N
Y
1
405,000
Commercial associate
Higher education
Married
House / apartment
-11,842
-2,016
1
0
0
0
Managers
3
5,008,842
M
N
Y
1
405,000
Commercial associate
Higher education
Married
House / apartment
-11,842
-2,016
1
0
0
0
Managers
3
5,008,843
M
N
Y
1
405,000
Commercial associate
Higher education
Married
House / apartment
-11,842
-2,016
1
0
0
0
Managers
3
5,008,844
M
Y
Y
0
112,500
Commercial associate
Secondary / secondary special
Married
House / apartment
-20,502
-4,450
1
0
1
0
Drivers
2
5,008,846
M
Y
Y
0
112,500
Commercial associate
Secondary / secondary special
Married
House / apartment
-20,502
-4,450
1
0
1
0
Drivers
2
5,008,847
M
Y
Y
0
112,500
Commercial associate
Secondary / secondary special
Married
House / apartment
-20,502
-4,450
1
0
1
0
Drivers
2
5,008,849
M
Y
Y
0
112,500
Commercial associate
Secondary / secondary special
Married
House / apartment
-20,502
-4,450
1
0
1
0
Drivers
2
5,008,850
M
Y
Y
0
112,500
Commercial associate
Secondary / secondary special
Married
House / apartment
-20,502
-4,450
1
0
1
0
Drivers
2
5,008,851
M
Y
Y
0
112,500
Commercial associate
Secondary / secondary special
Married
House / apartment
-20,502
-4,450
1
0
1
0
Drivers
2
5,008,852
M
Y
Y
0
112,500
Commercial associate
Secondary / secondary special
Married
House / apartment
-20,502
-4,450
1
0
1
0
Drivers
2
5,008,853
M
Y
Y
0
112,500
Commercial associate
Secondary / secondary special
Married
House / apartment
-20,502
-4,450
1
0
1
0
Drivers
2
6,153,733
M
Y
Y
0
112,500
Commercial associate
Secondary / secondary special
Married
House / apartment
-20,502
-4,450
1
0
1
0
Drivers
2
6,153,734
M
Y
Y
0
112,500
Commercial associate
Secondary / secondary special
Married
House / apartment
-20,502
-4,450
1
0
1
0
Drivers
2
6,153,735
M
Y
Y
0
112,500
Commercial associate
Secondary / secondary special
Married
House / apartment
-20,502
-4,450
1
0
1
0
Drivers
2
5,008,854
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,008,855
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,008,856
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,008,857
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,008,858
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,008,859
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,008,860
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,008,861
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,008,862
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,008,863
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,008,864
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,008,865
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,008,866
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,008,867
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,112,846
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
6,153,736
F
Y
Y
2
135,000
Working
Secondary / secondary special
Married
House / apartment
-15,761
-3,173
1
0
0
0
Laborers
4
5,008,868
F
N
Y
1
211,500
State servant
Secondary / secondary special
Civil marriage
House / apartment
-16,212
-7,099
1
0
0
0
Core staff
3
5,008,870
F
N
Y
1
211,500
State servant
Secondary / secondary special
Civil marriage
House / apartment
-16,212
-7,099
1
0
0
0
Core staff
3
5,008,872
M
Y
Y
0
360,000
Commercial associate
Secondary / secondary special
Married
House / apartment
-16,670
-5,364
1
0
1
0
Security staff
2
6,153,737
M
Y
Y
0
360,000
Commercial associate
Secondary / secondary special
Married
House / apartment
-16,670
-5,364
1
0
1
0
Security staff
2
5,008,873
F
N
Y
2
126,000
Commercial associate
Higher education
Married
House / apartment
-12,411
-1,773
1
0
0
1
Managers
4
5,008,874
F
N
Y
2
126,000
Commercial associate
Higher education
Married
House / apartment
-12,411
-1,773
1
0
0
1
Managers
4
5,008,875
F
N
Y
2
126,000
Commercial associate
Higher education
Married
House / apartment
-12,411
-1,773
1
0
0
1
Managers
4
5,008,876
F
N
Y
2
126,000
Commercial associate
Higher education
Married
House / apartment
-12,411
-1,773
1
0
0
1
Managers
4
5,008,877
F
N
Y
2
126,000
Commercial associate
Higher education
Married
House / apartment
-12,411
-1,773
1
0
0
1
Managers
4
5,008,878
F
N
Y
2
126,000
Commercial associate
Higher education
Married
House / apartment
-12,411
-1,773
1
0
0
1
Managers
4
5,008,879
F
N
Y
2
126,000
Commercial associate
Higher education
Married
House / apartment
-12,411
-1,773
1
0
0
1
Managers
4
5,008,880
F
N
Y
2
126,000
Commercial associate
Higher education
Married
House / apartment
-12,411
-1,773
1
0
0
1
Managers
4
5,008,881
F
N
Y
2
126,000
Commercial associate
Higher education
Married
House / apartment
-12,411
-1,773
1
0
0
1
Managers
4
5,008,882
F
N
Y
2
126,000
Commercial associate
Higher education
Married
House / apartment
-12,411
-1,773
1
0
0
1
Managers
4
5,008,884
F
N
Y
0
315,000
Pensioner
Secondary / secondary special
Widow
House / apartment
-20,186
365,243
1
0
0
0
null
1
5,008,888
F
N
Y
0
247,500
Commercial associate
Higher education
Separated
Rented apartment
-17,016
-1,347
1
0
0
0
Core staff
1
5,008,889
F
N
Y
0
247,500
Commercial associate
Higher education
Separated
Rented apartment
-17,016
-1,347
1
0
0
0
Core staff
1
5,008,890
F
N
Y
0
247,500
Commercial associate
Higher education
Separated
Rented apartment
-17,016
-1,347
1
0
0
0
Core staff
1
5,112,847
F
N
Y
0
247,500
Commercial associate
Higher education
Separated
Rented apartment
-17,016
-1,347
1
0
0
0
Core staff
1
5,008,891
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,892
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,893
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,894
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,895
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,896
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,897
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,898
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,899
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,900
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,901
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,902
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,903
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,904
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,905
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,906
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,907
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,908
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
5,008,909
F
N
Y
0
297,000
Commercial associate
Secondary / secondary special
Single / not married
Rented apartment
-15,519
-3,234
1
0
0
0
Laborers
1
End of preview.

No dataset card yet

Downloads last month
25