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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 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 |
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