nudb_use.quality.specific_variables package

nudb_use.quality.specific_variables.gro_elevstatus module

Validations for the gro_elevstatus variable.

check_gro_elevstatus(df, **kwargs)

Run all gro_elevstatus-specific checks on the provided DataFrame.

Parameters:

  • df (DataFrame) – DataFrame containing gro_elevstatus and supporting columns.

  • **kwargs (object) – Additional keyword arguments for future compatibility.

Returns:

Errors reported by the gro_elevstatus checks.

Return type:

list[NudbQualityError]

subcheck_elevstatus_utd_211(utd_utdanningstype, gro_elevstatus)

Ensure gro_elevstatus is ‘E’ whenever utd_utdanningstype equals 211.

Parameters:

  • utd_utdanningstype (Series | None) – Series containing the utdanningstype codes.

  • gro_elevstatus (Series | None) – Series containing gro_elevstatus values.

Returns:

Error when invalid combinations exist, else None.

Return type:

NudbQualityError | None

nudb_use.quality.specific_variables.grunnskolepoeng module

Validations for the gr_grunnskolepoeng variable.

check_grunnskolepoeng(df, **kwargs)

Run grunnskolepoeng validations on the provided dataset.

Parameters:

  • df (DataFrame) – DataFrame containing the grunnskolepoeng column.

  • **kwargs (object) – Currently unused keyword arguments. Passed in from parent function.

Returns:

Errors produced by the sub-checks.

Return type:

list[NudbQualityError]

subcheck_grunnskolepoeng_maxval(grunnskolepoeng, max_poeng=70.0)

Verify that grunnskolepoeng values stay within the allowed maximum.

Parameters:

  • grunnskolepoeng (Series | None) – Series containing grunnskolepoeng values.

  • max_poeng (float) – Maximum allowed points.

Returns:

Error when values exceed the maximum, else None.

Return type:

NudbQualityError | None

nudb_use.quality.specific_variables.kommune module

Validations for kommune-coded variables referencing KLASS 131.

check_kommune(df, **kwargs)

Run kommune-specific validations on all kommune columns in the DataFrame.

Parameters:

  • df (DataFrame) – DataFrame that may contain kommune columns.

  • **kwargs (object) – Placeholder for future configuration. Passed in from parent function.

Returns:

Errors aggregated from kommune checks.

Return type:

list[NudbQualityError]

subcheck_only_single_sentinel_value_9999_allowed(kommune_col)

Ensure kommune codes only use ‘9999’ as the sentinel value.

Parameters:

kommune_col (Series | None) – Series with kommune codes.

Returns:

Error when other sentinel values exist, else None.

Return type:

NudbQualityError | None

subcheck_single_kommune_oslo_svalbard_utland(kommune_col)

Ensure fylker with single municipality codes are mapped correctly.

Parameters:

kommune_col (Series | None) – Series with kommune codes.

Returns:

Error when illegal mappings exist, else None.

Return type:

NudbQualityError | None

nudb_use.quality.specific_variables.land module

Validations for country (land) variables mapping to KLASS 91.

check_land(df, **kwargs)

Run land-specific validations for all configured columns.

Parameters:

  • df (DataFrame) – DataFrame containing potential land variables.

  • **kwargs (object) – Placeholder for future options. Passed in from parent function.

Returns:

Errors aggregated from land checks.

Return type:

list[NudbQualityError]

subcheck_landkode_000(land_col, col_name)

Ensure the reserved land code 000 is not incorrectly used.

Parameters:

  • land_col (Series | None) – Series with land codes.

  • col_name (str) – Name of the land column for logging context.

Returns:

Error when invalid codes are present, else None.

Return type:

NudbQualityError | None

nudb_use.quality.specific_variables.nus2000 module

Validations for the nus2000 classification variable.

check_nus2000(df, **kwargs)

Run all nus2000-specific validation checks on the provided dataset.

Parameters:

  • df (DataFrame) – DataFrame that should contain nus2000-relevant columns.

  • **kwargs (object) – Additional keyword arguments forwarded to range validation. Passed in from parent function.

Returns:

Validation errors gathered from all sub-checks, or an empty list when the dataset passes cleanly.

Return type:

list[NudbQualityError]

subcheck_nus2000_uh_institusjon_id_against_nus(uh_institusjon_id_col, nus_col, utd_skoleaar_start_col)

Ensure UH institution id is populated when nus2000 starts with 6, 7, or 8.

Parameters:

  • uh_institusjon_id_col (Series | None) – Series with UH institution identifiers.

  • nus_col (Series | None) – Series containing nus2000 codes.

  • utd_skoleaar_start_col (Series | None) – Series with the school-year start used to determine when the validation applies.

Returns:

Validation error describing offending combinations, or None when every row satisfies the rule.

Return type:

NudbQualityError | None

subcheck_nus2000_valid_nus(col)

Validate that every nus2000 code is six digits and starts with 1-8.

Parameters:

col (Series | None) – Series containing nus2000 codes, or None if the column is missing.

Returns:

Validation error describing the invalid codes, or None when all codes satisfy the required format.

Return type:

NudbQualityError | None

subcheck_nus2000_valid_range(nus_col, range_valid_nus=None, dataset_name=None, **kwargs)

Check that nus2000 codes stay inside the configured numeric range.

Parameters:

  • nus_col (Series | None) – Series containing nus2000 codes to validate.

  • range_valid_nus (range | None) – Optional range describing the allowed first-digit span. When omitted, dataset-specific or default ranges are used.

  • dataset_name (str | None) – to look up configuration overrides.

  • **kwargs (Any) – Keyword arguments that may include range_valid_nus

Returns:

Validation error listing codes outside the allowed range, or None when no violations are detected.

Return type:

NudbQualityError | None

nudb_use.quality.specific_variables.run_all module

Entry point for executing all variable-specific validation checks.

run_all_specific_variable_tests(df, raise_errors=False, **kwargs)

Execute every registered variable-specific validation routine.

Parameters:

  • df (DataFrame) – DataFrame that should contain the required variables.

  • raise_errors (bool) – When True, raise grouped errors if any validations fail.

  • **kwargs (object) – Extra keyword arguments forwarded to each check.

Returns:

Errors aggregated from all specific checks, or an empty list when every check passes.

Return type:

list[NudbQualityError]

nudb_use.quality.specific_variables.skoleaar module

Validations for the skoleaar time variables.

check_skoleaar(df, **kwargs)

Validate skoleaar columns for invalid year formats.

Scans columns whose name includes “skoleaar” or “skolar” and aggregates any validation errors found.

Parameters:

  • df (DataFrame) – Input DataFrame to validate.

  • **kwargs (object) – Unused extra arguments for compatibility.

Returns:

Collected validation errors.

Return type:

list[NudbQualityError]

check_skoleaar_contains_one_year(col_name, col_series)

Validate that skoleaar values look like a single 4-digit year.

Parameters:

  • col_name (str) – Column name for error context.

  • col_series (Series) – Series of skoleaar values as strings.

Returns:

Error if invalid values are found, otherwise None.

Return type:

NudbQualityError | None

check_skoleaar_contains_sane_years(col_name, col_series)

Validate that skoleaar values fall within a sane year range.

Parameters:

  • col_name (str) – Column name for error context.

  • col_series (Series) – Series of skoleaar values as strings or integers.

Returns:

Error if out-of-range values are found, otherwise None.

Return type:

NudbQualityError | None

check_skoleaar_contains_two_years_one_offset(col_name, col_series)

Validate 8-digit skoleaar values have a +1 year offset.

Parameters:

  • col_name (str) – Column name for error context.

  • col_series (Series) – Series of skoleaar values as strings.

Returns:

Error if 8-digit values are not consecutive, otherwise None.

Return type:

NudbQualityError | None

check_skoleaar_is_string_dtype(col_name, col_series)

Ensure a skoleaar column uses a string dtype before further checks.

Parameters:

  • col_name (str) – Column name for error context.

  • col_series (Series) – Series of skoleaar values to validate.

Returns:

Error when dtype is not string, otherwise None.

Return type:

NudbQualityError | None

nudb_use.quality.specific_variables.sn07 module

Validations for the SN07 classification variable.

check_sn07(df, **kwargs)

Execute SN07-specific checks and return collected errors.

Parameters:

  • df (DataFrame) – DataFrame containing SN07 codes.

  • **kwargs (object) – Placeholder for future configuration. Passed in from parent function.

Returns:

Errors describing invalid SN07 codes.

Return type:

list[NudbQualityError]

subcheck_sn07_bad_value(sn07)

Detect disallowed SN07 codes.

Parameters:

sn07 (Series | None) – Series containing SN07 codes to inspect.

Returns:

Error when forbidden codes are present, else None.

Return type:

NudbQualityError | None

nudb_use.quality.specific_variables.snr_fnr module

Checks for required personal identifier columns.

check_has_personal_ids(df, **kwargs)

Ensure at least one personal identifier column is populated per row.

Parameters:

  • df (DataFrame) – DataFrame containing personal identifier columns.

  • **kwargs (object) – Placeholder for future options. Passed in from parent function.

Returns:

Errors for rows missing all identifier values.

Return type:

list[NudbQualityError]

nudb_use.quality.specific_variables.unique_per_person module

Validations ensuring certain columns are unique per person.

check_unique_per_person(df, **kwargs)

Ensure configured columns have at most one value per person.

Parameters:

  • df (DataFrame) – DataFrame containing personal identifier and value columns.

  • **kwargs (object) – Placeholder for future options. Passed in from parent function.

Returns:

Errors describing rows that violate uniqueness.

Return type:

list[NudbQualityError]

subcheck_unique_per_person(fnr, snr, unique_col, unique_col_name)

Check that a single column has unique values per person.

Parameters:

  • fnr (Series | None) – Series containing national identifiers.

  • snr (Series | None) – Series containing snr person identifiers.

  • unique_col (Series | None) – Column that should hold unique values per person.

  • unique_col_name (str) – Name of the column for logging context.

Returns:

Error when multiple values exist per person, else None.

Return type:

NudbQualityError | None

nudb_use.quality.specific_variables.utils module

Shared helpers for the variable-specific validation modules.

add_err2list(errors, error)

Append a validation error to a list if it is not None.

Return type:

None

Parameters:
get_column(df, col)

Return a DataFrame column or None when it is missing.

Return type:

Series | None

Parameters:

  • df (DataFrame)

  • col (str)

require_series_present(**series_by_name)

Ensure required pandas Series exist before continuing a validation step.

Return type:

dict[str, Series] | None

Parameters:

series_by_name (Series | None)

nudb_use.quality.specific_variables.vg_fullfoertkode_detaljert module

Validations for the vg_fullfoertkode_detaljert variable.

check_vg_fullfoertkode_detaljert(df, **kwargs)

Run all vg_fullfoertkode_detaljert-specific checks on the provided DataFrame.

Parameters:

  • df (DataFrame) – DataFrame containing vg_fullfoertkode_detaljert and supporting columns.

  • **kwargs (object) – Additional keyword arguments for future compatibility.

Returns:

Errors reported by the vg_fullfoertkode_detaljert checks.

Return type:

list[NudbQualityError]

subcheck_vg_fullfoertkode_detaljert_utd_211(utd_utdanningstype, vg_fullfoertkode_detaljert)

Ensure vg_fullfoertkode_detaljert is filled only for utd_utdanningstype 211, 212, 220 and 610.

Parameters:

  • utd_utdanningstype (Series | None) – Series containing the utdanningstype codes.

  • vg_fullfoertkode_detaljert (Series | None) – Series containing gro_elevstatus values.

Returns:

Error when invalid combinations exist, else None.

Return type:

NudbQualityError | None