nudb_use.variables.derive package

nudb_use.variables.derive.all_data_helpers module

enforce_datetime_s(series)

Enforce the datetime dtype to datetime64[s].

Return type:

Series

Parameters:

series (Series)

get_source_data(variable_name, df_left=None)

Load and prepare source data for deriving a variable.

This function reads one or more parquet datasets defined in config, selects only the columns needed for derivation, unions all datasets, and (optionally) filters rows down to only those whose join-key values overlap with df_left.

Filtering is performed inside DuckDB by registering a temporary in-memory key table derived from df_left[derived_join_keys] and performing an INNER JOIN on the derived join keys.

Parameters:

  • variable_name (str) – Name of the variable being derived (used for config lookup).

  • df_left (DataFrame | None) – If provided, limits source rows to those matching the join-key combinations present in this dataframe.

Returns:

A pandas DataFrame containing the unioned (and possibly filtered) source data.

Return type:

pd.DataFrame

Raises:

  • ValueError – If required config fields are missing or invalid.

  • KeyError – If df_left is missing required join key columns.

join_variable_data(variable_name, df_right, df_left)

Left-join a derived variable dataframe onto an input dataframe using config keys.

Parameters:

  • variable_name (str) – Name of the variable being derived (used for config lookup).

  • df_right (DataFrame) – Derived data containing at least the join keys and derived columns.

  • df_left (DataFrame) – Base dataframe to enrich.

Returns:

df_left with df_right merged in using a left join on the config keys.

Return type:

pd.DataFrame

Raises:

  • ValueError – If derived join keys are missing in config.

  • KeyError – If either dataframe is missing required join key columns.

nudb_use.variables.derive.derive_decorator module

class WrappedDerive(*args, **kwargs)

Bases: Protocol[P]

Arg types for the wrap_derive decorator.

class WrappedDeriveJoinAllData(*args, **kwargs)

Bases: Protocol[P]

Arg types for the wrap_derive_join_all_data decorator.

fillna_by_priority(newvals, oldvals, priority='old')

Fill missing values in prioritized order when a column already exists.

Parameters:

  • newvals (Series | None) – A pandas series with the newly added values.

  • oldvals (Series | None) – A pandas series with the old values.

  • priority (Literal['old', 'new']) – “old” if we should prioritze the old values, “new” if we should prioritize the new.

Returns:

The resulting merged columns using fillna-methods. Returns None if both newvals and oldvals is None.

Return type:

pd.Series | None

Raises:

ValueError – If you are sending in a non-specific Literal for the priority-arg.

get_derive_function(varname)

Return the derive function for a variable if it exists.

Parameters:

varname (str) – The name of the variable to get the derive function for.

Returns:

The derive function, or None if no function was found.

Return type:

Callable[…, pd.DataFrame] | None

wrap_derive(basefunc)

Decorator for derive functions that enforces config metadata and logging.

Notes

  • Validates that the variable exists in config and has a derived_from definition.

  • Recursively derives missing prerequisites before calling the decorated function.

  • Logs fill percentages and merges existing data with derived data based on priority.

Parameters:

basefunc (Callable[[Concatenate[DataFrame, ParamSpec(P, bound= None)]], Series | DataFrame]) – Function that derives a single variable from an input dataframe.

Returns:

Wrapped derive function that writes/updates the derived column.

Return type:

WrappedDerive[P]

Raises:

NudbDerivedFromNotFoundError – No matching entry can be found in the config for the function name.

wrap_derive_join_all_data(basefunc)

Decorator for derive functions that need specific data as a base, specified in the config.

Parameters:

basefunc (Callable[[Concatenate[DataFrame, ParamSpec(P, bound= None)]], DataFrame]) – Function that derives a single variable from an input dataframe.

Returns:

Wrapped derive function that writes/updates the derived column using whole NUDB-datasets.

Return type:

WrappedDeriveJoinAllData[P]

nudb_use.variables.derive.fullfoert module

gr_ergrunnskole_fullfoert(df, priority='old', *args, **kwargs)

Derive gr_ergrunnskole_fullfoert from nus2000, utd_fullfoertkode, utd_erutland.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_fullfoertkode’, ‘nus2000’, ‘utd_erutland’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing gr_ergrunnskole_fullfoert values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with gr_ergrunnskole_fullfoert added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_erbachelor_fullfoert(df, priority='old', *args, **kwargs)

Derive uh_erbachelor_fullfoert from uh_gradmerke_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_fullfoertkode’, ‘uh_gradmerke_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_erbachelor_fullfoert values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_erbachelor_fullfoert added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_erdoktorgrad_fullfoert(df, priority='old', *args, **kwargs)

Derive uh_erdoktorgrad_fullfoert from nus2000, utd_fullfoertkode.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_fullfoertkode’, ‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_erdoktorgrad_fullfoert values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_erdoktorgrad_fullfoert added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_erhoeyskolekandidat_fullfoert(df, priority='old', *args, **kwargs)

Derive vg_eryrkesfag_fullfoert from nus2000, vg_utdprogram, utd_fullfoertkode, vg_kompetanse_nus and utd_aktivitet_start.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_fullfoertkode’, ‘nus2000’, ‘uh_gruppering_nus’, ‘utd_klassetrinn’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_erhoeyskolekandidat_fullfoert values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_erhoeyskolekandidat_fullfoert added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_ermaster_fullfoert(df, priority='old', *args, **kwargs)

Derive uh_ermaster_fullfoert from nus2000, utd_fullfoertkode.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_fullfoertkode’, ‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_ermaster_fullfoert values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_ermaster_fullfoert added/updated when all prerequisites are available.

Return type:

pd.DataFrame

vg_erstudiespess_fullfoert(df, priority='old', *args, **kwargs)

Derive vg_erstudiespess_fullfoert from nus2000, vg_utdprogram, utd_fullfoertkode, vg_kompetanse_nus and utd_aktivitet_start.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_fullfoertkode’, ‘nus2000’, ‘vg_utdprogram’, ‘vg_kompetanse_nus’, ‘utd_aktivitet_start’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing vg_erstudiespess_fullfoert values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with vg_erstudiespess_fullfoert added/updated when all prerequisites are available.

Return type:

pd.DataFrame

vg_ervgo_fullfoert(df, priority='old', *args, **kwargs)

Derive vg_ervgo_fullfoert from nus2000, utd_fullfoertkode, vg_kompetanse_nus and utd_aktivitet_start.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_fullfoertkode’, ‘nus2000’, ‘vg_kompetanse_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing vg_ervgo_fullfoert values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with vg_ervgo_fullfoert added/updated when all prerequisites are available.

Return type:

pd.DataFrame

vg_eryrkesfag_fullfoert(df, priority='old', *args, **kwargs)

Derive vg_eryrkesfag_fullfoert from nus2000, vg_utdprogram, utd_fullfoertkode, vg_kompetanse_nus and utd_aktivitet_start.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_fullfoertkode’, ‘nus2000’, ‘vg_utdprogram’, ‘vg_kompetanse_nus’, ‘utd_aktivitet_start’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing vg_eryrkesfag_fullfoert values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with vg_eryrkesfag_fullfoert added/updated when all prerequisites are available.

Return type:

pd.DataFrame

nudb_use.variables.derive.fullfoert_foerste module

gr_foerste_fullfoert_dato(df=None, priority='old', *args, **kwargs)

Derive gr_foerste_fullfoert_dato from avslutta.

Parameters:

  • df (DataFrame | None) – Source dataset containing at least snr, gr_ergrunnskole_fullfoert, utd_aktivitet_slutt.

  • priority (Literal['old', 'new'])

  • args (~P)

  • kwargs (~P)

Returns:

A column suitable for adding as a new column to the df.

Args:

df: Dataframe that we should merge the variable data onto. priority: ‘old’ keeps existing gr_foerste_fullfoert_dato values when present, ‘new’ prefers freshly derived values.

Returns:

pd.DataFrame: The dataframe with gr_foerste_fullfoert_dato added/updated.

Return type:

pd.DataFrame

uh_bachelor_foerste_fullfoert_dato(df=None, priority='old', *args, **kwargs)

Derive uh_bachelor_foerste_fullfoert_dato from avslutta.

Parameters:

  • df (DataFrame | None) – Source dataset containing at least snr, uh_erbachelor_fullfoert, utd_aktivitet_slutt.

  • priority (Literal['old', 'new'])

  • args (~P)

  • kwargs (~P)

Returns:

A column suitable for adding as a new column to the df.

Args:

df: Dataframe that we should merge the variable data onto. priority: ‘old’ keeps existing uh_bachelor_foerste_fullfoert_dato values when present, ‘new’ prefers freshly derived values.

Returns:

pd.DataFrame: The dataframe with uh_bachelor_foerste_fullfoert_dato added/updated.

Return type:

pd.DataFrame

uh_doktorgrad_foerste_fullfoert_dato(df=None, priority='old', *args, **kwargs)

Derive uh_doktorgrad_foerste_fullfoert_dato from avslutta.

Parameters:

  • df (DataFrame | None) – Source dataset containing at least snr, uh_erdoktorgrad_fullfoert, utd_aktivitet_slutt.

  • priority (Literal['old', 'new'])

  • args (~P)

  • kwargs (~P)

Returns:

A column suitable for adding as a new column to the df.

Args:

df: Dataframe that we should merge the variable data onto. priority: ‘old’ keeps existing uh_doktorgrad_foerste_fullfoert_dato values when present, ‘new’ prefers freshly derived values.

Returns:

pd.DataFrame: The dataframe with uh_doktorgrad_foerste_fullfoert_dato added/updated.

Return type:

pd.DataFrame

uh_hoeyskolekandidat_foerste_fullfoert_dato(df=None, priority='old', *args, **kwargs)

Derive uh_hoeyskolekandidat_foerste_fullfoert_dato from avslutta.

Parameters:

  • df (DataFrame | None) – Source dataset containing at least snr, uh_erhoeyskolekandidat_fullfoert, utd_aktivitet_slutt.

  • priority (Literal['old', 'new'])

  • args (~P)

  • kwargs (~P)

Returns:

A column suitable for adding as a new column to the df.

Args:

df: Dataframe that we should merge the variable data onto. priority: ‘old’ keeps existing uh_hoeyskolekandidat_foerste_fullfoert_dato values when present, ‘new’ prefers freshly derived values.

Returns:

pd.DataFrame: The dataframe with uh_hoeyskolekandidat_foerste_fullfoert_dato added/updated.

Return type:

pd.DataFrame

uh_master_foerste_fullfoert_dato(df=None, priority='old', *args, **kwargs)

Derive uh_master_foerste_fullfoert_dato from avslutta.

Parameters:

  • df (DataFrame | None) – Source dataset containing at least snr, uh_ermaster_fullfoert, utd_aktivitet_slutt.

  • priority (Literal['old', 'new'])

  • args (~P)

  • kwargs (~P)

Returns:

A column suitable for adding as a new column to the df.

Args:

df: Dataframe that we should merge the variable data onto. priority: ‘old’ keeps existing uh_master_foerste_fullfoert_dato values when present, ‘new’ prefers freshly derived values.

Returns:

pd.DataFrame: The dataframe with uh_master_foerste_fullfoert_dato added/updated.

Return type:

pd.DataFrame

vg_foerste_fullfoert_dato(df=None, priority='old', *args, **kwargs)

Derive vg_foerste_fullfoert_dato from avslutta.

Parameters:

  • df (DataFrame | None) – Source dataset containing at least snr, vg_ervgo_fullfoert, utd_aktivitet_slutt.

  • priority (Literal['old', 'new'])

  • args (~P)

  • kwargs (~P)

Returns:

A column suitable for adding as a new column to the df.

Args:

df: Dataframe that we should merge the variable data onto. priority: ‘old’ keeps existing vg_foerste_fullfoert_dato values when present, ‘new’ prefers freshly derived values.

Returns:

pd.DataFrame: The dataframe with vg_foerste_fullfoert_dato added/updated.

Return type:

pd.DataFrame

vg_studiespess_foerste_fullfoert_dato(df=None, priority='old', *args, **kwargs)

Derive vg_studiespess_foerste_fullfoert_dato from avslutta.

Parameters:

  • df (DataFrame | None) – Source dataset containing at least snr, vg_erstudiespess_fullfoert, utd_aktivitet_slutt.

  • priority (Literal['old', 'new'])

  • args (~P)

  • kwargs (~P)

Returns:

A column suitable for adding as a new column to the df.

Args:

df: Dataframe that we should merge the variable data onto. priority: ‘old’ keeps existing vg_studiespess_foerste_fullfoert_dato values when present, ‘new’ prefers freshly derived values.

Returns:

pd.DataFrame: The dataframe with vg_studiespess_foerste_fullfoert_dato added/updated.

Return type:

pd.DataFrame

vg_yrkesfag_foerste_fullfoert_dato(df=None, priority='old', *args, **kwargs)

Derive vg_yrkesfag_foerste_fullfoert_dato from avslutta.

Parameters:

  • df (DataFrame | None) – Source dataset containing at least snr, vg_eryrkesfag_fullfoert, utd_aktivitet_slutt.

  • priority (Literal['old', 'new'])

  • args (~P)

  • kwargs (~P)

Returns:

A column suitable for adding as a new column to the df.

Args:

df: Dataframe that we should merge the variable data onto. priority: ‘old’ keeps existing vg_yrkesfag_foerste_fullfoert_dato values when present, ‘new’ prefers freshly derived values.

Returns:

pd.DataFrame: The dataframe with vg_yrkesfag_foerste_fullfoert_dato added/updated.

Return type:

pd.DataFrame

nudb_use.variables.derive.klass_correspondences_and_variants module

fa_erfagskole_nokut_nus(df, priority='old', *args, **kwargs)

Derive ‘fa_erfagskole_nokut_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing fa_erfagskole_nokut_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with fa_erfagskole_nokut_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

fa_studiepoeng_nus(df, priority='old', *args, **kwargs)

Derive ‘fa_studiepoeng_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing fa_studiepoeng_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with fa_studiepoeng_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_gradmerke_nus(df, priority='old', *args, **kwargs)

Derive ‘uh_gradmerke_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_gradmerke_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_gradmerke_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_gruppering_nus(df, priority='old', *args, **kwargs)

Derive ‘uh_gruppering_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_gruppering_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_gruppering_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_studiepoeng_nus(df, priority='old', *args, **kwargs)

Derive ‘uh_studiepoeng_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_studiepoeng_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_studiepoeng_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_foreldet_kode_nus(df, priority='old', *args, **kwargs)

Derive ‘utd_foreldet_kode_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_foreldet_kode_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_foreldet_kode_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_isced2011_attainment_nus(df, priority='old', *args, **kwargs)

Derive ‘utd_isced2011_attainment_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_isced2011_attainment_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_isced2011_attainment_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_isced2011_programmes_nus(df, priority='old', *args, **kwargs)

Derive ‘utd_isced2011_programmes_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_isced2011_programmes_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_isced2011_programmes_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_isced2013_fagfelt_nus(df, priority='old', *args, **kwargs)

Derive ‘utd_isced2013_fagfelt_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_isced2013_fagfelt_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_isced2013_fagfelt_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_klassetrinn_lav_hoey_nus(df, priority='old', *args, **kwargs)

Derive ‘utd_klassetrinn_lav_hoey_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_klassetrinn_lav_hoey_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_klassetrinn_lav_hoey_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_samle_eller_enkeltutd_nus(df, priority='old', *args, **kwargs)

Derive ‘utd_samle_eller_enkeltutd_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_samle_eller_enkeltutd_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_samle_eller_enkeltutd_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_utdanningsprogram_nus(df, priority='old', *args, **kwargs)

Derive ‘utd_utdanningsprogram_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_utdanningsprogram_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_utdanningsprogram_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_varighet_antall_mnd_nus(df, priority='old', *args, **kwargs)

Derive ‘utd_varighet_antall_mnd_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_varighet_antall_mnd_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_varighet_antall_mnd_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

vg_kompetanse_nus(df, priority='old', *args, **kwargs)

Derive ‘vg_kompetanse_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing vg_kompetanse_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with vg_kompetanse_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

vg_kurstrinn_nus(df, priority='old', *args, **kwargs)

Derive ‘vg_kurstrinn_nus’ from ‘[‘nus2000’]’.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing vg_kurstrinn_nus values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with vg_kurstrinn_nus added/updated when all prerequisites are available.

Return type:

pd.DataFrame

nudb_use.variables.derive.klass_labels module

fa_erfagskole_nokut_nus_label(df, priority='old', *args, **kwargs)

Derive fa_erfagskole_nokut_nus_label, with klass labels for fa_erfagskole_nokut_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘fa_erfagskole_nokut_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing fa_erfagskole_nokut_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with fa_erfagskole_nokut_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

fa_studiepoeng_nus_label(df, priority='old', *args, **kwargs)

Derive fa_studiepoeng_nus_label, with klass labels for fa_studiepoeng_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘fa_studiepoeng_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing fa_studiepoeng_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with fa_studiepoeng_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

fuh_nett_eller_stedbasert_label(df, priority='old', *args, **kwargs)

Derive fuh_nett_eller_stedbasert_label, with klass labels for fuh_nett_eller_stedbasert.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘fuh_nett_eller_stedbasert’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing fuh_nett_eller_stedbasert_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with fuh_nett_eller_stedbasert_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

fuh_opptaksgrunnlag_label(df, priority='old', *args, **kwargs)

Derive fuh_opptaksgrunnlag_label, with klass labels for fuh_opptaksgrunnlag.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘fuh_opptaksgrunnlag’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing fuh_opptaksgrunnlag_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with fuh_opptaksgrunnlag_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

fuh_utvekslingsland_label(df, priority='old', *args, **kwargs)

Derive fuh_utvekslingsland_label, with klass labels for fuh_utvekslingsland.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘fuh_utvekslingsland’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing fuh_utvekslingsland_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with fuh_utvekslingsland_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

gro_elevstatus_label(df, priority='old', *args, **kwargs)

Derive gro_elevstatus_label, with klass labels for gro_elevstatus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘gro_elevstatus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing gro_elevstatus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with gro_elevstatus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

nus2000_label(df, priority='old', *args, **kwargs)

Derive nus2000_label, with klass labels for nus2000.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing nus2000_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with nus2000_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

pers_invkat_label(df, priority='old', *args, **kwargs)

Derive pers_invkat_label, with klass labels for pers_invkat.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘pers_invkat’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing pers_invkat_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with pers_invkat_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

pers_kjoenn_label(df, priority='old', *args, **kwargs)

Derive pers_kjoenn_label, with klass labels for pers_kjoenn.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘pers_kjoenn’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing pers_kjoenn_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with pers_kjoenn_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_campus_kommune_label(df, priority='old', *args, **kwargs)

Derive uh_campus_kommune_label, with klass labels for uh_campus_kommune.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘uh_campus_kommune’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_campus_kommune_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_campus_kommune_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_foerste_nus2000_label(df, priority='old', *args, **kwargs)

Derive uh_foerste_nus2000_label, with klass labels for uh_foerste_nus2000.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘uh_foerste_nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_foerste_nus2000_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_foerste_nus2000_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_gradmerke_nus_label(df, priority='old', *args, **kwargs)

Derive uh_gradmerke_nus_label, with klass labels for uh_gradmerke_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘uh_gradmerke_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_gradmerke_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_gradmerke_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_gruppering_nus_label(df, priority='old', *args, **kwargs)

Derive uh_gruppering_nus_label, with klass labels for uh_gruppering_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘uh_gruppering_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_gruppering_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_gruppering_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_institusjon_id_label(df, priority='old', *args, **kwargs)

Derive uh_institusjon_id_label, with klass labels for uh_institusjon_id.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘uh_institusjon_id’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_institusjon_id_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_institusjon_id_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_statsborgerskap_inn_label(df, priority='old', *args, **kwargs)

Derive uh_statsborgerskap_inn_label, with klass labels for uh_statsborgerskap_inn.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘uh_statsborgerskap_inn’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_statsborgerskap_inn_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_statsborgerskap_inn_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_studgrunnlagsland_label(df, priority='old', *args, **kwargs)

Derive uh_studgrunnlagsland_label, with klass labels for uh_studgrunnlagsland.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘uh_studgrunnlagsland’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_studgrunnlagsland_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_studgrunnlagsland_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_studiepoeng_nus_label(df, priority='old', *args, **kwargs)

Derive uh_studiepoeng_nus_label, with klass labels for uh_studiepoeng_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘uh_studiepoeng_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_studiepoeng_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_studiepoeng_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_univ_eller_hoegskole_label(df, priority='old', *args, **kwargs)

Derive uh_univ_eller_hoegskole_label, with klass labels for uh_univ_eller_hoegskole.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘uh_univ_eller_hoegskole’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_univ_eller_hoegskole_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_univ_eller_hoegskole_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_aktivitetsnivaa_heltid_deltid_label(df, priority='old', *args, **kwargs)

Derive utd_aktivitetsnivaa_heltid_deltid_label, with klass labels for utd_aktivitetsnivaa_heltid_deltid.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_aktivitetsnivaa_heltid_deltid’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_aktivitetsnivaa_heltid_deltid_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_aktivitetsnivaa_heltid_deltid_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_datakilde_label(df, priority='old', *args, **kwargs)

Derive utd_datakilde_label, with klass labels for utd_datakilde.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_datakilde’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_datakilde_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_datakilde_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_foreldet_kode_nus_label(df, priority='old', *args, **kwargs)

Derive utd_foreldet_kode_nus_label, with klass labels for utd_foreldet_kode_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_foreldet_kode_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_foreldet_kode_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_foreldet_kode_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_hoeyeste_nus2000_label(df, priority='old', *args, **kwargs)

Derive utd_hoeyeste_nus2000_label, with klass labels for utd_hoeyeste_nus2000.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_hoeyeste_nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_hoeyeste_nus2000_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_hoeyeste_nus2000_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_isced2011_attainment_nus_label(df, priority='old', *args, **kwargs)

Derive utd_isced2011_attainment_nus_label, with klass labels for utd_isced2011_attainment_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_isced2011_attainment_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_isced2011_attainment_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_isced2011_attainment_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_isced2011_programmes_nus_label(df, priority='old', *args, **kwargs)

Derive utd_isced2011_programmes_nus_label, with klass labels for utd_isced2011_programmes_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_isced2011_programmes_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_isced2011_programmes_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_isced2011_programmes_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_isced2013_fagfelt_nus_label(df, priority='old', *args, **kwargs)

Derive utd_isced2013_fagfelt_nus_label, with klass labels for utd_isced2013_fagfelt_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_isced2013_fagfelt_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_isced2013_fagfelt_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_isced2013_fagfelt_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_klassetrinn_lav_hoey_nus_label(df, priority='old', *args, **kwargs)

Derive utd_klassetrinn_lav_hoey_nus_label, with klass labels for utd_klassetrinn_lav_hoey_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_klassetrinn_lav_hoey_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_klassetrinn_lav_hoey_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_klassetrinn_lav_hoey_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_samle_eller_enkeltutd_nus_label(df, priority='old', *args, **kwargs)

Derive utd_samle_eller_enkeltutd_nus_label, with klass labels for utd_samle_eller_enkeltutd_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_samle_eller_enkeltutd_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_samle_eller_enkeltutd_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_samle_eller_enkeltutd_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_skolekom_label(df, priority='old', *args, **kwargs)

Derive utd_skolekom_label, with klass labels for utd_skolekom.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_skolekom’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_skolekom_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_skolekom_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_studieland_label(df, priority='old', *args, **kwargs)

Derive utd_studieland_label, with klass labels for utd_studieland.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_studieland’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_studieland_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_studieland_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_utdanningsprogram_nus_label(df, priority='old', *args, **kwargs)

Derive utd_utdanningsprogram_nus_label, with klass labels for utd_utdanningsprogram_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_utdanningsprogram_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_utdanningsprogram_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_utdanningsprogram_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_utdanningstype_label(df, priority='old', *args, **kwargs)

Derive utd_utdanningstype_label, with klass labels for utd_utdanningstype.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_utdanningstype’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_utdanningstype_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_utdanningstype_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_utveksling_label(df, priority='old', *args, **kwargs)

Derive utd_utveksling_label, with klass labels for utd_utveksling.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_utveksling’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_utveksling_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_utveksling_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_varighet_antall_mnd_nus_label(df, priority='old', *args, **kwargs)

Derive utd_varighet_antall_mnd_nus_label, with klass labels for utd_varighet_antall_mnd_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_varighet_antall_mnd_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_varighet_antall_mnd_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_varighet_antall_mnd_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_viderutd_nettbasert_label(df, priority='old', *args, **kwargs)

Derive utd_viderutd_nettbasert_label, with klass labels for utd_viderutd_nettbasert.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_viderutd_nettbasert’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_viderutd_nettbasert_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_viderutd_nettbasert_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

vg_kompetanse_nus_label(df, priority='old', *args, **kwargs)

Derive vg_kompetanse_nus_label, with klass labels for vg_kompetanse_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘vg_kompetanse_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing vg_kompetanse_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with vg_kompetanse_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

vg_kontraktstype_label(df, priority='old', *args, **kwargs)

Derive vg_kontraktstype_label, with klass labels for vg_kontraktstype.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘vg_kontraktstype’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing vg_kontraktstype_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with vg_kontraktstype_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

vg_kurstrinn_nus_label(df, priority='old', *args, **kwargs)

Derive vg_kurstrinn_nus_label, with klass labels for vg_kurstrinn_nus.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘vg_kurstrinn_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing vg_kurstrinn_nus_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with vg_kurstrinn_nus_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

vg_rettstype_inntak_label(df, priority='old', *args, **kwargs)

Derive vg_rettstype_inntak_label, with klass labels for vg_rettstype_inntak.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘vg_rettstype_inntak’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing vg_rettstype_inntak_label values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with vg_rettstype_inntak_label added/updated when all prerequisites are available.

Return type:

pd.DataFrame

nudb_use.variables.derive.land module

utd_erutland(df, priority='old', *args, **kwargs)

Derive utd_erutland from utd_skolekom.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘utd_skolekom’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_erutland values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_erutland added/updated when all prerequisites are available.

Return type:

pd.DataFrame

nudb_use.variables.derive.nus_variants module

nudb_use.variables.derive.person module

pers_foedselsdato(df, priority='old', *args, **kwargs)

Derive pers_foedselsdato.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘snr’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing pers_foedselsdato values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with pers_foedselsdato added/updated when all prerequisites are available.

Return type:

pd.DataFrame

pers_invkat(df, priority='old', *args, **kwargs)

Derive pers_invkat.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘snr’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing pers_invkat values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with pers_invkat added/updated when all prerequisites are available.

Return type:

pd.DataFrame

pers_kjoenn(df, priority='old', *args, **kwargs)

Derive pers_kjoenn.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘snr’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing pers_kjoenn values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with pers_kjoenn added/updated when all prerequisites are available.

Return type:

pd.DataFrame

nudb_use.variables.derive.person_idents module

snr_mrk(df, priority='old', *args, **kwargs)

Derive the column snr_mrk from snr-column, True if values in snr_col is notna, has a length of 7 and are wholly alphanumeric.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘snr’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing snr_mrk values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with snr_mrk added/updated when all prerequisites are available.

Return type:

pd.DataFrame

nudb_use.variables.derive.registrert module

gr_ergrunnskole_registrering(df, priority='old', *args, **kwargs)

Derive gr_ergrunnskole_registrering from nus2000 and utland, as a boolean filter for registrations on gr-level.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’, ‘utd_erutland’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing gr_ergrunnskole_registrering values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with gr_ergrunnskole_registrering added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_erbachelor_registrering(df, priority='old', *args, **kwargs)

Derive uh_erbachelor_registrering from nus2000 as a boolean filter.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’, ‘uh_gradmerke_nus’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_erbachelor_registrering values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_erbachelor_registrering added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_erhoeyereutd_registrering(df, priority='old', *args, **kwargs)

Derive uh_erhoeyereutd_registrering from nus2000 as a boolean filter.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_erhoeyereutd_registrering values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_erhoeyereutd_registrering added/updated when all prerequisites are available.

Return type:

pd.DataFrame

uh_ermaster_registrering(df, priority='old', *args, **kwargs)

Derive uh_erbachelor_registrering from nus2000 as a boolean filter.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing uh_ermaster_registrering values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with uh_ermaster_registrering added/updated when all prerequisites are available.

Return type:

pd.DataFrame

vg_erstudiespess_registrering(df, priority='old', *args, **kwargs)

Derive vg_erstudiespess_registrering from nus2000 and vg_utdprogram, as a boolean filter.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’, ‘vg_utdprogram’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing vg_erstudiespess_registrering values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with vg_erstudiespess_registrering added/updated when all prerequisites are available.

Return type:

pd.DataFrame

vg_ervgo_registrering(df, priority='old', *args, **kwargs)

Derive vg_ervgo_registrering from nus2000, as a boolean filter for registrations on vg-level.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing vg_ervgo_registrering values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with vg_ervgo_registrering added/updated when all prerequisites are available.

Return type:

pd.DataFrame

vg_eryrkesfag_registrering(df, priority='old', *args, **kwargs)

Derive vg_eryrkesfag_registrering from nus2000 and vg_utdprogram, as a boolean filter.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’, ‘vg_utdprogram’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing vg_eryrkesfag_registrering values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with vg_eryrkesfag_registrering added/updated when all prerequisites are available.

Return type:

pd.DataFrame

nudb_use.variables.derive.registrert_foerste module

gr_foerste_registrert_dato(df=None, priority='old', *args, **kwargs)

Derive gr_foerste_registrert_dato from avslutta.

Parameters:

  • df (DataFrame | None) – Source dataset containing at least snr, gr_ergrunnskole_registrering, utd_aktivitet_start.

  • priority (Literal['old', 'new'])

  • args (~P)

  • kwargs (~P)

Returns:

A column suitable for adding as a new column to the df.

Args:

df: Dataframe that we should merge the variable data onto. priority: ‘old’ keeps existing gr_foerste_registrert_dato values when present, ‘new’ prefers freshly derived values.

Returns:

pd.DataFrame: The dataframe with gr_foerste_registrert_dato added/updated.

Return type:

pd.DataFrame

uh_bachelor_foerste_registrert_dato(df=None, priority='old', *args, **kwargs)

Derive uh_bachelor_foerste_registrert_dato from avslutta.

Parameters:

  • df (DataFrame | None) – Source dataset containing at least snr, uh_erbachelor_registrering, utd_aktivitet_start.

  • priority (Literal['old', 'new'])

  • args (~P)

  • kwargs (~P)

Returns:

A column suitable for adding as a new column to the df.

Args:

df: Dataframe that we should merge the variable data onto. priority: ‘old’ keeps existing uh_bachelor_foerste_registrert_dato values when present, ‘new’ prefers freshly derived values.

Returns:

pd.DataFrame: The dataframe with uh_bachelor_foerste_registrert_dato added/updated.

Return type:

pd.DataFrame

uh_foerste_nus2000(df=None, priority='old', *args, **kwargs)

Derive the first nus2000 a person has on UH-level.

Parameters:

  • df (DataFrame | None) – Source dataset containing at least snr, nus2000, utd_aktivitet_start.

  • priority (Literal['old', 'new'])

  • args (~P)

  • kwargs (~P)

Returns:

A column suitable for adding as a new column to the df.

Args:

df: Dataframe that we should merge the variable data onto. priority: ‘old’ keeps existing uh_foerste_nus2000 values when present, ‘new’ prefers freshly derived values.

Returns:

pd.DataFrame: The dataframe with uh_foerste_nus2000 added/updated.

Return type:

pd.DataFrame

uh_foerste_registrert_dato(df=None, priority='old', *args, **kwargs)

Derive uh_foerste_registrert_dato from avslutta.

Parameters:

  • df (DataFrame | None) – Source dataset containing at least snr, uh_erhoeyereutd_registrering, utd_aktivitet_start.

  • priority (Literal['old', 'new'])

  • args (~P)

  • kwargs (~P)

Returns:

A column suitable for adding as a new column to the df.

Args:

df: Dataframe that we should merge the variable data onto. priority: ‘old’ keeps existing uh_foerste_registrert_dato values when present, ‘new’ prefers freshly derived values.

Returns:

pd.DataFrame: The dataframe with uh_foerste_registrert_dato added/updated.

Return type:

pd.DataFrame

uh_master_foerste_registrert_dato(df=None, priority='old', *args, **kwargs)

Derive uh_master_foerste_registrert_dato from avslutta.

Parameters:

  • df (DataFrame | None) – Source dataset containing at least snr, uh_ermaster_registrering, utd_aktivitet_start.

  • priority (Literal['old', 'new'])

  • args (~P)

  • kwargs (~P)

Returns:

A column suitable for adding as a new column to the df.

Args:

df: Dataframe that we should merge the variable data onto. priority: ‘old’ keeps existing uh_master_foerste_registrert_dato values when present, ‘new’ prefers freshly derived values.

Returns:

pd.DataFrame: The dataframe with uh_master_foerste_registrert_dato added/updated.

Return type:

pd.DataFrame

vg_foerste_registrert_dato(df=None, priority='old', *args, **kwargs)

Derive vg_foerste_registrert_dato from avslutta.

Parameters:

  • df (DataFrame | None) – Source dataset containing at least snr, vg_ervgo_registrering, utd_aktivitet_start.

  • priority (Literal['old', 'new'])

  • args (~P)

  • kwargs (~P)

Returns:

A column suitable for adding as a new column to the df.

Args:

df: Dataframe that we should merge the variable data onto. priority: ‘old’ keeps existing vg_foerste_registrert_dato values when present, ‘new’ prefers freshly derived values.

Returns:

pd.DataFrame: The dataframe with vg_foerste_registrert_dato added/updated.

Return type:

pd.DataFrame

nudb_use.variables.derive.uh_univ_eller_hoegskole module

nudb_use.variables.derive.utd_foreldres_utdnivaa module

utd_foreldres_utdnivaa_16aar(df, priority='old', *args, **kwargs)

Derive utd_foreldres_utdnivaa_16aar.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘snr’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_foreldres_utdnivaa_16aar values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_foreldres_utdnivaa_16aar added/updated when all prerequisites are available.

Return type:

pd.DataFrame

nudb_use.variables.derive.utd_hoeyeste module

utd_hoeyeste_nus2000(df, priority='old', *args, **kwargs)

Derive utd_hoyeste_nus2000.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘snr’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_hoeyeste_nus2000 values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_hoeyeste_nus2000 added/updated when all prerequisites are available.

Return type:

pd.DataFrame

utd_hoeyeste_rangering(df, priority='old', *args, **kwargs)

Derive utd_hoyeste_rangering.

Parameters:

  • df (DataFrame) – Dataframe that should contain prerequisites listed in [‘nus2000’, ‘utd_klassetrinn’, ‘uh_gruppering_nus’, ‘utd_skoleaar_start’, ‘uh_eksamen_studpoeng’, ‘uh_eksamen_dato’, ‘utd_aktivitet_slutt’].

  • priority (Literal['old', 'new']) – ‘old’ keeps existing utd_hoeyeste_rangering values when present, ‘new’ prefers freshly derived values.

  • args (~P)

  • kwargs (~P)

Returns:

The dataframe with utd_hoeyeste_rangering added/updated when all prerequisites are available.

Return type:

pd.DataFrame

nudb_use.variables.derive.utd_skoleaar module