arbmark.groups package¶

Submodules¶

arbmark.groups.age module¶

alder_5grp(alder, display='label')¶

Categorize a pandas Series of person ages into predefined groups used in ARBLONN.

Parameters:

alder (Series) – A pandas Series containing the person ages.
display (str) – If ‘label’, returns group labels; if ‘number’, returns keys; for any other string, returns a combination of keys and labels.

Return type:

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

Returns:

A numpy Array where the original person ages are replaced by group labels, keys, or a combination.

alder_grp(alder, display='label')¶

Categorize a pandas Series of person ages into predefined groups used in SYKEFR.

Parameters:

alder (Series) – A pandas Series containing the person ages.
display (str) – If ‘label’, returns group labels; if ‘number’, returns keys; for any other string, returns a combination of keys and labels.

Return type:

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

Returns:

A numpy Array where the original person ages are replaced by group labels, keys, or a combination.

arbmark.groups.company_size module¶

virk_str_8grp(ansatte, display='label')¶

Categorize a pandas Series of employee counts into predefined groups.

Parameters:

ansatte (Series) – A pandas Series containing the employee counts.
display (str) – If ‘label’, returns group labels; if ‘number’, returns keys; for any other string, returns a combination of keys and labels.

Return type:

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

Returns:

A numpy Array where the original employee counts are replaced by group labels or keys.

arbmark.groups.country_origin module¶

landbakgrunn_grp(landbakgrunn, display='label')¶

Categorize a pandas Series of country origins from 3 generations into world regions.

Parameters:

landbakgrunn (Series) – A pandas Series containing the country origins.
display (str) – If ‘label’, returns group labels; if ‘number’, returns keys; if ‘arblonn’, returns specific labels for ARBLONN; for any other string, returns a combination of keys and labels.

Return type:

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

Returns:

A numpy Array where the original country origins are replaced by group labels or keys.

arbmark.groups.nace module¶

clean_nace_17_groups(val)¶

Cleans the NACE code value by removing redundant parts.

This function checks if the input string val contains a hyphen (‘-’) and if the parts before and after the hyphen are identical. If they are, it returns only the part before the hyphen. Otherwise, it returns the original input value.

Parameters:: val (str) – A string containing the NACE code to be cleaned.
Return type:: str
Returns:: A string with the cleaned NACE code.

nace_sn07_47grp(nace_sn07, display='label')¶

Categorize a pandas Series of NACE-codes (SN07) into predefined groups.

Parameters:

nace_sn07 (Series) – A pandas Series containing the NACE-codes.
display (str) – If ‘label’, returns group labels; if ‘number’, returns keys; for any other string, returns a combination of keys and labels.

Return type:

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

Returns:

A numpy Array where the original NACE-codes are replaced by group labels or keys.

nace_to_17_groups(nace, label=False)¶

Converts NACE codes in a Pandas Series to their corresponding group codes or labels.

NACE (Nomenclature of Economic Activities) is the European industry standard classification system. This function maps NACE codes to a higher-level group (level 2) and optionally returns the group’s name instead of its code.

Parameters:

nace (Series) – A Pandas Series containing NACE codes.
label (bool) – If True, returns the names of the groups instead of their codes. Defaults to False.

Return type:

Series

Returns:

A Pandas Series with the mapped group codes or names, depending on the ‘label’ argument.

Note

The function relies on a predefined mapping (‘KlassVariant(1616).data’) to perform the conversion. It assumes that this mapping has a specific structure, with ‘level’, ‘code’, and ‘parentCode’ (or ‘name’ if labels are requested) columns.

arbmark.groups.sector module¶

sektor2_grp(sektor, display='label')¶

Categorize a pandas Series of sectors into predefined groups.

Parameters:

sektor (Series) – A pandas Series containing the sector codes.
display (str) – If ‘label’, returns group labels; if ‘number’, returns keys; for any other string, returns a combination of keys and labels.

Return type:

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

Returns:

A numpy Array where the original sector is replaced by group labels or keys.

arbmark.groups.shift_work module¶

turnuskoder(arb_tid_ordning)¶

Assigns codes based on work schedule categories.

This function takes a pandas Series containing work schedule categories and assigns corresponding codes based on specific conditions. The conditions are as follows: - ‘20’ is assigned to categories [‘dogn355’, ‘helkont336’, ‘offshore336’, ‘skift365’, ‘andre_skift’] - ‘25’ is assigned to the ‘ikke_skift’ category - ‘99’ is assigned to values [‘-2’, ‘’, ‘-1’] or NaN values in the series Any value that doesn’t match these conditions will be assigned an empty string.

Parameters:: arb_tid_ordning (Series) – A pandas Series object containing strings that represent different work schedule categories.
Return type:: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]
Returns:: An array of strings, where each string is a code corresponding to the work schedule category in arb_tid_ordning.

Example

>>> arb_tid_ordning = pd.Series(['dogn355', 'helkont336', 'ikke_skift', '-2', 'offshore336', ''])
>>> turnuskoder(arb_tid_ordning)
array(['20', '20', '25', '99', '20', '99'], dtype='<U2')

Module contents¶

A collection of useful groups.