ebm.model.building_category module

class MyEnumType(cls, bases, classdict, *, boundary=None, _simple=False, **kwds)

Bases: EnumType

class BuildingCategory(value)

Bases: StrEnum

HOUSE = 'house'

APARTMENT_BLOCK = 'apartment_block'

KINDERGARTEN = 'kindergarten'

SCHOOL = 'school'

UNIVERSITY = 'university'

OFFICE = 'office'

RETAIL = 'retail'

HOTEL = 'hotel'

HOSPITAL = 'hospital'

NURSING_HOME = 'nursing_home'

CULTURE = 'culture'

SPORTS = 'sports'

STORAGE = 'storage_repairs'

yearly_construction_floor_area()

is_residential() → bool

is_non_residential() → bool

static from_string(category_name: str) → BuildingCategory

Create an enum object from category name :param category_name: :type category_name: str

Returns:

building_category (BuildingCategory (Enum))

Raises:

ValueError – category_name not found in BuildingCategory

from_norsk(norsk: str) → BuildingCategory

expand_building_category(row: Series) → DataFrame

Expand a row of data based on the building category into multiple rows, each representing a specific sub-category of either residential or non-residential buildings.

Parameters

rowpd.Series

A pandas Series containing the data for a single row, including a ‘building_category’ field.

Returns

pd.DataFrame

A DataFrame with expanded rows for each sub-category of the building category.

expand_building_categories(df: DataFrame, unique_columns: List[str] = None)

Transform input dataframe so that building_category within groups (residential/non-residential) are unpacked into all containing categories. Duplicates categories are removed. Specific categories with values area preferred over category groups when there is a conflict.

Parameters

df : pandas.core.frame.DataFrame unique_columns : str

list of column names that should be treated as joint unique. default: [‘building_category’]

Returns

pandas.core.frame.DataFrame

collapse_building_category(building_category: Series) → Series

Replace building category labels with building group labels where appropriate.

This function takes a pandas Series containing building category strings separated by either “+” or “,”. It normalizes token case, applies a set of category‑collapse rules, and returns a new Series with collapsed category labels.

building groups implemented:

• residential

  house, apartment_block

• non_residential

  culture, hospital, hotel, kindergarten, nursing_home, office, retail, school, sports, storage_repairs, university

• default

  All of the above

Parameters

building_categorypd.Series

Series of building category strings. Each value must be a

string containing tokens separated by either "+" or “,”`. Tokens may contain arbitrary casing; they are normalized to lowercase.

Returns

pd.Series

series with building categories collapsed into groups

Examples

>>> import pandas as pd
>>> collapse_building_category(pd.Series(["house+apartment_block"]))
0    residential
dtype: object

>>> collapse_building_category(pd.Series(
...     ["culture+hospital+hotel+kindergarten+nursing_home+office+retail"
...      "+school+sports+storage_repairs+university"]
... ))
0    non_residential
dtype: object