ebm.model.energy_need_filter module
- filter_original_condition(df: DataFrame, building_category: BuildingCategory | str, tek: str, purpose: str) DataFrame[source]
Explode and deduplicates DataFrame df and returns rows matching building_category, tek, and purpose
Convenience function that does
exploded = explode_dataframe(df) de_duped = de_dupe_dataframe(exploded) filtered = de_duped[(de_duped.building_category==building_category) & (de_duped.building_code==tek) & (de_duped.purpose == purpose)]
Parameters
df : pd.DataFrame building_category : BuildingCategory | str tek : str purpose : str
Returns
pd.DataFrame
- filter_improvement_building_upgrade(df: DataFrame, building_category: BuildingCategory | str, tek: str, purpose: str) DataFrame[source]
Explode and deduplicates DataFrame df and returns rows matching building_category, tek, and purpose
Convenience function that does
exploded = explode_dataframe(df) de_duped = de_dupe_dataframe(exploded) filtered = de_duped[(de_duped.building_category==building_category) & (de_duped.building_code==tek) & (de_duped.purpose == purpose)]
Parameters
df : pd.DataFrame building_category : BuildingCategory | str tek : str purpose : str
Returns
pd.DataFrame
- de_dupe_dataframe(df: DataFrame, unique_columns: list[str] | None = None) DataFrame[source]
Drops duplicate rows in df based on building_category, TEK and purpose same as
df.drop_duplicates(unique_columns)
Parameters
df : pd.DataFrame unique_columns : list[str], optional
default= [‘building_category’, ‘building_code’, ‘purpose’]
Returns
pd.DataFrame
- explode_dataframe(df: DataFrame, building_code_list: list[str] | None = None) DataFrame[source]
Explode column aliases for building_category, TEK, purpose in dataframe.
default in building_category is replaced with all options from BuildingCategory enum default in TEK is replaced with all elements in optional building_code_list parameter default in purpose is replaced with all options from EnergyPurpose enum
Parameters
df : pd.DataFrame building_code_list : list of TEK to replace default, Optional
default TEK49 PRE_TEK49 PRE_TEK49_RES_1950 TEK69 TEK87 TEK97 TEK07 TEK10 TEK17
Returns
pd.DataFrame