pudl.transform.eia176

Module to perform data cleaning functions on EIA176 data tables.

Attributes

logger

Functions

_core_eia176__data(→ tuple[dagster.Output, dagster.Output])	Take raw list and return two wide tables with primary keys and one column per variable.
get_wide_table(→ pandas.DataFrame)	Take a 'long' or entity-attribute-value table and return a wide table with one column per attribute/variable.
validate_totals(→ dagster.AssetCheckResult)	Compare reported and calculated totals for different geographical aggregates, report any differences.

Module Contents

pudl.transform.eia176.logger

pudl.transform.eia176._core_eia176__data(raw_eia176__data: pandas.DataFrame) → tuple[dagster.Output, dagster.Output]

Take raw list and return two wide tables with primary keys and one column per variable.

One table with data for each year and company, one with state- and US-level aggregates per year.

pudl.transform.eia176.get_wide_table(long_table: pandas.DataFrame, primary_key: list[str]) → pandas.DataFrame

Take a ‘long’ or entity-attribute-value table and return a wide table with one column per attribute/variable.

pudl.transform.eia176.validate_totals(_core_eia176__yearly_company_data: pandas.DataFrame, _core_eia176__yearly_aggregate_data: pandas.DataFrame) → dagster.AssetCheckResult

Compare reported and calculated totals for different geographical aggregates, report any differences.