pudl.extract.vcerare
====================

.. py:module:: pudl.extract.vcerare

.. autoapi-nested-parse::

   Extract VCE Resource Adequacy Renewable Energy (RARE) Power Dataset.

   This dataset has 1,000s of columns, so we don't want to manually specify a rename on
   import because we'll pivot these to a column in the transform step. We adapt the
   standard extraction infrastructure to simply read in the data.

   Each annual zip folder contains a folder with three files:
   Wind_Power_140m_Offshore_county.csv
   Wind_Power_100m_Onshore_county.csv
   Fixed_SolarPV_Lat_UPV_county.csv

   The drive also contains one more CSV file: vce_county_lat_long_fips_table.csv. This gets
   read in when the fips partition is set to True.



Attributes
----------

.. autoapisummary::

   pudl.extract.vcerare.logger
   pudl.extract.vcerare.VCERARE_PAGES


Functions
---------

.. autoapisummary::

   pudl.extract.vcerare._clean_column_names
   pudl.extract.vcerare.extract_vcerare
   pudl.extract.vcerare.raw_vcerare__lat_lon_fips


Module Contents
---------------

.. py:data:: logger

.. py:data:: VCERARE_PAGES

.. py:function:: _clean_column_names(table_relation: duckdb.DuckDBPyRelation) -> duckdb.DuckDBPyRelation

   Apply basic cleaning to column names.


.. py:function:: extract_vcerare(context) -> tuple[dict[int, pudl.helpers.ParquetData], dict[int, pudl.helpers.ParquetData], dict[int, pudl.helpers.ParquetData]]

   Extract data from all vcerare pages and write to parquet files.


.. py:function:: raw_vcerare__lat_lon_fips(context) -> pandas.DataFrame

   Extract lat/lon to FIPS and county mapping CSV.

   This dataframe is static, so it has a distinct partition from the other datasets and
   its extraction is controlled by a boolean in the ETL run.