pudl.extract.vcerare#

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#

`logger`
`VCERARE_PAGES`

Functions#

`_clean_column_names`(→ duckdb.DuckDBPyRelation)	Apply basic cleaning to column names.
`extract_vcerare`(→ tuple[dict[int, ...)	Extract data from all vcerare pages and write to parquet files.
`raw_vcerare__lat_lon_fips`(→ pandas.DataFrame)	Extract lat/lon to FIPS and county mapping CSV.

Module Contents#

pudl.extract.vcerare.logger[source]#

pudl.extract.vcerare.VCERARE_PAGES[source]#

pudl.extract.vcerare._clean_column_names(table_relation: duckdb.DuckDBPyRelation) → duckdb.DuckDBPyRelation[source]#: Apply basic cleaning to column names.

pudl.extract.vcerare.extract_vcerare(context) → tuple[dict[int, pudl.helpers.ParquetData], dict[int, pudl.helpers.ParquetData], dict[int, pudl.helpers.ParquetData]][source]#: Extract data from all vcerare pages and write to parquet files.

pudl.extract.vcerare.raw_vcerare__lat_lon_fips(context) → pandas.DataFrame[source]#

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.