pudl.extract.rus7#

Extract USDA RUS Form 7 data from CSVs.

Attributes#

`raw_rus7__all_dfs`
`raw_rus7_assets`

Classes#

Extractor

Extractor for USDA RUS Form 7.

Functions#

raw_rus7_asset_factory(table_name)

Create raw RUS Form 7 asset for a specific page.

Module Contents#

class pudl.extract.rus7.Extractor(*args, **kwargs)[source]#

Bases: pudl.extract.csv.CsvExtractor

Extractor for USDA RUS Form 7.

METADATA[source]#: Instance of metadata object to use with this extractor.

READ_CSV_KWARGS[source]#

Keyword arguments that are passed to pandas.read_csv().

These allow customization of the CSV parsing process. For example, you can specify the column delimiter, data types, date parsing, etc. This can greatly reduce peak memory usage and speed up the extraction process. Unfortunately you must refer to the column headers using their original names as they appear in the CSV.

TODO[zaneselvans] 2024-04-19: it would be useful to be able to specify different CSV reading options for different pages within the same dataset. At the moment the same arguments will be applied to all pages. This still allows some flexibility because some pandas.read_csv() arguments like dtype don’t raise errors if the columns they apply to aren’t present.

source_filename(page: str, **partition: pudl.extract.extractor.PartitionSelection) → str[source]#

Get the file name for the right page and part.

In this instance we are using the same methodology from the excel metadata extractor.

process_raw(df, page, **partition)[source]#: Adds source column and report_year column if missing.

pudl.extract.rus7.raw_rus7__all_dfs[source]#

pudl.extract.rus7.raw_rus7_asset_factory(table_name: str)[source]#: Create raw RUS Form 7 asset for a specific page.

pudl.extract.rus7.raw_rus7_assets[source]#