DataframeConversionValidator

Module for checking PySpark Dataframes before and after conversion.

Provides a way to quickly validate column conversions didn't result in null across large dataframes. Can also be used to find problem columns and rows.

Will need to specify or create a primary key column for matching before and after rows.

Use case

Converting StringType columns to TimestampType can cause null values in poorly formed data. It's not possible to check by hand. This works as an early warning system.

Example:

from DFCV import DataframeConversionValidator

dfcv = DataframeConversionValidator(_before_df=unmodified_df, _after_df=converted_df, _primary_key_column='pk')

---------------
Original Shape:
    rows    - 469221
    columns - 582
Problem Shape:
    rows    - 1
    columns - 3
Details:
    ['ImproperDate (1)', 'ImproperTimestamp (1)', 'BadUpdateTime (1)']
---------------