It provides a special namespace on your DataFrame, ww, which contains the physical, logical, and semantic data types. It can be used with Featuretools, EvalML, and general machine learning applications where logical and semantic typing information is important.
Woodwork provides simple interfaces for adding and updating logical and semantic typing information, as well as selecting data columns based on the types.
Below is an example of using Woodwork to automatically infer the Logical Types for a DataFrame and select columns with specific types.
import woodwork as ww
df = ww.demo.load_retail(nrows=100, init_woodwork=False)
filtered_df = df.ww.select(include=['numeric', 'Boolean'])
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-datatables/envs/v0.4.1/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-datatables/envs/v0.4.1/lib/python3.7/site-packages/woodwork/table_schema.py:462: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working
if not isinstance(selector, collections.Hashable):