Woodwork is a library that helps with data typing of 2-dimensional tabular data structures.

It provides a DataTable object, 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.

Quick Start

Below is an example of using a Woodwork DataTable to automatically infer the Logical Types for a data structure and select columns with specific types.

import woodwork as ww

data = ww.demo.load_retail(nrows=100, return_dataframe=True)

dt = ww.DataTable(data, name="retail")
dt.types

	Physical Type	Logical Type	Semantic Tag(s)
Data Column
order_product_id	Int64	WholeNumber	{numeric}
order_id	Int64	WholeNumber	{numeric}
product_id	category	Categorical	{category}
description	string	NaturalLanguage	{}
quantity	Int64	WholeNumber	{numeric}
order_date	datetime64[ns]	Datetime	{}
unit_price	float64	Double	{numeric}
customer_name	string	NaturalLanguage	{}
country	string	NaturalLanguage	{}
total	float64	Double	{numeric}
cancelled	boolean	Boolean	{}

filtered_dt = dt.select(include=['numeric', 'Boolean'])
filtered_dt.to_dataframe().head(5)

	total	unit_price	order_id	cancelled	quantity	order_product_id
0	25.245	4.2075	536365	False	6	0
1	33.561	5.5935	536365	False	6	1
2	36.300	4.5375	536365	False	8	2
3	33.561	5.5935	536365	False	6	3
4	33.561	5.5935	536365	False	6	4

Table of contents

• Install
• Development
• Start

• Guides
  • Understanding Types and Tags
  • Woodwork Global Configuration Options
  • Gain Statistical Insights into Your DataTable
  • Using Woodwork with Dask DataFrames

• API Reference
• Release Notes

Install