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Woodwork

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

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.

Quick Start¶

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

[1]:

import woodwork as ww

df = ww.demo.load_retail(nrows=100, init_woodwork=False)

df.ww.init(name="retail")
df.ww

[1]:

	Physical Type	Logical Type	Semantic Tag(s)
Column
order_product_id	int64	Integer	['numeric']
order_id	int64	Integer	['numeric']
product_id	string	Unknown	[]
description	string	Unknown	[]
quantity	int64	Integer	['numeric']
order_date	datetime64[ns]	Datetime	[]
unit_price	float64	Double	['numeric']
customer_name	category	Categorical	['category']
country	category	Categorical	['category']
total	float64	Double	['numeric']
cancelled	bool	Boolean	[]

[2]:

filtered_df = df.ww.select(include=['numeric', 'Boolean'])
filtered_df.head(5)

[2]:

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

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