Assembling

fuzzy_join(): Joining tables on non-normalized categories with approximate matching. Example

Joiner, AggJoiner: transformers for joining multiple tables together. Example

Encoding

TableVectorizer: turn a pandas dataframe into a numerical array for machine learning. Example

GapEncoder: OneHotEncoder but robust to typos or non-normalized categories. Example

Cleaning

deduplicate(): merge categories of similar morphology (spelling). Example

Less data wrangling, more machine learning

tabular_learner: easily create tabular-learning pipelines that wrangle complex dataframes.

Given, a complex dataframe df: (expand for full code)

>>> from skrub.datasets import fetch_employee_salaries
>>> dataset = fetch_employee_salaries()
>>> df = dataset.X
>>> y = dataset.y
>>> df

	gender	department	department_name	division	assignment_category	employee_position_title	date_first_hired	year_first_hired
0	F	POL	Department of Police	MSB Information Mgmt and...	Fulltime-Regular	Office Services Coordinator	09/22/1986	1986
1	M	POL	Department of Police	ISB Major Crimes...	Fulltime-Regular	Master Police Officer	09/12/1988	1988
...	...	...	...	...	...	...	...	...
9226	M	CCL	County Council	Council Central Staff	Fulltime-Regular	Manager II	09/05/2006	2006
9227	M	DLC	Department of Liquor Control	Licensure, Regulation...	Fulltime-Regular	Alcohol/Tobacco Enforcement Specialist II	01/30/2012	2012

>>> from sklearn.model_selection import cross_val_score
>>> from skrub import tabular_learner
>>> cross_val_score(tabular_learner('regressor'), df, y)
array([0.89370447, 0.89279068, 0.92282557, 0.92319094, 0.92162666])