Skrub workshop - Introducing Skrub

Less wrangling, more machine learning

Riccardo Cappuzzo, Jérôme Dockès, Vincent Maladiere

Inria, P16, Probabl.

2025-01-29

Plan for the presentation

• Introducing skrub
  • Example use case
  • Detailed explanation of the features
  • Getting involved

In the beginning…

Skrub stems from the development of dirty_cat, a package that provided support for handling dirty columns and perform fuzzy joins across tables.

It has since evolved into a package that provides:

• interactive data exploration
• automated pre-processing of pandas and polars dataframes
• powerful feature engineering

Skrub’s vision

The goal of skrub is to facilitate building and deploying machine-learning models on pandas and polars dataframes (later, SQL databases…)

Skrub is high-level, with a philosophy and an API matching that of scikit-learn. It strives to bridge the worlds of databases and machine-learning, enabling imperfect assembly and representations of the data when it is noisy.

Let’s set the stage

An example use case

1. Gather some data
   • Employee salaries, census, customer churn…
2. Explore the data
   • Null values, dtypes, correlated features…
3. Pre-process the data
4. Build a scikit-learn estimator
5. ???
6. Profit 📈

Exploring the data

import skrub
import pandas as pd
from skrub.datasets import fetch_employee_salaries

dataset = fetch_employee_salaries()
employees, salaries = dataset.X, dataset.y

employees.describe(include="all")

	gender	department	department_name	division	assignment_category	employee_position_title	date_first_hired	year_first_hired
count	9211	9228	9228	9228	9228	9228	9228	9228.000000
unique	2	37	37	694	2	443	2264	NaN
top	M	POL	Department of Police	School Health Services	Fulltime-Regular	Bus Operator	12/12/2016	NaN
freq	5481	1844	1844	300	8394	638	87	NaN
mean	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2003.597529
std	NaN	NaN	NaN	NaN	NaN	NaN	NaN	9.327078
min	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1965.000000
25%	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1998.000000
50%	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2005.000000
75%	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2012.000000
max	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2016.000000

Exploring the data… interactively!

from skrub import TableReport
TableReport(employee_salaries)

Preview

Main features:

• Obtain high-level statistics about the data (number of uniques, missing values…)
• Explore the distribution of values and find outliers
• Discover highly correlated columns
• Export and share the report as an html file

More examples here

Build a predictive pipeline

Build a predictive pipeline

from sklearn.linear_model import Ridge
model = Ridge()

Build a predictive pipeline

from sklearn.linear_model import Ridge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

model = make_pipeline(StandardScaler(), Ridge())

Build a predictive pipeline

from sklearn.linear_model import Ridge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer

model = make_pipeline(SimpleImputer(), StandardScaler(), Ridge())

Build a predictive pipeline

from sklearn.linear_model import Ridge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import make_column_selector as selector
from sklearn.compose import make_column_transformer

categorical_columns = selector(dtype_include=object)(employees)
numerical_columns = selector(dtype_exclude=object)(employees)

ct = make_column_transformer(
      (StandardScaler(),
       numerical_columns),
      (OneHotEncoder(handle_unknown="ignore"),
       categorical_columns))

model = make_pipeline(ct, SimpleImputer(), Ridge())

Build a predictive pipeline

from sklearn.linear_model import Ridge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import make_column_selector as selector
from sklearn.compose import make_column_transformer

categorical_columns = selector(dtype_include=object)(employees)
numerical_columns = selector(dtype_exclude=object)(employees)

ct = make_column_transformer(
      (StandardScaler(),
       numerical_columns),
      (OneHotEncoder(handle_unknown="ignore"),
       categorical_columns))

model = make_pipeline(ct, SimpleImputer(), Ridge())

Build a predictive pipeline

Pipeline(steps=[('columntransformer',
                 ColumnTransformer(transformers=[('standardscaler',
                                                  StandardScaler(),
                                                  ['year_first_hired']),
                                                 ('onehotencoder',
                                                  OneHotEncoder(handle_unknown='ignore'),
                                                  ['gender', 'department',
                                                   'department_name',
                                                   'division',
                                                   'assignment_category',
                                                   'employee_position_title',
                                                   'date_first_hired'])])),
                ('simpleimputer', SimpleImputer()), ('ridge', Ridge())])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Enter: tabular_learner

import skrub
from sklearn.linear_model import Ridge
tl = skrub.tabular_learner(Ridge())

Enter: tabular_learner

Pipeline(steps=[('tablevectorizer',
                 TableVectorizer(datetime=DatetimeEncoder(periodic_encoding='spline'))),
                ('simpleimputer', SimpleImputer(add_indicator=True)),
                ('standardscaler', StandardScaler()), ('ridge', Ridge())])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

SimpleImputer

?Documentation for SimpleImputer

Parameters

	missing_values	nan
	strategy	'mean'
	fill_value	None
	copy	True
	add_indicator	True
	keep_empty_features	False

StandardScaler

?Documentation for StandardScaler

Parameters

	copy	True
	with_mean	True
	with_std	True

Ridge

?Documentation for Ridge

Parameters

	alpha	1.0
	fit_intercept	True
	copy_X	True
	max_iter	None
	tol	0.0001
	solver	'auto'
	positive	False
	random_state	None