API

This page lists all available functions and classes of skrub.

Joining dataframes

Joiner	Augment features in a main table by fuzzy-joining an auxiliary table to it.
AggJoiner	Aggregate an auxiliary dataframe before joining it on a base dataframe.
MultiAggJoiner	Extension of the AggJoiner to multiple auxiliary tables.
AggTarget	Aggregate a target y before joining its aggregation on a base dataframe.
InterpolationJoiner	Join with a table augmented by machine-learning predictions.

fuzzy_join

Fuzzy (approximate) join.

Encoding a column

GapEncoder	Encode string columns by constructing latent topics.
MinHashEncoder	Encode string categorical features by applying the MinHash method to n-gram decompositions of strings.
SimilarityEncoder	Encode string categories to a similarity matrix, to capture fuzziness across a few categories.
DatetimeEncoder	Extract temporal features such as month, day of the week, … from a datetime column.
ToCategorical	Convert a string column to Categorical dtype.
ToDatetime	Parse datetimes represented as strings and return Datetime columns.

to_datetime

Convert DataFrame or column to Datetime dtype.

Deep Learning

These encoders require installing additional dependencies around torch. See the “deep learning dependencies” section in the Install guide for more details.

TextEncoder

Encode string features by applying a pretrained language model downloaded from the HuggingFace Hub.

Building a pipeline

TableVectorizer	Transform a dataframe to a numeric (vectorized) representation.
SelectCols	Select a subset of a DataFrame's columns.
DropCols	Drop a subset of a DataFrame's columns.

tabular_learner

Get a simple machine-learning pipeline for tabular data.

Generating an HTML report

TableReport

Summarize the contents of a dataframe.

patch_display	Replace the default DataFrame HTML displays with skrub.TableReport.
unpatch_display	Undo the effect of skrub.patch_display().
column_associations	Get measures of statistical associations between all pairs of columns.

Cleaning a dataframe

deduplicate

Deduplicate categorical data by hierarchically clustering similar strings.

Downloading a dataset

fetch_employee_salaries	Fetches the employee salaries dataset (regression), available at https://openml.org/d/42125
fetch_medical_charge	Fetches the medical charge dataset (regression), available at https://openml.org/d/42720
fetch_midwest_survey	Fetches the midwest survey dataset (classification), available at https://openml.org/d/42805
fetch_open_payments	Fetches the open payments dataset (classification), available at https://openml.org/d/42738
fetch_road_safety	Fetches the road safety dataset (classification), available at https://openml.org/d/42803
fetch_traffic_violations	Fetches the traffic violations dataset (classification), available at https://openml.org/d/42132
fetch_drug_directory	Fetches the drug directory dataset (classification), available at https://openml.org/d/43044
fetch_world_bank_indicator	Fetches a dataset of an indicator from the World Bank open data platform.
fetch_movielens	Fetches a dataset from Movielens.
fetch_credit_fraud	Fetch the credit fraud dataset from figshare.
fetch_ken_table_aliases	Get the supported aliases of embedded KEN entities tables.
fetch_ken_types	Helper function to search for KEN entity types.
fetch_ken_embeddings	Download Wikipedia embeddings by type.
make_deduplication_data	Duplicates examples with spelling mistakes.