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.
StringEncoder	Generate a lightweight string encoding of a given column using tf-idf vectorization and truncated singular value decomposition (SVD).

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.
Cleaner	Column-wise consistency checks and sanitization of dtypes, null values and dates.
SelectCols	Select a subset of a DataFrame's columns.
DropCols	Drop a subset of a DataFrame's columns.
DropUninformative	Drop column if it is found to be uninformative according to various criteria.

tabular_learner

Get a simple machine-learning pipeline for tabular data.

Skrub Expressions

The tabular_learner provides a pre-defined, default pipeline for datasets that contain a simple table. For more control or in order to build pipelines for more datasets, use the skrub expressions.

as_expr	Create an expression Expr that evaluates to the given value.
choose_bool	A choice between True and False.
choose_float	A choice of floating-point numbers from a numeric range.
choose_from	A choice among several possible outcomes.
choose_int	A choice of integers from a numeric range.
cross_validate	Cross-validate a pipeline built from an expression.
deferred	Wrap function calls in an expression Expr.
eval_mode	Return the mode in which the expression is currently being evaluated.
optional	A choice between value and None.
var	Create a skrub variable.
X	Create a skrub variable and mark it as being X.
y	Create a skrub variable and mark it as being y.

Expr	Representation of a computation that can be used to build ML pipelines.

Expr.skb.apply	Apply a scikit-learn estimator to a dataframe or numpy array.
Expr.skb.apply_func	Apply the given function.
Expr.skb.clone	Get an independent clone of the expression.
Expr.skb.concat	Concatenate dataframes vertically or horizontally.
Expr.skb.cross_validate	Cross-validate the expression.
Expr.skb.describe_defaults	Describe the hyper-parameters used by the default pipeline.
Expr.skb.describe_param_grid	Describe the hyper-parameters extracted from choices in the expression.
Expr.skb.describe_steps	Get a text representation of the computation graph.
Expr.skb.draw_graph	Get an SVG string representing the computation graph.
Expr.skb.drop	Drop some columns.
Expr.skb.eval	Evaluate the expression.
Expr.skb.freeze_after_fit	Freeze the result during pipeline fitting.
Expr.skb.full_report	Generate a full report of the expression's evaluation.
Expr.skb.get_data	Collect the values of the variables contained in the expression.
Expr.skb.get_pipeline	Get a skrub pipeline for this expression.
Expr.skb.get_grid_search	Find the best parameters with grid search.
Expr.skb.get_randomized_search	Find the best parameters with randomized search.
Expr.skb.if_else	Create a conditional expression.
Expr.skb.iter_pipelines_grid	Get pipelines with different parameter combinations.
Expr.skb.iter_pipelines_randomized	Get pipelines with different parameter combinations.
Expr.skb.mark_as_X	Mark this expression as being the X table.
Expr.skb.mark_as_y	Mark this expression as being the X table.
Expr.skb.match	Select based on the value of an expression.
Expr.skb.preview	Get the value computed for previews (shown when printing the expression).
Expr.skb.select	Select a subset of columns.
Expr.skb.set_description	Give a description to this expression.
Expr.skb.set_name	Give a name to this expression.
Expr.skb.subsample	Configure subsampling of a dataframe or numpy array.
Expr.skb.train_test_split	Split an environment into a training an testing environments.

Expr.skb.description	A user-defined description or comment about the expression.
Expr.skb.is_X	Whether this expression has been marked with .skb.mark_as_X().
Expr.skb.is_y	Whether this expression has been marked with .skb.mark_as_y().
Expr.skb.name	A user-chosen name for the expression.
Expr.skb.applied_estimator	Retrieve the estimator applied in the previous step, as an expression.

SkrubPipeline	Pipeline that evaluates a skrub expression.
ParamSearch	Pipeline that evaluates a skrub expression with hyperparameter tuning.

Selecting columns in a DataFrame

The srkub selectors provide a flexible way to specify the columns on which a transformation should be applied. They are meant to be used for the cols argument of Expr.skb.apply(), Expr.skb.select(), Expr.skb.drop(), SelectCols or DropCols.

selectors.all	Select all columns.
selectors.any_date	Select columns that have a Date or Datetime data type.
selectors.boolean	Select columns that have an Boolean data type.
selectors.cardinality_below	Select columns whose cardinality (number of unique values) is (strictly) below threshold.
selectors.categorical	Select columns that have a Categorical (or polars Enum) data type.
selectors.cols	Select columns by name.
selectors.Filter
selectors.filter	Select columns for which predicate returns True.
selectors.filter_names	Select columns based on their name.
selectors.float	Select columns that have a floating-point data type.
selectors.glob	Select columns by name with Unix shell style 'glob' pattern.
selectors.has_nulls	Select columns that contain at least one null value.
selectors.integer	Select columns that have an integer data type.
selectors.inv	Invert a selector.
selectors.make_selector	Transform a selector, column name or list of column names into a selector.
selectors.NameFilter
selectors.numeric	Select columns that have a numeric data type.
selectors.regex	Select columns by name with a regular expression.
selectors.select	Apply a selector to a dataframe and return the resulting dataframe.
selectors.Selector
selectors.string	Select columns that have a String data type.

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_bike_sharing	Fetch the bike sharing dataset (regression) available at skrub-data/skrub-data-files
fetch_country_happiness	Fetch the happiness index dataset (regression) available at skrub-data/skrub-data-files
fetch_credit_fraud	Fetch the credit fraud dataset (classification) available at skrub-data/skrub-data-files
fetch_drug_directory	Fetches the drug directory dataset (classification), available at skrub-data/skrub-data-files
fetch_employee_salaries	Fetches the employee salaries dataset (regression), available at skrub-data/skrub-data-files
fetch_flight_delays	Fetch the flight delays dataset (regression) available at skrub-data/skrub-data-files
fetch_ken_embeddings	Download Wikipedia embeddings by type.
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_medical_charge	Fetches the medical charge dataset (regression), available at skrub-data/skrub-data-files
fetch_midwest_survey	Fetches the midwest survey dataset (classification), available at skrub-data/skrub-data-files
fetch_movielens	Fetch the movielens dataset (regression) available at skrub-data/skrub-data-files
fetch_open_payments	Fetches the open payments dataset (classification), available at skrub-data/skrub-data-files
fetch_toxicity	Fetch the toxicity dataset (classification) available at skrub-data/skrub-data-files
fetch_traffic_violations	Fetches the traffic violations dataset (classification), available at skrub-data/skrub-data-files
fetch_videogame_sales	Fetch the videogame sales dataset (regression) available at skrub-data/skrub-data-files
get_data_dir	Returns the directory in which skrub looks for data.
make_deduplication_data	Duplicates examples with spelling mistakes.