Customizing the global configuration

Skrub includes a configuration manager that allows setting various parameters (see the set_config() documentation for more detail).

It is possible to change configuration options using the set_config() function:

>>> from skrub import set_config
>>> set_config(use_table_report=True)

This alters the behavior of skrub in the current script. Each configuration parameter has an environment variable that can be used to set it permanently.

Additionally, a config_context() is provided to allow temporarily altering the configuration:

>>> import skrub
>>> with skrub.config_context(max_plot_columns=1):
...     pass

Within this context, only the code executed inside the with statement is affected.

The get_config() function allows retrieving the current configuration.

Configuration parameters

The configuration parameters that can be set with set_config and config_context are available by using

>>> import skrub
>>> config = skrub.get_config()
>>> config.keys()
dict_keys(['use_table_report', 'use_table_report_data_ops', 'max_plot_columns', 'max_association_columns', 'subsampling_seed', 'enable_subsampling', 'float_precision', 'cardinality_threshold'])

These are the parameters currently available in the global configuration:

Skrub Configuration Parameters

Parameter Name	Default Value	Env Variable	Description
use_table_report	False	SKB_USE_TABLE_REPORT	If set to True, the HTML representation of Pandas and Polars dataframes is replaced with the TableReport.
use_table_report_data_ops	True	SKB_USE_TABLE_REPORT_DATA_OPS	Set the HTML representation used for the Data Ops previews. If True, use the TableReport, otherwise use the default Pandas or Polars representation.
max_plot_columns	30	SKB_MAX_PLOT_COLUMNS	If a dataframe has more columns than the value set here, the TableReport will skip generating the plots.
max_association_columns	30	SKB_MAX_ASSOCIATION_COLUMNS	If a dataframe has more columns than the value set here, the TableReport will skip computing the associations.
subsampling_seed	0	SKB_SUBSAMPLING_SEED	Set the random seed of subsampling in skrub.DataOp.skb.subsample(), when how="random" is passed.
enable_subsampling	"default"	SKB_ENABLE_SUBSAMPLING	Control the activation of subsampling in skrub.DataOp.skb.subsample(). If "default", the behavior of skrub.DataOp.skb.subsample() is used. If "disable", subsampling is never used, so skb.subsample becomes a no-op. If "force", subsampling is used in all DataOps evaluation modes (eval(), fit_transform, etc.).
float_precision	3	SKB_FLOAT_PRECISION	Control the number of significant digits shown when formatting floats. Applies overall precision rather than fixed decimal places.
cardinality_threshold	40	SKB_CARDINALITY_THRESHOLD	Set the cardinality_threshold argument of TableVectorizer. Additionally, set the threshold for warning the user about high cardinality features in the TableReport.