Version 1.6.X#

Version 1.6.2#

Deployed: 18th September 2023

New functionality#

  • MatchVariables() can now also match the dtypes of the variables (Kyle Gilde)

  • DatetimeFeatures() and DatetimeSubtraction() can now specify the format of the datetime variables (Soledad Galli)

  • Add inverse_transform method to YeoJohnsonTransformer() (Giorgio Segalla)

Bug fixes#

This bugs were introduced by the latest releases of pandas, Scikit-learn and Scipy.

Code improvements#

  • Routine in DatetimeFeatures() does not enter into our check for utc=True when working with different timezones any more (Soledad Galli)

  • Improve performance in OneHotEncoder() (Soledad Galli)

  • Add check for dupicated variable names in dataframe (David Cortes)


Version 1.6.1#

Deployed: 8th June 2023


In this release, we make Feature-engine compatible with pandas 2.0, extend the functionality of some transformers, and we fix bugs introduced in the previous release.

Thank you so much to all contributors, Gleb Levitski and Claudio Salvatore Arcidiacono for helping with review and to those of you who created issues flagging bugs or requesting new functionality.

New functionality#

Bug fixes#

Code improvements#

Version 1.6.0#

Deployed: 16th March 2023


In this release, we make Feature-engine transformers compatible with the set_output API from Scikit-learn, which was released in version 1.2.0. We also make Feature-engine compatible with the newest direction of pandas, in removing the inplace functionality that our transformers use under the hood.

We introduce a major change: most of the categorical encoders can now encode variables even if they have missing data.

We are also releasing 3 brand new transformers: One for discretization, one for feature selection and one for operations between datetime variables.

We also made a major improvement in the performance of the DropDuplicateFeatures and some smaller bug fixes here and there.

We’d like to thank all contributors for fixing bugs and expanding the functionality and documentation of Feature-engine.

Thank you so much to all contributors and to those of you who created issues flagging bugs or requesting new functionality.

New transformers#

  • ProbeFeatureSelection: introduces random features and selects variables whose importance is greater than the random ones (Morgan Sell and Soledad Galli)

  • DatetimeSubtraction: creates new features by subtracting datetime variables (Kyle Gilde and Soledad Galli)

  • GeometricWidthDiscretiser: sorts continuous variables into intervals determined by geometric progression (Gleb Levitski)

New functionality#

  • Allow categorical encoders to encode variables with NaN (Soledad Galli)

  • Make transformers compatible with new set_output functionality from sklearn (Soledad Galli)

  • The ArbitraryDiscretiser() now includes the lowest limits in the intervals (Soledad Galli)

New modules#

  • New Datasets module with functions to load specific datasets (Alfonso Tobar)

  • New variable_handling module with functions to automatically select numerical, categorical, or datetime variables (Soledad Galli)

Bug fixes#

  • Fixed bug in DropFeatures() (Luís Seabra)

  • Fixed bug in RecursiveFeatureElimination() caused when only 1 feature remained in data (Soledad Galli)



  • The class PRatioEncoder is no longer supported and was removed from the API (Soledad Galli)

Code improvements#