Examples

Hands-On with Column Selection and Transformers

Hands-On with Column Selection and Transformers

SquashingScaler: Robust numerical preprocessing for neural networks

SquashingScaler: Robust numerical preprocessing for neural networks

Sessions in time-based data: Predicting user purchases with the SessionEncoder

Sessions in time-based data: Predicting user purchases with the SessionEncoder

Encoding features

Encoding: from a dataframe to a numerical matrix for machine learning

Encoding: from a dataframe to a numerical matrix for machine learning

Various string encoders: a sentiment analysis example

Various string encoders: a sentiment analysis example

Handling datetime features with the DatetimeEncoder

Handling datetime features with the DatetimeEncoder

Skrub DataOps

Multiples tables: building machine learning pipelines with DataOps

Multiples tables: building machine learning pipelines with DataOps

Hyperparameter tuning with DataOps

Hyperparameter tuning with DataOps

Tuning DataOps with Optuna

Tuning DataOps with Optuna

Subsampling for faster development

Subsampling for faster development

Use case: developing locally and deploying to production

Use case: developing locally and deploying to production

Using PyTorch (via skorch) in DataOps

Using PyTorch (via skorch) in DataOps

Joining tables with imperfect data

Fuzzy joining dirty tables with the Joiner

Fuzzy joining dirty tables with the Joiner

Deduplicating misspelled categories

Deduplicating misspelled categories

Spatial join for flight data: Joining across multiple columns

Spatial join for flight data: Joining across multiple columns

AggJoiner on a credit fraud dataset

AggJoiner on a credit fraud dataset

Interpolation join: infer missing rows when joining two tables

Interpolation join: infer missing rows when joining two tables