Wrangling data with good defaults

This section covers how to build a predictive pipeline starting from a dataframe. The skrub objects described in this section can be used as strong defaults for building baseline pipelines, and can be customized for specific use cases.

• Cleaner: sanitizing a dataframe
  • Parsing numeric-looking strings with the Cleaner
  • Downcasting float dtypes to float32 with the Cleaner
• Transforming a table into an array of numeric features: TableVectorizer
  • Numeric strings and categorical encoding
• Building robust ML baselines with tabular_pipeline()
• The logic used by the tabular pipeline is quite simple
• Extending the pipeline with the .steps attribute
• Using a pipeline as the estimator
• Transforming selected columns with ApplyToCols
  • Dealing with columns that cannot be handled by a transformer
  • Advanced usage of ApplyToCols