Describe the workflow you want to enable

I am often using random datasets (typically with make_classification). However I often find myself having to add more realistic features to the dataset:

• missing data, sometime just to test the pipeline (missing at random would be fine), or sometimes to look for more complex phenomenons (missingnes not at random, possibly depending on the target)
• categorical: categoricals variables often need to be handled specifically. I usually introduce categoricals with binning a continuous value, then transforming to strings.
  It would be nice to have both of those in datasets generation.

Describe your proposed solution

Introduce parameters to allow for generation of missing data (proportion of missingness, type of missingness - at random, not at random).
Introduce parameters to allow for generation of categorical features (number of features, type of repartition in categories - even - uneven - pareto.

Describe alternatives you've considered, if relevant

I usually handle this by hand.

Additional context

Could be used to illustrate imputing techniques, encoding techniques.