Mention & differentiate from similar packages in README

[kwinkunks]

• Great Expectations - seems big and cumbersome - lesson: stay lean
• Evidently - "framework to evaluate, test and monitor ML models in production." - looks nice but quite plugged in to Jupyter, eg lots of plots
• ydata-quality - from the same people as profiling (below), and does not look well maintained
• Pandas Profiling - generates some alerts eg see below - should test this on my usual datasets

More specialized:

• Pandera - "statistical data validation for pandas" (and only pandas) - lesson: support other data formats
• pandas_dq - looks quite nice, pands only
• Spectacles - continuous integration tool for Looker and LookML (a GCP service?)py
• Datafold - time-series and point clouds?
• dbt (Data Build Tool) - not sure what this is
• Deequ - targeted at Spark/PySpark dataframes only I think

Couple of nice posts etc

• http://mfcabrera.com/blog/pandas-dataa-validation-machine-learning.html
• See #69