Data Validation with Pandera in Python
Data Validation is a crucial step for production applications. You need to ensure the data you are ingesting is compatible with your pipeline and that unexpected values aren't present. Moreover, validating the data is a security measure that prevents any corrupted or inaccurate information from being further processed, raising a flag on the first steps.
Python already counts with a great OS project for this task called Pydantic. However, when dealing with large dataframe-like objects such as in Machine Learning, Pandera is a much faster and scalable way of validating data (check this article with public notebooks).
In addition, Pandera offers support for a great variety of dataframe libraries like pandas, polars, dask, modin, and pyspark.pandas. For more information on these refer to Pandera's docs
