Datatype For Handling Missing Valued Columns in Pandas

If your data has NaN-valued columns, Pandas provides a datatype specifically for representing them - called the Sparse datatype.

This is especially handy when you are working with large data-driven projects with many missing values.

The snippet compares the memory usage of float and sparse datatype in Pandas.

Read more here: https://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#sparsedtype.