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.
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