Squeeze (gently) your data
summary
Describes techniques for reducing dataset size and memory usage using Python data tools such as NumPy and pandas, focusing on practical strategies to downcast types, compress data, and otherwise ‘squeeze’ tables for more efficient processing. Covers concrete approaches for changing dtypes, handling categorical data, and applying compression to make datasets fit into memory and speed up analysis.
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