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Repeated string columns waste RAM and slow down aggregations. Converting them to pandas Categorical stores unique values once and uses integer codes, cutting memory and speeding up groupby.
Learn how converting low‑cardinality string columns to pandas Categorical reduces memory and accelerates groupby operations, with a concrete example and practical verification steps.
Learn how converting low‑cardinality string columns to pandas categorical dtype can cut memory usage by up to 90 % and boost performance. Follow a quick checklist to decide when the conversion is worth it.
Convert repeated string columns to pandas Categorical to cut memory by up to 90%. This guide shows a worked example, explains how it works, and lists limits and common pitfalls.
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