Cutting Memory Use in Pandas with Categorical Data Types
Learn how to shrink pandas DataFrames by converting repeated string columns to categorical dtype, see a concrete example, and understand the trade‑offs.
Loading…
ReadMeFeed / Community knowledge
#categorical
Real questions. Useful conversations. Find the people who know your stack.
pandasUsing pandas Categorical to Reduce Memory and Speed 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.
Cut Pandas Memory with Categorical Columns: When Low‑Cardinality Strings Pay Off
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.