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Choosing Data Types to Save Memory

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Right-size columns safely.

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Help me reduce memory use of a DataFrame without changing results. Output of df.info(memory_usage="deep") and df.nunique(): [PASTE_INFO]. Value ranges per numeric column: [VALUE_RANGES]. How the data is used: [USE_CASE].

Provide a table: Column | Current dtype | Memory MB | Proposed dtype | Why it is safe | Check that proves it. Use category for text columns with a low share of distinct values, the smallest integer type that holds the range (and nullable Int64 when nulls exist), float32 only when the precision loss is acceptable, and boolean or datetime types where appropriate.
Add: a function that downcasts with pd.to_numeric(downcast=...) and verifies that values are unchanged; a warning about integer overflow when summing small integer types; a note on categoricals and groupby (observed=True); and a before and after memory comparison. Do not downcast columns used as join keys without checking the other table's dtype. Finish with the total memory saved, computed from my numbers.

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• memory