Observed behavior When loading a CSV with low_memory=False, columns that contain only integers but a few NaNs are promoted to float. The resulting DataFrame consumes noticeably more memory than expected, even though the flag is intended to reduce peak usage. Unresolved questions Does low_memory=False guarantee a single‑pass read, or does pandas still perform a second pass for dtype inference? How does the dtype inference heuristic treat mixed‑type columns when low_memory=False is set, especially regarding integer‑to‑float promotion? Is there a documented version change between pandas 1.3 and 2.x that alters the memory allocation strategy for low_memory=False?

Pandas low_memory flag: dtype promotion & memory usage questions

Pandas read_csv low_memory flag causes unexpected dtype promotion and memory spikes

25.5K reputation · 14 Mar 2021, 18:29 UTC

13.8K views0

Observed behavior

When loading a CSV with low_memory=False, columns that contain only integers but a few NaNs are promoted to float. The resulting DataFrame consumes noticeably more memory than expected, even though the flag is intended to reduce peak usage.

Unresolved questions

Does low_memory=False guarantee a single‑pass read, or does pandas still perform a second pass for dtype inference?
How does the dtype inference heuristic treat mixed‑type columns when low_memory=False is set, especially regarding integer‑to‑float promotion?
Is there a documented version change between pandas 1.3 and 2.x that alters the memory allocation strategy for low_memory=False?

Keep exploring this topic

Query vs Eval: Preventing Stale pandas Results

How to prevent stale results when using pandas DataFrame.query after the underlying data changes? Compare disabling numexpr caching with engine='python' versus using DataFrame.eval with a local dictionary, and assess memory trade‑offs.

Evaluation limits of pandas.DataFrame.assign keyword arguments

Question about whether pandas.DataFrame.assign should allow later keyword arguments to reference columns created earlier in the same call.

DataSpell DataFrame Preview vs pandas‑profiling: Which Avoids Memory Leaks for Large Datasets?

Choosing between DataSpell’s interactive DataFrame preview and an external pandas‑profiling report raises a memory‑leak concern for large datasets. Which method keeps RAM usage lower, and can DataSpell force cache eviction?

pandas DataFrame.to_csv missing built‑in encryption or permission control

Discusses the lack of built‑in encryption or permission controls in pandas DataFrame.to_csv and asks whether the library should add such features.

Pandas read_csv low_memory flag causes unexpected dtype promotion and memory spikes

Observed behavior

Unresolved questions

0 answers

Your perspective belongs here.

0 question comments