ARTICLE AD BOX
I'm working with large CSV datasets (10M+ rows, ~400MB-1GB) on low-resource cloud instances (1GB-2GB RAM). Standard pandas.read_csv is causing MemoryError because the "Object Tax" (wrapping every value in a Python object) bloats the memory usage to 3x-4x the file size.
Even using chunksize doesn't help with the processing latency, which is sitting around 10-12 seconds for basic aggregations.
Are there proven ways to bypass the Python heap entirely and process raw CSV data closer to the metal to reduce RAM overhead?
