Memory Mapped Out Of Core Dataframe Access

via “memory-mapped-out-of-core-dataframe-access”

Out-of-Core DataFrames to visualize and explore big tabular datasets

Unique: Implements transparent memory mapping via dataset_mmap.py abstraction that presents memory-mapped files as standard DataFrames, with the kernel handling page faults. This differs from Pandas (full load) and Dask (distributed) by using OS-level virtual memory directly, achieving billions of rows/second throughput on single machines.

vs others: Achieves 10-100x faster access to large datasets than Pandas (which requires full materialization) and lower latency than Dask (which adds distributed scheduling overhead), while maintaining single-machine simplicity.