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Same story for me, give or take a few percent. I have to recommend dask for python though, it made out of memory errors largely disappear for me. It allows para
by pletnes 9y ago
Same story for me, give or take a few percent. I have to recommend dask for python though, it made out of memory errors largely disappear for me. It allows parallel processing of disk-size datasets with the convenience of in-memory datasets (almost).
- joshu 9y agoHoly cow, dask looks useful
- pletnes 9y agoThe most impressive is how easy it is to use. Basically just reuse of the numpy API.
- FridgeSeal 9y agoThey replicate the Pandas API, not the numpy one. Just for clarity's sake.
- tkxxx7 9y agoWhat a peculiar comment. Their homepage has examples of both - with Numpy first.
- FridgeSeal 9y agoReally? It's been a while since I've used it, and I remember a good portion of the documentation talking about how they replicate some, but not all of Pandas' API's (because of the sheer number of them). They've probably updated it since then...
- stustustu 9y agoyup it really is