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For most pandas functions, I expected numpy to outperform regardless of data size.
I'm curious about the technical details behind this observation. Any information would be appreciated.
Thanks!
The text was updated successfully, but these errors were encountered:
@bscully27 > For most pandas functions, I expected numpy to outperform regardless of data size.
I'm curious about the technical details behind this observation. Any information would be appreciated.
Thanks!
Numpy is the fastest because it is C-compiled and stores data of same datatype (homogeneous arrays) and you get the benefits of principle of locality i.e., tendency of a processor to access the same set of memory locations repetitively over a short period of time. On the other hand pandas are flexible to store data of many datatypes which in turn decrease its performance.
For most pandas functions, I expected numpy to outperform regardless of data size.
I'm curious about the technical details behind this observation. Any information would be appreciated.
Thanks!
The text was updated successfully, but these errors were encountered: