Profiling and the need for speed

GDSP Conference 2018

a very quick demo of some ways to identify bottlenecks in Python code and ways to speed up these hotspots.

this repo includes :

jupyter notebook with code and some comments
Text version overview
jupyter notebook with plain python , cython and numba example comparison
jupyter notebook with showing how to include fortran code to be called as a python functionon a notebook comparison

Feel free to fork , add content and then do a pull request

Name		Name	Last commit message	Last commit date
Latest commit History 30 Commits
.ipynb_checkpoints		.ipynb_checkpoints
00-TLDR.md		00-TLDR.md
01-profiling.ipynb		01-profiling.ipynb
02-Memoization.ipynb		02-Memoization.ipynb
03-Cython_examples.ipynb		03-Cython_examples.ipynb
04-fortran_in_python.ipynb		04-fortran_in_python.ipynb
05-multithreading.ipynb		05-multithreading.ipynb
NFS.png		NFS.png
README.md		README.md
aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa.png		aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa.png
arraysum.png		arraysum.png
arraysumresults.csv		arraysumresults.csv
bubblepynumba.png		bubblepynumba.png
bubbleresults.csv		bubbleresults.csv
cython_annotation.png		cython_annotation.png
diceresults.csv		diceresults.csv
hot-output.gif		hot-output.gif
map-p.png		map-p.png
plain-numba-cython.png		plain-numba-cython.png
plain-numba-cython1.png		plain-numba-cython1.png
primesresults.csv		primesresults.csv
quicklistresult.csv		quicklistresult.csv
quicksortpynumba.png		quicksortpynumba.png