I learned about your blog post on Cython from the recent Python Bytes episode that covered it. It is a really nice write up and makes doing a little bit of Cython really accessible.

I just wanted to suggest one other point that I didn't see mentioned in your post -- often working with numpy is good enough without reaching for Cython. For example, for the case you considered, I tried this alternate version:

import numpy
from numpy.typing import NDArray

def count_increases(depths: NDArray[np.int32]) -> int:
    return np.sum(depths[1:] > depths[:-1])

For my testing on 5,000 item lists/arrays, I got:

So the Cythonized solution is the best but is only about a factor of 2.5 faster than calling standard numpy functions from Python. So there might be an extra point to consider in your conditions for when to use Cython -- has the code already been optimized to use numpy in a performant way? The cases that would benefit most from Cython might be ones where you need to operate on arrays with a little bit of unusual logic not likely to be captured by numpy functions.

@pmbaumgartner
Is there a way to write a rule based system to catch things like start/end dates from a contract text. Here are a few real examples. I am bolding the date entities which I want spacy to automatically detect. If you have other ideas different than spacy that is also OK!

1. The initial term of this Lease shall be for a period of Five (5) years commencing on
   February 1, 2012, (the “Lease Commencement Date”) and expiring on January 31, 2017
   (the “Initial Lease Term”).

2. Term: One (1) year commencing January 1, 2007 ("Commencement Date") and ending
   December 31, 2007 ("Expiration Date").

3. This Lease Agreement is entered into for term of 15 years, beginning January 1, 2014 and ending on December 31, 2028.