Reddit reviews Fundamentals of Matrix Computations

I have always struggled learning numerical analysis systematically. Could someone recommend books on Numerical Linear Algebra, Numerical Solutions to ODE's/PDE's, Numerical Analysis etc. that have good coding exercises, along with the necessary theory etc. The coding exercises should preferably be in Python, I suppose.


The best book on numerical linear algebra I have found matching this criteria is Watkins' Fundamentals of Matrix Computations, https://www.amazon.com/Fundamentals-Matrix-Computations-David-Watkins/dp/0470528338, but the exercises in Matlab.


Another book I seem to like is this physics book on Mathematica:


https://www.amazon.com/Introduction-Mathematica%C2%AE-Physicists-Graduate-Physics/dp/3319008935/ref=sr_1_3?keywords=physics+mathematica&qid=1557866324&s=books&sr=1-3-spell


In this case, project problems are derived from physical examples, making learning numerical methods worthwhile, I suppose.


So can someone recommend good books on the aforementioned topics that use Python etc. for project problems that have context as,as perhaps, applications.

There's a wealth of materials out there. Here's an open content HPC text i really like: http://pages.tacc.utexas.edu/~eijkhout/istc/istc.html

Watkisn is often referenced, I haven't read: https://www.amazon.com/Fundamentals-Matrix-Computations-David-Watkins/dp/0470528338

and some course notes: http://people.ds.cam.ac.uk/nmm1/arithmetic/na1.pdf

http://people.inf.ethz.ch/arbenz/ewp/Lnotes/

http://www.seas.ucla.edu/~vandenbe/103/

also, besides Strang, the most often recommended LA texts are Axler, Insel/Friedberg/Spence, Hoffman/Kunze, I think.