[cygnus_a]

Actually, even the subsection headings in bold give a very succinct summary:

- Python has easy development (https://xkcd.com/353/)

- Great libraries (ie, free matlab)

- Cython for efficiency via C

- The algorithms themselves determine speediness (ie numerical methods)

[dagw]

The algorithms themselves determine speediness

This is so important I wish people would focus more on it. I recently rewrote some Javascript code in (pure) python and got a good 2 orders of magnitude speed up on large inputs just by picking the right data structures and replacing an O(n^3) nested loop with an O(n log n) approach.

[fengwick3]

Yes, this is actually one of the rare moments where I find my past background in competitive programming helpful. There's a habit I've picked up of constantly running through techniques to speed up whenever I'm coding. Not necessarily the most efficient way to code, but it's a habit difficult to change.

[timClicks]

Which data structures did you use? In Python, I tend to rely on dict, list, and set for 90% or more of my code. I wouldn't want to rely on structures written in pure Python.

[pbowyer]

Have you written a blog post about this? If not, care to? Trying to speed up a codebase, it's of interest to me (and no doubt others too).

[anon4]

Let me just add Numba to the list. It compiles numpy-using code to native via llvm and can remove temporary arrays in the process.

I tried a very simple toy program the other day and while I had to write some things slightly un-pythonically (it can't deal with syntax like a[:] = b+c yet), it performed practically as good as hand-written C code.

[pbowyer]

If your code falls within the subset it supports :) I've not yet got it to run our code in nopython mode - I think the latest problem is a function expecting a function as an argument, but the error messages aren't helpful or enlightening, even with DEBUG turned on.

[okpatil]

Just typed "import antigravity" in python repl. It took me to the same link. :-)