This is again a post about python code speed, the data and inference are more than a few months old but still valid.

Here's a spreadsheet showing speed of array math operations (+, -, *, /) between numpy arrays and python lists.

Check this spreadsheet to see the timings of various operations

https://spreadsheets.google.com/ccc?key=0AomYDYyBBNkkdHAtMkdHMF9TZ29lMmZQV3UwYkxWNFE&hl=en

The operations I considered for comparison were:

- x+0.1
- x-0.1
- x*0.1
- x/0.1
- x*(1/0.1)
- x+y
- x-y
- x*y
- x/y
- [p+yp[j] for j,p in enumerate(xp)]
- [xp[j]+yp[j] for j in xrange(i)]

**x**and

**y**are numpy arrays,

**xp**and

**yp**are python lists, all of size

**N**which is varied for the comparison.

The raw timings data is available here:

See the timings plot yourself

**Conclusion:**

- Use numpy arrays for size > 10
- Avoid division as much as you can to improve the speed of your numerical codes
- Instead of
**x/0.1**do**x*(1/0.1)**. This itself causes large speedup as N is increased. **x/0.1**and**x/y**take almost the same time**+**,**-**,*****take almost same time,**/**takes much more time, and its expense increases as**N**is increased.- Once again,
**do not divide**. - The same thing is valid in cython code also. Avoid division even in cython code, and even if you are using
**double**instead of numpy arrays (buffer). Rewrite expressions to minimize the usage of division operator.

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