This page collects tips and tricks to increase the speed of your code using numpy/scipy.

For general tips and tricks to improve the performance of your Python programs see http://wiki.python.org/moin/PythonSpeed/PerformanceTips.

Python built-ins vs. numpy functions

Note that the built-in python min function can be much slower (up to 300-500 times) than using the .min() method of an array. I.e.: use x.min() instead of min(x).

The same applies to max.

This is also true for the new any and all functions for Python >=2.5.

Beyond pure Python

Sometimes there are tasks for which pure python code can be too slow.

Possible solutions can be obtained via:

• hand-written C extensions

• psyco
• pyrex
• ctypes
• f2py
• weave
• swig
• boost
• SIP
• CXX

For a full discussion with examples on performance gains through interfacing with other languages see this article.

Examples

Tips and tricks for specific situations.

Finding the row and column of the min or max value of an array or matrix

A slow, but straightforward, way to find the row and column indices of the minimum value of an array or matrix x:

import numpy as np

def min_ij(x):
    i, j = np.where(x == x.min())
    return i[0], j[0]

This can be made quite a bit faster:

def min_ij(x):
    i, j = divmod(x.argmin(), x.shape[1])
    return i, j

The fast method is about 4 times faster on a 500 by 500 array.

Removing the i-th row and j-th column of a 2d array or matrix

The slow way to remove the i-th row and j-th column from a 2d array or matrix:

import numpy as np

def remove_ij(x, i, j):

    # Remove the ith row
    idx = range(x.shape[0])
    idx.remove(i)
    x = x[idx,:]

    # Remove the jth column
    idx = range(x.shape[1])
    idx.remove(j)
    x = x[:,idx]

    return x

The fast way, because it avoids making copies, to remove the i-th row and j-th column from a 2d array or matrix:

def remove_ij(x, i, j):
    # Row i and column j divide the array into 4 quadrants
    y = x[:-1,:-1]
    y[:i,j:] = x[:i,j+1:]
    y[i:,:j] = x[i+1:,:j]
    y[i:,j:] = x[i+1:,j+1:]
    return y

For a 500 by 500 array the second method is over 25 times faster.