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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 [:Cookbook/C_Extensions: 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 [:PerformancePython: 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.