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NumPy’s ndarray indexing

In NumPy a new kind of array is provided: n-dimensional array or ndarray. It’s usually fixed-sized and accepts items of the same type and size. For example, to define a 2×3 matrix:

import numpy as np
a = np.array([[1,2,3,], [4,5,6]], np.int32)

When indexing ndarray, it supports “array indexing” other than single element indexing.  (See

It is possible to index arrays with other arrays for the purposes of selecting lists of values out of arrays into new arrays. There are two different ways of accomplishing this. One uses one or more arrays of index values. The other involves giving a boolean array of the proper shape to indicate the values to be selected. Index arrays are a very powerful tool that allow one to avoid looping over individual elements in arrays and thus greatly improve performance.

So you basically can do the following:

a = np.array([1, 2, 3], np.int32)
a[np.array([0, 2])) # Fetch the first the third elements, returns np.array([1, 3])
a[np.array([True, False, True])] # Same as the line above

Besides, when you do equals operation on ndarrays, another ndarray is returned by comparing each element:

a = np.array([1, 2, 3], np.int32)
a == 2 # Returns array([False,  True, False], dtype=bool)
a != 2 # Returns array([ True, False,  True], dtype=bool)
a[a != 2] # Returns a sub array that excludes elements with a value 2, in this case array([1, 3], dtype=int32)

Published in Miscellaneous


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