Random sampling in numpy | randint() function

Last Updated :26 Feb, 2019

numpy.random.randint() is one of the function for doing random sampling in numpy. It returns an array of specified shape and fills it with random integers from low (inclusive) to high (exclusive), i.e. in the interval [low, high).

Syntax : numpy.random.randint(low, high=None, size=None, dtype='l')Parameters :low : [int] Lowest (signed) integer to be drawn from the distribution.But, it works as a highest integer in the sample if high=None.high : [int, optional] Largest (signed) integer to be drawn from the distribution.size : [int or tuple of ints, optional] Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.dtype : [optional] Desired output data-type.Return : Array of random integers in the interval [low, high) or a single such random int if size not provided.

Code #1 :

Python3

# Python program explaining# numpy.random.randint() function# importing numpyimportnumpyasgeek# output arrayout_arr=geek.random.randint(low=0,high=3,size=5)print("Output 1D Array filled with random integers : ",out_arr)

Output :

Output 1D Array filled with random integers :  [1 1 0 1 1]

Code #2 :

Python3

# Python program explaining# numpy.random.randint() function# importing numpyimportnumpyasgeek# output arrayout_arr=geek.random.randint(low=4,size=(2,3))print("Output 2D Array filled with random integers : ",out_arr)

Output :

Output 2D Array filled with random integers :  [[1 1 0] [1 0 3]]

Code #3 :

Python3

# Python program explaining# numpy.random.randint() function# importing numpyimportnumpyasgeek# output arrayout_arr=geek.random.randint(2,10,(2,3,4))print("Output 3D Array filled with random integers : ",out_arr)

Output :

Output 3D Array filled with random integers :  [[[4 8 5 7]  [6 5 6 7]  [4 3 4 3]] [[2 9 2 2]  [3 2 2 3]  [6 8 3 2]]]

Basics of NumPy Arrays

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How to get the magnitude of a vector in NumPy?

TheÂ fundamental feature of linear algebra are vectors, these are the objects having both direction and magnitude. In Python, NumPy arrays can be used to depict a vector.Â There are mainly two ways of getting the magnitude of vector: By defining an explicit function which computes the magnitude of a

3 min read

How to compute the eigenvalues and right eigenvectors of a given square array using NumPY?

In this article, we will discuss how to compute the eigenvalues and right eigenvectors of a given square array using NumPy library.Â Example: Suppose we have a matrix as:Â [[1,2], [2,3]]Â Eigenvalue we get from this matrix or square array is:Â [-0.23606798 4.23606798] Eigenvectors of this matrix are

2 min read

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Random sampling in numpy | randint() function

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