Generate Random Numbers From The Uniform Distribution using NumPy Last Updated : 01 Mar, 2024 Comments Improve Suggest changes Like Article Like Report Random numbers are the numbers that cannot be predicted logically and in Numpy we are provided with the module called random module that allows us to work with random numbers. To generate random numbers from the Uniform distribution we will use random.uniform() method of random module. Syntax: numpy.random.uniform(low = 0.0, high = 1.0, size = None) In uniform distribution samples are uniformly distributed over the half-open interval [low, high) it includes low but excludes high interval. Examples: Python3 # importing module import numpy as np # numpy.random.uniform() method r = np.random.uniform(size=4) # printing numbers print(r) Output: [0.3829765 0.50958636 0.42844207 0.4260992 0.3513896 ] Example 2: Python3 # importing module import numpy as np # numpy.random.uniform() method random_array = np.random.uniform(0.0, 1.0, 5) # printing 1D array with random numbers print("1D Array with random values : \n", random_array) Output: 1D Array with random values : [0.2167103 0.07881761 0.89666672 0.31143605 0.31481039] Comment More infoAdvertise with us Next Article Generate Random Numbers From The Uniform Distribution using NumPy S simran_bhandari Follow Improve Article Tags : Python Python-numpy Python numpy-Random Practice Tags : python Similar Reads Generate five random numbers from the normal distribution using NumPy In Numpy we are provided with the module called random module that allows us to work with random numbers. The random module provides different methods for data distribution. In this article, we have to create an array of specified shape and fill it random numbers or values such that these values are 2 min read How to generate random numbers from a log-normal distribution in Python ? A continuous probability distribution of a random variable whose logarithm is usually distributed is known as a log-normal (or lognormal) distribution in probability theory. A variable x is said to follow a log-normal distribution if and only if the log(x) follows a normal distribution. The PDF is d 3 min read NumPy random.noncentral_f() | Get Random Samples from noncentral F distribution The NumPy random.noncentral_f() method returns the random samples from the noncentral F distribution. Example Python3 import numpy as np import matplotlib.pyplot as plt gfg = np.random.noncentral_f(1.24, 21, 3, 1000) count, bins, ignored = plt.hist(gfg, 50, density = True) plt.show() Output: Syntax 1 min read Random sampling in numpy | random_integers() function numpy.random.random_integers() 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.random_integers(low, high=None, size=No 2 min read Uniform Distribution in NumPy A Uniform Distribution is used when all the numbers in a range have the same chance of being picked. For example, if we choose a number between 10 and 20 and every number in that range is just as likely as any other. In Python's NumPy library you can generate random numbers following a Uniform Distr 2 min read Like