numpy.random.power() in Python Last Updated : 15 Jul, 2020 Comments Improve Suggest changes Like Article Like Report With the help of numpy.random.power() method, we can get the random samples from power distribution and return the random samples by using this method. power distribution Syntax : numpy.random.power(a, size=None) Return : Return the random samples as numpy array. Example #1 : In this example we can see that by using numpy.random.power() method, we are able to get the random samples from power distribution and return the random samples. Python3 # import numpy import numpy as np import matplotlib.pyplot as plt # Using power() method gfg = np.random.power(1.75, 1000) plt.figure() plt.hist(gfg, bins = 50, density = True) plt.show() Output : power plot Example #2 : Python3 # import numpy import numpy as np import matplotlib.pyplot as plt # Using power() method gfg = np.random.power(6.75, 500) plt.figure() plt.hist(gfg, bins = 50, density = True) plt.show() Output : power plot Comment More infoAdvertise with us Next Article numpy.random.power() in Python J Jitender_1998 Follow Improve Article Tags : Python Python-numpy Python numpy-Random Practice Tags : python Similar Reads numpy.power() in Python numpy.power(arr1, arr2, out = None, where = True, casting = 'same_kind', order = 'K', dtype = None) : Array element from first array is raised to the power of element from second element(all happens element-wise). Both arr1 and arr2 must have same shape and each element in arr1 must be raised to cor 3 min read Random Numbers in Python Python defines a set of functions that are used to generate or manipulate random numbers through the random module. Functions in the random module rely on a pseudo-random number generator function random(), which generates a random float number between 0.0 and 1.0. These particular type of functions 6 min read numpy.random.f() in Python With the help of numpy.random.f() method, we can get the random samples of F distribution and return the random samples of numpy array by using this method. Syntax : numpy.random.f(dfnum, dfden, size=None) Return : Return the random samples as numpy array. Example #1 : In this example we can see tha 1 min read numpy.random.permutation() in Python With the help of numpy.random.permutation() method, we can get the random samples of sequence of permutation and return sequence by using this method. Syntax : numpy.random.permutation(x) Return : Return the random sequence of permuted values. Example #1 : In this example we can see that by using nu 1 min read numpy.random.rand() in Python This article provides an in-depth exploration of the `numpy.random.rand()` function in Python. It covers the function's syntax, and definition, and includes illustrative examples with detailed explanations for better understanding. numpy.random.rand() Function Syntax The numpy.random.rand() function 3 min read numpy.random.wald() in Python With the help of numpy.random.wald() method, we can get the random samples from Wald or Inverse Gaussian distribution and return the random samples as numpy array by using this method. Inverse Gaussian distribution Syntax : numpy.random.wald(mean, scale, size=None) Return : Return the random samples 1 min read numpy.random.randn() in Python The numpy.random.randn() function creates an array of specified shape and fills it with random values as per standard normal distribution. If positive arguments are provided, randn generates an array of shape (d0, d1, ..., dn), filled with random floats sampled from a univariate “normal†(Gaussian) 3 min read numpy.random.gamma() in Python With the help of numpy.random.gamma() method, we can get the random samples of gamma distribution and return the random samples of numpy array by using this method. gamma distribution Syntax : numpy.random.gamma(shape, scale=1.0, size=None) Return : Return the random samples of numpy array. Example 1 min read numpy.random.choice() in Python With the help of choice() method, we can get the random samples of one dimensional array and return the random samples of numpy array. Syntax : numpy.random.choice(a, size=None, replace=True, p=None) Parameters: 1) a - 1-D array of numpy having random samples. 2) size - Output shape of random sample 1 min read numpy.random.shuffle() in python With the help of numpy.random.shuffle() method, we can get the random positioning of different integer values in the numpy array or we can say that all the values in an array will be shuffled randomly. Syntax : numpy.random.shuffle(x) Return : Return the reshuffled numpy array. Example #1 : In this 1 min read Like