numpy.random.standard_normal() in Python

numpy.random.standard_normal() in Python

Last Updated : 18 Aug, 2020

With the help of numpy.random.standard_normal() method, we can get the random samples from standard normal distribution and return the random samples as numpy array by using this method.

Syntax : numpy.random.standard_normal(size=None)

Return : Return the random samples as numpy array.

Example #1 :

In this example we can see that by using numpy.random.standard_normal() method, we are able to get the random samples of standard normal distribution.

Python3

# import numpy
import numpy as np
import matplotlib.pyplot as plt

# Using standard_normal() method
gfg = np.random.standard_normal(5000)

plt.hist(gfg, bins = 50, density = True)
plt.show()

Output :

Example #2 :

Python3

# import numpy
import numpy as np
import matplotlib.pyplot as plt

# Using standard_normal() method
gfg = np.random.standard_normal(10000)

plt.hist(gfg, bins = 100, density = True)
plt.show()

Output :

numpy.random.standard_normal() in Python

J

jitender_1998

Improve

Article Tags :

Practice Tags :

python

Similar Reads

numpy.random.standard_t() in Python

With the help of numpy.random.standard_t() method, we can get the random samples from standard T distribution having degree of freedom and return the random samples by using this method. Standard T distribution Syntax : numpy.random.standard_t(df, size=None) # Here df is degree of freedom. Return :

numpy.random.standard_gamma() in Python

With the help of numpy.random.standard_gamma() method, we can get the random samples from standard gamma distribution and return the random samples by using this method. Standard gamma distribution Syntax : numpy.random.standard_gamma(shape, size=None) Return : Return the random samples as numpy arr

numpy.random.standard_exponential() in Python

With the help of numpy.random.standard_exponential() method, we can get the random samples of standard exponential distribution and return the random samples. Syntax : numpy.random.standard_exponential(size=None) Return : Return the random samples as numpy array. Example #1 : In this example we can

rand vs normal in Numpy.random in Python

In this article, we will look into the principal difference between the Numpy.random.rand() method and the Numpy.random.normal() method in detail. About random: For random we are taking .rand() numpy.random.rand(d0, d1, ..., dn) : creates an array of specified shape and fills it with random values.

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