numpy.random.standard_exponential() in Python

numpy.random.standard_cauchy() in 1Python

Last Updated : 18 Aug, 2020

With the help of numpy.random.standard_cauchy() method, we can see get the random samples from a standard cauchy distribution and return the random samples.

Standard cauchy distribution

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

Return : Return the random samples as numpy array.

Example #1 :

In this example we can see that by using numpy.random.standard_cauchy() method, we are able to get the random samples of standard cauchy distribution and generate the random samples from it.

Python3

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

# Using standard_cauchy() method
gfg = np.random.standard_cauchy(100000)

gfg = gfg[(gfg>-25) & (gfg<25)]
plt.hist(gfg, bins = 100, density = True)
plt.show()

Output :

Example #2 :

Python3

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

# Using standard_cauchy() method
gfg = np.random.standard_cauchy(100000)
gfg1 = np.random.power([gfg>0], 100000)

plt.hist(gfg1, bins = 100, density = True)
plt.show()

Output :

numpy.random.standard_exponential() in Python

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