# numpy.random.triangular() in Python

• Last Updated : 18 Aug, 2020

With the help of numpy.random.triangular() method, we can get the random samples from triangular distribution from interval [left, right] and return the random samples by using this method.

Syntax : numpy.random.triangular(left, mode, right, size=None)

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Parameters :

1) left – lower limit of the triangle.

2) mode – peak value of the distribution.

3) right – upper limit of the triangle.

4) size – total number of samples required.

Return : Return the random samples as numpy array.

Example #1 :

In this example we can see that by using numpy.random.triangular() method, we are able to get the random samples of triangular distribution and return the numpy array.

## Python3

 `# import numpy``import` `numpy as np``import` `matplotlib.pyplot as plt`` ` `# Using triangular() method``gfg ``=` `np.random.triangular(``-``5``, ``0``, ``5``, ``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 triangular() method``gfg ``=` `np.random.triangular(``-``10``, ``8``, ``10``, ``15000``)`` ` `plt.hist(gfg, bins ``=` `100``, density ``=` `True``)``plt.show()`

Output : My Personal Notes arrow_drop_up