# numpy.random.choice() in Python

• Difficulty Level : Expert
• Last Updated : 15 Jul, 2020

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 samples of numpy array.

3) replace – Whether the sample is with or without replacement.

4) p – The probability attach with every samples in a.

Output : Return the numpy array of random samples.

Example #1 :

In this example we can see that by using choice() method, we are able to get the random samples of numpy array, it can generate uniform or non-uniform samples by using this method.

## Python3

 `# import choice``import` `numpy as np``import` `matplotlib.pyplot as plt`` ` `# Using choice() method``gfg ``=` `np.random.choice(``13``, ``5000``)`` ` `count, bins, ignored ``=` `plt.hist(gfg, ``25``, density ``=` `True``)``plt.show()`

Output :

Example #2 :

## Python3

 `# import choice``import` `numpy as np``import` `matplotlib.pyplot as plt`` ` `# Using choice() method``gfg ``=` `np.random.choice(``5``, ``1000``, p ``=``[``0.2``, ``0.1``, ``0.3``, ``0.4``, ``0``])`` ` `count, bins, ignored ``=` `plt.hist(gfg, ``14``, density ``=` `True``)``plt.show()`

Output :

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