How to choose elements from the list with different probability using NumPy?
Last Updated :
15 Jul, 2025
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We will see How to use numpy.random.choice() method to choose elements from the list with different probability.
Syntax: numpy.random.choice(a, size=None, replace=True, p=None)
Output: Return the numpy array of random samples.
Note: parameter p is probabilities associated with each entry in a(1d-array). If not given the sample assumes a uniform distribution over all entries in a.
Now, let's see the examples:
Example 1:
# import numpy library
import numpy as np
# create a list
num_list = [10, 20, 30, 40, 50]
# uniformly select any element
# from the list
number = np.random.choice(num_list)
print(number)
Output:
50
Example 2:
# import numpy library
import numpy as np
# create a list
num_list = [10, 20, 30, 40, 50]
# choose index number-3rd element
# with 100% probability and other
# elements probability set to 0
# using p parameter of the
# choice() method so only
# 3rd index element selected
# every time in the list size of 3.
number_list = np.random.choice(num_list, 3,
p = [0, 0, 0, 1, 0])
print(number_list)
Output:
[40 40 40]
In the above example, we want only to select the 3rd index element from the given list every time.
Example 3:
# import numpy library
import numpy as np
# create a list
num_list = [10, 20, 30, 40, 50]
# choose index number 2nd & 3rd element
# with 50%-50% probability and other
# elements probability set to 0
# using p parameter of the
# choice() method so 2nd &
# 3rd index elements selected
# every time in the list size of 3.
number_list = np.random.choice(num_list, 3,
p = [0, 0, 0.5, 0.5, 0])
print(number_list)
Output:
[30 40 30]
In the above example, we want to select 2nd & 3rd index elements from the given list every time.
