How to randomly select elements of an array with NumPy in Python ?

Prerequisites: Numpy

The random values are useful in data-related fields like machine learning, statistics and probability. The numpy.random.choice() function is used to get random elements from a NumPy array. It is a built-in function in the NumPy package of python.

Syntax: numpy.random.choice( a , size = None, replace = True, p = None)

Parameters:

• a: a one-dimensional array/list (random sample will be generated from its elements) or an integer (random samples will be generated in the range of this integer)
• size: int or tuple of ints (default is None where a single random value is returned). If the given shape is (m,n), then m x n  random samples are drawn.
• replace: (optional); the Boolean value that specifies whether the sample is drawn with or without replacement. When sample is larger than the population of the list, replace cannot be False.
• p: (optional); a 1-D array containing probabilities associated with each entry in a. If not given then sample assumes uniform distribution over all entries in a.

Approach

• Import module
• Create a sample array
• Randomly choose values from the array created
• Print the array so generated.

Given below is the implementation for 1D and 2D array.

Generating 1-D list of random samples

Example 1:

Python3

import numpy as np

prog_langs = ['python', 'c++', 'java', 'ruby']

# generating random samples
print(np.random.choice(prog_langs, size=8))

# generating random samples without replacement
print(np.random.choice(prog_langs, size=3, replace=False))

# generating random samples with probabilities
print(np.random.choice(prog_langs, size=10,
                       replace=True, p=[0.3, 0.5, 0.0, 0.2]))

Output :

Example 2:

Python3

import numpy as np

samples = 5
# generating random samples
print(np.random.choice(samples, size=10))

# generating random samples without replacement
print(np.random.choice(samples, size=5, replace=False))

# generating random samples with probabilities
print(np.random.choice(samples, size=5, replace=True))

# generating with probabilities
print(np.random.choice(samples, size=15,
                       replace=True, p=[0.2, 0.1, 0.1, 0.3, 0.3]))

Output:

Generating a 2-D list of random samples

Example:

Python3

import numpy as np

prog_langs = ['python', 'c++', 'java', 'ruby']

# generating random samples
print(np.random.choice(prog_langs, size=(4, 5)))

# generating random samples with probabilities
print('\n')
print(np.random.choice(prog_langs, size=(10, 2),
                       replace=True, p=[0.3, 0.5, 0.0, 0.2]))

Output:

Example 2:

Python3

import numpy as np

samples = 5

# generating random samples
print(np.random.choice(samples, size=(5, 5)))

# generating with probabilities
print('\n')
print(np.random.choice(samples, size=(8, 3),
                       replace=True,
                       p=[0.2, 0.1, 0.1, 0.3, 0.3]))

Output: