How to Use NumPy Random choice() in Python?

NumPy random.choice() function in Python is used to return a random sample from a given 1-D array. It creates an array and fills it with random samples. It has four parameters and using these parameters we can manipulate the random samples of an array.

1. Quick Examples of random.choice() Function

Following are quick examples of random.choice()


# Quick examples of random.choice() function

# Example 1: Get the single element from random choice
arr = np.random.choice(7)

# Example 2:  Get an array of uniform random samples 
arr = np.random.choice(5, 5) 

# Example 3: Get an array of non uniform random samples  
arr = np.random.choice(5, 5, p=[0.1, 0, 0.3, 0.6, 0])

# Example 4: Get the random values without replace
arr = np.random.choice(5, 5, replace = False)

# Example 5: Get the Non-random values without replace
arr = np.random.choice(5, 3, replace = False, p=[0.1, 0, 0.3, 0.6, 0])

2. Syntax of random.choice()

Following is the syntax of the NumPy random.choice() function.


# Syntax of random.choice
random.choice(arr, size=None, replace=True, p=None)

2.1 Parameters of random.choice()

Following are the parameters of random.choice() function.

arr – 1-D NumPy array or int. If a ndarray a random sample is generated from its elements.
size – (Optional) The shape of the output. If None, a single random element is returned. If an integer, size a number of random elements are generated. If a tuple of integers, the output will have that shape.
replace – (optional)Whether the random sample is with or without replacement. Default is True, meaning that a value of arr can be selected multiple times.
p – (Optional) The probabilities associated with each entry in the input array. If specified, the probabilities must sum to 1. If None, the function assumes a uniform distribution.

2.2 Return value

It returns an ndarray of random samples.

3. Usage of NumPy random.choice()

The NumPy random.choice() function is a built-in function in the NumPy module package and is used to create a one-dimensional NumPy array of random samples. We know that the NumPy module is a data manipulation library for Python. Some special tools of NumPy operate on arrays of numbers. For example, the manipulation of numeric data is a big task in data analysis and statistics for getting random data samples.

In the below example, np.random.choice(7) to generate a single random element from the numbers 0 to 6 (inclusive). In this case, np.random.choice(7) will randomly select a single integer from the range [0, 1, 2, 3, 4, 5, 6]. The selected random number will be stored in the variable arr, and it will be printed using the print() statement.


# Import numpy 
import numpy as np

# Get the single element from random choice
arr = np.random.choice(7)
print("After getting the random choice:", arr)

Yields below output.

Note that the output will be different each time you run the code because it’s a random choice.

4. Get Uniform Random Samples of NumPy Array

You can use np.random.choice(5, 5) to generate an array of 5 uniform random samples from the integers 0 to 4 (inclusive). In this case, np.random.choice(5, 5) will generate an array of 5 elements, each randomly chosen from the integers [0, 1, 2, 3, 4]. The resulting array arr will contain 5 random integers.


# Get an array of uniform random samples 
arr = np.random.choice(5, 5)
print("After getting an array of uniform random samples:\n",arr)

Yields below output.

5. Get Non-Uniform random samples of NumPy Array

You can also use np.random.choice(5, 5, p=[0.1, 0, 0.3, 0.6, 0]) to generate an array of 5 non-uniform random samples from the integers 0 to 4 (inclusive) with specified probabilities.

In this program, the p parameter specifies the probabilities associated with each element in the input array. The probabilities [0.1, 0, 0.3, 0.6, 0] indicate the likelihood of each element being chosen. In this example, the second and last elements have a probability of 0, so they will never be selected. The third element has a probability of 0.3, and the fourth element has a probability of 0.6, making them more likely to be chosen.


# Get an array of non uniform random samples  
arr = np.random.choice(5, 5, p=[0.1, 0, 0.3, 0.6, 0])
print("After getting an array of non uniform random samples:\n",arr)

# Output:
# After getting an array of non uniform random samples:
#  [2 3 2 2 2]

6. Get the Uniform Random Sample without Replacement

Create a uniform random sample from arange(5) of size 5 without replacement. which means the selected elements may be repeated, as you can see in the above output few elements are repeated in the randomly selected array. Whereas if replace=False then the elements will not repeat in the randomly selected array.


# Get the random values without replace
arr = np.random.choice(5, 5, replace = False)
print("After getting random values without replace:\n",arr)

# Output:
# After getting random values without replace:
#  [4 2 3 0 1]

7. Get the Non-Uniform Random Sample without Replacement

Create a non-uniform random sample from arange(5) of size 3 without replacement. For that, pass p parameter of the same size as the given array and set replace=False into this function, it will return Non-repeated and Non-uniform random samples of the given array.


# Get the Non-random values without replace
arr = np.random.choice(5, 3, replace = False, p=[0.1, 0, 0.3, 0.6, 0])
print("After getting non random values without replace:\n",arr)

# Output:
# After getting non random values without replace:
#  [3 2 0]

8. Get the Graphical presentation of Random Values

Let’s plot the graph of the random values using the matplotlib library.


import matplotlib.pyplot as plt
# Using choice() method
arr = np.random.choice(7, 300)
count, bins, ignored = plt.hist(arr, 25, density=True)
plt.show()

Frequently Asked Questions

What is the purpose of numpy.random.choice()?

numpy.random.choice() is used to generate random samples from a specified 1-D array-like object. It can be used to randomly select elements from an array, generate random integers, or perform random sampling with or without replacement.

How do I generate a random integer between a specific range using numpy.random.choice()?

You can generate a random integer between a specific range using numpy.random.choice() by providing an array-like object containing the range of integers. For example, np.random.choice(7) will generate a random integer between 0 and 6.

How do I generate random samples without replacement using numpy.random.choice()?

To generate random samples without replacement, set the replace parameter to False. For example, np.random.choice(5, 3, replace=False) will generate 3 random samples without replacement from the integers 0 to 4.

Can numpy.random.choice() be used with non-integer data types?

numpy.random.choice() can be used with non-integer data types. It works with any array-like object, including arrays of floats, strings, or other data types.

Can I generate non-uniform random samples using numpy.random.choice()?

You can generate non-uniform random samples by providing the p parameter, which specifies the probabilities associated with each element in the input array. The function will sample elements based on these probabilities.

Conclusion

In this article, I have explained the NumPy random.choice() function syntax, parameter, and usage of how to get the random samples of 1-D NumPy array with examples.

References

https://np.org/doc/stable/reference/random/generated/np.random.choice.html