How to Use NumPy Random choice() in Python? (2024)

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.

In this article, I will explain how to use the NumPy random.choice() function and using its syntax, parameters, and how to generate random samples of a given 1-D array with examples.

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 choicearr = 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 replacearr = np.random.choice(5, 5, replace = False)# Example 5: Get the Non-random values without replacearr = 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.choicerandom.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 ofarrcan 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 NumPymodule 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 indata analysisand statisticsfor 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 choicearr = 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 fromarange(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 replacearr = 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 fromarange(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 replacearr = 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 thematplotliblibrary.

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

Frequently Asked Questions

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.

Related Articles

• Python NumPy array copy
• NumPy Array Addition
• Python NumPy Concatenate() Function
• How to Transpose Matrix in NumPy
• How to use median() in NumPy
• How to Use NumPy clip() in Python
• How to Use NumPy pad() in Python
• How to Use NumPy logspace() in Python
• Python NumPy round() Array Function
• How to Use NumPy random.randint() in Python

References

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