How to Use NumPy random seed() in Python

The NumPy random seed() function is used to seed the random number generator in NumPy. Seeding the random number generator allows you to get reproducible results when generating random numbers. This function generates pseudo-random numbers based on a seed value. A pseudo-random number is a number that sorts random, but they are not really random numbers.

The NumPy random seed is a numerical value that generates a new set or repeats pseudo-random numbers. The value in the NumPy random seed saves the state of randomness. If we call the seed function using value 1 multiple times, the computer displays the same random numbers. When the value is not mentioned in the NumPy random seed, then the computer will use the current system time in seconds or milliseconds as a seed value to generate a different set of random numbers.

The NumPy random() function does not generate ‘truly’ random numbers but we used it to generate pseudo-random numbers. By Pseudo-random numbers we mean, they can be determined, not exactly generated randomly. We will explain pseudo-random numbers in detail in the next section. The random() function generates pseudo-random numbers based on a seed value.

1. What is the Pseudo-Random Number?

As the name signifies, the Pseudo-random number is not a ‘truly’ random number but a partial random number. The pseudo-random numbers are computer-generated numbers that look like they are random, but are actually pre-determined.

Our computer system works on algorithms. If we give the same input to an algorithm, the output remains the same.

A set of algorithms created by Computer Scientists to generate pseudo-random numbers, which approximates the properties of random numbers. These algorithms are called “pseudo-random number generators.”

NumPy random seed functions generate random numbers based on “pseudo-random number generators” algorithms.

2. Syntax of NumPy random.seed()

Following is the Syntax of np.random.seed().

# Syntax of Use seed() 
numpy.random.seed(seed=None)

2.1 Parameters of random.seed()

Following are the parameters of seed() function.

seed – This is an optional parameter. It is an integer or an array_like object that is used to seed the random number generator. If seed is not provided or is None, the generator is initialized based on the system time or entropy source, which means you’ll get different sequences of random numbers each time you run your program. If you provide a specific integer value as the seed, you’ll get the same sequence of random numbers every time you run your program with that seed.

3. Usage of NumPy seed()

The numpy.random.seed() function is used to seed the random number generator in NumPy. Seeding is important for reproducibility. By setting the seed, you ensure that the sequence of random numbers generated by NumPy is the same every time you run your code with that seed.

In the below example, np.random.seed(5) sets the seed for NumPy’s random number generator to the integer value 5. Setting the seed is crucial for reproducibility. If you run this code multiple times, you will get the same sequence of random numbers each time because the seed is fixed. np.random.random(5) generates an array of 5 random numbers from a uniform distribution between 0 (inclusive) and 1 (exclusive). The result is assigned to the variable arr.

# Import numpy
import numpy as np

# Generate random numbers 
np.random.seed(5)
arr = np.random.random(5)
print("The random numbers are:\n",arr)

Yields below output.

In the below example, np.random.seed() sets the seed for NumPy’s random number generator based on the system time or some other entropy source. When no seed value is provided within the parentheses, the seed is set dynamically each time the code is executed. As a result, you’ll get different sequences of random numbers each time you run your program. np.random.random(5) generates an array of 5 random numbers from a uniform distribution between 0 (inclusive) and 1 (exclusive). The result is assigned to the variable arr.

# Generate random numbers 
np.random.seed()
arr = np.random.random(5)
print("The random numbers are:\n",arr)

# Output:
# The random numbers are:
# [0.27711177 0.10360379 0.44506416 0.27977484 0.85190227]

4. Use numpy. random.seed() Function

Use the np.random.seed() function to set the seed for the random number generator. For instance, First, import the NumPy library and use the alias np. Set the seed to a specific value, in this case, 42. You can replace 42 with any integer. Generate a 3×3 array of random numbers from a uniform distribution between 0 and 1.

# Set the seed to a specific value
np.random.seed(42)

# Generate random numbers
arr = np.random.rand(3, 3)
print("The random numbers are:\n",arr)

# Output:
# The random numbers are:
# [[0.37454012 0.95071431 0.73199394]
#  [0.59865848 0.15601864 0.15599452]
#  [0.05808361 0.86617615 0.60111501]]

5. Graphical Representation of NumPy random.seed() Function

To make a histogram with the help pyplot library and print the graph of NumPy random.seed() function.

import numpy as np
import matplotlib.pyplot as plt

new_samp = np.random.choice(16, 3000)

num, b, i = plt.hist(new_samp, 21, density = True)
plt.show()

Frequently Asked Questions

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References

• https://numpy.org/doc/stable/reference/random/generated/numpy.random.sample.html