“Python’s random() method is utilized to produce random values. This will be applied to create pseudo-random values. That indicates that these values are randomly selected and could be identified. For several numbers, the random() method creates integers. This number is, however, known as the “seed number”. A random method’s state is stored using a seed method, enabling it to create similar random data during subsequent executions of the code on a very similar system or different machines. The preceding number produced by the creator serves as the seed number. If there is no prior number, it utilizes the latest system parameters for the first time.”

## Applications for seed() Function

To obtain a reliable secret decryption key semi-randomly and securely, the seed number is important in information security. Then we will configure the highly credible and durable pseudo-random value creator as we like with a customized seed number.

Additionally, the seed() method is effective for duplicating information from a pseudo-random value creator. We may recreate similar information repeatedly by using a seed number because numerous threads are not operational. We will always obtain similar values when we run the program if we give the random generator a particular seed; when we require a consistent element of random values, that is helpful.

This will be utilized to produce a pseudo-random encryption method. Information systems rely heavily on data encryption. These would be the types of private keys that are being utilized to encrypt information from being accessed by unauthorized parties digitally. Whenever random values are utilized for testing, it facilitates code efficiency.

Now we are going to discuss how to utilize the seed() function in python.

## Example no 1

While executing any additional random package methods, we have to give a similar seed number if we intend to create a similar value each time. Let’s examine how the Python pseudo-random value can be seeded.

print('Random values having seed 40')

for j in range(4):

random.seed(40)

print(random.randint(10, 65))

At the start of the program, we integrate the header file “random”. Then we utilize the print() function to display the message “Random values having seed 40”. In the next step, we have been applying for loop. Here we call the range() function and set the attribute of this function.

Further, we employ the seed() method. We have provided value 40 as its argument. This function is linked with the random module. In the end, we applied the print() statement to display the random values. Within the print() function, the randint() method of random header file is being used.

As we seeded them with almost a similar number while invoking a randint() function, which can be seen in the display, we obtained a similar value 4 times.

## Example no 2

We will acquire a unique value if we run the randint() function multiple times before executing the seed() function. Before invoking any other functionality of the random package, provide a new seed number if we want different information.

random.seed(40)

print("1st value:", random.randint(30, 60))

print("2nd Value:", random.randint(30, 60))

random.seed(30)

print("3rd Value:", random.randint(15, 55))

First of all, the random module will be imported. In the next step, we will apply the seed() function, which is associated with this library. We utilize print() three times. The first print() function is used to display the 1st random value between 30 to 60 as we apply the randint() method. So we specify the minimum and maximum limits as its parameters. We have taken the function randint() from the framework random.

Now we want to generate another random value, so the print() function and randint() function both will be employed. To terminate the code, we again call the seed() method. This function is being utilized to print a third random value. For this purpose, we will apply the randint() method.

In this instance, as we have used randint() two times without changing the seed number, the output’s second value was unique.

## Example no 3

Being able to duplicate information produced by a pseudo-random value creator can be helpful in some cases. A seed number is necessary for the production of random values. We may recreate relatively similar data continuously by utilizing a seed number because numerous threads are not effective when we want to duplicate the outcomes we are obtaining in a specific run. In these situations, the seed is applied to duplicate the outcome. Until we want data that can be repeated, the existing seed number is essential.

The Python Random creator will not retain the seed while utilizing a custom seed number. This doesn’t employ any means of obtaining the most recent seed number. Whenever we want to recover the seed, we have to save it. The automated seed would not be removed from the generator again. However, we can utilize this method.

import sys

s_num = random.randrange(sys.maxsize)

print('Seed Number:', s_num)

random.seed(s_num)

n = random.randint(20, 600)

print("Random Value", n)

Here, we are going to incorporate two required modules: random and sys. After this, we will create a seed, so we declare a variable “s_num”. Here we use the randrange() method. We will give maximum size as the arguments of the function. This function is related to the random header file.

In the next step, the print statement is used to show the seed number. Now we call the seed() function of the random library. The seed() function generates the random number. We will adjust the lowest and highest range as the attributes of the randint() function of the random library. Lastly, the print() function is called to show the random value.

The output shows the seed number first and then displays the random value, which lies between 20 to 600.

## Conclusion

In this article, we have talked about the random seed function. We will duplicate the information provided by a pseudo-random value generator by specifying the unique seed number. With the help of the seed() function, we select similar components from the list at irregular intervals. To obtain a similar random value each time, we utilize the seed() method. Additionally, using a random number creator, we obtain a seed number as an example in this guide.