[toc]

**Introduction**

**random** is an in-built module in Python which generates **pseudo-random** numbers. Now, the random data generated by this module is not completely random. Instead it is pseudo-random, as mentioned previously.

**Note: **A *“True Random Number” **can be generated by a TRNG (true random number generator) while a “pseudo-random number” is generated by a PRNG (pseudorandom number generator).*

⚠️ TRNG is outside the scope of the discussion in this article.

So, **what is a PRNG (pseudorandom number generator)?**

PRNG initially generates a random number known asseed. Then an algorithm is used to generate a pseudo-random sequence of bits based on it. In simple words,it is an algorithm that generates seemingly random numbers; however, these numbers are still reproducible.

The **random **module has a set of methods that help us to generate random elements (numbers). In this tutorial, we will be focusing on the **seed()** method of the **random** **module**.

**Random seed() Method in Python**

The random number generator needs a starting point, i.e., it needs a

seed valueto start generating a sequence of random numbers. Thus, it is the`seed()`

method that is used to initialize the random number generator.

By default, **current system time** is used by the random number generator as a start-point. To customize the start number of the random number generator, you must use the **seed()** method.

**Syntax:**

**Example:**

import random random.seed(10) print(random.random())

**Output:**

0.5714025946899135

**How To Generate The Same Random Integer Every Time?**

If you set the same **seed **value before calling any random module function, you will get the same number repeatedly.

**Example:**

import random for i in range(3): # setting seed value to 10 random.seed(10) print("i[{}]={}".format(i,random.randint(12, 30)))

**Output:**

i[0]=30 i[1]=30 i[2]=30

**Explanation: **In the above output, we got the same number as the output because the same seed was set before using `randint`

every time.

**random.seed() and random.choice()**

➥ `choice()`

is a method of the `random`

module that selects a random element from a specified sequence (`string`

, `range`

, `list`

, `tuple`

).

You can use a custom seed value to receive the same choice value again and again. Let’s have a look at the following example.

**Example**

import random x = "PNEUMONOULTRAMICROSCOPICSILICOVOLCANOCONIOSIS" print("Output Without Setting A Seed: ") for i in range(3): print(random.choice(x)) print("Output After Setting A Seed: ") for i in range(3): random.seed(5) print(random.choice(x))

**Output:**

Output Without Setting A Seed: R C N Output After Setting A Seed: N N N

**What is NumPy Random Seed?**

The`np.random.seed`

function provides a, i.e., a base input value toseed valueNumPy's pseudo-random number generatorin Python.

**Syntax:**

**Example 1:**

import numpy as np for i in range(3): np.random.seed(101) print('i[{}]={}'.format(i, np.random.randint(low=1, high=10, size=10)))

**Output:**

i[0]=[2 7 8 9 5 9 6 1 6 9] i[1]=[2 7 8 9 5 9 6 1 6 9] i[2]=[2 7 8 9 5 9 6 1 6 9]

Here’s another example for you to visualize the effects of `numpy.random.seed`

.

**Example 2:**

import numpy as np print("GENERATE SAME RANDOM NUMBER WITH NUMPY.RANDOM.RANDOM") for i in range(2): np.random.seed(101) print('i[{}]={}'.format(i, np.random.random())) print("\nSELECT A RANDOM SAMPLE FROM AN INPUT ARRAY") for i in range(2): np.random.seed(0) print('i[{}]={}'.format(i, np.random.choice(a=[1, 2, 3, 4, 5, 6], size=5)))

**Output:**

GENERATE SAME RANDOM NUMBER WITH NUMPY.RANDOM.RANDOM i[0]=0.5163986277024462 i[1]=0.5163986277024462 SELECT A RANDOM SAMPLE FROM AN INPUT ARRAY i[0]=[5 6 1 4 4] i[1]=[5 6 1 4 4]

**Application of numpy.random.seed**

**Machine Learning**- Splitting datasets into test set and training sets require random sampling. And random sampling, in turn, requires pseudo-random random numbers. Therefore if you play around with ML models, then Numpy’s random.seed() is almost a certainty!

**Deep Learning**- Just like ML problems, Deep Learning problems also require splitting the dataset into test set and training set with the help of pseudo-random numbers.

**Random Sampling****Probability and Statistics**

## Frequently Asked Questions

**Should I Use numpy.random.seed or random.seed?**

- The answer to this question depends on whether you are using Numpy’s random generator in your code or the one in the normal random module.
- The random generators in
`random`

and`numpy.random`

have completely different/separate internal states. This means`random.seed()`

won’t affect the random sequences generated by`numpy.random.randn()`

, etc. Similarly,`numpy.random.seed()`

will not affect the random sequences generated by`random.random()`

, etc. - In case you have used both
`numpy.random`

and`random`

in your code, then you have to separately set seeds for both.

**What Number Should I Use in random.seed?**

It does not matter what number you use within the` numpy.random.seed()`

method. Using different seeds will only cause `Random`

module (or Numpy in case of `numpy.random.seed`

) to generate different *pseudo-random numbers.* Thus, the output of a random function depends on the value of `random.seed()`

but the choice of the seed value is arbitrary.

**Example:**

import random print("SEED VALUE = 10") for i in range(3): # setting seed value to 10 random.seed(10) print("i[{}]={}".format(i,random.randint(12, 30))) print("SEED VALUE = 15") for i in range(3): # setting seed value to 15 random.seed(15) print("i[{}]={}".format(i,random.randint(12, 30)))

**Output:**

SEED VALUE = 10 i[0]=30 i[1]=30 i[2]=30 SEED VALUE = 15 i[0]=18 i[1]=18 i[2]=18

**How Do I Get random.seed() to Use System Time?**

Since time keeps changing, hence using it as a seed value to generate random numbers will ensure that the seed value keeps changing and you will get a different random sequence/number upon every execution.

**Example:**

import random import time random.seed(int(time.time())) c = 'abcdefghijklmnopqrstuvwxyz0123456789%^*(-_=+)' password = ''.join([c[random.randint(0, len(c) - 1)] for i in range(10)]) print("New Password: ", password)

**Output:**

New Password: za2arj+hjz

**Conclusion**

I hope this article helped you to understand the importance and uses of `random.seed`

in Python. Please **subscribe** and **stay tuned** for more interesting concepts. Happy coding!?

- Do you want to master the most popular Python IDE fast?
- This course will take you from beginner to expert in PyCharm in ~90 minutes.
- For any software developer, it is crucial to master the IDE well, to write, test and debug high-quality code with little effort.

**Join the PyCharm Masterclass** now, and master PyCharm by tomorrow!