Related Articles
Generating random number list in Python
• Last Updated : 17 Nov, 2020

Sometimes, in making programs for gaming or gambling, we come across the task of creating the list all with random numbers. This task is to perform in general using loop and appending the random numbers one by one. But there is always a requirement to perform this in most concise manner. Let’s discuss certain ways in which this can be done.

### Method #1 : Using list comprehension + randrange()

The naive method to perform this particular task can be shortened using the list comprehension. randrange function is used to perform the task of generating the random numbers.

## Python3

 `# Python3 code to demonstrate``# to generate random number list``# using list comprehension + randrange()``import` `random` `# using list comprehension + randrange()``# to generate random number list``res ``=` `[random.randrange(``1``, ``50``, ``1``) ``for` `i ``in` `range``(``7``)]` `# printing result``print` `(``"Random number list is : "` `+`  `str``(res))`
Output:
```Random number list is : [21, 27, 23, 20, 21, 25, 15]

```

### Method #2 : Using random.sample()

This single utility function performs the exact required as asked by the problem statement, it generated N no. of random numbers in a list in the specified range and returns the required list.

## Python3

 `# Python3 code to demonstrate``# to generate random number list``# using random.sample()``import` `random` `# using random.sample()``# to generate random number list``res ``=` `random.sample(``range``(``1``, ``50``), ``7``)` `# printing result``print` `(``"Random number list is : "` `+`  `str``(res))`
Output:

```Random number list is : [8, 46, 45, 49, 15, 6, 31]

```

### Method #3: Using numpy.random

The random function provided by the numpy module can be more useful for you as it provides little better functionality and performance as compare to the random module.

#### 1. Generating a list of random integers using numpy.random.randint function

This function returns random integers from the “discrete uniform” distribution of the integer data type.

## Python3

 `# importing numpy module``import` `numpy as np` `# print the list of 10 integers from 3  to 7``print``(``list``(np.random.randint(low ``=` `3``,high``=``8``,size``=``10``)))` `# print the list of 5 integers from 0 to 2``# if high parameter is not passed during``# function call then results are from [0, low)``print``(``list``(np.random.randint(low ``=` `3``,size``=``5``)))`
```Output:
[5, 3, 6, 7, 4, 5, 7, 7, 7, 7]
[0, 2, 1, 2, 1]

```

#### 2. Generating list of random floating values using numpy.random.random_sample function

This function return random float values in half open interval [0.0, 1.0).

## Python3

 `import` `numpy as np` `# generates list of 4 float values``print``(np.random.random_sample(size ``=` `4``))` `# generates 2d list of 4*4``print``(np.random.random_sample(size ``=` `(``4``,``4``)))`
```output:
[0.08035145 0.94966245 0.92860366 0.22102797]
[[0.02937499 0.50073572 0.58278742 0.02577903]
[0.37892104 0.60267882 0.33774815 0.28425059]
[0.57086088 0.07445422 0.86236614 0.33505317]
[0.83514508 0.82818536 0.1917555  0.76293027]]

```

The benefit of using numpy.random over the random module of python is that it provides few extra probability distributions which can help in scientific research.

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course

My Personal Notes arrow_drop_up