A random number in Python is any number between 0.0 to 1.0 generated with a pseudo-random number generator. It is possible to create integers, doubles, floats, and even longs using the pseudo-random generator in Python. Since it generates the numbers randomly, it is usually used in gaming and lottery applications. Another use of random numbers in Python is in Machine Learning, to create random weights for training the algorithms.

Python provides the random module that allows one to work with random numbers. The module defines some methods that use the pseudo-random generator to generate random numbers in Python. Using these methods, you can perform various random number operations.

To use random numbers in Python, you can use various methods declared in the random module. The table below describes all the methods along with their use.

Method |
Description |

seed() |
Initializes the pseudo-random generator |

getstate() |
Returns an object that shows the internal state of the generator |

setstate() |
Restores the generator’s internal state |

getrandbits() |
Returns an integer with k random bits |

randrange() |
Returns a random integer from the specified range |

randint() |
Returns a random integer from the specified inclusive range |

choice() |
Selects and gives a random element from the non-empty list |

choices() |
Selects and returns a list of random elements from the non-empty list |

shuffle() |
Shuffles any given sequence in a random order |

sample() |
Returns specified numbers of lists from the given sequence |

random() |
Gives a random floating point number within 0.0 and 1.0 |

uniform() |
Gives a random floating point number within an inclusive range |

triangular() |
Returns a random floating point number within the two specified parameters. It also allows you to set a midpoint parameter. |

betavariate() |
Gives a random floating point number from the beta distribution |

expovariate() |
Returns a random floating point number from the exponential distribution |

gammavariate() |
Returns a random floating point number from the gamma distribution |

gauss() |
Gives a random floating point number from a gaussian distribution |

lognormvariate() |
Gives a random floating point number from a log-normal distribution |

normalvariate() |
Returns a random floating point number from a normal distribution |

vonmisesvariate() |
Returns a random floating point number from von Mises distribution |

paretovariate() |
Gives a random floating point number from the Pareto distribution |

weibullvariate() |
Gives a random floating point number from the Weibull distribution |

Since you now know the methods that you can use with random numbers in Python, let’s use some of them.

To get a random number in Python from a list, string, or tuple, you can use the choice() function. Look at the example below to get a random number from a list, and a random character from a string.

# importing random to use the generator

import random

# Defining a list

list_exm = [5, 7, 11, 22, 46, 84]

# Getting a random number

print(random.choice(list_exm))

# Initiating a string

str_exm = "Simplilearn"

# Printing a random character

print(random.choice(str_exm))

Output:

Note: Your output may vary as the random number from the list and string is selected and printed.

The Python randrange(beg, end, step) function is used to generate a random number between a given range. The first parameter defines the beginning, while the second parameter specifies the end of the range. The third parameter is a skip step. If you pass 2 as the third parameter, then the generator will skip 2 numbers and return the third. Use the following function to generate a random number in Python.

import random

print("Random number from range 15 to 85 is : ", end="")

# using the randrange() function

print(random.randrange(15, 85, 4))

Output:

The randint() function will generate a random number between the specified range. Let’s have a look at an example for the same.

import random

i = random.randint(20, 80)

print(i)

Output:

The random() function is used to generate a number between 0.0 and 1.0. On the other hand, the Python seed() function is used to save the state, so that the random() function gives the same output on multiple executions. It seems fairly evident that it’s not that random, after all. Look at the following example to understand this better.

import random

# using random() function to generate a number

print("The random number is : ", end="")

print(random.random())

# using seed() function to save the state

random.seed(3)

# printing the seeded random number

print("The seeded number with 3 is : ", end="")

print(random.random())

# using seed() function again

random.seed(9)

# printing seeded random number

print("The mapped random number with 9 is : ", end="")

print(random.random())

# using the same seeds to print same numbers

random.seed(3)

print("The mapped random number with 3 is : ", end="")

print(random.random())

random.seed(9)

print("The mapped random number with 9 is : ", end="")

print(random.random())

Output:

As you can see in the output, the first number generation with a seed was random. But, when you use the same seed, the same number is printed.

The shuffle() function is used to shuffle a sequence randomly in this example, which is a list. The process of how it mixes with the list is depicted in the example below.

import random

example_list = [1, 3, 5, 7, 9, 11]

print("Original list : ")

print(example_list)

# Shuffling

random.shuffle(example_list)

print("\nAfter the shuffle : ")

print(example_list)

# Shuffling again

random.shuffle(example_list)

print("\nAfter the shuffle : ")

print(example_list)

Output:

The Python uniform() function takes two parameters: the lower limit and the upper limit, and returns a random number between the range. The lower limit is inclusive, but the upper limit is exclusive.

import random

# Using uniform() function

print("The random floating point number is : ", end="")

print(random.uniform(3, 7))

print("The random floating point number is : ", end="")

print(random.uniform(17, 23))

Output:

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In this article, you have looked into everything you need to about random numbers in Python. You also went through the random module and different methods defined in it. This article showed the use of some of the methods, but if you want, you can try all the methods one-by-one to see how it works.

The more you code, the more you learn. If you are new to Python and want to learn more about these basic concepts, Simplilearn’s Python Tutorial for Beginners is the right learning resource for you. If you’re looking to go pro, after clearing the basic concepts, you can opt for our Online Python Certification Course. If you are passionate about data science, you can also opt for our Data Science with Python Certification Course. This course is adept at helping you learn data analysis, machine learning, natural language processing, and more.

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