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From Random Import Randint | True Random Number Generators

random numbers in python

Bookkeeping functions¶

random.seed(a=None, version=2)¶

Initialize the random number generator.

If a is omitted or


None

, the current system time is used. If randomness sources are provided by the operating system, they are used instead of the system time (see the

os.urandom()

function for details on availability).

If a is an int, it is used directly.

With version 2 (the default), a


str

,

bytes

, or

bytearray

object gets converted to an

int

and all of its bits are used.

With version 1 (provided for reproducing random sequences from older versions of Python), the algorithm for


str

and

bytes

generates a narrower range of seeds.

Changed in version 3.2: Moved to the version 2 scheme which uses all of the bits in a string seed.

random.getstate()¶

Return an object capturing the current internal state of the generator. This object can be passed to


setstate()

to restore the state.

random.setstate(state)¶

state should have been obtained from a previous call to


getstate()

, and

setstate()

restores the internal state of the generator to what it was at the time

getstate()

was called.

Create a multidimensional array of random integers

Python’s NumPy module has a

numpy.random

package to generate random data. To create a random multidimensional array of integers within a given range, we can use the following NumPy methods:


  • randint()

  • random_integers()

  • np.randint(low[, high, size, dtype])

    to get random integers array from low (inclusive) to high (exclusive).

  • np.random_integers(low[, high, size])

    to get random integer’s array between low and high, inclusive.

Now, Let see the example.

Generate a 4 x 4 array of ints between 10 and 50, exclusive:


import numpy # 4 x 4 array newArray = numpy.random.randint(10, 50, size=(4, 4)) print(newArray)

Output:

[[10 48 30 24] [13 46 30 11] [12 28 49 26] [30 18 49 35]]

Generate a 5 x 3 array of random integers between 60 and 100, inclusive.


import numpy # 3 x 5 array of ints newArray = numpy.random.random_integers(60, 100, size=(3, 5)) print(newArray)

Output:

[[63 76 95 93 75] [71 84 63 99 93] [65 64 66 69 92]]

random numbers in python
random numbers in python

Python3


import


random


r1


random.randint(


1.23


9.34


print


(r1)

Output :

Traceback (most recent call last):
File “/home/f813370b9ea61dd5d55d7dadc8ed5171.py”, line 6, in
r1=random.randint(1.23, 9.34)
File “/usr/lib/python3.5/random.py”, line 218, in randint
return self.randrange(a, b+1)
File “/usr/lib/python3.5/random.py”, line 182, in randrange
raise ValueError(“non-integer arg 1 for randrange()”)
ValueError: non-integer arg 1 for randrange()

Program to Demonstrate the TypeError

In this example, we can see that if we pass string or character literals as parameters in the randint() function then a TypeError occurs.

Parameter Values

Parameter Description
start Required. An integer specifying at which position to start.
stop Required. An integer specifying at which position to end.

random — Generate pseudo-random numbers¶

Source code: Lib/random.py

This module implements pseudo-random number generators for various distributions.

For integers, there is uniform selection from a range. For sequences, there is uniform selection of a random element, a function to generate a random permutation of a list in-place, and a function for random sampling without replacement.

On the real line, there are functions to compute uniform, normal (Gaussian), lognormal, negative exponential, gamma, and beta distributions. For generating distributions of angles, the von Mises distribution is available.

Almost all module functions depend on the basic function

random()

, which
generates a random float uniformly in the half-open range

0.0 <= X < 1.0

.
Python uses the Mersenne Twister as the core generator. It produces 53-bit precision
floats and has a period of 2**19937-1. The underlying implementation in C is
both fast and threadsafe. The Mersenne Twister is one of the most extensively
tested random number generators in existence. However, being completely
deterministic, it is not suitable for all purposes, and is completely unsuitable
for cryptographic purposes.

The functions supplied by this module are actually bound methods of a hidden
instance of the

random.Random

class. You can instantiate your own
instances of

Random

to get generators that don’t share state.

Class

Random

can also be subclassed if you want to use a different
basic generator of your own devising: see the documentation on that class for
more details.

The

random

module also provides the

SystemRandom

class which
uses the system function

os.urandom()

to generate random numbers
from sources provided by the operating system.

Warning

The pseudo-random generators of this module should not be used for
security purposes. For security or cryptographic uses, see the

secrets

module.

See also

M. Matsumoto and T. Nishimura, “Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator”, ACM Transactions on Modeling and Computer Simulation Vol. 8, No. 1, January pp.3–30 1998.

Complementary-Multiply-with-Carry recipe for a compatible alternative random number generator with a long period and comparatively simple update operations.

Unit 3 - Built in Functions,  Importing Random, and Using randint function - Python
Unit 3 – Built in Functions, Importing Random, and Using randint function – Python

Alternative Generator¶

class random.Random([seed])¶

Class that implements the default pseudo-random number generator used by the


random

module.

Changed in version 3.11: Formerly the seed could be any hashable object. Now it is limited to:


None

,

int

,

float

,

str

,

bytes

, or

bytearray

.

Subclasses of


Random

should override the following methods if they wish to make use of a different basic generator:

seed(a=None, version=2)¶

Override this method in subclasses to customise the


seed()

behaviour of

Random

instances.

getstate()¶

Override this method in subclasses to customise the


getstate()

behaviour of

Random

instances.

setstate(state)¶

Override this method in subclasses to customise the


setstate()

behaviour of

Random

instances.

random()¶

Override this method in subclasses to customise the


random()

behaviour of

Random

instances.

Optionally, a custom generator subclass can also supply the following method:

getrandbits(k)¶

Override this method in subclasses to customise the


getrandbits()

behaviour of

Random

instances.

class random.SystemRandom([seed])¶

Class that uses the


os.urandom()

function for generating random numbers from sources provided by the operating system. Not available on all systems. Does not rely on software state, and sequences are not reproducible. Accordingly, the

seed()

method has no effect and is ignored. The

getstate()

and

setstate()

methods raise

NotImplementedError

if called.

Generate a list of random integer numbers

In this section, we will see how to generate multiple random numbers. Sometimes we need a sample list to perform testing. In this case, instead of creating it manually, we can create a list with random integers using a

randint()

or

randrange()

. In this example, we will see how to create a list of 10 random integers.


import random random_list = [] # Set a length of the list to 10 for i in range(0, 10): # any random numbers from 0 to 1000 random_list.append(random.randint(0, 1000)) print(random_list) # Output [994, 287, 65, 994, 936, 462, 839, 160, 689, 624]

Create a list of random numbers without duplicates

Note: In the above example, there is a chance of occurring a duplicate number in a list.

If you want to make sure each number in the list is unique, use the random.sample() method to generate a list of unique random numbers.

  • The

    sample()

    returns a sampled list of selected random numbers within a range of values.
  • It never repeats the element so that we can get a list of random numbers without duplicates


import random # Generate 10 unique random numbers within a range num_list = random.sample(range(0, 1000), 10) print(num_list) # Output [499, 580, 735, 784, 574, 511, 704, 637, 472, 211]

Note: You can also use the step parameter of the range() function to specify the increment. For example, you want a list of 10 random numbers, but each integer in a list must be divisible by 5, then use

random.sample(range(0, 1000, 5), 10)

Sort random numbers list

Use the

sort()

function to sort a list of random integers in ascending order


import random sample_list = random.sample(range(50, 500, 5), 5) # Before sorting print(sample_list) # Output [305, 240, 260, 235, 445] sample_list.sort() # After sorting print(sample_list) # Output [235, 240, 260, 305, 445]

python from random import randint
python from random import randint

Python3


import


random


r2


random.randint(


'a'


'z'


print


(r2)

Output :

Traceback (most recent call last):
File “/home/fb805b21fea0e29c6a65f62b99998953.py”, line 5, in
r2=random.randint(‘a’, ‘z’)
File “/usr/lib/python3.5/random.py”, line 218, in randint
return self.randrange(a, b+1)
TypeError: Can’t convert ‘int’ object to str implicitly

Applications : The randint() function can be used to simulate a lucky draw situation. Let’s say User has participated in a lucky draw competition. The user gets three chances to guess the number between 1 and 10. If guess is correct user wins, else loses the competition.

Real-valued distributions¶

The following functions generate specific real-valued distributions. Function parameters are named after the corresponding variables in the distribution’s equation, as used in common mathematical practice; most of these equations can be found in any statistics text.

random.random()¶

Return the next random floating point number in the range


0.0 <= X < 1.0

random.uniform(a, b)¶

Return a random floating point number N such that


a <= N <= b

for

a <= b

and

b <= N <= a

for

b < a

.

The end-point value

may or may not be included in the range depending on floating-point rounding in the equation

a + (b-a) * random()

.

random.triangular(low, high, mode)¶

Return a random floating point number N such that


low <= N <= high

and with the specified mode between those bounds. The low and high bounds default to zero and one. The mode argument defaults to the midpoint between the bounds, giving a symmetric distribution.

random.betavariate(alpha, beta)¶

Beta distribution. Conditions on the parameters are


alpha > 0

and

beta > 0

. Returned values range between 0 and 1.

random.expovariate(lambd=1.0)¶

Exponential distribution. lambd is 1.0 divided by the desired mean. It should be nonzero. (The parameter would be called “lambda”, but that is a reserved word in Python.) Returned values range from 0 to positive infinity if lambd is positive, and from negative infinity to 0 if lambd is negative.

Changed in version 3.12: Added the default value for


lambd

.

random.gammavariate(alpha, beta)¶

Gamma distribution. (Not the gamma function!) The shape and scale parameters, alpha and beta, must have positive values. (Calling conventions vary and some sources define ‘beta’ as the inverse of the scale).

The probability distribution function is:

x ** (alpha – 1) * math.exp(-x / beta) pdf(x) = ————————————– math.gamma(alpha) * beta ** alpha

random.gauss(mu=0.0, sigma=1.0)¶

Normal distribution, also called the Gaussian distribution. mu is the mean, and sigma is the standard deviation. This is slightly faster than the


normalvariate()

function defined below.

Multithreading note: When two threads call this function simultaneously, it is possible that they will receive the same return value. This can be avoided in three ways. 1) Have each thread use a different instance of the random number generator. 2) Put locks around all calls. 3) Use the slower, but thread-safe


normalvariate()

function instead.

Changed in version 3.11: mu and sigma now have default arguments.

random.lognormvariate(mu, sigma)¶

Log normal distribution. If you take the natural logarithm of this distribution, you’ll get a normal distribution with mean mu and standard deviation sigma. mu can have any value, and sigma must be greater than zero.

random.normalvariate(mu=0.0, sigma=1.0)¶

Normal distribution. mu is the mean, and sigma is the standard deviation.

Changed in version 3.11: mu and sigma now have default arguments.

random.vonmisesvariate(mu, kappa)¶

mu is the mean angle, expressed in radians between 0 and 2*pi, and kappa is the concentration parameter, which must be greater than or equal to zero. If kappa is equal to zero, this distribution reduces to a uniform random angle over the range 0 to 2*pi.

random.paretovariate(alpha)¶

Pareto distribution. alpha is the shape parameter.

random.weibullvariate(alpha, beta)¶

Weibull distribution. alpha is the scale parameter and beta is the shape parameter.

Python Tutorial: Generate Random Numbers and Data Using the random Module
Python Tutorial: Generate Random Numbers and Data Using the random Module

Points to remember about randint() and randrange()

  • Use

    randint()

    when you want to generate a random number from an inclusive range.
  • Use

    randrange()

    when you want to generate a random number within a range by specifying the increment. It produces a random number from an exclusive range.

You should be aware of some value constraints of a

randrange()

function.

  • The

    randint(

    ) rand

    randrange()

    works only with integers. You cannot use float numbers.
  • The step must not be 0. If it is set to 0, you will get a

    ValueError: zero step for randrange()
  • The start should not be greater than stop if you are using all positive numbers. If you set start greater than stop, you will get a ValueError: empty range for randrange()

Examples


import random # ValueError: empty range for randrange() print(random.randrange(100, 10, 2))

But, you can also set a start value greater than stop if you are using a negative step value.


import random print(random.randrange(100, 10, -2)) # output 60

Examples¶

Basic examples:

>>> random() # Random float: 0.0 <= x < 1.0 0.37444887175646646 >>> uniform(2.5, 10.0) # Random float: 2.5 <= x <= 10.0 3.1800146073117523 >>> expovariate(1 / 5) # Interval between arrivals averaging 5 seconds 5.148957571865031 >>> randrange(10) # Integer from 0 to 9 inclusive 7 >>> randrange(0, 101, 2) # Even integer from 0 to 100 inclusive 26 >>> choice([‘win’, ‘lose’, ‘draw’]) # Single random element from a sequence ‘draw’ >>> deck = ‘ace two three four’.split() >>> shuffle(deck) # Shuffle a list >>> deck [‘four’, ‘two’, ‘ace’, ‘three’] >>> sample([10, 20, 30, 40, 50], k=4) # Four samples without replacement [40, 10, 50, 30]

Simulations:

>>> # Six roulette wheel spins (weighted sampling with replacement) >>> choices([‘red’, ‘black’, ‘green’], [18, 18, 2], k=6) [‘red’, ‘green’, ‘black’, ‘black’, ‘red’, ‘black’] >>> # Deal 20 cards without replacement from a deck >>> # of 52 playing cards, and determine the proportion of cards >>> # with a ten-value: ten, jack, queen, or king. >>> deal = sample([‘tens’, ‘low cards’], counts=[16, 36], k=20) >>> deal.count(‘tens’) / 20 0.15 >>> # Estimate the probability of getting 5 or more heads from 7 spins >>> # of a biased coin that settles on heads 60% of the time. >>> sum(binomialvariate(n=7, p=0.6) >= 5 for i in range(10_000)) / 10_000 0.4169 >>> # Probability of the median of 5 samples being in middle two quartiles >>> def trial(): … return 2_500 <= sorted(choices(range(10_000), k=5))[2] < 7_500 … >>> sum(trial() for i in range(10_000)) / 10_000 0.7958

Example of statistical bootstrapping using resampling with replacement to estimate a confidence interval for the mean of a sample:

# https://www.thoughtco.com/example-of-bootstrapping-3126155 from statistics import fmean as mean from random import choices data = [41, 50, 29, 37, 81, 30, 73, 63, 20, 35, 68, 22, 60, 31, 95] means = sorted(mean(choices(data, k=len(data))) for i in range(100)) print(f’The sample mean of {mean(data):.1f} has a 90% confidence ‘ f’interval from {means[5]:.1f} to {means[94]:.1f}’)

Example of a resampling permutation test to determine the statistical significance or p-value of an observed difference between the effects of a drug versus a placebo:

# Example from “Statistics is Easy” by Dennis Shasha and Manda Wilson from statistics import fmean as mean from random import shuffle drug = [54, 73, 53, 70, 73, 68, 52, 65, 65] placebo = [54, 51, 58, 44, 55, 52, 42, 47, 58, 46] observed_diff = mean(drug) – mean(placebo) n = 10_000 count = 0 combined = drug + placebo for i in range(n): shuffle(combined) new_diff = mean(combined[:len(drug)]) – mean(combined[len(drug):]) count += (new_diff >= observed_diff) print(f'{n} label reshufflings produced only {count} instances with a difference’) print(f’at least as extreme as the observed difference of {observed_diff:.1f}.’) print(f’The one-sided p-value of {count / n:.4f} leads us to reject the null’) print(f’hypothesis that there is no difference between the drug and the placebo.’)

Simulation of arrival times and service deliveries for a multiserver queue:

from heapq import heapify, heapreplace from random import expovariate, gauss from statistics import mean, quantiles average_arrival_interval = 5.6 average_service_time = 15.0 stdev_service_time = 3.5 num_servers = 3 waits = [] arrival_time = 0.0 servers = [0.0] * num_servers # time when each server becomes available heapify(servers) for i in range(1_000_000): arrival_time += expovariate(1.0 / average_arrival_interval) next_server_available = servers[0] wait = max(0.0, next_server_available – arrival_time) waits.append(wait) service_duration = max(0.0, gauss(average_service_time, stdev_service_time)) service_completed = arrival_time + wait + service_duration heapreplace(servers, service_completed) print(f’Mean wait: {mean(waits):.1f} Max wait: {max(waits):.1f}’) print(‘Quartiles:’, [round(q, 1) for q in quantiles(waits)])

See also

Statistics for Hackers a video tutorial by Jake Vanderplas on statistical analysis using just a few fundamental concepts including simulation, sampling, shuffling, and cross-validation.

Economics Simulation a simulation of a marketplace by Peter Norvig that shows effective use of many of the tools and distributions provided by this module (gauss, uniform, sample, betavariate, choice, triangular, and randrange).

A Concrete Introduction to Probability (using Python) a tutorial by Peter Norvig covering the basics of probability theory, how to write simulations, and how to perform data analysis using Python.

random.randint error - python
random.randint error – python

How to use random.randint()

Syntax:


random.randint(start, stop)

This function returns a random integer between a given start and stop integer.

Parameters:

It takes two parameters. Both are mandatory.


  • start

    : It is the start position of a range. The default value is 0 if not specified.

  • stop

    : It is the end position of a range.

Return value:

It will generate any random integer number from the inclusive range. The

randint(start, stop)

consider both the start and stop numbers while generating random integers

How to use Python

randint()

and

randrange()

to get random integers

  1. Import random module

    Use Python’s random module to work with random data generation.

    import it using a

    import random

    statement.

  2. Use randint() Generate random integer

    Use a


    random.randint()

    function to get a random integer number from the inclusive range. For example,

    random.randint(0, 10)

    will return a random number from [0, 1, 2, 3, 4, 5, 6, 7, 8 ,9, 10].

  3. Use the randrnage() function to generate a random integer within a range

    Use a


    random.randrange()

    function to get a random integer number from the given exclusive range by specifying the increment. For example,

    random.randrange(0, 10, 2)

    will return any random number between 0 and 20 (like 0, 2, 4, 6, 8).

random.randint() example of generating random number


import random # random integer from 0 to 9 num1 = random.randint(0, 9) print(num1) # output 5 # Random integer from 10 to 100 num2 = random.randint(10, 100) print(num2) # Output 84

Note: You cannot use float numbers in

randint()

. It will raise a ValueError (

non-integer stop for randrange()

) if you use non-integer numbers. Please, read how to generate a random float number within a range.

Nguồn tham khảo

All rights reserved

Python is one of the programming languages and it defines a set of functions whose use takes place for the generation or manipulation of random numbers via the random module in python. Furthermore, the reliance of the functions in the random module is on a pseudo-random number generator function random(). Moreover, randint() refers to an inbuilt function belonging to the random module that is in Python3.

Random Module in Python

random() Function in Python

random(), simply speaking, is an inbuilt function of the random module in Python3 and it helps in the derivation of the python random list. Furthermore, the random module in Python provides access to some useful functions and one of them is able to facilitate the generation of random floating numbers, which is random(). Also, its syntax is random. random() python.

Syntax: random.random()

Parameters: Any parameter is not accepted by this method.

Returns: Random floating number between 0 and 1 is returned by this method.

First of all, example #1: Simple implementation of sample() function

# Python3 program to demonstrate # the use of random() function . # import random from random import random # Prints random item Moreover, print(random())

Furthermore, output:

0.41941790721207284

Moreover, Example 2:

# Python3 program to demonstrate # the use of random() function . # import random Also, from random import random lst = [] for i in range(10): lst.append(random()) # Prints random items Furthermore, print(lst)

Finally, Output:

[0.12144204979175777, 0.27614050014306335, 0.8217122381411321, 0.34259785168486445, 0.6119383347065234, 0.8527573184278889, 0.9741465121560601, 0.21663626227016142, 0.9381166706029976, 0.2785298315133211]

Browse more Topics Under Functions

randint() Function in Python

randint() refers to an inbuilt function belonging to the random module in Python3. Furthermore, access is provided by random module provides access to some beneficial functions. Moreover, one of the functions is to generate random numbers, which is randint().

Syntax :

randint(start, end)

Parameters :

(start, end) : Both of them have to be integer-type values.

Returns :

A random integer in the range [start, end] consisting of the endpoints.

Errors and Exceptions :

ValueError: Returns a ValueError when floating-point values are passed as parameters.

TypeError: TypeError is returned when anything other than

numeric values is passed as parameters.

Code #1 :

# Python3 program explaining work # of randint() function # imports random module import random # Generates a random number between # a given positive range r1 = random.randint(0, 10) Furthermore, another important point, print(“Random number between 0 and 10 is % s” % (r1)) # Generates a random number between # two given negative range r2 = random.randint(-10, -1) Also, another important point, print(“Random number between -10 and -1 is % d” % (r2)) # Generates a random number between # a positive and a negative range r3 = random.randint(-5, 5) Furthermore, another important point, print(“Random number existing between -5 and 5 is % d” % (r3))

So, output:

Random number between 0 and 10 is 5 Also, Random number between -10 and -1 is -7 Finally, Random number between -5 and 5 is 2

Furthermore, code #2 : Program demonstrating the ValueError.

# imports random module import random ”’If we pass floating point values as parameters in the randint() function”’ r1 = random.randint(1.23, 9.34) Moreover, print(r1)

So, output :

Traceback (most recent call last): File “/home/f813370b9ea61dd5d55d7dadc8ed5171.py”, line 6, in r1=random.randint(1.23, 9.34) File “/usr/lib/python3.5/random.py”, line 218, in randint return self.randrange(a, b+1) File “/usr/lib/python3.5/random.py”, line 182, in randrange raise ValueError(“non-integer arg 1 for randrange()”) ValueError: non-integer arg 1 for randrange()

FAQs on Using Random and Randint Functions

Question 1: Explain the working of randint in a simple manner?

Answer 1: randint(1,101) will facilitate the automatic selection of a random integer between 1 and 100 for you. Furthermore, the process is fairly simple. Moreover, a random number will be generated between 1 and 20 by this code, and that number will be multiplied by 5.

Question 2: What is meant by the random module in python?

Answer 2: The random module in python is a built-in module that leads to the generation of pseudo-random variables. Furthermore, its use can take place to perform some action randomly such as to randomly shuffle elements, selecting random elements from a list, getting a random number, etc.

Python Random Module Functions - Coding Examples (random, randint, choice, randrange)
Python Random Module Functions – Coding Examples (random, randint, choice, randrange)

True Random Number Generators

Các bộ tạo số ngẫu nhiên thực là các phương pháp trích rút tính ngẫu nhiên hoặc không thể tiên đoán từ các khía cạnh không thể đoán trước được của các tiến trình vật lý. Các phương thức này không trực tiếp tạo ra các số, mà là các trạng thái, sau đó có thể được diễn dịch sang dạng số – đây là lý do tại sao chúng thường được gọi là các trình tạo sự kiện ngẫu nhiên (Random Event Generators – REGs). Một số trong số chúng, sử dụng các sự kiện vĩ mô phổ biến, như là các phương pháp ném xúc xắc, lật đồng xu hoặc xáo trộn thẻ bài.
Những bộ tạo số ngẫu nhiên thực này thường sử dụng các hiện tượng vật lý phức tạp hơn. Một số trong số chúng, như phân rã phóng xạ, nhiễu nhiệt hoặc nhiễu vô tuyến, được trích xuất sự khó lường từ đặc thù của cơ học lượng tử. Các phương pháp khác sử dụng tính không thể đoán trước được của tiếng ồn trong khí quyển hoặc thậm chí là trạng thái của đèn đối lưu giọt dầu.

Python3


from


random


import


randint


def


generator():


return


randint(


10


def


rand_guess():


random_number


generator()


guess_left


flag


while


guess_left >


guess


int


input


"Pick your number to "


"enter the lucky draw\n"


))


if


guess


random_number:


flag


break


else


print


"Wrong Guess!!"


guess_left


if


flag


is


return


True


else


return


False


if


__name__


'__main__'


if


rand_guess()


is


True


print


"Congrats!! You Win."


else


print


"Sorry, You Lost!"

Output

Pick your number to enter the lucky draw
8
Wrong Guess!!
Pick your number to enter the lucky draw
9
Wrong Guess!!
Pick your number to enter the lucky draw
0
Congrats!! You Win.

Don’t miss your chance to ride the wave of the data revolution! Every industry is scaling new heights by tapping into the power of data. Sharpen your skills and become a part of the hottest trend in the 21st century.

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Last Updated :
30 Oct, 2023

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Python Random randint() Method

Example

Return a number between 3 and 9 (both included):

import randomprint(random.randint(3, 9))

Try it Yourself »

print(random.randint(3, 9))

Why Is \
Why Is \”random.seed()\” So Important In Python?

Parameters of randint() function in python

randint() function in Python includes two parameters:

  • start parameter, which is the starting point from and including which the random integer would be generated,
  • end parameter, which is the ending point up to which the function would return the random integer.

Both start and end must be integer type values.

Common Issues and Solutions with randint

While using Python’s

randint()

function, you might encounter certain issues. Let’s discuss some common problems and their solutions.

Forgetting to Import the Random Module

One of the most common mistakes is forgetting to import the random module. Without importing it, Python won’t recognize the

randint()

function, leading to an error.


number = randint(1, 10) print(number) # Output: # NameError: name 'randint' is not defined

In this example, we forgot to import the random module, so Python raises a

NameError

because it doesn’t recognize

randint()

. The solution is to import the random module at the beginning of your code.


import random number = random.randint(1, 10) print(number) # Output: # (A random number between 1 and 10)

By importing the random module with

import random

, we can now use the

randint()

function without any issues.

Using Incorrect Range Values

Another common mistake is using incorrect values for the range. Remember, the first parameter should be less than or equal to the second. If it’s greater, Python will raise a

ValueError

.


import random number = random.randint(10, 1) print(number) # Output: # ValueError: empty range for randrange() (10,1, -9)

In this example, we tried to generate a random number between 10 and 1, which is an empty range. The solution is to ensure the first parameter is less than or equal to the second.

Random number generator in Python
Random number generator in Python

Python3


import


random


beg,end


1000


for


in


range


):


print


(random.randint(beg, end))

Program to Demonstrate the ValueError

In this example, we are seeing that if we passes the floating point values as parameters in the randint() function then a ValueError occurs.

What Happens in Case of Multiple randint() Method Call?

You have seen that when you call the randint Python function; it generates and returns a random integer from the specified range. But what if you call it multiple times? Can it return the same value twice? If not, what happens if the number of calls is more than the available range of values? Let’s get all these queries answered.

Example: Calling the Randint Python Function Multiple Times

In this example, you will keep the available range larger. Let’s see the output.

import random

init=1

end=50

for x in range(5):

print(random.randint(init, end))

Output:

As you can see in the output, the randint() function could still give different random values. Now, let’s cut short the available range and see what happens.

import random

init=1

end=10

for x in range(5):

print(random.randint(init, end))

Output:

Since the range was shorter and the number of calls was still 5, the randint Python function gave a repeated value: 5. Thus, the randint() function can provide the same number twice or even more times. There are also chances that you might get a repeated value, even if the available range is more extensive.

Numpy Random ALL EXPLAINED!!!
Numpy Random ALL EXPLAINED!!!

What is randint() function in Python?

The randint() function in python is a built-in function of the random module that returns a random integer between the higher and lower limit passed as parameters. The randint() function returns an integer N where N>=beg and N<=end.

We must first import the random module in Python to begin using the randint() function. The module essentially creates pseudo-randomness.

Learn more about random modules from this article.

Wrapping Up: Python’s randint Function

Python’s

randint()

function is a powerful tool in the

random

module, providing a straightforward way to generate random integers within a specified range. From simple applications to more complex scenarios,

randint()

offers a reliable solution for random number generation.

While

randint()

is generally easy to use, common issues include forgetting to import the

random

module and using incorrect range values. These can be easily avoided by following best practices such as always importing necessary modules and ensuring correct parameter values.

Beyond

randint()

, Python offers other methods for random number generation. These include the

random()

and

uniform()

functions in the

random

module, and the

numpy.random.randint()

function in the NumPy module. Each method has its unique advantages and can be more suitable depending on your specific needs.

Random number generation is a fundamental aspect of programming with diverse applications. By mastering Python’s

randint()

function and understanding other random number generation methods, you can harness the power of randomness in your Python projects.

randint() Function in Python

Giáo sư Toán dạy bạn đánh Tài Xỉu thắng 100%, quay Gacha bách phát bách trúng
Giáo sư Toán dạy bạn đánh Tài Xỉu thắng 100%, quay Gacha bách phát bách trúng

Pseudo Random Number Generators

Sự thật là, việc thường xuyên tạo ra những con số phải thực sự ngẫu nhiên là không cần thiết. Trong nhiều trường hợp, tất cả những gì chúng ta cần là các bộ số “có vẻ ngẫu nhiên”. Loại dữ liệu này có thể được lấy từ các bộ tạo số giả ngẫu nhiên. Đây là các thuật toán, sử dụng một phần nhỏ thông tin (được gọi là seed) và sau đó áp dụng các công thức toán học phức tạp để tạo ra các bộ số xác định giống như các bộ thực sự ngẫu nhiên. Seed có thể là một giá trị được lấy từ một trình tạo số ngẫu nhiên thực sự hoặc một nguồn khác, như đồng hồ của hệ thống hoặc thời gian hiện tại.
Chạy bộ tạo số nhiều lần bằng cùng một seed sẽ dẫn đến cùng một output mỗi lần chạy. Các số kết quả hầu như không thể phân biệt được với các số có nguồn gốc từ các bộ tạo số ngẫu nhiên thực, mặc dù thực tế có một số quy tắc ẩn trong sự phân phối của chúng. Tuy nhiên, đối với nhiều ứng dụng, loại giả ngẫu nhiên xác định này là hoàn toàn đủ.

Return Value of the Python randint() Function

The randint Python function returns a randomly generated integer from the specified range, with both inclusive parameters.

Example: Demonstrating the Use of Randint Python Function

# importing the random module

import random

# using the randint function

int1 = random.randint(0, 5)

print(“Random number generated between 0 and 5 using the randint() function is % s” % (int1))

Output:

Here, you need to first import the random module in the above code, and then use the randint Python function to generate a random integer from 0 to 5.

Note: Your output may vary as a random integer is selected and returned by the randint() function.

Máy tính có random thật không?
Máy tính có random thật không?

Python randint() Method Syntax

Syntax: randint(start, end)

Parameters :

(start, end) : Both of them must be integer type values.

Returns :

A random integer in range [start, end] including the end points.

Errors and Exceptions :

ValueError : Returns a ValueError when floating point values are passed as parameters.

TypeError : Returns a TypeError when anything other than numeric values are passed as parameters.

How randint() in Python work?

In this example, we are using the randint() method in Python to find a random number in a given range.

Discrete distributions¶

The following function generates a discrete distribution.

random.binomialvariate(n=1, p=0.5)¶

Binomial distribution. Return the number of successes for n independent trials with the probability of success in each trial being p:

Mathematically equivalent to:

sum(random() < p for i in range(n))

The number of trials n should be a non-negative integer. The probability of success p should be between


0.0 <= p <= 1.0

. The result is an integer in the range

0 <= X <= n

.

New in version 3.12.

How To Predict Random Numbers Generated By A Computer
How To Predict Random Numbers Generated By A Computer

Real-World Applications of Python’s randint

Python’s

randint()

function isn’t just for academic exercises—it has practical applications in real-world scenarios. Let’s explore some of these applications.

Simulations

In simulations,

randint()

can be used to generate random inputs. For example, in a weather simulation,

randint()

could generate random temperatures or wind speeds.


import random random_temperature = random.randint(-10, 40) print('Random Temperature:', random_temperature, '°C') # Output: # Random Temperature: (A random number between -10 and 40) °C

In this code, we’re using

randint()

to generate a random temperature between -10 and 40 degrees Celsius.

Games

In games,

randint()

can be used to create unpredictable elements, making the game more exciting. For instance, in a dice game,

randint()

could be used to generate the dice roll.


import random dice_roll = random.randint(1, 6) print('Dice Roll:', dice_roll) # Output: # Dice Roll: (A random number between 1 and 6)

In this code, we’re using

randint()

to simulate a dice roll, generating a random number between 1 and 6.

Data Analysis

In data analysis,

randint()

can be used to generate random samples from a larger dataset. This can ensure a more representative sample and more accurate analysis.

random.randrange() to generate random integers within a range

Now let’s see how to use the

random.randrange()

function to get a random integer number from the given exclusive range by specifying the increment.

Syntax


random.randrange(start, stop[, step])

This function returns a random integer from a

range(start, stop, step)

. For example,

random.randrange(0, 10, 2)

will generate any random numbers from [0, 2, 4, 6, 8].

Parameters

It takes three parameters. Out of three, two parameters are optional. i.e.,

start

and

step

are optional.


  • start

    : it is the star number in a range. i.e., lower limit. The default value is 0 if not specified.

  • stop

    : It is the end/last number in a range. It is the upper limit.

  • step

    : Specify the increment value in range. The generated random number is divisible by step. The default value is 1 if not specified.

random.randrange() examples

In the following example, we are trying to print a random int in a given range. This example demonstrates all the variants of

random.randrange()

function.


import random # random integer from 0 to 9 num1 = random.randint(0, 9) print(num1) # output 5 # Random integer from 10 to 100 num2 = random.randint(10, 100) print(num2) # Output 84

Note:

  • The

    randrange()

    doesn’t consider the stop number while generating a random integer. It is an exclusive random range. For example,

    randrange(2, 20, 2)

    will return any random number between 2 to 20, such as 2, 4, 6, …18. It will never select 20.
  • Same as

    randint()

    , you cannot use float value in

    randrange()

    too. It will raise a

    ValueError

    (non-integer arg 1 for randrange()) if you use non-integers.

Random number of a specific length

Let’s see how to generate a random number of length n. For example, any random number of length four, such as 7523, 3674. We can accomplish this using both

randint()


randrange()

.


import random # random number of length 4 num1 = random.randint(1000, 9999) # random number of length 4 with step 2 num2 = random.randrange(1000, 10000, 2) print(num1, num2) # Output 3457 5116

Note: As you can see, we set a

start = 1000

and a

stop = 10000

because we want to generate the random number of length 4 (from 1000 to 9999).

Random integer number multiple of n

For example, let’s generate a random number between x and y multiple of 3 like 3, 6, 39, 66.


import random num = random.randrange(3, 300, 3) print(num) # output 144

Fix Python Relative Imports and Auto-completion in VSCode
Fix Python Relative Imports and Auto-completion in VSCode

What are the Applications of Randint Python Function?

Since the Python randint() function generates a pseudo-random integer, it is usually helpful in gaming and lottery applications. For instance, let’s say a participant gets three chances to guess the following random number generated within a range. If the person gets it correct within the three attempts, he wins, or else loses. Let’s simulate this situation using the randint Python function in the example below.

Example: Creating a Lucky Draw Game with the randint() Function

In the below code, you will generate a random number between 1 and 20 three times. You will then accept input from the user and check if the guess is correct and wins. Let’s begin with our example.

# importing randint function

from random import randint

# Function to generate new random integer

def random_generator():

return randint(1, 20)

# Function to take input from user and show results

def your_guess():

# calling function to generate random numbers

rand_number = random_generator()

# defining the number of

# guesses the user gets

remaining_gus = 3

# Setting win-condition checker flagship variable

flagship = 0

# Using loop to provide only three chances

while remaining_gus > 0:

# Taking user input

guess_num = int(input(“What’s your guess?\n”))

# checking if the guess is correct

if guess_num == rand_number

# setting flagship 1 for correct guess to

# break loop

flagship = 1

break

else:

# printing the failure message

print(“Oops, you missed!”)

# Decrementing guesses left

remaining_gus -= 1

# your_guess function returns True if win-condition

# is satisfied

if flagship == 1:

return True

# else returns False

else:

return False

# Final output code to decide win or lose

if __name__ == ‘__main__’:

if your_guess() is True:

print(“Wow, you hit the bull’s eye!”)

else :

print(“Sorry, better luck next time!”)

Output:

Second attempt output:

Well, as it is evident, you could not get it correct in the first game. But in the second game, you hit the bull’s eye in the very first attempt. Copy-paste the code in any Python compiler, and you too can enjoy the game. You can compete with your friends and family members, and make a day out of it.

Understanding Random Number Generation in Python

Random number generation is a fundamental concept in programming that has a variety of applications, from game development to data analysis. Python’s

random

module, which includes the

randint()

function, is a powerful tool for generating these random numbers.

The Role of Python’s Random Module

Python’s

random

module provides a suite of functions for generating random numbers. These functions include

randint()

,

random()

,

uniform()

, and many others. Each function generates a random number in a different way or within a different range.


import random random_integer = random.randint(1, 10) random_float = random.random() random_uniform = random.uniform(1.0, 10.0) print(random_integer, random_float, random_uniform) # Output: # (A random integer between 1 and 10, a random float between 0.0 and 1.0, a random float between 1.0 and 10.0)

In this code, we’re using three functions from Python’s

random

module to generate different types of random numbers. Each function provides a unique way to generate random numbers, making the

random

module a versatile tool for random number generation in Python.

The Importance of Randomness in Programming

Randomness plays a crucial role in many areas of programming. For instance, in game development, randomness can be used to create unpredictable gameplay elements. In data analysis, random sampling can help ensure a representative sample of data. By understanding how to generate random numbers in Python, you can harness the power of randomness in your own programming projects.

The Trick to Get Unlimited Datasets
The Trick to Get Unlimited Datasets

Further Reading: Probability Theory and Statistical Analysis

If you’re interested in the theory behind random number generation, consider studying probability theory. And if you want to apply random number generation in data analysis, look into statistical analysis in Python. Both topics will provide a deeper understanding of the power and potential of Python’s

randint()

function.

Further Resources for Python Modules

For a more profound understanding of Python Modules, we have gathered several insightful resources for you:

  • Python Modules Fundamentals Covered – Dive deep into Python’s module caching and reload mechanisms.
  • Implementing Queues in Python – Dive into various queue types, including FIFO and LIFO, in Python.

  • Simplifying Random Data Generation in Python – Learn how to add randomness to your Python programs with “random.”

  • Python’s Random Module – Learn about the random module and generating random numbers with this Programiz guide.

  • How to Create Random Numbers in Python – A Medium article that delves into generating random numbers in Python.

  • Python’s Random Tutorial – A tutorial by Real Python covering topics related to generating random numbers in Python.

Explore these resources, and you’ll be taking another stride towards expertise in Python and taking your coding abilities to the next level.

What Errors and Exceptions Can Occur While Using Randint in Python?

The Python randint() function can throw the following two errors:

  • ValueError: This error is thrown if you try to pass floating-point numbers as arguments
  • TypeError: This error is thrown if you try to pass anything except numeric values as arguments

Example: Getting ValueError for Randint Python Function

In the code below, you will try passing a floating-point value as a parameter to get the ValueError while compiling the program.

# importing the random module

import random

# using the randint function

int1 = random.randint(0.0, 4.9)

print(int1)

Output:

As you passed a floating-point value as the second (end) parameter, you got a ValueError while running the code.

Example: Getting TypeError for Randint Python Function

In the code below, you will try passing a string value as a parameter to get the TypeError while compiling the program intentionally.

# importing the random module

import random

# using the randint function

int1 = random.randint(‘x’, ‘y’)

print(int1)

Output:

As you can see in the output, this demo got a TypeError saying ‘can only concatenate str (not “int”) to str’ as you passed string values instead of integers.

3 Ways to Make a Custom AI Assistant | RAG, Tools, & Fine-tuning
3 Ways to Make a Custom AI Assistant | RAG, Tools, & Fine-tuning

Random negative integer

Let’s see how to generate a random negative integer between -60 to -6.


import random singed_int = random.randrange(-60, -6) print(singed_int) # Output -16

Generate random positive or negative integer


import random for i in range(5): print(random.randint(-10, 10), end=' ') # Output 10 -1 5 -10 -7

Randomly generate 1 or -1


import random num = random.choice([-1, 1]) print(num)

Note: we used random.choice() to choose a single number from the list of numbers. Here our list is

[-1, 1]

.

Conclusion

  • randint() function is used to generate the random integers between the two given numbers passed as parameters.
  • Randint is an in-built function of random library in python. So we have to import the random library at the start of our code to generate random numbers.
  • randint function returns:

    • ValueError, when floating point values are passed as parameters.
    • TypeErrorwhen anything other than numeric values is passed as parameters.

In this lesson, we will see how to use the

randrange()

and

randint()

functions of a Python random module to generate a random integer number.

Using

randrange()

and

randint()

functions of a random module, we can generate a random integer within a range. In this lesson, you’ll learn the following functions to generate random numbers in Python. We will see each one of them with examples.

Function Description
Returns any random integer from 0 to 9
Returns a random integer from 0 to 19
Returns a random integer from 2 to 19.
Returns any random integer from 100 to 999 with step 3. For example, any number from 100, 103, 106 … 994, 997.
Returns a random negative integer between -50 to -6.
Returns a list of random numbers
Returns a secure random number
Avoiding import loops in Python
Avoiding import loops in Python

Expanding the Range of randint

Python’s

randint()

function isn’t limited to small ranges. In fact, you can generate random integers in any range you like. For example, if you’re simulating a lottery draw, you might need to generate numbers between 1 and 1000:


import random lottery_number = random.randint(1, 1000) print(lottery_number) # Output: # (A random number between 1 and 1000)

In this example, we’ve expanded the range of

randint()

to generate a random number between 1 and 1000. This demonstrates the flexibility of the

randint()

function.

TL;DR: How Do I Use the randint Function in Python?

The randint function is part of Python’s random module, and it’s used to generate a random integer within a specified range. Here’s a simple example:


import random number = random.randint(1, 10) print(number) # Output: # (A random number between 1 and 10)

In this example, we’re using Python’s

random.randint()

function to generate a random number between 1 and 10. The

import random

line at the beginning is necessary because randint is part of the random module in Python. The function

random.randint(1, 10)

then generates a random integer within the range of 1 and 10.

If you’re interested in learning more about the randint function, including its more advanced uses and potential issues you might encounter, keep reading for a comprehensive exploration.

Table of Contents

  • Understanding Python’s randint Function
  • Expanding the Range of randint
  • Using randint in Loops
  • Exploring Alternatives to randint
  • Common Issues and Solutions with randint
  • Best Practices with randint
  • Understanding Random Number Generation in Python
  • Real-World Applications of Python’s randint
  • Further Reading: Probability Theory and Statistical Analysis
  • Wrapping Up: Python’s randint Function
How to implement Random Forest from scratch with Python
How to implement Random Forest from scratch with Python

Functions for integers¶

random.randrange(stop)¶
random.randrange(start, stop[, step])

Return a randomly selected element from


range(start, stop, step)

.

This is roughly equivalent to


choice(range(start, stop, step))

but supports arbitrarily large ranges and is optimized for common cases.

The positional argument pattern matches the


range()

function.

Keyword arguments should not be used because they can be interpreted in unexpected ways. For example


randrange(start=100)

is interpreted as

randrange(0, 100, 1)

.

Changed in version 3.2:


randrange()

is more sophisticated about producing equally distributed values. Formerly it used a style like

int(random()*n)

which could produce slightly uneven distributions.

Changed in version 3.12: Automatic conversion of non-integer types is no longer supported. Calls such as


randrange(10.0)

and

randrange(Fraction(10, 1))

now raise a

TypeError

.

random.randint(a, b)¶

Return a random integer N such that


a <= N <= b

. Alias for

randrange(a, b+1)

.

random.getrandbits(k)¶

Returns a non-negative Python integer with k random bits. This method is supplied with the Mersenne Twister generator and some other generators may also provide it as an optional part of the API. When available,


getrandbits()

enables

randrange()

to handle arbitrarily large ranges.

Changed in version 3.9: This method now accepts zero for k.

Next Steps

I want to hear from you. What do you think of this article on

randint()

and

randrange()

? Or maybe I missed one of the usages of those two functions. Either way, let me know by leaving a comment below.

Also, try to solve the following exercise and quiz to have a better understanding of working with random data in Python.

  • Python random data generation Exercise to practice and master the random data generation techniques in Python.
  • Python random data generation Quiz to test your random data generation concepts.

The random module in Python allows you to generate pseudo-random variables. The module provides various methods to get the random variables, one of which is the randint method. The randint Python function is a built-in method that lets you generate random integers using the random module.

AVOID Using: \
AVOID Using: \”import *\” At ALL Costs In Python

Understanding Python’s randint Function

Python’s

randint()

is a function that belongs to the

random

module. It is used to generate a random integer within a defined range. The function takes two parameters: the start and end of the range, inclusive.

Using randint: A Simple Example

Let’s look at a simple code example to understand how

randint()

works:


import random number = random.randint(1, 10) print(number) # Output: # (A random number between 1 and 10)

In this example,

import random

is used to import the random module, which contains the randint function. Next,

random.randint(1, 10)

is used to generate a random integer between 1 and 10, inclusive. The result is then stored in the variable

number

, which is printed out.

Parameters and Return Value

The

randint(a, b)

function takes two parameters:

  • : The lower limit of the range (inclusive).
  • : The upper limit of the range (inclusive).

The function returns a random integer such that

a <= N <= b

.

Understanding the Output

The output of the

randint()

function is a random integer within the specified range. In our example, the output is a random number between 1 and 10. Each time you run the code, you might get a different number because the selection is random.

By understanding the basics of Python’s

randint()

function, you can start to harness the power of random number generation in your coding projects.

Exploring Alternatives to randint

While

randint()

is a powerful tool for generating random integers, Python offers other functions and modules for random number generation. These include the

random()

function, the

uniform()

function, and the NumPy module.

The random Function

The

random()

function is another part of Python’s random module. Unlike

randint()

,

random()

generates a random floating-point number between 0.0 and 1.0.


import random random_number = random.random() print(random_number) # Output: # (A random floating-point number between 0.0 and 1.0)

The

random()

function doesn’t take any arguments and returns a random float in the range [0.0, 1.0).

The uniform Function

The

uniform(a, b)

function generates a random floating-point number between and . It’s similar to

randint()

, but it works with floating-point numbers.


import random random_number = random.uniform(1.0, 10.0) print(random_number) # Output: # (A random floating-point number between 1.0 and 10.0)

In this example,

random.uniform(1.0, 10.0)

generates a random float between 1.0 and 10.0.

The NumPy Module

NumPy, a powerful library for numerical computation in Python, also provides functions for random number generation. For instance,

numpy.random.randint()

generates random integers in a similar way to Python’s

randint()

.


import numpy random_number = numpy.random.randint(1, 10) print(random_number) # Output: # (A random integer between 1 and 10)

In this example,

numpy.random.randint(1, 10)

generates a random integer between 1 and 10, just like Python’s

randint()

function.

These alternative methods offer more flexibility and options for random number generation in Python. Depending on your specific needs, you might find one of these methods more suitable than

randint()

.

C++ Random Number Generator AKA STOP USING Rand()
C++ Random Number Generator AKA STOP USING Rand()

Functions for sequences¶

random.choice(seq)¶

Return a random element from the non-empty sequence seq. If seq is empty, raises


IndexError

.

random.choices(population, weights=None, *, cum_weights=None, k=1)¶

Return a k sized list of elements chosen from the population with replacement. If the population is empty, raises


IndexError

.

If a weights sequence is specified, selections are made according to the relative weights. Alternatively, if a cum_weights sequence is given, the selections are made according to the cumulative weights (perhaps computed using


itertools.accumulate()

). For example, the relative weights

[10, 5, 30, 5]

are equivalent to the cumulative weights

[10, 15, 45, 50]

. Internally, the relative weights are converted to cumulative weights before making selections, so supplying the cumulative weights saves work.

If neither weights nor cum_weights are specified, selections are made with equal probability. If a weights sequence is supplied, it must be the same length as the population sequence. It is a


TypeError

to specify both weights and cum_weights.

The weights or cum_weights can use any numeric type that interoperates with the


float

values returned by

random()

(that includes integers, floats, and fractions but excludes decimals). Weights are assumed to be non-negative and finite. A

ValueError

is raised if all weights are zero.

For a given seed, the


choices()

function with equal weighting typically produces a different sequence than repeated calls to

choice()

. The algorithm used by

choices()

uses floating point arithmetic for internal consistency and speed. The algorithm used by

choice()

defaults to integer arithmetic with repeated selections to avoid small biases from round-off error.

New in version 3.6.

Changed in version 3.9: Raises a


ValueError

if all weights are zero.

random.shuffle(x)¶

Shuffle the sequence x in place.

To shuffle an immutable sequence and return a new shuffled list, use


sample(x, k=len(x))

instead.

Note that even for small


len(x)

, the total number of permutations of x can quickly grow larger than the period of most random number generators. This implies that most permutations of a long sequence can never be generated. For example, a sequence of length 2080 is the largest that can fit within the period of the Mersenne Twister random number generator.

Changed in version 3.11: Removed the optional parameter random.

random.sample(population, k, *, counts=None)¶

Return a k length list of unique elements chosen from the population sequence. Used for random sampling without replacement.

Returns a new list containing elements from the population while leaving the original population unchanged. The resulting list is in selection order so that all sub-slices will also be valid random samples. This allows raffle winners (the sample) to be partitioned into grand prize and second place winners (the subslices).

Members of the population need not be hashable or unique. If the population contains repeats, then each occurrence is a possible selection in the sample.

Repeated elements can be specified one at a time or with the optional keyword-only counts parameter. For example,


sample(['red', 'blue'], counts=[4, 2], k=5)

is equivalent to

sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)

.

To choose a sample from a range of integers, use a


range()

object as an argument. This is especially fast and space efficient for sampling from a large population:

sample(range(10000000), k=60)

.

If the sample size is larger than the population size, a


ValueError

is raised.

Changed in version 3.9: Added the counts parameter.

Changed in version 3.11: The population must be a sequence. Automatic conversion of sets to lists is no longer supported.

Summing It Up

In this article, you learned everything about the randint Python function. You also learned something fun, which was creating a lucky draw game that you can enjoy in your free time. You can also tweak that code to make it a bit more complex for a bigger lucky draw game.

It is fascinating to see how one can do so much with Python programming. If you want to learn more about Python’s concepts, you can refer to Simplilearn’s Python Tutorial for Beginners. The tutorial is dedicated to newbies to help them get acquainted with Python’s basics. Once you are done with the basics, you can opt for our Post Graduate Program in Full Stack Web Development course to learn the advanced concepts and excel in Python development.

Have any questions for us? Leave them in the comments section, and our experts will answer them for you ASAP!

Hàm random() trong Python có thực sự “ngẫu nhiên”?

Chúng ta đều biết sự hữu ích của việc tạo ra số ngẫu nhiên trong một số trường hợp cụ thể dù là trong lập trình hay trong cuộc sống thường nhật. Trong các trò chơi, chúng ta ném xúc xắc để tạo ra một con số không thể đoán trước, xác định bước đi tiếp theo của người chơi. Ngoài ra, tất cả chúng ta đều sẽ đồng ý rằng việc chơi bất cứ một trò chơi bài nào mà không có sự xáo trộn ngẫu nhiên các lá bài sẽ là vô nghĩa.
Vậy chắc hẳn, khi các bạn xử lý với số ngẫu nhiên trong lập trình Python cũng sẽ có thắc mắc giống như mình. Liệu Python có sinh ra được những số thực sự ngẫu nhiên?

Ngẫu nhiên thựcGiả ngẫu nhiên

Số ngẫu nhiên có thể thu được do áp dụng các phương pháp toán học hay được gọi là bộ tạo số ngẫu nhiên (Random Number Generator – RNG). Nó có thể được chia thành 2 loại: bộ tạo số ngẫu nhiên thực (True Random Number Generators – TRNGs hay còn gọi là bộ tạo số ngẫu nhiên phần cứng) và bộ tạo số giả ngẫu nhiên (Pseudo-random Number Generator – PRNGS).

Generate random numbers in Python 🎲
Generate random numbers in Python 🎲

Table of contents

  • How to use random.randint()
  • random.randrange() to generate random integers within a range
  • Random negative integer
  • Generate a list of random integer numbers
  • Generate a secure random integer
  • Create a multidimensional array of random integers
  • Points to remember about randint() and randrange()
  • Next Steps

Module Random trong Python

Python cung cấp sẵn một module cực kỳ dễ sử dụng để xử lý với các số ngẫu nhiên. Module này gọi là

random

, được cài đặt một bộ tạo số giả ngẫu nhiên và chứa các hàm cho phép chúng ta giải quyết trực tiếp nhiều vấn đề lập trình khác nhau sử dụng đến tính ngẫu nhiên.
Module

random

dựa trên Marsenne Twister – một thuật toán rất phổ biến, là trình tạo số giả ngẫu nhiên mặc định không chỉ cho Python, mà còn cho nhiều hệ thống phần mềm phổ biến khác như Microsoft Excel, MATLAB, R hay PHP. Ưu điểm nổi bật của nó là việc được cấp phép chứng nhận, tính ngẫu nhiên được xác nhận bởi nhiều thử nghiệm thống kê và tốc độ tương đối cao so với các PRNG khác.

Hàm random()

Phương thức quan trọng nhất của module

random

là phương thức

random()

. Hầu hết các chức năng khác phụ thuộc vào nó. Phương thức

random()

tạo ra một số thực float ngẫu nhiên trong phạm vi (0.0, 1.0).


>>> import random >>> random.random() 0.8474337369372327

Hàm seed()

Nếu chúng ta không đặt một seed cho bộ tạo số giả ngẫu nhiên, thì seed mặc định là thời gian hệ thống hiện tại. Tuy nhiên, chúng ta có thể đặt giá trị chính xác của seed một cách thủ công, việc này sẽ rất hữu ích nếu chúng ta muốn sao chép kết quả giả ngẫu nhiên trong tương lai. Với mục đích như vậy, chúng ta có thể sử dụng phương thức

random.seed()


>>> random.seed(5) >>> random.random() 0.6229016948897019 >>> random.random() 0.7417869892607294 >>> random.random() 0.7951935655656966 >>> random.seed(5) >>> random.random() 0.6229016948897019

Phương thức

random.seed()

sẽ ảnh hưởng đến tất cả các phương thức của module

random

mà chúng ta sử dụng sau khi gọi nó. Trong đoạn code ví dụ ở trên, ta đặt seed là và sau đó gọi hàm

random.random()

nhiều lần. Điều quan trọng cần lưu ý ở đây là seed do người dùng định nghĩa sẽ chỉ được sử dụng lần đầu tiên khi có một phương thức

random

khác được thực thi – sau đó, các seed cho các phương thức sau sẽ thay đổi bằng cách sử dụng các giá trị ngẫu nhiên được tạo ra trước đó.
Điều này cho phép Python đưa ra được giá trị ngẫu nhiên mới mỗi lần. Tuy nhiên, sau khi thiết lập lại seed bằng phương thức

random.seed()

, chúng ta sẽ có thể sao chép chính xác chuỗi số giả ngẫu nhiên bất cứ lúc nào. Điều này rất hữu ích cho những việc như chạy thử nghiệm. Nếu bạn đưa ra cùng một seed mỗi khi bạn chạy một thử nghiệm có sử dụng một trong các phương pháp

random

thì bạn vẫn có thể biết đầu ra sẽ là gì cho các thử nghiệm này.

Một vài ví dụ cụ thể của module Random

Hàm randint()


>>> random.randint(1,10) 4

Phương thức

random.randint()

lấy hai đối số thể hiện phạm vi mà phương thức rút ra một số nguyên ngẫu nhiên. Ở ví dụ trên là số nguyên được chọn ngẫu nhiên từ 1 đến 9.

Hàm randrange()


>>> random.randrange(2,10,2) 2 >>> random.randrange(2,10,2) 4 >>> random.randrange(2,10,2) 8 >>> random.randrange(2,10,2) 6

Trong đoạn code trên, phương thức

random.randrange()

tương tự như

random.randint()

nhưng nó cũng cho phép chúng ta xác định đối số thứ ba, là bước nhảy trong phạm vi được xác định. Ở ví dụ này, ta chỉ yêu cầu đưa ra các số chẵn trong phạm vi từ 2 đến 9.

Hàm choice()


>>> cards = ['ace_spades','10_hearts','3_diamonds','king_hearts'] >>> random.choice(cards) '10_hearts'

Trong đoạn code này, phương thức

random.choice()

chọn một phần tử ngẫu nhiên thuộc danh sách.

Hàm shuffle()


>>> cards = ['ace_spades','10_hearts','3_diamonds','king_hearts'] >>> random.shuffle(cards) >>> print(cards) ['king_hearts', '3_diamonds', 'ace_spades', '10_hearts']

Trong đoạn code trên, phương thức

random.shuffle()

xáo trộn một danh sách các phần tử. Điều quan trọng cần lưu ý ở đây là nó xáo trộn chính ở trong danh sách đó. Có nghĩa là phương thức này trả về

None

và thực sự sửa đổi biến

cards

của chúng ta.

Python randint() function | random module | Amit Thinks
Python randint() function | random module | Amit Thinks

Recipes¶

These recipes show how to efficiently make random selections
from the combinatoric iterators in the

itertools

module:

def random_product(*args, repeat=1): “Random selection from itertools.product(*args, **kwds)” pools = [tuple(pool) for pool in args] * repeat return tuple(map(random.choice, pools)) def random_permutation(iterable, r=None): “Random selection from itertools.permutations(iterable, r)” pool = tuple(iterable) r = len(pool) if r is None else r return tuple(random.sample(pool, r)) def random_combination(iterable, r): “Random selection from itertools.combinations(iterable, r)” pool = tuple(iterable) n = len(pool) indices = sorted(random.sample(range(n), r)) return tuple(pool[i] for i in indices) def random_combination_with_replacement(iterable, r): “Choose r elements with replacement. Order the result to match the iterable.” # Result will be in set(itertools.combinations_with_replacement(iterable, r)). pool = tuple(iterable) n = len(pool) indices = sorted(random.choices(range(n), k=r)) return tuple(pool[i] for i in indices)

The default

random()

returns multiples of 2⁻⁵³ in the range
0.0 ≤ x < 1.0. All such numbers are evenly spaced and are exactly
representable as Python floats. However, many other representable
floats in that interval are not possible selections. For example,

0.05954861408025609

isn’t an integer multiple of 2⁻⁵³.

The following recipe takes a different approach. All floats in the interval are possible selections. The mantissa comes from a uniform distribution of integers in the range 2⁵² ≤ mantissa < 2⁵³. The exponent comes from a geometric distribution where exponents smaller than -53 occur half as often as the next larger exponent.

from random import Random from math import ldexp class FullRandom(Random): def random(self): mantissa = 0x10_0000_0000_0000 | self.getrandbits(52) exponent = -53 x = 0 while not x: x = self.getrandbits(32) exponent += x.bit_length() – 32 return ldexp(mantissa, exponent)

All real valued distributions in the class will use the new method:

>>> fr = FullRandom() >>> fr.random() 0.05954861408025609 >>> fr.expovariate(0.25) 8.87925541791544

The recipe is conceptually equivalent to an algorithm that chooses from
all the multiples of 2⁻¹⁰⁷⁴ in the range 0.0 ≤ x < 1.0. All such
numbers are evenly spaced, but most have to be rounded down to the
nearest representable Python float. (The value 2⁻¹⁰⁷⁴ is the smallest
positive unnormalized float and is equal to

math.ulp(0.0)

.)

See also

Generating Pseudo-random Floating-Point Values a
paper by Allen B. Downey describing ways to generate more
fine-grained floats than normally generated by

random()

.

By Sneh

While we believe that this content benefits our community, we have not yet thoroughly reviewed it. If you have any suggestions for improvements, please let us know by clicking the “report an issue“ button at the bottom of the tutorial.

In this tutorial, we are going to focus on the

randint()

method in Python. In our previous tutorials, we saw different random number generating methods defined inside the random module in our Random Number Tutorial in Python.

So, as you already know, we need to import the random module in Python first to begin using the randint() method. The module essentially creates pseudo-randomness.

Basically, the

randint()

method in Python returns a random integer value between the two lower and higher limits (including both limits) provided as two parameters.

It should be noted that this method is only capable of generating integer-type random value. Take a look at the syntax so that we can further incorporate the method.


#randint() Syntax randint(lower limit , upper limit)

Here,

The above example returns an integer N where N>=beg and N<=end.

It works in the same way

randrange(beg,end)

does, and hence is an alias for the same.

Let us look at the given code below, it illustrates the use and working of the

randint()

method.


import random beg=10 end=100 random_integer = random.randint(beg, end) print("The random integer is :", random_integer)

Output:

Clearly, we can see that the

randint()

method generates a random integer value within the limit 1-100.

Is this value random? What happens when we call the method multiple times? Does it return the same value?

The code snippet below answers all the above-mentioned questions and gives us a clear understanding.


import random beg=10 end=100 for i in range(5): print(random.randint(beg, end))

Output:

For the above code, repeating the

random.randint()

method gives us different random integers for each call within the limit 10 to 100.

Hence, we can infer that the values are random for each call and do not overlap in our case. Furthermore, when the number of calls is large and the range is quite smaller, in that case, the random values generated may collide or overlap.

As said earlier, one must ensure that the higher and lower limit parameters have to be an integer type. For other types, we get a ValueError as shown below.


import random beg=5.3 end=10.2 print(random.randint(beg, end))

Output:


Traceback (most recent call last): File "C:/Users/sneha/Desktop/test.py", line 4, in

print(random.randint(beg, end)) File "C:\Users\sneha\AppData\Local\Programs\Python\Python37-32\lib\random.py", line 222, in randint return self.randrange(a, b+1) File "C:\Users\sneha\AppData\Local\Programs\Python\Python37-32\lib\random.py", line 186, in randrange raise ValueError("non-integer arg 1 for randrange()") ValueError: non-integer arg 1 for randrange() Process finished with exit code 1

I hope this brief tutorial on the randint() method in Python has made the function clear for you. Your feedback is always welcome through the comments.

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Using Python’s randint for Random Number Generation

Python’s randint function is a powerful tool in your coding arsenal. It’s like a digital dice, capable of generating random numbers for a variety of applications.

This function can seem a bit confusing at first, but fear not! In this guide, we will dive deep into the ins and outs of using randint in Python. By the end of this guide, you will be able to confidently use the randint function in your own Python projects.

So, let’s roll the dice and start our journey into the world of Python’s randint function.

Python3


import


random


r1


random.randint(


10


print


"Random number between 0 and 10 is % s"


(r1))


r2


random.randint(


10


print


"Random number between -10 and -1 is % d"


(r2))


r3


random.randint(


print


"Random number between -5 and 5 is % d"


(r3))

Output

Random number between 0 and 10 is 2
Random number between -10 and -1 is -7
Random number between -5 and 5 is -3

Sıfırdan Python Dersleri Ders 12: Random modülü
Sıfırdan Python Dersleri Ders 12: Random modülü

Using randint in Loops

One of the powerful ways to use

randint()

is within loops. This allows you to generate multiple random numbers at once. For instance, if you need to generate a list of 5 random numbers between 1 and 10, you can use a for loop with

randint()

:


import random random_numbers = [random.randint(1, 10) for _ in range(5)] print(random_numbers) # Output: # (A list of 5 random numbers between 1 and 10)

In this code, we’re using a for loop to generate a list of 5 random numbers. The

random.randint(1, 10)

function is called 5 times, once for each iteration of the loop, generating a new random number each time. The result is a list of 5 random integers.

These examples demonstrate how you can use Python’s

randint()

function in more complex ways to suit your needs. By adjusting the range and using loops, you can generate a variety of random number sequences.

Generate a secure random integer

Above all, examples are not cryptographically secure. The cryptographically secure random generator generates random numbers using synchronization methods to ensure that no two processes can obtain the same number simultaneously.

If you are producing random numbers for a security-sensitive application, then you must use this approach.

Use the secrets module if you are using a Python version higher than 3.6.


import secrets # secure random integer # from 0 to 10 secure_num = secrets.randbelow(10) print(secure_num) # Output 5

If you are using Python version less than 3.6, then use the

random.SystemRandom().randint()

or

random.SystemRandom().randrange()

functions.

ytpype from numpy.random import randint
ytpype from numpy.random import randint

More Examples

Example 1: Working of randint() function

Code:

Output:

Explanation: The above program generates a random integer using the randint() function in python in 3 different cases that include

  • Case 1: Generates a random number between two positive integers
  • Case 2: Generates a random number between two negative integers.
  • Case 3: Generates a random number between one positive and other negative integer.

Example 2: ValueError in randint() function

Code:

Output:

Explanation:

The above program demonstrates how we can get ValueError while using randint() function in python. We get this error because we passed floating point values as parameters in the randint() function.

Example 3: TypeError in randint() function

Code:

Output:

Explanation: The above program demonstrates how we can get TypeError while using randint() function in python. We got this error because we passed string or character literals that are non-numeric values as parameters in the randint() function.

Click Here, To know about the int() function in python.

Notes on Reproducibility¶

Sometimes it is useful to be able to reproduce the sequences given by a pseudo-random number generator. By reusing a seed value, the same sequence should be reproducible from run to run as long as multiple threads are not running.

Most of the random module’s algorithms and seeding functions are subject to change across Python versions, but two aspects are guaranteed not to change:

  • If a new seeding method is added, then a backward compatible seeder will be offered.

  • The generator’s


    random()

    method will continue to produce the same sequence when the compatible seeder is given the same seed.

What is Random RandRange in Python?
What is Random RandRange in Python?

Parameters Used in Randint Python Function

As you can see in the syntax, the Python randint() function accepts two parameters, which are:

  • start: It is a required parameter that accepts integer value and determines the starting range from which it will generate the random integer.
  • end: It is a required parameter that accepts integer value and defines the ending of the range from which it will generate the random integer.

Keywords searched by users: from random import randint

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