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Python Program to Generate a Random Number

To understand this example, you should have the knowledge of the following Python programming topics:

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To generate random number in Python, randint() function is used. This function is defined in random module .

Source Code

Note that we may get different output because this program generates random number  in range 0 and 9. The syntax of this function is:

This returns a number N in the inclusive range [a,b] , meaning a <= N <= b , where the endpoints are included in the range.

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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.

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

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.

Bookkeeping functions ¶

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.

Changed in version 3.11: The seed must be one of the following types: None , int , float , str , bytes , or bytearray .

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

Functions for bytes ¶

Generate n random bytes.

This method should not be used for generating security tokens. Use secrets.token_bytes() instead.

New in version 3.9.

Functions for integers ¶

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 .

Return a random integer N such that a <= N <= b . Alias for randrange(a, b+1) .

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 .

Functions for sequences ¶

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

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.

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 .

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.

Discrete distributions ¶

The following function generates a discrete distribution.

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

Mathematically equivalent to:

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.

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.

Return the next random floating point number in the range 0.0 <= X < 1.0

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 b may or may not be included in the range depending on floating-point rounding in the equation a + (b-a) * random() .

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.

Beta distribution. Conditions on the parameters are alpha > 0 and beta > 0 . Returned values range between 0 and 1.

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 .

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:

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.

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.

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

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 .

Pareto distribution. alpha is the shape parameter.

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

Alternative Generator ¶

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:

Override this method in subclasses to customise the seed() behaviour of Random instances.

Override this method in subclasses to customise the getstate() behaviour of Random instances.

Override this method in subclasses to customise the setstate() behaviour of Random instances.

Override this method in subclasses to customise the random() behaviour of Random instances.

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

Override this method in subclasses to customise the getrandbits() behaviour of Random instances.

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.

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.

Basic examples:

Simulations:

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

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:

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

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.

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

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.

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

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) .)

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() .

Table of Contents

  • Bookkeeping functions
  • Functions for bytes
  • Functions for integers
  • Functions for sequences
  • Discrete distributions
  • Real-valued distributions
  • Alternative Generator
  • Notes on Reproducibility

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Random Numbers in Python

Python defines a set of functions that are used to generate or manipulate random numbers through the random module. 

Functions in the random module rely on a pseudo-random number generator function random(), which generates a random float number between 0.0 and 1.0. These particular type of functions is used in a lot of games, lotteries, or any application requiring a random number generation.

Let us see an example of generating a random number in Python using the random() function.

Output:  

Different Ways to Generate a Random Number in Python

There are a number of ways to generate a random numbers in Python using the functions of the Python random module. Let us see a few different ways.

Generating a Random number using choice()

Python random.choice() is an inbuilt function in the Python programming language that returns a random item from a list , tuple , or string .

Generating a Random Number using randrange()

The random module offers a function that can generate Python random numbers from a specified range and also allows room for steps to be included, called randrange() .

Generating a Random number using seed()

Python random.seed() function is used to save the state of a random function so that it can generate some random numbers in Python on multiple executions of the code on the same machine or on different machines (for a specific seed value). The seed value is the previous value number generated by the generator. For the first time when there is no previous value, it uses the current system time.

Generating a Random number using shuffle()

The shuffle() function is used to shuffle a sequence (list). Shuffling means changing the position of the elements of the sequence. Here, the shuffling operation is in place.

Generating a Random number using uniform()

The uniform() function is used to generate a floating point Python random number between the numbers mentioned in its arguments. It takes two arguments, lower limit(included in generation) and upper limit(not included in generation).

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Generate Random Numbers in Python

  • March 2, 2022 March 22, 2023

Generate random numbers in Python Cover Image

In this tutorial, you’ll learn how to generate random numbers in Python . Being able to generate random numbers in different ways can be an incredibly useful tool in many different domains. Python makes it very easy to generate random numbers in many different ways.

In order to do this, you’ll learn about the random and numpy modules, including the randrange, randint, random, and seed functions. You’ll also learn about the uniform and normal functions in order to create more controlled random values.

By the end of this tutorial, you’ll have learned:

  • How to generate random floating point values and integers
  • How to generate random numbers between different values
  • How to create lists of random numbers
  • How to generate a random number following a gaussian distribution

Table of Contents

Generate Random Floating Point Value in Python

Python comes with a package, random , built in. This means that you don’t need to install any additional libraries. The library makes it incredibly easy to generate random numbers. Let’s first see how to create a random floating point between 0 and 1 .

Let’s break down what we did here:

  • We imported the random library
  • We used the .random() function from the library

The random() function is used to generate a random float between 0 and 1.

Generate a Random Float Between 2 Numbers

While the random() function generates a random float between 0 and 1. However, there may be times you want to generate a random float between any two values. For this, you can use the .uniform() function . Let’s see how this works:

You can see that the random number that’s returned is between (and can include) the boundary numbers.

In the next section, you’ll learn how to generate a random integer in Python.

Generate Random Integer in Python

The random library makes it equally easy to generate random integer values in Python. For this, you can use the randint() function, which accepts two parameters:

  • a= is the low end of the range, which can be selected
  • b= is the high end of the range, which can also be selected

Let’s see how we can generate a random integer in Python:

Generate Random Numbers Between Two Values in Python

In the example above, we used 0 as the starting point. However, if we wanted to generate a random number between two values, we can simply specify a different value as the starting point (including negative values).

Let’s repeat this example by picking a random integer between -100 and 100:

Generate Random Numbers Between Two Values at Particular Steps in Python

In this section, you’ll learn how to generate random numbers between two values that increase at particular steps. This means that say you wanted to choose a random number between, say, 0 and 100, but only in multiples of 3.

For this, you can use the randrange() function. Let’s see how this works:

The important piece to note here is that the upper limit number is not included in the selection. In order to include it, simply add 1 to the value, such as random.randrange(0, 101, 3) .

Generate a List of Random Numbers in Python

In this section, you’ll learn how to generate a list of random numbers in Python. Because this can be done in different ways, we’ll split this section into multiple parts. By the end of this, you’ll learn how to select a list of random floats, random integers, and random integers without repetition.

Generate a List of Random Floats

In order to generate a list of random floats, we can simply call the .random() or .uniform() functions multiple times. We can use either a for loop or a list comprehension.

In the examples below, we’ll use a Python list comprehension to generate two lists: one with random floats between 0 and 1 and the other with random floats between two specified numbers:

  • We instantiated a new list, random_list which holds a list comprehension
  • The list comprehension repeats calling the .random() function 4 times.

We can apply the same approach to create a list of random floats between two given numbers:

Generate a List of Random Integers

We can apply the same approach to generate a list of random integers. To do this, we’ll create a list comprehension that calls the random.randint() function multiple times. Let’s see how we can create a list of 5 random integers between 50 and 100:

Generate a List of Random Integers without Substitution

In this section, you’ll learn how to generate a list of random integers without substitution. This means that you can select a number once, and only once. In order to do this, you can use the random.sample() function.

The function expects a list of values and the number of values to select. So, say you wanted to select five values without substitution between 0 and 15, you could write:

Generate a Random (Normal) Gaussian Distribution in Python

The random library also allows you to select a random value that follows a normal Gaussian distribution. In order to do this, you can use the gauss() function, which accepts both the mean and the standard deviation of the distribution .

Let’s see how you can generate a random value from a distribution with the mean of 10 and a standard deviation of 1:

Want to create an entire distribution? Check out this article here, which teaches you how to produce an entire Gaussian (Normal) distribution using Numpy .

The random library also comes with helpful ways to generate random numbers from other types of distributions. Check out the full list below:

  • Beta distribution:  random.betavariate()
  • Exponential distribution:  random.expovariate()
  • Gamma distribution:  random.gammavariate()
  • Gaussian distribution:  random.gauss()
  • Log normal distribution:  random.lognormvariate()
  • Normal distribution:  random.normalvariate()

Create Reproducible Random Numbers in Python

There will be many times when you want to generate a random number, but also want to be able to reproduce your result. This is where the random.seed() function come in. This allows you to set a seed that you can reproduce at any time.

Let’s see how we can do this:

Let’s break this down a little bit:

  • We imported the library
  • We then instantiated a random seed, using the value of 100
  • Then when we printed a random float, a value was returned
  • If we were to run this again, the same value would be returned

In this tutorial, you learned how to generate random numbers using Python. You learned how the random library works to generate random numbers, including floats and integers. You also learned how to generate random numbers from different ranges of numbers, including only multiples of numbers.

You then learned how to generate lists of random numbers, including floats and integers, as well as without substitution. Finally, you learned how to select random numbers from a normal distribution and how to reproduce your results.

Additional Resources

To learn more about related topics, check out the tutorials below:

  • Python: Select Random Element from a List
  • Python: Shuffle a List (Randomize Python List Elements)
  • NumPy for Data Science in Python
  • Python Random: Official Documentation

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Nik is the author of datagy.io and has over a decade of experience working with data analytics, data science, and Python. He specializes in teaching developers how to use Python for data science using hands-on tutorials. View Author posts

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Generate Random Numbers in Python

The module named random can be used to generate random numbers in Python. To use a module you need to type import module . This loads all of the functions inside the module.

Keep in mind that random numbers with the random module are pseudo-random numbers. For most programs this is fine.

Related course: Complete Python Programming Course & Exercises

The random module

The random module has pseudo-random number generators, this means they are not truly random.

Generate random numbers

In all of the cases we use the random module. Several random numbers are generated.

If you want a random float between 1 and 10 you can use this trick:

For random integers either case them to integers or use the randrange function.

If you are a beginner, then I highly recommend this book.

Study drill

Try the exercises below

  • Make a program that creates a random number and stores it into x.
  • Make a program that prints 3 random numbers.
  • Create a program that generates 100 random numbers and find the frequency of each number.

After completing these continue with the next exercise.

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How to Generate Random Numbers in Python

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Sometimes you need a random number or element. How can Python help?

The truth is that randomness is all around us. Think about the lottery, a dice roll, or the (extreme) randomness of your office's Secret Santa workings.

In this article, we'll discuss pseudo-randomness, how it's different from true randomness, and how it can be applied in Python to generate random numbers. We'll also delve into some more advanced topics, like reproducible coding with random numbers and using the choice() and choices() functions to return random string elements from a list. Finally, we'll show you how to randomize list order.

Red dice

What Are Pseudo-Random Numbers?

Pseudo-Random Number Generator Numbers

A pseudo-random number generator generates "randomness" with the help of a mathematical algorithm. This means that a random pick is produced with a computer program. To humans, this has the effect of randomness: the result appears completely arbitrary. A pseudo-random generator is almost as good as a true random generator (one that uses an unpredictable physical means to generate random numbers).

For the purposes of this article, when we talk about the randomness that is produced by Python, we're actually talking about pseudo-randomness. It is important to mention that we will not be using a true random generator, but pseudo-randomness is good enough for most of the business world's current needs.

Generating Pseudo-Random Numbers with Python's random Module

One of Python's best-known modules for generating random picks is random . Let's look at the most famous functions from this library.

Generating Pseudo-Random Numbers with Python

Picking a Random Integer

Imagine that you work in sales and you have 10 clients. You want to randomly choose one of these clients for a special offer. You can do a random pick in Python by using the randint() function.

We've created an example?—?10 clients are stored in a Pandas data frame. Each client has an ID and a name. The ID numbers go from 0 to 10 and uniquely identify each client.

Now a question arises: How can we use randint() to choose one client from the data frame? Easily:

In the code above, randint() is called with two arguments: the starting number (which in this case is 0) and the ending number (which is 9). By calling randint() with the arguments 0 and 9, we're telling Python to return a random integer number from 0,1,2,3,4,5,6,7,8, 9. Calling clients.loc[clients['client_id']== random_number] returns the randomly-selected client ID and the associated name:

Selecting a client at random

Try to repeat this process several times. You will not always get the same client because the ID is randomly chosen by the random module.

But what if you want to make your code reproducible? Maybe your boss wants to know how Bomy Ltd. was selected. And maybe he wants to run the code and get the same results again. This is possible because the random module generates pseudo-random numbers, not true random numbers. Randomly-generated numbers in Python can be determined. (There's a mathematical formula behind the choice). We can use the seed() function if we want to get reproducible results.

Reproducible Coding with the seed() Function

Image credit: "Scratch" from "Piled Higher and Deeper" by Jorge Cham www.phdcomics.com .

The seed() function is used to save the state of the random() function, allowing it to generate the same random number(s) on multiple executions of the code on the same or different machines for a specific seed value. Passing the same seed to random() and then calling that function will always give you the same set of numbers.

If we put random.seed(0) at the beginning of the code in the above example, calling randint(0,9) will always get us the same result:

Using seed() makes your code reproducible; each execution will produce the same result.

The Random Float Number Generator

Another useful function, random() , can be used for a random floating number generator.

Many famous algorithms today use a pseudo-random float number generator in one of their steps. For example, in neural networks , weights are initialized with small random numbers. The idea is to find a perfect set of weights, which a specific mapping function will use to make a good prediction for the output variable. The process of finding such weights is called 'learning';?many iterations of weight combinations are tried and the best combination (i.e. the set of weights that will give the most accurate predictions) is chosen with the help of a type of algorithm called 'stochastic gradient descent'.

The first step in the above process is to use randomly-generated floating numbers. They can be chosen with the help of the random() function. So, if you are in a situation where you need to generate a small number (between 0 and 1), you can call random() :

This will produce a random number – in our case, 0.5417604303861212.

The returned random number is always between 0 and 1. If you want to get a number from some other interval, you can just multiply your result. The code below will always generate a random number between 0 and 10:

There is another option if you want to choose a random number from a specific interval. It's the uniform() function, which takes two arguments: low (the lowest boundary of the interval) and high (the highest boundary of the interval).

If you want to choose a random number between 0 and 10, here's how you can do it with uniform() :

Now that we have shown you random() and uniform() , it's up to you which function to use. Either can be used to get a random number within a range.

But what about when you want to randomly choose one element from a tuple or a predefined list? There is also a function for that – it's called choice() . This function is used when you want to randomly select an item from a given list. Let's see how it works.

Using choice() to Return a Random Element from a List

Random Choice

Earlier, we gave the example of a salesman who needs to choose one of his clients from a list. Each client was represented with an integer from 0 to 10. Instead of using randint() to select a random integer, our salesman could just use the choice() function, like this:

In the code above, random.choice(clients['client_id']) picks a number from the list [0,1,2,3,4,5,6,7,8,9] . The line clients.loc[clients['client_id'] == random.choice(clients['client_id'])] retrieves the client name and ID that was randomly chosen with random.choice(['clients_id']) . This is a very elegant way to retrieve a random item.

It's also important to mention that choice() also works with a list of strings. Our salesman could also run random. choice (clients['client_name']) and a random name would be directly returned in the output. Instead of a random integer pick, choice() would be doing a random string pick.

Sometimes you may want to choose several items from a list. This can be done with the choices() function (note the 's'). We can randomly choose two clients from the list by using random.choices(clients['client_name'],k=2) . The k argument is used to define the number of elements that we want to randomly select.

The above code will return two randomly chosen names – just keep in mind that if you want to make your code reproducible, you must use the seed() function. (Otherwise, you will always get different names.)

Randomly Reordering a List with shuffle()

Shuffle

The last function that we are going to mention is shuffle() . You use this function when you need to return all elements from the list but in a different order. Maybe our salesman wants to shuffle his list of clients and use the reordered list to make sales calls. The list of client_ids looks like this: [0,1,2,3,4,5,6,7,8,9] . It can be reordered with random.shuffle(client_id) . After executing this line of code, the variable client_id would look something like [0, 4, 3, 2, 8, 9, 1, 6, 7, 5] . We randomly shuffled the list. Now our salesman can make calls by this random reorder.

Shuffle

Of course, we can similarly reorder a list of strings with shuffle() . If we have names stored in a list, like so:

client_name(['Mobili Ltd.', 'Tymy Ltd.', 'Lukas Ltd.', 'Brod Ltd.', 'Missyda Ltd.', 'Abiti Ltd.', 'Bomy Ltd.', 'Citiwi Ltd.', 'Dolphy Ltd.', 'Doper Ltd.'])

We can reorder this list by running random.shuffle(client_name) . It returns a shuffled list.

After calling random.shuffle() , the list was reordered. It would look something like this:

['Abiti Ltd.', 'Citiwi Ltd.', 'Dolphy Ltd.', 'Tymy Ltd.', 'Doper Ltd.', 'Missyda Ltd.', 'Mobili Ltd.', 'Lukas Ltd.', 'Brod Ltd.', 'Bomy Ltd.']

Random Numbers in Python Are Easier Than You Think

One of the most popular Python modules makes pseudo-random number generation easy. Some of the most frequently-used functions from the random module include those that handle a random integer pick ( randint() ), a random floating number ( random() , uniform() ), a random list item pick ( choice() , choice() ) and random list reordering ( shuffle() ). This article showed you how to use these functions and how to implement randomness in your code. Remember, when you use seed() , these pseudo-random results can be reproduced in your code – a very handy way to put random numbers in Python to work!

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  • Machine Learning

random number

Generating random numbers is a common task in Python. Whether it’s for simulations, gaming, or even testing, Python’s random module offers versatile functions to cater to different needs. This guide will explore the various methods to generate random numbers in Python.

Using the random module in Python, you can produce pseudo-random numbers. The function random() yields a number between 0 and 1, such as [0, 0.1 .. 1]. Although numbers generated using the random module aren’t truly random, they serve most use cases effectively.

Related Course: Python Programming Bootcamp: Go from zero to hero

Generating a Random Float Between 0 and 1

With just a few lines of code, you can generate a pseudo-random floating-point number. Here’s how:

Producing a Random Integer Between 1 and 100

If you need a whole number or integer between 1 and 100, use the following approach:

To store this random integer in a variable, follow this example:

Getting a Random Float Between 1 and 10

To generate a random floating point number between 1 and 10, the uniform() function is the right tool.

Manipulating Lists with Randomness

Lists are versatile data structures in Python, and sometimes you may want to introduce randomness when working with them.

Shuffling a List : To randomize the order of items in a list, use the following:

Selecting Random Items from a List : For selecting a single or multiple random items from a list, observe these examples:

The same method works for lists containing strings:

To get more practice with Python, you can download these Python exercises .

Navigate through our tutorials: < Back | Next >

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the function "sample" does not work on my laptop with windows9

from random import * items = ['Alissa','Alice','Marco','Melissa','Sandra','Steve'] x = sample(items, 1) # Pick a random item from the list print x[0]

Which Python version are you using? Try replacing by print(x[0])

Im getting error in randint( )

>>> from random import * >>> print randint(1,100) SyntaxError: invalid syntax I'm using Python 3.4.3

Hi Harsi, in Python 3 you need bracket around a print statement. Try this: >>> from random import * >>> print(randint(1,100))

Hmm could you guys tell me what does it: from random import * mean ? Thanks :D

Hi, this imports all the functions from a python file called random (actually random.py.

This file is one of the standard python modules. You can see the source code of random.py here: https://hg.python.org/cpython/file/2.7/Lib/random.py .

You can create your own module that way:

Create a file called test.py

Then create a file called app.py:

The program app.py now uses the code in test.py. The same principle applies when you use "from random import *"

from random import *

items = ['Alissa','Alice','Marco','Melissa','Sandra','Steve'] x = sample(items, 1) # Pick a random item from the list print x[0] why we put [0] here ? and what will happen if we change (items, 1) to (items, 2 or 3 or 4 ) In the presence of print x[0] This point needs to explain more...

The sample function can return a list of numbers. sample(items,2) would return a list of two random numbers. x[0] is simply the first random number in the list

Is the statement from random import * a seeding process? Does this mean that y = sample(items,4) will return the same 4 items each time it is used?

Hi Steve, the statement y = sample(items,4) will return new items every call. If you want you can call the random class seed() function, which uses the system time to initialize.

Just a quick question about sample() -- when used to fill values in an equation it returns:

TypeError: can't multiply sequence by non-int of type 'float'

I'm curious why this isn't allowed, and if there is an alternative that I haven't been able to find for extracting multiple random values from a series of value. For now, all I've come up with is using the choice() alternative with a loop for however large my sample pool needs to be, but I have a feeling there's a better way to do it. Admittedly, I haven't read through all of the documentation for random thoroughly, but I haven't found a better solution. I figured that someone with a bit of knowledge would be my best bet. Any suggestions? As an aside, I should note I'm using 2.7 to learn with. I know they changed some stuff with Python 3.x, so I'm not sure if this is even an issue with newer revisions.

seq = [1,2,3,4,5] y = seq * randomElement

from random import * items = [1, 2.13, 3.24, 4.25, 5.46, 6.57, 7.68, 8.79, 9.810, 10.911] seq = [1,2,3,4,5] # Get a list of 1 random numbers, convert to one random number. randomElement = sample(items, 1) randomElement = randomElement[0] # Multiple every element of the list for element in seq: y = element * randomElement print("y=", y)

from random import * seq = [1,2,3,4,5] # Get a list of 1 random numbers, convert to one random number. randomElement = sample(seq, 1) randomElement = randomElement[0] # Multiple every element of the list, using list comprehension l = [randomElement*x for x in seq] print(l) # Multiple every element using map l = list(map(lambda x:randomElement*x, seq)) print(l)

I hope that helps. If not, could you post the code?

I think you answered my question pretty well. My mistake was in not addressing the derived sample with an index value, so I was essentially attempting mathematics against a list rather than single values from within the list. It somehow slipped past me that sample returned a list. I guess it's a beginner mistake and lack of attention on my part.

Quick questions, though:

Is the behavior of a loop iteration of choice() essentially the same as the returned sample() list? For clarification, a simple example would be a loop that iterates 4 times using choice() and storing the returned values in a list, versus a sample() of 4 the same values. Would that essentially return the same chance of random values?

Also, supposing they essentially return the similar randomness in their values, is there a performance difference over larger iterations? Common sense tells me that since the looping choice() jumps right to the chase, but a sample() first stores the values in a new list and that list then needs to be iterated over, looping choice() would be the cleaner and more efficient alternative. Am I mistaken?

Thanks for the quick response, I appreciate your answer and it definitely helped me understand where I originally went wrong.

Apologies for the double response. The original response didn't exist on a new browser, and the original browser was stuck on the spinning dots for the comment section, so I assumed it hadn't gone through the first time. Feel free to delete whichever one you think is best.

No problem.

Practically that would do the same. There is a slight difference, choice() will raise an IndexError if the sequence is empty. Sample will throw a ValueError. This matters if you implement error handling (try/catch statement). Performance wise there is a big difference.

def choice(self, seq): """Choose a random element from a non-empty sequence.""" try: i = self._randbelow(len(seq)) except ValueError: raise IndexError('Cannot choose from an empty sequence') return seq[i] def sample(self, population, k): if isinstance(population, _collections.Set): population = tuple(population) if not isinstance(population, _collections.Sequence): raise TypeError("Population must be a sequence or Set. For dicts, use list(d).") randbelow = self._randbelow n = len(population) if not 0 <= k <= n: raise ValueError("Sample larger than population") result = [None] * k setsize = 21 # size of a small set minus size of an empty list if k > 5: setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets if n <= setsize: # An n-length list is smaller than a k-length set pool = list(population) for i in range(k): # invariant: non-selected at [0,n-i) j = randbelow(n-i) result[i] = pool[j] pool[j] = pool[n-i-1] # move non-selected item into vacancy else: selected = set() selected_add = selected.add for i in range(k): j = randbelow(n) while j in selected: j = randbelow(n) selected_add(j) result[i] = population[j] return result

from random import * import time def testSample(size): start_time = time.time() seq = [1,2,3,4,5] for i in range(0,size): randomElements = sample(seq, 1)[0] print("--- %s seconds ---" % (time.time() - start_time)) def testChoice(size): start_time = time.time() seq = [1,2,3,4,5] for i in range(0,size): randomElement = choice(seq) print("--- %s seconds ---" % (time.time() - start_time)) testSample(1000000) testChoice(1000000)

Output with Python 2.7: --- 5.25197696686 seconds --- --- 1.08564114571 seconds ---

Output with Python 3.4: --- 17.56459665298462 seconds --- --- 2.1325480937957764 seconds ---

I would say you are correct, choice() is faster.

In terms of randomness, all of the numbers generated by the random module as pseudo-random. Python 2.3 uses the Wichmann-Hill algorithm , later versions use the MersenneTwister algorithm (Python implementation below)

I'm assuming your response hit the maximum depth, so I'll respond to myself here for the sake of keeping it together...

I appreciate your response, and I'm actually relieved that my line of thought seems to be heading in the right direction. I just had a finishing question for clarity.

If I'm understanding you right, each time a number is selected, no matter which version of "random" you use, it runs the same algorithm at the core to return each value with additional modification for specific behavior. Meaning, the returned values essentially exist in the same frequencies of pseudo-randomness? And,

Yes, you are understanding right. The functions choice() and sample() use the same algorithm, which is implemented in the method random(). It is called indirectly from the method randbelow().

def choice(self, seq): return seq[int(self.random() * len(seq))] # raises IndexError if seq is empty def sample(self,population,k): -snip- j = _int(random() * (n-i)) -snip-

Is there a question after "And," ? It seems to have gone missing.

Quick thanks for the great comments thread - it elevated a my super basic reference search to a whole 'nother level - kudos!

Exploring the Random Module: Generating Basic Random Numbers in Python

RANDOM NUMBER GENERATOR

Are you a pro gamer? A game enthusiast? A geek? If you are, you might have observed the use of random numbers in almost every game. A dice game is one of the finest examples of random number generation. Suppose you are playing this game with a machine online. You roll the dice and get any number between 1 and 6. Similarly, even the machines get their own random numbers. The numbers that we see on the screen while playing games (on the dice) are numbers that are generated randomly. How do these games generate random numbers continuously, you may ask?

An algorithm called the Random Number Generator (RNG) is used to produce numbers randomly in these games. RNG finds its significance in modern video games to determine which rare items you collect or the obstacles you come across.

Wondering how to create easy games using Python? Follow this link.

It is also used in traditional games like poker, where the numbers decide the winner. Crafting working code in Python is crucial when it comes to RNG models. To enhance that, CasinoAlphaNZ’s analysis shows just how crucial it is to ensure flawless implementation. Even the tiniest error in the code can have catastrophic consequences, potentially reducing the chances of winning in games reliant on the RNG to a dismal zero. The implications are far worse for the establishments that run flawed and unmoderated random number generators. As these companies could face legal action if their flawed RNG code leads to skewed outcomes.

This underscores the vital role of rigorous testing, thorough debugging, and adherence to best practices in RNG coding to maintain both fairness and integrity within the gaming industry.

Let us learn more about these random number generators and the important Python module behind the Random Number Generators.

Exploring the Random Module

The random module is the Python module behind the random number generator(RNG) algorithm. Most of the RNG algorithms use the random module to generate pseudo-random numbers.

The random module can be imported into our environment with the help of this simple line of code.

When using the random module for the random number generators, we need to be cautious about what functions to use and know the result of the usage.

To learn more about the functions of the random module, consider reading this post .

Let me simplify for you. The table below consists of the functions of the random module we can use to generate random numbers.

RNG – Generating Random Numbers

I hope you have understood the significance of RNGs and what they use at the foundational level, let us try to create a random number generator that generates random numbers within the given range.

We are going to use Tkinter, a GUI tool of Python to create an interface of the Random Number Generator algorithm. Follow the code given below.

Lines 1 through 4 – We import the tkinter and random modules. Tkinter needs a window to display the output so we are creating one and also giving it a title.

Lines 5 through 9 – Here comes the main function- we take two values from the user (a minimum and a maximum value), pass them to the randint function, and generate a random number.

The next few lines are used to label the user’s inputs and create a button to generate random numbers. Here is a screengrab of the Tkinter window.

Random Numbers Generator

We also have a demo to help you use the RNG!

That’s a wrap! I hope this tutorial was helpful in learning about the random number generator algorithms and their applications in various video games. In this comprehensive tutorial, we have observed various use cases of random numbers with the help of a few examples and explored the random module to understand the generation of random numbers.

We used this prior knowledge to develop RNG and made it interactive using Tkinter.

Useful Links

Random Module Documentation

Tkinter- Python’s GUI Tool

Random Generator #

The Generator provides access to a wide range of distributions, and served as a replacement for RandomState . The main difference between the two is that Generator relies on an additional BitGenerator to manage state and generate the random bits, which are then transformed into random values from useful distributions. The default BitGenerator used by Generator is PCG64 . The BitGenerator can be changed by passing an instantized BitGenerator to Generator .

Construct a new Generator with the default BitGenerator (PCG64).

A seed to initialize the BitGenerator . If None, then fresh, unpredictable entropy will be pulled from the OS. If an int or array_like[ints] is passed, then it will be passed to SeedSequence to derive the initial BitGenerator state. One may also pass in a SeedSequence instance. Additionally, when passed a BitGenerator , it will be wrapped by Generator . If passed a Generator , it will be returned unaltered.

The initialized generator object.

If seed is not a BitGenerator or a Generator , a new BitGenerator is instantiated. This function does not manage a default global instance.

See Seeding and Entropy for more information about seeding.

default_rng is the recommended constructor for the random number class Generator . Here are several ways we can construct a random number generator using default_rng and the Generator class.

Here we use default_rng to generate a random float:

Here we use default_rng to generate 3 random integers between 0 (inclusive) and 10 (exclusive):

Here we specify a seed so that we have reproducible results:

If we exit and restart our Python interpreter, we’ll see that we generate the same random numbers again:

Container for the BitGenerators.

Generator exposes a number of methods for generating random numbers drawn from a variety of probability distributions. In addition to the distribution-specific arguments, each method takes a keyword argument size that defaults to None . If size is None , then a single value is generated and returned. If size is an integer, then a 1-D array filled with generated values is returned. If size is a tuple, then an array with that shape is filled and returned.

The function numpy.random.default_rng will instantiate a Generator with numpy’s default BitGenerator .

No Compatibility Guarantee

Generator does not provide a version compatibility guarantee. In particular, as better algorithms evolve the bit stream may change.

BitGenerator to use as the core generator.

Recommended constructor for Generator .

The Python stdlib module random contains pseudo-random number generator with a number of methods that are similar to the ones available in Generator . It uses Mersenne Twister, and this bit generator can be accessed using MT19937 . Generator , besides being NumPy-aware, has the advantage that it provides a much larger number of probability distributions to choose from.

Accessing the BitGenerator and Spawning #

Simple random data #, permutations #.

The methods for randomly permuting a sequence are

The following table summarizes the behaviors of the methods.

The following subsections provide more details about the differences.

In-place vs. copy #

The main difference between Generator.shuffle and Generator.permutation is that Generator.shuffle operates in-place, while Generator.permutation returns a copy.

By default, Generator.permuted returns a copy. To operate in-place with Generator.permuted , pass the same array as the first argument and as the value of the out parameter. For example,

Note that when out is given, the return value is out :

Handling the axis parameter #

An important distinction for these methods is how they handle the axis parameter. Both Generator.shuffle and Generator.permutation treat the input as a one-dimensional sequence, and the axis parameter determines which dimension of the input array to use as the sequence. In the case of a two-dimensional array, axis=0 will, in effect, rearrange the rows of the array, and axis=1 will rearrange the columns. For example

Note that the columns have been rearranged “in bulk”: the values within each column have not changed.

The method Generator.permuted treats the axis parameter similar to how numpy.sort treats it. Each slice along the given axis is shuffled independently of the others. Compare the following example of the use of Generator.permuted to the above example of Generator.permutation :

In this example, the values within each row (i.e. the values along axis=1 ) have been shuffled independently. This is not a “bulk” shuffle of the columns.

Shuffling non-NumPy sequences #

Generator.shuffle works on non-NumPy sequences. That is, if it is given a sequence that is not a NumPy array, it shuffles that sequence in-place. For example,

Distributions #

Codeforgeek

Python Random Module: 6 Essential Methods for Generating Random Numbers

Priyanshu Singh

  • Oct 24, 2023

Python Random Generating Random Numbers In Python

In our daily life when we play a ludo game and throw a dice any number can come between one to six. The number will be totally random we can not predict it. If we knew then definitely it would be no fun to us in playing those games. So that is what randomization is.

We can also develop these kinds of games by computer programming but how? For developing these kinds of games we need to create randomization and this can be achieved by an inbuilt module in Python which is called random module and it contains several functions through which we can create different random numbers.

Apart from this, we have one more important use of generating random numbers which is in machine learning as while creating datasets for the testing we need to generate random data so the random module plays an important role here as well.

In this article, we’ll look at six functions to generate random numbers using the Python random module. Let’s get started.

Python Random Module

The random module is a built-in module in Python’s Standard Library that provides functions for generating random numbers and working with randomness. It is an extremely practical module used for a lot of applications, games, and also in machine learning.

Random module functions for generating random numbers are given below:

  • randrange()

Let’s try to generate random numbers using them one by one.

1. Generating Random Numbers Using randint() Function

When we need to generate random integers between two numbers, let’s say a and b with both a and b being inclusive, we can use the function randint() of the random module.

In the above code, we first imported the random  module then we provided parameters a as 1 and b as 7 in the random.randint() function and saved the result into a variable random_num and then printed the variable which will give us the random integer number between 1 and 7. We can also get 1 and 7 as both a and b are inclusive.

Generating Random Numbers Using randint() Function

2. Generating Random Numbers Using randrange() Function

The randrange() function generates a random integer from the specified range, it is similar to the range() function. We can specify a starting point let’s say start , an ending point let’s say stop , and step value let’s step .

Here we have passed parameters start as 1 and stop as 3 in the random.randrange() function to get a random integer number between 1 and 3.

Generating Random Numbers Using randrange() Function

3. Generating Random Numbers Using random() Function

The random module also provides us with random() function which helps to generate random floating numbers between 0 and 1 where 0 will be inclusive and 1 will be exclusive. And the important thing is we don’t need to pass any argument in this function.

Here we just called the random.random() function and saved the result into the variable random_num and then printed the variable. The variable will contain a random float number between 0 and 1. Every time we run the above code we get different floating numbers.

Generating Random Numbers Using random() Function

4. Generating Random Numbers Using uniform() Function

The uniform() function is used to generate random floating numbers between two numbers, let’s say a and b which we need to pass as an argument in this function. Here a will be inclusive and b will be exclusive.

Here we have provided parameters a as 1 and b as 3 in the random.uniform() function which will return a random float number between 1 and 3.

Generating Random Numbers Using uniform() Function

5. Generating Random Numbers Using choice() Function

The choice() function offered by the random module is a little bit different from the other functions that we have discussed above. Here we have to provide a sequence as an argument and the sequence could be a list , tuple , or string and the choice function will return us any random element from the sequence.

Here we have passed a list to the random.choice() function. This function will return a random number from the list l . Every time we will run the above code we will get different random elements from the sequence.

Generating Random Numbers Using choice() Function

6. Generating Random Numbers Using shuffle() Function

The shuffle() function is used to shuffle the sequence that we have passed as an argument, i.e., it will change the position of the elements in the sequence.

Here we have shuffled or changed the position of the elements from the list l randomly. Every time we will run the above code the same element can be found at different positions in the sequence.

Generating Random Numbers Using shuffle() Function

The random module is very useful for a broad range of applications where randomness is required such as games, statistical analysis, and data science. In this tutorial, we have discussed six random module functions to generate random numbers with examples. After reading this tutorial, we hope you can easily generate random numbers in Python.

https://stackoverflow.com/questions/3996904/generate-random-integers-between-0-and-9

Priyanshu Singh

Priyanshu Singh

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Python’s Random Number Generator: Complete Guide

Python script using the random module to generate numbers with dice and mathematical symbols showcasing randomness in programming

Ever wondered how to generate random numbers in Python? It’s not as complicated as it might seem. Python, like a digital magician, can conjure up a variety of random numbers with just a few commands.

This article will guide you through the entire process, from the basic use to advanced techniques, of Python’s random number generation capabilities. Whether you’re a beginner just starting out with Python or an intermediate user looking to expand your knowledge, this guide has something for you.

Stay with us as we unravel the magic of random numbers in Python, complete with practical code examples and their expected outputs. Let’s dive in!

TL;DR: How Do I Generate Random Numbers in Python?

Python’s built-in random module allows you to generate random numbers. Here’s a simple example:

In the example above, we first import the random module. Then, we use the randint function from this module to generate a random integer between 1 and 10. Each time you run this code, Python will print a different random number in this range.

If you’re curious about generating random numbers in Python or want to learn more about advanced usage scenarios, keep reading. We have a lot more to share!

Table of Contents

Generating Random Numbers: The Basics

Advanced random number generation in python, alternative methods for random number generation, common issues and solutions in random number generation, understanding randomness and pseudo-randomness in python, expanding the horizon: random numbers in action, further resources for python modules, wrapping up: the magic of random numbers in python.

Python’s random module is a built-in library that allows us to generate random numbers. Before we can use it, we need to import it into our script. Here’s how you do that:

With the random module imported, we can now use the randint function to generate a random integer within a specified range. Here’s an example:

In this example, randint(1, 10) will generate a random integer between 1 and 10, both inclusive. Each time you run this code, you’ll get a different number within this range.

The random module is a powerful tool with many functions. However, it’s important to note that the numbers it generates are pseudo-random. This means they are generated by a deterministic process and are not truly random. For most applications, pseudo-random numbers are sufficient. But for tasks that require high levels of unpredictability, such as cryptography, you might need to use other methods, which we’ll discuss later in this guide.

Python’s random module goes beyond generating random integers. It can also generate random floats, choose random elements from a list, and shuffle a list randomly. Let’s explore these features.

Generating Random Floats

To generate a random float, you can use the random() function. This function returns a random float number between 0.0 and 1.0. Here’s an example:

Choosing Random Elements from a List

The random module can also select a random element from a list using the choice() function. Here’s how you can do it:

Shuffling a List Randomly

If you need to shuffle the elements in a list randomly, you can use the shuffle() function. This function reorders the elements in the list in place, meaning that no new list is created. Here’s an example:

These advanced features of the random module allow us to perform a wide range of tasks involving random number generation in Python. Remember, the numbers generated by this module are pseudo-random, which means they’re suitable for many applications but not all. For tasks requiring higher levels of unpredictability, other methods may be more appropriate, as we’ll discuss in the next section.

While Python’s built-in random module is powerful and versatile, there are alternative methods for generating random numbers that offer additional capabilities. Let’s explore two of these alternatives: the numpy library and the secrets module.

Generating Random Numbers with Numpy

The numpy library is a popular choice for numerical operations in Python, including random number generation. Its random module can generate arrays of random numbers and supports various probability distributions.

Here’s how you can generate a random integer within a range using numpy :

And here’s how you can generate an array of random floats:

Secure Random Numbers with Secrets

For tasks requiring high levels of unpredictability, such as cryptography, Python’s secrets module is a good choice. It generates random numbers suitable for security-sensitive applications.

Here’s how you can generate a random integer within a range using secrets :

The numpy library and the secrets module offer more options and flexibility for random number generation in Python. However, they may be overkill for simple tasks where the built-in random module is sufficient. Your choice of method should depend on your specific needs and the nature of your project.

While Python’s random module is versatile and easy to use, it’s not without its quirks. Let’s discuss some common issues you might encounter when generating random numbers in Python and how to address them.

Pseudo-Randomness and Predictability

The random module generates pseudo-random numbers. This means they are generated by a deterministic process and are not truly random. If you need to generate truly random numbers for applications like cryptography, you might need to use the secrets module or an external service.

Here’s how you can generate a random integer using the secrets module:

In the example above, the randbelow function generates a random integer that is less than the number provided.

Reproducibility with Random Seed

Sometimes, you might want to reproduce the same sequence of random numbers for debugging purposes. You can do this by setting a seed value with the seed() function before generating random numbers.

Here’s an example:

In this example, we set the seed to 1. This means that every time we run this code, we will get the same sequence of random numbers.

Remember, while Python’s random module is powerful and versatile, it might not be the best tool for every task. Depending on your specific needs, you might need to use alternative methods like the numpy library or the secrets module.

To fully grasp the concept of random number generation in Python, it’s important to understand the difference between randomness and pseudo-randomness.

What is Randomness?

In simple terms, randomness implies unpredictability. In a truly random sequence of numbers, there is no pattern that can be used to predict the next number in the sequence.

Pseudo-Randomness in Python

Python’s random module generates pseudo-random numbers. These numbers appear random and unpredictable, but they are generated by a deterministic process. If you know the process and its initial state (also known as the seed), you can predict all the numbers.

Here’s an example of generating pseudo-random numbers with a fixed seed in Python:

In this example, we set the seed to 1 using the seed() function. The sequence of random numbers generated by this code will be the same every time you run it.

The Underlying Algorithm of Python’s random Module

Python’s random module uses the Mersenne Twister algorithm to generate pseudo-random numbers. This algorithm is known for its long period (the sequence of numbers before it starts repeating) and high-quality random numbers.

In conclusion, while Python’s random module doesn’t generate truly random numbers, it generates high-quality pseudo-random numbers that are sufficient for most applications. For tasks requiring truly random numbers, such as cryptography, other methods like the secrets module or an external service may be more appropriate.

The application of random number generation isn’t limited to creating unpredictable sequences. It has a wide range of uses across various fields, including game development, cryptography, and data science.

Game Development

In game development, random numbers can add unpredictability and replayability. They can be used to generate random game scenarios, character attributes, or loot drops. Here’s a simple example of generating a random character attribute:

In this example, the character’s strength attribute is a random number between 1 and 10.

Cryptography

In cryptography, random numbers are essential for creating keys that are hard to predict. Python’s secrets module, which we discussed earlier, is designed for generating cryptographically strong random numbers.

Data Science

In data science, random numbers are used for tasks such as random sampling, bootstrapping, and Monte Carlo simulations. Python’s numpy library, which we also discussed earlier, is particularly useful for these tasks due to its ability to generate arrays of random numbers.

Random number generation is a fundamental concept that often accompanies other topics in Python programming.

For more in-depth information about these related topics, you might want to check out the following guides:

  • Python Modules Logic Simplified – Explore modules for working with regular expressions and data serialization.

Random Number Generation in Python: A Quick Guide – Explore Python’s “random” module for random number generation

Interacting with External Commands in Python covers subprocess management, input/output streams, and error handling.

Python Generate Random Number – A JavaTpoint tutorial that guides you through generating random numbers in Python.

Python NumPy Random – Learn about the NumPy random module to generate random numbers in Python with W3Schools’ guide.

Python’s Math Module – Explore Python’s math module with W3Schools’ quick reference guide.

To recap, we’ve explored the various aspects of random number generation in Python. We started with the basics, learning how to generate random integers using Python’s built-in random module. We then moved onto more advanced topics, such as generating random floats, selecting random elements from a list, and shuffling a list randomly.

We also discussed common issues you might encounter when using the random module and how to address them. We learned that the random module generates pseudo-random numbers, which are sufficient for many applications but not all. For tasks requiring truly random numbers, we explored alternatives such as the secrets module and the numpy library.

Here’s a quick comparison of the methods we discussed:

We hope this guide has helped you master the art of random number generation in Python. Remember, the method you choose should depend on your specific needs and the nature of your project. Happy coding!

About Author

Gabriel Ramuglia

Gabriel Ramuglia

Gabriel is the owner and founder of IOFLOOD.com , an unmanaged dedicated server hosting company operating since 2010.Gabriel loves all things servers, bandwidth, and computer programming and enjoys sharing his experience on these topics with readers of the IOFLOOD blog.

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Python random module

last modified January 29, 2024

Python random module tutorial shows how to generate pseudo-random numbers in Python.

Random number generator

Random number generator (RNG) generates a set of values that do not display any distinguishable patterns in their appearance. The random number generators are divided into two categories: hardware random-number generators and pseudo-random number generators. Hardware random-number generators are believed to produce genuine random numbers. Pseudo-random number generators generate values based on software algorithms. They produce values that look random. But these values are deterministic and can be reproduced, if the algorithm is known.

In computing, random generators are used in gambling, gaming, simulations, or cryptography.

To increase the quality of the pseudo random-number generators, operating systems use environmental noise collected from device drivers, user input latency, or jitter from one or more hardware components. This is the core of the cryptographically secure pseudo-random number generators.

The built-in Python random module implements pseudo-random number generators for various distributions. Python uses the Mersenne Twister algorithm to produce its pseudo-random numbers. This module is not suited for security. For security related tasks, the secrets module is recommended.

The seed is a value which initializes the random number generator. Random number generators produce values by performing some operation on a previous value. When the algorithm starts, the seed is the initial value on which the generator operates. The most important and difficult part of the generators is to provide a seed that is close to a truly random number.

In Python, the seed value is provided with the random.seed function. If the value is not explicitly given, Python uses either the system clock or other random source.

Python random - same seed

In the following example, we use the same seed.

The same seed value produces the same pseudo-random values.

Python random.randint

The random.randint function generates integers between values [x, y].

The example produces four random integers between numbers 1 and 10.

Python random.randrange

The random.randrange function excludes the right-hand side of the interval. It picks values between [x, y).

The example produces four random integers between numbers 1 and 10, where the value 10 is excluded.

Python random.uniform

The random.uniform function generates random floats between values [x, y].

The example produces four random floats between numbers 1 and 10.

Python random.choice

The random.choice function returns a random element from the non-empty sequence.

The example picks randomly a word from the list four times.

Python random.shuffle

The random.shuffle function shuffles the sequence in place.

The example shuffles the list of words twice.

Python random.sample

The random.sample allows to pick a random sample of n unique elements from a sequence.

The example picks randomly three elements twice from a list of words.

Python secrets

The secrets module is used for generating cryptographically strong random numbers suitable for managing data such as passwords, account authentication, or security tokens.

The token_hex function returns a random text string, in hexadecimal. The token_urlsafe function returns a random URL-safe text string.

Here we generate an eight-character alphanumeric password.

random — Generate pseudo-random numbers

In this article we have worked with the Python random module.

My name is Jan Bodnar and I am a passionate programmer with many years of programming experience. I have been writing programming articles since 2007. So far, I have written over 1400 articles and 8 e-books. I have over eight years of experience in teaching programming.

List all Python tutorials .

Python Tutorial

File handling, python modules, python numpy, python pandas, python matplotlib, python scipy, machine learning, python mysql, python mongodb, python reference, module reference, python how to, python examples, python random number, random number.

Python does not have a random() function to make a random number, but Python has a built-in module called random that can be used to make random numbers:

Import the random module, and display a random number between 1 and 9:

In our Random Module Reference you will learn more about the Random module.

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Random Generate Number

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Randomly Hilarious: The Wacky World of Generating Random Numbers in Python! 🎲

Hey there Python pals! 🐍 Let’s dive into the whimsical universe of generating random numbers in Python. 🎉 Because who doesn’t love a bit of chaos in their code, am I right? Let’s sprinkle some randomness into our programming salad and see what flavors we can cook up! 🥗🌶️

Generating Random Numbers in Python

Ah, the mysterious art of generating random numbers. It’s like trying to predict the weather in Delhi – utterly unpredictable! 🌦️ Let’s unveil the secrets, shall we?

Using the random module

So, picture this: you’re a Python wizard 🧙‍♂️ and you want to conjure up some random numbers. What do you do? Well, my friend, say hello to your magical wand – the random module! With a flick of your code wand, you can summon random numbers like a boss. 🔮✨

Setting boundaries for random number generation

Now, we can’t have these random numbers running amok, can we? We need to set some boundaries, just like how Delhi traffic needs some lanes (which are often ignored, but hey, we try!). 🚗🤪 Let’s corral those wild numbers within some limits and keep them in check.

Applications of Random Numbers in Python

Random numbers aren’t just for fun and games (though they’re great at that too!). They have some serious applications, believe it or not!

Simulations and modeling

It’s like playing The Sims, but with code. 🤖 Let’s create virtual worlds, run simulations, and model all sorts of crazy scenarios using these random shenanigans. Who knows what we might discover in our virtual laboratory? 🧪🔬

Randomized algorithms

Now, if you’re feeling a bit adventurous, why not dive into the rabbit hole of randomized algorithms? It’s like exploring unknown territories, but with a safety net made of code. 🐇⚔️ Let’s shake things up and see where these random paths take us!

Random Number Generation Techniques in Python

Ah, the art and science of generating random numbers. There’s more to it than meets the eye!

Pseudo-random number generation

Ever heard of pseudo-randomness? It’s like fake news, but in a good way! 🤫 Let’s learn how Python creates these pseudo-random numbers that can fool even the sharpest minds. It’s all smoke and mirrors, folks!

True random number generation

But wait, there’s more! True randomness exists too, like finding a hidden gem in Sarojini Nagar market. 💎 Let’s uncover the magic behind true random numbers and marvel at the chaos they bring. Embrace the unpredictability, my friends!

Seeding in Random Number Generation

Ah, the concept of seeding – it’s like planting a tiny code seed and watching it grow into a beautiful random flower! 🌱🌼 Let’s explore why seeding is crucial in the world of random number generation.

Importance of seeding

Seeding is like giving your code a starting point, a north star to guide the randomness ship. ⭐️ Without seeding, it’s like driving in Delhi without Google Maps – you might end up in places you never expected!

Using seed values for reproducibility

Want to share your random results with a friend? Or maybe just rerun your code and get the same random goodness? That’s where seed values come in! 🌾 Let’s see how we can use these magical seeds for reproducibility and consistency in our random adventures.

Best Practices for Using Random Numbers in Python

Now that we’ve dabbled in the art of randomness, let’s talk about some best practices to keep our random code in check!

Seeding before generating random numbers

Remember, always seed before you leap! 🌱 It’s like wearing your lucky socks before a big day – you want that extra bit of fortune on your side. Let’s make sure our random numbers start from a predictable point for sanity’s sake.

Handling random number generation in loops

Loops and random numbers – it’s a match made in coding heaven ! 🔄 But, be careful not to create a random mess in your loops. Let’s explore how we can generate random numbers gracefully within our loops without causing a circus!

Overall, folks, generating random numbers in Python is like juggling with invisible balls – you never know what you’ll catch! 🤹‍♀️ Embrace the chaos, dance with the uncertainty, and let the randomness guide you through the enchanted forest of code.

Thank you for joining me on this wacky journey! Stay random, stay Pythonic, and may your code always be delightfully unpredictable! 🎩✨

Keep coding, keep laughing, and remember – the only certainty in life is the uncertainty of random numbers! 🌈🐢🚀

Program Code – Random Generate Number

### code output:.

The output will display a list titled ‘Random Numbers:’ followed by five random numbers between 1 and 100, inclusive. Each time the program is run , a different set of numbers will be shown due to the random nature of the generation.

### Code Explanation:

The provided Python program is designed to generate a specific number of random integers within a defined range. It achieves this objective through the use of the random module, a cornerstone for randomness in Python, and a custom function called generate_random_numbers .

At the very beginning, the script imports the random module, crucial for leveraging the randint function which generates random integers between a specified range.

The heart of the program lies within the generate_random_numbers function. This function accepts three arguments: num_numbers , which determines how many random integers the function must generate; lower_bound , the smallest possible integer that can be randomly generated; and upper_bound , the largest possible integer that can be generated. Through the use of a for loop, the function iterates a specified number of times ( num_numbers ) and, during each iteration, generates a random integer between the lower_bound and upper_bound (inclusive) using the randint method. These integers are stored in a list called random_numbers .

After the completion of the loop, the list containing the generated random integers is returned.

In the example usage section, the script demonstrates how to use the generate_random_numbers function by specifying the desired number of random numbers ( num_numbers ), the minimum possible value ( lower_bound ), and the maximum possible value ( upper_bound ). Consequently, it calls the function with these parameters and prints the resulting list of random numbers.

The architecture of this program is simple yet effective, providing a reusable function that can be easily adapted for various applications requiring the generation of random numbers within a specific range. This demonstrates not just the power of Python’s built-in randomization capabilities but also how modular and flexible programming can achieve robust and dynamic outcomes.

F&Q (Frequently Asked Questions) on Random Generate Number with Python Program

Q: How can I generate a random number in Python?

A: You can use the random module in Python, specifically the randint() function, to generate a random integer within a specified range.

Q: Can I generate a random floating-point number in Python?

A: Yes, you can use the random module’s uniform() function to generate a random floating-point number within a specified range.

Q: Is there a way to get a random number from a specific distribution in Python?

A: Absolutely! Python’s random module provides functions like gauss() for a Gaussian distribution, expovariate() for an exponential distribution, and more.

Q: How can I generate a random number without repetition in Python?

A: To generate random numbers without repetition, you can use the random.sample() function to get a unique selection of numbers from a range.

Q: Can I seed the random number generator for reproducibility in Python?

A: Yes, you can use the random.seed() function to seed the random number generator with a particular value for reproducible results.

Q: Are there any libraries in Python for advanced random number generation?

A: Yes, you can explore libraries like NumPy and SciPy for advanced random number generation and statistical functions in Python.

Q: How efficient is Python in generating random numbers compared to other programming languages?

A: Python’s random number generation is efficient for most use cases, but for performance-critical applications, languages like C or C++ might offer faster random number generation .

Q: Can I use random numbers in Python for simulations and modeling purposes?

A: Yes, random number generation is a fundamental tool for simulations and modeling in fields like data science , machine learning, and statistics using Python.

Use these answers to guide you on your journey of mastering random number generation in Python ! 🚀

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  1. Python Program to Generate a Random Number

    To generate random number in Python, randint () function is used. This function is defined in random module. Source Code # Program to generate a random number between 0 and 9 # importing the random module import random print(random.randint(0,9)) Run Code Output 5

  2. random

    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.

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    There are a number of ways to generate a random numbers in Python using the functions of the Python random module. Let us see a few different ways. Generating a Random number using choice () Python random.choice () is an inbuilt function in the Python programming language that returns a random item from a list, tuple, or string. Python3

  4. Generating Random Data in Python (Guide)

    Generating Random Data in Python (Guide) by Brad Solomon data-science intermediate python Mark as Completed Share Share Table of Contents How Random Is Random? What Is "Cryptographically Secure?" What You'll Cover Here PRNGs in Python The random Module PRNGs for Arrays: numpy.random CSPRNGs in Python os.urandom (): About as Random as It Gets

  5. Generate Random Numbers in Python • datagy

    Learn how to generate random numbers in Python using the random and numpy modules, including the randrange, randint, random, and seed functions. See examples of how to create random floating point values, integers, lists, and distributions.

  6. How to Generate Random Numbers in Python

    Tutorial Overview This tutorial is divided into three parts; they are: Pseudorandom Number Generators Random Numbers with the Python Standard Library Random Numbers with NumPy 1. Pseudorandom Number Generators The source of randomness that we inject into our programs and algorithms is a mathematical trick called a pseudorandom number generator.

  7. Python Random Module: Generate Random Numbers and Data

    Example import random print("Printing random number using random.random ()") print(random.random()) Run As you can see in the result, we have got 0.50. You may get a different number. The random.random () is the most basic function of the random module. Almost all functions of the random module depend on the basic function random ().

  8. Generate Random Numbers in Python

    The random module. To create random numbers with Python code you can use the random module. To use it, simply type: 1. importrandom. This module has several functions, the most important one is just named random(). The random() function generates a floating point number between 0 and 1, [0.0, 1.0]. The random module has pseudo-random number ...

  9. How to Generate Random Numbers in Python

    You can do a random pick in Python by using the randint () function. We've created an example?—?10 clients are stored in a Pandas data frame. Each client has an ID and a name. The ID numbers go from 0 to 10 and uniquely identify each client. import pandas as pd clients = pd.DataFrame () clients ['client_id'] = [0,1,2,3,4,5,6,7,8,9]

  10. Python random randrange() & randint() to get Random Integer number

    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.

  11. random number

    Learn how to generate random numbers in Python using the random module, such as pseudo-random floats, integers, and lists. See examples of how to use the random module functions, such as random(), randint(), uniform(), shuffle(), and sample(), with syntax and output.

  12. Python random Module

    Python random Module Methods 1. seed() This initializes a random number generator. To generate a new random sequence, a seed must be set depending on the current system time. random.seed() sets the seed for random number generation. 2. getstate() This returns an object containing the current state of the generator.

  13. Exploring the Random Module: Generating Basic Random Numbers in Python

    Exploring the Random Module. The random module is the Python module behind the random number generator (RNG) algorithm. Most of the RNG algorithms use the random module to generate pseudo-random numbers. The random module can be imported into our environment with the help of this simple line of code. import random.

  14. Random Generator

    The Python stdlib module random contains pseudo-random number generator with a number of methods that are similar to the ones available in Generator. It uses Mersenne Twister, and this bit generator can be accessed using MT19937 .

  15. Introduction to Random Number Generators for Machine Learning in Python

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    How can I generate random integers between 0 and 9 (inclusive) in Python? For example, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 python random integer Share Follow edited Oct 18, 2018 at 8:08 user6269864 asked Oct 22, 2010 at 12:48 aneuryzm 63.8k 100 278 492

  17. Python Random Module: 6 Essential Methods for Generating Random Numbers

    shuffle () Let's try to generate random numbers using them one by one. 1. Generating Random Numbers Using randint () Function. When we need to generate random integers between two numbers, let's say a and b with both a and b being inclusive, we can use the function randint () of the random module.

  18. Python's Random Number Generator: Complete Guide

    Python Generate Random Number - A JavaTpoint tutorial that guides you through generating random numbers in Python. Python NumPy Random - Learn about the NumPy random module to generate random numbers in Python with W3Schools' guide. Python's Math Module - Explore Python's math module with W3Schools' quick reference guide.

  19. Python random module

    The built-in Python random module implements pseudo-random number generators for various distributions. Python uses the Mersenne Twister algorithm to produce its pseudo-random numbers. This module is not suited for security. For security related tasks, the secrets module is recommended.

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    11 Answers Sorted by: 268 You can use either of random.randint or random.randrange. So to get a random 3-digit number: from random import randint, randrange randint(100, 999) # randint is inclusive at both ends randrange(100, 1000) # randrange is exclusive at the stop * Assuming you really meant three digits, rather than "up to three digits".

  22. Efficient way to generate and use millions of random numbers in Python

    Python builtin random module, e.g. random.random(), random.randint(), (some distributions also available, you probably want gaussian) does about 300K samples/s.. Since you are doing numerical computation, you probably use numpy anyway, that offers better performance if you cook random number one array at a time instead of one number at a time and wider choice of distributions. 60K/s * 1024 ...

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    Instantly Download or Run the code at https://codegive.com title: a comprehensive guide to python's random number generatorintroduction:python provides a ve...

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  25. How to bring my Python code to work in Microsoft Excel?

    Currently, what the macro VBA does is to generate random numbers and perform complex calculations involving various sheets. I already successfully create that code. If I run that code in my IDE (Jupyter Notebook), Excel will process the operations exactly like what the VBA does.