Как заполнить массив случайными числами питон

How to Generate Random Numbers in Python?

Random numbers are all around us in the world of data science. Every so often I need to quickly draw up some random numbers to run a thought experiment, or to demonstrate a concept to an audience but without having to download big datasets.

From creating dummy data to shuffling the data for training and testing purposes or initializing weights of a neural network, we generate random numbers all the time in Python. You’ll love this concept once you have the hang of it after this article.

In my opinion, generating random numbers is a must-know topic for anyone in data science. I’ll guide you through the entire random number generation process in Python here and also demonstrate it using different techniques.

New to Python? These two free courses will get you started:

Table of Contents

• Random Library
• Seeding Random Numbers
• Generating Random Numbers in a Range
• Picking Up Randomly From a List
• Shuffling a List
• Generating Random Numbers According to Distributions

Generating Random Numbers in Python using the Random Library

Here’s the good news — there are various ways of generating random numbers in Python. The easiest method is using the random module. It is a built-in module in Python and requires no installation. This module uses a pseudo-random number generator (PRNG) known as Mersenne Twister for generating random numbers.

A pseudo-random number generator is a deterministic random number generator. It does not generate truly random numbers. It takes a number as an input and generates a random number for it.

Note: Do not use the random module for generating random numbers for security purposes. For security and cryptographic uses, you can use the module which uses true-random number generators (TRNG).

Seeding Random Numbers

As we discussed in the above section, the random module takes a number as input and generates a random number for it. This initial value is known as a seed, and the procedure is known as seeding.

The numbers we generate through pseudo-random number generators are deterministic. This means they can be replicated using the same seed.

Let’s understand it with an example:

Here, I am using the random() function which generates a random number in the range [0.0, 1.0]. Notice here that I haven’t mentioned the value of the seed. By default, the current system time in milliseconds is used as a seed. Let’s take a look at the output.

Both numbers are different because of the change in time during execution from the first statement to the second statement. Let’s see what happens if we seed the generators with the same value:

We get the same numbers here. That’s why pseudo-random number generators are deterministic and not used in security purposes because anyone with the seed can generate the same random number.

Generating Random Numbers in a Range

So far, we know about creating random numbers in the range [0.0, 1.0]. But what if we have to create a number in a range other than this?

One way is to multiply and add numbers to the number returned by the random() function. For example, random.random() * 3 + 2 will return numbers in the range [2.0, 5.0]. However, this is more of a workaround, not a straight solution.

Don’t worry! The random module has got your back here. It provides uniform() and randint() functions that we can use for this purpose. Let’s understand them one by one.

uniform()

The uniform() function of the random module takes starting and ending values of a range as arguments and returns a floating-point random number in the range [starting, ending]:

randint()

This function is similar to the uniform() function. The only difference is that the uniform() function returns floating-point random numbers, and the randint() function returns an integer. It also returns the number in the range [starting, ending]:

Picking Up Randomly From a List

choice() & choices() are the two functions provided by the random module that we can use for randomly selecting values from a list. Both of these functions take a list as an argument and randomly select a value(s) from it. Can you guess what the difference between choice() and choices() is?

choice() only picks a single value from a list whereas choices() picks multiple values from a list with replacement. One fantastic thing about these functions is that they work on a list containing strings too. Let’s see them in action:

As you can see, choice() returned a single value from a and choices() returned three values from a. Here, k is the length of the list returned by choices().

One more thing you can notice in the responses returned by choices() is that each value occurs only once. You can increase the probability of each value being picked by passing an array as weights to the choices() function. So, let’s increase the probability of 10 to as much as thrice of others and see the results:

Here, we can see that 10 occurred in every draw from the list. There also exists a sample() function in the random module that works similarly to the choices() function but takes random samples from a list without replacement.

Shuffling a List

Let’s say we don’t want to pick values from a list but you just want to reorder them. We can do this using the shuffle() function from the random module. This shuffle() function takes the list as an argument and shuffles the list in-place:

Note: The shuffle() function does not return a list.

Generating Random Numbers According to Distributions

One more amazing feature of the random module is that it allows us to generate random numbers based on different probability distributions. There are various functions like gauss(), expovariate(), etc. which help us in doing this.

If you are not familiar with probability distributions, then I highly recommend you to read this article: 6 Common Probability Distributions every data science professional should know.

gauss()

Let’s start with the most common probability distribution, i.e., normal distribution. gauss() is a function of the random module used for generating random numbers according to a normal distribution. It takes mean and standard deviation as an argument and returns a random number:

Here, I plotted 1000 random numbers generated by the gauss() function for mean equal to 0 and standard deviation as 1. You can see above that all the points are spread around the mean and they are not widely spread since the standard deviation is 1.

expovariate()

Exponential distribution is another very common probability distribution that you’ll encounter. The expovariate() function is used for getting a random number according to the exponential distribution. It takes the value of lambda as an argument and returns a value from 0 to positive infinity if lambda is positive, and from negative infinity to 0 if lambda is negative:

End Notes

I often use random numbers for creating dummy datasets and for random sampling. I’d love to know how you use random numbers in your projects so comment down below with your thoughts and share them with the community.

If you found this article informative, then please share it with your friends and comment below your queries and feedback. I have listed some amazing articles related to Python and data science below for your reference:

random — Generate pseudo-random numbers¶

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: in that case, override the random() , seed() , getstate() , and setstate() methods. Optionally, a new generator can supply a getrandbits() method — this allows randrange() to produce selections over an arbitrarily large range.

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: NoneType, 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.

random. setstate ( state ) ¶

state should have been obtained from a previous call to getstate() , and setstate() restores the internal state of the generator to what it was at the time getstate() was called.

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 equivalent to choice(range(start, stop, step)) , but doesn’t actually build a range object.

The positional argument pattern matches that of range() . Keyword arguments should not be used because the function may use them in unexpected ways.

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.

Deprecated since version 3.10: The automatic conversion of non-integer types to equivalent integers is deprecated. Currently randrange(10.0) is losslessly converted to randrange(10) . In the future, this will raise a TypeError .

Deprecated since version 3.10: The exception raised for non-integral values such as randrange(10.5) or randrange(’10’) will be changed from ValueError to TypeError .

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

random. getrandbits ( k ) ¶

Returns a non-negative Python integer with k random bits. This method is supplied with the MersenneTwister 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 .

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

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

If a weights sequence is specified, selections are made according to the relative weights. Alternatively, if a cum_weights sequence is given, the selections are made according to the cumulative weights (perhaps computed using itertools.accumulate() ). For example, the relative weights [10, 5, 30, 5] are equivalent to the cumulative weights [10, 15, 45, 50] . Internally, the relative weights are converted to cumulative weights before making selections, so supplying the cumulative weights saves work.

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

The weights or cum_weights can use any numeric type that interoperates with the float values returned by random() (that includes integers, floats, and fractions but excludes decimals). Weights are assumed to be non-negative and finite. A ValueError is raised if all weights are zero.

For a given seed, the choices() function with equal weighting typically produces a different sequence than repeated calls to choice() . The algorithm used by choices() uses floating point arithmetic for internal consistency and speed. The algorithm used by choice() defaults to integer arithmetic with repeated selections to avoid small biases from round-off error.

New in version 3.6.

Changed in version 3.9: Raises a ValueError if all weights are zero.

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.

Deprecated since version 3.9, removed in version 3.11: 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.

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() .

random. triangular ( low , high , mode ) ¶

Return a random floating point number N such that low <= N <= high and with the specified mode between those bounds. The low and high bounds default to zero and one. The mode argument defaults to the midpoint between the bounds, giving a symmetric distribution.

random. betavariate ( alpha , beta ) ¶

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

random. expovariate ( lambd ) ¶

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.

random. gammavariate ( alpha , beta ) ¶

Gamma distribution. (Not the gamma function!) Conditions on the parameters are alpha > 0 and beta > 0 .

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.

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

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

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

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

random. paretovariate ( alpha ) ¶

Pareto distribution. alpha is the shape parameter.

random. weibullvariate ( alpha , beta ) ¶

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

Alternative Generator¶

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

Deprecated since version 3.9: In the future, the seed must be one of the following types: NoneType , int , float , str , bytes , or bytearray .

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 re-using 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.

Заполнение списка рандомными значениями в Python

Статьи

Введение

В этой небольшой статье рассмотрим способы заполнения списка рандомными значениями.

В основном будем использовать функции модуля random.

Заполнение списка используя функции модуля random

Заполнение списка используя функцию randint()

Самая распространённая функция в модуле random это конечно же randint(), и именно им мы воспользуемся в первом способе!

Для начала мы его импортируем, после чего создаём пустой список, который в последствии будем заполнять:

Далее нам понадобится цикл, в котором мы будем заполнять список рандомными значениями:

Осталось только вывести результат:

При желании можно значительно сократить код, и сделать генератор списка:

Заполнение списка используя функцию sample()

Самой подходящей функцией является как раз таки sample(), но почему-то о ней говорят редко.

Для начала естественно импортируем модуль random, после чего сгенерируем список рандомных значений и выведем их:

В функции range() мы указали диапазон чисел, а через запятую количество генерируемых чисел.

Заполнение списка используя функции модуля numpy

Переходим к генерации рандомных чисел с помощью модуля numpy. Из него мы будем использовать функцию randint().

Для начала мы его конечно же импортируем:

Далее создаём список, который мы сразу же заполняем рандомными значениями и выведем его:

В первом параметре мы указываем минимальное значение, которое может быть сгенерировано, во втором – максимальное, и в третьем количество значений.

Заключение

В статье мы с Вами научились генерировать списки с рандомными значениями разными способами. Надеюсь Вам понравилась статья, удачи! ��

Python 3: Генерация случайных чисел (модуль random)¶

Python порождает случайные числа на основе формулы, так что они не на самом деле случайные, а, как говорят, псевдослучайные [1]. Этот способ удобен для большинства приложений (кроме онлайновых казино) [2].

Модуль random позволяет генерировать случайные числа. Прежде чем использовать модуль, необходимо подключить его с помощью инструкции:

random.random¶

random.random() — возвращает псевдослучайное число от 0.0 до 1.0

random.seed¶

random.seed(<Параметр>) — настраивает генератор случайных чисел на новую последовательность. По умолчанию используется системное время. Если значение параметра будет одиноким, то генерируется одинокое число:

random.uniform¶

random.uniform(<Начало>, <Конец>) — возвращает псевдослучайное вещественное число в диапазоне от <Начало> до <Конец> :

random.randint¶

random.randint(<Начало>, <Конец>) — возвращает псевдослучайное целое число в диапазоне от <Начало> до <Конец> :

random.choince¶

random.choince(<Последовательность>) — возвращает случайный элемент из любой последовательности (строки, списка, кортежа):

random.randrange¶

random.randrange(<Начало>, <Конец>, <Шаг>) — возвращает случайно выбранное число из последовательности.

random.shuffle¶

random.shuffle(<Список>) — перемешивает последовательность (изменяется сама последовательность). Поэтому функция не работает для неизменяемых объектов.

Вероятностные распределения¶

random.triangular(low, high, mode) — случайное число с плавающей точкой, low ≤ N ≤ high . Mode — распределение.

random.betavariate(alpha, beta) — бета-распределение. alpha>0 , beta>0 . Возвращает от 0 до 1.

random.expovariate(lambd) — экспоненциальное распределение. lambd равен 1/среднее желаемое. Lambd должен быть отличным от нуля. Возвращаемые значения от 0 до плюс бесконечности, если lambd положительно, и от минус бесконечности до 0, если lambd отрицательный.

random.gammavariate(alpha, beta) — гамма-распределение. Условия на параметры alpha>0 и beta>0 .

random.gauss(значение, стандартное отклонение) — распределение Гаусса.

random.lognormvariate(mu, sigma) — логарифм нормального распределения. Если взять натуральный логарифм этого распределения, то вы получите нормальное распределение со средним mu и стандартным отклонением sigma . mu может иметь любое значение, и sigma должна быть больше нуля.

random.normalvariate(mu, sigma) — нормальное распределение. mu — среднее значение, sigma — стандартное отклонение.

random.vonmisesvariate(mu, kappa) — mu — средний угол, выраженный в радианах от 0 до 2π, и kappa — параметр концентрации, который должен быть больше или равен нулю. Если каппа равна нулю, это распределение сводится к случайному углу в диапазоне от 0 до 2π.

random.paretovariate(alpha) — распределение Парето.

random.weibullvariate(alpha, beta) — распределение Вейбулла.

Примеры¶

Генерация произвольного пароля¶

Хороший пароль должен быть произвольным и состоять минимум из 6 символов, в нём должны быть цифры, строчные и прописные буквы. Приготовить такой пароль можно по следующему рецепту:

Этот же скрипт можно записать всего в две строки:

Данная команда является краткой записью цикла for, вместо неё можно было написать так:

Данный цикл повторяется 12 раз и на каждом круге добавляет к строке psw произвольно выбранный элемент из списка.