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This article mainly explains "the method of generating random data in Python". The content of the explanation is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "the method of generating random data in Python".

How random is it?

Most random data generated by Python are not completely random in a scientific sense. Instead, it is pseudo-random: generated using a pseudo-random number generator (PRNG), which is essentially any algorithm used to generate seemingly random but still reproducible data. A "true" random number can be generated by a true random number generator (TRNG).

You may have seen something like random.seed (999) in Python. This function calls the underlying random number generator used by the Python module random.seed (1234). Random makes random numbers generated by subsequent calls deterministic: input An always produces output B.

Perhaps the terms "random" and "deterministic" do not seem to coexist. To illustrate this more clearly, there is an extremely compact version of random () that creates a "random" number x = (x * 3)% 19 by using iterations. X is initially defined as a seed value, and then deforms into a deterministic sequence of numbers based on the seed.

Class NotSoRandom (object): def seed (self Random number generator "self.seedval = a def random (self):"Random number"self.seedval = (self.seedval * 3)% 19 return self.seedval_inst = NotSoRandom () seed = _ inst.seedrandom = _ inst.randomfor i in range (10): seed (123) print ([random () for _ in range (10)]) [8,5,15,7] 2, 6, 18, 16, 10, 11] [8, 5, 15, 7, 2, 6, 18, 16, 10, 11] [8, 5, 15, 7, 2, 6, 18, 16, 10, 11] [8, 5, 15, 7, 2, 6, 18, 16, 10, 11] [8, 5, 15, 7, 2, 6, 18, 16, 10, 11] [8, 5, 15, 7, 2, 6, 18, 16, 10, 11] [8, 5, 15, 7, 2, 6, 18 16, 10, 11] [8, 5, 15, 7, 2, 6, 18, 16, 10, 11] [8, 5, 15, 7, 2, 6, 5, 15, 7, 2, 6, 18, 16, 10, 11] encryption security

If you don't know enough about the acronym "RNG", add a CSPRNG, or encrypt the security PRNG. CSPRNG is suitable for generating sensitive data, such as passwords, authenticators, and tokens. Given a random string, it is virtually impossible to determine which string appears before or after the random string sequence.

Another term entropy, the amount of randomness introduced or expected. For example, a Python module that will be introduced defines DEFAULT_ENTROPY = 32, the number of bytes returned by default.

One of the key points about CSPRNG is that they are still pseudo-random. They are designed in some way of internal certainty, but add some other variables or properties that make them "random enough" to prohibit returning to any function that enforces certainty.

PRNG and CSPRNG in the Python tool:

PRNG options include the random module in the Python standard library and its array-based NumPy counterpart module numpy.random.

The os, secrets, and uuid modules of Python contain functions for generating encrypted security objects.

PRNG

Random module

The random module is the most well-known tool for generating random data in Python, using the Mersenne Twister PRNG algorithm as its core generator.

Build some random data that is not sown. The random.random () function returns a random floating point number in the interval [0.0,1.0).

Import randomrandom.random () 0.1250920165739744random.random () 0.7327868824782764

Using random.seed (), you can make the result reproducible, and the subsequent call chain random.seed () will produce the same data trajectory.

The sequence of random numbers becomes deterministic or completely determined by the seed value.

Random.seed (444) random.random () 0.3088946587429545random.random () 0.01323751590501987random.seed () random.random () 0.3088946587429545random.random () 0.0132375159050 1987

Using random.randint (), you can use this function to generate a random integer between two endpoints in Python. The data is in the entire [x, y] range and may include two endpoints.

> random.randint (0,10) 2 > random.randint (500,50000) 9991

Use random.randrange () to exclude the right side of the interval, where the resulting number is always within the [x, y) range and is always smaller than the right endpoint.

Random.randrange (1,10) 9

Using random.uniform (), a random floating point number is extracted from a continuous uniform distribution to generate a random floating point number in a specific [x, y] interval.

Random.uniform (20,30) 27.42639687016509random.uniform (30,40) 36.33865802745107

Use random.choice () to select random elements from a non-empty sequence, such as a list or tuple.

Items = ['Aids,' bundles, 'cations,' dudes,'E'] random.choice (items) 'B'random.choices (items, kryp2) [' Aids,'C'] random.choices (items, kryp3) ['cations,' Downs,'E']

Use random.sample () to simulate sampling without substitution.

Random.sample (items, 4) ['Aids,' Downs, 'Balls,' E']

Use random.shuffle () to modify sequence objects and randomize the order of elements.

Random.shuffle (items) items ['estranged,' baked, 'awaited,' clocked,'D']

An example of generating a series of unique random strings of the same length, commonly used for CAPTCHA.

From random import Random# randomly generates the random string def RandomsStr (random_length): Str =''chars =' AaBbCcDdEeFfGgHhIiJjKkLlMmNnOoPpQqRrSsTtUuVvWwXxYyZz0123456789' # to set the optional character length = len (chars)-1 random = Random () for i in range (random_length): Str + = chars [random.randint (0, length)] return StrRandomsStr (10) LhK3vFepchRandomsStr (16) iGy1g0FO54Cjx3WP array numpy.random

Most functions random return a scalar value (a single int, float, or other object). If you generate a sequence, you can use the method of list generation.

[random.random () for _ in range (5)] [0.7401011155476498,0.9892634439644596,0.36991622177966765,0.14950913503744223,0.4868906039708182]

Numpy.random uses its own PRNG, which is quite different from normal random.

Np.random.randn (5) array ([- 0.59656657,-0.6271152,-1.51244475,-1.02445644,-0.36722254]) np.random.randn (3,4) array ([[0.34054183, 1.59173609,-0.5257795,-0.86912511], [- 0.86855499,-0.64487065,1.47682128,1.8238103], [0.05477224] Np.random.choice ([0,1], p = [0.35452769, 0.55049185], size= (5, 4)) array ([[0,1,0,1], [0,1,1,1], [0,1,0,1], [1,0,0,0], [0,0,0,0] 0]]) "" create a series of random Boolean values "" np.random.randint (0,2, size=25, dtype=np.uint8) .view (bool) array ([True, False, True, True, False, True, False, True, True, False, True]) related data generation

Suppose you want to simulate two related time series. One way to solve this problem is to use NumPy's multivariate_normal () function, which takes into account the covariance matrix. In other words, to extract from a random variable of a single normal distribution, you need to specify its mean and variance (or standard deviation).

Def corr2cov (p, s): "" Covariance matrix of correlation and standard deviation "" d = np.diag (s) return d @ p @ dcorr = np.array ([1.40], [- 0.40,1.]]) stdev = np.array ([6.1,1.]) mean= np.array ([2.0.5]) cov= corr2cov (corr, stdev) data = np.random.multivariate_normal (mean=mean, cov=cov) Size=50) data [: 10] [[- 0.33377432 0.22889428] [- 1.5311996 0.31678635] [- 6.02684472 0.90562824] [5.2696086 0.86518295] [6.43832395 0.36507745] [- 8.49347011 0.68663565] [- 5.05968126 0.55214914] [2.02314646 1.32325775] [0.98705556-0.63118682] random module and NumPy comparison table random module NumPy pair The party shall specify random () rand () [0.0] Random floating point numbers in random floating point numbers randint (a, b) random_integers () [a, b] random integers randrange (a, b [, step]) randint () [a, b) random integers uniform (a, b) uniform () [a, b] random floating point numbers choice (seq) choice () random elements come from random k element seqsample (population) with substitution in seqchoices (seq, kumb1) choice () K) choice () and replace=False unsubstituted random k elements seqshuffle (x [, random]) shuffle () randomly disrupt the sequence normalvariate (mu, sigma) or gauss (mu, sigma) normal () mu normal distribution sample sigmaCSPRNG with mean and standard deviation

Os.urandom as randomly as possible ()

Without too much detail, generating random bytes that depend on the operating system, which can safely be called password secure secretsuuidos.urandom (), is still technically pseudorandom.

The only parameter is the number of bytes to return.

Os.urandom (3) b'\ xa2\ xe8\ x02roomx = os.urandom (6) xb'\ xce\ x11\ xe7 "!\ x84'type (x), len (x) (bytes, 6)

However, this save format does not quite meet the requirements of the development.

The best way to save secrets

The PEP,secrets module introduced in Python 3.6 + is intended to be a de facto Python module for generating encrypted and secure random bytes and strings.

Secrets is basically a wrapper os.urandom (). Only a few functions are derived for generating random numbers, bytes, and strings.

N = "generate security token secrets.token_bytes (n) BundA\ x8cz\ xe1o\ xf9token;\ x8b\ xf2\ x80pJ\ x8b\ xd4\ xd3'secrets.token_hex (n) '9cb190491e01230ec4239cae643f286f' secrets.token_urlsafe (n) the secure version secrets.choice (' rain') 'a'UUID of' MJoi7CknFu3YN41m88SEgQ'# `random.choice () `

The last option for generating random tokens is the uuid4 () function in Python's uuid module. UUID is a universal unique identifier, a 128-bit sequence (a 32-bit string) designed to "guarantee uniqueness across space and time". Uuid4 () is one of the most useful functions in this module, which also uses os.urandom ().

Import uuiduuid.uuid4 () UUID ('3e3ef28d-3ff0-4933-9bbamure5ee91ce0e7b') uuid.uuid4 () UUID (' 2e115fcb-5761-4fa1-8287-19f4ee2877ac')

You may also see some other variants: uuid1 (), uuid3 (), and uuid5 (). The main difference between them is that the three uuid4 () functions all take some form of input, which does not conform to the "guarantee uniqueness across space and time" of uuid4 ().

In addition to the security module (such as secrets), Python's random module actually has a rarely used class called SystemRandom, which uses os.urandom (). (in turn, SystemRandom is also used secretly. It's kind of like a network that goes back to urandom (). )

So why not "default" this version? Why not "always safe" instead of using deterministic random functions that are cryptographically insecure by default?

1. Because sometimes you want the data to be deterministic and repeatable for subsequent use by others.

two。 The problem of time efficiency.

"" CSPRNG is at least in Python and tends to be much slower than PRNG. Let's test it with the script timed.py, which uses timeit.repeat () to compare the PRNG and CSPRNG versions of randint (). "" The import randomimport timeit# CSPRNG version uses `SystemRandom () `and `os.urandom ()` in turn. _ sysrand = random.SystemRandom () def prng ()-> None: random.randint (0,95) def csprng ()-> None: _ sysrand.randint (0,95) setup = 'import random From _ _ main__ import prng, csprng'if _ _ name__ ='_ main__': print ('Best of 3 trials with 1000000 loops per trial:') for f in (' prng ()', 'csprng ()'): best = min (timeit.repeat (f, setup=setup)) print ('\ t {: 8s} {: 0.2f} seconds total time.'.format (f) Best) Best of 3 trials with 1000000 loops per trial: prng () 0.93 seconds total time. Csprng () 1.70 seconds total time. Engineering randomness comparison encapsulation / module description encryption security random uses Mersenne Twister fast and simple random data is not numpy.random like random but for (possibly multidimensional) arrays os does not contain urandom (). The other functions introduced here are based on the de facto module designed by secrets as Python Random numbers, bytes and strings are used to generate secure random numbers, bytes and strings are the location of some functions used by uuid to build 128bit identifiers uuid4 () is thank you for reading, the above is the content of "the method of generating random data in Python". After the study of this article, I believe you have a deeper understanding of the method of generating random data in Python, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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