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You can use the random module to generate random numbers in Python.
Python provides a module called random that can generate random numbers.
These are pseudo-random numbers because the generated number sequence depends on the seed.
If the seed value is the same, the sequence will be the same. For example, if using2As the seed value, the following sequence will always be seen.
import random random.seed(2) print(random.random()) print(random.random()) print(random.random())
The output will always follow the following order:
0.9560342718892494 0.9478274870593494 0.05655136772680869
Not so casual?Since this generator is completely deterministic, it must not be used for encryption purposes.
This is a list of functions defined in the random module, and a brief explanation of their functions.
| Function | Description |
|---|---|
| seed(a=None, version=2) | Initialize the random number generator |
| getstate() | Return an object capturing the current internal state of the generator |
| setstate(state) | Restore the internal state of the generator |
| getrandbits(k) | Return a Python integer with k random bits |
| randrange(start, stop[, step]) | Return a random integer within the range |
| randint(a, b) | Return a random integer between a and b |
| choice(seq) | Return a random element from a non-empty sequence |
| shuffle(seq) | Random sequence |
| sample(population, k) | Return a list of unique elements from the filled sequence with length ak |
| random() | Return a range of [0.0,1The next random floating-point number of the form .0) |
| uniform(a, b) | Return a random floating-point number between a and b |
| triangular(low, high, mode) | Return a random floating-point number between the lower and upper bounds, and specify the pattern within these boundaries |
| betavariate(alpha, beta) | Beta distribution |
| expovariate(lambd) | Exponential distribution |
| gammavariate(alpha, beta) | Gamma distribution |
| gauss(mu, sigma) | Gaussian distribution |
| lognormvariate(mu, sigma) | Lognormal distribution |
| normalvariate(mu, sigma) | Normal distribution |
| vonmisesvariate(mu, kappa) | Vonmises distribution |
| paretovariate(alpha) | Pareto distribution |
| weibullvariate(alpha, beta) | Weibull distribution |