Python offers random module that can generate random numbers.
These are pseudo-random number as the sequence of number generated depends on the seed.
If the seeding value is same, the sequence will be the same. For example, if you use 2 as the seeding value, you will always see the following sequence.
import random
random.seed(2)
print(random.random())
print(random.random())
print(random.random())
The output will always follow the sequence:
0.9560342718892494 0.9478274870593494 0.05655136772680869
Not so random eh? Since this generator is completely deterministic, it must not be used for encryption purpose.
Here is the list of all the functions defined in random module with a brief explanation of what they do.
| Function | Description |
|---|---|
| seed(a=None, version=2) | Initialize the random number generator |
| getstate() | Returns an object capturing the current internal state of the generator |
| setstate(state) | Restores the internal state of the generator |
| getrandbits(k) | Returns a Python integer with k random bits |
| randrange(start, stop[, step]) | Returns a random integer from the range |
| randint(a, b) | Returns a random integer between a and b inclusive |
| choice(seq) | Return a random element from the non-empty sequence |
| shuffle(seq) | Shuffle the sequence |
| sample(population, k) | Return a k length list of unique elements chosen from the population sequence |
| random() | Return the next random floating point number in the range [0.0, 1.0) |
| uniform(a, b) | Return a random floating point number between a and b inclusive |
| triangular(low, high, mode) | Return a random floating point number between low and high, with the specified mode between those bounds |
| betavariate(alpha, beta) | Beta distribution |
| expovariate(lambd) | Exponential distribution |
| gammavariate(alpha, beta) | Gamma distribution |
| gauss(mu, sigma) | Gaussian distribution |
| lognormvariate(mu, sigma) | Log normal distribution |
| normalvariate(mu, sigma) | Normal distribution |
| vonmisesvariate(mu, kappa) | Vonmises distribution |
| paretovariate(alpha) | Pareto distribution |
| weibullvariate(alpha, beta) | Weibull distribution |
Visit this page to learn more on how you can generate pseudo-random numbers in Python.