Random Numbers
Random numbers are important to many projects. Python has 2 (many) ways you can generate random numbers. 1) through numpy 2) through the random module.
np.random.random() and np.random.rand() are identical functions used to return numbers sampled uniformly from the half-open interval [0,1).
The random module provides a fast pseudorandom number generator as well.
Both are based on the based on the Mersenne Twister algorithm. (So if you a Cyber person .. these random numbers are not what you want... use the cyrpto modules.)
Again the NumPy one is usually better in most cases - faster, better memory, performance. (but numpy.random.seed() ) might not be fully thread safe... Ping me if this has been updated.)
NumPy Random Distributions
You have access to a large number of distributions Links to an external site. as well - binomial, exponential, uniform, logarithmic Weibull, etc....
Seeding
random() produces different values each time it is called and has a very large period before it repeats any numbers. This is useful for producing unique values or variations, but there are times when having the same data set available to be processed in different ways is useful. One technique is to use a program to generate random values and save them to be processed by a separate step. That may not be practical for large amounts of data, though, so random includes the seed() function for initializing the pseudorandom generator so that it produces an expected set of values.
We only change one line from the top example to add the seed. If you run this over and over again you get random numbers but now they always stay the same between runs.
Random Integers
You are not limited to only simulating random floats, you can also simulte random integers.
References:
- Random Sampling Links to an external site. - SciPy documention
- Python Random module Links to an external site. from PYMOTW