NumPy provides a wide range of functions to generate random numbers in arrays. These functions are available in the numpy.random module.
The random numbers are generated using the same, excellent pseudo-random number generator called Mersenne Twister that is also used in the normal random module. Mersenne Twister has a period of 2^19937-1 and is generally regarded as a very good pseudo-random number generator (for non-cryptographic purposes).
Several functions for constructing random arrays are provided, including:
- random: uniform random numbers
- normal: normal distribution
- choice: random sample from given array
a = numpy.random.random((2,2)) print(a) # output: # [[ 0.02909142 0.90848 ] # [ 0.9471314 0.31424393]] b = numpy.random.choice(numpy.arange(4), 10) print(b) # output: [0 1 1 2 1 1 2 0 2 3]
What kind of random numbers do you need in your won work? Can you find them in NumPy?
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