Python has a built-in module that you can use to make random numbers.
The random module has a set of methods:
| seed[] | Initialize the random number generator |
| getstate[] | Returns the current internal state of the random number generator |
| setstate[] | Restores the internal state of the random number generator |
| getrandbits[] | Returns a number representing the random bits |
| randrange[] | Returns a random number between the given range |
| randint[] | Returns a random number between the given range |
| choice[] | Returns a random element from the given sequence |
| choices[] | Returns a list with a random selection from the given sequence |
| shuffle[] | Takes a sequence and returns the sequence in a random order |
| sample[] | Returns a given sample of a sequence |
| random[] | Returns a random float number between 0 and 1 |
| uniform[] | Returns a random float number between two given parameters |
| triangular[] | Returns a random float number between two given parameters, you can also set a mode parameter to specify the midpoint between the two other parameters |
| betavariate[] | Returns a random float number between 0 and 1 based on the Beta distribution [used in statistics] |
| expovariate[] | Returns a random float number based on the Exponential distribution [used in statistics] |
| gammavariate[] | Returns a random float number based on the Gamma distribution [used in statistics] |
| gauss[] | Returns a random float number based on the Gaussian distribution [used in probability theories] |
| lognormvariate[] | Returns a random float number based on a log-normal distribution [used in probability theories] |
| normalvariate[] | Returns a random float number based on the normal distribution [used in probability theories] |
| vonmisesvariate[] | Returns a random float number based on the von Mises distribution [used in directional statistics] |
| paretovariate[] | Returns a random float number based on the Pareto distribution [used in probability theories] |
| weibullvariate[] | Returns a random float number based on the Weibull distribution [used in statistics] |
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Random Permutations of Elements
A permutation refers to an arrangement of elements. e.g. [3, 2, 1] is a permutation of [1, 2, 3] and vice-versa.
The NumPy Random module provides two methods for this: shuffle[] and permutation[].
Shuffling Arrays
Shuffle means changing arrangement of elements in-place. i.e. in the array itself.
Example
Randomly shuffle elements of following array:
from numpy import random
import numpy as np
arr = np.array[[1, 2, 3, 4, 5]]
random.shuffle[arr]
print[arr]
Try it Yourself »
The shuffle[] method makes changes to the original array.
Generating Permutation of Arrays
Example
Generate a random permutation of elements of following array:
from numpy import random
import numpy as np
arr = np.array[[1, 2, 3, 4, 5]]
print[random.permutation[arr]]
Try it Yourself »
The permutation[] method returns a re-arranged array [and leaves the original array un-changed].
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