Numpy¶

random module in Python uses the Mersenne Twister algorithm to generate random numbers.

The numpy library uses a more efficient PCG (Permuted Congruential Generator) algorithm.

Creating a basic array¶

import numpy as np
A = np.array([3, 7, 2, 4, 5])

>>> np.arange(10)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

>>> np.arange(2, 3, 0.1)
array([ 2., 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9])

range() vs numpy.arange()¶

Unlike Python’s built-in range() function, numpy.arange() supports non-integer step sizes and returns an array of floats when needed.

Built-in vs numpy's max() and min() functions¶

The built-in max() and min() functions work for one-dimensional lists or arrays, but do not handle multi-dimensional arrays. In such cases, numpy's max() and min() functions are useful.

numpy.nanmax()¶

If the array contains missing elements (common in real-world data), numpy.max() will return nan because the maximum cannot be determined.

nanmax() should be used in this case to find the maximum value while ignoring nan values.

Random Number Generators¶

Generate random numbers¶

import numpy as np

default_rng = np.random.default_rng()
default_rng
print(default_rng.random())

Generate random numbers between low and high value¶

.random and .uniform are exactly same unless the low and high values are provided.

import numpy as np

default_rng = np.random.default_rng()
print(default_rng.uniform(low = 3, high = 9))

Generate random integers¶

import numpy as np

default_rng = np.random.default_rng()
print(default_rng.integers(low = 0, high = 100))

More powerful random number generator¶

from numpy.random import Generator, PCG64DXSM
pcg64dxsm_rng = Generator(PCG64DXSM())
print(pcg64dxsm_rng.random())

Generate an array of random numbers¶

If you assign a tuple to size, then you’ll generate an array.

.random(size=(5,5)) 5X5 array
.uniform(low = 1, high = 10, size=(10,)) 1X10 array
.integer(size(50,32)) 50X32 array

Selecting a random element from an array¶

The Generator object’s .choice() method allows you to select random samples from an array.

>>> import numpy as np

>>> rng = np.random.default_rng()

>>> input_array_1d = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
>>> rng.choice(input_array_1d, size=3, replace=False)
array([ 6, 12, 10])

>>> rng.choice(input_array_1d, size=(2, 3), replace=False)
array([[ 8, 12, 11],
       [10,  7,  5]])

If you set replace=False, the same element cannot be selected more than once. By default, replace is True, so elements may be selected multiple times.

Generate normally distributed random numbers¶

import numpy as np

default_rng = np.random.default_rng()
print(default_rng.normal())