Random Arrays

It is often the case that we need to generate a random number or an array of random numbers. Luckily, NumPy has a module named random specifically for this purpose.

The two most commonly used functions of the random module are:

rand();
randint().

rand()

The numpy.random.rand() function is used to generate either a random float number or an array of random floats from a uniform distribution over [0, 1).

Note

Square brackets [ or ] in interval notation indicate that the value is included, while parentheses ( or ) indicate that the value is excluded. For example, [0, 1) means the interval includes 0 but not 1. Don't confuse this mathematical notation with brackets used in code — they serve different purposes.

Its only possible arguments are the dimensions of the array. If no argument is passed, rand() generates a random float number (scalar).

import numpy as np

# Generating a random number

random_number = np.random.rand()

print(random_number)

# Generating a random 1D array with 5 elements

random_array = np.random.rand(5)

print(random_array)

# Generating a random 2D array (matrix) of shape 4x3

random_matrix = np.random.rand(4, 3)

print(random_matrix)


              12345678910
            
import numpy as np
# Generating a random number
random_number = np.random.rand()
print(random_number)
# Generating a random 1D array with 5 elements
random_array = np.random.rand(5)
print(random_array)
# Generating a random 2D array (matrix) of shape 4x3
random_matrix = np.random.rand(4, 3)
print(random_matrix)

Note

Dimensions in the rand() function should be specified as separate integer parameters, not as a tuple of integers. For example, rand(4, 3) is correct, while rand((4, 3)) is incorrect.

randint()

The numpy.random.randint function is used to generate either a random integer or an array of random integers from a discrete uniform distribution within a specified interval.

Its three most important parameters are low (the only required parameter), high, and size. The interval is [low, high) (from low inclusive to high exclusive). However, if high is not specified, then the interval is [0, low).

import numpy as np

# Generating a random integer from 0 to 3 exclusive

random_integer = np.random.randint(3)

print(random_integer)

# Generating a 1D array of random integers in [0, 5) with 4 elements

random_int_array = np.random.randint(5, size=4)

print(random_int_array)

# Generating a 1D array of random integers in [2, 5) with 4 elements

random_int_array_2 = np.random.randint(2, 5, size=4)

print(random_int_array_2)

# Generating a random 2D array of random integers in [1, 6) of shape 4x2

random_int_matrix = np.random.randint(1, 6, size=(4, 2))

print(random_int_matrix)


              12345678910111213
            
import numpy as np
# Generating a random integer from 0 to 3 exclusive
random_integer = np.random.randint(3)
print(random_integer)
# Generating a 1D array of random integers in [0, 5) with 4 elements
random_int_array = np.random.randint(5, size=4)
print(random_int_array)
# Generating a 1D array of random integers in [2, 5) with 4 elements
random_int_array_2 = np.random.randint(2, 5, size=4)
print(random_int_array_2)
# Generating a random 2D array of random integers in [1, 6) of shape 4x2
random_int_matrix = np.random.randint(1, 6, size=(4, 2))
print(random_int_matrix)

Note

Unlike rand(), we specify the dimensions of the array via a single parameter size, passing either an integer or a tuple of integers.

Task

Swipe to start coding

Create a 1D array of random floats from a uniform distribution in [0, 1) with 4 elements for random_floats_array.
Create a 2D array of random floats from a uniform distribution in [0, 1) with a shape of 3x2 for random_floats_matrix.
Use the correct function to create a 2D array of random integers for random_integers_matrix.
Set the interval to [10, 21) (from 10 to 21 exclusive) by specifying the first two arguments of the function.
Set the shape of the random_integers_matrix to 3x2 by specifying the third keyword argument of the function.