02. NumPy Array Basics: Create and Handle Arrays in Python

1. Creating Arrays with np.array()

You can create arrays manually using Python lists.

import numpy as np

A = np.array([
    [1, -1, 2],
    [3, 2, 2],
    [4, 1, 2],
    [7, 5, 6]
])

print(A)

Output:

[[ 1 -1  2]
 [ 3  2  2]
 [ 4  1  2]
 [ 7  5  6]]

Here, A is a 4×3 array.

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2. Another Example of np.array()

B = np.array([
    [0, 1],
    [-1, 3],
    [5, 2]
])

print(B)

Output:

[[ 0  1]
 [-1  3]
 [ 5  2]]

B is a 3×2 array.

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3. Creating Random Arrays with np.random.rand()

The np.random.rand() function generates random numbers between 0 and 1.

C = np.random.rand(3, 5)
print(C)

Example Output:

[[0.7720613  0.34936775 0.55718465 0.83078859 0.84575107]
 [0.18550725 0.06023345 0.36876688 0.14548477 0.80038621]
 [0.69913545 0.09887855 0.38529517 0.65256723 0.48723025]]

This creates a 3×5 array filled with random floating-point numbers.

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4. Creating Zero Arrays with np.zeros()

To create an array filled with zeros:

D = np.zeros((2, 4))
print(D)

Output:

[[0. 0. 0. 0.]
 [0. 0. 0. 0.]]

D is a 2×4 array filled with zeros.

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5. Accessing Elements in an Array

You can access individual elements using indexing.

value = A[0][2]
print(value)

Output:

2

This gets the element from the first row (index 0) and third column (index 2) of array A.

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Summary

• np.array() → create arrays from lists
• np.random.rand() → generate random arrays
• np.zeros() → create arrays filled with zeros
• Indexing → access specific elements

NumPy makes numerical computation in Python fast and easy!

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