Python - NumPy Part 3: Array Operations

NumPy supports a wide range of mathematical operations, both element-wise and matrix-wise.

Examples and Explanation

Element-Wise Operations

a = np.array([1, 2, 3])

b = np.array([4, 5, 6])

print(a + b)  # Output: [5 7 9]

Explanation: Operations like addition, subtraction, and multiplication are applied element-wise.

Broadcasting

print(a * 2)  # Output: [2 4 6]

Explanation: Broadcasting allows operations between arrays of different shapes, making it efficient for scalar multiplication.

Matrix Multiplication

matrix1 = np.array([[1, 2], [3, 4]])

matrix2 = np.array([[5, 6], [7, 8]])

print(np.dot(matrix1, matrix2))

# Output:

# [[19 22]

#  [43 50]]

Explanation: Use np.dot() for matrix multiplication, essential in linear algebra applications.

Statistical Functions

print(np.mean(a))  # Output: 2.0

print(np.max(a))   # Output: 3

Explanation: NumPy provides statistical functions like mean, max, and min to analyze data arrays.

Aggregations

print(np.sum(matrix1))  # Output: 10

Explanation: Aggregations like sum and product are performed efficiently on entire arrays.