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Python Lab
03
FUNCTIONS AND NUMPY
MODULAR PROGRAMMING AND SCIENTIFI C
COMPUTING
Introduction to Python Functions
A function is a reusable block of code that performs a specific task. Functions
help make programs modular, readable, and easy to debug.
Basic syntax:
def function_name(parameters):
# code block
return result
Example:
def add(a, b):
return a + b
print(add(3, 5)) # Output: 8
Functions can take zero or more parameters, return values, and even return multiple values.
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Introduction to NumPy
NumPy (Numerical Python) is a library for fast numerical computation and array manipulation ideal for
data processing and scientific computing. It provides:
1.
Multidimensional array objects (ndarray)
2.
Mathematical operations
3.
Linear algebra tools
4.
Random number generation
Example:
import numpy as np
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b) # Output: [5 7 9]
NumPy arrays are faster than Python lists because operations are vectorized — executed directly in C
instead of Python loops.
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Experiment 01. Sum of Squares
Learning Objective: Write a function that takes two numbers and returns the sum of their squares.
Hint: Use the ** operator for power.
Code Hint Example:
def sum_of_squares(x, y):
"""
Returns the sum of the squares of two numbers.
Formula: x² + y²
"""
return (x ** 2) + (y ** 2)
# Test
a=3
b=4
result = sum_of_squares(a, b)
print(f"The sum of squares of {a} and {b} is {result}")
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Experiment 02: Element-wise Operations
Learning Objective: Use NumPy arrays to perform operations on lists efficiently.
import numpy as np
# Define arrays
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
# Perform element-wise operations
sum_result = a + b
# Display the results
print("Array A:", a)
print("Array B:", b)
print("\nSum (A + B):", sum_result)
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