Working with Functions and Modules

# CHAPTER 7

Working with Functions and Modules #

1. Chapter Introduction #

2. Defining Functions in Python #

A function is a block of code that only runs when it is called. You can pass data (parameters) into it, and it can return data back as a result.

Syntax:

• Use the def keyword.

• Name the function (using snake_case).

• Add parentheses () and a colon :.

• *Indent* the code block inside the function (Jupyter does this automatically).

Cell 1:

# Defining the function
def greet_user(name):
    """This is a docstring. It explains what the function does."""
    message = f"Hello there, {name}!"
    return message

# Calling the function
output1 = greet_user("Alice")
output2 = greet_user("Bob")

print(output1)
print(output2)

3. Multiple Arguments and Default Values #

Functions can accept multiple inputs, and you can provide default values so an argument becomes optional.

Cell 2:

def calculate_total(price, tax_rate=0.05):
    """Calculates total price with default 5% tax."""
    total = price + (price * tax_rate)
    return total

# Using the default tax rate
print(calculate_total(100))        # Output: 105.0

# Overriding the default tax rate (e.g., 20% tax)
print(calculate_total(100, 0.20))  # Output: 120.0

4. Importing Modules (The Standard Library) #

Python comes with a "Standard Library"—a collection of pre-written modules you can use for common tasks like math, dates, and random numbers. You must import them to use them.

*Best Practice:* In Jupyter, place all your import statements in the very first cell of the notebook.

Cell 3:

import math
import random

# Using the math module
root = math.sqrt(64)
print(f"Square root: {root}")

# Using the random module
dice_roll = random.randint(1, 6)
print(f"You rolled a {dice_roll}")

5. Different Ways to Import #

If a module's name is too long, or you only need one specific function, you can change how you import it.

Cell 4:

# Import with an alias (standard practice for data science)
import statistics as stats

data = [10, 20, 30, 40, 50]
print(stats.mean(data))

# Import ONE specific function from a module
from datetime import date

today = date.today()
print(f"Today's date is: {today}")

6. Package Management (pip) and Magic Commands #

The Standard Library is great, but data scientists use external, third-party libraries (like Pandas and Scikit-Learn). To install these, you use Python's package manager, pip.

In Jupyter, you can run terminal commands directly in a code cell by prefixing the command with an exclamation mark !.

Cell 5:

# Installing a package directly from a Jupyter cell
!pip install requests

*(After this finishes, you can import requests in the next cell).*

7. Mini Project: Utility Notebook Toolkit #

Imagine you are analyzing temperatures. Instead of writing the math every time, you build a toolkit function.

Cell 6:

def c_to_f(celsius):
    """Converts Celsius to Fahrenheit."""
    return (celsius * 9/5) + 32

def analyze_temp(temp_c):
    temp_f = c_to_f(temp_c)
    status = "Normal"
    
    if temp_c > 37.5:
        status = "Fever"
    elif temp_c < 35.0:
        status = "Hypothermia"
        
    return f"{temp_c}°C ({temp_f}°F) - Status: {status}"

# Test the toolkit
print(analyze_temp(36.5))
print(analyze_temp(39.0))

8. Common Mistakes #

• Indentation Errors: Python does not use curly brackets {} to define code blocks. It relies strictly on indentation (usually 4 spaces). If your return statement is not indented properly, it will break.

• Forgetting to run the definition cell: If you write a def function in Cell 1, and try to call it in Cell 2 *before running Cell 1*, Jupyter will say the function is not defined. The Kernel must "read" the definition first.

9. MCQs #

10. Interview Questions #

• Q: What is the difference between print() and return in a Python function?

• Q: Explain the difference between import math and from math import sqrt.

11. Summary #

12. Next Chapter Recommendation #