Lesson 3 · Data analytics zero to hero

Python Functions & Modules for Reusable Code | Data Analytics #3

Video three of the 30-part series: packaging reusable logic into functions, and pulling in Python's built-in modules. Builds directly on videos one and two,…

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CourseData analytics zero to hero
Lesson3 of 30
Video11 min
FormatJupyter notebook · 9 code cells

datetime

What you'll learn

Defining and Calling Functions
Return Values
Default and Keyword Arguments
Multiple Return Values and Docstrings
Importing Modules
Mini Project A Reusable Discount Calculator
Wrap-Up: What You Learned

Data

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Data Analytics Zero to Hero, Video 3: Functions and Modules#

Video three of the 30-part series: packaging reusable logic into functions, and pulling in Python's built-in modules.
Builds directly on videos one and two, variables and control flow.
Let's jump straight in.

Before You Start#

Open a new Jupyter Notebook in VS Code and select your Python interpreter as the kernel.

Part 1: Defining and Calling Functions#

def greet():
    print('Hello, analyst!')

greet()

Hello, analyst!

def greet_person(name):
    print(f'Hello, {name}!')

greet_person('Sam')
greet_person('Priya')

Hello, Sam!
Hello, Priya!

Return Values#

def add_tax(price, tax_rate):
    return price * (1 + tax_rate)

total = add_tax(50, 0.08)
print(total)

54.0

Part 2: Default and Keyword Arguments#

def add_tax(price, tax_rate=0.08):
    return price * (1 + tax_rate)

print(add_tax(100))
print(add_tax(100, 0.15))
print(add_tax(price=100, tax_rate=0.05))

108.0
114.99999999999999
105.0

Part 3: Multiple Return Values and Docstrings#

def price_stats(prices):
    """Return the minimum, maximum, and average of a list of prices."""
    lowest = min(prices)
    highest = max(prices)
    average = sum(prices) / len(prices)
    return lowest, highest, average

low, high, avg = price_stats([19.99, 45.50, 12.75, 88.00])
print(f'Low: {low}, High: {high}, Avg: {avg:.2f}')

Low: 12.75, High: 88.0, Avg: 41.56

Part 4: Importing Modules#

import math
print(math.sqrt(64))
print(math.pi)
print(math.ceil(4.2))
print(math.floor(4.8))

8.0
3.141592653589793
5
4

import random
print(random.randint(1, 100))
print(random.choice(['A', 'B', 'C', 'D']))

22
C

from datetime import date
today = date.today()
print(today)
print(today.year)

2026-08-16
2026

Part 5: Mini Project A Reusable Discount Calculator#

import math

def apply_discount(price, discount_pct=10, round_up=False):
    """Apply a percentage discount to a price, optionally rounding the result up."""
    discounted = price * (1 - discount_pct / 100)
    if round_up:
        return math.ceil(discounted)
    return round(discounted, 2)

print(apply_discount(59.99))
print(apply_discount(59.99, discount_pct=25))
print(apply_discount(59.99, discount_pct=25, round_up=True))

53.99
44.99
45

Wrap-Up: What You Learned#

Defining and calling functions, with parameters and return values.
Default arguments, keyword arguments, and returning multiple values at once.
Docstrings for documenting what a function does.
Importing built-in modules like math, random, and datetime.
A small reusable tool combining all of it into one function.
Functions are how you'll organize every real analysis from here on. Video four covers Python's core data structures lists, dicts, tuples, and sets the containers you'll store real data in. Subscribe so it lands automatically.

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