Python Basics for Data Science: Variables, Loops, and Functions

Hello, Learners! Let’s Lay the Foundation for Data Science

To master Data Science, you need to understand the basics of Python. Variables, loops, and functions are the building blocks of Python programming. In this article, we’ll dive deep into these concepts with simple explanations and practical examples.

Let’s get started!

1. What are Variables in Python?

A variable is like a container that holds data. You can think of it as a labeled box where you store information to use later.

How to Create a Variable:

Simply assign a value to a name:

age = 25
name = "Alice"
is_student = True

Rules for Naming Variables:

1. Variable names can only contain letters, numbers, and underscores (_).
2. They can’t start with a number.
3. They are case-sensitive (age and Age are different).

Example:

x = 10
y = 5
total = x + y
print(total)  # Output: 15

2. Data Types in Python

Python automatically assigns the type of data stored in a variable. Here are the common types:

1. int: Integer (whole numbers).
2. float: Decimal numbers.
3. str: Text (strings).
4. bool: True/False values.

Example:

price = 19.99  # float
item = "Notebook"  # str
in_stock = True  # bool

Check a Variable’s Type:

Use the type() function:

print(type(price))  # Output: <class 'float'>

3. What are Loops?

Loops let you repeat a block of code multiple times. Python has two main types of loops: for and while.

For Loop:

Used to iterate over a sequence (like a list or range of numbers).

for i in range(5):
    print(f"Iteration {i}")

Output:

Iteration 0
Iteration 1
Iteration 2
Iteration 3
Iteration 4

While Loop:

Repeats as long as a condition is True.

count = 0
while count < 5:
    print(f"Count is {count}")
    count += 1

4. Functions in Python

Functions are reusable blocks of code that perform a specific task. They help you avoid repeating code.

How to Create a Function:

Use the def keyword:

def greet():
    print("Hello, Data Scientists!")

Calling a Function:

Simply use its name followed by parentheses:

greet()  # Output: Hello, Data Scientists!

Functions with Parameters:

Pass values to a function to customize its behavior:

def add(a, b):
    return a + b

result = add(3, 7)
print(result)  # Output: 10

5. Combining Variables, Loops, and Functions

Let’s see how these concepts work together with an example.

Example: Calculate the Average of a List

def calculate_average(numbers):
    total = sum(numbers)
    count = len(numbers)
    return total / count

grades = [85, 90, 78, 92]
average = calculate_average(grades)
print(f"Average Grade: {average}")

Mini Project: Simple Expense Tracker

Goal: Track and calculate the total expenses for a week.

Steps:

1. Create a list of daily expenses.
2. Use a for loop to iterate through the list.
3. Use a function to calculate the total.

Code Example:

def total_expenses(expenses):
    return sum(expenses)

weekly_expenses = [200, 150, 300, 250, 100, 50, 75]
total = total_expenses(weekly_expenses)
print(f"Total Weekly Expenses: ${total}")

Quiz Time

Questions:

1. Which of the following is a valid variable name?
   a) 2name
   b) name_2
   c) name-2
2. What will this code output?

   for i in range(3):
       print(i * 2)

a) 0, 1, 2
b) 0, 2, 4
c) 2, 4, 6

3. Write a function to multiply two numbers.

Answers:

1-b, 2-b, 3 (Open-ended).

Tips for Beginners

1. Practice creating variables with different data types to understand them better.
2. Start using loops to automate repetitive tasks.
3. Write small functions to break complex problems into manageable parts.

Key Takeaways

1. Variables store data, loops automate repetition, and functions make your code reusable.
2. These basic concepts are the foundation of Python programming.
3. Mastering them will make it easier to tackle advanced Data Science topics.

Next Steps

• Practice writing loops and functions.
• Build small projects using these concepts.
• Stay tuned for the next article: “Getting Started with NumPy for Numerical Computations.”