Session 9 Python Data Types and Data Structures

Published

July 15, 2026

Introduction to Data Types and Data Structures in Python

1. Understanding Data Types

Programming languages are designed to accommodate real-world data. A language’s effectiveness often depends on how many real-world data types it can handle. Python includes several built-in basic data types: * Strings (str): Textual data, such as “hello” or usernames. These are always placed in quotes. * Integers (int): Whole numbers, both positive and negative (e.g., 5, -15). * Floating Point (float): Decimal numbers like 3.14 (pi). * Complex Numbers: Numbers with real and imaginary parts. This is an advanced feature that many other languages do not include.

2. The “Big Four” Data Structures

Data structures are used to store and organize collections of data. Python automatically interprets the structure based on the brackets used.

Structure Brackets Characteristics Best Use Case
List [ ] Ordered, changeable, allows duplicates. General tasks, items that need frequent modification.
Tuple ( ) Ordered, unchangeable, allows duplicates. Fixed data that should not change, like coordinates.
Set { } Unordered, changeable, unique values only. Removing duplicates or membership testing.
Dictionary { : } Key-Value pairs, unordered. Lookups, such as student IDs or passwords.

3. Core List Operations

Lists are the most commonly used structure for data manipulation. Key operations include:

  • Indexing: Accessing an item by its position. Indexing starts at 0.
    • list is the first element.
    • list[-1] is the last element.
  • Appending: Adding a single item to the end of a list using the .append() method.
  • Slicing: Extracting a sublist using the format [start:stop:step].
    • Example: fruits[:2] prints everything from the start up to, but excluding, index 2.
    • The “step” allows you to skip items (e.g., taking every second element).
  • Membership Testing: Checking if an item exists in a collection (e.g., blue in colors). This is much faster in sets than in lists.

4. Algorithmic Thinking & Pseudo-code

To become a proficient programmer, you must understand the logic before writing the code.

Finding the Largest Number in a List

Instead of just using the built-in max() function, we can use a comparison loop: 1. Create a variable (e.g., max_value) and assign it the first element of the list. 2. Loop through the rest of the list. 3. Compare each element to max_value. 4. If the current element is greater, replace max_value with that element. 5. After the loop finishes, the variable holds the largest number.

Removing Duplicates

While converting a list to a set automatically removes duplicates, doing it manually requires a specific logical flow:

  1. Create an empty list for the results.
  2. Create an empty set to track “seen” items (sets are faster for checking existence).
  3. Loop through the original list.
  4. If an item is not in the “seen” set, add it to both the set and the result list.
  5. If it is already in the set, ignore it and move to the next item.

5. Important Programming Concepts

  • Object-Oriented Syntax: Python often uses the object.method() format. For example, fruits.append("orange") tells the computer to take the “fruits” object and apply the “append” command to it.
  • Memory and Efficiency: Different structures have different impacts on memory and speed. For instance, inserting an element at the beginning of a long list is difficult because every other item must be reassigned to a new position.
  • Learning via AI: Tools like AI can help explain code or suggest beginner-friendly versions of programs, but it is vital to type out code manually to truly understand how it works.