Data Structures

Data structures are ways to organize data so your program can use it fast and clearly. Picking the right one saves time and prevents bugs.

1. List

A list is an ordered, changeable collection.

1temps = [24.5, 25.1, 26.0]
2
3temps.append(26.3)
4print(temps[0])
5print(temps[-1])

What it is: A flexible sequence of items.

How it is used: Store many values and access by index.

When it is used: Sensor readings, marks list, batched samples.

2. Tuple

A tuple is like a list but immutable (cannot be changed).

1resolution = (1920, 1080)
2width, height = resolution
3print(width, height)

What it is: A fixed ordered collection.

How it is used: Store values that should not change.

When it is used: Coordinates, RGB values, fixed config pairs.

3. Dictionary

A dictionary stores key-value pairs.

1device = {
2    "id": "esp32-01",
3    "temp": 27.2,
4    "active": True
5}
6
7print(device["temp"])
8device["temp"] = 27.8
9device["room"] = "lab"

What it is: A lookup table.

How it is used: Find data by key instead of index.

When it is used: JSON data, API responses, configuration, metadata.

4. Set

A set stores unique values only.

1names = {"A", "B", "A", "C"}
2print(names)  # {'A', 'B', 'C'}

What it is: An unordered unique collection.

How it is used: Remove duplicates and test membership quickly.

When it is used: Unique IDs, tags, visited nodes.

5. Common Operations You Should Know

Slicing (parts of a list or string)

1nums = [10, 20, 30, 40, 50]
2print(nums[1:4])   # [20, 30, 40]
3print(nums[:3])    # [10, 20, 30]
4print(nums[::2])   # [10, 30, 50]

Membership check

1if 30 in nums:
2    print("Found")

Length

1print(len(nums))

6. List Comprehension

Fast way to create lists.

1squares = [x * x for x in range(6)]
2print(squares)

With condition:

1evens = [x for x in range(20) if x % 2 == 0]
2print(evens)

What it is: Compact list-building syntax.

How it is used: Transform or filter data in one line.

When it is used: Feature engineering and preprocessing.

7. Dictionary Comprehension

1prices = {"pen": 10, "book": 80, "bag": 500}
2with_tax = {k: v * 1.18 for k, v in prices.items()}
3print(with_tax)

8. Stack and Queue in Python

Stack (LIFO: last in, first out)

1stack = []
2stack.append("task1")
3stack.append("task2")
4print(stack.pop())  # task2

Queue (FIFO: first in, first out)

1from collections import deque
2
3queue = deque()
4queue.append("student1")
5queue.append("student2")
6print(queue.popleft())  # student1

9. Choosing the Right Data Structure

10. Tiny Complexity Intuition (No heavy math)

• List indexing arr[i] is usually fast.
• Dictionary key lookup is usually very fast.
• Searching in plain list can be slower for big lists.

For beginner projects, readability first. Optimize only when needed.

11. Real Example: Sensor Packet

1packet = {
2    "device_id": "esp32-01",
3    "time": "2026-05-09T11:30:00Z",
4    "readings": [24.8, 25.0, 25.1, 24.9],
5    "labels": {"room": "lab", "floor": 2}
6}
7
8avg_temp = sum(packet["readings"]) / len(packet["readings"])
9print(avg_temp)

You used dictionary + list in one structure. This is very common in ML data pipelines.

Practice Tasks

1. Remove duplicates from a list using a set.
2. Build a dictionary from a list of names and marks.
3. Create a queue simulation for customer tokens.
4. Extract top 3 values from a list and print average.

Master these and your Python foundation becomes much stronger.