M.T.Ramkrushna

Posted on Nov 27

5. PYTHON ESSENTIALS FOR AI/ML (List & Dictionary Comprehensions)

#ai #python #tutorial

🛒 1. What Are Comprehensions?

Comprehensions are a compact and expressive way to create lists and dictionaries.

Instead of writing long loops, you write everything in one clean line.

Classic loop vs comprehension

Without comprehension:

squares = []
for n in range(1, 6):
    squares.append(n*n)

With comprehension:

squares = [n*n for n in range(1, 6)]

Cleaner, faster, more readable.

🍫 2. List Comprehension (Core Formula)

General pattern

[new_item   for item in iterable   if condition]

Visual

Imagine you have a conveyor belt of items.
Comprehension → picks items → maybe filters → transforms → outputs a new list.

🥤 3. Real-Life Examples (Very Relatable)

🍟 Example 1: Filter items from a food list

Suppose you ordered a mixed snack bundle and want to keep only healthy items.

foods = ["chips", "salad", "pizza", "fruits"]

healthy = [f for f in foods if f in ["salad", "fruits"]]
print(healthy)   # ['salad', 'fruits']

🎧 Example 2: Increase volume of all songs

Imagine a music app increases the volume of every song by 10%.

volumes = [40, 60, 80]
new_volumes = [v + 10 for v in volumes]

🛍️ Example 3: Add “₹” symbol to all prices

You grab prices from a website:

prices = [200, 450, 1200]

formatted = [f"₹{p}" for p in prices]

🚗 Example 4: Extract only electric cars

cars = [
    {"model": "Tesla", "type": "EV"},
    {"model": "BMW", "type": "Petrol"},
    {"model": "Tata Nexon", "type": "EV"}
]

ev_models = [car["model"] for car in cars if car["type"] == "EV"]
print(ev_models)  # ['Tesla', 'Tata Nexon']

🧮 4. Nested List Comprehension

🎒 Example: Flatten a list of student groups

groups = [[1, 2], [3, 4], [5, 6]]

flat = [student for group in groups for student in group]
print(flat)  # [1, 2, 3, 4, 5, 6]

Think of unzipping multiple packets into one tray.

🗃️ 5. Dictionary Comprehensions

Structure:

{key: value   for item in iterable   if condition}

🧾 6. Real-Life Dictionary Examples

📦 Example 1: Price after discount

You have a price list and want to apply 10% discount.

prices = {"shoes": 2000, "shirt": 1200, "watch": 5000}

discounted = {item: price*0.9 for item, price in prices.items()}

📱 Example 2: Count lengths of all usernames

users = ["ali", "coder_123", "sara"]

length_map = {u: len(u) for u in users}

⭐ Example 3: Create mapping of product → “In Stock”/“Low Stock”

stock = {"laptop": 5, "mouse": 0, "keyboard": 2}

status = {
    item: ("In Stock" if qty > 0 else "Out of Stock")
    for item, qty in stock.items()
}

🧩 7. Combined Example

(Super realistic & used in ML/Data)

🧪 Convert a list of temperatures into a dict

temps = [28, 31, 36]

weather = {t: "Hot" if t > 30 else "Cool" for t in temps}

🏁 8. When to Use Comprehensions

✔ transforming data
✔ filtering data
✔ flattening lists
✔ building dicts from large datasets
✔ feature preprocessing in ML

Avoid when:
✘ The logic becomes too long or unreadable
✘ Multiple if/loops make it messy
(then use normal loops)

🧪 9. Mini Exercises (Try These!)

Send me your answers — I’ll check and correct if needed.

Exercise 1

Create a new list of squares from 1 to 20 only for even numbers.

Exercise 2

Given a list of names, create a dict → name → uppercase version.

Exercise 3

You have this dict:

products = {"mouse": 500, "laptop": 60000, "usb": 200}

Create a new dict where:

items > 1000 become “Expensive”
else “Cheap”

Exercise 4

Flatten this list using comprehension:

matrix = [[1,2,3], [4,5,6]]

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