List Comprehensions

In Python, there is a short and powerful way to create new lists from existing data — it is called a List Comprehension. It does the same thing as a for loop but in just one line.

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1. The Problem: Too Many Lines for Simple Tasks

Imagine you want to create a list of squares from 1 to 5.

The Long Way (Using a Loop):

squares = []
for num in range(1, 6):
    squares.append(num ** 2)

print(squares)
# Output: [1, 4, 9, 16, 25]

That works, but it takes 4 lines for a simple task.

The Short Way (List Comprehension):

squares = [num ** 2 for num in range(1, 6)]

print(squares)
# Output: [1, 4, 9, 16, 25]

Same result, just 1 line! This is a list comprehension.

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2. The Basic Formula

new_list = [expression for item in iterable]

• expression → What you want to do with each item (e.g., num ** 2)
• item → The variable name for each element (e.g., num)
• iterable → The data you are looping through (e.g., range(1, 6) or a list)

More Examples:

# Double every number
numbers = [1, 2, 3, 4, 5]
doubled = [n * 2 for n in numbers]
print(doubled)  # Output: [2, 4, 6, 8, 10]

# Convert names to uppercase
names = ["sai", "ravi", "priya"]
upper_names = [name.upper() for name in names]
print(upper_names)  # Output: ['SAI', 'RAVI', 'PRIYA']

# Get the length of each word
words = ["python", "ai", "ml"]
lengths = [len(word) for word in words]
print(lengths)  # Output: [6, 2, 2]

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3. Adding a Condition (Filtering)

You can add an if condition to pick only certain items:

new_list = [expression for item in iterable if condition]

Examples:

# Only even numbers
numbers = [1, 2, 3, 4, 5, 6, 7, 8]
evens = [n for n in numbers if n % 2 == 0]
print(evens)  # Output: [2, 4, 6, 8]

# Only names with more than 3 letters
names = ["sai", "ravi", "priya", "raj"]
long_names = [name for name in names if len(name) > 3]
print(long_names)  # Output: ['ravi', 'priya']

# Squares of only odd numbers
odd_squares = [n ** 2 for n in range(1, 11) if n % 2 != 0]
print(odd_squares)  # Output: [1, 9, 25, 49, 81]

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4. If-Else Inside a Comprehension

When you want to do different things based on a condition:

# Mark "pass" or "fail" based on marks
marks = [85, 40, 72, 30, 91]
results = ["pass" if m >= 50 else "fail" for m in marks]
print(results)
# Output: ['pass', 'fail', 'pass', 'fail', 'pass']

Note: When using if-else together, the condition goes before the for. When using only if (filtering), it goes after the for.

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5. Nested List Comprehensions

You can also loop through 2D data (a list inside a list):

# Flatten a 2D list into a 1D list
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
flat = [num for row in matrix for num in row]
print(flat)
# Output: [1, 2, 3, 4, 5, 6, 7, 8, 9]

Tip: Read nested comprehensions from left to right — the outer loop comes first.

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6. Why This Matters for AI & ML

In AI and ML code, you will see list comprehensions everywhere:

# Clean a dataset: Remove empty strings
raw_data = ["hello", "", "world", "", "python"]
clean_data = [item for item in raw_data if item != ""]
print(clean_data)  # Output: ['hello', 'world', 'python']

# Extract specific features from data
students = [
    {"name": "Sai", "score": 85},
    {"name": "Ravi", "score": 72},
    {"name": "Priya", "score": 91},
]
scores = [student["score"] for student in students]
print(scores)  # Output: [85, 72, 91]

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Quick Summary

• List Comprehension: A concise, one-line syntax for creating transformed lists from loops.
• Basic Formula: [expression for item in iterable].
• Filtered Formula: [expression for item in iterable if condition].
• Conditional Transformation: [value_if_true if condition else value_if_false for item in iterable].
• AI & Data Science: Extensively used for data preprocessing, tokenization, and feature extraction.

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What's Next?

Let's explore how to create dictionaries and sets dynamically in a single line using Dictionary & Set Comprehensions in the next lesson!