Python Loops - Complete Guide

Understanding Loops in Python

Loops in Python allow you to execute a block of code repeatedly. They are essential for automating repetitive tasks, processing collections of data, and creating iterative algorithms.

Python supports two primary types of loops:

for loops: Used for iterating over a sequence (list, tuple, string, etc.)
while loops: Used for executing code as long as a condition is true

# Basic for loop example
for i in range(5):
    print(i)  # Prints 0, 1, 2, 3, 4

# Basic while loop example
count = 0
while count < 5:
    print(count)  # Prints 0, 1, 2, 3, 4
    count += 1

For Loops

The for loop in Python is designed to iterate over a sequence (like a list, tuple, dictionary, set, or string). This makes it perfect for processing collections of data.

Basic For Loop Syntax

# Iterating over a list
fruits = ["apple", "banana", "cherry"]
for fruit in fruits:
    print(fruit)

# Iterating over a string
for char in "Python":
    print(char)

# Iterating with range()
for i in range(5):  # 0 to 4
    print(i)

# Range with start and stop
for i in range(2, 6):  # 2 to 5
    print(i)

# Range with start, stop, and step
for i in range(1, 10, 2):  # 1, 3, 5, 7, 9
    print(i)

Iterating Over Collections

# Iterating over a dictionary
person = {"name": "Alice", "age": 30, "city": "New York"}

# Iterating over keys (default)
for key in person:
    print(key, person[key])

# Iterating over key-value pairs
for key, value in person.items():
    print(f"{key}: {value}")

# Iterating over values only
for value in person.values():
    print(value)

# Iterating with index using enumerate()
fruits = ["apple", "banana", "cherry"]
for index, fruit in enumerate(fruits):
    print(f"{index}: {fruit}")  # 0: apple, 1: banana, 2: cherry

# Iterating over multiple lists with zip()
names = ["Alice", "Bob", "Charlie"]
ages = [25, 30, 35]
for name, age in zip(names, ages):
    print(f"{name} is {age} years old")

While Loops

The while loop executes a block of code as long as a specified condition is true. It's useful when you don't know in advance how many times a loop needs to run.

Basic While Loop Syntax

# Simple countdown
count = 5
while count > 0:
    print(count)
    count -= 1
print("Blast off!")

# Loop with user input
user_input = ""
while user_input.lower() != "quit":
    user_input = input("Enter a command (type 'quit' to exit): ")
    print(f"You entered: {user_input}")

# Infinite loop with break
while True:
    user_input = input("Enter 'quit' to exit: ")
    if user_input.lower() == "quit":
        break
    print(f"You entered: {user_input}")

Use Cases for While Loops

When the number of iterations is unknown
When you need to continue until a specific condition is met
For implementing retry logic or polling
Game loops that run until the player quits

Loop Control Statements

Python provides several statements to control the flow of loops:

break: Exits the loop completely
continue: Skips the current iteration and moves to the next one
else: Executes when the loop completes normally (not after a break)

Break Statement

# Break when a condition is met
for i in range(1, 11):
    if i == 5:
        print("Found 5! Breaking the loop.")
        break
    print(i)
# Output: 1, 2, 3, 4, Found 5! Breaking the loop.

# Break in a while loop
number = 0
while True:
    number += 1
    if number > 10:
        break
    print(number)

Continue Statement

# Skip even numbers
for i in range(1, 11):
    if i % 2 == 0:
        continue  # Skip the rest of the code and go to next iteration
    print(i)
# Output: 1, 3, 5, 7, 9

# Skip specific values
fruits = ["apple", "banana", "cherry", "date", "elderberry"]
for fruit in fruits:
    if fruit == "cherry":
        continue
    print(fruit)

Else Clause in Loops

# Else clause executes after loop completes
for i in range(5):
    print(i)
else:
    print("Loop completed successfully!")

# Else won't execute if loop is broken
for i in range(5):
    print(i)
    if i == 3:
        break
else:
    print("This won't print because the loop was broken")

Nested Loops

Loops can be nested inside one another. This is useful for working with multi-dimensional data or when you need to perform complex iterations.

# Simple nested loop
for i in range(3):
    for j in range(3):
        print(f"({i}, {j})", end=" ")
    print()  # New line after inner loop

# Multiplication table
for i in range(1, 6):
    for j in range(1, 6):
        print(f"{i * j:2d}", end=" ")
    print()

# Nested loop with 2D list
matrix = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
]

for row in matrix:
    for element in row:
        print(element, end=" ")
    print()

Performance Considerations

Nested loops can be computationally expensive. The time complexity is typically O(n²) for doubly nested loops, where n is the number of iterations in each loop.

List Comprehensions

List comprehensions provide a concise way to create lists based on existing lists or other iterable objects. They're a powerful Python feature that can replace certain types of for loops.

Basic List Comprehension

# Creating a list of squares
# Regular for loop approach
squares = []
for x in range(10):
    squares.append(x**2)
print(squares)  # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

# Using list comprehension
squares = [x**2 for x in range(10)]
print(squares)  # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

# Filtering with list comprehension
even_squares = [x**2 for x in range(10) if x % 2 == 0]
print(even_squares)  # [0, 4, 16, 36, 64]

Advanced List Comprehensions

# Nested list comprehension
matrix = [[j for j in range(3)] for i in range(3)]
print(matrix)  # [[0, 1, 2], [0, 1, 2], [0, 1, 2]]

# Multiple conditions
values = [x for x in range(100) if x % 2 == 0 if x % 5 == 0]
print(values)  # [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]

# If-else in list comprehension
numbers = [-4, -2, 0, 2, 4]
absolute = [x if x >= 0 else -x for x in numbers]
print(absolute)  # [4, 2, 0, 2, 4]

Other Comprehensions

# Dictionary comprehension
squares_dict = {x: x**2 for x in range(6)}
print(squares_dict)  # {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

# Set comprehension
unique_lengths = {len(word) for word in ["hello", "world", "python", "code"]}
print(unique_lengths)  # {5, 4, 6}

# Generator expression (parentheses instead of brackets)
numbers_gen = (x**2 for x in range(5))
print(numbers_gen)  #  at 0x...>
print(list(numbers_gen))  # [0, 1, 4, 9, 16]

Loop Optimization Techniques

Loops can sometimes be performance bottlenecks. Here are techniques to optimize loops in Python.

Performance Tips

# 1. Avoid expensive operations in loops
# Poor performance
import time
start = time.time()
for i in range(1000000):
    i / 2  # Performs division 1,000,000 times
print(f"Time: {time.time() - start:.4f} seconds")

# 2. Use list comprehensions for simple loops
# list comprehension is generally faster than for loop for creating lists
start = time.time()
squares = [x**2 for x in range(1000000)]
print(f"Comprehension time: {time.time() - start:.4f} seconds")

start = time.time()
squares = []
for x in range(1000000):
    squares.append(x**2)
print(f"For loop time: {time.time() - start:.4f} seconds")

# 3. Move constant operations outside the loop
import math
# Inefficient
for i in range(1000):
    result = i * math.pi  # math.pi calculated in each iteration

# More efficient
pi_value = math.pi
for i in range(1000):
    result = i * pi_value  # Constant value calculated once

Using Specialized Tools

# Using NumPy for faster array operations
import numpy as np

# NumPy vectorization vs. loop
arr = np.array(range(1000000))

# Using loop (slower)
start = time.time()
result = []
for i in arr:
    result.append(i**2)
print(f"Loop time: {time.time() - start:.4f} seconds")

# Using NumPy vectorization (faster)
start = time.time()
result = arr**2
print(f"NumPy time: {time.time() - start:.4f} seconds")

Common Loop Patterns

Searching and Filtering

# Finding an element
numbers = [4, 7, 2, 9, 1, 5]
target = 9
found = False

for num in numbers:
    if num == target:
        found = True
        break

print(f"Found: {found}")

# Filtering elements
numbers = [4, 7, 2, 9, 1, 5]
even_numbers = []

for num in numbers:
    if num % 2 == 0:
        even_numbers.append(num)

print(f"Even numbers: {even_numbers}")

# More concise with list comprehension
even_numbers = [num for num in numbers if num % 2 == 0]

Accumulating Results

# Sum of elements
numbers = [4, 7, 2, 9, 1, 5]
total = 0

for num in numbers:
    total += num

print(f"Sum: {total}")  # 28

# Building a new collection
words = ["hello", "world", "python"]
capitalized = []

for word in words:
    capitalized.append(word.capitalize())

print(capitalized)  # ['Hello', 'World', 'Python']

Processing File Data

# Reading a file line by line
with open('example.txt', 'r') as file:
    for line in file:
        line = line.strip()  # Remove leading/trailing whitespace
        print(line)

# Process CSV data
import csv
with open('data.csv', 'r') as file:
    csv_reader = csv.reader(file)
    for row in csv_reader:
        print(row)  # row is a list of values

Practice Exercises

Exercise 1: FizzBuzz

# Write a program that prints the numbers from 1 to 100.
# But for multiples of three print "Fizz" instead of the number
# and for the multiples of five print "Buzz".
# For numbers which are multiples of both three and five print "FizzBuzz".

# Your code here

Exercise 2: Nested Loop Pattern

# Write a program that prints the following pattern:
# *
# **
# ***
# ****
# *****

# Your code here

Exercise 3: Prime Number Checker

# Write a function that checks whether a number is prime.
# Then use it to find all prime numbers between 1 and 100.

# Your code here