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List Comprehension and Functions - Python Typing CST Test

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List Comprehension and Functions — Python Code

Demonstrates Python's list comprehension, type hints, and function definitions with docstrings.

import math
from typing import List, Optional

def fibonacci(n: int) -> List[int]:
	"""Generate fibonacci sequence up to n numbers"""
	if n <= 0:
		return []
	elif n == 1:
		return [0]

	fib = [0, 1]
	for i in range(2, n):
		fib.append(fib[i-1] + fib[i-2])
	return fib

def find_primes(limit: int) -> List[int]:
	"""Find all prime numbers up to limit using sieve"""
	primes = []
	sieve = [True] * (limit + 1)

	for i in range(2, int(math.sqrt(limit)) + 1):
		if sieve[i]:
		for j in range(i * i, limit + 1, i):
		sieve[j] = False

	return [i for i in range(2, limit + 1) if sieve[i]]

# Main execution
if __name__ == "__main__":
	fib_sequence = fibonacci(10)
	print(f"Fibonacci: {fib_sequence}")

	primes = find_primes(30)
	print(f"Primes up to 30: {primes}")

	# List comprehension example
	squares = [x**2 for x in range(1, 11) if x % 2 == 0]
	print(f"Even squares: {squares}")

Python Language Guide

Python is a high-level, dynamically typed, multi-paradigm programming language known for simplicity, readability, and massive ecosystem support. It powers web development, data science, machine learning, automation, scripting, backend systems, and more.

Primary Use Cases

  • ▸Backend web development
  • ▸Machine learning & AI
  • ▸Data analysis & visualization
  • ▸Automation & scripting
  • ▸API development
  • ▸Scientific computing
  • ▸DevOps tooling
  • ▸Cybersecurity scripting

Notable Features

  • ▸Simple, readable syntax
  • ▸Massive standard library
  • ▸Dynamically typed
  • ▸Extensive third-party ecosystem (PyPI)
  • ▸Cross-platform
  • ▸Strong scientific & ML libraries

Origin & Creator

Created by Guido van Rossum in 1991, inspired by ABC language with a vision of a simple, readable language for everyday programming tasks.

Industrial Note

Python dominates in AI/ML research, automation-heavy engineering teams, fast MVP prototyping, data-driven industries, hybrid cloud pipelines, ETL scripting, scientific computing, and large-scale analytical workflows.

More Python Typing Exercises

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