Learn Python Coding
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Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills. Admin: @HusseinSheikho || @Hussein_Sheikho
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“Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills.
Admin: @HusseinSheikho || @Hussein_Sheikho”
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Postlar arxiv
40 055
Accelerating the Sieve of Eratosthenes
1. Quickly recall the algorithm
Classic implementation:
def eratosthenes(n):
is_prime = [True] * (n + 1)
is_prime[0] = is_prime[1] = False
for i in range(2, int(n ** 0.5) + 1):
if is_prime[i]:
for j in range(i * i, n + 1, i):
is_prime[j] = False
return is_primedef eratosthenes_odd(n):
if n < 2:
return []
size = (n + 1) // 2
is_prime = [True] * size
is_prime[0] = False
limit = int(n ** 0.5) // 2
for i in range(1, limit + 1):
if is_prime[i]:
p = 2 * i + 1
start = (p * p) // 2
for j in range(start, size, p):
is_prime[j] = False
return is_primedef eratosthenes_bytearray(n):
is_prime = bytearray(b"\x01") * (n + 1)
is_prime[0:2] = b"\x00\x00"
for i in range(2, int(n ** 0.5) + 1):
if is_prime[i]:
for j in range(i * i, n + 1, i):
is_prime[j] = 0
return is_primedef eratosthenes_fast(n):
if n < 2:
return []
size = (n + 1) // 2
is_prime = bytearray(b"\x01") * size
is_prime[0] = 0
limit = int(n ** 0.5) // 2
for i in range(1, limit + 1):
if is_prime[i]:
p = 2 * i + 1
start = (p * p) // 2
is_prime[start::p] = b"\x00" * (((size - start - 1) // p) + 1)
return is_primen = 10_000_000:
>>> eratosthenes.py
real 0.634s
>>> eratosthenes_odd.py
real 0.245s
>>> eratosthenes_bytearray.py
real 0.801s
>>> eratosthenes_fast.py
real 0.028s
Conclusions:
* skipping even numbers (#1) gives ~2.6× speedup
* bytearray itself doesn't speed up — it's more about memory
* the hybrid (#3) gives ~22.6× speedup
Key trick in #3:
is_prime[start::p] = b"\x00" * (((size - start - 1) // p) + 1)
40 055
✨ dataframe | Python Glossary ✨
📖 A data structure for working with tabular data in Python.
🏷️ #Python
40 055
✨ assertion | Python Glossary ✨
📖 A debugging aid that tests a condition as an internal self-check.
🏷️ #Python
40 055
✨ Reference: Python Best Practices ✨
📖 Widely accepted and established guidelines, conventions, tips, and best practices for Python programmers.
🏷️ #23_terms
40 055
✨ Reference: Python’s Built-in Exceptions ✨
📖 Predefined error classes that the Python interpreter uses to handle various error conditions.
🏷️ #47_terms
40 055
✨ Python's deque: Implement Efficient Queues and Stacks ✨
📖 Use a Python deque to efficiently append and pop elements from both ends of a sequence, build queues and stacks, and set maxlen for history buffers.
🏷️ #intermediate #data-structures #python #stdlib
40 055
✨ Dependency Management | Python Best Practices ✨
📖 Guidelines and best practices for dependency management in Python.
🏷️ #Python
40 055
✨ How to Integrate ChatGPT's API With Python Projects ✨
📖 Learn how to use the ChatGPT Python API with the OpenAI library to build AI-powered features in your Python applications.
🏷️ #intermediate #ai #api
40 055
✨ Constants | Python Best Practices ✨
📖 Guidelines and best practices for using constants in your Python code.
🏷️ #Python
40 055
✨ Optimization | Python Best Practices ✨
📖 Guidelines and best practices for optimizing your Python code.
🏷️ #Python
40 055
✨ Project Layout | Python Best Practices ✨
📖 Guidelines and best practices for structuring and organizing your Python projects effectively.
🏷️ #Python
40 055
✨ Public API Surface | Python Best Practices ✨
📖 Guidelines and recommendations for using public and non-public names in your Python code.
🏷️ #Python
40 055
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