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Learn Python Coding

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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📈 Analytical overview of Telegram channel Learn Python Coding

Channel Learn Python Coding (@pythonre) in the English language segment is an active participant. Currently, the community unites 39 139 subscribers, ranking 3 511 in the Technologies & Applications category and 10 584 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 39 139 subscribers.

According to the latest data from 06 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 433 over the last 30 days and by 10 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.57%. Within the first 24 hours after publication, content typically collects 1.00% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 004 views. Within the first day, a publication typically gains 393 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
  • Thematic interests: Content is focused on key topics such as math, harvard, oxford, supervision, waybienad.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
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

Thanks to the high frequency of updates (latest data received on 08 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

39 139
Subscribers
+1024 hours
+887 days
+43330 days
Posts Archive
Sorting Algorithm #python #datastructures
+9
Sorting Algorithm #python #datastructures

✨ Quiz: How to Create a Django Project ✨ 📖 Check your Django setup skills. Install safely and pin requirements, create a pro
Quiz: How to Create a Django Project ✨ 📖 Check your Django setup skills. Install safely and pin requirements, create a project and an app. Start building your first site. 🏷️ #basics #best-practices #django #web-dev

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Boolean flag | Python Glossary ✨ 📖 A variable or function parameter that you set to either True or False. 🏷️ #Python

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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_prime
Time — O(N log log N). We're not interested in the asymptotics, but in how much we can speed up the implementation itself. 2. Optimization #1 — don't bother with even numbers The idea is simple: * all even numbers except 2 are composite * if we only work with odd numbers, we reduce the array size and the number of iterations by about half Implementation:
def 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_prime
3. Optimization #2 — use bytearray instead of list[bool] Thought: * bool in Python is an object * bytearray is a tightly packed buffer * less overhead and better fits into the CPU cache Example:
def 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_prime
4. Optimization #3 — a hybrid of the two approaches
def 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_prime
5. Time comparison Test with n = 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)
There's no Python loop here — everything is done by a C-level operation on the slice. On such tasks, this makes a huge difference. General idea: in Python, we often speed up not the asymptotics, but the memory model and the number of passes over the data. Loops + memory → the main factors. 👉 @DataScience4

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dataframe | Python Glossary ✨ 📖 A data structure for working with tabular data in Python. 🏷️ #Python

assertion | Python Glossary ✨ 📖 A debugging aid that tests a condition as an internal self-check. 🏷️ #Python

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Reference: Python Best Practices ✨ 📖 Widely accepted and established guidelines, conventions, tips, and best practices for Python programmers. 🏷️ #23_terms

Reference: Python’s Built-in Exceptions ✨ 📖 Predefined error classes that the Python interpreter uses to handle various error conditions. 🏷️ #47_terms

✨ Python's deque: Implement Efficient Queues and Stacks ✨ 📖 Use a Python deque to efficiently append and pop elements from b
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

Dependency Management | Python Best Practices ✨ 📖 Guidelines and best practices for dependency management in Python. 🏷️ #Python

✨ How to Integrate ChatGPT's API With Python Projects ✨ 📖 Learn how to use the ChatGPT Python API with the OpenAI library to
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

Constants | Python Best Practices ✨ 📖 Guidelines and best practices for using constants in your Python code. 🏷️ #Python

Optimization | Python Best Practices ✨ 📖 Guidelines and best practices for optimizing your Python code. 🏷️ #Python

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