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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 40 879 subscribers, ranking 3 292 in the Technologies & Applications category and 9 353 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 879 subscribers.

According to the latest data from 27 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 395 over the last 30 days and by -12 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 3.98%. Within the first 24 hours after publication, content typically collects 1.48% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 628 views. Within the first day, a publication typically gains 604 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
  • 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 28 October, 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.

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❔ Interview question What tools are used for error monitoring in Python services? Answer: Most often, Sentry, centralized logging, and metrics are used. Sentry collects stack traces, context, and shows the frequency of errors. It's also important to set up alerts - a sharp increase in exceptions usually signals problems after a release or a service degradation. tags: #interview https://t.me/pythonRe ✅

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Exploring pathlib for Working with Paths! Many projects still use os.path for path operations: join, dirname, exists, and more. It works, but the code quickly becomes cluttered with string manipulations and harder to read — especially when there are many paths being actively combined. Since Python 3.4, there's pathlib — an object-oriented API for working with files and directories. Importing the module is simple:
from pathlib import Path
You can create a path like any regular object:
path = Path("data/users.json")
When working with Path and the / operator, the correct separators for the current OS are used automatically. This keeps the code portable between Linux, macOS, and Windows without extra checks. If you need an absolute path, use resolve():
print(path.resolve())
Very often when working with files, you need to check if a path exists:
if path.exists():
    print("File found")
Pathlib also lets you quickly determine the type of file system object:
path.is_file()
path.is_dir()
The Path object has convenient properties for getting path parts. This eliminates manual string parsing and working with split().
print(path.name)    # users.json
print(path.stem)    # users
print(path.suffix)  # .json
print(path.parent)  # data
For joining paths, the / operator is used, which looks noticeably cleaner and is easier to read compared to os.path.join:
base = Path("logs")
file_path = base / "2026" / "app.log"
Creating directories is also compact and convenient:
Path("backup/archive").mkdir(parents=True, exist_ok=True)
Here: parents=True creates nested directories; exist_ok=True doesn't raise an error if the folder already exists. For reading and writing text files, there are built-in methods that cover most everyday tasks:
config = Path("config.txt")

config.write_text("debug=true", encoding="utf-8")

content = config.read_text(encoding="utf-8")
print(content)
For binary data, read_bytes() and write_bytes() methods are available. You can iterate through directory contents using iterdir():
for file in Path("logs").iterdir():
    print(file)
If you need to search for files by pattern, use glob():
for py_file in Path(".").glob("*.py"):
    print(py_file)
And for recursive directory traversal, there's rglob():
for file in Path(".").rglob("*.json"):
    print(file)
Practical example — finding logs older than a certain date. This is a more real-world task:
from pathlib import Path
from datetime import datetime

logs = Path("logs")
limit_date = datetime(2026, 1, 1)

for file in logs.glob("*.log"):
    modified = datetime.fromtimestamp(file.stat().st_mtime)

    if modified < limit_date:
        print(file.name, modified)
The stat() method lets you get file metadata: size, modification time, permissions, and other system data. Deleting files and directories is also built directly into the Path API:
path.unlink()  # file
path.rmdir()   # empty directory
It's important to note that pathlib doesn't fully replace shutil or os. For example, for copying files, recursive directory deletion, or complex permission operations, additional modules are usually used. 🔥 pathlib makes working with the file system noticeably cleaner: less string operations, better readability, and more predictable code when working with paths and files. #Python #Pathlib #Programming #Coding #Developer #SoftwareEngineering #TechTips #LearnPython #PythonTips #FileSystem

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Why can't you use mutable default values in constructors? If you set a list or dictionary as the default value, the object is
Why can't you use mutable default values in constructors? If you set a list or dictionary as the default value, the object is created once and then reused by all instances.
class User:
    def __init__(self, tags=[]):
        self.tags = tags
This results in a change in one instance affecting the others:
u1 = User(); u2 = User()
u1.tags.append("x"); print(u2.tags)
default_factory creates a new object each time the constructor is called, eliminating shared state: field(default_factory=list) Thus, each instance receives an independent data structure: User().tags is User().tags 🔥 Using default_factory is an important practice when working with mutable types and prevents hard-to-detect state errors.

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Python: simple things that improve code If you write like this: if type(x) == str: print("This is a string") it might work, b
Python: simple things that improve code If you write like this:
if type(x) == str:
    print("This is a string")
it might work, but it breaks on subclasses of str. It's better to use isinstance(). It takes into account inheritance and is more consistent with polymorphism.
if isinstance(x, str):
    print("This is a string")
This variant will work for str and its subclasses. Conclusion: type(x) == str is only suitable for simple cases, but it's fragile. isinstance(x, str) is a more stable and correct option almost always.

Python Basics Arrays & Loops 🐍 Essential you need to start strong 💪 https://t.me/pythonRe 🔗
Python Basics Arrays & Loops 🐍 Essential you need to start strong 💪 https://t.me/pythonRe 🔗

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Unlock Your AI Career Join our Data Science Full Stack with AI Course – a real-time, project-based online training designed f
Unlock Your AI Career Join our Data Science Full Stack with AI Course – a real-time, project-based online training designed for hands-on mastery. Core Topics Covered •  Data Science using Python with Generative AI: Build end-to-end data pipelines, from data wrangling to deploying AI models with Python libraries like Pandas, Scikit-learn, and Hugging Face transformers. •  Prompt Engineering: Craft precise prompts to maximize output from models like GPT and Gemini for accurate, creative results. •  AI Agents & Agentic AI: Develop autonomous agents that reason, plan, and act using frameworks like Lang Chain for real-world automation. Why Choose This Course? This training emphasizes live sessions, industry projects, and practical skills for immediate job impact, similar to top programs offering 100+ hours of Python-to-AI progression. Ready to start? Call/WhatsApp: (+91)-7416877757 WhatsApp Link:- http://wa.me/+917416877757

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