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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شبکه:📈 تحلیل کانال تلگرام Learn Python Coding
کانال Learn Python Coding (@pythonre) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 39 629 مشترک است و جایگاه 3 400 را در دسته فناوری و برنامهها و رتبه 9 883 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 39 629 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 13 ژوئیه, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 401 و در ۲۴ ساعت گذشته برابر 21 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 1.75% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.15% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 692 بازدید دریافت میکند. در اولین روز معمولاً 455 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 3 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند math, harvard, oxford, supervision, waybienad تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“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”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 14 ژوئیه, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
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39 632
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✨ Unpacking the remaining elements 🧩
Sometimes you need to extract the first and last elements from a list, while grouping everything in the middle separately. Instead of struggling with slicing ([1:-1]), use the asterisk (*). ⭐️
data = ["CEO", "Middle Python Dev", "Junior Dev", "QA", "HR"]
# The asterisk automatically collects everything "extra" into a separate list.
boss, *team, hr = data
print(boss) # CEO
print(team) # ['Middle Python Dev', 'Junior Dev', 'QA']
print(hr) # HR39 632
Python has a built-in topological dependency sorter!🚀
If you're working with tasks that have dependencies — for example, in build systems, CI/CD pipelines, or workflow orchestration — the order of execution often has to be determined manually.
Usually through graphs, DFS,, or custom execution order logic.
But Python's standard library already has graphlib.TopologicalSorter.
ts = TopologicalSorter()
ts.add("deploy", "test")
ts.add("test", "build")tuple(ts.static_order())("build", "test", "deploy")39 632
💡 Replacing if-else with Match-Case
Starting with Python 3.10, we have a powerful tool: Structural Pattern Matching (match-case). This is not just an analog of switch-case from other languages; it's much more flexible. 🚀
Imagine you're writing a command handler for a bot. 🤖
❌ How NOT to do it:
def handle_command(command):
if command == "start":
return "Hello! I'm a bot."
elif command == "help":
return "Here's a list of available commands..."
elif command == "stop":
return "Goodbye!"
else:
return "Unknown command."def handle_command(command):
match command:
case "start":
return "Hello! I'm a bot."
case "help":
return "Here's a list of available commands..."
case "stop":
return "Goodbye!"
case _: # The underscore symbol catches everything else (default)
return "Unknown command."elif statements. 🧐
You can pass data structures in the case statements and check their structure and content on the fly. 🔍
It's easy to combine cases. 🧩
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What's the difference between is and == in Python?
The == operator checks whether the values of two objects are equal. In contrast, is determines whether variables refer to same object in memory. That is, == compares the content, while is checks the identity of the objects 🐍🔍
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📌 How to make code cleaner with any() and all() 🐍
Do you often have to check lists for compliance with conditions? Forget about cumbersome loops! 🚫🔄
any() — returns True if at least one element is true. ✅
all() — returns True only if all elements are true. 🔒
# Example: checking if there are negative numbers
numbers = [1, 5, -3, 7]
# Bad: through a loop
has_negative = False
for num in numbers:
if num < 0:
has_negative = True
# Beautiful:
has_negative = any(num < 0 for num in numbers) # True ✨
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collections.Counter — counting elements in a single line. 📊
Counting elements without loops with Counter 🔄
Do you need to count how many times each word appears in a text or how many duplicates there are in a list? Don't reinvent the wheel with for loops and dictionaries. The built-in collections module will do everything for you. 🚀
🛠 Code:
from collections import Counter
words = ["apple", "banana", "apple", "cherry", "banana", "apple"]
word_counts = Counter(words)
print(word_counts)
# Output: Counter({'apple': 3, 'banana': 2, 'cherry': 1})
# Bonus: the top 2 most frequent elements
print(word_counts.most_common(2))
# Output: [('apple', 3), ('banana', 2)]
