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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Network:📈 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 39 629 subscribers, ranking 3 400 in the Technologies & Applications category and 9 883 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 39 629 subscribers.
According to the latest data from 13 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 401 over the last 30 days and by 21 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 1.75%. Within the first 24 hours after publication, content typically collects 1.15% reactions from the total number of subscribers.
- Post reach: On average, each post receives 692 views. Within the first day, a publication typically gains 455 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 14 July, 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.
39 629
Subscribers
+2124 hours
+1397 days
+40130 days
Posts Archive
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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":
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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 ✨
#python #coding #pythonprogramming #learnpython #codeoptimization #programmingtips
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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)]
