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:π 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 40 869 subscribers, ranking 3 287 in the Technologies & Applications category and 9 131 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 40 869 subscribers.
According to the latest data from 31 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 306 over the last 30 days and by -6 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 5.91%. Within the first 24 hours after publication, content typically collects 1.63% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 414 views. Within the first day, a publication typically gains 665 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.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βLearn 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_Sheikhoβ
Thanks to the high frequency of updates (latest data received on 01 November, 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.
40 869
Subscribers
-624 hours
-237 days
+30630 days
Posts Archive
40 869
Tip: Efficiently Slice Iterators and Large Sequences with
itertools.islice
Explanation:
Traditional list slicing (my_list[start:end]) creates a new list in memory containing the sliced elements. While convenient for small lists, this becomes memory-inefficient for very large lists and is impossible for pure iterators (like generators or file objects) that don't support direct indexing.
itertools.islice provides a memory-optimized solution by returning an iterator that yields elements from a source iterable (list, generator, file, etc.) between specified start, stop (exclusive), and step indices, without first materializing the entire slice into a new collection. This "lazy" consumption of the source iterable is crucial for processing massive datasets, infinite sequences, or streams where only a portion is needed, preventing excessive memory usage and improving performance. It behaves syntactically similar to standard slicing but operates at the iterator level.
Example:
import itertools
import sys
# A generator for a very large sequence
def generate_large_sequence(count):
for i in range(count):
yield f"Data_Item_{i}"
# Imagine needing to process only a small segment of 10 million items
total_items = 10**7
data_stream = generate_large_sequence(total_items)
# Get items from index 500 to 509 (inclusive)
# Using islice:
print("--- Using itertools.islice ---")
# islice(iterable, [start], stop, [step])
# Here, start=500, stop=510 (exclusive)
for item in itertools.islice(data_stream, 500, 510):
print(item)
# Compare memory usage (conceptual, as actual list materialization would be massive)
# If you tried:
# large_list = list(generate_large_sequence(total_items)) # <-- HUGE memory consumption here!
# for item in large_list[500:510]:
# print(item)
# islice consumes minimal memory, only holding iterator state.
# The `data_stream` generator itself only holds its current state, not the whole sequence.
print("\n`itertools.islice` memory footprint is negligible compared to creating a full list slice.")40 869
β¨ Quiz: Writing DataFrame-Agnostic Python Code With Narwhals β¨
π If you're a Python library developer wondering how to write DataFrame-agnostic code, the Narwhals library is the solution you're looking for.
π·οΈ #advanced #data-science #python
40 869
β¨ Pylint | Python Tools β¨
π A static code checker for Python.
π·οΈ #Python
40 869
β¨ pytest | Python Tools β¨
π A test runner and framework for Python.
π·οΈ #Python
40 869
Repost from Machine Learning with Python
Real Python - Pocket Reference (Important)
#python #py #PythonTips #programming
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β¨ flake8 | Python Tools β¨
π A command-line Python linter.
π·οΈ #Python
40 869
β¨ Git | Python Tools β¨
π A distributed version control system.
π·οΈ #Python
40 869
β¨ dependency | Python Glossary β¨
π An external package that your project needs in order to run, build, or be developed.
π·οΈ #Python
40 869
β¨ abstract method | Python Glossary β¨
π A method that is marked with @abstractmethod.
π·οΈ #Python
40 869
This channels is for Programmers, Coders, Software Engineers.
0οΈβ£ Python
1οΈβ£ Data Science
2οΈβ£ Machine Learning
3οΈβ£ Data Visualization
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5οΈβ£ Data Analysis
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β
https://t.me/addlist/8_rRW2scgfRhOTc0
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40 869
β¨ Python Inner Functions: What Are They Good For? β¨
π Learn how to create inner functions in Python to access nonlocal names, build stateful closures, and create decorators.
π·οΈ #intermediate #python
40 869
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Everyone can join his channel and make money! He gives away from $200 to $5.000 every day in his channel
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40 869
β¨ Quiz: Python Inner Functions: What Are They Good For? β¨
π Test inner functions, closures, nonlocal, and decorators in Python. Build confidence and learn to keep state across calls. Try the quiz now.
π·οΈ #intermediate #python
40 869
β¨ setuptools | Python Tools β¨
π A packaging library and build backend for Python.
π·οΈ #Python
40 869
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Admin: @HusseinSheikho
40 869
β¨ Conda | Python Tools β¨
π A cross-platform package and environment manager for Python.
π·οΈ #Python
