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

Network:Machine Learning with Python India10 515 Technologies & Applications3 501...

📈 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 39 175 subscribers, ranking 3 501 in the Technologies & Applications category and 10 515 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 39 175 subscribers.

According to the latest data from 09 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 443 over the last 30 days and by 15 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.52%. Within the first 24 hours after publication, content typically collects 0.96% reactions from the total number of subscribers.
Post reach: On average, each post receives 988 views. Within the first day, a publication typically gains 374 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
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 10 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.

39 175

Subscribers

+1524 hours

+827 days

+44330 days

988

Post views

~ 37424 hours

~ 47148 hours

2.52%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

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If you're working with data pipelines, these repositories are very useful: 🚀📊 ibis: A Python API that allows you to write queries once and run them on different data backends, such as DuckDB, BigQuery, and Snowflake. 🐍🔗 https://github.com/ibis-project/ibis pygwalker: Instantly turns a DataFrame into an interactive UI for visual data exploration. 📈🖥️ https://github.com/Kanaries/pygwalker katana: A fast and scalable web crawler, often used for security testing and large-scale data collection/search. 🕷️🔒 https://github.com/projectdiscovery/katana #dataengineering #python #opensource #devtools #dataviz #security

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📂 Reminder about Python map()! map() — a built-in function that applies the specified function to each element of an iterable object (list, tuple, set, etc.). The picture shows the basic syntax, an example of use with lambda, and a typical case — data transformation without a manual for loop. Save it to quickly remember the syntax! 🐍💻🗺️ #Python #Coding #Programming #LearnToCode #DevTips #Tech

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"Open Data Structures" is another very useful free resource for anyone studying data structures and algorithms. 📚✨ The book discusses the implementation and analysis of basic structures: array-based lists, linked lists, hash tables, binary trees, red-black trees, heaps, sorting algorithms, graphs, and data structures for working with integers. 🔍🧮 This is a full-fledged open textbook for studying one of the fundamental topics of computer science and a good reference that's worth keeping on hand. 💻🌟 https://opendatastructures.org/ods-python.pdf 📄 👉 @PythonRe #DataStructures #Algorithms #Python #ComputerScience #OpenSource #Learning

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Do you know that Python can shift sequences without slicing and creating new lists? 🤔 When you need to cyclically shift data, many use slicing:

data = data[-1:] + data[:-1]

But deque.rotate() does this at the level of the data structure and usually works more efficiently for cyclical operations. 🚀

q.rotate(1)

A negative value rotates the queue in the other direction. ⬅️

q.rotate(-2)

This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms. 🔄

workers.rotate(-1)

🔥 deque.rotate() allows you to implement cyclical data structures without manual index logic and without creating new lists. 💡 #Python #Programming #Deque #CodingTips #Tech #DevCommunity

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The Python library itertools contains many useful functions. 🐍✨ One of them is compress(), which returns an iterator over the elements from data, for which the corresponding element in selectors is equal to True. 🔍💻 Here's an example: 📝👇 #Python #Programming #Itertools #Coding #Tech #DataScience

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Many applications require mapping strings to integers. In Python, this usually looks like:

d = {"apple": 100, "banana": 200, "cherry": 300}

If there are 1 million keys, this can consume a lot of memory — more than 100 bytes per key. Our elephant has published a new library that uses about 9 bytes per key. Yes, only 9 bytes. Usage looks like this:

from fastconstmap import ConstMap

d = {"apple": 100, "banana": 200, "cherry": 300}
m = ConstMap(d)

m["apple"]                  # -> 100
m.get_many(["banana", "cherry"])  # -> [200, 300]

It can be significantly faster (for example, up to 2 times in some cases) than the standard dictionary. It can also be serialized and deserialized to disk or network for convenient reuse. https://pypi.org/project/fastconstmap/ github: https://github.com/lemire/fastconstmap 👉 @PythonRe

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Repost from Machine Learning with Python

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Python Basics Notes 🐍📚 https://t.me/pythonRe 🔗 #Python #Coding #Programming #LearnPython #Tech #DevCommunity

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Repost from Machine Learning with Python

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