Python/ django

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по всем вопросам @haarrp @itchannels_telegram - 🔥 все ит каналы @ai_machinelearning_big_data -ML @ArtificialIntelligencedl -AI @datascienceiot - 📚 @pythonlbooks РКН: clck.ru/3FmxmM

Network:Machinelearning Russia10 245 Technologies & Applications2 215...

📈 Analytical overview of Telegram channel Python/ django

Channel Python/ django (@pythonl) in the Russian language segment is an active participant. Currently, the community unites 59 935 subscribers, ranking 2 215 in the Technologies & Applications category and 10 245 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 59 935 subscribers.

According to the latest data from 15 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -583 over the last 30 days and by -23 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.35%. Within the first 24 hours after publication, content typically collects 3.13% reactions from the total number of subscribers.
Post reach: On average, each post receives 4 404 views. Within the first day, a publication typically gains 1 878 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 22.
Thematic interests: Content is focused on key topics such as github, claude, контекст, архитектура, api.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“по всем вопросам @haarrp @itchannels_telegram - 🔥 все ит каналы @ai_machinelearning_big_data -ML @ArtificialIntelligencedl -AI @datascienceiot - 📚 @pythonlbooks РКН: clck.ru/3Fmxm...”

Thanks to the high frequency of updates (latest data received on 16 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.

59 935

Subscribers

-2324 hours

-1107 days

-58330 days

4 404

Post views

~ 1 87824 hours

~ 2 38348 hours

7.35%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

59 935

Repost from Python tests

@python_testit

59 935

An introduction to Pydbantic — a single model solution to Data Verification & Storage https://itnext.io/an-introduction-to-pydbantic-a-single-model-solution-to-data-verification-storage-254cfe9e757f @pythonl

59 935

Empty a Dictionary in Python https://www.pythonforbeginners.com/basics/empty-a-dictionary-in-python @pythonl

59 935

A Gentle Introduction to Serialization for Python https://machinelearningmastery.com/a-gentle-introduction-to-serialization-for-python/ @pythonl

59 935

How to create a generator function https://www.pythonmorsels.com/how-to-create-a-generator-function/ @pythonl

59 935

Repost from Python tests

result of code ?

Anonymous voting

59 935

Repost from Python tests

@python_testit

59 935

Python List index() https://itsmycode.com/python-list-index/ @pythonl

59 935

Argos Translate Open-source offline translation library written in Python Github: https://github.com/argosopentech/argos-translate Docs: https://argos-translate.readthedocs.io/en/latest/ Project: https://www.argosopentech.com/ @pythonl

59 935

Intersection of Lists in Python https://www.pythonforbeginners.com/basics/intersection-of-lists-in-python @pythonl

59 935

Hello everyone. My name is Andrew and for several years I've been working on to make the learning path for ML easier. I wrote a manual on machine learning that everyone understands - Machine Learning Simplified Book. The main purpose of my book is to build an intuitive understanding of how algorithms work through basic examples. In order to understand the presented material, it is enough to know basic mathematics and linear algebra. After reading this book, you will know the basics of supervised learning, understand complex mathematical models, understand the entire pipeline of a typical ML project, and also be able to share your knowledge with colleagues from related industries and with technical professionals. And for those who find the theoretical part not enough - I supplemented the book with a repository on GitHub, which has Python implementation of every method and algorithm that I describe in each chapter. You can read the book absolutely free at the link below: -> https://themlsbook.com I’ve also started my Instagram page - feel free to subscribe! it’s mostly in Russian but I’ll be posting in English too. -> https://instagram.com/5x12

59 935

⬛️ Test-Driven Data Analysis (Python TDDA library) Code: https://github.com/tdda/tdda Docs: http://tdda.readthedocs.io @pythonl

59 935

📖 How to Download Books Concurrently from Project Gutenberg https://superfastpython.com/threadpoolexecutor-download-books/ @pythonl

59 935

How to Deploy a Jupyter Notebook to Docker https://dev.to/edemgold/how-to-deploy-a-jupyter-notebook-to-docker-4glb @pythonl

59 935

Deploying a Django Application to Elastic Beanstalk https://testdriven.io/blog/django-elastic-beanstalk/ @pythonl

59 935

Select Random Element from A List in Python https://www.pythonforbeginners.com/basics/select-random-element-from-a-list-in-python @pythonl

59 935

Web Scraper with Python in 5 Minutes https://www.kdnuggets.com/2022/02/build-web-scraper-python-5-minutes.html @pythonl

59 935

How to Use MySQL Connector/Python https://www.pythonforbeginners.com/code-snippets-source-code/how-use-mysql-connectorpython @pythonl

59 935

«Лаборатория Касперского» ищет аналитиков информационной безопасности со знанием Python/PHP/Perl и опытом в ИБ от 3-х лет. 1. Senior Security Services Analyst — будет оказывать аналитическую поддержку проектов по security assessment: пентесты, веб анализ, ред тим, социальная инженерия. 2. Аналитик по утечкам данных — будет отслеживать тематические ресурсы, в том числе сегмента deep web, darknet, чтобы изучать новые схемы совершения противоправных действий в Интернете. А еще — работать с threat intelligence данными, проводить исследования на основе входных данных от заказчика, совершенствовать методологическую базу сбора и анализа данных. 3. Digital Footprint Analyst — будет собирать информацию из множества источников, анализировать ее и подготавливать отчеты об угрозах для заказчиков. Переходите по ссылкам и присоединяйтесь к команде лучших экспертов.

59 935

10 Unknown Security Pitfalls for Python https://blog.sonarsource.com/10-unknown-security-pitfalls-for-python @pythonl