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Python/ django

Python/ django

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📈 Analytical overview of Telegram channel Python/ django

Channel Python/ django (@pythonl) in the Russian language segment is an active participant. Currently, the community unites 59 836 subscribers, ranking 2 219 in the Technologies & Applications category and 10 249 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 59 836 subscribers.

According to the latest data from 21 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -518 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 8.80%. Within the first 24 hours after publication, content typically collects 3.51% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 5 267 views. Within the first day, a publication typically gains 2 101 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 25.
  • 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 22 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.

59 836
Subscribers
-2324 hours
-1217 days
-51830 days
Posts Archive
#books_channel📚📚📚📚 #python #deep_learning - #CNN - #LSTM - #Capsulenet #deep_tools - #tensorflow - #theano #data_mining -
#books_channel📚📚📚📚 #python #deep_learning - #CNN - #LSTM - #Capsulenet #deep_tools - #tensorflow - #theano #data_mining - #implementation . 🇯‌🇴‌🇮‌🇳 ↯ @Machine_learn

Torchdata is PyTorch oriented library focused on data processing and input pipelines in general https://github.com/szymonmaszke/torchdata

Great tool for pytorch @ai_machinelearning_big_data

Python Multiprocessing Tutorial: Run Code in Parallel Using the Multiprocessing Module https://www.youtube.com/watch?v=fKl2JW_qrso

Custom Application Metrics with Django, Prometheus, and Kubernetes https://labs.meanpug.com/custom-application-metrics-with-django-prometheus-and-kubernetes/

5 Reasons to Learn Probability for Machine Learning https://machinelearningmastery.com/why-learn-probability-for-machine-learning/

Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples» https://github.com/vandit15/Class-balanced-loss-pytorch Class-Balanced Loss Based on Effective Number of Samples https://github.com/richardaecn/class-balanced-loss

Django + Elasticsearch. Searching for awesome TED Talks https://apirobot.me/posts/django-elasticsearch-searching-for-awesome-ted-talks

SuperSQLite: a supercharged SQLite library for Python https://github.com/plasticityai/supersqlite

Dagster is a system for building modern data applications. https://github.com/dagster-io/dagster

Useful String Methods in Python Learn about some of Python’s built-in methods that can be used on strings https://towardsdatascience.com/useful-string-methods-in-python-5047ea4d3f90