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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 59 814 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 59 814 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.

59 814
Subscribers
-2324 hours
-1217 days
-51830 days
Posts Archive
Text can be beautiful How visualisation can uncover hidden patterns in text data https://towardsdatascience.com/text-can-be-beautiful-226ea089513a?source=---------17---------------------

How to Automate Tasks on GitHub With Machine Learning for Fun and Profit https://towardsdatascience.com/mlapp-419f90e8f007?source=collection_home---4------0---------------------

The most important concepts and features of scaPy: Advanced NLP in Python https://www.datacamp.com/community/blog/spacy-cheatsheet

Python Face Recognition Tutorial https://www.youtube.com/watch?v=QSTnwsZj2yc

How to Calculate Precision, Recall, F1, and More for Deep Learning Models https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/

Data Science with Python explained An overview of using Python for data science including Numpy, Scipy, pandas, Scikit-Learn, XGBoost, TensorFlow and Keras https://towardsdatascience.com/data-science-with-python-explained-9333b7cef747

Complete Python Tutorial for Beginners | Learn Python from Scratch | Python Training https://www.youtube.com/watch?v=4_6CHpzwljQ

10 Python Tips and Tricks For Writing Better Code https://www.youtube.com/watch?v=C-gEQdGVXbk

#books_channel📚📚📚📚 #python #deep_learning - #CNN - #LSTM - #Capsulenet #deep_tools - #keras - #tensorflow - #theano #data
#books_channel📚📚📚📚 #python #deep_learning - #CNN - #LSTM - #Capsulenet #deep_tools - #keras - #tensorflow - #theano #data_mining - #slide - #implementation . . 🇯‌🇴‌🇮‌🇳 ↯ @Machine_learn

How to Load and Manipulate Images for Deep Learning in Python With PIL/Pillow https://machinelearningmastery.com/how-to-load-and-manipulate-images-for-deep-learning-in-python-with-pil-pillow/

Loguru is a library which aims to bring enjoyable logging in Python. https://github.com/Delgan/loguru?utm_source=mybridge&utm_medium=blog&utm_campaign=read_more