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#python Faster conda https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community

#python Guidelines for research coding. It is not the highest standard but is easy to follow. https://goodresearch.dev/

#python This post is a retro on how I learned Python. Disclaimer: I can not claim that I am a master of Python. This post is a retrospective of how I learned Python in different stages. I started using Python back in 2012. Before this, I was mostly a Matlab/C user. Python is easy to get started, yet it is hard to master. People coming from other languages can easily make it work but will write some "disgusting" python code. And this is because Python people talk about "pythonic" all the time. Instead of being an actual style guide, it is rather a philosophy of styles. When we get started, we are most likely not interested in [PEP8](https://peps.python.org/pep-0008/) and [PEP257](https://peps.python.org/pep-0257/). Instead, we focus on making things work. After some lectures from the university (or whatever sources), we started to get some sense of styles. Following these lectures, people will probably write code and use Python in some projects. Then we began to realize that Python is strange, sometimes even doesn't make sense. Then we started leaning about the philosophy behind it. At some point, we will get some peer reviews and probably fight against each other on some philosophies we accumulated throughout the years. The attached drawing (in comments) somehow captures this path that I went through. It is not a monotonic path of any sort. This path is most likely to be permutation invariant and cyclic. But the bottom line is that mastering Python requires a lot of struggle, fights, and relearning. And one of the most effective methods is peer review, just as in any other learning task in our life. Peer review makes us think, and it is very important to find some good reviewers. Don't just stay in a silo and admire our own code. To me, the whole journey helped me building one of the most important philosophies of my life: embrace open source and collaborate.

#python Anaconda open sourced this... I have no idea what this is for... https://github.com/pyscript/pyscript

#python I find poetry a great tool to manage Python requirements. I used to manage Python requirements using requirements.txt(environment.yaml) and install them using pip(conda). The thing is, in this stack, we have to pin the version ranges manually. It is quite tedious, and we easily run into version problems for a large project. Poetry is the savior here. When developing a package, we add some initial dependencies to the pyproject.yaml, a PEP standard. Whenever a new package is needed, we run poetry add package-name. Poetry tries to figure out the compatible versions. A lock file for the dependencies with restricted versions will be created or updated. To recreate an identical python environment, we only need to run poetry install. There's one drawback and may be quite painful at some point. Recreating the lock file for dependencies is extremely slow when the complexity grows in the requirements. But this is not a problem if poetry but rather constraints from pypi. One solution to this problem is to use cache. https://python-poetry.org/

#python I thought it was a trivial talk in the beginning. But I quickly realized that I may know every each piece of the code mentioned in the video but the philosophy is what makes it exciting. He talked about some fundamental ideas of Python, e.g., protocols. After watching this video, an idea came to me. Pytorch lightning has implanted a lot of hooks in a very pythonic way. This is what makes pytorch lightning easy to use. (So if you do a lot of machine learning experiments, pytorch lightning is worth a try.) https://youtu.be/cKPlPJyQrt4

Генерация облака слов☁️ Завершающий пост серии про анализ тг-канала⚡️ После сбора всех данных можно наконец-то приступить к визуализации. В данном случае разбираю создание облака слов с помощью библиотеки wordcloud, т.к. в базовом экселе / гугл-таблицах его не сделаешь😣 Файл ipynb с кодом и примечаниями на github: https://github.com/alsosha/word_cloud_generator/blob/main/word_cloud_generator.ipynb Предыдущие посты: 1. Парсинг тг-чата 2. Анализ текста Примеры проектов с использованием кода из постов: 1. Дата-открытки для коллеги 2. Анализ новогодних открыток 3. Подборка инфографики с анализом текстов 💞 #python

Анализ текста Второй пост из серии про анализ тг-чата. В первом показывала, как получить архив чата и вытащить из него сообщения. На этот раз разбираю, как посчитать частотность слов и определить части речи✒️ Файл ipynb с кодом и примечаниями выложила на github: https://github.com/alsosha/text_analysis/blob/main/text_analysis.ipynb Остается пост про генерацию облака слов, постараюсь с ним не затягивать😜 P.S. В карточках упомянула, что кроме pymorphy3 есть и другие подобные библиотеки. Например, spacy, natasha, nltk и т.д. Сравнение результатов лемматизации нашла вот тут, а общее сравнение библиотек тут. Можно попробовать разное и подобрать удобный вариант под себя💞 #python

Парсинг тг-чата Показывала тут дата-открытки с облаками слов из всех сообщений коллеги и обещала рассказать о процессе парсинга тг-чата и анализе текста с помощью python. Рассказываю и делюсь кодом👾 Файл ipynb выложила на github. Внутри не только код, но и комментарии с объяснением алгоритма: https://github.com/alsosha/tg_chat_parser/blob/main/tg_chat_parser.ipynb Для наглядности также сделала карточки — изучайте и задавайте вопросы в комментариях, если будут💞 Впереди еще два поста: про анализ текста и генерацию облака слов в svg бесплатно и без регистрации😄 P.S. Еще недавно выложила код для анализа видео и рассказала про алгоритм в карточках. #python

Закинула на github файл с кодом (python) для анализа цвета видео👾 https://github.com/alsosha/video_color_analysis/blob/main/video_analysis.ipynb В прошлом году постила алгоритм и его описание тремя отдельными частями: 1. Подготовка данных 2. Анализ данных 3. Визуализация данных Пример применения кода — мой небольшой проект про цвета трилогии "Матрица". #python

📰 JDownloader site hacked to replace installers with Python RAT malware The website for the popular JDownloader download manager was compromised earlier this week to distribute malicious Windows and Linux installers, with the Windows payload found deploying a Python-based remote access trojan. 🔗 Source: https://www.bleepingcomputer.com/news/security/jdownloader-site-hacked-to-replace-installers-with-python-rat-malware/ #linux#python

📰 PyPI Packages Deliver ZiChatBot Malware via Zulip APIs on Windows and Linux Cybersecurity researchers have discovered three packages on the Python Package Index (PyPI) repository that are designed to stealthily deliver a previously unknown malware family called ZiChatBot on Windows and Linux systems."While these wheel packages do implement the features described on their PyPI web pages, their true purpose is to covertly deliver malicious files," Kaspersky. 🔗 Source: https://thehackernews.com/2026/05/pypi-packages-deliver-zichatbot-malware.html #linux#python