Auto copy paste using Python
#Python #Programming #Coding #DataScience #MachineLearning #AI #WebDevelopment #Automation #OpenSource #TechSkills
https://t.iss.one/DataScience4
#Python #Programming #Coding #DataScience #MachineLearning #AI #WebDevelopment #Automation #OpenSource #TechSkills
https://t.iss.one/DataScience4
👍5
"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
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 📄
#DataStructures #Algorithms #Python #ComputerScience #OpenSource #Learning
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❤7
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
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
❤3
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❤️ Architecture Patterns — an informative repository on backend architecture in Python!
Here, they excellently demonstrate how to properly separate application logic, work with complex architecture, build a scalable backend, and maintain a codebase in an adequate state as the project grows. Instead of dry theory, the authors gradually build a full-fledged application and show how the architecture evolves as the project grows.
I'll leave a link: https://github.com/cosmicpython/book
#Python #Backend #Architecture #Coding #DevCommunity #OpenSource
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Here, they excellently demonstrate how to properly separate application logic, work with complex architecture, build a scalable backend, and maintain a codebase in an adequate state as the project grows. Instead of dry theory, the authors gradually build a full-fledged application and show how the architecture evolves as the project grows.
I'll leave a link: https://github.com/cosmicpython/book
#Python #Backend #Architecture #Coding #DevCommunity #OpenSource
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❤2
When you're doing a parser or migrating a site, there's often a pile of unreadable HTML markup on the screen. Converting this into neat Markdown is usually a hassle.
In the open code, I found a convenient tool called python-markdownify, which precisely solves the problem of converting HTML to Markdown.
The logic is simple: you take bulky HTML and get a clear and well-structured Markdown as a result.
The tool is easily customizable. You can clean up the necessary tags, change the format of headings, and neatly process tables and images. All of this can be configured.
It's installed via pip. It can be used both from Python code and from the command line, converting files in batches.
If desired, you can inherit and redefine the conversion rules for your own cases. The extensibility is fine there.
If you have to process large amounts of text or migrate a blog, the library saves a lot of time that would otherwise be spent on tedious work with regular expressions.
➡️ Link to GitHub
https://github.com/matthewwithanm/python-markdownify
#python #markdown #html #coding #devtools #opensource
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In the open code, I found a convenient tool called python-markdownify, which precisely solves the problem of converting HTML to Markdown.
The logic is simple: you take bulky HTML and get a clear and well-structured Markdown as a result.
The tool is easily customizable. You can clean up the necessary tags, change the format of headings, and neatly process tables and images. All of this can be configured.
It's installed via pip. It can be used both from Python code and from the command line, converting files in batches.
pip install python-markdownify
If desired, you can inherit and redefine the conversion rules for your own cases. The extensibility is fine there.
If you have to process large amounts of text or migrate a blog, the library saves a lot of time that would otherwise be spent on tedious work with regular expressions.
➡️ Link to GitHub
https://github.com/matthewwithanm/python-markdownify
#python #markdown #html #coding #devtools #opensource
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❤3