In the previous article, I have described my approach to configure Python workspace. I mentioned there that I do not use poetry because it “cannot be used to specify dependencies when you work with Jupyter notebooks”. However, people ( @BasicWolf и @iroln) from the russian tech website Habr recommended me to look at poetry closer, as it apparently can fulfil all my requirements. “Two heads are better than one”, and I started to explore this tool deeper.
I like Python. For the last several years, I have used it extensively in my research. There are a lot of useful libraries, and it is an equally powerful language for writing simple scripts, producing large systems, doing data analysis and machine learning. It is very laconic and allows you to use different programming paradigms. According to Tiobe Index, currently (January 2020) Python is on the third place among the most popular programming languages, with year-to-year gain of 1.