There is a fast selection of editors, IDEs and tools out there to develop your Python projects. This blog post will aims at providing a pragmatic hands-on guide for data scientists that transfer from tools like SPSS, STATA, RStudio etc.
MS Visual Studio
There is a fast selection of editors, IDEs and tools out there to developyour Python projects. This blog post will aims at providing a prgmatic hand-on guide for data scientists that transfer from tools like SPSS, STATA, RStudio etc.
https://visualstudio.microsoft.com/de/
Notepad++
This is where the discussion IDE vs. editor comes into play. If you want to keep it simple, Notepad++ gives you the unspoiled and down-to-earth experience without any distractions.
https://www.heise.de/download/product/notepad-26659
Jupyter Notebook
If collaboration is key for you and you are a great fan to work from anywhere in your browser, especially on smaller projects, Jupyter is a fantastic tool. However, note my comments on how Spyder and VS Code integrate Jupyter notebooks.
https://jupyter.org/
Sublime Text
Sublime Text 3 is a super fast environment. However, it is not completely free, harder to build a great setup and therefore I am already out. Full disclosure, I have ruled out this tool for my projects very early. Therefore, I do not want to extend my criticism much further.
https://www.sublimetext.com/
PyCharm
Spyder is the best IDE for data scientist I have come across so far. Great editor, great data browser and variable control. Build in console, build in graphic support and super great set-up experience and package management via Anaconda. I like the Jupyter Notebook integration, the mark-up editor – just everything.
For someone transferring from RStudio etc. it feels right from the start, and provides everything you need out of the box.
https://www.jetbrains.com/de-de/pycharm/