It is easier to install Spyder (at least in Linux) but P圜harm is not that hard to install. P圜harm is one of the best IDEs I have seen, JupyterLab notebooks are good for capturing reproducible research and mixing documentation and code, Black greatly improves your code, and Anaconda makes environment management easy.Spyder help/object explorer Spyder vs Pycharm My current “data science in Python” goto tools are: P圜harm, JupyterLab, Black, and Anaconda.

Dash “a framework for building analytical web applications”.

elpy “Emacs Python Development Environment”.

VS Code for Python a Python IDE based on Visual Studio.

Apache Zeppelin “Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more”.

Spyder “a powerful scientific environment written in Python”.

The Anaconda distribution, a great package set and package manager.

JupyterLab “a web-based interactive development environment for Jupyter notebooks, code, and data” (the successor to Jupyter Notebook and IPython Notebook).

Black “The uncompromising code formatter”.

This IDE has amazing re-factoring and completion abilities, and automatically criticizes your code relative the PEP8 code style recommendations.

P圜harm, both Community Edition “The Python IDE for Professional Developers”, and Professional Edition “For both Scientific and Web Python development.

Off the top of my head I remember the following Python tools:

Please read on for a small list of tools, and my recommendations for a specific data science in Python toolchain. “What’s the Best Statistical Software? A Comparison of R, Python, SAS, SPSS and STATA” Amit GhoshĪctually, Python has a large number of very capable integrated development environments, some of which are specifically tailored for data science. Data science advising, consulting, and trainingĪ Comment on Data Science Integrated Development EnvironmentsĪ point that differs from our experience struck us in the recent note regarding doing data science in Python:Ī development environment specifically tailored to the data science sector on the level of RStudio, for example, does not (yet) exist.

Spyder rstudio