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Tag: PyCharm

JetBrains Releases IDE “DataSpell” Especially For Data Scientists

JetBrains recently announced the official release of DataSpell, its new data science IDE. DataSpell by JetBrains is designed specifically for those involved in exploratory data analysis and prototyping ML models. It combines the interactivity of Jupyter notebooks with the intelligent Python and R coding assistance of PyCharm in one ergonomic environment.
DataSpell supports both local and remote Jupyter notebooks. It is possible to work with them right inside the IDE exactly as using traditional web-based notebooks. The main advantage of DataSpell over Jupyter or JupyterLab is the…

Will You Switch From PyCharm to DataSpell — the Latest Data Science IDE from JetBrains?

Review of the key features for the DataSpell IDE

Photo by Nick Fewings on Unsplash

Among the common Python IDEs, PyCharm is my favorite for several reasons, just to name a few: 1). PyCharm gives me a more coherent user experience because I used to use AndroidStudio a lot; 2). Great auto-completion intelligence for high productivity; 3). Native integration of version control tools (e.g., GitHub); 4). Easy management of virtual environment; and 5) Refactoring and debugging is painless.

Although less known than other big tech enterprises, JetBrains is a highly innovative company that is behind the well-regarded Python IDE — PyCharm, together with several industry-leading IDEs for other specialty developments, such as WebStorm for web development.


Jupyter Notebook vs PyCharm

  1. Introduction
  2. Jupyter Notebook
  3. PyCharm
  4. Summary
  5. References

As a data scientist still learning in an educational setting, you might use one main tool, while you may focus on another, different one as a professional data scientist. Of course, using multiple tools or platforms is beneficial, but there is a time and place for specific ones. Two beneficial and important tools that many data scientists use are Jupyter Notebook and PyCharm. Each has its own respective functions, but the end goal can be surprisingly similar, which is to organize and execute code for data science processes (referring to just data science for the sake of this article). With that being said, I want to highlight the benefits of both and when to use one over the other below.