What is JetBrains DataSpell?
It has met the strict standards set by its developer; JetBrains DataSpellprovides an integrated development environment that can satisfy the needs and requirements of data scientists. The new IDE provides capabilities designed to aid in the process of exploratory data analysis and machine learning. The JetBrains team is proud of providing a user-friendly environment in which users can benefit from the advanced code tools offered within PyCharm and Jupyter's notebooks with interactive features.
Compatible with Python and numerous other languages
JetBrains DataSpell relies on the Python interpreter and offers support with Conda, Markdown, and the R language. It comes with tools for debugging and virtualization, a dataset Explorer, a manager of packages, and reliable support for programming.
Like you think of JetBrains products, its IDE features a chic appearance and an ample editing space. If you're working using Python scripts or Jupyter notebooks. In this case, you'll be able to have a productive environment with line numbers and code completion along with intelligent suggestions and syntax highlighter.
Utilizing Python scripts in JetBrains DataSpell lets you access all Python science libraries. Python scripts can be split into code cells and run on their own like Jupyter. Inbuilt Python console displays output in real-time.
Support for local and remote Jupyter notebooks
One of the main points is that it integrates Jupyter. It allows you to establish connections to Jupyter notebooks locally and remote ones to JupyterLab and JupyterHub.
DataSpell allows you to operate in the editor as well as command mode, and it's compatible with all Jupyter standard shortcuts. It can also produce interactive outputs, and the IDE allows you to complete codes and quickly detect errors to speed up the process.
The Terminal is a version control tool, as are the versions and databases tools.
In addition to the features previously discussed, JetBrains DataSpell comes with options typically found in important IDEs, such as version control, a built-in terminal, and database tools.
Version control helps you manage your Git project's changes commits, commits, and changes like a professional. The Terminal helps you work on the command line. Likewise, the database tools allow you to connect to and query databases via the IDE.
An IDE is dedicated to data science and not as much development
JetBrains DataSpell was designed to be an IDE designed to work with data. PyCharm is not only focused on development but also includes features to work with data scientists. JetBrains DataSpell is suitable for professionals who are data scientists, not just developers.
Intelligent Jupyter notebooks
High Interactivity Tuned at the top possible.
Switch between command and editor modes by pressing a single keystroke. Use Arrow keys to navigate cells. Keys. Utilize all the typical Jupyter shortcuts--experience fully interactive outputs directly under the cell.
Assistance via smart coding
When making edits to code, cells take advantage of intelligent completeness of code, quick error correction, quick fixes, easy navigation, and more.
Notebooks that are remote, but local
It is possible to use local Jupyter notebooks or direct connections to remote Jupyter, JupyterHub, or JupyterLab servers through the IDE.
Interactive Python scripts
Scientific Python console
Run Python scripts or any other expressions that you want to interact with by using the Python Console. Review the outputs and the status of variables at any point.
Cells are a part of Python scripts.
Separate Python scripts into code cells using the "#%% " Separator. After that, you can run them as you would in the Jupyter notebook.
Data visualization and outputs
Explore DataFrames, and view them all in one place with interactive controls. The well-known Python libraries for scientific research are accessible with Plotly, Bokeh, Altair, IPS widgets, and other widgets.
Tools and integrated tools
Control of Version
Clone Git projects to move and commit changes while managing multiple branches. Manage change lists, and then the changes are staged before their commit.
Use Command Line Shell via the built-in Terminal that supports the identical commands available when you run your OS.
You can connect to and query databases right through the IDE. Benefit from the assistance of intelligent programming as you edit SQL code, and running queries search data, or modify schemas.
JetBrains DataSpellGreat Features:
If you're working on your own or are working in Jupyter notebooks or Python scripts. With Python, you'll be able to count on the intelligent development of your code. It also offers immediate error-checking, as well as quick fixes, and also easy navigation of your code.
DataSpell lets you edit and render Markdown inside notebooks as well as separate files. LaTeX support isn't yet available; however it'll be shortly.
Conda's built-in support of Conda allows you to build and manage environments and dependencies.
The Debugger can be located in both notebooks created using Jupyter or Python scripts. Stop at breakpoints and go to the script, browse and monitor the state of variables.
Connect to your database to analyze tables, work on refactorings and import and export data, etc.
Support for the essential tasks in R is provided by Debugger visualization of datasets, Explorer and Package Manager, and smart assistance in coding, and others.
Its Vim Emulation Docker, additional VCS, custom-designed theme for appearance, and other features are available through various plugins.
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