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Introducing JetBrains DataSpell

It was upholding the high requirements set by its developer, JetBrains DataSpellprovides an integrated development environment that can meet the demands and demands of professionals working as data scientists. This brand new IDE offers capabilities that aim to facilitate the process of exploratory data analysis and machine learning. The JetBrains team takes pride in creating a user-friendly environment where users can benefit from the smart coding tools that are available in PyCharm and Jupyter's interactive notebooks.

Compatibility with Python and many other languages

JetBrains DataSpell relies on the Python interpreter and offers assistance for Conda, Markdown, and the R language. It includes tools for debugging and a virtualization and dataset Explorer, a manager of packages and reliable assistance with coding.

Like you would think of JetBrains products its IDE has a stylish design and an ample editing space. Suppose you are working with Python scripts or Jupyter notebooks. In that case, you'll be able to enjoy a developing environment that features line numbers and code completion, as well as intelligent suggestions, and syntax highlighting.

Using Python scripts within JetBrains DataSpell lets you access all Python science libraries. Python scripts can be separated into code cells, and run independently, similar to Jupyter. The built-in Python console shows the output in real-time.

Support for remote and local Jupyter notebooks

One of the most prominent highlights is the integration with Jupyter. You can establish connections to Jupyter notebooks locally or remote ones to JupyterLab and JupyterHub.

DataSpell lets you operate in both editor and command mode. It is compatible with all of the Jupyter standard shortcuts and also generates interactive outputs, and the IDE lets you complete code and rapid error detection to speed up your work.

A terminal, version control, and database tools

Apart from the specifics previously mentioned, JetBrains DataSpell comes with options usually included in most significant IDEs including version control and a built-in terminal and databases tools.

Version control lets you organize your Git project changes, commits, and commits as a professional. The Terminal makes it easier to work with the command line, while the tools for databases allow you to access and query databases through the IDE.

An IDE is devoted to data science and less development

JetBrains DataSpell was designed to be an IDE geared towards working with data. PyCharm is also focused on development but also offers capabilities for data science. JetBrains DataSpell is suitable for professional data scientists and not just developers.

Intelligent Jupyter notebooks

High Interactivity Tuned to the highest level.

Switch between editor and command modes with just one keystroke. Use arrow keys to navigate through cells. Keys. Utilize all the typical Jupyter shortcuts—experience fully interactive outputs directly under the cell.

Assistance with smart coding

When editing code, cells take advantage of intelligent code completion, instant errors and quick fixes, Easy navigation, and so much more.

Notebooks that are remote and local

You can use local Jupyter notebooks or connect directly to distant Jupyter, JupyterHub, or JupyterLab servers from within the IDE.

Interactive Python scripts

Scientific Python console

Run Python scripts or any other arbitrary expressions interactively using a Python Console. Check the outputs and the status of the variables at any time.

Cells are included in Python scripts.

Separate Python scripts in code cells using The"#%% " separator. Then, run them like you would in a Jupyter notebook.

Visualization and data outputs

Browse DataFrames and visualize them in the same place using interactive controls. The popular Python library for science is available, including Plotly, Bokeh, Altair, IPS widgets, and others.

Integrated tools and tools

Control of version

Clone Git projects, commit and push changes, and manage multiple branches. Manage change lists and then stage changes before they are committed.

Terminal

Use Command Line Shell via the built-in Terminal, which supports all the same commands that are available on you use your OS.

Database tools

You can access and query databases directly from the IDE. Benefit from intelligent programming assistance while editing SQL code and running queries browsing data, or changing schemas.

Features

Python

Whether you are working, regardless of whether you work in Jupyter notebooks and Python scripts, you'll always be able to rely on the intelligent completion of your code and on-the-fly error-checking and quick fixes, as well as simple navigation of code.

Markdown

DataSpell allows editing and render Markdown in notebook cells as well as in separate files. LaTeX support isn't available yet, but it will be shortly.

Interactive outputs

DataSpell is fully compatible with the static and JavaScript-based inputs utilized by libraries for scientific research like Plotly, Bokeh, Altair Ipywidgets, and more. In the case of DataFrames, DataSpell offers rich interactive table control.

Conda

Conda's built-in support for Conda allows you to create, manage, and reuse dependencies and environments.

Debugger

The Debugger can be found in both notebooks created with Jupyter and Python scripts. Stop at breakpoints, go into the script, and then browse and control the status of variables.

SQL

Connect with your database to study tables, perform refactorings, export and import data, etc.

R

Support for essential functions of R includes Debugger, datasets and visualization Explorer, Package Manager and intelligent assistance with coding, and many more.

Plugins

The Vim Emulation Docker, additional VCS, custom-designed appearance themes, and many more are accessible through various plugins.

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