DataSpell 2022.2: Visual Merge for Jupyter Notebook, UX Enhancements for Working With Cells, WSL Support

The second EAP build for DataSpell 2022.3 contains some exciting new features, including two highly requested additions: the ability to configure a remote interpreter using SSH and debugging for Jupyter notebooks running on remote machines. In addition, we’ve greatly expanded the options for working with DataFrames, including by making improvements to how you can display and export your data. Finally, we’ve added enhancements to help with coding, such as autocompletion for pandas column names when using Python scripts and Data Vision, which displays helpful in-line information about variables.

DataSpell 2022.2.3 implements an important fix – editing the search query no longer results in cell code deletion. There are also more improvements to the notebook editor in this build. Read on to learn more. You can download the new version from our website, update directly from the IDE, update via the free Toolbox App, or use snaps for Ubuntu. DOWNLOAD DATASPELL 2022.2.3 The Terminal now respects the Python environment In DataSpell you can set up a dedicated Python environment for any folder you attach to the workspace.  In earlier versions, when you opened the built-in

In the first EAP build for DataSpell 2022.3, you will find the ability to track and revert changes made to Jupyter notebooks, as well as a better in-editor experience for formatting multiline code blocks in Jupyter notebooks and searching within them. This first build also enriches support for web outputs by making it possible to use the Output widget.  Please give the new functionality a try and share your feedback! The Toolbox App is the easiest way to get the EAP builds and keep your stable and EAP versions up to date. You can also manually download the EAP builds from our we

DataSpell 2022.2.2 introduces enhancements for the Python interpreter selector, addresses a bug affecting the display of image outputs in the Python Console, and fixes the way metadata from externally created notebooks is handled.  You can download the new version from our website, update directly from the IDE, update via the free Toolbox App, or use snaps for Ubuntu. DOWNLOAD DATASPELL 2022.2.2 Python interpreter selector DataSpell has an Interpreter selector in the bottom right-hand corner of the IDE that helps you see and change the Python interpreter you are currently wo

Get up and running with version control using Git when working with Jupyter notebooks.

The first minor update to DataSpell 2022.2 brings the ability to copy-paste directories between your local machine and remote Jupyter servers, as well as better rendering for Markdown and HTML tables in the Jupyter editor. Let’s take a closer look! You can download the new version from our website, update directly from the IDE, update via the free Toolbox App, or use snaps for Ubuntu. Copy-paste directories between the local machine and the remote Jupyter server You can now copy-paste both files and directories from your local machine to remote Jupyter servers, and vice versa. To

对于使用 Windows 的数据科学家或机器学习工程师来说，Windows Subsystem for Linux 2 (WSL 2) 的功能一定不算陌生。 WSL 2 允许使用 Hyper-V 虚拟化在 Windows 10 或 11 上完全安装 Linux 发行版。 涉及数据科学或机器学习任务时，这提供了来自两个方面的优势：在 Windows 上访问所有应用程序或文件，以及使用 Linux 运行 Python 代码。 这完美契合使用 Linux 专属软件包的分析或模型，无需手动编译。 WSL 2 比传统虚拟机更轻量，仅提供命令行功能，一直将 Python 中的开发环境选项限制为 Jupyter Notebook。 好在 DataSpell 2022.2 具有通过 WSL 2 配置 Python 解释器的功能，您可以在 Windows 上通过 DataSpell 轻松运行所有想要使用的 Linux 兼容软件包。 我们来看看它是如何运作的。 设置 WSL 2 使用 WSL 2 之前，需要确保使用的是兼容版本的 Windows。 Windows 10 版本 2004 及更高版本（家庭版及更高版本）和所有版本的 Windows 11 都支持 WSL 2。 然后，安装 Ubuntu 或首

Learn more about using Windows Subsystem for Linux with Jupyter Notebooks, including how to run Python code from Windows using Linux.

Easy access to relevant, safe data is a major bottleneck hindering developers and data scientists. But what if you could generate your own accurate, privacy-protected, shareable data?  Synthetic data can provide an inexpensive alternative to real sets of data that can’t be used due to its sensitivity or regulations. Such data is used for training machine learning models, testing, and performing quality assurance.  In this webinar with Mason Egger, we'll learn about using Synthetic Data, and we’ll learn how to get started creating our own Synthetic Data. Join us on July 2

This new build is equipped with a bunch of improvements to the Jupyter cells UX. It also provides a new capability to set up and use WSL-based interpreters for your projects from inside DataSpell. Here is an overview of what you can expect from the second EAP for DataSpell 2022.2. You can download the EAP build from our website, get it from the free Toolbox App, or use snaps if you are using Ubuntu. Cell selection while invoking the context menu We changed the behavior of cell selection in command mode while invoking the context menu to make it more obvious. Now if the context

On Tuesday, June 21, 2022, Matt Harrison joined Jodie Burchell for a webinar about Pandas, and how to be more effective while working with this popular library. Here’s the recording for you to watch if you missed the live stream. https://youtu.be/7nhG8SKkh5E Description In this webinar Matt Harrison and Jodie Burchell discussed tips for making Pandas more memory-friendly, getting the best performance possible when applying operations to Series and DataFrames, keeping your Pandas code as reproducible and tidy as possible, and how using the right tooling can make working with Pandas e