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DataSpell 2023.1.1 provides more precise measurement of cell execution time, fixes for disappearing DataFrame tables, and more. 

DataSpell 2023.1 is out! Automatically convert a Jupyter Notebook into a Python script, drag and drop a CSV file to create a pandas DataFrame, and more

Many DataSpell users requested the ability to organize their work into multiple projects with completely separate environments and in DataSpell 2023.1 EAP 2 we have delivered! This second EAP build also contains new features to convert a Jupyter Notebook into a Python script and vice versa, to drag and drop a CSV to create a Pandas DataFrame, and to change the default number of rows displayed for a DataFrame. Finally, debugging and package management just got easier with an interactive debug console in the Jupyter Notebook Debugger and a fully functional Python Packages Tool Window.

DataSpell 2022.3.3 gets GitHub Copilot back on board and includes fixes for remote Jupyter issues, overenthusiastic Notebook updates and the DataSpell onboarding tour.  Download the new version from our website, update directly from the IDE, via the free Toolbox App, or use snaps for Ubuntu. Download DataSpell 2022.3.3 Your Copilot Is Back On Board! In DataSpell 2022.3, GitHub Copilot worked in Python script files (.py), but not with Jupyter Notebooks. The issue was caused by changes in the Jupyter editor-to-file relationship made in DataSpell 2022.2. The DataSpell

The first EAP build for DataSpell 2023.1 brings you Jupyter Notebook cell execution time and duration, improved code completion for Jupyter Notebooks, better Data Vision and an enhanced interpreter widget.

DataSpell 2022.3.2 brings you fixes to ensure that Jupyter Notebooks are consistently reloaded from disk and that your typing doesn’t get stuck in reverse.

Get a fix for interpreter widget woes, an uninterrupted onboarding tour, simplified settings sync and code completion for column names in Python scripts.

DataSpell 2022.3 has been released! The most important new features include: - Remote development via SSH - Remote Jupyter debugging - Automatic tracking of important code changes - Enhanced DataFrame interactivity - In-line data about Jupyter variables

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.

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

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