DataSpell 2022.2.2 Is Out
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. 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 working with. In DataS
How to Use Git With Jupyter Notebooks in DataSpell
Get up and running with version control using Git when working with Jupyter notebooks.
DataSpell 2022.2: Visual Merge for Jupyter Notebook, UX Enhancements for Working With Cells, WSL Support
DataSpell 2022.2 is here and ready to give your data science work an efficiency boost. Merging diverged notebooks is now straightforward with the visual merge tool, as DataSpell 2022.2 allows you to review them as two notebooks open side by side, highlighting the changes in the cells. Support for WSL allows you to create WSL-based projects as well as interpreters and to work in the Python console and the terminal directly in DataSpell. Code insight for your Python and Jupyter files is provided too.
Using Jupyter Notebooks With WSL 2 in DataSpell
Learn more about using Windows Subsystem for Linux with Jupyter Notebooks, including how to run Python code from Windows using Linux.
Webinar: I Can’t Believe It’s Not Real Data! An Introduction to Synthetic Data
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
DataSpell 2022.2 EAP 2 Is Out!
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
Webinar Recording: “Idiomatic Pandas: 5 Tips for Better Pandas”, With Matt Harrison
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
Webinar: “Idiomatic Pandas: 5 tips for better Pandas”, With Matt Harrison and Jodie Burchell
I’ve been a long-time Pandas user, relying on it heavily since the start of my data science career. However, up until the last couple of years, I struggled with certain issues, such as not being able to work with very large DataFrames or efficiently run heavy data processing tasks. I’d also often find my Jupyter notebooks cluttered with intermediate DataFrames after applying transformations, making it harder to read the code and keep my notebook tidy. For a long time, I had thought that these issues were just endemic to Pandas and accepted there wasn’t a better way; however, there is! If yo
The DataSpell 2022.2 EAP Has Started!
We’re opening the Early Access Program (EAP) for DataSpell 2022.2. In this first build, we’ve improved the appearance of outputs in the console, introduced the ability to resize image outputs in a mouse click, and bundled the Diagrams plugin.
DataSpell 2022.1.1 Is Out!
With DataSpell 2022.1.1, we have worked hard to improve the overall DataSpell user experience. You can download the build from the website, update via the Toolbox App, apply a patch (go to DataSpell | Check for Updates), or use a snap package (for Ubuntu). For Jupyter notebooks, we concentrated on multiple issues involving the display of cell outputs. When there are multiple outputs, they will now appear without delay, and outputs will no longer be resized on text selection. You can now connect to Jupyter servers that have paths in their URLs – a setup that is quite common from self-h
What’s new in DataSpell 2022.1
JupyterHub 2.0 support, the ability to copy files to remote Jupyter servers, runtime completion, and the DataSpell Onboarding Tour It's been quite a while since we released the first public version of DataSpell back in November 2021. We’ve received a lot of feedback since then and are doing our best to address it with new features and fixes coming with the new DataSpell 2022.1 release. Improvements to the way DataSpell interacts with remote Jupyter servers brought the inclusion of JupyterHub 2.0 support, as well as the ability to copy files from the local machine to remote Jupyter right i
Live Stream: From Jupyter Notebooks to JetBrains DataSpell
Join us Tuesday, August 17, 6 pm – 7 pm (CEST) / 12 am – 1 pm (EDT), for our live webinar “From Jupyter Notebooks to JetBrains DataSpell”. In this webinar, Andrey Cheptsov, Product Manager for PyCharm and DataSpell, will preview JetBrains DataSpell – a brand new IDE for data scientists. Over the last few years, PyCharm users have often requested better support for Jupyter notebooks. So a year ago, part of the PyCharm team sat together and decided to rebuild what PyCharm offers to data scientists from scratch. They decided not to just improve the support for Jupyter notebooks, but also t