The DataSpell Blog : The IDE for Data Scientists

The DataSpell Blog

The IDE for Data Scientists

Latest posts

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

You can get the new build from our website, via the free Toolbox App, or by using snaps for Ubuntu. To familiarize yourself with the most notable updates in v.2022.2, browse the DataSpell 2022.2 EAP section of our blog. This release candidate also delivers some updates we haven’t previously covered, so read on for the latest news. New action to generate tables of contents in Markdown files It’s now possible to easily generate tables of contents in Markdown files based on the document headers. This new action is available from the Insert and Generate popup menus that you can invoke v

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

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

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.

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

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

In the previous article devoted to full-line code completion, we looked into the vocabulary that the neural net of our full line completion plugin uses for Python. However, just having 16384 tokens like self., or, s.append(, return value, and others described in the article is not enough to generate even a single line. We need a way to combine these tokens together to write chunks of code. In today’s article, we will discuss how the algorithm constructs longer phrases using the elements of the vocabulary. The first idea that deserves mentioning is autoregression. Autoregression Autore

DataSpell 2022.1 EAP 2 is now available, and you can try the newly added features right away. Get the latest version using the ToolBox App by clicking Update – this will update your existing installation. You can also download the new EAP as a standalone version using this button: DOWNLOAD DATASPELL 2022.1 EAP 2 New features overview Bundled Grazie plugin The DataSpell 2022.1 EAP 2 build includes Grazie, a plugin that provides spelling and grammar checks for the text that you write in the IDE. Grazie can provide support for 15 languages (go to Settings | Editor | Natural Languages

This is a simplified image of a computer display. It also includes the following text: Looking at Python through the eyes of a neural net.

The JetBrains full line code completion plugin for Python is now available as a public beta. We would like to talk about some of the technologies and algorithms used to create the plugin and share statistics about Python programming that we’ve collected in the process. What is “full line code completion?” You are probably already familiar with code completion, the kind that suggests the next word the user is typing. If you are not, we have covered it in a series of articles (one, two, three, four). Full line code completion extends the service by suggesting larger fragments of code.