DataSpell 2023.2 EAP 1 Is Out!
The Early Access Program (EAP) for DataSpell 2023.2 is now open! The EAP gives you access to pre-release versions of DataSpell, allowing you to evaluate new features, test issues that have been resol…
DataSpell 2023.1.1 Is Out!
DataSpell 2023.1.1 provides more precise measurement of cell execution time, fixes for disappearing DataFrame tables, and more.
DataSpell 2023.1: Support for Multiple Projects, Notebook Productivity Boosters, and DataFrame Enhancements
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
DataSpell 2023.1 EAP 2 is Out!
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.
Picking the Perfect Data Visualization: Barplots
Use barplots to gain insight into the differences between groups.
DataSpell 2022.3.3 Is Out!
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
DataSpell 2023.1: Early Access Program is Open!
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 Is Out!
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.
Hit the Ground Running With Pandas
If you’re doing any work with data in Python, it’s only a matter of time before you come across pandas. This popular package provides you with many options for reading in, processing, and writing data; however, it’s not the most intuitive package to use and beginners might find it a bit overwhelming at first. In this blog post, we’ll cover the basics of what you need to know to get up and running with the powerful pandas library, including some of my favorite methods for these operations. So, let’s get you on your way! Reading in and writing data In order to start working with data,
DataSpell 2022.3.1 Is Out!
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: Support for Remote Interpreter Connections via SSH, Remote Jupyter Debugging, Local History for Tracking and Reverting Changes
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
DataSpell 2022.3 EAP 2 Is Out!
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.