Fixes

• When pasting text into a new notebook cell added using Select Cell | Add Code Cell Below, the new cell disappears. This behavior is corrected in this release.
• When a DataFrame is displayed in table form, a horizontal scroll bar overlaps the last displayed row. In this release the table no longer has to be manually adjusted to view the final row.
• When the user edited a code cell in a Jupyter notebook, selected text in this cell and then created a new cell of any type, the cursor moved to a new cell but focus erroneously remained in the code cell. When the user then typed in the new cell, the typed text would appear in the old cell.
• When text was selected in a cell and a new cell was then added using an action on the toolbar, pasted text incorrectly appeared in the first cell, instead of in the new cell.

Features

• Execution time updates - Since some Jupyter Notebook cells run for a long time, it can be useful to know the execution time of a cell. DataSpell displays both the last time a code cell was executed and the execution time (duration) directly below every cell.
  • This release provides more precise measurement of execution time, displaying the number of days, hours, minutes, seconds, and milliseconds it took to execute a cell, instead of a single unit like minutes.

Fixes

• Missing DataFrame table data - DataSpell displays pandas DataFrames in tabular form. In DataSpell 2022.3.2 and later, the table for a dataFrame is sometimes not displayed, or a static or truncated table is displayed. Users often encountered the error "Table data could not be loaded". While these issues have not been completely eliminated, the probability that users will encounter them has been greatly reduced in DataSpell 2023.1.1.
• Fixed a number of bugs related to execution time, including a bug that caused the execution time to disappear when a Jupyter Notebook file was closed and reopened and one that prevented the execution time from clearing in Jupyter Notebook metadata.
• When you select the output of a Jupyter Notebook cell, Copy Output, Save as, and Clear Output items are now available in the context menu that appears. Previously, you could only copy the output of a cell by manually selecting the text and using Edit | Copy from the main menu.
• The Jupyter Notebook debugger now works correctly when using a Python interpreter with WSL (Windows Subsystem for Linux) or SSH.
• DataSpell can now connect to a JupyterHub whose URL contains a prefix.

Features

• Multiple Projects With Separate Environments
  • Manage multiple separate projects - DataSpell now enables you to organize your work into multiple, completely separate projects, each of which has its own virtual environment or Python interpreter. Select the Projects option in the left pane of DataSpell’s Welcome screen to see the list of existing projects, open these projects, or create a new project. You can also create and manage projects from the File menu.
  • Workspace with attached directories - Alternatively, you can continue to use a single workspace with attached directories. The environment or interpreter configured for the workspace is inherited by the directories and projects you attach to the workspace by default.
• Jupyter Notebook Productivity Boosters
  • Jupyter Notebook to Python script - Switching back and forth between Jupyter Notebooks and Python scripts is a common workflow in data science. You can now convert a Jupyter Notebook (.ipynb file) to a Python script (.py file) and vice versa in just a few clicks.
  • Cell execution start time and duration - Since Jupyter Notebook cells are often executed out of order and some run for a long time, both the last time a code cell was executed and the duration of the execution are now displayed directly below the cell.
  • Jupyter Notebook code completion - The ineffective code completion provided by Jupyter Notebooks has been disabled. Instead, use the new and improved DataFrame column name completion, autocompletion for dynamic classes, path completion for remote Jupyter servers, and more.
  • Markdown user experience - Several improvements are available for Markdown cells in Jupyter Notebooks and Markdown files, including an intention action to correct the formatting of tables, the Fill Paragraph editor action to break up long texts, and a Smart Keys settings page for Markdown files.
• DataFrame Enhancements
  • CSV to pandas DataFrame - Creating a pandas DataFrame from the data in a CSV file is a common data science task. Drag and drop a CSV (.csv file) into a Jupyter Notebook and a pandas DataFrame will be automatically created from the contents of the file.
  • Change default rows displayed - DataSpell now displays the contents of pandas DataFrames in tabular form. To browse large DataFrame tables more comfortably, update the number of rows displayed per page to your preferred page size using the Change Default dialog.
• User Experience
  • Python packages tool window - The Python Packages tool window is the quickest way to manage packages and preview package documentation for a particular environment or Python interpreter.
  • Debug console in the Jupyter Notebook debugger - The interactive debug console can now be used to send commands to the Jupyter Debugger and view outputs and error messages while debugging Jupyter Notebook cells.
  • Enhanced interpreter widget - You can now add a new Python interpreter directly from the interpreter widget in DataSpell's status bar. Open the widget, select the relevant directory, and then a popup will open with an option to add a new interpreter.
• New UI
  • New UI - Added a new UI designed to reduce visual complexity, provide easy access to essential features, and progressively disclose complex functionality. The new UI has a simplified main toolbar, a new tool window layout, an updated icon set, new light and dark color themes, and more.