DataSpell can now connect to a JupyterHub whose URL contains a prefix.The Jupyter Notebook debugger now works correctly when using a Python interpreter with WSL (Windows Subsystem for Linux) or SSH.Previously, you could only copy the output of a cell by manually selecting the text and using Edit | Copy from the main menu. 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.While these issues have not been completely eliminated, the probability that users will encounter them has been greatly reduced in DataSpell 2023.1.1. Users often encountered the error “Table data could not be loaded”. In DataSpell 2022.3.2 and later, the table for a dataFrame is sometimes not displayed, or a static or truncated table is displayed. Missing DataFrame table dataĭataSpell displays pandas DataFrames in tabular form. We’ve also 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. DataSpell 2023.1.1 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. DataSpell 2023.1 displays both the last time a code cell was executed and the execution time (duration) directly below every cell. Since some Jupyter Notebook cells run for a long time, it can be useful to know the execution time of a cell. DataSpell 2023.1.1 provides more precise measurement of cell execution time, fixes for missing DataFrame table data, and more.ĭownload the new version from our website, directly from the IDE, via the free Toolbox App, or use snaps for Ubuntu.ĭownload DataSpell 2023.1.1 Execution time updates
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