In DataSpell 2023.1, if you drag and drop a CSV file (.csv) into a Jupyter Notebook, a pandas DataFrame will be automatically created from the contents of the file. CSV file to pandas DataFrameĪnother common data science task is creating and populating a pandas DataFrame with the data in a CSV file. You can speed up this repetitive task with a new feature to convert a Jupyter Notebook (.ipynb file) to a Python script (.py file) and vice versa in just a few clicks. Switching back and forth between Jupyter Notebooks and Python scripts is a common workflow in data science. Alternatively, you can continue to use a single workspace with attached directories. This model is more in line with other JetBrains IDEs like P圜harm. In version 2022.3, DataSpell’s workspace is, in essence, the default project.īy popular demand, DataSpell 2023.1 enables you to organize your work into multiple, completely separate projects, each of which has its own environment or Python interpreter. By default, all directories and projects in the workspace share its environment or Python interpreter. Existing projects are attached to the workspace as directories. 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.ĭownload the new version from our website, update directly from the IDE or via the free Toolbox App, or use snaps for Ubuntu.ĭownload DataSpell 2023.1 Use multiple projects or a single workspaceĭataSpell 2022.3 has a single workspace, to which you can attach notebooks and other files, directories, and projects. Many DataSpell users requested the ability to organize their work into multiple, separate projects in line with other JetBrains IDEs, and in this release we have delivered! Speed up tedious tasks by automatically converting a Jupyter Notebook into a Python script and vice versa, drag and drop a CSV file to create a pandas DataFrame, view cell execution start time and duration, and more. DataSpell 2023.1 brings you productivity boosting features for Jupyter Notebooks and pandas DataFrames, as well as a batch of user experience improvements.
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