This release comes with a built-in Variable Explorer along with a Data Viewer, which will help the users to easily view, inspect and filter the variables in the application, including lists, NumPy arrays, pandas data frames, and more. This release shows a section for variables while running code and cells in the Python Interactive window. On expanding it, users can see a list of the variables in the current Jupyter session. More variables will automatically show up as they get used in the code. And users can sort the variables in columns by clicking on each column header.
Users can now double-click on each row or use the “Show variable in Data Viewer” button in order to view full data of each variable in the newly-added Data Viewer and can perform a simple search over its values.
In this release, the process of configuring the debugger has now been simplified. If a user starts debugging through the Debug Panel and no debug configuration exists, then the users will now be prompted to create a debug configuration for their application. Instead of manually configuring the launch.json file, users can now create a debug configuration through a set of menus.
This release comes with fixes and improvements to the Python Language Server. The team has added back the features that were removed in the 0.2 release including “Rename Symbol”, “Go to Definition” and “Find All References”. Also, the loading time and memory usage have been improved while importing scientific libraries such as pandas, Plotly, PyQt5, especially while running in full Anaconda environments.
Read Also: Visualizing data in R and Python using Anaconda [Tutorial]
To know more about this news, check out Microsoft’s official blog post.
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