Ctrl/Command
+ Shift
+ P
Create New Blank Notebook
Open the command palette with the shortcut: Ctrl/Command
+ Shift
+ P
Search for the command Python: Open Start Page
You are currently viewing what we call our Notebook Editor. It is an interactive document based on Jupyter Notebooks that supports the intermixing of code, outputs and markdown documentation.
This cell is a markdown cell. To edit the text in this cell, simply double click on the cell to change it into edit mode.
The next cell below is a code cell. You can switch a cell between code and markdown by clicking on the code /markdown
icons or using the keyboard shortcut
M
and Y
respectively.
print("hello world")
Ctrl/Command
+ Enter
.Add Cell
icon ESC
Key and then use the keyboard shortcut B
to create the new cell below.Variable explorer
To view all your active variables and their current values in the notebook, click on the variable explorer icon in the top toolbar.
Data Viewer
To view your data frame in a more visual "Excel" like format, open the variable explorer and to the left of any dataframe object, you will see the data viewer icon which you can click to open the data viewer.
Convert to Python File
To export your notebook to a Python file (.py), click on the Convert to Python script
icon in the top toolbar
Plot Viewer
If you have a graph (such as matplotlib) in your output, you'll notice if you hover over the graph, the Plot Viewer
icon will appear in the top left. Click the icon to open up the graph in the Plotviewer which allows you to zoom on your plots and export it in formats such as png and jpeg.
Switching Kernels
The notebook editor will detect all kernels in your system by default. To change your notebook kernel, click on the kernel status in the top toolbar at the far right. For example, your kernel status may say "Python 3: Idle". This will open up the kernel selector where you can choose your desired kernel.
Remote Jupyter Server
To connect to a remote Jupyter server, open the command prompt and search for the command Specify remote or local Jupyter server for connections
. Then select Existing
and enter the remote Jupyter server URL. Afterwards, you'll be prompted to reload the window and the Notebook will be opened connected to the remote Jupyter server.