Jupyter (IPython)

You can now run Python code directly in a Jupyter (IPython) kernel. More information on Jupyter (IPython) can be found here

Running a line of Python code in a Kernel
Running selected Pytohn code in a Kernel
Running a block of code (cell) in a Kernel
Restart, Interrupt and Shutdown a kernel
Selecting a kernel
Viewing output within Visual Studio Code (Images, Html, Graphs, LaTeX, SVG, and more)

Before using Jupyter (IPython), you will need to ensure the prerequisites are installed and setup.
Once you have the necessary prerequisites installed and setup, you are ready to evaluate code in a Jupyter kernel from within Visual Studio Code.
For instance you can select a line or a block of code and evaluate it in a kernel and view the results
Evaluating a block of code is what starts a kernel, and results in the displaying of the results and a status bar item to manage the kernel
If selecting blocks of code is too cumbersome you can always use comment blocks to define a block of code also known as a cell for easy execution.

Running a line or selection of code in a kernel
- You could select a block of code and execute it in a kernel
- You could place the cursor at a line and execute just that line
Running a cell in a kernel
- Provided you have defined a block of code using the necessary characters, code lenses will automatically appear allowing you to run that cell in a kernel
Managing kernels
- Running a block of code in a kernel results in a kernel being Started
- The kernel can be managed via the quick pick options displayed when clicking a status bar item
- You could restart, interrupt, shutdown or even select a different kernel (e.g. you could have two kernels, one for Python 2.7 and another for Python 3.5)
Results are displayed within Visual Studio Code (with support for various interactive graphs)

At the moment, the following configuration options are supported. More details on setting up the following configuration settings can be found here.

Defining the default kernel
Defining startup code for the jupyter kernel
- The default startup code is %matplotlib inline
Controlling whether results are appended to existing results or not.

Topics:

You can now run Python code directly in a Jupyter (IPython) kernel. More information on Jupyter (IPython) can be found here

Running a line of Python code in a Kernel
Running selected Pytohn code in a Kernel
Running a block of code (cell) in a Kernel
Restart, Interrupt and Shutdown a kernel
Selecting a kernel
Viewing output within Visual Studio Code (Images, Html, Graphs, LaTeX, SVG, and more)

Before using Jupyter (IPython), you will need to ensure the prerequisites are installed and setup.
Once you have the necessary prerequisites installed and setup, you are ready to evaluate code in a Jupyter kernel from within Visual Studio Code.
For instance you can select a line or a block of code and evaluate it in a kernel and view the results
Evaluating a block of code is what starts a kernel, and results in the displaying of the results and a status bar item to manage the kernel
If selecting blocks of code is too cumbersome you can always use comment blocks to define a block of code also known as a cell for easy execution.

Running a line or selection of code in a kernel
- You could select a block of code and execute it in a kernel
- You could place the cursor at a line and execute just that line
Running a cell in a kernel
- Provided you have defined a block of code using the necessary characters, code lenses will automatically appear allowing you to run that cell in a kernel
Managing kernels
- Running a block of code in a kernel results in a kernel being Started
- The kernel can be managed via the quick pick options displayed when clicking a status bar item
- You could restart, interrupt, shutdown or even select a different kernel (e.g. you could have two kernels, one for Python 2.7 and another for Python 3.5)
Results are displayed within Visual Studio Code (with support for various interactive graphs)

At the moment, the following configuration options are supported. More details on setting up the following configuration settings can be found here.

Defining the default kernel
Defining startup code for the jupyter kernel
- The default startup code is %matplotlib inline
Controlling whether results are appended to existing results or not.