Rich Outputs
One of the main feature of IPython when used as a kernel is its ability to show rich output. This means that object that can be representing as image, sounds, animation, (etc...) can be shown this way if the frontend support it.
In order for this to be possible, you need to use the display() function, that should be available by default on IPython 5.4+ and 6.1+, or that you can import with from IPython.display import display. Then use display(<your object>) instead of print(), and if possible your object will be displayed with a richer representation. In the terminal of course, there won't be much difference as object are most of the time represented by text, but in notebook and similar interface you will get richer outputs.
Plotting
Note
Starting with IPython 5.0 and matplotlib 2.0 you can avoid the use of IPython's specific magic and use matplotlib.pyplot.ion()/matplotlib.pyplot.ioff() which have the advantages of working outside of IPython as well.
One major feature of the IPython kernel is the ability to display plots that are the output of running code cells. The IPython kernel is designed to work seamlessly with the matplotlib_ plotting library to provide this functionality.
To set this up, before any plotting or import of matplotlib is performed you may execute the %matplotlib :ref:`magic command <magics_explained>`. This performs the necessary behind-the-scenes setup for IPython to work correctly hand in hand with matplotlib; it does not, however, actually execute any Python import commands, that is, no names are added to the namespace.
If you do not use the %matplotlib magic or you call it without an argument, the output of a plotting command is displayed using the default matplotlib backend, which may be different depending on Operating System and whether running within Jupyter or not.
Alternatively, the backend can be explicitly requested using, for example:
%matplotlib gtk
The argument passed to the %matplotlib magic command may be the name of any backend understood by matplotlib or it may the name of a GUI loop such as qt or osx, in which case an appropriate backend supporting that GUI loop will be selected. To obtain a full list of all backends and GUI loops understood by matplotlib use %matplotlib --list.
There are some specific backends that are used in the Jupyter ecosystem:
- The inline backend is provided by IPython and can be used in Jupyter Lab, Notebook and QtConsole; it is the default backend when using Jupyter. The outputs of plotting commands are displayed inline within frontends like Jupyter Notebook, directly below the code cells that produced them. The resulting plots will then also be stored in the notebook document.
- The notebook or nbagg backend is built into matplotlib and can be used with Jupyter notebook <7 and nbclassic. Plots are interactive so they can be zoomed and panned.
- The ipympl or widget backend is for use with Jupyter lab and notebook >=7. It is in a separate ipympl module that must be installed using pip or conda in the usual manner. Plots are interactive so they can be zoomed and panned.
See the matplotlib_ documentation for more information, in particular the section on backends.