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
that should be available by default on IPython 5.4+ and 6.1+, or that you can
from IPython.display import display. Then use
object>) instead of
print(), and if possible your object will be displayed
with a richer representation. In the terminal of course, there wont be much
difference as object are most of the time represented by text, but in notebook
and similar interface you will get richer outputs.
Starting with IPython 5.0 and matplotlib 2.0 you can avoid the use of
IPython’s specific magic and use
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
must execute the
%matplotlib magic command. 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
import commands, that is, no names are added to the namespace.
%matplotlib magic is called without an argument, the
output of a plotting command is displayed using the default
backend in a separate window. Alternatively, the backend can be explicitly
requested using, for example:
A particularly interesting backend, provided by IPython, is the
backend. This is available only for the Jupyter Notebook and the
Jupyter QtConsole. It can be invoked as follows:
With this backend, the output of plotting commands is displayed inline within frontends like the Jupyter notebook, directly below the code cell that produced it. The resulting plots will then also be stored in the notebook document.
Plotting with Matplotlib example notebook
The matplotlib library also ships with
%matplotlib notebook command that
allows interactive figures if your environment allows it.
See the matplotlib documentation for more information.