For more on all the wild and wonderful things you can do with annotations, including fancy arrows, see :ref:`plotting-guide-annotation` and :ref:`pylab_examples-annotation_demo`.

There are two types of ``Artists``: primitives and containers. The primitives represent the standard graphical objects we want to paint onto our canvas: :class:`~matplotlib.lines.Line2D`, :class:`~matplotlib.patches.Rectangle`, :class:`~matplotlib.text.Text`, :class:`~matplotlib.image.AxesImage`, etc., and the containers are places to put them (:class:`~matplotlib.axis.Axis`, :class:`~matplotlib.axes.Axes` and :class:`~matplotlib.figure.Figure`). The standard use is to create a :class:`~matplotlib.figure.Figure` instance, use the ``Figure`` to create one or more :class:`~matplotlib.axes.Axes` or :class:`~matplotlib.axes.Subplot` instances, and use the ``Axes`` instance helper methods to create the primitives. In the example below, we create a ``Figure`` instance using :func:`matplotlib.pyplot.figure`, which is a convenience method for instantiating ``Figure`` instances and connecting them with your user interface or drawing toolkit ``FigureCanvas``. As we will discuss below, this is not necessary -- you can work directly with PostScript, PDF Gtk+, or wxPython ``FigureCanvas`` instances, instantiate your ``Figures`` directly and connect them yourselves -- but since we are focusing here on the ``Artist`` API we'll let :mod:`~matplotlib.pyplot` handle some of those details for us::

returned by the ``line, = ax.plot...`` call:

Each ``Axis`` object contains a :attr:`~matplotlib.axis.Axis.label` attribute (this is what :mod:`~matplotlib.pylab` modifies in calls to :func:`~matplotlib.pylab.xlabel` and :func:`~matplotlib.pylab.ylabel`) as well as a list of major and minor ticks. The ticks are :class:`~matplotlib.axis.XTick` and :class:`~matplotlib.axis.YTick` instances, which contain the actual line and text primitives that render the ticks and ticklabels. Because the ticks are dynamically created as needed (eg. when panning and zooming), you should access the lists of major and minor ticks through their accessor methods :meth:`~matplotlib.axis.Axis.get_major_ticks` and :meth:`~matplotlib.axis.Axis.get_minor_ticks`. Although the ticks contain all the primitives and will be covered below, the ``Axis`` methods contain accessor methods to return the tick lines, tick labels, tick locations etc.:

Here is an example which sets the formatter for the right side ticks with

Andrew Straw provided much of the log scaling architecture, the fill

command, PIL support for imshow, and provided many examples. He

also wrote the support for dropped axis spines.

added support for "Dashed Text". See `dashpointlabel.py

contributed work to matplotlib's Cocoa support and has done a lot of work on the OSX and win32 binary releases.

Jouni K. Seppaenen wrote the PDF backend and contributed numerous

fixes to the code, to tex support and to the get_sample_data handler

Jae-Joon Lee implemented fancy arrows and boxes, rewrote the legend

support to handle multiple columns and fancy text boxes, wrote the

axes grid toolkit, and has made numerous contributions to the code

and documentation