########
Plotting
########

.. code-links:: clear

******************************
Take home message for the lazy
******************************

To use plotting in a Notebook, add the following cell::

    import matplotlib.pyplot as plt
    % matplotlib inline

Then use commands like ``plt.hist(data)`` and ``plt.scatter(x_data, y_data)``.

**************
In more detail
**************

`Matplotlib`_ is a popular plotting library for Python.

In particular, there is a sub-module of Matplotlib, that has many useful
plotting functions.

You can import this in the usual way:

.. nbplot::

    >>> import matplotlib.pyplot
    >>> type(matplotlib.pyplot)
    <class 'module'>

By default, Matplotlib likes to make a new window for each plot.  That's not
what we want in the Notebook, because we want the plots to appear inside the
Notebook.  To make that happen we use a special command in the Notebook, like
this::

    %matplotlib inline

Once we've done this, the plot come up inside the Notebook, and can be stored
with the notebook.

Here's an example histogram from the :doc:`brexit_analysis` data.  I'm using
Pandas to load the data, but that's only to get the data; it is not important
for the plotting:

.. nbplot::

    >>> # Get the data - don't worry about the details of this cell
    >>> import pandas
    >>> remain_leave = pandas.read_csv('remain_leave.csv')
    >>> leavers_ages = remain_leave.loc[remain_leave['brexit'] == 2, 'age']

.. nbplot::

    >>> # Do a default histogram of the leavers' ages
    >>> matplotlib.pyplot.hist(leavers_ages)
    <...>

It is boring to have to type ``matplotlib.pyplot.hist`` when you want a
histogram, so we often make a shortcut to the `plt` module, like this:

.. nbplot::

    >>> # Make a shortcut to the pyplot submodule, and use it
    >>> plt = matplotlib.pyplot
    >>> plt.hist(leavers_ages)
    <...>

In fact, there's a shortcut to making the shortcut, which is to rename the
submodule when we import it.  That looks like this:

.. nbplot::

    >>> # Import the pyplot submodule with it's own shortcut
    >>> import matplotlib.pyplot as plt
    >>> plt.hist(leavers_ages)
    <...>

You'll see this command ``import matplotlib.pyplot as plt`` in a lot of code
online, it's a very popular idiom.

Another useful plot is a scatterplot. Here I go back to the Brexit
questionnaire, and plot the persons age (on the x axis) against a weighting
score used in the survey:

.. nbplot::

    >>> # Get all the data again
    >>> audit_data = pandas.read_table('audit_of_political_engagement_14_2017.tab')
    >>> plt.scatter(audit_data['numage'], audit_data['wts'])
    <...>

.. include:: links_names.inc