.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/introductory/lifecycle.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_tutorials_introductory_lifecycle.py>`
to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_tutorials_introductory_lifecycle.py:
=======================
The Lifecycle of a Plot
=======================
This tutorial aims to show the beginning, middle, and end of a single
visualization using Matplotlib. We'll begin with some raw data and
end by saving a figure of a customized visualization. Along the way we try
to highlight some neat features and best-practices using Matplotlib.
.. currentmodule:: matplotlib
.. note::
This tutorial is based on
`this excellent blog post <http://pbpython.com/effective-matplotlib.html>`_
by Chris Moffitt. It was transformed into this tutorial by Chris Holdgraf.
A note on the Object-Oriented API vs. Pyplot
============================================
Matplotlib has two interfaces. The first is an object-oriented (OO)
interface. In this case, we utilize an instance of :class:`axes.Axes`
in order to render visualizations on an instance of :class:`figure.Figure`.
The second is based on MATLAB and uses a state-based interface. This is
encapsulated in the :mod:`.pyplot` module. See the :doc:`pyplot tutorials
</tutorials/introductory/pyplot>` for a more in-depth look at the pyplot
interface.
Most of the terms are straightforward but the main thing to remember
is that:
* The Figure is the final image that may contain 1 or more Axes.
* The Axes represent an individual plot (don't confuse this with the word
"axis", which refers to the x/y axis of a plot).
We call methods that do the plotting directly from the Axes, which gives
us much more flexibility and power in customizing our plot.
.. note::
In general, try to use the object-oriented interface over the pyplot
interface.
Our data
========
We'll use the data from the post from which this tutorial was derived.
It contains sales information for a number of companies.
.. GENERATED FROM PYTHON SOURCE LINES 52-71
.. code-block:: default
import numpy as np
import matplotlib.pyplot as plt
data = {'Barton LLC': 109438.50,
'Frami, Hills and Schmidt': 103569.59,
'Fritsch, Russel and Anderson': 112214.71,
'Jerde-Hilpert': 112591.43,
'Keeling LLC': 100934.30,
'Koepp Ltd': 103660.54,
'Kulas Inc': 137351.96,
'Trantow-Barrows': 123381.38,
'White-Trantow': 135841.99,
'Will LLC': 104437.60}
group_data = list(data.values())
group_names = list(data.keys())
group_mean = np.mean(group_data)
.. GENERATED FROM PYTHON SOURCE LINES 73-87
Getting started
===============
This data is naturally visualized as a barplot, with one bar per
group. To do this with the object-oriented approach, we first generate
an instance of :class:`figure.Figure` and
:class:`axes.Axes`. The Figure is like a canvas, and the Axes
is a part of that canvas on which we will make a particular visualization.
.. note::
Figures can have multiple axes on them. For information on how to do this,
see the :doc:`Tight Layout tutorial
</tutorials/intermediate/tight_layout_guide>`.
.. GENERATED FROM PYTHON SOURCE LINES 87-90
.. code-block:: default
fig, ax = plt.subplots()
.. image:: /tutorials/introductory/images/sphx_glr_lifecycle_001.png
:alt: lifecycle
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 91-92
Now that we have an Axes instance, we can plot on top of it.
.. GENERATED FROM PYTHON SOURCE LINES 92-96
.. code-block:: default
fig, ax = plt.subplots()
ax.barh(group_names, group_data)
.. image:: /tutorials/introductory/images/sphx_glr_lifecycle_002.png
:alt: lifecycle
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
<BarContainer object of 10 artists>
.. GENERATED FROM PYTHON SOURCE LINES 97-103
Controlling the style
=====================
There are many styles available in Matplotlib in order to let you tailor
your visualization to your needs. To see a list of styles, we can use
:mod:`.style`.
.. GENERATED FROM PYTHON SOURCE LINES 103-106
.. code-block:: default
print(plt.style.available)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']
.. GENERATED FROM PYTHON SOURCE LINES 107-108
You can activate a style with the following:
.. GENERATED FROM PYTHON SOURCE LINES 108-111
.. code-block:: default
plt.style.use('fivethirtyeight')
.. GENERATED FROM PYTHON SOURCE LINES 112-113
Now let's remake the above plot to see how it looks:
.. GENERATED FROM PYTHON SOURCE LINES 113-117
.. code-block:: default
fig, ax = plt.subplots()
ax.barh(group_names, group_data)
.. image:: /tutorials/introductory/images/sphx_glr_lifecycle_003.png
:alt: lifecycle
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
<BarContainer object of 10 artists>
.. GENERATED FROM PYTHON SOURCE LINES 118-128
The style controls many things, such as color, linewidths, backgrounds,
etc.
Customizing the plot
====================
Now we've got a plot with the general look that we want, so let's fine-tune
it so that it's ready for print. First let's rotate the labels on the x-axis
so that they show up more clearly. We can gain access to these labels
with the :meth:`axes.Axes.get_xticklabels` method:
.. GENERATED FROM PYTHON SOURCE LINES 128-133
.. code-block:: default
fig, ax = plt.subplots()
ax.barh(group_names, group_data)
labels = ax.get_xticklabels()
.. image:: /tutorials/introductory/images/sphx_glr_lifecycle_004.png
:alt: lifecycle
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 134-137
If we'd like to set the property of many items at once, it's useful to use
the :func:`pyplot.setp` function. This will take a list (or many lists) of
Matplotlib objects, and attempt to set some style element of each one.
.. GENERATED FROM PYTHON SOURCE LINES 137-143
.. code-block:: default
fig, ax = plt.subplots()
ax.barh(group_names, group_data)
labels = ax.get_xticklabels()
plt.setp(labels, rotation=45, horizontalalignment='right')
.. image:: /tutorials/introductory/images/sphx_glr_lifecycle_005.png
:alt: lifecycle
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]
.. GENERATED FROM PYTHON SOURCE LINES 144-150
It looks like this cut off some of the labels on the bottom. We can
tell Matplotlib to automatically make room for elements in the figures
that we create. To do this we set the ``autolayout`` value of our
rcParams. For more information on controlling the style, layout, and
other features of plots with rcParams, see
:doc:`/tutorials/introductory/customizing`.
.. GENERATED FROM PYTHON SOURCE LINES 150-158
.. code-block:: default
plt.rcParams.update({'figure.autolayout': True})
fig, ax = plt.subplots()
ax.barh(group_names, group_data)
labels = ax.get_xticklabels()
plt.setp(labels, rotation=45, horizontalalignment='right')
.. image:: /tutorials/introductory/images/sphx_glr_lifecycle_006.png
:alt: lifecycle
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]
.. GENERATED FROM PYTHON SOURCE LINES 159-162
Next, we add labels to the plot. To do this with the OO interface,
we can use the :meth:`.Artist.set` method to set properties of this
Axes object.
.. GENERATED FROM PYTHON SOURCE LINES 162-170
.. code-block:: default
fig, ax = plt.subplots()
ax.barh(group_names, group_data)
labels = ax.get_xticklabels()
plt.setp(labels, rotation=45, horizontalalignment='right')
ax.set(xlim=[-10000, 140000], xlabel='Total Revenue', ylabel='Company',
title='Company Revenue')
.. image:: /tutorials/introductory/images/sphx_glr_lifecycle_007.png
:alt: Company Revenue
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[(-10000.0, 140000.0), Text(0.5, 44.000000000000014, 'Total Revenue'), Text(43.999999999999986, 0.5, 'Company'), Text(0.5, 1.0, 'Company Revenue')]
.. GENERATED FROM PYTHON SOURCE LINES 171-180
We can also adjust the size of this plot using the :func:`pyplot.subplots`
function. We can do this with the ``figsize`` kwarg.
.. note::
While indexing in NumPy follows the form (row, column), the figsize
kwarg follows the form (width, height). This follows conventions in
visualization, which unfortunately are different from those of linear
algebra.
.. GENERATED FROM PYTHON SOURCE LINES 180-188
.. code-block:: default
fig, ax = plt.subplots(figsize=(8, 4))
ax.barh(group_names, group_data)
labels = ax.get_xticklabels()
plt.setp(labels, rotation=45, horizontalalignment='right')
ax.set(xlim=[-10000, 140000], xlabel='Total Revenue', ylabel='Company',
title='Company Revenue')
.. image:: /tutorials/introductory/images/sphx_glr_lifecycle_008.png
:alt: Company Revenue
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[(-10000.0, 140000.0), Text(0.5, 44.0, 'Total Revenue'), Text(44.00000000000004, 0.5, 'Company'), Text(0.5, 1.0, 'Company Revenue')]
.. GENERATED FROM PYTHON SOURCE LINES 189-198
For labels, we can specify custom formatting guidelines in the form of
functions. Below we define a function that takes an integer as input, and
returns a string as an output. When used with `.Axis.set_major_formatter` or
`.Axis.set_minor_formatter`, they will automatically create and use a
:class:`ticker.FuncFormatter` class.
For this function, the ``x`` argument is the original tick label and ``pos``
is the tick position. We will only use ``x`` here but both arguments are
needed.
.. GENERATED FROM PYTHON SOURCE LINES 198-208
.. code-block:: default
def currency(x, pos):
"""The two args are the value and tick position"""
if x >= 1e6:
s = '${:1.1f}M'.format(x*1e-6)
else:
s = '${:1.0f}K'.format(x*1e-3)
return s
.. GENERATED FROM PYTHON SOURCE LINES 209-212
We can then apply this function to the labels on our plot. To do this,
we use the ``xaxis`` attribute of our axes. This lets you perform
actions on a specific axis on our plot.
.. GENERATED FROM PYTHON SOURCE LINES 212-222
.. code-block:: default
fig, ax = plt.subplots(figsize=(6, 8))
ax.barh(group_names, group_data)
labels = ax.get_xticklabels()
plt.setp(labels, rotation=45, horizontalalignment='right')
ax.set(xlim=[-10000, 140000], xlabel='Total Revenue', ylabel='Company',
title='Company Revenue')
ax.xaxis.set_major_formatter(currency)
.. image:: /tutorials/introductory/images/sphx_glr_lifecycle_009.png
:alt: Company Revenue
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 223-229
Combining multiple visualizations
=================================
It is possible to draw multiple plot elements on the same instance of
:class:`axes.Axes`. To do this we simply need to call another one of
the plot methods on that axes object.
.. GENERATED FROM PYTHON SOURCE LINES 229-254
.. code-block:: default
fig, ax = plt.subplots(figsize=(8, 8))
ax.barh(group_names, group_data)
labels = ax.get_xticklabels()
plt.setp(labels, rotation=45, horizontalalignment='right')
# Add a vertical line, here we set the style in the function call
ax.axvline(group_mean, ls='--', color='r')
# Annotate new companies
for group in [3, 5, 8]:
ax.text(145000, group, "New Company", fontsize=10,
verticalalignment="center")
# Now we move our title up since it's getting a little cramped
ax.title.set(y=1.05)
ax.set(xlim=[-10000, 140000], xlabel='Total Revenue', ylabel='Company',
title='Company Revenue')
ax.xaxis.set_major_formatter(currency)
ax.set_xticks([0, 25e3, 50e3, 75e3, 100e3, 125e3])
fig.subplots_adjust(right=.1)
plt.show()
.. image:: /tutorials/introductory/images/sphx_glr_lifecycle_010.png
:alt: Company Revenue
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 255-261
Saving our plot
===============
Now that we're happy with the outcome of our plot, we want to save it to
disk. There are many file formats we can save to in Matplotlib. To see
a list of available options, use:
.. GENERATED FROM PYTHON SOURCE LINES 261-264
.. code-block:: default
print(fig.canvas.get_supported_filetypes())
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
{'eps': 'Encapsulated Postscript', 'jpg': 'Joint Photographic Experts Group', 'jpeg': 'Joint Photographic Experts Group', 'pdf': 'Portable Document Format', 'pgf': 'PGF code for LaTeX', 'png': 'Portable Network Graphics', 'ps': 'Postscript', 'raw': 'Raw RGBA bitmap', 'rgba': 'Raw RGBA bitmap', 'svg': 'Scalable Vector Graphics', 'svgz': 'Scalable Vector Graphics', 'tif': 'Tagged Image File Format', 'tiff': 'Tagged Image File Format'}
.. GENERATED FROM PYTHON SOURCE LINES 265-272
We can then use the :meth:`figure.Figure.savefig` in order to save the figure
to disk. Note that there are several useful flags we show below:
* ``transparent=True`` makes the background of the saved figure transparent
if the format supports it.
* ``dpi=80`` controls the resolution (dots per square inch) of the output.
* ``bbox_inches="tight"`` fits the bounds of the figure to our plot.
.. GENERATED FROM PYTHON SOURCE LINES 272-275
.. code-block:: default
# Uncomment this line to save the figure.
# fig.savefig('sales.png', transparent=False, dpi=80, bbox_inches="tight")
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 5.545 seconds)
.. _sphx_glr_download_tutorials_introductory_lifecycle.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: lifecycle.py <lifecycle.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: lifecycle.ipynb <lifecycle.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
Keywords: matplotlib code example, codex, python plot, pyplot
`Gallery generated by Sphinx-Gallery
<https://sphinx-gallery.readthedocs.io>`_