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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

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<h3><a href="../../contents.html">Table of Contents</a></h3>

<ul>

<li><a class="reference internal" href="#">The Lifecycle of a Plot</a><ul>

<li><a class="reference internal" href="#a-note-on-the-object-oriented-api-vs-pyplot">A note on the Object-Oriented API vs Pyplot</a></li>

<li><a class="reference internal" href="#getting-started">Getting started</a></li>

<li><a class="reference internal" href="#controlling-the-style">Controlling the style</a></li>

<li><a class="reference internal" href="#customizing-the-plot">Customizing the plot</a></li>

<li><a class="reference internal" href="#combining-multiple-visualizations">Combining multiple visualizations</a></li>

<li><a class="reference internal" href="#saving-our-plot">Saving our plot</a></li>

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</div>

<h1>The Lifecycle of a Plot<a class="headerlink" href="#the-lifecycle-of-a-plot" title="Permalink to this headline">¶</a></h1>

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'll try

to highlight some neat features and best-practices using Matplotlib.

Note

This tutorial is based off of

<a class="reference external" href="http://pbpython.com/effective-matplotlib.html">this excellent blog post</a>

by Chris Moffitt. It was transformed into this tutorial by Chris Holdgraf.

</div>

<h2>A note on the Object-Oriented API vs Pyplot<a class="headerlink" href="#a-note-on-the-object-oriented-api-vs-pyplot" title="Permalink to this headline">¶</a></h2>

Matplotlib has two interfaces. The first is an object-oriented (OO)

interface. In this case, we utilize an instance of <a class="reference internal" href="../../api/axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-class docutils literal notranslate">axes.Axes</code></a>

in order to render visualizations on an instance of <a class="reference internal" href="../../api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><code class="xref py py-class docutils literal notranslate">figure.Figure</code></a>.

The second is based on MATLAB and uses a state-based interface. This is

encapsulated in the <code class="xref py py-mod docutils literal notranslate">pyplot</code> module. See the <a class="reference internal" href="pyplot.html">pyplot tutorials</a> for a more in-depth look at the pyplot

interface.

Most of the terms are straightforward but the main thing to remember

is that:

<li>The Figure is the final image that may contain 1 or more Axes.</li>

<li>The Axes represent an individual plot (don't confuse this with the word

"axis", which refers to the x/y axis of a plot).</li>

</ul>

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.

</div>

We'll use the data from the post from which this tutorial was derived.

It contains sales information for a number of companies.

<div class="highlight-default notranslate"><div class="highlight"><pre># sphinx_gallery_thumbnail_number = 10

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.ticker import <a href="../../api/ticker_api.html#matplotlib.ticker.FuncFormatter" title="View documentation for matplotlib.ticker.FuncFormatter">FuncFormatter</a>

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 = <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html#numpy.mean" title="View documentation for numpy.mean">np.mean</a>(group_data)

</pre></div>

</div>

<h2>Getting started<a class="headerlink" href="#getting-started" title="Permalink to this headline">¶</a></h2>

This data is naturally visualized as a barplot, with one bar per

group. To do this with the object-oriented approach, we'll first generate

an instance of <a class="reference internal" href="../../api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><code class="xref py py-class docutils literal notranslate">figure.Figure</code></a> and

<a class="reference internal" href="../../api/axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-class docutils literal notranslate">axes.Axes</code></a>. 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 <a class="reference internal" href="../intermediate/tight_layout_guide.html">Tight Layout tutorial</a>.

</div>

<div class="highlight-default notranslate"><div class="highlight"><pre>fig, ax = <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="View documentation for matplotlib.pyplot.subplots">plt.subplots</a>()

</pre></div>

</div>

Now that we have an Axes instance, we can plot on top of it.

ax.barh(group_names, group_data)

</pre></div>

</div>

</div>

<h2>Controlling the style<a class="headerlink" href="#controlling-the-style" title="Permalink to this headline">¶</a></h2>

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

<code class="xref py py-mod docutils literal notranslate">pyplot.style</code>.

<div class="highlight-default notranslate"><div class="highlight"><pre>print(plt.style.available)

</pre></div>

</div>

Out:

<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre>['seaborn-ticks', 'ggplot', 'dark_background', 'bmh', 'seaborn-poster', 'seaborn-notebook', 'fast', 'seaborn', 'classic', 'Solarize_Light2', 'seaborn-dark', 'seaborn-pastel', 'seaborn-muted', '_classic_test', 'seaborn-paper', 'seaborn-colorblind', 'seaborn-bright', 'seaborn-talk', 'seaborn-dark-palette', 'tableau-colorblind10', 'seaborn-darkgrid', 'seaborn-whitegrid', 'fivethirtyeight', 'grayscale', 'seaborn-white', 'seaborn-deep']

</pre></div>

</div>

You can activate a style with the following:

<div class="highlight-default notranslate"><div class="highlight"><pre>plt.style.use('fivethirtyeight')

</pre></div>

</div>

Now let's remake the above plot to see how it looks:

</pre></div>

</div>

The style controls many things, such as color, linewidths, backgrounds,

etc.

</div>

<h2>Customizing the plot<a class="headerlink" href="#customizing-the-plot" title="Permalink to this headline">¶</a></h2>

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 <a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.get_xticklabels.html#matplotlib.axes.Axes.get_xticklabels" title="matplotlib.axes.Axes.get_xticklabels"><code class="xref py py-meth docutils literal notranslate">axes.Axes.get_xticklabels()</code></a> method:

labels = ax.get_xticklabels()

</pre></div>

</div>

If we'd like to set the property of many items at once, it's useful to use

the <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.setp.html#matplotlib.pyplot.setp" title="matplotlib.pyplot.setp"><code class="xref py py-func docutils literal notranslate">pyplot.setp()</code></a> function. This will take a list (or many lists) of

Matplotlib objects, and attempt to set some style element of each one.

labels = ax.get_xticklabels()

<a href="../../api/_as_gen/matplotlib.pyplot.setp.html#matplotlib.pyplot.setp" title="View documentation for matplotlib.pyplot.setp">plt.setp</a>(labels, rotation=45, horizontalalignment='right')

</pre></div>

</div>

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'll set the <code class="docutils literal notranslate">autolayout</code> value of our

rcParams. For more information on controlling the style, layout, and

other features of plots with rcParams, see

<a class="reference internal" href="customizing.html">Customizing Matplotlib with style sheets and rcParams</a>.

<div class="highlight-default notranslate"><div class="highlight"><pre>plt.rcParams.update({'figure.autolayout': True})

fig, ax = <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="View documentation for matplotlib.pyplot.subplots">plt.subplots</a>()

labels = ax.get_xticklabels()

</pre></div>

</div>

Next, we'll add labels to the plot. To do this with the OO interface,

we can use the <a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.set.html#matplotlib.axes.Axes.set" title="matplotlib.axes.Axes.set"><code class="xref py py-meth docutils literal notranslate">axes.Axes.set()</code></a> method to set properties of this

Axes object.

labels = ax.get_xticklabels()

ax.set(xlim=[-10000, 140000], xlabel='Total Revenue', ylabel='Company',

title='Company Revenue')

</pre></div>

</div>

We can also adjust the size of this plot using the <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots"><code class="xref py py-func docutils literal notranslate">pyplot.subplots()</code></a>

function. We can do this with the <code class="docutils literal notranslate">figsize</code> 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.

</div>

labels = ax.get_xticklabels()

title='Company Revenue')

</pre></div>

</div>

For labels, we can specify custom formatting guidelines in the form of

functions by using the <a class="reference internal" href="../../api/ticker_api.html#matplotlib.ticker.FuncFormatter" title="matplotlib.ticker.FuncFormatter"><code class="xref py py-class docutils literal notranslate">ticker.FuncFormatter</code></a> class. Below we'll

define a function that takes an integer as input, and returns a string

as an output.

<div class="highlight-default notranslate"><div class="highlight"><pre>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

formatter = <a href="../../api/ticker_api.html#matplotlib.ticker.FuncFormatter" title="View documentation for matplotlib.ticker.FuncFormatter">FuncFormatter</a>(currency)

</pre></div>

</div>

We can then apply this formatter to the labels on our plot. To do this,

we'll use the <code class="docutils literal notranslate">xaxis</code> attribute of our axis. This lets you perform

actions on a specific axis on our plot.

labels = ax.get_xticklabels()

title='Company Revenue')

ax.xaxis.set_major_formatter(formatter)

</pre></div>

</div>

</div>

<h2>Combining multiple visualizations<a class="headerlink" href="#combining-multiple-visualizations" title="Permalink to this headline">¶</a></h2>

It is possible to draw multiple plot elements on the same instance of

the plot methods on that axes object.

labels = ax.get_xticklabels()

# 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'll move our title up since it's getting a little cramped

ax.title.set(y=1.05)

title='Company Revenue')

ax.set_xticks([0, 25e3, 50e3, 75e3, 100e3, 125e3])

fig.subplots_adjust(right=.1)

<a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show">plt.show</a>()

</pre></div>

</div>

</div>

<h2>Saving our plot<a class="headerlink" href="#saving-our-plot" title="Permalink to this headline">¶</a></h2>

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:

<div class="highlight-default notranslate"><div class="highlight"><pre>print(fig.canvas.get_supported_filetypes())

</pre></div>

</div>

Out:

<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre>{'ps': 'Postscript', 'eps': 'Encapsulated Postscript', 'pdf': 'Portable Document Format', 'pgf': 'PGF code for LaTeX', 'png': 'Portable Network Graphics', 'raw': 'Raw RGBA bitmap', 'rgba': 'Raw RGBA bitmap', 'svg': 'Scalable Vector Graphics', 'svgz': 'Scalable Vector Graphics', 'jpg': 'Joint Photographic Experts Group', 'jpeg': 'Joint Photographic Experts Group', 'tif': 'Tagged Image File Format', 'tiff': 'Tagged Image File Format'}

</pre></div>

</div>

We can then use the <a class="reference internal" href="../../api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.savefig" title="matplotlib.figure.Figure.savefig"><code class="xref py py-meth docutils literal notranslate">figure.Figure.savefig()</code></a> in order to save the figure

to disk. Note that there are several useful flags we'll show below:

<li><code class="docutils literal notranslate">transparent=True</code> makes the background of the saved figure transparent

if the format supports it.</li>

<li><code class="docutils literal notranslate">dpi=80</code> controls the resolution (dots per square inch) of the output.</li>

<li><code class="docutils literal notranslate">bbox_inches="tight"</code> fits the bounds of the figure to our plot.</li>

</ul>

<div class="highlight-default notranslate"><div class="highlight"><pre># Uncomment this line to save the figure.

# fig.savefig('sales.png', transparent=False, dpi=80, bbox_inches="tight")

</pre></div>

</div>

<a class="reference download internal" download="" href="../../_downloads/0aa526fb17d2197164296ca813288a5d/lifecycle.py"><code class="xref download docutils literal notranslate">Download Python source code: lifecycle.py</code></a></div>

<a class="reference download internal" download="" href="../../_downloads/f3fe9e6beb4769177a4ad3bcabea54eb/lifecycle.ipynb"><code class="xref download docutils literal notranslate">Download Jupyter notebook: lifecycle.ipynb</code></a></div>

</div>

Keywords: matplotlib code example, codex, python plot, pyplot

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