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<li><a class="reference internal" href="#">Usage Guide</a><ul>
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<li><a class="reference internal" href="#parts-of-a-figure">Parts of a Figure</a><ul>
<li><a class="reference internal" href="#figure"><code class="xref py py-class docutils literal notranslate"><span class="pre">Figure</span></code></a></li>
<li><a class="reference internal" href="#axes"><code class="xref py py-class docutils literal notranslate"><span class="pre">Axes</span></code></a></li>
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<div class="sphx-glr-example-title section" id="usage-guide">
<span id="sphx-glr-tutorials-introductory-usage-py"></span><h1>Usage Guide<a class="headerlink" href="#usage-guide" title="Permalink to this headline">¶</a></h1>
<p>This tutorial covers some basic usage patterns and best-practices to
help you get started with Matplotlib.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
</pre></div>
</div>
<div class="section" id="a-simple-example">
<h2>A simple example<a class="headerlink" href="#a-simple-example" title="Permalink to this headline">¶</a></h2>
<p>Matplotlib graphs your data on <a class="reference internal" href="../../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Figure</span></code></a>s (i.e., windows, Jupyter
widgets, etc.), each of which can contain one or more <a class="reference internal" href="../../api/axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Axes</span></code></a> (i.e., an
area where points can be specified in terms of x-y coordinates, or theta-r
in a polar plot, or x-y-z in a 3D plot, etc.). The simplest way of
creating a figure with an axes is using <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyplot.subplots</span></code></a>. We can then use
<a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Axes.plot</span></code></a> to draw some data on the axes:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span> <span class="c1"># Create a figure containing a single axes.</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="c1"># Plot some data on the axes.</span>
</pre></div>
</div>
<img alt="usage" class="sphx-glr-single-img" src="../../_images/sphx_glr_usage_001.png" />
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[<matplotlib.lines.Line2D object at 0x7f57e5661610>]
</pre></div>
</div>
<p>Many other plotting libraries or languages do not require you to explicitly
create an axes. For example, in MATLAB, one can just do</p>
<div class="highlight-matlab notranslate"><div class="highlight"><pre><span></span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="c">% MATLAB plot.</span>
</pre></div>
</div>
<p>and get the desired graph.</p>
<p>In fact, you can do the same in Matplotlib: for each <a class="reference internal" href="../../api/axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Axes</span></code></a> graphing
method, there is a corresponding function in the <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot" title="matplotlib.pyplot"><code class="xref py py-mod docutils literal notranslate"><span class="pre">matplotlib.pyplot</span></code></a>
module that performs that plot on the "current" axes, creating that axes (and
its parent figure) if they don't exist yet. So, the previous example can be
written more shortly as</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="c1"># Matplotlib plot.</span>
</pre></div>
</div>
<img alt="usage" class="sphx-glr-single-img" src="../../_images/sphx_glr_usage_002.png" />
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[<matplotlib.lines.Line2D object at 0x7f57e5782e80>]
</pre></div>
</div>
</div>
<div class="section" id="parts-of-a-figure">
<span id="figure-parts"></span><h2>Parts of a Figure<a class="headerlink" href="#parts-of-a-figure" title="Permalink to this headline">¶</a></h2>
<p>Now, let's have a deeper look at the components of a Matplotlib figure.</p>
<img alt="../../_images/anatomy.png" src="../../_images/anatomy.png" />
<div class="section" id="figure">
<h3><a class="reference internal" href="../../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><code class="xref py py-class docutils literal notranslate"><span class="pre">Figure</span></code></a><a class="headerlink" href="#figure" title="Permalink to this headline">¶</a></h3>
<p>The <strong>whole</strong> figure. The figure keeps
track of all the child <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"><span class="pre">Axes</span></code></a>, a smattering of
'special' artists (titles, figure legends, etc), and the <strong>canvas</strong>.
(Don't worry too much about the canvas, it is crucial as it is the
object that actually does the drawing to get you your plot, but as the
user it is more-or-less invisible to you). A figure can contain any
number 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"><span class="pre">Axes</span></code></a>, but will typically have
at least one.</p>
<p>The easiest way to create a new figure is with pyplot:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span> <span class="c1"># an empty figure with no Axes</span>
<a href="../../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span> <span class="c1"># a figure with a single Axes</span>
<a href="../../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="c1"># a figure with a 2x2 grid of Axes</span>
</pre></div>
</div>
<p>It's convenient to create the axes together with the figure, but you can
also add axes later on, allowing for more complex axes layouts.</p>
</div>
<div class="section" id="axes">
<h3><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"><span class="pre">Axes</span></code></a><a class="headerlink" href="#axes" title="Permalink to this headline">¶</a></h3>
<p>This is what you think of as 'a plot', it is the region of the image
with the data space. A given figure
can contain many Axes, but a given <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"><span class="pre">Axes</span></code></a>
object can only be in one <a class="reference internal" href="../../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><code class="xref py py-class docutils literal notranslate"><span class="pre">Figure</span></code></a>. The
Axes contains two (or three in the case of 3D)
<a class="reference internal" href="../../api/axis_api.html#matplotlib.axis.Axis" title="matplotlib.axis.Axis"><code class="xref py py-class docutils literal notranslate"><span class="pre">Axis</span></code></a> objects (be aware of the difference
between <strong>Axes</strong> and <strong>Axis</strong>) which take care of the data limits (the
data limits can also be controlled via the <a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.set_xlim.html#matplotlib.axes.Axes.set_xlim" title="matplotlib.axes.Axes.set_xlim"><code class="xref py py-meth docutils literal notranslate"><span class="pre">axes.Axes.set_xlim()</span></code></a> and
<a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.set_ylim.html#matplotlib.axes.Axes.set_ylim" title="matplotlib.axes.Axes.set_ylim"><code class="xref py py-meth docutils literal notranslate"><span class="pre">axes.Axes.set_ylim()</span></code></a> methods). Each <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"><span class="pre">Axes</span></code></a> has a title
(set via <a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.set_title.html#matplotlib.axes.Axes.set_title" title="matplotlib.axes.Axes.set_title"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_title()</span></code></a>), an x-label (set via
<a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_xlabel()</span></code></a>), and a y-label set via
<a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_ylabel()</span></code></a>).</p>
<p>The <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"><span class="pre">Axes</span></code></a> class and its member functions are the primary entry
point to working with the OO interface.</p>
</div>
<div class="section" id="axis">
<h3><a class="reference internal" href="../../api/axis_api.html#matplotlib.axis.Axis" title="matplotlib.axis.Axis"><code class="xref py py-class docutils literal notranslate"><span class="pre">Axis</span></code></a><a class="headerlink" href="#axis" title="Permalink to this headline">¶</a></h3>
<p>These are the number-line-like objects. They take
care of setting the graph limits and generating the ticks (the marks
on the axis) and ticklabels (strings labeling the ticks). The location of
the ticks is determined by a <a class="reference internal" href="../../api/ticker_api.html#matplotlib.ticker.Locator" title="matplotlib.ticker.Locator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Locator</span></code></a> object and the
ticklabel strings are formatted by a <a class="reference internal" href="../../api/ticker_api.html#matplotlib.ticker.Formatter" title="matplotlib.ticker.Formatter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Formatter</span></code></a>. The
combination of the correct <a class="reference internal" href="../../api/ticker_api.html#matplotlib.ticker.Locator" title="matplotlib.ticker.Locator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Locator</span></code></a> and <a class="reference internal" href="../../api/ticker_api.html#matplotlib.ticker.Formatter" title="matplotlib.ticker.Formatter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Formatter</span></code></a> gives very fine
control over the tick locations and labels.</p>
</div>
<div class="section" id="artist">
<h3><a class="reference internal" href="../../api/artist_api.html#matplotlib.artist.Artist" title="matplotlib.artist.Artist"><code class="xref py py-class docutils literal notranslate"><span class="pre">Artist</span></code></a><a class="headerlink" href="#artist" title="Permalink to this headline">¶</a></h3>
<p>Basically, everything you can see on the figure is an artist (even the
<a class="reference internal" href="../../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Figure</span></code></a>, <a class="reference internal" href="../../api/axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Axes</span></code></a>, and <a class="reference internal" href="../../api/axis_api.html#matplotlib.axis.Axis" title="matplotlib.axis.Axis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Axis</span></code></a> objects). This includes
<a class="reference internal" href="../../api/text_api.html#matplotlib.text.Text" title="matplotlib.text.Text"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Text</span></code></a> objects, <a class="reference internal" href="../../api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Line2D</span></code></a> objects, <a class="reference internal" href="../../api/collections_api.html#module-matplotlib.collections" title="matplotlib.collections"><code class="xref py py-mod docutils literal notranslate"><span class="pre">collections</span></code></a> objects, <a class="reference internal" href="../../api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Patch</span></code></a>
objects ... (you get the idea). When the figure is rendered, all of the
artists are drawn to the <strong>canvas</strong>. Most Artists are tied to an Axes; such
an Artist cannot be shared by multiple Axes, or moved from one to another.</p>
</div>
</div>
<div class="section" id="types-of-inputs-to-plotting-functions">
<span id="input-types"></span><h2>Types of inputs to plotting functions<a class="headerlink" href="#types-of-inputs-to-plotting-functions" title="Permalink to this headline">¶</a></h2>
<p>All of plotting functions expect <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.array</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ma.masked_array.html#numpy.ma.masked_array" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ma.masked_array</span></code></a> as
input. Classes that are 'array-like' such as <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/index.html#module-pandas" title="(in pandas v1.2.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas</span></code></a> data objects
and <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.matrix.html#numpy.matrix" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.matrix</span></code></a> may or may not work as intended. It is best to
convert these to <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.array</span></code></a> objects prior to plotting.</p>
<p>For example, to convert a <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v1.2.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">columns</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="s1">'abcde'</span><span class="p">))</span>
<span class="n">a_asarray</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">values</span>
</pre></div>
</div>
<p>and to convert a <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.matrix.html#numpy.matrix" title="(in NumPy v1.20)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.matrix</span></code></a></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matrix</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="n">b_asarray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="the-object-oriented-interface-and-the-pyplot-interface">
<span id="coding-styles"></span><h2>The object-oriented interface and the pyplot interface<a class="headerlink" href="#the-object-oriented-interface-and-the-pyplot-interface" title="Permalink to this headline">¶</a></h2>
<p>As noted above, there are essentially two ways to use Matplotlib:</p>
<ul class="simple">
<li>Explicitly create figures and axes, and call methods on them (the
"object-oriented (OO) style").</li>
<li>Rely on pyplot to automatically create and manage the figures and axes, and
use pyplot functions for plotting.</li>
</ul>
<p>So one can do (OO-style)</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="c1"># Note that even in the OO-style, we use `.pyplot.figure` to create the figure.</span>
<a href="../../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span> <span class="c1"># Create a figure and an axes.</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'linear'</span><span class="p">)</span> <span class="c1"># Plot some data on the axes.</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'quadratic'</span><span class="p">)</span> <span class="c1"># Plot more data on the axes...</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="o">**</span><span class="mi">3</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'cubic'</span><span class="p">)</span> <span class="c1"># ... and some more.</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s1">'x label'</span><span class="p">)</span> <span class="c1"># Add an x-label to the axes.</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">'y label'</span><span class="p">)</span> <span class="c1"># Add a y-label to the axes.</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.set_title.html#matplotlib.axes.Axes.set_title" title="matplotlib.axes.Axes.set_title" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_title</span></a><span class="p">(</span><span class="s2">"Simple Plot"</span><span class="p">)</span> <span class="c1"># Add a title to the axes.</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span> <span class="c1"># Add a legend.</span>
</pre></div>
</div>
<img alt="Simple Plot" class="sphx-glr-single-img" src="../../_images/sphx_glr_usage_003.png" />
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><matplotlib.legend.Legend object at 0x7f57e54e4580>
</pre></div>
</div>
<p>or (pyplot-style)</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'linear'</span><span class="p">)</span> <span class="c1"># Plot some data on the (implicit) axes.</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'quadratic'</span><span class="p">)</span> <span class="c1"># etc.</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="o">**</span><span class="mi">3</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'cubic'</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="matplotlib.pyplot.xlabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s1">'x label'</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel" title="matplotlib.pyplot.ylabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s1">'y label'</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="s2">"Simple Plot"</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
</pre></div>
</div>
<img alt="Simple Plot" class="sphx-glr-single-img" src="../../_images/sphx_glr_usage_004.png" />
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><matplotlib.legend.Legend object at 0x7f57e56704c0>
</pre></div>
</div>
<p>In addition, there is a third approach, for the case when embedding
Matplotlib in a GUI application, which completely drops pyplot, even for
figure creation. We won't discuss it here; see the corresponding section in
the gallery for more info (<a class="reference internal" href="../../gallery/index.html#user-interfaces"><span class="std std-ref">Embedding Matplotlib in graphical user interfaces</span></a>).</p>
<p>Matplotlib's documentation and examples use both the OO and the pyplot
approaches (which are equally powerful), and you should feel free to use
either (however, it is preferable pick one of them and stick to it, instead
of mixing them). In general, we suggest to restrict pyplot to interactive
plotting (e.g., in a Jupyter notebook), and to prefer the OO-style for
non-interactive plotting (in functions and scripts that are intended to be
reused as part of a larger project).</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p>In older examples, you may find examples that instead used the so-called
<code class="docutils literal notranslate"><span class="pre">pylab</span></code> interface, via <code class="docutils literal notranslate"><span class="pre">from</span> <span class="pre">pylab</span> <span class="pre">import</span> <span class="pre">*</span></code>. This star-import
imports everything both from pyplot and from <a class="reference external" href="https://numpy.org/doc/stable/reference/index.html#module-numpy" title="(in NumPy v1.20)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy</span></code></a>, so that one
could do</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a> <span class="o">=</span> <span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">plot</span><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'linear'</span><span class="p">)</span>
<span class="o">...</span>
</pre></div>
</div>
<p class="last">for an even more MATLAB-like style. This approach is strongly discouraged
nowadays and deprecated. It is only mentioned here because you may still
encounter it in the wild.</p>
</div>
<p>Typically one finds oneself making the same plots over and over
again, but with different data sets, which leads to needing to write
specialized functions to do the plotting. The recommended function
signature is something like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">my_plotter</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data1</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data2</span></a><span class="p">,</span> <span class="n">param_dict</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> A helper function to make a graph</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> ax : Axes</span>
<span class="sd"> The axes to draw to</span>
<span class="sd"> data1 : array</span>
<span class="sd"> The x data</span>
<span class="sd"> data2 : array</span>
<span class="sd"> The y data</span>
<span class="sd"> param_dict : dict</span>
<span class="sd"> Dictionary of kwargs to pass to ax.plot</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> out : list</span>
<span class="sd"> list of artists added</span>
<span class="sd"> """</span>
<span class="n">out</span> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data1</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data2</span></a><span class="p">,</span> <span class="o">**</span><span class="n">param_dict</span><span class="p">)</span>
<span class="k">return</span> <span class="n">out</span>
</pre></div>
</div>
<p>which you would then use as:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data1</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data2</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data3</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data4</span></a> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/random/generated/numpy.random.randn.html#numpy.random.randn" title="numpy.random.randn" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<a href="../../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">my_plotter</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data1</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data2</span></a><span class="p">,</span> <span class="p">{</span><span class="s1">'marker'</span><span class="p">:</span> <span class="s1">'x'</span><span class="p">})</span>
</pre></div>
</div>
<img alt="usage" class="sphx-glr-single-img" src="../../_images/sphx_glr_usage_005.png" />
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[<matplotlib.lines.Line2D object at 0x7f57e5466640>]
</pre></div>
</div>
<p>or if you wanted to have 2 sub-plots:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">my_plotter</span><span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data1</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data2</span></a><span class="p">,</span> <span class="p">{</span><span class="s1">'marker'</span><span class="p">:</span> <span class="s1">'x'</span><span class="p">})</span>
<span class="n">my_plotter</span><span class="p">(</span><span class="n">ax2</span><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data3</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data4</span></a><span class="p">,</span> <span class="p">{</span><span class="s1">'marker'</span><span class="p">:</span> <span class="s1">'o'</span><span class="p">})</span>
</pre></div>
</div>
<img alt="usage" class="sphx-glr-single-img" src="../../_images/sphx_glr_usage_006.png" />
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[<matplotlib.lines.Line2D object at 0x7f57e5428310>]
</pre></div>
</div>
<p>For these simple examples this style seems like overkill, however
once the graphs get slightly more complex it pays off.</p>
</div>
<div class="section" id="backends">
<span id="id1"></span><h2>Backends<a class="headerlink" href="#backends" title="Permalink to this headline">¶</a></h2>
<div class="section" id="what-is-a-backend">
<span id="id2"></span><h3>What is a backend?<a class="headerlink" href="#what-is-a-backend" title="Permalink to this headline">¶</a></h3>
<p>A lot of documentation on the website and in the mailing lists refers
to the "backend" and many new users are confused by this term.
Matplotlib targets many different use cases and output formats. Some
people use Matplotlib interactively from the python shell and have
plotting windows pop up when they type commands. Some people run
<a class="reference external" href="https://jupyter.org">Jupyter</a> notebooks and draw inline plots for
quick data analysis. Others embed Matplotlib into graphical user
interfaces like PyQt or PyGObject to build rich applications. Some
people use Matplotlib in batch scripts to generate postscript images
from numerical simulations, and still others run web application
servers to dynamically serve up graphs.</p>
<p>To support all of these use cases, Matplotlib can target different
outputs, and each of these capabilities is called a backend; the
"frontend" is the user facing code, i.e., the plotting code, whereas the
"backend" does all the hard work behind-the-scenes to make the figure.
There are two types of backends: user interface backends (for use in
PyQt/PySide, PyGObject, Tkinter, wxPython, or macOS/Cocoa); also referred to
as "interactive backends") and hardcopy backends to make image files
(PNG, SVG, PDF, PS; also referred to as "non-interactive backends").</p>
</div>
<div class="section" id="selecting-a-backend">
<h3>Selecting a backend<a class="headerlink" href="#selecting-a-backend" title="Permalink to this headline">¶</a></h3>
<p>There are three ways to configure your backend:</p>
<ol class="arabic simple">
<li>The <code class="docutils literal notranslate"><a class="reference external" href="../../tutorials/introductory/customizing.html?highlight=backend#a-sample-matplotlibrc-file"><span class="pre">rcParams["backend"]</span></a></code> (default: <code class="docutils literal notranslate"><span class="pre">'agg'</span></code>) parameter in your <code class="file docutils literal notranslate"><span class="pre">matplotlibrc</span></code> file</li>
<li>The <span class="target" id="index-0"></span><a class="reference internal" href="../../faq/environment_variables_faq.html#envvar-MPLBACKEND"><code class="xref std std-envvar docutils literal notranslate"><span class="pre">MPLBACKEND</span></code></a> environment variable</li>
<li>The function <a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.use" title="matplotlib.use"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.use()</span></code></a></li>
</ol>
<p>A more detailed description is given below.</p>
<p>If multiple of these are configurations are present, the last one from the
list takes precedence; e.g. calling <a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.use" title="matplotlib.use"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.use()</span></code></a> will override
the setting in your <code class="file docutils literal notranslate"><span class="pre">matplotlibrc</span></code>.</p>
<p>If no backend is explicitly set, Matplotlib automatically detects a usable
backend based on what is available on your system and on whether a GUI event
loop is already running. On Linux, if the environment variable
<span class="target" id="index-1"></span><a class="reference internal" href="../../faq/environment_variables_faq.html#envvar-DISPLAY"><code class="xref std std-envvar docutils literal notranslate"><span class="pre">DISPLAY</span></code></a> is unset, the "event loop" is identified as "headless",
which causes a fallback to a noninteractive backend (agg).</p>
<p>Here is a detailed description of the configuration methods:</p>
<ol class="arabic">
<li><p class="first">Setting <code class="docutils literal notranslate"><a class="reference external" href="../../tutorials/introductory/customizing.html?highlight=backend#a-sample-matplotlibrc-file"><span class="pre">rcParams["backend"]</span></a></code> (default: <code class="docutils literal notranslate"><span class="pre">'agg'</span></code>) in your <code class="file docutils literal notranslate"><span class="pre">matplotlibrc</span></code> file:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">backend</span> <span class="p">:</span> <span class="n">qt5agg</span> <span class="c1"># use pyqt5 with antigrain (agg) rendering</span>
</pre></div>
</div>
<p>See also <a class="reference internal" href="customizing.html"><span class="doc">Customizing Matplotlib with style sheets and rcParams</span></a>.</p>
</li>
<li><p class="first">Setting the <span class="target" id="index-2"></span><a class="reference internal" href="../../faq/environment_variables_faq.html#envvar-MPLBACKEND"><code class="xref std std-envvar docutils literal notranslate"><span class="pre">MPLBACKEND</span></code></a> environment variable:</p>
<p>You can set the environment variable either for your current shell or for
a single script.</p>
<p>On Unix:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">></span> <span class="n">export</span> <span class="n">MPLBACKEND</span><span class="o">=</span><span class="n">qt5agg</span>
<span class="o">></span> <span class="n">python</span> <span class="n">simple_plot</span><span class="o">.</span><span class="n">py</span>
<span class="o">></span> <span class="n">MPLBACKEND</span><span class="o">=</span><span class="n">qt5agg</span> <span class="n">python</span> <span class="n">simple_plot</span><span class="o">.</span><span class="n">py</span>
</pre></div>
</div>
<p>On Windows, only the former is possible:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">></span> <span class="nb">set</span> <span class="n">MPLBACKEND</span><span class="o">=</span><span class="n">qt5agg</span>
<span class="o">></span> <span class="n">python</span> <span class="n">simple_plot</span><span class="o">.</span><span class="n">py</span>
</pre></div>
</div>
<p>Setting this environment variable will override the <code class="docutils literal notranslate"><span class="pre">backend</span></code> parameter
in <em>any</em> <code class="file docutils literal notranslate"><span class="pre">matplotlibrc</span></code>, even if there is a <code class="file docutils literal notranslate"><span class="pre">matplotlibrc</span></code> in
your current working directory. Therefore, setting <span class="target" id="index-3"></span><a class="reference internal" href="../../faq/environment_variables_faq.html#envvar-MPLBACKEND"><code class="xref std std-envvar docutils literal notranslate"><span class="pre">MPLBACKEND</span></code></a>
globally, e.g. in your <code class="file docutils literal notranslate"><span class="pre">.bashrc</span></code> or <code class="file docutils literal notranslate"><span class="pre">.profile</span></code>, is discouraged
as it might lead to counter-intuitive behavior.</p>
</li>
<li><p class="first">If your script depends on a specific backend you can use the function
<a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.use" title="matplotlib.use"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.use()</span></code></a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib</span>
<a href="../../api/matplotlib_configuration_api.html#matplotlib.use" title="matplotlib.use" class="sphx-glr-backref-module-matplotlib sphx-glr-backref-type-py-function"><span class="n">matplotlib</span><span class="o">.</span><span class="n">use</span></a><span class="p">(</span><span class="s1">'qt5agg'</span><span class="p">)</span>
</pre></div>
</div>
<p>This should be done before any figure is created, otherwise Matplotlib may
fail to switch the backend and raise an ImportError.</p>
<p>Using <a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.use" title="matplotlib.use"><code class="xref py py-obj docutils literal notranslate"><span class="pre">use</span></code></a> will require changes in your code if users want to
use a different backend. Therefore, you should avoid explicitly calling
<a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.use" title="matplotlib.use"><code class="xref py py-obj docutils literal notranslate"><span class="pre">use</span></code></a> unless absolutely necessary.</p>
</li>
</ol>
</div>
<div class="section" id="the-builtin-backends">
<span id="id3"></span><h3>The builtin backends<a class="headerlink" href="#the-builtin-backends" title="Permalink to this headline">¶</a></h3>
<p>By default, Matplotlib should automatically select a default backend which
allows both interactive work and plotting from scripts, with output to the
screen and/or to a file, so at least initially, you will not need to worry
about the backend. The most common exception is if your Python distribution
comes without <a class="reference external" href="https://docs.python.org/3/library/tkinter.html#module-tkinter" title="(in Python v3.9)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">tkinter</span></code></a> and you have no other GUI toolkit installed.
This happens on certain Linux distributions, where you need to install a
Linux package named <code class="docutils literal notranslate"><span class="pre">python-tk</span></code> (or similar).</p>
<p>If, however, you want to write graphical user interfaces, or a web
application server
(<a class="reference internal" href="../../gallery/user_interfaces/web_application_server_sgskip.html"><span class="doc">Embedding in a web application server (Flask)</span></a>), or need a
better understanding of what is going on, read on. To make things a little
more customizable for graphical user interfaces, Matplotlib separates
the concept of the renderer (the thing that actually does the drawing)
from the canvas (the place where the drawing goes). The canonical
renderer for user interfaces is <code class="docutils literal notranslate"><span class="pre">Agg</span></code> which uses the <a class="reference external" href="http://antigrain.com/">Anti-Grain
Geometry</a> C++ library to make a raster (pixel) image of the figure; it
is used by the <code class="docutils literal notranslate"><span class="pre">Qt5Agg</span></code>, <code class="docutils literal notranslate"><span class="pre">Qt4Agg</span></code>, <code class="docutils literal notranslate"><span class="pre">GTK3Agg</span></code>, <code class="docutils literal notranslate"><span class="pre">wxAgg</span></code>, <code class="docutils literal notranslate"><span class="pre">TkAgg</span></code>, and
<code class="docutils literal notranslate"><span class="pre">macosx</span></code> backends. An alternative renderer is based on the Cairo library,
used by <code class="docutils literal notranslate"><span class="pre">Qt5Cairo</span></code>, <code class="docutils literal notranslate"><span class="pre">Qt4Cairo</span></code>, etc.</p>
<p>For the rendering engines, one can also distinguish between <a class="reference external" href="https://en.wikipedia.org/wiki/Vector_graphics">vector</a> or <a class="reference external" href="https://en.wikipedia.org/wiki/Raster_graphics">raster</a> renderers. Vector
graphics languages issue drawing commands like "draw a line from this
point to this point" and hence are scale free, and raster backends
generate a pixel representation of the line whose accuracy depends on a
DPI setting.</p>
<p>Here is a summary of the Matplotlib renderers (there is an eponymous
backend for each; these are <em>non-interactive backends</em>, capable of
writing to a file):</p>
<table border="1" class="docutils align-default">
<colgroup>
<col width="11%" />
<col width="13%" />
<col width="76%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Renderer</th>
<th class="head">Filetypes</th>
<th class="head">Description</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>AGG</td>
<td>png</td>
<td><a class="reference external" href="https://en.wikipedia.org/wiki/Raster_graphics">raster</a> graphics -- high quality images using the
<a class="reference external" href="http://antigrain.com/">Anti-Grain Geometry</a> engine</td>
</tr>
<tr class="row-odd"><td>PDF</td>
<td>pdf</td>
<td><a class="reference external" href="https://en.wikipedia.org/wiki/Vector_graphics">vector</a> graphics -- <a class="reference external" href="https://en.wikipedia.org/wiki/Portable_Document_Format">Portable Document Format</a></td>
</tr>
<tr class="row-even"><td>PS</td>
<td>ps, eps</td>
<td><a class="reference external" href="https://en.wikipedia.org/wiki/Vector_graphics">vector</a> graphics -- <a class="reference external" href="https://en.wikipedia.org/wiki/PostScript">Postscript</a> output</td>
</tr>
<tr class="row-odd"><td>SVG</td>
<td>svg</td>
<td><a class="reference external" href="https://en.wikipedia.org/wiki/Vector_graphics">vector</a> graphics -- <a class="reference external" href="https://en.wikipedia.org/wiki/Scalable_Vector_Graphics">Scalable Vector Graphics</a></td>
</tr>
<tr class="row-even"><td>PGF</td>
<td>pgf, pdf</td>
<td><a class="reference external" href="https://en.wikipedia.org/wiki/Vector_graphics">vector</a> graphics -- using the <a class="reference external" href="https://ctan.org/pkg/pgf">pgf</a> package</td>
</tr>
<tr class="row-odd"><td>Cairo</td>
<td>png, ps,
pdf, svg</td>
<td><a class="reference external" href="https://en.wikipedia.org/wiki/Raster_graphics">raster</a> or <a class="reference external" href="https://en.wikipedia.org/wiki/Vector_graphics">vector</a> graphics -- using the <a class="reference external" href="https://www.cairographics.org">Cairo</a> library</td>
</tr>
</tbody>
</table>
<p>To save plots using the non-interactive backends, use the
<code class="docutils literal notranslate"><span class="pre">matplotlib.pyplot.savefig('filename')</span></code> method.</p>
<p>And here are the user interfaces and renderer combinations supported;
these are <em>interactive backends</em>, capable of displaying to the screen
and of using appropriate renderers from the table above to write to
a file:</p>
<table border="1" class="docutils align-default">
<colgroup>
<col width="12%" />
<col width="88%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Backend</th>
<th class="head">Description</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>Qt5Agg</td>
<td>Agg rendering in a <a class="reference external" href="https://doc.qt.io/qt-5/index.html">Qt5</a> canvas (requires <a class="reference external" href="https://riverbankcomputing.com/software/pyqt/intro">PyQt5</a>). This
backend can be activated in IPython with <code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">qt5</span></code>.</td>
</tr>
<tr class="row-odd"><td>ipympl</td>
<td>Agg rendering embedded in a Jupyter widget. (requires ipympl).
This backend can be enabled in a Jupyter notebook with
<code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">ipympl</span></code>.</td>
</tr>
<tr class="row-even"><td>GTK3Agg</td>
<td>Agg rendering to a <a class="reference external" href="https://www.gtk.org/">GTK</a> 3.x canvas (requires <a class="reference external" href="https://wiki.gnome.org/action/show/Projects/PyGObject">PyGObject</a>,
and <a class="reference external" href="https://www.cairographics.org/pycairo/">pycairo</a> or <a class="reference external" href="https://pythonhosted.org/cairocffi/">cairocffi</a>). This backend can be activated in
IPython with <code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">gtk3</span></code>.</td>
</tr>
<tr class="row-odd"><td>macosx</td>
<td>Agg rendering into a Cocoa canvas in OSX. This backend can be
activated in IPython with <code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">osx</span></code>.</td>
</tr>
<tr class="row-even"><td>TkAgg</td>
<td>Agg rendering to a <a class="reference external" href="https://www.tcl.tk/">Tk</a> canvas (requires <a class="reference external" href="https://docs.python.org/3/library/tk.html">TkInter</a>). This
backend can be activated in IPython with <code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">tk</span></code>.</td>
</tr>
<tr class="row-odd"><td>nbAgg</td>
<td>Embed an interactive figure in a Jupyter classic notebook. This
backend can be enabled in Jupyter notebooks via
<code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">notebook</span></code>.</td>
</tr>
<tr class="row-even"><td>WebAgg</td>
<td>On <code class="docutils literal notranslate"><span class="pre">show()</span></code> will start a tornado server with an interactive
figure.</td>
</tr>
<tr class="row-odd"><td>GTK3Cairo</td>
<td>Cairo rendering to a <a class="reference external" href="https://www.gtk.org/">GTK</a> 3.x canvas (requires <a class="reference external" href="https://wiki.gnome.org/action/show/Projects/PyGObject">PyGObject</a>,
and <a class="reference external" href="https://www.cairographics.org/pycairo/">pycairo</a> or <a class="reference external" href="https://pythonhosted.org/cairocffi/">cairocffi</a>).</td>
</tr>
<tr class="row-even"><td>Qt4Agg</td>
<td>Agg rendering to a <a class="reference external" href="https://doc.qt.io/archives/qt-4.8/index.html">Qt4</a> canvas (requires <a class="reference external" href="https://riverbankcomputing.com/software/pyqt/intro">PyQt4</a> or
<code class="docutils literal notranslate"><span class="pre">pyside</span></code>). This backend can be activated in IPython with
<code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">qt4</span></code>.</td>
</tr>
<tr class="row-odd"><td>wxAgg</td>
<td>Agg rendering to a <a class="reference external" href="https://www.wxwidgets.org/">wxWidgets</a> canvas (requires <a class="reference external" href="https://www.wxpython.org/">wxPython</a> 4).
This backend can be activated in IPython with <code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">wx</span></code>.</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">The names of builtin backends case-insensitive; e.g., 'Qt5Agg' and
'qt5agg' are equivalent.</p>
</div>
<div class="section" id="ipympl">
<h4>ipympl<a class="headerlink" href="#ipympl" title="Permalink to this headline">¶</a></h4>
<p>The Jupyter widget ecosystem is moving too fast to support directly in
Matplotlib. To install ipympl</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip install ipympl
jupyter nbextension <span class="nb">enable</span> --py --sys-prefix ipympl
</pre></div>
</div>
<p>or</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda install ipympl -c conda-forge
</pre></div>
</div>
<p>See <a class="reference external" href="https://github.com/matplotlib/jupyter-matplotlib">jupyter-matplotlib</a>
for more details.</p>
</div>
<div class="section" id="how-do-i-select-pyqt4-or-pyside">
<span id="qt-api-usage"></span><h4>How do I select PyQt4 or PySide?<a class="headerlink" href="#how-do-i-select-pyqt4-or-pyside" title="Permalink to this headline">¶</a></h4>
<p>The <span class="target" id="index-4"></span><a class="reference internal" href="../../faq/environment_variables_faq.html#envvar-QT_API"><code class="xref std std-envvar docutils literal notranslate"><span class="pre">QT_API</span></code></a> environment variable can be set to either <code class="docutils literal notranslate"><span class="pre">pyqt</span></code> or
<code class="docutils literal notranslate"><span class="pre">pyside</span></code> to use <code class="docutils literal notranslate"><span class="pre">PyQt4</span></code> or <code class="docutils literal notranslate"><span class="pre">PySide</span></code>, respectively.</p>
<p>Since the default value for the bindings to be used is <code class="docutils literal notranslate"><span class="pre">PyQt4</span></code>, Matplotlib
first tries to import it. If the import fails, it tries to import <code class="docutils literal notranslate"><span class="pre">PySide</span></code>.</p>
</div>
</div>
<div class="section" id="using-non-builtin-backends">
<h3>Using non-builtin backends<a class="headerlink" href="#using-non-builtin-backends" title="Permalink to this headline">¶</a></h3>
<p>More generally, any importable backend can be selected by using any of the
methods above. If <code class="docutils literal notranslate"><span class="pre">name.of.the.backend</span></code> is the module containing the
backend, use <code class="docutils literal notranslate"><span class="pre">module://name.of.the.backend</span></code> as the backend name, e.g.
<code class="docutils literal notranslate"><span class="pre">matplotlib.use('module://name.of.the.backend')</span></code>.</p>
</div>
</div>
<div class="section" id="what-is-interactive-mode">
<span id="interactive-mode"></span><h2>What is interactive mode?<a class="headerlink" href="#what-is-interactive-mode" title="Permalink to this headline">¶</a></h2>
<p>Use of an interactive backend (see <a class="reference internal" href="#what-is-a-backend"><span class="std std-ref">What is a backend?</span></a>)
permits--but does not by itself require or ensure--plotting
to the screen. Whether and when plotting to the screen occurs,
and whether a script or shell session continues after a plot
is drawn on the screen, depends on the functions and methods
that are called, and on a state variable that determines whether
Matplotlib is in "interactive mode". The default Boolean value is set
by the <code class="file docutils literal notranslate"><span class="pre">matplotlibrc</span></code> file, and may be customized like any other
configuration parameter (see <a class="reference internal" href="customizing.html"><span class="doc">Customizing Matplotlib with style sheets and rcParams</span></a>). It
may also be set via <a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.interactive" title="matplotlib.interactive"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.interactive()</span></code></a>, and its
value may be queried via <a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.is_interactive" title="matplotlib.is_interactive"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.is_interactive()</span></code></a>. Turning
interactive mode on and off in the middle of a stream of plotting
commands, whether in a script or in a shell, is rarely needed
and potentially confusing. In the following, we will assume all
plotting is done with interactive mode either on or off.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Major changes related to interactivity, and in particular the
role and behavior of <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show"><code class="xref py py-func docutils literal notranslate"><span class="pre">show()</span></code></a>, were made in the
transition to Matplotlib version 1.0, and bugs were fixed in
1.0.1. Here we describe the version 1.0.1 behavior for the
primary interactive backends, with the partial exception of
<em>macosx</em>.</p>
</div>
<p>Interactive mode may also be turned on via <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.ion.html#matplotlib.pyplot.ion" title="matplotlib.pyplot.ion"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.pyplot.ion()</span></code></a>,
and turned off via <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.ioff.html#matplotlib.pyplot.ioff" title="matplotlib.pyplot.ioff"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.pyplot.ioff()</span></code></a>.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Interactive mode works with suitable backends in ipython and in
the ordinary python shell, but it does <em>not</em> work in the IDLE IDE.
If the default backend does not support interactivity, an interactive
backend can be explicitly activated using any of the methods discussed
in <a class="reference internal" href="#id2">What is a backend?</a>.</p>
</div>
<div class="section" id="interactive-example">
<h3>Interactive example<a class="headerlink" href="#interactive-example" title="Permalink to this headline">¶</a></h3>
<p>From an ordinary python prompt, or after invoking ipython with no options,
try this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ion</span><span class="p">()</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">([</span><span class="mf">1.6</span><span class="p">,</span> <span class="mf">2.7</span><span class="p">])</span>
</pre></div>
</div>
<p>This will pop up a plot window. Your terminal prompt will remain active, so
that you can type additional commands such as:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="s2">"interactive test"</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="matplotlib.pyplot.xlabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s2">"index"</span><span class="p">)</span>
</pre></div>
</div>
<p>On most interactive backends, the figure window will also be updated if you
change it via the object-oriented interface. E.g. get a reference to the
<a class="reference internal" href="../../api/axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Axes</span></code></a> instance, and call a method of that instance:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">([</span><span class="mf">3.1</span><span class="p">,</span> <span class="mf">2.2</span><span class="p">])</span>
</pre></div>
</div>
<p>If you are using certain backends (like <code class="docutils literal notranslate"><span class="pre">macosx</span></code>), or an older version
of Matplotlib, you may not see the new line added to the plot immediately.
In this case, you need to explicitly call <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.draw.html#matplotlib.pyplot.draw" title="matplotlib.pyplot.draw"><code class="xref py py-func docutils literal notranslate"><span class="pre">draw()</span></code></a>
in order to update the plot:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">draw</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="non-interactive-example">
<h3>Non-interactive example<a class="headerlink" href="#non-interactive-example" title="Permalink to this headline">¶</a></h3>
<p>Start a fresh session as in the previous example, but now
turn interactive mode off:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ioff</span><span class="p">()</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">([</span><span class="mf">1.6</span><span class="p">,</span> <span class="mf">2.7</span><span class="p">])</span>
</pre></div>
</div>
<p>Nothing happened--or at least nothing has shown up on the
screen (unless you are using <em>macosx</em> backend, which is
anomalous). To make the plot appear, you need to do this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<p>Now you see the plot, but your terminal command line is
unresponsive; <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyplot.show()</span></code></a> <em>blocks</em> the input
of additional commands until you manually kill the plot
window.</p>
<p>What good is this--being forced to use a blocking function?
Suppose you need a script that plots the contents of a file
to the screen. You want to look at that plot, and then end
the script. Without some blocking command such as <code class="docutils literal notranslate"><span class="pre">show()</span></code>, the
script would flash up the plot and then end immediately,
leaving nothing on the screen.</p>
<p>In addition, non-interactive mode delays all drawing until
<code class="docutils literal notranslate"><span class="pre">show()</span></code> is called; this is more efficient than redrawing
the plot each time a line in the script adds a new feature.</p>
<p>Prior to version 1.0, <code class="docutils literal notranslate"><span class="pre">show()</span></code> generally could not be called
more than once in a single script (although sometimes one
could get away with it); for version 1.0.1 and above, this
restriction is lifted, so one can write a script like this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ioff</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<p>This makes three plots, one at a time. I.e., the second plot will show up
once the first plot is closed.</p>
</div>
<div class="section" id="summary">
<h3>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h3>
<p>In interactive mode, pyplot functions automatically draw
to the screen.</p>
<p>When plotting interactively, if using
object method calls in addition to pyplot functions, then
call <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.draw.html#matplotlib.pyplot.draw" title="matplotlib.pyplot.draw"><code class="xref py py-func docutils literal notranslate"><span class="pre">draw()</span></code></a> whenever you want to
refresh the plot.</p>
<p>Use non-interactive mode in scripts in which you want to
generate one or more figures and display them before ending
or generating a new set of figures. In that case, use
<a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show"><code class="xref py py-func docutils literal notranslate"><span class="pre">show()</span></code></a> to display the figure(s) and
to block execution until you have manually destroyed them.</p>
</div>
</div>
<div class="section" id="performance">
<span id="id4"></span><h2>Performance<a class="headerlink" href="#performance" title="Permalink to this headline">¶</a></h2>
<p>Whether exploring data in interactive mode or programmatically
saving lots of plots, rendering performance can be a painful
bottleneck in your pipeline. Matplotlib provides a couple
ways to greatly reduce rendering time at the cost of a slight
change (to a settable tolerance) in your plot's appearance.
The methods available to reduce rendering time depend on the
type of plot that is being created.</p>
<div class="section" id="line-segment-simplification">
<h3>Line segment simplification<a class="headerlink" href="#line-segment-simplification" title="Permalink to this headline">¶</a></h3>
<p>For plots that have line segments (e.g. typical line plots, outlines
of polygons, etc.), rendering performance can be controlled by
<code class="docutils literal notranslate"><a class="reference external" href="../../tutorials/introductory/customizing.html?highlight=path.simplify#a-sample-matplotlibrc-file"><span class="pre">rcParams["path.simplify"]</span></a></code> (default: <code class="docutils literal notranslate"><span class="pre">True</span></code>) and <code class="docutils literal notranslate"><a class="reference external" href="../../tutorials/introductory/customizing.html?highlight=path.simplify_threshold#a-sample-matplotlibrc-file"><span class="pre">rcParams["path.simplify_threshold"]</span></a></code> (default: <code class="docutils literal notranslate"><span class="pre">0.111111111111</span></code>), which
can be defined e.g. in the <code class="file docutils literal notranslate"><span class="pre">matplotlibrc</span></code> file (see
<a class="reference internal" href="customizing.html"><span class="doc">Customizing Matplotlib with style sheets and rcParams</span></a> for more information about
the <code class="file docutils literal notranslate"><span class="pre">matplotlibrc</span></code> file). <code class="docutils literal notranslate"><a class="reference external" href="../../tutorials/introductory/customizing.html?highlight=path.simplify#a-sample-matplotlibrc-file"><span class="pre">rcParams["path.simplify"]</span></a></code> (default: <code class="docutils literal notranslate"><span class="pre">True</span></code>) is a boolean
indicating whether or not line segments are simplified at all.
<code class="docutils literal notranslate"><a class="reference external" href="../../tutorials/introductory/customizing.html?highlight=path.simplify_threshold#a-sample-matplotlibrc-file"><span class="pre">rcParams["path.simplify_threshold"]</span></a></code> (default: <code class="docutils literal notranslate"><span class="pre">0.111111111111</span></code>) controls how much line segments are simplified;
higher thresholds result in quicker rendering.</p>
<p>The following script will first display the data without any
simplification, and then display the same data with simplification.
Try interacting with both of them:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
<span class="c1"># Setup, and create the data to plot</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
<span class="n">y</span><span class="p">[</span><span class="mi">50000</span><span class="p">:]</span> <span class="o">*=</span> <span class="mi">2</span>
<span class="n">y</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">geomspace</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">50000</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'path.simplify'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'path.simplify_threshold'</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'path.simplify_threshold'</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<p>Matplotlib currently defaults to a conservative simplification
threshold of <code class="docutils literal notranslate"><span class="pre">1/9</span></code>. If you want to change your default settings
to use a different value, you can change your <code class="file docutils literal notranslate"><span class="pre">matplotlibrc</span></code>
file. Alternatively, you could create a new style for
interactive plotting (with maximal simplification) and another
style for publication quality plotting (with minimal
simplification) and activate them as necessary. See
<a class="reference internal" href="customizing.html"><span class="doc">Customizing Matplotlib with style sheets and rcParams</span></a> for
instructions on how to perform these actions.</p>
<p>The simplification works by iteratively merging line segments
into a single vector until the next line segment's perpendicular
distance to the vector (measured in display-coordinate space)
is greater than the <code class="docutils literal notranslate"><span class="pre">path.simplify_threshold</span></code> parameter.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Changes related to how line segments are simplified were made
in version 2.1. Rendering time will still be improved by these
parameters prior to 2.1, but rendering time for some kinds of
data will be vastly improved in versions 2.1 and greater.</p>
</div>
</div>
<div class="section" id="marker-simplification">
<h3>Marker simplification<a class="headerlink" href="#marker-simplification" title="Permalink to this headline">¶</a></h3>
<p>Markers can also be simplified, albeit less robustly than
line segments. Marker simplification is only available
to <a class="reference internal" href="../../api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D"><code class="xref py py-class docutils literal notranslate"><span class="pre">Line2D</span></code></a> objects (through the
<code class="docutils literal notranslate"><span class="pre">markevery</span></code> property). Wherever
<a class="reference internal" href="../../api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D"><code class="xref py py-class docutils literal notranslate"><span class="pre">Line2D</span></code></a> construction parameters
are passed through, such as
<a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.pyplot.plot()</span></code></a> and
<a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot"><code class="xref py py-meth docutils literal notranslate"><span class="pre">matplotlib.axes.Axes.plot()</span></code></a>, the <code class="docutils literal notranslate"><span class="pre">markevery</span></code>
parameter can be used:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">markevery</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">markevery</span></code> argument allows for naive subsampling, or an
attempt at evenly spaced (along the <em>x</em> axis) sampling. See the
<a class="reference internal" href="../../gallery/lines_bars_and_markers/markevery_demo.html"><span class="doc">Markevery Demo</span></a>
for more information.</p>
</div>
<div class="section" id="splitting-lines-into-smaller-chunks">
<h3>Splitting lines into smaller chunks<a class="headerlink" href="#splitting-lines-into-smaller-chunks" title="Permalink to this headline">¶</a></h3>
<p>If you are using the Agg backend (see <a class="reference internal" href="#what-is-a-backend"><span class="std std-ref">What is a backend?</span></a>),
then you can make use of <code class="docutils literal notranslate"><a class="reference external" href="../../tutorials/introductory/customizing.html?highlight=agg.path.chunksize#a-sample-matplotlibrc-file"><span class="pre">rcParams["agg.path.chunksize"]</span></a></code> (default: <code class="docutils literal notranslate"><span class="pre">0</span></code>)
This allows you to specify a chunk size, and any lines with
greater than that many vertices will be split into multiple
lines, each of which has no more than <code class="docutils literal notranslate"><span class="pre">agg.path.chunksize</span></code>
many vertices. (Unless <code class="docutils literal notranslate"><span class="pre">agg.path.chunksize</span></code> is zero, in
which case there is no chunking.) For some kind of data,
chunking the line up into reasonable sizes can greatly
decrease rendering time.</p>
<p>The following script will first display the data without any
chunk size restriction, and then display the same data with
a chunk size of 10,000. The difference can best be seen when
the figures are large, try maximizing the GUI and then
interacting with them:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'path.simplify_threshold'</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="c1"># Setup, and create the data to plot</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
<span class="n">y</span><span class="p">[</span><span class="mi">50000</span><span class="p">:]</span> <span class="o">*=</span> <span class="mi">2</span>
<span class="n">y</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">geomspace</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">50000</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'path.simplify'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'agg.path.chunksize'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'agg.path.chunksize'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">10000</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="legends">
<h3>Legends<a class="headerlink" href="#legends" title="Permalink to this headline">¶</a></h3>
<p>The default legend behavior for axes attempts to find the location
that covers the fewest data points (<code class="docutils literal notranslate"><span class="pre">loc='best'</span></code>). This can be a
very expensive computation if there are lots of data points. In
this case, you may want to provide a specific location.</p>
</div>
<div class="section" id="using-the-fast-style">
<h3>Using the <em>fast</em> style<a class="headerlink" href="#using-the-fast-style" title="Permalink to this headline">¶</a></h3>
<p>The <em>fast</em> style can be used to automatically set
simplification and chunking parameters to reasonable
settings to speed up plotting large amounts of data.
It can be used simply by running:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.style</span> <span class="k">as</span> <span class="nn">mplstyle</span>
<span class="n">mplstyle</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">'fast'</span><span class="p">)</span>
</pre></div>
</div>
<p>It is very lightweight, so it plays nicely with other
styles, just make sure the fast style is applied last
so that other styles do not overwrite the settings:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mplstyle</span><span class="o">.</span><span class="n">use</span><span class="p">([</span><span class="s1">'dark_background'</span><span class="p">,</span> <span class="s1">'ggplot'</span><span class="p">,</span> <span class="s1">'fast'</span><span class="p">])</span>
</pre></div>
</div>
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