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

"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<head>

<title>Sample plots in Matplotlib — Matplotlib 2.0.2.post4416.dev0+g1df513f documentation</title>

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

<ul>

<li><a class="reference internal" href="#">Sample plots in Matplotlib</a><ul>

<li><a class="reference internal" href="#subplot-demo">Subplot demo</a></li>

<li><a class="reference internal" href="#histograms">Histograms</a></li>

<li><a class="reference internal" href="#mplot3d">mplot3d</a></li>

<li><a class="reference internal" href="#streamplot">Streamplot</a></li>

<li><a class="reference internal" href="#ellipses">Ellipses</a></li>

<li><a class="reference internal" href="#bar-charts">Bar charts</a></li>

<li><a class="reference internal" href="#pie-charts">Pie charts</a></li>

<li><a class="reference internal" href="#table-demo">Table demo</a></li>

<li><a class="reference internal" href="#scatter-demo">Scatter demo</a></li>

<li><a class="reference internal" href="#slider-demo">Slider demo</a></li>

<li><a class="reference internal" href="#log-plots">Log plots</a></li>

<li><a class="reference internal" href="#polar-plots">Polar plots</a></li>

<li><a class="reference internal" href="#legends">Legends</a></li>

<li><a class="reference internal" href="#mathtext-examples">Mathtext_examples</a></li>

<li><a class="reference internal" href="#native-tex-rendering">Native TeX rendering</a></li>

<li><a class="reference internal" href="#xkcd-style-sketch-plots">XKCD-style sketch plots</a></li>

</ul>

</li>

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<h3>Related Topics</h3>

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<li><a href="../../contents.html">Documentation overview</a><ul>

</ul></li>

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<span id="sphx-glr-tutorials-01-introductory-sample-plots-py"></span><h1>Sample plots in Matplotlib<a class="headerlink" href="#sample-plots-in-matplotlib" title="Permalink to this headline">¶</a></h1>

<p>Here you’ll find a host of example plots with the code that

generated them.</p>

<p>Here’s how to create a line plot with text labels using

<p class="caption"><span class="caption-text">Simple Plot</span></p>

</div>

<span id="screenshots-subplot-demo"></span><h2>Subplot demo<a class="headerlink" href="#subplot-demo" title="Permalink to this headline">¶</a></h2>

<p>Multiple axes (i.e. subplots) are created with the

<a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot"><code class="xref py py-func docutils literal"><span class="pre">subplot()</span></code></a> command:</p>

<p class="caption"><span class="caption-text">Subplot</span></p>

</div>

<span id="screenshots-histogram-demo"></span><h2>Histograms<a class="headerlink" href="#histograms" title="Permalink to this headline">¶</a></h2>

<p>The <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.hist.html#matplotlib.pyplot.hist" title="matplotlib.pyplot.hist"><code class="xref py py-func docutils literal"><span class="pre">hist()</span></code></a> command automatically generates

histograms and returns the bin counts or probabilities:</p>

<p class="caption"><span class="caption-text">Histogram Features</span></p>

</div>

<p>You can add arbitrary paths in Matplotlib using the

<a class="reference internal" href="../../api/path_api.html#module-matplotlib.path" title="matplotlib.path"><code class="xref py py-mod docutils literal"><span class="pre">matplotlib.path</span></code></a> module:</p>

<p class="caption"><span class="caption-text">Path Patch</span></p>

</div>

<span id="screenshots-mplot3d-surface"></span><h2>mplot3d<a class="headerlink" href="#mplot3d" title="Permalink to this headline">¶</a></h2>

<p>The mplot3d toolkit (see <a class="reference internal" href="../toolkits/mplot3d.html#toolkit-mplot3d-tutorial"><span class="std std-ref">Getting started</span></a> and

<a class="reference internal" href="../../gallery/index.html#mplot3d-examples-index"><span class="std std-ref">mplot3d toolkit</span></a>) has support for simple 3d graphs

including surface, wireframe, scatter, and bar charts.</p>

<p class="caption"><span class="caption-text">Surface3d</span></p>

</div>

<p>Thanks to John Porter, Jonathon Taylor, Reinier Heeres, and Ben Root for

the <code class="xref py py-obj docutils literal"><span class="pre">mplot3d</span></code> toolkit. This toolkit is included with all standard Matplotlib

installs.</p>

</div>

<span id="screenshots-ellipse-demo"></span><h2>Streamplot<a class="headerlink" href="#streamplot" title="Permalink to this headline">¶</a></h2>

<p>The <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.streamplot.html#matplotlib.pyplot.streamplot" title="matplotlib.pyplot.streamplot"><code class="xref py py-meth docutils literal"><span class="pre">streamplot()</span></code></a> function plots the streamlines of

a vector field. In addition to simply plotting the streamlines, it allows you

to map the colors and/or line widths of streamlines to a separate parameter,

such as the speed or local intensity of the vector field.</p>

<p class="caption"><span class="caption-text">Plot Streamplot</span></p>

</div>

<p>This feature complements the <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.quiver.html#matplotlib.pyplot.quiver" title="matplotlib.pyplot.quiver"><code class="xref py py-meth docutils literal"><span class="pre">quiver()</span></code></a> function for

plotting vector fields. Thanks to Tom Flannaghan and Tony Yu for adding the

streamplot function.</p>

</div>

<h2>Ellipses<a class="headerlink" href="#ellipses" title="Permalink to this headline">¶</a></h2>

<p>In support of the

<a class="reference external" href="http://www.jpl.nasa.gov/news/phoenix/main.php">Phoenix</a> mission to

Mars (which used Matplotlib to display ground tracking of spacecraft),

Michael Droettboom built on work by Charlie Moad to provide an extremely

accurate 8-spline approximation to elliptical arcs (see

<a class="reference internal" href="../../api/_as_gen/matplotlib.patches.Arc.html#matplotlib.patches.Arc" title="matplotlib.patches.Arc"><code class="xref py py-class docutils literal"><span class="pre">Arc</span></code></a>), which are insensitive to zoom level.</p>

<p class="caption"><span class="caption-text">Ellipse Demo</span></p>

</div>

<span id="screenshots-barchart-demo"></span><h2>Bar charts<a class="headerlink" href="#bar-charts" title="Permalink to this headline">¶</a></h2>

<p>Use the <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.bar.html#matplotlib.pyplot.bar" title="matplotlib.pyplot.bar"><code class="xref py py-func docutils literal"><span class="pre">bar()</span></code></a> command to make bar charts, which

includes customizations such as error bars:</p>

<p class="caption"><span class="caption-text">Barchart Demo</span></p>

</div>

<p>You can also create stacked bars

(<a class="reference external" href="../../gallery/pylab_examples/bar_stacked.html">bar_stacked.py</a>),

or horizontal bar charts

(<a class="reference external" href="../../gallery/lines_bars_and_markers/barh.html">barh.py</a>).</p>

</div>

<span id="screenshots-pie-demo"></span><h2>Pie charts<a class="headerlink" href="#pie-charts" title="Permalink to this headline">¶</a></h2>

<p>The <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.pie.html#matplotlib.pyplot.pie" title="matplotlib.pyplot.pie"><code class="xref py py-func docutils literal"><span class="pre">pie()</span></code></a> command allows you to create pie

charts. Optional features include auto-labeling the percentage of area,

exploding one or more wedges from the center of the pie, and a shadow effect.

Take a close look at the attached code, which generates this figure in just

a few lines of code.</p>

<p class="caption"><span class="caption-text">Pie Features</span></p>

</div>

<span id="screenshots-table-demo"></span><h2>Table demo<a class="headerlink" href="#table-demo" title="Permalink to this headline">¶</a></h2>

<p>The <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.table.html#matplotlib.pyplot.table" title="matplotlib.pyplot.table"><code class="xref py py-func docutils literal"><span class="pre">table()</span></code></a> command adds a text table

to an axes.</p>

<p class="caption"><span class="caption-text">Table Demo</span></p>

</div>

<span id="screenshots-scatter-demo"></span><h2>Scatter demo<a class="headerlink" href="#scatter-demo" title="Permalink to this headline">¶</a></h2>

<p>The <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter" title="matplotlib.pyplot.scatter"><code class="xref py py-func docutils literal"><span class="pre">scatter()</span></code></a> command makes a scatter plot

with (optional) size and color arguments. This example plots changes

in Google’s stock price, with marker sizes reflecting the

trading volume and colors varying with time. Here, the

alpha attribute is used to make semitransparent circle markers.</p>

<p class="caption"><span class="caption-text">Scatter Demo2</span></p>

</div>

<span id="screenshots-slider-demo"></span><h2>Slider demo<a class="headerlink" href="#slider-demo" title="Permalink to this headline">¶</a></h2>

<p>Matplotlib has basic GUI widgets that are independent of the graphical

user interface you are using, allowing you to write cross GUI figures

and widgets. See <a class="reference internal" href="../../api/widgets_api.html#module-matplotlib.widgets" title="matplotlib.widgets"><code class="xref py py-mod docutils literal"><span class="pre">matplotlib.widgets</span></code></a> and the

<a class="reference external" href="../../gallery/index.html">widget examples</a>.</p>

<p class="caption"><span class="caption-text">Slider Demo</span></p>

</div>

<p>The <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.fill.html#matplotlib.pyplot.fill" title="matplotlib.pyplot.fill"><code class="xref py py-func docutils literal"><span class="pre">fill()</span></code></a> command lets you

plot filled curves and polygons:</p>

</div>

<p>Thanks to Andrew Straw for adding this function.</p>

</div>

<p>You can plot timeseries data with major and minor ticks and custom

tick formatters for both.</p>

</div>

<p>See <a class="reference internal" href="../../api/ticker_api.html#module-matplotlib.ticker" title="matplotlib.ticker"><code class="xref py py-mod docutils literal"><span class="pre">matplotlib.ticker</span></code></a> and <a class="reference internal" href="../../api/dates_api.html#module-matplotlib.dates" title="matplotlib.dates"><code class="xref py py-mod docutils literal"><span class="pre">matplotlib.dates</span></code></a> for details and usage.</p>

</div>

<span id="screenshots-log-demo"></span><h2>Log plots<a class="headerlink" href="#log-plots" title="Permalink to this headline">¶</a></h2>

<p>The <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.semilogx.html#matplotlib.pyplot.semilogx" title="matplotlib.pyplot.semilogx"><code class="xref py py-func docutils literal"><span class="pre">semilogx()</span></code></a>,

<a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.semilogy.html#matplotlib.pyplot.semilogy" title="matplotlib.pyplot.semilogy"><code class="xref py py-func docutils literal"><span class="pre">semilogy()</span></code></a> and

<a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.loglog.html#matplotlib.pyplot.loglog" title="matplotlib.pyplot.loglog"><code class="xref py py-func docutils literal"><span class="pre">loglog()</span></code></a> functions simplify the creation of

logarithmic plots.</p>

</div>

<p>Thanks to Andrew Straw, Darren Dale and Gregory Lielens for contributions

log-scaling infrastructure.</p>

</div>

<span id="screenshots-polar-demo"></span><h2>Polar plots<a class="headerlink" href="#polar-plots" title="Permalink to this headline">¶</a></h2>

<p>The <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.polar.html#matplotlib.pyplot.polar" title="matplotlib.pyplot.polar"><code class="xref py py-func docutils literal"><span class="pre">polar()</span></code></a> command generates polar plots.</p>

<p class="caption"><span class="caption-text">Polar Demo</span></p>

</div>

<span id="screenshots-legend-demo"></span><h2>Legends<a class="headerlink" href="#legends" title="Permalink to this headline">¶</a></h2>

<p>The <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend"><code class="xref py py-func docutils literal"><span class="pre">legend()</span></code></a> command automatically

generates figure legends, with MATLAB-compatible legend placement

commands.</p>

<p class="caption"><span class="caption-text">Legend</span></p>

</div>

<p>Thanks to Charles Twardy for input on the legend command.</p>

</div>

<span id="screenshots-mathtext-examples-demo"></span><h2>Mathtext_examples<a class="headerlink" href="#mathtext-examples" title="Permalink to this headline">¶</a></h2>

<p>Below is a sampling of the many TeX expressions now supported by Matplotlib’s

internal mathtext engine. The mathtext module provides TeX style mathematical

expressions using <a class="reference external" href="https://www.freetype.org/">FreeType</a>

and the DejaVu, BaKoMa computer modern, or <a class="reference external" href="http://www.stixfonts.org">STIX</a>

fonts. See the <a class="reference internal" href="../../api/mathtext_api.html#module-matplotlib.mathtext" title="matplotlib.mathtext"><code class="xref py py-mod docutils literal"><span class="pre">matplotlib.mathtext</span></code></a> module for additional details.</p>

<p class="caption"><span class="caption-text">Mathtext Examples</span></p>

</div>

<p>Matplotlib’s mathtext infrastructure is an independent implementation and

does not require TeX or any external packages installed on your computer. See

the tutorial at <a class="reference internal" href="../text/mathtext.html#sphx-glr-tutorials-text-mathtext-py"><span class="std std-ref">Writing mathematical expressions</span></a>.</p>

</div>

<span id="screenshots-tex-demo"></span><h2>Native TeX rendering<a class="headerlink" href="#native-tex-rendering" title="Permalink to this headline">¶</a></h2>

<p>Although Matplotlib’s internal math rendering engine is quite

powerful, sometimes you need TeX. Matplotlib supports external TeX

rendering of strings with the <em>usetex</em> option.</p>

</div>

<p>You can embed Matplotlib into pygtk, wx, Tk, or Qt applications.

Here is a screenshot of an EEG viewer called <a class="reference external" href="https://github.com/nipy/pbrain">pbrain</a>.</p>

<p>The lower axes uses <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.specgram.html#matplotlib.pyplot.specgram" title="matplotlib.pyplot.specgram"><code class="xref py py-func docutils literal"><span class="pre">specgram()</span></code></a>

to plot the spectrogram of one of the EEG channels.</p>

<p>For examples of how to embed Matplotlib in different toolkits, see:</p>

<li><a class="reference internal" href="../../gallery/user_interfaces/embedding_in_gtk2_sgskip.html#sphx-glr-gallery-user-interfaces-embedding-in-gtk2-sgskip-py"><span class="std std-ref">Embedding In GTK2</span></a></li>

<li><a class="reference internal" href="../../gallery/user_interfaces/embedding_in_wx2_sgskip.html#sphx-glr-gallery-user-interfaces-embedding-in-wx2-sgskip-py"><span class="std std-ref">Embedding In Wx2</span></a></li>

<li><a class="reference internal" href="../../gallery/user_interfaces/mpl_with_glade_sgskip.html#sphx-glr-gallery-user-interfaces-mpl-with-glade-sgskip-py"><span class="std std-ref">Matplotlib With Glade</span></a></li>

<li><a class="reference internal" href="../../gallery/user_interfaces/embedding_in_qt4_sgskip.html#sphx-glr-gallery-user-interfaces-embedding-in-qt4-sgskip-py"><span class="std std-ref">Embedding In QT4</span></a></li>

<li><a class="reference internal" href="../../gallery/user_interfaces/embedding_in_tk_sgskip.html#sphx-glr-gallery-user-interfaces-embedding-in-tk-sgskip-py"><span class="std std-ref">Embedding In Tk</span></a></li>

</ul>

</div></blockquote>

</div>

<h2>XKCD-style sketch plots<a class="headerlink" href="#xkcd-style-sketch-plots" title="Permalink to this headline">¶</a></h2>

<p>Matplotlib supports plotting in the style of <code class="xref py py-obj docutils literal"><span class="pre">xkcd</span></code>.</p>

</div>

<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># An extra set of sample plots for the thumbnail image.</span>

<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>

<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">19680801</span><span class="p">)</span>

<span class="n">data</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">randn</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">fig</span><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="View documentation for matplotlib.pyplot.subplots"><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="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>

<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>

</pre></div>

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<p><strong>Total running time of the script:</strong> ( 0 minutes 0.104 seconds)</p>

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<a class="reference download internal" href="../../_downloads/sample_plots.ipynb" download=""><code class="xref download docutils literal"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">sample_plots.ipynb</span></code></a></div>

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