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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>Pyplot tutorial — Matplotlib 2.0.2.post4416.dev0+g1df513f documentation</title>

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SOURCELINK_SUFFIX: '.txt'

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title="Search within Matplotlib 2.0.2.post4416.dev0+g1df513f documentation"

href="../../_static/opensearch.xml"/>

</head>

<body>

</map>

</div>

<h3>Navigation</h3>

<ul>

<a href="../../genindex.html" title="General Index"

accesskey="I">index</a></li>

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>modules</a> |</li>

<li><a href="../../gallery/index.html">examples</a>| </li>

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</ul>

</div>

</a>

</a>

Travis-CI: <a href="https://travis-ci.org/matplotlib/matplotlib">

</a>

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

<ul>

<li><a class="reference internal" href="#">Pyplot tutorial</a><ul>

<li><a class="reference internal" href="#intro-to-pyplot">Intro to pyplot</a><ul>

<li><a class="reference internal" href="#formatting-the-style-of-your-plot">Formatting the style of your plot</a></li>

</ul>

</li>

<li><a class="reference internal" href="#plotting-with-keyword-strings">Plotting with keyword strings</a></li>

<li><a class="reference internal" href="#plotting-with-categorical-variables">Plotting with categorical variables</a></li>

<li><a class="reference internal" href="#controlling-line-properties">Controlling line properties</a></li>

<li><a class="reference internal" href="#working-with-multiple-figures-and-axes">Working with multiple figures and axes</a></li>

<li><a class="reference internal" href="#working-with-text">Working with text</a><ul>

<li><a class="reference internal" href="#using-mathematical-expressions-in-text">Using mathematical expressions in text</a></li>

<li><a class="reference internal" href="#annotating-text">Annotating text</a></li>

</ul>

</li>

<li><a class="reference internal" href="#logarithmic-and-other-nonlinear-axes">Logarithmic and other nonlinear axes</a></li>

</ul>

</li>

</ul>

<h3>Related Topics</h3>

<ul>

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

</ul></li>

</ul>

</div>

<li><a href="../../_sources/tutorials/01_introductory/pyplot.rst.txt"

rel="nofollow">Show Source</a></li>

</ul>

</div>

<h3>Quick search</h3>

</form>

</div>

</div>

<h1>Pyplot tutorial<a class="headerlink" href="#pyplot-tutorial" title="Permalink to this headline">¶</a></h1>

An introduction to the pyplot interface.

<h2>Intro to pyplot<a class="headerlink" href="#intro-to-pyplot" title="Permalink to this headline">¶</a></h2>

<code class="xref py py-mod docutils literal">matplotlib.pyplot</code> is a collection of command style functions

that make matplotlib work like MATLAB.

Each <code class="docutils literal">pyplot</code> function makes

some change to a figure: e.g., creates a figure, creates a plotting area

in a figure, plots some lines in a plotting area, decorates the plot

with labels, etc.

In <code class="xref py py-mod docutils literal">matplotlib.pyplot</code> various states are preserved

across function calls, so that it keeps track of things like

the current figure and plotting area, and the plotting

functions are directed to the current axes (please note that “axes” here

and in most places in the documentation refers to the axes

<a class="reference external" href="http://matplotlib.org/faq/usage_faq.html#parts-of-a-figure">part of a figure</a>

and not the strict mathematical term for more than one axis).

Note

the pyplot API is generally less-flexible than the object-oriented API.

Most of the function calls you see here can also be called as methods

from an <code class="docutils literal">Axes</code> object. We recommend browsing the tutorials and

examples to see how this works.

</div>

Generating visualizations with pyplot is very quick:

<div class="highlight-python"><div class="highlight"><pre>import matplotlib.pyplot as plt

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

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

</pre></div>

</div>

You may be wondering why the x-axis ranges from 0-3 and the y-axis

from 1-4. If you provide a single list or array to the

<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">plot()</code></a> command, matplotlib assumes it is a

sequence of y values, and automatically generates the x values for

you. Since python ranges start with 0, the default x vector has the

same length as y but starts with 0. Hence the x data are

<code class="docutils literal">[0,1,2,3]</code>.

<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">plot()</code></a> is a versatile command, and will take

an arbitrary number of arguments. For example, to plot x versus y,

you can issue the command:

<div class="highlight-default"><div class="highlight"><pre>.. code-block:: python

</pre></div>

</div>

<h3>Formatting the style of your plot<a class="headerlink" href="#formatting-the-style-of-your-plot" title="Permalink to this headline">¶</a></h3>

For every x, y pair of arguments, there is an optional third argument

which is the format string that indicates the color and line type of

the plot. The letters and symbols of the format string are from

MATLAB, and you concatenate a color string with a line style string.

The default format string is ‘b-‘, which is a solid blue line. For

example, to plot the above with red circles, you would issue

<div class="highlight-python"><div class="highlight"><pre><a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot">plt.plot</a>([1, 2, 3, 4], [1, 4, 9, 16], 'ro')

<a href="../../api/axis_api.html#matplotlib.axis" title="View documentation for matplotlib.pyplot.axis">plt.axis</a>([0, 6, 0, 20])

</pre></div>

</div>

See the <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">plot()</code></a> documentation for a complete

list of line styles and format strings. The

<a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.axis.html#matplotlib.pyplot.axis" title="matplotlib.pyplot.axis"><code class="xref py py-func docutils literal">axis()</code></a> command in the example above takes a

list of <code class="docutils literal">[xmin, xmax, ymin, ymax]</code> and specifies the viewport of the

axes.

If matplotlib were limited to working with lists, it would be fairly

useless for numeric processing. Generally, you will use <a class="reference external" href="http://www.numpy.org">numpy</a> arrays. In fact, all sequences are

converted to numpy arrays internally. The example below illustrates a

plotting several lines with different format styles in one command

using arrays.

<div class="highlight-python"><div class="highlight"><pre>import numpy as np

# evenly sampled time at 200ms intervals

t = np.arange(0., 5., 0.2)

# red dashes, blue squares and green triangles

<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot">plt.plot</a>(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')

</pre></div>

</div>

</div>

<h2>Plotting with keyword strings<a class="headerlink" href="#plotting-with-keyword-strings" title="Permalink to this headline">¶</a></h2>

There are some instances where you have data in a format that lets you

access particular variables with strings. For example, with

<a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html#numpy.recarray" title="(in NumPy v1.13)"><code class="xref py py-class docutils literal">numpy.recarray</code></a> or <a class="reference external" href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v0.20.3)"><code class="xref py py-class docutils literal">pandas.DataFrame</code></a>.

Matplotlib allows you provide such an object with

the <code class="docutils literal">data</code> keyword argument. If provided, then you may generate plots with

the strings corresponding to these variables.

<div class="highlight-python"><div class="highlight"><pre>data = {'a': np.arange(50),

'c': np.random.randint(0, 50, 50),

'd': np.random.randn(50)}

data['b'] = data['a'] + 10 * np.random.randn(50)

data['d'] = np.abs(data['d']) * 100

<a href="../../api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter" title="View documentation for matplotlib.pyplot.scatter">plt.scatter</a>('a', 'b', c='c', s='d', data=data)

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

</pre></div>

</div>

</div>

<h2>Plotting with categorical variables<a class="headerlink" href="#plotting-with-categorical-variables" title="Permalink to this headline">¶</a></h2>

It is also possible to create a plot using categorical variables.

Matplotlib allows you to pass categorical variables directly to

many plotting functions. For example:

<div class="highlight-python"><div class="highlight"><pre>names = ['group_a', 'group_b', 'group_c']

values = [1, 10, 100]

<a href="../../api/_as_gen/matplotlib.figure.figaspect.html#matplotlib.figure" title="View documentation for matplotlib.pyplot.figure">plt.figure</a>(1, figsize=(9, 3))

<a href="../../api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="View documentation for matplotlib.pyplot.subplot">plt.subplot</a>(131)

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

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

</pre></div>

</div>

</div>

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

Lines have many attributes that you can set: linewidth, dash style,

antialiased, etc; see <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">matplotlib.lines.Line2D</code></a>. There are

several ways to set line properties

<ul>

<li>Use keyword args:

<div class="highlight-default"><div class="highlight"><pre><a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot">plt.plot</a>(x, y, linewidth=2.0)

</pre></div>

</div>

</li>

<li>Use the setter methods of a <code class="docutils literal">Line2D</code> instance. <code class="docutils literal">plot</code> returns a list

of <code class="docutils literal">Line2D</code> objects; e.g., <code class="docutils literal">line1, line2 = plot(x1, y1, x2, y2)</code>. In the code

below we will suppose that we have only

one line so that the list returned is of length 1. We use tuple unpacking with

<code class="docutils literal">line,</code> to get the first element of that list:

<div class="highlight-default"><div class="highlight"><pre>line, = <a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot">plt.plot</a>(x, y, '-')

line.set_antialiased(False) # turn off antialising

</pre></div>

</div>

</li>

<li>Use the <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.setp.html#matplotlib.pyplot.setp" title="matplotlib.pyplot.setp"><code class="xref py py-func docutils literal">setp()</code></a> command. The example below

uses a MATLAB-style command to set multiple properties

on a list of lines. <code class="docutils literal">setp</code> works transparently with a list of objects

or a single object. You can either use python keyword arguments or

MATLAB-style string/value pairs:

<div class="highlight-default"><div class="highlight"><pre>lines = <a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot">plt.plot</a>(x1, y1, x2, y2)

# use keyword args

plt.setp(lines, color='r', linewidth=2.0)

# or MATLAB style string value pairs

plt.setp(lines, 'color', 'r', 'linewidth', 2.0)

</pre></div>

</div>

</li>

</ul>

Here are the available <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">Line2D</code></a> properties.

</colgroup>

<tr class="row-odd"><th class="head">Property</th>

<th class="head">Value Type</th>

</tr>

</thead>

<tr class="row-even"><td>alpha</td>

<td>float</td>

</tr>

<tr class="row-odd"><td>animated</td>

<td>[True | False]</td>

</tr>

<tr class="row-even"><td>antialiased or aa</td>

<td>[True | False]</td>

</tr>

<td>a matplotlib.transform.Bbox instance</td>

</tr>

<td>[True | False]</td>

</tr>

<td>a Path instance and a Transform instance, a Patch</td>

</tr>

<tr class="row-even"><td>color or c</td>

<td>any matplotlib color</td>

</tr>

<tr class="row-odd"><td>contains</td>

<td>the hit testing function</td>

</tr>

<tr class="row-even"><td>dash_capstyle</td>

<td>[<code class="docutils literal">'butt'</code> | <code class="docutils literal">'round'</code> | <code class="docutils literal">'projecting'</code>]</td>

</tr>

<tr class="row-odd"><td>dash_joinstyle</td>

<td>[<code class="docutils literal">'miter'</code> | <code class="docutils literal">'round'</code> | <code class="docutils literal">'bevel'</code>]</td>

</tr>

<tr class="row-even"><td>dashes</td>

<td>sequence of on/off ink in points</td>

</tr>

<td>(np.array xdata, np.array ydata)</td>

</tr>

<tr class="row-even"><td>figure</td>

<td>a matplotlib.figure.Figure instance</td>

</tr>

<tr class="row-odd"><td>label</td>

<td>any string</td>

</tr>

<tr class="row-even"><td>linestyle or ls</td>

</tr>

<tr class="row-odd"><td>linewidth or lw</td>

<td>float value in points</td>

</tr>

<td>[True | False]</td>

</tr>

<tr class="row-odd"><td>marker</td>

</tr>

<tr class="row-even"><td>markeredgecolor or mec</td>

<td>any matplotlib color</td>

</tr>

<tr class="row-odd"><td>markeredgewidth or mew</td>

<td>float value in points</td>

</tr>

<tr class="row-even"><td>markerfacecolor or mfc</td>

<td>any matplotlib color</td>

</tr>

<tr class="row-odd"><td>markersize or ms</td>

<td>float</td>

</tr>

<tr class="row-even"><td>markevery</td>

<td>[ None | integer | (startind, stride) ]</td>

</tr>

<tr class="row-odd"><td>picker</td>

<td>used in interactive line selection</td>

</tr>

<tr class="row-even"><td>pickradius</td>

<td>the line pick selection radius</td>

</tr>

<tr class="row-odd"><td>solid_capstyle</td>

</tr>

<tr class="row-even"><td>solid_joinstyle</td>

</tr>

<tr class="row-odd"><td>transform</td>

<td>a matplotlib.transforms.Transform instance</td>

</tr>

<tr class="row-even"><td>visible</td>

<td>[True | False]</td>

</tr>

<tr class="row-odd"><td>xdata</td>

<td>np.array</td>

</tr>

<tr class="row-even"><td>ydata</td>

<td>np.array</td>

</tr>

<tr class="row-odd"><td>zorder</td>

<td>any number</td>

</tr>

</tbody>

</table>

To get a list of settable line properties, call the

<a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.setp.html#matplotlib.pyplot.setp" title="matplotlib.pyplot.setp"><code class="xref py py-func docutils literal">setp()</code></a> function with a line or lines

as argument

<div class="highlight-ipython"><div class="highlight"><pre>In [69]: lines = <a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot">plt.plot</a>([1, 2, 3])

In [70]: plt.setp(lines)

alpha: float

animated: [True | False]

antialiased or aa: [True | False]

...snip

</pre></div>

</div>

<h2>Working with multiple figures and axes<a class="headerlink" href="#working-with-multiple-figures-and-axes" title="Permalink to this headline">¶</a></h2>

MATLAB, and <code class="xref py py-mod docutils literal">pyplot</code>, have the concept of the current

figure and the current axes. All plotting commands apply to the

current axes. The function <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.gca.html#matplotlib.pyplot.gca" title="matplotlib.pyplot.gca"><code class="xref py py-func docutils literal">gca()</code></a> returns the

current axes (a <a class="reference internal" href="../../api/axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-class docutils literal">matplotlib.axes.Axes</code></a> instance), and

<a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.gcf.html#matplotlib.pyplot.gcf" title="matplotlib.pyplot.gcf"><code class="xref py py-func docutils literal">gcf()</code></a> returns the current figure

(<a class="reference internal" href="../../api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><code class="xref py py-class docutils literal">matplotlib.figure.Figure</code></a> instance). Normally, you don’t have

to worry about this, because it is all taken care of behind the

scenes. Below is a script to create two subplots.

<div class="highlight-python"><div class="highlight"><pre>def f(t):

return np.exp(-t) * np.cos(2*np.pi*t)

t1 = np.arange(0.0, 5.0, 0.1)

t2 = np.arange(0.0, 5.0, 0.02)

<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot">plt.plot</a>(t1, f(t1), 'bo', t2, f(t2), 'k')

<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot">plt.plot</a>(t2, np.cos(2*np.pi*t2), 'r--')

</pre></div>

</div>

The <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure"><code class="xref py py-func docutils literal">figure()</code></a> command here is optional because

<code class="docutils literal">figure(1)</code> will be created by default, just as a <code class="docutils literal">subplot(111)</code>

will be created by default if you don’t manually specify any axes. 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">subplot()</code></a> command specifies <code class="docutils literal">numrows,

numcols, fignum</code> where <code class="docutils literal">fignum</code> ranges from 1 to

<code class="docutils literal">numrows*numcols</code>. The commas in the <code class="docutils literal">subplot</code> command are

optional if <code class="docutils literal">numrows*numcols<10</code>. So <code class="docutils literal">subplot(211)</code> is identical

to <code class="docutils literal">subplot(2, 1, 1)</code>.

You can create an arbitrary number of subplots

and axes. If you want to place an axes manually, i.e., not on a

rectangular grid, use the <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.axes.html#matplotlib.pyplot.axes" title="matplotlib.pyplot.axes"><code class="xref py py-func docutils literal">axes()</code></a> command,

which allows you to specify the location as <code class="docutils literal">axes([left, bottom,

width, height])</code> where all values are in fractional (0 to 1)

coordinates. See <a class="reference internal" href="../../gallery/pylab_examples/axes_demo.html#sphx-glr-gallery-pylab-examples-axes-demo-py">Axes Demo</a> for an example of

placing axes manually and <a class="reference internal" href="../../gallery/subplots_axes_and_figures/subplot_demo.html#sphx-glr-gallery-subplots-axes-and-figures-subplot-demo-py">Subplot Demo</a> for an

example with lots of subplots.

You can create multiple figures by using multiple

<a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure"><code class="xref py py-func docutils literal">figure()</code></a> calls with an increasing figure

number. Of course, each figure can contain as many axes and subplots

as your heart desires:

<div class="highlight-default"><div class="highlight"><pre>import matplotlib.pyplot as plt

<a href="../../api/_as_gen/matplotlib.figure.figaspect.html#matplotlib.figure" title="View documentation for matplotlib.pyplot.figure">plt.figure</a>(1) # the first figure

<a href="../../api/_as_gen/matplotlib.figure.figaspect.html#matplotlib.figure" title="View documentation for matplotlib.pyplot.figure">plt.figure</a>(2) # a second figure

<a href="../../api/_as_gen/matplotlib.figure.figaspect.html#matplotlib.figure" title="View documentation for matplotlib.pyplot.figure">plt.figure</a>(1) # figure 1 current; subplot(212) still current

<a href="../../api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="View documentation for matplotlib.pyplot.title">plt.title</a>('Easy as 1, 2, 3') # subplot 211 title

</pre></div>

</div>

You can clear the current figure with <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.clf.html#matplotlib.pyplot.clf" title="matplotlib.pyplot.clf"><code class="xref py py-func docutils literal">clf()</code></a>

and the current axes with <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.cla.html#matplotlib.pyplot.cla" title="matplotlib.pyplot.cla"><code class="xref py py-func docutils literal">cla()</code></a>. If you find

it annoying that states (specifically the current image, figure and axes)

are being maintained for you behind the scenes, don’t despair: this is just a thin

stateful wrapper around an object oriented API, which you can use

instead (see <a class="reference internal" href="../02_intermediate/artists.html#sphx-glr-tutorials-02-intermediate-artists-py">Artist tutorial</a>)

If you are making lots of figures, you need to be aware of one

more thing: the memory required for a figure is not completely

released until the figure is explicitly closed with

<a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.close.html#matplotlib.pyplot.close" title="matplotlib.pyplot.close"><code class="xref py py-func docutils literal">close()</code></a>. Deleting all references to the

figure, and/or using the window manager to kill the window in which

the figure appears on the screen, is not enough, because pyplot

maintains internal references until <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.close.html#matplotlib.pyplot.close" title="matplotlib.pyplot.close"><code class="xref py py-func docutils literal">close()</code></a>

is called.

</div>

<h2>Working with text<a class="headerlink" href="#working-with-text" title="Permalink to this headline">¶</a></h2>

The <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.text.html#matplotlib.pyplot.text" title="matplotlib.pyplot.text"><code class="xref py py-func docutils literal">text()</code></a> command can be used to add text in

an arbitrary location, and the <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="matplotlib.pyplot.xlabel"><code class="xref py py-func docutils literal">xlabel()</code></a>,

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

are used to add text in the indicated locations (see <a class="reference internal" href="../text/text_intro.html#sphx-glr-tutorials-text-text-intro-py">Text introduction</a>

for a more detailed example)

<div class="highlight-python"><div class="highlight"><pre>mu, sigma = 100, 15

x = mu + sigma * np.random.randn(10000)

# the histogram of the data

n, bins, patches = <a href="../../api/_as_gen/matplotlib.pyplot.hist.html#matplotlib.pyplot.hist" title="View documentation for matplotlib.pyplot.hist">plt.hist</a>(x, 50, normed=1, facecolor='g', alpha=0.75)

<a href="../../api/text_api.html#matplotlib.text" title="View documentation for matplotlib.pyplot.text">plt.text</a>(60, .025, r'$\mu=100,\ \sigma=15$')

<a href="../../api/axis_api.html#matplotlib.axis" title="View documentation for matplotlib.pyplot.axis">plt.axis</a>([40, 160, 0, 0.03])

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

</pre></div>

</div>

All of the <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.text.html#matplotlib.pyplot.text" title="matplotlib.pyplot.text"><code class="xref py py-func docutils literal">text()</code></a> commands return an

<a class="reference internal" href="../../api/text_api.html#matplotlib.text.Text" title="matplotlib.text.Text"><code class="xref py py-class docutils literal">matplotlib.text.Text</code></a> instance. Just as with with lines

above, you can customize the properties by passing keyword arguments

into the text functions or using <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.setp.html#matplotlib.pyplot.setp" title="matplotlib.pyplot.setp"><code class="xref py py-func docutils literal">setp()</code></a>:

<div class="highlight-default"><div class="highlight"><pre>t = <a href="../../api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="View documentation for matplotlib.pyplot.xlabel">plt.xlabel</a>('my data', fontsize=14, color='red')

</pre></div>

</div>

These properties are covered in more detail in <a class="reference internal" href="../text/text_props.html#sphx-glr-tutorials-text-text-props-py">Text properties and layout</a>.

<h3>Using mathematical expressions in text<a class="headerlink" href="#using-mathematical-expressions-in-text" title="Permalink to this headline">¶</a></h3>

matplotlib accepts TeX equation expressions in any text expression.

For example to write the expression <img src="../../_images/mathmpl/math-4cd9a23707.png" style="position: relative; bottom: -6px"/> in the title,

you can write a TeX expression surrounded by dollar signs:

<div class="highlight-default"><div class="highlight"><pre><a href="../../api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="View documentation for matplotlib.pyplot.title">plt.title</a>(r'$\sigma_i=15$')

</pre></div>

</div>

The <code class="docutils literal">r</code> preceding the title string is important – it signifies

that the string is a raw string and not to treat backslashes as

python escapes. matplotlib has a built-in TeX expression parser and

layout engine, and ships its own math fonts – for details see

<a class="reference internal" href="../text/mathtext.html#sphx-glr-tutorials-text-mathtext-py">Writing mathematical expressions</a>. Thus you can use mathematical text across platforms

without requiring a TeX installation. For those who have LaTeX and

dvipng installed, you can also use LaTeX to format your text and

incorporate the output directly into your display figures or saved

postscript – see <a class="reference internal" href="../text/usetex.html#sphx-glr-tutorials-text-usetex-py">Text rendering With LaTeX</a>.

</div>

<h3>Annotating text<a class="headerlink" href="#annotating-text" title="Permalink to this headline">¶</a></h3>

The uses of the basic <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.text.html#matplotlib.pyplot.text" title="matplotlib.pyplot.text"><code class="xref py py-func docutils literal">text()</code></a> command above

place text at an arbitrary position on the Axes. A common use for

text is to annotate some feature of the plot, and the

<a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.annotate.html#matplotlib.pyplot.annotate" title="matplotlib.pyplot.annotate"><code class="xref py py-func docutils literal">annotate()</code></a> method provides helper

functionality to make annotations easy. In an annotation, there are

two points to consider: the location being annotated represented by

the argument <code class="docutils literal">xy</code> and the location of the text <code class="docutils literal">xytext</code>. Both of

these arguments are <code class="docutils literal">(x,y)</code> tuples.

<div class="highlight-python"><div class="highlight"><pre>ax = <a href="../../api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="View documentation for matplotlib.pyplot.subplot">plt.subplot</a>(111)

t = np.arange(0.0, 5.0, 0.01)

s = np.cos(2*np.pi*t)

line, = <a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot">plt.plot</a>(t, s, lw=2)

<a href="../../api/_as_gen/matplotlib.pyplot.annotate.html#matplotlib.pyplot.annotate" title="View documentation for matplotlib.pyplot.annotate">plt.annotate</a>('local max', xy=(2, 1), xytext=(3, 1.5),

arrowprops=dict(facecolor='black', shrink=0.05),

)

<a href="../../api/_as_gen/matplotlib.pyplot.ylim.html#matplotlib.pyplot.ylim" title="View documentation for matplotlib.pyplot.ylim">plt.ylim</a>(-2, 2)

</pre></div>

</div>

In this basic example, both the <code class="docutils literal">xy</code> (arrow tip) and <code class="docutils literal">xytext</code>

locations (text location) are in data coordinates. There are a

variety of other coordinate systems one can choose – see

<a class="reference internal" href="../text/annotations.html#annotations-tutorial">Basic annotation</a> and <a class="reference internal" href="../text/annotations.html#plotting-guide-annotation">Advanced Annotation</a> for

details. More examples can be found in

<a class="reference internal" href="../../gallery/text_labels_and_annotations/annotation_demo.html#sphx-glr-gallery-text-labels-and-annotations-annotation-demo-py">Annotating Plots</a>.

</div>

<h2>Logarithmic and other nonlinear axes<a class="headerlink" href="#logarithmic-and-other-nonlinear-axes" title="Permalink to this headline">¶</a></h2>

<code class="xref py py-mod docutils literal">matplotlib.pyplot</code> supports not only linear axis scales, but also

logarithmic and logit scales. This is commonly used if data spans many orders

of magnitude. Changing the scale of an axis is easy:

<div>plt.xscale(‘log’)</div></blockquote>

An example of four plots with the same data and different scales for the y axis

is shown below.

<div class="highlight-python"><div class="highlight"><pre>from matplotlib.ticker import <a href="../../api/ticker_api.html#matplotlib.ticker.NullFormatter" title="View documentation for matplotlib.ticker.NullFormatter">NullFormatter</a> # useful for `logit` scale

# Fixing random state for reproducibility

np.random.seed(19680801)

# make up some data in the interval ]0, 1[

y = np.random.normal(loc=0.5, scale=0.4, size=1000)

y = y[(y > 0) & (y < 1)]

y.sort()

x = np.arange(len(y))

# plot with various axes scales

# linear

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

# log

# symmetric log

# logit

# Format the minor tick labels of the y-axis into empty strings with

# `NullFormatter`, to avoid cumbering the axis with too many labels.

<a href="../../api/_as_gen/matplotlib.pyplot.gca.html#matplotlib.pyplot.gca" title="View documentation for matplotlib.pyplot.gca">plt.gca</a>().yaxis.set_minor_formatter(<a href="../../api/ticker_api.html#matplotlib.ticker.NullFormatter" title="View documentation for matplotlib.ticker.NullFormatter">NullFormatter</a>())

# Adjust the subplot layout, because the logit one may take more space

# than usual, due to y-tick labels like "1 - 10^{-3}"

<a href="../../api/_as_gen/matplotlib.pyplot.subplots_adjust.html#matplotlib.pyplot.subplots_adjust" title="View documentation for matplotlib.pyplot.subplots_adjust">plt.subplots_adjust</a>(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,

wspace=0.35)

</pre></div>

</div>

It is also possible to add your own scale, see <a class="reference internal" href="../../devel/add_new_projection.html#adding-new-scales">Developer’s guide for creating scales and transformations</a> for

details.

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