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.. redirect-from:: /tutorials/introductory/pyplot
.. _pyplot_tutorial:
===============
Pyplot tutorial
===============
An introduction to the pyplot interface.  Please also see
:ref:`quick_start` for an overview of how Matplotlib
works and :ref:`api_interfaces` for an explanation of the trade-offs between the
supported user APIs.
# Introduction to pyplot
# ======================
# :mod:`matplotlib.pyplot` is a collection of functions that make matplotlib
# work like MATLAB.  Each ``pyplot`` 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 :mod:`matplotlib.pyplot` 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 we use uppercase
# Axes to refer to the `~.axes.Axes` concept, which is a central
# :ref:`part of a figure <figure_parts>`
# and not only the plural of *axis*).
# .. note::
#    The implicit pyplot API is generally less verbose but also not as flexible as the
#    explicit API.  Most of the function calls you see here can also be called
#    as methods from an ``Axes`` object. We recommend browsing the tutorials
#    and examples to see how this works. See :ref:`api_interfaces` for an
#    explanation of the trade-off of the supported user APIs.
# Generating visualizations with pyplot is very quick:
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
# 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
# `~.pyplot.plot`, 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; therefore, the x data are
# ``[0, 1, 2, 3]``.
# `~.pyplot.plot` is a versatile function, and will take an arbitrary number of
# arguments.  For example, to plot x versus y, you can write:
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
# Formatting the style of your plot
# ---------------------------------
# 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
plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')
plt.axis((0, 6, 0, 20))
# See the `~.pyplot.plot` documentation for a complete
# list of line styles and format strings.  The
# `~.pyplot.axis` function in the example above takes a
# list of ``[xmin, xmax, ymin, ymax]`` and specifies the viewport of the
# If matplotlib were limited to working with lists, it would be fairly
# useless for numeric processing.  Generally, you will use `numpy
# <https://numpy.org/>`_ arrays.  In fact, all sequences are
# converted to numpy arrays internally.  The example below illustrates
# plotting several lines with different format styles in one function call
# using arrays.
import numpy as np
# evenly sampled time at 200ms intervals
t = np.arange(0., 5., 0.2)
# red dashes, blue squares and green triangles
plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
# .. _plotting-with-keywords:
# Plotting with keyword strings
# =============================
# There are some instances where you have data in a format that lets you
# access particular variables with strings. For example, with `structured arrays`_
# or `pandas.DataFrame`.
# .. _structured arrays: https://numpy.org/doc/stable/user/basics.rec.html#structured-arrays
# Matplotlib allows you to provide such an object with
# the ``data`` keyword argument. If provided, then you may generate plots with
# the strings corresponding to these variables.
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
plt.scatter('a', 'b', c='c', s='d', data=data)
plt.xlabel('entry a')
plt.ylabel('entry b')
# .. _plotting-with-categorical-vars:
# Plotting with categorical variables
# ===================================
# 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:
names = ['group_a', 'group_b', 'group_c']
values = [1, 10, 100]
plt.figure(figsize=(9, 3))
plt.subplot(131)
plt.bar(names, values)
plt.subplot(132)
plt.scatter(names, values)
plt.subplot(133)
plt.plot(names, values)
plt.suptitle('Categorical Plotting')
# .. _controlling-line-properties:
# Controlling line properties
# ===========================
# Lines have many attributes that you can set: linewidth, dash style,
# antialiased, etc; see `matplotlib.lines.Line2D`.  There are
# several ways to set line properties
# * Use keyword arguments::
#       plt.plot(x, y, linewidth=2.0)
# * Use the setter methods of a ``Line2D`` instance.  ``plot`` returns a list
#   of ``Line2D`` objects; e.g., ``line1, line2 = plot(x1, y1, x2, y2)``.  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
#   ``line,`` to get the first element of that list::
#       line, = plt.plot(x, y, '-')
#       line.set_antialiased(False) # turn off antialiasing
# * Use `~.pyplot.setp`.  The example below
#   uses a MATLAB-style function to set multiple properties
#   on a list of lines.  ``setp`` works transparently with a list of objects
#   or a single object.  You can either use python keyword arguments or
#   MATLAB-style string/value pairs::
#       lines = plt.plot(x1, y1, x2, y2)
#       # use keyword arguments
#       plt.setp(lines, color='r', linewidth=2.0)
#       # or MATLAB style string value pairs
#       plt.setp(lines, 'color', 'r', 'linewidth', 2.0)
# Here are the available `~.lines.Line2D` properties.
# ======================  ==================================================
# Property                Value Type
# ======================  ==================================================
# alpha                   float
# animated                [True | False]
# antialiased or aa       [True | False]
# clip_box                a matplotlib.transform.Bbox instance
# clip_on                 [True | False]
# clip_path               a Path instance and a Transform instance, a Patch
# color or c              any matplotlib color
# contains                the hit testing function
# dash_capstyle           [``'butt'`` | ``'round'`` | ``'projecting'``]
# dash_joinstyle          [``'miter'`` | ``'round'`` | ``'bevel'``]
# dashes                  sequence of on/off ink in points
# data                    (np.array xdata, np.array ydata)
# figure                  a matplotlib.figure.Figure instance
# label                   any string
# linestyle or ls         [ ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'steps'`` | ...]
# linewidth or lw         float value in points
# marker                  [ ``'+'`` | ``','`` | ``'.'`` | ``'1'`` | ``'2'`` | ``'3'`` | ``'4'`` ]
# markeredgecolor or mec  any matplotlib color
# markeredgewidth or mew  float value in points
# markerfacecolor or mfc  any matplotlib color
# markersize or ms        float
# markevery               [ None | integer | (startind, stride) ]
# picker                  used in interactive line selection
# pickradius              the line pick selection radius
# solid_capstyle          [``'butt'`` | ``'round'`` | ``'projecting'``]
# solid_joinstyle         [``'miter'`` | ``'round'`` | ``'bevel'``]
# transform               a matplotlib.transforms.Transform instance
# visible                 [True | False]
# xdata                   np.array
# ydata                   np.array
# zorder                  any number
# ======================  ==================================================
# To get a list of settable line properties, call the
# `~.pyplot.setp` function with a line or lines as argument
# .. sourcecode:: ipython
#     In [69]: lines = plt.plot([1, 2, 3])
#     In [70]: plt.setp(lines)
#       alpha: float
#       animated: [True | False]
#       antialiased or aa: [True | False]
#       ...snip
# .. _multiple-figs-axes:
# Working with multiple figures and Axes
# ======================================
# MATLAB, and :mod:`.pyplot`, have the concept of the current figure
# and the current Axes.  All plotting functions apply to the current
# Axes.  The function `~.pyplot.gca` returns the current Axes (a
# `matplotlib.axes.Axes` instance), and `~.pyplot.gcf` returns the current
# figure (a `matplotlib.figure.Figure` 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.
    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)
plt.figure()
plt.subplot(211)
plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')
plt.subplot(212)
plt.plot(t2, np.cos(2*np.pi*t2), 'r--')
# The `~.pyplot.figure` call here is optional because a figure will be created
# if none exists, just as an Axes will be created (equivalent to an explicit
# ``subplot()`` call) if none exists.
# The `~.pyplot.subplot` call specifies ``numrows,
# numcols, plot_number`` where ``plot_number`` ranges from 1 to
# ``numrows*numcols``.  The commas in the ``subplot`` call are
# optional if ``numrows*numcols<10``.  So ``subplot(211)`` is identical
# to ``subplot(2, 1, 1)``.
# 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 `~.pyplot.axes`,
# which allows you to specify the location as ``axes([left, bottom,
# width, height])`` where all values are in fractional (0 to 1)
# coordinates.  See :doc:`/gallery/subplots_axes_and_figures/axes_demo` for an example of
# placing Axes manually and :doc:`/gallery/subplots_axes_and_figures/subplot` for an
# example with lots of subplots.
# You can create multiple figures by using multiple
# `~.pyplot.figure` calls with an increasing figure
# number.  Of course, each figure can contain as many Axes and subplots
# as your heart desires::
#     import matplotlib.pyplot as plt
#     plt.figure(1)                # the first figure
#     plt.subplot(211)             # the first subplot in the first figure
#     plt.plot([1, 2, 3])
#     plt.subplot(212)             # the second subplot in the first figure
#     plt.plot([4, 5, 6])
#     plt.figure(2)                # a second figure
#     plt.plot([4, 5, 6])          # creates a subplot() by default
#     plt.figure(1)                # first figure current;
#                                  # subplot(212) still current
#     plt.subplot(211)             # make subplot(211) in the first figure
#                                  # current
#     plt.title('Easy as 1, 2, 3') # subplot 211 title
# You can clear the current figure with `~.pyplot.clf`
# and the current Axes with `~.pyplot.cla`.  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 :ref:`artists_tutorial`)
# 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
# `~.pyplot.close`.  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 `~.pyplot.close`
# is called.
# .. _working-with-text:
# Working with text
# =================
# `~.pyplot.text` can be used to add text in an arbitrary location, and
# `~.pyplot.xlabel`, `~.pyplot.ylabel` and `~.pyplot.title` are used to add
# text in the indicated locations (see :ref:`text_intro` for a
# more detailed example)
mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)
# the histogram of the data
n, bins, patches = plt.hist(x, 50, density=True, facecolor='g', alpha=0.75)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title('Histogram of IQ')
plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
# All of the `~.pyplot.text` functions return a `matplotlib.text.Text`
# instance.  Just as with lines above, you can customize the properties by
# passing keyword arguments into the text functions or using `~.pyplot.setp`::
#   t = plt.xlabel('my data', fontsize=14, color='red')
# These properties are covered in more detail in :ref:`text_props`.
# Using mathematical expressions in text
# --------------------------------------
# Matplotlib accepts TeX equation expressions in any text expression.
# For example to write the expression :math:`\sigma_i=15` in the title,
# you can write a TeX expression surrounded by dollar signs::
#     plt.title(r'$\sigma_i=15$')
# The ``r`` 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
# :ref:`mathtext`.  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 :ref:`usetex`.
# Annotating text
# ---------------
# The uses of the basic `~.pyplot.text` function 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
# `~.pyplot.annotate` 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 ``xy`` and the location of the text ``xytext``.  Both of
# these arguments are ``(x, y)`` tuples.
ax = plt.subplot()
t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2*np.pi*t)
line, = plt.plot(t, s, lw=2)
plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
             arrowprops=dict(facecolor='black', shrink=0.05),
plt.ylim(-2, 2)
# In this basic example, both the ``xy`` (arrow tip) and ``xytext``
# locations (text location) are in data coordinates.  There are a
# variety of other coordinate systems one can choose -- see
# :ref:`annotations-tutorial` and :ref:`plotting-guide-annotation` for
# details.  More examples can be found in
# :doc:`/gallery/text_labels_and_annotations/annotation_demo`.
# Logarithmic and other nonlinear axes
# ====================================
# :mod:`matplotlib.pyplot` 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::
#     plt.xscale('log')
# An example of four plots with the same data and different scales for the y-axis
# is shown below.
# Fixing random state for reproducibility
np.random.seed(19680801)
# make up some data in the open interval (0, 1)
y = np.random.normal(loc=0.5, scale=0.4, size=1000)
y = y[(y > 0) & (y < 1)]
x = np.arange(len(y))
# plot with various axes scales
plt.figure()
plt.subplot(221)
plt.plot(x, y)
plt.yscale('linear')
plt.title('linear')
plt.grid(True)
plt.subplot(222)
plt.plot(x, y)
plt.yscale('log')
plt.title('log')
plt.grid(True)
# symmetric log
plt.subplot(223)
plt.plot(x, y - y.mean())
plt.yscale('symlog', linthresh=0.01)
plt.title('symlog')
plt.grid(True)
plt.subplot(224)
plt.plot(x, y)
plt.yscale('logit')
plt.title('logit')
plt.grid(True)
# Adjust the subplot layout, because the logit one may take more space
# than usual, due to y-tick labels like "1 - 10^{-3}"
plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,
                    wspace=0.35)
# It is also possible to add your own scale, see `matplotlib.scale` for
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