matplotlib/tutorials/introductory/quick_start.py at v3.7.x · matplotlib/matplotlib · GitHub

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"""

*****************

Quick start guide

*****************

This tutorial covers some basic usage patterns and best practices to

help you get started with Matplotlib.

.. redirect-from:: /tutorials/introductory/usage

"""

# sphinx_gallery_thumbnail_number = 3

import matplotlib as mpl

import matplotlib.pyplot as plt

import numpy as np

##############################################################################

# A simple example

# ================

# Matplotlib graphs your data on `.Figure`\s (e.g., windows, Jupyter

# widgets, etc.), each of which can contain one or more `~.axes.Axes`, an

# area where points can be specified in terms of x-y coordinates (or theta-r

# in a polar plot, x-y-z in a 3D plot, etc.). The simplest way of

# creating a Figure with an Axes is using `.pyplot.subplots`. We can then use

# `.Axes.plot` to draw some data on the Axes:

fig, ax = plt.subplots() # Create a figure containing a single axes.

ax.plot([1, 2, 3, 4], [1, 4, 2, 3]) # Plot some data on the axes.

# %%

# Note that to get this Figure to display, you may have to call ``plt.show()``,

# depending on your backend. For more details of Figures and backends, see

# :ref:`figure_explanation`.

# .. _figure_parts:

# Parts of a Figure

# =================

# Here are the components of a Matplotlib Figure.

# .. image:: ../../_static/anatomy.png

# :class:`~matplotlib.figure.Figure`

# ----------------------------------

# The **whole** figure. The Figure keeps

# track of all the child :class:`~matplotlib.axes.Axes`, a group of

# 'special' Artists (titles, figure legends, colorbars, etc), and

# even nested subfigures.

# The easiest way to create a new Figure is with pyplot::

# fig = plt.figure() # an empty figure with no Axes

# fig, ax = plt.subplots() # a figure with a single Axes

# fig, axs = plt.subplots(2, 2) # a figure with a 2x2 grid of Axes

# # a figure with one axes on the left, and two on the right:

# fig, axs = plt.subplot_mosaic([['left', 'right_top'],

# ['left', 'right_bottom']])

# It is often convenient to create the Axes together with the Figure, but you

# can also manually add Axes later on. Note that many

# :doc:`Matplotlib backends </users/explain/backends>` support zooming and

# panning on figure windows.

# For more on Figures, see :ref:`figure_explanation`.

# :class:`~matplotlib.axes.Axes`

# ------------------------------

# An Axes is an Artist attached to a Figure that contains a region for

# plotting data, and usually includes two (or three in the case of 3D)

# :class:`~matplotlib.axis.Axis` objects (be aware of the difference

# between **Axes** and **Axis**) that provide ticks and tick labels to

# provide scales for the data in the Axes. Each :class:`~.axes.Axes` also

# has a title

# (set via :meth:`~matplotlib.axes.Axes.set_title`), an x-label (set via

# :meth:`~matplotlib.axes.Axes.set_xlabel`), and a y-label set via

# :meth:`~matplotlib.axes.Axes.set_ylabel`).

# The :class:`~.axes.Axes` class and its member functions are the primary

# entry point to working with the OOP interface, and have most of the

# plotting methods defined on them (e.g. ``ax.plot()``, shown above, uses

# the `~.Axes.plot` method)

# :class:`~matplotlib.axis.Axis`

# ------------------------------

# These objects set the scale and limits and generate ticks (the marks

# on the Axis) and ticklabels (strings labeling the ticks). The location

# of the ticks is determined by a `~matplotlib.ticker.Locator` object and the

# ticklabel strings are formatted by a `~matplotlib.ticker.Formatter`. The

# combination of the correct `.Locator` and `.Formatter` gives very fine

# control over the tick locations and labels.

# :class:`~matplotlib.artist.Artist`

# ----------------------------------

# Basically, everything visible on the Figure is an Artist (even

# `.Figure`, `Axes <.axes.Axes>`, and `~.axis.Axis` objects). This includes

# `.Text` objects, `.Line2D` objects, :mod:`.collections` objects, `.Patch`

# objects, etc. When the Figure is rendered, all of the

# Artists are drawn to the **canvas**. Most Artists are tied to an Axes; such

# an Artist cannot be shared by multiple Axes, or moved from one to another.

# .. _input_types:

# Types of inputs to plotting functions

# =====================================

# Plotting functions expect `numpy.array` or `numpy.ma.masked_array` as

# input, or objects that can be passed to `numpy.asarray`.

# Classes that are similar to arrays ('array-like') such as `pandas`

# data objects and `numpy.matrix` may not work as intended. Common convention

# is to convert these to `numpy.array` objects prior to plotting.

# For example, to convert a `numpy.matrix` ::

# b = np.matrix([[1, 2], [3, 4]])

# b_asarray = np.asarray(b)

# Most methods will also parse an addressable object like a *dict*, a

# `numpy.recarray`, or a `pandas.DataFrame`. Matplotlib allows you to

# provide the ``data`` keyword argument and generate plots passing the

# strings corresponding to the *x* and *y* variables.

np.random.seed(19680801) # seed the random number generator.

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

fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')

ax.scatter('a', 'b', c='c', s='d', data=data)

ax.set_xlabel('entry a')

ax.set_ylabel('entry b')

##############################################################################

# .. _coding_styles:

# Coding styles

# =============

# The explicit and the implicit interfaces

# ----------------------------------------

# As noted above, there are essentially two ways to use Matplotlib:

# - Explicitly create Figures and Axes, and call methods on them (the

# "object-oriented (OO) style").

# - Rely on pyplot to implicitly create and manage the Figures and Axes, and

# use pyplot functions for plotting.

# See :ref:`api_interfaces` for an explanation of the tradeoffs between the

# implicit and explicit interfaces.

# So one can use the OO-style

x = np.linspace(0, 2, 100) # Sample data.

# Note that even in the OO-style, we use `.pyplot.figure` to create the Figure.

fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')

ax.plot(x, x, label='linear') # Plot some data on the axes.

ax.plot(x, x**2, label='quadratic') # Plot more data on the axes...

ax.plot(x, x**3, label='cubic') # ... and some more.

ax.set_xlabel('x label') # Add an x-label to the axes.

ax.set_ylabel('y label') # Add a y-label to the axes.

ax.set_title("Simple Plot") # Add a title to the axes.

ax.legend() # Add a legend.

###############################################################################

# or the pyplot-style:

x = np.linspace(0, 2, 100) # Sample data.

plt.figure(figsize=(5, 2.7), layout='constrained')

plt.plot(x, x, label='linear') # Plot some data on the (implicit) axes.

plt.plot(x, x**2, label='quadratic') # etc.

plt.plot(x, x**3, label='cubic')

plt.xlabel('x label')

plt.ylabel('y label')

plt.title("Simple Plot")

plt.legend()

###############################################################################

# (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. See the corresponding section in the gallery for more info:

# :ref:`user_interfaces`.)

# Matplotlib's documentation and examples use both the OO and the pyplot

# styles. In general, we suggest using the OO style, particularly for

# complicated plots, and functions and scripts that are intended to be reused

# as part of a larger project. However, the pyplot style can be very convenient

# for quick interactive work.

# .. note::

# You may find older examples that use the ``pylab`` interface,

# via ``from pylab import *``. This approach is strongly deprecated.

# Making a helper functions

# -------------------------

# If you need to make the same plots over and over again with different data

# sets, or want to easily wrap Matplotlib methods, use the recommended

# signature function below.

def my_plotter(ax, data1, data2, param_dict):

"""

A helper function to make a graph.

"""

out = ax.plot(data1, data2, **param_dict)

return out

###############################################################################

# which you would then use twice to populate two subplots:

data1, data2, data3, data4 = np.random.randn(4, 100) # make 4 random data sets

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(5, 2.7))

my_plotter(ax1, data1, data2, {'marker': 'x'})

my_plotter(ax2, data3, data4, {'marker': 'o'})

###############################################################################

# Note that if you want to install these as a python package, or any other

# customizations you could use one of the many templates on the web;

# Matplotlib has one at `mpl-cookiecutter

# <https://github.com/matplotlib/matplotlib-extension-cookiecutter>`_

# Styling Artists

# ===============

# Most plotting methods have styling options for the Artists, accessible either

# when a plotting method is called, or from a "setter" on the Artist. In the

# plot below we manually set the *color*, *linewidth*, and *linestyle* of the

# Artists created by `~.Axes.plot`, and we set the linestyle of the second line

# after the fact with `~.Line2D.set_linestyle`.

fig, ax = plt.subplots(figsize=(5, 2.7))

x = np.arange(len(data1))

ax.plot(x, np.cumsum(data1), color='blue', linewidth=3, linestyle='--')

l, = ax.plot(x, np.cumsum(data2), color='orange', linewidth=2)

l.set_linestyle(':')

###############################################################################

# Colors

# ------

# Matplotlib has a very flexible array of colors that are accepted for most

# Artists; see the :doc:`colors tutorial </tutorials/colors/colors>` for a

# list of specifications. Some Artists will take multiple colors. i.e. for

# a `~.Axes.scatter` plot, the edge of the markers can be different colors

# from the interior:

fig, ax = plt.subplots(figsize=(5, 2.7))

ax.scatter(data1, data2, s=50, facecolor='C0', edgecolor='k')

###############################################################################

# Linewidths, linestyles, and markersizes

# ---------------------------------------

# Line widths are typically in typographic points (1 pt = 1/72 inch) and

# available for Artists that have stroked lines. Similarly, stroked lines

# can have a linestyle. See the :doc:`linestyles example

# </gallery/lines_bars_and_markers/linestyles>`.

# Marker size depends on the method being used. `~.Axes.plot` specifies

# markersize in points, and is generally the "diameter" or width of the

# marker. `~.Axes.scatter` specifies markersize as approximately

# proportional to the visual area of the marker. There is an array of

# markerstyles available as string codes (see :mod:`~.matplotlib.markers`), or

# users can define their own `~.MarkerStyle` (see

# :doc:`/gallery/lines_bars_and_markers/marker_reference`):

fig, ax = plt.subplots(figsize=(5, 2.7))

ax.plot(data1, 'o', label='data1')

ax.plot(data2, 'd', label='data2')

ax.plot(data3, 'v', label='data3')

ax.plot(data4, 's', label='data4')

ax.legend()

###############################################################################

# Labelling plots

# ===============

# Axes labels and text

# --------------------

# `~.Axes.set_xlabel`, `~.Axes.set_ylabel`, and `~.Axes.set_title` are used to

# add text in the indicated locations (see :doc:`/tutorials/text/text_intro`

# for more discussion). Text can also be directly added to plots using

# `~.Axes.text`:

mu, sigma = 115, 15

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

fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')

# the histogram of the data

n, bins, patches = ax.hist(x, 50, density=True, facecolor='C0', alpha=0.75)

ax.set_xlabel('Length [cm]')

ax.set_ylabel('Probability')

ax.set_title('Aardvark lengths\n (not really)')

ax.text(75, .025, r'$\mu=115,\ \sigma=15$')

ax.axis([55, 175, 0, 0.03])

ax.grid(True)

###############################################################################

# All of the `~.Axes.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::

# t = ax.set_xlabel('my data', fontsize=14, color='red')

# These properties are covered in more detail in

# :doc:`/tutorials/text/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::

# ax.set_title(r'$\sigma_i=15$')

# where the ``r`` preceding the title string 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

# :doc:`/tutorials/text/mathtext`. You can also use LaTeX directly to format

# your text and incorporate the output directly into your display figures or

# saved postscript – see :doc:`/tutorials/text/usetex`.

# Annotations

# -----------

# We can also annotate points on a plot, often by connecting an arrow pointing

# to *xy*, to a piece of text at *xytext*:

fig, ax = plt.subplots(figsize=(5, 2.7))

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

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

line, = ax.plot(t, s, lw=2)

ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),

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

ax.set_ylim(-2, 2)

###############################################################################

# In this basic example, both *xy* and *xytext* 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 also can be found in

# :doc:`/gallery/text_labels_and_annotations/annotation_demo`.

# Legends

# -------

# Often we want to identify lines or markers with a `.Axes.legend`:

fig, ax = plt.subplots(figsize=(5, 2.7))

ax.plot(np.arange(len(data1)), data1, label='data1')

ax.plot(np.arange(len(data2)), data2, label='data2')

ax.plot(np.arange(len(data3)), data3, 'd', label='data3')

ax.legend()

##############################################################################

# Legends in Matplotlib are quite flexible in layout, placement, and what

# Artists they can represent. They are discussed in detail in

# :doc:`/tutorials/intermediate/legend_guide`.

# Axis scales and ticks

# =====================

# Each Axes has two (or three) `~.axis.Axis` objects representing the x- and

# y-axis. These control the *scale* of the Axis, the tick *locators* and the

# tick *formatters*. Additional Axes can be attached to display further Axis

# objects.

# Scales

# ------

# In addition to the linear scale, Matplotlib supplies non-linear scales,

# such as a log-scale. Since log-scales are used so much there are also

# direct methods like `~.Axes.loglog`, `~.Axes.semilogx`, and

# `~.Axes.semilogy`. There are a number of scales (see

# :doc:`/gallery/scales/scales` for other examples). Here we set the scale

# manually:

fig, axs = plt.subplots(1, 2, figsize=(5, 2.7), layout='constrained')

xdata = np.arange(len(data1)) # make an ordinal for this

data = 10**data1

axs[0].plot(xdata, data)

axs[1].set_yscale('log')

axs[1].plot(xdata, data)

##############################################################################

# The scale sets the mapping from data values to spacing along the Axis. This

# happens in both directions, and gets combined into a *transform*, which

# is the way that Matplotlib maps from data coordinates to Axes, Figure, or

# screen coordinates. See :doc:`/tutorials/advanced/transforms_tutorial`.

# Tick locators and formatters

# ----------------------------

# Each Axis has a tick *locator* and *formatter* that choose where along the

# Axis objects to put tick marks. A simple interface to this is

# `~.Axes.set_xticks`:

fig, axs = plt.subplots(2, 1, layout='constrained')

axs[0].plot(xdata, data1)

axs[0].set_title('Automatic ticks')

axs[1].plot(xdata, data1)

axs[1].set_xticks(np.arange(0, 100, 30), ['zero', '30', 'sixty', '90'])

axs[1].set_yticks([-1.5, 0, 1.5]) # note that we don't need to specify labels

axs[1].set_title('Manual ticks')

##############################################################################

# Different scales can have different locators and formatters; for instance

# the log-scale above uses `~.LogLocator` and `~.LogFormatter`. See

# :doc:`/gallery/ticks/tick-locators` and

# :doc:`/gallery/ticks/tick-formatters` for other formatters and

# locators and information for writing your own.

# Plotting dates and strings

# --------------------------

# Matplotlib can handle plotting arrays of dates and arrays of strings, as

# well as floating point numbers. These get special locators and formatters

# as appropriate. For dates:

fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')

dates = np.arange(np.datetime64('2021-11-15'), np.datetime64('2021-12-25'),

np.timedelta64(1, 'h'))

data = np.cumsum(np.random.randn(len(dates)))

ax.plot(dates, data)

cdf = mpl.dates.ConciseDateFormatter(ax.xaxis.get_major_locator())

ax.xaxis.set_major_formatter(cdf)

##############################################################################

# For more information see the date examples

# (e.g. :doc:`/gallery/text_labels_and_annotations/date`)

# For strings, we get categorical plotting (see:

# :doc:`/gallery/lines_bars_and_markers/categorical_variables`).

fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')

categories = ['turnips', 'rutabaga', 'cucumber', 'pumpkins']

ax.bar(categories, np.random.rand(len(categories)))

##############################################################################

# One caveat about categorical plotting is that some methods of parsing

# text files return a list of strings, even if the strings all represent

# numbers or dates. If you pass 1000 strings, Matplotlib will think you

# meant 1000 categories and will add 1000 ticks to your plot!

# Additional Axis objects

# ------------------------

# Plotting data of different magnitude in one chart may require

# an additional y-axis. Such an Axis can be created by using

# `~.Axes.twinx` to add a new Axes with an invisible x-axis and a y-axis

# positioned at the right (analogously for `~.Axes.twiny`). See

# :doc:`/gallery/subplots_axes_and_figures/two_scales` for another example.

# Similarly, you can add a `~.Axes.secondary_xaxis` or

# `~.Axes.secondary_yaxis` having a different scale than the main Axis to

# represent the data in different scales or units. See

# :doc:`/gallery/subplots_axes_and_figures/secondary_axis` for further

# examples.

fig, (ax1, ax3) = plt.subplots(1, 2, figsize=(7, 2.7), layout='constrained')

l1, = ax1.plot(t, s)

ax2 = ax1.twinx()

l2, = ax2.plot(t, range(len(t)), 'C1')

ax2.legend([l1, l2], ['Sine (left)', 'Straight (right)'])

ax3.plot(t, s)

ax3.set_xlabel('Angle [rad]')

ax4 = ax3.secondary_xaxis('top', functions=(np.rad2deg, np.deg2rad))

ax4.set_xlabel('Angle [°]')

##############################################################################

# Color mapped data

# =================

# Often we want to have a third dimension in a plot represented by a colors in

# a colormap. Matplotlib has a number of plot types that do this:

X, Y = np.meshgrid(np.linspace(-3, 3, 128), np.linspace(-3, 3, 128))

Z = (1 - X/2 + X**5 + Y**3) * np.exp(-X**2 - Y**2)

fig, axs = plt.subplots(2, 2, layout='constrained')

pc = axs[0, 0].pcolormesh(X, Y, Z, vmin=-1, vmax=1, cmap='RdBu_r')

fig.colorbar(pc, ax=axs[0, 0])

axs[0, 0].set_title('pcolormesh()')

co = axs[0, 1].contourf(X, Y, Z, levels=np.linspace(-1.25, 1.25, 11))

fig.colorbar(co, ax=axs[0, 1])

axs[0, 1].set_title('contourf()')

pc = axs[1, 0].imshow(Z**2 * 100, cmap='plasma',

norm=mpl.colors.LogNorm(vmin=0.01, vmax=100))

fig.colorbar(pc, ax=axs[1, 0], extend='both')

axs[1, 0].set_title('imshow() with LogNorm()')

pc = axs[1, 1].scatter(data1, data2, c=data3, cmap='RdBu_r')

fig.colorbar(pc, ax=axs[1, 1], extend='both')

axs[1, 1].set_title('scatter()')

##############################################################################

# Colormaps

# ---------

# These are all examples of Artists that derive from `~.ScalarMappable`

# objects. They all can set a linear mapping between *vmin* and *vmax* into

# the colormap specified by *cmap*. Matplotlib has many colormaps to choose

# from (:doc:`/tutorials/colors/colormaps`) you can make your

# own (:doc:`/tutorials/colors/colormap-manipulation`) or download as

# `third-party packages

# <https://matplotlib.org/mpl-third-party/#colormaps-and-styles>`_.

# Normalizations

# --------------

# Sometimes we want a non-linear mapping of the data to the colormap, as

# in the ``LogNorm`` example above. We do this by supplying the

# ScalarMappable with the *norm* argument instead of *vmin* and *vmax*.

# More normalizations are shown at :doc:`/tutorials/colors/colormapnorms`.

# Colorbars

# ---------

# Adding a `~.Figure.colorbar` gives a key to relate the color back to the

# underlying data. Colorbars are figure-level Artists, and are attached to

# a ScalarMappable (where they get their information about the norm and

# colormap) and usually steal space from a parent Axes. Placement of

# colorbars can be complex: see

# :doc:`/gallery/subplots_axes_and_figures/colorbar_placement` for

# details. You can also change the appearance of colorbars with the

# *extend* keyword to add arrows to the ends, and *shrink* and *aspect* to

# control the size. Finally, the colorbar will have default locators

# and formatters appropriate to the norm. These can be changed as for

# other Axis objects.

# Working with multiple Figures and Axes

# ======================================

# You can open multiple Figures with multiple calls to

# ``fig = plt.figure()`` or ``fig2, ax = plt.subplots()``. By keeping the

# object references you can add Artists to either Figure.

# Multiple Axes can be added a number of ways, but the most basic is

# ``plt.subplots()`` as used above. One can achieve more complex layouts,

# with Axes objects spanning columns or rows, using `~.pyplot.subplot_mosaic`.

fig, axd = plt.subplot_mosaic([['upleft', 'right'],

['lowleft', 'right']], layout='constrained')

axd['upleft'].set_title('upleft')

axd['lowleft'].set_title('lowleft')

axd['right'].set_title('right')

###############################################################################

# Matplotlib has quite sophisticated tools for arranging Axes: See

# :doc:`/tutorials/intermediate/arranging_axes` and

# :doc:`/gallery/subplots_axes_and_figures/mosaic`.

# More reading

# ============

# For more plot types see :doc:`Plot types </plot_types/index>` and the

# :doc:`API reference </api/index>`, in particular the

# :doc:`Axes API </api/axes_api>`.

matplotlib/tutorials/introductory/quick_start.py at v3.7.x · matplotlib/matplotlib · GitHub

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