3232# | Explicit, Object Oriented | Implicit, ``pyplot`` |
3333# | Programming (OOP) | |
3434# +====================================+====================================+
35- # | Users explicitly create and manage |Automatically manages Figure and |
36- # | all Figure elements. |Axes. |
35+ # | Users explicitly create and manage |The Matplotlib library implicitly |
36+ # | all Figure elements. |manages Figure and Axes. |
3737# +------------------------------------+------------------------------------+
3838# | Useful for repeated code use, | Helpful for quickly graphing data |
3939# | generalization, robust | when using interactive |
5555#
5656# Implicit programming with ``pyplot`` is simpler. It is helpful for basic
5757# plots and for interactive environments, such as Jupyter Notebooks. Users
58- # familiar with MATLAB orwish to have Matplotlib create and manage parts of
58+ # familiar with MATLAB orwishing to have Matplotlib create and manage parts of
5959# the visualization in state-based programming benefit from using ``pyplot``.
6060# Using implicit programming acts as a convenient shortcut for generating
6161# visualizations. New users to Matplotlib may experience difficulty
7575# +====================================+====================================+
7676# | :: | :: |
7777# | | |
78- # | fig, ax = plt.subplots() | plt.plot([1, 2, 3],[1, 2, 3])
79- # | ax.plot([1, 2, 3],[1, 2, 3])
78+ # | fig, ax = plt.subplots() | plt.plot([1, 2, 3],
79+ # | ax.plot([1, 2, 3],
8080# | | |
8181# +------------------------------------+------------------------------------+
8282# | `.pyplot.subplots` generates a | :mod:`matplotlib.pyplot` creates |
8383# | `~.figure.Figure` and one or | implicit Figure and Axes if |
8484# | more `~.axes.Axes` explicitly. | there are no pre-existing |
8585# | `.Axes.plot` plots the data. | elements and `.pyplot.plot` plots |
86- # | | the data. |
86+ # | | the data. This also plots over any |
87+ # | | existing Figure if applicable. |
8788# +------------------------------------+------------------------------------+
8889# | .. plot:: | .. plot:: |
8990# | | |
90- # | fig, ax = plt.subplots() | plt.plot([1,2, 3],[1,2, 3])
91- # | ax.plot([1, 2, 3],[1, 2, 3])
91+ # | fig, ax = plt.subplots() | plt.plot([1, 2, 3], 2, 3]) |
92+ # | ax.plot([1, 2, 3],
9293# | | |
9394# +------------------------------------+------------------------------------+
9495#
103104# higher* is required. Depending on your operating system, Python may already
104105# be installed on your machine.
105106#
106- # InstallingMaptlotlib is required in order to generate graphs with the
107+ # InstallingMatplotlib is required in order to generate graphs with the
107108# library. Install Matplotlib for your own development environment manually or
108109# use a third-party package distribution.
109110#
140141#
141142# - The ``pyplot`` module in Matplotlib is a collection of functions. The
142143# module's functions create, manage, and manipulate the current Figure and
143- # the plotting area. Theabbreviation as ``plt`` is the standard shortcut.
144+ # the plotting area. The ``plt`` abbreviation is the standard shortcut.
144145#
145146
146147import numpy as np
149150
150151##############################################################################
151152#
152- # - NumPy is a common scientific Python library that benefits users with
153- #additional tools for manipulating data.
153+ # -` NumPy<https://numpy.org/doc/stable/index.html#>`_ is a common scientific
154+ #Python library that benefits users working with numerical data.
154155# - The ``functools`` module helps manage functions that act on or return
155156# other functions. The `Pie Chart Examples`_ section note contains more
156157# information about the purpose of this module.
157158#
159+ #
160+ #
158161# Two Approaches for Creating Graphs
159162# ----------------------------------
160163#
227230# Explicit: Object Oriented Programming (OOP)
228231# --------------------------------------------
229232#
230- # Explicit programming for Matplotlib involves calling the method ``subplots``
231- # in the ``pyplot`` module once. This unpacks a group of an explicit Figure and
232- # Axes. More than one Axes is configurable; however, each Axes only corresponds
233- # to a single Figure.
233+ # Explicit programming for Matplotlib involves calling the function
234+ # `pyploy.subplots` in the ``pyplot`` module once. This returns a group of an
235+ # explicit Figure and Axes to be unpacked as part of variable assignment. More
236+ # than one Axes is configurable; however, each Axes only corresponds to a
237+ # single Figure.
234238#
235239# Each Axes has its own methods to graph data. In addition, each Axes
236- # also uses separate methods to create and manageparts of a Figure. These
237- # methods are different from those of the implicit programming approach.
240+ # also uses separate methods to create and manageobjects within a Figure.
241+ #These methods are different from those of the implicit programming approach.
238242
239243# Explicit programming with OOP
240244
241- # Assigning sample data tolabeled variables.
245+ # Assigning sample data to variables.
242246x = months
243247y1 = income
244248y2 = chk_acct_09
266270
267271##############################################################################
268272#
269- # The module ``pyplot`` for theOOP exampleunpacks the Figure and Axes.
270- # This convention uses ``plt.subplots()`` and defaults to one Figure, ``fig``,
271- # and one Axes, ``ax``. The variable names are common shorthand terms. Any
272- # naming conventions also work.
273+ # The module ``pyplot`` for theexplicit exampleuses a function that returns
274+ #the Figure and Axes. This convention uses ``plt.subplots()``. It defaults
275+ #to one Figure, ``fig``, and one Axes, ``ax``. The variable names are common
276+ #shorthand terms and any naming conventions also work.
273277#
274278# The `Configuration`_ section below contains additional information about
275279# manipulating visuals, multiple visualizations, and other modifications.
276280#
277281# Using explicit programming allows for ``fig`` and ``ax`` to use separate
278- # methods tomanipulate the visualization. Specific Figures and Axes manage
279- # data components with their own respective methods.
282+ # methods tomanage objects within the visualization. Specific Figures and
283+ #Axes manage data components with their own respective methods.
280284#
281285#
282286# Implicit: ``pyplot``
283287# --------------------
284288#
285289# Implicit programming for Matplotlib centers around using the ``pyplot``
286- # module. The moduleautomatically generates Figure and Axes. Methods and
290+ # module. The moduleimplicitly generates the Figure and Axes. Methods and
287291# functions within the module take incoming data as arguments. Additional parts
288292# of the Figure are also available through the module methods.
289293
307311
308312##############################################################################
309313#
310- # In the example above, the ``pyplot`` module contains its ownmethods of
314+ # In the example above, the ``pyplot`` module contains its ownfunctions of
311315# actionable tasks for the data. The ``plt.plot`` plots data as a line graph
312316# with various keyword arguments as customizable options. The module also
313317# includes other methods for generating parts of the visualization. These parts
364368#
365369# :class:`~matplotlib.axes.Axes`
366370#
367- # Axes are subplots within the Figure. They containFigure elements and
371+ # Axes are subplots within the Figure. They containMatplotlib objects and
368372# are responsible for plotting and configuring additional details. Each
369373# Figure can contain multiple Axes, but each Axes is specific to one
370374# Figure.
371375#
372376# In a Figure, each Axes contains any number of visual elements. Axes are
373377# configurable for more than one type of visualization of data. From the
374378# `Plotting`_ section above, the Axes in both explicit and implicit strategies
375- # contain multiple types of visualizations of data on a single Axes. Each of
376- # these types are specific to the Axes they are in.
379+ # contain multiple types of visualizations of data on a single Axes.
380+
381+ # Each of these types are specific to the Axes they are in. In the example, the
382+ # two plots each have one Axes. These Axes each have multiple plot lines. The
383+ # lines as objects are not shared between the two plots even though the data is
384+ # shared.
377385#
378386# Matplotlib Axes also integrate with other Python libraries. In Axes-based
379387# interfaces, other libraries take an Axes object as input. Libraries such as
472480# | | :doc:`/tutorials/text/annotations` |
473481# +------------------------------+--------------------------------------------+
474482#
475- # For complete information about available methods forArtists, refer to the
476- # table below.
483+ # For complete information about available methods forcreating new Artists,
484+ #refer to the table below.
477485#
478486# +------------------------------------+------------------------------------+
479487# | Explicit | Implicit |
@@ -594,7 +602,7 @@ def autopct_format(percent, group):
594602#
595603# The pie chart below adds configurations with keyword arguments for
596604# ``explode``, ``autopct``, ``startangle``, and ``shadow``. These keyword
597- # arguments help tomanipulate the Artists.
605+ # arguments help todefine the display of Artists.
598606
599607# Explicit
600608
@@ -603,7 +611,7 @@ def autopct_format(percent, group):
603611# The explode keyword argument uses explode variable data to separate
604612# respective wedges from center.
605613# The autopct keyword argument takes formatting strings and functions to
606- # generate text within wedge. '%1.1f%%' is the string formatter.
614+ # generate text withineach wedge. '%1.1f%%' is the string formatter.
607615# The startangle keyword argument changes where first wedge spans. Angles start
608616# at 0 degrees on X-axis and move counterclockwise.
609617# The shadow keyword argument toggles a shadow on the visual.
@@ -676,8 +684,8 @@ def autopct_format(percent, group):
676684ax .set_title ('Average Monthly Income Expenses' )
677685ax .axis ('equal' )
678686
679- # The Figure method tight_layout()manages space between all Artists to
680- # maximize visiblity on Figure. This method also contains various
687+ # The Figure method tight_layout()adjusts spacing between all Artists to
688+ # maximize visiblity onthe Figure. This method also contains various
681689# parameters for configuration.
682690fig .tight_layout ()
683691
@@ -759,18 +767,18 @@ def autopct_format(percent, group):
759767# the explicit approach refers to an explicitly generated Axes after creating
760768# both the Figure and Axes.
761769#
762- # In the unpacking process,numerous Axes are assigned to a single variable.
770+ # In the unpacking process,multiple Axes are assigned to a single variable.
763771# To reference a specific Axes, indexing the location of the respective Axes
764772# as a matrix through the single variable works as well.
765773#
766774# The code below demonstrates indexing multiple Axes::
767775#
768- # fig, ax = plt.subplots(2,2)
776+ # fig, ax = plt.subplots(2,
769777#
770- # ax[0,0].bar([1,2, 3],[1,2, 3])
771- # ax[0,1].plot([3,2, 1],[3,2, 1])
778+ # ax[0,0].bar([1, 2, 3], 2, 3])
779+ # ax[0,1].plot([3, 2, 1], 2, 1])
772780# ax[1,0].hist(hist_data)
773- # ax[1,1].imshow([[1,2], [2,1]])
781+ # ax[1,1].imshow([[1,
774782#
775783#
776784# The method `matplotlib.figure.Figure.subplot_mosaic` also generates Axes in
@@ -783,10 +791,10 @@ def autopct_format(percent, group):
783791# ax_dict = fig.subplot_mosaic([['bar', 'plot'],
784792# ['hist', 'image']])
785793#
786- # ax_dict['bar'].bar([1,2, 3],[1,2, 3])
787- # ax_dict['plot'].plot([3,2, 1],[3,2, 1])
794+ # ax_dict['bar'].bar([1, 2, 3], 2, 3])
795+ # ax_dict['plot'].plot([3, 2, 1], 2, 1])
788796# ax_dict['hist'].hist(hist_data)
789- # ax_dict['image'].imshow([[1,2], [2,1]])
797+ # ax_dict['image'].imshow([[1,
790798#
791799# Implicit
792800# ^^^^^^^^
@@ -816,7 +824,7 @@ def my_plotter(ax, data1, data2, param_dict):
816824 X data
817825 data2 : array
818826 Y data
819- param_dict : dict()
827+ param_dict : dict
820828 Dictionary of keyword arguments passes to method
821829
822830 Returns