matplotlib · timhoffm · Jan 15, 2026 · Jan 15, 2026
diff --git a/galleries/tutorials/coding_shortcuts.py b/galleries/tutorials/coding_shortcuts.py
@@ -0,0 +1,172 @@
+"""
+================
+Coding shortcuts
+================
+
+Matplotlib's primary and universal API is the :ref:`Axes interface <api_interfaces>`.
+While it is clearly structured and powerful, it can sometimes feel overly verbose and
+thus cumbersome to write. This page collects patterns for condensing the code
+of the Axes-based API and achieving the same results with less typing for many simpler
+plots.
+
+.. note::
+
+    The :ref:`pyplot interface <pyplot_interface>` is an alternative more compact
+    interface, and was historically modeled to be similar to MATLAB. It remains a
+    valid approach for those who want to use it. However, it has the disadvantage that
+    it achieves its brevity through implicit assumptions that you have to understand.
+
+    Since it follows a different paradigm, switching between the Axes interface and
+    the pyplot interface requires a shift of the mental model, and some code rewrite,
+    if the code develops to a point at which pyplot no longer provides enough
+    flexibility.
+
+This tutorial goes the other way round, starting from the standard verbose Axes
+interface and using its capabilities for shortcuts when you don't need all the
+generality.
+
+Let's assume we want to make a plot of the number of daylight hours per day over the
+year in London.
+
+The standard approach with the Axes interface looks like this.
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+day = np.arange(365)
+hours = 4.276 * np.sin(2 * np.pi * (day - 80)/365) + 12.203
+
+fig, ax = plt.subplots()
+ax.plot(day, hours, color="orange")
+ax.set_xlabel("day")
+ax.set_ylabel("daylight hours")
+ax.set_title("London")
+plt.show()
+
+# %%
+# Note that we've included ``plt.show()`` here. This is needed to show the plot window
+# when running from a command line or in a Python script. If you run a Jupyter notebook,
+# this command is automatically executed at the end of each cell.
+#
+# For the rest of the tutorial, we'll assume that we are in a notebook and leave this
+# out for brevity. Depending on your context you may still need it.
+#
+# If you instead want to save to a file, use ``fig.savefig("daylight.png")``.
+#
+#
+# Collect Axes properties into a single ``set()`` call
+# ====================================================
+#
+# The properties of Matplotlib Artists can be modified through their respective
+# ``set_*()`` methods. Artists additionally have a generic ``set()`` method, that takes
+# keyword arguments and is equivalent to calling all the respective ``set_*()`` methods.
+# ::
+#
+#     ax.set_xlabel("day")
+#     ax.set_ylabel("daylight hours")
+#
+# can also be written as ::
+#
+#     ax.set(xlabel="day", ylabel="daylight hours")
+#
+# This is the most simple and effective reduction you can do. With that we can shorten
+# the above plot to
+
+fig, ax = plt.subplots()
+ax.plot(day, hours, color="orange")
+ax.set(xlabel="day", ylabel="daylight hours", title="London")
+
+# %%
+#
+# This works as long as you only need to pass one parameter to the ``set_*()`` function.
+# The individual functions are still necessary if you want more control, e.g.
+# ``set_title("London", fontsize=16)``.
+#
+#
+# Not storing a reference to the figure
+# =====================================
+# Another nuisance of ``fig, ax = plt.subplots()`` is that you always create a ``fig``
+# variable, even if you don't use it. A slightly shorter version would be using the
+# standard variable for unused value in Python (``_``): ``_, ax = plt.subplots()``.
+# However, that's only marginally better.
+#
+# You can work around this by separating figure and Axes creation and chaining them ::
+#
+#     ax = plt.figure().add_subplot()
+#
+# This is a bit cleaner logically and has the slight advantage that you could set
+# figure properties inline as well; e.g. ``plt.figure(facecolor="lightgoldenrod")``.
+# But it has the disadvantage that it's longer than ``fig, ax = plt.subplots()``.
+#
+# You can still obtain the figure from the Axes if needed, e.g. ::
+#
+#    ax.figure.savefig("daylight_hours.png")
+#
+# The example code now looks like this:
+
+ax = plt.figure().add_subplot()
+ax.plot(day, hours, color="orange")
+ax.set(xlabel="day", ylabel="daylight hours", title="London")
+
+# %%
+# Define Axes properties during axes creation
+# ===========================================
+# The ``set_*`` methods as well as ``set`` modify existing objects. You can
+# alternatively define them right at creation. Since you typically don't instantiate
+# classes yourself in Matplotlib, but rather call some factory function that creates
+# the object and wires it up correctly with the plot, this may seem less obvious. But
+# in fact you just pass the desired properties to the factory functions. You are likely
+# doing this already in some places without realizing. Consider the function to create
+# a line ::
+#
+#     ax.plot(x, y, color="orange")
+#
+# This is equivalent to ::
+#
+#     line, = ax.plot(x, y)
+#     line.set_color("orange")
+#
+# The same can be done with functions that create Axes.
+
+ax = plt.figure().add_subplot(xlabel="day", ylabel="daylight hours", title="London")
+ax.plot(day, hours, color="orange")
+
+# %%
+# .. important::
+#    The Axes properties are only accepted as keyword arguments by
+#    `.Figure.add_subplot`, which creates a single Axes.
+#
+#    For `.Figure.subplots` and `.pyplot.subplots`, you'd have to pass the properties
+#    as a dict via the keyword argument ``subplot_kw``. The limitation here is that
+#    such parameters are given to all Axes. For example, if you need two subplots
+#    (``fig, (ax1, ax2) = plt.subplots(1, 2)``) with different labels, you have to
+#    set them individually.
+#
+# Defining Axes properties during creation is best used for single subplots or when
+# all subplots share the same properties.
+#
+#
+# Using implicit figure creation
+# ==============================
+# You can go even further by tapping into the pyplot logic and use `.pyplot.axes` to
+# create the axes:
+
+ax = plt.axes(xlabel="day", ylabel="daylight hours", title="London")
+ax.plot(day, hours, color="orange")
+
+# %%
+# .. warning::
+#    When using this, you have to be aware of the implicit figure semantics of pyplot.
+#    ``plt.axes()`` will only create a new figure if no figure exists. Otherwise, it
+#    will add the Axes to the current existing figure, which is likely not what you
+#    want.
+#
+# Not storing a reference to the Axes
+# ===================================
+# If you only need to visualize one dataset, you can append the plot command
+# directly to the Axes creation. This may be useful e.g. in notebooks,
+# where you want to create a plot with some configuration, but as little distracting
+# code as possible:
+
+plt.axes(xlabel="day", ylabel="daylight hours").plot(day, hours, color="orange")
diff --git a/galleries/tutorials/index.rst b/galleries/tutorials/index.rst
@@ -32,6 +32,23 @@ a :ref:`FAQ <howto-faq>` in our :ref:`user guide <users-guide-index>`.
     </div>
 
 
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Coding shortcuts">
+
+.. only:: html
+
+  .. image:: /tutorials/images/thumb/sphx_glr_coding_shortcuts_thumb.png
+    :alt: Coding shortcuts
+
+  :ref:`sphx_glr_tutorials_coding_shortcuts.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Coding shortcuts</div>
+    </div>
+
+
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="A short tutorial on plotting images with Matplotlib.">
@@ -92,6 +109,7 @@ a :ref:`FAQ <howto-faq>` in our :ref:`user guide <users-guide-index>`.
    :hidden:
 
    /tutorials/pyplot
+   /tutorials/coding_shortcuts
    /tutorials/images
    /tutorials/lifecycle
    /tutorials/artists