diff --git a/.flake8 b/.flake8
index ee739cdf4231..c11645341933 100644
--- a/.flake8
+++ b/.flake8
@@ -60,6 +60,7 @@ per-file-ignores =
     galleries/users_explain/artists/transforms_tutorial.py: E402, E501
     galleries/users_explain/colors/colormaps.py: E501
     galleries/users_explain/colors/colors.py: E402
+    galleries/users_explain/quick_start.py: E402
     galleries/tutorials/artists.py: E402
     galleries/users_explain/axes/constrainedlayout_guide.py: E402
     galleries/users_explain/axes/legend_guide.py: E402
diff --git a/galleries/users_explain/artists/index.rst b/galleries/users_explain/artists/index.rst
index d3f2918c9a91..66cab003da63 100644
--- a/galleries/users_explain/artists/index.rst
+++ b/galleries/users_explain/artists/index.rst
@@ -21,3 +21,4 @@ and :doc:`Axes <../axes/index>` are Artists, and generally contain
     Path effects guide <patheffects_guide>
     Understanding the extent keyword argument of imshow <imshow_extent>
     transforms_tutorial
+    patches
diff --git a/galleries/users_explain/artists/patches.py b/galleries/users_explain/artists/patches.py
new file mode 100644
index 000000000000..50fcf1fb1b96
--- /dev/null
+++ b/galleries/users_explain/artists/patches.py
@@ -0,0 +1,28 @@
+"""
+.. _patches_artists:
+
+=======
+Patches
+=======
+
+:mod:`Patches <matplotlib.patches>` are a family of Artists that can be used
+when drawing arbitrary two-dimensional regions. In addition to the general
+Patch Artists :class:`~.patches.PathPatch` and :class:`~.patches.Polygon`,
+common shapes have corresponding Patch Artists such as
+:class:`~.patches.Circle`, :class:`~.patches.Rectangle`,
+:class:`~.patches.Ellipse`, etc.
+"""
+
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+import numpy as np
+
+fig, ax = plt.subplots()
+
+polygon = mpl.patches.Polygon(np.array([[1, 0], [0.5, 1.5], [2, 1]]), closed=True)
+ax.add_patch(polygon)
+
+circle = mpl.patches.Circle((2, 2), 0.5, facecolor='orange', edgecolor='black')
+ax.add_patch(circle)
+
+ax.set(xlim=(0, 3), ylim=(0, 3), box_aspect=1)
diff --git a/galleries/users_explain/quick_start.py b/galleries/users_explain/quick_start.py
index cf2d5850e6e5..8f6f6c1e5f6c 100644
--- a/galleries/users_explain/quick_start.py
+++ b/galleries/users_explain/quick_start.py
@@ -111,6 +111,45 @@
 # 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.
 #
+# .. _plot_types_quickstart:
+#
+# Plot types
+# ==========
+#
+# For an overview of the different types of plots you can create with Matplotlib,
+# see the :ref:`Plot types gallery <plot_types>`. If you don't find the plot
+# type you need, it may still be possible to create the visualization you want
+# For example, Matplotlib does not provide a method to automatically annotate a
+# distribution plot. Instead, you can create this visualization by combining the
+# `~.matplotlib.axes.Axes.plot` method to draw the distribution, the
+# `~.matplotlib.axes.Axes.axhline` method to draw the mean, and
+# `~matplotlib.axes.Axes.axhspan` to shade the standard deviation region.
+
+x = np.array([3, 4, 9, 8, 9, 8, 0, 8, 4, 8])
+fig, ax = plt.subplots()
+
+# Compute the mean and standard deviation of the data
+mean = np.mean(x)
+std = np.std(x)
+
+# Add a horizontal line for the mean, and a rectangle representing the
+# standard deviation of the data
+ln_data = ax.plot(x, label='Data')
+ln_mean = ax.axhline(mean, color='red', label='Mean')
+ln_std = ax.axhspan(mean-std, mean+std, alpha=0.1, label=r'$\sigma$')
+
+ax.legend()
+
+# Now, you can use object methods to directly customize your plot.
+# Note that ln_data is a list of `~.matplotlib.lines.Line2D` objects - we'll
+# modify the first entry in this list.
+ln_data[0].set_color('orange')
+ln_mean.set_linestyle(':')
+ln_std.set_hatch('oo')
+# %%
+# For more information on creating artists from their constructors, see
+# :ref:`artists_tutorial`.
+#
 # .. _input_types:
 #
 # Types of inputs to plotting functions
@@ -206,8 +245,8 @@
 #    You may find older examples that use the ``pylab`` interface,
 #    via ``from pylab import *``. This approach is strongly deprecated.
 #
-# Making a helper functions
-# -------------------------
+# Making 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
@@ -558,8 +597,8 @@ def my_plotter(ax, data1, data2, param_dict):
 # 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
 # ======================================
 #
@@ -588,3 +627,6 @@ def my_plotter(ax, data1, data2, param_dict):
 # 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>`.
+#
+# For more information on coordinate systems and transformations, see the
+# :ref:`transforms_tutorial`.