DOC: more updates

jklymak · jklymak · commit a30517758c85 · 2023-12-05T16:52:18.000-08:00
diff --git a/galleries/examples/statistics/histogram_normalization.py b/galleries/examples/statistics/histogram_normalization.py
@@ -9,11 +9,11 @@
 which is flexible and helpful, but can also lead to confusion.  In particular,
 you can:
 
-  - bin the data as you want, either with an automatically chosen number of
-    bins, or with fixed bin edges,
-  - normalize the histogram so that its integral is one,
-  - and assign weights to the data points, so that each data point affects the
-    count in its bin differently.
+- bin the data as you want, either with an automatically chosen number of
+  bins, or with fixed bin edges,
+- normalize the histogram so that its integral is one,
+- and assign weights to the data points, so that each data point affects the
+  count in its bin differently.
 
 The Matplotlib ``hist`` method calls `numpy.histogram` and plots the results,
 therefore users should consult the numpy documentation for a definitive guide.
@@ -92,10 +92,12 @@
 # %%
 # This normalization can be a little hard to interpret when just exploring the
 # data. The value attached to each bar is divided by the total number of data
-# points _and_ the width of the bin, and thus the values _integrate_ to one
+# points *and* the width of the bin, and thus the values _integrate_ to one
 # when integrating across the full range of data.
-# e.g. (``density = counts / (sum(counts) * np.diff(bins))``),
-# and (``np.sum(density * np.diff(bins)) == 1``).
+# e.g. ::
+#
+#     (``density = counts / (sum(counts) * np.diff(bins))``)
+#     (``np.sum(density * np.diff(bins)) == 1``).
 #
 # This normalization is how `probability density functions
 # <https://en.wikipedia.org/wiki/Probability_density_function>`_ are defined in
