DOC: normalizing histograms

jklymak · jklymak · commit e00fe2e59b47 · 2023-12-03T16:28:44.000-08:00
diff --git a/galleries/examples/statistics/histogram_normalization.py b/galleries/examples/statistics/histogram_normalization.py
@@ -159,10 +159,11 @@
 ax['True'].legend(fontsize='small')
 
 # %%
+
 # Sometimes people want to normalize so that the sum of counts is one.  This is
-# _not_ done with the *density* kwarg, but instead we can set the *weights* to
-# 1/N.  Note, however, that the amplitude of the histogram still depends on
-# width of the bins
+# not done with the *density* kwarg, but rather we can get this effects if we
+# set the *weights* to 1/N.  Note, however, that the amplitude of the histogram
+# still depends on width of the bins:
 
 fig, ax = plt.subplots(layout='constrained', figsize=(3.5, 3))
 
@@ -176,7 +177,8 @@
 
 # %%
 # The true value of normalizing is if you do want to compare two distributions
-# that have different sized populations:
+# that have different sized populations.  Here we compare the distribution of
+# ``xdata`` with a population of 1000, and ``xdata2`` with 100 members.
 
 xdata2 = rng.normal(size=100)
 
