Address comments for Longitudinal Data Example

* Added blank blockquote lines to make markdown more readable.
* move comma so it does not start the line.
* use NumPy functions instead of methods.

Author: Stephen Childs

_episodes/01-numpy.md

@@ -1146,6 +1146,7 @@ the graphs will actually be squeezed together more closely.)
 >difference along a specified axis.
 >
 >Which axis would it make sense to use this function along?
+>
 > > ## Solution
 > > Since the row axis (0) is patients, it does not make sense to get the
 > > difference between two arbitrary patients. The column axis (1) is in
@@ -1158,23 +1159,27 @@ the graphs will actually be squeezed together more closely.)
 > > {: .python}
 > {: .solution}
 >
->If the shape of an individual data file is `(60, 40)` (60 rows and 40 columns)
->, what would the shape of the array be after you run the `diff()` function and
->why?
+>If the shape of an individual data file is `(60, 40)` (60 rows and 40
+>columns), what would the shape of the array be after you run the `diff()`
+>function and why?
+>
 > > ## Solution
 > > The shape will be `(60, 39)` because there is one fewer difference between
 > > columns than there are columns in the data.
 > {: .solution}
 >
 >How would you find the largest change in inflammation for each patient? Does
 >it matter if the change in inflammation is an increase or a decrease?
+>
 > > ## Solution
-> > By using the `max()` function after you apply the `diff()` function, you
-> > will get the largest difference between days.
+> > By using the `numpy.max()` function after you apply the `numpy.diff()`
+> > function, you will get the largest difference between days.
+> >
 > > ~~~
-> > numpy.diff(data, axis=1).max(axis=1)
+> > numpy.max(numpy.diff(data, axis=1), axis=1)
 > > ~~~
 > > {: .python}
+> >
 > > ~~~
 > > array([  7.,  12.,  11.,  10.,  11.,  13.,  10.,   8.,  10.,  10.,   7.,
 > >          7.,  13.,   7.,  10.,  10.,   8.,  10.,   9.,  10.,  13.,   7.,
@@ -1184,16 +1189,19 @@ the graphs will actually be squeezed together more closely.)
 > >          8.,  12.,  10.,   7.,  12.])
 > > ~~~
 > > {: .python}
+> >
 > > If a difference is a *decrease*, then the difference will be negative. If
 > > you are interested in the **magnitude** of the change and not just the
 > > direction, the `numpy.absolute()` function will provide that.
 > >
 > > Notice the difference if you get the largest _absolute_ difference
 > > between readings.
+> >
 > > ~~~
-> > numpy.absolute(numpy.diff(data, axis=1)).max(axis=1)
+> > numpy.max(numpy.absolute(numpy.diff(data, axis=1)), axis=1)
 > > ~~~
 > > {: .python}
+> >
 > > ~~~
 > > array([ 12.,  14.,  11.,  13.,  11.,  13.,  10.,  12.,  10.,  10.,  10.,
 > >         12.,  13.,  10.,  11.,  10.,  12.,  13.,   9.,  10.,  13.,   9.,
@@ -1203,5 +1211,6 @@ the graphs will actually be squeezed together more closely.)
 > >         11.,  13.,  10.,  10.,  12.])
 > > ~~~
 > > {: .python}
+> >
 > {: .solution}
 {: .challenge}