Added numba as an argument

doc/source/user_guide/computation.rst

@@ -361,6 +361,9 @@ compute the mean absolute deviation on a rolling basis:
  @savefig rolling_apply_ex.png
  s.rolling(window=60).apply(mad, raw=True).plot(style='k')
 
+Numba as an argument
+~~~~~~~~~~~~~~~~~~~~
+
 .. versionadded:: 1.0
 
 Additionally, :meth:`~Rolling.apply` can leverage `Numba <https://numba.pydata.org/>`__

doc/source/user_guide/enhancingperf.rst

@@ -373,6 +373,14 @@ nicer interface by passing/returning pandas objects.
 
 In this example, using Numba was faster than Cython.
 
+Numba as an argument
+~~~~~~~~~~~~~~~~~~~~
+
+Additionally, we can leverage the power of `Numba <https://numba.pydata.org/>`__
+by calling it as an argument in :meth:`~Rolling.apply`. See `Computation tools
+<https://pandas.pydata.org/pandas-docs/stable/user_guide/computation.html#rolling-apply>`__
+for an extensive example.
+
 Vectorize
 ~~~~~~~~~