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3 changes: 3 additions & 0 deletions doc/source/user_guide/computation.rst
Original file line number Diff line number Diff line change
Expand Up @@ -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')

Using the Numba engine
~~~~~~~~~~~~~~~~~~~~~~

.. versionadded:: 1.0

Additionally, :meth:`~Rolling.apply` can leverage `Numba <https://numba.pydata.org/>`__
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7 changes: 7 additions & 0 deletions doc/source/user_guide/enhancingperf.rst
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Expand Up @@ -373,6 +373,13 @@ 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 :ref:`Computation tools
<stats.rolling_apply>` for an extensive example.

Vectorize
~~~~~~~~~

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