Why does autocorrelation_plot() generate plots where auto-correlation proportionally decrease with lag?
#### Problem description

I can observe ``autocorrelation_plot()`` generates plots where value importance proportionally decrease with lag.

This can be visually verified in documentation example (https://pandas.pydata.org/pandas-docs/stable/visualization.html#visualization-autocorrelation) were auto-correlation decreases linearly with lag.

Looking into ``autocorrelation_plot()`` implementation the reason is ``r(h)`` closure where ``(data[h:] - mean)).sum()`` will proportionally decrease with ``h``.

I would like to know the reason of this implementation choice that is not equivalent to applying ``autocorr()`` with different lag values. 

I suspect the reason behind is to limit the importance of auto-correlation terms calculated with few values.

Thanks for your feedback.