[Numpy-discussion] Coverting ranks to a Gaussian

Pierre GM pgmdevlist@gmail....
Mon Jun 9 21:35:01 CDT 2008

On Monday 09 June 2008 22:30:09 Keith Goodman wrote:
> On Mon, Jun 9, 2008 at 7:02 PM, Pierre GM <pgmdevlist@gmail.com> wrote:
> > There's a scipy.stats.mstats.rankdata() that take care of both ties and
> > missing data. Missing data are allocated a rank of either 0 or the
> > average rank, depending on some parameter.
> That sounds interesting. But I can't find it:
> >> import scipy
> >> from scipy import stats

Yes, you should do
>>> import scipy.stats.mstats as mstats
>>> mstats.rankdata

> In my implementation I leave the missing values as missing. I think
> that would be a nice option for rankdata.

Handling missing data is why I needed a tailored rankdata. 
In mstats.rankdata, if you set the use_missing optional parameter to False 
(the default), they will have a rank of 0. As no other value will have a rank 
of zero, you can then remask with masked_values or masked_equal.

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