scipy.stats.rankdata

scipy.stats.rankdata(a, method='average')

Assign ranks to data, dealing with ties appropriately.

Ranks begin at 1. The method argument controls how ranks are assigned to equal values. See [1] for further discussion of ranking methods.

Parameters:

• a (array_like) -- The array of values to be ranked. The array is first flattened.
• method (str, optional) --

  The method used to assign ranks to tied elements. The options are 'average', 'min', 'max', 'dense' and 'ordinal'.

  'average':

  The average of the ranks that would have been assigned to all the tied values is assigned to each value.

  'min':

  The minimum of the ranks that would have been assigned to all the tied values is assigned to each value. (This is also referred to as "competition" ranking.)

  'max':

  The maximum of the ranks that would have been assigned to all the tied values is assigned to each value.

  'dense':

  Like 'min', but the rank of the next highest element is assigned the rank immediately after those assigned to the tied elements.

  'ordinal':

  All values are given a distinct rank, corresponding to the order that the values occur in a.

  The default is 'average'.

Returns:

ranks -- An array of length equal to the size of a, containing rank scores.

Return type:

ndarray

Notes

All floating point types are converted to numpy.float64 before ranking. This may result in spurious ties if an input array of floats has a wider data type than numpy.float64 (e.g. numpy.float128).

References

[1]	"Ranking", http://en.wikipedia.org/wiki/Ranking

Examples

>>> rankdata([0, 2, 3, 2])
array([ 1. ,  2.5,  4. ,  2.5])
>>> rankdata([0, 2, 3, 2], method='min')
array([ 1.,  2.,  4.,  2.])
>>> rankdata([0, 2, 3, 2], method='max')
array([ 1.,  3.,  4.,  3.])
>>> rankdata([0, 2, 3, 2], method='dense')
array([ 1.,  2.,  3.,  2.])
>>> rankdata([0, 2, 3, 2], method='ordinal')
array([ 1.,  2.,  4.,  3.])