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# 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 [R629] 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 : ndarray An array of length equal to the size of a, containing rank scores.

  References

  [R629] | (1, 2) “Ranking”, http://en.wikipedia.org/wiki/Ranking

  Examples

      >>> from scipy.stats import rankdata
      >>> 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])