class_docstr: Tests for multiple comparisons
  
  Parameters
  ----------
  data : ndarray
      independent data samples
  groups : ndarray
      group labels corresponding to each data point
  group_order : list[str], optional
      the desired order for the group mean results to be reported in. If
      not specified, results are reported in increasing order.
      If group_order does not contain all labels that are in groups, then
      only those observations are kept that have a label in group_order.
class_docstr_tok: ["Tests","for","multiple","comparisons","Parameters","--","--","--","--","--","data",":","ndarray","independent","data","samples","groups",":","ndarray","group","labels","corresponding","to","each","data","point","group_order",":","list","[","str","]",",","optional","the","desired","order","for","the","group","mean","results","to","be","reported","in",".","If","not","specified",",","results","are","reported","in","increasing","order",".","If","group_order","does","not","contain","all","labels","that","are","in","groups",",","then","only","those","observations","are","kept","that","have","a","label","in","group_order","."]
docstr: convert data to rankdata and attach
  
  
  This creates rankdata as it is used for non-parametric tests, where
  in the case of ties the average rank is assigned.
docstr_tok: ["convert","data","to","rankdata","and","attach","This","creates","rankdata","as","it","is","used","for","non-parametric","tests",",","where","in","the","case","of","ties","the","average","rank","is","assigned","."]
code: def getranks(self):
      """convert data to rankdata and attach
  
  
      This creates rankdata as it is used for non-parametric tests, where
      in the case of ties the average rank is assigned.
  
  
      """
      # bug: the next should use self.groupintlab instead of self.groups
      # update: looks fixed
      # self.ranks = GroupsStats(np.column_stack([self.data, self.groups]),
      self.ranks = GroupsStats(
          np.column_stack([self.data, self.groupintlab]), useranks=True
      )
      self.rankdata = self.ranks.groupmeanfilter
  
imports: from collections import namedtuple
  from statsmodels.compat.python import lzip, lrange
  import copy
  import math
  import numpy
  from numpy.testing import assert_almost_equal, assert_equal
  from scipy import stats, interpolate
  from statsmodels.iolib.table import SimpleTable
  from statsmodels.stats.multitest import multipletests, _ecdf, fdrcorrection, fdrcorrection_twostage
  from statsmodels.graphics import utils
  from statsmodels.tools.sm_exceptions import ValueWarning
label_desc: Use image node_id 2 for calling the MultiComparison obj's underlying member method code with example usage: obj.getranks() without return types