code: def posthoc_anderson(
      a: Union[list, np.ndarray, DataFrame],
      val_col: str = None,
      group_col: str = None,
      midrank: bool = True,
      p_adjust: str = None,
      sort: bool = False,
  ) -> DataFrame:
      """Anderson-Darling Pairwise Test for k-samples. Tests the null hypothesis
      that k-samples are drawn from the same population without having to specify
      the distribution function of that population [1]_.
  
      Parameters
      ----------
      a : array_like or pandas DataFrame object
          An array, any object exposing the array interface or a pandas
          DataFrame.
  
      val_col : str, optional
          Name of a DataFrame column that contains dependent variable values (test
          or response variable). Values should have a non-nominal scale. Must be
          specified if `a` is a pandas DataFrame object.
  
      group_col : str, optional
          Name of a DataFrame column that contains independent variable values
          (grouping or predictor variable). Values should have a nominal scale
          (categorical). Must be specified if `a` is a pandas DataFrame object.
  
      midrank : bool, optional
          Type of Anderson-Darling test which is computed. If set to True (default), the
          midrank test applicable to continuous and discrete populations is performed. If
          False, the right side empirical distribution is used.
  
      sort : bool, optional
          If True, sort data by block and group columns.
  
      p_adjust : str, optional
          Method for adjusting p values. See statsmodels.sandbox.stats.multicomp for details. Available methods are:
          'bonferroni' : one-step correction
          'sidak' : one-step correction
          'holm-sidak' : step-down method using Sidak adjustments
          'holm' : step-down method using Bonferroni adjustments
          'simes-hochberg' : step-up method  (independent)
          'hommel' : closed method based on Simes tests (non-negative)
          'fdr_bh' : Benjamini/Hochberg  (non-negative)
          'fdr_by' : Benjamini/Yekutieli (negative)
          'fdr_tsbh' : two stage fdr correction (non-negative)
          'fdr_tsbky' : two stage fdr correction (non-negative)
  
      Returns
      -------
      result : pandas.DataFrame
          P values.
  
      References
      ----------
      .. [1] F.W. Scholz, M.A. Stephens (1987), K-Sample Anderson-Darling Tests,
          Journal of the American Statistical Association, Vol. 82, pp. 918-924.
  
      Examples
      --------
      >>> x = np.array([[2.9, 3.0, 2.5, 2.6, 3.2], [3.8, 2.7, 4.0, 2.4], [2.8, 3.4, 3.7, 2.2, 2.0]])
      >>> sp.posthoc_anderson(x)
      """
      x, _val_col, _group_col = __convert_to_df(a, val_col, group_col)
      x = x.sort_values(by=[_group_col], ascending=True) if sort else x
  
      groups = x[_group_col].unique()
      k = groups.size
      vs = np.zeros((k, k), dtype=float)
      combs = it.combinations(range(k), 2)
  
      tri_upper = np.triu_indices(vs.shape[0], 1)
      tri_lower = np.tril_indices(vs.shape[0], -1)
      vs[:, :] = 0
  
      for i, j in combs:
          vs[i, j] = ss.anderson_ksamp(
              [
                  x.loc[x[_group_col] == groups[i], _val_col],
                  x.loc[x[_group_col] == groups[j], _val_col],
              ],
              midrank=midrank,
          )[2]
  
      if p_adjust:
          vs[tri_upper] = multipletests(vs[tri_upper], method=p_adjust)[
              1
          ]
  
      vs[tri_lower] = np.transpose(vs)[tri_lower]
      np.fill_diagonal(vs, 1)
      return DataFrame(vs, index=groups, columns=groups)
  
imports: import itertools
  from typing import Tuple, Union
  import numpy
  import scipy.stats
  from statsmodels.sandbox.stats.multicomp import multipletests
  from statsmodels.stats.multicomp import pairwise_tukeyhsd
  from statsmodels.stats.libqsturng import psturng
  from pandas import DataFrame
label_desc: Use image node_id 12 for calling a global function with example usage: posthoc_anderson(a, val_col, group_col, midrank, p_adjust, sort) and returns: DataFrame