docs: def ssad(patch, cols, splits):
      """
      Calculates an empirical intra-specific spatial abundance distribution
  
      Parameters
      ----------
      {0}
  
      Returns
      -------
      {1} Result has one column giving the individuals of species in each
      subplot.
  
      Notes
      -----
      {2}
  
      {3}
  
      Examples
      --------
  
      {4}
  
      >>> # Get the spatial abundance distribution for all species for each of
      >>> # the cells in the ANBO plot
      >>> all_spp_ssads = meco.empirical.ssad(pat, cols='spp_col:spp; count_col:count', splits='row:4; column:4')
  
      >>> # Convert to dict for easy searching
      >>> all_ssads_dict = dict(all_spp_ssads)
  
      >>> # Look up the spatial abundance distribution for 'grass'
      >>> all_ssads_dict['grass']
           y
      0    42
      1    20
      2    60
      3    60
      4    88
      5    86
      6    20
      7     0
      8   110
      9    12
      10  115
      11  180
      12  160
      13  120
      14   26
      15   11
  
      >>> # Each value in 'y' gives the abundance of grass in one of the 16 cells
  
      See http://www.macroeco.org/tutorial_macroeco.html for additional
      examples and explanation
  
      """
  
      # Get and check SAD
      sad_results = sad(patch, cols, splits, clean=False)
  
      # Create dataframe with col for spp name and numbered col for each split
      for i, sad_result in enumerate(sad_results):
          if i == 0:  # For first result, create dataframe
              fulldf = sad_result[1]
              fulldf.columns = ['spp', '0']  # Renames y col to 0
          else:  # For other results, append col to dataframe, named by num
              fulldf[str(i)] = sad_result[1]['y']
  
      # Get each spp SSAD (row of fulldf) and append as tuple in result_list
      result_list = []
      for _, row in fulldf.iterrows():
          row_values_array = np.array(row[1:], dtype=float)
          result_list.append((row[0], pd.DataFrame({'y': row_values_array})))
  
      # Return all results
      return result_list