docs: def histogram_bin_edges_mincount(data, min_count, bins):
    r'''
  Merge bins with right-neighbour until each bin has a minimum number of data-points.
  
  :arguments:
  
    **data** (``<array_like>``)
      Input data. The histogram is computed over the flattened array.
  
    **bins** (``<array_like>`` | ``<int>``)
      The bin-edges (or the number of bins, automatically converted to equal-sized bins).
  
    **min_count** (``<int>``)
      The minimum number of data-points per bin.
    '''
  
    # escape
    if min_count is None : return bins
    if min_count is False: return bins
  
    # check
    if type(min_count) != int: raise IOError('"min_count" must be an integer number')
  
    # keep removing where needed
    while True:
  
      P, _ = np.histogram(data, bins=bins, density=False)
  
      idx = np.where(P < min_count)[0]
  
      if len(idx) == 0: return bins
  
      idx = idx[0]
  
      if idx+1 == len(P): bins = np.hstack(( bins[:(idx)  ], bins[-1]       ))
      else              : bins = np.hstack(( bins[:(idx+1)], bins[(idx+2):] ))