docs: def median_grouped(data, interval=1):
      """"Return the 50th percentile (median) of grouped continuous data.
  
      >>> median_grouped([1, 2, 2, 3, 4, 4, 4, 4, 4, 5])
      3.7
      >>> median_grouped([52, 52, 53, 54])
      52.5
  
      This calculates the median as the 50th percentile, and should be
      used when your data is continuous and grouped. In the above example,
      the values 1, 2, 3, etc. actually represent the midpoint of classes
      0.5-1.5, 1.5-2.5, 2.5-3.5, etc. The middle value falls somewhere in
      class 3.5-4.5, and interpolation is used to estimate it.
  
      Optional argument ``interval`` represents the class interval, and
      defaults to 1. Changing the class interval naturally will change the
      interpolated 50th percentile value:
  
      >>> median_grouped([1, 3, 3, 5, 7], interval=1)
      3.25
      >>> median_grouped([1, 3, 3, 5, 7], interval=2)
      3.5
  
      This function does not check whether the data points are at least
      ``interval`` apart.
      """
      data = sorted(data)
      n = len(data)
      if n == 0:
          raise StatisticsError("no median for empty data")
      elif n == 1:
          return data[0]
      # Find the value at the midpoint. Remember this corresponds to the
      # centre of the class interval.
      x = data[n//2]
      for obj in (x, interval):
          if isinstance(obj, (str, bytes)):
              raise TypeError('expected number but got %r' % obj)
      try:
          L = x - interval/2  # The lower limit of the median interval.
      except TypeError:
          # Mixed type. For now we just coerce to float.
          L = float(x) - float(interval)/2
      cf = data.index(x)  # Number of values below the median interval.
      # FIXME The following line could be more efficient for big lists.
      f = data.count(x)  # Number of data points in the median interval.
      return L + interval*(n/2 - cf)/f