code: def seasonal_plot(grouped_x, xticklabels, ylabel=None, ax=None):
      """
      Consider using one of month_plot or quarter_plot unless you need
      irregular plotting.
  
      Parameters
      ----------
      grouped_x : iterable of DataFrames
          Should be a GroupBy object (or similar pair of group_names and groups
          as DataFrames) with a DatetimeIndex or PeriodIndex
      xticklabels : list of str
          List of season labels, one for each group.
      ylabel : str
          Lable for y axis
      ax : AxesSubplot, optional
          If given, this subplot is used to plot in instead of a new figure being
          created.
      """
      fig, ax = utils.create_mpl_ax(ax)
      start = 0
      ticks = []
      for season, df in grouped_x:
          df = df.copy()  # or sort balks for series. may be better way
          df.sort_index()
          nobs = len(df)
          x_plot = np.arange(start, start + nobs)
          ticks.append(x_plot.mean())
          ax.plot(x_plot, df.values, "k")
          ax.hlines(
              df.values.mean(),
              x_plot[0],
              x_plot[-1],
              colors="r",
              linewidth=3,
          )
          start += nobs
  
      ax.set_xticks(ticks)
      ax.set_xticklabels(xticklabels)
      ax.set_ylabel(ylabel)
      ax.margins(0.1, 0.05)
      return fig
  
file_path: statsmodels/statsmodels/graphics/tsaplots.py
imports: from statsmodels.compat.pandas import deprecate_kwarg
  import calendar
  import numpy
  import pandas
  from statsmodels.graphics import utils
  from statsmodels.tools.validation import array_like
  from statsmodels.tsa.stattools import acf, pacf, ccf
label_desc: Use image node_id 7 for calling a global function with example usage: seasonal_plot(grouped_x, xticklabels, ylabel, ax) and returns: fig