content: 
  from __future__ import print_function
  import numpy as np
  import matplotlib.pyplot as plt
  
  import statsmodels.api as sm
  
  
  # Necessary to make horizontal axis labels fit
  plt.rcParams['figure.subplot.bottom'] = 0.23
  
  data = sm.datasets.anes96.load_pandas()
  party_ID = np.arange(7)
  labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat",
            "Independent-Independent", "Independent-Republican",
            "Weak Republican", "Strong Republican"]
  
  # Group age by party ID.
  age = [data.exog['age'][data.endog == id] for id in party_ID]
  
  
  # Create a violin plot.
  fig = plt.figure()
  ax = fig.add_subplot(111)
  
  sm.graphics.violinplot(age, ax=ax, labels=labels,
                         plot_opts={'cutoff_val':5, 'cutoff_type':'abs',
                                    'label_fontsize':'small',
                                    'label_rotation':30})
  
  ax.set_xlabel("Party identification of respondent.")
  ax.set_ylabel("Age")
  ax.set_title("US national election '96 - Age & Party Identification")
  
  
  # Create a bean plot.
  fig2 = plt.figure()
  ax = fig2.add_subplot(111)
  
  sm.graphics.beanplot(age, ax=ax, labels=labels,
                      plot_opts={'cutoff_val':5, 'cutoff_type':'abs',
                                 'label_fontsize':'small',
                                 'label_rotation':30})
  
  ax.set_xlabel("Party identification of respondent.")
  ax.set_ylabel("Age")
  ax.set_title("US national election '96 - Age & Party Identification")
  
  
  # Create a jitter plot.
  fig3 = plt.figure()
  ax = fig3.add_subplot(111)
  
  plot_opts={'cutoff_val':5, 'cutoff_type':'abs', 'label_fontsize':'small',
             'label_rotation':30, 'violin_fc':(0.8, 0.8, 0.8),
             'jitter_marker':'.', 'jitter_marker_size':3, 'bean_color':'#FF6F00',
             'bean_mean_color':'#009D91'}
  sm.graphics.beanplot(age, ax=ax, labels=labels, jitter=True,
                      plot_opts=plot_opts)
  
  ax.set_xlabel("Party identification of respondent.")
  ax.set_ylabel("Age")
  ax.set_title("US national election '96 - Age & Party Identification")
  
  
  # Create an asymmetrical jitter plot.
  ix = data.exog['income'] < 16  # incomes < $30k
  age = data.exog['age'][ix]
  endog = data.endog[ix]
  age_lower_income = [age[endog == id] for id in party_ID]
  
  ix = data.exog['income'] >= 20  # incomes > $50k
  age = data.exog['age'][ix]
  endog = data.endog[ix]
  age_higher_income = [age[endog == id] for id in party_ID]
  
  fig = plt.figure()
  ax = fig.add_subplot(111)
  
  plot_opts['violin_fc'] = (0.5, 0.5, 0.5)
  plot_opts['bean_show_mean'] = False
  plot_opts['bean_show_median'] = False
  plot_opts['bean_legend_text'] = 'Income < \$30k'
  plot_opts['cutoff_val'] = 10
  sm.graphics.beanplot(age_lower_income, ax=ax, labels=labels, side='left',
                       jitter=True, plot_opts=plot_opts)
  plot_opts['violin_fc'] = (0.7, 0.7, 0.7)
  plot_opts['bean_color'] = '#009D91'
  plot_opts['bean_legend_text'] = 'Income > \$50k'
  sm.graphics.beanplot(age_higher_income, ax=ax, labels=labels, side='right',
                       jitter=True, plot_opts=plot_opts)
  
  ax.set_xlabel("Party identification of respondent.")
  ax.set_ylabel("Age")
  ax.set_title("US national election '96 - Age & Party Identification")
  
  
  # Show all plots.
  plt.show()
  
  