Question: I want a grouped axis label for box-plots for example a bit like this bar chart where the x axis is hierarchical:  I am struggling to work with groupby objects to extract the values for the box plot. I have found this heatmap example which references this stacked bar answer from @Stein but I can't get it to work for my box plots (I know I don't want the 'sum' of the groups but can't figure out how to get the values I want grouped correctly). In my real data the group sizes will be different, not all the same as in the example data. I don't want to use seaborn's 'hue' as a solution as I want all the boxes the same color. This is the closest I have got, thanks: import numpy as np import seaborn as sns import matplotlib.pyplot as plt from itertools import groupby def test_table(): data_table = pd.DataFrame({'Room':['Room A']*24 + ['Room B']*24, 'Shelf':(['Shelf 1']*12 + ['Shelf 2']*12)*2, 'Staple':['Milk','Water','Sugar','Honey','Wheat','Corn']*8, 'Quantity':np.random.randint(1, 20, 48), }) return data_table def add_line(ax, xpos, ypos): line = plt.Line2D([xpos, xpos], [ypos + .1, ypos], transform=ax.transAxes, color='black') line.set_clip_on(False) ax.add_line(line) def label_len(my_index,level): labels = my_index.get_level_values(level) return [(k, sum(1 for i in g)) for k,g in groupby(labels)] def label_group_bar_table(ax, df): ypos = -.1 scale = 1./df.index.size for level in range(df.index.nlevels)[::-1]: pos = 0 for label, rpos in label_len(df.index,level): lxpos = (pos + .5 * rpos)*scale ax.text(lxpos, ypos, label, ha='center', transform=ax.transAxes) add_line(ax, pos*scale, ypos) pos += rpos add_line(ax, pos*scale , ypos) ypos -= .1 df = test_table().groupby(['Room','Shelf','Staple']).sum() fig = plt.figure() fig = plt.figure(figsize = (15, 10)) ax = fig.add_subplot(111) sns.boxplot(x=df.Quantity, y=df.Quantity,data=df) #Below 3 lines remove default labels labels = ['' for item in ax.get_xticklabels()] ax.set_xticklabels(labels) ax.set_xlabel('') label_group_bar_table(ax, df) fig.subplots_adjust(bottom=.1*df.index.nlevels) plt.show()  Which gives: 

Answer: You can use: import numpy as np import seaborn as sns import matplotlib.pyplot as plt from itertools import groupby def test_table(): data_table = pd.DataFrame({'Room':['Room A']*24 + ['Room B']*24, 'Shelf':(['Shelf 1']*12 + ['Shelf 2']*12)*2, 'Staple':['Milk','Water','Sugar','Honey','Wheat','Corn']*8, 'Quantity':np.random.randint(1, 20, 48), }) return data_table def add_line(ax, xpos, ypos): line = plt.Line2D([xpos, xpos], [ypos + .1, ypos], transform=ax.transAxes, color='black') line.set_clip_on(False) ax.add_line(line) def label_len(my_index,level): labels = my_index.get_level_values(level) return [(k, sum(1 for i in g)) for k,g in groupby(labels)] # HERE: Replace all df.index occurrences with levels def label_group_bar_table(ax, levels): ypos = -.1 scale = 1./levels.size for level in range(levels.nlevels)[::-1]: pos = 0 for label, rpos in label_len(levels, level): lxpos = (pos + .5 * rpos)*scale ax.text(lxpos, ypos, label, ha='center', transform=ax.transAxes) add_line(ax, pos*scale, ypos) pos += rpos add_line(ax, pos*scale , ypos) ypos -= .1 # HERE: Don't aggregate your data df = test_table() # HERE: Create a unique group identifier df['ID'] = df.groupby(['Room', 'Shelf', 'Staple']).ngroup() # HERE: Create an ordered MultiIndex for label_group_bar_table levels = df.drop_duplicates('ID').sort_values('ID')[['Room', 'Shelf', 'Staple']] levels = pd.MultiIndex.from_frame(levels) fig = plt.figure(figsize = (15, 10)) ax = fig.add_subplot(111) # HERE: Set 'ID' as x-axis sns.boxplot(x='ID', y='Quantity', data=df) #Below 3 lines remove default labels labels = ['' for item in ax.get_xticklabels()] ax.set_xticklabels(labels) ax.set_xlabel('') ax.set_xticks([]) # HERE: Remove xticks label_group_bar_table(ax, levels) fig.subplots_adjust(bottom=.1*levels.nlevels) plt.show()  To get: