How to add group labels for bar charts in matplotlib

Since I could not find a built-in solution for this in matplotlib, I coded my own:

#!/usr/bin/env pythonfrom matplotlib import pyplot as pltdef mk_groups(data):    try:        newdata = data.items()    except:        return    thisgroup = []    groups = []    for key, value in newdata:        newgroups = mk_groups(value)        if newgroups is None:            thisgroup.append((key, value))        else:            thisgroup.append((key, len(newgroups[-1])))            if groups:                groups = [g + n for n, g in zip(newgroups, groups)]            else:                groups = newgroups    return [thisgroup] + groupsdef 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_group_bar(ax, data):    groups = mk_groups(data)    xy = groups.pop()    x, y = zip(*xy)    ly = len(y)    xticks = range(1, ly + 1)    ax.bar(xticks, y, align='center')    ax.set_xticks(xticks)    ax.set_xticklabels(x)    ax.set_xlim(.5, ly + .5)    ax.yaxis.grid(True)    scale = 1. / ly    for pos in xrange(ly + 1):  # change xrange to range for python3        add_line(ax, pos * scale, -.1)    ypos = -.2    while groups:        group = groups.pop()        pos = 0        for label, rpos in group:            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 -= .1if __name__ == '__main__':    data = {'Room A':               {'Shelf 1':                   {'Milk': 10,                    'Water': 20},                'Shelf 2':                   {'Sugar': 5,                    'Honey': 6}               },            'Room B':               {'Shelf 1':                   {'Wheat': 4,                    'Corn': 7},                'Shelf 2':                   {'Chicken': 2,                    'Cow': 1}               }           }    fig = plt.figure()    ax = fig.add_subplot(1,1,1)    label_group_bar(ax, data)    fig.subplots_adjust(bottom=0.3)    fig.savefig('label_group_bar_example.png')

The mk_groups function takes a dictionary (or anything with an items() method, like collections.OrderedDict) and converts it to a data format that is then used to create the chart. It is basically a list of the form:

[ [(label, bars_to_span), ...], ..., [(tick_label, bar_value), ...] ]

The add_line function creates a vertical line in the subplot at the specified positions (in axes coordinates).

The label_group_bar function takes a dictionary and creates the bar chart in the subplot with the labels beneath. The result from the example then looks like this.

Easier or better solutions and suggestions are still very much appreciated.

I was looking for this solution for a while.I modified it some to work with a pandas data table.Only fair to share.

import pandas as pdimport numpy as npfrom matplotlib import pyplot as pltfrom itertools import groupbydef test_table():    data_table = pd.DataFrame({'Room':['Room A']*4 + ['Room B']*4,                               'Shelf':(['Shelf 1']*2 + ['Shelf 2']*2)*2,                               'Staple':['Milk','Water','Sugar','Honey','Wheat','Corn','Chicken','Cow'],                               'Quantity':[10,20,5,6,4,7,2,1],                               'Ordered':np.random.randint(0,10,8)                               })    return data_tabledef 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 -= .1df = test_table().groupby(['Room','Shelf','Staple']).sum()fig = plt.figure()ax = fig.add_subplot(111)df.plot(kind='bar',stacked=True,ax=fig.gca())#Below 3 lines remove default labelslabels = ['' 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()