How to add group labels for bar charts in matplotl

问题:

I want to plot data of the following form using matplotlib\'s bar plot feature:

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}
           }
       }

The bar chart is supposed to look like this. The bar groups should be visible from the labels on the x axis. Is there any way to do this with matplotlib?

回答1:

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

#!/usr/bin/env python

from matplotlib import pyplot as plt

def 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] + groups

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_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):
        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 -= .1

if __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.

回答2:

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 pd
import numpy as np
from matplotlib import pyplot as plt
from itertools import groupby

def 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_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()
ax = fig.add_subplot(111)
df.plot(kind=\'bar\',stacked=True,ax=fig.gca())
#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()