This visualization was the first draft of the last chart in this post. I added extra spacing between the bars to provide space for annotations; eventually I decided this was unnecessary.
#y-positions for the next graph. Add empty spaces (label='' and width=0) under each bar.
ypos = np.arange(chapterdata.shape[0]*2 - 1, 0, -1)
widths = list(itertools.chain.from_iterable((x, 0) for x in chapterdata['new_vocabulary']))[:-1]
labels = list(itertools.chain.from_iterable((str(x), '') for x in chapterdata.index))[:-1]
#Don't need extra room after the first two chapters.
ypos = np.delete(ypos, (0,1))
del(widths[1])
del(widths[2])
del(labels[1])
del(labels[2])
MOST_COMMON_COUNT = 5
fig, ax = plt.subplots(figsize=(14, 40))
#plt.barh('new_vocabulary', chapterdata.index, data=chapterdata,orient='h', color=mycolor)
ax.barh(ypos, widths, .8, tick_label=labels, color=mycolor)
plt.ylim([0, ypos.max()+1])
plt.grid(axis='x')
xax = ax.get_xaxis()
xax.set_label_position('top')
plt.xlabel("Count of New Vocabulary")
ax.xaxis.tick_top()
for chapnum in range(1, 58):
chaplbl = str(chapnum)
#print(str(widths[labels.index(chaplbl)]))
plt.text(10, ypos[labels.index(chaplbl)], str(widths[labels.index(chaplbl)]),\
weight='bold', color='white', verticalalignment='center')
if chapnum >= 3:
newwords = ', '.join(['"%s" (%d)'%word for word in newvocab[chapnum].most_common(MOST_COMMON_COUNT)])
#print(newwords)
plt.text(10, ypos[labels.index(chaplbl)]-1.1, 'Major new vocabulary:', weight='bold',\
bbox=dict(facecolor='#ffffff99'))
plt.text(500, ypos[labels.index(chaplbl)]-1.1, newwords,\
bbox=dict(facecolor='#ffffff99'))
plt.show()