Q:

How do I plot hatched bars using pandas?

I am trying to achieve differentiation by hatch pattern instead of by (just) colour. How do I do it using pandas?
It's possible in matplotlib, by passing the hatch optional argument as discussed here. I know I can also pass that option to a pandas plot, but I don't know how to tell it to use a different hatch pattern for each DataFrame column.
df = pd.DataFrame(rand(10, 4), columns=['a', 'b', 'c', 'd'])
df.plot(kind='bar', hatch='/');

For colours, there is the colormap option described here. Is there something similar for hatching? Or can I maybe set it manually by modifying the Axes object returned by plot?

A:

This is kind of hacky but it works:
df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
ax = plt.figure(figsize=(10, 6)).add_subplot(111)
df.plot(ax=ax, kind='bar', legend=False)

bars = ax.patches
hatches = ''.join(h*len(df) for h in 'x/O.')

for bar, hatch in zip(bars, hatches):
    bar.set_hatch(hatch)

ax.legend(loc='center right', bbox_to_anchor=(1, 1), ncol=4)

A:

This code allows you a little more freedom when defining the patterns, so you can have '//', etc.
bars = ax.patches
patterns =('-', '+', 'x','/','//','O','o','\\','\\\\')
hatches = [p for p in patterns for i in range(len(df))]
for bar, hatch in zip(bars, hatches):
    bar.set_hatch(hatch)

