Using pandas.TimeSeries_DateFormatter in bar plot?

xref #5774

When time series is set as index, line plot uses the TimeSeries_DateFormatter for recognizable date formatting. However, bar plot use the FixedFormatter which outputs the datetime as it is. Can bar plot also use the DateFormatter?

In [1]: from pandas import *
In [2]: from numpy.random import *
In [3]: ts = Series(randn(100), index=date_range('1/1/2000', periods=100))
In [4]: df = DataFrame(randn(100, 4), index=ts.index, columns=['A', 'B', 'C', 'D'])
In [5]: df = df.cumsum()

In [7]: a = df.plot(kind='bar')
In [9]: a.get_xaxis().get_major_formatter()
Out[9]: <matplotlib.ticker.FixedFormatter instance at 0x108700f38>

In [11]: a = df.plot()
In [12]: a.get_xaxis().get_major_formatter()
Out[12]: <pandas.tseries.converter.TimeSeries_DateFormatter instance at 0x10877ccf8>

[pwaller]

I found a workaround, by applying this at the end:

In this case, d is the DataFrame I’m plotting, and d.index is the DatetimeIndex.

plt.gca().xaxis.set_major_formatter(plt.FixedFormatter(d.index.to_series().dt.strftime("%b %Y")))

[pwaller]

I’m not a huge fan of the workaround and hit this quite frequently still. It also doesn’t produce x-axis labels which are as good a data.plot() does, because the latter does Year labels on a separate row than Month labels, and I don’t know how to achieve the same effect. I’d love for bar chars to be able to display time series data in the same way that line plots do, sometimes it is a less misleading way to present the data.