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Showing content from https://github.com/pandas-dev/pandas/issues/8614 below:

Plotting of DatetimeIndex directly with matplotlib no longer gives datetime formatted axis (0.15) · Issue #8614 · pandas-dev/pandas · GitHub

From http://stackoverflow.com/questions/26526230/plotting-datetimeindex-on-x-axis-with-matplotlib-creates-wrong-ticks-in-pandas-0

df = pd.DataFrame({'RandomValues': np.random.randint(1, 50, 60)},
                  index=pd.date_range("2012-01-01", periods=60))

Plotting of course works:

In [79]: df.plot()
Out[79]: <matplotlib.axes._subplots.AxesSubplot at 0xf571550>

But plotting with matplotlib's plot functions gives no longer a datetime formatted x-axis, but just ints:

In [80]: plt.plot(df.index, df.RandomValues)
Out[80]: [<matplotlib.lines.Line2D at 0xfb5f748>]

Reason: matplotlibs plot calls np.atleast_1d on the input (https://github.com/matplotlib/matplotlib/blob/v1.4.0/lib/matplotlib/axes/_base.py#L220). Previously this returned an Index, now it returns a array of datetime64 values, which matplotlib can't handle.

Issues:

• This is actually a problem of matplotlib (they should support datetime64), but of course: a lot of users rely now on the behaviour that it did work with a pandas Index also.
• Is there a way to restore this behaviour? I don't see directly a solution?
• We should update our docs, as this behaviour is also mentioned there briefly: http://pandas.pydata.org/pandas-docs/stable/visualization.html#plotting-directly-with-matplotlib + figure out why this didn't fail in the doc build (the figure seems not updated with the 0.15 doc build)

It seems there was some discussion about this in the Index-no-subclass PR: #7891 (comment) and #7891 (comment) (I commented there about exactly this issue, but I don't know why we didn't look further in it then)

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