Weird DatetimeIndex + secondary y axis plotting issue

Today I stumbled on weird issue with plotting two series with a datetimeindex and a secondary axis.

Code that illustrates the problem:

a = pd.Series(
    [16, 13, 11],
    index=pd.to_datetime(['2017-09-13', '2017-09-14', '2017-09-16'], format='%Y-%m-%d')
)
b = pd.Series(
    [23, 27, 25],
    index=pd.to_datetime(['2017-09-13', '2017-09-14', '2017-09-15'], format='%Y-%m-%d')
)

# Combinations of `secondary_y` values to try out.
secondary_ys = [(False, False), (False, True), (True, False), (True, True)]

fig, axes = plt.subplots(figsize=(10, 2), ncols=4, nrows=1)
for (sya, syb), ax in zip(secondary_ys, axes.flat):
    a.plot(ax=ax, style='o-', secondary_y=sya)
    b.plot(ax=ax, style='x-', secondary_y=syb)
    ax.set_title('a:%r - b:%r' % (sya, syb))

• b is a straightforward time series with successive days
• a has a one day jump in the last item
• the for loop tries out all the possible combinations of assigning these two series to the primary or secondary y axis in a plot
• Whenever one or more series is assigned to the secondary y axis, the x axis is completely confused:

More in depth experimentation (and version information) can be found in the notebook at https://gist.github.com/soxofaan/9fdfdeafb8fb555dd8547bc48a27e2f3 (also notice the inconsistant x axes label formatting and orientation)

[biddisco]

Is there any kind of workaround that can be used to disable the labels on either the primary or secondary plot so that we can can get what we want with the first dataset and then overlay the second without the labels disappearing? (I experimented with different formatters/locators without success)

[soxofaan]

Ok, this was quite a rabbit hole to debug, let’s see if I can manage to write down what I found.

Main reason that the lines in graph with primary and secondary axis do not properly cover the same x range, is the difference in conversion between:

• pandas.plotting._converter.DatetimeConverter converts date to number of days since 0001-01-01 plus one. Example: 2017-12-16 -> 736679
• pandas.plotting._converter.PeriodConverter convers to number of days since 1970-01-01. Example: 2017-12-16 -> 17516

Short description what is happening, starting with the same data as above:

a = pd.Series([16, 13, 11], index=pd.to_datetime(['2017-09-13', '2017-09-14', '2017-09-16'], format='%Y-%m-%d'))
b = pd.Series([23, 27, 25], index=pd.to_datetime(['2017-09-13', '2017-09-14', '2017-09-15'], format='%Y-%m-%d'))

First plot on primary axis:

a.plot(ax=ax, style='o-', secondary_y=False)

in LinePlot._make_plot the self._is_ts_plot() check returns False https://github.com/pandas-dev/pandas/blob/265e3272b8709a7be274321ee8b505a0e74b8e10/pandas/plotting/_core.py#L947 because (among other reasons) in pandas.plotting._timeseries._use_dynamic_x the frequency variables freq (and ax_freq) are None for (“irregular”) series a. https://github.com/pandas-dev/pandas/blob/265e3272b8709a7be274321ee8b505a0e74b8e10/pandas/plotting/_timeseries.py#L211 As a result of this a DatetimeConverter is set up as converter for the x axis .

Second plot on secondary axis:

b.plot(ax=ax, style='o-', secondary_y=True)

For the secondary axis twin axes is created. Now, in LinePlot._make_plot, the self._is_ts_plot() check returns True:

• in pandas.plotting._timeseries._use_dynamic_x we have now freq='D' (and ax_freq = None ) because the b series has a clean successive day index.
• a bit further there is https://github.com/pandas-dev/pandas/blob/265e3272b8709a7be274321ee8b505a0e74b8e10/pandas/plotting/_timeseries.py#L217 which, if I understand the comment correctly, seems to want to cover the problem of mixing “irregular” and “clean” time indexes. However: there is an additional check len(ax.get_lines()) > 0 which evaluates to False because the previous plot is done on the primary axes, and we can not reach that from here and we count zero lines.
• the control flow in _use_dynamic_x goes a bit further, but finally returns True

Back in LinePlot._make_plot, the series with DatetimeIndex is now converted to one with PeriodIndex, and as a result a PeriodConverter is associated now with the axes.

Summary

During plot of a the axes are initially set up using the DatetimeConverter mapping (e.g. for xlim stuff). During plot of b a non compatible PeriodConverter enters the stage. The end result is confusion in x axis conversion for the two line plots.

I guess there are several possible solutions (but my understanding of the inner workings of pandas and matplotlib are limited, so I don’t really know the best way forward in terms of breaking things and keeping things managable):

• make sure DatetimeConverter and PeriodConverter are “more compatible”
• for pandas.plotting._timeseries._use_dynamic_x in a secondary axes context: make it possible to check if there are already lines in the corresponding primary axes, to avoid replacing the DatetimeConverter with PeriodConverter
• don’t do the conversion to a PeriodIndex series/dataframe in LinePlot._make_plot in the first place

I hope this sheds some light on what is happening here.

(note that I didn’t look into the case where both plots are done on the secondary axes, but I guess it is related)