[Matplotlib-users] Less tedious way to change the x-axis scale intervals with time series data?

[pybokeh <py...@gm...>]

Hello,
I'm trying to learn if there is a better or less tedious way of changing the
x-axis time scale interval size when plotting time series data using
MATPLOTLIB.

To account for all the different x-axis intervals that I may end up using, I
usually do the following imports:
from matplotlib.dates import YearLocator, MonthLocator, WeekdayLocator,
DateFormatter, DayLocator
from matplotlib.dates import MO, TU, WE, TH, FR, SA, SU

Then I specify specific intervals by doing the following:
year   = YearLocator()
month  = MonthLocator(bymonth=range(1,13),  bymonthday=1, interval=1)
week   = WeekdayLocator(byweekday=MO)  # Every MOnday
day    = DayLocator(bymonthday=range(1,32), interval=1)

Then apply any of the above formats by doing:
axes.xaxis.set_major_locator(year)
axes.xaxis.set_major_locator(month)

I find all that above too tedious since I may then decide to change the
intervals to something different to experiment.

With R's ggplot2 or Yhat's ggplot, it is very simple and intuitive to change
the x-axis scale interval.  You just need to specify the breaks and
minor_breaks interval like so:
scale_x_datetime(labels=date_format("%Y-%m"), breaks="3 months",
minor_breaks="1 month")

I've quit using MATPLOTLIB for plotting time series data because of this. 
However, I thought perhaps there is a better way that I am not aware of.

If there is a better way, please educate me!  Thanks!



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