How can I convert pandas date time xticks to readable format?

I am plotting a time series with a date time index. The plot needs to be a particular size for the journal format. Consequently, the sticks are not readable since they span many years.

Here is a data sample

2013-02-10  0.7714492098202259
2013-02-11  0.7709101833765016
2013-02-12  0.7704911332770049
2013-02-13  0.7694975914173087
2013-02-14  0.7692108921323576

The data is a series with a datetime index and spans from 2013 to 2016. I use

data.plot(ax = ax)

to plot the data.

How can I format my xticks to read like '13 instead of 2013?

It seems there is some incompatibility between pandas and matplotlib formatters/locators when it comes to dates. See e.g. those questions:

• Pandas plot - modify major and minor xticks for dates

• Pandas Dataframe line plot display date on xaxis

I'm not entirely sure why it still works in some cases to use matplotlib formatters and not in others. However because of those issues, the bullet-proof solution is to use matplotlib to plot the graph instead of the pandas plotting function. This allows to use locators and formatters just as seen in the matplotlib example.

Here the solution to the question would look as follows:

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np

dates = pd.date_range("2013-01-01", "2017-06-20" )
y = np.cumsum(np.random.normal(size=len(dates)))

s = pd.Series(y, index=dates)

fig, ax = plt.subplots()
ax.plot(s.index, s.values)

ax.xaxis.set_major_locator(mdates.YearLocator())
ax.xaxis.set_minor_locator(mdates.MonthLocator())
yearFmt = mdates.DateFormatter("'%y")
ax.xaxis.set_major_formatter(yearFmt)

plt.show()