Pandas timeseries plot setting x-axis major and minor ticks and labels

Question

I want to be able to set the major and minor xticks and their labels for a time series graph plotted from a Pandas time series object.

The Pandas 0.9 "what's new" page says:

"you can either use to_pydatetime or register a converter for the Timestamp type"

but I can't work out how to do that so that I can use the matplotlib ax.xaxis.set_major_locator and ax.xaxis.set_major_formatter (and minor) commands.

If I use them without converting the pandas times, the x-axis ticks and labels end up wrong.

By using the 'xticks' parameter, I can pass the major ticks to pandas' .plot, and then set the major tick labels. I can't work out how to do the minor ticks using this approach (I can set the labels on the default minor ticks set by pandas' .plot).

Here is my test code:

Graph with strange dates on xaxis

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

dateIndex = pd.date_range(start='2011-05-01', end='2011-07-01', freq='D')
testSeries = pd.Series(data=np.random.randn(len(dateIndex)), index=dateIndex)    

ax = plt.figure(figsize=(7,4), dpi=300).add_subplot(111)
testSeries.plot(ax=ax, style='v-', label='first line')    

# using MatPlotLib date time locators and formatters doesn't work with new
# pandas datetime index
ax.xaxis.set_minor_locator(mdates.WeekdayLocator())
ax.xaxis.set_minor_formatter(mdates.DateFormatter('%d\n%a'))
ax.xaxis.grid(True, which="minor")
ax.xaxis.grid(False, which="major")
ax.xaxis.set_major_formatter(mdates.DateFormatter('\n\n\n%b%Y'))
plt.show()    

Graph with correct dates (without minor ticks)

# set the major xticks and labels through pandas
ax2 = plt.figure(figsize=(7,4), dpi=300).add_subplot(111)
xticks = pd.date_range(start='2011-05-01', end='2011-07-01', freq='W-Tue')
testSeries.plot(ax=ax2, style='-v', label='second line', xticks=xticks.to_pydatetime())
ax2.set_xticklabels([x.strftime('%a\n%d\n%h\n%Y') for x in xticks]);
# remove the minor xtick labels set by pandas.plot 
ax2.set_xticklabels([], minor=True)
# turn the minor ticks created by pandas.plot off 
plt.show()

Update: I've been able to get closer to the layout I wanted by using a loop to build the major xtick labels:

# only show month for first label in month
month = dStart.month - 1
xticklabels = []
for x in xticks:
    if  month != x.month :
        xticklabels.append(x.strftime('%d\n%a\n%h'))
        month = x.month
    else:
        xticklabels.append(x.strftime('%d\n%a'))

However, this is a bit like doing the x-axis using ax.annotate: possible but not ideal.

How do I set the major and minor ticks when plotting pandas time-series data?

Answer 1

Both pandas and matplotlib.dates use matplotlib.units for locating the ticks.

But while matplotlib.dates has convenient ways to set the ticks manually, pandas seems to have the focus on auto formatting so far (you can have a look at the code for date conversion and formatting in pandas).

So for the moment it seems more reasonable to use matplotlib.dates (as mentioned by @BrenBarn in his comment).

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

idx = pd.date_range('2011-05-01', '2011-07-01')
s = pd.Series(np.random.randn(len(idx)), index=idx)

fig, ax = plt.subplots()
ax.plot_date(idx.to_pydatetime(), s, 'v-')
ax.xaxis.set_minor_locator(dates.WeekdayLocator(byweekday=(1),
                                                interval=1))
ax.xaxis.set_minor_formatter(dates.DateFormatter('%d\n%a'))
ax.xaxis.grid(True, which="minor")
ax.yaxis.grid()
ax.xaxis.set_major_locator(dates.MonthLocator())
ax.xaxis.set_major_formatter(dates.DateFormatter('\n\n\n%b\n%Y'))
plt.tight_layout()
plt.show()

(my locale is German, so that Tuesday [Tue] becomes Dienstag [Di])