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:

import pandas print 'pandas.__version__ is ', pandas.__version__ print 'matplotlib.__version__ is ', matplotlib.__version__      dStart = datetime.datetime(2011,5,1) # 1 May dEnd = datetime.datetime(2011,7,1) # 1 July      dateIndex = pandas.date_range(start=dStart, end=dEnd, freq='D') print "1 May to 1 July 2011", dateIndex        testSeries = pandas.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(matplotlib.dates.WeekdayLocator(byweekday=(1),                                                            interval=1)) ax.xaxis.set_minor_formatter(matplotlib.dates.DateFormatter('%d\n%a')) ax.xaxis.grid(True, which="minor") ax.xaxis.grid(False, which="major") ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('\n\n\n%b%Y')) plt.show()      # set the major xticks and labels through pandas ax2 = plt.figure(figsize=(7,4), dpi=300).add_subplot(111) xticks = pandas.date_range(start=dStart, end=dEnd, freq='W-Tue') print "xticks: ", xticks 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]); # set the text of the first few minor ticks created by pandas.plot #    ax2.set_xticklabels(['a','b','c','d','e'], minor=True) # remove the minor xtick labels set by pandas.plot  ax2.set_xticklabels([], minor=True) # turn the minor ticks created by pandas.plot off  # plt.minorticks_off() plt.show() print testSeries['6/4/2011':'6/7/2011'] 

and its output:

pandas.__version__ is  0.9.1.dev-3de54ae matplotlib.__version__ is  1.1.1 1 May to 1 July 2011 <class 'pandas.tseries.index.DatetimeIndex'> [2011-05-01 00:00:00, ..., 2011-07-01 00:00:00] Length: 62, Freq: D, Timezone: None 

xticks:  <class 'pandas.tseries.index.DatetimeIndex'> [2011-05-03 00:00:00, ..., 2011-06-28 00:00:00] Length: 9, Freq: W-TUE, Timezone: None 

2011-06-04   -0.199393 2011-06-05   -0.043118 2011-06-06    0.477771 2011-06-07   -0.033207 Freq: D 

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.

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])

Answer 2

To turn off Pandas Datetime tick adjustment, you have to add the argument x_compat=True

Example:

ds.plot(x_compat=True) 

See more examples in the Pandas documentation: Suppressing tick resolution adjustment