Reduce number of x tick marks and remove zero-padding

Question

I am new to matplotlib and pyplot and trying to plot a large data set. The below is a small snippet.

The plot works, but the xtick marks are crowded.

How can I reduce the number of tick marks?

Using plt.locator_params(nbins=4) returned an error:

AttributeError: 'FixedLocator' object has no attribute 'set_params'

And is there a way to remove the 0 padding from the date labels within pyplot?

import matplotlib.pyplot as plt


x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]
xticks = ['01/01', '01/02', '01/03', '01/04', '01/05', '01/06', '01/07', '01/08', '01/09', '01/10', '01/11', '01/12', '01/13', '01/14', '01/15', '01/16', '01/17', '01/18', '01/19', '01/20', '01/21', '01/22', '01/23', '01/24', '01/25', '01/26', '01/27', '01/28', '01/29', '01/30']
y = [80, 80, 60, 30, 90, 50, 200, 300, 200, 150, 10, 80, 20, 30, 40, 150, 160, 170, 180, 190, 20, 210, 220, 20, 20, 20, 200, 270, 280, 90, 00]
y2 = [100, 100, 200, 300, 40, 50, 60, 70, 80, 90, 100, 110, 12, 13, 10, 110, 16, 170, 80, 90, 20, 89, 28, 20, 20, 28, 60, 70, 80, 90, 30]


plt.plot(x, y)
plt.plot(x, y2)
plt.xticks(x, xticks, rotation=90)
plt.show()

Answer 1

Since matplotlib has some really nice tools for dates I think it makes sense to convert your date strings to datetime.datetime objects.

Then you can use one of the handy date-locators; in this case DayLocator makes the most sense. To get that to skip some of the labels you use the interval kwarg.

Then to drop the leading zero from your xticklabels you need a custom formatting function.

import datetime as dt

import matplotlib.pyplot as plt
import matplotlib.dates as mdates 
import matplotlib.ticker as tkr

def xfmt(x,pos=None):
    ''' custom date formatting '''
    x = mdates.num2date(x)
    label = x.strftime('%m/%d')
    label = label.lstrip('0')
    return label

x = ['01/01', '01/02', '01/03', '01/04', '01/05', '01/06', '01/07', '01/08', '01/09', '01/10', '01/11', '01/12', '01/13', '01/14', '01/15', '01/16', '01/17', '01/18', '01/19', '01/20', '01/21', '01/22', '01/23', '01/24', '01/25', '01/26', '01/27', '01/28', '01/29', '01/30', '01/31']
xdates = [dt.datetime.strptime(i,'%m/%d') for i in x]
y = [80, 80, 60, 30, 90, 50, 200, 300, 200, 150, 10, 80, 20, 30, 40, 150, 160, 170, 180, 190, 20, 210, 220, 20, 20, 20, 200, 270, 280, 90, 00]
y2 = [100, 100, 200, 300, 40, 50, 60, 70, 80, 90, 100, 110, 12, 13, 10, 110, 16, 170, 80, 90, 20, 89, 28, 20, 20, 28, 60, 70, 80, 90, 30]

plt.plot(xdates,y)
plt.plot(xdates,y2)
plt.setp(plt.gca().xaxis.get_majorticklabels(),rotation=90)
plt.gca().xaxis.set_major_formatter(tkr.FuncFormatter(xfmt))
plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=4))
plt.gca().xaxis.set_minor_locator(mdates.DayLocator())
plt.show()

The code above produces the following plot:

Answer 2

you use the maxNLocator

fig, ax = plt.subplots()
locator = MaxNLocator(nbins=3) # with 3 bins you will have 4 ticks
ax.xaxis.set_major_locator(locator)

alternatively see https://stackoverflow.com/a/13418954/541038