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Changing the "tick frequency" on x or y axis in matplotlib?


Question

I am trying to fix how python plots my data.

Say

x = [0,5,9,10,15]

and

y = [0,1,2,3,4]

Then I would do:

matplotlib.pyplot.plot(x,y)
matplotlib.pyplot.show()

and the x axis' ticks are plotted in intervals of 5. Is there a way to make it show intervals of 1?

2020/07/06
1
501
7/6/2020 12:49:24 PM

Accepted Answer

You could explicitly set where you want to tick marks with plt.xticks:

plt.xticks(np.arange(min(x), max(x)+1, 1.0))

For example,

import numpy as np
import matplotlib.pyplot as plt

x = [0,5,9,10,15]
y = [0,1,2,3,4]
plt.plot(x,y)
plt.xticks(np.arange(min(x), max(x)+1, 1.0))
plt.show()

(np.arange was used rather than Python's range function just in case min(x) and max(x) are floats instead of ints.)


The plt.plot (or ax.plot) function will automatically set default x and y limits. If you wish to keep those limits, and just change the stepsize of the tick marks, then you could use ax.get_xlim() to discover what limits Matplotlib has already set.

start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, stepsize))

The default tick formatter should do a decent job rounding the tick values to a sensible number of significant digits. However, if you wish to have more control over the format, you can define your own formatter. For example,

ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))

Here's a runnable example:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]
fig, ax = plt.subplots()
ax.plot(x,y)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, 0.712123))
ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
plt.show()
2013/10/01
611
10/1/2013 5:50:35 PM

Another approach is to set the axis locator:

import matplotlib.ticker as plticker

loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)

There are several different types of locator depending upon your needs.

Here is a full example:

import matplotlib.pyplot as plt
import matplotlib.ticker as plticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]
fig, ax = plt.subplots()
ax.plot(x,y)
loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)
plt.show()
2019/05/15

I like this solution (from the Matplotlib Plotting Cookbook):

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]

tick_spacing = 1

fig, ax = plt.subplots(1,1)
ax.plot(x,y)
ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
plt.show()

This solution give you explicit control of the tick spacing via the number given to ticker.MultipleLocater(), allows automatic limit determination, and is easy to read later.

2016/03/25

In case anyone is interested in a general one-liner, simply get the current ticks and use it to set the new ticks by sampling every other tick.

ax.set_xticks(ax.get_xticks()[::2])
2016/04/15

This is a bit hacky, but by far the cleanest/easiest to understand example that I've found to do this. It's from an answer on SO here:

Cleanest way to hide every nth tick label in matplotlib colorbar?

for label in ax.get_xticklabels()[::2]:
    label.set_visible(False)

Then you can loop over the labels setting them to visible or not depending on the density you want.

edit: note that sometimes matplotlib sets labels == '', so it might look like a label is not present, when in fact it is and just isn't displaying anything. To make sure you're looping through actual visible labels, you could try:

visible_labels = [lab for lab in ax.get_xticklabels() if lab.get_visible() is True and lab.get_text() != '']
plt.setp(visible_labels[::2], visible=False)
2017/05/23

This is an old topic, but I stumble over this every now and then and made this function. It's very convenient:

import matplotlib.pyplot as pp
import numpy as np

def resadjust(ax, xres=None, yres=None):
    """
    Send in an axis and I fix the resolution as desired.
    """

    if xres:
        start, stop = ax.get_xlim()
        ticks = np.arange(start, stop + xres, xres)
        ax.set_xticks(ticks)
    if yres:
        start, stop = ax.get_ylim()
        ticks = np.arange(start, stop + yres, yres)
        ax.set_yticks(ticks)

One caveat of controlling the ticks like this is that one does no longer enjoy the interactive automagic updating of max scale after an added line. Then do

gca().set_ylim(top=new_top) # for example

and run the resadjust function again.

2015/04/02