reducing number of plot ticks

I have too ma开发者_StackOverflow中文版ny ticks on my graph and they are running into each other.

How can I reduce the number of ticks?

For example, I have ticks:

1E-6, 1E-5, 1E-4, ... 1E6, 1E7

And I only want:

1E-5, 1E-3, ... 1E5, 1E7

I've tried playing with the LogLocator, but I haven't been able to figure this out.

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Alternatively, if you want to simply set the number of ticks while allowing matplotlib to position them (currently only with MaxNLocator), there is pyplot.locator_params,

pyplot.locator_params(nbins=4)

You can specify specific axis in this method as mentioned below, default is both:

# To specify the number of ticks on both or any single axes
pyplot.locator_params(axis='y', nbins=6)
pyplot.locator_params(axis='x', nbins=10)

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To solve the issue of customisation and appearance of the ticks, see the Tick Locators guide on the matplotlib website

ax.xaxis.set_major_locator(plt.MaxNLocator(3))

would set the total number of ticks in the x-axis to 3, and evenly distribute them across the axis.

There is also a nice tutorial about this

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If somebody still gets this page in search results:

fig, ax = plt.subplots()

plt.plot(...)

every_nth = 4
for n, label in enumerate(ax.xaxis.get_ticklabels()):
    if n % every_nth != 0:
        label.set_visible(False)

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There's a set_ticks() function for axis objects.

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in case somebody still needs it, and since nothing here really worked for me, i came up with a very simple way that keeps the appearance of the generated plot "as is" while fixing the number of ticks to exactly N:

import numpy as np
import matplotlib.pyplot as plt

f, ax = plt.subplots()
ax.plot(range(100))

ymin, ymax = ax.get_ylim()
ax.set_yticks(np.round(np.linspace(ymin, ymax, N), 2))

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The solution @raphael gave is straightforward and quite helpful.

Still, the displayed tick labels will not be values sampled from the original distribution but from the indices of the array returned by np.linspace(ymin, ymax, N).

To display N values evenly spaced from your original tick labels, use the set_yticklabels() method. Here is a snippet for the y axis, with integer labels:

import numpy as np
import matplotlib.pyplot as plt

ax = plt.gca()

ymin, ymax = ax.get_ylim()
custom_ticks = np.linspace(ymin, ymax, N, dtype=int)
ax.set_yticks(custom_ticks)
ax.set_yticklabels(custom_ticks)

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If you need one tick every N=3 ticks :

N = 3  # 1 tick every 3
xticks_pos, xticks_labels = plt.xticks()  # get all axis ticks
myticks = [j for i,j in enumerate(xticks_pos) if not i%N]  # index of selected ticks
newlabels = [label for i,label in enumerate(xticks_labels) if not i%N]

or with fig,ax = plt.subplots() :

N = 3  # 1 tick every 3
xticks_pos = ax.get_xticks()
xticks_labels = ax.get_xticklabels()
myticks = [j for i,j in enumerate(xticks_pos) if not i%N]  # index of selected ticks
newlabels = [label for i,label in enumerate(xticks_labels) if not i%N]

(obviously you can adjust the offset with (i+offset)%N).

Note that you can get uneven ticks if you wish, e.g. myticks = [1, 3, 8].

Then you can use

plt.gca().set_xticks(myticks)  # set new X axis ticks

or if you want to replace labels as well

plt.xticks(myticks, newlabels)  # set new X axis ticks and labels

Beware that axis limits must be set after the axis ticks.

Finally, you may wish to draw only an arbitrary set of ticks :

mylabels = ['03/2018', '09/2019', '10/2020']
plt.draw()  # needed to populate xticks with actual labels
xticks_pos, xticks_labels = plt.xticks()  # get all axis ticks
myticks = [i for i,j in enumerate(b) if j.get_text() in mylabels]
plt.xticks(myticks, mylabels)

(assuming mylabels is ordered ; if it is not, then sort myticks and reorder it).

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xticks function auto iterates with range function

start_number = 0

end_number = len(data you have)

step_number = how many skips to make from strat to end

rotation = 90 degrees tilt will help with long ticks

plt.xticks(range(start_number,end_number,step_number),rotation=90)

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if you want 10 ticks:

for y axis: ax.set_yticks(ax.get_yticks()[::len(ax.get_yticks())//10])

for x axis: ax.set_xticks(ax.get_xticks()[::len(ax.get_xticks())//10])

this simply gets your ticks and chooses every 10th of the list and sets it back to your ticks. you can change the number of ticks as you wish.

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When a log scale is used the number of major ticks can be fixed with the following command

import matplotlib.pyplot as plt

....

plt.locator_params(numticks=12)
plt.show()

The value set to numticks determines the number of axis ticks to be displayed.

Credits to @bgamari's post for introducing the locator_params() function, but the nticks parameter throws an error when a log scale is used.