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Modify tick label text

I want to make some modifications to a few selected tick labels in a plot.

For example, if I do:

label = axes.yaxis.get_major_ticks()[2].label
label.set_fontsize(size)
label.set_rotation('vertical')

the font size and the orientation of the tick label is changed.

However, if try:

label.set_text('Foo')

the tick label is not modified. Also if I do:

print label.get_text()

nothing is printed.

Here's some more strangeness. When I tried this:

 from pylab import *
 axes = figure().add_subplot(111)
 t = arange(0.0, 2.0, 0.01)
 s = sin(2*pi*t)
 axes.plot(t, s)
 for ticklabel in axes.get_xticklabels():
     print ticklabel.get_text()

Only empty strings are printed, but the plot contains ticks labeled as '0.0', '0.5', '1.0', '1.5', and '2.0'.

Can you supply the plot you used for getting the label from?
You're getting blank labels because you haven't drawn the canvas yet. If you call draw() before trying to print the labels, you'll get what you expect. Setting individual tick labels is unfortunately a touch more difficult (what's happening is that the tick locator and formatter hasn't been reset and it overrides things when you set_text). I'll add an example in a bit, if someone doesn't beat me to it. I have to catch the bus, at the moment, though.
@JoeKington: Great! Looking forward to see your fix.
@repoman - Well, it seems I spoke a touch too soon. What I had in mind works for older versions of matplotlib, but not the latest version. I need to do a bit more digging. That having been said, this shouldn't be as complicated as it is...

j
joelostblom

Caveat: Unless the ticklabels are already set to a string (as is usually the case in e.g. a boxplot), this will not work with any version of matplotlib newer than 1.1.0. If you're working from the current github master, this won't work. I'm not sure what the problem is yet... It may be an unintended change, or it may not be...

Normally, you'd do something along these lines:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

# We need to draw the canvas, otherwise the labels won't be positioned and 
# won't have values yet.
fig.canvas.draw()

labels = [item.get_text() for item in ax.get_xticklabels()]
labels[1] = 'Testing'

ax.set_xticklabels(labels)

plt.show()

https://i.stack.imgur.com/5m900.png

To understand the reason why you need to jump through so many hoops, you need to understand a bit more about how matplotlib is structured.

Matplotlib deliberately avoids doing "static" positioning of ticks, etc, unless it's explicitly told to. The assumption is that you'll want to interact with the plot, and so the bounds of the plot, ticks, ticklabels, etc will be dynamically changing.

Therefore, you can't just set the text of a given tick label. By default, it's re-set by the axis's Locator and Formatter every time the plot is drawn.

However, if the Locators and Formatters are set to be static (FixedLocator and FixedFormatter, respectively), then the tick labels stay the same.

This is what set_*ticklabels or ax.*axis.set_ticklabels does.

Hopefully that makes it slighly more clear as to why changing an individual tick label is a bit convoluted.

Often, what you actually want to do is just annotate a certain position. In that case, look into annotate, instead.


this does not appear to work with the current version (1.20)!
If the ticklabels are already set to a string as in e.g. a boxplot, this is still working. This might be obvious, but since the first line of the answer is that it is not working on newer matplotlib versions, users might skip it completely (I did initially). Maybe mention this briefly.
i think you can condense it down to plt.gca().set_xticklabels(labels)
what if I wanna the fontweight of "testing" to be bold while others use fontweight of "light". Is there a way to do it?
for those using this in a jupyter notebook may benefit from noting that fig.canvas.draw() is crucial
b
blueshift

One can also do this with pylab and xticks

import matplotlib
import matplotlib.pyplot as plt
x = [0,1,2]
y = [90,40,65]
labels = ['high', 'low', 37337]
plt.plot(x,y, 'r')
plt.xticks(x, labels, rotation='vertical')
plt.show()

https://matplotlib.org/stable/gallery/ticks_and_spines/ticklabels_rotation.html


This is a simple solution and works with pyplot 1.5.1. This should be upvoted.
Although the question didn't ask for this, I appreciate that this example lets you set the location of the ticks at the same time as modifying their labels.
From modern matplotlib docs: "pylab is deprecated and its use is strongly discouraged because of namespace pollution. Use pyplot instead."
C
CT Zhu

In newer versions of matplotlib, if you do not set the tick labels with a bunch of str values, they are '' by default (and when the plot is draw the labels are simply the ticks values). Knowing that, to get your desired output would require something like this:

>>> from pylab import *
>>> axes = figure().add_subplot(111)
>>> a=axes.get_xticks().tolist()
>>> a[1]='change'
>>> axes.set_xticklabels(a)
[<matplotlib.text.Text object at 0x539aa50>, <matplotlib.text.Text object at 0x53a0c90>, 
<matplotlib.text.Text object at 0x53a73d0>, <matplotlib.text.Text object at 0x53a7a50>, 
<matplotlib.text.Text object at 0x53aa110>, <matplotlib.text.Text object at 0x53aa790>]
>>> plt.show()

https://i.stack.imgur.com/YiGb5.png

and now if you check the _xticklabels, they are no longer a bunch of ''.

>>> [item.get_text() for item in axes.get_xticklabels()]
['0.0', 'change', '1.0', '1.5', '2.0']

It works in the versions from 1.1.1rc1 to the current version 2.0.


Thanks, this is the answer I was looking for. Cleanest solution IMO; just supply set_xticklabels with a mixed list of strings and tick objects.
Be aware that if the tick labels are set as ints, this will change them to floats. Easily worked around but worth noting.
This one worked (ax.get_xticks().tolist()). The most voted solution did not (ax.get_xtickslabels()). Somehow it was not able to extract the labels before the plt.show() was excuted, although I used fig.canvas.draw() as suggested.
FYI, from modern matplotlib docs: "pylab is deprecated and its use is strongly discouraged because of namespace pollution. Use pyplot instead."
UserWarning: FixedFormatter should only be used together with FixedLocator
i
iipr

It's been a while since this question was asked. As of today (matplotlib 2.2.2) and after some reading and trials, I think the best/proper way is the following:

Matplotlib has a module named ticker that "contains classes to support completely configurable tick locating and formatting". To modify a specific tick from the plot, the following works for me:

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

def update_ticks(x, pos):
    if x == 0:
        return 'Mean'
    elif pos == 6:
        return 'pos is 6'
    else:
        return x

data = np.random.normal(0, 1, 1000)
fig, ax = plt.subplots()
ax.hist(data, bins=25, edgecolor='black')
ax.xaxis.set_major_formatter(mticker.FuncFormatter(update_ticks))
plt.show()

https://i.stack.imgur.com/AuRQR.png

Caveat! x is the value of the tick and pos is its relative position in order in the axis. Notice that pos takes values starting in 1, not in 0 as usual when indexing.

In my case, I was trying to format the y-axis of a histogram with percentage values. mticker has another class named PercentFormatter that can do this easily without the need to define a separate function as before:

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

data = np.random.normal(0, 1, 1000)
fig, ax = plt.subplots()
weights = np.ones_like(data) / len(data)
ax.hist(data, bins=25, weights=weights, edgecolor='black')
ax.yaxis.set_major_formatter(mticker.PercentFormatter(xmax=1.0, decimals=1))
plt.show()

https://i.stack.imgur.com/PKy37.png

In this case xmax is the data value that corresponds to 100%. Percentages are computed as x / xmax * 100, that's why we fix xmax=1.0. Also, decimals is the number of decimal places to place after the point.


S
Soham

This works:

import matplotlib.pyplot as plt

fig, ax1 = plt.subplots(1,1)

x1 = [0,1,2,3]
squad = ['Fultz','Embiid','Dario','Simmons']

ax1.set_xticks(x1)
ax1.set_xticklabels(squad, minor=False, rotation=45)

https://i.stack.imgur.com/qQx6q.png


s
stanri

The axes class has a set_yticklabels function which allows you to set the tick labels, like so:

#ax is the axes instance
group_labels = ['control', 'cold treatment',
             'hot treatment', 'another treatment',
             'the last one']

ax.set_xticklabels(group_labels)

I'm still working on why your example above didn't work.


But I only want to alter a single label. The above trick requires that you extract all tick labels and set the desired one to a new value. But how can I extract the tick labels when label.get_text() returns nothing?
M
MichaelSB

This also works in matplotlib 3:

x1 = [0,1,2,3]
squad = ['Fultz','Embiid','Dario','Simmons']

plt.xticks(x1, squad, rotation=45)

d
dopexxx

If you do not work with fig and ax and you want to modify all labels (e.g. for normalization) you can do this:

labels, locations = plt.yticks()
plt.yticks(labels, labels/max(labels))


A
Aymen Alsaadi

Try this :

  fig,axis = plt.subplots(nrows=1,ncols=1,figsize=(13,6),sharex=True)
  axis.set_xticklabels(['0', 'testing', '10000', '20000', '30000'],fontsize=22)

J
Jason

I noticed that all the solutions posted here that use set_xticklabels() are not preserving the offset, which is a scaling factor applied to the ticks values to create better-looking tick labels. For instance, if the ticks are on the order of 0.00001 (1e-5), matplotlib will automatically add a scaling factor (or offset) of 1e-5, so the resultant tick labels may end up as 1 2 3 4, rather than 1e-5 2e-5 3e-5 4e-5.

Below gives an example:

The x array is np.array([1, 2, 3, 4])/1e6, and y is y=x**2. So both are very small values.

Left column: manually change the 1st and 3rd labels, as suggested by @Joe Kington. Note that the offset is lost.

Mid column: similar as @iipr suggested, using a FuncFormatter.

Right column: My suggested offset-preserving solution.

https://i.stack.imgur.com/ZZAOV.png

Complete code here:

import matplotlib.pyplot as plt
import numpy as np

# create some *small* data to plot
x = np.arange(5)/1e6
y = x**2

fig, axes = plt.subplots(1, 3, figsize=(10,6))

#------------------The set_xticklabels() solution------------------
ax1 = axes[0]
ax1.plot(x, y)
fig.canvas.draw()
labels = [item.get_text() for item in ax1.get_xticklabels()]

# Modify specific labels
labels[1] = 'Testing'
labels[3] = 'Testing2'
ax1.set_xticklabels(labels)
ax1.set_title('set_xticklabels()')

#--------------FuncFormatter solution--------------
import matplotlib.ticker as mticker

def update_ticks(x, pos):
    if pos==1:
        return 'testing'
    elif pos==3:
        return 'testing2'
    else:
        return x

ax2=axes[1]
ax2.plot(x,y)
ax2.xaxis.set_major_formatter(mticker.FuncFormatter(update_ticks))
ax2.set_title('Func Formatter')

#-------------------My solution-------------------
def changeLabels(axis, pos, newlabels):
    '''Change specific x/y tick labels

    Args:
        axis (Axis): .xaxis or .yaxis obj.
        pos (list): indices for labels to change.
        newlabels (list): new labels corresponding to indices in <pos>.
    '''

    if len(pos) != len(newlabels):
        raise Exception("Length of <pos> doesn't equal that of <newlabels>.")

    ticks = axis.get_majorticklocs()
    # get the default tick formatter
    formatter = axis.get_major_formatter()
    # format the ticks into strings
    labels = formatter.format_ticks(ticks)

    # Modify specific labels
    for pii, lii in zip(pos, newlabels):
        labels[pii] = lii

    # Update the ticks and ticklabels. Order is important here.
    # Need to first get the offset (1e-6 in this case):
    offset = formatter.get_offset()
    # Then set the modified labels:
    axis.set_ticklabels(labels)
    # In doing so, matplotlib creates a new FixedFormatter and sets it to the xaxis
    # and the new FixedFormatter has no offset. So we need to query the
    # formatter again and re-assign the offset:
    axis.get_major_formatter().set_offset_string(offset)

    return

ax3 = axes[2]
ax3.plot(x, y)

changeLabels(ax3.xaxis, [1, 3], ['Testing', 'Testing2'])
ax3.set_title('With offset')

fig.show()
plt.savefig('tick_labels.png')

Caveat: it appears that solutions that use set_xticklabels(), including my own, relies on FixedFormatter, which is static and doesn't respond to figure resizing. To observe the effect, change the figure to a smaller size, e.g. fig, axes = plt.subplots(1, 3, figsize=(6,6)) and enlarge the figure window. You will notice that that only the mid column responds to resizing and adds more ticks as the figure gets larger. The left and right column will have empty tick labels (see figure below).

Caveat 2: I also noticed that if your tick values are floats, calling set_xticklabels(ticks) directly might give you ugly-looking strings, like 1.499999999998 instead of 1.5.

https://i.stack.imgur.com/chgXN.png


H
Hachi Manzur

you can do:

for k in ax.get_xmajorticklabels():
    if some-condition:
        k.set_color(any_colour_you_like)

draw()

Can someone explain why this answer is so heavily downvoted?
@HlynurDavíðHlynsson I think it has not really something to do with the question. Well, maybe with some editing... Please edit it, if you get to relate it to the question. ;)