[python] 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'.

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()

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()

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.

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.

Figure here:

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.

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()

and the result:

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.

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()

http://matplotlib.org/examples/ticks_and_spines/ticklabels_demo_rotation.html

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)

you can do:

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

draw()

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))

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.

This also works in matplotlib 3:

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

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

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)