你可以:

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

draw()

试试这个:

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

matplotlib.axes.Axes.set_xticks, or matplotlib.axes.Axes.set_yticks for the y-axis, can be used to change the ticks and labels beginning with matplotlib 3.5.0. These are for the object oriented interface. If using the pyplot state-based interface, use plt.xticks or plt.yticks, as shown in other answers. In general terms, pass a list / array of numbers to the ticks parameter, and a list / array strings to the labels parameter. In this case, the x-axis is comprised of continuous numeric values, so there are no set Text labels, as thoroughly explained in this answer. This is not the case when plots have discrete ticks (e.g. boxplot, barplot). [Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, '')] is returned by ax.get_xticklabels() [-0.25 0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2. 2.25] is returned by ax.get_xticks() type(ax.get_xticks()) is <class 'numpy.ndarray'> type(ax.get_xticks()[0]) is <class 'numpy.float64'> Since the OP is trying to replace a numeric label with a str, all of the values in the ndarray must be converted to str type, and the value to be changed can be updated. Tested in python 3.10 and matplotlib 3.5.2

import numpy as np
import matplotlib.pyplot as plt

# create figure and axes
fig, ax = plt.subplots(figsize=(8, 6))

# plot data
t = np.arange(0.0, 2.0, 0.01)
s = np.sin(2*np.pi*t)

# plot
ax.plot(t, s)

# get the xticks, which are the numeric location of the ticks
xticks = ax.get_xticks()

# get the xticks and convert the values in the array to str type
xticklabels = list(map(str, ax.get_xticks()))

# update the string to be changed
xticklabels[1] = 'Test'

# set the xticks and the labels
_ = ax.set_xticks(xticks, xticklabels)

注意，更改xticklabels时，x轴偏移量不会保留。但是，正确的值没有偏移量。

# create figure and axes
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 6), sharex=False)

# plot data
t = np.linspace(0, 1500000, 100)
s = t**2

# plot
ax1.plot(t, s)
ax2.plot(t, s)

# get the xticks, which are the numeric location of the ticks
xticks = ax2.get_xticks()

# get the xticks and convert the values in the array to str type
xticklabels = list(map(str, ax2.get_xticks()))

# update the string to be changed
xticklabels[1] = 'Test'

# set the xticks and the labels
_ = ax2.set_xticks(xticks, xticklabels, rotation=90)

我注意到这里发布的所有使用set_xticklabels()的解决方案都没有保留偏移量，偏移量是应用于tick值以创建更好看的tick标签的缩放因子。例如，如果刻度在0.00001 (1e-5)量级，matplotlib将自动添加1e-5的缩放因子(或偏移量)，因此最终的刻度标签可能是1 2 3 4，而不是1e-5 2e-5 3e-5 4e-5。

下面是一个例子:

x数组是np。数组([1,2,3,4])/1e6，并且y=x**2。所以两个值都很小。

左栏:按照@Joe Kington的建议手动更改第1和第3个标签。注意，偏移量丢失了。

中间列:类似于@iipr的建议，使用FuncFormatter。

右栏:我建议的保留偏移量的解决方案。

图:

完整代码:

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')

注意:使用set_xticklabels()的解决方案，包括我自己的解决方案，似乎都依赖于FixedFormatter，它是静态的，不响应图形调整大小。为了观察效果，将图形更改为较小的尺寸，例如fig, axes = plt。Subplots (1,3, figsize=(6,6))并放大图形窗口。您将注意到，只有中间列响应调整大小，并随着图形变大而添加更多刻度。左边和右边的列将有空的标记(见下图)。

警告2:我还注意到，如果tick值是浮点数，直接调用set_xticklabels(ticks)可能会得到难看的字符串，比如1.499999999998而不是1.5。

axes类有一个set_yticklabels函数，它允许你设置tick标签，如下所示:

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

ax.set_xticklabels(group_labels)

我仍在研究为什么你上面的例子不起作用。

这个问题被问到已经有一段时间了。截至今天(matplotlib 2.2.2)，经过一些阅读和试验，我认为最佳/适当的方式如下:

Matplotlib有一个名为ticker的模块，它“包含支持完全可配置的标记定位和格式化的类”。为了从图中修改一个特定的tick，以下对我来说是有效的:

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()

警告!X是tick的值，pos是它在坐标轴上的相对位置。注意，pos的值从1开始，而不是在索引时通常从0开始。

在我的例子中，我试图用百分比值格式化直方图的y轴。mticker有另一个名为PercentFormatter的类，它可以很容易地做到这一点，而不需要像以前那样定义一个单独的函数:

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()

在本例中，xmax是对应于100%的数据值。百分比计算为x / xmax * 100，这就是为什么我们修正xmax=1.0。此外，decimals是指在该点后放置的小数位数。