Matplotlib.axes.axes.set_xticks()

Matplotlib是Python中的一个库，它是NumPy库的数值-数学扩展。Axes包含了大多数图形元素:Axis、Tick、Line2D、Text、Polygon等，并设置坐标系。Axes的实例通过callbacks属性支持回调。

函数:Matplotlib.axes.axes.set_xticks()

matplotlib库的Axes模块中的Axes.set_xticks()函数用于使用刻度列表设置x刻度。

Axes.set_xticks(self, ticks, minor=False)

参数:该方法接受以下参数。

ticks:该参数是x轴滴答位置的列表。

minor:设置主要节拍和次要节拍都使用该参数

返回值:该方法不返回任何值。

下面的例子演示了matplotlib.axes.axes.set_xticks()函数在matplotlib.axes中的作用:

示例1

# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
   
   
def func(x):
    return (x - 4) * (x - 6) * (x - 5) + 100
   
   
a, b = 2, 9  # integral limits
x = np.linspace(0, 10)
y = func(x)
   
fig, ax = plt.subplots()
ax.plot(x, y, "k", linewidth = 2)
ax.set_ylim(bottom = 0)
   
# Make the shaded region
ix = np.linspace(a, b)
iy = func(ix)
verts = [(a, 0), *zip(ix, iy), (b, 0)]
poly = Polygon(verts, facecolor ='green',
               edgecolor ='0.5', alpha = 0.4)
ax.add_patch(poly)
   
ax.text(0.5 * (a + b), 30,
        r"\int_a ^ b f(x)\mathrm{d}x",
        horizontalalignment ='center',
        fontsize = 20)
   
fig.text(0.9, 0.05, 'x')
fig.text(0.1, 0.9, 'y')
   
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False) 
ax.set_xticks((a, b))
   
fig.suptitle('matplotlib.axes.Axes.set_xticks()\
 function Example\n\n', fontweight ="bold")
fig.canvas.draw()
plt.show()

输出:

Matplotlib.axes.axes.set_xticks()

示例2

# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt
   
# Fixing random state for reproducibility
np.random.seed(19680801)
   
x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)
y2 = y + 0.2 * np.random.normal(size = x.shape)
   
fig, ax = plt.subplots()
ax.plot(x, y)
ax.plot(x, y2)
  
ax.set_xticks([0, np.pi, 2 * np.pi])
   
ax.spines['left'].set_bounds(-1, 1)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
   
fig.suptitle('matplotlib.axes.Axes.set_xticks() \
function Example\n\n', fontweight ="bold")
fig.canvas.draw()
plt.show()