Python + matplotlib更改纵横坐标刻度颜色

2D坐标系

1 修改全部坐标颜色

import matplotlib.pyplot as plt
import numpy as np
#显示静态图像
%matplotlib inline#jupyter notebok语句
x=np.linspace(-1,1,50)#-1到1中画50个点
y=x**2
plt.plot(x,y,)
###################以下两条语句用于更改颜色#######################
plt.tick_params(axis='x',colors='red')
plt.tick_params(axis='y',colors='red')
plt.show()

2 修改某一点坐标颜色

import matplotlib.pyplot as plt
 
fig, ax = plt.subplots(figsize=(5,4))
ax.plot([1,2,3])
###################更改某一点刻度颜色#######################
ax.get_xticklabels()[3].set_color("red")  # 这里的数字3是表示第几个点，不是坐标刻度值
ax.get_yticklabels()[5].set_color("red")
plt.show()

3D坐标系

1 修改全部坐标颜色

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig=plt.figure(num=1,figsize=(8,6))
ax = Axes3D(fig)
# X, Y value
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X ** 2 + Y ** 2)
# height value
Z = np.cos(R)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'),edgecolors='black')
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=plt.get_cmap('rainbow'))#投影等高线,改变zdir='x', offset=-4实现投影到不同坐标轴     
ax.set_zlim(-2, 2)

###################以下三条语句用于更改颜色#######################
ax.tick_params(axis='x',colors='red')
ax.tick_params(axis='y',colors='red')
ax.tick_params(axis='z',colors='red')

plt.show()

2 修改某一点坐标颜色

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig=plt.figure(num=1,figsize=(8,6))
ax = Axes3D(fig)
# X, Y value
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X ** 2 + Y ** 2)
# height value
Z = np.cos(R)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'),edgecolors='black')
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=plt.get_cmap('rainbow'))#投影等高线,改变zdir='x', offset=-4实现投影到不同坐标轴     
ax.set_zlim(-2, 2)

###################以下三条语句用于更改颜色#######################
ax.get_xticklabels()[3].set_color("red")
ax.get_yticklabels()[5].set_color("red")
ax.get_zticklabels()[7].set_color("red")

plt.show()