Python的Matplotlib库应用实例超详细教程<

pip install matplotlib

pip install seaborn

pip install scikit-image

import matplotlib.pyplot as plt
import numpy as np

X = np.linspace(1, 15)
Y = np.sin(X)
# 图像大小设置
plt.figure(figsize=(10,8))
# 绘制线
plt.plot(X, Y, color='red')
plt.xlabel('X')
plt.ylabel('Y')
# 设置图像标题名
plt.title("y = sin(X)")
# 是否添加网格
plt.grid(True)
# 绘制图像
plt.show()

import matplotlib.pyplot as plt
import numpy as np

X = np.linspace(1, 15)
Y = np.sin(X)
# 图像大小设置
plt.figure(figsize=(10,8))
# 绘制线 蓝色 虚线
plt.plot(X, Y, "b-.")
plt.xlabel(r"$\alpha$")
plt.ylabel(r"$\beta$")
# 设置图像标题名
plt.title("$y=\sum sin(x)$")
# 是否添加网格
plt.grid(True)
# 绘制图像
plt.show()

import matplotlib.pyplot as plt
import numpy as np
import matplotlib
matplotlib.rcParams['axes.unicode_minus'] = False
import seaborn as sns
sns.set(font = "Kaiti", style = "ticks", font_scale = 1.4)

X = np.linspace(1, 15)
Y = np.sin(X)
# 图像大小设置
plt.figure(figsize=(10,8))
# 生成数据
data = np.random.randn(200, 1)
# 可视化
plt.hist(data, 10)
plt.xlabel("取值")
plt.ylabel("频数")
plt.title("直方")
# 绘制图像
plt.show()

import matplotlib.pyplot as plt
import numpy as np
import matplotlib
from matplotlib.pyplot import xticks

matplotlib.rcParams['axes.unicode_minus'] = False
import seaborn as sns
sns.set(font = "Kaiti", style = "ticks", font_scale = 1.4)

X = np.linspace(1, 15)
Y = np.sin(X)
# 图像大小设置
plt.figure(figsize=(10,8))
# 阶梯图设置
plt.step(X, Y, c = "r", label = "sin(x)", linewidth = 3)
# 添加辅助线
plt.plot(X, Y, "o--", color = "grey", alpha = 0.5)
plt.xlabel("X")
plt.ylabel("Y")
plt.title("Bar")
# 设置图例位置及大小
plt.legend(loc = "lower right", fontsize = "small")
# 设置X轴坐标系取值
xtick = [0, 5, 10, 15]
xticklabels = [str(x) + "万" for x in xtick]
# x轴的坐标取值，倾斜度为45°
plt.xticks(xtick, xticklabels, rotation = 45)
# 调整水平空间距离
plt.subplots_adjust(hspace = 0.5)
plt.show()

import matplotlib.pyplot as plt
import numpy as np
import matplotlib
from matplotlib.pyplot import xticks
from pyparsing import alphas
matplotlib.rcParams['axes.unicode_minus'] = False
import seaborn as sns
sns.set(font = "Kaiti", style = "ticks", font_scale = 1.4)

x = np.linspace(-4, 4, num = 50)
y = np.linspace(-4, 4, num = 50)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))
# 3D曲面图可视化
fig = plt.figure(figsize=(6, 5))
# 设置3D坐标
ax1 = fig.add_subplot(1, 1, 1, projection = '3d')
# 绘制曲面图, rstride：行的跨度 cstride：列的跨度 alpha：透明度 cmap：颜色
ax1.plot_surface(X, Y, Z, rstride = 1, cstride = 1, alpha = 0.5, cmap = plt.cm.coolwarm)
# 绘制z轴方向的等高线
cset = ax1.contourf(X, Y, Z, zdir = 'z', offset = 1, cmap = plt.cm.CMRmap)
ax1.set_xlabel("X")
ax1.set_xlim(-4, 4)
ax1.set_ylabel("Y")
ax1.set_ylim(-4, 4)
ax1.set_zlabel("Z")
ax1.set_zlim(-1, 1)
ax1.set_title("曲面图和等高线")
plt.show()

import matplotlib.pyplot as plt
import numpy as np
import matplotlib
from matplotlib.pyplot import xticks
from pyparsing import alphas
matplotlib.rcParams['axes.unicode_minus'] = False
import seaborn as sns
sns.set(font = "Kaiti", style = "ticks", font_scale = 1.4)

theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
x = np.cos(theta)
y = np.sin(theta)
z = np.linspace(-2, 2, 100)
r = z ** 2 + 1
# 在子图中绘制三维图像
fig = plt.figure(figsize=(10, 10))
# 将坐标系设置为3D坐标系
ax1 = fig.add_subplot(1, 1, 1, projection='3d')
ax1.plot(x, y, z, "b-")
ax1.view_init(elev = 20, azim = 25)
ax1.set_title("3D曲线图")
plt.show()

import matplotlib.pyplot as plt
import numpy as np
import matplotlib
from matplotlib.pyplot import xticks
from pyparsing import alphas
matplotlib.rcParams['axes.unicode_minus'] = False
import seaborn as sns
sns.set(font = "Kaiti", style = "ticks", font_scale = 1.4)

theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
x = np.cos(theta)
y = np.sin(theta)
z = np.linspace(-2, 2, 100)
r = z ** 2 + 1
# 在子图中绘制三维图像
fig = plt.figure(figsize=(10, 10))
# 将坐标系设置为3D坐标系
ax1 = plt.subplot(1, 1, 1, projection='3d')
ax1.scatter3D(x, y, z, c = "r", s = 20)
ax1.view_init(elev = 20, azim = 25)
ax1.set_title("3D散点图")
plt.show()