Python的Matplotlib库应用实例超详细教程

前言

Matplotlib库是一款功能强大且灵活的Python数据可视化软件包，它支持跨平台运行，能够根据NumPy ndarray数组绘制高质量的2D图像（也支持部分3D图像）。Matplotlib提供了类MATLAB的绘图API，使得绘图过程简单直观，代码清晰易懂。它广泛应用于数据分析、科学研究、报告生成以及教育与培训等领域，用户可以通过它创建多样化的图表类型，如折线图、柱状图、散点图等，并对图表的各个元素进行高度定制化的调整。无论是简单的图表还是复杂的可视化需求，Matplotlib都能提供高质量的输出。

一、环境搭建

1.1 配置matplotlib库

pip install matplotlib

安装成功展示图：

1.2 配置seaborn库

pip install seaborn

安装成功展示图：

1.3 配置Skimage库

pip install scikit-image

安装成功展示图：

二、二维图像

2.1 曲线（直线）可视化

import matplotlib.pyplot as pltimport numpy as npX = 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()

2.2 曲线（虚线）可视化

import matplotlib.pyplot as pltimport numpy as npX = 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()

2.3 直方图

import matplotlib.pyplot as pltimport numpy as npimport matplotlibmatplotlib.rcParams['axes.unicode_minus'] = Falseimport seaborn as snssns.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()

2.4 阶梯图

import matplotlib.pyplot as pltimport numpy as npimport matplotlibfrom matplotlib.pyplot import xticksmatplotlib.rcParams['axes.unicode_minus'] = Falseimport seaborn as snssns.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()

三、三维图像

3.1 3D曲面图

import matplotlib.pyplot as pltimport numpy as npimport matplotlibfrom matplotlib.pyplot import xticksfrom pyparsing import alphasmatplotlib.rcParams['axes.unicode_minus'] = Falseimport seaborn as snssns.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()

3.2 3D散点图

import matplotlib.pyplot as pltimport numpy as npimport matplotlibfrom matplotlib.pyplot import xticksfrom pyparsing import alphasmatplotlib.rcParams['axes.unicode_minus'] = Falseimport seaborn as snssns.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()

3.3 3D散点图

import matplotlib.pyplot as pltimport numpy as npimport matplotlibfrom matplotlib.pyplot import xticksfrom pyparsing import alphasmatplotlib.rcParams['axes.unicode_minus'] = Falseimport seaborn as snssns.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()