matplotlib模块数据可视化-3D图

1 matplotlib绘制3D图形

matplotlib可以绘制3D图形，有的版本中不具备该模块，可以进入python环境，输入from mpl_toolkits.mplot3d import Axes3D进行测试，如果导入成功则可以，否则需要安装matplotlib其他版本，这里我用的是2.0.2版本。

2 绘制3D画面图

2.1 源码

import numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dfig = plt.figure()# 创建3d图形的两种方式# ax = Axes3D(fig)ax = fig.add_subplot(111, projection='3d')# X, Y valueX = np.arange(-4, 4, 0.25)Y = np.arange(-4, 4, 0.25)X, Y = np.meshgrid(X, Y)    # x-y 平面的网格R = np.sqrt(X ** 2 + Y ** 2)# height valueZ = np.sin(R)# rstride:行之间的跨度  cstride:列之间的跨度# rcount:设置间隔个数，默认50个，ccount:列的间隔个数  不能与上面两个参数同时出现#vmax和vmin  颜色的最大值和最小值ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'))# zdir : 'z' | 'x' | 'y' 表示把等高线图投射到哪个面# offset : 表示等高线图投射到指定页面的某个刻度ax.contourf(X,Y,Z,zdir='z',offset=-2)# 设置图像z轴的显示范围，x、y轴设置方式相同ax.set_zlim(-2,2)plt.show()

2.2 效果图

3 绘制散点图

3.1 源码

import numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dfig = plt.figure()ax = Axes3D(fig)x = np.arange(0, 200)y = np.arange(0, 100)x, y = np.meshgrid(x, y)z = np.random.randint(0, 200, size=(100, 200))y3 = np.arctan2(x,y)ax.scatter(x, y, z, c=y3, marker='.', s=50, label='')plt.show()

3.2 效果图

4 绘制多边形

4.1 源码

from mpl_toolkits.mplot3d import Axes3Dfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection,Line3DCollectionfig = plt.figure()ax = fig.gca(projection='3d')# 正文体顶点和面verts = [(0, 0, 0), (0, 1, 0), (1, 1, 0), (1, 0, 0), (0, 0, 1), (0, 1, 1), (1, 1, 1), (1, 0, 1)]faces = [[0, 1, 2, 3], [4, 5, 6, 7], [0, 1, 5, 4], [1, 2, 6, 5], [2, 3, 7, 6], [0, 3, 7, 4]]# 四面体顶点和面# verts = [(0, 0, 0), (1, 0, 0), (1, 1, 0), (1, 0, 1)]# faces = [[0, 1, 2], [0, 1, 3], [0, 2, 3], [1, 2, 3]]# 获得每个面的顶点poly3d = [[verts[vert_id] for vert_id in face] for face in faces]# print(poly3d)# 绘制顶点x, y, z = zip(*verts)ax.scatter(x, y, z)# 绘制多边形面ax.add_collection3d(Poly3DCollection(poly3d, facecolors='w', linewidths=1, alpha=0.3))# 绘制对变形的边ax.add_collection3d(Line3DCollection(poly3d, colors='k', linewidths=0.5, linestyles=':')) # 设置图形坐标范围ax.set_xlabel('X')ax.set_xlim3d(-0.5, 1.5)ax.set_ylabel('Y')ax.set_ylim3d(-0.5, 1.5)ax.set_zlabel('Z')ax.set_zlim3d(-0.5, 1.5)plt.show()

4.2 效果图

5 三个方向有等高线的3D图

5.1 源码

from mpl_toolkits.mplot3d import Axes3Dfrom matplotlib import cmfrom mpl_toolkits.mplot3d import axes3dfig = plt.figure()ax = fig.gca(projection='3d')X, Y, Z = axes3d.get_test_data(0.05)ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.5,color='b')cset = ax.contour(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)cset = ax.contour(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)cset = ax.contour(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)ax.set_xlabel('X')ax.set_xlim(-40, 40)ax.set_ylabel('Y')ax.set_ylim(-40, 40)ax.set_zlabel('Z')ax.set_zlim(-100, 100)plt.show()

5.2 效果图

6 三维柱状图

6.1 源码

import randomimport matplotlib as mplimport matplotlib.dates as mdatesfrom mpl_toolkits.mplot3d import Axes3Dmpl.rcParams['font.size'] = 10fig = plt.figure()ax = fig.add_subplot(111, projection='3d')for z in [2011, 2012, 2013, 2014]:    xs = range(1,13)    ys = 1000 * np.random.rand(12)    color = plt.cm.Set2(random.choice(range(plt.cm.Set2.N)))    ax.bar(xs, ys, zs=z, zdir='y', color=color, alpha=0.8)ax.xaxis.set_major_locator(mpl.ticker.FixedLocator(xs))ax.yaxis.set_major_locator(mpl.ticker.FixedLocator(ys))ax.set_xlabel('Month')ax.set_ylabel('Year')ax.set_zlabel('Sales Net [usd]')plt.show()

6.2 效果图

7 补充图

7.1 源码

import numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dfrom matplotlib import cmn_angles = 36n_radii = 8# An array of radii# Does not include radius r=0, this is to eliminate duplicate pointsradii = np.linspace(0.125, 1.0, n_radii)# An array of anglesangles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)# Repeat all angles for each radiusangles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)# Convert polar (radii, angles) coords to cartesian (x, y) coords# (0, 0) is added here. There are no duplicate points in the (x, y) planex = np.append(0, (radii * np.cos(angles)).flatten())y = np.append(0, (radii * np.sin(angles)).flatten())# Pringle surfacez = np.sin(-x * y)fig = plt.figure()ax = fig.gca(projection='3d')ax.plot_trisurf(x, y, z, cmap=cm.jet, linewidth=0.2)plt.show()

7.2 效果图

说明：内容太多，这里都是做了源码和效果图展示，如果有朋友不懂的，或者有我漏掉的东西，大家可以在下面留言交流，我会及时回复大家的，记得在使用中导入import matplotlib.pyplot as plt，否则会报错；对于import numpy as np模块根据实际情况导入，如果没有使用该模块构造数据的，可以不导入。