python机器学习-Python MatPlotLib

绘制二维图形

二维或二维图是那些只有 2 个轴即 x 和 y 的图，它们是平面图

1. 线二维图

折线图或折线图或折线图或曲线图是一种将信息显示为由直线段连接的称为“标记”的一系列数据点的图表。它是许多领域中常见的一种基本类型的图表。

5. 散点二维图

散点图是一种绘图或数学图，使用笛卡尔坐标显示一组数据的典型两个变量的值。如果点被编码，则可以显示一个额外的变量

import matplotlib.pyplot as plt   
  
# x-axis values   
x = [2,4,5,7,6,8,9,11,12,12]  
# y-axis values   
y = [1,2,3,4,5,6,7,8,9,10]  
  
# plotting points as a scatter plot   
plt.scatter(x, y, label= "stars", color= "green",   
            marker= "x", s=30)   
  
# x-axis label   
plt.xlabel('x - axis')   
# frequency label   
plt.ylabel('y - axis')   
# plot title   
plt.title('Scatter plot!')   
# showing legend   
plt.legend()   
  
# function to show the plot   
plt.show()   

输出

6. 饼图二维图

饼图是一种圆形统计图形，它被分成多个切片以说明数字比例。在饼图中，每个切片的弧长与其代表的数量成正比。

import matplotlib.pyplot as plt   
  
# defining labels   
activities = ['eat', 'sleep', 'work', 'play']   
  
# portion covered by each label   
slices = [3, 7, 8, 6]   
  
# color for each label   
colors = ['r', 'y', 'g', 'b']   
  
# plotting the pie chart   
plt.pie(slices, labels = activities, colors=colors,   
        startangle=90, shadow = True, explode = (0.3, 0, 0.1, 0),   
        radius = 1.2, autopct = '%1.1f%%')   
  
# plotting legend   
plt.legend()   
  
# showing the plot   
plt.show()   

输出

绘制 3D 图形

工具包是扩展 Matplotlib 的特定于应用程序的函数的集合。

mplot3d

mplot3d 工具包通过提供可以创建 3D 场景的 2D 投影的轴对象，为 matplotlib 添加了简单的 3D 绘图功能。生成的图形将具有与常规 2D 绘图相同的外观和感觉。

1. 线 3D 绘图

折线图或折线图或折线图或曲线图是一种将信息显示为由直线段连接的称为“标记”的一系列数据点的图表。它是许多领域中常见的一种基本类型的图表。

# This import registers the 3D projection but is otherwise unused.  
from mpl_toolkits.mplot3d import Axes3D  #F401 unused import  
  
import numpy as np  
import matplotlib.pyplot as plt  
  
  
plt.rcParams['legend.fontsize'] = 10  
  
fig = plt.figure()  
ax = fig.gca(projection='3d')  
  
# Prepare arrays x, y, z  
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)  
z = np.linspace(-2, 2, 100)  
r = z**2 + 1  
x = r * np.sin(theta)  
y = r * np.cos(theta)  
  
ax.plot(x, y, z, label='parametric curve')  
ax.legend()  
  
plt.show()  

输出

2. 散点图 3D 图

散点图是一种绘图或数学图，使用笛卡尔坐标显示一组数据的典型两个变量的值。如果点被编码，则可以显示一个额外的变量

# This import registers the 3D projection but is otherwise unused.  
from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import  
  
import matplotlib.pyplot as plt  
import numpy as np  
  
# Fixing random state for reproducibility  
np.random.seed(19680801)  
  
  
def randrange(n, vmin, vmax):  
    ''''' 
    Helper function to make an array of random numbers having shape (n, ) 
    with each number distributed Uniform(vmin, vmax). 
    '''  
    return (vmax - vmin)*np.random.rand(n) + vmin  
  
fig = plt.figure()  
ax = fig.add_subplot(111, projection='3d')  
  
n = 100  
  
# For each set of style and range settings, plot n random points in the box  
# defined by x in [23, 32], y in [0, 100], z in [zlow, zhigh].  
for m, zlow, zhigh in [('o', -50, -25), ('^', -30, -5)]:  
    xs = randrange(n, 23, 32)  
    ys = randrange(n, 0, 100)  
    zs = randrange(n, zlow, zhigh)  
    ax.scatter(xs, ys, zs, marker=m)  
  
ax.set_xlabel('X Label')  
ax.set_ylabel('Y Label')  
ax.set_zlabel('Z Label')  
  
plt.show()  

输出

3. 线框 3D 绘图

线框图采用值网格并将其投影到指定的三维表面上，并且可以使生成的三维形式非常容易可视化。

4. 表面 3D 绘图

曲面图是三维数据的图表。表面图不是显示单个数据点，而是显示指定因变量 (Y) 和两个自变量（X 和 Z）之间的函数关系。该图是等高线图的配套图。

# This import registers the 3D projection but is otherwise unused.  
from mpl_toolkits.mplot3d import Axes3D  # F401 unused import  
  
import matplotlib.pyplot as plt  
from matplotlib import cm  
from matplotlib.ticker import LinearLocator, FormatStrFormatter  
import numpy as np  
  
  
fig = plt.figure()  
ax = fig.gca(projection='3d')  
  
# Make data.  
X = np.arange(-5, 5, 0.5)  
Y = np.arange(-5, 5, 0.5)  
X, Y = np.meshgrid(X, Y)  
R = np.sqrt(X**2 + Y**2)  
Z = np.sin(R)  
  
# Plot the surface.  
surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,  
                       linewidth=0, antialiased=False)  
  
# Customize the z axis.  
ax.set_zlim(-1.01, 1.01)  
ax.zaxis.set_major_locator(LinearLocator(10))  
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))  
  
# Add a color bar that maps values to colors.  
fig.colorbar(surf, shrink=0.5, aspect=5)  
  
plt.show() 

输出

5. 三曲面 3D 绘图

紧凑曲面的三角剖分是覆盖曲面的有限三角形集合，曲面上的每个点都在一个三角形中，并且任何两个三角形的交点要么是空的，要么是公共边，要么是公共顶点. 三角面称为三角面。

from mpl_toolkits.mplot3d import Axes3D  
import matplotlib.pyplot as plt  
import numpy as np  
  
  
n_radii = 16  
n_angles = 16  
  
# Make radii and angles spaces (radius r=0 omitted to eliminate duplication).  
radii = np.linspace(0.125, 1.0, n_radii)  
angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False)  
  
# Repeat all angles for each radius.  
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)  
  
# Convert polar (radii, angles) coords to cartesian (x, y) coords.  
# (0, 0) is manually added at this stage,  so there will be no duplicate  
# points in the (x, y) plane.  
x = np.append(0, (radii*np.cos(angles)).flatten())  
y = np.append(0, (radii*np.sin(angles)).flatten())  
  
# Compute z to make the pringle surface.  
z = np.sin(-x*y)  
  
fig = plt.figure()  
ax = fig.gca(projection='3d')  
  
ax.plot_trisurf(x, y, z, linewidth=0.2, antialiased=True)  
  
plt.show()  

输出

from mpl_toolkits.mplot3d import axes3d  
import matplotlib.pyplot as plt  
from matplotlib import cm  
  
fig = plt.figure()  
ax = fig.gca(projection='3d')  
X, Y, Z = axes3d.get_test_data(0.005)  
cset = ax.contour(X, Y, Z, extend3d=True, cmap=cm.coolwarm)  
ax.clabel(cset, fontsize=9, inline=1)  
  
plt.show()  

从 mpl_toolkits.mplot3d 导入 axes3d  
导入 matplotlib.pyplot 作为 plt  
从 matplotlib 导入 cm  
  
fig = plt.figure()  
ax = fig.gca(projection= '3d' )  
X, Y, Z = axes3d.get_test_data( 0.005 )  
cset = ax.contourf(X, Y, Z, cmap=cm.coolwarm)  
ax.clabel(cset, fontsize= 9 , inline= 1 )  
  
plt.show()  

from mpl_toolkits.mplot3d import Axes3D  
from matplotlib.collections import PolyCollection  
import matplotlib.pyplot as plt  
from matplotlib import colors as mcolors  
import numpy as np  
  
  
fig = plt.figure()  
ax = fig.gca(projection='3d')  
  
  
def cc(arg):  
    return mcolors.to_rgba(arg, alpha=0.9)  
  
xs = np.arange(0, 10, 0.2)  
verts = []  
zs = [0.0, 1.0, 2.0, 3.0]  
for z in zs:  
    ys = np.random.rand(len(xs))  
    ys[0], ys[-1] = 0, 0  
    verts.append(list(zip(xs, ys)))  
  
poly = PolyCollection(verts, facecolors=[cc('r'), cc('g'), cc('b'),  
                                         cc('y')])  
poly.set_alpha(0.9)  
ax.add_collection3d(poly, zs=zs, zdir='y')  
  
ax.set_xlabel('X')  
ax.set_xlim3d(0, 10)  
ax.set_ylabel('Y')  
ax.set_ylim3d(-1, 4)  
ax.set_zlabel('Z')  
ax.set_zlim3d(0, 1)  
  
plt.show()  

from mpl_toolkits.mplot3d import Axes3D  
import matplotlib.pyplot as plt  
import numpy as np  
  
fig = plt.figure()  
ax = fig.add_subplot(111, projection='3d')  
for c, z in zip(['r', 'g', 'b', 'y'], [30, 20, 10, 0]):  
    xs = np.arange(100)  
    ys = np.random.rand(100)  
  
    # You can provide either a single color or an array. To demonstrate this,  
    # the first bar of each set will be colored cyan.  
    cs = [c] * len(xs)  
    cs[0] = 'c'  
    ax.bar(xs, ys, zs=z, zdir='y', color=cs, alpha=0.8)  
  
ax.set_xlabel('X')  
ax.set_ylabel('Y')  
ax.set_zlabel('Z')  
  
plt.show()  

from mpl_toolkits.mplot3d import axes3d  
import matplotlib.pyplot as plt  
import numpy as np  
  
fig = plt.figure()  
ax = fig.gca(projection='3d')  
  
# Make the grid  
x, y, z = np.meshgrid(np.arange(-0.8, 1, 0.4),  
                      np.arange(-0.8, 1, 0.3),  
                      np.arange(-0.8, 1, 0.3))  
  
# Make the direction data for the arrows  
u = np.sin(np.pi * x) * np.cos(np.pi * y) * np.cos(np.pi * z)  
v = -np.cos(np.pi * x) * np.sin(np.pi * y) * np.cos(np.pi * z)  
w = (np.sqrt(2.0 / 3.0) * np.cos(np.pi * x) * np.cos(np.pi * y) *  
     np.sin(np.pi * z))  
  
ax.quiver(x, y, z, u, v, w, length=0.2, normalize=True)  
  
plt.show()