python matplotlib模块

matplotlib是基于Python语言的开源项目，旨在为Python提供一个数据绘图包。我将在这篇文章中介绍matplotlib API的核心对象，并介绍如何使用这些对象来实现绘图。实际上，matplotlib的对象体系严谨而有趣，为使用者提供了巨大的发挥空间。用户在熟悉了核心对象之后，可以轻易的定制图像。matplpotlib是基于numpy的，所以需要先安装numpy

pip install numpy           #安装numpypip install matplotlib    #安装matplotlibpip show matplotlib   #查看matplotlib的信息

matplotlib使用numpy进行数组运算，并调用一系列其他的Python库来实现硬件交互。matplotlib的核心是一套由对象构成的绘图API。
- 最简单的显示一条直线

import matplotlib.pyplot as pltimport numpy as npx = np.linspace(-3,3,50)y = 2*x+1plt.figure()plt.plot(x,y)plt.show()

你将看到

• 同时显示两条线

import matplotlib.pyplot as pltimport numpy as npx = np.linspace(-3,3,50)y1 = 2*x+1y2 = x**2plt.figure()plt.plot(x,y1)plt.plot(x,y2,color='red',linewidth=1.0,linestyle='--')plt.show()

你将看到

• 坐标轴取值范围

import matplotlib.pyplot as pltimport numpy as npx = np.linspace(-3,3,50)y1 = 2*x+1y2 = x**2plt.figure()plt.plot(x,y1)plt.plot(x,y2,color='red',linewidth=1.0,linestyle='--')plt.xlim((-1,2))   #x轴范围plt.ylim((-2,3))    #y轴范围plt.show()

你将看到

• 描述 xlabel ylabel

import matplotlib.pyplot as pltimport numpy as npx = np.linspace(-3,3,50)y1 = 2*x+1y2 = x**2plt.figure()plt.plot(x,y1)plt.plot(x,y2,color='red',linewidth=1.0,linestyle='--')plt.xlim((-1,2))plt.ylim((-2,3))plt.xlabel('X')plt.ylabel('Y')plt.show()

你将看到

• 更换标签

import matplotlib.pyplot as pltimport numpy as npx = np.linspace(-3,3,50)y1 = 2*x+1y2 = x**2plt.figure()plt.plot(x,y1)plt.plot(x,y2,color='red',linewidth=1.0,linestyle='--')plt.xlim((-1,2))plt.ylim((-2,3))plt.xlabel('X')plt.ylabel('Y')new_ticks = np.linspace(-1,2,5)print(new_ticks)plt.xticks(new_ticks)plt.yticks([-2,-1,1,3,],['bad','really bad','good','really good',])plt.show()

你将看到

• 修改坐标轴的位置

import matplotlib.pyplot as pltimport numpy as npx = np.linspace(-3,3,50)y1 = 2*x+1y2 = x**2plt.figure()plt.plot(x,y1)plt.plot(x,y2,color='red',linewidth=1.0,linestyle='--')plt.xlim((-1,2))plt.ylim((-2,3))plt.xlabel('X')plt.ylabel('Y')new_ticks = np.linspace(-1,2,5)print(new_ticks)plt.xticks(new_ticks)plt.yticks([-2,-1,1,3,],[r'$bad$','really bad','good','really good',])ax = plt.gca()ax.spines['right'].set_color('none')   #把右侧坐标轴去掉ax.spines['top'].set_color('none')     #把上面坐标轴去掉ax.xaxis.set_ticks_position('bottom')   #x轴为下面的轴ax.yaxis.set_ticks_position('left')        #y轴为左侧的轴ax.spines['bottom'].set_position(('data',0))    #x轴绑定在y轴0处ax.spines['left'].set_position(('data',0))      #y轴绑定在x轴0处plt.show()

你将看到

• 图例

import matplotlib.pyplot as pltimport numpy as npx = np.linspace(-3,3,50)y1 = 2*x+1y2 = x**2plt.figure()plt.plot(x,y1,label='y1')plt.plot(x,y2,color='red',linewidth=1.0,linestyle='--',label='y2')plt.xlim((-1,2))plt.ylim((-2,3))plt.xlabel('X')plt.ylabel('Y')new_ticks = np.linspace(-1,2,5)plt.xticks(new_ticks)plt.yticks(np.linspace(-1,3,5))ax = plt.gca()ax.spines['right'].set_color('none')   #把右侧坐标轴去掉ax.spines['top'].set_color('none')     #把上面坐标轴去掉ax.xaxis.set_ticks_position('bottom')   #x轴为下面的轴ax.yaxis.set_ticks_position('left')        #y轴为左侧的轴ax.spines['bottom'].set_position(('data',0))    #x轴绑定在y轴0处ax.spines['left'].set_position(('data',0))      #y轴绑定在x轴0处plt.legend(loc='best')     plt.show()

你将看到

• 标注
  在图片中添加注解

import matplotlib.pyplot as pltimport numpy as npx = np.linspace(-3,3,50)y1 = 2*x+1y2 = x**2plt.figure()plt.plot(x,y1,label='y1')# plt.plot(x,y2,color='red',linewidth=1.0,linestyle='--',label='y2')plt.xlim((-1,2))plt.ylim((-2,3))plt.xlabel('X')plt.ylabel('Y')new_ticks = np.linspace(-1,2,5)ax = plt.gca()ax.spines['right'].set_color('none')   #把右侧坐标轴去掉ax.spines['top'].set_color('none')     #把上面坐标轴去掉ax.xaxis.set_ticks_position('bottom')   #x轴为下面的轴ax.yaxis.set_ticks_position('left')        #y轴为左侧的轴ax.spines['bottom'].set_position(('data',0))    #x轴绑定在y轴0处ax.spines['left'].set_position(('data',0))      #y轴绑定在x轴0处plt.legend(loc='best')x0=0.5y0=2*x0+1plt.scatter(x0,y0,c = 'r',marker = 'o')plt.plot([x0,x0],[0,y0],'k--',lw=2.5)    #(x0,0)与（x0,y0）之间的虚线连接#标注#方法1plt.annotate(r'$(0.5,2)$',xy=(x0,y0))#方法2plt.text(1,2,u'(0.5,2)')plt.show()

你将看到

• 坐标轴刻度调整

import matplotlib.pyplot as pltimport numpy as npx = np.linspace(-3,3,50)y1 = 2*x+1y2 = x**2plt.figure()plt.plot(x,y1,label='y1',lw=1)# plt.plot(x,y2,color='red',linewidth=1.0,linestyle='--',label='y2')plt.xlim((-1,2))plt.ylim((-2,3))plt.xlabel('X')plt.ylabel('Y')new_ticks = np.linspace(-1,2,5)ax = plt.gca()ax.spines['right'].set_color('none')   #把右侧坐标轴去掉ax.spines['top'].set_color('none')     #把上面坐标轴去掉ax.xaxis.set_ticks_position('bottom')   #x轴为下面的轴ax.yaxis.set_ticks_position('left')        #y轴为左侧的轴ax.spines['bottom'].set_position(('data',0))    #x轴绑定在y轴0处ax.spines['left'].set_position(('data',0))      #y轴绑定在x轴0处plt.legend(loc='best')plt.xticks(new_ticks)plt.yticks(np.linspace(-1,3,5))for label in ax.get_xticklabels()+ax.get_yticklabels():    label.set_fontsize(12)   #设置字体    label.set_bbox(dict(facecolor='white',edgecolor='None',alpha=0.8))   #alpha表示透明度plt.show()

你将看到

• 散点数据

import matplotlib.pyplot as pltimport numpy as npn = 1204X = np.random.normal(0,1,n)Y = np.random.normal(0,1,n)T = np.arctan2(Y,X)plt.scatter(X,Y,s=75,c=T,alpha=0.5)plt.xlim((-1,1))plt.ylim((-1,1))plt.xticks(())plt.yticks(())plt.show()

你将看到

• 条形图（bar）

import matplotlib.pyplot as pltimport numpy as npn = 12X = np.arange(n)Y1 = (1-X/(float(n))*np.random.uniform(0.5,1.0,n))Y2 = (1-X/(float(n))*np.random.uniform(0.5,1.0,n))plt.bar(X,+Y1,facecolor='r',edgecolor='w')plt.bar(X,-Y2,facecolor='b',edgecolor='w')for x,y in zip(X,Y1):    plt.text(x-0.1,y+0.05,'%.2f'%y,ha='center',va='bottom')  #居中对齐for x,y in zip(X,Y2):    plt.text(x+0.1,-y-0.05,'-%.2f'%y,ha='center',va='top')plt.xlim((-0.5,n))plt.ylim((-2,2))plt.xticks(())plt.yticks(())plt.show()

你将看到

• 等高线

import matplotlib.pyplot as pltimport numpy as npdef f(x,y):    return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)n = 256x = np.linspace(-3,3,n)y = np.linspace(-3,3,n)X,Y = np.meshgrid(x,y)plt.contourf(X,Y,f(X,Y),8,alpha=0.75,cmap=plt.cm.hot)C = plt.contour(X,Y,f(X,Y),8,colors='black',lw=0.5)    #等高线分多少部分plt.clabel(C,inline=True,fontsize=10)plt.xticks(())plt.yticks(())plt.show()

你将看到

• 3D

import matplotlib.pyplot as pltimport numpy as npfrom mpl_toolkits.mplot3d import Axes3Dfig = plt.figure()ax = Axes3D(fig)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)#Z是高度值Z = np.sin(R)ax.plot_surface(X,Y,Z,rstride=1,cstride=1,cmap=plt.get_cmap('rainbow'))  #stride表示跨度# ax.contourf(X,Y,Z,zdir='z',offset=-2,cmap='rainbow')# ax.set_zlim(-2,2)plt.show()

你将看到

• 多个显示

第1种

import matplotlib.pyplot as pltimport numpy as npplt.figure()plt.subplot(2,2,1)plt.plot([0,1],[0,1])plt.subplot(2,2,2)plt.plot([0,1],[0,2])plt.subplot(2,2,3)plt.plot([0,1],[0,3])plt.subplot(2,2,4)plt.plot([0,1],[0,4])plt.show()

第2种

import matplotlib.pyplot as pltimport numpy as npplt.figure()plt.subplot(2,1,1)plt.plot([0,1],[0,1])plt.subplot(2,3,4)plt.plot([0,1],[0,2])plt.subplot(2,3,5)plt.plot([0,1],[0,3])plt.subplot(2,3,6)plt.plot([0,1],[0,4])plt.show()

参考资料：https://morvanzhou.github.io/tutorials/data-manipulation/plt/