pycharm使用matplotlib绘图学习笔记

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import numpy as npdef main():import matplotlib.pyplot as plt##lesson1:画图# x = np.linspace(-1, 1, 50)# x=np.linspace(-np.pi,np.pi,256,endpoint=True)# c,s=np.cos(x),np.sin(x)# plt.figure(1)# plt.plot(x,c,color="blue",lineCOS",alpha=0.5)# plt.plot(x,s,"r*",label="SIN")# plt.title("COS & SIN")# ##设置坐标轴# ax=plt.gca()# ax.spines["right"].set_color("none")# ax.spines["top"].set_color("none")# ax.spines["left"].set_position(("data",0))# ax.spines["bottom"].set_position(("data",0))# ax.xaxis.set_ticks_position("bottom")# ax.yaxis.set_ticks_position("left")# ##网格填充# plt.grid()# plt.fill_between(x,np.abs(x)<0.5,c,c>0.5,color="green",alpha=0.25)# ##注释# t=1# plt.plot([t,t],[0,np.cos(t)],"y",linecos(1)",xy=(t,np.cos(1)),xycoords="data",xytext=(+10,+30),textcoords="offset points",arrowprops=dict(arrow,connection))## plt.show()##lesson2:数据分析常见图形##散点图scatter# fig=plt.figure()# ax=fig.add_subplot(3,3,1)# n=128# X=np.random.normal(0,1,n)#随机数# Y=np.random.normal(0,1,n)#随机数# T=np.arctan2(Y,X)#上色# #plt.axes([0.025,0.025,0.95,0.95])#指定现实范围# #plt.scatter(X,Y,s=75,c=T,alpha=0.5)#画散点# ax.scatter(X, Y, s=75, c=T, alpha=0.5) # 画散点# plt.xlim(-1.5,1.5),plt.xticks([])#X范围# plt.ylim(-1.5,1.5),plt.yticks([])#Y范围# plt.axis()# plt.title("scatter")# plt.xlabel("x")# plt.ylabel("y")# plt.show()# #bar柱状图# fig = plt.figure()# fig.add_subplot(332)# n=10# X=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=&＃39;#9999ff&＃39;,edgecolor=&＃39;white&＃39;)# plt.bar(X,-Y2,facecolor=&＃39;#ff9999&＃39;,edgecolor=&＃39;white&＃39;)# for x,y in zip(X,Y1):# plt.text(x+0.4,y+0.05,&＃39;%.2f&＃39;%y,ha=&＃39;center&＃39;,va=&＃39;bottom&＃39;)## for x,y in zip(X,Y2):# plt.text(x+0.4,-y-0.05,&＃39;%.2f&＃39;%y,ha=&＃39;center&＃39;,va=&＃39;top&＃39;)## plt.show()# #pie饼状图# fig = plt.figure()# fig.add_subplot(333)# n=20# Z=np.ones(n)# Z[-1]*=2# plt.pie(Z,explode=Z*.05,colors=[&＃39;%f&＃39;%(i/float(n)) for i in range(n)],# labels=[&＃39;%.2f&＃39;%(i/float(n)) for i in range(n)])# plt.gca().set_aspect(&＃39;equal&＃39;)# plt.xticks([]),plt.yticks([])# plt.show()# #polar极线图# fig = plt.figure()# fig.add_subplot(334,polar=True)# n=20# theta=np.arange(0.0,2*np.pi,2*np.pi/n)# radii=10*np.random.rand(n)# #plt.plot(theta,radii)# plt.polar(theta,radii)# plt.show()#heatmap热力图# fig=plt.figure()# fig.add_subplot(335)# from matplotlib import cm# data=np.random.rand(3,3)# cmap=cm.Blues# map=plt.imshow(data,interpolation=&＃39;nearest&＃39;,cmap=cmap,aspect=&＃39;auto&＃39;,vmin=0,vmax=1)# plt.show()# #3D图# fig=plt.figure()# ax=fig.add_subplot(336,projection="3d")# ax.scatter(1,1,3,s=100)# plt.show()# #hot map热图# fig=plt.figure()# fig.add_subplot(313)# def f(x,y):# return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)# n=256# x=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=.75,cmap=plt.cm.hot)# plt.show()## ##保存图片# plt.savefig("./data/fig.png")if __name__ == &＃39;__main__&＃39;:main()