x = np.linspace(0, 2, 100)
plt.plot(x, x, label='linear')  # Plot some data on the (implicit) axes.plt.plot(x, x**2, label='quadratic')  # etc.plt.plot(x, x**3, label='cubic')plt.xlabel('x label')plt.ylabel('y label')plt.title("Simple Plot")plt.legend()

import matplotlib.pyplot as pltimport numpy as np
# Data for plottingt = np.arange(0.0, 2.0, 0.01)s = 1 + np.sin(2 * np.pi * t)
fig, ax = plt.subplots()ax.plot(t, s)
ax.set(xlabel='time (s)', ylabel='voltage (mV)',       title='About as simple as it gets, folks')ax.grid()
fig.savefig("test.png")plt.show()

import numpy as npimport matplotlib.pyplot as plt
np.random.seed(19680801)
# example datamu = 100  # mean of distributionsigma = 15  # standard deviation of distributionx = mu + sigma * np.random.randn(437)
num_bins = 50
fig, ax = plt.subplots()
# the histogram of the datan, bins, patches = ax.hist(x, num_bins, density=True)
# add a 'best fit' liney = ((1 / (np.sqrt(2 * np.pi) * sigma)) *     np.exp(-0.5 * (1 / sigma * (bins - mu))**2))ax.plot(bins, y, '--')ax.set_xlabel('Smarts')ax.set_ylabel('Probability density')ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
# Tweak spacing to prevent clipping of ylabelfig.tight_layout()plt.show()

import matplotlib.path as mpathimport matplotlib.patches as mpatchesimport matplotlib.pyplot as plt
fig, ax = plt.subplots()
Path = mpath.Pathpath_data = [    (Path.MOVETO, (1.58, -2.57)),    (Path.CURVE4, (0.35, -1.1)),    (Path.CURVE4, (-1.75, 2.0)),    (Path.CURVE4, (0.375, 2.0)),    (Path.LINETO, (0.85, 1.15)),    (Path.CURVE4, (2.2, 3.2)),    (Path.CURVE4, (3, 0.05)),    (Path.CURVE4, (2.0, -0.5)),    (Path.CLOSEPOLY, (1.58, -2.57)),    ]codes, verts = zip(*path_data)path = mpath.Path(verts, codes)patch = mpatches.PathPatch(path, facecolor='r', alpha=0.5)ax.add_patch(patch)
# plot control points and connecting linesx, y = zip(*path.vertices)line, = ax.plot(x, y, 'go-')
ax.grid()ax.axis('equal')plt.show()

import numpy as npimport matplotlib.pyplot as pltr = np.arange(0, 2, 0.01)theta = 2 * np.pi * r
fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})ax.plot(theta, r)ax.set_rmax(2)ax.set_rticks([0.5, 1, 1.5, 2])  # Less radial ticksax.set_rlabel_position(-22.5)  # Move radial labels away from plotted lineax.grid(True)
ax.set_title("A line plot on a polar axis", va='bottom')plt.show()

import matplotlib.pyplot as plt
# Pie chart, where the slices will be ordered and plotted counter-clockwise:labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'sizes = [15, 30, 45, 10]explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')
fig1, ax1 = plt.subplots()ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',        shadow=True, startangle=90)ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()

import matplotlib.pyplot as pltfrom matplotlib import cmfrom matplotlib.ticker import LinearLocatorimport numpy as np
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
# Make data.X = np.arange(-5, 5, 0.25)Y = np.arange(-5, 5, 0.25)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))# A StrMethodFormatter is used automaticallyax.zaxis.set_major_formatter('{x:.02f}')
# Add a color bar which maps values to colors.fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()

#使用numpy产生数据x=np.arange(-5,5,0.1)y=x*3
#创建窗口、子图#方法1：先创建窗口，再创建子图。（一定绘制）fig = plt.figure(num=1, figsize=(15, 8),dpi=80)     #开启一个窗口，同时设置大小，分辨率ax1 = fig.add_subplot(2,1,1)  #通过fig添加子图，参数：行数，列数，第几个。ax2 = fig.add_subplot(2,1,2)  #通过fig添加子图，参数：行数，列数，第几个。print(fig,ax1,ax2)#方法2：一次性创建窗口和多个子图。（空白不绘制）fig,axarr = plt.subplots(4,1)  #开一个新窗口，并添加4个子图，返回子图数组ax1 = axarr[0]    #通过子图数组获取一个子图print(fig,ax1)#方法3：一次性创建窗口和一个子图。（空白不绘制）ax1 = plt.subplot(1,1,1,facecolor='white')      #开一个新窗口，创建1个子图。facecolor设置背景颜色print(ax1)#获取对窗口的引用，适用于上面三种方法# fig = plt.gcf()   #获得当前figure# fig=ax1.figure   #获得指定子图所属窗口
# fig.subplots_adjust(left=0)                         #设置窗口左内边距为0，即左边留白为0。
#设置子图的基本元素ax1.set_title('python-drawing')            #设置图体，plt.titleax1.set_xlabel('x-name')                    #设置x轴名称,plt.xlabelax1.set_ylabel('y-name')                    #设置y轴名称,plt.ylabelplt.axis([-6,6,-10,10])                  #设置横纵坐标轴范围，这个在子图中被分解为下面两个函数ax1.set_xlim(-5,5)                           #设置横轴范围，会覆盖上面的横坐标,plt.xlimax1.set_ylim(-10,10)                         #设置纵轴范围，会覆盖上面的纵坐标,plt.ylim
xmajorLocator = MultipleLocator(2)   #定义横向主刻度标签的刻度差为2的倍数。就是隔几个刻度才显示一个标签文本ymajorLocator = MultipleLocator(3)   #定义纵向主刻度标签的刻度差为3的倍数。就是隔几个刻度才显示一个标签文本
ax1.xaxis.set_major_locator(xmajorLocator) #x轴 应用定义的横向主刻度格式。如果不应用将采用默认刻度格式ax1.yaxis.set_major_locator(ymajorLocator) #y轴 应用定义的纵向主刻度格式。如果不应用将采用默认刻度格式
ax1.xaxis.grid(True, which='major')      #x坐标轴的网格使用定义的主刻度格式ax1.yaxis.grid(True, which='major')      #x坐标轴的网格使用定义的主刻度格式
ax1.set_xticks([])     #去除坐标轴刻度ax1.set_xticks((-5,-3,-1,1,3,5))  #设置坐标轴刻度ax1.set_xticklabels(labels=['x1','x2','x3','x4','x5'],rotation=-30,fontsize='small')  #设置刻度的显示文本，rotation旋转角度，fontsize字体大小
plot1=ax1.plot(x,y,marker='o',color='g',label='legend1')   #点图：marker图标plot2=ax1.plot(x,y,linestyle='--',alpha=0.5,color='r',label='legend2')   #线图：linestyle线性，alpha透明度，color颜色，label图例文本
ax1.legend(loc='upper left')            #显示图例,plt.legend()ax1.text(2.8, 7, r'y=3*x')                #指定位置显示文字,plt.text()ax1.annotate('important point', xy=(2, 6), xytext=(3, 1.5),  #添加标注，参数：注释文本、指向点、文字位置、箭头属性            arrowprops=dict(facecolor='black', shrink=0.05),            )#显示网格。which参数的值为major(只绘制大刻度)、minor(只绘制小刻度)、both，默认值为major。axis为'x','y','both'ax1.grid(b=True,which='major',axis='both',alpha= 0.5,color='skyblue',linestyle='--',linewidth=2)
axes1 = plt.axes([.2, .3, .1, .1], facecolor='y')       #在当前窗口添加一个子图，rect=[左, 下, 宽, 高]，是使用的绝对布局，不和以存在窗口挤占空间axes1.plot(x,y)  #在子图上画图plt.savefig('aa.jpg',dpi=400,bbox_inches='tight')   #savefig保存图片，dpi分辨率，bbox_inches子图周边白色空间的大小plt.show()    #打开窗口，对于方法1创建在窗口一定绘制，对于方法2方法3创建的窗口，若坐标系全部空白，则不绘制