1、盒形图绘制：
%matplotlib inlineimport matplotlib.pyplot as pltimport numpy as nptang_data = [np.random.normal(0,std,100) for std in range(1,4)]fig = plt.figure(figsize = (8,6)) #区域大小plt.boxplot(tang_data,notch=False,sym='s',vert=True) #notch=False基本形状，sym=‘s’方块，vert=True竖着画plt.xticks([y+1 for y in range(len(tang_data))],['x1','x2','x3'])plt.xlabel('x')plt.title('box plot')更改盒形图颜色：
for components in bplot.keys(): for line in bplot[components]: line.set_color('black')也可以横着画，只需要vert=False：
tang_data = [np.random.normal(0,std,100) for std in range(1,4)]fig = plt.figure(figsize = (8,6))plt.boxplot(tang_data,notch=False,sym='s',vert=False)plt.yticks([y+1 for y in range(len(tang_data))],['x1','x2','x3'])plt.ylabel('x')plt.title('box plot')当然你要实在闲着没事干，甚至可以把盒子形状改一下，notch=True。
或者更改一下盒子的颜色：
2、小提琴图：violinplot
fig,axes = plt.subplots(nrows=1,ncols=2,figsize=(12,5))tang_data = [np.random.normal(0,std,100) for std in range(6,10)]axes[0].violinplot(tang_data,showmeans=False,showmedians=True)axes[0].set_title('violin plot')axes[1].boxplot(tang_data)axes[1].set_title('box plot')for ax in axes: ax.yaxis.grid(True) ax.set_xticks([y+1 for y in range(len(tang_data))])plt.setp(axes,xticks=[y+1 for y in range(len(tang_data))],xticklabels=['x1','x2','x3','x4'])3、对于轴的操作：
x = range(10)y = range(10)fig = plt.gca() #对轴操作plt.plot(x,y)fig.axes.get_xaxis().set_visible(False) #消去轴的刻度fig.axes.get_yaxis().set_visible(False)4、去掉上右两轴且带网格的直方图：
import mathx = np.random.normal(loc = 0.0,scale=1.0,size=300)width = 0.5bins = np.arange(math.floor(x.min())-width,math.ceil(x.max())+width,width)ax = plt.subplot(111)ax.spines['top'].set_visible(False) #去除上轴ax.spines['right'].set_visible(False) #去除右轴plt.tick_params(bottom='off',top='off',left = 'off',right='off') #消除轴上的指示线plt.grid() #加上网格plt.hist(x,alpha = 0.5,bins = bins) #hist直方图5、轴标签：
x = range(10)y = range(10)labels = ['godx' for i in range(10)]fig,ax = plt.subplots()plt.plot(x,y)plt.title('godx')ax.set_xticklabels(labels,rotation = 45,horizontalalignment='right')#rotation = 45倾斜角，horizontalalignment='right'右对齐import matplotlib as mplmpl.rcParams['axes.titlesize'] = '10' #更改图标题的大小6、一个图多条曲线+图例：
x = np.arange(10)for i in range(1,4): plt.plot(x,i*x**2,label = 'Group %d'%i) plt.legend(loc='best') #在图里加上图例，loc='best'放在最好的位置也可指定其他位置upper center、lower left等
ax.legend(loc='upper center',bbox_to_anchor = (0.5,1.15) ,ncol=3)#把图例放在图外面，上方，(0.5,1.15)表示位置，nocl=3表示横着写（3列）ax.legend(loc='upper center',bbox_to_anchor = (1.15,1) ,ncol=1)#ncol=1竖着写（1列）plt.legend(loc='upper right',framealpha = 0.1) #将图例设置为透明7、直方图：
最简单的：
两个变量的直方图：
import randomdata1 = [random.gauss(15,10) for i in range(500)]data2 = [random.gauss(5,5) for i in range(500)]bins = np.arange(-50,50,2.5)plt.hist(data1,bins=bins,label='class 1',alpha = 0.3) #设置alpha可以看两组数据堆叠程度plt.hist(data2,bins=bins,label='class 2',alpha = 0.3)plt.legend(loc='best') #图例plt.show()8、散点图的绘制：
mu_vec1 = np.array([0,0])cov_mat1 = np.array([[2,0],[0,2]]) #协方差矩阵x1_samples = np.random.multivariate_normal(mu_vec1, cov_mat1, 100)x2_samples = np.random.multivariate_normal(mu_vec1+0.2, cov_mat1+0.2, 100)x3_samples = np.random.multivariate_normal(mu_vec1+0.4, cov_mat1+0.4, 100)plt.figure(figsize = (8,6))plt.scatter(x1_samples[:,0],x1_samples[:,1],marker ='x',color='blue',alpha=0.6,label='x1')plt.scatter(x2_samples[:,0],x2_samples[:,1],marker ='o',color='red',alpha=0.6,label='x2')plt.scatter(x3_samples[:,0],x3_samples[:,1],marker ='^',color='green',alpha=0.6,label='x3')plt.legend(loc='best')plt.show()也可以在散点图中加入每个点的坐标：
x_coords = [0.13, 0.22, 0.39, 0.59, 0.68, 0.74, 0.93]y_coords = [0.75, 0.34, 0.44, 0.52, 0.80, 0.25, 0.55]plt.figure(figsize = (8,6))plt.scatter(x_coords,y_coords,marker='s',s=50) #s=50点的大小for x,y in zip(x_coords,y_coords): plt.annotate('(%s,%s)'%(x,y),xy=(x,y),xytext=(0,-15),textcoords = 'offset points',ha='center') #加注释，textcoords = 'offset points'加坐标,ha='center'对齐plt.show()离某个点越远点越大：
mu_vec1 = np.array([0,0])cov_mat1 = np.array([[1,0],[0,1]])X = np.random.multivariate_normal(mu_vec1, cov_mat1, 500)fig = plt.figure(figsize=(8,6))R=X**2 R_sum=R.sum(axis = 1)plt.scatter(X[:,0],X[:,1],color='grey',marker='o',s=20*R_sum,alpha=0.5)plt.show()