data = [5, 20, 15, 25, 10] labels = ['Tom', 'Dick', 'Harry', 'Slim', 'Jim'] plt.bar(range(len(data)), data, tick_label=labels) plt.show() ``` 示例: import matplotlib.pyplot as plt labels = [0.3, 0.5, 1.0, 1.5, 3.0] bottom_means = [20, 80, 160, 250, 400] top...
which的默认值为'major'，就是仅对主刻度的字体大小、刻度朝向等进行设置。而因为你训练编写的代码应该...
plt.show() 1. 2. 3. 4. 5. 6. 7. 8. 9. 1.4 Matplotlib-画布与子图 画布:figure fig = plt.figure() 子图:subplot ax1 = fig.add_subplot(2,2,1) 调节子图间距: subplots_adjust(left, bottom, right, top, wspace, hspace) fig = plt.figure() ax1 = fig.add_subplot(2, 2, 1) # ...
data)forbarinbars:yval=bar.get_height()ax.text(bar.get_x()+bar.get_width()/2,yval,yval,ha='center',va='bottom',rotation=45)plt.title("Rotated Value Labels - how2matplotlib.com")plt.show()
# 创建直方图counts,bins,_=plt.hist(data,bins=10,edgecolor='black')# 添加标签forcount,bininzip(counts,bins[:-1]):plt.text(bin,count,f'{count:.0f}',ha='center',va='bottom')plt.title('Histogram with Labels - how2matplotlib.com')plt.xlabel('Value')plt.ylabel('Frequency')plt.show(...
axesax = fig.add_axes()l1 = ax.plot(x1,y,'ys-')l2 = ax.plot(x2,y,'go--')# add additional parametersax.legend(labels = ('line 1', 'line 2'), loc = 'lower right')ax.set_title("usage of add axes function")ax.set_xlabel('x-axix')ax.set_ylabel('y-axis')plt.show()...
ax_main.title.set_fontsize(20) for item in ([ax_main.xaxis.label, ax_main.yaxis.label] + ax_main.get_xticklabels + ax_main.get_yticklabels): item.set_fontsize(14) xlabels = ax_main.get_xticks.tolist ax_main.set_xticklabels(xlabels) plt.show ...
plt.show()#显示图像 matplotlib的figure为单独图像窗口,小窗口内还可以有更多的小图片。 代码语言:javascript 复制 x=np.linspace(-3,3,50)#50为生成的样本数 y1=2*x+1y2=x**2plt.figure(num=1,figsize=(8,5))#定义编号为1大小为(8,5)plt.plot(x,y1,color='red',linewidth=2,linestyle='--')...
linspace(0, 2 * np.pi, 50)offsets = np.linspace(0, 2 * np.pi, 4, endpoint=False)yy = np.transpose([np.sin(x + phi) for phi in offsets])fig, ax = plt.subplots(figsize=(8, 4))ax.set_prop_cycle(line_prop_cycler) # Set propcycle before plottingax.plot(x, yy)plt.show...
plt.show() 输出结果: 三、饼图 饼图用于实现可视化离散型变量的分布的一种图形 plt.pie(x,explode,labels,colors,autopct=,pctdistance,shadow,startangle,radius,wedgeprops,textprops,center) --explode:突出显示 --labels:标签 --colors:颜色 --autopct:百分比 ...