参考:Add a vertical slider with matplotlib Matplotlib是Python中最流行的数据可视化库之一,它提供了丰富的绘图功能。然而,在某些情况下,我们可能希望能够动态地调整图表的某些参数,以便更好地探索和分析数据。这就是滑块控件发挥作用的地方。在本文中,我们将深入探讨如何在Matplotlib中添加垂直滑块,以增强图表的交互性和...
y="Sales", x="Name", ax=ax)avg = top_10['Sales'].mean()# Set limits and labelsax.set_xlim([-10000,140000])ax.set(title='2014 Revenue', xlabel='Total Revenue', ylabel='Customer')# Add a line for the averageax.axvline(x=avg, color='b...
linestyle='--',linewidth=2)plt.axvline(x=0.7,ymin=0.1,ymax=0.9,color='blue',linestyle='-',linewidth=2)plt.title('Customized Vertical Line Range - how2matplotlib.com')plt.xlabel('X-axis')plt.ylabel('Y-axis')plt.ylim(0,1)plt.show()...
# add a horizontal line across the axis plt.axhline(y=0.0,c="r",ls="--",lw=2) # add a vertical span across the axis plt.axvspan(xmin=1.0,xmax=2.0,facecolor="y",alpha=.3) # set annotating info plt.annotate("maximum", xy=(np.pi/2,1.0), xytext=((np.pi/2)+1.0,0.8),...
By using axvline() In matplotlib, theaxvline()method is used to add vertical lines to the plot. The syntax of the axvline() method is as given below: matplotlib.pyplot.axvline(x, ymin, ymax, color, linestyle) The above-used parameters are described as below: ...
ax = fig.add_axes([0.15, 0.1, 0.7, 0.3]) #然后调用Axes对象的plot()来绘制曲线,并且返回表示此曲线的Line2D对象 line = ax.plot([l, 2, 3], [1, 2, 1])[0] #返回的是只有一个元素的列表 #Axes对象的lines属性是一个包含所有曲线的列表,如果继续运行ax.plot(),所创建的Line2D 对象都会添加...
→ ax.add_patch(plt.Rectangle((0, 0),1,1) … draw a vertical line? → ax.axvline(x=0.5) … draw outside frame? → ax.plot(…, clip_on=False) … use transparency? → ax.plot(…, alpha=0.25) … convert an RGB image into a gray image? → gray = 0.2989*R+0.5870*G+0.1140...
formatter=FuncFormatter(currency)fig,ax=plt.subplots(figsize=(8,8))ax.barh(group_names,group_data)labels=ax.get_xticklabels()plt.setp(labels,rotation=45,horizontalalignment='right')# Add a vertical line, here we set the style in the function callax.axvline(group_mean,ls='--',color='...
ax.add_line(l) returnl fig, ax = plt.subplots(1,1,figsize=(14,14), dpi=80) # Vertical Lines ax.vlines(x=1, ymin=500, ymax=13000, color='black', alpha=0.7, linewidth=1, linestyles='dotted') ax.vlines(x=3, ymin=500,...
ax2 = fig.add_subplot(222) ax3 = fig.add_subplot(223) ax4 = fig.add_subplot(224) plt.show() 效果如下: 可以看到图中的x,y轴坐标都是从0到1,当然有时候我们需要其他的坐标起始值。 此时可以使用语句指定: ax1.axis([-1, 1, -1, 1]) ...