3.1 使用按钮控制可见性 importmatplotlib.pyplotaspltfrommatplotlib.widgetsimportButtonfig,ax=plt.subplots()line,=ax.plot([1,2,3,4],[1,4,2,3],label='Data - how2matplotlib.com')deftoggle_visibility(event):line.set_visible(notline.get_visible())fig.canvas.draw()ax_button=plt.axes([0.8...
importmatplotlib.pyplotaspltimportnumpyasnp fig,ax=plt.subplots()ax.set_title("how2matplotlib.com Dynamic Update Example")line,=ax.plot([],[],'r-')line.set_gid("dynamic_line")defupdate_line(num,line):ifline.get_gid()=="dynamic_line":x=np.linspace(0,10,100)y=np.sin(x+nu...
Method#2 – Update Data for Plot There is a better and faster way of updating a plot in matplotlib. This technique does not involve clearing our plot, instead it directly updates the part that need to be updated. It is faster because there is less time wasted in clearing and redrawing th...
matplotlib.pyplot.plot(x,y) matplotlib.pyplot.show() 1. 2. and the x axis' ticks are plotted in intervals of 5. Is there a way to make it show intervals of 1?并且x轴的刻度线以5的间隔绘制。是否有办法使其显示1的间隔? #1楼
fig,ax=plt.subplots()ax.set_title("how2matplotlib.com - Boolean Picker Example")line1,=ax.plot([1,2,3,4],[1,4,2,3],label='Selectable',picker=True)line2,=ax.plot([1,2,3,4],[4,3,2,1],label='Not Selectable',picker=False)defon_pick(event):thisline=event.artistprint(f...
importmatplotlib.pyplotaspltdefon_click(event):ifevent.inaxes:print(f'Clicked in Axes: x={event.xdata:.2f}, y={event.ydata:.2f}')print(f'Button:{event.button}')else:print('Clicked outside Axes')fig,ax=plt.subplots()ax.plot([1,2,3,4],[1,4,2,3])ax.set_title('Click inside ...
=== Demonstration of a basic scatterplot in 3D. ''' from mpl_toolkits.mplot3d import Axes3D...
Updating amatplotlibplot is straightforward. Create the data, the plot and update in a loop. Setting interactive mode on is essential: plt.ion(). This controls if the figure is redrawn every draw() command. If it is False (the default), then the figure does not update itself. ...
它支持4个框架，包括tkinter，Qt，WxPython和Remi。与直接使用基础框架编写代码相比，PySimpleGUI代码更简单...
df_titanic['Age'] = df_titanic['Age'].fillna(df_titanic['Age'].mode().iloc[0]) # data to plot: age distribution survived vs not-sutvived surv = df_titanic[df_titanic['Survived'] == 1]['Age'] vict = df_titanic[df_titanic['Survived'] == 0]['Age'] group_labels = ['Survi...