Then, you should be able to update the example.txt file with new coordinates. The result of running this graph should give you a graph as usual. We run the animation, putting the animation to the figure (fig), running the animation function of "animate," and then finally we have an interval of 1000, which is 1000 milliseconds, or one second. Then: ani = animation.FuncAnimation(fig, animate, interval=1000) We open the above file, and then store each line, split by comma, into xs and ys, which we'll plot. We read data from an example file, which has the contents of: 1,5 What we're doing here is building the data and then plotting it. Graph_data = open('example.txt','r').read() Now we write the animation function: def animate(i): Next, we'll add some code that you should be familiar with if you're following this series: e('fivethirtyeight') plt.scatter(X:, 0, X:, 1) plt.xscale('symlog') plt.show() Share. This is the module that will allow us to animate the figure after it has been shown. Since you have some points with negative first coordinates, you would need to use the symmetric logarithmic scale - which is logarithmic in both positive and negative directions of the x-axis.: import matplotlib.pyplot as plt. Here, the only new import is the matplotlib.animation as animation. To start: import matplotlib.pyplot as plt Plot 2D data on 3D plot Demo of 3D bar charts Create 2D bar graphs in different planes 3D box surface plot Plot contour (level) curves in 3D Plot contour (level) curves in 3D using the extend3d option Project contour profiles onto a graph Filled contours Project filled contour onto a graph Custom hillshading in a 3D. To do this, we use the animation functionality with Matplotlib. You may want to use this for something like graphing live stock pricing data, or maybe you have a sensor connected to your computer, and you want to display the live sensor data. In this Matplotlib tutorial, we're going to cover how to create live updating graphs that can update their plots live as the data-source updates.