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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. plt.scatter(X:, 0, X:, 1) plt.xscale('symlog') plt.show() Share. Then: ani = animation.FuncAnimation(fig, animate, interval=1000) 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. The scatter() function plots one dot for each observation. We open the above file, and then store each line, split by comma, into xs and ys, which we'll plot. With Pyplot, you can use the scatter() function to draw a scatter 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() scatter plots, bar charts, histograms, stem plots, and spectrograms. Now we write the animation function: def animate(i): create webbased reporting templates 2D and 3D Chart options. Next, we'll add some code that you should be familiar with if you're following this series: e('fivethirtyeight') This is the module that will allow us to animate the figure after it has been shown. Here, the only new import is the matplotlib.animation as animation. To start: import matplotlib.pyplot as plt 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.