
        
        
            
                
                
                    fig, ax = plt.subplots()
ax.scatter([5,2,3], [1,2,4])
plt.show()

The first argument is an array of your xs, and the second is an array of your ys.

This produces the following:

Using the plot function

To draw a basic 2D scatter plot in Matplotlib, we could also use the plot(~) function like so:


        
        
            
                
                
                    plt.plot([5,2,3], [1,2,4], "o")
plt.show()

Here, the third argument "o" indicates that you don't want the data points to be connected. This is needed since the default behavior is to connect the dots.

This produces the following:

Customizing our plots

Both the scatter(~) and plot(~) methods allow for flexible customization.

Marker size

Pass in the markersize parameter for plot(~) and the s parameter for scatter(~):


        
        
            
                
                
                    plt.scatter([3,4],[5,6], s=10)
plt.plot([3,4],[5,6], markersize=10)

Marker color

Pass in the color parameter for both plot(~) and scatter(~):


        
        
            
                
                
                    plt.plot([3,4], [5,6], color="r")
plt.plot([3,4], [5,6], color="red")
plt.plot([3,4], [5,6], color="#FF0000")
plt.plot([3,4], [5,6], color=(1,0,0))      # Warning: this isn't your typical (0-255) interval

Marker style

Pass in the marker parameter for both plot(~) and scatter(~):


        
        
            
                
                
                    plt.scatter([1], [1], marker="*", s=300)   # star
plt.scatter([1], [2], marker="o", s=300)   # circle
plt.scatter([1], [3], marker="+", s=300)   # plus
plt.scatter([2], [1], marker="^", s=300)   # triangle
plt.scatter([2], [2], marker="D", s=300)   # diamond
plt.scatter([2], [3], marker="s", s=300)   # square

This produces the following:

The colors are automatically chosen by Matplotlib.

Difference between functions scatter and plot

Both functions can be used to draw scatter plots, but the difference is that the plot(~) function is more efficient but less flexible than the scatter(~) function. The plot(~) function does not allow for customization of individual points (e.g. changing color and size), while the scatter(~) allows for this. The implication of such a difference is that the plot(~) function has far less jobs to handle, making it more efficient.

As a rule of thumb, if you do not need to customize individual points, then opt for the plot(~) function.

Published by Isshin Inada

Edited by 0 others

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Official Matplotlib Documentation

https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.scatter.html

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