["python","matplotlib","google-maps-markers","scatter-plot"]
pyplot scatter plot marker size

In the pyplot document for scatter plot: matplotlib.pyplot.scatter(x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, faceted=True, verts=None, hold=None, **kwargs) The marker size s: size in points^2. It is a scalar or an array of the same length as x and y. What kind of unit is points^2? What does it mean? Does s=100 mean 10 pixel x 10 pixel? Basically I'm trying to make scatter plots with different marker sizes, and I want to figure out what does the s number mean.
s is an area s is an area (measured in pt2), and is the square of a length (measured in pt): s = area = length**2 For example, these are all proportional to length**2: square_area = w**2 --> 1 * length**2 circle_area = π * r**2 --> π/4 * length**2 To double the width and height of any marker, multiply s by a factor of 4: A = W * H --> (2W) * (2H) = 4A (Warning: s is proportional to the marker's shaded area, but is usually not equal to it.) Why is it like this? There is a reason, however, that the size of markers is defined in this way. Because of the scaling of area as the square of width, doubling the width actually appears to increase the size by more than a factor 2 (in fact it increases it by a factor of 4). To see this consider the following two examples and the output they produce. # doubling the width of markers x = [0,2,4,6,8,10] y = [0]*len(x) s = [20*4**n for n in range(len(x))] plt.scatter(x,y,s=s) plt.show() gives Notice how the size increases very quickly. If instead we have # doubling the area of markers x = [0,2,4,6,8,10] y = [0]*len(x) s = [20*2**n for n in range(len(x))] plt.scatter(x,y,s=s) plt.show() gives Now the apparent size of the markers increases roughly linearly in an intuitive fashion. As for the exact meaning of what a 'point' is, it is fairly arbitrary for plotting purposes, you can just scale all of your sizes by a constant until they look reasonable. Edit: (In response to comment from @Emma) It's probably confusing wording on my part. The question asked about doubling the width of a circle so in the first picture for each circle (as we move from left to right) it's width is double the previous one so for the area this is an exponential with base 4. Similarly the second example each circle has area double the last one which gives an exponential with base 2. However it is the second example (where we are scaling area) that doubling area appears to make the circle twice as big to the eye. Thus if we want a circle to appear a factor of n bigger we would increase the area by a factor n not the radius so the apparent size scales linearly with the area. Edit to visualize the comment by @TomaszGandor: This is what it looks like for different functions of the marker size: x = [0,2,4,6,8,10,12,14,16,18] s_exp = [20*2**n for n in range(len(x))] s_square = [20*n**2 for n in range(len(x))] s_linear = [20*n for n in range(len(x))] plt.scatter(x,[1]*len(x),s=s_exp, label='$s=2^n$', lw=1) plt.scatter(x,[0]*len(x),s=s_square, label='$s=n^2$') plt.scatter(x,[-1]*len(x),s=s_linear, label='$s=n$') plt.ylim(-1.5,1.5) plt.legend(loc='center left', bbox_to_anchor=(1.1, 0.5), labelspacing=3) plt.show()