![]() Of course the units of points**2 are area units. So why do other answers and even the documentation speak about "area" when it comes to the s parameter? In order to produce a scatter marker of the same size as a plot marker of size 10 points you would hence call scatter(. So the relationship between the markersize of a line plot and the scatter size argument is the square. In order to obtain a marker which is x points large, you need to square that number and give it to the s argument. S : scalar or array_like, shape (n, ), optional The argument s in plt.scatter denotes the markersize**2. Plt.legend(loc='center left', bbox_to_anchor=(1.1, 0.5), labelspacing=3)īecause other answers here claim that s denotes the area of the marker, I'm adding this answer to clearify that this is not necessarily the case. 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.Įdit to visualize the comment by is what it looks like for different functions of the marker size: 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. Similarly the second example each circle has area double the last one which gives an exponential with base 2. 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. Now the apparent size of the markers increases roughly linearly in an intuitive fashion.Īs 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.Įdit: (In response to comment from probably confusing wording on my part. If instead we have # doubling the area of markers Notice how the size increases very quickly. To see this consider the following two examples and the output they produce. ![]() 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). There is a reason, however, that the size of markers is defined in this way. ![]() This means, to double the width (or height) of the marker you need to increase s by a factor of 4. Let’s visualize the heights of basketball players again.This can be a somewhat confusing way of defining the size but you are basically specifying the area of the marker. But you can specify a different color for each dot. So far, we’ve been drawing dots with the same color. For example, what if you want to keep the y-axis? You could draw it using Line2D, as we did above for the x-axis. The plot looks cleaner without the surrounding box and the y-axis. xlabel( "Final Exam Scores", labelpad = 20) So Matplotlib won't show these # two values on the x-axis # Below code ensures that every possible value in the score # range is visible on the x-axis (xmin, xmax), (ymin, ymin), linewidth = 2, color = 'black' # Removing frame also removed x-axis line # let's add it back # Seaborn for better styling import seaborn as sns # Line2D will be needed to draw x-axis line from matplotlib.lines import Line2D Suppose the below list contains the heights (in inches) of 50 high school basketball players: Let’s see a few ways in which you can use this function. Thus, you can customize the dot plot using any parameters that work with scatter(). ![]() Notice that the function passes all the inputs ( **args) to scatter().Finally, it uses Matplotlib’s scatter() and the 2D array to draw the dot plot.For example, if the value 60 appears three times, we’ll have three 2D points - (60, 1), (60, 2), and (60, 3). ![]() It counts how many times each unique value occurs and creates as many 2D points.It transforms the given list input_x into a 2D array.# Optional - show all unique values on x-axis. Scatter_y = # corresponding y values for idx, value in enumerate(unique_values):įor counter in range( 1, counts + 1): # Count how many times does each value occur # standard numpy and matplotlib library imports import numpy as np import matplotlib.pyplot as plt def dotplot(input_x, **args): ![]()
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