Note Click here to download the full example code
To create a scatter plot with a legend one may use a loop and create one
scatter
plot per item to appear in the legend and set the label
accordingly.
The following also demonstrates how transparency of the markers can be adjusted by giving alpha
a value between 0 and 1.
import numpy as np import matplotlib.pyplot as plt np.random.seed[19680801] fig, ax = plt.subplots[] for color in ['tab:blue', 'tab:orange', 'tab:green']: n = 750 x, y = np.random.rand[2, n] scale = 200.0 * np.random.rand[n] ax.scatter[x, y, c=color, s=scale, label=color, alpha=0.3, edgecolors='none'] ax.legend[] ax.grid[True] plt.show[]
Automated legend creation#
Another option for creating a legend for a scatter is to use the PathCollection.legend_elements
method. It will automatically
try to determine a useful number of legend entries to be shown and return a tuple of handles and labels. Those can be passed to the call to legend
.
N = 45 x, y = np.random.rand[2, N] c = np.random.randint[1, 5, size=N] s = np.random.randint[10, 220, size=N] fig, ax = plt.subplots[] scatter = ax.scatter[x, y, c=c, s=s] # produce a legend with the unique colors from the scatter legend1 = ax.legend[*scatter.legend_elements[], loc="lower left", title="Classes"] ax.add_artist[legend1] # produce a legend with a cross section of sizes from the scatter handles, labels = scatter.legend_elements[prop="sizes", alpha=0.6] legend2 = ax.legend[handles, labels, loc="upper right", title="Sizes"] plt.show[]
Further arguments to the
PathCollection.legend_elements
method can be used to steer how many legend entries are to be created and how they should be labeled. The following shows how to use some of them.
volume = np.random.rayleigh[27, size=40] amount = np.random.poisson[10, size=40] ranking = np.random.normal[size=40] price = np.random.uniform[1, 10, size=40] fig, ax = plt.subplots[] # Because the price is much too small when being provided as size for ``s``, # we normalize it to some useful point sizes, s=0.3*[price*3]**2 scatter = ax.scatter[volume, amount, c=ranking, s=0.3*[price*3]**2, vmin=-3, vmax=3, cmap="Spectral"] # Produce a legend for the ranking [colors]. Even though there are 40 different # rankings, we only want to show 5 of them in the legend. legend1 = ax.legend[*scatter.legend_elements[num=5], loc="upper left", title="Ranking"] ax.add_artist[legend1] # Produce a legend for the price [sizes]. Because we want to show the prices # in dollars, we use the *func* argument to supply the inverse of the function # used to calculate the sizes from above. The *fmt* ensures to show the price # in dollars. Note how we target at 5 elements here, but obtain only 4 in the # created legend due to the automatic round prices that are chosen for us. kw = dict[prop="sizes", num=5, color=scatter.cmap[0.7], fmt="$ {x:.2f}", func=lambda s: np.sqrt[s/.3]/3] legend2 = ax.legend[*scatter.legend_elements[**kw], loc="lower right", title="Price"] plt.show[]
Total running time of the script: [ 0 minutes 1.840 seconds]
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