带图例的散点图

要创建带有图例的散点图，可以使用循环并创建一个 scatter 绘制每个项目以显示在图例中，并设置 label 因此。

下面还演示了如何通过 alpha 介于0和1之间的值。

import numpy as np
np.random.seed(19680801)
import matplotlib.pyplot as plt


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()

自动创建图例

为散点图创建图例的另一个选项是使用 PathCollection.legend_elements 方法。它将自动尝试确定要显示的图例项的有用数量，并返回句柄和标签的元组。这些可以传递给 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()

关于 PathCollection.legend_elements 方法可用于确定要创建多少个图例项以及如何标记它们。下面介绍如何使用其中的一些。

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()

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工具书类

以下函数和方法的用法如本例所示：

import matplotlib
matplotlib.axes.Axes.scatter
matplotlib.pyplot.scatter
matplotlib.axes.Axes.legend
matplotlib.pyplot.legend
matplotlib.collections.PathCollection.legend_elements

出：

<function PathCollection.legend_elements at 0x7faa16688f28>