如何在 matplotlib 中制作按密度着色的散点图? [英] How can I make a scatter plot colored by density in matplotlib?

问题描述

我想制作一个散点图，其中每个点都由附近点的空间密度着色.

I'd like to make a scatter plot where each point is colored by the spatial density of nearby points.

我遇到了一个非常相似的问题，它显示了一个使用 R 的例子:

I've come across a very similar question, which shows an example of this using R:

R 散点图:符号颜色代表重叠点的数量

使用 matplotlib 在 python 中完成类似任务的最佳方法是什么?

What's the best way to accomplish something similar in python using matplotlib?

推荐答案

除了@askewchan 建议的 hist2d 或 hexbin 之外，您还可以使用与您链接到的问题中已接受的答案.

In addition to hist2d or hexbin as @askewchan suggested, you can use the same method that the accepted answer in the question you linked to uses.

如果你想这样做:

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde

# Generate fake data
x = np.random.normal(size=1000)
y = x * 3 + np.random.normal(size=1000)

# Calculate the point density
xy = np.vstack([x,y])
z = gaussian_kde(xy)(xy)

fig, ax = plt.subplots()
ax.scatter(x, y, c=z, s=100)
plt.show()

如果您希望按密度顺序绘制点，以便最密集的点始终位于顶部(类似于链接示例)，只需按 z 值对它们进行排序.我还将在这里使用较小的标记尺寸，因为它看起来更好一些:

If you'd like the points to be plotted in order of density so that the densest points are always on top (similar to the linked example), just sort them by the z-values. I'm also going to use a smaller marker size here as it looks a bit better:

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde

# Generate fake data
x = np.random.normal(size=1000)
y = x * 3 + np.random.normal(size=1000)

# Calculate the point density
xy = np.vstack([x,y])
z = gaussian_kde(xy)(xy)

# Sort the points by density, so that the densest points are plotted last
idx = z.argsort()
x, y, z = x[idx], y[idx], z[idx]

fig, ax = plt.subplots()
ax.scatter(x, y, c=z, s=50)
plt.show()

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