如何在散点图Pylab中的不同点使用不同的标记 [英] How to use different marker for different point in scatter plot pylab

问题描述

我要使用pylab的散点图功能

I want to use the scatter plot function of pylab

x = [1,2,3,4,5]
y = [2,1,3,6,7]

在这5个点中有两个聚类，索引1-2(集群1)和索引2-4(集群2).簇 1 中的点应使用标记 '^'，而簇 2 中的点应使用标记 's'.所以

there are two clusters in this 5 points, index 1-2(cluster 1) and index 2-4 (cluster 2). The point in cluster 1 should use marker '^', whereas the point in cluster 2 should use marker 's'. so

cluster = ['^','^','^','s','s']

我试过了

fig, ax = pl.subplots()
ax.scatter(x,y,marker=cluster)
pl.show()

这是一个玩具示例，真实数据有10000多个样本

This is a toy example, real data have more than 10000 samples

推荐答案

要获得此结果，您需要在同一轴上多次调用 scatter .好消息是您可以针对给定数据自动执行此操作:

To achieve this result you need to call scatter multiple times on the same axis. The good news is you can automate this for your given data:

import matplotlib.pyplot as plt

x = [1,2,3,4,5]
y = [2,1,3,6,7]

cluster = ['^','^','^','s','s']

fig, ax = plt.subplots()

for xp, yp, m in zip(x, y, cluster):
    ax.scatter([xp],[yp], marker=m)

plt.show()

一个更整洁的解决方案是使用您的集群信息过滤您的输入数据.我们可以使用 numpy 做到这一点.

A neater solution would be to filter your input data using your cluster information. We can do that using numpy.

import matplotlib.pyplot as plt
import numpy as np

x = np.array([1,2,3,4,5])
y = np.array([2,1,3,6,7])

cluster = np.array([1,1,1,2,2]) 

fig, ax = plt.subplots()

ax.scatter(x[cluster==1],y[cluster==1], marker='^')
ax.scatter(x[cluster==2],y[cluster==2], marker='s')

plt.show()

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