如何在散点图中将离群值更改为其他颜色 [英] How to change outliers to some other colors in a scatter plot

问题描述

如果我有这样的散点图

If I have a scatter plot like this

我想知道是否有办法将明显的离群值(如顶部的三个)更改为同一图中的其他一些颜色?

I was wondering is there any way to change the obvious outliers, like the three on the top, to some other colors in the same plot?

推荐答案

首先，您需要找到异常值"的条件.一旦有了这些，就可以掩盖绘图中那些不需要的点. 根据条件选择数组的子集可以很容易地以numpy的方式完成，例如如果a是一个numpy数组，则a[a <= 1]将返回所有值都大于1的数组"cut out".

First, you need to find a criterion for "outliers". Once you have that, you could mask those unwanted points in your plot. Selecting a subset of an array based on a condition can be easily done in numpy, e.g. if a is a numpy array, a[a <= 1] will return the array with all values bigger than 1 "cut out".

然后可以按照以下步骤进行绘制

Plotting could then be done as follows

import numpy as np
import matplotlib.pyplot as plt

num= 1000
x= np.linspace(0,100, num=num)
y= np.random.normal(size=num)

fig=plt.figure()
ax=fig.add_subplot(111)
# plot points inside distribution's width
ax.scatter(x[np.abs(y)<1], y[np.abs(y)<1], marker="s", color="#2e91be")
# plot points outside distribution's width
ax.scatter(x[np.abs(y)>=1], y[np.abs(y)>=1], marker="d", color="#d46f9f")
plt.show()

生产

在这里，我们根据正态分布绘制点，对分布宽度以外的所有点进行不同的着色.

Here, we plot points from a normal distribution, colorizing all points outside the distribution's width differently.

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