python编程基础双色版_R或python中的双色散点图

使用python&＃xff0c;matplotlib(pylab)绘制&＃xff0c;使用scipy&＃xff0c;numpy来拟合数据。numpy的诀窍是创建一个索引或掩码来过滤出您想要的结果。在

编辑&＃xff1a;要选择性地为顶部和底部的异常值着色&＃xff1f;这是我们创建的两个面具的简单组合&＃xff1a;import scipy as sci

import numpy as np

import pylab as plt

# Create some data

N &＃61; 1000

X &＃61; np.random.normal(5,1,size&＃61;N)

Y &＃61; X &＃43; np.random.normal(0,5.5,size&＃61;N)/np.random.normal(5,.1)

NAMES &＃61; ["foo"]*1000 # Customize names here

# Fit a polynomial

(a,b)&＃61;sci.polyfit(X,Y,1)

# Find all points above the line

idx &＃61; (X*a &＃43; b)

# Scatter according to that index

plt.scatter(X[idx],Y[idx], color&＃61;&＃39;r&＃39;)

plt.scatter(X[~idx],Y[~idx], color&＃61;&＃39;g&＃39;)

# Find top 10 outliers

err &＃61; ((X*a&＃43;b) - Y) ** 2

idx_L &＃61; np.argsort(err)[-10:]

for i in idx_L:

plt.text(X[i], Y[i], NAMES[i])

# Color the outliers purple or black

top &＃61; idx_L[idx[idx_L]]

bot &＃61; idx_L[~idx[idx_L]]

plt.scatter(X[top],Y[top], color&＃61;&＃39;purple&＃39;)

plt.scatter(X[bot],Y[bot], color&＃61;&＃39;black&＃39;)

XF &＃61; np.linspace(0,10,1000)

plt.plot(XF, XF*a &＃43; b, &＃39;k &＃39;)

plt.axis(&＃39;tight&＃39;)

plt.show()