如何计算 python 中多列中 2 列的协方差？

Question

我在下面提供了示例数据。它包含 8x10 矩阵，其中包含二维正态分布。例如，col1 和 col2 是一组，col3/col4 是另一组，依此类推。我正在尝试计算 python 中单个集合的协方差。到目前为止，我一直没有成功，而且我是 python 的新手。但是，以下是我尝试过的：

import pandas
import numpy
import matplotlib.pyplot as plg    
data = pandas.read_excel("testfile.xlsx", header=None)
dataNpy = pandas.DataFrame.to_numpy(data)

mean = numpy.mean(dataNpy, axis=0)
dataAWithoutMean = dataNpy - mean
covB = numpy.cov(dataAWithoutMean)

print("cov is: " + str(covB))

我的任务是计算 4 个独立的协方差矩阵并绘制每组的协方差值。此外，绘制每个集合的方差。

数据集：

5.583566716 -0.441667252 -0.663300181 -1.249623134 -6.530464227 -4.984165997 2.594874802    2.646629654
6.129721509 2.374902708 -2.583949571 -2.224729817 0.279965502 -0.850298098 -1.542499771 -2.686894831
5.793226266 1.133844629 -1.939493549 1.570726544 -2.125423302 -1.33966397 -0.42901856   -0.09814741
3.413049714 -0.1133744  -0.032092831 -0.122147373 2.063549449 0.685517481 5.887909556   4.056242954
-2.639701885 -0.716557389 -0.851273969 -0.522784614 -7.347432606 -2.653482175 1.043389849   0.774192416
-1.84827484 -0.636893709 -2.223488277 -1.227420764 0.253999505 0.540299783 -1.593071594 -0.70980532
0.754029441 1.427571018 5.486147486 2.956320758 2.054346142 1.939929175 -3.559875405    -3.074861749
2.009806308 1.916796155 7.820990369 2.953681659 2.071682641 0.105056782 -1.120995825    -0.036335483
1.875128481 1.785216268 -2.607698929 0.244415372 -0.793431956 -1.598343481 -2.120852679 -2.777871862
0.168442246 0.324606905 0.53741174  0.274617158 -2.99037756 -3.323958514 -3.288399345   -2.482277047

感谢您的提前帮助:)

Answer 1

这是你需要的吗？

import pandas
import numpy
import matplotlib.pyplot as plt

data = pandas.read_excel("Book1.xlsx", header=None)

mean = data.mean(axis=0)
dataAWithoutMean = data - mean

# Variance of each set
dataAWithoutMean.var()

# Covariance matrix
cov = dataAWithoutMean.cov()
plt.matshow(cov)
plt.show()

如何计算 python 中多列中 2 列的协方差？

How to calculate covariance on 2 columns out of multiple columns in python?

python

macos

matrix

covariance

variance