Calculating Covariance with Python and Numpy

Question

I am trying to figure out how to calculate covariance with the Python Numpy function cov   When I pass it two one-dimentional arrays  I get back a 2x2 matrix of results   I don t know what to do with that   I m not great at statistics  but I believe covariance in such a situation should be a single number   This is what I am looking for   I wrote my own   def cov a  b        if len a     len b           return      a mean   np mean a      b mean   np mean b       sum   0      for i in range 0  len a            sum      a i  - a mean     b i  - b mean        return sum  len a -1    That works  but I figure the Numpy version is much more efficient  if I could figure out how to use it   Does anybody know how to make the Numpy cov function perform like the one I wrote   Thanks   Dave

User · Answer

Thanks to unutbu for the explanation  By default numpy cov calculates the sample covariance  To obtain the population covariance you can specify normalisation by the total N samples like this   Covariance   numpy cov a  b  bias True  0  1  print Covariance    or like this   Covariance   numpy cov a  b  ddof 0  0  1  print Covariance

User · Answer

When a and b are 1-dimensional sequences  numpy cov a b  0  1  is equivalent to your cov a b    The 2x2 array returned by np cov a b  has elements equal to  cov a a   cov a b   cov a b   cov b b     where  again  cov is the function you defined above

[python] Calculating Covariance with Python and Numpy

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