Calculating Covariance with Python and Numpy
covariance, numpy, python
Solution
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.)
Problem
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