title: Approximation of covariance for differently sized arrays
body: <p>Are there any common tools in NumPy/SciPy for computing a correlation measure that works even when the input variables are differently sized? In the standard formulation of covariance and correlation, one is required to have the same number of observations for each different variable under test. Typically, you must pass a matrix where each row is a different variable and each column represents a distinct observation.</p>
  
  <p>In my case, I have 9 different variables, but for each variable the number of observations is not constant. Some variables have more observations than others. I know that there are fields like sensor fusion which study problems like this, so what standard tools are out there for computing relational statistics on data series of differing lengths (preferably in Python)?</p>