@r-beer I checked back on this and realized I didn't reply to your question: yes re completing #2652, if you're up for giving this a push

@r-beer would be great to finish this off! I think this would be a popular feature. You could take @hrishikeshac 's code (which is close!) and make the final changes.

Would be great to get this in, if anyone wants to have a go. A small, focused, PR would be a good start.

In the meantime you can use one of the solutions above...

@hrishikeshac that looks great! Well done for getting an MVP running.

Do you want to do a PR from this? Should be v close from here.

Others can comment from there. I'd suggest we get something close to this in and iterate from there. How abstract do we want the dimensions to be (i.e. currently we can only pass one dimension in, which is fine, but potentially we could enable multiple).

One nit - no need to use np.sum - that may cause issues with dask arrays - .sum will work fine

Yes for useful, but not sure whether they should be on the same method. They're also fairly easy for a user to construct (call correlation on a .shift copy of the array).

And increments are easy to build on! I'm the worst offender, but don't let completeness get in the way of incremental improvement

(OK, I'll go and finish the fill_value branch...)

And one that handles NaNs:

```python

untested!

def covariance(x, y, dim=None): valid_values = x.notnull() & y.notnull() valid_count = valid_values.sum(dim)

demeaned_x = (x - x.mean(dim)).fillna(0)
demeaned_y = (y - y.mean(dim)).fillna(0)

return xr.dot(demeaned_x, demeaned_y, dims=dim) / valid_count

def correlation(x, y, dim=None): # dim should default to the intersection of x.dims and y.dims return covariance(x, y, dim) / (x.std(dim) * y.std(dim)) ```

For posterity, I made a small adjustment to @shoyer 's draft:

```python

untested!

def covariance(x, y, dim=None): # need to ensure the dim lengths are the same - i.e. no auto-aligning # could use count-1 for sample return xr.dot(x - x.mean(dim), y - y.mean(dim), dims=dim) / x.count(dim)

def correlation(x, y, dim=None): # dim should default to the intersection of x.dims and y.dims return covariance(x, y, dim) / (x.std(dim) * y.std(dim)) ```

Great! Ping me / the issues with any questions at all!

@hrishikeshac if you'd like to contribute, we can help you along - xarray is a v welcoming project!

And from mvstats it looks like you're already up to speed

Let us know

I'm up for adding .corr to xarray

What do want this to look like? It's a bit different from most xarray functions, which either return the same shape or reduce one dimension. - The basic case here would take a n x m array and return an m x m correlation matrix. We could easily wrap https://docs.scipy.org/doc/numpy/reference/generated/numpy.corrcoef.html - Another case would be take two similarly sized arrays (with the option of broadcasting) and return an array with one dimension reduced. For example 200 x 10 and 200, return a 10 array. - I need to think about how those extrapolate to multiple dimensions

Should I start with the first case and then we can expand as needed?