issue_comments: 398931150

This data as json

html_url issue_url id node_id user created_at updated_at author_association body reactions performed_via_github_app issue
https://github.com/pydata/xarray/pull/2236#issuecomment-398931150 https://api.github.com/repos/pydata/xarray/issues/2236 398931150 MDEyOklzc3VlQ29tbWVudDM5ODkzMTE1MA== 1217238 2018-06-20T23:42:04Z 2018-06-20T23:42:04Z MEMBER

A module of bottleneck/numpy functions that act on numpy arrays only. A module of functions that act on numpy or dask arrays (or these could be moved into duck_array_ops).

Could you explain more detail about this idea?

OK, let me try:

  1. On numpy arrays, we use bottleneck eqiuvalents of numpy functions when possible because bottleneck is faster than numpy
  2. On dask arrays, we use dask equivalents of numpy functions.
  3. We also want to add some extra features on top of what numpy/dask/bottleneck provide, e.g., handling of min_count

We could implement this with: - nputils.nansum() is equivalent to numpy.nansum() but uses bottleneck.nansum() internally instead when possible. - duck_array_ops.nansum() uses numpy_nansum() or dask.array.nansum(), based upon the type of the inputs. - duck_array_ops.sum() uses numpy.sum() or dask.array.sum(), based upon the type of the inputs. - duck_array_ops.sum_with_mincount() adds mincount and skipna support and is used in the Dataset.sum() implementation. Its is written using duck_array_ops.nansum(), duck_array_ops.sum(), duck_array_ops.where() and duck_array_ops.isnull().

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   333248242