ENH: missingdata: trying some more functions to see how they treat NAs

3 files changed, 15 insertions, 5 deletions

diff --git a/doc/neps/missing-data.rst b/doc/neps/missing-data.rst
index 478fe81bc..49528da41 100644
--- a/doc/neps/missing-data.rst
+++ b/doc/neps/missing-data.rst
@@ -477,7 +477,7 @@ Some examples::
     >>> np.sum(a, skipna=True)
     11.0
     >>> np.mean(a)
-    NA('<f8')
+    NA(dtype='<f8')
     >>> np.mean(a, skipna=True)
     3.6666666666666665
 
@@ -487,7 +487,7 @@ Some examples::
     >>> np.max(a, skipna=True)
     array(NA, dtype='<f8', maskna=True)
     >>> np.mean(a)
-    NA('<f8')
+    NA(dtype='<f8')
     >>> np.mean(a, skipna=True)
     /home/mwiebe/virtualenvs/dev/lib/python2.7/site-packages/numpy/core/fromnumeric.py:2374: RuntimeWarning: invalid value encountered in double_scalars
       return mean(axis, dtype, out)
diff --git a/doc/release/2.0.0-notes.rst b/doc/release/2.0.0-notes.rst
index 0ba7594fe..d50797cb3 100644
--- a/doc/release/2.0.0-notes.rst
+++ b/doc/release/2.0.0-notes.rst
@@ -26,9 +26,10 @@ What works with NA:
     * Basic indexing and slicing, as well as full boolean mask indexing.
     * All element-wise ufuncs.
     * UFunc.reduce methods, with a new skipna parameter.
+    * The nditer object.
     * Array methods:
        + ndarray.clip, ndarray.min, ndarray.max, ndarray.sum, ndarray.prod,
-         ndarray.conjugate, ndarray.diagonal
+         ndarray.conjugate, ndarray.diagonal, ndarray.flatten
        + numpy.concatenate, numpy.column_stack, numpy.hstack,
          numpy.vstack, numpy.dstack
 
@@ -36,6 +37,8 @@ What doesn't work with NA:
     * Fancy indexing, such as with lists and partial boolean masks.
     * ndarray.flat and any other methods that use the old iterator
       mechanism instead of the newer nditer.
+    * Struct dtypes, which will have corresponding struct masks with
+      one mask value per primitive field of the struct dtype.
     * UFunc.reduce of multi-dimensional arrays, with skipna=True and a ufunc
       that doesn't have an identity.
     * UFunc.accumulate, UFunc.reduceat.
@@ -43,7 +46,10 @@ What doesn't work with NA:
       rules NA | True == True and NA & False == False yet.
     * Array methods:
        + ndarray.argmax, ndarray.argmin,
-       + numpy.repeat
+       + numpy.repeat, numpy.delete (relies on fancy indexing),
+         numpy.append, numpy.insert (relies on fancy indexing),
+         numpy.where,
+
 
 
 Custom formatter for printing arrays
@@ -66,7 +72,7 @@ where possible, and performs much faster as well.
 
 The function np.concatenate tries to match the layout of its input
 arrays. Previously, the layout did not follow any particular reason,
-and depended in an undesirable on the particular axis chosen for
+and depended in an undesirable way on the particular axis chosen for
 concatenation. A bug was also fixed which silently allowed out of bounds
 axis arguments.
 
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index caef5c709..411a86955 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -3317,6 +3317,7 @@ def delete(arr, obj, axis=None):
                     "invalid entry")
         newshape[axis]-=1;
         new = empty(newshape, arr.dtype, arr.flags.fnc)
+        new.flags.maskna = arr.flags.maskna
         slobj[axis] = slice(None, obj)
         new[slobj] = arr[slobj]
         slobj[axis] = slice(obj,None)
@@ -3333,6 +3334,7 @@ def delete(arr, obj, axis=None):
                 return arr.copy()
         newshape[axis] -= numtodel
         new = empty(newshape, arr.dtype, arr.flags.fnc)
+        new.flags.maskna = arr.flags.maskna
         # copy initial chunk
         if start == 0:
             pass
@@ -3464,6 +3466,7 @@ def insert(arr, obj, values, axis=None):
                     "in dimension %d" % (obj, N, axis))
         newshape[axis] += 1;
         new = empty(newshape, arr.dtype, arr.flags.fnc)
+        new.flags.maskna = arr.flags.maskna
         slobj[axis] = slice(None, obj)
         new[slobj] = arr[slobj]
         slobj[axis] = obj
@@ -3490,6 +3493,7 @@ def insert(arr, obj, values, axis=None):
     index2 = setdiff1d(arange(numnew+N),index1)
     newshape[axis] += numnew
     new = empty(newshape, arr.dtype, arr.flags.fnc)
+    new.flags.maskna = arr.flags.maskna
     slobj2 = [slice(None)]*ndim
     slobj[axis] = index1
     slobj2[axis] = index2