diff options
| -rw-r--r-- | doc/neps/missing-data.rst | 4 | ||||
| -rw-r--r-- | doc/release/2.0.0-notes.rst | 12 | ||||
| -rw-r--r-- | numpy/lib/function_base.py | 4 |
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 |