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authorSebastian Berg <sebastian@sipsolutions.net>2013-04-01 23:06:38 +0200
committerSebastian Berg <sebastian@sipsolutions.net>2013-04-11 18:52:03 +0200
commit0350d5e2194494dc1bd8bb10759557e30980fef0 (patch)
treefcca8a49d8e850583839604f45bb140024462595
parent6d305e49c155b744a699e9d09ca9132ee663018a (diff)
downloadnumpy-0350d5e2194494dc1bd8bb10759557e30980fef0.tar.gz
DOC: Fixup of delete/insert changes in release notes
-rw-r--r--doc/release/1.8.0-notes.rst20
1 files changed, 13 insertions, 7 deletions
diff --git a/doc/release/1.8.0-notes.rst b/doc/release/1.8.0-notes.rst
index 823697236..ad33b5513 100644
--- a/doc/release/1.8.0-notes.rst
+++ b/doc/release/1.8.0-notes.rst
@@ -85,13 +85,19 @@ The function np.take now allows 0-d arrays as indices.
The separate compilation mode is now enabled by default.
-The functions np.insert and np.delete now handle out of bound indices like
-normal indexing instead of ignoring them, this strongly affects negative ones.
-FutureWarnings will be raised for boolean indices which will change behaviour.
-Insert allows multiple insertions at single scalar since numpy 1.7. and now
-further allows this for a one item sequence. There are subtle differences
-because the scalar expects N arrays, while the sequence expects size N along
-the axis.
+Several changes to np.insert and np.delete:
+* Previously, negative indices and indices that pointed past the end of
+ the array were simply ignored. Now, this will raise a Future or Deprecation
+ Warning. In the future they will be treated like normal indexing treats
+ them -- negative indices will wrap around, and out-of-bound indices will
+ generate an error.
+* Previously, boolean indices were treated as if they were integers (always
+ referring to either the 0th or 1st item in the array). In the future, they
+ will be treated as masks. In this release, they raise a FutureWarning
+ warning of this coming change.
+* In Numpy 1.7. np.insert already allowed the syntax
+ `np.insert(arr, 3, [1,2,3])` to insert multiple items at a single position.
+ In Numpy 1.8. this is also possible for `np.insert(arr, [3], [1, 2, 3])`.
C-API
~~~~~