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author | Sebastian Berg <sebastian@sipsolutions.net> | 2013-04-01 23:06:38 +0200 |
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committer | Sebastian Berg <sebastian@sipsolutions.net> | 2013-04-11 18:52:03 +0200 |
commit | 0350d5e2194494dc1bd8bb10759557e30980fef0 (patch) | |
tree | fcca8a49d8e850583839604f45bb140024462595 | |
parent | 6d305e49c155b744a699e9d09ca9132ee663018a (diff) | |
download | numpy-0350d5e2194494dc1bd8bb10759557e30980fef0.tar.gz |
DOC: Fixup of delete/insert changes in release notes
-rw-r--r-- | doc/release/1.8.0-notes.rst | 20 |
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 ~~~~~ |