Sebastian has done a lot of work to refactor/rationalize numpy indexing. The changes are extensive enough that it would be good to have more public review, so here is the release note.
The NumPy indexing has seen a complete rewrite in this version. This makes > most advanced integer indexing operations much faster and should have no > other implications. > However some subtle changes and deprecations were introduced in advanced > indexing operations: > > * Boolean indexing into scalar arrays will always return a new 1-d array. > This means that ``array(1)[array(True)]`` gives ``array([1])`` and > not the original array. > * Advanced indexing into one dimensional arrays used to have > (undocumented) > special handling regarding repeating the value array in assignments > when the shape of the value array was too small or did not match. > Code using this will raise an error. For compatibility you can use > ``arr.flat[index] = values``, which uses the old code branch. > * The iteration order over advanced indexes used to be always C-order. > In NumPy 1.9. the iteration order adapts to the inputs and is not > guaranteed (with the exception of a *single* advanced index which is > never reversed for compatibility reasons). This means that the result > is > undefined if multiple values are assigned to the same element. > An example for this is ``arr[[0, 0], [1, 1]] = [1, 2]``, which may > set ``arr[0, 1]`` to either 1 or 2. > * Equivalent to the iteration order, the memory layout of the advanced > indexing result is adapted for faster indexing and cannot be predicted. > * All indexing operations return a view or a copy. No indexing operation > will return the original array object. > * In the future Boolean array-likes (such as lists of python bools) > will always be treated as Boolean indexes and Boolean scalars > (including > python `True`) will be a legal *boolean* index. At this time, this is > already the case for scalar arrays to allow the general > ``positive = a[a > 0]`` to work when ``a`` is zero dimensional. > * In NumPy 1.8 it was possible to use `array(True)` and `array(False)` > equivalent to 1 and 0 if the result of the operation was a scalar. > This will raise an error in NumPy 1.9 and, as noted above, treated as a > boolean index in the future. > * All non-integer array-likes are deprecated, object arrays of custom > integer like objects may have to be cast explicitly. > * The error reporting for advanced indexing is more informative, however > the error type has changed in some cases. (Broadcasting errors of > indexing arrays are reported as `IndexError`) > * Indexing with more then one ellipsis (`...`) is deprecated. > Thoughts? Chuck
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