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| -rw-r--r-- | doc/source/reference/arrays.indexing.rst | 12 | ||||
| -rw-r--r-- | numpy/doc/structured_arrays.py | 9 |
2 files changed, 13 insertions, 8 deletions
diff --git a/doc/source/reference/arrays.indexing.rst b/doc/source/reference/arrays.indexing.rst index 5f67a788a..3a319ecca 100644 --- a/doc/source/reference/arrays.indexing.rst +++ b/doc/source/reference/arrays.indexing.rst @@ -514,14 +514,10 @@ only the part of the data in the specified field. Also :ref:`record array <arrays.classes.rec>` scalars can be "indexed" this way. Indexing into a structured array can also be done with a list of field names, -*e.g.* ``x[['field-name1','field-name2']]``. Currently this returns a new -array containing a copy of the values in the fields specified in the list. -As of NumPy 1.7, returning a copy is being deprecated in favor of returning -a view. A copy will continue to be returned for now, but a FutureWarning -will be issued when writing to the copy. If you depend on the current -behavior, then we suggest copying the returned array explicitly, i.e. use -x[['field-name1','field-name2']].copy(). This will work with both past and -future versions of NumPy. +*e.g.* ``x[['field-name1','field-name2']]``. As of NumPy 1.16 this returns a +view containing only those fields. In older versions of numpy it returned a +copy. See the user guide section on :ref:`structured_arrays` for more +information on multifield indexing. If the accessed field is a sub-array, the dimensions of the sub-array are appended to the shape of the result. diff --git a/numpy/doc/structured_arrays.py b/numpy/doc/structured_arrays.py index 0fcdecf00..e92a06124 100644 --- a/numpy/doc/structured_arrays.py +++ b/numpy/doc/structured_arrays.py @@ -397,6 +397,15 @@ typically a non-structured array, except in the case of nested structures. >>> y.dtype, y.shape, y.strides (dtype('float32'), (2,), (12,)) +If the accessed field is a subarray, the dimensions of the subarray +are appended to the shape of the result:: + + >>> x = np.zeros((2,2), dtype=[('a', np.int32), ('b', np.float64, (3,3))]) + >>> x['a'].shape + (2, 2) + >>> x['b'].shape + (2, 2, 3, 3) + Accessing Multiple Fields ``````````````````````````` |