diff options
| -rw-r--r-- | doc/source/reference/arrays.indexing.rst | 10 | ||||
| -rw-r--r-- | numpy/core/src/multiarray/arrayobject.c | 7 |
2 files changed, 14 insertions, 3 deletions
diff --git a/doc/source/reference/arrays.indexing.rst b/doc/source/reference/arrays.indexing.rst index 8da4ecca7..bc12c5d0e 100644 --- a/doc/source/reference/arrays.indexing.rst +++ b/doc/source/reference/arrays.indexing.rst @@ -335,6 +335,16 @@ sub-array) but of data type ``x.dtype['field-name']`` and contains only the part of the data in the specified field. Also record array scalars can be "indexed" this way. +Indexing into a record 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 DeprecationWarning +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. + 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/core/src/multiarray/arrayobject.c b/numpy/core/src/multiarray/arrayobject.c index 4a219e70e..f0e9e36a5 100644 --- a/numpy/core/src/multiarray/arrayobject.c +++ b/numpy/core/src/multiarray/arrayobject.c @@ -694,10 +694,11 @@ array_might_be_written(PyArrayObject *obj) { const char *msg = "Numpy has detected that you (may be) writing to an array returned\n" - "by numpy.diagonal or by selecting multiple fields in a structured\n" + "by numpy.diagonal or by selecting multiple fields in a record\n" "array. This code will likely break in the next numpy release --\n" - "see numpy.diagonal or structured array docs for details. The quick\n" - "fix is to make an explicit copy (e.g., do arr.diagonal().copy())."; + "see numpy.diagonal or arrays.indexing reference docs for details.\n" + "The quick fix is to make an explicit copy (e.g., do\n" + "arr.diagonal().copy() or arr[['f0','f1']].copy())."; if (PyArray_FLAGS(obj) & NPY_ARRAY_WARN_ON_WRITE) { if (DEPRECATE_FUTUREWARNING(msg) < 0) { return -1; |