diff options
| author | Allan Haldane <allan.haldane@gmail.com> | 2015-01-16 23:53:41 -0500 |
|---|---|---|
| committer | Allan Haldane <allan.haldane@gmail.com> | 2015-01-22 17:36:43 -0500 |
| commit | 1bd0b4e8f176cd80e81b5f50832db5f8ba1ee1e9 (patch) | |
| tree | fce876400e049c7927cfe4b62ee4d1ca00a8ed7b /doc/source/reference/arrays.scalars.rst | |
| parent | b69035e8ea28bd759b929822aaba544d3c5f8c30 (diff) | |
| download | numpy-1bd0b4e8f176cd80e81b5f50832db5f8ba1ee1e9.tar.gz | |
DOC: improve record/structured array nomenclature & guide
This update adds a section better describing record arrays in the user
guide (numpy/doc/structured_arrays.py).
It also corrects nomenclature, such that "structured array" refers to
ndarrays with structured dtype, "record array" refers to modified
ndarrays as created by np.rec.array, and "recarray" refers to ndarrays
viewed as np.recarray. See the note at the end of the structured
array user guide.
Diffstat (limited to 'doc/source/reference/arrays.scalars.rst')
| -rw-r--r-- | doc/source/reference/arrays.scalars.rst | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/doc/source/reference/arrays.scalars.rst b/doc/source/reference/arrays.scalars.rst index f229efb07..652fa62e1 100644 --- a/doc/source/reference/arrays.scalars.rst +++ b/doc/source/reference/arrays.scalars.rst @@ -250,7 +250,7 @@ array scalar, - ``x[()]`` returns a 0-dimensional :class:`ndarray` - ``x['field-name']`` returns the array scalar in the field *field-name*. - (*x* can have fields, for example, when it corresponds to a record data type.) + (*x* can have fields, for example, when it corresponds to a structured data type.) Methods ======= @@ -282,10 +282,10 @@ Defining new types ================== There are two ways to effectively define a new array scalar type -(apart from composing record :ref:`dtypes <arrays.dtypes>` from the built-in -scalar types): One way is to simply subclass the :class:`ndarray` and -overwrite the methods of interest. This will work to a degree, but -internally certain behaviors are fixed by the data type of the array. -To fully customize the data type of an array you need to define a new -data-type, and register it with NumPy. Such new types can only be -defined in C, using the :ref:`Numpy C-API <c-api>`. +(apart from composing structured types :ref:`dtypes <arrays.dtypes>` from +the built-in scalar types): One way is to simply subclass the +:class:`ndarray` and overwrite the methods of interest. This will work to +a degree, but internally certain behaviors are fixed by the data type of +the array. To fully customize the data type of an array you need to +define a new data-type, and register it with NumPy. Such new types can only +be defined in C, using the :ref:`Numpy C-API <c-api>`. |