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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>`. |