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diff --git a/trunk/source/reference/arrays.rst b/trunk/source/reference/arrays.rst deleted file mode 100644 index b6d28fe2c..000000000 --- a/trunk/source/reference/arrays.rst +++ /dev/null @@ -1,46 +0,0 @@ -.. _arrays: - -************* -Array objects -************* - -.. currentmodule:: numpy - -NumPy provides an N-dimensional array type, the :ref:`ndarray -<arrays.ndarray>`, which describes a collection of "items" of the same -type. The items can be :ref:`indexed <arrays.indexing>` using for -example N integers. - -All ndarrays are :term:`homogenous`: every item takes up the same size -block of memory, and all blocks are interpreted in exactly the same -way. How each item in the array is to be interpreted is specified by a -separate :ref:`data-type object <arrays.dtypes>`, one of which is associated -with every array. In addition to basic types (integers, floats, -*etc.*), the data type objects can also represent data structures. - -An item extracted from an array, *e.g.*, by indexing, is represented -by a Python object whose type is one of the :ref:`array scalar types -<arrays.scalars>` built in Numpy. The array scalars allow easy manipulation -of also more complicated arrangements of data. - -.. figure:: figures/threefundamental.png - - **Figure** - Conceptual diagram showing the relationship between the three - fundamental objects used to describe the data in an array: 1) the - ndarray itself, 2) the data-type object that describes the layout - of a single fixed-size element of the array, 3) the array-scalar - Python object that is returned when a single element of the array - is accessed. - - - -.. toctree:: - :maxdepth: 2 - - arrays.ndarray - arrays.scalars - arrays.dtypes - arrays.indexing - arrays.classes - arrays.interface |