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diff --git a/doc/source/reference/arrays.rst b/doc/source/reference/arrays.rst new file mode 100644 index 000000000..b6d28fe2c --- /dev/null +++ b/doc/source/reference/arrays.rst @@ -0,0 +1,46 @@ +.. _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 |