:class:`dpnp.ndarray` is the DPNP counterpart of NumPy :class:`numpy.ndarray`.
For the basic concept of ndarrays, please refer to the NumPy documentation.
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray dpnp.dpnp_array.dpnp_array
New arrays can be constructed using the routines detailed in :ref:`Array Creation Routines <routines.array-creation>`, and also by using the low-level :class:`dpnp.ndarray` constructor:
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray
Arrays can be indexed using an extended Python slicing syntax,
array[selection].
.. seealso:: :ref:`Indexing routines <routines.indexing>`.
Array attributes reflect information that is intrinsic to the array itself. Generally, accessing an array through its attributes allows you to get and sometimes set intrinsic properties of the array without creating a new array. The exposed attributes are the core parts of an array and only some of them can be reset meaningfully without creating a new array. Information on each attribute is given below.
The following attributes contain information about the memory layout of the array:
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.flags dpnp.ndarray.shape dpnp.ndarray.strides dpnp.ndarray.ndim dpnp.ndarray.data dpnp.ndarray.size dpnp.ndarray.itemsize dpnp.ndarray.nbytes dpnp.ndarray.device dpnp.ndarray.sycl_context dpnp.ndarray.sycl_device dpnp.ndarray.sycl_queue dpnp.ndarray.usm_type
.. seealso:: :ref:`Available array data types <Data types>`
The data type object associated with the array can be found in the :attr:`dtype <dpnp.ndarray.dtype>` attribute:
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.dtype
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.T dpnp.ndarray.mT dpnp.ndarray.real dpnp.ndarray.imag dpnp.ndarray.flat
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.__sycl_usm_array_interface__ dpnp.ndarray.__usm_ndarray__
An :class:`dpnp.ndarray` object has many methods which operate on or with the array in some fashion, typically returning an array result. These methods are briefly explained below. (Each method's docstring has a more complete description.)
For the following methods there are also corresponding functions in :mod:`dpnp`: :func:`all <dpnp.all>`, :func:`any <dpnp.any>`, :func:`argmax <dpnp.argmax>`, :func:`argmin <dpnp.argmin>`, :func:`argpartition <dpnp.argpartition>`, :func:`argsort <dpnp.argsort>`, :func:`choose <dpnp.choose>`, :func:`clip <dpnp.clip>`, :func:`compress <dpnp.compress>`, :func:`copy <dpnp.copy>`, :func:`cumprod <dpnp.cumprod>`, :func:`cumsum <dpnp.cumsum>`, :func:`diagonal <dpnp.diagonal>`, :func:`imag <dpnp.imag>`, :func:`max <dpnp.max>`, :func:`mean <dpnp.mean>`, :func:`min <dpnp.min>`, :func:`nonzero <dpnp.nonzero>`, :func:`partition <dpnp.partition>`, :func:`prod <dpnp.prod>`, :func:`put <dpnp.put>`, :func:`ravel <dpnp.ravel>`, :func:`real <dpnp.real>`, :func:`repeat <dpnp.repeat>`, :func:`reshape <dpnp.reshape>`, :func:`round <dpnp.around>`, :func:`searchsorted <dpnp.searchsorted>`, :func:`sort <dpnp.sort>`, :func:`squeeze <dpnp.squeeze>`, :func:`std <dpnp.std>`, :func:`sum <dpnp.sum>`, :func:`swapaxes <dpnp.swapaxes>`, :func:`take <dpnp.take>`, :func:`trace <dpnp.trace>`, :func:`transpose <dpnp.transpose>`, :func:`var <dpnp.var>`.
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.item dpnp.ndarray.tolist dpnp.ndarray.tobytes dpnp.ndarray.tofile dpnp.ndarray.dump dpnp.ndarray.dumps dpnp.ndarray.astype dpnp.ndarray.byteswap dpnp.ndarray.copy dpnp.ndarray.view dpnp.ndarray.getfield dpnp.ndarray.setflags dpnp.ndarray.fill dpnp.ndarray.get_array
For reshape, resize, and transpose, the single tuple argument may be
replaced with n integers which will be interpreted as an n-tuple.
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.reshape dpnp.ndarray.resize dpnp.ndarray.transpose dpnp.ndarray.swapaxes dpnp.ndarray.flatten dpnp.ndarray.ravel dpnp.ndarray.squeeze
For array methods that take an axis keyword, it defaults to None. If axis is None, then the array is treated as a 1-D array. Any other value for axis represents the dimension along which the operation should proceed.
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.take dpnp.ndarray.put dpnp.ndarray.repeat dpnp.ndarray.choose dpnp.ndarray.sort dpnp.ndarray.argsort dpnp.ndarray.partition dpnp.ndarray.argpartition dpnp.ndarray.searchsorted dpnp.ndarray.nonzero dpnp.ndarray.compress dpnp.ndarray.diagonal
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.max dpnp.ndarray.argmax dpnp.ndarray.min dpnp.ndarray.argmin dpnp.ndarray.clip dpnp.ndarray.conj dpnp.ndarray.conjugate dpnp.ndarray.round dpnp.ndarray.trace dpnp.ndarray.sum dpnp.ndarray.cumsum dpnp.ndarray.mean dpnp.ndarray.var dpnp.ndarray.std dpnp.ndarray.prod dpnp.ndarray.cumprod dpnp.ndarray.all dpnp.ndarray.any
Arithmetic and comparison operations on :class:`dpnp.ndarrays <dpnp.ndarray>` are defined as element-wise operations, and generally yield :class:`dpnp.ndarray` objects as results.
Each of the arithmetic operations (+, -, *, /, //,
%, divmod(), ** or pow(), <<, >>, &,
^, |, ~) and the comparisons (==, <, >,
<=, >=, !=) is equivalent to the corresponding
universal function (or :term:`ufunc` for short) in DPNP. For
more information, see the section on :ref:`Universal Functions
<ufuncs>`.
Comparison operators:
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.__lt__ dpnp.ndarray.__le__ dpnp.ndarray.__gt__ dpnp.ndarray.__ge__ dpnp.ndarray.__eq__ dpnp.ndarray.__ne__
Truth value of an array (:class:`bool() <bool>`):
.. autosummary:: :toctree: generated/ dpnp.ndarray.__bool__
Note
Truth-value testing of an array invokes
:meth:`dpnp.ndarray.__bool__`, which raises an error if the number of
elements in the array is not 1, because the truth value
of such arrays is ambiguous. Use :meth:`.any() <dpnp.ndarray.any>` and
:meth:`.all() <dpnp.ndarray.all>` instead to be clear about what is meant
in such cases. (If you wish to check for whether an array is empty,
use for example .size > 0.)
Unary operations:
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.__neg__ dpnp.ndarray.__pos__ dpnp.ndarray.__abs__ dpnp.ndarray.__invert__
Arithmetic:
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.__add__ dpnp.ndarray.__sub__ dpnp.ndarray.__mul__ dpnp.ndarray.__truediv__ dpnp.ndarray.__floordiv__ dpnp.ndarray.__mod__ dpnp.ndarray.__divmod__ dpnp.ndarray.__pow__ dpnp.ndarray.__lshift__ dpnp.ndarray.__rshift__ dpnp.ndarray.__and__ dpnp.ndarray.__or__ dpnp.ndarray.__xor__
Arithmetic, reflected:
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.__radd__ dpnp.ndarray.__rsub__ dpnp.ndarray.__rmul__ dpnp.ndarray.__rtruediv__ dpnp.ndarray.__rfloordiv__ dpnp.ndarray.__rmod__ dpnp.ndarray.__rpow__ dpnp.ndarray.__rlshift__ dpnp.ndarray.__rrshift__ dpnp.ndarray.__rand__ dpnp.ndarray.__ror__ dpnp.ndarray.__rxor__
Arithmetic, in-place:
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.__iadd__ dpnp.ndarray.__isub__ dpnp.ndarray.__imul__ dpnp.ndarray.__itruediv__ dpnp.ndarray.__ifloordiv__ dpnp.ndarray.__imod__ dpnp.ndarray.__ipow__ dpnp.ndarray.__ilshift__ dpnp.ndarray.__irshift__ dpnp.ndarray.__iand__ dpnp.ndarray.__ior__ dpnp.ndarray.__ixor__
Matrix Multiplication:
.. autosummary:: :toctree: generated/ dpnp.ndarray.__matmul__ dpnp.ndarray.__rmatmul__ dpnp.ndarray.__imatmul__
For standard library functions:
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.__copy__ dpnp.ndarray.__deepcopy__ .. dpnp.ndarray.__reduce__ dpnp.ndarray.__setstate__
Basic customization:
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.__new__ dpnp.ndarray.__array__ dpnp.ndarray.__array_namespace__ dpnp.ndarray.__array_wrap__ dpnp.ndarray.__dlpack__ dpnp.ndarray.__dlpack_device__
Container customization: (see :ref:`Indexing <routines.indexing>`)
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.__len__ dpnp.ndarray.__iter__ dpnp.ndarray.__getitem__ dpnp.ndarray.__setitem__ dpnp.ndarray.__contains__
Conversion; the operations :class:`int() <int>`, :class:`float() <float>`, :class:`complex() <complex>` and :func:`operator.index() <index>`. They work only on arrays that have one element in them and return the appropriate scalar.
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.__index__ dpnp.ndarray.__int__ dpnp.ndarray.__float__ dpnp.ndarray.__complex__
String representations:
.. autosummary:: :toctree: generated/ :nosignatures: dpnp.ndarray.__str__ dpnp.ndarray.__repr__