.. currentmodule:: numpy
.. _arrays.dtypes:
**********************************
Data type objects (:class:`dtype`)
**********************************
A data type object (an instance of :class:`numpy.dtype` class)
describes how the bytes in the fixed-size block of memory
corresponding to an array item should be interpreted. It describes the
following aspects of the data:
1. Type of the data (integer, float, Python object, etc.)
2. Size of the data (how many bytes is in *e.g.* the integer)
3. Byte order of the data (:term:`little-endian` or :term:`big-endian`)
4. If the data type is a :term:`record`, an aggregate of other
data types, (*e.g.*, describing an array item consisting of
an integer and a float),
1. what are the names of the ":term:`fields `" of the record,
by which they can be :ref:`accessed `,
2. what is the data-type of each :term:`field`, and
3. which part of the memory block each field takes.
5. If the data type is a sub-array, what is its shape and data type.
.. index::
pair: dtype; scalar
To describe the type of scalar data, there are several :ref:`built-in
scalar types ` in Numpy for various precision
of integers, floating-point numbers, *etc*. An item extracted from an
array, *e.g.*, by indexing, will be a Python object whose type is the
scalar type associated with the data type of the array.
Note that the scalar types are not :class:`dtype` objects, even though
they can be used in place of one whenever a data type specification is
needed in Numpy.
.. index::
pair: dtype; field
pair: dtype; record
Struct data types are formed by creating a data type whose
:term:`fields` contain other data types. Each field has a name by
which it can be :ref:`accessed `. The parent data
type should be of sufficient size to contain all its fields; the
parent is nearly always based on the :class:`void` type which allows
an arbitrary item size. Struct data types may also contain nested struct
sub-array data types in their fields.
.. index::
pair: dtype; sub-array
Finally, a data type can describe items that are themselves arrays of
items of another data type. These sub-arrays must, however, be of a
fixed size.
If an array is created using a data-type describing a sub-array,
the dimensions of the sub-array are appended to the shape
of the array when the array is created. Sub-arrays in a field of a
record behave differently, see :ref:`arrays.indexing.rec`.
Sub-arrays always have a C-contiguous memory layout.
.. admonition:: Example
A simple data type containing a 32-bit big-endian integer:
(see :ref:`arrays.dtypes.constructing` for details on construction)
>>> dt = np.dtype('>i4')
>>> dt.byteorder
'>'
>>> dt.itemsize
4
>>> dt.name
'int32'
>>> dt.type is np.int32
True
The corresponding array scalar type is :class:`int32`.
.. admonition:: Example
A record data type containing a 16-character string (in field 'name')
and a sub-array of two 64-bit floating-point number (in field 'grades'):
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
>>> dt['name']
dtype('|S16')
>>> dt['grades']
dtype(('float64',(2,)))
Items of an array of this data type are wrapped in an :ref:`array
scalar ` type that also has two fields:
>>> x = np.array([('Sarah', (8.0, 7.0)), ('John', (6.0, 7.0))], dtype=dt)
>>> x[1]
('John', [6.0, 7.0])
>>> x[1]['grades']
array([ 6., 7.])
>>> type(x[1])
>>> type(x[1]['grades'])
.. _arrays.dtypes.constructing:
Specifying and constructing data types
======================================
Whenever a data-type is required in a NumPy function or method, either
a :class:`dtype` object or something that can be converted to one can
be supplied. Such conversions are done by the :class:`dtype`
constructor:
.. autosummary::
:toctree: generated/
dtype
What can be converted to a data-type object is described below:
:class:`dtype` object
.. index::
triple: dtype; construction; from dtype
Used as-is.
:const:`None`
.. index::
triple: dtype; construction; from None
The default data type: :class:`float_`.
.. index::
triple: dtype; construction; from type
Array-scalar types
The 24 built-in :ref:`array scalar type objects
` all convert to an associated data-type object.
This is true for their sub-classes as well.
Note that not all data-type information can be supplied with a
type-object: for example, :term:`flexible` data-types have
a default *itemsize* of 0, and require an explicitly given size
to be useful.
.. admonition:: Example
>>> dt = np.dtype(np.int32) # 32-bit integer
>>> dt = np.dtype(np.complex128) # 128-bit complex floating-point number
Generic types
The generic hierarchical type objects convert to corresponding
type objects according to the associations:
===================================================== ===============
:class:`number`, :class:`inexact`, :class:`floating` :class:`float`
:class:`complexfloating` :class:`cfloat`
:class:`integer`, :class:`signedinteger` :class:`int\_`
:class:`unsignedinteger` :class:`uint`
:class:`character` :class:`string`
:class:`generic`, :class:`flexible` :class:`void`
===================================================== ===============
Built-in Python types
Several python types are equivalent to a corresponding
array scalar when used to generate a :class:`dtype` object:
================ ===============
:class:`int` :class:`int\_`
:class:`bool` :class:`bool\_`
:class:`float` :class:`float\_`
:class:`complex` :class:`cfloat`
:class:`str` :class:`string`
:class:`unicode` :class:`unicode\_`
:class:`buffer` :class:`void`
(all others) :class:`object_`
================ ===============
.. admonition:: Example
>>> dt = np.dtype(float) # Python-compatible floating-point number
>>> dt = np.dtype(int) # Python-compatible integer
>>> dt = np.dtype(object) # Python object
Types with ``.dtype``
Any type object with a ``dtype`` attribute: The attribute will be
accessed and used directly. The attribute must return something
that is convertible into a dtype object.
.. index::
triple: dtype; construction; from string
Several kinds of strings can be converted. Recognized strings can be
prepended with ``'>'`` (:term:`big-endian`), ``'<'``
(:term:`little-endian`), or ``'='`` (hardware-native, the default), to
specify the byte order.
One-character strings
Each built-in data-type has a character code
(the updated Numeric typecodes), that uniquely identifies it.
.. admonition:: Example
>>> dt = np.dtype('b') # byte, native byte order
>>> dt = np.dtype('>H') # big-endian unsigned short
>>> dt = np.dtype('>> dt = np.dtype('d') # double-precision floating-point number
Array-protocol type strings (see :ref:`arrays.interface`)
The first character specifies the kind of data and the remaining
characters specify how many bytes of data. The supported kinds are
================ ========================
``'b'`` Boolean
``'i'`` (signed) integer
``'u'`` unsigned integer
``'f'`` floating-point
``'c'`` complex-floating point
``'S'``, ``'a'`` string
``'U'`` unicode
``'V'`` raw data (:class:`void`)
================ ========================
.. admonition:: Example
>>> dt = np.dtype('i4') # 32-bit signed integer
>>> dt = np.dtype('f8') # 64-bit floating-point number
>>> dt = np.dtype('c16') # 128-bit complex floating-point number
>>> dt = np.dtype('a25') # 25-character string
String with comma-separated fields
Numarray introduced a short-hand notation for specifying the format
of a record as a comma-separated string of basic formats.
A basic format in this context is an optional shape specifier
followed by an array-protocol type string. Parenthesis are required
on the shape if it has more than one dimension. NumPy allows a modification
on the format in that any string that can uniquely identify the
type can be used to specify the data-type in a field.
The generated data-type fields are named ``'f0'``, ``'f1'``, ...,
``'f'`` where N (>1) is the number of comma-separated basic
formats in the string. If the optional shape specifier is provided,
then the data-type for the corresponding field describes a sub-array.
.. admonition:: Example
- field named ``f0`` containing a 32-bit integer
- field named ``f1`` containing a 2 x 3 sub-array
of 64-bit floating-point numbers
- field named ``f2`` containing a 32-bit floating-point number
>>> dt = np.dtype("i4, (2,3)f8, f4")
- field named ``f0`` containing a 3-character string
- field named ``f1`` containing a sub-array of shape (3,)
containing 64-bit unsigned integers
- field named ``f2`` containing a 3 x 4 sub-array
containing 10-character strings
>>> dt = np.dtype("a3, 3u8, (3,4)a10")
Type strings
Any string in :obj:`numpy.sctypeDict`.keys():
.. admonition:: Example
>>> dt = np.dtype('uint32') # 32-bit unsigned integer
>>> dt = np.dtype('Float64') # 64-bit floating-point number
.. index::
triple: dtype; construction; from tuple
``(flexible_dtype, itemsize)``
The first argument must be an object that is converted to a
zero-sized flexible data-type object, the second argument is
an integer providing the desired itemsize.
.. admonition:: Example
>>> dt = np.dtype((void, 10)) # 10-byte wide data block
>>> dt = np.dtype((str, 35)) # 35-character string
>>> dt = np.dtype(('U', 10)) # 10-character unicode string
``(fixed_dtype, shape)``
.. index::
pair: dtype; sub-array
The first argument is any object that can be converted into a
fixed-size data-type object. The second argument is the desired
shape of this type. If the shape parameter is 1, then the
data-type object is equivalent to fixed dtype. If *shape* is a
tuple, then the new dtype defines a sub-array of the given shape.
.. admonition:: Example
>>> dt = np.dtype((np.int32, (2,2))) # 2 x 2 integer sub-array
>>> dt = np.dtype(('S10', 1)) # 10-character string
>>> dt = np.dtype(('i4, (2,3)f8, f4', (2,3))) # 2 x 3 record sub-array
.. index::
triple: dtype; construction; from list
``[(field_name, field_dtype, field_shape), ...]``
*obj* should be a list of fields where each field is described by a
tuple of length 2 or 3. (Equivalent to the ``descr`` item in the
:obj:`__array_interface__` attribute.)
The first element, *field_name*, is the field name (if this is
``''`` then a standard field name, ``'f#'``, is assigned). The
field name may also be a 2-tuple of strings where the first string
is either a "title" (which may be any string or unicode string) or
meta-data for the field which can be any object, and the second
string is the "name" which must be a valid Python identifier.
The second element, *field_dtype*, can be anything that can be
interpreted as a data-type.
The optional third element *field_shape* contains the shape if this
field represents an array of the data-type in the second
element. Note that a 3-tuple with a third argument equal to 1 is
equivalent to a 2-tuple.
This style does not accept *align* in the :class:`dtype`
constructor as it is assumed that all of the memory is accounted
for by the array interface description.
.. admonition:: Example
Data-type with fields ``big`` (big-endian 32-bit integer) and
``little`` (little-endian 32-bit integer):
>>> dt = np.dtype([('big', '>i4'), ('little', '>> dt = np.dtype([('R','u1'), ('G','u1'), ('B','u1'), ('A','u1')])
.. index::
triple: dtype; construction; from dict
``{'names': ..., 'formats': ..., 'offsets': ..., 'titles': ..., 'itemsize': ...}``
This style has two required and three optional keys. The *names*
and *formats* keys are required. Their respective values are
equal-length lists with the field names and the field formats.
The field names must be strings and the field formats can be any
object accepted by :class:`dtype` constructor.
When the optional keys *offsets* and *titles* are provided,
their values must each be lists of the same length as the *names*
and *formats* lists. The *offsets* value is a list of byte offsets
(integers) for each field, while the *titles* value is a list of
titles for each field (:const:`None` can be used if no title is
desired for that field). The *titles* can be any :class:`string`
or :class:`unicode` object and will add another entry to the
fields dictionary keyed by the title and referencing the same
field tuple which will contain the title as an additional tuple
member.
The *itemsize* key allows the total size of the dtype to be
set, and must be an integer large enough so all the fields
are within the dtype. If the dtype being constructed is aligned,
the *itemsize* must also be divisible by the struct alignment.
.. admonition:: Example
Data type with fields ``r``, ``g``, ``b``, ``a``, each being
a 8-bit unsigned integer:
>>> dt = np.dtype({'names': ['r','g','b','a'],
... 'formats': [uint8, uint8, uint8, uint8]})
Data type with fields ``r`` and ``b`` (with the given titles),
both being 8-bit unsigned integers, the first at byte position
0 from the start of the field and the second at position 2:
>>> dt = np.dtype({'names': ['r','b'], 'formats': ['u1', 'u1'],
... 'offsets': [0, 2],
... 'titles': ['Red pixel', 'Blue pixel']})
``{'field1': ..., 'field2': ..., ...}``
This usage is discouraged, because it is ambiguous with the
other dict-based construction method. If you have a field
called 'names' and a field called 'formats' there will be
a conflict.
This style allows passing in the :attr:`fields `
attribute of a data-type object.
*obj* should contain string or unicode keys that refer to
``(data-type, offset)`` or ``(data-type, offset, title)`` tuples.
.. admonition:: Example
Data type containing field ``col1`` (10-character string at
byte position 0), ``col2`` (32-bit float at byte position 10),
and ``col3`` (integers at byte position 14):
>>> dt = np.dtype({'col1': ('S10', 0), 'col2': (float32, 10),
'col3': (int, 14)})
``(base_dtype, new_dtype)``
This usage is discouraged. In NumPy 1.7 and later, it is possible
to specify struct dtypes with overlapping fields, functioning like
the 'union' type in C. The union mechanism is preferred.
Both arguments must be convertible to data-type objects in this
case. The *base_dtype* is the data-type object that the new
data-type builds on. This is how you could assign named fields to
any built-in data-type object.
.. admonition:: Example
32-bit integer, whose first two bytes are interpreted as an integer
via field ``real``, and the following two bytes via field ``imag``.
>>> dt = np.dtype((np.int32,{'real':(np.int16, 0),'imag':(np.int16, 2)})
32-bit integer, which is interpreted as consisting of a sub-array
of shape ``(4,)`` containing 8-bit integers:
>>> dt = np.dtype((np.int32, (np.int8, 4)))
32-bit integer, containing fields ``r``, ``g``, ``b``, ``a`` that
interpret the 4 bytes in the integer as four unsigned integers:
>>> dt = np.dtype(('i4', [('r','u1'),('g','u1'),('b','u1'),('a','u1')]))
:class:`dtype`
==============
Numpy data type descriptions are instances of the :class:`dtype` class.
Attributes
----------
The type of the data is described by the following :class:`dtype` attributes:
.. autosummary::
:toctree: generated/
dtype.type
dtype.kind
dtype.char
dtype.num
dtype.str
Size of the data is in turn described by:
.. autosummary::
:toctree: generated/
dtype.name
dtype.itemsize
Endianness of this data:
.. autosummary::
:toctree: generated/
dtype.byteorder
Information about sub-data-types in a :term:`record`:
.. autosummary::
:toctree: generated/
dtype.fields
dtype.names
For data types that describe sub-arrays:
.. autosummary::
:toctree: generated/
dtype.subdtype
dtype.shape
Attributes providing additional information:
.. autosummary::
:toctree: generated/
dtype.hasobject
dtype.flags
dtype.isbuiltin
dtype.isnative
dtype.descr
dtype.alignment
Methods
-------
Data types have the following method for changing the byte order:
.. autosummary::
:toctree: generated/
dtype.newbyteorder
The following methods implement the pickle protocol:
.. autosummary::
:toctree: generated/
dtype.__reduce__
dtype.__setstate__