numpy.dtype#

class numpy.dtype(dtype, align=False, copy=False)[source]#

Create a data type object.

A numpy array is homogeneous, and contains elements described by a dtype object. A dtype object can be constructed from different combinations of fundamental numeric types.

Parameters

dtype: Object to be converted to a data type object.
alignbool, optional: Add padding to the fields to match what a C compiler would output for a similar C-struct. Can be True only if obj is a dictionary or a comma-separated string. If a struct dtype is being created, this also sets a sticky alignment flag isalignedstruct.
copybool, optional: Make a new copy of the data-type object. If False, the result may just be a reference to a built-in data-type object.

See also

result_type

Examples

Using array-scalar type:

>>> np.dtype(np.int16)
dtype('int16')

Structured type, one field name ‘f1’, containing int16:

>>> np.dtype([('f1', np.int16)])
dtype([('f1', '<i2')])

Structured type, one field named ‘f1’, in itself containing a structured type with one field:

>>> np.dtype([('f1', [('f1', np.int16)])])
dtype([('f1', [('f1', '<i2')])])

Structured type, two fields: the first field contains an unsigned int, the second an int32:

>>> np.dtype([('f1', np.uint64), ('f2', np.int32)])
dtype([('f1', '<u8'), ('f2', '<i4')])

Using array-protocol type strings:

>>> np.dtype([('a','f8'),('b','S10')])
dtype([('a', '<f8'), ('b', 'S10')])

Using comma-separated field formats. The shape is (2,3):

>>> np.dtype("i4, (2,3)f8")
dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))])

Using tuples. int is a fixed type, 3 the field’s shape. void is a flexible type, here of size 10:

>>> np.dtype([('hello',(np.int64,3)),('world',np.void,10)])
dtype([('hello', '<i8', (3,)), ('world', 'V10')])

Subdivide int16 into 2 int8’s, called x and y. 0 and 1 are the offsets in bytes:

>>> np.dtype((np.int16, {'x':(np.int8,0), 'y':(np.int8,1)}))
dtype((numpy.int16, [('x', 'i1'), ('y', 'i1')]))

Using dictionaries. Two fields named ‘gender’ and ‘age’:

>>> np.dtype({'names':['gender','age'], 'formats':['S1',np.uint8]})
dtype([('gender', 'S1'), ('age', 'u1')])

Offsets in bytes, here 0 and 25:

>>> np.dtype({'surname':('S25',0),'age':(np.uint8,25)})
dtype([('surname', 'S25'), ('age', 'u1')])

Attributes

alignment: The required alignment (bytes) of this data-type according to the compiler.
base: Returns dtype for the base element of the subarrays, regardless of their dimension or shape.
byteorder: A character indicating the byte-order of this data-type object.
char: A unique character code for each of the 21 different built-in types.
descr: __array_interface__ description of the data-type.
fields: Dictionary of named fields defined for this data type, or None.
flags: Bit-flags describing how this data type is to be interpreted.
hasobject: Boolean indicating whether this dtype contains any reference-counted objects in any fields or sub-dtypes.
isalignedstruct: Boolean indicating whether the dtype is a struct which maintains field alignment.
isbuiltin: Integer indicating how this dtype relates to the built-in dtypes.
isnative: Boolean indicating whether the byte order of this dtype is native to the platform.
itemsize: The element size of this data-type object.
kind: A character code (one of ‘biufcmMOSUV’) identifying the general kind of data.
metadata: Either None or a readonly dictionary of metadata (mappingproxy).
name: A bit-width name for this data-type.
names: Ordered list of field names, or None if there are no fields.
ndim: Number of dimensions of the sub-array if this data type describes a sub-array, and 0 otherwise.
num: A unique number for each of the 21 different built-in types.
shape: Shape tuple of the sub-array if this data type describes a sub-array, and () otherwise.
str: The array-protocol typestring of this data-type object.
subdtype: Tuple (item_dtype, shape) if this dtype describes a sub-array, and None otherwise.
type

Methods

newbyteorder([new_order])

Return a new dtype with a different byte order.

Data type objects (dtype)

numpy.dtype.newbyteorder