numpy.dtype¶

class numpy.dtype(obj, 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:

Parameters:	obj Object to be converted to a data type object. align : bool, 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`. copy : bool, 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.

obj

Object to be converted to a data type object.

align : bool, 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.

copy : bool, 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.uint), ('f2', np.int32)])
dtype([('f1', '<u4'), ('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',(int,3)),('world',np.void,10)])
dtype([('hello', '<i4', 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(('<i2', [('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

`base`
`descr`	PEP3118 interface description of the data-type.
`fields`	Dictionary of named fields defined for this data type, or `None`.
`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.
`metadata`
`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.
`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.

Methods

newbyteorder([new_order]) Return a new dtype with a different byte order.