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_DTypeMeta

numpy:dtype

source: /dev/numpy/build-install/usr/lib/python3.14/site-packages/numpy/__init__.py

Summary

Create a data type object.

Extended Summary

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.

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.

metadata : dict, optional

An optional dictionary with dtype metadata.

Examples

Using array-scalar type: 
import numpy as np
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')])

See also

result_type

Aliases

  • numpy.dtype