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dtype(dtype, align=False, copy=False)

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 :None:None:`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.

Create a data type object.

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')])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy

56 Elements

numpy.common_type

numpy.ascontiguousarray

numpy.issubdtype

numpy.asarray

numpy.nanstd

numpy.rec.array

numpy.memmap

numpy.mean

numpy.lib.format.descr_to_dtype

numpy.nanmean

numpy.rec.fromstring

numpy.find_common_type

numpy.result_type

numpy.nancumsum

numpy.core._multiarray_umath.min_scalar_type

numpy.geomspace

numpy.ma.core.make_mask

numpy.rec.fromrecords

numpy.promote_types

numpy.fromstring

numpy.ma.core._recursive_fill_value

numpy.ma.core.MaskedArray

numpy.lib.format.dtype_to_descr

numpy.logspace

numpy.std

numpy.lib._iotools.easy_dtype

numpy.array

numpy.core.multiarray.scalar

numpy.ma.core.array

numpy.rec.fromarrays

numpy.linspace

numpy.arange

numpy.lib._iotools.StringConverter

numpy.fromregex

numpy.mintypecode

numpy.fromfunction

numpy.core._multiarray_umath.can_cast

numpy.core._multiarray_umath.result_type

numpy.ma.core.MaskedArray.view

numpy.lib._iotools.StringConverter.update

numpy.lib.npyio.recfromcsv

numpy.ma.extras.masked_all

numpy.min_scalar_type

numpy.maximum_sctype

numpy.ndarray

numpy.asfarray

numpy.typename

numpy.format_parser

numpy.lib.npyio.recfromtxt

numpy.can_cast

numpy.ma.core.choose

numpy.lib.format.open_memmap

numpy.rec.fromfile

numpy.recarray

numpy.ma.core.make_mask_none

numpy.cumsum

pandas

pandas.core.dtypes.cast.maybe_infer_dtype_type

pandas.core.generic.NDFrame.convert_dtypes

pandas.core.dtypes.common.is_timedelta64_ns_dtype

pandas.core.dtypes.missing.na_value_for_dtype

pandas.core.arrays.sparse.array.SparseArray.astype

pandas.core.construction.array

pandas.core.dtypes.dtypes.PandasDtype

networkx

networkx.convert_matrix.to_numpy_array

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