numpy.dtype#

classnumpy.dtype(dtype,align=False,copy=False[,metadata])[source]#

Create a data type object.

A numpy array is homogeneous, and contains elements described by adtype object. A dtype object can be constructed from differentcombinations 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 outputfor a similar C-struct. Can beTrue only ifobj is a dictionaryor a comma-separated string. If a struct dtype is being created,this also sets a sticky alignment flagisalignedstruct.

copybool, optional

Make a new copy of the data-type object. IfFalse, the resultmay just be a reference to a built-in data-type object.

metadatadict, optional

An optional dictionary with dtype metadata.

See also

result_type

Examples

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Using array-scalar type:

>>>importnumpyasnp>>>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 structuredtype with one field:

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

Structured type, two fields: the first field contains an unsigned int, thesecond 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.voidis a flexible type, here of size 10:

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

Subdivideint16 into 2int8’s, called x and y. 0 and 1 arethe 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')])

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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, orNone.

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 ‘biufcmMOSTUV’) identifying the general kind of data.

metadata

EitherNone or a readonly dictionary of metadata (mappingproxy).

name

A bit-width name for this data-type.

names

Ordered list of field names, orNone if there are no fields.

ndim

Number of dimensions of the sub-array if this data type describes a sub-array, and0 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 thisdtype describes a sub-array, and None otherwise.

type

Methods

newbyteorder([new_order])

Return a new dtype with a different byte order.