Scalars#

Integer types#

class numpy.integer[source]#

Abstract base class of all integer scalar types.

Inexact types#

class numpy.inexact[source]#

Abstract base class of all numeric scalar types with a (potentially) inexact representation of the values in its range, such as floating-point numbers.

>>> f16 = np.float16("0.1")
>>> f32 = np.float32(f16)
>>> f64 = np.float64(f32)
>>> f16 == f32 == f64
True
>>> f16, f32, f64
(0.1, 0.099975586, 0.0999755859375)

>>> f16 = np.float16("0.1")
>>> f32 = np.float32("0.1")
>>> f64 = np.float64("0.1")
>>> f16 == f32 == f64
False
>>> f16, f32, f64
(0.1, 0.1, 0.1)

Other types#

numpy.bool_[source]#

alias of bool

class numpy.bool[source]#

Boolean type (True or False), stored as a byte.

Warning

The bool type is not a subclass of the int_ type (the bool is not even a number type). This is different than Python’s default implementation of bool as a sub-class of int.

Character code:

'?'

class numpy.datetime64[source]#

If created from a 64-bit integer, it represents an offset from 1970-01-01T00:00:00. If created from string, the string can be in ISO 8601 date or datetime format.

When parsing a string to create a datetime object, if the string contains a trailing timezone (A ‘Z’ or a timezone offset), the timezone will be dropped and a User Warning is given.

Datetime64 objects should be considered to be UTC and therefore have an offset of +0000.

>>> np.datetime64(10, 'Y')
numpy.datetime64('1980')
>>> np.datetime64('1980', 'Y')
numpy.datetime64('1980')
>>> np.datetime64(10, 'D')
numpy.datetime64('1970-01-11')

See Datetimes and timedeltas for more information.

Character code:

'M'

class numpy.timedelta64[source]#

A timedelta stored as a 64-bit integer.

See Datetimes and timedeltas for more information.

Character code:

'm'

class numpy.object_[source]#

Any Python object.

Character code:

'O'

The following data types are flexible: they have no predefined size and the data they describe can be of different length in different arrays. (In the character codes # is an integer denoting how many elements the data type consists of.)

class numpy.flexible[source]#

Abstract base class of all scalar types without predefined length. The actual size of these types depends on the specific numpy.dtype instantiation.

class numpy.character[source]#

Abstract base class of all character string scalar types.

class numpy.bytes_[source]#

A byte string.

When used in arrays, this type strips trailing null bytes.

Character code:

'S'

class numpy.str_[source]#

A unicode string.

This type strips trailing null codepoints.

>>> s = np.str_("abc\x00")
>>> s
'abc'

Unlike the builtin str, this supports the Buffer Protocol, exposing its contents as UCS4:

>>> m = memoryview(np.str_("abc"))
>>> m.format
'3w'
>>> m.tobytes()
b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00'

Character code:

'U'

class numpy.void(length_or_data, /, dtype=None)[source]#

Create a new structured or unstructured void scalar.

Parameters:

length_or_dataint, array-like, bytes-like, object

One of multiple meanings (see notes). The length or bytes data of an unstructured void. Or alternatively, the data to be stored in the new scalar when dtype is provided. This can be an array-like, in which case an array may be returned.

dtypedtype, optional

If provided the dtype of the new scalar. This dtype must be “void” dtype (i.e. a structured or unstructured void, see also Structured datatypes).

New in version 1.24.

Notes

For historical reasons and because void scalars can represent both arbitrary byte data and structured dtypes, the void constructor has three calling conventions:

1. np.void(5) creates a dtype="V5" scalar filled with five \0 bytes. The 5 can be a Python or NumPy integer.

2. np.void(b"bytes-like") creates a void scalar from the byte string. The dtype itemsize will match the byte string length, here "V10".

3. When a dtype= is passed the call is roughly the same as an array creation. However, a void scalar rather than array is returned.

Please see the examples which show all three different conventions.

Examples

>>> np.void(5)
np.void(b'\x00\x00\x00\x00\x00')
>>> np.void(b'abcd')
np.void(b'\x61\x62\x63\x64')
>>> np.void((3.2, b'eggs'), dtype="d,S5")
np.void((3.2, b'eggs'), dtype=[('f0', '<f8'), ('f1', 'S5')])
>>> np.void(3, dtype=[('x', np.int8), ('y', np.int8)])
np.void((3, 3), dtype=[('x', 'i1'), ('y', 'i1')])

Character code:

'V'

Sized aliases#

Along with their (mostly) C-derived names, the integer, float, and complex data-types are also available using a bit-width convention so that an array of the right size can always be ensured. Two aliases (numpy.intp and numpy.uintp) pointing to the integer type that is sufficiently large to hold a C pointer are also provided.

numpy.int8[source]#

numpy.int16#

numpy.int32#

numpy.int64#

Aliases for the signed integer types (one of numpy.byte, numpy.short, numpy.intc, numpy.int_, numpy.long and numpy.longlong) with the specified number of bits.

Compatible with the C99 int8_t, int16_t, int32_t, and int64_t, respectively.

numpy.uint8[source]#

numpy.uint16#

numpy.uint32#

numpy.uint64#

Alias for the unsigned integer types (one of numpy.ubyte, numpy.ushort, numpy.uintc, numpy.uint, numpy.ulong and numpy.ulonglong) with the specified number of bits.

Compatible with the C99 uint8_t, uint16_t, uint32_t, and uint64_t, respectively.

numpy.intp[source]#

Alias for the signed integer type (one of numpy.byte, numpy.short, numpy.intc, numpy.int_, numpy.long and numpy.longlong) that is used as a default integer and for indexing.

Compatible with the C Py_ssize_t.

Character code:

'n'

Changed in version 2.0: Before NumPy 2, this had the same size as a pointer. In practice this is almost always identical, but the character code 'p' maps to the C intptr_t. The character code 'n' was added in NumPy 2.0.

numpy.uintp[source]#

Alias for the unsigned integer type that is the same size as intp.

Compatible with the C size_t.

Character code:

'N'

Changed in version 2.0: Before NumPy 2, this had the same size as a pointer. In practice this is almost always identical, but the character code 'P' maps to the C uintptr_t. The character code 'N' was added in NumPy 2.0.

numpy.float16[source]#

alias of half

numpy.float32[source]#

alias of single

numpy.float64[source]#

alias of double

numpy.float96#

numpy.float128[source]#

Alias for numpy.longdouble, named after its size in bits. The existence of these aliases depends on the platform.

numpy.complex64[source]#

alias of csingle

numpy.complex128[source]#

alias of cdouble

numpy.complex192#

numpy.complex256[source]#

Alias for numpy.clongdouble, named after its size in bits. The existence of these aliases depends on the platform.