# 数组中的数据类型

NumPy是用C语言来实现的，我们可以对标一下NumPy中数组中的数据类型跟C语言中的数据类型：

Numpy 中的类型 C 中的类型 说明
np.bool_ bool Boolean (True or False) stored as a byte
np.byte signed char Platform-defined
np.ubyte unsigned char Platform-defined
np.short short Platform-defined
np.ushort unsigned short Platform-defined
np.intc int Platform-defined
np.uintc unsigned int Platform-defined
np.int_ long Platform-defined
np.uint unsigned long Platform-defined
np.longlong long long Platform-defined
np.ulonglong unsigned long long Platform-defined
np.half / np.float16 Half precision float: sign bit, 5 bits exponent, 10 bits mantissa
np.single float Platform-defined single precision float: typically sign bit, 8 bits exponent, 23 bits mantissa
np.double double Platform-defined double precision float: typically sign bit, 11 bits exponent, 52 bits mantissa.
np.longdouble long double Platform-defined extended-precision float
np.csingle float complex Complex number, represented by two single-precision floats (real and imaginary components)
np.cdouble double complex Complex number, represented by two double-precision floats (real and imaginary components).
np.clongdouble long double complex Complex number, represented by two extended-precision floats (real and imaginary components).

import numpy as np

In [26]: np.byte
Out[26]: numpy.int8

In [27]: np.bool_
Out[27]: numpy.bool_

In [28]: np.ubyte
Out[28]: numpy.uint8

In [29]: np.short
Out[29]: numpy.int16

In [30]: np.ushort
Out[30]: numpy.uint16


Numpy 类型 C 类型 说明
np.int8 int8_t Byte (-128 to 127)
np.int16 int16_t Integer (-32768 to 32767)
np.int32 int32_t Integer (-2147483648 to 2147483647)
np.int64 int64_t Integer (-9223372036854775808 to 9223372036854775807)
np.uint8 uint8_t Unsigned integer (0 to 255)
np.uint16 uint16_t Unsigned integer (0 to 65535)
np.uint32 uint32_t Unsigned integer (0 to 4294967295)
np.uint64 uint64_t Unsigned integer (0 to 18446744073709551615)
np.intp intptr_t Integer used for indexing, typically the same as ssize_t
np.uintp uintptr_t Integer large enough to hold a pointer
np.float32 float
np.float64 / np.float_ double Note that this matches the precision of the builtin python float.
np.complex64 float complex Complex number, represented by two 32-bit floats (real and imaginary components)
np.complex128 / np.complex_ double complex Note that this matches the precision of the builtin python complex.

>>> import numpy as np
>>> x = np.float32(1.0)
>>> x
1.0
>>> y = np.int_([1,2,4])
>>> y
array([1, 2, 4])
>>> z = np.arange(3, dtype=np.uint8)
>>> z
array([0, 1, 2], dtype=uint8)



>>> np.array([1, 2, 3], dtype='f')
array([ 1.,  2.,  3.], dtype=float32)


## 类型转换

In [33]: z = np.arange(3, dtype=np.uint8)

In [34]: z
Out[34]: array([0, 1, 2], dtype=uint8)

In [35]: z.astype(float)
Out[35]: array([0., 1., 2.])

In [36]: np.int8(z)
Out[36]: array([0, 1, 2], dtype=int8)


## 查看类型

In [37]: z.dtype
Out[37]: dtype('uint8')


dtype作为一个对象，本身也可以进行一些类型判断操作：

>>> d = np.dtype(int)
>>> d
dtype('int32')

>>> np.issubdtype(d, np.integer)
True

>>> np.issubdtype(d, np.floating)
False


# 数据溢出

In [38]: a= 1000000000000000000000000000000000000000000000000000000000000000000000000000000

In [39]: a
Out[39]: 1000000000000000000000000000000000000000000000000000000000000000000000000000000

In [40]: np.int(1000000000000000000000000000000000000000000000000000000)
Out[40]: 1000000000000000000000000000000000000000000000000000000

In [41]: np.int32(1000000000000000000000000000000000000000000000000000000)
---------------------------------------------------------------------------
OverflowError                             Traceback (most recent call last)
<ipython-input-41-71feb4433730> in <module>()
----> 1 np.int32(1000000000000000000000000000000000000000000000000000000)


In [43]: np.power(100, 8, dtype=np.int32)
Out[43]: 1874919424

In [44]: np.power(100, 8, dtype=np.int64)
Out[44]: 10000000000000000


NumPy提供了两个方法来测量int和float的范围，numpy.iinfo 和 numpy.finfo ：

In [45]:  np.iinfo(int)
Out[45]: iinfo(min=-9223372036854775808, max=9223372036854775807, dtype=int64)

In [46]: np.iinfo(np.int32)
Out[46]: iinfo(min=-2147483648, max=2147483647, dtype=int32)

In [47]: np.iinfo(np.int64)
Out[47]: iinfo(min=-9223372036854775808, max=9223372036854775807, dtype=int64)


In [48]: np.power(100, 100, dtype=np.int64)
Out[48]: 0

In [49]: np.power(100, 100, dtype=np.float64)
Out[49]: 1e+200


posted @ 2021-04-23 09:42  flydean  阅读(475)  评论(1编辑  收藏  举报