本机int类型和numpy.int类型之间有什么区别? [英] What is the difference between native int type and the numpy.int types?
问题描述
您能否帮助您理解本机int类型与numpy.int32或numpy.int64类型之间的主要区别(如果有)吗?
Can you please help understand what are the main differences (if any) between the native int type and the numpy.int32 or numpy.int64 types?
推荐答案
查看差异的另一种方法是询问这两种对象有哪些方法.
Another way to look at the differences is to ask what methods do the 2 kinds of objects have.
在Ipython中，我可以使用制表符complete来查看方法:
In Ipython I can use tab complete to look at methods:
In [1277]: x=123; y=np.int32(123)
int方法和属性:
In [1278]: x.<tab>
x.bit_length x.denominator x.imag x.numerator x.to_bytes
x.conjugate x.from_bytes x.real
int'操作员'
In [1278]: x.__<tab>
x.__abs__ x.__init__ x.__rlshift__
x.__add__ x.__int__ x.__rmod__
x.__and__ x.__invert__ x.__rmul__
x.__bool__ x.__le__ x.__ror__
...
x.__gt__ x.__reduce_ex__ x.__xor__
x.__hash__ x.__repr__
x.__index__ x.__rfloordiv__
np.int32方法和属性(或属性).其中一些是相同的，但更多的是，基本上都是所有ndarray的:
np.int32 methods and attributes (or properties). Some of the same, but a lot more, basically all the ndarray ones:
In [1278]: y.<tab>
y.T y.denominator y.ndim y.size
y.all y.diagonal y.newbyteorder y.sort
y.any y.dtype y.nonzero y.squeeze
...
y.cumsum y.min y.setflags
y.data y.nbytes y.shape
y.__方法看起来很像int方法.他们可以做同样的数学.
the y.__ methods look a lot like the int ones. They can do the same math.
In [1278]: y.__<tab>
y.__abs__ y.__getitem__ y.__reduce_ex__
y.__add__ y.__gt__ y.__repr__
...
y.__format__ y.__rand__ y.__subclasshook__
y.__ge__ y.__rdivmod__ y.__truediv__
y.__getattribute__ y.__reduce__ y.__xor__
y在许多方面与0d数组相同.不一样，但是很接近.
y is in many ways the same as a 0d array. Not identical, but close.
In [1281]: z=np.array(123,dtype=np.int32)
np.int32是索引该类型的数组时得到的:
np.int32 is what I get when I index an array of that type:
In [1300]: A=np.array([0,123,3])
In [1301]: A[1]
Out[1301]: 123
In [1302]: type(A[1])
Out[1302]: numpy.int32
我必须使用item删除所有numpy包装.
I have to use item to remove all of the numpy wrapping.
In [1303]: type(A[1].item())
Out[1303]: int
作为numpy用户，np.int32是具有numpy包装器的int.或者相反，是ndarray的单个元素.通常，我不会关注A[0]是给我本机int还是numpy等效项.与某些新用户相反，我很少使用np.int32(123)；相反，我很少使用np.int32(123).我会改用np.array(123).
As a numpy user, an np.int32 is an int with a numpy wrapper. Or conversely a single element of an ndarray. Usually I don't pay attention as to whether A[0] is giving me the 'native' int or the numpy equivalent. In contrast to some new users, I rarely use np.int32(123); I would use np.array(123) instead.
A = np.array([1,123,0], np.int32)
不包含3个np.int32对象.而是其数据缓冲区为3 * 4 = 12字节长.是数组开销将它解释为1d中的3个int.并且view向我展示了具有不同解释的相同数据缓冲区:
does not contain 3 np.int32 objects. Rather its data buffer is 3*4=12 bytes long. It's the array overhead that interprets it as 3 ints in a 1d. And view shows me the same databuffer with different interpretations:
In [1307]: A.view(np.int16)
Out[1307]: array([ 1, 0, 123, 0, 0, 0], dtype=int16)
In [1310]: A.view('S4')
Out[1310]: array([b'\x01', b'{', b''], dtype='|S4')
只有当我为单个元素建立索引时，我才会获得一个np.int32对象.
It's only when I index a single element that I get a np.int32 object.
列表L=[1, 123, 0]是不同的；它是一个指针列表-指向int对象的指针，位于内存中的其他位置.对于dtype = object数组也是如此.
The list L=[1, 123, 0] is different; it's a list of pointers - pointers to int objects else where in memory. Similarly for a dtype=object array.
这篇关于本机int类型和numpy.int类型之间有什么区别?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
