问题
I have different types of data. most of them are int
and sometimes float
. The int
is different in size so 8/ 16/ 32 bits are the sizes.
For this situation I'm creating a numerical type converter. therefore i check the type by using isinstence()
. This because I have read that isinstance()
is less worse than type()
.
The point is that a lot of data i get is numpy arrays. I use spyder as IDE and then i see by the variables also a type. but when i type isinstance(var,'type i read')
i get False
.
I did some checks:
a = 2.17
b = 3
c = np.array(np.random.rand(2, 8))
d = np.array([1])
for there isinstance(var,type)
i get:
isinstance(a, float)
True
isinstance(b, int)
True
isinstance(c, float) # or isinstance(c, np.float64)
False
isinstance(d, int) # or isinstance(c, np.int32)
False
c
and d
are True when i ask
isinstance(c, np.ndarray)
True
isinstance(d, np.ndarray)
True
i can check with step in the ndarray
by
isinstance(c[i][j], np.float64)
True
isinstance(d[i], np.int32)
True
but this means that for every dimension i have to add a new index otherwise it is False
again.
I can check there type with dtype
like c.dtype == 'float64'
...
Oke so for what i have find and tried... My questions are basicly:
- how is the
var.dtype
method compared toisinstance()
andtype()
(worst/ better etc)? - if
var.dtype
is even worse asisinstance()
is there some method in theisinstance()
without all the manual indexing? (autoindexing etc)?
回答1:
An array is an object of type np.ndarray
. Its values or elements are stored in a data buffer, which can be thought of as a contiguous block of memory bytes. The bytes in the data buffer do not have a type, because they are not Python objects.
The array has a dtype
parameter, which is used to interpret those bytes. If dtype
is int32
(there are various synonyms), 4 bytes are interpreted as an integer. Accessing an element, say c[0]
gives a new object that depends on the dtype, e.g. an object type np.int32
.
c[0].item
will give an Python object of the corresponding type:
In [2102]: c=np.array([1])
In [2103]: c.dtype
Out[2103]: dtype('int32')
In [2104]: type(c)
Out[2104]: numpy.ndarray
In [2105]: type(c[0])
Out[2105]: numpy.int32
In [2107]: c[0].item()
Out[2107]: 1
In [2108]: type(c[0].item())
Out[2108]: int
(And c[0].dtype
is the same as for c.dtype
; you don't need to index individual elements of an array to check their dtype).
The same 4 bytes of this array can be viewed as dtype
int8
- a single byte integer.
In [2112]: c.view('b')
Out[2112]: array([1, 0, 0, 0], dtype=int8)
A single element of this alternate view is np.int8
, but when I take item()
, I get a Python integer. There isn't a int8
Python numeric type.
In [2113]: type(c.view('b')[0])
Out[2113]: numpy.int8
In [2115]: type(c.view('b')[0].item())
Out[2115]: int
A list contains pointers to Python objects, each of which has a type. So does an array of dtype=object
. But the common numeric array does not contain Python integers or floats. It has a data buffer that can interpreted in various ways according to the dtype
. Python integers don't come in different sizes, at least not to the same extent as numpy dtypes.
So the isinstance
and type()
stuff does not apply to the contents of an ndarray
.
====================
From the comments I gather you are trying to convert integer arrays to float. You aren't converting scalars. If so then dtype
is all that matters; an array always has a dtype
. It's unclear whether you are ok with casting a np.float32
to np.float64
.
I'd suggest studying, and experimenting with the np.can_cast
function and the x.astype
method.
x.astype(np.float64, copy=False)
for example will convert all int dtypes to float, without copying the ones that are already float64. It may copy and convert np.float32
ones.
Look also at the casting
parameter of these functions.
===========================
I found in scipy.optimize.minimize
another testing tool
In [156]: np.typecodes
Out[156]:
{'All': '?bhilqpBHILQPefdgFDGSUVOMm',
'AllFloat': 'efdgFDG',
'AllInteger': 'bBhHiIlLqQpP',
'Character': 'c',
'Complex': 'FDG',
'Datetime': 'Mm',
'Float': 'efdg',
'Integer': 'bhilqp',
'UnsignedInteger': 'BHILQP'}
It can be used to check for integers with:
if x0.dtype.kind in np.typecodes["AllInteger"]:
x0 = np.asarray(x0, dtype=float)
回答2:
All entries in a numpy array are of the same type. The numpy type and the Python type are not the same thing. This can be a bit confusing, but the type numpy refers to is more like the types used by languages like C - you might say more low level closer the the machine.
You can not say which type is better, because it would be like comparing apple and oranges.
回答3:
To directly answer the question, you can do this:
isinstance(arr.flat[0], np.floating)
.flat
will collapse any number of dimensions down, so you can then access the 0th element easily.np.floating
will match any numpy float type
来源:https://stackoverflow.com/questions/40312013/check-type-within-numpy-array