blocks|key|91561|text|虽然，@jamylak的方法是更好的方法，但这里有一个替代方法|type|unstyled|depth|inlineStyleRanges|entityRanges|data|91562|>>>+N=[2,3,5]
>>>+P+=+5
>>>+type(P)+in+(tuple,+list)
False
>>>+type(N)+in+(tuple,+list)
True|code-block|syntax|javascript|91563|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

While, @jamylak's approach is the better one, here is an alternative approach

<pre><code>&gt;&gt;&gt; N=[2,3,5]
&gt;&gt;&gt; P = 5
&gt;&gt;&gt; type(P) in (tuple, list)
False
&gt;&gt;&gt; type(N) in (tuple, list)
True
</code></pre>

blocks|key|91571|text|>>>+N=[2,3,5]
>>>+P+=+5
>>>+type(P)==type(0)
True
>>>+type([1,2])==type(N)
True
>>>+type(P)==type([1,2])
False|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|91572|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>&gt;&gt;&gt; N=[2,3,5]
&gt;&gt;&gt; P = 5
&gt;&gt;&gt; type(P)==type(0)
True
&gt;&gt;&gt; type([1,2])==type(N)
True
&gt;&gt;&gt; type(P)==type([1,2])
False
</code></pre>

blocks|key|308640|text|您可以检查变量的数据类型。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|308641|N+=+[2,3,5]
P+=+5
type(P)|code-block|syntax|javascript|308642|它将给出输出为P的数据类型。|308643|<type+'int'>|308644|因此您可以区分它是整数还是数组。|308645|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|S|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

You can check data type of variable.

<pre><code>N = [2,3,5]
P = 5
type(P)
</code></pre>

It will give you out put as data type of P.

<pre><code>&lt;type 'int'&gt;
</code></pre>

So that you can differentiate that it is an integer or an array.

blocks|key|91626|text|前面的答案假设数组是一个python标准列表。作为经常使用numpy的人，我推荐一个非常严格的测试：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|91627|if+hasattr(N,+"__len__")|code-block|syntax|javascript|91628|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Previous answers assume that the array is a python standard list. As someone who uses numpy often, I'd recommend a very pythonic test of:

<pre><code>if hasattr(N, "__len__")
</code></pre>

blocks|key|91682|text|将@jamylak和@jpaddison3的答案结合在一起，如果您需要健壮地将numpy数组作为输入，并以与列表相同的方式处理它们，那么您应该使用|type|unstyled|depth|inlineStyleRanges|entityRanges|data|91683|import+numpy+as+np
isinstance(P,+(list,+tuple,+np.ndarray))|code-block|syntax|javascript|91684|这对于list，tuple和numpy数组的子类是健壮的。|91685|如果您还想对sequence的所有其他子类(不仅仅是列表和元组)保持健壮，请使用|91686|import+collections
import+numpy+as+np
isinstance(P,+(collections.Sequence,+np.ndarray))|91687|为什么要用isinstance这样做，而不是将type(P)与目标值进行比较？这里有一个例子，我们制作并研究了NewList的行为，list的一个无关紧要的子类。|offset|length|style|CODE|91688|>>>+class+NewList(list):
...+++++isThisAList+=+'???'
...+
>>>+x+=+NewList([0,1])
>>>+y+=+list([0,1])
>>>+print+x
[0,+1]
>>>+print+y
[0,+1]
>>>+x==y
True
>>>+type(x)
<class+'__main__.NewList'>
>>>+type(x)+is+list
False
>>>+type(y)+is+list
True
>>>+type(x).__name__
'NewList'
>>>+isinstance(x,+list)
True|91689|尽管将x和y相提并论，但type对它们的处理会导致不同的行为。但是，由于x是list子类的一个实例，因此使用isinstance(x,list)可以提供所需的行为，并以相同的方式处理x和y。|91690|entityMap^0|0|0|0|0|0|5|A|N|7|1J|7|0|0|3|1|5|1|C|4|10|1|12|4|1I|I|2J|1|2L|1|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|Z|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|10|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|11|8|@]|9|@]|A|$]]|$1|K|3|L|5|D|7|12|8|@]|9|@]|A|$E|F]]|$1|M|3|N|5|6|7|13|8|@$O|14|P|15|Q|R]|$O|16|P|17|Q|R]|$O|18|P|19|Q|R]]|9|@]|A|$]]|$1|S|3|T|5|D|7|1A|8|@]|9|@]|A|$E|F]]|$1|U|3|V|5|6|7|1B|8|@$O|1C|P|1D|Q|R]|$O|1E|P|1F|Q|R]|$O|1G|P|1H|Q|R]|$O|1I|P|1J|Q|R]|$O|1K|P|1L|Q|R]|$O|1M|P|1N|Q|R]|$O|1O|P|1P|Q|R]|$O|1Q|P|1R|Q|R]]|9|@]|A|$]]|$1|W|3|-4|5|6|7|1S|8|@]|9|@]|A|$]]]|X|$]]

Combining @jamylak and @jpaddison3's answers together, if you need to be robust against numpy arrays as the input and handle them in the same way as lists, you should use

<pre><code>import numpy as np
isinstance(P, (list, tuple, np.ndarray))
</code></pre>

This is robust against subclasses of list, tuple and numpy arrays.

And if you want to be robust against all other subclasses of sequence as well (not just list and tuple), use

<pre><code>import collections
import numpy as np
isinstance(P, (collections.Sequence, np.ndarray))
</code></pre>

Why should you do things this way with <code>isinstance</code> and not compare <code>type(P)</code> with a target value? Here is an example, where we make and study the behaviour of <code>NewList</code>, a trivial subclass of list.

<pre><code>&gt;&gt;&gt; class NewList(list):
... isThisAList = '???'
... 
&gt;&gt;&gt; x = NewList([0,1])
&gt;&gt;&gt; y = list([0,1])
&gt;&gt;&gt; print x
[0, 1]
&gt;&gt;&gt; print y
[0, 1]
&gt;&gt;&gt; x==y
True
&gt;&gt;&gt; type(x)
&lt;class '__main__.NewList'&gt;
&gt;&gt;&gt; type(x) is list
False
&gt;&gt;&gt; type(y) is list
True
&gt;&gt;&gt; type(x).__name__
'NewList'
&gt;&gt;&gt; isinstance(x, list)
True
</code></pre>

Despite <code>x</code> and <code>y</code> comparing as equal, handling them by <code>type</code> would result in different behaviour. However, since <code>x</code> is an instance of a subclass of <code>list</code>, using <code>isinstance(x,list)</code> gives the desired behaviour and treats <code>x</code> and <code>y</code> in the same manner.

blocks|key|91744|text|令我惊讶的是，这样一个基本的问题在python中似乎没有直接的答案。在我看来，几乎所有提出的答案都使用某种类型检查，这在python中通常是不建议的，而且它们似乎仅限于特定的情况(对于不同的数值类型或不是元组或列表的泛型可迭代对象，它们都会失败)。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|91745|对我来说，更好的做法是导入numpy并使用array.size，例如：|91746|>>>+a=1
>>>+np.array(a)
Out[1]:+array(1)

>>>+np.array(a).size
Out[2]:+1

>>>+np.array([1,2]).size
Out[3]:+2

>>>+np.array('125')
Out[4]:+1|code-block|syntax|javascript|91747|另请注意：|91748|>>>+len(np.array([1,2]))

Out[5]:+2|91749|但是：|91750|>>>+len(np.array(a))
---------------------------------------------------------------------------
TypeError+++++++++++++++++++++++++++++++++Traceback+(most+recent+call+last)
<ipython-input-40-f5055b93f729>+in+<module>()
---->+1+len(np.array(a))

TypeError:+len()+of+unsized+object|91751|entityMap^0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|T|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|V|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|W|8|@]|9|@]|A|$G|H]]|$1|M|3|N|5|6|7|X|8|@]|9|@]|A|$]]|$1|O|3|P|5|F|7|Y|8|@]|9|@]|A|$G|H]]|$1|Q|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|R|$]]

I am surprised that such a basic question doesn't seem to have an immediate answer in python.
It seems to me that nearly all proposed answers use some kind of type
checking, that is usually not advised in python and they seem restricted to a specific case (they fail with different numerical types or generic iteratable objects that are not tuples or lists). 

For me, what works better is importing numpy and using array.size, for example: 

<pre><code>&gt;&gt;&gt; a=1
&gt;&gt;&gt; np.array(a)
Out[1]: array(1)

&gt;&gt;&gt; np.array(a).size
Out[2]: 1

&gt;&gt;&gt; np.array([1,2]).size
Out[3]: 2

&gt;&gt;&gt; np.array('125')
Out[4]: 1
</code></pre>

Note also:

<pre><code>&gt;&gt;&gt; len(np.array([1,2]))

Out[5]: 2
</code></pre>

but:

<pre><code>&gt;&gt;&gt; len(np.array(a))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
&lt;ipython-input-40-f5055b93f729&gt; in &lt;module&gt;()
----&gt; 1 len(np.array(a))

TypeError: len() of unsized object
</code></pre>

blocks|key|308798|text|numpy中是否有等效于isscalar()的函数？是。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|308799|>>>+np.isscalar(3.1)
True
>>>+np.isscalar([3.1])
False
>>>+np.isscalar(False)
True
>>>+np.isscalar('abcd')
True|code-block|syntax|javascript|308800|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Is there an equivalent to isscalar() in numpy? Yes.
<pre><code>&gt;&gt;&gt; np.isscalar(3.1)
True
&gt;&gt;&gt; np.isscalar([3.1])
False
&gt;&gt;&gt; np.isscalar(False)
True
&gt;&gt;&gt; np.isscalar('abcd')
True
</code></pre>

blocks|key|308814|text|只需使用size而不是len！|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|308815|>>>+from+numpy+import+size
>>>+N+=+[2,+3,+5]
>>>+size(N)
3
>>>+N+=+array([2,+3,+5])
>>>+size(N)
3
>>>+P+=+5
>>>+size(P)
1|code-block|syntax|javascript|308816|entityMap^0|4|4|B|3|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|S|8|@]|D|@]|E|$]]]|L|$]]

Simply use <code>size</code> instead of <code>len</code>!

<pre><code>&gt;&gt;&gt; from numpy import size
&gt;&gt;&gt; N = [2, 3, 5]
&gt;&gt;&gt; size(N)
3
&gt;&gt;&gt; N = array([2, 3, 5])
&gt;&gt;&gt; size(N)
3
&gt;&gt;&gt; P = 5
&gt;&gt;&gt; size(P)
1
</code></pre>

blocks|key|91810|text|preds_test的形状是(128,128,1)，让我们使用isinstance()函数来检查它的数据类型，isinstance接受2个参数。第一个参数是数据，第二个参数是数据类型isinstance(preds_test，np.ndarray)输出为真。这意味着preds_test是一个数组。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|91811|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

preds_test[0] is of shape (128,128,1)
Lets check its data type using isinstance() function
isinstance takes 2 arguments.
1st argument is data
2nd argument is data type
isinstance(preds_test[0], np.ndarray) gives Output as True. It means preds_test[0] is an array.

blocks|key|91840|text|要回答标题中的问题，判断变量是否为标量的直接方法是尝试将其转换为浮点数。如果你得到了TypeError，它就不是了。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|91841|N+=+[1,+2,+3]
try:
++++float(N)
except+TypeError:
++++print('it+is+not+a+scalar')
else:
++++print('it+is+a+scalar')|code-block|syntax|javascript|91842|entityMap^0|16|9|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

To answer the question in the title, a direct way to tell if a variable is a scalar is to try to convert it to a float. If you get <code>TypeError</code>, it's not.

<pre><code>N = [1, 2, 3]
try:
 float(N)
except TypeError:
 print('it is not a scalar')
else:
 print('it is a scalar')
</code></pre>

blocks|key|91866|text|由于Python中的一般指导原则是请求宽恕而不是权限，因此我认为从序列中检测字符串/标量的最典型的方法是检查它是否包含整数：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|91867|try:
++++1+in+a
++++print('{}+is+a+sequence'.format(a))
except+TypeError:
++++print('{}+is+a+scalar+or+string'.format(a))|code-block|syntax|javascript|91868|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Since the general guideline in Python is to ask for forgiveness rather than permission, I think the most pythonic way to detect a string/scalar from a sequence is to check if it contains an integer:
<pre><code>try:
 1 in a
 print('{} is a sequence'.format(a))
except TypeError:
 print('{} is a scalar or string'.format(a))
</code></pre>

I have a function that takes the argument <code>NBins</code>. I want to make a call to this function with a scalar <code>50</code> or an array <code>[0, 10, 20, 30]</code>. How can I identify within the function, what the length of <code>NBins</code> is? or said differently, if it is a scalar or a vector? 

I tried this:

<pre><code>&gt;&gt;&gt; N=[2,3,5]
&gt;&gt;&gt; P = 5
&gt;&gt;&gt; len(N)
3
&gt;&gt;&gt; len(P)
Traceback (most recent call last):
 File "&lt;stdin&gt;", line 1, in &lt;module&gt;
TypeError: object of type 'int' has no len()
&gt;&gt;&gt; 
</code></pre>

As you see, I can't apply <code>len</code> to <code>P</code>, since it's not an array.... Is there something like <code>isarray</code> or <code>isscalar</code> in python?

thanks

python: how to identify if a variable is an array or a scalar

Python

我有一个接受参数NBins的函数。我想使用标量50或数组[0, 10, 20, 30]调用此函数。如何在函数中识别NBins的长度？或者换一种说法，它是一个标量还是一个向量？我试过这个：>>> N=[2,3,5]>>> P = 5>>> len(N)3>>> len(P)Traceback (most recent c...

问python:如何识别变量是数组还是标量
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