blocks|key|639626|text|这样如何：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|639627|In+[51]:+dict([(d,+type(np.zeros(1,d).tolist()[0]))+for+d+in+(np.float32,np.float64,np.uint32,+np.int16)])
Out[51]:+
{<type+'numpy.int16'>:+<type+'int'>,
+<type+'numpy.uint32'>:+<type+'long'>,
+<type+'numpy.float32'>:+<type+'float'>,
+<type+'numpy.float64'>:+<type+'float'>}|code-block|syntax|javascript|639628|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|$]]

How about:

<pre><code>In [51]: dict([(d, type(np.zeros(1,d).tolist()[0])) for d in (np.float32,np.float64,np.uint32, np.int16)])
Out[51]: 
{&lt;type 'numpy.int16'&gt;: &lt;type 'int'&gt;,
 &lt;type 'numpy.uint32'&gt;: &lt;type 'long'&gt;,
 &lt;type 'numpy.float32'&gt;: &lt;type 'float'&gt;,
 &lt;type 'numpy.float64'&gt;: &lt;type 'float'&gt;}
</code></pre>

blocks|key|425255|text|我认为你可以像这样编写通用的类型转换函数：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|425256|import+numpy+as+np

def+get_type_convert(np_type):
+++convert_type+=+type(np.zeros(1,np_type).tolist()[0])
+++return+(np_type,+convert_type)

print+get_type_convert(np.float32)
>>+(<type+'numpy.float32'>,+<type+'float'>)

print+get_type_convert(np.float64)
>>+(<type+'numpy.float64'>,+<type+'float'>)|code-block|syntax|javascript|425257|这意味着没有固定的列表，您的代码将扩展到更多的类型。|425258|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

I think you can just write general type convert function like so:

<pre><code>import numpy as np

def get_type_convert(np_type):
 convert_type = type(np.zeros(1,np_type).tolist()[0])
 return (np_type, convert_type)

print get_type_convert(np.float32)
&gt;&gt; (&lt;type 'numpy.float32'&gt;, &lt;type 'float'&gt;)

print get_type_convert(np.float64)
&gt;&gt; (&lt;type 'numpy.float64'&gt;, &lt;type 'float'&gt;)
</code></pre>

This means there is no fixed lists and your code will scale with more types.

blocks|key|425303|text|使用val.item()将大多数Python值转换为原生NumPy类型：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|425304|import+numpy+as+np

#+for+example,+numpy.float32+->+python+float
val+=+np.float32(0)
pyval+=+val.item()
print(type(pyval))+++++++++#+<class+'float'>

#+and+similar...
type(np.float64(0).item())+#+<class+'float'>
type(np.uint32(0).item())++#+<class+'int'>
type(np.int16(0).item())+++#+<class+'int'>
type(np.cfloat(0).item())++#+<class+'complex'>
type(np.datetime64(0,+'D').item())++#+<class+'datetime.date'>
type(np.datetime64('2001-01-01+00:00:00').item())++#+<class+'datetime.datetime'>
type(np.timedelta64(0,+'D').item())+#+<class+'datetime.timedelta'>
...|code-block|syntax|javascript|425305|(另一种方法是np.asscalar(val)，但是从NumPy+1.16开始就不推荐使用它了)。|425306|425307|出于好奇心，为您的系统构建一个NumPy+array+scalars转换表：|425308|for+name+in+dir(np):
++++obj+=+getattr(np,+name)
++++if+hasattr(obj,+'dtype'):
++++++++try:
++++++++++++if+'time'+in+name:
++++++++++++++++npn+=+obj(0,+'D')
++++++++++++else:
++++++++++++++++npn+=+obj(0)
++++++++++++nat+=+npn.item()
++++++++++++print('{0}+({1!r})+->+{2}'.format(name,+npn.dtype.char,+type(nat)))
++++++++except:
++++++++++++pass|425309|有一些NumPy类型在某些系统上没有原生Python等效项，包括：clongdouble、clongfloat、complex192、complex256、float128、longcomplex、longdouble和longfloat。在使用.item()之前，需要将它们转换为最接近的NumPy等效项。|425310|entityMap|0|LINK|mutability|MUTABLE|url|https://numpy.org/doc/stable/reference/generated/numpy.ndarray.item.html|1|https://numpy.org/doc/stable/reference/generated/numpy.asscalar.html|2|https://numpy.org/doc/stable/reference/arrays.scalars.html^0|2|A|2|A|0|0|0|7|G|7|G|1|0|0|F|J|2|0|0|X|B|19|A|1K|A|1V|A|26|8|2F|B|2R|A|32|9|3F|7|0^^$0|@$1|2|3|4|5|6|7|15|8|@$9|16|A|17|B|C]]|D|@$9|18|A|19|1|1A]]|E|$]]|$1|F|3|G|5|H|7|1B|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1C|8|@$9|1D|A|1E|B|C]]|D|@$9|1F|A|1G|1|1H]]|E|$]]|$1|M|3|-4|5|6|7|1I|8|@]|D|@]|E|$]]|$1|N|3|O|5|6|7|1J|8|@]|D|@$9|1K|A|1L|1|1M]]|E|$]]|$1|P|3|Q|5|H|7|1N|8|@]|D|@]|E|$I|J]]|$1|R|3|S|5|6|7|1O|8|@$9|1P|A|1Q|B|C]|$9|1R|A|1S|B|C]|$9|1T|A|1U|B|C]|$9|1V|A|1W|B|C]|$9|1X|A|1Y|B|C]|$9|1Z|A|20|B|C]|$9|21|A|22|B|C]|$9|23|A|24|B|C]|$9|25|A|26|B|C]]|D|@]|E|$]]|$1|T|3|-4|5|6|7|27|8|@]|D|@]|E|$]]]|U|$V|$5|W|X|Y|E|$Z|10]]|11|$5|W|X|Y|E|$Z|12]]|13|$5|W|X|Y|E|$Z|14]]]]

Use <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.item.html" rel="noreferrer"><code>val.item()</code></a> to convert most NumPy values to a native Python type:
<pre><code>import numpy as np

# for example, numpy.float32 -&gt; python float
val = np.float32(0)
pyval = val.item()
print(type(pyval)) # &lt;class 'float'&gt;

# and similar...
type(np.float64(0).item()) # &lt;class 'float'&gt;
type(np.uint32(0).item()) # &lt;class 'int'&gt;
type(np.int16(0).item()) # &lt;class 'int'&gt;
type(np.cfloat(0).item()) # &lt;class 'complex'&gt;
type(np.datetime64(0, 'D').item()) # &lt;class 'datetime.date'&gt;
type(np.datetime64('2001-01-01 00:00:00').item()) # &lt;class 'datetime.datetime'&gt;
type(np.timedelta64(0, 'D').item()) # &lt;class 'datetime.timedelta'&gt;
...
</code></pre>
(Another method is <a href="https://numpy.org/doc/stable/reference/generated/numpy.asscalar.html" rel="noreferrer"><code>np.asscalar(val)</code></a>, however it is deprecated since NumPy 1.16).
<hr />
For the curious, to build a table of conversions of <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html" rel="noreferrer">NumPy array scalars</a> for your system:
<pre><code>for name in dir(np):
 obj = getattr(np, name)
 if hasattr(obj, 'dtype'):
 try:
 if 'time' in name:
 npn = obj(0, 'D')
 else:
 npn = obj(0)
 nat = npn.item()
 print('{0} ({1!r}) -&gt; {2}'.format(name, npn.dtype.char, type(nat)))
 except:
 pass
</code></pre>
There are a few NumPy types that have no native Python equivalent on some systems, including: <code>clongdouble</code>, <code>clongfloat</code>, <code>complex192</code>, <code>complex256</code>, <code>float128</code>, <code>longcomplex</code>, <code>longdouble</code> and <code>longfloat</code>. These need to be converted to their nearest NumPy equivalent before using <code>.item()</code>.

blocks|key|425388|text|我发现自己有一组混合的numpy类型和标准的python。由于所有的numpy类型都是从numpy.generic派生的，下面是如何将所有内容转换为python标准类型的方法：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|425389|if+isinstance(obj,+numpy.generic):
++++return+numpy.asscalar(obj)|code-block|syntax|javascript|425390|entityMap^0|18|D|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|$]]

found myself having mixed set of numpy types and standard python. as all numpy types derive from <code>numpy.generic</code>, here's how you can convert everything to python standard types:

<pre><code>if isinstance(obj, numpy.generic):
 return numpy.asscalar(obj)
</code></pre>

blocks|key|639897|text|您还可以调用要转换的对象的item()+method：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|639898|>>>+from+numpy+import+float32,+uint32
>>>+type(float32(0).item())
<type+'float'>
>>>+type(uint32(0).item())
<type+'long'>|code-block|syntax|javascript|639899|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.item.html^0|D|6|D|D|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@$9|V|A|W|1|X]]|E|$]]|$1|F|3|G|5|H|7|Y|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

You can also call the <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.item.html" rel="noreferrer"><code>item()</code> method</a> of the object you want to convert:

<pre><code>&gt;&gt;&gt; from numpy import float32, uint32
&gt;&gt;&gt; type(float32(0).item())
&lt;type 'float'&gt;
&gt;&gt;&gt; type(uint32(0).item())
&lt;type 'long'&gt;
</code></pre>

blocks|key|639964|text|numpy将该信息保存在一个公开为typeDict的映射中，因此您可以执行类似以下操作：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|639965|>>>+import+__builtin__
>>>+import+numpy+as+np
>>>+{v:+k+for+k,+v+in+np.typeDict.items()+if+k+in+dir(__builtin__)}
{numpy.object_:+'object',
+numpy.bool_:+'bool',
+numpy.string_:+'str',
+numpy.unicode_:+'unicode',
+numpy.int64:+'int',
+numpy.float64:+'float',
+numpy.complex128:+'complex'}|code-block|syntax|javascript|639966|如果您想要实际的python类型而不是它们的名称，您可以这样做：|639967|>>>+{v:+getattr(__builtin__,+k)+for+k,+v+in+np.typeDict.items()+if+k+in+vars(__builtin__)}
{numpy.object_:+object,
+numpy.bool_:+bool,
+numpy.string_:+str,
+numpy.unicode_:+unicode,
+numpy.int64:+int,
+numpy.float64:+float,
+numpy.complex128:+complex}|639968|entityMap^0|H|8|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|U|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|V|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|W|8|@]|D|@]|E|$]]]|P|$]]

numpy holds that information in a mapping exposed as <code>typeDict</code> so you could do something like the below::

<pre><code>&gt;&gt;&gt; import __builtin__
&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; {v: k for k, v in np.typeDict.items() if k in dir(__builtin__)}
{numpy.object_: 'object',
 numpy.bool_: 'bool',
 numpy.string_: 'str',
 numpy.unicode_: 'unicode',
 numpy.int64: 'int',
 numpy.float64: 'float',
 numpy.complex128: 'complex'}
</code></pre>

If you want the actual python types rather than their names, you can do ::

<pre><code>&gt;&gt;&gt; {v: getattr(__builtin__, k) for k, v in np.typeDict.items() if k in vars(__builtin__)}
{numpy.object_: object,
 numpy.bool_: bool,
 numpy.string_: str,
 numpy.unicode_: unicode,
 numpy.int64: int,
 numpy.float64: float,
 numpy.complex128: complex}
</code></pre>

blocks|key|425671|text|如果您想要将(numpy.array或numpy标量或本机类型或numpy.darray)转换为本机类型，您可以简单地执行以下操作：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|425672|converted_value+=+getattr(value,+"tolist",+lambda:+value)()|code-block|syntax|javascript|425673|tolist会将你的标量或数组转换为python原生类型。默认的lambda函数负责处理值已经是本机的情况。|425674|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

If you want to convert (numpy.array OR numpy scalar OR native type OR numpy.darray) TO native type you can simply do :

<pre><code>converted_value = getattr(value, "tolist", lambda: value)()
</code></pre>

tolist will convert your scalar or array to python native type. The default lambda function takes care of the case where value is already native.

blocks|key|425701|text|tolist()是一种更通用的方法来实现这一点。它可以在任何原始数据类型中工作，也可以在数组或矩阵中工作。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|425702|如果从原始类型调用，我实际上不会生成列表：|425703|numpy+==+1.15.2|425704|>>>+import+numpy+as+np

>>>+np_float+=+np.float64(1.23)
>>>+print(type(np_float),+np_float)
<class+'numpy.float64'>+1.23

>>>+listed_np_float+=+np_float.tolist()
>>>+print(type(listed_np_float),+listed_np_float)
<class+'float'>+1.23

>>>+np_array+=+np.array([[1,2,3.],+[4,5,6.]])
>>>+print(type(np_array),+np_array)
<class+'numpy.ndarray'>+[[1.+2.+3.]
+[4.+5.+6.]]

>>>+listed_np_array+=+np_array.tolist()
>>>+print(type(listed_np_array),+listed_np_array)
<class+'list'>+[[1.0,+2.0,+3.0],+[4.0,+5.0,+6.0]]|code-block|syntax|javascript|425705|entityMap^0|0|8|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|T|8|@]|D|@]|E|$]]|$1|H|3|I|5|6|7|U|8|@]|D|@]|E|$]]|$1|J|3|K|5|L|7|V|8|@]|D|@]|E|$M|N]]|$1|O|3|-4|5|6|7|W|8|@]|D|@]|E|$]]]|P|$]]

<code>tolist()</code> is a more general approach to accomplish this. It works in any primitive dtype and also in arrays or matrices.

I doesn't actually yields a list if called from primitive types:

numpy == 1.15.2

<pre><code>&gt;&gt;&gt; import numpy as np

&gt;&gt;&gt; np_float = np.float64(1.23)
&gt;&gt;&gt; print(type(np_float), np_float)
&lt;class 'numpy.float64'&gt; 1.23

&gt;&gt;&gt; listed_np_float = np_float.tolist()
&gt;&gt;&gt; print(type(listed_np_float), listed_np_float)
&lt;class 'float'&gt; 1.23

&gt;&gt;&gt; np_array = np.array([[1,2,3.], [4,5,6.]])
&gt;&gt;&gt; print(type(np_array), np_array)
&lt;class 'numpy.ndarray'&gt; [[1. 2. 3.]
 [4. 5. 6.]]

&gt;&gt;&gt; listed_np_array = np_array.tolist()
&gt;&gt;&gt; print(type(listed_np_array), listed_np_array)
&lt;class 'list'&gt; [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
</code></pre>

blocks|key|425772|text|转换整个ndarray而不是一个单元数据对象：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|425773|def+trans(data):
"""
translate+numpy.int/float+into+python+native+data+type
"""
result+=+[]
for+i+in+data.index:
++++#+i+=+data.index[0]
++++d0+=+data.iloc[i].values
++++d+=+[]
++++for+j+in+d0:
++++++++if+'int'+in+str(type(j)):
++++++++++++res+=+j.item()+if+'item'+in+dir(j)+else+j
++++++++elif+'float'+in+str(type(j)):
++++++++++++res+=+j.item()+if+'item'+in+dir(j)+else+j
++++++++else:
++++++++++++res+=+j
++++++++d.append(res)
++++d+=+tuple(d)
++++result.append(d)
result+=+tuple(result)
return+result|code-block|syntax|javascript|425774|但是，在处理大型数据帧时需要几分钟的时间。我也在寻找一个更有效的解决方案。希望有一个更好的答案。|425775|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Translate the whole ndarray instead one unit data object:

<pre><code>def trans(data):
"""
translate numpy.int/float into python native data type
"""
result = []
for i in data.index:
 # i = data.index[0]
 d0 = data.iloc[i].values
 d = []
 for j in d0:
 if 'int' in str(type(j)):
 res = j.item() if 'item' in dir(j) else j
 elif 'float' in str(type(j)):
 res = j.item() if 'item' in dir(j) else j
 else:
 res = j
 d.append(res)
 d = tuple(d)
 result.append(d)
result = tuple(result)
return result
</code></pre>

However, it takes some minutes when handling large dataframes. I am also looking for a more efficient solution.
Hope a better answer.

blocks|key|640181|text|我的方法有点强势，但似乎对所有情况都很好：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|640182|def+type_np2py(dtype=None,+arr=None):
++++'''Return+the+closest+python+type+for+a+given+numpy+dtype'''

++++if+((dtype+is+None+and+arr+is+None)+or
++++++++(dtype+is+not+None+and+arr+is+not+None)):
++++++++raise+ValueError(
++++++++++++"Provide+either+keyword+argument+`dtype`+or+`arr`:+a+numpy+dtype+or+a+numpy+array.")

++++if+dtype+is+None:
++++++++dtype+=+arr.dtype

++++#1)+Make+a+single-entry+numpy+array+of+the+same+dtype
++++#2)+force+the+array+into+a+python+'object'+dtype
++++#3)+the+array+entry+should+now+be+the+closest+python+type
++++single_entry+=+np.empty([1],+dtype=dtype).astype(object)

++++return+type(single_entry[0])|code-block|syntax|javascript|640183|用法：|640184|>>>+type_np2py(int)
<class+'int'>

>>>+type_np2py(np.int)
<class+'int'>

>>>+type_np2py(str)
<class+'str'>

>>>+type_np2py(arr=np.array(['hello']))
<class+'str'>

>>>+type_np2py(arr=np.array([1,2,3]))
<class+'int'>

>>>+type_np2py(arr=np.array([1.,2.,3.]))
<class+'float'>|640185|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

My approach is a bit forceful, but seems to play nice for all cases:

<pre><code>def type_np2py(dtype=None, arr=None):
 '''Return the closest python type for a given numpy dtype'''

 if ((dtype is None and arr is None) or
 (dtype is not None and arr is not None)):
 raise ValueError(
 "Provide either keyword argument `dtype` or `arr`: a numpy dtype or a numpy array.")

 if dtype is None:
 dtype = arr.dtype

 #1) Make a single-entry numpy array of the same dtype
 #2) force the array into a python 'object' dtype
 #3) the array entry should now be the closest python type
 single_entry = np.empty([1], dtype=dtype).astype(object)

 return type(single_entry[0])
</code></pre>

Usage:

<pre><code>&gt;&gt;&gt; type_np2py(int)
&lt;class 'int'&gt;

&gt;&gt;&gt; type_np2py(np.int)
&lt;class 'int'&gt;

&gt;&gt;&gt; type_np2py(str)
&lt;class 'str'&gt;

&gt;&gt;&gt; type_np2py(arr=np.array(['hello']))
&lt;class 'str'&gt;

&gt;&gt;&gt; type_np2py(arr=np.array([1,2,3]))
&lt;class 'int'&gt;

&gt;&gt;&gt; type_np2py(arr=np.array([1.,2.,3.]))
&lt;class 'float'&gt;
</code></pre>

blocks|key|640243|text|如果您有一个numpy类型的数组list_numpy_numbers，请执行以下操作：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|640244|list_native_numbers+=+[i.item()+for+i+in+list_numpy_numbers]|code-block|syntax|javascript|640245|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|$]]

If you have an array list_numpy_numbers of numpy types, do the following:
<pre><code>list_native_numbers = [i.item() for i in list_numpy_numbers]
</code></pre>

If I have a numpy dtype, how do I automatically convert it to its closest python data type? For example,

<pre><code>numpy.float32 -&gt; "python float"
numpy.float64 -&gt; "python float"
numpy.uint32 -&gt; "python int"
numpy.int16 -&gt; "python int"
</code></pre>

I could try to come up with a mapping of all of these cases, but does numpy provide some automatic way of converting its dtypes into the closest possible native python types? This mapping need not be exhaustive, but it should convert the common dtypes that have a close python analog. I think this already happens somewhere in numpy.

Converting numpy dtypes to native python types

Python

如果我有一个numpy dtype，我如何自动将它转换为最接近的python数据类型？例如,numpy.float32 -> "python float"numpy.float64 -> "python float"numpy.uint32  -> "python int"numpy.int16   -> "pytho...

问将numpy dtype转换为本机python类型
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