< type'numpy.string _'>类型之间有什么区别?和< type'str'&gt ;? [英] What is the difference between the types <type 'numpy.string_'> and <type 'str'>?

问题描述

<type 'numpy.string_'>和<type 'str'>类型之间有区别吗?

Is there a difference between the types <type 'numpy.string_'> and <type 'str'>?

推荐答案

numpy.string_是用于包含固定宽度字节字符串的数组的NumPy数据类型.另一方面，str是本机Python类型，不能用作NumPy数组的数据类型*.

numpy.string_ is the NumPy datatype used for arrays containing fixed-width byte strings. On the other hand, str is a native Python type and can not be used as a datatype for NumPy arrays*.

如果创建一个包含字符串的NumPy数组，则该数组将使用numpy.string_类型(或Python 3中的numpy.unicode_类型).更准确地说，该数组将使用np.string_的子数据类型:

If you create a NumPy array containing strings, the array will use the numpy.string_ type (or the numpy.unicode_ type in Python 3). More precisely, the array will use a sub-datatype of np.string_:

>>> a = np.array(['abc', 'xy'])
>>> a
array(['abc', 'xy'], dtype='<S3')
>>> np.issubdtype('<S3', np.string_)
True

在这种情况下，数据类型为'<S3':<表示字节顺序(小尾数)，S表示字符串类型，并且3表示数组中的每个值最多包含三个字符(或字节).

In this case the datatype is '<S3': the < denotes the byte-order (little-endian), S denotes the string type and 3 indicates that each value in the array holds up to three characters (or bytes).

np.string_和str共享的一个属性是不变性.尝试增加Python str对象的长度将在内存中创建一个新对象.同样，如果您希望定宽NumPy数组容纳更多字符，则必须在内存中创建一个新的更大数组.

One property that np.string_ and str share is immutability. Trying to increase the length of a Python str object will create a new object in memory. Similarly, if you want fixed-width NumPy array to hold more characters, a new larger array will have to be created in memory.

*请注意，可以创建一个NumPy object数组，其中包含对Python str对象的引用，但此类数组的行为与普通数组完全不同.

* Note that it is possible to create a NumPy object array which contains references to Python str objects, but such arrays behave quite differently to normal arrays.

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