What is the difference between types <type 'numpy.string _'> and <type 'str'>?

numpy.string_

is a NumPy data type used for arrays containing fixed-width byte strings. On the other hand, it str

is a native Python type and cannot be used as a data type for NumPy * arrays.

If you create a NumPy array containing strings, the array will use the type numpy.string_

(or type numpy.unicode_

in Python 3). More precisely, the array will use a subtype like np.string_

:

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

      

        
        
        
      

    

In this case, the data type '<S3'

: <

denotes byte order (little-endian), S

denotes the string type, and 3

indicates that each value in the array contains up to three characters (or bytes).

One property that separates np.string_

and str

is immutable. Attempting to increase the length of a Python object str

will create a new object in memory. Likewise, if you want a fixed-width NumPy array to hold more characters, a new larger array must be created in memory.

* Note that it is possible to create a NumPy array object

that contains references to Python objects str

, but such arrays behave differently to regular arrays.