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Is there a difference between the types <type 'numpy.string_'> and <type 'str'>?
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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*.
There are 5 basic numerical types representing booleans (bool), integers (int), unsigned integers (uint) floating point (float) and complex.
1 answer. type is a built-in name of the Python language - it is a call that either identifies the type of an object, or creates a new type. dtype is the name of a parameter / member of numerical numpy library objects.
The main difference is that array (by default) will make a copy of the object, while asarray will not unless necessary.
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*.
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 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).
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.
* 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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