What does dtype=object mean while creating a numpy array?
Question:
I was experimenting with numpy arrays and created a numpy array of strings:
ar1 = np.array(['avinash', 'jay'])
As I have read from from their official guide, operations on numpy array are propagated to individual elements. So I did this:
ar1 * 2
But then I get this error:
TypeError Traceback (most recent call last)
<ipython-input-22-aaac6331c572> in <module>()
----> 1 ar1 * 2
TypeError: unsupported operand type(s) for *: 'numpy.ndarray' and 'int'
But when I used dtype=object
ar1 = np.array(['avinash', 'jay'], dtype=object)
while creating the array I am able to do all operations.
Can anyone tell me why this is happening?
Answers:
NumPy arrays are stored as contiguous blocks of memory. They usually have a single datatype (e.g. integers, floats or fixed-length strings) and then the bits in memory are interpreted as values with that datatype.
Creating an array with dtype=object is different. The memory taken by the array now is filled with pointers to Python objects which are being stored elsewhere in memory (much like a Python list is really just a list of pointers to objects, not the objects themselves).
Arithmetic operators such as * don’t work with arrays such as ar1 which have a string_ datatype (there are special functions instead – see below). NumPy is just treating the bits in memory as characters and the * operator doesn’t make sense here. However, the line
np.array(['avinash','jay'], dtype=object) * 2
works because now the array is an array of (pointers to) Python strings. The * operator is well defined for these Python string objects. New Python strings are created in memory and a new object array with references to the new strings is returned.
If you have an array with string_ or unicode_ dtype and want to repeat each string, you can use np.char.multiply:
In [52]: np.char.multiply(ar1, 2)
Out[52]: array(['avinashavinash', 'jayjay'],
dtype='<U14')
NumPy has many other vectorised string methods too.
I was experimenting with numpy arrays and created a numpy array of strings:
ar1 = np.array(['avinash', 'jay'])
As I have read from from their official guide, operations on numpy array are propagated to individual elements. So I did this:
ar1 * 2
But then I get this error:
TypeError Traceback (most recent call last)
<ipython-input-22-aaac6331c572> in <module>()
----> 1 ar1 * 2
TypeError: unsupported operand type(s) for *: 'numpy.ndarray' and 'int'
But when I used dtype=object
ar1 = np.array(['avinash', 'jay'], dtype=object)
while creating the array I am able to do all operations.
Can anyone tell me why this is happening?
NumPy arrays are stored as contiguous blocks of memory. They usually have a single datatype (e.g. integers, floats or fixed-length strings) and then the bits in memory are interpreted as values with that datatype.
Creating an array with dtype=object is different. The memory taken by the array now is filled with pointers to Python objects which are being stored elsewhere in memory (much like a Python list is really just a list of pointers to objects, not the objects themselves).
Arithmetic operators such as * don’t work with arrays such as ar1 which have a string_ datatype (there are special functions instead – see below). NumPy is just treating the bits in memory as characters and the * operator doesn’t make sense here. However, the line
np.array(['avinash','jay'], dtype=object) * 2
works because now the array is an array of (pointers to) Python strings. The * operator is well defined for these Python string objects. New Python strings are created in memory and a new object array with references to the new strings is returned.
If you have an array with string_ or unicode_ dtype and want to repeat each string, you can use np.char.multiply:
In [52]: np.char.multiply(ar1, 2)
Out[52]: array(['avinashavinash', 'jayjay'],
dtype='<U14')
NumPy has many other vectorised string methods too.