What does dtype=object mean while creating a numpy array?

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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.

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