Question

I have two different arrays, one with strings and another with ints. I want to concatenate them, into one array where each column has the original datatype. My current solution for doing this (see below) converts the entire array into dtype = string, which seems very memory inefficient.

combined_array = np.concatenate((A, B), axis = 1)

Is it possible to mutiple dtypes in combined_array when A.dtype = string and B.dtype = int?

Answer 1

One approach might be to use a record array. The "columns" won’t be like the columns of standard numpy arrays, but for most use cases, this is sufficient:

>>> a = numpy.array(['a', 'b', 'c', 'd', 'e'])
>>> b = numpy.arange(5)
>>> records = numpy.rec.fromarrays((a, b), names=('keys', 'data'))
>>> records
rec.array([('a', 0), ('b', 1), ('c', 2), ('d', 3), ('e', 4)], 
      dtype=[('keys', '|S1'), ('data', '<i8')])
>>> records['keys']
rec.array(['a', 'b', 'c', 'd', 'e'], 
      dtype='|S1')
>>> records['data']
array([0, 1, 2, 3, 4])

Note that you can also do something similar with a standard array by specifying the datatype of the array. This is known as a "structured array":

>>> arr = numpy.array([('a', 0), ('b', 1)], 
                      dtype=([('keys', '|S1'), ('data', 'i8')]))
>>> arr
array([('a', 0), ('b', 1)], 
      dtype=[('keys', '|S1'), ('data', '<i8')])

The difference is that record arrays also allow attribute access to individual data fields. Standard structured arrays do not.

>>> records.keys
chararray(['a', 'b', 'c', 'd', 'e'], 
      dtype='|S1')
>>> arr.keys
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'numpy.ndarray' object has no attribute 'keys'