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?
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'
arr = np.array([('cat', 5), ('dog', 20)], dtype=[('name', np.object), ('age',np.int)]) name column can be accessed by arr['name'] in structured arrayA simple solution: convert your data to object 'O' type
z = np.zeros((2,2), dtype='U2')
o = np.ones((2,1), dtype='O')
np.hstack([o, z])
creates the array:
array([[1, '', ''],
[1, '', '']], dtype=object)
df.loc[(df.col == item)] well that would not work because when pandas does the filtering it expects all the items to be of the same type. So if, for example, you were to mix strings and integers in the same column then you would be comparing apples and oranges effectively. And hence pandas would throw an error.Refering Numpy doc, there is a function named numpy.lib.recfunctions.merge_arraysfunction which can be used to merge numpy arrays in different data type into either structured array or record array.
Example:
>>> from numpy.lib import recfunctions as rfn
>>> A = np.array([1, 2, 3])
>>> B = np.array(['a', 'b', 'c'])
>>> b = rfn.merge_arrays((A, B))
>>> b
array([(1, 'a'), (2, 'b'), (3, 'c')], dtype=[('f0', '<i4'), ('f1', '<U1')])
For more detail please refer the link above.
