How to Concatenate Arrays with Different Datatypes and Maintain Memory Efficiency?

Concatenating Arrays with Multiple Datatypes

When dealing with data of different types, it is often necessary to combine them into a single array. This can be done efficiently without converting the entire array to a single datatype.

Consider the following scenario: You have two arrays, A containing strings and B containing integers. The goal is to create a combined array combined_array where each column retains its original datatype.

While concatenating A and B with np.concatenate as combined_array = np.concatenate((A, B), axis = 1) appears straightforward, it converts the entire array to dtype=string by default, resulting in memory inefficiencies.

Solution: Record Arrays and Structured Arrays

An effective approach is to utilize record arrays or structured arrays.

Record Arrays:

Record arrays offer a flexible way to store multiple data types in a single array. The individual fields can be accessed through attributes:

import numpy as np

a = np.array(['a', 'b', 'c', 'd', 'e'])
b = np.arange(5)
records = np.rec.fromarrays((a, b), names=('keys', 'data'))

print(records)
# rec.array([('a', 0), ('b', 1), ('c', 2), ('d', 3), ('e', 4)], 
#   dtype=[('keys', '|S1'), ('data', '<i8')])
print(records['keys'])
# rec.array(['a', 'b', 'c', 'd', 'e'], 
#   dtype='|S1')
print(records['data'])
# array([0, 1, 2, 3, 4])

Structured Arrays:

Similar to record arrays, structured arrays allow for the specification of a datatype for each field:

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

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

Note that record arrays provide attribute access while structured arrays do not.

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