SciPy Recipes

Using object arrays to store heterogeneous data

Up to this point, we only considered arrays that contained native data types, such as floats or integers. If we need an array containing heterogeneous data, we can create an array with arbitrary Python objects as elements, as shown in the following code:

x = np.array([2.5, 'a string', [2,4], {'a':0, 'b':1}])

This will result in an array with the np.object data type, as indicated in the output line reproduced as follows:

array([2.5, 'string', [2, 4], {'a': 0, 'b': 1}], dtype=object)

We mentioned that all elements in a NumPy array must be of the same type. In the case of arrays of objects, NumPy wraps the data in each array item with a common object type. The objects are unwrapped when accessed, ...