Using object arrays to store heterogeneous data
Up to this point, we only considered arrays that contained native elementary data types.
How to do it...
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 as follows:
array([2.5, 'string', [2, 4], {'a': 0, 'b': 1}], dtype=object)If the objects to be contained in the array are not known at construction time, we can create an empty array of objects with the following code:
x = np.empty((2,2), dtype=np.object)
The first argument, (2,2), in the call to empty(), specifies the shape of the array, and dtype=np.object says that we want an array of objects. The resulting array is not really empty but has every entry set as equal to None. We can then assign arbitrary objects to the entries of x.
Note
In a NumPy array...
