Efficient way to convert numpy record array to a list of dictionary

I am not sure there is built-in function for that or not, but following could do the work.

>>> [dict(zip(r.dtype.names,x)) for x  in r]
[{'age': 31, 'name': 'Bill', 'weight': 260.0}, 
{'age': 15, 'name': 'Fred', 'weight': 145.0}]

Here's a solution that works in the following cases not covered by the other answers:

• 0-dimensional arrays (scalars). e.g. np.array((1, 2), dtype=[('a', 'float32'), ('b', 'float32')])
• elements of type np.void (result of indexing a record array)
• multi-dimensional arrays of structs
• structs containing structs, (e.g. structured_to_dict(np.zeros((), dtype=[('a', [('b', 'float32', (2,))])])))
• Combinations of any of the above.

def structured_to_dict(arr: np.ndarray):
    import numpy as np

    if np.ndim(arr) == 0:
        if arr.dtype.names == None:
            return arr.item()
        # accessing by int does *not* work when arr is a zero-dimensional array!
        return {k: structured_to_dict(arr[k]) for k in arr.dtype.names}
    return [structured_to_dict(v) for v in arr]

This depends on the final structure required. This example shows a numpy recarray composed of several 1D subsets. To have a python dictionary instead, a possible conversion is:

import numpy as np

a = np.rec.array([np.array([1,3,6]), np.array([3.4,4.2,-1.2])], names=['t', 'x'])

b = {name:a[name] for name in a.dtype.names}