I have a list of lists that looks something like:
data = [
[1., np.array([2., 3., 4.]), ...],
[5., np.array([6., 7., 8.]), ...],
...
]
where each of the internal lists is the same length and contains the same data type/shape at each entry. I would like to calculate the mean over corresponding entries and return something of the same structure as the internal lists. For example, in the above case (assuming only two entries) I want the result to be:
[3., np.array([4., 5., 6.]), ...]
What is the best way to do this with Python?
data is a list, so a list comprehension seems like a natural option. Even if it were a numpy array, given that it's a jagged array, it wouldn't benefit from being wrapped in an ndarray anyway, so a list comp would still be the best option, in my opinion.
Anyway, use zip() to "transpose" data and call np.mean() in a loop to find mean along the first axis.
[np.mean(x, axis=0) for x in zip(*data)]
# [3.0, array([4., 5., 6.]), array([[2., 2.], [2., 2.]])]