python - Why is there a large overhead in pickling numpy arrays? -

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suppose have simple array in python:

>>> x = [1.0, 2.0, 3.0, 4.0]

when pickled, reasonably small size:

>>> pickle.dumps(x).__len__() 44

how come if use numpy array, size larger?

>>> xn = np.array(x) >>> pickle.dumps(xn).__len__() 187

converting less precise data type helps little bit...

>>> x16 = xn.astype('float16') >>> pickle.dumps(x16).__len__() 163

other numpy/scipy data structures sparse matrices don't pickle well. why?

checking in debugger, numpy array has fields max, min, type etc apart data, not sure python list has.

a complete list can found on http://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html

as pickling binary copying, these other fields being copied, resulting in larger size.


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