How to flatten a numpy array of dtype object

Question

I'm taking ndarray slices with different length and I want my result to be flat. For example:

a = np.array(((np.array((1,2)), np.array((1,2,3))), (np.array((1,2)), np.array((1,2,3,4,5,6,7,8)))))

Is there any straight way to make this array flat by using numpy functionalities (without loop)?

Using a NumPy array of dtype object doesn't help to speed things up. They are similar to Python lists, and they are only useful if you need something exposing the same interface as a NumPy array. What is your aim with using this weird data structure? — Sven Marnach
– Sven Marnach, Commented Dec 18, 2012 at 22:57
I have tags for my signal in random intervals. Slicing returns me this type (since when slices have different lengths). — jgrynczewski
– jgrynczewski, Commented Dec 18, 2012 at 23:37

SeoFernando · Accepted Answer · 2020-09-21 08:15:37Z

25

You could try flattening it and then using hstack, which stacks the array in sequence horizontally.

>>> a = np.array(((np.array((1,2)), np.array((1,2,3))), (np.array((1,2)), np.array((1,2,3,4,5,6,7,8)))))
>>> np.hstack(a.flatten())
array([1, 2, 1, 2, 3, 1, 2, 1, 2, 3, 4, 5, 6, 7, 8])

edited Sep 21, 2020 at 8:15

SeoFernando

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answered Dec 18, 2012 at 22:55

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