correct and efficient way to flatten array in numpy in python? [duplicate]

I have:

a = array([[1,2,3],[4,5,6]]) 

and I'd like to flatten it, joining the two inner lists into one flat array entry. I can do:

array(list(flatten(a))) 

but that seems inefficient due to the list cast (I want to end up with an array and not a generator.)

Also, how can this be generalized to an array like this:

b = array([[[1,2,3],[4,5,6]], [[10,11,12],[13,14,15]]]) 

where the result should be:

b = array([[1,2,3,4,5,6], [10,11,12,13,14,15]]) 

are there builtin/efficient numpy/scipy operators for this? thanks.

4 Answers

You might need to check out numpy.flatten and numpy.ravel, both return a 1-d array from an n-d array.

Furthermore, if you're not going to modify the returned 1-d array, I suggest you use numpy.ravel, since it doesn't make a copy of the array, but just return a view of the array, which is much faster than numpy.flatten.

>>>a = np.arange(10000).reshape((100,100)) >>>%timeit a.flatten() 100000 loops, best of 3: 4.02 µs per loop >>>%timeit a.ravel() 1000000 loops, best of 3: 412 ns per loop 

Also check out this post.

You can use the reshape method.

>>> import numpy >>> b = numpy.array([[[1,2,3],[4,5,6]], [[10,11,12],[13,14,15]]]) >>> b.reshape([2, 6]) array([[ 1, 2, 3, 4, 5, 6], [10, 11, 12, 13, 14, 15]]) 

How about:

>>> import numpy as np >>> a=np.arange(1,7).reshape((2,3)) >>> a array([[1, 2, 3], [4, 5, 6]]) >>> a.flatten() array([1, 2, 3, 4, 5, 6]) 

and

>>> import numpy as np >>> b=np.arange(1,13).reshape((2,2,3)) >>> b array([[[ 1, 2, 3], [ 4, 5, 6]], [[ 7, 8, 9], [10, 11, 12]]]) >>> b.reshape((2,6)) array([[ 1, 2, 3, 4, 5, 6], [ 7, 8, 9, 10, 11, 12]]) 

a = np.arange(10000) %timeit a.reshape(100,100) 1000000 loops, best of 3: 517 ns per loop %timeit a.resize(100,100) 1000000 loops, best of 3: 428 ns per loop 

I wonder reshape should take far lesser time but its almost similar