CODEWITHSUNDEEP
pythonarraysnumpyreshapenumpy-ndarray
user3119875
user3119875

Reputation: 161

Reshape multiple numpy array

I have the following numpy array:

X = [[1],
     [2],
     [3],
     [4]]

Y = [[5],
     [6],
     [7],
     [8]]

Z = [[9],
     [10],
     [11],
     [12]]

I would like to get the following output:

H = [[1,5,9],
     [2,6,10],
     [3,7,11]
     [4,8,12]]

Is there a way get this result using numpy.reshape?

Upvotes: 0

Views: 2623

Answers (3)

kmario23
kmario23

Reputation: 61355

How about this (faster) solution?

In [16]: np.array([x.squeeze(), y.squeeze(), z.squeeze()]).T
Out[16]: 
array([[ 1,  5,  9],
       [ 2,  6, 10],
       [ 3,  7, 11],
       [ 4,  8, 12]])

Efficiency (descending order)

# proposed (faster) solution
In [17]: %timeit np.array([x.squeeze(), y.squeeze(), z.squeeze()]).T
The slowest run took 7.40 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 7.36 µs per loop

# Other solutions
In [18]: %timeit np.column_stack((x, y, z))
The slowest run took 5.18 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 9.18 µs per loop

In [19]: %timeit np.hstack((x, y, z))
The slowest run took 4.49 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 16 µs per loop

In [20]: %timeit np.reshape((x,y,z),(3,4)).T
10000 loops, best of 3: 21.6 µs per loop

In [20]: %timeit np.c_[x, y, z]
10000 loops, best of 3: 55.9 µs per loop

Upvotes: 1

Paul Panzer
Paul Panzer

Reputation: 53029

And don't forget np.c_ (I don't see the need for np.reshape):

np.c_[X,Y,Z]
# array([[ 1,  5,  9],
#        [ 2,  6, 10],
#        [ 3,  7, 11],
#        [ 4,  8, 12]])

Upvotes: 1

Divakar
Divakar

Reputation: 221564

You can use np.column_stack -

np.column_stack((X,Y,Z))

Or np.concatenate along axis=1 -

np.concatenate((X,Y,Z),axis=1)

Or np.hstack -

np.hstack((X,Y,Z))

Or np.stack along axis=0 and then do multi-dim transpose -

np.stack((X,Y,Z),axis=0).T

Reshape applies on an array, not to stack or concatenate arrays together. So, reshape alone doesn't make sense here.

One could argue using np.reshape to give us the desired output, like so -

np.reshape((X,Y,Z),(3,4)).T

But, under the hoods its doing the stacking operation, which AFAIK is something to convert to array with np.asarray -

In [453]: np.asarray((X,Y,Z))
Out[453]: 
array([[[ 1],
        [ 2],
        [ 3],
        [ 4]],

       [[ 5],
        [ 6],
        [ 7],
        [ 8]],

       [[ 9],
        [10],
        [11],
        [12]]])

We just need to use multi-dim transpose on it, to give us a 3D array version of the expected output -

In [454]: np.asarray((X,Y,Z)).T
Out[454]: 
array([[[ 1,  5,  9],
        [ 2,  6, 10],
        [ 3,  7, 11],
        [ 4,  8, 12]]])

Upvotes: 4

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