How to alternate numpy.ndarrays?

Question

I'm currently looking for a method in which I can alternate 2 numpy.ndarray. such that

>>> a = np.random.rand(5,5) >>> print a [[ 0.83367208 0.29507876 0.41849799 0.58342521 0.81810562] [ 0.31363351 0.69468009 0.14960363 0.7685722 0.56240711] [ 0.49368821 0.46409791 0.09042236 0.68706312 0.98430387] [ 0.21816242 0.87907115 0.49534121 0.60453302 0.75152033] [ 0.10510938 0.55387841 0.37992348 0.6754701 0.27095986]] >>> b = np.random.rand(5,5) >>> print b [[ 0.52237011 0.75242666 0.39895415 0.66519185 0.87043142] [ 0.08624797 0.66193953 0.80640822 0.95403594 0.33977566] [ 0.13789573 0.84868366 0.09734757 0.06010175 0.48043968] [ 0.28871551 0.62186888 0.44603741 0.3351644 0.6417847 ] [ 0.85745394 0.93179792 0.62535765 0.96625077 0.86880908]] >>> 

print c shoule will alternate with each line as with matrices

 [ 0.83367208 0.52237011 0.29507876 0.75242666 0.41849799 0.39895415 0.58342521 0.66519185 0.81810562 0.87043142] 

I have three in general that should alternate, but I think it would be easier to do this two at a time.

but how can I do this easily. I read some method that used arrays, but I'm not sure what to do with ndarrays?

Answer 1

Lay them along the third axis np.dstack and go back to 2D -

 np.dstack((a,b)).reshape(a.shape[0],-1) 

With three arrays or even more arrays, just add them. So for three arrays use: np.dstack((a,b,c)) and change the form with c being the third array.

Run Example -

 In [99]: a Out[99]: array([[8, 4, 0, 5, 6], [0, 2, 3, 0, 6], [4, 4, 0, 6, 5], [7, 5, 0, 7, 0], [6, 7, 4, 7, 2]]) In [100]: b Out[100]: array([[3, 5, 8, 6, 5], [5, 6, 8, 8, 4], [8, 3, 3, 3, 5], [2, 1, 1, 1, 3], [5, 7, 7, 5, 7]]) In [101]: np.dstack((a,b)).reshape(a.shape[0],-1) Out[101]: array([[8, 3, 4, 5, 0, 8, 5, 6, 6, 5], [0, 5, 2, 6, 3, 8, 0, 8, 6, 4], [4, 8, 4, 3, 0, 3, 6, 3, 5, 5], [7, 2, 5, 1, 0, 1, 7, 1, 0, 3], [6, 5, 7, 7, 4, 7, 7, 5, 2, 7]]) 

Answer 2

np.c_ is good for this

 a = np.arange(25).reshape(5, 5) b = -np.arange(25).reshape(5, 5) c = np.ones((5, 5)) d = np.zeros((5, 5)) np.c_[a.ravel(), b.ravel(), c.ravel(), d.ravel()].ravel() 

--->

 array([ 0., 0., 1., 0., 1., -1., 1., 0., 2., -2., 1., 0., 3., -3., 1., 0., 4., -4., 1., 0., 5., -5., 1., 0., 6., -6., 1., 0., 7., -7., 1., 0., 8., -8., 1., 0., 9., -9., 1., 0., 10., -10., 1., 0., 11., -11., 1., 0., 12., -12., 1., 0., 13., -13., 1., 0., 14., -14., 1., 0., 15., -15., 1., 0., 16., -16., 1., 0., 17., -17., 1., 0., 18., -18., 1., 0., 19., -19., 1., 0., 20., -20., 1., 0., 21., -21., 1., 0., 22., -22., 1., 0., 23., -23., 1., 0., 24., -24., 1., 0.])