multiply numpy row with all elements in list

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how to multiply all rows in numpy array with list elements one by one like first row in array with first tuple in list , second with second and so on.

i am doing this

utl  = np.array([[  3,         12.      ],
                 [  3.    ,  17.        ]])

all_ltp = ([(0, 134.30000305175778), (1, 133.80000305175778)])

a=np.array(list(itertools.product(utl, all_ltp)))
a = np.reshape(a, (-1,4))
print(a)

output is  - 
[[  3.          12.           0.         134.30000305]
 [  3.          12.           1.         133.80000305]
 [  3.          17.           0.         134.30000305]
 [  3.          17.           1.         133.80000305]]

it only works but if i increase the values of array then

utl  = np.array([[  3,         12.  , 99   ],
                 [  3.    ,  17.   , 99    ]])

all_ltp = ([(0, 134.30000305175778), (1, 133.80000305175778)])

a=np.array(list(itertools.product(utl, all_ltp)))
a = np.reshape(a, (-1,2))
print(a)

output is - 

[[array([ 3., 12., 99.]) (0, 134.30000305175778)]
 [array([ 3., 12., 99.]) (1, 133.80000305175778)]
 [array([ 3., 17., 99.]) (0, 134.30000305175778)]
 [array([ 3., 17., 99.]) (1, 133.80000305175778)]]

it is also working but not combining elements

output must be -

[[  3.          12.     99      0.         134.30000305]
 [  3.          12.     99      1.         133.80000305]
 [  3.          17.     99      0.         134.30000305]
 [  3.          17.     99      1.         133.80000305]]
numpy-ndarray numpy python
1 Answers

First convert all_ltp to a Numpy array:

b = np.array(all_ltp)

Then generate 2 intermediate arrays, by repeating utl and tiling b:

wrk1 = np.repeat(utl, repeats=b.shape[0], axis=0)
wrk2 = np.tile(b, reps=(utl.shape[0], 1))

(print both of them to see the result).

And to get the final result, horizontally stack both these tables:

result = np.hstack((wrk1, wrk2))

The result, for your source data, is:

[[  3.          12.          99.           0.         134.30000305]
 [  3.          12.          99.           1.         133.80000305]
 [  3.          17.          99.           0.         134.30000305]
 [  3.          17.          99.           1.         133.80000305]]

Or, to have more concise code, run:

result = np.hstack((np.repeat(utl, repeats=b.shape[0], axis=0),
    np.tile(b, reps=(utl.shape[0], 1))))
user_73884770
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