Multiplying across in a numpy array

Question

I m trying to multiply each of the terms in a 2D array by the corresponding terms in a 1D array  This is very easy if I want to multiply every column by the 1D array  as shown in the numpy multiply function  But I want to do the opposite  multiply each term in the row  In other words I want to multiply    1 2 3     0   4 5 6     1   7 8 9     2    and get   0 0 0   4 5 6   14 16 18    but instead I get   0 2 6   0 5 12   0 8 18    Does anyone know if there s an elegant way to do that with numpy  Thanks a lot  Alex

User · Answer

Yet another trick  as of v1 6   A np arange 1 10  reshape 3 3  b np arange 3   np einsum  ij i- gt ij  A b    I m proficient with the numpy broadcasting  newaxis   but I m still finding my way around this new einsum tool   So I had play around a bit to find this solution   Timings  using Ipython timeit    einsum  4 9 micro transpose  8 1 micro newaxis  8 35 micro dot-diag  10 5 micro   Incidentally  changing a i to j  np einsum  ij j- gt ij  A b   produces the matrix that Alex does not want   And np einsum  ji j- gt ji  A b  does  in effect  the double transpose

User · Answer

You could also use matrix multiplication  aka dot product    a     1 2 3   4 5 6   7 8 9   b    0 1 2  c   numpy diag b   numpy dot c a    Which is more elegant is probably a matter of taste

User · Answer

For those lost souls on google  using numpy expand dims then numpy repeat will work  and will also work in higher dimensional cases  i e  multiplying a shape  10  12  3  by a  10  12      gt  gt  gt  import numpy  gt  gt  gt  a   numpy array   1 2 3   4 5 6   7 8 9     gt  gt  gt  b   numpy array  0 1 2    gt  gt  gt  b0   numpy expand dims b  axis   0   gt  gt  gt  b0   numpy repeat b0  a shape 0   axis   0   gt  gt  gt  b1   numpy expand dims b  axis   1   gt  gt  gt  b1   numpy repeat b1  a shape 1   axis   1   gt  gt  gt  a b0 array    0   2   6        0   5  12        0   8  18     gt  gt  gt  a b1 array    0   0   0        4   5   6       14  16  18

User · Answer

Normal multiplication like you showed    gt  gt  gt  import numpy as np  gt  gt  gt  m   np array   1 2 3   4 5 6   7 8 9     gt  gt  gt  c   np array  0 1 2    gt  gt  gt  m   c array    0   2   6            0   5  12            0   8  18      If you add an axis  it will multiply the way you want    gt  gt  gt  m   c    np newaxis  array    0   0   0            4   5   6           14  16  18      You could also transpose twice    gt  gt  gt   m T   c  T array    0   0   0            4   5   6           14  16  18

User · Answer

I ve compared the different options for speed and found that     much to my surprise     all options  except diag  are equally fast  I personally use A   b    None    or  A T   b  T  because it s short    Code to reproduce the plot  import numpy import perfplot   def newaxis data       A  b   data     return A   b    numpy newaxis    def none data       A  b   data     return A   b    None    def double transpose data       A  b   data     return  A T   b  T   def double transpose contiguous data       A  b   data     return numpy ascontiguousarray  A T   b  T    def diag dot data       A  b   data     return numpy dot numpy diag b   A    def einsum data       A  b   data     return numpy einsum  quot ij i- gt ij quot   A  b    perfplot save       quot p png quot       setup lambda n   numpy random rand n  n   numpy random rand n        kernels           newaxis          none          double transpose          double transpose contiguous          diag dot          einsum             n range  2    k for k in range 13        xlabel  quot len A   len b  quot

User · Answer

Why don t you just do   gt  gt  gt  m   np array   1 2 3   4 5 6   7 8 9     gt  gt  gt  c   np array  0 1 2    gt  gt  gt   m T   c  T

[python] Multiplying across in a numpy array

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