Numpy Divide each row by a vector element

Question

Suppose I have a numpy array   data   np array   1 1 1   2 2 2   3 3 3      and I have a corresponding  vector    vector   np array  1 2 3     How do I operate on data along each row to either subtract or divide so the result is   sub result     0 0 0    0 0 0    0 0 0   div result     1 1 1    1 1 1    1 1 1     Long story short  How do I perform an operation on each row of a 2D array with a 1D array of scalars that correspond to each row

User · Accepted Answer

Here you go  You just need to use None  or alternatively np newaxis  combined with broadcasting   In  6   data - vector   None  Out 6   array   0  0  0           0  0  0           0  0  0     In  7   data   vector   None  Out 7   array   1  1  1           1  1  1           1  1  1

User · Answer

Pythonic way to do this is      np divide data T vector  T   This takes care of reshaping and also the results are in floating point format  In other answers results are in rounded integer format    NOTE  No of columns in both data and vector should match

User · Answer

As has been mentioned  slicing with None or with np newaxes is a great way to do this  Another alternative is to use transposes and broadcasting  as in   data T - vector  T   and   data T   vector  T   For higher dimensional arrays you may want to use the swapaxes method of NumPy arrays or the NumPy rollaxis function  There really are a lot of ways to do this   For a fuller explanation of broadcasting  see http   docs scipy org doc numpy user basics broadcasting html

User · Answer

JoshAdel s solution uses np newaxis to add a dimension  An alternative is to use reshape   to align the dimensions in preparation for broadcasting   data   np array   1 1 1   2 2 2   3 3 3    vector   np array  1 2 3    data   array   1  1  1             2  2  2             3  3  3    vector   array  1  2  3    data shape    3  3  vector shape    3    data   vector reshape  3 1     array   1  1  1             1  1  1             1  1  1      Performing the reshape   allows the dimensions to line up for broadcasting   data             3 x 3 vector               3 vector reshaped  3 x 1   Note that data vector is ok  but it doesn t get you the answer that you want  It divides each column of array  instead of each row  by each corresponding element of vector  It s what you would get if you explicitly reshaped vector to be 1x3 instead of 3x1   data   vector   array   1  0  0             2  1  0             3  1  1    data   vector reshape  1 3     array   1  0  0             2  1  0             3  1  1

User · Answer

Adding to the answer of stackoverflowuser2010  in the general case you can just use  data   np array   1 1 1   2 2 2   3 3 3     vector   np array  1 2 3    data   vector reshape -1 1    This will turn your vector into a column matrix vector  Allowing you to do the elementwise operations as you wish  At least to me  this is the most intuitive way going about it and since  in most cases  numpy will just use a view of the same internal memory for the reshaping it s efficient too

[python] Numpy: Divide each row by a vector element

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