Python Differentiating between row and column vectors

Question

Is there a good way of differentiating between row and column vectors in python  So far I m using numpy and scipy and what I see so far is that If I was to give one a vector  say  from numpy import   Vector   array  1 2 3     they wouldn t be able to say weather I mean a row or a column vector  Moreover   array  1 2 3      array  1 2 3   transpose   True   Which in  real world  is simply untrue  I realize that most of the functions on vectors from the mentioned modules don t need the differentiation  For example outer a b  or a dot b  but I d like to differentiate for my own convenience

User · Answer

The vector you are creating is neither row nor column  It actually has 1 dimension only  You can verify that by   checking the number of dimensions myvector ndim which is 1 checking the myvector shape  which is  3    a tuple with one element only   For a row vector is should be  1  3   and for a column  3  1    Two ways to handle this   create an actual row or column vector reshape your current one   You can explicitly create a row or column  row   np array        one row with 3 elements     1  2  3    column   np array      3 rows  with 1 element each      1        2        3       or  with a shortcut  row   np r   r    1 2 3         shape   1  3  column   np r   c    1 2 3      shape   3 1    Alternatively  you can reshape it to  1  n  for row  or  n  1  for column  row   my vector reshape 1  -1  column   my vector reshape -1  1    where the -1 automatically finds the value of n

User · Answer

When I tried to compute w T   x using numpy  it was super confusing for me as well  In fact  I couldn t implement it myself  So  this is one of the few gotchas in NumPy that we need to acquaint ourselves with   As far as 1D array is concerned  there is no distinction between a row vector and column vector  They are exactly the same   Look at the following examples  where we get the same result in all cases  which is not true in  the theoretical sense of  linear algebra   In  37   w Out 37   array  0  1  2  3  4    In  38   x Out 38   array  1  2  3  4  5    In  39   np dot w  x  Out 39   40  In  40   np dot w transpose    x  Out 40   40  In  41   np dot w transpose    x transpose    Out 41   40  In  42   np dot w  x transpose    Out 42   40     With that information  now let s try to compute the squared length of the vector  w  2   For this  we need to transform w to 2D array   In  51   wt   w    np newaxis   In  52   wt Out 52    array   0           1           2           3           4      Now  let s compute the squared length  or squared magnitude  of the vector w    In  53   np dot w  wt  Out 53   array  30     Note that we used w  wt instead of wt  w  like in theoretical linear algebra  because of shape mismatch with the use of np dot wt  w   So  we have the squared length of the vector as  30   Maybe this is one of the ways to distinguish  numpy s interpretation of  row and column vector   And finally  did I mention that I figured out the way to implement w T   x   Yes  I did    In  58   wt Out 58    array   0           1           2           3           4     In  59   x Out 59   array  1  2  3  4  5    In  60   np dot x  wt  Out 60   array  40     So  in NumPy  the order of the operands is reversed  as evidenced above  contrary to what we studied in theoretical linear algebra     P S    potential gotchas in numpy

User · Answer

It looks like Python s Numpy doesn t distinguish it unless you use it in context    You can have standard vectors or row column vectors if you like             You can treat rank-1 arrays as either row or column vectors  dot A v  treats v as a column vector  while dot v A  treats v as a row vector  This can save you having to type a lot of transposes     Also  specific to your code   Transpose on a rank-1 array does nothing    Source  http   wiki scipy org NumPy for Matlab Users

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You can store the array s elements in a row or column as follows    gt  gt  gt  a   np array  1  2  3      None    stores in rows  gt  gt  gt  a array   1           2           3      gt  gt  gt  b   np array  1  2  3   None       stores in columns  gt  gt  gt  b array   1  2  3

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The excellent Pandas library adds features to numpy that make these kinds of operations more intuitive IMO  For example   import numpy as np import pandas as pd    column df   pd DataFrame  1 2 3      row df2   pd DataFrame   1 2 3      You can even define a DataFrame and make a spreadsheet-like pivot table

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You can make the distinction explicit by adding another dimension to the array    gt  gt  gt  a   np array  1  2  3    gt  gt  gt  a array  1  2  3    gt  gt  gt  a transpose   array  1  2  3    gt  gt  gt  a dot a transpose    14   Now force it to be a column vector    gt  gt  gt  a shape    3 1   gt  gt  gt  a array   1           2           3     gt  gt  gt  a transpose   array   1  2  3     gt  gt  gt  a dot a transpose    array   1  2  3           2  4  6           3  6  9      Another option is to use np newaxis when you want to make the distinction    gt  gt  gt  a   np array  1  2  3    gt  gt  gt  a array  1  2  3    gt  gt  gt  a    np newaxis  array   1           2           3     gt  gt  gt  a np newaxis     array   1  2  3

User · Answer

row vectors are  1 0  tensor  vectors are  0  1  tensor  if using v   np array   1 2 3     v become  0 2  tensor  Sorry  i am confused

User · Answer

If you want a distiction for this case I would recommend to use a matrix instead  where   matrix  1 2 3      matrix  1 2 3   transpose     gives   matrix    True  False  False            False   True  False            False  False   True    dtype bool    You can also use a ndarray explicitly adding a second dimension   array  1 2 3   None     array   1  2  3      and       array  1 2 3     None   array   1            2            3

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I think you can use ndmin option of numpy array  Keeping it to 2 says that it will be a  4 1  and transpose will be  1 4      gt  gt  gt  a   np array  12  3  4  5   ndmin 2   gt  gt  gt  print a shape  gt  gt  gt   1 4   gt  gt  gt  print a T shape  gt  gt  gt   4 1

User · Answer

Use double    when writing your vectors   Then  if you want a row vector   row vector   array   1  2  3         shape  1  3    Or if you want a column vector   col vector   array   1  2  3    T    shape  3  1

User · Answer

If I want a 1x3 array  or 3x1 array       import numpy as np row arr   np array  1 2 3   reshape  1 3   col arr   np array  1 2 3   reshape  3 1      Check your work   row arr shape   returns  1 3  col arr shape   returns  3 1    I found a lot of answers here are helpful  but much too complicated for me  In practice I come back to shape and reshape and the code is readable  very simple and explicit

User · Answer

Here s another intuitive way  Suppose we have    gt  gt  gt  a   np array  1  3  4    gt  gt  gt  a array  1  3  4     First we make a 2D array with that as the only row    gt  gt  gt  a   np array  a    gt  gt  gt  a array   1  3  4      Then we can transpose it    gt  gt  gt  a T array   1           3           4

[python] Python: Differentiating between row and column vectors

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