Transposing a 1D NumPy array

Question

I use Python and NumPy and have some problems with  transpose    import numpy as np a   np array  5 4   print a  print a T    Invoking a T is not transposing the array   If a is for example         then it transposes correctly  but I need the transpose of

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numpy 1D array --  column row matrix    gt  gt  gt  a np array  1 2 4    gt  gt  gt  a    None       col array   1           2           4     gt  gt  gt  a None          row  or faster  a None   array   1  2  4      And as  joe-kington said  you can replace None with np newaxis for readability

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instead use arr   None  to create column vector

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The name of the function in numpy is column stack     gt  gt  gt a np array  5 4    gt  gt  gt np column stack a  array   5  4

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Use two bracket pairs instead of one  This creates a 2D array  which can be transposed  unlike the 1D array you create if you use one bracket pair   import numpy as np     a   np array   5  4    a T   More thorough example    gt  gt  gt  a    3 6 9   gt  gt  gt  b   np array a   gt  gt  gt  b T array  3  6  9            Here it didn t transpose because  a  is 1 dimensional  gt  gt  gt  b   np array  a    gt  gt  gt  b T array   3                 Here it did transpose because a is 2 dimensional         6           9      Use numpy s shape method to see what is going on here    gt  gt  gt  b   np array  10 20 30    gt  gt  gt  b shape  3    gt  gt  gt  b   np array   10 20 30     gt  gt  gt  b shape  1  3

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You can only transpose a 2D array  You can use numpy matrix to create a 2D array  This is three years late  but I am just adding to the possible set of solutions   import numpy as np m   np matrix  2  3   m T

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It s working exactly as it s supposed to  The transpose of a 1D array is still a 1D array    If you re used to matlab  it fundamentally doesn t have a concept of a 1D array  Matlab s  1D  arrays are 2D    If you want to turn your 1D vector into a 2D array and then transpose it  just slice it with np newaxis  or None  they re the same  newaxis is just more readable    import numpy as np a   np array  5 4   np newaxis  print a  print a T    Generally speaking though  you don t ever need to worry about this  Adding the extra dimension is usually not what you want  if you re just doing it out of habit   Numpy will automatically broadcast a 1D array when doing various calculations  There s usually no need to distinguish between a row vector and a column vector  neither of which are vectors  They re both 2D   when you just want a vector

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I am just consolidating the above post  hope it will help others to save some time   The below array has  2   dimension  it s a 1-D array   b new   np array  2j  3j       There are two ways to transpose a 1-D array     slice it with  np newaxis  or none    print b new np newaxis  T shape  print b new None  T shape    other way of writing  the above without T operation    print b new    np newaxis  shape  print b new    None  shape    Wrapping     or using np matrix   means adding a new dimension    print np array  b new   T shape  print np matrix b new  T shape

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For 1D arrays   a   np array  1  2  3  4   a   a reshape  -1  1      lt --- THIS IS IT  print a array   1           2           3           4      Once you understand that -1 here means  as many rows as needed   I find this to be the most readable way of  transposing  an array  If your array is of higher dimensionality simply use a T

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To  transpose  a 1d array to a 2d column  you can use numpy vstack    gt  gt  gt  numpy vstack numpy array  1 2 3    array   1           2           3      It also works for vanilla lists    gt  gt  gt  numpy vstack  1 2 3   array   1           2           3

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The way I ve learned to implement this in a compact and readable manner for 1-D arrays  so far   h   np array  1 2 3 4 5    v1   np vstack h  v2   np c  h   h1   np hstack v1  h2   np r  v2   0     numpy r  and numpy c  translate slice objects to concatenation along the first and second axis  respectively  Therefore the slicing v2   0  in transposing back the vertical array v2 into the horizontal array h2  numpy vstack is equivalent to concatenation along the first axis after 1-D arrays of shape  N   have been reshaped to  1 N   Rebuilds arrays divided by vsplit

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Basically what the transpose function does is to swap the shape and strides of the array    gt  gt  gt  a   np ones  1 2 3     gt  gt  gt  a shape  1  2  3    gt  gt  gt  a T shape  3  2  1    gt  gt  gt  a strides  48  24  8    gt  gt  gt  a T strides  8  24  48    In case of 1D numpy array  rank-1 array  the shape and strides are 1-element tuples and cannot be swapped  and the transpose of such an 1D array returns it unchanged  Instead  you can transpose a  row-vector   numpy array of shape  1  n   into a  column-vector   numpy array of shape  n  1    To achieve this you have to first convert your 1D numpy array into row-vector and then swap the shape and strides  transpose it   Below is a function that does it   from numpy lib stride tricks import as strided  def transpose a       a   np atleast 2d a      return as strided a  shape a shape   -1   strides a strides   -1     Example    gt  gt  gt  a   np arange 3   gt  gt  gt  a array  0  1  2     gt  gt  gt  transpose a  array   0           1           2      gt  gt  gt  a   np arange 1  7  reshape 2 3   gt  gt  gt  a      array   1  2  3           4  5  6      gt  gt  gt  transpose a  array   1  4           2  5           3  6      Of course you don t have to do it this way since you have a 1D array and you can directly reshape it into  n  1  array by a reshape  -1  1   or a    None   I just wanted to demonstrate how transposing an array works

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As some of the comments above mentioned  the transpose of 1D arrays are 1D arrays  so one way to transpose a 1D array would be to convert the array to a matrix like so   np transpose a reshape len a   1

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There is a method not described in the answers but described in the documentation for the numpy ndarray transpose method      For a 1-D array this has no effect  as a transposed vector is simply the same vector  To convert a 1-D array into a 2D column vector  an additional dimension must be added  np atleast2d a  T achieves this  as does a    np newaxis     One can do   import numpy as np a   np array  5 4   print a  print np atleast 2d a  T    Which  imo  is nicer than using newaxis

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The transpose of   x     0 1         2 3     is  xT     0 2          1 3     well the code is   x   array   0  1   2  3     np transpose x            this a link for more information   http   docs scipy org doc numpy reference generated numpy transpose html

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You can convert an existing vector into a matrix by wrapping it in an extra set of square brackets     from numpy import   v array  5 4      create a numpy vector array  v   T    transpose a vector into a matrix   numpy also has a matrix class  see array vs  matrix      matrix v  T    transpose a vector into a matrix

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Another solution      -   import numpy as np  a    1 2 4        1  2  4    b   np array  a   T      array   1             2             4

[python] Transposing a 1D NumPy array

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