How to add an extra column to a NumPy array

Question

Let   s say I have a NumPy array  a     a   np array        1  2  3        2  3  4           And I would like to add a column of zeros to get an array  b     b   np array        1  2  3  0        2  3  4  0           How can I do this easily in NumPy

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I like JoshAdel s answer because of the focus on performance  A minor performance improvement is to avoid the overhead of initializing with zeros  only to be overwritten  This has a measurable difference when N is large  empty is used instead of zeros  and the column of zeros is written as a separate step   In  1   import numpy as np  In  2   N   10000  In  3   a   np ones  N N    In  4    timeit b   np zeros  a shape 0  a shape 1  1    b    -1    a 1 loops  best of 3  492 ms per loop  In  5    timeit b   np empty  a shape 0  a shape 1  1    b    -1    a  b   -1    np zeros  a shape 0     1 loops  best of 3  407 ms per loop

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There is a function specifically for this  It is called numpy pad  a   np array   1 2 3    2 3 4    b   np pad a    0  0    0  1    mode  constant   constant values 0  print b  gt  gt  gt  array   1  2  3  0               2  3  4  0      Here is what it says in the docstring   Pads an array   Parameters ---------- array   array like of rank N     Input array pad width    sequence  array like  int      Number of values padded to the edges of each axis        before 1  after 1        before N  after N   unique pad widths     for each axis        before  after    yields same before and after pad for each axis       pad   or int is a shortcut for before   after   pad width for all     axes  mode   str or function     One of the following string values or a user supplied function        constant          Pads with a constant value       edge          Pads with the edge values of array       linear ramp          Pads with the linear ramp between end value and the         array edge value       maximum          Pads with the maximum value of all or part of the         vector along each axis       mean          Pads with the mean value of all or part of the         vector along each axis       median          Pads with the median value of all or part of the         vector along each axis       minimum          Pads with the minimum value of all or part of the         vector along each axis       reflect          Pads with the reflection of the vector mirrored on         the first and last values of the vector along each         axis       symmetric          Pads with the reflection of the vector mirrored         along the edge of the array       wrap          Pads with the wrap of the vector along the axis          The first values are used to pad the end and the         end values are used to pad the beginning       lt function gt          Padding function  see Notes  stat length   sequence or int  optional     Used in  maximum    mean    median   and  minimum    Number of     values at edge of each axis used to calculate the statistic value         before 1  after 1        before N  after N   unique statistic     lengths for each axis         before  after    yields same before and after statistic lengths     for each axis        stat length   or int is a shortcut for before   after   statistic     length for all axes       Default is   None    to use the entire axis  constant values   sequence or int  optional     Used in  constant    The values to set the padded values for each     axis         before 1  after 1        before N  after N   unique pad constants     for each axis         before  after    yields same before and after constants for each     axis        constant   or int is a shortcut for before   after   constant for     all axes       Default is 0  end values   sequence or int  optional     Used in  linear ramp    The values used for the ending value of the     linear ramp and that will form the edge of the padded array         before 1  after 1        before N  after N   unique end values     for each axis         before  after    yields same before and after end values for each     axis        constant   or int is a shortcut for before   after   end value for     all axes       Default is 0  reflect type     even    odd    optional     Used in  reflect   and  symmetric    The  even  style is the     default with an unaltered reflection around the edge value   For     the  odd  style  the extented part of the array is created by     subtracting the reflected values from two times the edge value   Returns ------- pad   ndarray     Padded array of rank equal to  array  with shape increased     according to  pad width    Notes -----    versionadded   1 7 0  For an array with rank greater than 1  some of the padding of later axes is calculated from padding of previous axes   This is easiest to think about with a rank 2 array where the corners of the padded array are calculated by using padded values from the first axis   The padding function  if used  should return a rank 1 array equal in length to the vector argument with padded values replaced  It has the following signature        padding func vector  iaxis pad width  iaxis  kwargs   where      vector   ndarray         A rank 1 array already padded with zeros   Padded values are         vector  pad tuple 0   and vector -pad tuple 1         iaxis pad width   tuple         A 2-tuple of ints  iaxis pad width 0  represents the number of         values padded at the beginning of vector where         iaxis pad width 1  represents the number of values padded at         the end of vector      iaxis   int         The axis currently being calculated      kwargs   dict         Any keyword arguments the function requires   Examples --------  gt  gt  gt  a    1  2  3  4  5   gt  gt  gt  np pad a   2 3    constant   constant values  4  6   array  4  4  1  2  3  4  5  6  6  6     gt  gt  gt  np pad a   2  3    edge   array  1  1  1  2  3  4  5  5  5  5     gt  gt  gt  np pad a   2  3    linear ramp   end values  5  -4   array   5   3   1   2   3   4   5   2  -1  -4     gt  gt  gt  np pad a   2     maximum   array  5  5  1  2  3  4  5  5  5     gt  gt  gt  np pad a   2     mean   array  3  3  1  2  3  4  5  3  3     gt  gt  gt  np pad a   2     median   array  3  3  1  2  3  4  5  3  3     gt  gt  gt  a     1  2    3  4    gt  gt  gt  np pad a    3  2    2  3     minimum   array   1  1  1  2  1  1  1           1  1  1  2  1  1  1           1  1  1  2  1  1  1           1  1  1  2  1  1  1           3  3  3  4  3  3  3           1  1  1  2  1  1  1           1  1  1  2  1  1  1      gt  gt  gt  a    1  2  3  4  5   gt  gt  gt  np pad a   2  3    reflect   array  3  2  1  2  3  4  5  4  3  2     gt  gt  gt  np pad a   2  3    reflect   reflect type  odd   array  -1   0   1   2   3   4   5   6   7   8     gt  gt  gt  np pad a   2  3    symmetric   array  2  1  1  2  3  4  5  5  4  3     gt  gt  gt  np pad a   2  3    symmetric   reflect type  odd   array  0  1  1  2  3  4  5  5  6  7     gt  gt  gt  np pad a   2  3    wrap   array  4  5  1  2  3  4  5  1  2  3     gt  gt  gt  def pad with vector  pad width  iaxis  kwargs           pad value   kwargs get  padder   10          vector  pad width 0     pad value         vector -pad width 1      pad value         return vector  gt  gt  gt  a   np arange 6   gt  gt  gt  a   a reshape  2  3    gt  gt  gt  np pad a  2  pad with  array   10  10  10  10  10  10  10           10  10  10  10  10  10  10           10  10   0   1   2  10  10           10  10   3   4   5  10  10           10  10  10  10  10  10  10           10  10  10  10  10  10  10     gt  gt  gt  np pad a  2  pad with  padder 100  array   100  100  100  100  100  100  100           100  100  100  100  100  100  100           100  100    0    1    2  100  100           100  100    3    4    5  100  100           100  100  100  100  100  100  100           100  100  100  100  100  100  100

User · Answer

I liked this  new column   np zeros  len a   1   b   np block  a  new column

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I find the following most elegant   b   np insert a  3  values 0  axis 1    Insert values before column 3   An advantage of insert is that it also allows you to insert columns  or rows  at other places inside the array  Also instead of inserting a single value you can easily insert a whole vector  for instance duplicate the last column   b   np insert a  insert index  values a   2   axis 1    Which leads to   array   1  2  3  3           2  3  4  4      For the timing  insert might be slower than JoshAdel s solution   In  1   N   10  In  2   a   np random rand N N   In  3    timeit b   np hstack  a  np zeros  a shape 0   1     100000 loops  best of 3  7 5   s per loop  In  4    timeit b   np zeros  a shape 0   a shape 1  1    b    -1    a 100000 loops  best of 3  2 17   s per loop  In  5    timeit b   np insert a  3  values 0  axis 1  100000 loops  best of 3  10 2   s per loop

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Assuming M is a  100 3  ndarray and y is a  100   ndarray append can be used as follows   M numpy append M y   None  1    The trick is to use   y    None    This converts y to a  100  1  2D array   M shape   now gives   100  4

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np r         and np c         are useful alternatives to vstack and hstack  with square brackets    instead of round     A couple of examples     import numpy as np   N   3   A   np eye N     np c   A  np ones N                   add a column array    1    0    0    1             0    1    0    1             0    0    1    1        np c   np ones N   A  np ones N       or two array    1    1    0    0    1             1    0    1    0    1             1    0    0    1    1        np r   A   A 1                    add a row array    1    0    0             0    1    0             0    0    1             0    1    0         not np r   A  A 1       np r   A 0   1  2  3  A 1         mix vecs and scalars   array   1    0    0    1    2    3    0    1    0       np r   A 0    1  2  3   A 1       lists   array   1    0    0    1    2    3    0    1    0       np r   A 0    1  2  3   A 1       tuples   array   1    0    0    1    2    3    0    1    0       np r   A 0   1 4  A 1             same  1 4    arange 1 4     1 2 3   array   1    0    0    1    2    3    0    1    0       The reason for square brackets    instead of round    is that Python expands e g  1 4 in square -- the wonders of overloading

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np concatenate also works   gt  gt  gt  a   np array   1 2 3   2 3 4     gt  gt  gt  a array   1  2  3           2  3  4     gt  gt  gt  z   np zeros  2 1    gt  gt  gt  z array    0             0      gt  gt  gt  np concatenate  a  z   axis 1  array    1    2    3    0             2    3    4    0

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Add an extra column to a numpy array   Numpy s np append method takes three parameters  the first two are 2D numpy arrays and the 3rd is an axis parameter instructing along which axis to append   import numpy as np   x   np array   1 2 3    4 5 6     print  Original x     print x    y   np array   1    1     print  Original y     print y    print  x appended to y on axis of 1     print np append x  y  axis 1      Prints   Original x    1 2 3    4 5 6   Original y    1    1   x appended to y on axis of 1    1 2 3 1    4 5 6 1

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One way  using hstack  is   b   np hstack  a  np zeros  a shape 0   1   dtype a dtype

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I think   np column stack  a  zeros shape a  0       is more elegant

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For me  the next way looks pretty intuitive and simple   zeros   np zeros  2 1    2 is a number of rows in your array     b   np hstack  a  zeros

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np insert also serves the purpose    matA   np array   1 2 3                      2 3 4    idx   3 new col   np array  0  0   np insert matA  idx  new col  axis 1   array   1  2  3  0           2  3  4  0      It inserts values  here new col  before a given index  here idx along one axis  In other words  the newly inserted values will occupy the idx column and move what were originally there at and after idx backward

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Use numpy append    gt  gt  gt  a   np array   1 2 3   2 3 4     gt  gt  gt  a array   1  2  3           2  3  4      gt  gt  gt  z   np zeros  2 1   dtype int64   gt  gt  gt  z array   0           0      gt  gt  gt  np append a  z  axis 1  array   1  2  3  0           2  3  4  0

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I was also interested in this question and compared the speed of  numpy c  a  a  numpy stack  a  a   T numpy vstack  a  a   T numpy ascontiguousarray numpy stack  a  a   T                 numpy ascontiguousarray numpy vstack  a  a   T  numpy column stack  a  a   numpy concatenate  a   None   a   None    axis 1  numpy concatenate  a None   a None    axis 0  T   which all do the same thing for any input vector a  Timings for growing a     Note that all non-contiguous variants  in particular  stack vstack  are eventually faster than all contiguous variants  column stack  for its clarity and speed  appears to be a good option if you require contiguity     Code to reproduce the plot   import numpy import perfplot  perfplot save       out png       setup lambda n  numpy random rand n       kernels           lambda a  numpy c  a  a           lambda a  numpy ascontiguousarray numpy stack  a  a   T           lambda a  numpy ascontiguousarray numpy vstack  a  a   T           lambda a  numpy column stack  a  a            lambda a  numpy concatenate  a    None   a    None    axis 1           lambda a  numpy ascontiguousarray              numpy concatenate  a None   a None    axis 0  T                    lambda a  numpy stack  a  a   T          lambda a  numpy vstack  a  a   T          lambda a  numpy concatenate  a None   a None    axis 0  T             labels            c             ascont stack             ascont vstack             column stack            concat            ascont concat             stack  non-cont             vstack  non-cont             concat  non-cont               n range  2    k for k in range 20        xlabel  len a        logx True      logy True

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A bit late to the party  but nobody posted this answer yet  so for the sake of completeness  you can do this with list comprehensions  on a plain Python array   source   a tolist   result    row    0  for row in source  b   np array result

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I think a more straightforward solution and faster to boot is to do the following   import numpy as np N   10 a   np random rand N N  b   np zeros  N N 1   b    -1    a   And timings   In  23   N   10  In  24   a   np random rand N N   In  25    timeit b   np hstack  a np zeros  a shape 0  1     10000 loops  best of 3  19 6 us per loop  In  27    timeit b   np zeros  a shape 0  a shape 1  1    b    -1    a 100000 loops  best of 3  5 62 us per loop

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In my case  I had to add a column of ones to a NumPy array  X   array   6 1101  5 5277         X shape   gt   97   X   np concatenate  np ones  m 1   dtype np int   X reshape m 1    axis 1    After     X shape     97  2   array    1    6 1101            1    5 5277

[python] How to add an extra column to a NumPy array

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