From ND to 1D arrays

Question

Say I have an array a   a   np array   1 2 3    4 5 6     array   1  2  3           4  5  6      I would like to convert it to a 1D array  i e  a column vector    b   np reshape a   1 np product a shape      but this returns  array   1  2  3  4  5  6      which is not the same as   array  1  2  3  4  5  6     I can take the first element of this array to manually convert it to a 1D array   b   np reshape a   1 np product a shape    0    but this requires me to know how many dimensions the original array has  and concatenate  0  s when working with higher dimensions   Is there a dimensions-independent way of getting a column row vector from an arbitrary ndarray

User · Answer

For list of array with different size use following   import numpy as np    ND array list with different size a     1   2 3 4 5   6 7 8      stack them b   np hstack a   print b    Output    1 2 3 4 5 6 7 8

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In  14   b   np reshape a   np product a shape      In  15   b Out 15   array  1  2  3  4  5  6     or  simply   In  16   a flatten   Out 16   array  1  2  3  4  5  6

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Although this isn t using the np array format   to lazy to modify my code  this should do what you want     If  you truly want a column vector you will want to transpose the vector result   It all depends on how you are planning to use this   def getVector data array col       vector          imax   len data array      for i in range imax           vector append data array i  col       return   vector   a     1 2 3    4 5 6   b   getVector a 1  print b   Out gt  2 5    So if you need to transpose  you can do something like this   def transposeArray data array         need to test if this is a 1D array        can t do a len data array 0   if it s 1D     two d   True     if isinstance data array 0   list           dimx   len data array 0       else          dimx   1         two d   False     dimy   len data array        init output transposed array     data array t     0 for row in range dimx   for col in range dimy         fill output transposed array     for i in range dimx           for j in range dimy               if two d                  data array t j  i    data array i  j              else                  data array t j  i    data array j      return data array t

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Use np ravel  for a 1D view  or np ndarray flatten  for a 1D copy  or np ndarray flat  for an 1D iterator    In  12   a   np array   1 2 3    4 5 6     In  13   b   a ravel    In  14   b Out 14   array  1  2  3  4  5  6     Note that ravel   returns a view of a when possible  So modifying b also modifies a  ravel   returns a view when the 1D elements are contiguous in memory  but would return a copy if  for example  a were made from slicing another array using a non-unit step size  e g  a   x   2     If you want a copy rather than a view  use  In  15   c   a flatten     If you just want an iterator  use np ndarray flat   In  20   d   a flat  In  21   d Out 21    lt numpy flatiter object at 0x8ec2068 gt   In  22   list d  Out 22    1  2  3  4  5  6

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One of the simplest way is to use flatten    like this example     import numpy as np   batch y  train output iloc sample      batch y   np array batch y  flatten     My array it was like this         0 0   6 1   6 2   5 3   4 4   3         After using flatten      array  6  6  5       5  3  6     It s also the solution of errors of this type    Cannot feed value of shape  100  1  for Tensor  input Y 0   which has shape

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I wanted to see a benchmark result of functions mentioned in answers including unutbu s   Also want to point out that numpy doc recommend to use arr reshape -1  in case view is preferable   even though ravel is tad faster in the following result        TL DR  np ravel is the most performant  by very small amount     Benchmark  Functions    np ravel  returns view  if possible np reshape -1   returns view  if possible np flatten  returns copy np flat  returns numpy flatiter  similar to iterable   numpy version   1 18 0   Execution times on different ndarray sizes   ------------- ---------- ----------- ----------- -------------     function       10x10     100x100    1000x1000   10000x10000    ------------- ---------- ----------- ----------- -------------    ravel         0 002073    0 002123    0 002153      0 002077     reshape -1    0 002612    0 002635    0 002674      0 002701     flatten       0 000810    0 007467    0 587538    107 321913     flat          0 000337    0 000255    0 000227      0 000216    ------------- ---------- ----------- ----------- -------------    Conclusion     ravel and reshape -1  s execution time was consistent and independent from ndarray size    However  ravel is tad faster  but reshape provides flexibility in reshaping size   maybe that s why numpy doc recommend to use it instead  Or there could be some cases where reshape returns view and ravel doesn t     If you are dealing with large size ndarray  using flatten can cause a performance issue  Recommend not to use it  Unless you need a copy of the data to do something else     Used code  import timeit setup       import numpy as np nd   np random randint 10  size  10  10        timeit timeit  nd   np reshape nd  -1    setup setup  number 1000  timeit timeit  nd   np ravel nd    setup setup  number 1000  timeit timeit  nd   nd flatten     setup setup  number 1000  timeit timeit  nd flat   setup setup  number 1000

[python] From ND to 1D arrays

For list of array with different size use following:

Output:

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