How does numpy newaxis work and when to use it

Question

When I try numpy newaxis  the result gives me a 2-d plot frame with x-axis from 0 to 1  However  when I try using numpy newaxis to slice a vector  vector 0 4     0 04965172  0 04979645  0 04994022  0 05008303  vector    np newaxis  0 4      0 04965172    0 04979645    0 04994022    0 05008303    Is it the same thing except that it changes a row vector to a column vector  Generally  what is the use of numpy newaxis  and in which circumstances should we use it

User · Answer

You started with a one-dimensional list of numbers. Once you used numpy.newaxis, you turned it into a two-dimensional matrix, consisting of four rows of one column each.

You could then use that matrix for matrix multiplication, or involve it in the construction of a larger 4 x n matrix.

User · Answer

Simply put  numpy newaxis is used to increase the dimension of the existing array by one more dimension  when used once  Thus   1D array will become 2D array  2D array will become 3D array  3D array will become 4D array  4D array will become 5D array   and so on   Here is a visual illustration which depicts promotion of 1D array to 2D arrays    Scenario-1  np newaxis might come in handy when you want to explicitly convert a 1D array to either a row vector or a column vector  as depicted in the above picture  Example    1D array In  7   arr   np arange 4  In  8   arr shape Out 8    4      make it as row vector by inserting an axis along first dimension In  9   row vec   arr np newaxis           arr None     In  10   row vec shape Out 10    1  4     make it as column vector by inserting an axis along second dimension In  11   col vec   arr    np newaxis        arr    None  In  12   col vec shape Out 12    4  1    Scenario-2  When we want to make use of numpy broadcasting as part of some operation  for instance while doing addition of some arrays  Example  Let s say you want to add the following two arrays   x1   np array  1  2  3  4  5    x2   np array  5  4  3    If you try to add these just like that  NumPy will raise the following ValueError   ValueError  operands could not be broadcast together with shapes  5    3    In this situation  you can use np newaxis to increase the dimension of one of the arrays so that NumPy can broadcast  In  2   x1 new   x1    np newaxis       x1    None    now  the shape of x1 new is  5  1    array   1             2             3             4             5     Now  add  In  3   x1 new   x2 Out 3   array    6   5   4            7   6   5            8   7   6            9   8   7           10   9   8      Alternatively  you can also add new axis to the array x2  In  6   x2 new   x2    np newaxis       x2    None  In  7   x2 new       shape is  3  1  Out 7    array   5           4           3     Now  add  In  8   x1   x2 new Out 8    array    6   7   8   9  10            5   6   7   8   9            4   5   6   7   8     Note  Observe that we get the same result in both cases  but one being the transpose of the other    Scenario-3  This is similar to scenario-1  But  you can use np newaxis more than once to promote the array to higher dimensions  Such an operation is sometimes needed for higher order arrays  i e  Tensors   Example  In  124   arr   np arange 5 5  reshape 5 5   In  125   arr shape Out 125    5  5     promoting 2D array to a 5D array In  126   arr 5D   arr np newaxis       np newaxis  np newaxis       arr None       None  None   In  127   arr 5D shape Out 127    1  5  5  1  1   As an alternative  you can use numpy expand dims that has an intuitive axis kwarg    adding new axes at 1st  4th  and last dimension of the resulting array In  131   newaxes    0  3  -1  In  132   arr 5D   np expand dims arr  axis newaxes  In  133   arr 5D shape Out 133    1  5  5  1  1    More background on np newaxis vs np reshape newaxis is also called as a pseudo-index that allows the temporary addition of an axis into a multiarray  np newaxis uses the slicing operator to recreate the array while numpy reshape reshapes the array to the desired layout  assuming that the dimensions match  And this is must for a reshape to happen   Example In  13   A   np ones  3 4 5 6   In  14   B   np ones  4 6   In  15    A   B    np newaxis      shape       B    None     Out 15    3  4  5  6   In the above example  we inserted a temporary axis between the first and second axes of B  to use broadcasting   A missing axis is filled-in here using np newaxis to make the broadcasting operation work   General Tip  You can also use None in place of np newaxis  These are in fact the same objects  In  13   np newaxis is None Out 13   True  P S  Also see this great answer  newaxis vs reshape to add dimensions

User · Answer

newaxis object in the selection tuple serves to expand the dimensions of the resulting selection by one unit-length dimension   It is not just conversion of row matrix to column matrix   Consider the example below   In  1  x1   np arange 1 10  reshape 3 3         print x1  Out 1   array   1  2  3                   4  5  6                   7  8  9      Now lets add new dimension to our data   In  2  x1 new   x1   np newaxis         print x1 new  Out 2  array    1  2  3                     4  5  6                     7  8  9       You can see that newaxis added the extra dimension here  x1 had dimension  3 3  and X1 new has dimension  3 1 3    How our new dimension enables us to different operations   In  3  x2   np arange 11 20  reshape 3 3         print x2  Out 3  array   11  12  13                  14  15  16                  17  18  19       Adding x1 new and x2  we get   In  4  x1 new x2 Out 4  array    12  14  16                   15  17  19                   18  20  22                     15  17  19                   18  20  22                   21  23  25                     18  20  22                   21  23  25                   24  26  28       Thus  newaxis is not just conversion of row to column matrix  It increases the dimension of matrix  thus enabling us to do more operations on it

User · Answer

What is np newaxis   The np newaxis is just an alias for the Python constant None  which means that wherever you use np newaxis you could also use None    gt  gt  gt  np newaxis is None True   It s just more descriptive if you read code that uses np newaxis instead of None   How to use np newaxis   The np newaxis is generally used with slicing  It indicates that you want to add an additional dimension to the array  The position of the np newaxis represents where I want to add dimensions    gt  gt  gt  import numpy as np  gt  gt  gt  a   np arange 10   gt  gt  gt  a array  0  1  2  3  4  5  6  7  8  9    gt  gt  gt  a shape  10     In the first example I use all elements from the first dimension and add a second dimension    gt  gt  gt  a    np newaxis  array   0           1           2           3           4           5           6           7           8           9     gt  gt  gt  a    np newaxis  shape  10  1    The second example adds a dimension as first dimension and then uses all elements from the first dimension of the original array as elements in the second dimension of the result array    gt  gt  gt  a np newaxis        The output has 2    pairs  array   0  1  2  3  4  5  6  7  8  9     gt  gt  gt  a np newaxis     shape  1  10    Similarly you can use multiple np newaxis to add multiple dimensions    gt  gt  gt  a np newaxis     np newaxis     note the 3    pairs in the output array    0            1            2            3            4            5            6            7            8            9      gt  gt  gt  a np newaxis     np newaxis  shape  1  10  1    Are there alternatives to np newaxis   There is another very similar functionality in NumPy  np expand dims  which can also be used to insert one dimension    gt  gt  gt  np expand dims a  1     like a    np newaxis   gt  gt  gt  np expand dims a  0     like a np newaxis       But given that it just inserts 1s in the shape you could also reshape the array to add these dimensions    gt  gt  gt  a reshape a shape    1       like a    np newaxis   gt  gt  gt  a reshape  1     a shape     like a np newaxis       Most of the times np newaxis is the easiest way to add dimensions  but it s good to know the alternatives   When to use np newaxis   In several contexts is adding dimensions useful    If the data should have a specified number of dimensions  For example if you want to use matplotlib pyplot imshow to display a 1D array  If you want NumPy to broadcast arrays  By adding a dimension you could for example get the difference between all elements of one array  a - a    np newaxis   This works because NumPy operations broadcast starting with the last dimension 1  To add a necessary dimension so that NumPy can broadcast arrays  This works because each length-1 dimension is simply broadcast to the length of the corresponding1 dimension of the other array      1 If you want to read more about the broadcasting rules the NumPy documentation on that subject is very good  It also includes an example with np newaxis     gt  gt  gt  a   np array  0 0  10 0  20 0  30 0    gt  gt  gt  b   np array  1 0  2 0  3 0    gt  gt  gt  a    np newaxis    b array     1     2     3             11    12    13             21    22    23             31    32    33

[python] How does numpy.newaxis work and when to use it?

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