What does -1 mean in numpy reshape

Question

A numpy matrix can be reshaped into a vector using reshape function with parameter -1  But I don t know what -1 means here   For example    a   numpy matrix   1  2  3  4    5  6  7  8    b   numpy reshape a  -1    The result of b is  matrix   1  2  3  4  5  6  7  8     Does anyone know what -1 means here  And it seems python assign -1 several meanings  such as  array -1  means the last element  Can you give an explanation

User · Answer

The criterion to satisfy for providing the new shape is that 'The new shape should be compatible with the original shape'

numpy allow us to give one of new shape parameter as -1 (eg: (2,-1) or (-1,3) but not (-1, -1)). It simply means that it is an unknown dimension and we want numpy to figure it out. And numpy will figure this by looking at the 'length of the array and remaining dimensions' and making sure it satisfies the above mentioned criteria

Now see the example.

z = np.array([[1, 2, 3, 4],
         [5, 6, 7, 8],
         [9, 10, 11, 12]])
z.shape
(3, 4)

Now trying to reshape with (-1) . Result new shape is (12,) and is compatible with original shape (3,4)

z.reshape(-1)
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

Now trying to reshape with (-1, 1) . We have provided column as 1 but rows as unknown . So we get result new shape as (12, 1).again compatible with original shape(3,4)

z.reshape(-1,1)
array([[ 1],
   [ 2],
   [ 3],
   [ 4],
   [ 5],
   [ 6],
   [ 7],
   [ 8],
   [ 9],
   [10],
   [11],
   [12]])

The above is consistent with numpy advice/error message, to use reshape(-1,1) for a single feature; i.e. single column

Reshape your data using array.reshape(-1, 1) if your data has a single feature

New shape as (-1, 2). row unknown, column 2. we get result new shape as (6, 2)

z.reshape(-1, 2)
array([[ 1,  2],
   [ 3,  4],
   [ 5,  6],
   [ 7,  8],
   [ 9, 10],
   [11, 12]])

Now trying to keep column as unknown. New shape as (1,-1). i.e, row is 1, column unknown. we get result new shape as (1, 12)

z.reshape(1,-1)
array([[ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]])

The above is consistent with numpy advice/error message, to use reshape(1,-1) for a single sample; i.e. single row

Reshape your data using array.reshape(1, -1) if it contains a single sample

New shape (2, -1). Row 2, column unknown. we get result new shape as (2,6)

z.reshape(2, -1)
array([[ 1,  2,  3,  4,  5,  6],
   [ 7,  8,  9, 10, 11, 12]])

New shape as (3, -1). Row 3, column unknown. we get result new shape as (3,4)

z.reshape(3, -1)
array([[ 1,  2,  3,  4],
   [ 5,  6,  7,  8],
   [ 9, 10, 11, 12]])

And finally, if we try to provide both dimension as unknown i.e new shape as (-1,-1). It will throw an error

z.reshape(-1, -1)
ValueError: can only specify one unknown dimension

User · Answer

The final outcome of the conversion is that the number of elements in the final array is same as that of the initial array or data frame  -1 corresponds to the unknown count of the row or column  We can think of it as x unknown   x is obtained by dividing the number of elements in the original array by the other value of the ordered pair with -1  Examples  12 elements with reshape -1 1  corresponds to an array with x 12 1 12 rows and 1 column   12 elements with reshape 1 -1  corresponds to an array with 1 row and x 12 1 12 columns

User · Answer

I didn t manage to understand what np reshape   does until I read this article  Mechanically it is clear what reshape   does  But how do we interpret the data before and after reshape  The missing piece for me was   When we train a machine learning model  the nesting levels of arrays have precisely defined meaning   This means that the reshape operation has to be keenly aware both points below before the operation has any meaning   the data it operates on  how the reshape intput looks like  how the algorithm model expects the reshaped data to be  how the reshape output looks like   For example   The external array contains observations rows  The inner array contains columns features  This causes two special cases when we have either an array of multiple observations of only one feature or a single observation of multiple features   For more advanced example  See this stackoverflow question

User · Answer

Long story short  you set some dimensions and let NumPy set the remaining s     userDim1  userDim2       -1  -- gt  gt    userDim1  userDim1       TOTAL DIMENSION -  userDim1   userDim2

User · Answer

numpy reshape a newshape order     check the below link for more info  https   docs scipy org doc numpy reference generated numpy reshape html for the below example you mentioned the output explains the resultant vector to be a single row  -1  indicates the number of rows to be 1  if the a   numpy matrix   1  2  3  4    5  6  7  8    b   numpy reshape a  -1   output  matrix   1  2  3  4  5  6  7  8     this can be explained more precisely with another example  b   np arange 10  reshape  -1 1    output  is a 1 dimensional columnar array  array   0           1           2           3           4           5           6           7           8           9     or b   np arange 10  reshape  1 -1    output  is a 1 dimensional row array  array   0  1  2  3  4  5  6  7  8  9

User · Answer

It simply means that you are not sure about what number of rows  or columns you can give and you are asking numpy to suggest number of column or rows to get reshaped in   numpy provides last example for -1 https   docs scipy org doc numpy reference generated numpy reshape html  check below code and its output to better understand about  -1    CODE -  import numpy a   numpy matrix   1  2  3  4    5  6  7  8    print  Without reshaping  - gt     print a  b   numpy reshape a  -1  print  HERE We don t know about what number we should give to row col   print  Reshaping as  a -1    print b  c   numpy reshape a   -1 2   print  HERE We just know about number of columns   print  Reshaping as  a  -1 2     print c  d   numpy reshape a   2 -1   print  HERE We just know about number of rows   print  Reshaping as  a  2 -1     print d    OUTPUT  -  Without reshaping  - gt     1 2 3 4    5 6 7 8   HERE We don t know about what number we should give to row col Reshaping as  a -1    1 2 3 4 5 6 7 8   HERE We just know about number of columns Reshaping as  a  -1 2     1 2    3 4    5 6    7 8   HERE We just know about number of rows Reshaping as  a  2 -1     1 2 3 4    5 6 7 8

User · Answer

According to the documentation      newshape   int or tuple of ints      The new shape should be compatible with the original shape  If an   integer  then the result will be a 1-D array of that length  One shape   dimension can be -1  In this case  the value is inferred from the   length of the array and remaining dimensions

User · Answer

import numpy as np x   np array   2 3 4    5 6 7        Convert any shape to 1D shape x   np reshape x   -1     Making it 1 row - gt   6      When you don t care about rows and just want to fix number of columns x   np reshape x   -1  1     Making it 1 column - gt   6  1  x   np reshape x   -1  2     Making it 2 column - gt   3  2  x   np reshape x   -1  3     Making it 3 column - gt   2  3     When you don t care about columns and just want to fix number of rows x   np reshape x   1  -1     Making it 1 row - gt   1  6  x   np reshape x   2  -1     Making it 2 row - gt   2  3  x   np reshape x   3  -1     Making it 3 row - gt   3  2

User · Answer

Used to reshape an array   Say we have a 3 dimensional array of dimensions 2 x 10 x 10   r   numpy random rand 2  10  10     Now we want to reshape to 5 X 5 x 8   numpy reshape r  shape  5  5  8      will do the job   Note that  once you fix first dim   5 and second dim   5  you don t need to determine third dimension  To assist your laziness  python gives the option of -1   numpy reshape r  shape  5  5  -1      will give you an array of shape    5  5  8    Likewise    numpy reshape r  shape  50  -1      will give you an array of shape    50  4   You can read more at http   anie me numpy-reshape-transpose-theano-dimshuffle

User · Answer

It is fairly easy to understand  The  -1  stands for  unknown dimension  which can should be infered from another dimension   In this case  if you set your matrix like this   a   numpy matrix   1  2  3  4    5  6  7  8      Modify your matrix like this   b   numpy reshape a  -1    It will call some deafult operations to the matrix a  which will return a 1-d numpy array martrix   However  I don t think it is a good idea to use code like this  Why not try   b   a reshape 1 -1    It will give you the same result and it s more clear for readers to understand  Set b as another shape of a  For a  we don t how much columns it should have set it to -1    but we want a 1-dimension array set the first parameter to 1

[python] What does -1 mean in numpy reshape?

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