De numpy.reshape() funktion formar en array utan att ändra arrayens data.
Syntax:
mvc för java
numpy.reshape(array, shape, order = 'C')>
Parametrar:
array : [array_like]Input array shape : [int or tuples of int] e.g. if we are arranging an array with 10 elements then shaping it like numpy.reshape(4, 8) is wrong; we can do numpy.reshape(2, 5) or (5, 2) order : [C-contiguous, F-contiguous, A-contiguous; optional] C-contiguous order in memory(last index varies the fastest) C order means that operating row-rise on the array will be slightly quicker FORTRAN-contiguous order in memory (first index varies the fastest). F order means that column-wise operations will be faster. ‘A’ means to read / write the elements in Fortran-like index order if, array is Fortran contiguous in memory, C-like order otherwise>
Returtyp:
Array which is reshaped without changing the data.>
Exempel
Pytonorm
# Python Program illustrating> # numpy.reshape() method> > import> numpy as geek> > # array = geek.arrange(8)> # The 'numpy' module has no attribute 'arrange'> array1> => geek.arange(> 8> )> print> (> 'Original array :
'> , array1)> > # shape array with 2 rows and 4 columns> array2> => geek.arange(> 8> ).reshape(> 2> ,> 4> )> print> (> '
array reshaped with 2 rows and 4 columns :
'> ,> > array2)> > # shape array with 4 rows and 2 columns> array3> => geek.arange(> 8> ).reshape(> 4> ,> 2> )> print> (> '
array reshaped with 4 rows and 2 columns :
'> ,> > array3)> > # Constructs 3D array> array4> => geek.arange(> 8> ).reshape(> 2> ,> 2> ,> 2> )> print> (> '
Original array reshaped to 3D :
'> ,> > array4)> |
>
>
Utgång:
Original array : [0 1 2 3 4 5 6 7] array reshaped with 2 rows and 4 columns : [[0 1 2 3] [4 5 6 7]] array reshaped with 4 rows and 2 columns : [[0 1] [2 3] [4 5] [6 7]] Original array reshaped to 3D : [[[0 1] [2 3]] [[4 5] [6 7]]] [[0 1 2 3] [4 5 6 7]]>
Referenser:
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