The numpy.reshape() funcția modelează o matrice fără a modifica datele matricei.
Sintaxă:
verificarea nulă în java
numpy.reshape(array, shape, order = 'C')>
Parametri:
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>
Tip returnare:
Array which is reshaped without changing the data.>
Exemplu
Piton
# 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)> |
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>
Ieșire:
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]]>
Referinte:
Notă: Aceste coduri nu vor rula pe IDE-uri online. Așa că, vă rog, rulați-le pe sistemele dvs. pentru a explora funcționarea.
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