NUMPY.RESHAPE() У PYTHON

The numpy.reshape() функція формує масив без зміни даних масиву.

Синтаксис:

numpy.reshape(array, shape, order = 'C')

Параметри:

 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

Тип повернення:

Array which is reshaped without changing the data.

приклад

Python

# 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)>

Вихід:

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]]

Література: