關於:
numpy.reshape(array, shape, order = ‘C’):在不更改數組數據的情況下對數組進行整形。
參數:
array : [array_like]Input array shape : [int or tuples of int] e.g. if we are aranging an array with 10 elements then shaping it like numpy.reshape(4, 8) is wrong; we can 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
返回:
Array which is reshaped without changing the data.
# Python Program illustrating
# numpy.reshape() method
import numpy as geek
array = geek.arange(8)
print("Original array : \n", array)
# shape array with 2 rows and 4 columns
array = geek.arange(8).reshape(2, 4)
print("\narray reshaped with 2 rows and 4 columns : \n", array)
# shape array with 2 rows and 4 columns
array = geek.arange(8).reshape(4 ,2)
print("\narray reshaped with 2 rows and 4 columns : \n", array)
# Constructs 3D array
array = geek.arange(8).reshape(2, 2, 2)
print("\nOriginal array reshaped to 3D : \n", array)
輸出:
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 2 rows and 4 columns : [[0 1] [2 3] [4 5] [6 7]] Original array reshaped to 3D : [[[0 1] [2 3]] [[4 5] [6 7]]]
參考文獻:
https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.reshape.html
相關用法
注:本文由純淨天空篩選整理自 numpy.reshape() in Python。非經特殊聲明,原始代碼版權歸原作者所有,本譯文未經允許或授權,請勿轉載或複製。