>>> np.reshape(a, (2, 3)) # C-like index ordering
array([[0, 1, 2],
[3, 4, 5]])
>>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
array([[0, 1, 2],
[3, 4, 5]])
>>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
array([[0, 4, 3],
[2, 1, 5]])
>>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
array([[0, 4, 3],
[2, 1, 5]])
np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2
array([[1, 2],
[3, 4],
[5, 6]])
z.reshape(-1,1)
array([[ 1],
[ 2],
[ 3],
[ 4],
[ 5],
[ 6],
[ 7],
[ 8],
[ 9],
[10],
[11],
[12]])
a = np.array([[1,2,3], [4,5,6]])
>>> np.reshape(a, 6)
array([1, 2, 3, 4, 5, 6])
>>> np.reshape(a, 6, order='F')
array([1, 4, 2, 5, 3, 6])
# 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(-1, 1)
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 2 rows and 4 columns :
",
array3)
# Constructs 3D array
array4 = geek.arange(8).reshape(2, 2, 2)
print("
Original array reshaped to 3D :
",
array4)