np.reshape ()
a = np.arange(6).reshape((3, 2))
>>> a
array([[0, 1],
[2, 3],
[4, 5]])
Careful Caterpillar
a = np.arange(6).reshape((3, 2))
>>> a
array([[0, 1],
[2, 3],
[4, 5]])a = np.arange(6)
np.reshape(a, newshape=(1, 6))
""" assuming that v is a numpy array with shape (N, ) """
new_v = v.reshape(-1, 1) # new_v.shape is (N, 1)>>> 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])
>>> np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2
array([[1, 2],
[3, 4],
[5, 6]])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])# Welcome to softhunt.net
# Python Program illustrating
# numpy.reshape() method
import numpy as np
# array = np.arrange(8)
# The 'numpy' module has no attribute 'arrange'
array1 = np.arange(8)
print("Original array : \n", array1)
# shape array with 3 rows and 3 columns
array2 = np.arange(8).reshape(2, 4)
print("\narray reshaped with 2 rows and 4 columns : \n",
array2)
# shape array with 4 rows and 2 columns
array3 = np.arange(8).reshape(4, 2)
print("\narray reshaped with 4 rows and 2 columns : \n",
array3)
# Constructs 3D array
array4 = np.arange(8).reshape(2, 2, 2)
print("\nOriginal array reshaped to 3D : \n",
array4)