NumPy for Beginners: Reshaping NumPy Arrays

gist_numpy_reshape.py

import numpy as np

# one dimensional array
np1 = np.arange(0, 12)
np1

# reshape() method
# modify to 3 x 4 two dimensional array
np1.reshape(3, 4)

# np1 is still a one dimensional array
np1

# reshape can be used to reduce the dimensions as well
# two dimensional array
np2 = np.array([[1, 2, 3], [4, 5, 6]])

# use reshape to make it one dimensional
np2.reshape(6)

# resize() method
# original array
np1

# modify the dimension using the resize() method
np1.resize(3, 4)
np1

# retrieve the dimension of an array using shape() method
np1.shape

# modify the dimension of the array
np1.shape = (4, 3)
np1

# the ravel method
np1.ravel()

# the flatten method
np1.flatten()

# the dimension of np1 is not modified
np1

# difference between reshape(), ravel() and flatten()
np1

# create 3 new arrays 
np_reshape = np1.reshape(3, 4)
np_ravel = np1.ravel()
np_flatten = np1.flatten()

# np1 is not modified
np1

# make changes to np_reshape
# set the element in the third row/column to 25
np_reshape[2, 2] = 25

# test if the change is reflected in np1
np1

# test if the change is reflected in np_ravel
np_ravel

# test if the change is reflected in np_flatten
np_flatten

# transpose method()
np1.transpose()

# T property
np1.T