In [1]:

    

import numpy as np


    

In [2]:

    

a = np.arange(24)


    

In [3]:

    

print(a)


    

    




[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]

In [4]:

    

print(a.shape)


    

    




(24,)

In [5]:

    

print(a.ndim)


    

    




1

In [6]:

    

a_4_6 = a.reshape([4, 6])


    

In [7]:

    

print(a_4_6)


    

    




[[ 0  1  2  3  4  5]
 [ 6  7  8  9 10 11]
 [12 13 14 15 16 17]
 [18 19 20 21 22 23]]

In [8]:

    

print(a_4_6.shape)


    

    




(4, 6)

In [9]:

    

print(a_4_6.ndim)


    

    




2

In [10]:

    

a_2_3_4 = a.reshape([2, 3, 4])


    

In [11]:

    

print(a_2_3_4)


    

    




[[[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]]

 [[12 13 14 15]
  [16 17 18 19]
  [20 21 22 23]]]

In [12]:

    

print(a_2_3_4.shape)


    

    




(2, 3, 4)

In [13]:

    

print(a_2_3_4.ndim)


    

    




3

In [14]:

    

# a_5_6 = a.reshape([5, 6])
# ValueError: cannot reshape array of size 24 into shape (5,6)


    

In [15]:

    

print(a.reshape(4, 6))


    

    




[[ 0  1  2  3  4  5]
 [ 6  7  8  9 10 11]
 [12 13 14 15 16 17]
 [18 19 20 21 22 23]]

In [16]:

    

print(a.reshape(2, 3, 4))


    

    




[[[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]]

 [[12 13 14 15]
  [16 17 18 19]
  [20 21 22 23]]]

In [17]:

    

print(np.reshape(a, [4, 6]))


    

    




[[ 0  1  2  3  4  5]
 [ 6  7  8  9 10 11]
 [12 13 14 15 16 17]
 [18 19 20 21 22 23]]

In [18]:

    

print(np.reshape(a, [2, 3, 4]))


    

    




[[[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]]

 [[12 13 14 15]
  [16 17 18 19]
  [20 21 22 23]]]

In [19]:

    

# print(np.reshape(a, [5, 6]))
# ValueError: cannot reshape array of size 24 into shape (5,6)


    

In [20]:

    

print(a.reshape(4, 6))


    

    




[[ 0  1  2  3  4  5]
 [ 6  7  8  9 10 11]
 [12 13 14 15 16 17]
 [18 19 20 21 22 23]]

In [21]:

    

# print(np.reshape(a, 4, 6))
# ValueError: cannot reshape array of size 24 into shape (4,)


    

In [22]:

    

print(a.reshape([4, 6], order='C'))


    

    




[[ 0  1  2  3  4  5]
 [ 6  7  8  9 10 11]
 [12 13 14 15 16 17]
 [18 19 20 21 22 23]]

In [23]:

    

print(a.reshape([4, 6], order='F'))


    

    




[[ 0  4  8 12 16 20]
 [ 1  5  9 13 17 21]
 [ 2  6 10 14 18 22]
 [ 3  7 11 15 19 23]]

In [24]:

    

print(a.reshape([2, 3, 4], order='C'))


    

    




[[[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]]

 [[12 13 14 15]
  [16 17 18 19]
  [20 21 22 23]]]

In [25]:

    

print(a.reshape([2, 3, 4], order='F'))


    

    




[[[ 0  6 12 18]
  [ 2  8 14 20]
  [ 4 10 16 22]]

 [[ 1  7 13 19]
  [ 3  9 15 21]
  [ 5 11 17 23]]]

In [26]:

    

print(np.reshape(a, [4, 6], order='F'))


    

    




[[ 0  4  8 12 16 20]
 [ 1  5  9 13 17 21]
 [ 2  6 10 14 18 22]
 [ 3  7 11 15 19 23]]

In [27]:

    

# print(a.reshape([4, 6], 'F'))
# TypeError: 'list' object cannot be interpreted as an integer


    

In [28]:

    

print(np.reshape(a, [4, 6], 'F'))


    

    




[[ 0  4  8 12 16 20]
 [ 1  5  9 13 17 21]
 [ 2  6 10 14 18 22]
 [ 3  7 11 15 19 23]]

In [29]:

    

print(a.reshape([4, -1]))


    

    




[[ 0  1  2  3  4  5]
 [ 6  7  8  9 10 11]
 [12 13 14 15 16 17]
 [18 19 20 21 22 23]]

In [30]:

    

print(a.reshape([2, -1, 4]))


    

    




[[[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]]

 [[12 13 14 15]
  [16 17 18 19]
  [20 21 22 23]]]

In [31]:

    

# print(a.reshape([2, -1, -1]))
# ValueError: can only specify one unknown dimension


    

In [32]:

    

# print(a.reshape([2, -1, 5]))
# ValueError: cannot reshape array of size 24 into shape (2,newaxis,5)


    

In [33]:

    

a = np.arange(8)
print(a)


    

    




[0 1 2 3 4 5 6 7]

In [34]:

    

a_2_4 = a.reshape([2, 4])
print(a_2_4)


    

    




[[0 1 2 3]
 [4 5 6 7]]

In [35]:

    

print(np.shares_memory(a, a_2_4))


    

    




True

In [36]:

    

a[0] = 100
print(a)


    

    




[100   1   2   3   4   5   6   7]

In [37]:

    

print(a_2_4)


    

    




[[100   1   2   3]
 [  4   5   6   7]]

In [38]:

    

a_2_4[0, 0] = 0
print(a_2_4)


    

    




[[0 1 2 3]
 [4 5 6 7]]

In [39]:

    

print(a)


    

    




[0 1 2 3 4 5 6 7]

In [40]:

    

a_2_4_copy = a.reshape([2, 4]).copy()
print(a_2_4_copy)


    

    




[[0 1 2 3]
 [4 5 6 7]]

In [41]:

    

print(np.shares_memory(a, a_2_4_copy))


    

    




False

In [42]:

    

a[0] = 100
print(a)


    

    




[100   1   2   3   4   5   6   7]

In [43]:

    

print(a_2_4_copy)


    

    




[[0 1 2 3]
 [4 5 6 7]]

In [44]:

    

a_2_4_copy[0, 0] = 200
print(a_2_4_copy)


    

    




[[200   1   2   3]
 [  4   5   6   7]]

In [45]:

    

print(a)


    

    




[100   1   2   3   4   5   6   7]

In [46]:

    

a = np.arange(6).reshape(2, 3)
print(a)


    

    




[[0 1 2]
 [3 4 5]]

In [47]:

    

a_step = a[:, ::2]
print(a_step)


    

    




[[0 2]
 [3 5]]

In [48]:

    

print(a_step.reshape(-1))


    

    




[0 2 3 5]

In [49]:

    

print(np.shares_memory(a_step, a_step.reshape(-1)))


    

    




False

In [50]:

    

np.info(a)


    

    




class:  ndarray
shape:  (2, 3)
strides:  (24, 8)
itemsize:  8
aligned:  True
contiguous:  True
fortran:  False
data pointer: 0x7fb49bf71950
byteorder:  little
byteswap:  False
type: int64

In [51]:

    

np.info(a_step)


    

    




class:  ndarray
shape:  (2, 2)
strides:  (24, 16)
itemsize:  8
aligned:  True
contiguous:  False
fortran:  False
data pointer: 0x7fb49bf71950
byteorder:  little
byteswap:  False
type: int64

In [52]:

    

np.info(a_step.reshape(-1))


    

    




class:  ndarray
shape:  (4,)
strides:  (8,)
itemsize:  8
aligned:  True
contiguous:  True
fortran:  True
data pointer: 0x7fb49e162210
byteorder:  little
byteswap:  False
type: int64

In [53]:

    

a = np.arange(8).reshape(2, 4)
print(a)


    

    




[[0 1 2 3]
 [4 5 6 7]]

In [54]:

    

a_step = a[:, ::2]
print(a_step)


    

    




[[0 2]
 [4 6]]

In [55]:

    

print(a_step.reshape(-1))


    

    




[0 2 4 6]

In [56]:

    

print(np.shares_memory(a_step, a_step.reshape(-1)))


    

    




True

In [57]:

    

np.info(a)


    

    




class:  ndarray
shape:  (2, 4)
strides:  (32, 8)
itemsize:  8
aligned:  True
contiguous:  True
fortran:  False
data pointer: 0x7fb49e0e1c40
byteorder:  little
byteswap:  False
type: int64

In [58]:

    

np.info(a_step)


    

    




class:  ndarray
shape:  (2, 2)
strides:  (32, 16)
itemsize:  8
aligned:  True
contiguous:  False
fortran:  False
data pointer: 0x7fb49e0e1c40
byteorder:  little
byteswap:  False
type: int64

In [59]:

    

np.info(a_step.reshape(-1))


    

    




class:  ndarray
shape:  (4,)
strides:  (16,)
itemsize:  8
aligned:  True
contiguous:  False
fortran:  False
data pointer: 0x7fb49e0e1c40
byteorder:  little
byteswap:  False
type: int64