Python array concatenation, combination, connection

Transferred from: Https://www.douban.com/note/518335786/?type=like

============ changing the dimension of an array ==================
Known reshape function can have a one-dimensional array to form a multidimensional array
Ravel function to flatten an array
B.ravel ()
The flatten () function can also achieve the same function
Difference: Ravel only provides view views, while flatten allocates memory storage

Reshaping: Setting a dimension with a meta-ancestor
>>> b.shape= (4,2,3)
>>> b
Array ([[0, 1, 2],
[3, 4, 5],

[6, 7, 8],
[9, 10, 11],

[12, 13, 14],
[15, 16, 17],

[18, 19, 20],
[21, 22, 23]])




Transpose:
>>> b
Array ([0, 1],
[2, 3])
>>> B.transpose ()
Array ([0, 2],
[1, 3])

A combination of ============= arrays ==============
>>> A
Array ([0, 1, 2],
[3, 4, 5],
[6, 7, 8])
>>> B = a*2
>>> b
Array ([0, 2, 4],
[6, 8, 10],
[12, 14, 16])

1. Horizontal Combination
>>> Np.hstack ((b))
Array ([0, 1, 2, 0, 2, 4],
[3, 4, 5, 6, 8, 10],
[6, 7, 8, 12, 14, 16]
>>> Np.concatenate ((A, B), Axis=1)
Array ([0, 1, 2, 0, 2, 4],
[3, 4, 5, 6, 8, 10],
[6, 7, 8, 12, 14, 16]

2. Vertical combination
>>> Np.vstack ((b))
Array ([0, 1, 2],
[3, 4, 5],
[6, 7, 8],
[0, 2, 4],
[6, 8, 10],
[12, 14, 16])
>>> Np.concatenate ((A, B), axis=0)
Array ([0, 1, 2],
[3, 4, 5],
[6, 7, 8],
[0, 2, 4],
[6, 8, 10],
[12, 14, 16])

3. Depth combination: Along the longitudinal axis direction
>>> Np.dstack ((b))
Array ([[0, 0],
[1, 2],
[2, 4],

[3, 6],
[4, 8],
[5, 10],

[6, 12],
[7, 14],
[8, 16]])

4. Column Combination Column_stack ()
One-dimensional arrays: combination by column direction
Two-dimensional arrays: Same as Hstack

5. Line Combination Row_stack ()
Think of arrays: Group by row direction
Two-dimensional arrays: Same as Vstack

6.== to compare two arrays
>>> a==b
Array ([True, False, false],
[False, False, false],
[False, False, false], Dtype=bool)
#True那个因为都是0啊

================== the segmentation of an array ===============
>>> A
Array ([0, 1, 2],
[3, 4, 5],
[6, 7, 8])
>>> B = a*2
>>> b
Array ([0, 2, 4],
[6, 8, 10],
[12, 14, 16])

1. Horizontal segmentation (not vertical division??? ）
>>> Np.hsplit (a,3)
[Array ([0],
[3],
[6]),
Array ([1],
[4],
[7]),
Array ([2],
[5],
[8]]
Split (A,3,axis=1) to achieve the same goal

2. Vertical segmentation
>>> Np.vsplit (a,3)
[Array ([0, 1, 2]), Array ([3, 4, 5]), Array ([6, 7, 8])]

Split (a,3,axis=0) to achieve the same goal

3. Deep segmentation
A three-dimensional array:::
>>> d = np.arange (reshape) (3,3,3)
>>> D
Array ([[0, 1, 2],
[3, 4, 5],
[6, 7, 8],

[9, 10, 11],
[12, 13, 14],
[15, 16, 17],

[18, 19, 20],
[21, 22, 23],
[24, 25, 26]])

Deep segmentation (i.e. split in the direction of depth)
Note: Dsplite only works on arrays of more than 3 dimensions
Raise ValueError (' dsplit works on arrays of 3 or more dimensions ')
Valueerror:dsplit only works on arrays of 3 or more dimensions

>>> Np.dsplit (d,3)
[Array ([[0],
[3],
[6],

[9],
[12],
[15],

[18],
[21],
[[[1]]), Array (
[4],
[7],

[10],
[13],
[16],

[19],
[22],
[+]]), Array ([[2],
[5],
[8],

[11],
[14],
[17],

[20],
[23],
[26]])

=================== the properties of an array =================
>>> A.shape #数组维度
(3, 3)
>>> A.dtype #元素类型
Dtype (' int32 ')
>>> a.size #数组元素个数
9
>>> a.itemsize #元素占用字节数
4
>>> a.nbytes #整个数组占用存储空间 =itemsize*size
36
>>> A.T #转置 =transpose
Array ([0, 3, 6],
[1, 4, 7],
[2, 5, 8])

Flat property
......

Python array concatenation, combination, connection