numpy array merge and matrix splice implementation

Numpy provides concatenate, append, stack class (including hsatck, vstack, dstack, row_stack, column_stack), r_ and c_ and other classes and functions for array stitching operations.

The characteristics and differences of the various functions are labeled below:

concatenate	The axis parameter is provided to specify the direction of the splice
append	The default is to ravel and then splice into one-dimensional arrays, but you can also specify axis
stack	The axis parameter is provided for generating new dimensions
hstack	Horizontal splicing, splicing the columns along the rows
vstack	Vertical splicing, splicing rows along columns
dstack	Splice along the third axis (depth direction)
column_stack	Horizontal splicing, splicing the columns along the rows
row_stack	Vertical splicing, splicing rows along columns
r_	Vertical splicing, splicing rows along columns
c_	Horizontal splicing, splicing the columns along the rows

Direct Merge

Combines two one-dimensional arrays into a two-dimensional array:

import torch
import numpy as np
import  as plt
a = (0,15,0.1)
b = 1.088 * a + 0.638 + () * 10

print(,)
points = ([a,b])
print()


(150,) (150,)
(2, 150)

append splice

append(arr, values, axis=None)

arr	Duplication of the arrays to be merged (the special home page is a duplicate, so it takes a lot more memory)
values	Used to merge in the values copied from the above array. These values must match the shape of arr if the following parameter axis is specified (everything outside of shape[axis] is equal), otherwise there is no requirement.
axis	Axes to be merged.

>>> import numpy as np
>>> ar1 = ([[1,2,3], [4,5,6]])
>>> ar2 = ([[7,8,9], [11,12,13]])

>>> (ar1, ar2) # ravel flattens and splices, so the return value is a 1-dimensional array.
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13])

>>> (ar1, ar2, axis=0)  # Splice along the first axis, in this case the direction of the rows
array([[ 1, 2, 3],
  [ 4, 5, 6],
  [ 7, 8, 9],
  [11, 12, 13]])

>>> (ar1, ar2, axis=1)  # Splice along the second axis, in this case the direction of the columns
array([[ 1, 2, 3, 7, 8, 9],
  [ 4, 5, 6, 11, 12, 13]])

concatenate splice

concatenate(a_tuple, axis=0, out=None)

a_tuple:	The arrays to be merged are given in the form of tuples
axis	The axis to be merged, default is 0

 >>> import numpy as np
>>> ar1 = ([[1,2,3], [4,5,6]])
>>> ar2 = ([[7,8,9], [11,12,13]])
>>> ar1
array([[1, 2, 3],
  [4, 5, 6]])
>>> ar2
array([[ 7, 8, 9],
  [11, 12, 13]])

>>> ((ar1, ar2)) # Here the first axis (axis 0) is the row direction
array([[ 1, 2, 3],
  [ 4, 5, 6],
  [ 7, 8, 9],
  [11, 12, 13]])

>>> ((ar1, ar2),axis=1) # Here's a splice along the second axis, the column direction #
array([[ 1, 2, 3, 7, 8, 9],
  [ 4, 5, 6, 11, 12, 13]])

>>> ar3 = ([[14,15,16]]) # 2-dimensional array with shape (1, 3)
>>> ((ar1, ar3)) # General concatenate operation of the array shape needs to be consistent, of course, if the array in the splicing axis direction of the size is not the same, you can also complete the
>>> ((ar1, ar3)) # ar3 is consistent in the axis1 direction although the length is not consistent in the axis0 direction, so it can be spliced along axis0
array([[ 1, 2, 3],
  [ 4, 5, 6],
  [14, 15, 16]])
>>> ((ar1, ar3), axis=1) # ar3 and ar1 are not the same length in axis0 direction, so an error is reported

hstack

>>> ((ar1,ar2)) # Horizontal splicing, splicing of columns along the rows
array([[ 1, 2, 3, 7, 8, 9],
  [ 4, 5, 6, 11, 12, 13]])

vstack

>>> ((ar1,ar2)) # Vertical splicing, splicing rows along columns
array([[ 1, 2, 3],
  [ 4, 5, 6],
  [ 7, 8, 9],
  [11, 12, 13]])

vstack

>>> ((ar1,ar2)) # For a 2-dimensional array, splice along the third axis (depth direction), which is equivalent to stack(axis=-1)
array([[[ 1, 7],
  [ 2, 8],
  [ 3, 9]],
  [[ 4, 11],
  [ 5, 12],
  [ 6, 13]]])

column_stack and row_stack

>>> np.column_stack((ar1,ar2)) # Horizontal splicing, splicing of columns along the rows
array([[ 1, 2, 3, 7, 8, 9],
  [ 4, 5, 6, 11, 12, 13]])

>>> np.row_stack((ar1,ar2)) # Vertical splicing, splicing rows along columns
array([[ 1, 2, 3],
  [ 4, 5, 6],
  [ 7, 8, 9],
  [11, 12, 13]])

np.r_ and np.c_

Commonly used to quickly generate ndarray data

>>> np.r_[ar1,ar2]  # Vertical splicing, splicing rows along columns
array([[ 1, 2, 3],
  [ 4, 5, 6],
  [ 7, 8, 9],
  [11, 12, 13]])
 
>>> np.c_[ar1,ar2] # Horizontal splicing, splicing of columns along the rows
array([[ 1, 2, 3, 7, 8, 9],
  [ 4, 5, 6, 11, 12, 13]])

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