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Python numpy.append()用法及代码示例


关于:
numpy.append(array,values,axis = None):沿提到的轴在数组的末尾附加值
参数:

array   : [array_like]Input array. 
values  : [array_like]values to be added in the arr. Values should be 
     shaped so that arr[...,obj,...] = values. If the axis is defined values can be of any
     shape as it will be flattened before use.
axis    : Axis along which we want to insert the values. By default, array
     is flattened.    

返回:

An copy of array with values being appended at the end as per the mentioned object
along a given axis. 

代码1:追加数组


# Python Program illustrating 
# numpy.append() 
  
import numpy as geek 
  
#Working on 1D 
arr1 = geek.arange(5) 
print("1D arr1 : ", arr1) 
print("Shape : ", arr1.shape) 
  
  
arr2 = geek.arange(8, 12) 
print("\n1D arr2 : ", arr2) 
print("Shape : ", arr2.shape) 
  
  
# appending the arrays 
arr3 = geek.append(arr1, arr2) 
print("\nAppended arr3 : ", arr3)

输出:

1D arr1 :  [0 1 2 3 4]
Shape :  (5,)

1D arr2 :  [ 8  9 10 11]
Shape :  (4,)

Appended arr3 :  [ 0  1  2  3  4  8  9 10 11]

代码2:使用轴

# Python Program illustrating 
# numpy.append() 
  
import numpy as geek 
  
#Working on 1D 
arr1 = geek.arange(8).reshape(2, 4) 
print("2D arr1 : \n", arr1) 
print("Shape : ", arr1.shape) 
  
  
arr2 = geek.arange(8, 16).reshape(2, 4) 
print("\n2D arr2 : \n", arr2) 
print("Shape : ", arr2.shape) 
  
  
# appending the arrays 
arr3 = geek.append(arr1, arr2) 
print("\nAppended arr3 by flattened : ", arr3) 
  
# appending the arrays with axis = 0 
arr3 = geek.append(arr1, arr2, axis = 0) 
print("\nAppended arr3 with axis 0 : \n", arr3) 
  
# appending the arrays with axis = 1 
arr3 = geek.append(arr1, arr2, axis = 1) 
print("\nAppended arr3 with axis 1 : \n", arr3)

输出:

2D arr1 : 
 [[0 1 2 3]
 [4 5 6 7]]
Shape :  (2, 4)

2D arr2 : 
 [[ 8  9 10 11]
 [12 13 14 15]]
Shape :  (2, 4)

Appended arr3 by flattened :  [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]

Appended arr3 with axis 0 : 
 [[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]
 [12 13 14 15]]

Appended arr3 with axis 1 : 
 [[ 0  1  2  3  8  9 10 11]
 [ 4  5  6  7 12 13 14 15]]


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