Python numpy.append()用法及代碼示例

關於：
numpy.append(array，values，axis = None)：沿提到的軸在數組的末尾附加值
參數：

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.

代碼1：追加數組

# 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)

輸出：

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]

代碼2：使用軸

# 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)

輸出：

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]]

相關用法

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