numpy.atleast_3d() in Python

numpy.atleast_3d() function is used when we want to Convert inputs to arrays with at least three dimension. Scalar, 1 and 2 dimensional inputs are converted to 3-dimensional arrays, whilst higher-dimensional inputs are preserved.

Input includes scalar, lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays.

Syntax : numpy.atleast_3d(*arrays)

Parameters :
arrays1, arrays2, … : [array_like] One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have three or more dimensions are preserved.

Return : An array, or list of arrays, each with arr.ndim >= 3. Copies are avoided where possible, and views with three or more dimensions are returned. For example, a 1-D array of shape (N, ) becomes a view of shape (1, N, 1), and a 2-D array of shape (M, N) becomes a view of shape (M, N, 1).

Code #1 : Working

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# Python program explaining
# numpy.atleast_3d() function
   
import numpy as geek
in_num = 10
   
print ("Input  number : ", in_num)
    
     
out_arr = geek.atleast_3d(in_num)
print ("output 3d array from input number : ", out_arr) 

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Output :

Input  number :  10
output 3d array from input number :  [[[10]]]

 
Code #2 : Working

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# Python program explaining
# numpy.atleast_3d() function
  
import numpy as geek
  
my_list = [[2, 6, 10], 
          [8, 12, 16]]
   
print ("Input  list : ", my_list)
    
out_arr = geek.atleast_3d(my_list) 
print ("output array : ", out_arr) 

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Output :

Input  list :  [[2, 6, 10], [8, 12, 16]]
output array :  [[[ 2]
  [ 6]
  [10]]

 [[ 8]
  [12]
  [16]]]

 
Code #3 : Working

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# Python program explaining
# numpy.atleast_3d() function
# when inputs are in high dimension
  
import numpy as geek
  
in_arr = geek.arange(16).reshape(1, 4, 4)
print ("Input  array :\n ", in_arr)
  
out_arr = geek.atleast_3d(in_arr)
print ("output  array :\n ", out_arr)
print(in_arr is out_arr)

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Output :

Input  array :
  [[[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]
  [12 13 14 15]]]
output  array :
  [[[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]
  [12 13 14 15]]]
True


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