numpy.atleast_3d() in Python

Last Updated : 28 Nov, 2018

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

Python

# 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)

Output :

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

Code #2 : Working

Python

# 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)

Output :

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

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

Code #3 : Working