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
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
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
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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