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
# 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 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
# 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) |
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
