numpy.vander() function | Python

numpy.vander() function is used to generate a Vandermonde matrix.

Syntax : numpy.vander(arr, N = None, increasing = False)
Parameters :
arr : [ array_like] 1-D input array.
N : [int, optional] Number of columns in the output. If N is not specified, a square array is returned (N = len(x)).
increasing : [bool, optional] Order of the powers of the columns. If True, the powers increase from left to right, if False (the default) they are reversed.
Return : [ndarray] dVandermonde matrix. If increasing is False, the first column is x^(N-1), the second x^(N-2) and so forth. If increasing is True, the columns are x^0, x^1, …, x^(N-1).

Code #1 :

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# Python program explaining
# numpy.vander() function
  
# importing numpy as geek 
import numpy as geek
  
arr = geek.array([1, 2, 3, 4, 5])
  
gfg = geek.vander(arr)
  
print (gfg)

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Output :

[[  1   1   1   1   1]
 [ 16   8   4   2   1]
 [ 81  27   9   3   1]
 [256  64  16   4   1]
 [625 125  25   5   1]]

 
Code #2 :



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# Python program explaining
# numpy.vander() function
  
# importing numpy as geek 
import numpy as geek
  
arr = geek.array([1, 2, 3, 4, 5])
N = 3
  
gfg = geek.vander(arr, N)
  
print (gfg)

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Output :

[[ 1  1  1]
 [ 4  2  1]
 [ 9  3  1]
 [16  4  1]
 [25  5  1]]

 
Code #3 :

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# Python program explaining
# numpy.vander() function
  
# importing numpy as geek 
import numpy as geek
  
arr = geek.array([1, 2, 3, 4, 5])
  
gfg = geek.vander(arr, increasing = True)
  
print (gfg)

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Output :

[[  1   1   1   1   1]
 [  1   2   4   8  16]
 [  1   3   9  27  81]
 [  1   4  16  64 256]
 [  1   5  25 125 625]]



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