Bias and Variance in Machine Learning

Numpy MaskedArray asanyarray() method | Python

Last Updated : 16 Nov, 2021

numpy.ma.asanyarray() function is used when we want to convert input to a masked array, conserving subclasses.
If arr is a subclass of MaskedArray, its class is conserved. No copy is performed if the input is already an ndarray.

Syntax : numpy.ma.asanyarray(arr, dtype=None)

Parameters :
arr : [array_like] Input data, in any form that can be converted to a masked array.
dtype : [data-type, optional] By default, the data-type is inferred from the input data.
order : Whether to use row-major (C-style) or column-major (Fortran-style) memory representation. Defaults to ‘C’.

Return : [MaskedArray] Masked array interpretation of arr.

Code #1 :

# Python program explaining 
# numpy.ma.asanyarray() function 
import numpy as geek 
my_list = [1, 4, 8, 7, 2, 5] 
  
print ("Input list : ", my_list) 
  
  
out_arr = geek.ma.asanyarray(my_list) 
print ("output array from input list : ", out_arr)  

Output :

Input list :  [1, 4, 8, 7, 2, 5]
output array from input list :  [1 4 8 7 2 5]

Code #2 :

# Python program explaining 
# numpy.ma.asanyarray() function 
  
import numpy as geek 
  
my_tuple = ([1, 4, 8], [7, 2, 5]) 
  
print ("Input tuple : ", my_tuple) 
  
out_arr = geek.ma.asanyarray(my_tuple)  
print ("output array from input tuple : ", out_arr)  

Output :

Input tuple :  ([1, 4, 8], [7, 2, 5])
output array from input tuple :  [[1 4 8]
 [7 2 5]]

Bias and Variance in Machine Learning

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