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Numpy MaskedArray.flatten() function | Python

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numpy.MaskedArray.flatten() function is used to return a copy of the input masked array collapsed into one dimension.

Syntax : numpy.ma.flatten(order='C')

Parameters:
order : [‘C’, ‘F’, ‘A’, ‘K’, optional] Whether to flatten in C (row-major), Fortran (column-major) order, or preserve the C/Fortran ordering from a. The default is ‘C’.

Return : [ ndarray] A copy of the input array, flattened to one dimension.

Code #1 :




# Python program explaining
# numpy.MaskedArray.flatten() method 
    
# importing numpy as geek  
# and numpy.ma module as ma 
import numpy as geek 
import numpy.ma as ma 
    
# creating input array of 2 * 2  
in_arr = geek.array([[10, 20], [-10, 40]]) 
print ("Input array : ", in_arr) 
    
# Now we are creating a masked array 
# by making one entry as invalid.  
mask_arr = ma.masked_array(in_arr, mask =[[ 1, 0], [ 0, 0]]) 
print ("Masked array : ", mask_arr) 
    
# applying MaskedArray.flatten methods to make  
# it a 1D flattened array
out_arr = mask_arr.flatten() 
print ("Output flattened masked array : ", out_arr) 


Output:

Input array :  [[ 10  20]
 [-10  40]]
Masked array :  [[-- 20]
 [-10 40]]
Output flattened masked array :  [-- 20 -10 40]

 

Code #2 :




# Python program explaining
# numpy.MaskedArray.flatten() method 
    
# importing numpy as geek  
# and numpy.ma module as ma 
import numpy as geek 
import numpy.ma as ma 
    
# creating input array 
in_arr = geek.array([[[ 2e8, 3e-5]], [[ -4e-6, 2e5]]])
print ("Input array : ", in_arr) 
    
# Now we are creating a masked array 
# by making one entry as invalid.  
mask_arr = ma.masked_array(in_arr, mask =[[[ 1, 0]], [[ 0, 0]]]) 
print ("Masked array : ", mask_arr) 
    
# applying MaskedArray.flatten methods to make  
# it a 1D masked array
out_arr = mask_arr.flatten(order ='F'
print ("Output flattened masked array : ", out_arr)


Output:

Input array :  [[[ 2.e+08  3.e-05]]

 [[-4.e-06  2.e+05]]]
Masked array :  [[[-- 3e-05]]

 [[-4e-06 200000.0]]]
Output flattened masked array :  [-- -4e-06 3e-05 200000.0]



Last Updated : 03 Oct, 2019
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