Numpy MaskedArray.transpose () Function | python



numpy.MaskedArray.transpose() is used to resize the masked array.

Syntax: numpy.ma.transpose(axis)

Parameters:
axis: [list of ints, optional ] By default, reverse the dimensions, otherwise permute the axes according to the values ​​given.

Return: [ndarray] Resultant array with its axes permuted ..

Code # 1:

# Python program explaining
# numpy.MaskedArray.transpose () method

 
# importing numy as a geek
# and the numpy.ma module as a ma

import numpy as geek 

import numpy.ma as ma 

 
# create input array

in_arr = geek.array ([[ 1 , 2 ], [ 3 , - 1 ], [ 5 , - 3 ]])

print ( "Input array:" , in_arr) 

 
# Now we create a masked mac siv.
# invalidating the post.

mask_arr = ma.masked_array (in_arr, mask = [[ 1 , 0 ], [ 0 , 1 ], [ 0 , 0 ]]) 

print ( "Masked array:" , mask_arr) 

 
# applying the MaskedArray.transpose methods
# into the masked array

out_arr = mask_arr.transpose () 

print ( "Output transposed masked array:" , out_arr) 

Output:

 Input array: [[1 2] [3 -1] [5 -3]] Masked array: [[- 2] [3 -] [5 -3]] Output transposed masked array: [[- 3 5] 

Code # 2:

# Python program explaining
# numpy.MaskedArray.transpose () method

 
# import numy as a geek
# and the numpy.ma module as a ma

import numpy as geek 

import numpy.ma as ma 

  
# create input array

in_arr = geek.array ([[[ 2e8 , 3e - 5 ]], [[ - 45.0 , 2e5 ]]])

print ( "Input array:" , in_arr)

 
# Now we create a masked array.
# invalidating one entry.

mask_arr = ma.masked_array (in_arr, mask = [[[ 1 , 0 ]], [[ 0 , 0 ]]]) 

print ( "3D Masked array:" , mask_arr) 

 
# applying MaskedArray.transpose methods
# to masked array

out_arr = mask_arr.transpose ( ) 

print ( "Output transposed masked array: " , out_arr)

Output:

 Input array: [[[2.0e + 08 3.0e-05]] [[-4.5e + 01 2.0e + 05]]] 3D Masked array: [[[- 3e- 05]] [[-45.0 200000.0]]] Output transposed masked array: [[[- -45.0]] [[3e-05 200000.0]]]