numpy.ma.masked_all_like() function | Python

numpy.ma.is_masked() function | Python

Last Updated : 05 May, 2020

numpy.ma.is_masked() function determine whether input has masked values & accepts any object as input, but always returns False unless the input is a MaskedArray containing masked values.

Syntax : numpy.ma.is_masked(arr) Parameters : arr : [array_like] Array to check for masked values. Return : [bool] True if arr is a MaskedArray with masked values, False otherwise.

Code #1 :

Python3

# Python program explaining
# numpy.ma.is_masked() function

# importing numpy as geek 
# and numpy.ma module as ma 
import numpy as geek 
import numpy.ma as ma 

arr = ma.masked_equal([0, 1, 2, 0, 3], 0)

gfg = ma.is_masked(arr)


print (gfg)

Output :

True

Code #2 :

Python3

# Python program explaining
# numpy.ma.is_masked() function

# importing numpy as geek 
# and numpy.ma module as ma 
import numpy as geek 
import numpy.ma as ma 

arr = [True, False, True]

# always returns False unless
# the input is a MaskedArray 
gfg = ma.is_masked(arr)

print (gfg)

Output :

False

numpy.ma.masked_all_like() function | Python

sanjoy_62

Improve

Article Tags :

Practice Tags :

Similar Reads

numpy.ma.is_mask() function | Python

numpy.ma.is_mask() function return True if parameter m is a valid, standard mask. This function does not check the contents of the input, only that the type is MaskType. In particular, this function returns False if the mask has a flexible dtype. Syntax : numpy.ma.is_mask(m) Parameter : m : [array_l

numpy.ma.masked_all() function | Python

numpy.ma.masked_all() function return an empty masked array of the given shape and dtype, where all the data are masked. Syntax : numpy.ma.masked_all(shape, dtype) Parameter : shape : [tuple] Shape of the required MaskedArray. dtype : [dtype, optional] Data type of the output. Return : [MaskedArray]

numpy.ma.masked_all_like() function | Python

numpy.ma.masked_all_like() function return an empty masked array of the same shape and dtype as the array arr, where all the data are masked. Syntax : numpy.ma.masked_all_like(arr) Parameter : arr : [ndarray] An array describing the shape and dtype of the required MaskedArray. Return : [MaskedArray]

numpy.ma.mask_or() function | Python

numpy.ma.mask_or() function combine two masks with the logical_or operator. The result may be a view on m1 or m2 if the other is nomask (i.e. False). Syntax : numpy.ma.mask_or(m1, m2, copy = False, shrink = True) Parameters : m1, m2 : [ array_like] Input masks. copy : [bool, optional] If copy is Fal

numpy.ma.make_mask() function | Python

numpy.ma.make_mask() function is used to create a boolean mask from an array. This function can accept any sequence that is convertible to integers, or nomask. It does not require that contents must be 0s and 1s, values of 0 are interpreted as False, everything else as True. Return m as a boolean ma

numpy.ma.clump_masked() function | Python

numpy.ma.clump_masked() function returns a list of slices corresponding to the masked clumps of a 1-D array. Syntax : numpy.ma.clump_masked(arr) Parameters : arr : [ndarray] A one-dimensional masked array. Return : [list of slice] The list of slices, one for each continuous region of masked elements

numpy.ma.masked_values() function | Python

numpy.ma.masked_values() function return a MaskedArray, masked where the data in array arr are approximately equal to value, determined using isclose. The default tolerances for masked_values are the same as those for isclose. Syntax : numpy.ma.masked_values(arr, value, rtol = 1e-05, atol = 1e-08, c

numpy.ma.make_mask_none() function | Python

numpy.ma.make_mask_none() function return a boolean mask of the given shape, filled with False. This function returns a boolean ndarray with all entries False, that can be used in common mask manipulations. If a complex dtype is specified, the type of each field is converted to a boolean type. Synta

numpy.ma.mask_rows() function | Python

In this numpy.ma.mask_rows() function, mask rows of a 2D array that contain masked values. This function is a shortcut to mask_rowcols with axis equal to 0. Syntax : numpy.ma.mask_rows(arr, axis = None) Parameters : arr : [array_like, MaskedArray] The array to mask. The result is a MaskedArray. axis

numpy.ma.mask_cols() function | Python

In thisnumpy.ma.mask_cols() function, mask columns of a 2D array that contain masked values. This function is a shortcut to mask_rowcols with axis equal to 1. Syntax : numpy.ma.mask_cols(arr, axis = None) Parameters : arr : [array_like, MaskedArray] The array to mask. axis : [int, optional] Axis alo