numpy.vander() function | Python

numpy.mask_indices() function | Python

Last Updated : 22 Apr, 2020

numpy.mask_indices() function return the indices to access (n, n) arrays, given a masking function.

Syntax : numpy.mask_indices(n, mask_func, k = 0)
Parameters :
n : [int] The returned indices will be valid to access arrays of shape (n, n).
mask_func : [callable] A function whose call signature is similar to that of triu, tril.
k : [scalar] An optional argument which is passed through to mask_func.
Return : [tuple of arrays] The n arrays of indices corresponding to the locations where mask_func(np.ones((n, n)), k) is True.

Code #1 :

# Python program explaining 
# numpy.mask_indices() function 
  
# importing numpy as geek  
import numpy as geek 
  
gfg = geek.mask_indices(3, geek.triu) 
  
print (gfg) 

Output :

(array([0, 0, 0, 1, 1, 2]), array([0, 1, 2, 1, 2, 2]))

Code #2 :

# Python program explaining 
# numpy.mask_indices() function 
  
# importing numpy as geek  
import numpy as geek 
  
gfg = geek.mask_indices(3, geek.triu, 1) 
  
print (gfg) 

Output :

(array([0, 0, 1]), array([1, 2, 2]))

numpy.vander() 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.is_masked() function | Python

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 :

numpy.tril_indices() function | Python

numpy.tril_indices() function return the indices for the lower-triangle of an (n, m) array. Syntax : numpy.tril_indices(n, k = 0, m = None) Parameters : n : [int] The row dimension of the arrays for which the returned indices will be valid. k : [int, optional] Diagonal offset. m : [int, optional] Th

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.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.fix_invalid() function | Python

numpy.ma.fix_invalid() function return input with invalid data masked and replaced by a fill value. Where invalid data means values of nan, inf, etc. Syntax : numpy.ma.fix_invalid(arr, mask = False, copy = True, fill_value = None) Parameter : arr : [array_like] Input array. mask : [sequence, optiona

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

Numpy MaskedArray.masked_inside() function | Python

In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arra

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.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]