numpy.ma.mask_rowcols

numpy.ma.mask_rowcols(a, axis=None) [source]

Mask rows and/or columns of a 2D array that contain masked values.

Mask whole rows and/or columns of a 2D array that contain masked values. The masking behavior is selected using the axis parameter.

• If axis is None, rows and columns are masked.
• If axis is 0, only rows are masked.
• If axis is 1 or -1, only columns are masked.

Parameters:	a : array_like, MaskedArray The array to mask. If not a MaskedArray instance (or if no array elements are masked). The result is a MaskedArray with mask set to nomask (False). Must be a 2D array. axis : int, optional Axis along which to perform the operation. If None, applies to a flattened version of the array.
Returns:	a : MaskedArray A modified version of the input array, masked depending on the value of the axis parameter.
Raises:	NotImplementedError If input array a is not 2D.

See also

mask_rows

Mask rows of a 2D array that contain masked values.

mask_cols

Mask cols of a 2D array that contain masked values.

masked_where

Mask where a condition is met.

Notes

The input array’s mask is modified by this function.

Examples

>>> import numpy.ma as ma
>>> a = np.zeros((3, 3), dtype=int)
>>> a[1, 1] = 1
>>> a
array([[0, 0, 0],
       [0, 1, 0],
       [0, 0, 0]])
>>> a = ma.masked_equal(a, 1)
>>> a
masked_array(data =
 [[0 0 0]
 [0 -- 0]
 [0 0 0]],
      mask =
 [[False False False]
 [False  True False]
 [False False False]],
      fill_value=999999)
>>> ma.mask_rowcols(a)
masked_array(data =
 [[0 -- 0]
 [-- -- --]
 [0 -- 0]],
      mask =
 [[False  True False]
 [ True  True  True]
 [False  True False]],
      fill_value=999999)