numpy.ma.mask_rowcols

numpy.ma.mask_rowcols(a, axis=None)

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=1)
>>> ma.mask_rowcols(a)
masked_array(
  data=[[0, --, 0],
        [--, --, --],
        [0, --, 0]],
  mask=[[False,  True, False],
        [ True,  True,  True],
        [False,  True, False]],
  fill_value=1)