numpy.ma.mask_rowcols#

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:

aarray_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.
axisint, optional: Axis along which to perform the operation. If None, applies to a flattened version of the array.

Returns:

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