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
>>> 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)