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/NumPy 1.17

`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. `a : MaskedArray` A modified version of the input array, masked depending on the value of the `axis` parameter. NotImplementedError If input array `a` is not 2D.

`mask_rows`
`mask_cols`
`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
data=[[0, 0, 0],
[0, --, 0],
[0, 0, 0]],
[False,  True, False],
[False, False, False]],
fill_value=1)