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 containmasked values. The masking behavior is selected using theaxis parameter.

  • Ifaxis is None, rowsand columns are masked.

  • Ifaxis is 0, only rows are masked.

  • Ifaxis is 1 or -1, only columns are masked.

Parameters:
aarray_like, MaskedArray

The array to mask. If not a MaskedArray instance (or if no arrayelements are masked), the result is a MaskedArray withmask settonomask (False). Must be a 2D array.

axisint, optional

Axis along which to perform the operation. If None, applies to aflattened version of the array.

Returns:
aMaskedArray

A modified version of the input array, masked depending on the valueof theaxis parameter.

Raises:
NotImplementedError

If input arraya 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

>>>importnumpyasnp>>>a=np.zeros((3,3),dtype=int)>>>a[1,1]=1>>>aarray([[0, 0, 0],       [0, 1, 0],       [0, 0, 0]])>>>a=np.ma.masked_equal(a,1)>>>amasked_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)
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