-
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 tonomask
(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=np.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=999999) >>> ma.mask_rowcols(a) masked_array(data = [[0 -- 0] [-- -- --] [0 -- 0]], mask = [[False True False] [ True True True] [False True False]], fill_value=999999)
- If
numpy.ma.mask_rowcols()
2017-01-10 18:15:39
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