numpy.ma.mask_rowcols — NumPy v1.15 Manual (original) (raw)
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. |
|---|---|
| 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 of a 2D array that contain masked values.
Mask cols of a 2D array that contain masked values.
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 = 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)