Mask rows of a 2D array that contain masked values. This function is a shortcut to ``mask_rowcols`` with `axis` equal to 0. See Also -------- mask_rowcols : Mask rows and/or columns of a 2D array. masked_where : Mask where a condition is met. Examples --------
(a, axis=np._NoValue)
| 1044 | |
| 1045 | |
| 1046 | def mask_rows(a, axis=np._NoValue): |
| 1047 | """ |
| 1048 | Mask rows of a 2D array that contain masked values. |
| 1049 | |
| 1050 | This function is a shortcut to ``mask_rowcols`` with `axis` equal to 0. |
| 1051 | |
| 1052 | See Also |
| 1053 | -------- |
| 1054 | mask_rowcols : Mask rows and/or columns of a 2D array. |
| 1055 | masked_where : Mask where a condition is met. |
| 1056 | |
| 1057 | Examples |
| 1058 | -------- |
| 1059 | >>> import numpy.ma as ma |
| 1060 | >>> a = np.zeros((3, 3), dtype=int) |
| 1061 | >>> a[1, 1] = 1 |
| 1062 | >>> a |
| 1063 | array([[0, 0, 0], |
| 1064 | [0, 1, 0], |
| 1065 | [0, 0, 0]]) |
| 1066 | >>> a = ma.masked_equal(a, 1) |
| 1067 | >>> a |
| 1068 | masked_array( |
| 1069 | data=[[0, 0, 0], |
| 1070 | [0, --, 0], |
| 1071 | [0, 0, 0]], |
| 1072 | mask=[[False, False, False], |
| 1073 | [False, True, False], |
| 1074 | [False, False, False]], |
| 1075 | fill_value=1) |
| 1076 | |
| 1077 | >>> ma.mask_rows(a) |
| 1078 | masked_array( |
| 1079 | data=[[0, 0, 0], |
| 1080 | [--, --, --], |
| 1081 | [0, 0, 0]], |
| 1082 | mask=[[False, False, False], |
| 1083 | [ True, True, True], |
| 1084 | [False, False, False]], |
| 1085 | fill_value=1) |
| 1086 | |
| 1087 | """ |
| 1088 | if axis is not np._NoValue: |
| 1089 | # remove the axis argument when this deprecation expires |
| 1090 | # NumPy 1.18.0, 2019-11-28 |
| 1091 | warnings.warn( |
| 1092 | "The axis argument has always been ignored, in future passing it " |
| 1093 | "will raise TypeError", DeprecationWarning, stacklevel=2) |
| 1094 | return mask_rowcols(a, 0) |
| 1095 | |
| 1096 | |
| 1097 | def mask_cols(a, axis=np._NoValue): |
no test coverage detected