Mask columns of a 2D array that contain masked values. This function is a shortcut to ``mask_rowcols`` with `axis` equal to 1. 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)
| 1095 | |
| 1096 | |
| 1097 | def mask_cols(a, axis=np._NoValue): |
| 1098 | """ |
| 1099 | Mask columns of a 2D array that contain masked values. |
| 1100 | |
| 1101 | This function is a shortcut to ``mask_rowcols`` with `axis` equal to 1. |
| 1102 | |
| 1103 | See Also |
| 1104 | -------- |
| 1105 | mask_rowcols : Mask rows and/or columns of a 2D array. |
| 1106 | masked_where : Mask where a condition is met. |
| 1107 | |
| 1108 | Examples |
| 1109 | -------- |
| 1110 | >>> import numpy.ma as ma |
| 1111 | >>> a = np.zeros((3, 3), dtype=int) |
| 1112 | >>> a[1, 1] = 1 |
| 1113 | >>> a |
| 1114 | array([[0, 0, 0], |
| 1115 | [0, 1, 0], |
| 1116 | [0, 0, 0]]) |
| 1117 | >>> a = ma.masked_equal(a, 1) |
| 1118 | >>> a |
| 1119 | masked_array( |
| 1120 | data=[[0, 0, 0], |
| 1121 | [0, --, 0], |
| 1122 | [0, 0, 0]], |
| 1123 | mask=[[False, False, False], |
| 1124 | [False, True, False], |
| 1125 | [False, False, False]], |
| 1126 | fill_value=1) |
| 1127 | >>> ma.mask_cols(a) |
| 1128 | masked_array( |
| 1129 | data=[[0, --, 0], |
| 1130 | [0, --, 0], |
| 1131 | [0, --, 0]], |
| 1132 | mask=[[False, True, False], |
| 1133 | [False, True, False], |
| 1134 | [False, True, False]], |
| 1135 | fill_value=1) |
| 1136 | |
| 1137 | """ |
| 1138 | if axis is not np._NoValue: |
| 1139 | # remove the axis argument when this deprecation expires |
| 1140 | # NumPy 1.18.0, 2019-11-28 |
| 1141 | warnings.warn( |
| 1142 | "The axis argument has always been ignored, in future passing it " |
| 1143 | "will raise TypeError", DeprecationWarning, stacklevel=2) |
| 1144 | return mask_rowcols(a, 1) |
| 1145 | |
| 1146 | |
| 1147 | #####-------------------------------------------------------------------------- |
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