numpy.ma.masked_array.put¶

method

ma.masked_array.put(indices, values, mode='raise')[source]¶

Set storage-indexed locations to corresponding values.

Sets self._data.flat[n] = values[n] for each n in indices. If values is shorter than indices then it will repeat. If values has some masked values, the initial mask is updated in consequence, else the corresponding values are unmasked.

Parameters

indices1-D array_like: Target indices, interpreted as integers.
valuesarray_like: Values to place in self._data copy at target indices.
mode{‘raise’, ‘wrap’, ‘clip’}, optional: Specifies how out-of-bounds indices will behave. ‘raise’ : raise an error. ‘wrap’ : wrap around. ‘clip’ : clip to the range.

Notes

values can be a scalar or length 1 array.

Examples

>>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
>>> x
masked_array(
  data=[[1, --, 3],
        [--, 5, --],
        [7, --, 9]],
  mask=[[False,  True, False],
        [ True, False,  True],
        [False,  True, False]],
  fill_value=999999)
>>> x.put([0,4,8],[10,20,30])
>>> x
masked_array(
  data=[[10, --, 3],
        [--, 20, --],
        [7, --, 30]],
  mask=[[False,  True, False],
        [ True, False,  True],
        [False,  True, False]],
  fill_value=999999)

>>> x.put(4,999)
>>> x
masked_array(
  data=[[10, --, 3],
        [--, 999, --],
        [7, --, 30]],
  mask=[[False,  True, False],
        [ True, False,  True],
        [False,  True, False]],
  fill_value=999999)