numpy.ma.masked_array.mini

method

masked_array.mini(self, axis=None)

Return the array minimum along the specified axis.

Deprecated since version 1.13.0: This function is identical to both:

• self.min(keepdims=True, axis=axis).squeeze(axis=axis)
• np.ma.minimum.reduce(self, axis=axis)

Typically though, self.min(axis=axis) is sufficient.

Parameters:

axis : int, optional

The axis along which to find the minima. Default is None, in which case the minimum value in the whole array is returned.

Returns:

min : scalar or MaskedArray

If axis is None, the result is a scalar. Otherwise, if axis is given and the array is at least 2-D, the result is a masked array with dimension one smaller than the array on which mini is called.

Examples

>>> x = np.ma.array(np.arange(6), mask=[0 ,1, 0, 0, 0 ,1]).reshape(3, 2)
>>> x
masked_array(
  data=[[0, --],
        [2, 3],
        [4, --]],
  mask=[[False,  True],
        [False, False],
        [False,  True]],
  fill_value=999999)
>>> x.mini()
masked_array(data=0,
             mask=False,
       fill_value=999999)
>>> x.mini(axis=0)
masked_array(data=[0, 3],
             mask=[False, False],
       fill_value=999999)
>>> x.mini(axis=1)
masked_array(data=[0, 2, 4],
             mask=[False, False, False],
       fill_value=999999)

There is a small difference between mini and min:

>>> x[:,1].mini(axis=0)
masked_array(data=3,
             mask=False,
       fill_value=999999)
>>> x[:,1].min(axis=0)
3