numpy.amin

numpy.amin(a, axis=None, out=None, keepdims=False)

Return the minimum of an array or minimum along an axis.

Parameters :

a : array_like

Input data.

axis : int, optional

Axis along which to operate. By default a flattened input is used.

out : ndarray, optional

Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. See doc.ufuncs (Section “Output arguments”) for more details.

keepdims : bool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original arr.

Returns :

amin : ndarray

A new array or a scalar array with the result.

See also

nanmin

nan values are ignored instead of being propagated

fmin

same behavior as the C99 fmin function

argmin

Return the indices of the minimum values.

amax, nanmax, fmax

Notes

NaN values are propagated, that is if at least one item is nan, the corresponding min value will be nan as well. To ignore NaN values (matlab behavior), please use nanmin.

Examples

>>> a = np.arange(4).reshape((2,2))
>>> a
array([[0, 1],
       [2, 3]])
>>> np.amin(a)           # Minimum of the flattened array
0
>>> np.amin(a, axis=0)         # Minima along the first axis
array([0, 1])
>>> np.amin(a, axis=1)         # Minima along the second axis
array([0, 2])

>>> b = np.arange(5, dtype=np.float)
>>> b[2] = np.NaN
>>> np.amin(b)
nan
>>> np.nanmin(b)
0.0