# numpy.amin¶

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

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. amin : ndarray A new array or a scalar array with the result.

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
```

numpy.digitize

numpy.amax