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 : None or int or tuple of ints, optional
Axis or axes along which to operate. By default, flattened input is used.
If this is a tuple of ints, the minimum is selected over multiple axes, instead of a single axis or all the axes as before.
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 or scalar
Minimum of a. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension a.ndim - 1.
See also
- amax
- The maximum value of an array along a given axis, propagating any NaNs.
- nanmin
- The minimum value of an array along a given axis, ignoring any NaNs.
- minimum
- Element-wise minimum of two arrays, propagating any NaNs.
- fmin
- Element-wise minimum of two arrays, ignoring any NaNs.
- argmin
- Return the indices of the minimum values.
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.
Don’t use amin for element-wise comparison of 2 arrays; when a.shape[0] is 2, minimum(a[0], a[1]) is faster than amin(a, axis=0).
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