- numpy.min(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)#
Return the minimum of an array or minimum along an axis.
- axisNone or int or tuple of ints, optional
Axis or axes along which to operate. By default, flattened input is used.
New in version 1.7.0.
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.
- outndarray, optional
Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. See Output type determination for more details.
- keepdimsbool, 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 input array.
If the default value is passed, then keepdims will not be passed through to the
minmethod of sub-classes of
ndarray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.
- initialscalar, optional
The maximum value of an output element. Must be present to allow computation on empty slice. See
New in version 1.15.0.
- wherearray_like of bool, optional
Elements to compare for the minimum. See
New in version 1.17.0.
- minndarray or scalar
Minimum of a. If axis is None, the result is a scalar value. If axis is an int, the result is an array of dimension
a.ndim - 1. If axis is a tuple, the result is an array of dimension
a.ndim - len(axis).
The maximum value of an array along a given axis, propagating any NaNs.
The minimum value of an array along a given axis, ignoring any NaNs.
Element-wise minimum of two arrays, propagating any NaNs.
Element-wise minimum of two arrays, ignoring any NaNs.
Return the indices of the minimum values.
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.
minfor element-wise comparison of 2 arrays; when
minimum(a, a)is faster than
>>> a = np.arange(4).reshape((2,2)) >>> a array([[0, 1], [2, 3]]) >>> np.min(a) # Minimum of the flattened array 0 >>> np.min(a, axis=0) # Minima along the first axis array([0, 1]) >>> np.min(a, axis=1) # Minima along the second axis array([0, 2]) >>> np.min(a, where=[False, True], initial=10, axis=0) array([10, 1])
>>> b = np.arange(5, dtype=float) >>> b = np.NaN >>> np.min(b) nan >>> np.min(b, where=~np.isnan(b), initial=10) 0.0 >>> np.nanmin(b) 0.0
>>> np.min([[-50], ], axis=-1, initial=0) array([-50, 0])
Notice that the initial value is used as one of the elements for which the minimum is determined, unlike for the default argument Python’s max function, which is only used for empty iterables.
Notice that this isn’t the same as Python’s
>>> np.min(, initial=5) 5 >>> min(, default=5) 6