python numpy amin amax

1. np.amax

np.amax(a) # Maximum of the flattened array>>> np.amax(a, axis=0) # 这个的用法是，把每个元素都取最大值，最后组成一个数组>>> np.amax(a, axis=1) # 这个的用法是把每一个元素中的最大值找到，然后把这些最大值找出来组成一个数组

2. np.amin

numpy. amin ( a,  axis=None,  out=None,  keepdims= ) [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. 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. 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. Seedoc.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 input array. If the default value is passed, then keepdims will not be passed through to the amin method of sub-classes of ndarray, however any non-default value will be. If the sub-classes sum method does not implement keepdims any exceptions will be raised.

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.

nanmax, maximum, fmax

>>> 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