文档走一波
Help on function sum in module numpy.core.fromnumeric:
sum(a, axis=None, dtype=None, out=None, keepdims=<class 'numpy._globals._NoValue'>)
Sum of array elements over a given axis.
Parameters
----------
a : array_like
Elements to sum.
axis : None or int or tuple of ints, optional
Axis or axes along which a sum is performed. The default,
axis=None, will sum all of the elements of the input array. If
axis is negative it counts from the last to the first axis.
.. versionadded:: 1.7.0
If axis is a tuple of ints, a sum is performed on all of the axes
specified in the tuple instead of a single axis or all the axes as
before.
dtype : dtype, optional
The type of the returned array and of the accumulator in which the
elements are summed. The dtype of `a` is used by default unless `a`
has an integer dtype of less precision than the default platform
integer. In that case, if `a` is signed then the platform integer
is used while if `a` is unsigned then an unsigned integer of the
same precision as the platform integer is used.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape as the expected output, but the type of the output
values will be cast if necessary.
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 `sum` 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
-------
sum_along_axis : ndarray
An array with the same shape as `a`, with the specified
axis removed. If `a` is a 0-d array, or if `axis` is None, a scalar
is returned. If an output array is specified, a reference to
`out` is returned.
See Also
--------
ndarray.sum : Equivalent method.
cumsum : Cumulative sum of array elements.
trapz : Integration of array values using the composite trapezoidal rule.
mean, average
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.
The sum of an empty array is the neutral element 0:
>>> np.sum([])
0.0
Examples
--------
>>> np.sum([0.5, 1.5])
2.0
>>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
1
>>> np.sum([[0, 1], [0, 5]])
6
>>> np.sum([[0, 1], [0, 5]], axis=0)
array([0, 6])
>>> np.sum([[0, 1], [0, 5]], axis=1)
array([1, 5])
If the accumulator is too small, overflow occurs:
>>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)
-128
所有的参数都是可选的(optional)，默认下都是取None。这里暂时只说明参数axis，其他的以后用到再补充。
看一个例子就懂了
c = array([[[0, 1, 2, 0, 1, 2]],
[[0, 1, 2, 0, 1, 2]]])
print('{0}\n'.format(c.shape))
print('{0}\n'.format(c.sum())) //①
print('{0}\n'.format(c.sum(axis=0))) //②
print('{0}\n'.format(c.sum(axis=1))) //③
print('{0}\n'.format(c.sum(axis=2))) //④
结果
(2, 1, 6)
12
[[0, 2, 4, 0, 2, 4]]
[[0, 1, 2, 0, 1, 2]
[0, 1, 2, 0, 1, 2]]
[[6]
[6]]
①不带参数，数组内的所有元素相加得到
②axis=0，在第一维度上执行相加，我们已经知道这个数组的shape，第一维度对应2个元素，所以是两个元素的相加，即[0, 1, 2, 0, 1, 2]和[0, 1, 2, 0, 1, 2]相加，得到[0, 2, 4, 0, 2, 4]。
③axis=1，在第二维度上执行相加，第二维度对应1个元素，所以只有[0, 1, 2, 0, 1, 2]自己玩加法，自然得到的还是自己咯
④axis=2，在第三维度上执行相加，第三维度对应6个元素，所以是6个元素的相加。
观察到，执行完sum函数后，结果都降且仅降一个维度。
