numpy

np.sum()

http://blog.youkuaiyun.com/ikerpeng/article/details/17026011

我们平时用的sum应该是默认的axis=0 就是普通的相加 （对不起，写的不好，看下面的）

 

而当加入axis=1以后就是将一个矩阵的每一行向量相加

 

例如：

import numpy as np

np.sum([[0,1,2],[2,1,3]],axis=1)的结果就是：array（[3,6]）

 

希望可以帮到你 呵呵

 

Sorry，以前学习阶段写东西比较随意，现在补充完善一下：

1. python 自己的sum（）

输入的参数首先是［］

 

[python]  view plain  copy

 

1. >>> sum([0,1,2])  
2. 3  
3. >>> sum([0,1,2],3)  
4. 6  
5. >>> sum([0,1,2],[3,2,1])  
6. Traceback (most recent call last):  
7.   File "<stdin>", line 1, in <module>  
8. TypeError: can only concatenate list (not "int") to list  

 

2.python的 numpy当中

现在对于数据的处理更多的还是numpy。没有axis参数表示全部相加，axis＝0表示按列相加，axis＝1表示按照行的方向相加

 

[python]  view plain  copy

 

1. 1. >>> import numpy as np  
   2. >>> a=np.sum([[0,1,2],[2,1,3]])  
   3. >>> a  
   4. 9  
   5. >>> a.shape  
   6. ()  
   7. >>> a=np.sum([[0,1,2],[2,1,3]],axis=0)  
   8. >>> a  
   9. array([2, 2, 5])  
   10. >>> a.shape  
   11. (3,)  
   12. >>> a=np.sum([[0,1,2],[2,1,3]],axis=1)  
   13. >>> a  
   14. array([3, 6])  
   15. >>> a.shape  
   16. (2,)  

http://www.cnblogs.com/100thMountain/p/4719488.html

numpy.sum

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

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) is perform a sum over all the dimensions of the input array. axis may be negative, in which case it counts from the last to the first axis. New in version 1.7.0. If this is a tuple of ints, a sum is performed on multiple axes, 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. By default, the dtype of a is used. An exception is when a has an integer type with less precision than the default platform integer. In that case, the default platform integer is used instead. out : ndarray, optional Array into which the output is placed. By default, a new array is created. If out is given, it must be of the appropriate shape (the shape of a with axis removed, i.e., numpy.delete(a.shape, axis)). Its type is preserved. 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:

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.

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) #axis=0是按列求和 array([0, 6]) >>> np.sum([[0, 1], [0, 5]], axis=1) #axis=1 是按行求和 array([1, 5]) 

If the accumulator is too small, overflow occurs:

>>>

>>> np.ones(128, dtype=np.int8).sum(dtype=np.int8) -128

 

转载于:https://www.cnblogs.com/JZ-Ser/p/7593797.html