python numpy sum函数用法
2015-08-10 22:25
836 查看
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. |
ndarray.sumEquivalent method.cumsumCumulative sum of array elements.trapzIntegration 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
相关文章推荐
- python numpy argsort函数用法
- python __name__用法
- Apriori算法
- 升级MAC OX上的Python到3.4
- 自动化运维工具Ansible之Python API
- machine learning in coding(python):拼接原始数据;生成高次特征
- Python(x,y)科学计算包的安装
- python安装psycopg2
- python基础教程学习笔记 — 准备Windows下开发环境
- python:5、函数2
- Python中的布尔类型
- 实习小记-python中不可哈希对象设置为可哈希对象
- [Python基础]010.os模块(2)
- python:5、函数1
- 《机器学习系统设计》之k-近邻分类算法
- python tile函数用法
- python发送各类邮件的主要方法 smpt
- python连接ftp上传下载
- Python多线程爬取知乎获赞过千的答案链接
- python2/3差异之——字符串差异