numpy库应用04

7.Numpy基本的组合运算

1. 数学函数应用（exp-e底数，sqrt-平方根函数）

    import numpy as np//设置宏定义
    B=np.arange(3)
    print (B)//结果[0 1 2]
    print (np.exp(B))//结果：[1. exp数据]
    print (np.sqrt(B))//结果：[0. sqrt数据]
   

2. 数据取整，维数转换

   import numpy as np//设置宏定义
   a=np.floor(10*np.random.random((3,4)))//np.random(3,4)的数据，矩阵3X4
                                        //numpy.floor-向下取整（数据取整数处理）
                                       //numpy.ceil-向上取整
   print (a)//结果:[6. 7. 2. 9]
   [6. 0. 5. 2]
   [9. 0. 9. 6]
   print(“------------”)
   print (a.ravel)//结果:[6. 7. 2. 9. 6. 0. 5. 2. 9. 0. 9. 6]
                  //.ravel-将多维数组降位一维
                 //.flattenh-将多维数组降位一维(优先级高于ravel，并保持原有矩阵)
   print(“------------”)
   a.shape=(6,2)//a:矩阵定义名称 shape：矩阵打印函数
   print (a)//结果：[6. 7]
                   [2. 9]
                   [6. 0]
                   [5. 2]
                   [9. 0]
                   [9. 6]
   print(“------------”)
   print (a,T)//结果:[[6. 2. 6. 5. 9. 9.][7. 9. 0. 2. 0. 6.]]
   

numpy.floor

numpy.floor(x, /, out=None, *, where=True, casting=‘same_kind’, order=‘K’, dtype=None, subok=True[, signature, extobj]) =
Return the floor of the input, element-wise.

The floor of the scalar x is the largest integer i, such that i <= x. It is often denoted as \lfloor x \rfloor.

Parameters:
x : array_like
Input data.
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
where : array_like, optional
Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.
**kwargs
For other keyword-only arguments, see the ufunc docs.
Returns:
y : ndarray or scalar
The floor of each element in x. This is a scalar if x is a scalar.
See also
ceil, trunc, rint

Notes

Some spreadsheet programs calculate the “floor-towards-zero”, in other words floor(-2.5) == -2. NumPy instead uses the definition of floor where floor(-2.5) == -3.

Examples

a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
np.floor(a)
array([-2., -2., -1., 0., 1., 1., 2.])

8.矩阵数据组合

import numpy as np//设置宏定义
a = np.floor(10*np.random.random((2,2)))//数据内容10*（2-2）的随机；数据矩阵2X2
b = np.floor(10*np.random.random((2,2)))//数据内容10*（2-2）的随机；数据矩阵2X2
print (a)//结果:[3. 1]
       //      [0. 1]
print(“------------”)
print (b)//结果:[4. 2]
[9. 2]
print(“------------”)
print (np.hstack((a,b)))//结果:[3. 1. 4. 2]
                       // [0. 1. 9. 2]

.hstack(a,b)-stack针对数据(a,b)叠加。hstack叠加方向行向(h)
.vstack(a,b)-stack针对数据(a,b)叠加。vstack叠加方向纵向(v)

9.矩阵数据拆分组合

import numpy as np//设置宏定义
a = np.floor(10*np.random.random((2,12)))//数据内容10*（2-12）的随机；数据矩阵2X12
print (a)//结果:数据内容10*（2-12）的随机；数据矩阵2X12
print(“------------”)
print (np.hsplit(a,3))//矩阵数据a拆分，拆分数量为3（3等份的矩阵），方向为行
print(“------------”)
print (np.hsplit(a,(3,4)))//矩阵数据a拆分，拆分数量为(3,4)（矩阵第3，4行），方向为行
a = np.floor(10*np.random.random((12,2)))//数据内容10*（12-2）的随机；数据矩阵12X2
print(“------------”)
print (a)//结果:数据内容10*（2-12）的随机；数据矩阵12X2
np.vsplit(a,3)//矩阵数据a拆分，拆分数量为3（3等份的矩阵），方向为纵

.hsplit(a,b)-split针对矩阵数据a拆分，拆分数量为b（b等份的矩阵）。hspli拆方向行向(h)
.hsplit(a,(b,c))-split针对矩阵数据a拆分，拆分数量为(b,c)（矩阵第b，c行）。hspli拆方向行向(h)
.vsplit(a,b)-split针对矩阵数据a拆分，拆分数量为b（b等份的矩阵）。vspli拆方向纵向(v)
.vsplit(a,(b,c))-split针对矩阵数据a拆分，拆分数量为(b,c)（矩阵第b，c列）。hspli拆方向纵向(v)

—未完待续（2018.10.16.21点11分）