Python numpy 转置、逆、去掉一列、按列取出、矩阵拼接、矩阵排序、矩阵相等、np.where，一维转二维

转置

例如：

from numpy import *
import numpy as np
>>> c = [[1,2,5],[4,5,8]]
>>> print c
[[1, 2, 5], [4, 5, 8]]

先mat,然后转置T

>>> print mat(c).T
[[1 4]
 [2 5]
 [5 8]]

或者：先转为array，然后T(最好不用这个)

>>> d = np.array(c)
>>> print d
[[1 2 5] [4 5 8]]
>>> print d.T
[[1 4]
 [2 5]
 [5 8]]

逆：

用mat以后，然后用I

>>> a = [[1,3,4],[2,5,9],[4,9,8]]
>>> print mat(a).I
[[-3.72727273  1.09090909  0.63636364]
 [ 1.81818182 -0.72727273 -0.09090909]
 [-0.18181818  0.27272727 -0.09090909]]

但是这种方法不行：先转为array，然后再I

>>> print np.array(a).I
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: "numpy.ndarray" object has no attribute "I"

numpy的列操作：

numpy类型去掉一列(例子中为倒数第一列)：

cut_data = np.delete(mydata, -1, axis=1)

numpy按类标取出：

dataone = list(d for d in raw_data[:] if d[mark_line] == 0)
datatwo = list(d for d in raw_data[:] if d[mark_line] == 1)
datathree = list(d for d in raw_data[:] if d[mark_line] == 2)

矩阵拼接：

list先转化为list形式，然后用mat转为矩阵，再用 c = np.hstack((a,b))   d = np.vstack((a,b))

>>> a = [[1,2,3],[4,5,6]]
>>> b = [[11,22,33],[44,55,66]]
>>> a_a = mat(a)
>>> b_b = mat(b)
>>> print a_a
[[1 2 3]
 [4 5 6]]
>>> print b_b
[[11 22 33]
 [44 55 66]]
>>> c = np.hstack((a,b))
>>> d = np.vstack((a,b))
>>> print c
[[ 1  2  3 11 22 33]
 [ 4  5  6 44 55 66]]
>>> print d
[[ 1  2  3]
 [ 4  5  6]
 [11 22 33]
 [44 55 66]]

矩阵排序：

list也可以这样做，只是返回值仍然是一个排好序的list

 a = [[4,1,5],[1,2,5]]
>>> c = sorted(a,key = operator.itemgetter(1),reverse = True)
>>> print c
[[1, 2, 5], [4, 1, 5]]

import operator
a = [[4,1,5],[1,2,5]]
>>> b = np.array(a)
>>> print b
[[4 1 5]
 [1 2 5]]
## 注意必须在b前面也加上c变量用于记录位置，否则的话b是不变的
>>> c = sorted(b,key = operator.itemgetter(1),reverse = True)  #按照第二列进行排序，并按高到低排序
[array([1, 2, 5]), array([4, 1, 5])]
>>> sorted(b,key = operator.itemgetter(0),reverse = True)
[array([4, 1, 5]), array([1, 2, 5])]
>>> sorted(b,key = operator.itemgetter(0),reverse = True)
[array([4, 1, 5]), array([1, 2, 5])]
>>> c = sorted(a ,key = operator.itemgetter(1),reverse = True)        # list 也可以排序，但是里面的类型不同
>>> print c
[[1, 2, 5], [4, 1, 5]]

寻找位置：

>>> a = [[1,2,3],[4,5,6],[7,8,9]]
>>> b = np.array(a)
>>> print b
[[1 2 3]
 [4 5 6]
 [7 8 9]]
>>> np.where(b = 5)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: where() takes no keyword arguments
>>> np.where(5)
(array([0]),)
>>> np.where(b == 5)
(array([1]), array([1]))
>>> np.where(b == 6)
(array([1]), array([2]))

判断两个矩阵是不是相等:

注意不能直接用 == 号

>>> a = [[1,2,3],[4,5,6],[7,8,9]]
>>> b = [[1,2,3],[4,5,6],[7,8,9]]
>>> c = np.array(a)
>>> d = np.array(b)
>>> print c
[[1 2 3]
 [4 5 6]
 [7 8 9]]
>>> print d
[[1 2 3]
 [4 5 6]
 [7 8 9]]
>>> if c == d:
...     print "yes"
... 
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
>>> if (c == d).all():
...     print "yes"
... 
yes

若还是报错的话，则使用np.close(a, b)：

>>> a = [array([ 4.90312812,  0.31002876, -3.41898514]), array([ 16.02316243,   1.51557803,  82.28424555])]
>>> b = [array([ 1.57286264,  2.1289384 , -1.57890685]), array([ 10.22050379,   6.02365441,  48.91208021])]
>>> a == b
Traceback (most recent call last):
  File "<input>", line 1, in <module>
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
>>> (a == b).all()
Traceback (most recent call last):
  File "<input>", line 1, in <module>
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
>>> np.allclose(a,b)
False

矩阵的copy问题：

当用copy()的时候相当于另外开辟了一个空间存储这个变量与copy过来的值，否则的话仍然在以前变量的基础上修改！

>>> a = [[1,2,3],[4,5,6],[7,8,9]]
>>> b = np.array(a)
>>> print b
[[1 2 3]
 [4 5 6]
 [7 8 9]]
>>> c = b.copy()
>>> c[1,1] = 100
>>> print c
[[  1   2   3]
 [  4 100   6]
 [  7   8   9]]
>>> print b
[[1 2 3]
 [4 5 6]
 [7 8 9]]
>>> d = b
>>> d[1,1] = 99
>>> print d
[[ 1  2  3]
 [ 4 99  6]
 [ 7  8  9]]
>>> print b
[[ 1  2  3]
 [ 4 99  6]
 [ 7  8  9]]

从numpy中取出数据，可以传入list

In[17]: a = [1,4,5,6,9,6,7]
In[18]: b = [0,1,5]
In[20]: a = np.array(a)
In[21]: a[b]
Out[21]: 
array([1, 4, 6])

numpy一维转二维

例如：对于二维数组而言，(3,1)与(3,)是不同的

>>> a = [[1],[2],[3]]
>>> a = np.array(a)