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)

>>> a

array([[1],

      [2],

      [3]])

>>> np.shape(a)

(3, 1)

>>> b = a[:,0]

>>> b

array([1, 2, 3])

>>> np.shape(a=b)

(3,)

矩阵包

附录：

>>> print a

[[1, 3, 4], [2, 5, 9], [4, 9, 8]]

>>> print type(a)

<type 'list'>

>>> print np.array(a)

[[1 3 4]

[2 5 9]

[4 9 8]]

>>> print mat(a)

[[1 3 4]

[2 5 9]

[4 9 8]]

>>> print type(a[0])

<type 'list'>

>>> print type(np.array(a)[0])

<type 'numpy.ndarray'>

>>> print type(mat(a)[0])

<class 'numpy.matrixlib.defmatrix.matrix'>

>>> print np.array(a)[0,0]

1

>>> print mat(a)[0,0]