1. What is NumPy
Very simply, NumPy is a scientific computing library of Python that provides the function of matrix operations, which are commonly used with scipy and matplotlib. In fact, the list already provides a representation similar to a matrix, but NumPy provides us with more functions. If contacted Matlab, Scilab, then numpy very good start. In the following code example, the NumPy is always imported first:
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>>> Import NumPy as NP
>>> Print Np.version.version
1.6.2
2, multidimensional array
The type of multidimensional array is: Numpy.ndarray.
Using the Numpy.array method
To produce a one-dimensional array of parameters as a list or tuple variable:
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>>> print Np.array ([1,2,3,4])
[1 2 3 4]
>>> Print Np.array ((1.2,2,3,4))
[1.2 2. 3.4. ]
>>> Print Type (Np.array (1.2,2,3,4))
<type ' Numpy.ndarray ' >
To produce a two-dimensional array of elements in a list or tuple variable:
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>>> print Np.array ([[[1,2],[3,4]])
[[1 2]
[3 4]]
When generating an array, you can specify the data type, such as Numpy.int32, Numpy.int16, and Numpy.float64:
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>>> Print Np.array ((1.2,2,3,4), Dtype=np.int32)
[1 2 3 4]
using the Numpy.arange method
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>>> print Np.arange (15)
[0 1 2 3 4 5 6 7 8 9 10 11 12 13-14]
>>> Print Type (Np.arange (15))
<type ' Numpy.ndarray ' >
>>> print Np.arange. Reshape (3,5)
[[0 1 2 3 4]
[5 6 7 8 9]
[10 11 12 13 14]]
>>> Print type (np.arange. Reshape (3,5))
<type ' Numpy.ndarray ' >
using the Numpy.linspace method
For example, generate 9 numbers from 1 through 3:
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>>> print Np.linspace (1,3,9)
[1.1.25 1.5 1.75 2. 2.25 2.5 2.75 3. ]
a specific matrix can be constructed using methods such as Numpy.zeros,numpy.ones,numpy.eye
For example:
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>>> Print Np.zeros ((3,4))
[[0.0. 0.0.]
[0.0. 0.0.]
[0.0. 0.0.]]
>>> Print Np.ones ((3,4))
[[1.1. 1.1.]
[1.1. 1.1.]
[1.1. 1.1.]]
>>> print Np.eye (3)
[[1.0. 0.]
[0.1. 0.]
[0.0. 1.]]
Create a three-dimensional array:
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>>> Print Np.zeros ((2,2,2))
[[[0.0.]
[0.0.]]
[[0.0.]
[0.0.]]]
to get the properties of an array:
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>>> a = Np.zeros ((2,2,2))
>>> Print A.ndim #数组的维数
3
>>> Print A.shape #数组每一维的大小
(2, 2, 2)
>>> Print A.size #数组的元素数
8
>>> Print A.dtype #元素类型
Float64
>>> Print A.itemsize #每个元素所占的字节数
8
Array indexes, slices, assigning values
Example:
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>>> a = Np.array ([[[2,3,4],[5,6,7]])
>>> Print a
[[2 3 4]
[5 6 7]]
>>> Print a[1,2]
7
>>> print a[1,:]
[5 6 7]
>>> Print A[1,1:2]
[6]
>>> a[1,:] = [8,9,10]
>>> Print a
[[2 3 4]
[8 9 10]]
using the For Action element
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>>> for X in Np.linspace (1,3,3):
... print X
...
1.0
2.0
3.0
Basic array Operations
First construct array A, B:
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>>> a = Np.ones ((2,2))
>>> B = Np.eye (2)
>>> Print a
[[1.1.]
[1.1.]]
>>> Print B
[[1.0.]
[0.1.]]
Subtraction of the array:
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>>> Print a > 2
[[False false]
[False false]]
>>> Print A+b
[[2.1.]
[1.2.]]
>>> Print A-b
[[0.1.]
[1.0.]]
>>> Print B*2
[[2.0.]
[0.2.]]
>>> Print (a*2) * (b*2)
[[4.0.]
[0.4.]]
>>> Print b/(a*2)
[[0.5 0.]
[0.0.5]]
>>> print (a*2) **4
[[16.16.]
[16.16.]]
Ways to use an array object from the band:
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>>> A.sum ()
4.0
>>> a.sum (axis=0) #计算每一列 (columns in a two-dimensional array similar to matrices)
Array ([2., 2.])
>>> A.min ()
1.0
>>> A.max ()
1.0
Use the method under NumPy:
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>>> Np.sin (a)
Array ([[0.84147098, 0.84147098],
[0.84147098, 0.84147098]])
>>> Np.max (a)
1.0
>>> Np.floor (a)
Array ([[1., 1.],
[1., 1.]]
>>> Np.exp (a)
Array ([[2.71828183, 2.71828183],
[2.71828183, 2.71828183]])
>>> Np.dot (a,a) # #矩阵乘法
Array ([[2., 2.],
[2., 2.]]
Merging arrays
Use the Vstack and Hstack functions under NumPy:
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>>> a = Np.ones ((2,2))
>>> B = Np.eye (2)
>>> Print Np.vstack ((a,b))
[[1.1.]
[1.1.]
[1.0.]
[0.1.]]
>>> Print Np.hstack ((a,b))
[[1.1. 1.0.]
[1.1. 0.1.]]
See if these two functions involve a shallow copy of the problem:
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>>> C = np.hstack ((a,b))
>>> Print C
[[1.1. 1.0.]
[1.1. 0.1.]]
>>> a[1,1] = 5
>>> b[1,1] = 5
>>> Print C
[[1.1. 1.0.]
[1.1. 0.1.]]
As you can see, the change of elements in A and B does not affect C.
Deep Copy Array
The array object has a method of shallow copy and deep copy, but generally uses more deep copies:
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>>> a = Np.ones ((2,2))
>>> B = A
>>> B is a
True
>>> C = a.copy () #深拷贝
>>> C is a
False
Basic matrix Operations
Transpose:
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>>> a = Np.array ([[[1,0],[2,3]])
>>> Print a
[[1 0]
[2 3]]
>>> Print A.transpose ()
[[1 2]
[0 3]]
Trace:
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There are a number of methods for matrix operations in the NUMPY.LINALG module:
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>>> Import Numpy.linalg as NPLG
Eigenvalues, eigenvectors:
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>>> print Nplg.eig (a)
(Array ([3., 1.]), array ([[0. , 0.70710678],
[1.,-0.70710678]])
3, Matrix
NumPy can also construct matrix objects, which are not discussed here.
