NumPy常用方法总结

numpy中的数据类型，ndarray类型，和标准库中的array.array并不一样。

ndarray的创建

>>> import numpy as np
>>> a = np.array([2,3,4])
>>> a
array([2, 3, 4])
>>> a.dtype
dtype('int64')
>>> b = np.array([1.2, 3.5, 5.1])
>>> b.dtype
dtype('float64')

二维的数组

>>> b = np.array([(1.5,2,3), (4,5,6)])
>>> b
array([[ 1.5, 2. , 3. ],
 [ 4. , 5. , 6. ]])

创建时指定类型

>>> c = np.array( [ [1,2], [3,4] ], dtype=complex )
>>> c
array([[ 1.+0.j, 2.+0.j],
 [ 3.+0.j, 4.+0.j]])

创建一些特殊的矩阵

>>> np.zeros( (3,4) )
array([[ 0., 0., 0., 0.],
 [ 0., 0., 0., 0.],
 [ 0., 0., 0., 0.]])
>>> np.ones( (2,3,4), dtype=np.int16 ) # dtype can also be specified
array([[[ 1, 1, 1, 1],
 [ 1, 1, 1, 1],
 [ 1, 1, 1, 1]],
 [[ 1, 1, 1, 1],
 [ 1, 1, 1, 1],
 [ 1, 1, 1, 1]]], dtype=int16)
>>> np.empty( (2,3) ) # uninitialized, output may vary
array([[ 3.73603959e-262, 6.02658058e-154, 6.55490914e-260],
 [ 5.30498948e-313, 3.14673309e-307, 1.00000000e+000]])

创建一些有特定规律的矩阵

>>> np.arange( 10, 30, 5 )
array([10, 15, 20, 25])
>>> np.arange( 0, 2, 0.3 ) # it accepts float arguments
array([ 0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8])
>>> from numpy import pi
>>> np.linspace( 0, 2, 9 ) # 9 numbers from 0 to 2
array([ 0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ])
>>> x = np.linspace( 0, 2*pi, 100 ) # useful to evaluate function at lots of points
>>> f = np.sin(x)

一些基本的运算

加减乘除三角函数逻辑运算

>>> a = np.array( [20,30,40,50] )
>>> b = np.arange( 4 )
>>> b
array([0, 1, 2, 3])
>>> c = a-b
>>> c
array([20, 29, 38, 47])
>>> b**2
array([0, 1, 4, 9])
>>> 10*np.sin(a)
array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854])
>>> a<35
array([ True, True, False, False], dtype=bool)

矩阵运算

matlab中有.* ,./等等

但是在numpy中，如果使用+，-，×，/优先执行的是各个点之间的加减乘除法

如果两个矩阵（方阵）可既以元素之间对于运算，又能执行矩阵运算会优先执行元素之间的运算

>>> import numpy as np
>>> A = np.arange(10,20)
>>> B = np.arange(20,30)
>>> A + B
array([30, 32, 34, 36, 38, 40, 42, 44, 46, 48])
>>> A * B
array([200, 231, 264, 299, 336, 375, 416, 459, 504, 551])
>>> A / B
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
>>> B / A
array([2, 1, 1, 1, 1, 1, 1, 1, 1, 1])

如果需要执行矩阵运算，一般就是矩阵的乘法运算

>>> A = np.array([1,1,1,1])
>>> B = np.array([2,2,2,2])
>>> A.reshape(2,2)
array([[1, 1],
 [1, 1]])
>>> B.reshape(2,2)
array([[2, 2],
 [2, 2]])
>>> A * B
array([2, 2, 2, 2])
>>> np.dot(A,B)
8
>>> A.dot(B)
8

一些常用的全局函数

>>> B = np.arange(3)
>>> B
array([0, 1, 2])
>>> np.exp(B)
array([ 1. , 2.71828183, 7.3890561 ])
>>> np.sqrt(B)
array([ 0. , 1. , 1.41421356])
>>> C = np.array([2., -1., 4.])
>>> np.add(B, C)
array([ 2., 0., 6.])

矩阵的索引分片遍历

>>> a = np.arange(10)**3
>>> a
array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729])
>>> a[2]
8
>>> a[2:5]
array([ 8, 27, 64])
>>> a[:6:2] = -1000 # equivalent to a[0:6:2] = -1000; from start to position 6, exclusive, set every 2nd element to -1000
>>> a
array([-1000, 1, -1000, 27, -1000, 125, 216, 343, 512, 729])
>>> a[ : :-1] # reversed a
array([ 729, 512, 343, 216, 125, -1000, 27, -1000, 1, -1000])
>>> for i in a:
... print(i**(1/3.))
...
nan
1.0
nan
3.0
nan
5.0
6.0
7.0
8.0
9.0

矩阵的遍历

>>> import numpy as np
>>> b = np.arange(16).reshape(4, 4)
>>> for row in b:
... print(row)
... 
[0 1 2 3]
[4 5 6 7]
[ 8 9 10 11]
[12 13 14 15]
>>> for node in b.flat:
... print(node)
... 
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

矩阵的特殊运算

改变矩阵形状--reshape

>>> a = np.floor(10 * np.random.random((3,4)))
>>> a
array([[ 6., 5., 1., 5.],
 [ 5., 5., 8., 9.],
 [ 5., 5., 9., 7.]])
>>> a.ravel()
array([ 6., 5., 1., 5., 5., 5., 8., 9., 5., 5., 9., 7.])
>>> a
array([[ 6., 5., 1., 5.],
 [ 5., 5., 8., 9.],
 [ 5., 5., 9., 7.]])

resize和reshape的区别

resize会改变原来的矩阵，reshape并不会

>>> a
array([[ 6., 5., 1., 5.],
 [ 5., 5., 8., 9.],
 [ 5., 5., 9., 7.]])
>>> a.reshape(2,-1)
array([[ 6., 5., 1., 5., 5., 5.],
 [ 8., 9., 5., 5., 9., 7.]])
>>> a
array([[ 6., 5., 1., 5.],
 [ 5., 5., 8., 9.],
 [ 5., 5., 9., 7.]])
>>> a.resize(2,6)
>>> a
array([[ 6., 5., 1., 5., 5., 5.],
 [ 8., 9., 5., 5., 9., 7.]])

矩阵的合并

>>> a = np.floor(10*np.random.random((2,2)))
>>> a
array([[ 8., 8.],
 [ 0., 0.]])
>>> b = np.floor(10*np.random.random((2,2)))
>>> b
array([[ 1., 8.],
 [ 0., 4.]])
>>> np.vstack((a,b))
array([[ 8., 8.],
 [ 0., 0.],
 [ 1., 8.],
 [ 0., 4.]])
>>> np.hstack((a,b))
array([[ 8., 8., 1., 8.],
 [ 0., 0., 0., 4.]])