Python Numpy

一些Numpy常用的概念和方法

1.数组

# 1D Array
a = np.array([0, 1, 2, 3, 4])
b = np.array((0, 1, 2, 3, 4))
c = np.arange(5)
d = np.linspace(0, 2*np.pi, 5) #在0到2*np.pi之间平均生成5个数

print(a) # >>>[0 1 2 3 4]
print(b) # >>>[0 1 2 3 4]
print(c) # >>>[0 1 2 3 4]
print(d) # >>>[ 0.          1.57079633  3.14159265  4.71238898  6.28318531]
print(a[3]) # >>>3

下面是二维数组

# MD Array,
a = np.array([[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
[26, 27, 28 ,29, 30],
[31, 32, 33, 34, 35]])

print(a[2,4]) # >>>25

2.数组切片

print(a[0, 1:4]) # >>>[12 13 14]
print(a[1:4, 0]) # >>>[16 21 26]
print(a[::2,::2]) # >>>[[11 13 15]
#     [21 23 25]
#     [31 33 35]]
print(a[:, 1]) # >>>[12 17 22 27 32]

这幅图很好的解释了切片操作

3.数组操作

a = np.arange(25)
a = a.reshape((5, 5))

b = np.array([10, 62, 1, 14, 2, 56, 79, 2, 1, 45,
4, 92, 5, 55, 63, 43, 35, 6, 53, 24,
56, 3, 56, 44, 78])
b = b.reshape((5,5))

print(a + b) #两个数组对应元素进行+-*/
print(a - b)
print(a * b)
print(a / b)
print(a ** 2)
print(a < b) print(a > b)#返回布尔值

print(a.dot(b))#点积

a = np.arange(10)

print(a.sum())
print(a.min())
print(a.max())

a = np.arange(0, 100, 10)
b = a[:5]
c = a[a >= 50]
print(b) # >>>[ 0 10 20 30 40]
print(c) # >>>[50 60 70 80 90]

这里具体说一下reshape

numpy.reshape(a, newshape, order='C')#a就是要被reshape的数组，newshape是新数组形状int
or tuple of ints.

例子：

>>> a = np.arange(6).reshape((3, 2))
>>> a
array([[0, 1],
[2, 3],
[4, 5]])

>>> a = np.array([[1,2,3], [4,5,6]])
>>> np.reshape(a, 6)
array([1, 2, 3, 4, 5, 6])
>>> np.reshape(a, 6, order='F')
array([1, 4, 2, 5, 3, 6])

>>> np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
array([[1, 2],
[3, 4],
[5, 6]])