numpy

numpy初探：

import random

import numpy as np
t1 = np.array([1,2,3])
print(t1)
print(type(t1))

t2 = np.array(range(10),dtype='i2')
print(t2)
print(type(t2))
print(t2.dtype)

t3 = t2.astype(dtype='i1')
print(t3)
print(t3.dtype)

t4 = np.arange(10)
print(t4)


t5 = np.array( [round(random.random() , 3) for i in range(10)] )
print(t5)

进阶：

import random
import numpy as np

t1 = np.array([[1,2,3],[4,5,6]])
print(t1.shape)

t1.reshape(3,2)
print(t1)
print(t1.shape)

print(t1.reshape(3,2))#这种有return的函数，一般不对原数组做改变，extend等方法是例外
print(t1.reshape(3,2).shape)

print(t1.shape[0] * t1.shape[1])
print(t1.flatten())#转换成一维
print(t1.flatten().shape[0])

print(t1+2)#广播机制
print(t1*2)

print(t1 + t1)

t2 = np.array([[[1,2,3],[4,5,6],[7,8,9]],
               [[1,2,3],[4,5,6],[7,8,9]],
               [[1,2,3],[4,5,6],[7,8,9]]])
t3 = np.array([[0,1,2],[3,4,5],[7,8,9]])
print(t2 - t3)

t2 = np.array([[[1,2],[4,5],[7,8]],
               [[1,2],[4,5],[7,8]],
               [[1,2],[4,5],[7,8]]])
t3 = np.array([[0],[1],[2]])
print("此之谓广播机制，尾号相同或者为1即可运算")
print(t2.shape)
print(t3.shape)
print(t2-t3)

索引与切片：