1.shape
#1.shape
#一维数组
a = [1,2,3,4,5,6,7,8,9,10,11,12]
b = np.array(a)
print(b.shape[0])#最外层有12个元素
#print(b.shape[1])#次外层，#IndexError: tuple index out of range
#为什么不直接 a.shape[0],因为 'list' object has no attribute 'shape'
#二维数组
a = [[1,2,3,4],[5,6,7,8],[9,10,11,12]]
b = np.array(a)
print(b)
print(b.shape[0],b.shape[1])#最外层3个，里边4个
#output：
12
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]
3 4
2.reshape
#2.reshape
a = [1,2,3,4,5,6,7,8,9,10,11,12]
b = np.array(a).reshape(2,6) #2行6列
print(b)
print(a)
b = np.array(a).reshape(2,3,2) #2行3列的两个矩阵
print(b)
print(np.array(a))
#reshape新生成数组和原数组公用一个内存，不管改变哪个都会互相影响。
#输出：
[[ 1 2 3 4 5 6]
[ 7 8 9 10 11 12]]
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
[[[ 1 2]
[ 3 4]
[ 5 6]]
[[ 7 8]
[ 9 10]
[11 12]]]
[ 1 2 3 4 5 6 7 8 9 10 11 12]
#3.reshape(-1,1) 解释为：-1行 == 没有行；1 == 1列，那么这个就是1个列向量
a = [1,2,3,4,5,6,7,8,9,10,11,12]
b = np.array(a).reshape(-1,1) #12 * 1
print(b)
a = [1,2,3,4,5,6,7,8,9,10,11,12]
b = np.array(a).reshape(-1,2) # 6*2
print(b)
a = [1,2,3,4,5,6,7,8,9,10,11,12]
b = np.array(a).reshape(1,-1) #1 * 12
print(b)
a = [1,2,3,4,5,6,7,8,9,10,11,12]
b = np.array(a).reshape(2,-1) #2 *6
print(b)
#结果：
[[ 1]
[ 2]
[ 3]
[ 4]
[ 5]
[ 6]
[ 7]
[ 8]
[ 9]
[10]
[11]
[12]]
[[ 1 2]
[ 3 4]
[ 5 6]
[ 7 8]
[ 9 10]
[11 12]]
[[ 1 2 3 4 5 6 7 8 9 10 11 12]]
[[ 1 2 3 4 5 6]
[ 7 8 9 10 11 12]]
>>>
3.学这个的时候好像参考了那个网址，有些不记得了，如果有侵犯到您的著作权，立删！
