numpy的形状

Python 数据分析 之numpy的形状

—b站 python数据分析(黑马程序员)

1.查看数组的形状,shape

t1 = np.arange(12)
print(t1.shape)

t2 = np.array([[1, 2, 3, 4, 5],
               [2, 3, 4, 5, 6]])
print(t2.shape)

(12,)
(2, 5)

Process finished with exit code 0

调用shape返回一个类型为元组的对象,元组的第一个值代表数组的行,第二个值代表数组的列
特别注意: 这里的一维数组形状为(12,)而不是(12,1)或(1,12)

2.修改数组的形状,reshape

reshape中放的参数为数据的类型,可以写reshape(3,4)也可以写reshape((3,4))
并且reshape函数有返回值,不改变原来的t1

t3 = t1.reshape((3, 4))
# t3 = t1.reshape(3, 4)
print(t3)
t4 = t1.reshape(2, 3, 2) #三维
# t4 = t1.reshape((2, 3, 2))
print(t4)

print(t1)

t3:
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
 
t4:
[[[ 0  1]
  [ 2  3]
  [ 4  5]]

 [[ 6  7]
  [ 8  9]
  [10 11]]]
  
t1:
 [ 0  1  2  3  4  5  6  7  8  9 10 11]
 
Process finished with exit code 0

3.将多维数组修改为一维

flatten()函数的运用

t5 = t4.flatten()
print(t5)  # [ 0  1  2  3  4  5  6  7  8  9 10 11]
t6 = t4.reshape(t4.shape[0] * t4.shape[1] * t4.shape[2], )
print(t6)  # [ 0  1  2  3  4  5  6  7  8  9 10 11]