# 1.shape
# One-dimensional array
a = [1,2,3,4,5,6,7,8,9,10,11,12 ]
b = np.array (a)
print (b.shape [0 ]) # outermost elements 12
# Print (b.shape [. 1]) times the outer #, # IndexError: OUT index tuple of Range
# Why not a.shape [0], because the 'list' object has no attribute 'shape'
# Two-dimensional array
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]) # 3 outermost layers, 4 inner ones
#output：
12
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]
3 4
# 2.reshape
a = [1,2,3,4,5,6,7,8,9,10,11,12 ]
b = np.array (a) .reshape (2,6) # 2 lines 6 Column
print (b)
print (a)
b = np.array (a) .reshape (2,3,2) # 2 rows and 3 columns of two matrices
print (b)
print (np.array (a))
# reshape The newly generated array and the original array share the same memory, no matter which changes will affect each other.
# Output:
[[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) is interpreted as: -1 row == no row; 1 == 1 column, then this is 1 column vector
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)
#Result :
[[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]]
>>>