Understanding the ndarray and its shape

The best way to think about NumPy arrays is that they consist of two parts, a data buffer which is just a block of raw elements, and a view which describes how to interpret the data buffer. The data buffer is a contiguous block of memory that contains the actual data, and the view is a way of accessing the data in a different way.

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import numpy as np    # (assuming you imported numpy)

a = np.arange(12)
print(a)
# Output: [ 0  1  2  3  4  5  6  7  8  9 10 11]

a is a 1-dimensional array with 12 consecutive integers from 0 to 11.

b = a.reshape(3, 4)
print(b)

This reshapes the same data into a 2-dimensional array with 3 rows and 4 columns:

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

Important notes:

• b is a new view of the same data buffer as a (not a copy) whenever possible. This means:

  b[0, 0] = 99
  print(a[0])   # → 99   (modifying b also modifies a)

NumPy 默认使用 C-order（行优先，row-major） 存储二维数组

a 在内存的表现

Index	Data
0	0
1	1
2	2
3	3
4	4
5	5
6	6
7	7
8	8
9	9
10	10
11	11

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b 在内存的表现

默认行有限

row_index	col_index	Data
0	0	0
0	1	1
0	2	2
0	3	3
1	0	4
1	1	5
1	2	6
1	3	7
2	0	8
2	1	9
2	2	10
2	3	11

b 的 ndarray 对象中的 data 指针指向 a 的存储区域