import numpy as np
import pandas as pd

################# 准备数据 #################
a1 = np.arange(1,101)
a3 = a1.reshape((2,5,10))
a3
'''
array([[[ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
  [ 11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
  [ 21, 22, 23, 24, 25, 26, 27, 28, 29, 30],
  [ 31, 32, 33, 34, 35, 36, 37, 38, 39, 40],
  [ 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]],  
  [[ 51, 52, 53, 54, 55, 56, 57, 58, 59, 60],
  [ 61, 62, 63, 64, 65, 66, 67, 68, 69, 70],
  [ 71, 72, 73, 74, 75, 76, 77, 78, 79, 80],
  [ 81, 82, 83, 84, 85, 86, 87, 88, 89, 90],
  [ 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]]])
'''

################# 准备标签 #################
# 第 1 维的标签
index1 = pd.Series(np.arange(1,11))
index1 = index1.astype(str)
index1 = 'A'+index1
index1
'''
0  A1
1  A2
2  A3
3  A4
4  A5
5  A6
6  A7
7  A8
8  A9
9 A10
'''

# 第 2 维的标签
index2 = pd.Series(np.arange(1,6))
index2 = index2.astype(str)
index2 = 'B'+index2
index2
'''
0 B1
1 B2
2 B3
3 B4
4 B5
'''

# 第 3 维的标签
index3 = pd.Series(np.arange(1,3))
index3 = index3.astype(str)
index3 = 'C'+index3
index3
'''
0 C1
1 C2
'''

################# 展开数据 #################
# 把三维数组展开
value = a3.flatten()
value = pd.Series(value)
value.name = 'value'
value
'''
0  1
1  2
2  3
  ... 
97  98
98  99
99 100
Name: value, Length: 100, dtype: int64
'''

################# 展开标签 #################
import itertools

# index的笛卡尔乘积。注意：高维在前，低维在后
prod = itertools.product(index3, index2, index1 )
# 转换为DataFrame
prod = pd.DataFrame([x for x in prod])
prod.columns = ['C', 'B', 'A']
prod.T
'''
 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 C C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 ... C2 C2 C2 C2 C2 C2 C2 
B B1 B1 B1 B1 B1 B1 B1 B1 B1 B1 ... B5 B5 B5 B5 B5 B5 B5 
A A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 ... A1 A2 A3 A4 A5 A6 A7 
 97 98 99 
C C2 C2 C2 
B B5 B5 B5 
A A8 A9 A10 
[3 rows x 100 columns]
'''

################# 最终数据 #################
# 合并成一个DataFrame
pd.concat([prod, value], axis=1)
'''
  C B A value
0 C1 B1 A1  1
1 C1 B1 A2  2
2 C1 B1 A3  3
3 C1 B1 A4  4
4 C1 B1 A5  5
5 C1 B1 A6  6
6 C1 B1 A7  7
7 C1 B1 A8  8
8 C1 B1 A9  9
9 C1 B1 A10  10
10 C1 B2 A1  11
11 C1 B2 A2  12
12 C1 B2 A3  13
13 C1 B2 A4  14
14 C1 B2 A5  15
15 C1 B2 A6  16
16 C1 B2 A7  17
17 C1 B2 A8  18
18 C1 B2 A9  19
19 C1 B2 A10  20
20 C1 B3 A1  21
21 C1 B3 A2  22
22 C1 B3 A3  23
23 C1 B3 A4  24
24 C1 B3 A5  25
25 C1 B3 A6  26
26 C1 B3 A7  27
27 C1 B3 A8  28
28 C1 B3 A9  29
29 C1 B3 A10  30
.. .. .. ... ...
70 C2 B3 A1  71
71 C2 B3 A2  72
72 C2 B3 A3  73
73 C2 B3 A4  74
74 C2 B3 A5  75
75 C2 B3 A6  76
76 C2 B3 A7  77
77 C2 B3 A8  78
78 C2 B3 A9  79
79 C2 B3 A10  80
80 C2 B4 A1  81
81 C2 B4 A2  82
82 C2 B4 A3  83
83 C2 B4 A4  84
84 C2 B4 A5  85
85 C2 B4 A6  86
86 C2 B4 A7  87
87 C2 B4 A8  88
88 C2 B4 A9  89
89 C2 B4 A10  90
90 C2 B5 A1  91
91 C2 B5 A2  92
92 C2 B5 A3  93
93 C2 B5 A4  94
94 C2 B5 A5  95
95 C2 B5 A6  96
96 C2 B5 A7  97
97 C2 B5 A8  98
98 C2 B5 A9  99
99 C2 B5 A10 100
[100 rows x 4 columns]
'''