Python实现把多维数组展开成DataFrame
如下所示：
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]
'''
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