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Python實(shí)現(xiàn)把多維數(shù)組展開(kāi)成DataFrame

更新時(shí)間：2019年11月30日 09:22:31 作者：lyghe

今天小編就為大家分享一篇Python實(shí)現(xiàn)把多維數(shù)組展開(kāi)成DataFrame，具有很好的參考價(jià)值，希望對(duì)大家有所幫助。一起跟隨小編過(guò)來(lái)看看吧

如下所示：

import numpy as np
import pandas as pd

################# 準(zhǔn)備數(shù)據(jù) #################
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]]])
'''

################# 準(zhǔn)備標(biāo)簽 #################
# 第 1 維的標(biāo)簽
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 維的標(biāo)簽
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 維的標(biāo)簽
index3 = pd.Series(np.arange(1,3))
index3 = index3.astype(str)
index3 = 'C'+index3
index3
'''
0 C1
1 C2
'''

################# 展開(kāi)數(shù)據(jù) #################
# 把三維數(shù)組展開(kāi)
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
'''

################# 展開(kāi)標(biāo)簽 #################
import itertools

# index的笛卡爾乘積。注意：高維在前，低維在后
prod = itertools.product(index3, index2, index1 )
# 轉(zhuǎn)換為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]
'''

################# 最終數(shù)據(jù) #################
# 合并成一個(gè)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]
'''

以上這篇Python實(shí)現(xiàn)把多維數(shù)組展開(kāi)成DataFrame就是小編分享給大家的全部?jī)?nèi)容了，希望能給大家一個(gè)參考，也希望大家多多支持腳本之家。

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