一 、加法贡献度计算
import pandas as pd
import numpy as np
df = pd.read_csv("C:/Users/supaur/Desktop/test.csv")
df
|
date
|
a
|
b
|
c
|
d
|
target
|
|
0
|
2022/10/21
|
40
|
141
|
241
|
50
|
472
|
|
1
|
2022/10/22
|
40
|
120
|
241
|
50
|
451
|
|
2
|
2022/10/23
|
39
|
142
|
239
|
50
|
470
|
|
3
|
2022/10/24
|
40
|
141
|
241
|
50
|
472
|
|
4
|
2022/10/25
|
41
|
60
|
230
|
51
|
382
|
|
5
|
2022/10/26
|
42
|
141
|
241
|
52
|
476
|
|
6
|
2022/10/27
|
43
|
141
|
241
|
53
|
478
|
|
7
|
2022/10/28
|
44
|
141
|
239
|
58
|
482
|
|
8
|
2022/10/29
|
45
|
141
|
238
|
55
|
479
|
|
9
|
2022/10/30
|
46
|
142
|
239
|
56
|
483
|
|
10
|
2022/10/31
|
47
|
143
|
240
|
57
|
487
|
|
11
|
2022/11/1
|
48
|
144
|
241
|
58
|
491
|
|
12
|
2022/11/2
|
49
|
145
|
242
|
59
|
495
|
|
13
|
2022/11/3
|
50
|
146
|
243
|
60
|
499
|
|
14
|
2022/11/4
|
51
|
147
|
244
|
61
|
503
|
|
15
|
2022/11/5
|
52
|
148
|
245
|
62
|
507
|
|
16
|
2022/11/6
|
53
|
149
|
246
|
63
|
511
|
|
17
|
2022/11/7
|
54
|
150
|
247
|
64
|
515
|
# 对df的数值列错位相减
df_diff = df.loc[:,"a":"target"].diff()
df_diff
|
a
|
b
|
c
|
d
|
target
|
|
0
|
NaN
|
NaN
|
NaN
|
NaN
|
NaN
|
|
1
|
0.0
|
-21.0
|
0.0
|
0.0
|
-21.0
|
|
2
|
-1.0
|
22.0
|
-2.0
|
0.0
|
19.0
|
|
3
|
1.0
|
-1.0
|
2.0
|
0.0
|
2.0
|
|
4
|
1.0
|
-81.0
|
-11.0
|
1.0
|
-90.0
|
|
5
|
1.0
|
81.0
|
11.0
|
1.0
|
94.0
|
|
6
|
1.0
|
0.0
|
0.0
|
1.0
|
2.0
|
|
7
|
1.0
|
0.0
|
-2.0
|
5.0
|
4.0
|
|
8
|
1.0
|
0.0
|
-1.0
|
-3.0
|
-3.0
|
|
9
|
1.0
|
1.0
|
1.0
|
1.0
|
4.0
|
|
10
|
1.0
|
1.0
|
1.0
|
1.0
|
4.0
|
|
11
|
1.0
|
1.0
|
1.0
|
1.0
|
4.0
|
|
12
|
1.0
|
1.0
|
1.0
|
1.0
|
4.0
|
|
13
|
1.0
|
1.0
|
1.0
|
1.0
|
4.0
|
|
14
|
1.0
|
1.0
|
1.0
|
1.0
|
4.0
|
|
15
|
1.0
|
1.0
|
1.0
|
1.0
|
4.0
|
|
16
|
1.0
|
1.0
|
1.0
|
1.0
|
4.0
|
|
17
|
1.0
|
1.0
|
1.0
|
1.0
|
4.0
|
# 套用公式
df["a_contribution"] = df_diff["a"]/df_diff["target"]*100
df["b_contribution"] = df_diff["b"]/df_diff["target"]*100
df["c_contribution"] = df_diff["c"]/df_diff["target"]*100
df["d_contribution"] = df_diff["d"]/df_diff["target"]*100
df
|
date
|
a
|
b
|
c
|
d
|
target
|
a_contribution
|
b_contribution
|
c_contribution
|
d_contribution
|
|
0
|
2022/10/21
|
40
|
141
|
241
|
50
|
472
|
NaN
|
NaN
|
NaN
|
NaN
|
|
1
|
2022/10/22
|
40
|
120
|
241
|
50
|
451
|
-0.000000
|
100.000000
|
-0.000000
|
-0.000000
|
|
2
|
2022/10/23
|
39
|
142
|
239
|
50
|
470
|
-5.263158
|
115.789474
|
-10.526316
|
0.000000
|
|
3
|
2022/10/24
|
40
|
141
|
241
|
50
|
472
|
50.000000
|
-50.000000
|
100.000000
|
0.000000
|
|
4
|
2022/10/25
|
41
|
60
|
230
|
51
|
382
|
-1.111111
|
90.000000
|
12.222222
|
-1.111111
|
|
5
|
2022/10/26
|
42
|
141
|
241
|
52
|
476
|
1.063830
|
86.170213
|
11.702128
|
1.063830
|
|
6
|
2022/10/27
|
43
|
141
|
241
|
53
|
478
|
50.000000
|
0.000000
|
0.000000
|
50.000000
|
|
7
|
2022/10/28
|
44
|
141
|
239
|
58
|
482
|
25.000000
|
0.000000
|
-50.000000
|
125.000000
|
|
8
|
2022/10/29
|
45
|
141
|
238
|
55
|
479
|
-33.333333
|
-0.000000
|
33.333333
|
100.000000
|
|
9
|
2022/10/30
|
46
|
142
|
239
|
56
|
483
|
25.000000
|
25.000000
|
25.000000
|
25.000000
|
|
10
|
2022/10/31
|
47
|
143
|
240
|
57
|
487
|
25.000000
|
25.000000
|
25.000000
|
25.000000
|
|
11
|
2022/11/1
|
48
|
144
|
241
|
58
|
491
|
25.000000
|
25.000000
|
25.000000
|
25.000000
|
|
12
|
2022/11/2
|
49
|
145
|
242
|
59
|
495
|
25.000000
|
25.000000
|
25.000000
|
25.000000
|
|
13
|
2022/11/3
|
50
|
146
|
243
|
60
|
499
|
25.000000
|
25.000000
|
25.000000
|
25.000000
|
|
14
|
2022/11/4
|
51
|
147
|
244
|
61
|
503
|
25.000000
|
25.000000
|
25.000000
|
25.000000
|
|
15
|
2022/11/5
|
52
|
148
|
245
|
62
|
507
|
25.000000
|
25.000000
|
25.000000
|
25.000000
|
|
16
|
2022/11/6
|
53
|
149
|
246
|
63
|
511
|
25.000000
|
25.000000
|
25.000000
|
25.000000
|
|
17
|
2022/11/7
|
54
|
150
|
247
|
64
|
515
|
25.000000
|
25.000000
|
25.000000
|
25.000000
|
二 、乘法贡献度计算
df2 = pd.read_excel("C:/Users/supaur/Desktop/乘法.xlsx")
df2
|
date
|
a
|
b
|
target
|
|
0
|
2022-10-21
|
0.283688
|
0.207469
|
0.058856
|
|
1
|
2022-10-22
|
0.333333
|
0.207469
|
0.069156
|
|
2
|
2022-10-23
|
0.274648
|
0.209205
|
0.057458
|
|
3
|
2022-10-24
|
0.283688
|
0.207469
|
0.058856
|
|
4
|
2022-10-25
|
0.683333
|
0.221739
|
0.151522
|
|
5
|
2022-10-26
|
0.297872
|
0.215768
|
0.064271
|
|
6
|
2022-10-27
|
0.304965
|
0.219917
|
0.067067
|
|
7
|
2022-10-28
|
0.312057
|
0.242678
|
0.075729
|
|
8
|
2022-10-29
|
0.319149
|
0.231092
|
0.073753
|
|
9
|
2022-10-30
|
0.323944
|
0.234310
|
0.075903
|
|
10
|
2022-10-31
|
0.328671
|
0.237500
|
0.078059
|
|
11
|
2022-11-01
|
0.333333
|
0.240664
|
0.080221
|
|
12
|
2022-11-02
|
0.337931
|
0.243802
|
0.082388
|
|
13
|
2022-11-03
|
0.342466
|
0.246914
|
0.084559
|
|
14
|
2022-11-04
|
0.346939
|
0.250000
|
0.086735
|
|
15
|
2022-11-05
|
0.351351
|
0.253061
|
0.088913
|
|
16
|
2022-11-06
|
0.355705
|
0.256098
|
0.091095
|
|
17
|
2022-11-07
|
0.360000
|
0.259109
|
0.093279
|
# 位置移动,整体上移一行
df2_shift = df2.shift(-1)
df2_shift
|
date
|
a
|
b
|
target
|
|
0
|
2022-10-22
|
0.333333
|
0.207469
|
0.069156
|
|
1
|
2022-10-23
|
0.274648
|
0.209205
|
0.057458
|
|
2
|
2022-10-24
|
0.283688
|
0.207469
|
0.058856
|
|
3
|
2022-10-25
|
0.683333
|
0.221739
|
0.151522
|
|
4
|
2022-10-26
|
0.297872
|
0.215768
|
0.064271
|
|
5
|
2022-10-27
|
0.304965
|
0.219917
|
0.067067
|
|
6
|
2022-10-28
|
0.312057
|
0.242678
|
0.075729
|
|
7
|
2022-10-29
|
0.319149
|
0.231092
|
0.073753
|
|
8
|
2022-10-30
|
0.323944
|
0.234310
|
0.075903
|
|
9
|
2022-10-31
|
0.328671
|
0.237500
|
0.078059
|
|
10
|
2022-11-01
|
0.333333
|
0.240664
|
0.080221
|
|
11
|
2022-11-02
|
0.337931
|
0.243802
|
0.082388
|
|
12
|
2022-11-03
|
0.342466
|
0.246914
|
0.084559
|
|
13
|
2022-11-04
|
0.346939
|
0.250000
|
0.086735
|
|
14
|
2022-11-05
|
0.351351
|
0.253061
|
0.088913
|
|
15
|
2022-11-06
|
0.355705
|
0.256098
|
0.091095
|
|
16
|
2022-11-07
|
0.360000
|
0.259109
|
0.093279
|
|
17
|
NaT
|
NaN
|
NaN
|
NaN
|
# 对应相除得到a/b
df2_move = df2_shift.iloc[:,1:3].div(df2.iloc[:,1:3],axis=0)
df2_move
|
a
|
b
|
|
0
|
1.175000
|
1.000000
|
|
1
|
0.823944
|
1.008368
|
|
2
|
1.032915
|
0.991701
|
|
3
|
2.408750
|
1.068783
|
|
4
|
0.435911
|
0.973070
|
|
5
|
1.023810
|
1.019231
|
|
6
|
1.023256
|
1.103497
|
|
7
|
1.022727
|
0.952260
|
|
8
|
1.015023
|
1.013922
|
|
9
|
1.014594
|
1.013616
|
|
10
|
1.014184
|
1.013322
|
|
11
|
1.013793
|
1.013038
|
|
12
|
1.013419
|
1.012764
|
|
13
|
1.013061
|
1.012500
|
|
14
|
1.012719
|
1.012245
|
|
15
|
1.012390
|
1.011998
|
|
16
|
1.012075
|
1.011760
|
|
17
|
NaN
|
NaN
|
# 整体下移一行
df2_move.shift(1)
|
a
|
b
|
|
0
|
NaN
|
NaN
|
|
1
|
1.175000
|
1.000000
|
|
2
|
0.823944
|
1.008368
|
|
3
|
1.032915
|
0.991701
|
|
4
|
2.408750
|
1.068783
|
|
5
|
0.435911
|
0.973070
|
|
6
|
1.023810
|
1.019231
|
|
7
|
1.023256
|
1.103497
|
|
8
|
1.022727
|
0.952260
|
|
9
|
1.015023
|
1.013922
|
|
10
|
1.014594
|
1.013616
|
|
11
|
1.014184
|
1.013322
|
|
12
|
1.013793
|
1.013038
|
|
13
|
1.013419
|
1.012764
|
|
14
|
1.013061
|
1.012500
|
|
15
|
1.012719
|
1.012245
|
|
16
|
1.012390
|
1.011998
|
|
17
|
1.012075
|
1.011760
|
# 索引连接
df_merge=pd.merge(df2,df2_move.shift(1),left_index=True,right_index=True,suffixes=("","_move"))
df_merge
|
date
|
a
|
b
|
target
|
a_move
|
b_move
|
|
0
|
2022-10-21
|
0.283688
|
0.207469
|
0.058856
|
NaN
|
NaN
|
|
1
|
2022-10-22
|
0.333333
|
0.207469
|
0.069156
|
1.175000
|
1.000000
|
|
2
|
2022-10-23
|
0.274648
|
0.209205
|
0.057458
|
0.823944
|
1.008368
|
|
3
|
2022-10-24
|
0.283688
|
0.207469
|
0.058856
|
1.032915
|
0.991701
|
|
4
|
2022-10-25
|
0.683333
|
0.221739
|
0.151522
|
2.408750
|
1.068783
|
|
5
|
2022-10-26
|
0.297872
|
0.215768
|
0.064271
|
0.435911
|
0.973070
|
|
6
|
2022-10-27
|
0.304965
|
0.219917
|
0.067067
|
1.023810
|
1.019231
|
|
7
|
2022-10-28
|
0.312057
|
0.242678
|
0.075729
|
1.023256
|
1.103497
|
|
8
|
2022-10-29
|
0.319149
|
0.231092
|
0.073753
|
1.022727
|
0.952260
|
|
9
|
2022-10-30
|
0.323944
|
0.234310
|
0.075903
|
1.015023
|
1.013922
|
|
10
|
2022-10-31
|
0.328671
|
0.237500
|
0.078059
|
1.014594
|
1.013616
|
|
11
|
2022-11-01
|
0.333333
|
0.240664
|
0.080221
|
1.014184
|
1.013322
|
|
12
|
2022-11-02
|
0.337931
|
0.243802
|
0.082388
|
1.013793
|
1.013038
|
|
13
|
2022-11-03
|
0.342466
|
0.246914
|
0.084559
|
1.013419
|
1.012764
|
|
14
|
2022-11-04
|
0.346939
|
0.250000
|
0.086735
|
1.013061
|
1.012500
|
|
15
|
2022-11-05
|
0.351351
|
0.253061
|
0.088913
|
1.012719
|
1.012245
|
|
16
|
2022-11-06
|
0.355705
|
0.256098
|
0.091095
|
1.012390
|
1.011998
|
|
17
|
2022-11-07
|
0.360000
|
0.259109
|
0.093279
|
1.012075
|
1.011760
|
df_merge["con_a"]= 1-df_merge.loc[:,"a_move"]
df_merge["con_b"]= 1-df_merge.loc[:,"b_move"]
df_merge
|
date
|
a
|
b
|
target
|
a_move
|
b_move
|
con_a
|
con_b
|
|
0
|
2022-10-21
|
0.283688
|
0.207469
|
0.058856
|
NaN
|
NaN
|
NaN
|
NaN
|
|
1
|
2022-10-22
|
0.333333
|
0.207469
|
0.069156
|
1.175000
|
1.000000
|
-0.175000
|
0.000000
|
|
2
|
2022-10-23
|
0.274648
|
0.209205
|
0.057458
|
0.823944
|
1.008368
|
0.176056
|
-0.008368
|
|
3
|
2022-10-24
|
0.283688
|
0.207469
|
0.058856
|
1.032915
|
0.991701
|
-0.032915
|
0.008299
|
|
4
|
2022-10-25
|
0.683333
|
0.221739
|
0.151522
|
2.408750
|
1.068783
|
-1.408750
|
-0.068783
|
|
5
|
2022-10-26
|
0.297872
|
0.215768
|
0.064271
|
0.435911
|
0.973070
|
0.564089
|
0.026930
|
|
6
|
2022-10-27
|
0.304965
|
0.219917
|
0.067067
|
1.023810
|
1.019231
|
-0.023810
|
-0.019231
|
|
7
|
2022-10-28
|
0.312057
|
0.242678
|
0.075729
|
1.023256
|
1.103497
|
-0.023256
|
-0.103497
|
|
8
|
2022-10-29
|
0.319149
|
0.231092
|
0.073753
|
1.022727
|
0.952260
|
-0.022727
|
0.047740
|
|
9
|
2022-10-30
|
0.323944
|
0.234310
|
0.075903
|
1.015023
|
1.013922
|
-0.015023
|
-0.013922
|
|
10
|
2022-10-31
|
0.328671
|
0.237500
|
0.078059
|
1.014594
|
1.013616
|
-0.014594
|
-0.013616
|
|
11
|
2022-11-01
|
0.333333
|
0.240664
|
0.080221
|
1.014184
|
1.013322
|
-0.014184
|
-0.013322
|
|
12
|
2022-11-02
|
0.337931
|
0.243802
|
0.082388
|
1.013793
|
1.013038
|
-0.013793
|
-0.013038
|
|
13
|
2022-11-03
|
0.342466
|
0.246914
|
0.084559
|
1.013419
|
1.012764
|
-0.013419
|
-0.012764
|
|
14
|
2022-11-04
|
0.346939
|
0.250000
|
0.086735
|
1.013061
|
1.012500
|
-0.013061
|
-0.012500
|
|
15
|
2022-11-05
|
0.351351
|
0.253061
|
0.088913
|
1.012719
|
1.012245
|
-0.012719
|
-0.012245
|
|
16
|
2022-11-06
|
0.355705
|
0.256098
|
0.091095
|
1.012390
|
1.011998
|
-0.012390
|
-0.011998
|
|
17
|
2022-11-07
|
0.360000
|
0.259109
|
0.093279
|
1.012075
|
1.011760
|
-0.012075
|
-0.011760
|
# 套用公式
df_merge["a_contribution"]=df_merge.loc[:,"con_a"]/(df_merge.loc[:,"con_a"]+df_merge.loc[:,"con_b"])*100
df_merge["b_contribution"]=df_merge.loc[:,"con_b"]/(df_merge.loc[:,"con_a"]+df_merge.loc[:,"con_b"])*100
df_merge
|
date
|
a
|
b
|
target
|
a_move
|
b_move
|
con_a
|
con_b
|
a_contribution
|
b_contribution
|
|
0
|
2022-10-21
|
0.283688
|
0.207469
|
0.058856
|
NaN
|
NaN
|
NaN
|
NaN
|
NaN
|
NaN
|
|
1
|
2022-10-22
|
0.333333
|
0.207469
|
0.069156
|
1.175000
|
1.000000
|
-0.175000
|
0.000000
|
100.000000
|
-0.000000
|
|
2
|
2022-10-23
|
0.274648
|
0.209205
|
0.057458
|
0.823944
|
1.008368
|
0.176056
|
-0.008368
|
104.990336
|
-4.990336
|
|
3
|
2022-10-24
|
0.283688
|
0.207469
|
0.058856
|
1.032915
|
0.991701
|
-0.032915
|
0.008299
|
133.712411
|
-33.712411
|
|
4
|
2022-10-25
|
0.683333
|
0.221739
|
0.151522
|
2.408750
|
1.068783
|
-1.408750
|
-0.068783
|
95.344765
|
4.655235
|
|
5
|
2022-10-26
|
0.297872
|
0.215768
|
0.064271
|
0.435911
|
0.973070
|
0.564089
|
0.026930
|
95.443421
|
4.556579
|
|
6
|
2022-10-27
|
0.304965
|
0.219917
|
0.067067
|
1.023810
|
1.019231
|
-0.023810
|
-0.019231
|
55.319149
|
44.680851
|
|
7
|
2022-10-28
|
0.312057
|
0.242678
|
0.075729
|
1.023256
|
1.103497
|
-0.023256
|
-0.103497
|
18.347335
|
81.652665
|
|
8
|
2022-10-29
|
0.319149
|
0.231092
|
0.073753
|
1.022727
|
0.952260
|
-0.022727
|
0.047740
|
-90.863612
|
190.863612
|
|
9
|
2022-10-30
|
0.323944
|
0.234310
|
0.075903
|
1.015023
|
1.013922
|
-0.015023
|
-0.013922
|
51.903311
|
48.096689
|
|
10
|
2022-10-31
|
0.328671
|
0.237500
|
0.078059
|
1.014594
|
1.013616
|
-0.014594
|
-0.013616
|
51.733471
|
48.266529
|
|
11
|
2022-11-01
|
0.333333
|
0.240664
|
0.080221
|
1.014184
|
1.013322
|
-0.014184
|
-0.013322
|
51.568219
|
48.431781
|
|
12
|
2022-11-02
|
0.337931
|
0.243802
|
0.082388
|
1.013793
|
1.013038
|
-0.013793
|
-0.013038
|
51.407329
|
48.592671
|
|
13
|
2022-11-03
|
0.342466
|
0.246914
|
0.084559
|
1.013419
|
1.012764
|
-0.013419
|
-0.012764
|
51.250589
|
48.749411
|
|
14
|
2022-11-04
|
0.346939
|
0.250000
|
0.086735
|
1.013061
|
1.012500
|
-0.013061
|
-0.012500
|
51.097804
|
48.902196
|
|
15
|
2022-11-05
|
0.351351
|
0.253061
|
0.088913
|
1.012719
|
1.012245
|
-0.012719
|
-0.012245
|
50.948791
|
49.051209
|
|
16
|
2022-11-06
|
0.355705
|
0.256098
|
0.091095
|
1.012390
|
1.011998
|
-0.012390
|
-0.011998
|
50.803379
|
49.196621
|
|
17
|
2022-11-07
|
0.360000
|
0.259109
|
0.093279
|
1.012075
|
1.011760
|
-0.012075
|
-0.011760
|
50.661409
|
49.338591
|
三 、除法贡献度计算
df3 = pd.read_excel("C:/Users/supaur/Desktop/除法.xlsx")
df3
|
date
|
a1
|
a2
|
b1
|
b2
|
c1
|
c2
|
d1
|
d2
|
target
|
|
0
|
2022-10-21
|
684
|
775
|
714
|
975
|
758
|
815
|
654
|
908
|
0.809099
|
|
1
|
2022-10-22
|
794
|
997
|
134
|
653
|
417
|
651
|
804
|
929
|
0.665325
|
|
2
|
2022-10-23
|
388
|
557
|
309
|
743
|
294
|
912
|
714
|
804
|
0.565318
|
|
3
|
2022-10-24
|
413
|
740
|
119
|
674
|
658
|
895
|
657
|
809
|
0.592367
|
|
4
|
2022-10-25
|
644
|
810
|
798
|
867
|
169
|
558
|
816
|
999
|
0.750464
|
|
5
|
2022-10-26
|
326
|
597
|
408
|
869
|
753
|
830
|
481
|
664
|
0.664865
|
|
6
|
2022-10-27
|
171
|
930
|
667
|
881
|
567
|
744
|
249
|
601
|
0.524081
|
|
7
|
2022-10-28
|
149
|
659
|
403
|
773
|
110
|
317
|
569
|
598
|
0.524499
|
|
8
|
2022-10-29
|
455
|
938
|
668
|
963
|
646
|
879
|
497
|
608
|
0.668831
|
|
9
|
2022-10-30
|
348
|
998
|
698
|
965
|
105
|
716
|
125
|
238
|
0.437436
|
|
10
|
2022-10-31
|
484
|
560
|
220
|
968
|
348
|
615
|
266
|
536
|
0.491975
|
|
11
|
2022-11-01
|
438
|
750
|
589
|
843
|
762
|
942
|
715
|
807
|
0.749252
|
|
12
|
2022-11-02
|
641
|
714
|
296
|
436
|
230
|
633
|
755
|
939
|
0.706098
|
|
13
|
2022-11-03
|
543
|
746
|
746
|
951
|
132
|
703
|
323
|
661
|
0.569748
|
|
14
|
2022-11-04
|
378
|
414
|
228
|
926
|
399
|
808
|
611
|
898
|
0.530532
|
|
15
|
2022-11-05
|
611
|
624
|
304
|
641
|
675
|
909
|
325
|
577
|
0.696111
|
|
16
|
2022-11-06
|
675
|
687
|
405
|
639
|
798
|
836
|
388
|
463
|
0.863238
|
|
17
|
2022-11-07
|
189
|
465
|
417
|
542
|
203
|
293
|
348
|
720
|
0.572772
|
# 对各维度数据求和 添加两列 sum_1,sum_2 其中target=sum_1/sum_2
df3["sum_1"] = df3.loc[:,"a1"]+df3.loc[:,"b1"]+df3.loc[:,"c1"]+df3.loc[:,"d1"]
df3["sum_2"] = df3.loc[:,"a2"]+df3.loc[:,"b2"]+df3.loc[:,"c2"]+df3.loc[:,"d2"]
df3
|
date
|
a1
|
a2
|
b1
|
b2
|
c1
|
c2
|
d1
|
d2
|
target
|
sum_1
|
sum_2
|
|
0
|
2022-10-21
|
684
|
775
|
714
|
975
|
758
|
815
|
654
|
908
|
0.809099
|
2810
|
3473
|
|
1
|
2022-10-22
|
794
|
997
|
134
|
653
|
417
|
651
|
804
|
929
|
0.665325
|
2149
|
3230
|
|
2
|
2022-10-23
|
388
|
557
|
309
|
743
|
294
|
912
|
714
|
804
|
0.565318
|
1705
|
3016
|
|
3
|
2022-10-24
|
413
|
740
|
119
|
674
|
658
|
895
|
657
|
809
|
0.592367
|
1847
|
3118
|
|
4
|
2022-10-25
|
644
|
810
|
798
|
867
|
169
|
558
|
816
|
999
|
0.750464
|
2427
|
3234
|
|
5
|
2022-10-26
|
326
|
597
|
408
|
869
|
753
|
830
|
481
|
664
|
0.664865
|
1968
|
2960
|
|
6
|
2022-10-27
|
171
|
930
|
667
|
881
|
567
|
744
|
249
|
601
|
0.524081
|
1654
|
3156
|
|
7
|
2022-10-28
|
149
|
659
|
403
|
773
|
110
|
317
|
569
|
598
|
0.524499
|
1231
|
2347
|
|
8
|
2022-10-29
|
455
|
938
|
668
|
963
|
646
|
879
|
497
|
608
|
0.668831
|
2266
|
3388
|
|
9
|
2022-10-30
|
348
|
998
|
698
|
965
|
105
|
716
|
125
|
238
|
0.437436
|
1276
|
2917
|
|
10
|
2022-10-31
|
484
|
560
|
220
|
968
|
348
|
615
|
266
|
536
|
0.491975
|
1318
|
2679
|
|
11
|
2022-11-01
|
438
|
750
|
589
|
843
|
762
|
942
|
715
|
807
|
0.749252
|
2504
|
3342
|
|
12
|
2022-11-02
|
641
|
714
|
296
|
436
|
230
|
633
|
755
|
939
|
0.706098
|
1922
|
2722
|
|
13
|
2022-11-03
|
543
|
746
|
746
|
951
|
132
|
703
|
323
|
661
|
0.569748
|
1744
|
3061
|
|
14
|
2022-11-04
|
378
|
414
|
228
|
926
|
399
|
808
|
611
|
898
|
0.530532
|
1616
|
3046
|
|
15
|
2022-11-05
|
611
|
624
|
304
|
641
|
675
|
909
|
325
|
577
|
0.696111
|
1915
|
2751
|
|
16
|
2022-11-06
|
675
|
687
|
405
|
639
|
798
|
836
|
388
|
463
|
0.863238
|
2266
|
2625
|
|
17
|
2022-11-07
|
189
|
465
|
417
|
542
|
203
|
293
|
348
|
720
|
0.572772
|
1157
|
2020
|
#各列数据错位相减
df3_move = df3.loc[:,"a1":"d2"].diff()
df3_move = df3_move.shift(-1)
df3_move
|
a1
|
a2
|
b1
|
b2
|
c1
|
c2
|
d1
|
d2
|
|
0
|
110.0
|
222.0
|
-580.0
|
-322.0
|
-341.0
|
-164.0
|
150.0
|
21.0
|
|
1
|
-406.0
|
-440.0
|
175.0
|
90.0
|
-123.0
|
261.0
|
-90.0
|
-125.0
|
|
2
|
25.0
|
183.0
|
-190.0
|
-69.0
|
364.0
|
-17.0
|
-57.0
|
5.0
|
|
3
|
231.0
|
70.0
|
679.0
|
193.0
|
-489.0
|
-337.0
|
159.0
|
190.0
|
|
4
|
-318.0
|
-213.0
|
-390.0
|
2.0
|
584.0
|
272.0
|
-335.0
|
-335.0
|
|
5
|
-155.0
|
333.0
|
259.0
|
12.0
|
-186.0
|
-86.0
|
-232.0
|
-63.0
|
|
6
|
-22.0
|
-271.0
|
-264.0
|
-108.0
|
-457.0
|
-427.0
|
320.0
|
-3.0
|
|
7
|
306.0
|
279.0
|
265.0
|
190.0
|
536.0
|
562.0
|
-72.0
|
10.0
|
|
8
|
-107.0
|
60.0
|
30.0
|
2.0
|
-541.0
|
-163.0
|
-372.0
|
-370.0
|
|
9
|
136.0
|
-438.0
|
-478.0
|
3.0
|
243.0
|
-101.0
|
141.0
|
298.0
|
|
10
|
-46.0
|
190.0
|
369.0
|
-125.0
|
414.0
|
327.0
|
449.0
|
271.0
|
|
11
|
203.0
|
-36.0
|
-293.0
|
-407.0
|
-532.0
|
-309.0
|
40.0
|
132.0
|
|
12
|
-98.0
|
32.0
|
450.0
|
515.0
|
-98.0
|
70.0
|
-432.0
|
-278.0
|
|
13
|
-165.0
|
-332.0
|
-518.0
|
-25.0
|
267.0
|
105.0
|
288.0
|
237.0
|
|
14
|
233.0
|
210.0
|
76.0
|
-285.0
|
276.0
|
101.0
|
-286.0
|
-321.0
|
|
15
|
64.0
|
63.0
|
101.0
|
-2.0
|
123.0
|
-73.0
|
63.0
|
-114.0
|
|
16
|
-486.0
|
-222.0
|
12.0
|
-97.0
|
-595.0
|
-543.0
|
-40.0
|
257.0
|
|
17
|
NaN
|
NaN
|
NaN
|
NaN
|
NaN
|
NaN
|
NaN
|
NaN
|
# 利用公式
df3["a_contribution"]=(df3.loc[:,"sum_1"]+df3_move.loc[:,"a1"])/(df3.loc[:,"sum_2"]+df3_move.loc[:,"a2"])-df3.loc[:,"target"]
df3["b_contribution"]=(df3.loc[:,"sum_1"]+df3_move.loc[:,"b1"])/(df3.loc[:,"sum_2"]+df3_move.loc[:,"b2"])-df3.loc[:,"target"]
df3["c_contribution"]=(df3.loc[:,"sum_1"]+df3_move.loc[:,"c1"])/(df3.loc[:,"sum_2"]+df3_move.loc[:,"c2"])-df3.loc[:,"target"]
df3["d_contribution"]=(df3.loc[:,"sum_1"]+df3_move.loc[:,"d1"])/(df3.loc[:,"sum_2"]+df3_move.loc[:,"d2"])-df3.loc[:,"target"]
df3
|
date
|
a1
|
a2
|
b1
|
b2
|
c1
|
c2
|
d1
|
d2
|
target
|
sum_1
|
sum_2
|
a_contribution
|
b_contribution
|
c_contribution
|
d_contribution
|
|
0
|
2022-10-21
|
684
|
775
|
714
|
975
|
758
|
815
|
654
|
908
|
0.809099
|
2810
|
3473
|
-0.018842
|
-0.101387
|
-0.062952
|
0.038068
|
|
1
|
2022-10-22
|
794
|
997
|
134
|
653
|
417
|
651
|
804
|
929
|
0.665325
|
2149
|
3230
|
-0.040594
|
0.034675
|
-0.084976
|
-0.002201
|
|
2
|
2022-10-23
|
388
|
557
|
309
|
743
|
294
|
912
|
714
|
804
|
0.565318
|
1705
|
3016
|
-0.024524
|
-0.051236
|
0.124578
|
-0.019804
|
|
3
|
2022-10-24
|
413
|
740
|
119
|
674
|
658
|
895
|
657
|
809
|
0.592367
|
1847
|
3118
|
0.059452
|
0.170545
|
-0.104053
|
0.014042
|
|
4
|
2022-10-25
|
644
|
810
|
798
|
867
|
169
|
558
|
816
|
999
|
0.750464
|
2427
|
3234
|
-0.052351
|
-0.120983
|
0.108350
|
-0.028836
|
|
5
|
2022-10-26
|
326
|
597
|
408
|
869
|
753
|
830
|
481
|
664
|
0.664865
|
1968
|
2960
|
-0.114303
|
0.084462
|
-0.044823
|
-0.065624
|
|
6
|
2022-10-27
|
171
|
930
|
667
|
881
|
567
|
744
|
249
|
601
|
0.524081
|
1654
|
3156
|
0.041603
|
-0.068044
|
-0.085459
|
0.101989
|
|
7
|
2022-10-28
|
149
|
659
|
403
|
773
|
110
|
317
|
569
|
598
|
0.524499
|
1231
|
2347
|
0.060801
|
0.065173
|
0.082926
|
-0.032773
|
|
8
|
2022-10-29
|
455
|
938
|
668
|
963
|
646
|
879
|
497
|
608
|
0.668831
|
2266
|
3388
|
-0.042671
|
0.008455
|
-0.133947
|
-0.041263
|
|
9
|
2022-10-30
|
348
|
998
|
698
|
965
|
105
|
716
|
125
|
238
|
0.437436
|
1276
|
2917
|
0.132149
|
-0.164148
|
0.101982
|
0.003311
|
|
10
|
2022-10-31
|
484
|
560
|
220
|
968
|
348
|
615
|
266
|
536
|
0.491975
|
1318
|
2679
|
-0.048615
|
0.168558
|
0.084206
|
0.107008
|
|
11
|
2022-11-01
|
438
|
750
|
589
|
843
|
762
|
942
|
715
|
807
|
0.749252
|
2504
|
3342
|
0.069562
|
0.004070
|
-0.099071
|
-0.016955
|
|
12
|
2022-11-02
|
641
|
714
|
296
|
436
|
230
|
633
|
755
|
939
|
0.706098
|
1922
|
2722
|
-0.043789
|
0.026679
|
-0.052803
|
-0.096442
|
|
13
|
2022-11-03
|
543
|
746
|
746
|
951
|
132
|
703
|
323
|
661
|
0.569748
|
1744
|
3061
|
0.008852
|
-0.165928
|
0.065438
|
0.046383
|
|
14
|
2022-11-04
|
378
|
414
|
228
|
926
|
399
|
808
|
611
|
898
|
0.530532
|
1616
|
3046
|
0.037343
|
0.082290
|
0.070676
|
-0.042458
|
|
15
|
2022-11-05
|
611
|
624
|
304
|
641
|
675
|
909
|
325
|
577
|
0.696111
|
1915
|
2751
|
0.007159
|
0.037247
|
0.064905
|
0.053984
|
|
16
|
2022-11-06
|
675
|
687
|
405
|
639
|
798
|
836
|
388
|
463
|
0.863238
|
2266
|
2625
|
-0.122497
|
0.037869
|
-0.060644
|
-0.090858
|
|
17
|
2022-11-07
|
189
|
465
|
417
|
542
|
203
|
293
|
348
|
720
|
0.572772
|
1157
|
2020
|
NaN
|
NaN
|
NaN
|
NaN
|
df3_shift = df3.loc[:,"a_contribution":"d_contribution"].shift(1)
df3_shift
|
a_contribution
|
b_contribution
|
c_contribution
|
d_contribution
|
|
0
|
NaN
|
NaN
|
NaN
|
NaN
|
|
1
|
-0.018842
|
-0.101387
|
-0.062952
|
0.038068
|
|
2
|
-0.040594
|
0.034675
|
-0.084976
|
-0.002201
|
|
3
|
-0.024524
|
-0.051236
|
0.124578
|
-0.019804
|
|
4
|
0.059452
|
0.170545
|
-0.104053
|
0.014042
|
|
5
|
-0.052351
|
-0.120983
|
0.108350
|
-0.028836
|
|
6
|
-0.114303
|
0.084462
|
-0.044823
|
-0.065624
|
|
7
|
0.041603
|
-0.068044
|
-0.085459
|
0.101989
|
|
8
|
0.060801
|
0.065173
|
0.082926
|
-0.032773
|
|
9
|
-0.042671
|
0.008455
|
-0.133947
|
-0.041263
|
|
10
|
0.132149
|
-0.164148
|
0.101982
|
0.003311
|
|
11
|
-0.048615
|
0.168558
|
0.084206
|
0.107008
|
|
12
|
0.069562
|
0.004070
|
-0.099071
|
-0.016955
|
|
13
|
-0.043789
|
0.026679
|
-0.052803
|
-0.096442
|
|
14
|
0.008852
|
-0.165928
|
0.065438
|
0.046383
|
|
15
|
0.037343
|
0.082290
|
0.070676
|
-0.042458
|
|
16
|
0.007159
|
0.037247
|
0.064905
|
0.053984
|
|
17
|
-0.122497
|
0.037869
|
-0.060644
|
-0.090858
|
df3_merge = pd.merge(df3.iloc[:,1:12],df3_shift,left_index=True,right_index=True)
df3_merge
|
a1
|
a2
|
b1
|
b2
|
c1
|
c2
|
d1
|
d2
|
target
|
sum_1
|
sum_2
|
a_contribution
|
b_contribution
|
c_contribution
|
d_contribution
|
|
0
|
684
|
775
|
714
|
975
|
758
|
815
|
654
|
908
|
0.809099
|
2810
|
3473
|
NaN
|
NaN
|
NaN
|
NaN
|
|
1
|
794
|
997
|
134
|
653
|
417
|
651
|
804
|
929
|
0.665325
|
2149
|
3230
|
-0.018842
|
-0.101387
|
-0.062952
|
0.038068
|
|
2
|
388
|
557
|
309
|
743
|
294
|
912
|
714
|
804
|
0.565318
|
1705
|
3016
|
-0.040594
|
0.034675
|
-0.084976
|
-0.002201
|
|
3
|
413
|
740
|
119
|
674
|
658
|
895
|
657
|
809
|
0.592367
|
1847
|
3118
|
-0.024524
|
-0.051236
|
0.124578
|
-0.019804
|
|
4
|
644
|
810
|
798
|
867
|
169
|
558
|
816
|
999
|
0.750464
|
2427
|
3234
|
0.059452
|
0.170545
|
-0.104053
|
0.014042
|
|
5
|
326
|
597
|
408
|
869
|
753
|
830
|
481
|
664
|
0.664865
|
1968
|
2960
|
-0.052351
|
-0.120983
|
0.108350
|
-0.028836
|
|
6
|
171
|
930
|
667
|
881
|
567
|
744
|
249
|
601
|
0.524081
|
1654
|
3156
|
-0.114303
|
0.084462
|
-0.044823
|
-0.065624
|
|
7
|
149
|
659
|
403
|
773
|
110
|
317
|
569
|
598
|
0.524499
|
1231
|
2347
|
0.041603
|
-0.068044
|
-0.085459
|
0.101989
|
|
8
|
455
|
938
|
668
|
963
|
646
|
879
|
497
|
608
|
0.668831
|
2266
|
3388
|
0.060801
|
0.065173
|
0.082926
|
-0.032773
|
|
9
|
348
|
998
|
698
|
965
|
105
|
716
|
125
|
238
|
0.437436
|
1276
|
2917
|
-0.042671
|
0.008455
|
-0.133947
|
-0.041263
|
|
10
|
484
|
560
|
220
|
968
|
348
|
615
|
266
|
536
|
0.491975
|
1318
|
2679
|
0.132149
|
-0.164148
|
0.101982
|
0.003311
|
|
11
|
438
|
750
|
589
|
843
|
762
|
942
|
715
|
807
|
0.749252
|
2504
|
3342
|
-0.048615
|
0.168558
|
0.084206
|
0.107008
|
|
12
|
641
|
714
|
296
|
436
|
230
|
633
|
755
|
939
|
0.706098
|
1922
|
2722
|
0.069562
|
0.004070
|
-0.099071
|
-0.016955
|
|
13
|
543
|
746
|
746
|
951
|
132
|
703
|
323
|
661
|
0.569748
|
1744
|
3061
|
-0.043789
|
0.026679
|
-0.052803
|
-0.096442
|
|
14
|
378
|
414
|
228
|
926
|
399
|
808
|
611
|
898
|
0.530532
|
1616
|
3046
|
0.008852
|
-0.165928
|
0.065438
|
0.046383
|
|
15
|
611
|
624
|
304
|
641
|
675
|
909
|
325
|
577
|
0.696111
|
1915
|
2751
|
0.037343
|
0.082290
|
0.070676
|
-0.042458
|
|
16
|
675
|
687
|
405
|
639
|
798
|
836
|
388
|
463
|
0.863238
|
2266
|
2625
|
0.007159
|
0.037247
|
0.064905
|
0.053984
|
|
17
|
189
|
465
|
417
|
542
|
203
|
293
|
348
|
720
|
0.572772
|
1157
|
2020
|
-0.122497
|
0.037869
|
-0.060644
|
-0.090858
|
# 各维度贡献度求和
df3_merge["sum_contribution"] = df3_merge.loc[:,"a_contribution"]+df3_merge.loc[:,"b_contribution"]+df3_merge.loc[:,"c_contribution"]+df3_merge.loc[:,"d_contribution"]
# 各维度贡献度归一化
df3_merge["a_nor_con"] = df3_merge.loc[:,"a_contribution"]/df3_merge["sum_contribution"]*100
df3_merge["b_nor_con"] = df3_merge.loc[:,"b_contribution"]/df3_merge["sum_contribution"]*100
df3_merge["c_nor_con"] = df3_merge.loc[:,"c_contribution"]/df3_merge["sum_contribution"]*100
df3_merge["d_nor_con"] = df3_merge.loc[:,"d_contribution"]/df3_merge["sum_contribution"]*100
#得到各维度归一化后的贡献度
df3_merge
|
a1
|
a2
|
b1
|
b2
|
c1
|
c2
|
d1
|
d2
|
target
|
sum_1
|
sum_2
|
a_contribution
|
b_contribution
|
c_contribution
|
d_contribution
|
sum_contribution
|
a_nor_con
|
b_nor_con
|
c_nor_con
|
d_nor_con
|
|
0
|
684
|
775
|
714
|
975
|
758
|
815
|
654
|
908
|
0.809099
|
2810
|
3473
|
NaN
|
NaN
|
NaN
|
NaN
|
NaN
|
NaN
|
NaN
|
NaN
|
NaN
|
|
1
|
794
|
997
|
134
|
653
|
417
|
651
|
804
|
929
|
0.665325
|
2149
|
3230
|
-0.018842
|
-0.101387
|
-0.062952
|
0.038068
|
-0.145113
|
12.984158
|
69.867731
|
43.381390
|
-26.233279
|
|
2
|
388
|
557
|
309
|
743
|
294
|
912
|
714
|
804
|
0.565318
|
1705
|
3016
|
-0.040594
|
0.034675
|
-0.084976
|
-0.002201
|
-0.093096
|
43.604496
|
-37.246550
|
91.277729
|
2.364325
|
|
3
|
413
|
740
|
119
|
674
|
658
|
895
|
657
|
809
|
0.592367
|
1847
|
3118
|
-0.024524
|
-0.051236
|
0.124578
|
-0.019804
|
0.029014
|
-84.524979
|
-176.589615
|
429.369192
|
-68.254598
|
|
4
|
644
|
810
|
798
|
867
|
169
|
558
|
816
|
999
|
0.750464
|
2427
|
3234
|
0.059452
|
0.170545
|
-0.104053
|
0.014042
|
0.139985
|
42.470418
|
121.830207
|
-74.331525
|
10.030900
|
|
5
|
326
|
597
|
408
|
869
|
753
|
830
|
481
|
664
|
0.664865
|
1968
|
2960
|
-0.052351
|
-0.120983
|
0.108350
|
-0.028836
|
-0.093820
|
55.799213
|
128.952739
|
-115.487176
|
30.735225
|
|
6
|
171
|
930
|
667
|
881
|
567
|
744
|
249
|
601
|
0.524081
|
1654
|
3156
|
-0.114303
|
0.084462
|
-0.044823
|
-0.065624
|
-0.140288
|
81.477285
|
-60.206169
|
31.950721
|
46.778163
|
|
7
|
149
|
659
|
403
|
773
|
110
|
317
|
569
|
598
|
0.524499
|
1231
|
2347
|
0.041603
|
-0.068044
|
-0.085459
|
0.101989
|
-0.009911
|
-419.790657
|
686.586903
|
862.304523
|
-1029.100768
|
|
8
|
455
|
938
|
668
|
963
|
646
|
879
|
497
|
608
|
0.668831
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2266
|
3388
|
0.060801
|
0.065173
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0.082926
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-0.032773
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0.176128
|
34.521140
|
37.003425
|
47.082666
|
-18.607232
|
|
9
|
348
|
998
|
698
|
965
|
105
|
716
|
125
|
238
|
0.437436
|
1276
|
2917
|
-0.042671
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0.008455
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-0.133947
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-0.041263
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-0.209427
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20.375175
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-4.037194
|
63.959077
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19.702943
|
|
10
|
484
|
560
|
220
|
968
|
348
|
615
|
266
|
536
|
0.491975
|
1318
|
2679
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0.132149
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-0.164148
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0.101982
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0.003311
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0.073293
|
180.301053
|
-223.960180
|
139.141968
|
4.517158
|
|
11
|
438
|
750
|
589
|
843
|
762
|
942
|
715
|
807
|
0.749252
|
2504
|
3342
|
-0.048615
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0.168558
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0.084206
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0.107008
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0.311158
|
-15.623749
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54.171136
|
27.062240
|
34.390373
|
|
12
|
641
|
714
|
296
|
436
|
230
|
633
|
755
|
939
|
0.706098
|
1922
|
2722
|
0.069562
|
0.004070
|
-0.099071
|
-0.016955
|
-0.042393
|
-164.088714
|
-9.600687
|
233.694988
|
39.994413
|
|
13
|
543
|
746
|
746
|
951
|
132
|
703
|
323
|
661
|
0.569748
|
1744
|
3061
|
-0.043789
|
0.026679
|
-0.052803
|
-0.096442
|
-0.166356
|
26.322555
|
-16.037199
|
31.741207
|
57.973437
|
|
14
|
378
|
414
|
228
|
926
|
399
|
808
|
611
|
898
|
0.530532
|
1616
|
3046
|
0.008852
|
-0.165928
|
0.065438
|
0.046383
|
-0.045255
|
-19.559587
|
366.647041
|
-144.596862
|
-102.490592
|
|
15
|
611
|
624
|
304
|
641
|
675
|
909
|
325
|
577
|
0.696111
|
1915
|
2751
|
0.037343
|
0.082290
|
0.070676
|
-0.042458
|
0.147850
|
25.257313
|
55.657621
|
47.802382
|
-28.717316
|
|
16
|
675
|
687
|
405
|
639
|
798
|
836
|
388
|
463
|
0.863238
|
2266
|
2625
|
0.007159
|
0.037247
|
0.064905
|
0.053984
|
0.163295
|
4.383994
|
22.809629
|
39.747091
|
33.059286
|
|
17
|
189
|
465
|
417
|
542
|
203
|
293
|
348
|
720
|
0.572772
|
1157
|
2020
|
-0.122497
|
0.037869
|
-0.060644
|
-0.090858
|
-0.236130
|
51.877062
|
-16.037558
|
25.682637
|
38.477859
|
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