我正在从数据框中选择专用数据，并希望根据定义的公式进行线性插值：
y = y0 + (y1 - y0) * [(x-x0)/(x1-x0)]
我想在等级2.0和3.0之间进行插值，其中所需等级为2.5。计算结果如下：
y = -9.080002 +（-9.039993-（-9.080002））* [（2.5-2）/（3-2）] = -9.059997500000
这些值在代码中的定义如下：
该代码如下所示：
import pandas as pd
#define path where the Excel File is
path = 'Desktop'
#define file name
filename = 'Test_Calculation.xlsx'
#add to full path
final_path = path + '/' + filename
print(final_path)
#define excel worksheet name
ws_name = 'Test_Sheet'
df = pd.read_excel(final_path, sheet_name=ws_name)
#sort column DataPoint and inclkude column rank
df.sort_values('DataPoint', inplace = True)
df['Rank'] = df['DataPoint'].rank()
#define the VaR function
def Calc(data):
Rank = 2.5
Rank_Up = 3.0
Rank_Down = 2.0
Calculation = 0.0
check_int = isinstance(Rank, int)
if not check_int:
for ind in data.index:
if (data['Rank'][ind] in (Rank_Down, Rank_Up)):
Calculation = data['DataPoint'][ind] + (data['DataPoint'][ind+1]- data['DataPoint'][ind]) *((Rank-Rank_Down)/(Rank_Up-Rank_Down))
print(data['DataPoint'][ind], data['Rank'][ind])
print(data['DataPoint'][ind+1], data['Rank'][ind+1])
break
return Calculation
Calc_int = Calc(df)
print(Calc_int)
结果如下所示：
-9.080002000000007 2.0
-9.360001000000011 1.0
-9.22000150000001
Excel文件如下所示：
Number DataPoint
1 -5.910004
2 -9.080002
3 -9.360001
4 -0.779999
5 0.389999
6 0.089996
7 0.009995
8 -0.380005
9 1.139999
10 2.389999
11 2.279999
12 0.089996
13 -1.220001
14 -0.960007
15 -5.419998
16 -6.410004
17 -5.029999
18 -7.529999
19 -7.479996
20 -4.580002
21 -5.589996
22 -6.339996
23 -5.570007
24 -3.520004
25 -5.490005
26 -1.860001
27 -1.589996
28 -2.470001
29 -4.350006
30 -2.630005
31 -3.800003
32 -4.949997
33 -2.770004
34 -6.300003
35 -3.220001
36 -4.949997
37 -7.320007
38 -2.110001
39 -2.020004
40 -1.460007
41 -3.300003
42 -9.039993
43 -6.529999
44 -1.149994
45 2.660004
46 3.940002
47 6.009995
48 6.309998
49 5.75
50 5.070007
51 6.160004
52 5.509995
53 6.75
54 5.119995
55 6.320007
56 6.599992
57 6.302474
58 7.403336
59 10.65633
60 9.634812
61 9.763731
62 8.7323
63 7.760373
64 8.434767
65 10.398461
66 9.029833
67 11.72744
68 21.793869
69 21.089708
70 19.592145
71 17.142479
72 17.221823
73 17.985487
74 15.575486
75 12.501017
76 11.816696
77 13.641543
78 13.373762
79 13.730799
80 13.135748
81 11.320812
82 11.628258
83 14.097763
84 14.712653
85 15.595325
86 17.231735
87 16.349063
88 15.615165
89 16.864786
90 15.793676
91 15.466391
92 14.891164
93 15.615165
94 17.390421
95 17.529268
96 14.315938
97 13.978741
98 16.021778
99 16.99372
100 18.521035
101 18.253254
102 18.778889
103 18.253254
104 20.296306
105 20.583912
106 18.729313
107 20.802102
108 21.912876
109 22.329416
110 22.051723
111 21.635182
112 21.833533
113 22.617037
114 20.861605
115 19.145853
116 19.711152
117 21.258306
118 19.582233
119 18.649969
120 20.365723
121 21.241765
122 20.946467
123 18.603786
124 21.665015
125 21.192546
126 18.05257
127 17.117471
128 17.570264
129 12.766789
130 11.684033
131 13.554246
132 8.150336
133 10.571764
134 11.556082
135 9.754779
136 9.833526
137 10.305994
138 9.794145
139 9.311839
140 9.52839
141 9.498846
142 7.933785
143 6.683697
144 7.983004
145 10.118971
146 7.884566
147 8.071589
148 6.703387
149 10.443798
150 12.048241
151 10.325685
152 7.303822
153 7.303822
154 4.498525
155 4.616639
156 1.860546
157 5.502519
158 4.341031
159 5.335186
160 8.248774
161 6.014368
162 8.652332
163 6.919939
164 8.504691
165 7.963313
166 9.400424
167 8.347196
168 10.256775
169 12.245101
170 10.355213
171 11.083599
172 11.31984
173 9.646503
174 8.435796
175 7.884566
176 6.034059
177 5.71907
178 6.073425
179 3.307494
180 4.262284
181 4.350876
182 3.422291
183 4.898252
184 1.174128
185 -0.14545
186 1.975638
187 -1.865776
188 0.206436
189 2.835809
190 2.356848
191 1.88767
192 -1.337954
193 3.412507
194 3.598227
195 1.08616
196 2.288433
197 3.275663
198 2.914007
199 5.523827
200 4.849376
201 1.555337
202 2.552352
203 5.914805
204 8.407334
205 6.823852
206 6.442642
207 6.892266
208 8.681022
209 8.172753
210 8.583285
211 7.801313
212 4.937344
213 4.12605
214 -1.494351
215 -2.00262
216 -0.389816
217 1.877901
218 7.439657
219 6.892266
220 9.31638
221 8.211845
222 9.590068
223 10.352487
224 10.811894
225 9.003586
226 7.89905
227 8.514855
228 8.299812
229 8.114092
230 8.495302
231 7.293029
232 7.019341
233 7.791529
234 8.241167
235 10.460008
236 10.460008
237 9.120876
238 8.27049
239 9.619391
240 9.805111
241 8.417118
242 7.820866
243 7.097551
244 7.903316
245 9.563419
246 9.602245
247 9.553716
248 8.68969
249 8.825604
250 7.14608
IndexError是由于引起的[1:]。
def Calc(data):
Rank = 2.5
Rank_Up = 3.0
Rank_Down = 2.0
Calculation = 0.0
check_int = isinstance(Rank, int)
if not check_int:
for ind in data.Rank:
if (ind in (Rank_Down, Rank_Up)):
index0 = data.Rank.index[ind-1]
index1 = data.Rank.index[ind]
Calculation = data['DataPoint'][index0] + (data['DataPoint'][index1]- data['DataPoint'][index0]) *((Rank-Rank_Down)/(Rank_Up-Rank_Down))
print(data['DataPoint'][index0], ind)
print(data['DataPoint'][index1], ind+1)
break
return Calculation
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