I have a simple dataframe df that contains three columns:
Time: expressed in secondsA: set of values that can vary between -inf to +infB: set of angles (degrees) which range between 0 and 359 Here is the dataframe
df = pd.DataFrame({'Time':[0,12,23,25,44,50], 'A':[5,7,9,8,11,6], 'B':[300,358,4,10,2,350]})
And it looks like this:
Time A B
0 0 5 300
1 12 7 358
2 23 9 4
3 25 8 10
4 44 11 2
5 50 6 350
My idea is to interpolate the data from 0 to 50 seconds and I was able to achieve my goal using the following lines of code:
y = pd.DataFrame({'Time':list(range(df['Time'].iloc[0], df['Time'].iloc[-1]))})
df = pd.merge(left=y, right=df, on='Time', how='left').interpolate()
Problem: even though column A is interpolated correctly, column B is wrong because the interpolation of an angle between 360 degrees is not performed! Here is an example:
Time A B
12 12 7.000000 358.000000
13 13 7.181818 325.818182
14 14 7.363636 293.636364
15 15 7.545455 261.454545
16 16 7.727273 229.272727
17 17 7.909091 197.090909
18 18 8.090909 164.909091
19 19 8.272727 132.727273
20 20 8.454545 100.545455
21 21 8.636364 68.363636
22 22 8.818182 36.181818
23 23 9.000000 4.000000
Question: can you suggest me a smart and efficient way to solve this issue and being able to interpolate correctly the angles between 0/360 degrees?
You should be able to use the method described in this question for the angle column:
import numpy as np
import pandas as pd
df = pd.DataFrame({'Time':[0,12,23,25,44,50], 'A':[5,7,9,8,11,6], 'B':[300,358,4,10,2,350]})
df['B'] = np.rad2deg(np.unwrap(np.deg2rad(df['B'])))
y = pd.DataFrame({'Time':list(range(df['Time'].iloc[0], df['Time'].iloc[-1]))})
df = pd.merge(left=y, right=df, on='Time', how='left').interpolate()
df['B'] %= 360
print(df)
Output:
Time A B
0 0 5.000000 300.000000
1 1 5.166667 304.833333
2 2 5.333333 309.666667
3 3 5.500000 314.500000
4 4 5.666667 319.333333
5 5 5.833333 324.166667
6 6 6.000000 329.000000
7 7 6.166667 333.833333
8 8 6.333333 338.666667
9 9 6.500000 343.500000
10 10 6.666667 348.333333
11 11 6.833333 353.166667
12 12 7.000000 358.000000
13 13 7.181818 358.545455
14 14 7.363636 359.090909
15 15 7.545455 359.636364
16 16 7.727273 0.181818
17 17 7.909091 0.727273
18 18 8.090909 1.272727
19 19 8.272727 1.818182
20 20 8.454545 2.363636
21 21 8.636364 2.909091
22 22 8.818182 3.454545
23 23 9.000000 4.000000
24 24 8.500000 7.000000
25 25 8.000000 10.000000
26 26 8.157895 9.578947
27 27 8.315789 9.157895
28 28 8.473684 8.736842
29 29 8.631579 8.315789
30 30 8.789474 7.894737
31 31 8.947368 7.473684
32 32 9.105263 7.052632
33 33 9.263158 6.631579
34 34 9.421053 6.210526
35 35 9.578947 5.789474
36 36 9.736842 5.368421
37 37 9.894737 4.947368
38 38 10.052632 4.526316
39 39 10.210526 4.105263
40 40 10.368421 3.684211
41 41 10.526316 3.263158
42 42 10.684211 2.842105
43 43 10.842105 2.421053
44 44 11.000000 2.000000
45 45 11.000000 2.000000
46 46 11.000000 2.000000
47 47 11.000000 2.000000
48 48 11.000000 2.000000
49 49 11.000000 2.000000