如何使用多个信号重新采样 pandas dataframe

Question

I have aa pandas dataframe, df_phys , structured as in the below example. I would like to resample all signals (in this case EngineSpeed and WheelBasedVehicleSpeed ) to a 1 second frequency. I can do so via a signal-by-signal loop as below:

for signal, group in df_phys.groupby("Signal")["Physical Value"]:
            df_signal = group.to_frame()
            df_signal = df_signal.resample("1S").pad().dropna()

However, I would prefer resampling the entire dataframe (with the Physical Value being the value that should be resampled), rather than doing so in a signal-by-signal loop. Is this possible to achieve?

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df_phys data structure (pre resampling)

TimeStamp,CAN ID,PGN,Source Address,Signal,Raw Value,Physical Value
2020-01-13 14:47:09.816750+00:00,217056256,61444,0,EngineSpeed,13596,1699.5
2020-01-13 14:47:09.826750+00:00,217056256,61444,0,EngineSpeed,13554,1694.25
2020-01-13 14:47:09.836800+00:00,217056256,61444,0,EngineSpeed,13495,1686.875
2020-01-13 14:47:09.846750+00:00,217056256,61444,0,EngineSpeed,13418,1677.25
2020-01-13 14:47:09.856850+00:00,217056256,61444,0,EngineSpeed,13331,1666.375
2020-01-13 14:47:09.867200+00:00,217056256,61444,0,EngineSpeed,13228,1653.5
2020-01-13 14:47:09.868950+00:00,419361024,65265,0,WheelBasedVehicleSpeed,4864,19.0
2020-01-13 14:47:09.876850+00:00,217056256,61444,0,EngineSpeed,13117,1639.625
2020-01-13 14:47:09.886800+00:00,217056256,61444,0,EngineSpeed,13004,1625.5
2020-01-13 14:47:09.896800+00:00,217056256,61444,0,EngineSpeed,12893,1611.625
2020-01-13 14:47:09.907300+00:00,217056256,61444,0,EngineSpeed,12814,1601.75
2020-01-13 14:47:09.916750+00:00,217056256,61444,0,EngineSpeed,12730,1591.25
2020-01-13 14:47:09.926750+00:00,217056256,61444,0,EngineSpeed,12663,1582.875
2020-01-13 14:47:09.936850+00:00,217056256,61444,0,EngineSpeed,12605,1575.625
2020-01-13 14:47:09.946800+00:00,217056256,61444,0,EngineSpeed,12552,1569.0
2020-01-13 14:47:09.956900+00:00,217056256,61444,0,EngineSpeed,12506,1563.25
2020-01-13 14:47:09.967300+00:00,217056256,61444,0,EngineSpeed,12452,1556.5
2020-01-13 14:47:09.969000+00:00,419361024,65265,0,WheelBasedVehicleSpeed,4946,19.3203125
2020-01-13 14:47:09.976850+00:00,217056256,61444,0,EngineSpeed,12393,1549.125
2020-01-13 14:47:09.986850+00:00,217056256,61444,0,EngineSpeed,12356,1544.5
2020-01-13 14:47:09.996800+00:00,217056256,61444,0,EngineSpeed,12303,1537.875
2020-01-13 14:47:10.007900+00:00,217056256,61444,0,EngineSpeed,12241,1530.125
2020-01-13 14:47:10.017200+00:00,217056256,61444,0,EngineSpeed,12187,1523.375
2020-01-13 14:47:10.026900+00:00,217056256,61444,0,EngineSpeed,12147,1518.375
2020-01-13 14:47:10.036900+00:00,217056256,61444,0,EngineSpeed,12095,1511.875
2020-01-13 14:47:10.046800+00:00,217056256,61444,0,EngineSpeed,12040,1505.0
2020-01-13 14:47:10.056950+00:00,217056256,61444,0,EngineSpeed,11983,1497.875
2020-01-13 14:47:10.067400+00:00,217056256,61444,0,EngineSpeed,11937,1492.125
2020-01-13 14:47:10.069150+00:00,419361024,65265,0,WheelBasedVehicleSpeed,5222,20.3984375

Answer 1

It seems you are trying to resample your data with groupby . If this is the intention, you can simply resample the groupby object, when setting TimeStamp as the index:

import pandas as pd
df_phys = pd.read_csv("test.txt", sep = ",", parse_dates=["TimeStamp"], index_col="TimeStamp")

df_res = df_phys.groupby("Signal").resample("1S").mean()

print(df_res)

Sample output:

                                                       CAN ID  ...  Physical Value
Signal                 TimeStamp                               ...                
EngineSpeed            2020-01-13 14:47:09+00:00  217056256.0  ...     1611.907895
                       2020-01-13 14:47:10+00:00  217056256.0  ...     1511.250000
WheelBasedVehicleSpeed 2020-01-13 14:47:09+00:00  419361024.0  ...       19.160156
                       2020-01-13 14:47:10+00:00  419361024.0  ...       20.398438

[4 rows x 5 columns]