Say I have a dataframe with several timestamps and values. I would like to measure Δ values / Δt
every 2.5
seconds. Does Pandas provide any utilities for time differentiation?
time_stamp values
19492 2014-10-06 17:59:40.016000-04:00 1832128
167106 2014-10-06 17:59:41.771000-04:00 2671048
202511 2014-10-06 17:59:43.001000-04:00 2019434
161457 2014-10-06 17:59:44.792000-04:00 1294051
203944 2014-10-06 17:59:48.741000-04:00 867856
The diff() method returns a DataFrame with the difference between the values for each row and, by default, the previous row. Which row to compare with can be specified with the periods parameter.
It most certainly does. First, you'll need to convert your indices into pandas date_range
format and then use the custom offset functions available to series/dataframes indexed with that class. Helpful documentation here. Read more here about offset aliases.
This code should resample your data to 2.5s intervals
#df is your dataframe
index = pd.date_range(df['time_stamp'])
values = pd.Series(df.values, index=index)
#Read above link about the different Offset Aliases, S=Seconds
resampled_values = values.resample('2.5S')
resampled_values.diff() #compute the difference between each point!
That should do it.
If you really want the time derivative, then you also need to divide by the time difference (delta time, dt) since last sample
An example:
dti = pd.DatetimeIndex([
'2018-01-01 00:00:00',
'2018-01-01 00:00:02',
'2018-01-01 00:00:03'])
X = pd.DataFrame({'data': [1,3,4]}, index=dti)
X.head()
data
2018-01-01 00:00:00 1
2018-01-01 00:00:02 3
2018-01-01 00:00:03 4
You can find the time delta by using the diff()
on the DatetimeIndex. This gives you a series of type Time Deltas. You only need the values in seconds, though
dt = pd.Series(df.index).diff().dt.seconds.values
dXdt = df.diff().div(dt, axis=0, )
dXdt.head()
data
2018-01-01 00:00:00 NaN
2018-01-01 00:00:02 1.0
2018-01-01 00:00:03 1.0
As you can see, this approach takes into account that there are two seconds between the first two values, and only one between the two last values. :)
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