Downsampling data frome 25hz to 10hz

Question

I have some time series data sampled at 25hz (25 samples per second)

time_in_secconds    data_vals
199.655    0.549038
199.696    0.83472
199.736    0.478569
199.776    0.114157
199.817    0.217603
199.858    0.701952
199.898    0.23409
199.938   -0.237923
199.979    0.337316
200.019    1.17735
200.059    1.42538

and I want to go down to 10 samples per second. I've been looking at doing this with pandas and have a few questions. The first one is what index object do I use for the times? is it https://pandas.pydata.org/docs/reference/api/pandas.TimedeltaIndex.seconds.html ?

The second is, after doing so, how do I actually go about resampling to get both time_in_seconds and data_vals at the new 10hz.

Answer 1

The documentation on the resample function contains much more useful information.

Since time_in_secods measure times since some event, a TimedeltaIndex is the most appropriate here. Here's how to resample by averaging the original data points:

# Original data @ 25Hz
t = np.arange(199, 201, .04)
df = pd.DataFrame({
    'time_in_seconds': t,
    'data_vals': np.random.uniform(-1, 1, len(t))
})

# Resample to 10Hz
output = (
    df.set_index(pd.to_timedelta(df['time_in_seconds'], unit='s'))
      ['data_vals']
      .resample('100ms')
      .mean()
)
output.index = output.index.total_seconds()

There are other resampling methods, such as min, max or weighted mean. Consult the documentation for more details.