Resampling Hz in a pandas dataframe

Question

I'm working on a project in pandas on python. I receive as input a .csv file like this:

Name   Timestamp       Data
A1       259           [1.1,1.0,0.1]
A1       260           [-0.1,1.2,0.3]
A1       261           [0.1,0.2,-0.3]
...
A1       14895         [1.4,0.3,1.8]
...      
A2       278           [-1.1,1.2,0.4]
A2       353           [-0.1,1.2,0.3]
A2       409           [-0.1,1.2,0.3]
...
A2       14900         [-0.1,1.2,0.3]
...
A1140    107           [-0.5,-1.0,-1.0]
A1140    107           [0.6,0.1,0.3]
A1140    114           [-1.1,-1.2,0.3] 
... 
A1140    14995         [-1,1.2,0.4]

I've 1140+ names and hundreds/thousands of data for each name. Data was recorded at 200 Hz and I think that the timestamp numbers indicates milliseconds, though I' m not sure, i don't have access to this information. I've to resample to 50 Hz frequency.

How can I do this? Do I need to convert Timestamp into actual seconds and then use the .resample() function with 0.25s? And should i use a .groupby["Name"] function? Thank you in advance!

Answer 1

I cannot answer the question exactly in its entirety as not even you are sure about the timestamp, but I will try to give you some general guidelines.
What you have here is called panel data , many different time series for each "name".
groupby(['Name']).apply(<func>) can indeed be a useful method, as it allows for manipulation of each of the different names separately, allowing you to work with the simpler data type of a time series .
A time series is data of the type:

Date                  Value
2000-01-01 00:00:00   3
2000-01-01 00:03:00   12
2000-01-01 00:06:00   21

As you can see, the time period in which each sample is taken, is 3 minutes. We could call resample() and convert it to 10 minutes like this:

series.resample('10T').mean()

Note that instead of mean you could use .apply(<func>) to choose the downsampling method. For more info on the frequency, consider this question.

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To conclude, your best bet would be to try and find out what exactly is timestamp, convert it to a DateTime and then use either

 df.groupby(['Name']).resample('20L').mean() 

or with a for loop iterate through each name and use resample to each series individually.