Simple Time Series Data Smoothing of Python Panda Columns (By Group ID) and New Field Creation/Calculation

Independent project. Test file has data that I want to be grouped by the HAUL_ID field. I wish to apply a Hamming Filter (available in Numpy) using a window=25 to smooth the X and Y fields, but treat the smoothing independently for each HAUL_ID. The Date field can be used for the time series domain, obviously.

I then wish to create a new Field named SIN_Bearing, and calculate the field using the following formula:

SIN_Bearing = SIN(Bearing*(2*PI/360)). This step can be done on all the rows and does not need to be grouped by HAUL_ID first. Note, 'Bearing' is a field in the provided csv.

Finally export the results with all the original fields, the new smoothed XY fields, and the newly calculated SIN_Bearing field into a separate CSV file for each HAUL_ID. In this test file, there are 2 unique hauls - which of course should result in two files. For my later work I will have many more HAUL_IDs, and create additional field calculations. This will get me started.

I use Jupyter Notebook and Python3.

Python Arkitekturë softuerësh Përpunim të dhënash Statistikë Signal Processing

ID Projekti: #30177812

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