How to use "scipy.signal.butter" function for doing low-pass filtering?

Question

How should I use the "scipy.signal.butter" for low pass filtering my SST data from the year 1870-2000 for retaining the signal above 8 years? I searched for this:- scipy.signal.butter(N, Wn, btype='low', analog=False, output='ba', fs=None)

but what should I choose N, Wn particularly?

It would be helpful if someone could explain the procedure to do this. And if there is any other way to do this. Please let me know.

Answer 1

Here 's the scipy documentation on butter .

Per that page, N is an int specifying the order of the filter and Wn is a list, tuple, or something similar which specifies "critical frequencies". What those "critical frequencies" mean for a Butterworth filter is briefly described in the documentation.

Does that help clear up your confusion, or are you looking for more information about how Butterworth filters themselves work? If you understand the signal processing you're trying to do and you just want help with the code, the documentation should be enough. If you need help learning about how Butterworth filters work, it's probably best if you google it. If you understand what Butterworth filters are and what types of stuff they're used for, and you keep having trouble with using the Butterworth filter to do what you want, be aware that there's a signal processing StackExchange - that's a better place to ask questions about signal processing itself (StackOverflow is just for programming).