import pandas as pd import numpy as np import scipy.stats def circ_mean(data, dummy_kwarg=0): # print(data) return 180/np.pi*scipy.stats.circmean(data*np.pi/180) def numpy_mean(data, dummy_kwarg=0): return np.mean(data) @pd.api.extensions.register_dataframe_accessor("my") class CircstatsAccessor(object): def __init__(self, pandas_obj): self._obj = pandas_obj def circ_mean(self, axis=0, level=None, **kwargs): df = self._obj if axis != 0 or level is not None: df = df.groupby(axis=axis, level=level) return df.agg(circ_mean, **kwargs) def numpy_mean(self, axis=0, level=None, **kwargs): df = self._obj if axis != 0 or level is not None: df = df.groupby(axis=axis, level=level) return df.agg(numpy_mean, **kwargs) df = pd.DataFrame( data={ "col1": [10, 11, 12, 13], "col2": [20, 21, 22, 23], }, index=[1, 2, 3, 4] ) # Compute results with the standard `df.mean` call # I'd like my custom mean function to do a similar thing df.mean(level=0, axis=0) # If I don't pass in any kwargs, `df.my.circ_mean` behaves as expected # Results approximately match those from `df.mean` df.my.circ_mean(level=0, axis=0) # If I pass in a kwarg that is not ever used, `df.my.circ_mean` # returns unusual results - the returned values in `col1` are  # identical to those in `col2`, whereas they were # different before df.my.circ_mean(level=0, axis=0, dummy_kwarg=0) # If I call `df.my.numpy_mean`, results are identical # without or without providing the kwarg df.my.numpy_mean(level=0, axis=0) df.my.numpy_mean(level=0, axis=0, dummy_kwarg=0)