missing-data moving-average pandas python

Replace NaN or missing values with rolling mean or other interpolation

I have a pandas dataframe with monthly data that I want to compute a 12 months moving average for. Data for for every month of January is missing, however (NaN), so I am using

pd.rolling_mean(data["variable"]), 12, center=True)

but it just gives me all NaN values.

Is there a simple way that I can ignore the NaN values? I understand that in practice this would become a 11-month moving average.

The dataframe has other variables which have January data, so I don’t want to just throw out the January columns and do an 11 month moving average.