exponential smoothening is also called exponentially weighted moving average or ewma for short single exponential smoothening is one of the simplest to get started with the general formula is given as et yt - ( et - whereet is the th smoothened observationy is the actual observed value at - instanceand is smoothing constant between and there are different methods to bootstrap the value of (the time period from which smoothing beginsit can be done by setting it to or an average of first time periods and so on alsothe value of determines how much of the past is accounted for value closer to dampens out the past observations quickly while values closer to dampen out slowly the following snippet uses the pandas ewm(function to calculate the smoothened series for visits the parameter halflife is used to calculate in this case in [ input_df['ewma'input_df['visits'ewm(halflife= ignore_na=falsemin_periods= adjust=truemean(plt plot(input_df visits,'-',color='black',alpha= plt plot(input_df ewma,color=' 'plt title('website visit and exponential smoothening'plt legend(plt show(the plot depicted in figure - showcases the ewma smoothened series along with the original one figure - smoothening using ewma