Methods to detect this kind of outliers
I recently had a similar problem (removing abnormal peaks from a time series). That's what I suggest you:


Get the smoothed trend. There are several techniques you can employ, such as various forms of exponential smoothing.
Find the difference between actual trend observations and smoothed ones.
Normalize this distribution of distances (using Z-score, i.e. sklearn's StandardScaler)
Substitute the observations that lie k standard deviations away from the mean (that is 0). The choice of x can be arbitrary or data-driven; in my case I chose k = 3 (i.e. a very conservative anomaly removal). You can use the smoothed values as a substitute. In your case, the interpolation could be perhaps a good choice (that's up to your preference).


This will automatically remove abnormal peaks like the one you displayed.