Kalman Filter implementation is too slow
Maybe what is extremely slow is python, if you are comparing with a rolling average function implemented in some library.Specially if the dimension of the observations large, each step it will invert a matrix, $O(n^3)$, it seems you have only one observation in your example. Even for similar implementations Kalman filter will be slower than moving average.
The Kalman filter uses the model of the system, from one state it can get a reasonable estimate, for instance if you are tracking a device with GPS data, you may create a model that assumes that the acceleration has limited variance, from the previous measurements of position you can have an estimate of velocity (part of the state), and if from there you can predict where the device will be.
The things you can tweak in Kalman filter is the covariances of the process and the covariances of the observation, this will tell how much the filter should rely in the estimate vs. the observation. For instance if you don't trust your model you can set the model covariance $Q$ large, if your observations are not that accurate you acn increase the observation covariance $R$.