Markov Chains for sequential data

Question

I am new to Markov chains and HMM and I am looking for help in developing a program (in python) that predicts the next state based on 20 previous states (lets say 20 states in last 20 months). I have a sequential dataset with 50 customers i.e. the rows contain sequence of 20 states for each of the 50 customers (dataset has 50 rows and 20 columns excluding the headers). I am trying to determine the next state using markov chains and all the literature in the web is focused around examples of text strings. I am looking something specific to the kind of example I have. Can somebody please help me come up with the initial probability matrix and then consider the 20 states to predict the next state?