Deriving Markov Transition Matrices


I had a need at work to understand the flow of a system. Knowing that a transition matrix would be perfect to understand state transitions, I researched available libraries within the Python ecosystem. Unfortunately, I could not find a suitable solution.

My desires in a library included:

  • It must capture states as strings or numbers for easy analysis.
  • Store both frequencies and probabilities of state transitions.
  • Allow simple filtering of frequencies and probabilities.
  • Output the derived matrix as a panadas dataframe or numpy matrix.
  • Plot the matrix as a Digraph.

This blog post shows my method of deriving a Markov transition matrix that covers all of those desires. This blog post DOES NOT discuss what a Markov transition matrix is in depth. You should read more about them on your own.


The code block below contains two important classes; Transition and TransitionMatrix. Transition is a class that keeps track of the current state and next state along with the frequency and probability. The TransitionMatrix stores all state transitions and provides methods for plotting and merging.

Note The graphviz and pandas modules are required to run this code.

In [1]:
This module is used to compute Markov Chains.
import itertools
import copy

import pandas as pd

from graphviz import Digraph
from graphviz import Source

class Transition(object):
    This class hold meta-data about a transition between a Markov stochastic process.
    Essentially it is used to keep track of frequencies and to compute probability of the
    def __init__(self, current_state, next_state):
        Creates a transition object given the current_state and next_state as strings.

        current_state : str
            The current state of this transition.
        next_state : str
            The next state of this transition.
        self.current_state = current_state
        self.next_state = next_state
        self.prob = 0
        self.freq = 0
    def increment_freq(self):
        self.freq += 1
    def increment_freq_by(self, x):
        self.freq += x    
    def compute_prob(self, current_state_freqs):
            self.prob = self.freq / current_state_freqs
        except ZeroDivisionError:
    def __hash__(self):
        return hash((self.current_state, self.next_state))
    def __eq__(self, other):
        if not isinstance(other, Transition):
            return False
        return self.current_state == other.current_state \
            and self.next_state == other.next_state
    def __str__(self):
        return '{} -> {}'.format(self.current_state, self.next_state)
    def __copy__(self):
        tmp = Transition(self.current_state, self.next_state)
        return tmp

class TransitionMatrix(object):
    The transition matrix is an object that is used to derive the Stochastic matrix
    of a Markov chain. Internally, everything is storing in a dictionary of transitions.
    This class also provides methods to obtain dict of dict, pandas dataframe, dotlang or
    a graph representation of the matrix.
    def __init__(self, states, valid_states=None):
        This kicks off the computation of the matrix given states and optionally valid states.
        states : :obj:`list` of :obj:`obj`
            The states to derive the matrix from. These states can be either int or str.
        valid_states : :obj:`list of :obj:`obj`, optional
            Optionally include valid states that are used to derive all valid transitions.
        self.transitions = {}
        self.state_frequencies = {}
        if valid_states:
    def __generate_transitions(self, states):
        Internal method that generates all permutations of transitions.
        tmp = list(itertools.permutations(states, 2))

        current = None
        for t in tmp:
            if t[0] not in self.state_frequencies:
                self.state_frequencies[t[0]] = 0
            if current != t[0]:
                transition = Transition(t[0], t[0])
                self.transitions[transition] = transition
                current = t[0]

            transition = Transition(t[0], t[1])
            self.transitions[transition] = transition
    def __compute_matrix(self, states):
        Internal method that computes frequencies and probability of the matrix.
        for i, j in zip(states, states[1:]):        
            self.transitions[Transition(i, j)].increment_freq()
            self.state_frequencies[i] += 1
    def __compute_prob(self):
        Internal method that computes the probability of all transitions.
        for transition in self.transitions:            
    def __recompute_from_transitions(self):
        Internal method that is primarily used when filtering and/or combining transition
        matrices together. It is used to derive current state frequencies from transitions.
        This can then be used to compute the correct probabilities.
        self.state_frequencies = {}
        for t in self.transitions:    
            c = self.transitions[t].current_state
            freq = self.transitions[t].freq
            if c not in self.state_frequencies:
                self.state_frequencies[c] = 0

            if freq > 0:
                self.state_frequencies[c] += freq
    def as_dict(self, prob=False, include_all=False):
        Converts the transition matrix into a dict of dicts.
        tmp = {}
        for t in self.transitions:
            c = self.transitions[t].current_state
            n = self.transitions[t].next_state
            val = self.transitions[t].freq
            if prob:
                val = self.transitions[t].prob                        
            if val <= 0 and not include_all:
            if c not in tmp:
                tmp[c] = {}
            tmp[c][n] = val
        return tmp
    def as_dataframe(self, prob=False, include_all=False):
        return pd.DataFrame(self.as_dict(prob=prob, include_all=include_all))
    def as_dotlang(self):
        data = []
        for t in self.transitions:
            c = self.transitions[t].current_state
            n = self.transitions[t].next_state
            prob = self.transitions[t].prob
            if prob > 0:
                t = '   "{}" -> "{}" [label = "{:2.4f}"]'.format(c, n, prob)

        return "digraph G {\n%s\n}" % ('\n'.join(data))
    def as_graph(self):
        g = Digraph(format='svg', engine='dot')

        for t in self.transitions:
            c = self.transitions[t].current_state
            n = self.transitions[t].next_state
            prob = self.transitions[t].prob

            if prob > 0:
                g.edge(c, n, label=" {:2.4f} ".format(prob))                

        return g


In this example, I will generate a handful of states that occur linearly. The TransitionMatrix expects a list of values as the states or a string. If a string is passed, it treats each character in the string as a state.

In [2]:
import numpy as np
In [3]:
states_a = [np.random.choice(['A', 'B', 'C'], p=[0.2, 0.5, 0.3]) for i in range(1000)]

Passing the states into the matrix derives the frequencies and probabilities.

In [4]:
transition_matrix = TransitionMatrix(states_a)


To obtain the matrix as a pandas dataframe we call the as_dataframe() method. By default we show the frequencies, however we can also get the probabilities.

In [5]:
A 41 108 59
B 103 238 146
C 63 142 99
In [6]:
A 0.198068 0.221311 0.194079
B 0.497585 0.487705 0.480263
C 0.304348 0.290984 0.325658


We are able to visualize the transition matrix as a Digraph by calling the as_graph() method. If we wanted to export a dotfile, we can call the as_dotlang() method to obtain the dotlang string.

In [7]:
%3 B BB->B 0.4877 A AB->A 0.2213 C CB->C 0.2910 A->B 0.4976 A->A 0.1981 A->C 0.3043 C->B 0.4803 C->A 0.1941 C->C 0.3257


Filtering is easily accomplished with a pandas dataframe.

In [8]:
df = transition_matrix.as_dataframe(prob=True)
In [9]:
df[df > 0.2]
A NaN 0.221311 NaN
B 0.497585 0.487705 0.480263
C 0.304348 0.290984 0.325658

You can see that we filtered on a probability of 0.2 creating a NaN probability between C and A.

Contents © 2020 Tyler Marrs