Advanced
Making computer generated text mimic human speech is fascinating and actually not that difficult for an effect that is sometimes convincing, but certainly entertaining. Markov Chain’s is one way to do this. It works by generating new text based on historical texts where the original sequencing of neighboring words (or groups of words) is used to generate meaningful sentences. Read the below guide on how to code a Markov Chain text generator (code example in python) including explanation of the concept.
What’s really interesting, is that you can take historical texts of a person, then generate new sentences which can sound similar to the way that person speaks. Alternatively, you can combine texts from two different people and get a mixed “voice”.
I played around this with texts of speeches from two great presidents:
What my Markov Chain generated which was “trained” using the combination of texts from Obama speeches and Bartlet scripts, is as follows:
- ‘Can I burn my mother in North Carolina for giving us a great night planned.’
- ‘And so going forward, I believe that we can build a bomb into their church.’
- ‘’Charlie, my father had grown up in the Situation Room every time I came in.’’
- ‘This campaign must be ballistic.’,
What is a Markov Chain in the context of a text generation?
For a more technical explanation, I think you can find plenty of resources out there. In simple terms, it is an algorithm which is used to generate a new outcome from a weighted list of words based on historical texts. Now that’s rather abstract. In more practical terms, in the scenario for text generation, it is a way to use historical texts, chop it up into individual words (or sets of words), and then randomly chose a given word then randomly chose the next likely words based on historical sequences. For example:

This doesn’t just apply in text as well (although one of the most popular applications is in your smart phone where there’s predictive text), it can be used for any scenario where you use historical information to define next steps for a given state. For example, you could codify a given stock market pattern (such as the % daily changes for the last 30 days), then use that to see historically what was the likely next day outcome (example only.. I’m very doubtful how effective it would be).
Why are Markov Chain Text Generators so fun?
I’ve always wanted to build a text generator as it’s just an awesome way to see how you could mimic intelligence using a very cheap shortcut. You’ll see the algorithm below, and it is super simple. The other fact is that, like above example, you can use it to mix the ‘voice’ from two different persons and see the outcome.
How does the Markov Chain Text Generator work?
There are two phases for text generation with Markov Chains. There’s first the ‘dictionary build phase’ which involves gathering the historical texts, and then generating a dictionary with the key being a given word in a sentence, and then having the resultant being the natural follow-up words.

The second is the execution, where you start from a given word, then use that word to see what the next word would be in a probabilistic way. For example:

Now, there are some tricks which you need to be mindful of ( I found this out the hard way):
- You can’t start from any random word — if you do, then you’ll get sentences like this: “ate the cat.” . You have to keep track of “starting words” to keep things simple — hence you can have: “John ate the cat”.
- Don’t ignore punctuation— if you do remove punctuation, you’ll get sentence like this: “The dog barked at John cat”. Instead keep them there so that you can have a better chance to have a more realistic sentence — i.e. “The dog barked at John’s ca”
- End on a full-stop word. When you go through and start from a word, then find the next word, then find the next word and so on, you can continue until you reach a specified length, but then you’ll end up stopping in mid-sentence such as this: “The cat ate John’s”. Instead, simply end when you have a word that has a full stop (another reason not to remove the punctuation) — i.e. “The cat ate John’s boots.”
Markov Chain Example Code Source texts
I played around with different texts including: Eddie Murphy stand-up routines, Donald Trump tweets, Obama speeches, and Jed Bartlet dialogue. You can find the the markov chain example source text here. It’s great to use one source and then generate the dictionary, but then you can mix and match and use two sources (e.g. Obama and Bartlet) and then create the one dictionary file. Then when you traverse the dictionary you get the both voices.
It is important to make sure that you can balance the text — e.g. if you had a 8000 text from Obama and only 1000 text from Eddie Murphy, it’s likely that you would see more of the Obama words. Of course, when you build the dictionary, you can also add some artificial weighting towards the lighter text source to balance things out.
Markov Chain Summary
The Markov Chain text generator is not perfect — you’ll see when you create your own, that some text is just gibberish. The more text that you have the better. Secondly, using single words is not helpful in the dictionary — you should use groups of 2–3 words. The actual number depends on how much historical text you have.
You can find all the python code, source texts and Markov Chain python example code here. Good luck!
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How To Use Python zipapp to Create Executable Python Archives
Intermediate
Sharing a Python script with a colleague is easy until it has dependencies. Suddenly you need to say “first install Python, then create a virtual environment, then pip install these five packages, then run the script.” Half the time something breaks on their machine because they are on a different OS version, have conflicting packages, or simply misread one of the steps. You end up spending more time on setup instructions than on the actual tool you built.
Python’s built-in zipapp module solves the distribution problem for CLI tools and internal utilities. It bundles your application code — and optionally its dependencies — into a single .pyz file that runs with python myapp.pyz on any machine that has Python installed. No virtual environment, no pip install, no path configuration. One file, one command.
In this article you will learn how to create a basic zipapp from the command line, bundle dependencies into the archive, set a custom entry point, add a shebang line for Unix direct execution, work around the limitations of compiled extensions, and finish with a real-life example: packaging a CSV-to-JSON converter as a portable .pyz file. By the end you will be distributing Python tools as single files the way Go developers take for granted.
zipapp Quick Example
Here is the minimal workflow — a Python script bundled into a .pyz archive that runs on any machine with Python 3:
# Step 1: create the source directory
# myapp/
# __main__.py
# utils.py
# myapp/__main__.py
def main():
from myapp.utils import greet
greet("World")
if __name__ == "__main__":
main()
# myapp/utils.py
def greet(name: str) -> None:
print(f"Hello, {name}! Running from a .pyz archive.")
# quick_build.py -- run this once to create the archive
import zipapp
zipapp.create_archive("myapp", target="myapp.pyz", interpreter="/usr/bin/env python3")
print("Created myapp.pyz")
Then run it:
$ python myapp.pyz
Hello, World! Running from a .pyz archive.
The .pyz file is a ZIP archive with a Python shebang prepended. Python’s import system knows how to find modules inside ZIP archives (PEP 302), so all imports work exactly as if the files were on disk. The __main__.py file is the entry point that runs when you execute the archive.
What Is zipapp and When Should You Use It?
A Python zip application (.pyz) is a ZIP file that starts with a shebang line (#!/usr/bin/env python3) and contains a __main__.py as its entry point. Python has supported executing ZIP archives since Python 2.6 (PEP 441). The zipapp module, added in Python 3.5, automates the creation process so you do not have to build the ZIP manually.
| Distribution method | Single file | Dependencies bundled | Python required | Cross-platform |
|---|---|---|---|---|
| zipapp (.pyz) | Yes | Yes (pure Python) | Yes | Yes |
| PyInstaller | Yes | Yes (incl. C extensions) | No | No (per-OS build) |
| pip package | No | Via dependencies | Yes | Yes |
| Docker image | No | Yes (everything) | No | Partial |
zipapp is the right choice for internal tools, CI scripts, and developer utilities where you know the target machine has Python and you want to share one file over Slack or email. It is not suitable for end-user consumer apps (use PyInstaller or packaging) or for tools that require C extension dependencies (use Docker).
Creating Archives from the Command Line
The zipapp module has a command-line interface that handles the most common cases without writing any Python:
# Command-line usage (run in your terminal, not Python)
# Basic: package a directory into a .pyz
$ python -m zipapp myapp -o myapp.pyz
# With a custom entry point (module:callable syntax)
$ python -m zipapp myapp -o myapp.pyz -m "myapp.cli:main"
# With a shebang for direct Unix execution (chmod +x myapp.pyz first)
$ python -m zipapp myapp -o myapp.pyz -p "/usr/bin/env python3" -m "myapp.cli:main"
# Inspect an existing archive's entry point
$ python -m zipapp --info myapp.pyz
Interpreter: /usr/bin/env python3
Main: myapp.cli:main
The -m "module:callable" flag writes a __main__.py shim that imports and calls the specified function. This means you do not need to write a __main__.py yourself — zipapp generates it. The generated __main__.py looks like:
# Generated __main__.py (created by -m flag)
import sys
sys.exit(__import__("myapp.cli").cli.main())
This is exactly what you would write manually, but you get it for free by specifying the entry point flag.
Bundling Dependencies into the Archive
To bundle third-party pure-Python packages, install them into a subdirectory, then include that directory when building the archive. The key is that everything inside the archive ZIP is available on the import path when the archive runs:
# build_with_deps.py
import subprocess
import zipapp
import shutil
import os
APP_DIR = "myapp_with_deps"
BUILD_DIR = "build_temp"
# Clean build dir
shutil.rmtree(BUILD_DIR, ignore_errors=True)
os.makedirs(BUILD_DIR)
# Copy application source
shutil.copytree(APP_DIR, os.path.join(BUILD_DIR, "myapp_with_deps"))
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Frequently Asked Questions
What is a Markov chain in simple terms?
A Markov chain is a mathematical model where the next state depends only on the current state, not on the sequence of events that preceded it. In text generation, this means the next word is predicted based only on the current word or phrase.
How does a Markov chain text generator work in Python?
A Python Markov chain text generator builds a dictionary of word transitions from training text. Each word maps to a list of words that follow it. The generator then randomly selects next words based on these observed probabilities to create new text.
What are the limitations of Markov chain text generation?
Markov chains produce text that can be grammatically inconsistent over long passages because they only consider local context (the previous few words). They lack understanding of meaning, coherence, and long-range dependencies that modern language models handle better.
Can I use Markov chains for purposes other than text generation?
Yes. Markov chains are used in weather prediction, stock market modeling, DNA sequence analysis, game AI, PageRank algorithms, and many simulation scenarios. Any system where transitions between states follow probabilistic rules can be modeled with Markov chains.
How do I improve the quality of Markov chain generated text?
Increase the chain order (use pairs or triples of words instead of single words as keys), use larger and higher-quality training data, add post-processing to fix grammar, and filter out nonsensical outputs. Higher-order chains produce more coherent text but require more training data.