The Python Journey - Getting Started with Python

Useful Links

Python Beginner's Guide The Python Handbook The Official Python Tutorial Getting started with Python in VS Code Tutorial Git Virtual Environment Conda

If you’re new to Python, then you’ll want to start here. If you’re already comfortable using Python, then feel free to dive straight into the AoC walkthroughts, using the links above.

Why Python?

If you’re a newbie to programming, then Python is a great language to start with. If you have a lot of programming experience, then guess what? Python is an amazing language to learn, and you’ll love it.

Many years ago, I used to write software for a living. Back then, my preferred languages were Java and C#. These days, I don’t write code in my job, though I do still need to know my way around. Now I write code as a hobby. But I can honestly say: of all the programming languages I’ve ever used, Python is my favourite.

For those of you coming from a background with languages like C, C#, Java, Javascript… You may find Python’s syntax and structure a bit jarring. But trust me: once you get your head around the differences, you’ll love it, just like I do.

Here are some of Python’s strengths:

It’s extremely readable. It’s syntax tends to be aligned to natural language.
It’s highly portable. Your Python programs will run on just about any operating system.
It’s really easy to install and use.
It’s open source: free to use, and free to distribute.
It’s extremely versatile. You can use it for console applications, web applications, automation, APIs, data science, machine learning, maths and statistics, graphs, visualisation, writing games, and so much more.
There are a massive number open source libraries available, to do just about any task.

My Recommendations for Getting Started

I recommend working through the following, to get started:

Install Python
Get a development environment / editor
Get Git
Learn the Python basics
Python Virtual Environments
Using Conda

Getting and Installing Python

To execute Python programs, you need to install Python on your machine. It’s possible you already have Python installed. To check, open your command prompt (or shell) and run:

> python --version

If you get a response that shows you have any Python 3.x version installed, then you’re good to go. E.g.

Verify Python Installation

If you haven’t got Python installed, you’ll want the latest available Python 3 version, which you can download from here. It’s really quick and easy to install. It should only take a couple of minutes. Take a look here if you need any guidance on installing Python.

Once you’ve installed it, check the installation as described above.

Development Environment / Editor

You can write and run Python programs without a development environment. But it’s a much better - and more effective - experience, if you have one.

A development environment adds a wealth of features, such as:

The ability to conveniently organise files and folders into projects.
Syntax colouring.
Linting - flagging of problems in your code, such as syntax errors, warnings, stylistic errors, or something potentially dangerous.
Autocompletion - the ability to offer likely completion of what you’re typing.
Refactoring - the ability to rename symbols, extract methods, etc.
Debugging - the ability to step through code, show the current value of variables, stop at break points, etc.
Keyboard commands and shortcuts that greatly improve productivity.
Integration with terminals / consoles / shells.
The ability install a number of freely available extensions and plugins, e.g. to integrate git, Docker, Jupyter Notebooks, enhanced visualisation of certain file types and formats, and so on.

Visual Studio Code

My favourite development environment is Visual Studio Code - aka VS Code - which can be downloaded and installed from here. (Note: VS Code is not the same as Visual Studio, which is a different product.)

Some benefits of VS Code:

It is open source and free.
It is platform-agnostic. I.e. you can run it on a wide variety of operating systems, including Windows, Linux and Mac.
Unlike some development environments, VS Code is a very lightweight and fast editor.
It is language-agnostic. You can use it write much more than just Python. E.g. if you want to write HTML, Javascript, markdown, yaml… No problem!
It is supported by a massive library of free extensions and plugins.
Really swish Git integration, out-of-the-box.
It looks great.
It is highly configurable.

Visual Studio Code

Installing and Setting Up VS Code

Check out the following useful links:

Install Useful Extensions

Now you’ll want to install some extensions that will make your Python - and general development - experience better. In VS Code, click on the Extensions button in the left hand panel of buttons. I’d recommend installing the following:

Python (Microsoft) - provides rich support for the Python language, including features such as IntelliSense (provided through Pylance), linting, debugging, code navigation, code formatting, refactoring, variable explorer, test explorer, and more. Note that when you install this extension, it automatically installs Pylance and Jupyter (for Jupyter Notebook support).
Better Comments - a cool plugin that enhances your comments.
Git Graph - for visualing your Git repos.
Jupyter - for running Jupyter notebooks inside of VS Code.
Python Docstring Generator - a cool plugin that helps with the creation of docstrings. If you don’t know what these are, then don’t worry about this just yet!
Rainbow CSV - Colourises any CSV files you open in VS Code.
Reload - Adds a button to reload your VS Code environment.
Python Indent - auto-identation for Python.

Note: if you’ve followed the Getting started with Python in VS Code Tutorial, this will have guided you through installing some of these extensions.

Git

Git is an extremely popular version control system (VCS). A VCS provides capabilities like these:

The ability to store and track all the changes to your code or project.
The ability to revert to your project to any point in its history. E.g. to get your code back to a previous state, before you made some catastrophic error!
The ability to share and collaborate with others.
The ability to obtain code from others.
The ability branch code, e.g. for experimenting, building new features, fixing bugs, etc.
The ability to integrate with other tools, like bug tracking and CI/CD pipelines.

So Why Git?

Git is an open source VCS, created by the legendary Linus Torvalds. You know… The guy that created Linux.

Git is:

Really easy to use.
Platform-agnostic.
Extremely fast.
Distributed! This means that you always have a complete copy of a proejct’s history on your own machine! But you can (and should) make use of a centralised, cloud-based repo service, like GitHub. In fact, all of my AoC source code is stored on GitHub, which is how I make it available to you.

Installing Git

Download and install git.
See the Git Getting Started documentation.
Git Installation Guide
Git Initial Setup

Python Basics

Finally, if you’re completely new to Python, you might want to learn some basics before you begin with AoC. Here’s a list of some decent learning material to help you get started:

Python Virtual Environments

Virtual environments are isolated Python contexts in which we can install Python packages. This allows specific and explicit combinations of Python runtimes, packages and frameworks, that may be different from other versions installed in the host OS. Basically, it gives us the ability to install packages in one environment without impacting another environment.

In Python, you should ALWAYS work inside a virtual environment (venv). Always install your packages into a venv, rather than at the global OS level.

General Steps for Creating a Virtual Environment

You need to do this once, per project, per development machine you’re working with.

# From the root of your project folder
python -m venv .venv # where .venv is the name of the virtual env you are creating

Note that the .venv folder should NOT be checked-in to source control. You will always create the venv on any given development machine.

Activating the Virtual Environment

A venv must be activated. VS Code will usually detect any virtual environments installed within the project folder that has been opened in VS Code. But outside of VS Code, you activate a venv like this:

source .venv/bin/activate # Linux
.venv\Scripts\activate.ps1 # Windows

Installing Packages to the Environment

To install packages to an environment for the first time:

python -m pip install some-package

But once you’ve installed packages, you can “freeze” your current package installation, like this:

python -m pip freeze > path/to/requirements.txt

This file WILL be checked-in to source control. So we can use this file to repeat the same package installations on any other machine, i.e.

python3 -m pip install -r requirements.txt # install application dependencies

Using Conda

Conda is a powerful environment and package manager. It has a number of advantages over using pip and Python virtual environments:

It can install packages and dependencies that are not just pure Python. This can be particularly useful if you’re using applications that need to make use of system-level libraries or tools, like ffmpeg and graphviz.
It has better dependency management and conflict resolution.
It provides pre-compiled and optimised versions of many packages; particularly those related to data science, such as NumPy, Pandas, PyTorch, etc.

A Conda environment is a separate Python installation with a specific set of packages; much like a Python virtual environment. We can work with Conda environments like this:

conda env list # see environments and current active env

export AOC_ENV="aoc-conda-env"

# Create and activate an environment
conda create --name $AOC_ENV
conda activate $AOC_ENV

# Install some core packages
# Before trying to run any Python code or Jupyter cells in this env
conda install -y -c conda-forge python jupyter jupyterlab

# Additional packages - or we could install these from (say) a Jupyter notebook
conda install pandas hvplot mathjax matplotlib networkx numpy plotly scipy

# Export the current environment config - for source control
conda env export > $AOC_ENV.yml

# To delete a Conda env
conda remove --name $AOC_ENV --all

›