Adventures in Machine Learning

Python Versions and Docker: Managing Your Development Environment Efficiently

Welcome to our comprehensive guide on understanding Python versions and Docker. In this article, we will explore the different versions of Python, its implementations, and how to use Docker to manage these versions efficiently.

We will also dive into the basics of using Docker, from installing it to building your own images. So grab a cup of coffee and let’s dive right in.

Part 1: Understanding Python Versions and Docker

Python is an interpreted, high-level programming language that is widely used in web development, scientific computing, artificial intelligence, and machine learning. Over the years, Python has undergone several changes and improvements, leading to different versions with different features.

Different Versions of Python and Their Features

Python currently has two active versions Python 2 and Python 3. Python 2 was released in 2000, while Python 3 was released in 2008.

Python 3 is the recommended version for new projects, and it has several features that are not present in Python 2. These features include:

Improved Unicode support

More efficient string handling

Better handling of exceptions

A simplified syntax for function annotations

Extended support for asynchronous programming

On the other hand, Python 2 is still popular and has several libraries and frameworks that are yet to support Python 3.

Therefore, it is important to know the differences and features of each version, depending on your project needs.

Implementations of Python

Python has several implementations, which are different ways of running Python code. The most common implementations are CPython, Jython, IronPython, and PyPy.

CPython is the default and most widely used implementation of Python.

It is written in C and compiles Python code to bytecode. Jython is an implementation of Python that runs on the Java Virtual Machine (JVM), while IronPython runs on the .NET framework.

PyPy is a fast and efficient implementation that uses a Just-In-Time (JIT) compiler to speed up Python code execution.

Using Docker to Manage Python Versions

Docker is a containerization tool that allows you to run isolated environments, called containers, on your computer. Docker makes it easy to manage multiple Python versions and their dependencies without affecting your system’s configuration.

With Docker, you can create a container for each Python version and run your code in a controlled environment. To use Docker for Python development, you need to follow these steps:


Install Docker on your computer. 2.

Pull the Python image for the version you want to use. 3.

Run a container using the pulled image. 4.

Install any additional dependencies you need. 5.

Mount your project directory to the container and run your code. Part 2: Using Docker

Now that we have covered Python’s different versions and Docker’s basics let’s dive into using Docker.

Overview of Docker and Its Concepts

Docker is a framework that uses containerization technology to allow developers to create, deploy and run applications in an isolated environment. Docker has two main concepts: images and containers.

An image is a template for creating a Docker container. It contains everything that is needed to run the application, including the code, libraries, and dependencies.

Containers, on the other hand, are instances of an image that can be run on your computer.

Installing Docker

Before using Docker, you need to install it on your computer. The installation process depends on your operating system.

You can find detailed installation instructions on the Docker website.

Running Containers

To run a container, you need to use the docker run command. When you run the command, Docker will check if the required image is available locally, and if it’s not, it will pull it from the Docker registry.

Here is an example of how to run a container using the Python 3 image:

docker run -it –rm python:3 bash

The -it flag tells Docker to run the container in interactive mode. The –rm flag tells Docker to remove the container when you exit it.

Finally, the python:3 argument specifies the image you want to use, and the bash command tells Docker to start a Bash shell inside the container.

Building Your Own Images Using Dockerfiles

Dockerfiles are files that contain instructions for Docker to build a custom image. You can use Dockerfiles to create images that are tailored to your project needs.

Here is an example of a Dockerfile that creates an image with Python 3 installed:

FROM python:3

RUN mkdir /app

COPY . /app


RUN pip install -r requirements.txt

CMD [“python”, “”]

In this Dockerfile, we start with the official Python 3 image (FROM python:3), create a directory for our application (/app), copy the contents of our project folder to the container (COPY .

/app), set the working directory to /app (WORKDIR /app), install the dependencies listed in the requirements file (RUN pip install -r requirements.txt), and finally, run our application (CMD [“python”, “”]).


Understanding Python versions and Docker is essential for modern software development. With this guide, you now have a better understanding of the different versions of Python and their features, how to use Docker to manage Python versions, and the basics of using Docker.

We hope that this article has provided you with some valuable insights and inspired you to start using these powerful tools in your development projects. Part 3: Running Python in a Docker Container

Running Python in a Docker container is a great way to isolate your Python development environment and avoid conflicts with your system’s configuration.

Docker containers also provide a consistent environment for your project, ensuring that your code works seamlessly across different platforms. In this section, we will discuss various ways to run Python in a Docker container, beginning with the Python REPL.

Playing With The REPL

The Python REPL is an interactive environment that allows you to experiment with Python code. It is a quick way to test small code snippets and try out new features.

You can run the Python REPL inside a Docker container by following the steps below:

1. Pull the Python image for the version you want to use.

docker pull python:3.9

2. Run a container and start the Python REPL.

docker run -it –rm python:3.9 python

This command creates a container, starts it in interactive mode (-it), and runs the Python interpreter (python). With this setup, you can play around with the Python REPL, test different Python versions, and learn about Python features.

Setting Up Your Python Environment

To set up your Python environment in a Docker container, you need to create a Dockerfile that specifies the dependencies and configuration for your project. Here is an example of a Dockerfile that sets up a Python development environment:

FROM python:3.9

# Set working directory


# Copy project files to the container


. # Install dependencies

RUN pip install –upgrade pip

RUN pip install -r requirements.txt

# Expose port


# Set default command to run the application

CMD [“python”, “”, “runserver”, “”]

In this Dockerfile, we start with the Python 3.9 image, set the working directory to /app, copy the project files to the container, install the dependencies listed in the requirements.txt file using pip, expose port 8000, and set the default command to run the Django server.

You can build the Docker image using the following command:

docker build -t my-python-app . The -t flag sets the name for the image, and the period (.) at the end specifies the build context.

Running Python Scripts Using Docker

You can also use Docker to run Python scripts without having to install Python and its dependencies on your machine. Here’s an example using a script called “”:


Create a Dockerfile that installs Python and copies the script to the container. FROM python:3.9


2. Build the Docker image.

docker build -t my-python-script . 3.

Run the Docker container and pass the script as an argument. docker run my-python-script python

This command runs a container using the “my-python-script” image and runs the “” script using the “python” interpreter.

Part 4: Running the Latest Alpha

Docker makes it easy to test and preview new versions of Python, including alpha and pre-release versions. Many developers use image repositories like, which provides a wide range of container images.

Here’s how you can use to preview a new version of Python:

1. Search for the Python image you want to test on the website.

2. Pull the image using the Docker pull command.

docker pull

3. Run the container and start the Python REPL or run your scripts.

docker run -it –rm python

This command creates a container and starts the Python REPL using the specified image, where you can experiment with the new Python version. In conclusion, Docker provides a powerful environment for Python development that allows you to easily test and experiment with the language and its various versions.

Whether you are developing a small script or a complex application, Docker can help you isolate and manage your Python environment efficiently. Additionally, Docker’s compatibility with various platforms and repositories like allows you to preview pre-release versions of Python, enabling you to stay ahead of the game in the constantly evolving world of software development.

In summary, Docker provides a powerful environment for Python development, making it easy to isolate and manage your Python environment efficiently. This article covered many important topics, including the different versions and implementations of Python, how to use Docker to manage your Python environment, running Python in a Docker container, setting up your Python environment, and testing and previewing new versions of Python supported by repositories like

The article highlights the importance of using Docker containers to avoid conflicts with your system’s configuration and ensure a consistent environment across different platforms. The takeaway is that implementing Docker containers for Python development can make your coding more organized, efficient, and easier to test and promote.

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