In this chapter, we'll pack into the container not only Python and Python script but also several Python libraries.

```Dockerfile
FROM python:3.9-slim

# Installing additional packages
RUN pip install numpy pandas scikit-learn

COPY . .
CMD ["python", "app.py"]
```

After specifying the base image, you use the `RUN pip install` command to install additional Python packages using the `pip` package manager. In this case, you're installing the `numpy`, `pandas`, and `scikit-learn` packages, which for example, might be crucial for data analysis and machine learning.

This Dockerfile demonstrates how to install dependencies and additional files in your Docker container for use in your data analysis or machine learning projects.

# Installing Requirements

Here's the revised Dockerfile where libraries are installed using a requirements file:




```Dockerfile
FROM python:3.9-slim

# Copying all files 
COPY . .

# Installing dependencies from requirements file
RUN pip3 install -r requirements.txt

CMD ["python", "app.py"]
```

You also need to create a `requirements.txt` file listing the libraries you want to install. For example:

```
numpy
pandas
scikit-learn
```

After that, you can build the Docker image using the command `docker build -t <image_name> .`, where `<image_name>` is the name you want to give to your image.

This course is designed for Python developers looking to master Docker for efficient application containerization. You will learn how to create and configure Dockerfiles, optimize images, and use Docker Compose. The course covers Docker basics, container and network management, container security, and access control. It is ideal for Python developers who want to enhance the development and deployment efficiency of their Python applications.

In this section, we'll explore the fundamental concepts of Docker - a powerful tool for managing containers. Understanding these concepts will help you gain a deeper insight into how Docker works and how to effectively utilize it in your software development and deployment processes.

This section will guide you step by step through the process of creating your first Dockerfiles - text files that contain instructions for automating the building of Docker images. You will learn how to create a Dockerfile for your application, including defining the base image, adding necessary components, configuring the environment, and installing dependencies.

The section titled "Docker for Flask Developers" offers an in-depth exploration of utilizing Docker for developing web applications based on the Flask framework for Python. In this section, you'll learn how to effectively use Docker to create, deploy, and manage Flask applications in an isolated and scalable container environment.

This section delves into Docker Compose, a tool designed to simplify the management of multi-container Docker applications. By utilizing Docker Compose, users can define and run multi-container Docker applications with ease, streamlining the process of container orchestration.

Docker for Python Developers

Installing Libraries

Installing Requirements