> For the complete documentation index, see [llms.txt](https://dockgispy.gitbook.io/dockgispy/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dockgispy.gitbook.io/dockgispy/about-dockgispy.md).

# About Dockgispy

**Dockgispy** is a fast Python-based Docker Image environment designed for quick GIS development and workflow. In a nutshell, it helps you with your workflow without the hassle of installing Python modules. It solves a common challenge GIS beginners and professionals face: installing Python modules across different operating systems.

To start GIS development on any operating system, you must account for the underlying system requirements. However, installing specific Python modules, particularly on Windows, can be challenging.

The usual steps for starting any GIS workflow with Python are:

* Check your underlying infrastructure and operating system.
* Find and download a compatible Python version.
* Use `pip` to install the required modules.
* Anticipate a smooth installation of the module.
* Start your favourite development platform (e.g. Jupyter Notebook, Jupyter Lab, VsCode, etc.).
* Start your GIS analysis or Geospatial Workflow.

### How does Dockgispy Work?

If you have read up to this point, you would have wondered how Dockgispy solves common installation challenges. So, Dockgispy is a Docker-based image with a preinstalled GIS Python module which runs in a separate environment from your operating system.

Your next question will be, what is Docker? Docker is a tool that solves the problem of "it works on my computer, but not on another system". Docker runs containers in isolated processes on the host system without affecting any process or files on the host system. Docker was initially built on Linux, hence, whatever runs on a Linux machine will also run on any docker image.

#### Why was Docker Chosen for Dockgispy?

[Docker](https://www.docker.com/) was chosen because it provides a lightweight version of [Linux](https://en.wikipedia.org/wiki/Linux) that can run in a separate process. With a lightweight version of Linux and the ease of installing applications on Linux, Docker is the perfect tool for building Dockgispy. Docker has a concept called images, which contains the installed modules and applications in the lightweight Linux environment. Containers are created when you run the image, which gives you access to the installed modules and packages. The docker image can be run multiple times as containers, and the containers does not affect the underlying image properties. The features of Docker makes it the perfect choice for Dockgispy, which has a preinstalled GIS-based Python module in an image that can be run multiple times and provides a quick development environment.

#### Why you should migrate your workflow to Dockgispy?

An important question on your mind is why should I leave my traditional method of starting Python environment for GIS workflow for Dockgispy.

* 🫙 Low code Python Based Environment for GIS Development and workflow. Dockgispy provides zero code-based environmental setup for your GIS workflow. You do not need to battle the manual code creation of a virtual environment or the issues of installing packages with `pip` or `conda` in a virtual environment.
* 🥰 Faster and easier way to go from ideation to development without worrying about your underlying operating system. With Dockgispy, your speed of going from when an idea comes to your head to start your development environment is faster than the speed of light 😉.
* ⚔ Ability to be creative and explore without fear of destroying your local system. Dockgispy is based on docker, which opens the development environment in an isolated environment away from your local filesystem and processes.
* 💰 Access to different GIS, Python, and big data analytics modules based on your preference.
* 🔥 Fast image build with [UV](https://astral.sh/blog/uv), the fastest Python package manager and installer worldwide. Dockgispy installed Python modules were installed with UV, which sped up the image build. Hence, rebuilding the image for your desired use is blazing fast.
* 🥰 Faster and easier way to go from ideation to development without worrying about your underlying operating system.
* ⚔ Ability to be creative and explore without fear of destroying your local system. The workflow is opened in an isolated environment away from your local filesytem.
* 🔥 Build images at lightning speed with the fastest package manager and installer available, all thanks to [UV](https://astral.sh/blog/uv)!