Create a Python GIS environment#

Python environments#

Especially on Windows, installing Python packages can sometimes become tricky, as dependencies between packages and dependencies on external libraries are difficult to manage there. At times, a specific tool might require an older version of a package, or even an older Python version. As mentioned earlier, it is also good practice to install packages from one source, primarily, such as from the same conda channel. Fortunately, there is a mechanism that allows us to keep the packages needed for different Python projects separated: Python environments.

Generally speaking, a Python environment is a separate local installation of all tools required for a particular task, including a Python interpreter, Python packages, and underlying libraries. Python has built-in, offical tools for managing environments: the venv module and the virtualenv package. However, there has been a time when GIS packages installed using these tools did not always function properly on Windows, because they relied on external libraries. This is why there exists a historically grown tendency to use conda to manage Python environments for geo-spatial applications.

Also during this course, we will use conda to install and manage Python packages. Read below how to create a Python environment that includes all tools needed for this course.

Creating a conda environment from scratch#

Conda’s documentation has an excellent section on how to create and manage conda environments. Please check it for reference on the commands we use below.

Creating a conda environment entails three basic steps:

  1. Create an environment,

  2. activate the environment, and

  3. install packages into the environment.

The command below work across different operating systems, granted that Anaconda or Miniconda have been installed.

Create an environment and give it a name:

conda create --name autogis

Activate the newly created environment:

conda activate autogis

You should now see the name of the environment at the command line prompt (at the beginning of the line).

Install packages into the activated environment:

# Install jupyter lab + git extension
conda install -c conda-forge jupyterlab jupyterlab-git

# Install packages
conda install -c conda-forge geopandas
conda install -c conda-forge matplotlib
conda install -c conda-forge geojson
conda install -c conda-forge folium
# ... install other packages

After you have installed all required packages, you can start working in a local JupyterLab environment that is linked to your autogis conda environment:

jupyter lab


It is a good idea to navigate to the folder of your Jupyter Notebook files before launching JupyterLab.

Create a conda environment from a YAML file#

It is also possible to create a conda environment based on a pre-defined configuration file. Requirements for the conda environment can be written into a YAML-file (file extension .yaml or .yml).

You can find and download a configuration file that contains all required packages needed during the Automating GIS processes course from its GitHub repository:

The configuration file looks like this:

name: autogis

    - conda-forge

    - python=3.10

    # sphinx + dependencies
    - myst-nb
    - sphinx
    - sphinx-book-theme

    # JupyterLab
    - jupyterlab
    - jupyterlab-git

    # lessons
    - bokeh
    - folium
    - geojson
    - geopandas
    - geopy
    - matplotlib
    - osmnx
    - pyrosm
    - r5py

Once you have downloaded the file, navigate to its folder, and run the following command to create a new conda environment according to the specifications set forward in the file:

conda env create --file=environment.yml

Solving all dependencies and installing the required packages might take a surprisingly long time, please remain patient. Once the installation has finished, you are ready to use your shiny new GIS package environment. Activate it using conda:

conda activate autogis

Docker environments#

Docker is a platform that can be used to ‘package’ computing tools into a so called container. Docker allows to develop applications and computing environments that are ‘ready-to-run’ without further hassle with installations.

For example, the instances at CSC notebooks are based on a docker image that contains a ubuntu operating system with Jupyter Lab, Python and relevant Python packages for this course. Dockerfiles used for setting up the CSC notebooks environments for Geo-Python and AutoGIS are documented at