Install Python locally (optional)¶
Although we will use the dedicated course environment to do all the programming, it is also possible to complete the tutorials and programming exercises on your own computer. The purpose of this page is to help you to install Python and required Python packages locally. We recommend using Miniconda for installing Python (i.e. not the one available from the Python website). Miniconda comes with Python and a small number of useful packages. Additional packages can be installed using the package management system conda.
Miniconda is a light-weight version of Anaconda which is an open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment. In short, it makes life much easier when installing new tools to your Python.
In case you already have Anaconda installed on your computer, you can continue using that during the course without a problem. If you are new to Python and have not yet installed anything, we recommend you to start by installing Miniconda.
Is it necessary to install software?
No! You can also complete the course without installing anything on your own computer. Each lesson contains a launch button for running the code interactively in the browser without a need to install anything.
You can find the latest version of Miniconda for different operating systems in the Miniconda download page. Tips and tricks for Windows, macOS and Linux users below.
Visit the Miniconda download page and download the latest Miniconda3 Windows 64-bit installer for Windows.
Install Miniconda to your computer by double clicking the installer and install it into a directory you want (you might need admin rights). Install it to all users and use default settings.
After the installation is completed, test that the package manager
conda works by
opening a command prompt as a admin user
and running command
conda --version. If the command returns a version number of conda (e.g.
conda 4.5.9) everything is working correctly.
Install Miniconda 3 and add it to system path using Terminal:
# Download and install miniconda (first sudo wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh sudo bash Miniconda3-latest-Linux-x86_64.sh # Add miniconda installation permanently to PATH variable nano ~/.bashrc # Add following line at the end of the file and save (EDIT ACCORDING YOUR INSTALLATION PATH) export PATH=$PATH:/PATH_TO_MINICONDA/miniconda3/bin:/PATH_TO_MINICONDA/miniconda3/lib/python3.9/site-packages
Install the course environment¶
Installing various GIS packages in Python can be sometimes a bit tricky due to various dependencies
between the packages. Sometimes an older version of the package, or even an older Python version might be required for a
specific tool to work. The recommended way to get the installation working smoothly is to create a dedicated
Python environment for the selected Python packages (e.g. for the ones used during this course).
A python environment is a separate installation including all required libraries as well as
the Python interpreter. It is a good practice to install all packages (if possible) from the same
conda channel (e.g.
conda-forge which we recommend), and not to mix conda and pip for installations
if not strictly necessary.
geo environment for the course
Conda has an excellent documentation about creating and managing conda environments where you can check details of the used commands.
Main steps for creating and using a conda environment:
create the environment from environment.yml file,
activate the environment
start using the environment (e.g. launch the JupyterLab and start coding, see below)
We have prepared a ready-made environment file for this course (called
environment.yml). You can DOWNLOAD IT FROM HERE.
After downloading the environment file, run the following commands on the same folder where you downloaded it.
The commands work similarly in all operating systems where you have Miniconda (or Anaconda) installed:
Create the Python environment based on the file that you downloaded:
conda env create -f environment.yml
Activate the environment:
conda activate spatial-analytics
You should now see the name of the environment at the start of the command line.
Launch JupyterLab IDE
After you have installed all required packages, you can start working in a local Jupyter Lab environment that is
linked to your
geo conda environment by launching jupyter lab on the command line.
It’s a good idea to first navigate to the folder where your Jupyter Notebook -files are located before launching Jupyter Lab.
Note, Jupyter Lab will probably prompt you to “Build” the installation in order to get the git-plugin to show.
If you want to install some additional packages to your conda environment, ensure you have activated it (step 2 above) and install the package that you wish to install following the guidelines below.
General guide for installing packages with Conda¶
Conda has an excellent online user guide which covers most of the basic things, such as installing new packages.
You can install new packages using the conda install
command. The basic syntax for installing packages is
conda install package-name.
In addition, we also want to specify the conda channel from where the package is downloaded using the parameter -c.
Installing Pandas package from the conda-forge channel:
conda install -c conda-forge pandas
Once you run this command, you will see also other packages getting installed and/or updated as conda checks for dependencies of the installed package. Read more about package installations in the conda documentation It’s a good idea to search for installation instructions for each package online.
You can install other useful packages in a similar way:
conda install -c conda-forge matplotlib conda install -c conda-forge bokeh conda install -c conda-forge geopandas
Conda channels are remote locations where packages are stored. During this course (and in general when installing packages for scientific computing and GIS analysis) we download most packages from the conda-forge channel.
A good rule of thumb is to always install packages from the same channel (for this course, we prefer the conda-forge channel). In case you encounter an error message when installing new packages, you might want to first check the versions and channels of existing packages using the conda list command before trying again.
We use JupyterLab as the main programming environment during this course. JupyterLab can be installed as a 1) desktop application or 2) like any other packages using the conda install command. For more information, take a look at the JupyterLab installation instructions.
Install JupyterLab using Desktop application:
Install JupyterLab from the conda-forge channel:
conda install -c conda-forge jupyterlab
After installation is completed, you can start a JupyterLab instance by running this command (notice the space between the words!):
After running the command, JupyterLab should open up automatically in a browser window.
Git extension for JupyterLab¶
After you have installed JupyterLab, you can also add the JupyterLab Git extension to your environment:
conda install -c conda-forge jupyterlab-git