The assignments of this course will require the students to use several libraries that have different APIs between versions. A managed Python environment is a simple and effective way to keep the dependencies of different projects separately by creating environments for each of them. 

There are different ways to create and manage virtual environments, we will introduce two main ways here:

• virtualenv
• conda

We can install virtualenv using pip, check pip version first by using pip —version to make sure it’s installed.

If no version version shows up, install pip by following the link here.

With pip installed, we can now install virtualenv using commands below:

        python3 -m pip install —user virtualenv #Linux, macOS
        py -m pip install —user virtualenv #Windows

To create a virtual environment and name it envname (or whatever you prefer):

        python3 -m venv envname #Linux, macOS
        py -m venv envname #Windows

To use your environment, we need to activate it first:

        source envname/bin/activate #Linux, macOS
        .\envname\Scripts\activate #Windows

(Check the location of your python interpreter if you are not sure about the path, it should point to the envname directory.)

After it is activated, we can now install packages using pip install some_package within this environment.

To install entire environments with respective requirements.txt files, create a new virtualenv environment and run pip install -r requirements.txt.

After finish using the environment, call deactivate to leave it.

To learn more about the use of virtualenv, check here.

Using conda to manage the environments requires us to install Miniconda (or Anaconda) first.

Anaconda comes with a lot of data science packages and is intended to use as a pre-installed environment, while Miniconda only comes with the minimal Python environment and the conda tool.

Installing Miniconda is simple, you can just download the right package for your OS and follow the instructions here.

To create a virtual environment, use

        conda create -n envname
         # if you want to pass a specific python version i.e. 3.6
        conda create -n envname python=3.6

To activate the environment, use

        conda activate envname #Linux, macOS
        activate envname #Windows

Then similarly you can use pip install some_package or conda install some_package to install your packages. However, we would recommend you to use the conda tool to install all of your packages.

To install entire environments with respective environment.yml files, run conda env create -f environment.yml. This will create a new conda environment with specifications of the environment.yml file. To change the name of the environment, modify the first line of the environment.yml file before you install it.

After finish using the environment, call conda deactivate to leave it.

For more information using conda, check conda's documentation here.