Virtual Environments For Software Development
OverviewTeaching: 30 min
Exercises: 0 minQuestions
What are virtual environments in software development and why you should use them?
How can we manage Python virtual environments and external (third-party) libraries?Objectives
Set up a Python virtual environment for our software project using
Run our software from the command line.
So far we have cloned our software project from GitHub and inspected its contents and architecture a bit. We now want to run our code to see what it does - let’s do that from the command line. For the most part of the course we will run our code and interact with Git from the command line. While we will develop and debug our code using the PyCharm IDE and it is possible to use Git from PyCharm too, typing commands in the command line allows you to familiarise yourself and learn it well. A bonus is that this knowledge is transferable to running code in other programming languages and is independent from any IDE you may use in the future.
If you have a little peak into our code (e.g. run
cat inflammation/views.py from the project root), you will see the
following two lines somewhere at the top.
from matplotlib import pyplot as plt
import numpy as np
This means that our code requires two external libraries (also called third-party packages or dependencies) -
Python applications often use external libraries that don’t come as part of the standard Python distribution. This means
that you will have to use a package manager tool to install them on your system.
Applications will also sometimes need a
specific version of an external library (e.g. because they were written to work with feature, class, or function that may have been updated in more recent versions), or a specific version of Python interpreter.
This means that each Python application you work with may require a different setup and a set of dependencies so it
is useful to be able to keep these configurations separate to avoid confusion between projects.
The solution for this problem is to create a self-contained virtual
environment per project, which contains a particular version of Python installation plus a number of
additional external libraries.
Virtual environments are not just a feature of Python - most modern programming languages use them to isolate libraries for a specific project and make it easier to develop, run, test and share code with others. Even languages that don’t explicitly have virtual environments have other mechanisms that promote per-project library collections. In this episode, we learn how to set up a virtual environment to develop our code and manage our external dependencies.
So what exactly are virtual environments, and why use them?
A Python virtual environment helps us create an isolated working copy of a software project that uses a specific version of Python interpreter together with specific versions of a number of external libraries installed into that virtual environment. Python virtual environments are implemented as directories with a particular structure within software projects, containing links to specified dependencies allowing isolation from other software projects on your machine that may require different versions of Python or external libraries.
As more external libraries are added to your Python project over time, you can add them to its specific virtual environment and avoid a great deal of confusion by having separate (smaller) virtual environments for each project rather than one huge global environment with potential package version clashes. Another big motivator for using virtual environments is that they make sharing your code with others much easier (as we will see shortly). Here are some typical scenarios where the usage of virtual environments is highly recommended (almost unavoidable):
- You have an older project that only works under Python 2. You do not have the time to migrate the project to Python 3 or it may not even be possible as some of the third party dependencies are not available under Python 3. You have to start another project under Python 3. The best way to do this on a single machine is to set up two separate Python virtual environments.
- One of your Python 3 projects is locked to use a particular older version of a third party dependency. You cannot use the latest version of the dependency as it breaks things in your project. In a separate branch of your project, you want to try and fix problems introduced by the new version of the dependency without affecting the working version of your project. You need to set up a separate virtual environment for your branch to ‘isolate’ your code while testing the new feature.
You do not have to worry too much about specific versions of external libraries that your project depends on most of the time. Virtual environments also enable you to always use the latest available version without specifying it explicitly. They also enable you to use a specific older version of a package for your project, should you need to.
A Specific Python or Package Version is Only Ever Installed Once
Note that you will not have a separate Python or package installations for each of your projects - they will only ever be installed once on your system but will be referenced from different virtual environments.
Managing Python Virtual Environments
There are several commonly used command line tools for managing Python virtual environments:
venv, available by default from the standard
virtualenv, needs to be installed separately but supports both
pipenv, created to fix certain shortcomings of
conda, package and environment management system (also included as part of the Anaconda Python distribution often used by the scientific community)
poetry, a modern Python packaging tool which handles virtual environments automatically
While there are pros and cons for using each of the above, all will do the job of managing Python
virtual environments for you and it may be a matter of personal preference which one you go for.
In this course, we will use
venv to create and manage our
virtual environment (which is the preferred way for Python 3.3+). The upside is that
venv virtual environments created from the command line are
also recognised and picked up automatically by PyCharm IDE, as we will see in the next episode.
Managing External Packages
Part of managing your (virtual) working environment involves installing, updating and removing external packages
on your system. The Python package manager tool
pip is most commonly used for this - it interacts
and obtains the packages from the central repository called Python Package Index (PyPI).
pip can now be used with all Python distributions (including Anaconda).
A Note on Anaconda and
Anaconda is an open source Python distribution commonly used for scientific programming - it conveniently installs Python, package and environment management
conda, and a number of commonly used scientific computing packages so you do not have to obtain them separately.
condais an independent command line tool (available separately from the Anaconda distribution too) with dual functionality: (1) it is a package manager that helps you find Python packages from remote package repositories and install them on your system, and (2) it is also a virtual environment manager. So, you can use
condafor both tasks instead of using
Many Tools for the Job
Installing and managing Python distributions, external libraries and virtual environments is, well,
complex. There is an abundance of tools for each task, each with its advantages and disadvantages, and there are different
ways to achieve the same effect (and even different ways to install the same tool!).
Note that each Python distribution comes with its own version of
pip - and if you have several Python versions installed you have to be extra careful to use the correct
manage external packages for that Python version.
pip are considered the de facto standards for virtual environment and package management for Python 3.
However, the advantages of using Anaconda and
conda are that you get (most of the) packages needed for
scientific code development included with the distribution. If you are only collaborating with others who are also using
Anaconda, you may find that
conda satisfies all your needs. It is good, however, to be aware of all these tools,
and use them accordingly. As you become more familiar with them you will realise that equivalent tools work in a similar
way even though the command syntax may be different (and that there are equivalent tools for other programming languages
too to which your knowledge can be ported).
Python Environment Hell
From XKCD (Creative Commons Attribution-NonCommercial 2.5 License)
Let us have a look at how we can create and manage virtual environments from the command line using
venv and manage packages using
Creating Virtual Environments Using
Creating a virtual environment with
venv is done by executing the following command:
$ python3 -m venv /path/to/new/virtual/environment
/path/to/new/virtual/environment is a path to a directory where you want to place it - conventionally within
your software project so they are co-located.
This will create the target directory for the virtual environment (and any parent directories that don’t exist already).
For our project let’s create a virtual environment called “venv”. First, ensure you are within the project root directory, then:
$ python3 -m venv venv
If you list the contents of the newly created directory “venv”, on a Mac or Linux system (slightly different on Windows as explained below) you should see something like:
$ ls -l venv
drwxr-xr-x 12 alex staff 384 5 Oct 11:47 bin
drwxr-xr-x 2 alex staff 64 5 Oct 11:47 include
drwxr-xr-x 3 alex staff 96 5 Oct 11:47 lib
-rw-r--r-- 1 alex staff 90 5 Oct 11:47 pyvenv.cfg
So, running the
python3 -m venv venv command created the target directory called “venv”
pyvenv.cfgconfiguration file with a home key pointing to the Python installation from which the command was run,
Scriptson Windows) containing a symlink of the Python interpreter binary used to create the environment and the standard Python library,
Lib\site-packageson Windows) to contain its own independent set of installed Python packages isolated from other projects,
- various other configuration and supporting files and subdirectories.
Naming Virtual Environments
What is a good name to use for a virtual environment? Using “venv” or “.venv” as the name for an environment and storing it within the project’s directory seems to be the recommended way - this way when you come across such a subdirectory within a software project, by convention you know it contains its virtual environment details. A slight downside is that all different virtual environments on your machine then use the same name and the current one is determined by the context of the path you are currently located in. A (non-conventional) alternative is to use your project name for the name of the virtual environment, with the downside that there is nothing to indicate that such a directory contains a virtual environment. In our case, we have settled to use the name “venv” instead of “.venv” since it is not a hidden directory and we want it to be displayed by the command line when listing directory contents (the “.” in its name that would, by convention, make it hidden). In the future, you will decide what naming convention works best for you. Here are some references for each of the naming conventions:
Once you’ve created a virtual environment, you will need to activate it.
On Mac or Linux, it is done as:
$ source venv/bin/activate
On Windows, recall that we have
Scripts directory instead of
bin and activating a virtual environment is done as:
$ source venv/Scripts/activate
Activating the virtual environment will change your command line’s prompt to show what virtual environment you are currently using (indicated by its name in round brackets at the start of the prompt), and modify the environment so that running Python will get you the particular version of Python configured in your virtual environment.
You can verify you are using your virtual environment’s version of Python by checking the path using the command
(venv) $ which python3
When you’re done working on your project, you can exit the environment with:
(venv) $ deactivate
If you’ve just done the
deactivate, ensure you reactivate the environment ready for the next part:
$ source venv/bin/activate
Python Within A Virtual Environment
Within a virtual environment, commands
pipwill refer to the version of Python you created the environment with. If you create a virtual environment with
python3 -m venv venv,
pythonwill refer to
pipwill refer to
On some machines with Python 2 installed,
pythoncommand may refer to the copy of Python 2 installed outside of the virtual environment instead, which can cause confusion. You can always check which version of Python you are using in your virtual environment with the command
which pythonto be absolutely sure. We continue using
pip3in this material to avoid confusion for those users, but commands
pipmay work for you as expected.
Note that, since our software project is being tracked by Git, the newly created virtual environment will show up in version control - we will see how to handle it using Git in one of the subsequent episodes.
Installing External Packages Using
We noticed earlier that our code depends on two external packages/libraries -
matplotlib. In order
for the code to run on your machine, you need to
install these two dependencies into your virtual environment.
To install the latest version of a package with
pip you use pip’s
install command and specify the package’s name, e.g.:
(venv) $ pip3 install numpy
(venv) $ pip3 install matplotlib
or like this to install multiple packages at once for short:
(venv) $ pip3 install numpy matplotlib
python3 -m pip install?
Why are we not using
pipas an argument to
python3command, in the same way we did with
python3 -m venv)?
python3 -m pip installshould be used according to the official Pip documentation; other official documentation still seems to have a mixture of usages. Core Python developer Brett Cannon offers a more detailed explanation of edge cases when the two options may produce different results and recommends
python3 -m pip install. We kept the old-style command (
pip3 install) as it seems more prevalent among developers at the moment - but it may be a convention that will soon change and certainly something you should consider.
If you run the
pip3 install command on a package that is already installed,
pip will notice this and do nothing.
To install a specific version of a Python package give the package name followed by
== and the version number, e.g.
pip3 install numpy==1.21.1.
To specify a minimum version of a Python package, you can
pip3 install numpy>=1.20.
To upgrade a package to the latest version, e.g.
pip3 install --upgrade numpy.
To display information about a particular installed package do:
(venv) $ pip3 show numpy
Summary: NumPy is the fundamental package for array computing with Python.
Author: Travis E. Oliphant et al.
To list all packages installed with
pip (in your current virtual environment):
(venv) $ pip3 list
To uninstall a package installed in the virtual environment do:
pip3 uninstall package-name.
You can also supply a list of packages to uninstall at the same time.
Exporting/Importing Virtual Environments Using
You are collaborating on a project with a team so, naturally, you will want to share your environment with your
collaborators so they can easily ‘clone’ your software project with all of its dependencies and everyone
can replicate equivalent virtual environments on their machines.
pip has a handy way of exporting,
saving and sharing virtual environments.
To export your active environment - use
pip3 freeze command to
produce a list of packages installed in the virtual environment.
A common convention is to put this list in a
(venv) $ pip3 freeze > requirements.txt
(venv) $ cat requirements.txt
The first of the above commands will create a
requirements.txt file in your current directory. Yours may look a little different, depending on the version of the packages you have installed, as well as any differences in the packages that they themselves use.
requirements.txt file can then be committed to a version control system (we will see how to do this using Git in
one of the following episodes) and
get shipped as part of your software and shared with collaborators and/or users. They can then replicate your environment and
install all the necessary packages from the project root as follows:
(venv) $ pip3 install -r requirements.txt
As your project grows - you may need to update your environment for a variety of reasons. For example, one of your project’s dependencies has
just released a new version (dependency version number update), you need an additional package for data analysis
(adding a new dependency) or you have found a better package and no longer need the older package (adding a new and
removing an old dependency). What you need to do in this case (apart from installing the new and removing the
packages that are no longer needed from your virtual environment) is update the contents of the
accordingly by re-issuing
pip freeze command and propagate the updated
requirements.txt file to your collaborators
via your code sharing platform (e.g. GitHub).
For a full list of options and commands, consult the official
venvdocumentation and the Installing Python Modules with
pipguide. Also check out the guide “Installing packages using
pipand virtual environments”.
Running Python Scripts From Command Line
Congratulations! Your environment is now activated and set up to run our
from the command line.
You should already be located in the root of the
(if not, please navigate to it from the command line now). To run the script, type the following command:
(venv) $ python3 inflammation-analysis.py
usage: inflammation-analysis.py [-h] infiles [infiles ...]
inflammation-analysis.py: error: the following arguments are required: infiles
In the above command, we tell the command line two things:
- to find a Python interpreter (in this case, the one that was configured via the virtual environment), and
- to use it to run our script
inflammation-analysis.py, which resides in the current directory.
As we can see, the Python interpreter ran our script, which threw an error -
inflammation-analysis.py: error: the following arguments are required: infiles. It looks like the script expects
a list of input files to process, so this is expected behaviour since we don’t supply any. We will fix this error in a
Virtual environments keep Python versions and dependencies required by different projects separate.
A virtual environment is itself a directory structure.
venvto create and manage Python virtual environments.
pipto install and manage Python external (third-party) libraries.
pipallows you to declare all dependencies for a project in a separate file (by convention called
requirements.txt) which can be shared with collaborators/users and used to replicate a virtual environment.
pip3 freeze > requirements.txtto take snapshot of your project’s dependencies.
pip3 install -r requirements.txtto replicate someone else’s virtual environment on your machine from the