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Virtual Environments For Software Development

Overview

Teaching: 30 min
Exercises: 0 min
Questions

  • 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 venv and pip.

  • Run our software from the command line.

Introduction

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 Jupyter Lab and it is possible to use Git with a Jupyter Lab extension (and many other IDEs have built-in functionality for this too), typing commands in the command line allows you to familiarise yourself and learn it well. Running Git from the command line does not depend on the IDE and for the most part, uses the same commands in different OS, so it is the most universal way of using it.

If you have a little peek into our code (e.g. run cat lcanalyzer/views.py from the project root), you will see the following two lines somewhere at the top.

from matplotlib import pyplot as plt
import pandas as pd

This means that our code requires two external libraries (also called third-party packages or dependencies) - pandas and matplotlib. 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.

Virtual Environments

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 use of virtual environments is highly recommended (almost unavoidable):

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:

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 default virtual environment manager for Python 3.3+).

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 conda

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. conda is 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 conda for both tasks instead of using venv and pip. However, there are some differences in the way pip and conda work. Quoting Jake VanderPlas, “pip installs python packages in any environment. conda installs any package in conda environments. If your project is purely Python, venv is a cleaner and more lightweight tool. conda is more convenient if you need to install non-Python packages. Here is more in-depth analysis of the topic.

Another case when conda is more convenient is when you need to create many environments with different versions of Python. Instead of installing the needed Python version manually, with conda you can do it with a one-liner:

$ conda create -n envname python=*.**

If you have conda installed on your PC, make sure to deactivate conda environments before using venv

$ conda deactivate

While you can, in principle, have both conda and venv virtual environments activated, you should avoid this situation as it is likely to produce issues. The names of the active environments are listed in parenthesis before your current location path, so if there are two environments listed, deactivate one of them.

(conda_base) (venv) alex@Serenity:/mnt/Data/Work/GitHub/InterPython_Workshop_Example$

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 pip to manage external packages for that Python version.

venv and 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 XKCD comic

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 pip.

Creating Virtual Environments Using venv

Creating a virtual environment with venv is done by executing the following command:

$ python3 -m venv /path/to/new/virtual/environment

where /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

total 8
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” containing:

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
(venv) $

On Windows, recall that we have Scripts directory instead of bin and activating a virtual environment is done as:

$ source venv/Scripts/activate
(venv) $

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 which:

(venv) $ which python3

/home/alex/InterPython_Workshop_Example/venv/bin/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
(venv) $

Python Within A Virtual Environment

Within a virtual environment, commands python and pip will refer to the version of Python you created the environment with. If you create a virtual environment with python3 -m venv venv, python will refer to python3 and pip will refer to pip3.

On some machines with Python 2 installed, python command 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 python to be absolutely sure. We continue using python3 and pip3 in this material to avoid confusion for those users, but commands python and pip may 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 pip

We noticed earlier that our code depends on two external packages/libraries - pandas and 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 pandas
(venv) $ pip3 install matplotlib

or like this to install multiple packages at once for short:

(venv) $ pip3 install pandas matplotlib

How About python3 -m pip install?

Why are we not using pip as an argument to python3 command, in the same way we did with venv (i.e. python3 -m venv)? python3 -m pip install should 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 pandas==2.1.2.

To specify a minimum version of a Python package, you can do pip3 install pandas>=2.1.0.

To upgrade a package to the latest version, e.g. pip3 install --upgrade pandas.

To display information about a particular installed package do:

(venv) $ pip3 show pandas

Name: pandas
Version: 2.1.4
Summary: Powerful data structures for data analysis, time series, and statistics
Home-page: https://pandas.pydata.org
Author: 
Author-email: The Pandas Development Team <pandas-dev@python.org>
License: BSD 3-Clause License
...
Requires: numpy, python-dateutil, pytz, tzdata
Required-by:

To list all packages installed with pip (in your current virtual environment):

(venv) $ pip3 list

Package         Version
--------------- -------
contourpy       1.2.0
cycler          0.12.1
fonttools       4.47.2
kiwisolver      1.4.5
matplotlib      3.8.2
numpy           1.26.3
packaging       23.2
pandas          2.1.4
pillow          10.2.0
pip             23.3.2
pyparsing       3.1.1
python-dateutil 2.8.2
pytz            2023.3.post1
setuptools      65.5.0
six             1.16.0
tzdata          2023.4

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 pip

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 requirements.txt file:

(venv) $ pip3 freeze > requirements.txt
(venv) $ cat requirements.txt

contourpy==1.2.0
cycler==0.12.1
fonttools==4.47.2
kiwisolver==1.4.5
matplotlib==3.8.2
numpy==1.26.3
packaging==23.2
pandas==2.1.4
pillow==10.2.0
pyparsing==3.1.1
python-dateutil==2.8.2
pytz==2023.3.post1
six==1.16.0
tzdata==2023.4

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.

The 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 requirements.txt file 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).

Official Documentation

For a full list of options and commands, consult the official venv documentation and the Installing Python Modules with pip guide. Also check out the guide “Installing packages using pip and virtual environments”.

Installing Jupyter Lab

Jupyter Lab itself comes as a Python package. Therefore, we have to install it in the environment as well. Another package that we will need for our project is astropy, which provides a lot of functions, useful for writing astronomical software and data processing.

(venv) $ pip3 install astropy
(venv) $ pip3 install jupyterlab

Do not forget to update the requirements.txt file after the installation is finished. If you run pip freeze, you will see that Jupyter Lab installed a lot of dependencies libraries, so the list of requirements is now much larger.

Key Points

  • Virtual environments keep Python versions and dependencies required by different projects separate.

  • A virtual environment is itself a directory structure.

  • Use venv to create and manage Python virtual environments.

  • Use pip to install and manage Python external (third-party) libraries.

  • pip allows 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.

  • Use pip3 freeze > requirements.txt to take snapshot of your project’s dependencies.

  • Use pip3 install -r requirements.txt to replicate someone else’s virtual environment on your machine from the requirements.txt file.

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