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 Visual Studio Code (VS Code) IDE and it is possible to use Git from VS Code 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. do cat catchment/views.py and cat catchment/models.py from the project root), you will see some of the following lines somewhere at the top of the code.

from matplotlib import pyplot as plt
import numpy as np
import pandas as pd

Although not every file has the same lines, taken together these mean that our project requires three external libraries (also called third-party packages or dependencies) - numpy, 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 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 Python distribution from Python 3.3+
• virtualenv, needs to be installed separately but supports both Python 2.7+ and Python 3.3+versions
• pipenv, created to fix certain shortcomings of virtualenv
• 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 VS Code 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 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.

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
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.

Making Sure You Can Invoke Python

You can test your Python installation from the command line with:

$ python3 --version # on Mac/Linux
$ python --version # on Windows — Windows installation comes with a python.exe file rather than a python3.exe file 

If you are using Windows and invoking python command causes your Git Bash terminal to hang with no error message or output, you may need to create an alias for the python executable python.exe, as explained in the troubleshooting section.

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).

What is -m Flag in python3 Command?

The Python -m flag means “module” and tells the Python interpreter to treat what follows -m as the name of a module and not as a single, executable program with the same name. Some modules (such as venv or pip) have main entry points and the -m flag can be used to invoke them on the command line via the python command. The main difference between running such modules as standalone programs (e.g. executing “venv” by running the venv command directly) versus using python3 -m command seems to be that with latter you are in full control of which Python module will be invoked (the one that came with your environment’s Python interpreter vs. some other version you may have on your system). This makes it a more reliable way to set things up correctly and avoid issues that could prove difficult to trace and debug.

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:

• pyvenv.cfg configuration file with a home key pointing to the Python installation from which the command was run,
• bin subdirectory (called Scripts on Windows) containing a symlink of the Python interpreter binary used to create the environment and the standard Python library,
• lib/pythonX.Y/site-packages subdirectory (called Lib\site-packages on 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:

• The Hitchhiker’s Guide to Python notes that “venv” is the general convention used globally
• The Python Documentation indicates that “.venv” is common
• “venv” vs “.venv” discussion

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/python-intermediate-rivercatchment/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 - numpy 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) $ python3 -m pip install numpy
(venv) $ python3 -m pip install pandas
(venv) $ python3 -m pip install matplotlib

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

(venv) $ python3 -m pip install numpy pandas matplotlib

How About pip3 install <package-name> Command?

You may have seen or used the pip3 install <package-name> command in the past, which is shorter and perhaps more intuitive than python3 -m pip install. However, the official Pip documentation recommends python3 -m pip install and core Python developer Brett Cannon offers a more detailed explanation of edge cases when the two commands may produce different results and why python3 -m pip install is recommended. In this material, we will use python3 -m whenever we have to invoke a Python module from command line.

If you run the python3 -m pip 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. python3 -m pip install numpy==1.21.1.

To specify a minimum version of a Python package, you can do python3 -m pip install numpy>=1.20.

To upgrade a package to the latest version, e.g. python3 -m pip install --upgrade numpy.

To display information about a particular installed package do:

(venv) $ python3 -m pip show numpy

Name: numpy
Version: 1.26.2
Summary: Fundamental package for array computing in Python
Home-page: https://numpy.org
Author: Travis E. Oliphant et al.
Author-email: 
License: Copyright (c) 2005-2023, NumPy Developers.
All rights reserved.
...
Required-by: contourpy, matplotlib

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

(venv) $ python3 -m pip list

Package         Version
--------------- -------
contourpy       1.0.5
cycler          0.11.0
fonttools       4.37.4
kiwisolver      1.4.4
matplotlib      3.6.1
numpy           1.23.4
packaging       21.3
pandas          1.5.0
Pillow          9.2.0
pip             20.2.3
pyparsing       3.0.9
python-dateutil 2.8.2
pytz            2022.4
setuptools      49.2.1
six             1.16.0

To uninstall a package installed in the virtual environment do: python3 -m pip 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 python3 -m pip 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) $ python3 -m pip freeze > requirements.txt
(venv) $ cat requirements.txt

contourpy==1.0.5
cycler==0.11.0
fonttools==4.37.4
kiwisolver==1.4.4
matplotlib==3.6.1
numpy==1.23.4
packaging==21.3
pandas==1.5.0
Pillow==9.2.0
pyparsing==3.0.9
python-dateutil==2.8.2
pytz==2022.4
six==1.16.0

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) $ python3 -m pip 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”.

Running Python Scripts From Command Line

Congratulations! Your environment is now activated and set up to run our catchment-analysis.py script from the command line.

You should already be located in the root of the python-intermediate-rivercatchment directory (if not, please navigate to it from the command line now). To run the script, type the following command:

(venv) $ python3 catchment-analysis.py

usage: catchment-analysis.py [-h] infiles [infiles ...]
catchment-analysis.py: error: the following arguments are required: infiles

In the above command, we tell the command line two things:

1. to find a Python interpreter (in this case, the one that was configured via the virtual environment), and
2. to use it to run our script catchment-analysis.py, which resides in the current directory.

As we can see, the Python interpreter ran our script, which threw an error - catchment-analysis.py: error: the following arguments are required: infiles. It looks like the script expects a list of one or more input files to process, as indicated by the infiles [infiles ...] text at the end of the usage statement.

We can solve this problem simply by providing those input files. Let’s start with the rainfall data in the file data/rain_data_2015-12.csv:

(venv) $ python3 catchment-analysis.py data/rain_data_2015-12.csv

This will produce the following figure: We can see now that the software calculates, and plots, for each site the daily sum, average, maximum and minimum values of the data provided. The presentation of the data is not perfect, but it is still a helpful overview of the data.

Let’s now provide the river data in the file data/river_data_2015-12.csv:

(venv) $ python3 catchment-analysis.py data/rain_data_2015-12.csv

This time, however, we get the following error message:

Traceback (most recent call last):
  File "/Users/mbessdl2/work/manchester/Course_Material/Intermediate_Programming_Skills/python-intermediate-rivercatchment-template/catchment-analysis.py", line 39, in <module>
    main(args)
  File "/Users/mbessdl2/work/manchester/Course_Material/Intermediate_Programming_Skills/python-intermediate-rivercatchment-template/catchment-analysis.py", line 22, in main
    measurement_data = models.read_variable_from_csv(filename)
  File "/Users/mbessdl2/work/manchester/Course_Material/Intermediate_Programming_Skills/python-intermediate-rivercatchment-template/catchment/models.py", line 22, in read_variable_from_csv
    dataset = pd.read_csv(filename, usecols=['Date', 'Site', 'Rainfall (mm)'])
...
ValueError: Usecols do not match columns, columns expected but not found: ['Rainfall (mm)']

We can see that error is caused by a mismatch between the columns of data the program expects to find, and what columns of data are present in the input file. The traceback information also shows the cause of the error, which is that the columns of data expected are hard-coded in the models.py file:

dataset = pd.read_csv(filename, usecols=['Date', 'Site', 'Rainfall (mm)'])

Hard-coding values like this into your code is problematic because it ultimately reduces the flexibility of your programs. We cannot correct this error right now, but will revisit the problem in a later episode.

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 python3 -m pip freeze > requirements.txt to take snapshot of your project’s dependencies.

• Use python3 -m pip install -r requirements.txt to replicate someone else’s virtual environment on your machine from the requirements.txt file.