What Is Python Virtual Environment and why we need it?
Virtual environment helps to create a sandbox by isolating dependencies required by a specific python project. As a result each python project can have an isolated environment / sandbox with it’s own dependencies, regardless of the decencies a-viable elsewhere on the same system.
Why Use Virtual Environment?
In this section I will list some of reasons and benefits of creating and managing multiple virtual environments for Python development.
- Major operating systems like macOS come with pre-installed version of Python, however the version installed with OS might be an older one and in some cases upgrading this default installation my compromise the proper working on system components.
- With default global installation using pip, it’s not possible to install different packages with
- Virtual environment enables you to use different version of a same package for different projects.
- By default pip installs the python packages globally, this may result in conflicts. same name. However virtual environment makes it easy to install packages within the project sandbox.
I prefer Python’s Anaconda distribution, you can follow official documentation to install it on different platforms. Alternatively you can get latest official python distribution for different platforms.
virtualenv can be installed using pip python package manager as following.
pip install virtualenv
Official installation documentation of virtualenv can be found here.
Creating a Virtual Environment using virtualenv
- Create your project directory.
mkdir myproject cd myproject
- Create virtual environment using virtualenv.
virtualenv -p python3 venvNotes
-p python3specifies the version of python to be used for newly created virtual environment, you can skip
-p python3option to use the default version of python installed on your system.
venvis the name of directory for virtual environment.
Activating Virtual Environment
We can use
source command to activate a virtual environment of systems like Linux and Mac.
activate.bat to activate virtual environment on Windows OS.
Installing Packages using pip
pip is the default package manager for python, we can install a package named camelcase using pip as following.
pip install camelcase
You can install a specific version of package as following.
pip install camelcase==0.2
Installing multiple packages using requirements.txt
You can store list of all packages required by your project in a file named
requirements.txt and then pip command can be used to install all these packages at once. Let’s do it.
- At the root of your project directory, create a new file named
requirements.txtwith following contents.
- Install the
requirements.txtusing pip command as following.
pip install -r requirements.txtAbove command will install all two packages listed in our
Uninstalling Packages using pip
You can uninstall a package as following.
pip uninstall camelcase
Searching Packages using pip
You can find python packages using pip as following.
pip search math
Above command will return available packages relating to keyword math as following.
math-addition (3.0) - Math Addition some-math (0.0.3) - some math routines animals-math (0.0.7) - A package for animals and their math math-fold (0.1.5) - back math notaion in CLI micropython-math (0.0.0) - Dummy math module for MicroPython blockdiagcontrib-math (0.9.0) - LaTeX math plugin for blockdiag mo-math (2.40.19027) - More Math! Many of the aggregates you are familiar with, but they ignore Nones scry-math (0.5) - A simple SCRY service to extend SPARQL with basic math procedures python-markdown-math (0.6) - Math extension for Python-Markdown django-math-captcha (0.1) - Simple, secure math captcha for django forms ntcir10-math-converter (0.2.2) - The NTCIR-10 Math Converter package converts NTCIR-10 Math XHTML dataset and relevance judgements to the NTCIR-11 Math-2, and NTCIR-12 MathIR XHTML5 format. pelican-render-math (0.3.0) - Pelican math rendering plugin modified to work with nice-blog theme ntcir-math-density (0.2.1) - The NTCIR Math Density Estimator package uses datasets, and judgements in the NTCIR-11 Math-2, and NTCIR-12 MathIR XHTML5 format to compute density, and probability estimates. wagtail-simple-math-captcha (0.1.2) - A simple math captcha field for Wagtail Form Pages based on Django Simple Math Captcha. django-simple-math-captcha (1.0.8) - An easy-to-use math field/widget captcha for Django forms.
This article is part of Python Programming, IoT, Big Data, Data Science, AI and Machine Learning Tutorials Series, please click here to visit the complete list of articles and tutorials in this series.
That’s it, hope you enjoyed it. You like this article, have any questions or suggestions please let us know in the comments section.
Thanks and Happy Learning!