Create a Virtual Environment with Python

The present gist is a hybrid between a 'go-to' cheat sheet and a tutorial when starting a new Data Science Project.

Its purpose is to create a virtual environment with Python using the 'venv' module.

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Table of contents

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System Settings

Settings at the time of writing this gist (12th of January 2021).

Microsoft Windows Operating System

Edition: Windows 10 Home
Version: 1909
OS build: 18363.1256
System type: 64-bits operating, x64-based processor

Microsoft Visual Studio Code

Version: 1.52.1 (user setup)
Electron: 9.3.5
Chrome: 83.0.4103.122
Node.js: 12.14.1
V8: 8.3.110.13-electron.0
OS: Windows_NT x64 10.0.18363

Python

Version:  Python 3.9.1

NOTE: this gist does not explain how to install Python.

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Steps to creating a 'venv'

Create a folder for the project

There are 2 options:

1. Create a repository on GitHub.com BEFORE creating the project folder on the local machine.

   Once the repository is created, clone it onto the local machine.

   NOTE: This procedure will not be covered here but it will be in a future Gist.

2. Create the project folder locally (see below gist)

   • First, open a Terminal Prompt within VS Code.

   • Then, go to the folder where the new project is to be created, i.e. go to the 'working folder'.

     For example, if the main folder is called python_projects, go to

        C:\python_projects
     

     Hence, for the relative path, type:

        cd /python_projects
     

   • Create a folder for the project called project_name and check if it is present in the working folder

        mkdir project_name && dir
     

   • cd into this folder

        cd project_name
     

   • Open the project folder project_name from the menu in VS Code

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Create a virtual environment for the project

• Create a .py python file into project_name and cd into it

    echo >> python_script.py
  

  Alternatively, create the .py file from VS Code menu.

• Open python_script.py file

  NOTE: it is important to create AND open a .py file BEFORE creating and activating the venv, as it helps with its activation

• Create a virtual environment called venv_name

  The command below will create a new folder called venv_name

    python -m venv venv_name
  

  NOTE: the virtual environment name can be anything (like banana). However, it is common practice to use just venv. This is handy when copying/pasting code snippets.

  Hence, use the following command:

    python -m venv venv
  

  NOTE: a message will appear (bottom right) asking to use the new environment, click 'Yes'.

  Alternatively, choose from the list of environments (bottom left). The Python version should be listed with venv in parentheses, e.g.

    Python 3.9.1 64-bit ('venv')
  

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Activate the new virtual environment venv

If using the default command prompt (cmd), type:

.\venv\Scripts\activate

If using Windows PowerShell (PS), type:

.\venv\Scripts\Activate.ps1

IMPORTANT: if no changes seem to occur, open a new terminal prompt to activate the environment.

The active path should now be preceded by (venv) or (banana) if a specific venv name was chosen.

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Create 'default' folders and files

These will be needed for the project, e.g.:

mkdir data, docs, tests, sources, scripts, figures

echo >> __init__.py
echo >> main.py
echo >> config.py
echo >> setup.py
echo >> requirements.txt

OPTION: if the project is to be hosted on GitHub, the following files can be created, or done automatically when creating a repository.

echo >> README.md
echo >> LICENSE
echo >> .gitignore
echo >> .gitkeep

NOTE 1: the 'LICENSE' and '.gitignore' files do NOT take a file extension. Only 'README.md' does.

NOTE 2: add a copy of .gitkeep into each folder in order to commit empty folders to GitHub.

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Record the dependencies for the project

• First, update the pip library

    python -m pip install --upgrade pip
  

• Then, create the dependencies file named requirements.txt

    pip list > requirements.txt
  

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Install a Python Library

pip install package_name

If installing more than one package, type:

pip install package_name_1 package_name_2

If installing a specific version of a package, type:

pip install package_name=1.6

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Save the new dependencies

It is better to use pip freeze instead of pip list as it allows to pin the dependencies version.

pip freeze > requirements.txt

Install dependencies from a file

The requirements.txt file can be used within a new environment to install dependencies cleanly with the following command:

python -m pip install -r requirements.txt

IMPORTANT: this only works if requirements.txt was produce with pip freeze and NOT pip list.

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Check dependency clashes after installing all packages

pip check

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Deactivate the virtual environment

deactivate

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Bonus: Linting & Formatting

The following linters can be "pip-installed" and ran from the terminal:

pydocstyle main.py
pycodestyle main.py
autoflake main.py
flake8 main.py

Similarly, the formatter called black can be run:

black main.py

NOTE: do not forget to pip freeze the linter/formatter libraries to requirements.txt.