Python > Deployment and Distribution > Dependency Management > Virtual Environments (`venv`, `conda`)

Creating and Using a Virtual Environment with `venv`

This snippet demonstrates how to create and activate a virtual environment using Python's built-in `venv` module. Virtual environments isolate project dependencies, preventing conflicts between different projects.

Creating a Virtual Environment

This command creates a new virtual environment in a directory named `.venv`. It uses the `venv` module, which is part of the standard Python library. The `.venv` directory will contain a copy of the Python interpreter and the `pip` package manager.

python3 -m venv .venv

Activating the Virtual Environment (Linux/macOS)

This command activates the virtual environment on Linux and macOS systems. Activating the environment modifies your shell's `PATH` variable so that when you run `python` or `pip`, you're using the versions within the virtual environment.

source .venv/bin/activate

Activating the Virtual Environment (Windows)

This command activates the virtual environment on Windows systems. The activation script is located in the `Scripts` directory within the virtual environment folder.

.venv\Scripts\activate

Installing Packages

After activating the environment, you can install project dependencies using `pip`. This command installs the `requests` and `flask` packages into the virtual environment. These packages are now available for use in your project without affecting the global Python installation.

pip install requests flask

Deactivating the Virtual Environment

When you're finished working on the project, you can deactivate the virtual environment. This removes the virtual environment's path from your shell's `PATH` variable, restoring the global Python environment. Simply type `deactivate` in your terminal.

deactivate

Why use Virtual Environments?

Virtual environments are crucial for managing dependencies in Python projects. They isolate project-specific packages, preventing conflicts that can arise when different projects require different versions of the same package. This ensures that your project has a consistent and predictable environment across different machines and deployments.

Real-Life Use Case

Imagine you have two projects: Project A, which uses Flask version 1.x, and Project B, which uses Flask version 2.x. Without virtual environments, installing Flask 2.x globally would break Project A. With virtual environments, each project can have its own Flask version, preventing conflicts and ensuring that both projects work correctly.

Best Practices

  • Always create a virtual environment for each new Python project.
  • Include the virtual environment directory (e.g., `.venv`) in your `.gitignore` file to prevent it from being committed to version control. Dependencies should be managed using `requirements.txt` or similar.
  • Activate the virtual environment before installing any packages or running your project.

Interview Tip

Be prepared to explain the purpose of virtual environments and how they solve dependency management problems. Mention the `venv` and `conda` tools, and be able to demonstrate how to create and activate an environment.

When to use them

Use virtual environments for every Python project, regardless of size. It's good practice to isolate dependencies from the start, even if your project initially has very few dependencies.

Memory footprint

Virtual environments do take up some disk space, as they contain a copy of the Python interpreter and installed packages. However, the benefits of isolation and dependency management far outweigh the relatively small cost of storage.

Alternatives

While `venv` is Python's standard tool, other options exist, such as `conda`, `virtualenv`, and `pipenv`. `conda` is particularly useful for managing non-Python dependencies (e.g., system libraries) and creating reproducible environments across different operating systems.

Pros of `venv`

  • Part of the Python standard library (no extra installation required).
  • Lightweight and easy to use.

Cons of `venv`

  • Doesn't manage non-Python dependencies (e.g., system libraries).
  • Less sophisticated dependency resolution than `conda`.

← Creating and Managing Environments with Conda Dependency Pinning with `pip freeze` →

FAQ

  • Why is the virtual environment directory often named `.venv`?

    Using a dot `.` at the beginning of the directory name makes it a hidden directory in Linux/macOS systems, which helps to keep the project directory clean. It's a convention, but you can name it anything you like.
  • How do I manage project dependencies in a virtual environment?

    Use `pip freeze > requirements.txt` to generate a `requirements.txt` file containing a list of all installed packages and their versions. You can then install these dependencies on another machine using `pip install -r requirements.txt`.