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For a straightforward Python-only project, start with Python’s built-in venv. Choose Pipenv when you want a project dependency file and lock workflow alongside a venv-based environment. Choose conda when the environment must manage Python itself or non-Python dependencies as well. These tools overlap, but they do not solve exactly the same problem.
What a Python virtual environment does
A virtual environment gives a project its own package-installation location so its dependencies can be kept separate from other projects. That separation helps prevent one project’s package choices from changing another’s. The term can refer to tools with different scopes, though: venv isolates packages around an existing Python installation, Pipenv layers project dependency management over a venv-based environment, and conda can manage Python and non-Python dependencies together.
For venv, create the environment with the Python interpreter you intend to use. The environment includes configuration, an executable location (bin on many Unix-like systems or Scripts on Windows), and a site-packages directory. Use pip through that environment to install packages. See the Python 3.14 venv documentation.
venv, Pipenv, and conda compared
| Decision | venv |
Pipenv | conda |
|---|---|---|---|
| Scope | Isolates Python packages using an existing Python installation. | Provides a venv-based environment plus project dependency management. | Can manage Python and non-Python or system-level dependencies in an environment. |
| Dependency workflow | Use pip in the environment. Choose a separate project-file and locking approach if needed. |
Uses Pipfile and Pipfile.lock; includes install, lock, and sync workflows. |
Install and manage packages with conda; conda documentation also describes extending an environment with pip. |
| Python version | Uses the interpreter from which the environment is created. | Can request a Python version when creating an environment and record a project requirement. | Python can be installed as a dependency inside the environment. |
| Environment location | Often a project directory such as .venv; recreate it rather than moving it. |
Centralized by default, with a project-local .venv option. |
Managed by conda; it is not the same implementation as Python’s built-in venv. |
The distinctions reflect the tools’ documented behavior: Python’s venv guide, Pipenv virtual environments, Pipfile and Pipfile.lock, and conda environments.
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When to choose each tool
Choose venv for a simple Python-only project
venv is a good fit when you already have the Python version you need and want a lightweight isolated place for packages. It is built into Python, so you do not need to adopt another environment manager just to keep project packages separate. You will still need to decide how your project records dependencies, for example in a requirements file or another workflow.
Choose Pipenv for a Pipfile and lock-file workflow
Pipenv is useful when you want project dependency declarations and lock data integrated with environment commands. The Pipfile describes project requirements; Pipfile.lock records resolved dependencies for repeatable installation. Pipenv also supports specifying a Python version for the project and running commands through pipenv run or an interactive shell via pipenv shell. See Pipenv’s Pipfile documentation and environment guide.
Choose conda when dependencies extend beyond Python packages
Conda is the stronger fit when the environment needs Python itself as a managed dependency, or when it must include non-Python or system-level dependencies alongside Python packages. That broader scope is the key reason to choose it over venv; it is not merely another name for a built-in Python environment. Conda documents using pip to extend an environment when appropriate, but its environment model remains distinct.
Create and use a venv environment
-
From the project directory, create an environment using the Python installation you want:
python -m venv .venv. If your system uses a versioned command such aspython3, substitute that command.Recommended Free Tools
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Activate it using the command for your shell and operating system. On Windows, the activation scripts are under
.venvScripts; on Unix-like systems, they are under.venv/bin. The exact activation command varies by shell; use Python’s activation instructions. -
Install project packages with
pipwhile the environment is active. Alternatively, call the environment’s Python executable directly, which avoids relying on activation. -
When you need the environment again, recreate it from the project’s dependency record rather than copying the environment directory.
Use Pipenv for project dependencies
-
Install Pipenv following its current installation instructions. Installation details can depend on platform policy: on modern Linux distributions enforcing PEP 668, Pipenv recommends an isolated installation, and user-level pip installation may be restricted.
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From the project directory, run
pipenv installto install or declare dependencies through Pipenv’s project workflow. -
Specify the project’s Python version in the Pipfile, as recommended in Pipenv best practices. The appropriate version constraint depends on whether you are managing an application or a library: applications often need exact or compatible versions, while libraries may allow minimum versions.
-
Run a command with
pipenv run, or enter the environment withpipenv shell. Use Pipenv’s lock and sync commands as appropriate to maintain and reproduce the dependency set.
Where environments live—and what to commit
Python describes virtual environments as disposable: they should not be committed to version control and are not intended to be portable directories that can simply be moved or copied. Keep the project’s dependency description and lock data instead, then recreate the environment at its destination. Python explains this in its venv documentation.
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Quick Recap
A quick decision checklist
- Existing Python version, Python packages only: use
venvandpip. - Want Pipfile declarations and a lock file with environment commands: use Pipenv.
- Need conda-managed Python or non-Python/system dependencies: use conda.
- Moving a project or setting up a teammate’s machine: recreate its environment from committed dependency files; do not commit or transport the environment directory.
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