A review of Python package managers in 2025

This is an overview of the current state of Python package management in 2025. You can find this and other posts by browsing the Python tag.

Package management is a critical part of modern software engineering. It allows developers to declare, install, and isolate project dependencies in a reliable and repeatable way. This is critical when working on software that you expect others to use, because if any dependency is missing or the wrong version, your code may not run correctly.

In this article, we’ll take a look at four popular Python package managers: Conda, Pipenv, Poetry, and UV. Each has its own strengths, weaknesses, and ideal use cases.

We’ll explore how to install each tool, how to define dependencies, and what to do when preparing a project for production deployment. Whether you’re managing a data science environment, a web application, or a CLI tool, there’s a package manager best suited for your needs.

Let’s dive in.

Comparison Overview

Conda

Conda is a cross-platform package and environment manager that is especially popular in the data science and machine learning communities. It supports not just Python packages, but also native libraries and packages from other languages such as R, C, and C++. This makes it particularly useful when working with scientific libraries that have complex dependencies.

Conda Strengths

Conda environments are self-contained and can include compiled binaries and non-Python dependencies. This makes it easier to set up reproducible environments across different systems. The conda tool is generally user-friendly and supports creating, exporting, and cloning environments.

Conda can also install packages from both its own repositories and PyPI, giving it broad package coverage.

Conda Weaknesses

Conda environments can be large and sometimes slower to create compared to other tools. While the core functionality is robust, some packages are only available in specific Conda channels like conda-forge, requiring additional configuration. It’s also less commonly used for lightweight applications or simple Python scripts.

Licensing Note

If you’re using Conda in a commercial or enterprise setting, it’s important to be aware of licensing restrictions. The Anaconda distribution and the default Conda channel provided by Anaconda, Inc. now require a paid commercial license for business use. This can lead to compliance issues if you’re installing packages without proper licensing.

To avoid this, you can switch to using the community-maintained conda-forge channel, which is freely available and not subject to the same restrictions. To do this, configure your environment to use conda-forge by default:

conda config --add channels conda-forge
conda config --set channel_priority strict

This ensures that your packages are pulled from conda-forge instead of the commercial defaults channel. The strict priority setting avoids mixing packages from multiple sources, which can reduce dependency conflicts.

Installing Conda

The easiest way to install Conda is via Miniconda, a minimal installer that includes just the Conda tool and Python. Avoid the full Anaconda distribution unless you have a commercial license or are using it personally.

After downloading and running the installer, you’ll have access to the conda command.

conda --version

Creating a Conda Environment

To create a new environment:

conda create --name myenv python=3.12

To activate the environment:

conda activate myenv

To install dependencies:

conda install numpy pandas matplotlib

If you’ve configured your channels correctly, these will be installed from conda-forge.

Managing Conda Dependencies

To export the environment so it can be reproduced elsewhere:

conda env export > environment.yml

To recreate the environment on another system:

conda env create -f environment.yml

Deploying Conda Environments to Production

In production, you can use the environment.yml file to ensure your application runs with the exact same dependencies. This is useful for deploying to servers or running batch jobs where reproducibility is key.

Some production environments, especially in data science and machine learning, support running entire Conda environments directly using tools like conda-pack or Docker with Conda.

Pipenv

Pipenv is a Python packaging tool that combines pip and virtualenv into one simple command line interface. It is designed to provide a more manageable way to handle dependencies for Python applications. Pipenv automatically creates and manages a virtual environment for your projects, as well as adds/removes packages from your Pipfile and Pipfile.lock.

Pipenv Strengths

Pipenv is easy to use and provides a clear separation of development and production dependencies. It integrates well with pip and virtualenv, so you don’t need to worry about manually managing virtual environments. The Pipfile.lock ensures that everyone working on the project uses the same version of dependencies, which is important for reproducibility.

Additionally, Pipenv supports installing from both PyPI and GitHub repositories, making it versatile.

Pipenv Weaknesses

Pipenv is not as robust when it comes to handling non-Python dependencies, and it can be a bit slower compared to Conda. It also lacks some of the more advanced features of other package managers, such as automatic resolution of conflicting dependencies. For larger or more complex projects, you may encounter issues with dependency resolution.

Installing Pipenv

To install Pipenv, you can use pip:

pip install pipenv

You can verify the installation by checking the version:

pipenv --version

Creating a Pipenv Environment

Pipenv automatically creates a virtual environment when you first install a package, so the following command will set up both the environment and the Pipfile:

pipenv install numpy pandas

To activate the environment:

pipenv shell

To install development dependencies (e.g., testing tools):

pipenv install --dev pytest

Managing Pipenv Dependencies

To check the installed dependencies and their versions, you can view the Pipfile and Pipfile.lock.

To install all dependencies listed in the Pipfile:

pipenv install

To update a package:

pipenv update numpy

To uninstall a package:

pipenv uninstall numpy

Deploying Pipenv Environments to Production

To deploy a project to production, you can use the Pipfile.lock to ensure that the exact versions of all dependencies are installed. To generate the Pipfile.lock:

pipenv lock

In production, you can use the following to install the exact dependencies:

pipenv install --deploy --ignore-pipfile

This command will only use the Pipfile.lock to ensure that the same versions are installed on production machines.

Poetry

Poetry is a modern Python dependency management and packaging tool that simplifies the management of project dependencies, packaging, and versioning. It uses a pyproject.toml file for configuration, which is the new standard in Python packaging. Poetry is designed to make it easy to create and maintain Python projects by providing a consistent, intuitive interface for managing dependencies.

Poetry Strengths

Poetry excels at dependency resolution and version management, ensuring that all of your project’s dependencies are compatible with each other. It provides an automatic way to create and manage virtual environments, and it handles both development and production dependencies with ease. Poetry’s pyproject.toml is a standardized file format that works with many modern Python tools, and it allows for easy packaging and distribution of your projects.

Like Pipenv, Poetry locks dependency versions with a poetry.lock file, ensuring reproducible builds across different environments.

Poetry Weaknesses

Poetry is still relatively new compared to other package managers, so it may not be as well-integrated with some older tools or libraries. It also has a steeper learning curve for beginners, as it introduces new concepts like pyproject.toml and a different approach to dependency resolution. While it supports non-Python dependencies, it doesn’t handle them as well as Conda, so it’s better suited for Python-centric projects.

Installing Poetry

To install Poetry, use the following command to download and run the installer:

curl -sSL https://install.python-poetry.org | python3 -

You can verify the installation by checking the version:

poetry --version

Creating a Poetry Environment

To create a new Poetry project, navigate to your project directory and run:

poetry new my_project

This will generate a new directory with the necessary files for your project, including the pyproject.toml file.

To add a dependency (e.g., numpy):

poetry add numpy

To install all dependencies from the pyproject.toml:

poetry install

Managing Poetry Dependencies

Poetry provides simple commands for adding, updating, and removing dependencies.

To add a development dependency (e.g., pytest):

poetry add --dev pytest

To update a specific package:

poetry update numpy

To remove a package:

poetry remove numpy

Deploying Poetry to Production

To prepare for deployment, you should ensure that your poetry.lock file is up-to-date. To generate the poetry.lock:

poetry lock

In production, you can use the following command to install the exact versions specified in the lock file:

poetry install --no-dev

This will ensure that only the production dependencies are installed on the production machine, ensuring consistency and reproducibility across environments.

UV

UV is a fast, lightweight Python package manager developed by Astral, designed for speed and simplicity. It aims to be a drop-in replacement for pip and virtualenv, with dramatically faster dependency resolution and installation. UV is written in Rust and is focused on Python-only workflows.

Because of its speed, UV is ideal for developers who value quick iteration and minimal overhead.

UV Strengths

The standout feature of UV is performance: dependency resolution and package installation are significantly faster than traditional tools like pip. UV integrates tightly with Python’s pyproject.toml standard, making it a natural fit for modern Python projects. It supports both installing dependencies and creating isolated environments, replacing both pip and virtualenv.

UV also provides better caching and deterministic installs via its lock file, which improves reproducibility.

UV Weaknesses

UV is still relatively new, and while it’s rapidly improving, it lacks some of the more advanced features of tools like Poetry or Conda. It doesn’t handle non-Python dependencies (e.g., system libraries) at all, so it’s not suitable for environments that need compiled libraries or system-level packages. The ecosystem around UV is still maturing, so some tooling and documentation may be limited compared to more established managers.

Installing UV

You can install UV using a pre-built binary via the install.sh script from the official source:

curl -Ls https://astral.sh/uv/install.sh | sh

After installation, you can verify it’s working:

uv --version

UV can also be installed via Homebrew on macOS:

brew install astral-sh/uv/uv

Creating a UV Environment

UV doesn’t use the traditional virtualenv. Instead, it creates and manages its own isolated environment in the .venv directory.

To create an environment and add packages from pyproject.toml:

uv venv
uv pip install -r requirements.txt

Or, if you’re using a pyproject.toml file (as you would with Poetry):

uv pip install .

To install new packages into your environment:

uv pip install requests

Managing UV Dependencies

To freeze your environment into a lock file:

uv pip freeze > requirements.txt

To install all dependencies from a lock file:

uv pip install -r requirements.txt

Since UV is compatible with pip, you can manage dependencies using standard pip commands. However, because UV is so fast, these operations complete much more quickly than with vanilla pip.

Deploying UV to Production

For production environments, the recommended workflow is to:

1. Create a lock file with exact versions:

   uv pip freeze > requirements.txt
   

2. Commit the requirements.txt to your repository.

3. In production, recreate the environment and install dependencies:

   uv venv
   uv pip install -r requirements.txt
   

This ensures fast, reproducible installs that work seamlessly in CI/CD pipelines or cloud deployments.

Summary

Each Python package manager we’ve explored offers different strengths depending on your needs:

• Conda is ideal for scientific computing and data science workflows, especially when non-Python dependencies are involved.
• Pipenv provides a user-friendly interface for managing virtual environments and dependencies in general-purpose Python apps.
• Poetry shines in Python-centric projects with clean dependency resolution, packaging, and publishing workflows.
• UV is a promising newcomer focused on speed and simplicity, great for lightweight development and fast installs.

Choosing the right tool depends on your project requirements — whether you need fast installs, cross-language support, ease of packaging, or compatibility with legacy tooling.

Wrapping Up

Python continues to thrive in 2025, and the ecosystem of tools around it keeps evolving. Package managers play a crucial role in project setup, development, and deployment, and knowing the trade-offs of each tool helps you make smarter choices.

Whether you’re building a quick prototype, deploying a web application, or managing a complex data pipeline, there’s a tool that fits your workflow. Experiment with a few and find the one that aligns with your priorities — speed, simplicity, cross-language support, or full-featured dependency management.

Happy coding!