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# Introduction
It all started when I was clearing out my computer storage and found that Anaconda was taking up 20GB of space. He thought I was strange. After some digging, I learned that Anaconda comes with many unused Python packages and tools, which explains its large installation size.
I then started looking for better alternatives that are faster and more lightweight. This led me to discover a number of Python package managers, and I decided to list them.
In this article, we’ll explore seven of the most popular and advanced package managers for Python, complete with installation commands for Linux systems.
# 1. UV
UV Next-generation Python is a package and environment manager designed for speed. Written in Rust, it aims to improve traditional tools like PIP and Rhyme while maintaining full compatibility with the Python ecosystem.
This is by far one of my favorite tools, as it provides fast installation of new Python packages. It is lightweight and works best when used in a virtual environment.
To install, please enter the following command in your terminal:
curl -LsSf | sh# 2. Pipe
Pipe Is Python’s default package manager, which includes most Python installations. It allows users to install, upgrade, and manage packages from the Python Package Index (PYPI), which forms the backbone of most Python environments.
Every Python developer starts here, as it is the default package manager and comes with many handy tools. However, it is relatively slow compared to the alternatives uv.
To install, please enter the following command in your terminal:
sudo apt update
sudo apt install python3-pip -y# 3. Poetry
Poetry is a dependency and packaging tool that simplifies project management in Python. It manages virtual environments, resolves dependencies, and handles publishing seamlessly, all called through a single configuration file. pyproject.toml.
Poetry is popular with software engineers because it gives them more control over their Python projects.
To install, please enter the following command in your terminal:
curl -sSL | python3 -or
# 4. Konda (Anaconda)
Konda is a cross-platform package and environment manager widely used in data science and machine learning. It is capable of managing both Python and non-Python dependencies, such as CUDA, R, or system libraries, and comes preloaded with the Anaconda distribution.
However, there are some downsides to using Konda. It can be slow and take up a significant amount of storage on your computer. Additionally, it often comes with pre-installed software that you may never use or even be familiar with.
To install, please enter the following command in your terminal:
wget
bash Anaconda3-2025.06-1-Linux-x86_64.sh# 5. Minikonda
Minikonda Anaconda is a lightweight version that includes Conda and its essential tools. It allows users to create custom environments without an excess of pre-installed packages, making it ideal for efficient and reproducible setups.
If you’re looking for a similar workflow to Anaconda but prefer a leaner and faster option, Miniconda is the perfect alternative.
To install, please enter the following command in your terminal:
wget
bash Miniconda3-latest-Linux-x86_64.sh# 6. Mumba
Mumba Written in C++, is a fast, drop-in alternative to Conda. It dramatically speeds up dependency resolution and environment creation, making it a favorite among data scientists who work with large environments. It has largely replaced Miniconda as the go-to tool for a fast and robust Python package manager, especially for machine learning and data science workflows.
To install, please enter the following command in your terminal:
curl micro.mamba.pm/install.sh | bash(or inside a comma)
conda install mamba -n base -c conda-forge# 7. Pixie
Pixie Conda is an advanced package manager built in Rust by the community to unify environment management across different programming languages. It is fully reproducible, cross-platform, and extremely fast, making it ideal for teams managing mixed technology stacks.
Although the pixie is impressive, it has not gained as much popularity uv. Likewise uvPixie offers quick and powerful Python dependency management, but it also provides language support.
To install, please enter the following command in your terminal:
curl -fsSL | bash# The result
If you are a beginner in data science, start with Anaconda. It’s beginner-friendly and allows you to be productive quickly because many of the necessary tools and libraries are already exposed. That way, you can focus on learning instead of spending time on setup.
As you gain more experience, consider using uv Smoother and faster for all-day work flow. If you prefer to live in the Konda ecosystem, mamba Data science is a powerful alternative to workflows.
Ultimately, the best Python package manager depends on your personal preferences, project needs, team conventions, reproducibility needs, and the balance you seek between convenience and performance. Choose the option that best suits your current stage, and be prepared to adapt as your skills and projects evolve.
Abid Ali Owan For centuries.@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunication Engineering. His vision is to create an AI product using graph neural networks for students with mental illness.