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# Entry
It all started when I was cleaning out my computer’s memory and discovered that Anaconda was taking up 20 GB of space. It seemed strange to me. After some thorough research, I found out that Anaconda includes many unused Python packages and tools, which explains the gigantic installation size.
I then started looking for better alternatives that are faster and lighter. This led me to discover several Python package managers and I decided to create a list of them.
In this article, we will discuss seven of the most popular and contemporary Python package managers, along with installation commands for Linux systems.
# 1. UV
UV is a next-generation Python package and environment manager designed for speed. Written in Rust, it aims to outperform customary tools like pip and Poetry while maintaining full compatibility with the Python ecosystem.
This is one of my favorite tools so far because it allows you to quickly install recent Python packages. It is lightweight and works best when used in a virtual environment.
To install, enter the following command in the terminal:
curl -LsSf https://astral.sh/uv/install.sh | sh# 2. Pip
pip is the default Python package manager, included with most Python installations. It allows users to install, update, and manage packages from the Python Package Index (PyPI), which forms the backbone of most Python environments.
Every Python developer starts here because it is the default package manager and contains many useful tools. However, it is relatively tardy compared to alternatives such as uv.
To install, enter the following command in the 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. Manages virtual environments, resolves dependencies, and seamlessly handles publishing, all from a single configuration file called pyproject.toml.
Poetry is popular among software engineers because it gives them more control over their Python projects.
To install, enter the following command in the terminal:
curl -sSL https://install.python-poetry.org | python3 -Or
# 4. Konda (Anaconda)
Konda is a cross-platform package and environment manager that is widely used in data science and machine learning. It is able to manage dependencies in both Python and other languages such as CUDA, R or system libraries, and comes with pre-installed Anaconda distributions.
However, there are some disadvantages to using Conda. It can be tardy and can take up a significant amount of space on your computer. Additionally, it often comes with a lot of pre-installed software that you may never operate or even be aware of.
To install, enter the following command in the terminal:
wget https://repo.anaconda.com/archive/Anaconda3-2025.06-1-Linux-x86_64.sh
bash Anaconda3-2025.06-1-Linux-x86_64.sh# 5. Miniconda
Miniconda is a lightweight version of Anaconda that includes Conda and its imperative tools. It allows users to create custom environments without the glut of pre-installed packages, making it ideal for productive and repeatable configurations.
If you’re looking for a workflow similar to Anaconda, but prefer a thinner and faster option, Miniconda is the best alternative.
To install, enter the following command in the terminal:
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh# 6. Mamba
Mamba is a brisk, easily available replacement for Conda, written in C++. It significantly speeds up dependency resolution and environment creation, making it a favorite among data scientists working in gigantic environments. It has largely replaced Miniconda as the go-to tool for a brisk and reliable Python package manager, especially for machine learning and data science workflows.
To install, enter the following command in the terminal:
curl micro.mamba.pm/install.sh | bash(or inside Conda)
conda install mamba -n base -c conda-forge# 7. Pixi
Pixi is a state-of-the-art package manager built in Rust by the Conda community to unify environment management across programming languages. It is fully reproducible, cross-platform, and incredibly brisk, making it ideal for teams managing mixed technology stacks.
Although Pixi is impressive, it has not gained as much popularity as uv. Similar to uvPixi offers brisk and productive Python dependency management, but also provides multi-language support.
To install, enter the following command in the terminal:
curl -fsSL https://pixi.sh/install.sh | bash# Application
If you are recent to data science, start with Anaconda. It is beginner-friendly and allows you to quickly escalate your productivity as many of the necessary tools and libraries are pre-installed. This way you can focus on learning instead of wasting time on setup.
As you gain more experience, consider using uv to improve and speed up everyday work. If you prefer to stay within the Conda ecosystem, mamba is a forceful alternative to data analytics workflows.
Ultimately, choosing the best Python package manager depends on your personal preferences, project needs, team conventions, reproducibility requirements, and the balance you’re looking for 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 Awan (@1abidaliawan) is a certified data science professional who loves building machine learning models. Currently, he focuses on creating content and writing technical blogs about machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunications Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.