Top 7 Python package managers

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The obvious Getting started

It all started when I cleaned my computer's storage and found that anaconda was taking up 20GB of space. This struck me as odd. After doing some digging, I learned that anaconda comes with many unused python packages, which explains its large installation size.

Then I started looking for better, faster and lighter alternatives. This led me to find several Python package managers, and I decided to create a list of them.

In this article, we will examine seven of the most popular and modern Python package managers, complete with installation instructions for Linux systems.

The obvious 1. UV

uv The next generation Python package and environment manager designed for speed. Written in Rust, it aims to warm to traditional tools like pip and poetry while maintaining full compatibility with the Python ecosystem.

It's one of my favorite tools so far, because it provides quick installation of new Python packages. It is lightweight and works best when used in a physical environment.

To install, please enter the following command in your directory:

curl -LsSf  | sh

The obvious 2. The PIP

peach Is the default Python package manager, included with most Python installations. It allows users to install, develop, and manage packages from the Python Package Index (PYPI), which forms the backbone of many Python environments.

Every Python developer starts here, because it is the default package manager and comes with many useful tools. However, it is slow compared to other methods uv.

To install, please enter the following command in your directory:

sudo apt update
sudo apt install python3-pip -y

The obvious 3. Poetry

You were a poet is a dependency and packaging tool that simplifies project management in Python. It manages virtual environments, resolves dependencies, and handles publishing seamlessly, all in a single configuration file called pyproject.toml.

Poetics is popular among software developers because it gives them more control over their Python projects.

To install, please enter the following command in your directory:

curl -sSL  | python3 -

or

The obvious 4. Colla (anaconda)

Listen Is a cross-platform package and Environment Manager widely used in data science and machine learning. It can handle both Python and non-python dependencies, such as Cuda, R, or libraries, and comes with the anaconda distribution.

However, there are downsides to using colla. It can be slow and can take up a significant amount of storage on your computer. Additionally, it often comes with a lot of pre-installed software that you may not use or even realize.

To install, please enter the following command in your directory:

wget 
bash Anaconda3-2025.06-1-Linux-x86_64.sh

The obvious 5. Miniconda

Mimon is a lightweight version of anaconda that combines conda with its essential tools. It allows users to create custom environments without prepackaged packages, making it ideal for functional setups and redevelopment.

If you want the same workflow in anaconda but prefer a faster and faster option, miniconda is the best option.

To install, please enter the following command in your directory:

wget 
bash Miniconda3-latest-Linux-x86_64.sh

The obvious 6. Mamba

Mumba A fast, foolproof replacement for conda, written in C++. It is very fast to solve the problem to create and create environment, which makes it a favorite among data scientists working in large areas. It has replaced miniconda as the go-to tool for Py-torthon Packar Manager, especially for machine learning and data science applications.

To install, please enter the following command in your directory:

curl micro.mamba.pm/install.sh | bash

(or inside conda)

conda install mamba -n base -c conda-forge

The obvious 7. PIXI

A type of news ball is an edge package manager built on rust by the colla community to integrate environment management in different programming languages. It is fully customizable, cross-platform, and very fast, which makes it ideal for teams managing mixed technology stacks.

While the Pixi was impressive, it didn't gain as much popularity as it did uv. Similar to uvPIXI provides fast and powerful management of Python axcurencrenctions, but also provides cross-language support.

To install, please enter the following command in your directory:

curl -fsSL  | bash

The obvious Lasting

If you are a beginner in data science, start with anaconda. It becomes easy to use and allows you to produce quickly because many important tools and libraries are available. This way, you can focus on learning rather than spending time on setup.

As you gain more experience, think about using it uv more refreshing and faster road skiing. If you choose to stay within the colla condacostem, mamba A robust workflow for data science.

Ultimately, the Python Packal package manager depends on your personal preferences, project needs, group conferences, productivity needs, and the balance you want between ease of use and functionality. Choose the option that best suits your current stage, and be ready to adapt as your skills and projects emerge.

Abid Awan Awan (@ 1Abidaliawan) is a certified trainer for a scientist with a passion for machine learning models. Currently, he specializes in content creation and technical blogging on machine learning and data science technologies. Avid holds a master's degree in technology management and a bachelor's degree in telecommunication engineering. His idea is to build an AI product using a graph neural network for students struggling with mental illness.