Python Package Managers¶

A comprehensive guide to Python package managers: pip, pipx, poetry, uv, and others.

Overview¶

Python has multiple package managers, each designed for different use cases. Understanding when to use each tool is crucial for efficient Python development.

pip - The Standard Package Manager¶

pip is Python's default package installer, included with Python 3.4+.

Installation¶

# pip comes with Python, but you can upgrade it
python -m pip install --upgrade pip

# Check version
pip --version

Basic Usage¶

# Install a package
pip install django

# Install specific version
pip install django==4.2.0

# Install with version constraints
pip install "django>=4.0,<5.0"

# Install from requirements.txt
pip install -r requirements.txt

# Install in editable mode (for development)
pip install -e .

# Uninstall package
pip uninstall django

# List installed packages
pip list

# Show package information
pip show django

# Search for packages (deprecated, use PyPI website)
# pip search django

Requirements Files¶

# requirements.txt
django==4.2.0
djangorestframework>=3.14.0
python-dotenv==1.0.0
psycopg2-binary==2.9.9
gunicorn==21.2.0

# Install all requirements
pip install -r requirements.txt

# Generate requirements from current environment
pip freeze > requirements.txt

Development vs Production Requirements¶

# requirements/base.txt
django==4.2.0
djangorestframework>=3.14.0
python-dotenv==1.0.0

# requirements/dev.txt
-r base.txt
pytest==7.4.0
black==23.7.0
flake8==6.0.0

# requirements/prod.txt
-r base.txt
gunicorn==21.2.0
psycopg2-binary==2.9.9

# Install for development
pip install -r requirements/dev.txt

# Install for production
pip install -r requirements/prod.txt

Virtual Environments with pip¶

# Create virtual environment
python -m venv venv

# Activate (Linux/Mac)
source venv/bin/activate

# Activate (Windows)
venv\Scripts\activate

# Deactivate
deactivate

# Install packages in virtual environment
pip install django

Pros and Cons¶

Pros: - Built into Python - Universal standard - Simple and straightforward - Huge ecosystem support

Cons: - No dependency resolution (until pip 20.3) - Manual virtual environment management - No lock file by default - Package conflicts can occur

pipx - Install Python CLI Tools¶

pipx installs Python applications in isolated environments while making them globally available.

Installation¶

# Install pipx
python -m pip install --user pipx
python -m pipx ensurepath

# Or via package manager (Ubuntu/Debian)
sudo apt install pipx
pipx ensurepath

# Or via Homebrew (Mac)
brew install pipx
pipx ensurepath

Basic Usage¶

# Install a Python CLI tool
pipx install black

# Install specific version
pipx install black==23.7.0

# Run a tool without installing
pipx run black --check .

# List installed applications
pipx list

# Upgrade an application
pipx upgrade black

# Upgrade all applications
pipx upgrade-all

# Uninstall
pipx uninstall black

# Inject additional packages into an app's environment
pipx inject black colorama

Common Use Cases¶

# Code formatters
pipx install black
pipx install isort

# Linters
pipx install flake8
pipx install pylint

# Type checkers
pipx install mypy

# Documentation tools
pipx install mkdocs
pipx install sphinx

# Build tools
pipx install build
pipx install twine

# Django development tools
pipx install django-extensions

Pros and Cons¶

Pros: - Isolated environments per application - No virtual environment activation needed - Global CLI tool access - Prevents dependency conflicts

Cons: - Only for CLI applications, not libraries - Not suitable for project dependencies - Adds overhead for simple scripts

Poetry - Modern Dependency Management¶

Poetry handles dependency management, packaging, and publishing in one tool.

Installation¶

# Install via official installer (recommended)
curl -sSL https://install.python-poetry.org | python3 -

# Or via pipx
pipx install poetry

# Check installation
poetry --version

Initialize Project¶

# Create new project
poetry new myproject

# Initialize in existing project
cd myproject
poetry init

# This creates pyproject.toml

pyproject.toml Example¶

[tool.poetry]
name = "myproject"
version = "0.1.0"
description = "My Django project"
authors = ["Your Name <you@example.com>"]
readme = "README.md"

[tool.poetry.dependencies]
python = "^3.11"
django = "^4.2"
djangorestframework = "^3.14"
python-dotenv = "^1.0"

[tool.poetry.group.dev.dependencies]
pytest = "^7.4"
black = "^23.7"
flake8 = "^6.0"
pytest-django = "^4.5"

[tool.poetry.group.prod.dependencies]
gunicorn = "^21.2"
psycopg2-binary = "^2.9"

[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

Basic Usage¶

# Install all dependencies
poetry install

# Install without dev dependencies
poetry install --without dev

# Install with specific groups
poetry install --with prod

# Add a dependency
poetry add django

# Add dev dependency
poetry add --group dev pytest

# Add with version constraint
poetry add "django>=4.2,<5.0"

# Remove dependency
poetry remove django

# Update dependencies
poetry update

# Update specific package
poetry update django

# Show installed packages
poetry show

# Show dependency tree
poetry show --tree

# Lock dependencies (update poetry.lock)
poetry lock

# Run command in virtual environment
poetry run python manage.py runserver

# Activate virtual environment
poetry shell

# Export to requirements.txt
poetry export -f requirements.txt --output requirements.txt
poetry export -f requirements.txt --without-hashes --output requirements.txt

Virtual Environment Management¶

# Poetry creates virtual environments automatically

# Show virtual environment info
poetry env info

# List virtual environments
poetry env list

# Use specific Python version
poetry env use python3.11
poetry env use /usr/bin/python3.11

# Remove virtual environment
poetry env remove python3.11

Build and Publish¶

# Build package
poetry build

# Publish to PyPI
poetry publish

# Build and publish
poetry publish --build

# Configure PyPI credentials
poetry config pypi-token.pypi your-token-here

Pros and Cons¶

Pros: - Automatic virtual environment management - Lock file (poetry.lock) for reproducible installs - Dependency resolution built-in - Publishing and packaging tools included - Clear separation of dev/prod dependencies

Cons: - Learning curve - Different from pip workflow - Can be slow on large projects - pyproject.toml format differs from other tools

uv - Ultra-fast Package Installer¶

uv is an extremely fast Python package installer and resolver written in Rust.

Installation¶

# Install via standalone installer (recommended)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Or via pipx
pipx install uv

# Or via Homebrew (Mac)
brew install uv

# Check installation
uv --version

Basic Usage¶

# Create virtual environment
uv venv

# Activate virtual environment
source .venv/bin/activate  # Linux/Mac
.venv\Scripts\activate     # Windows

# Install package
uv pip install django

# Install from requirements.txt
uv pip install -r requirements.txt

# Install with constraints
uv pip install "django>=4.2,<5.0"

# Compile requirements.txt (like pip-compile)
uv pip compile requirements.in -o requirements.txt

# Sync environment with requirements.txt
uv pip sync requirements.txt

# List installed packages
uv pip list

# Freeze installed packages
uv pip freeze

# Uninstall package
uv pip uninstall django

Using with pyproject.toml¶

# pyproject.toml
[project]
name = "myproject"
version = "0.1.0"
requires-python = ">=3.11"
dependencies = [
    "django>=4.2,<5.0",
    "djangorestframework>=3.14",
    "python-dotenv>=1.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=7.4",
    "black>=23.7",
    "flake8>=6.0",
]
prod = [
    "gunicorn>=21.2",
    "psycopg2-binary>=2.9",
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

# Install from pyproject.toml
uv pip install -e .

# Install with optional dependencies
uv pip install -e ".[dev]"
uv pip install -e ".[prod]"

Lock Files with uv¶

# Generate uv.lock file
uv lock

# Install from lock file
uv sync

# Update lock file
uv lock --upgrade

# Install without dev dependencies
uv sync --no-dev

Running Scripts¶

# Run Python script with uv
uv run python script.py

# Run with specific Python version
uv run --python 3.11 python script.py

# Run Django management command
uv run python manage.py runserver

Pros and Cons¶

Pros: - Extremely fast (10-100x faster than pip) - Drop-in replacement for pip - Built-in dependency resolution - Lock file support - Written in Rust for speed and reliability

Cons: - Relatively new tool - Smaller community compared to pip/poetry - Some edge cases may not be handled - Still evolving rapidly

Other Package Managers¶

pip-tools¶

Combines pip with dependency locking:

# Install
pip install pip-tools

# Create requirements.in
# requirements.in
django>=4.2
djangorestframework

# Compile to requirements.txt
pip-compile requirements.in

# Sync environment
pip-sync requirements.txt

# Update dependencies
pip-compile --upgrade

Conda¶

Package manager for data science:

# Create environment
conda create -n myenv python=3.11

# Activate environment
conda activate myenv

# Install package
conda install django

# Install from conda-forge
conda install -c conda-forge django

# Export environment
conda env export > environment.yml

# Create from environment file
conda env create -f environment.yml

PDM¶

Modern Python package manager:

# Install
pipx install pdm

# Initialize project
pdm init

# Add dependency
pdm add django

# Install dependencies
pdm install

# Run script
pdm run python manage.py runserver

Comparison Table¶

Feature	pip	pipx	Poetry	uv	pip-tools	Conda
Speed	Medium	Medium	Slow	Very Fast	Medium	Slow
Lock Files	No	N/A	Yes	Yes	Yes	Yes
Dependency Resolution	Basic	Basic	Advanced	Advanced	Advanced	Advanced
Virtual Envs	Manual	Auto	Auto	Manual	Manual	Auto
Publishing	No	No	Yes	No	No	No
Use Case	General	CLI Tools	Projects	General	Projects	Data Science
Learning Curve	Easy	Easy	Medium	Easy	Medium	Medium

When to Use Which Tool¶

Use pip when:¶

Working on simple projects
Following tutorials
Need maximum compatibility
Working in legacy environments
Quick prototyping

Use pipx when:¶

Installing CLI tools globally
Need isolated tool environments
Want tools available system-wide
Installing development utilities

Use Poetry when:¶

Starting new Python projects
Need dependency management and packaging
Publishing to PyPI
Want deterministic builds
Working on libraries

Use uv when:¶

Need maximum installation speed
Want pip compatibility with performance
Working with large dependency trees
Need lock file support
CI/CD optimization

Use pip-tools when:¶

Want to stick with pip workflow
Need lock files
Simple projects with deterministic builds
Gradual migration from pip

Use Conda when:¶

Data science projects
Need non-Python dependencies
Working with scientific libraries
Need environment isolation with system packages

Django Project Recommendations¶

Small Django Projects¶

# Use pip with virtual environment
python -m venv venv
source venv/bin/activate
pip install django
pip freeze > requirements.txt

Medium Django Projects¶

# Use Poetry
poetry init
poetry add django djangorestframework
poetry add --group dev pytest black flake8
poetry install

Large Django Projects¶

# Use uv for speed
uv venv
source .venv/bin/activate
uv pip install -r requirements.txt
uv sync  # with pyproject.toml

Team Django Projects¶

# Use Poetry with lock file
# pyproject.toml
[tool.poetry]
name = "myproject"
version = "0.1.0"

[tool.poetry.dependencies]
python = "^3.11"
django = "4.2.0"  # Exact versions for team consistency

[tool.poetry.group.dev.dependencies]
pytest = "^7.4"

# Team members run:
poetry install
# This installs exact versions from poetry.lock

Migration Between Tools¶

From pip to Poetry¶

# Create poetry project
poetry init

# Import from requirements.txt
cat requirements.txt | grep -v "^#" | xargs -n 1 poetry add

# Or manually edit pyproject.toml
poetry install

From pip to uv¶

# uv is a drop-in replacement
# Replace 'pip' with 'uv pip'
uv pip install -r requirements.txt

From Poetry to pip¶

# Export from poetry
poetry export -f requirements.txt --output requirements.txt

# Use with pip
pip install -r requirements.txt

From Poetry to uv¶

# Poetry's pyproject.toml is compatible
uv pip install -e .
uv pip install -e ".[dev]"

Best Practices¶

1. Always Use Virtual Environments¶

# Never install packages globally (except with pipx)
python -m venv venv
source venv/bin/activate

2. Pin Dependencies in Production¶

# requirements.txt for production
django==4.2.0  # Exact version, not >=4.2.0
gunicorn==21.2.0
psycopg2-binary==2.9.9

3. Separate Dev and Prod Dependencies¶

# requirements/base.txt
django==4.2.0

# requirements/dev.txt
-r base.txt
pytest==7.4.0

# requirements/prod.txt
-r base.txt
gunicorn==21.2.0

4. Use Lock Files¶

# Poetry
poetry lock

# uv
uv lock

# pip-tools
pip-compile requirements.in

5. Keep Dependencies Updated¶

# Check for outdated packages
pip list --outdated

# Update with Poetry
poetry update

# Update with uv
uv lock --upgrade

6. Document Installation Steps¶

# README.md
## Installation

### Using Poetry
```bash
poetry install
poetry run python manage.py runserver

Using pip¶

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python manage.py runserver

## Troubleshooting

### Dependency Conflicts

```bash
# With pip - check conflicts
pip check

# With Poetry - resolve conflicts
poetry lock
poetry install

# With uv - fast resolution
uv pip install -r requirements.txt

Corrupted Environment¶

# Delete and recreate

# pip
rm -rf venv
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Poetry
poetry env remove python3.11
poetry install

# uv
rm -rf .venv
uv venv
uv sync

SSL Certificate Errors¶

# pip
pip install --trusted-host pypi.org --trusted-host files.pythonhosted.org django

# Set globally
pip config set global.trusted-host "pypi.org files.pythonhosted.org"

Slow Installation¶

# Use uv for speed
pip install uv
uv pip install -r requirements.txt

# Or use pip cache
pip cache dir
pip cache list
pip cache remove *

Python Package Managers¶

Overview¶

pip - The Standard Package Manager¶

Installation¶

Basic Usage¶

Requirements Files¶

Development vs Production Requirements¶

Virtual Environments with pip¶

Pros and Cons¶

pipx - Install Python CLI Tools¶

Installation¶

Basic Usage¶

Common Use Cases¶

Pros and Cons¶

Poetry - Modern Dependency Management¶

Installation¶

Initialize Project¶

pyproject.toml Example¶

Basic Usage¶

Virtual Environment Management¶

Build and Publish¶

Pros and Cons¶

uv - Ultra-fast Package Installer¶

Installation¶

Basic Usage¶

Using with pyproject.toml¶

Lock Files with uv¶

Running Scripts¶

Pros and Cons¶

Other Package Managers¶

pip-tools¶

Conda¶

PDM¶

Comparison Table¶

When to Use Which Tool¶

Use pip when:¶

Use pipx when:¶

Use Poetry when:¶

Use uv when:¶

Use pip-tools when:¶

Use Conda when:¶

Django Project Recommendations¶

Small Django Projects¶

Medium Django Projects¶

Large Django Projects¶

Team Django Projects¶

Migration Between Tools¶

From pip to Poetry¶

From pip to uv¶

From Poetry to pip¶

From Poetry to uv¶

Best Practices¶

1. Always Use Virtual Environments¶

2. Pin Dependencies in Production¶

3. Separate Dev and Prod Dependencies¶

4. Use Lock Files¶

5. Keep Dependencies Updated¶

6. Document Installation Steps¶

Using pip¶

Corrupted Environment¶

SSL Certificate Errors¶

Slow Installation¶

Additional Resources¶