Pip vs Pipx: Complete Guide to Python Package Management
Pip vs Pipx: Complete Guide to Python Package Management
Python has two main tools for installing packages: pip and pipx. They both install Python packages, but they work very differently and solve different problems.
pip is Python's standard package installer. It comes built into Python and handles all kinds of packages - from libraries you use in your code to development tools. You'll use pip when you need to install packages for your Python projects.
pipx takes a different approach. It only installs Python applications that you run from the command line. Each app gets its own isolated environment, so they can't interfere with each other or break your other tools.
This guide will show you when to use each tool and help you avoid common mistakes that can mess up your Python setup.
What is pip?
pip is Python's main package installer. It's been around since 2008 and comes pre-installed with Python versions 3.4 and newer.
When you install Python, you automatically get pip. It can install packages from PyPI (the Python Package Index) and other sources. pip handles dependencies, manages different package versions, and works with virtual environments.
pip gives you complete control over what gets installed and where. You can install packages globally on your system, in virtual environments, or with specific version requirements. This flexibility makes pip essential for Python development, but it also means you need to understand how to use it properly.
What is pipx?
pipx solves a specific problem: installing Python command-line tools without breaking your system or other tools.
When you install a tool like black or flake8 with pipx, it creates a separate virtual environment just for that tool. The tool becomes available system-wide, but its dependencies stay isolated. This means you can install multiple tools that need different versions of the same library without any conflicts.
pipx focuses only on applications - tools you run from the command line. You can't use pipx to install libraries that you import in your Python code. It's designed for end-user applications like code formatters, linters, and other command-line utilities.
pip vs. pipx: a detailed comparison
Understanding when to use each tool requires looking at what they're designed to do. While both install Python packages, they serve completely different purposes.
Here are the key differences that matter:
| Aspect | pip | pipx |
|---|---|---|
| Primary purpose | Library and package management | Application installation and execution |
| Installation scope | Project-specific or global | Isolated applications with global access |
| Dependency management | Manual virtual environment handling | Automatic isolation per application |
| Target use case | Development dependencies and libraries | Command-line tools and standalone apps |
| Environment handling | Requires manual venv management | Creates isolated environments automatically |
| Package types | Any Python package or library | Applications with console scripts |
| Upgrade strategy | Manual dependency resolution | Per-application upgrade isolation |
| Configuration complexity | Flexible but requires setup | Minimal configuration needed |
| Development workflow | Integrates with requirements.txt | Designed for end-user tool installation |
| Conflict resolution | User manages conflicts manually | Prevents conflicts through isolation |
| System integration | Installs to active environment | Creates system-wide accessible commands |
| Temporary execution | Requires installation first | Supports run-once execution with --run |
| Maintenance overhead | Requires environment management | Self-contained application management |
Installation methods
Installing these tools shows you their fundamental differences and where you'll use them.
pip comes with Python, so you probably already have it. If you're using Python 3.4 or newer, pip is ready to use. For older Python versions or custom setups, you might need to install it separately.
# Check if pip is installed
python -m pip --version
# Install pip if you don't have it
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
python get-pip.py
# Install via system package managers
# Ubuntu/Debian
sudo apt update && sudo apt install python3-pip
# macOS with Homebrew
brew install python3 # includes pip
pip works directly with Python's import system. When you install a package with pip, it goes into your current Python environment - whether that's your system Python, a virtual environment, or a conda environment.
pipx is a separate tool that you need to install. The easiest way is to use pip to install pipx, then set up the command paths.
# Install pipx using pip
python -m pip install --user pipx
python -m pipx ensurepath
# Alternative installation methods
# Ubuntu/Debian
sudo apt install pipx
# macOS with Homebrew
brew install pipx
# Windows with Scoop
scoop install pipx
# Check that it works
pipx --version
After you install pipx, it creates its own directory structure to manage isolated applications. This keeps pipx-managed tools completely separate from your development environments and system Python.
Package installation patterns
The way you install packages with each tool shows their different purposes and philosophies.
pip installs packages into whatever Python environment is currently active. This could be your system Python, a virtual environment, or any other Python setup you're using.
# Basic package installation
pip install requests
# Install with version constraints
pip install "django>=3.2,<4.0"
# Install from requirements file
pip install -r requirements.txt
# Install in development mode
pip install -e .
# Install from Git repository
pip install git+https://github.com/user/repo.git
# Install with optional features
pip install "fastapi[all]"
pip's flexibility supports complex scenarios. You can install packages for development, production, testing, or any other purpose. The packages become part of your Python environment and you can import them in your code.
pipx takes a completely different approach. It only installs applications that provide command-line tools. Each application gets its own isolated environment, but the commands become available system-wide.
# Install a command-line tool
pipx install black
# Install with specific pip arguments
pipx install --pip-args="--pre" black
# Install from Git repository
pipx install git+https://github.com/psf/black.git
# Install with optional features
pipx install "jupyter[all]"
# Run a tool without installing it
pipx run black --check .
# Install with specific Python version
pipx install --python python3.9 black
When you install black with pipx, you can't import it in your Python code. Instead, you get a black command that you can run from anywhere on your system. The tool and all its dependencies stay isolated from everything else.
Virtual environment management
Virtual environments are where pip and pipx show their biggest differences. This affects how much work you need to do and how likely you are to run into problems.
pip requires you to manage virtual environments manually if you want to isolate your projects. This gives you complete control but means you need to understand and remember to use virtual environments properly.
# Manual virtual environment workflow
python -m venv project_env
source project_env/bin/activate # On Windows: project_env\Scripts\activate
# Install project dependencies
pip install -r requirements.txt
# Work on your project
python main.py
# Deactivate when finished
deactivate
# Development dependencies
pip install -r requirements-dev.txt
When you're working on multiple projects, you need to manage multiple virtual environments. Each project should have its own environment to avoid conflicts.
# Managing multiple projects
python -m venv project1_env
python -m venv project2_env
python -m venv experiment_env
# Switch between environments
source project1_env/bin/activate
# ... work on project1
deactivate
source project2_env/bin/activate
# ... work on project2
deactivate
You can use tools like virtualenvwrapper or conda to make this easier, but they add complexity to your setup.
pipx handles all virtual environment management automatically. You never need to create, activate, or deactivate environments. Every application you install gets its own environment behind the scenes.
# No environment management needed
pipx install black # Creates isolated environment automatically
pipx install flake8 # Creates separate isolated environment
pipx install mypy # Creates another separate isolated environment
# All tools work from anywhere
black --version
flake8 --version
mypy --version
# pipx manages everything for you
pipx list # Shows all installed applications
pipx upgrade black # Upgrades black in its isolated environment
pipx uninstall flake8 # Removes flake8 and its environment completely
You never have to think about environments with pipx. You can install tools that need conflicting dependencies without any problems because each tool lives in complete isolation.
Dependency conflict resolution
The approaches to managing dependency conflicts show the core differences between these tools and reveal their appropriate use cases.
pip resolves dependencies within whatever environment you're using. When multiple packages need different versions of the same dependency, pip tries to find a version that works for everyone. Sometimes this fails or forces upgrades that break your existing code.
# Potential conflict scenario
pip install requests==2.25.1 # Installs requests 2.25.1
pip install some-package # Might require requests>=2.26.0
# pip tries to resolve by upgrading requests
# This could break code that depends on requests 2.25.1 behavior
When conflicts happen, pip either refuses to install the package or forces changes that might break your existing setup. You need to carefully manage versions to avoid these problems.
# Complex dependency management
pip install "django>=3.2,<3.3"
pip install "requests>=2.25.0,<2.26.0"
pip install "urllib3>=1.26.0,<1.27.0"
# Your requirements file gets complicated
echo "django>=3.2,<3.3" >> requirements.txt
echo "requests>=2.25.0,<2.26.0" >> requirements.txt
echo "urllib3>=1.26.0,<1.27.0" >> requirements.txt
Even when you're careful, indirect dependencies can cause unexpected conflicts that are hard to debug.
pipx eliminates dependency conflicts completely through isolation. Each application has its own environment with its own dependency versions. Applications can't interfere with each other.
# No conflicts possible - each app is isolated
pipx install tool-requiring-requests-2.25
pipx install tool-requiring-requests-2.28
pipx install tool-requiring-old-urllib3
# Each tool runs in its own environment
pipx list
# Shows:
# tool-requiring-requests-2.25 (with requests 2.25.1)
# tool-requiring-requests-2.28 (with requests 2.28.0)
# tool-requiring-old-urllib3 (with urllib3 1.25.0)
This isolation works for all dependencies, including complex chains of requirements. The trade-off is that you use more disk space because each application stores its own copy of dependencies.
Use case scenarios
Understanding when to use each tool helps you pick the right one for your specific needs and avoid common mistakes.
Use pip when you need to install libraries that your Python code will import and use. This includes packages for data analysis, web development, machine learning, or any other functionality you want to add to your programs.
# Development scenario - these need pip installation
import requests # HTTP library for API calls
import pandas # Data manipulation
import numpy # Numerical computing
import django # Web framework
from myproject import utils # Your project's modules
# These packages become part of your application
response = requests.get('https://api.example.com/data')
df = pandas.DataFrame(response.json())
pip also works for project-specific development tools that need to work with your codebase:
# Development tools installed with pip in project environment
pip install pytest pytest-cov # Testing framework
pip install black isort # Code formatting (project-specific versions)
pip install mypy # Type checking
pip install sphinx # Documentation generation
# These tools work with your project's configuration files
pytest tests/
black --config pyproject.toml src/
mypy --config-file mypy.ini src/
Use pipx when you want to install standalone command-line applications. These are tools that you run from the terminal, not libraries that you import in your code.
# System-wide development tools
pipx install black # Code formatter
pipx install flake8 # Linter
pipx install mypy # Type checker
pipx install pre-commit # Git hooks manager
pipx install cookiecutter # Project template generator
# Utility and multimedia tools
pipx install youtube-dl # Video downloader
pipx install speedtest-cli # Internet speed testing
pipx install httpie # HTTP client
pipx install rich-cli # Terminal formatting
# Data science and analysis tools
pipx install jupyter # Notebook environment
pipx install streamlit # Web app framework
pipx install datasette # Database exploration
The key difference: pip-installed packages become part of your Python environment and you can import them in your code. pipx-installed applications give you commands that work independently of your development environment.
Performance and resource considerations
These tools use system resources differently, which affects storage requirements and performance in different ways.
pip shares dependencies between packages in the same environment. When multiple packages need the same library, pip installs it once and all packages use that shared copy. This saves disk space but can create version conflicts.
# Shared dependencies with pip
pip install django # Installs Django and its dependencies
pip install django-rest-framework # Reuses the Django installation
pip install celery # Might share some dependencies with Django
# Efficient storage but potential for conflicts
pip list | grep -E "(django|celery|rest)"
# Shows the shared dependency versions
When you use virtual environments with pip, you create separate copies of dependencies for each project. This uses more storage but gives you proper isolation.
# Multiple projects with pip
# Project 1
python -m venv project1_env
source project1_env/bin/activate
pip install django==3.2 # Django installed in project1_env
# Project 2
deactivate
python -m venv project2_env
source project2_env/bin/activate
pip install django==4.0 # Django installed separately in project2_env
pipx prioritizes isolation over storage efficiency. Each application gets its own environment with its own copy of all dependencies. This uses more disk space but prevents all conflicts.
# pipx resource usage
pipx install black # Creates environment with black and all its dependencies
pipx install isort # Creates separate environment with isort and all its dependencies
pipx install mypy # Creates separate environment with mypy and all its dependencies
# Each application is completely isolated
pipx list --verbose
# Shows detailed information about each isolated environment
The storage cost becomes noticeable with applications that have many dependencies:
# Applications with heavy dependencies
pipx install jupyter # Creates large environment with notebook ecosystem
pipx install tensorflow-cpu # Creates massive environment with ML libraries
pipx install ansible # Creates environment with automation dependencies
# Each maintains complete isolation
du -sh ~/.local/share/pipx/venvs/*
# Shows disk usage for each application environment
Integration with development workflows
The way these tools fit into your daily development work affects your productivity and how easy it is to maintain your projects.
pip integrates deeply with Python development workflows. It supports requirements files, editable installations, and complex dependency specifications that you need for reproducible development environments.
# Development workflow integration
pip install -e . # Editable install for development
pip install -r requirements.txt # Production dependencies
pip install -r requirements-dev.txt # Development dependencies
pip freeze > requirements-lock.txt # Create lock file for exact versions
# CI/CD integration
pip install --no-deps -r requirements-lock.txt # Exact reproduction
pip check # Verify dependencies are compatible
pip supports sophisticated dependency management through configuration files:
# setup.py or pyproject.toml integration
[build-system]
requires = ["setuptools>=45", "wheel"]
[project]
dependencies = [
"requests>=2.25.0",
"click>=8.0.0",
]
[project.optional-dependencies]
dev = [
"pytest>=6.0",
"black>=22.0",
"mypy>=0.900",
]
pipx complements development workflows by giving you consistent tooling that doesn't interfere with your project dependencies. You can have stable tool versions regardless of what your individual projects need.
# Stable development tooling
pipx install black==22.3.0 # Same formatter version across all projects
pipx install mypy==0.950 # Same type checker version everywhere
pipx install pre-commit==2.19.0 # Same git hooks version for all projects
# Project-specific configurations still work
# .pre-commit-config.yaml
repos:
- repo: local
hooks:
- id: black
name: black
entry: black # Uses pipx-installed black
language: system
This separation lets teams standardize on tool versions while allowing projects to have different library requirements:
# Team standardization with pipx
pipx install black==22.3.0 # Same formatter across the entire team
pipx install isort==5.10.1 # Same import sorter for everyone
# Project flexibility with pip
# Project A
pip install django==3.2.13 # Older Django version for legacy project
# Project B
pip install django==4.0.4 # Newer Django version for new project
Package discovery and exploration
Package discovery and exploration approaches reflect the different use cases and target audiences of each tool.
pip gives you comprehensive package discovery through direct PyPI integration. You can search for packages, get detailed information, and explore dependencies.
# Package discovery with pip
pip show django # Detailed package information
pip show --verbose requests # Extended package details including files
pip list # List all installed packages
pip list --outdated # Show packages that need updates
# Dependency exploration
pip show --verbose django | grep Requires
pip show --verbose django | grep Required-by
pip works with tools that help you understand and manage dependencies:
# Advanced dependency exploration
pip install pipdeptree # Tool for visualizing dependencies
pipdeptree # Show complete dependency tree
pipdeptree --packages django # Focus on specific package dependencies
pipdeptree --reverse # Show what depends on each package
pipx focuses on application discovery and management. It provides tools specifically designed for exploring and managing command-line applications.
# Application discovery with pipx
pipx list # Show all installed applications
pipx list --verbose # Detailed information about each application
pipx list --include-deps # Show applications and their dependencies
# Application exploration
pipx info black # Detailed information about black
pipx info --verbose jupyter # Extended details about jupyter installation
pipx has a unique feature that lets you try applications without installing them permanently:
# Try before installing
pipx run black --help # Run black without installing it
pipx run --spec black==22.3.0 black --version # Run specific version temporarily
pipx run cowsay "Hello World" # Try novelty applications without commitment
# Temporary exploration
pipx run httpie httpie.io/hello # Make HTTP request without installing httpie
pipx run youtube-dl --help # Explore youtube-dl options without installing
Upgrade and maintenance strategies
Package maintenance approaches show how each tool handles the ongoing challenge of keeping your software current and secure.
pip gives you detailed control over upgrades, but you need to manually manage dependency relationships and potential conflicts that might arise.
# Individual package upgrades
pip install --upgrade requests # Upgrade single package
pip install --upgrade --no-deps numpy # Upgrade without touching dependencies
pip install --upgrade-strategy eager # Upgrade all dependencies aggressively
# Bulk upgrade operations
pip list --outdated # Show packages needing updates
pip freeze | grep -v "^-e" | cut -d = -f 1 | xargs pip install -U # Upgrade everything
# Careful upgrade with constraints
pip install --upgrade "django>=3.2,<4.0" # Constrained upgrade to avoid breaking changes
Managing upgrades safely with pip requires understanding how changes might affect your dependencies:
# Safe upgrade workflow
pip check # Verify current installation is consistent
pip list --outdated > outdated.txt # Record what needs updating
pip install --upgrade package-name # Upgrade individual packages
pip check # Verify no conflicts were introduced
pipx simplifies upgrades through application-level isolation. Since each application lives in its own environment, upgrades can't cause conflicts between different tools.
# Simple application upgrades
pipx upgrade black # Upgrade single application safely
pipx upgrade-all # Upgrade all applications at once
pipx upgrade --pip-args="--pre" mypy # Upgrade with specific pip arguments
# Safe upgrade rollback
pipx list # Note current versions before upgrading
pipx upgrade jupyter # Upgrade jupyter
# If problems occur:
pipx uninstall jupyter # Remove problematic version completely
pipx install jupyter==previous.version # Reinstall known good version
pipx's isolation means you can upgrade tools fearlessly without worrying about breaking other applications:
# Fearless upgrades
pipx upgrade black # Won't affect any other tools
pipx upgrade --include-deps mypy # Upgrades mypy and dependencies safely
pipx upgrade pre-commit # Won't interfere with other project tooling
This isolation lets you use more aggressive update strategies without fear of breaking your existing setup.
Security considerations
Security implications differ between the tools based on how they install packages and manage environments.
pip installations share the security context of their target environment. If a compromised package gets installed, it can potentially affect other packages in the same environment or access system resources.
# Security considerations with pip
pip install --user package-name # Install to user directory (safer than system-wide)
pip install --no-deps package-name # Skip dependencies for manual security review
pip install --require-hashes -r requirements.txt # Verify package integrity with hashes
# Security scanning
pip-audit # Scan installed packages for known vulnerabilities
pip show package-name # Verify package details and origin
Global pip installations create particular security risks because they affect your entire system:
# Risky global installation
sudo pip install package-name # Installs system-wide with elevated privileges
# A compromised package could affect system Python or other global packages
Virtual environments provide some protection but still share the base Python installation:
# Safer pip usage with virtual environments
python -m venv secure_env
source secure_env/bin/activate
pip install package-name # Isolated from system but shares Python interpreter
pipx provides stronger security isolation through complete environment separation. Each application runs in its own sandbox, limiting the damage a compromised package can cause.
# Enhanced security through isolation
pipx install untrusted-tool # Runs in completely isolated environment
pipx run suspicious-package --help # Temporary execution without permanent installation
pipx install --pip-args="--no-deps" careful-package # Skip dependencies if needed
Each pipx application runs in complete isolation, so a compromised application can't directly access or modify other applications or your system Python:
# Isolated security contexts
pipx install tool-a # Runs in environment A
pipx install tool-b # Runs in environment B
# tool-a cannot access tool-b's dependencies, data, or functionality
This isolation also makes security incident response much simpler:
# Security incident response
pipx uninstall compromised-tool # Completely removes tool and its environment
pipx install --force-reinstall safe-tool # Clean reinstall if needed
pipx list --verbose # Audit all installed applications and their isolation
Final thoughts
We compared pip and pipx to help you understand when to use each tool in your Python workflow.
Use pip when you need packages for your Python projects. Install requests, pandas, django, and other libraries with pip because your code needs to import them.
Go with pipx for standalone tools you run from the command line. Install black, flake8, jupyter, and similar applications with pipx so they work everywhere without interfering with each other.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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