Why uv?

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See the difference

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With pip (traditional)

pip install pandas
# ⏱️ Takes 15-30 seconds

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With uv (modern)

uv pip install pandas
# ⚡ Takes 1-2 seconds

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Real-world comparison

# Create virtual environment
python -m venv .venv

# Activate it (different per OS!)
# Windows: .venv\Scripts\activate
# Mac/Linux: source .venv/bin/activate

# Install packages
pip install requests pandas numpy

# Save dependencies
pip freeze > requirements.txt

# Everything in one command
uv init
uv add requests pandas numpy

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Why is uv so fast?

1. Written in Rust - Compiled language, not interpreted Python
2. Smart caching - Reuses downloaded files intelligently
3. Parallel downloads - Gets multiple packages at once
4. Better algorithms - Optimized dependency resolution

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More than just speed

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One tool, not five

• pip for packages
• venv for virtual environments
• pip-compile for lock files
• pyenv for Python versions
• pipx for global tools

• uv does it all

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It just works

• “Did I activate my venv?”
• “Which Python am I using?”
• “Why is this package conflicting?”

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Modern features

• Lock files - Exact reproducible installs
• Workspace support - Multiple related projects
• Script running - Built-in task runner
• Python management - Install Python versions too

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Installing uv

curl -LsSf https://astral.sh/uv/install.sh | sh

powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

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Quick commands

# Create new project
uv init my-project

# Add packages
uv add requests pandas

# Install from existing project
uv sync

# Run Python
uv run python script.py

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Should you switch?

• Faster development
• Simpler commands
• Less confusion
• Modern tooling

• You’re in a team using pip
• You have complex legacy setups
• You’re just starting (learn basics first)

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What’s next?