Chapter 1.2: Installing UV & Creating Your First Project

Get UV Running and Build a Data Analysis Project

🚀 PROJECT 1.2 | Difficulty: Intermediate | Time: 15 minutes

📊 Complexity Level: Intermediate ⭐⭐

Hands-on installation and project setup. You’ll learn terminal commands and project structure—essential skills for any developer!

💻 Interactive Options:

📓 Open in JupyterLite - Full Jupyter environment in your browser
- 📥 Download Notebook - For use in local Jupyter or Google Colab
💡 Note: Installing UV requires a terminal, so we’ll show examples but you’ll run this locally!

📖 Installation: Getting UV on Your Computer

UV works on macOS, Linux, and Windows. Let’s get it installed!

Step 1: Install UV

Open your terminal and run:

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

After installation, restart your terminal or run:

source $HOME/.cargo/env

Open PowerShell and run:

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

Step 2: Verify Installation

Check that UV is installed correctly:

uv --version

You should see something like: uv 0.1.x

✅ Success! If you see a version number, UV is installed and ready to use!

🏗️ Creating Your First Project

Now let’s create a data analysis project. We’ll analyze student grades and create visualizations!

Initialize a New Project

# Create a new directory and navigate into it
mkdir student-grades-analyzer
cd student-grades-analyzer

# Initialize UV project
uv init

This creates a basic project structure:

student-grades-analyzer/
├── pyproject.toml    # Project configuration
├── README.md         # Project description
└── .python-version   # Python version lock

📝 What’s pyproject.toml?

This is your project’s configuration file. It tells UV:

What your project is called
Which Python version to use
Which packages your project depends on

Think of it as your project’s ID card!

📦 Adding Dependencies

Now let’s add the packages we need for data analysis:

# Add pandas for data manipulation
uv add pandas

# Add matplotlib for creating charts
uv add matplotlib

# Add numpy for numerical operations
uv add numpy

UV will: 1. ⚡ Download and install each package (super fast!) 2. 🔒 Create a uv.lock file to freeze exact versions 3. 📋 Update pyproject.toml with your dependencies

⏱️ Speed Comparison:

Traditional pip might take 1-2 minutes to install these packages.

UV does it in 5-10 seconds! 🚀

📁 Understanding the Project Structure

After adding dependencies, your project looks like this:

student-grades-analyzer/
├── .venv/              # Virtual environment (UV creates this)
├── pyproject.toml      # Project config with dependencies
├── uv.lock             # Locked versions for reproducibility
├── README.md
└── .python-version

🎯 Let’s Write Our First Script

Create a file called analyze_grades.py:

# analyze_grades.py
# Our first UV-managed project!

import pandas as pd
import numpy as np

def create_sample_data():
    """Create sample student grades data"""
    students = ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve']
    
    data = {
        'Student': students,
        'Math': np.random.randint(70, 100, 5),
        'Science': np.random.randint(70, 100, 5),
        'English': np.random.randint(70, 100, 5),
        'History': np.random.randint(70, 100, 5)
    }
    
    return pd.DataFrame(data)

# Create and display the data
df = create_sample_data()
print("📊 Student Grades:")
print(df)

# Calculate average for each student
df['Average'] = df[['Math', 'Science', 'English', 'History']].mean(axis=1)
print("\n📈 With Averages:")
print(df)

# Find the top student
top_student = df.loc[df['Average'].idxmax()]
print(f"\n🏆 Top Student: {top_student['Student']} with {top_student['Average']:.2f} average!")

▶️ Running Your Project

To run your script with UV:

# UV automatically uses the right Python and packages!
uv run python analyze_grades.py

No need to activate a virtual environment—UV handles it automatically!

💡 Pro Tip: The uv run command automatically uses your project’s virtual environment. No more source venv/bin/activate needed!

🔄 Common UV Commands

Here are the most useful UV commands:

# Initialize a new project
uv init

# Add a package
uv add package-name

# Remove a package
uv remove package-name

# Run a script
uv run python script.py

# Show installed packages
uv pip list

# Update all packages
uv lock --upgrade

🎮 Try It Yourself!

Create a new UV project called my-data-project
Add pandas and matplotlib
Create a simple script that prints “Hello from UV!”
Run it with uv run python your_script.py

🏆 Challenge:

Modify the analyze_grades.py script to:

Add a ‘Grade Letter’ column (A for 90+, B for 80+, etc.)
Calculate class averages per subject
Find which subject has the highest class average

🚀 What’s Next?

Now that you have UV set up and a working project, in the next slide we’ll:

Dive deeper into pandas for data manipulation
Load data from files (CSV, Excel)
Clean and transform real-world data

--- title: "Chapter 1.2: Installing UV & Creating Your First Project" subtitle: "Get UV Running and Build a Data Analysis Project" format: live-html: code-tools: true execute: eval: false --- ::: {.quest-badge} 🚀 PROJECT 1.2 | Difficulty: Intermediate | Time: 15 minutes ::: ::: {.concept-box} **📊 Complexity Level: Intermediate ⭐⭐** Hands-on installation and project setup. You'll learn terminal commands and project structure—essential skills for any developer! ::: ::: {.tip-box} **💻 Interactive Options:** - 📓 **[Open in JupyterLite](../../jupyterlite/lab/index.html?path=projects/uv/02-setup-uv.ipynb)** - Full Jupyter environment in your browser - 📥 **[Download Notebook](../../files/projects/uv/02-setup-uv.ipynb)** - For use in local Jupyter or Google Colab - 💡 **Note**: Installing UV requires a terminal, so we'll show examples but you'll run this locally! ::: ## 📖 Installation: Getting UV on Your Computer UV works on macOS, Linux, and Windows. Let's get it installed! ### Step 1: Install UV ::: {.panel-tabset} ## macOS/Linux Open your terminal and run: ```bash curl -LsSf https://astral.sh/uv/install.sh | sh ``` After installation, restart your terminal or run: ```bash source $HOME/.cargo/env ``` ## Windows Open PowerShell and run: ```powershell powershell -c "irm https://astral.sh/uv/install.ps1 | iex" ``` ::: ### Step 2: Verify Installation Check that UV is installed correctly: ```bash uv --version ``` You should see something like: `uv 0.1.x` ::: {.success-box} **✅ Success!** If you see a version number, UV is installed and ready to use! ::: ## 🏗️ Creating Your First Project Now let's create a data analysis project. We'll analyze student grades and create visualizations! ### Initialize a New Project ```bash # Create a new directory and navigate into it mkdir student-grades-analyzer cd student-grades-analyzer # Initialize UV project uv init ``` This creates a basic project structure: ``` student-grades-analyzer/ ├── pyproject.toml # Project configuration ├── README.md # Project description └── .python-version # Python version lock ``` ::: {.info-box} **📝 What's `pyproject.toml`?** This is your project's configuration file. It tells UV: - What your project is called - Which Python version to use - Which packages your project depends on Think of it as your project's ID card! ::: ## 📦 Adding Dependencies Now let's add the packages we need for data analysis: ```bash # Add pandas for data manipulation uv add pandas # Add matplotlib for creating charts uv add matplotlib # Add numpy for numerical operations uv add numpy ``` UV will: 1. ⚡ Download and install each package (super fast!) 2. 🔒 Create a `uv.lock` file to freeze exact versions 3. 📋 Update `pyproject.toml` with your dependencies ::: {.comparison-box} **⏱️ Speed Comparison**: Traditional pip might take **1-2 minutes** to install these packages. UV does it in **5-10 seconds**! 🚀 ::: ## 📁 Understanding the Project Structure After adding dependencies, your project looks like this: ``` student-grades-analyzer/ ├── .venv/ # Virtual environment (UV creates this) ├── pyproject.toml # Project config with dependencies ├── uv.lock # Locked versions for reproducibility ├── README.md └── .python-version ``` ## 🎯 Let's Write Our First Script Create a file called `analyze_grades.py`: ```{python} # analyze_grades.py # Our first UV-managed project! import pandas as pd import numpy as np def create_sample_data(): """Create sample student grades data""" students = ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'] data = { 'Student': students, 'Math': np.random.randint(70, 100, 5), 'Science': np.random.randint(70, 100, 5), 'English': np.random.randint(70, 100, 5), 'History': np.random.randint(70, 100, 5) } return pd.DataFrame(data) # Create and display the data df = create_sample_data() print("📊 Student Grades:") print(df) # Calculate average for each student df['Average'] = df[['Math', 'Science', 'English', 'History']].mean(axis=1) print("\n📈 With Averages:") print(df) # Find the top student top_student = df.loc[df['Average'].idxmax()] print(f"\n🏆 Top Student: {top_student['Student']} with {top_student['Average']:.2f} average!") ``` ## ▶️ Running Your Project To run your script with UV: ```bash # UV automatically uses the right Python and packages! uv run python analyze_grades.py ``` No need to activate a virtual environment—UV handles it automatically! ::: {.pro-tip} **💡 Pro Tip**: The `uv run` command automatically uses your project's virtual environment. No more `source venv/bin/activate` needed! ::: ## 🔄 Common UV Commands Here are the most useful UV commands: ```bash # Initialize a new project uv init # Add a package uv add package-name # Remove a package uv remove package-name # Run a script uv run python script.py # Show installed packages uv pip list # Update all packages uv lock --upgrade ``` ## 🎮 Try It Yourself! 1. **Create a new UV project** called `my-data-project` 2. **Add pandas and matplotlib** 3. **Create a simple script** that prints "Hello from UV!" 4. **Run it** with `uv run python your_script.py` ::: {.challenge-box} **🏆 Challenge**: Modify the `analyze_grades.py` script to: - Add a 'Grade Letter' column (A for 90+, B for 80+, etc.) - Calculate class averages per subject - Find which subject has the highest class average ::: ## 🚀 What's Next? Now that you have UV set up and a working project, in the next slide we'll: - Dive deeper into pandas for data manipulation - Load data from files (CSV, Excel) - Clean and transform real-world data --- ::: {.navigation-box} **Previous**: [1.1 Meet UV](01-intro-uv.qmd) | **Next**: [1.3 Data Magic with Pandas](03-pandas-basics.qmd) :::