Tutorial Notebooks

Learn repoindex interactively with our comprehensive Jupyter notebook tutorials. These notebooks provide hands-on experience with all major features, from basic commands to advanced integrations.

Overview

Our tutorial notebooks are designed to:

Teach by Example: Real-world scenarios with actual code
Progressive Learning: Start simple, build complexity
Interactive Exploration: Modify and experiment with code
Visual Learning: Charts, graphs, and visualizations
Best Practices: Learn optimal patterns and techniques

Prerequisites

Required Software

# Install Jupyter and dependencies
pip install jupyter notebook pandas matplotlib seaborn

# Install repoindex with all features
pip install repoindex[all]

# Or install specific features
pip install repoindex[clustering,visualization]

Setup Verification

# Verify installation (run in notebook)
import repoindex
import pandas as pd
import matplotlib.pyplot as plt

print(f"repoindex version: {repoindex.__version__}")
print("Setup complete!")

Notebook Collection

1. Getting Started (01_getting_started.ipynb)

Learn the Basics

Topics covered: - Installing and configuring repoindex - Basic commands and operations - Understanding JSONL output - Working with the metadata store - Query language fundamentals

Key exercises:

# List all repositories
repos = repoindex.list_repositories()

# Filter and query
python_repos = repoindex.query("language == 'Python'")

# Check status
status = repoindex.get_status(recursive=True)

Duration: 30-45 minutes Level: Beginner

2. Clustering Analysis (02_clustering_analysis.ipynb)

Machine Learning for Repository Management

Topics covered: - Feature extraction from repositories - Clustering algorithms comparison - Duplicate detection - Visualization techniques - Cluster interpretation

Key exercises:

# Perform clustering
from repoindex.integrations.clustering import ClusterAnalyzer

analyzer = ClusterAnalyzer()
clusters = analyzer.fit_predict(repos, algorithm='kmeans', n_clusters=5)

# Visualize results
analyzer.visualize(clusters, method='tsne')

# Find duplicates
duplicates = analyzer.find_duplicates(threshold=0.85)

Duration: 45-60 minutes Level: Intermediate

3. Workflow Orchestration (03_workflow_orchestration.ipynb)

Automate Complex Tasks

Topics covered: - Creating YAML workflows - DAG visualization - Conditional execution - Error handling patterns - Workflow composition

Key exercises:

# Load and run workflow
from repoindex.integrations.workflow import Workflow

workflow = Workflow.from_file('morning-routine.yaml')
result = workflow.run(variables={'env': 'production'})

# Create workflow programmatically
workflow = Workflow(name="Custom Workflow")
workflow.add_step("list", action="repoindex.list")
workflow.add_step("filter", action="repoindex.filter", depends_on=["list"])

Duration: 60 minutes Level: Intermediate

4. Advanced Integrations (04_advanced_integrations.ipynb)

Extend repoindex with Custom Features

Topics covered: - Creating custom integrations - Plugin development - API extensions - Performance optimization - Testing integrations

Key exercises:

# Create custom integration
from repoindex.integrations.base import Integration

class SecurityAnalyzer(Integration):
    def analyze(self, repos):
        for repo in repos:
            repo['security_score'] = self.calculate_score(repo)
            yield repo

# Register and use
repoindex.register_integration('security', SecurityAnalyzer)
results = repoindex.run_integration('security', repos)

Duration: 60-90 minutes Level: Advanced

5. Data Visualization (05_data_visualization.ipynb)

Visualize Your Repository Portfolio

Topics covered: - Portfolio statistics - Time series analysis - Technology landscapes - Contribution patterns - Interactive dashboards

Key exercises:

# Create visualizations
import matplotlib.pyplot as plt
import seaborn as sns

# Language distribution
lang_dist = pd.DataFrame(repos).groupby('language').size()
plt.pie(lang_dist.values, labels=lang_dist.index, autopct='%1.1f%%')

# Activity heatmap
activity = repoindex.get_activity_matrix(repos)
sns.heatmap(activity, cmap='YlOrRd')

# Interactive dashboard
from repoindex.visualizations import Dashboard
dashboard = Dashboard(repos)
dashboard.show()

Duration: 45 minutes Level: Intermediate

Running the Notebooks

Local Execution

# Clone repository with notebooks
git clone https://github.com/queelius/repoindex.git
cd repoindex/notebooks

# Start Jupyter
jupyter notebook

# Or use JupyterLab
jupyter lab

Online Execution

Run notebooks in your browser without installation:

Notebook Structure

Each notebook follows a consistent structure:

Introduction: Overview and learning objectives
Setup: Import statements and configuration
Concepts: Explanation with visual aids
Examples: Working code demonstrations
Exercises: Hands-on practice problems
Solutions: Detailed solutions with explanations
Summary: Key takeaways and next steps

Interactive Features

Code Cells

Modify and experiment with code:

# Original
repos = repoindex.list_repositories()

# Try modifying:
repos = repoindex.list_repositories(
    filter_language='Python',
    min_stars=10
)

Markdown Cells

Rich documentation with: - Formatted text and lists - LaTeX equations: $f(x) = x^2 + 2x + 1$ - Tables and diagrams - Links and references

Visualization Cells

Interactive plots with: - Matplotlib/Seaborn static plots - Plotly interactive charts - Network graphs with NetworkX - 3D visualizations

import ipywidgets as widgets

# Create interactive controls
algorithm = widgets.Dropdown(
    options=['kmeans', 'dbscan', 'hierarchical'],
    description='Algorithm:'
)

n_clusters = widgets.IntSlider(
    value=5, min=2, max=20,
    description='Clusters:'
)

# Interactive clustering
@widgets.interact(algorithm=algorithm, n_clusters=n_clusters)
def cluster_interactive(algorithm, n_clusters):
    result = cluster_repos(algorithm, n_clusters)
    visualize(result)

Exercise Examples

Beginner Exercise

Task: Find all Python repositories updated in the last week

# Your code here
# Hint: Use repoindex.query() with appropriate conditions

Solution:

recent_python = repoindex.query(
    "language == 'Python' and days_since_commit < 7"
)
print(f"Found {len(list(recent_python))} repositories")

Intermediate Exercise

Task: Create a workflow that audits repositories and generates a report

# Your code here
# Hint: Create workflow with audit and export steps

Solution:

workflow_yaml = """
name: Audit and Report
steps:
  - id: audit
    action: repoindex.audit
    parameters:
      check: [license, security]

  - id: report
    action: repoindex.export
    parameters:
      format: markdown
    depends_on: [audit]
"""

workflow = Workflow.from_yaml(workflow_yaml)
result = workflow.run()

Advanced Exercise

Task: Implement custom clustering based on code complexity

# Your code here
# Hint: Extract complexity metrics and use sklearn

Solution:

from sklearn.cluster import KMeans
import numpy as np

def extract_complexity_features(repos):
    features = []
    for repo in repos:
        features.append([
            repo.get('cyclomatic_complexity', 0),
            repo.get('lines_of_code', 0),
            repo.get('dependencies_count', 0),
            repo.get('file_count', 0)
        ])
    return np.array(features)

features = extract_complexity_features(repos)
kmeans = KMeans(n_clusters=4)
clusters = kmeans.fit_predict(features)

Best Practices

Notebook Organization

Clear Structure: Use headings to organize sections
Documentation: Explain what each cell does
Clean Output: Clear output before sharing
Version Control: Track notebooks in git
Reproducibility: Set random seeds for consistency

Performance Tips

# Use generators for large datasets
repos = repoindex.list_repositories()  # Generator
repos_list = list(repos)           # Only if needed

# Cache expensive operations
import functools

@functools.lru_cache(maxsize=128)
def expensive_analysis(repo_name):
    return analyze_repository(repo_name)

# Profile code performance
%timeit cluster_repos(algorithm='kmeans')

Debugging in Notebooks

# Enable debugging
%debug

# Verbose output
import logging
logging.basicConfig(level=logging.DEBUG)

# Step through code
%pdb on

# Inspect variables
%whos

Export Formats

# Convert to HTML
jupyter nbconvert --to html notebook.ipynb

# Convert to PDF
jupyter nbconvert --to pdf notebook.ipynb

# Convert to Python script
jupyter nbconvert --to python notebook.ipynb

# Convert to Markdown
jupyter nbconvert --to markdown notebook.ipynb

Notebook Hosting

GitHub: Renders notebooks automatically
nbviewer: Share via URL
Google Colab: Free cloud execution
Binder: Reproducible environments

Learning Path

Suggested Order

Week 1: Complete 01_getting_started.ipynb
Week 2: Work through 02_clustering_analysis.ipynb
Week 3: Master 03_workflow_orchestration.ipynb
Week 4: Explore 04_advanced_integrations.ipynb
Week 5: Create visualizations with 05_data_visualization.ipynb

Certification Track

Complete all notebooks and exercises to earn: - repoindex Practitioner: Notebooks 1-3 - repoindex Expert: All notebooks + custom integration - repoindex Master: Contribute your own notebook

Troubleshooting

Common Issues

Kernel Dies

# Increase memory limit
import resource
resource.setrlimit(resource.RLIMIT_AS, (2147483648, 2147483648))

Import Errors

# Check installation
import sys
!{sys.executable} -m pip install repoindex[all]

Slow Execution

# Use sampling for large datasets
sample_repos = list(repos)[:100]

Contributing Notebooks

We welcome notebook contributions! Guidelines:

Follow the standard structure
Include exercises and solutions
Add visualizations where helpful
Test on multiple platforms
Submit via pull request

Additional Resources

Video Tutorials: YouTube Channel
Blog Posts: repoindex Blog
Community: Discord Server
Office Hours: Weekly Q&A sessions

Next Steps

After completing the notebooks:

Build your own workflows
Create custom integrations
Contribute to repoindex
Share your experience
Teach others

Happy learning!

Tutorial Notebooks

Overview

Prerequisites

Required Software

Setup Verification

Notebook Collection

1. Getting Started (01_getting_started.ipynb)

2. Clustering Analysis (02_clustering_analysis.ipynb)

3. Workflow Orchestration (03_workflow_orchestration.ipynb)

4. Advanced Integrations (04_advanced_integrations.ipynb)

5. Data Visualization (05_data_visualization.ipynb)

Running the Notebooks

Local Execution

Online Execution

Notebook Structure

Interactive Features

Code Cells

Markdown Cells

Visualization Cells

Widget Integration

Exercise Examples

Beginner Exercise

Intermediate Exercise

Advanced Exercise

Best Practices

Notebook Organization

Performance Tips

Debugging in Notebooks

Sharing Notebooks

Export Formats

Notebook Hosting

Learning Path

Suggested Order

Certification Track

Troubleshooting

Common Issues

Kernel Dies

Import Errors

Slow Execution

Contributing Notebooks

Additional Resources

Next Steps