🏎️ Formula 1 Data Engineering & Analysis Pipeline

This repository contains an end-to-end pipeline for retrieving, cleaning, storing, and analyzing Formula 1 historical racing data.
It supports structured data ingestion from the Ergast Developer API, transformation into consistent tabular formats, and exploration through summary statistics and visualizations.

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🚦 Key Features

✔ Programmatic Data Retrieval

Uses the Ergast F1 API to fetch:

• Race results
• Driver standings
• Constructor standings
• Circuits and tracks
• Qualifying results
• Pit stop summaries
• Race schedules

✔ Local Caching

Fetched data is stored in:

• CSV files
• SQLite databases
• Local API cache files

This avoids redundant API calls and ensures efficient reproducibility.

✔ Clean & Unified Data

Raw API responses are standardized, merged, and validated into structured datasets suitable for analysis.

✔ Reproducible Analysis Workflow

Includes:

• Step-by-step notebooks
• Documented methodology in Quarto
• Visualizations and summary analytics

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📂 Repository Structure

At a high level, the repository contains the following components:

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📘 Quarto Documents

A collection of .qmd files that document the full project workflow, including:

• Data source documentation
• Retrieval logic
• Cleaning and transformation steps
• Analysis methodology
• Testing approach
• Reproducibility practices

These provide a narrative explanation of the pipeline from start to finish.

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📓 Jupyter Notebooks

Interactive notebooks used for structured data processing and exploratory analysis:

• data_extract.ipynb — Retrieves raw Formula 1 datasets from the Ergast API and caches them locally.
• clean.ipynb — Cleans, standardizes, and merges raw API data into structured analytical tables.
• summarystats.ipynb — Generates descriptive statistics and season-level summary metrics.
• viz.ipynb — Creates exploratory visualizations of driver, constructor, and race performance.

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📁 Data and Cache Files

Includes raw, intermediate, and processed data:

• CSV files with race results, standings, schedules, circuits, pit stops, and merged datasets
• SQLite databases (f1_data.db, etc.) for efficient local storage
• API cache files (e.g., ergast_cache.sqlite) to prevent repeated data downloads

These assets support reproducibility and fast iteration.

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🛠 Python Utilities and Tests

• data_loading_functions.py — Contains reusable helper functions for loading and querying datasets.
• test_data_processing.py — Basic tests verifying data integrity, schema consistency, and correctness of the cleaning steps.

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📎 Supporting Files

• requirements.txt — Python dependencies for running notebooks and scripts
• styles.css — Styling for Quarto-rendered documents
• Additional project artifacts such as poster.pdf and commitgraph.jpg

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