README.md

Project 74 – Analyzing LEGO Sets 🧱

Overview

This project explores a comprehensive LEGO dataset to uncover trends in LEGO’s history, product growth, and set complexity over time. Using Python and data analysis libraries, the notebook answers questions about LEGO’s origins, themes, and how sets have evolved across decades.

The analysis focuses on understanding both historical context and quantitative patterns in LEGO set production.

Key Questions Explored

What is the largest LEGO set ever created, and how many parts does it contain?
In what year did LEGO begin selling sets, and how many sets were sold in its first year?
Which LEGO theme contains the most sets?
How has LEGO’s product offering expanded over time?
Have LEGO sets become larger and more complex as the years progressed?

Data Source

The dataset used in this project comes from Rebrickable, a community-driven LEGO database that provides detailed CSV files on:

LEGO sets
Themes
Parts
Release years

Source: Rebrickable LEGO Downloads

Tools & Technologies

Python
Pandas – data manipulation and aggregation
Matplotlib – data visualization
Jupyter Notebook

Project Structure

Data importing and cleaning
Exploratory data analysis (EDA)
Grouping and aggregation by year and theme
Visualization of trends over time
Interpretation of results

How to Run

Open the .ipynb file in Jupyter Notebook or JupyterLab
Ensure required libraries are installed:
pandas
matplotlib
Run cells from top to bottom

Notes

This project is intended for educational and analytical purposes.
Results depend on the completeness and accuracy of the Rebrickable dataset.

Authorship

📌 This document was written by AI.

Project completed as Project 74.