## 1. Introduction

Everyone loves Lego (unless you ever stepped on one). Did you know by the way that "Lego" was derived from the Danish phrase leg godt, which means "play well"? Unless you speak Danish, probably not.

In this project, we will analyze a fascinating dataset on every single lego block that has ever been built!

A comprehensive database of lego blocks is provided by Rebrickable. The data is available as csv files and the schema is shown below.

Let us start by reading in the colors data to get a sense of the diversity of lego sets!

import pandas as pd

# Print the first few rows

id name rgb is_trans
0 -1 Unknown 0033B2 f
1 0 Black 05131D f
2 1 Blue 0055BF f
3 2 Green 237841 f
4 3 Dark Turquoise 008F9B f

## 3. Exploring Colors

Now that we have read the colors data, we can start exploring it! Let us start by understanding the number of colors available.

num_colors = len(colors.name.unique())
print(num_colors)

135


## 4. Transparent Colors in Lego Sets

The colors data has a column named is_trans that indicates whether a color is transparent or not. It would be interesting to explore the distribution of transparent vs. non-transparent colors.

colors_summary = colors.groupby('is_trans').count()
colors_summary

id name rgb
is_trans
f 107 107 107
t 28 28 28

## 5. Explore Lego Sets

Another interesting dataset available in this database is the sets data. It contains a comprehensive list of sets over the years and the number of parts that each of these sets contained.

Let us use this data to explore how the average number of parts in Lego sets has varied over the years.

%matplotlib inline
# Read sets data as sets

# Create a summary of average number of parts by year: parts_by_year
parts_by_year = sets.groupby('year').num_parts.mean()

# Plot trends in average number of parts by year
parts_by_year.plot(kind='hist');


## 6. Lego Themes Over Years

Lego blocks ship under multiple themes. Let us try to get a sense of how the number of themes shipped has varied over the years.

themes_by_year = sets[['year', 'theme_id']].\
groupby('year', as_index=False).agg({'theme_id': pd.Series.nunique})