Exploring 67 years of LEGO

This is another small project in datacamp. Even though i have done complex projects earlier, i am considering these projects refreshes my memory on the beautiful pandas. 

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!

lego

In [1]:
# Nothing to do here

2. Reading Data

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

schema

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

In [2]:
# Import modules
import pandas as pd

# Read colors data
colors = pd.read_csv('datasets/colors.csv')

# Print the first few rows
colors.head()
Out[2]:
idnamergbis_trans
0-1Unknown0033B2f
10Black05131Df
21Blue0055BFf
32Green237841f
43Dark Turquoise008F9Bf

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.

In [3]:
# How many distinct colors are available?
num_colors = len(colors.name.unique())
num_colors
Out[3]:
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.

In [4]:
# colors_summary: Distribution of colors based on transparency
colors_summary = colors.groupby('is_trans')['id', 'name', 'rgb'].count()
colors_summary
Out[4]:
idnamergb
is_trans
f107107107
t282828

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.

sets_data

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

In [5]:
%matplotlib inline
# Read sets data as `sets`
sets = pd.read_csv('datasets/sets.csv')
# 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()
Out[5]:
<matplotlib.axes._subplots.AxesSubplot at 0x2a7c395fa58>

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.

In [6]:
# themes_by_year: Number of themes shipped by year
themes_by_year = sets[['year', 'theme_id']].groupby('year', as_index=False).\
                agg({"theme_id":pd.Series.nunique})
themes_by_year.head()
Out[6]:
yeartheme_id
019502
119531
219542
319554
419563

7. Wrapping It All Up!

Lego blocks offer an unlimited amount of fun across ages. We explored some interesting trends around colors, parts, and themes.

In [7]:
# Nothing to do here

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