Today we'll dive deep into a dataset all about LEGO. From the dataset we can ask whole bunch of interesting questions about the history of the LEGO company, their product offering, and which LEGO set ultimately rules them all:
Data Source
Rebrickable has compiled data on all the LEGO pieces in existence. I recommend you use download the .csv files provided in this lesson.
import pandas as pd
import matplotlib.pyplot as plt
Challenge: How many different colours does the LEGO company produce? Read the colors.csv file in the data folder and find the total number of unique colours. Try using the .nunique() method to accomplish this.
colors = pd.read_csv("data/colors.csv")
colors.head()
| 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 |
# Number of Unique Entries in rgb (Colour)
colors.rgb.nunique()
124
# Number of Unique Entries in name (Colour Name)
colors["name"].nunique()
135
Challenge: Find the number of transparent colours where is_trans == 't' versus the number of opaque colours where is_trans == 'f'. See if you can accomplish this in two different ways.
colors.groupby("is_trans").count()
| id | name | rgb | |
|---|---|---|---|
| is_trans | |||
| f | 107 | 107 | 107 |
| t | 28 | 28 | 28 |
# .value_counts() Function
colors["is_trans"].value_counts()
f 107 t 28 Name: is_trans, dtype: int64
Walk into a LEGO store and you will see their products organised by theme. Their themes include Star Wars, Batman, Harry Potter and many more.
A lego set is a particular box of LEGO or product. Therefore, a single theme typically has many different sets.
The sets.csv data contains a list of sets over the years and the number of parts that each of these sets contained.
Challenge: Read the sets.csv data and take a look at the first and last couple of rows.
sets = pd.read_csv("data/sets.csv")
sets.head()
| set_num | name | year | theme_id | num_parts | |
|---|---|---|---|---|---|
| 0 | 001-1 | Gears | 1965 | 1 | 43 |
| 1 | 0011-2 | Town Mini-Figures | 1978 | 84 | 12 |
| 2 | 0011-3 | Castle 2 for 1 Bonus Offer | 1987 | 199 | 0 |
| 3 | 0012-1 | Space Mini-Figures | 1979 | 143 | 12 |
| 4 | 0013-1 | Space Mini-Figures | 1979 | 143 | 12 |
Challenge: In which year were the first LEGO sets released and what were these sets called?
sets["year"].min()
1949
Challenge: How many different sets did LEGO sell in their first year? How many types of LEGO products were on offer in the year the company started?
sets[sets["year"] == 1949]
| set_num | name | year | theme_id | num_parts | |
|---|---|---|---|---|---|
| 9521 | 700.1-1 | Extra-Large Gift Set (ABB) | 1949 | 365 | 142 |
| 9534 | 700.2-1 | Large Gift Set (ABB) | 1949 | 365 | 178 |
| 9539 | 700.3-1 | Medium Gift Set (ABB) | 1949 | 365 | 142 |
| 9544 | 700.A-1 | Small Brick Set (ABB) | 1949 | 371 | 24 |
| 9545 | 700.B-1 | Small Doors and Windows Set (ABB) | 1949 | 371 | 12 |
Challenge: Find the top 5 LEGO sets with the most number of parts.
sets.sort_values("num_parts", ascending=False)
| set_num | name | year | theme_id | num_parts | |
|---|---|---|---|---|---|
| 15004 | BIGBOX-1 | The Ultimate Battle for Chima | 2015 | 571 | 9987 |
| 11183 | 75192-1 | UCS Millennium Falcon | 2017 | 171 | 7541 |
| 10551 | 71043-1 | Hogwarts Castle | 2018 | 246 | 6020 |
| 295 | 10256-1 | Taj Mahal | 2017 | 673 | 5923 |
| 221 | 10189-1 | Taj Mahal | 2008 | 673 | 5922 |
| ... | ... | ... | ... | ... | ... |
| 1782 | 20216-1 | MBA Robot & Micro Designer (Kits 2 - 3 Redesign) | 2013 | 432 | 0 |
| 1780 | 20214-1 | MBA Adventure Designer (Kits 7 - 9 Redesign) | 2013 | 432 | 0 |
| 6822 | 5005539-1 | Brick Pouch (Yellow) | 2018 | 501 | 0 |
| 9026 | 66319-1 | Power Miners 3 in 1 Superpack | 2009 | 439 | 0 |
| 12946 | 853471-1 | C-3PO Key Chain | 2015 | 503 | 0 |
15710 rows × 5 columns
Challenge: Use .groupby() and .count() to show the number of LEGO sets released year-on-year. How do the number of sets released in 1955 compare to the number of sets released in 2019?
sets_by_year = sets.groupby("year").count()
sets_by_year
| set_num | name | theme_id | num_parts | |
|---|---|---|---|---|
| year | ||||
| 1949 | 5 | 5 | 5 | 5 |
| 1950 | 6 | 6 | 6 | 6 |
| 1953 | 4 | 4 | 4 | 4 |
| 1954 | 14 | 14 | 14 | 14 |
| 1955 | 28 | 28 | 28 | 28 |
| ... | ... | ... | ... | ... |
| 2017 | 786 | 786 | 786 | 786 |
| 2018 | 816 | 816 | 816 | 816 |
| 2019 | 840 | 840 | 840 | 840 |
| 2020 | 674 | 674 | 674 | 674 |
| 2021 | 3 | 3 | 3 | 3 |
71 rows × 4 columns
sets_by_year["set_num"].head()
year 1949 5 1950 6 1953 4 1954 14 1955 28 Name: set_num, dtype: int64
Challenge: Show the number of LEGO releases on a line chart using Matplotlib.
Note that the .csv file is from late 2020, so to plot the full calendar years, you will have to exclude some data from your chart. Can you use the slicing techniques covered in Day 21 to avoid plotting the last two years? The same syntax will work on Pandas DataFrames.
plt.plot(sets_by_year.index, sets_by_year.set_num)
# This is misleading because the last two entries in the column (2021, 2020) are estimates
[<matplotlib.lines.Line2D at 0x7fb49034ea90>]
sets_by_year.tail()
| set_num | name | theme_id | num_parts | |
|---|---|---|---|---|
| year | ||||
| 2017 | 786 | 786 | 786 | 786 |
| 2018 | 816 | 816 | 816 | 816 |
| 2019 | 840 | 840 | 840 | 840 |
| 2020 | 674 | 674 | 674 | 674 |
| 2021 | 3 | 3 | 3 | 3 |
plt.plot(sets_by_year.index[:-2], sets_by_year.set_num[:-2])
[<matplotlib.lines.Line2D at 0x7fb4a107a5b0>]
Let's work out the number of different themes shipped by year. This means we have to count the number of unique theme_ids per calendar year.
theme_by_year = sets.groupby("year").agg({"theme_id": pd.Series.nunique})
theme_by_year.head()
| theme_id | |
|---|---|
| year | |
| 1949 | 2 |
| 1950 | 1 |
| 1953 | 2 |
| 1954 | 2 |
| 1955 | 4 |
theme_by_year.rename(columns={"theme_id": "nr_themes"}, inplace=True)
theme_by_year.head()
| nr_themes | |
|---|---|
| year | |
| 1949 | 2 |
| 1950 | 1 |
| 1953 | 2 |
| 1954 | 2 |
| 1955 | 4 |
Challenge: Plot the number of themes released by year on a line chart. Only include the full calendar years (i.e., exclude 2020 and 2021).
plt.plot(theme_by_year.index[:-2], theme_by_year.nr_themes[:-2])
[<matplotlib.lines.Line2D at 0x7fb4901cfb50>]
# Number of themes & Number of Sets in same plot with same axes
plt.plot(sets_by_year.index[:-2], sets_by_year.set_num[:-2])
plt.plot(theme_by_year.index[:-2], theme_by_year.nr_themes[:-2])
[<matplotlib.lines.Line2D at 0x7fb4a110d190>]
# We use axis object in Matplotlib
ax1 = plt.gca() # Get Current Axis
ax2 = plt.twinx() # Create another axis which shares the same x-axis
ax1.plot(sets_by_year.index[:-2], sets_by_year.set_num[:-2], color="b")
ax2.plot(theme_by_year.index[:-2], theme_by_year.nr_themes[:-2], color="orange")
ax1.set_xlabel("Year")
ax1.set_ylabel("Number of Sets", color="blue")
ax2.set_ylabel("Number of Themes", color="orange")
Text(0, 0.5, 'Number of Themes')
Challenge: Use the .groupby() and .agg() function together to figure out the average number of parts per set. How many parts did the average LEGO set released in 1954 compared to say, 2017?
parts_per_year = sets.groupby("year").agg({"num_parts": pd.Series.mean})
parts_per_year.head()
| num_parts | |
|---|---|
| year | |
| 1949 | 99.600000 |
| 1950 | 1.000000 |
| 1953 | 13.500000 |
| 1954 | 12.357143 |
| 1955 | 36.607143 |
parts_per_year.tail()
| num_parts | |
|---|---|
| year | |
| 2017 | 221.840967 |
| 2018 | 213.618873 |
| 2019 | 207.510714 |
| 2020 | 259.732938 |
| 2021 | 0.000000 |
Challenge: Has the size and complexity of LEGO sets increased over time based on the number of parts? Plot the average number of parts over time using a Matplotlib scatter plot. See if you can use the scatter plot documentation before I show you the solution. Do you spot a trend in the chart?
plt.scatter(parts_per_year.index[:-2], parts_per_year.num_parts[:-2])
<matplotlib.collections.PathCollection at 0x7fb4c254f880>
LEGO has licensed many hit franchises from Harry Potter to Marvel Super Heros to many others. But which theme has the largest number of individual sets?
set_theme_count = sets["theme_id"].value_counts()
set_theme_count[:5]
158 753 501 656 494 398 435 356 503 329 Name: theme_id, dtype: int64
The themes.csv file has the actual theme names. The sets .csv has theme_ids which link to the id column in the themes.csv.
Challenge: Explore the themes.csv. How is it structured? Search for the name 'Star Wars'. How many ids correspond to this name in the themes.csv? Now use these ids and find the corresponding the sets in the sets.csv (Hint: you'll need to look for matches in the theme_id column)
themes = pd.read_csv("data/themes.csv")
themes.head()
| id | name | parent_id | |
|---|---|---|---|
| 0 | 1 | Technic | NaN |
| 1 | 2 | Arctic Technic | 1.0 |
| 2 | 3 | Competition | 1.0 |
| 3 | 4 | Expert Builder | 1.0 |
| 4 | 5 | Model | 1.0 |
themes[themes["name"] == "Star Wars"]
| id | name | parent_id | |
|---|---|---|---|
| 17 | 18 | Star Wars | 1.0 |
| 150 | 158 | Star Wars | NaN |
| 174 | 209 | Star Wars | 207.0 |
| 211 | 261 | Star Wars | 258.0 |
sets[sets.theme_id == 18]
| set_num | name | year | theme_id | num_parts | |
|---|---|---|---|---|---|
| 8786 | 65081-1 | R2-D2 / C-3PO Droid Collectors Set | 2002 | 18 | 1 |
| 12051 | 8000-1 | Pit Droid | 2000 | 18 | 223 |
| 12058 | 8001-1 | Battle Droid | 2000 | 18 | 336 |
| 12066 | 8002-1 | Destroyer Droid | 2000 | 18 | 567 |
| 12071 | 8007-1 | C-3PO | 2001 | 18 | 339 |
| 12072 | 8008-1 | Stormtrooper | 2001 | 18 | 360 |
| 12073 | 8009-1 | R2-D2 | 2002 | 18 | 239 |
| 12074 | 8010-1 | Darth Vader | 2002 | 18 | 388 |
| 12081 | 8011-1 | Jango Fett | 2002 | 18 | 425 |
| 12083 | 8012-1 | Super Battle Droid | 2002 | 18 | 378 |
| 15273 | K8008-1 | Darth Vader / Stormtrooper Kit | 2002 | 18 | 0 |
sets[sets.theme_id == 209]
| set_num | name | year | theme_id | num_parts | |
|---|---|---|---|---|---|
| 11013 | 75023-1 | Star Wars Advent Calendar 2013 | 2013 | 209 | 254 |
| 11046 | 75056-1 | Star Wars Advent Calendar 2014 | 2014 | 209 | 273 |
| 11080 | 75097-1 | Star Wars Advent Calendar 2015 | 2015 | 209 | 291 |
| 11131 | 75146-1 | Star Wars Advent Calendar 2016 | 2016 | 209 | 282 |
| 11173 | 75184-1 | Star Wars Advent Calendar 2017 | 2017 | 209 | 309 |
| 11206 | 75213-1 | Star Wars Advent Calendar 2018 | 2018 | 209 | 307 |
| 11245 | 75245-1 | Star Wars Advent Calendar 2019 | 2019 | 209 | 280 |
| 11281 | 75279-1 | Star Wars Advent Calendar 2020 | 2020 | 209 | 312 |
| 12019 | 7958-1 | Star Wars Advent Calendar 2011 | 2011 | 209 | 267 |
| 14352 | 9509-1 | Star Wars Advent Calendar 2012 | 2012 | 209 | 235 |
set_theme_df = pd.DataFrame({
"id": set_theme_count.index,
"set_count": set_theme_count.values,
})
set_theme_df.head()
| id | set_count | |
|---|---|---|
| 0 | 158 | 753 |
| 1 | 501 | 656 |
| 2 | 494 | 398 |
| 3 | 435 | 356 |
| 4 | 503 | 329 |
merged_df = pd.merge(set_theme_df, themes, on="id")
merged_df.head()
| id | set_count | name | parent_id | |
|---|---|---|---|---|
| 0 | 158 | 753 | Star Wars | NaN |
| 1 | 501 | 656 | Gear | NaN |
| 2 | 494 | 398 | Friends | NaN |
| 3 | 435 | 356 | Ninjago | NaN |
| 4 | 503 | 329 | Key Chain | 501.0 |
plt.bar(merged_df.name[:10], merged_df.set_count[:10])
# X-Axis is clustered
<BarContainer object of 10 artists>
plt.figure(figsize=(14,8))
plt.xticks(fontsize=14, rotation=45)
plt.yticks(fontsize=14)
plt.ylabel('Nr of Sets', fontsize=14)
plt.xlabel('Theme Name', fontsize=14)
plt.bar(merged_df.name[:10], merged_df.set_count[:10])
<BarContainer object of 10 artists>
print("That's It!")
That's It!