Video Game Sales DataViz

• Created by Andrés Segura Tinoco
• Created on Mar 17, 2020
• Updated on Sep 07, 2021

Visual Analytics project to analyze and discovery insights of video game sales in recent years, with the high-level API plotly.express.

# Load the Pandas libraries
import pandas as pd

# Load Plot libraries
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

Loading raw data

The first step is to load the dataset into a pandas dataframe. Here you can see the dataset.

dataURL = "../data/vgsales_2020.csv"
raw_data = pd.read_csv(dataURL)

raw_data

	Rank	Name	Platform	Year	Genre	Publisher	Developer	Global_Sales
0	1	Wii Sports	Wii	2006	Sports	Nintendo	NaN	82.74
1	2	Super Mario Bros.	NES	1985	Platform	Nintendo	NaN	40.24
2	3	Mario Kart Wii	Wii	2008	Racing	Nintendo	NaN	35.82
3	4	Wii Sports Resort	Wii	2009	Sports	Nintendo	NaN	33.00
4	5	Pokemon Red/Pokemon Blue	GB	1996	Role-Playing	Nintendo	NaN	31.37
...	...	...	...	...	...	...	...	...
17806	17807	This is the Police	NS	2017	Simulation	THQ Nordic	Weappy Studio	0.00
17807	17808	Starry Sky: Summer Stories	PSV	2017	Adventure	honeybee	honeybee	0.00
17808	17809	Kingdom Hearts HD I.5 + II.5 ReMIX	PS4	2017	Action	Square Enix	Square Enix	0.00
17809	17810	DOOM VFR	PC	2017	Shooter	Bethesda Softworks	id Software	0.00
17810	17811	Marvel vs. Capcom: Infinite	PC	2017	Fighting	Capcom	Capcom	0.00

17811 rows × 8 columns

Now the basic statistics of the numeric fields are shown, to have a quick understanding of the behavior of the data.

raw_data.describe()

	Rank	Year	Global_Sales
count	17811.000000	17811.000000	17811.000000
mean	8906.000000	2007.319409	0.518108
std	5141.737158	6.370235	1.522937
min	1.000000	1980.000000	0.000000
25%	4453.500000	2004.000000	0.050000
50%	8906.000000	2008.000000	0.160000
75%	13358.500000	2011.000000	0.450000
max	17811.000000	2020.000000	82.740000

1. Video Games Sales per Release Year

Important Note: Sales will be grouped by release year of game, not by real sale date as we don't have such historical data.

# Total sales
gd_sales = raw_data.groupby(["Year"]).sum()
gd_sales.reset_index(inplace=True)

# Plot global trend
fig = px.line(gd_sales, x="Year", y="Global_Sales")
fig.add_shape(dict(type="line", x0=2008, y0=0, x1=2008, y1=700, line=dict(color="RoyalBlue", width=2, dash="dot")))
fig.update_layout(height=400)
fig.update_xaxes(title_text="Release Year")
fig.update_yaxes(title_text="# Global Sales")
fig.show()

Insights

• It is clearly observed that the release year with the subsequent highest sales of video games was 2008. Keep in mind that for many of the games released later, sales have not been reported or are still selling.

2. Top 50 Best-Selling Video Games

Now we can plot the top 50 best-selling video games in the world.

# Data
top_games = 50
raw_data.head(10)

	Rank	Name	Platform	Year	Genre	Publisher	Developer	Global_Sales
0	1	Wii Sports	Wii	2006	Sports	Nintendo	NaN	82.74
1	2	Super Mario Bros.	NES	1985	Platform	Nintendo	NaN	40.24
2	3	Mario Kart Wii	Wii	2008	Racing	Nintendo	NaN	35.82
3	4	Wii Sports Resort	Wii	2009	Sports	Nintendo	NaN	33.00
4	5	Pokemon Red/Pokemon Blue	GB	1996	Role-Playing	Nintendo	NaN	31.37
5	6	Tetris	GB	1989	Puzzle	Nintendo	NaN	30.26
6	7	New Super Mario Bros.	DS	2006	Platform	Nintendo	NaN	30.01
7	8	Wii Play	Wii	2006	Misc	Nintendo	NaN	29.02
8	9	New Super Mario Bros. Wii	Wii	2009	Platform	Nintendo	NaN	28.62
9	10	Duck Hunt	NES	1984	Shooter	Nintendo	NaN	28.31

# Plot the best-selling video games, colored by Publisher
fig = px.bar(raw_data.head(top_games), x = 'Global_Sales', y = 'Name', color='Publisher', 
             orientation='h', hover_data=["Platform"])
fig.update_layout(yaxis={'categoryorder':'total ascending'}, showlegend=True)
fig.update_layout(height=800, title_text="Top 50 Best-Selling Video Games")
fig.update_xaxes(title_text="# Global Sales")
fig.update_yaxes(title_text="")
fig.show()

Insights

• The best-selling game is Wii Sports with approximately 80M copies. Keep in mind, that this game came by default with the first Wii console.
• The second best-selling game is GTA V, on 3 different platforms: PS3, PS4, Xbox 360.
• Also, the best-selling video game company is Nintendo (Royal Blue color).

3. Video Game Sales grouped by Platform

# Grouped data
gd = raw_data.groupby(['Platform', 'Publisher']).agg({'Global_Sales':'sum'})
gd.reset_index(inplace=True)
gd

	Platform	Publisher	Global_Sales
0	2600	20th Century Fox Video Games	1.94
1	2600	Activision	18.38
2	2600	Answer Software	0.50
3	2600	Atari	41.30
4	2600	Avalon Interactive	0.17
...	...	...	...
2072	XOne	Ubisoft	27.58
2073	XOne	Unknown	0.03
2074	XOne	Villa Gorilla	0.02
2075	XOne	Warner Bros. Interactive Entertainment	14.67
2076	XOne	Wired Productions	0.02

2077 rows × 3 columns

# Plot Video Game Sales grouped by Platform
fig = px.treemap(gd, path=['Platform', 'Publisher'], values='Global_Sales')
fig.show()

Insights

• The 4 platforms that have contributed the most to video game sales are: PS2, PS3, X360 and Wii.
• Closely followed by Nintendo DS and PS (1).

4. Video Game Sales grouped by Publisher

# Plot Video Game Sales grouped by Publisher
fig = px.treemap(gd, path=['Publisher', 'Platform'], values='Global_Sales')
fig.show()

Insights

• Regarding the biggest publishers of video games, Nintendo clearly wins.
• Other great cross-platform publishers are: Electronic Arts, Activision and Ubisoft.

5. Sales Trends of Top 10 Publishers

Important Note: Sales will be grouped by release year of game, not by real sale date as we don't have such historical data.

top_companies = 10

# Top 10 Companies
gd = raw_data.groupby(['Publisher']).agg({'Global_Sales':'sum'})
gd = gd.sort_values(by='Global_Sales', ascending=False)
top_companies = list(gd.head(top_companies).index)
top_companies

['Nintendo',
 'Electronic Arts',
 'Activision',
 'Sony Computer Entertainment',
 'Ubisoft',
 'Take-Two Interactive',
 'THQ',
 'Konami Digital Entertainment',
 'Sega',
 'Namco Bandai Games']

# Grouped data
gd = raw_data[raw_data["Publisher"].isin(top_companies)].groupby(['Year', 'Publisher']).agg({'Global_Sales':'sum'})
gd = gd.sort_values(by='Year', ascending=True)
gd.reset_index(inplace=True)
gd

	Year	Publisher	Global_Sales
0	1980	Activision	3.02
1	1981	Activision	8.50
2	1982	Activision	1.86
3	1982	Sega	0.40
4	1983	Activision	1.94
...	...	...	...
281	2020	Sony Computer Entertainment	0.26
282	2020	THQ	0.01
283	2020	Activision	0.01
284	2020	Namco Bandai Games	0.01
285	2020	Ubisoft	0.29

286 rows × 3 columns

Plotting Sales Trends of Top 10 Publishers

fig = px.line(gd, x="Year", y="Global_Sales", color='Publisher')
fig.update_layout(title_text="Sales Trends of Top 10 Publishers")
fig.update_xaxes(title_text="Release Year")
fig.update_yaxes(title_text="# Global Sales")
fig.show()

This multi-line chart confirms the insights obtained in point 4.

6. Distribution of Video Game Sales

Regarding Platform and Genre from 2013.

# Parallel Categories Diagram
fig = px.parallel_categories(raw_data.query("Year >= 2013"), dimensions=["Platform", "Genre"])
fig.show()

Insights

• As of 2013, the majority of video games sold were of the genres: Action, Role-Playing (RPG) and Adventure, closely followed by the genres: Sports and Shooter.
• Regarding the platforms that sold the most video games as of 2013, PlayStation (PS3, PS4 and PSV) is the clear winner, contributing approximately 40% of video games sold.
• The participation of games by gender is practically distributed uniformly by platform.

7. Evolution of Video Game Sales by Genre

# Top 10 Companies
gd = raw_data.groupby(['Year', 'Genre']).agg({'Global_Sales':'sum'})
gd.reset_index(inplace=True)
gd

	Year	Genre	Global_Sales
0	1980	Action	0.34
1	1980	Fighting	0.77
2	1980	Misc	2.71
3	1980	Shooter	7.07
4	1980	Sports	0.49
...	...	...	...
434	2020	Racing	0.11
435	2020	Role-Playing	1.64
436	2020	Simulation	0.29
437	2020	Sports	0.03
438	2020	Strategy	0.15

439 rows × 3 columns

# Parallel Categories Diagram
fig = px.area(raw_data, x='Year', y='Global_Sales', color='Genre')
fig.update_layout(title_text="Evolution of Video Game Sales by Genre")
fig.update_xaxes(title_text="Release Year")
fig.update_yaxes(title_text="# Global Sales")
fig.show()

Video Game Sales by Genre by Decades

# Cook the data
gd_1980s = raw_data.query("Year>=1980 and Year<1990")[["Genre", "Global_Sales"]].groupby(['Genre']).sum()
gd_1980s.reset_index(inplace=True)

gd_1990s = raw_data.query("Year>=1900 and Year<2000")[["Genre", "Global_Sales"]].groupby(['Genre']).sum()
gd_1990s.reset_index(inplace=True)

gd_2000s = raw_data.query("Year>=2000 and Year<2010")[["Genre", "Global_Sales"]].groupby(['Genre']).sum()
gd_2000s.reset_index(inplace=True)

gd_2010s = raw_data.query("Year>=2010")[["Genre", "Global_Sales"]].groupby(['Genre']).sum()
gd_2010s.reset_index(inplace=True)

# Create subplots: use 'domain' type for Pie subplot
labels = ["US", "China", "European Union", "Russian Federation", "Brazil", "India", "Rest of World"]
fig = make_subplots(rows=2, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}], [{'type':'domain'}, {'type':'domain'}]])
fig.add_trace(go.Pie(labels=gd_1980s["Genre"], values=gd_1980s["Global_Sales"], name="1980s", title="1980s", hole=.3), 1, 1)
fig.add_trace(go.Pie(labels=gd_1990s["Genre"], values=gd_1990s["Global_Sales"], name="1990s", title="1990s", hole=.3), 1, 2)
fig.add_trace(go.Pie(labels=gd_2000s["Genre"], values=gd_2000s["Global_Sales"], name="2000s", title="2000s", hole=.3), 2, 1)
fig.add_trace(go.Pie(labels=gd_2010s["Genre"], values=gd_2010s["Global_Sales"], name="2010s", title="2010s", hole=.3), 2, 2)
fig.update_layout(height=800, title_text="Video Game Sales by Genre by Decades")
fig.show()

Insights

Finally, how the video game genre trend has changed in each decade:

• In the 1980s, Platform games were the most played, with 32.5%, now (in the 2010s) they only represent 4.86%.
• Instead, the Action genre is the most famous now with a 25.4% share.
• The 1990s was one of the most balanced, where RPG games had more prominence (12.1%) and Platform games began to decline.
• In the 2000s it was when eSport games had their biggest boom, representing 17.6% of the gaming market.

---

« Home