Featured Photo by Element5 Digital on Pexels.
- Video games are an integral part of our lives. Expected to reach a value of $314bn by 2027, registering a CAGR of 9% over 2022-2027, gaming is set to become the new battleground for BigTech.
- Also, the number of new games released every year is increasing exponentially with 10,963 games released in 2022 alone.
- This has resulted in a large amount of data available that can be used to gain important BI insights.
- In this post, we will analyze video game sales and perform gaming industry market analysis in Python.
- This analysis will enable a game start-up firm launch a profitable product.
- The dataset contains a list of video games with sales greater than 100,000 copies. It was generated by a scrape of vgchartz.com. There are 16,598 records/rows and 11 attributes/columns
- Rank – Ranking based on overall sales
- Name – Name of the video game
- Platform – Platform on which the game is release
- Year – Year of the game’s release
- Genre – Genre of the game
- Publisher – Publisher of the game
- NA_Sales – Sales in North America (in millions)
- EU_Sales – Sales in Europe (in millions)
- JP_Sales – Sales in Japan (in millions)
- Other_Sales – Sales in the rest of the world (in millions)
- Global_Sales – Total worldwide sales.
- The script to scrape the data is available at https://github.com/GregorUT/vgchartzScrape.
- It is based on BeautifulSoup using Python.
Specific Questions:
- The game genre that generated the highest profit.
- Which Platforms have the highest sales price?
- Top Publishers and Total Revenue by Region
Import Modules
Let’s set the working directory
import os
os.chdir(‘VIDEOGAMES’)
os. getcwd()
and import the necessary modules/libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set_style(‘darkgrid’)
Input Dataset
Let’s read the dataset
df = pd.read_csv(‘vgsales.csv’)
df.head()
Dataset shape
df.shape
(16598, 11)
Dataset type
df.dtypes
Rank int64 Name object Platform object Year float64 Genre object Publisher object NA_Sales float64 EU_Sales float64 JP_Sales float64 Other_Sales float64 Global_Sales float64 dtype: object
Column names
df.columns
Index(['Rank', 'Name', 'Platform', 'Year', 'Genre', 'Publisher', 'NA_Sales',
'EU_Sales', 'JP_Sales', 'Other_Sales', 'Global_Sales'],
dtype='object')
Dataset summary
df.describe(include=’all’).T
Data Wrangling
Check for missing values
df.isnull().sum()
Rank 0 Name 0 Platform 0 Year 271 Genre 0 Publisher 58 NA_Sales 0 EU_Sales 0 JP_Sales 0 Other_Sales 0 Global_Sales 0 dtype: int64
Drop missing values
df = df.dropna()
(16291 rows × 11 columns)
Check if missing values have been removed
df.isnull().sum()
Rank 0 Name 0 Platform 0 Year 0 Genre 0 Publisher 0 NA_Sales 0 EU_Sales 0 JP_Sales 0 Other_Sales 0 Global_Sales 0 dtype: int64
Count number of entries in each column
df.count()
Rank 16291 Name 16291 Platform 16291 Year 16291 Genre 16291 Publisher 16291 NA_Sales 16291 EU_Sales 16291 JP_Sales 16291 Other_Sales 16291 Global_Sales 16291 dtype: int64
Check for duplicates
df.duplicated().any()
False
Convert year column from float to integers
df.Year = df.Year.astype(int)
Data Visualization Part 1
df_sum = df[df.columns.difference([‘Rank’,’Name’,’Platform’,’Genre’,’Publisher’])].sum()
df_sum
EU_Sales 2406.69 Global_Sales 8811.97 JP_Sales 1284.27 NA_Sales 4327.65 Other_Sales 788.91 Year 32686353.00 dtype: float64
Let’s check the Number of video games vs Video Games Publishers
BIGGER_SIZE = 24
plt.rc(‘font’, size=BIGGER_SIZE) # controls default text sizes
plt.rc(‘axes’, titlesize=BIGGER_SIZE) # fontsize of the axes title
plt.rc(‘axes’, labelsize=BIGGER_SIZE) # fontsize of the x and y labels
plt.rc(‘xtick’, labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc(‘ytick’, labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc(‘legend’, fontsize=BIGGER_SIZE) # legend fontsize
plt.rc(‘figure’, titlesize=BIGGER_SIZE) # fontsize of the figure title
plt.figure(figsize=(30, 15))
df.Publisher.value_counts().nlargest(50).plot(kind=’bar’)
plt.title(‘Video Games Publishers’)
plt.xlabel(‘Name of Publisher’)
plt.ylabel(‘Number of video games’)
Let’s check video game genres
df.Genre.value_counts().nlargest(10).plot(kind=’bar’,figsize=(10,5))
plt.title(‘Video Game Genres’)
plt.xlabel(‘Genres’)
plt.ylabel(‘Number of video games’)
plt.savefig(‘numbergamesgenre.png’)
Let’s check the correlation between numeric columns in the dataset
plt.figure(figsize=(10,7))
v = df.corr()
sns.heatmap(v, cmap=’BrBG’,annot=True)
The Pie chart below is just the % distribution of Platforms in the Dataset
plt.figure(figsize=(20,10))
plt.title(‘Platform’)
plt.pie(df.Genre.value_counts(), labels=df.Genre.value_counts().index, autopct=’%1.1f%%’, startangle=180);
Let’s look at value_counts() of Publishers
df[‘Publisher’].value_counts()
Electronic Arts 1339
Activision 966
Namco Bandai Games 928
Ubisoft 918
Konami Digital Entertainment 823
...
Detn8 Games 1
Pow 1
Navarre Corp 1
MediaQuest 1
UIG Entertainment 1
Name: Publisher, Length: 576, dtype: int64
Let’s check the total number of Platforms
df.Platform.nunique()
31
Let’s look at value_counts() of Genre
genre_counts = df.Genre.value_counts()
genre_counts
Action 3251 Sports 2304 Misc 1686 Role-Playing 1470 Shooter 1282 Adventure 1274 Racing 1225 Platform 875 Simulation 848 Fighting 836 Strategy 670 Puzzle 570 Name: Genre, dtype: int64
Let’s look at value_counts() of Platform
df[‘Platform’].value_counts()
DS 2131 PS2 2127 PS3 1304 Wii 1290 X360 1234 PSP 1197 PS 1189 PC 938 XB 803 GBA 786 GC 542 3DS 499 PSV 410 PS4 336 N64 316 SNES 239 XOne 213 SAT 173 WiiU 143 2600 116 NES 98 GB 97 DC 52 GEN 27 NG 12 SCD 6 WS 6 3DO 3 TG16 2 GG 1 PCFX 1 Name: Platform, dtype: int64
Data Visualization Part 2
Let’s look at the Video Games Sales dataset in data.world.
import warnings
warnings.filterwarnings(“ignore”)
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
%config InlineBackend.figure_format = ‘retina’
plt.rcParams[“figure.figsize”] = 8, 5
plt.rcParams[“image.cmap”] = “viridis”
import pandas as pd
df = pd.read_csv(“Video_Games.csv”).dropna()
print(df.shape)
(6825, 16)
Let’s edit the columns
df[“User_Score”] = df[“User_Score”].astype(“float64”)
df[“Year_of_Release”] = df[“Year_of_Release”].astype(“int64”)
df[“User_Count”] = df[“User_Count”].astype(“int64”)
df[“Critic_Count”] = df[“Critic_Count”].astype(“int64”)
useful_cols = [
“Name”,
“Platform”,
“Year_of_Release”,
“Genre”,
“Global_Sales”,
“Critic_Score”,
“Critic_Count”,
“User_Score”,
“User_Count”,
“Rating”,
]
df[useful_cols].head()
Let’s check top 5 platforms
top_platforms = (
df[“Platform”].value_counts().sort_values(ascending=False).head(5).index.values
)
sns.boxplot(
y=”Platform”,
x=”Critic_Score”,
data=df[df[“Platform”].isin(top_platforms)],
orient=”h”,
);
Let’s look at the pivot table Platform-Genre-Sales
platform_genre_sales = (
df.pivot_table(
index=”Platform”, columns=”Genre”, values=”Global_Sales”, aggfunc=sum
)
.fillna(0)
.applymap(float)
)
sns.heatmap(platform_genre_sales, annot=True, fmt=”.1f”, linewidths=0.5);
Let’s import plotly
import plotly
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, iplot, plot
init_notebook_mode(connected=True)
Let’s create a box trace of Critic_Score per genre in our dataset and visualize
data = []
for genre in df.Genre.unique():
data.append(go.Box(y=df[df.Genre == genre].Critic_Score, name=genre))
iplot(data, show_link=False)
Let’s look at the bar chart of genres vs total number of games released
rel = df.groupby([‘Genre’]).count().iloc[:,0]
rel = pd.DataFrame(rel.sort_values(ascending=False))
genres = rel.index
rel.columns = [‘Releases’]
colors = sns.color_palette(“summer”, len(rel))
plt.figure(figsize=(12,8))
ax = sns.barplot(y = genres , x = ‘Releases’, data=rel, orient=’h’, palette=colors)
ax.set_xlabel(xlabel=’Number of Releases’, fontsize=16)
ax.set_ylabel(ylabel=’Genre’, fontsize=16)
ax.set_title(label=’Genres by Total Number of Games Released’, fontsize=20)
ax.set_yticklabels(labels = genres, fontsize=14)
plt.show();
Let’s look at the bar chart of genres vs total revenue
rev = df.groupby([‘Genre’]).sum()[‘Global_Sales’]
rev = pd.DataFrame(rev.sort_values(ascending=False))
genres = rev.index
rev.columns = [‘Revenue’]
colors = sns.color_palette(‘Set3’, len(rev))
plt.figure(figsize=(12,8))
ax = sns.barplot(y = genres , x = ‘Revenue’, data=rev, orient=’h’, palette=colors)
ax.set_xlabel(xlabel=’Revenue in $ Millions’, fontsize=16)
ax.set_ylabel(ylabel=’Genre’, fontsize=16)
ax.set_title(label=’Genres by Total Revenue Generated in $ Millions’, fontsize=20)
ax.set_yticklabels(labels = genres, fontsize=14)
plt.show();
Let’s look at the bar chart revenue per release vs genre
data = pd.concat([rev, rel], axis=1)
data = pd.DataFrame(data[‘Revenue’] / data[‘Releases’])
data.columns = [‘Revenue Per Release’]
data = data.sort_values(by=’Revenue Per Release’,ascending=False)
genres = data.index
colors = sns.color_palette(“CMRmap”, len(data))
plt.figure(figsize=(12,8))
ax = sns.barplot(y = genres , x = ‘Revenue Per Release’, data=data, orient=’h’, palette=colors)
ax.set_xlabel(xlabel=’Revenue per Release in $ Millions’, fontsize=16)
ax.set_ylabel(ylabel=’Genre’, fontsize=16)
ax.set_title(label=’Revenue Per Release Generated in $ Millions Per Genre’, fontsize=20)
ax.set_yticklabels(labels = genres, fontsize=14)
plt.show();
Let’s look at the total revenue of top 10 games
data = pd.concat([df[‘Name’][0:10], df[‘Global_Sales’][0:10]], axis=1)
plt.figure(figsize=(12,8))
colors = sns.color_palette(“gist_earth”, len(data))
ax = sns.barplot(y = ‘Name’ , x = ‘Global_Sales’, data=data, orient=’h’, palette=colors)
ax.set_xlabel(xlabel=’Revenue in $ Millions’, fontsize=16)
ax.set_ylabel(ylabel=’Name’, fontsize=16)
ax.set_title(label=’Top 10 Games by Revenue Generated in $ Millions’, fontsize=20)
ax.set_yticklabels(labels = games, fontsize=14)
plt.show();
Data Visualization Part 3
Let’s look at the dataset vgsales.csv using plotly
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
import plotly
import matplotlib.pyplot as plt
import re
from plotly.subplots import make_subplots
from scipy.stats import chi2_contingency
from wordcloud import wordcloud
df_train = pd.read_csv(‘vgsales.csv’)
df_train.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 16598 entries, 0 to 16597 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Rank 16598 non-null int64 1 Name 16598 non-null object 2 Platform 16598 non-null object 3 Year 16327 non-null float64 4 Genre 16598 non-null object 5 Publisher 16540 non-null object 6 NA_Sales 16598 non-null float64 7 EU_Sales 16598 non-null float64 8 JP_Sales 16598 non-null float64 9 Other_Sales 16598 non-null float64 10 Global_Sales 16598 non-null float64 dtypes: float64(6), int64(1), object(4) memory usage: 1.4+ MB
Let’s edit the data
df_train = df_train.dropna()
df_train = df_train.reset_index(drop = True)
df_train.loc[:, ‘Rank’] = np.arange(df_train.shape[0])+1
df_train[‘Year’] = df_train[‘Year’].astype(int)
Let’s look at the platform counts
df_platcount = df_train.groupby(df_train[‘Platform’])[[‘Rank’]].count().rename(columns = {‘Rank’:’counts’}).sort_values(‘counts’, ascending = False)
fig = px.bar(df_platcount, x=df_platcount.index, y=’counts’, color=’counts’,color_continuous_scale=[‘rgba(17, 171, 122, 0.6)’, ‘rgba(17, 145, 171, 0.6)’],
height=400)
fig.show()
The following plotly bar chart is representative of the platforms linked to the top 100 games
df_platcount100 = df_train[0:100].groupby(df_train[‘Platform’])[[‘Rank’]].count().rename(columns = {‘Rank’:’counts’}).sort_values(‘counts’, ascending = False)
fig = px.bar(df_platcount100, x = df_platcount100.index, y = ‘counts’, color = ‘counts’, color_continuous_scale = [‘rgba(17, 171, 122. 0.6)’, ‘rgba(17, 145, 171, 0.6)’], height = 400)
fig.show()
Let’s check the Publisher rank counts
df_pubcount = df_train.groupby(df_train[‘Publisher’])[[‘Rank’]].count().rename(columns = {‘Rank’:’counts’}).sort_values(‘counts’, ascending = False)[:10]
fig = px.bar(df_pubcount, x = df_pubcount.index, y=’counts’, color=’counts’,color_continuous_scale=[‘rgb(17, 171, 122, 0.6)’, ‘rgb(17, 145, 171, 0.6)’],
height=400)
fig.show()
Let’s plot the plotly pie chart of Publisher vs Rank counts for the top 100 games
df_pubcount100 = df_train[:100].groupby(df_train[‘Publisher’])[[‘Rank’]].count().rename(columns = {‘Rank’:’counts’}).sort_values(‘counts’, ascending = False)
fig = px.pie(df_pubcount100 , names=df_pubcount100.index, values=’counts’, template=’seaborn’)
fig.update_traces(pull=[0.06,0.06,0.06,0.06,0.06], textinfo=”percent+label”)
fig.show()
Let’s plot the plotly pie chart of Publisher vs Rank counts for the top 50 games
df_pubcount100 = df_train[:50].groupby(df_train[‘Publisher’])[[‘Rank’]].count().rename(columns = {‘Rank’:’counts’}).sort_values(‘counts’, ascending = False)
fig = px.pie(df_pubcount100 , names=df_pubcount100.index, values =’counts’, template=’seaborn’)
fig.update_traces(pull=[0.06,0.06,0.06,0.06,0.06], textinfo=”percent+label”)
fig.show()
Let’s examine the treemap Genre vs Rank and Sales
df_genrecount = df_train.groupby(df_train[‘Genre’])[[‘Rank’]].count().rename(columns = {‘Rank’:’counts’}).sort_values(‘counts’, ascending = False)
df_genresales = df_train.groupby(df_train[‘Genre’])[[‘Global_Sales’]].sum().sort_values(‘Global_Sales’, ascending = False)
fig = px.treemap(
names = df_genrecount.index, parents = [‘total’]*12,
values = df_genrecount[‘counts’],color=df_genresales[‘Global_Sales’],
color_continuous_scale=’jet’,
color_continuous_midpoint=np.average(df_genresales[‘Global_Sales’])
)
fig.show()
Let’s examine the treemap Genre vs Rank and Sales for the top 100 games
df_genrecount100 = df_train[0:100].groupby(df_train[‘Genre’])[[‘Rank’]].count().rename(columns = {‘Rank’:’counts’}).sort_values(‘counts’, ascending = False)
df_genresales100 = df_train[0:100].groupby(df_train[‘Genre’])[[‘Global_Sales’]].sum().sort_values(‘Global_Sales’, ascending = False)
fig = px.treemap(
names = df_genrecount100.index, parents = [‘total’]*11,
values = df_genrecount100[‘counts’],color=df_genresales100[‘Global_Sales’],
color_continuous_scale=’jet’,
color_continuous_midpoint=np.average(df_genresales100[‘Global_Sales’])
)
fig.show()
Let’s plot the treemap Genre vs Rank and Sales for the top 10 games
df_genrecount10 = df_train[0:10].groupby(df_train[‘Genre’])[[‘Rank’]].count().rename(columns = {‘Rank’:’counts’}).sort_values(‘counts’, ascending = False)
df_genresales10 = df_train[0:10].groupby(df_train[‘Genre’])[[‘Global_Sales’]].sum().sort_values(‘Global_Sales’, ascending = False)
fig = px.treemap(
names = df_genrecount10. index, parents = [‘total’]*7,
values = df_genrecount10[‘counts’],color=df_genresales10[‘Global_Sales’],
color_continuous_scale=’jet’,
color_continuous_midpoint=np.average(df_genresales10[‘Global_Sales’])
)
fig.show()
Let’s compare game sales per region
region_sec = df_train[[‘NA_Sales’, ‘EU_Sales’, ‘JP_Sales’, ‘Other_Sales’]].apply(lambda x: x. sum (), axis = 0)
region_sum = pd.DataFrame.from_dict(region_sec.to_dict(), orient = ‘index’, columns = [‘sum’]).sort_values(‘sum’, ascending = False)
fig = px.pie(region_sum , names=region_sum.index, values=’sum’, template=’seaborn’)
fig.update_traces(pull=[0,0.01,0.01,0.01],textinfo=”percent+label”)
fig.show()
Let’s check regional sales per genre
genre = df_train[‘Genre’].unique()
genre_s = sorted(genre)
na_sales=[]
eu_sales=[]
jp_sales=[]
other_sales=[]
global_sales=[]
for i in genre_s:
val= df_train[df_train.Genre==i]
na_sales.append(val.NA_Sales.sum())
eu_sales.append(val.EU_Sales.sum())
jp_sales.append(val.JP_Sales.sum())
other_sales.append(val.Other_Sales.sum())
fig = go.Figure()
fig.add_trace(go.Bar(x=na_sales,
y=genre_s,
name=’North America Sales’,
marker_color=’teal’,
orientation=’h’))
fig.add_trace(go.Bar(x=eu_sales,
y=genre_s,
name=’Europe Sales’,
marker_color=’purple’,
orientation=’h’))
fig.add_trace(go.Bar(x=jp_sales,
y=genre_s,
name=’Japan Sales’,
marker_color=’gold’,
orientation=’h’))
fig.add_trace(go.Bar(x=other_sales,
y=genre_s,
name=’Other Region Sales’,
marker_color=’deepskyblue’,
orientation=’h’))
fig.update_layout(title_text=’Regional Sales by Genre’,xaxis_title=”Sales in $M”,yaxis_title=”Genre”,
barmode=’stack’)
fig.show()
Data Visualization Part 4
Let’s continue our analysis of the dataset vgsales.csv by comparing sns plots
import numpy as np
import pandas as pd
import scipy.stats as st
pd.set_option(‘display.max_columns’, None)
import math
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set_style(‘whitegrid’)
import missingno as msno
from sklearn.preprocessing import StandardScaler
from scipy import stats
data = pd.read_csv(‘vgsales.csv’)
data.head()
drop_row_index = data[data[‘Year’] > 2015].index
data = data.drop(drop_row_index)
data.shape
(16250, 11)
data.isnull().sum()
Rank 0 Name 0 Platform 0 Year 271 Genre 0 Publisher 56 NA_Sales 0 EU_Sales 0 JP_Sales 0 Other_Sales 0 Global_Sales 0 dtype: int64
Let’s count Genre
data[‘Genre’].value_counts()
Action 3196 Sports 2308 Misc 1721 Role-Playing 1446 Shooter 1278 Adventure 1252 Racing 1229 Platform 876 Simulation 857 Fighting 834 Strategy 671 Puzzle 582 Name: Genre, dtype: int64
Let’s look at the sns countplot of Genre
BIGGER_SIZE = 18
plt.rc(‘font’, size=BIGGER_SIZE) # controls default text sizes
plt.rc(‘axes’, titlesize=BIGGER_SIZE) # fontsize of the axes title
plt.rc(‘axes’, labelsize=BIGGER_SIZE) # fontsize of the x and y labels
plt.rc(‘xtick’, labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc(‘ytick’, labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc(‘legend’, fontsize=BIGGER_SIZE) # legend fontsize
plt.rc(‘figure’, titlesize=BIGGER_SIZE) # fontsize of the figure title
plt.figure(figsize=(15, 10))
sns.countplot(x=”Genre”, data=data, order = data[‘Genre’].value_counts().index)
plt.xticks(rotation=90)
(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]), [Text(0, 0, 'Action'), Text(1, 0, 'Sports'), Text(2, 0, 'Misc'), Text(3, 0, 'Role-Playing'), Text(4, 0, 'Shooter'), Text(5, 0, 'Adventure'), Text(6, 0, 'Racing'), Text(7, 0, 'Platform'), Text(8, 0, 'Simulation'), Text(9, 0, 'Fighting'), Text(10, 0, 'Strategy'), Text(11, 0, 'Puzzle')])
Let’s check the sns barplot of Sales vs Genre
data_genre = data.groupby(by=[‘Genre’])[‘Global_Sales’].sum()
data_genre = data_genre.reset_index()
data_genre = data_genre.sort_values(by=[‘Global_Sales’], ascending=False)
plt.figure(figsize=(15, 10))
sns.barplot(x=”Genre”, y=”Global_Sales”, data=data_genre)
plt.xticks(rotation=90)
Let’s check the sns barplot of Sales vs Platform
data_platform = data.groupby(by=[‘Platform’])[‘Global_Sales’].sum()
data_platform = data_platform.reset_index()
data_platform = data_platform.sort_values(by=[‘Global_Sales’], ascending=False)
plt.figure(figsize=(15, 10))
sns.barplot(x=”Platform”, y=”Global_Sales”, data=data_platform)
plt.xticks(rotation=90)
Let’s plot the sns heatmap Genre vs Region
comp_genre = data[[‘Genre’, ‘NA_Sales’, ‘EU_Sales’, ‘JP_Sales’, ‘Other_Sales’]]
comp_map = comp_genre.groupby(by=[‘Genre’]).sum()
plt.figure(figsize=(15, 10))
sns.set(font_scale=1)
sns.heatmap(comp_map, annot=True, fmt = ‘.1f’)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.show()
Let’s create the following table
comp_table = comp_map.reset_index()
comp_table = pd.melt(comp_table, id_vars=[‘Genre’], value_vars=[‘NA_Sales’, ‘EU_Sales’, ‘JP_Sales’, ‘Other_Sales’], var_name=’Sale_Area’, value_name=’Sale_Price’)
comp_table.head()
and plot x=’Genre’, y=’Sale_Price’, hue=’Sale_Area’
plt.figure(figsize=(15, 10))
sns.barplot(x=’Genre’, y=’Sale_Price’, hue=’Sale_Area’, data=comp_table)
Let’s create the following table
comp_platform = data[[‘Platform’, ‘NA_Sales’, ‘EU_Sales’, ‘JP_Sales’, ‘Other_Sales’]]
comp_platform.head()
Let’s look at sns.barplot with x=’Platform’, y=’Sale_Price’, hue=’Sale_Area’
BIGGER_SIZE = 24
plt.rc(‘font’, size=BIGGER_SIZE) # controls default text sizes
plt.rc(‘axes’, titlesize=BIGGER_SIZE) # fontsize of the axes title
plt.rc(‘axes’, labelsize=BIGGER_SIZE) # fontsize of the x and y labels
plt.rc(‘xtick’, labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc(‘ytick’, labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc(‘legend’, fontsize=BIGGER_SIZE) # legend fontsize
plt.rc(‘figure’, titlesize=BIGGER_SIZE) # fontsize of the figure title
plt.figure(figsize=(30, 15))
sns.barplot(x=’Platform’, y=’Sale_Price’, hue=’Sale_Area’, data=comp_table)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.show()
Let’s look at top 20 publishers
top_publisher = data.groupby(by=[‘Publisher’])[‘Year’].count().sort_values(ascending=False).head(20)
top_publisher = pd.DataFrame(top_publisher).reset_index()
BIGGER_SIZE = 18
plt.rc(‘font’, size=BIGGER_SIZE) # controls default text sizes
plt.rc(‘axes’, titlesize=BIGGER_SIZE) # fontsize of the axes title
plt.rc(‘axes’, labelsize=BIGGER_SIZE) # fontsize of the x and y labels
plt.rc(‘xtick’, labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc(‘ytick’, labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc(‘legend’, fontsize=BIGGER_SIZE) # legend fontsize
plt.rc(‘figure’, titlesize=BIGGER_SIZE) # fontsize of the figure title
plt.figure(figsize=(15, 10))
sns.countplot(x=”Publisher”, data=data, order = data.groupby(by=[‘Publisher’])[‘Year’].count().sort_values(ascending=False).iloc[:20].index)
plt.xticks(rotation=90)
Let’s create the sns barplot x=’Publisher’, y=’Global_Sales’
sale_pbl = data[[‘Publisher’, ‘Global_Sales’]]
sale_pbl = sale_pbl.groupby(‘Publisher’)[‘Global_Sales’].sum().sort_values(ascending=False).head(20)
sale_pbl = pd.DataFrame(sale_pbl).reset_index()
plt.figure(figsize=(15, 10))
sns.barplot(x=’Publisher’, y=’Global_Sales’, data=sale_pbl)
plt.xticks(rotation=90)
Let’s plot sns.barplot with x=’Publisher’, y=’Sale_Price’, hue=’Sale_Area’ for top 20 Publishers
comp_publisher = data[[‘Publisher’, ‘NA_Sales’, ‘EU_Sales’, ‘JP_Sales’, ‘Other_Sales’, ‘Global_Sales’]]
comp_publisher.head()
comp_publisher = comp_publisher.groupby(by=[‘Publisher’]).sum().reset_index().sort_values(by=[‘Global_Sales’], ascending=False)
comp_publisher = comp_publisher.head(20)
comp_publisher = pd.melt(comp_publisher, id_vars=[‘Publisher’], value_vars=[‘NA_Sales’, ‘EU_Sales’, ‘JP_Sales’, ‘Other_Sales’], var_name=’Sale_Area’, value_name=’Sale_Price’)
comp_publisher
BIGGER_SIZE = 24
plt.rc(‘font’, size=BIGGER_SIZE) # controls default text sizes
plt.rc(‘axes’, titlesize=BIGGER_SIZE) # fontsize of the axes title
plt.rc(‘axes’, labelsize=BIGGER_SIZE) # fontsize of the x and y labels
plt.rc(‘xtick’, labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc(‘ytick’, labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc(‘legend’, fontsize=BIGGER_SIZE) # legend fontsize
plt.rc(‘figure’, titlesize=BIGGER_SIZE) # fontsize of the figure title
plt.figure(figsize=(20, 10))
sns.barplot(x=’Publisher’, y=’Sale_Price’, hue=’Sale_Area’, data=comp_publisher)
plt.xticks(fontsize=14, rotation=90)
plt.yticks(fontsize=14)
plt.show()
Let’s create the table
top_sale_reg = data[[‘NA_Sales’, ‘EU_Sales’, ‘JP_Sales’, ‘Other_Sales’]]
top_sale_reg = top_sale_reg.sum().reset_index()
top_sale_reg = top_sale_reg.rename(columns={“index”: “region”, 0: “sale”})
top_sale_reg
and get the sns barplot region vs sale
plt.figure(figsize=(12, 8))
sns.barplot(x=’region’, y=’sale’, data = top_sale_reg)
Let’s look at the % region vs sale as plt pie chart
labels = top_sale_reg[‘region’]
sizes = top_sale_reg[‘sale’]
plt.figure(figsize=(10, 8))
plt.pie(sizes, labels=labels, autopct=’%1.1f%%’, shadow=True, startangle=90)
Let’s compute the data correlation matrix
plt.figure(figsize=(13,10))
sns.heatmap(data.corr(), cmap = “Blues”, annot=True, linewidth=3)
Subsector Analysis
- The gaming industry roughly splits into 3 main subgroups: console, PC, and smartphone.
- The console subsector is the oldest and most consolidated with the largest global players being Nintendo, Sony, and Microsoft.
- Japanese Nintendo targets casual gamers between the ages of 15-30 and has been consistently outselling Xbox and PS5 for the past two years.
- In its PS5 launch, Sony confirmed that users will only be able to play on Sony’s console.
- The PC’s subsector, emerged later in time, and as opposed to the console industry is highly fragmented. Companies offer products like keyboards, advanced PC components and monitors to support gaming.
- Smartphone gaming is the newest sector within the industry, recording $79bn in revenues in 2021, and is also the most profitable subsector.
US Outlook 2023
- In 2022, we estimated that more than half (54.2%) of the US population were digital gamers.
- 2023 could be a banner year for the gaming industry, with advancing technology supercharging game product sales.
- A combination of AI and virtual reality (VR) innovations have improved the gaming experience significantly. The use of AI are giving rise to enhanced artistic qualities and photorealistic animation, as well as originality in dialogue and character depth.
- As companies invest in AI and VR, and even 5G infrastructure, expect more impressive-looking games to drive adoption, especially among Gen Z.
- Microsoft’s attempt to acquire Activision Blizzard. The $68.7 billion acquisition would give Microsoft a leadership position.
- Other major media powerhouses such as Netflix are investing in the gaming industry, too. Netflix is adding two more games (Kentucky Route Zero and Twelve Minutes) to its platform, bringing the total number of games available on the service to 48.
Inferences
- Most Popular Genre: Action.
- Action and Sport have highest sales.
- North America have highest sales for every genre.
- Best choice of Platform: DS offers the highest number of games with a total of 2131.
- Highest global sales is PS2. Console launched in 2000 and yet still the unbeaten compared to the rest and even to its upgrade PS3.
- Best choice of Publisher: Electronic Arts sold the highest number of unique games with a frequency of 1364.
- Highest correlations between NA/EU and Global Sales.
Source: adityavipradas
Sources
- Explore Video Game Sales Data
- Video Game Sales EDA, Visualizations, ML Models
- Bilal Data Wrangling and EDA Video Games Sales
- Video Games Sales Analysis
- EXPLORATORY DATA ANALYSIS ON VIDEO GAMES SALES
- Video Games Sales
- World Economic Forum
- BSIC
- Insider Intelligence
- Market Research
Explore More
- Trending YouTube Video Data Science, NLP Predictions & Sentiment Analysis
- NETFLIX Interactive Visualization with Plotly
- Textual Genres Analysis using the Carloto’s NLP Algorithm
- Webscraping in R – IMDb ETL Showcase
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