This is an Exploratory Data Analysis and simple visualization for video games sales by genre, platform and region.
This dataset contains a list of video games with sales greater than 100,000 copies from the year 1980 to 2016. It was generated by a scrape of vgchartz.com.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
vgsales_df = pd.read_csv('vgsales.csv')
vgsales_df.head()
| Rank | Name | Platform | Year | Genre | Publisher | NA_Sales | EU_Sales | JP_Sales | Other_Sales | Global_Sales | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Wii Sports | Wii | 2006.0 | Sports | Nintendo | 41.49 | 29.02 | 3.77 | 8.46 | 82.74 |
| 1 | 2 | Super Mario Bros. | NES | 1985.0 | Platform | Nintendo | 29.08 | 3.58 | 6.81 | 0.77 | 40.24 |
| 2 | 3 | Mario Kart Wii | Wii | 2008.0 | Racing | Nintendo | 15.85 | 12.88 | 3.79 | 3.31 | 35.82 |
| 3 | 4 | Wii Sports Resort | Wii | 2009.0 | Sports | Nintendo | 15.75 | 11.01 | 3.28 | 2.96 | 33.00 |
| 4 | 5 | Pokemon Red/Pokemon Blue | GB | 1996.0 | Role-Playing | Nintendo | 11.27 | 8.89 | 10.22 | 1.00 | 31.37 |
vgsales_df.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
vgsales_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
vgsales_df['Genre'].value_counts()
Genre Action 3316 Sports 2346 Misc 1739 Role-Playing 1488 Shooter 1310 Adventure 1286 Racing 1249 Platform 886 Simulation 867 Fighting 848 Strategy 681 Puzzle 582 Name: count, dtype: int64
# Plot a countplot of the best selling games by genre
sns.countplot(data=vgsales_df, x = 'Genre', order = vgsales_df['Genre'].value_counts().index)
plt.title('BEST SELLING GAMES BY GENRE')
plt.xticks(rotation=60)
plt.show()
# Plot a countplot of the most played platform
sns.countplot(data=vgsales_df, x = 'Platform', order = vgsales_df['Platform'].value_counts().index)
plt.title('WHAT PLATFORM DO THE GAMES GET PLAYED THE MOST')
plt.xticks(rotation=90)
plt.show()
# Plot a scatterplot of the global sales by genre
sns.scatterplot(x="Genre", y="Global_Sales", data=vgsales_df)
plt.title("Global Sales by Genre")
plt.xticks(rotation=90)
plt.show()
# Plot a countplot of the game genres
sns.countplot(x="Genre", data=vgsales_df)
plt.title("Game Genre Count")
plt.xticks(rotation=90)
plt.show()
# Aggregate and sort the data based on the sales in each region or globally
# NA_Sales
t_v_name_NA = vgsales_df.groupby('Name').agg({'NA_Sales': 'sum'}).reset_index()
t_v_name_NA = t_v_name_NA.sort_values(by='NA_Sales', ascending=False)
# EU_Sales
t_v_name_EU = vgsales_df.groupby('Name').agg({'EU_Sales': 'sum'}).reset_index()
t_v_name_EU = t_v_name_EU.sort_values(by='EU_Sales', ascending=False)
# JP_Sales
t_v_name_JP = vgsales_df.groupby('Name').agg({'JP_Sales': 'sum'}).reset_index()
t_v_name_JP = t_v_name_JP.sort_values(by='JP_Sales', ascending=False)
# Other_Sales
t_v_name_Other = vgsales_df.groupby('Name').agg({'Other_Sales': 'sum'}).reset_index()
t_v_name_Other = t_v_name_Other.sort_values(by='Other_Sales', ascending=False)
# Global_Sales
t_v_name_Global = vgsales_df.groupby('Name').agg({'Global_Sales': 'sum'}).reset_index()
t_v_name_Global = t_v_name_Global.sort_values(by='Global_Sales', ascending=False)
# Plotting the 10 best selling games in North America
plt.figure(figsize=(14, 6))
sns.barplot(x="Name", y="NA_Sales", data=t_v_name_NA.head(10), palette="dark",
alpha=0.7)
plt.xlabel("")
plt.ylabel("Sales in North America (in millions)")
plt.title("The 10 best selling games in North America", fontsize=22, fontweight="bold")
plt.xticks(rotation=60)
sns.despine()
# Plotting the 10 best selling games in Europe
plt.figure(figsize=(14, 6))
sns.barplot(x="Name", y="EU_Sales", data=t_v_name_EU.head(10), palette="dark",
alpha=0.7)
plt.xlabel("")
plt.ylabel("Sales in Europe (in millions)")
plt.title("The 10 best selling games in Europe", fontsize=22, fontweight="bold")
plt.xticks(rotation=60)
sns.despine()
# Plotting the 10 best selling games in Japan
plt.figure(figsize=(14, 6))
sns.barplot(x="Name", y="JP_Sales", data=t_v_name_JP.head(10), palette="dark",
alpha=0.7)
plt.xlabel("")
plt.ylabel("Sales in Japan (in millions)")
plt.title("The 10 best selling games in Japan", fontsize=22, fontweight="bold")
plt.xticks(rotation=60)
sns.despine()
# Plotting the 10 best selling games in the rest of the world
plt.figure(figsize=(14, 6))
sns.barplot(x="Name", y="Other_Sales", data=t_v_name_Other.head(10), palette="dark",
alpha=0.7)
plt.xlabel("")
plt.ylabel("Sales in the rest of the world (in millions)")
plt.title("The 10 best selling games in rest of the world", fontsize=22, fontweight="bold")
plt.xticks(rotation=60)
sns.despine()
# Aggregate and sort the data based on platforms with the highest number of game sales globally
p_name_Global = vgsales_df.groupby('Platform')['Global_Sales'].sum().reset_index()
p_name_Global = p_name_Global.sort_values(by='Global_Sales', ascending=False)
p_name_Global = pd.DataFrame(p_name_Global)
p_name_Global.index = range(1, len(p_name_Global)+1)
p_name_Global.head(10)
| Platform | Global_Sales | |
|---|---|---|
| 1 | PS2 | 1255.64 |
| 2 | X360 | 979.96 |
| 3 | PS3 | 957.84 |
| 4 | Wii | 926.71 |
| 5 | DS | 822.49 |
| 6 | PS | 730.66 |
| 7 | GBA | 318.50 |
| 8 | PSP | 296.28 |
| 9 | PS4 | 278.10 |
| 10 | PC | 258.82 |
# Plotting the 10 platforms with the highest number of game sales in the world
sns.set_style("whitegrid")
sns.barplot(x="Platform", y="Global_Sales", data=p_name_Global.head(10), palette="dark")
plt.xlabel("")
plt.ylabel("")
plt.title("The 10 platforms with the highest number of game sales in the world", fontsize=15, fontweight="bold")
plt.xticks(fontsize=10, fontweight="bold")
plt.yticks(fontsize=10, fontweight="bold")
sns.despine(left=True, bottom=True)
plt.tight_layout()
plt.show()
# Aggregate and sort the data based on genres with the highest number of game sales globally
g_name_Global = vgsales_df.groupby('Genre')['Global_Sales'].sum().reset_index()
g_name_Global = g_name_Global.sort_values(by='Global_Sales', ascending=False)
g_name_Global = pd.DataFrame(g_name_Global)
g_name_Global.index = range(1, len(g_name_Global)+1)
g_name_Global.head(10)
| Genre | Global_Sales | |
|---|---|---|
| 1 | Action | 1751.18 |
| 2 | Sports | 1330.93 |
| 3 | Shooter | 1037.37 |
| 4 | Role-Playing | 927.37 |
| 5 | Platform | 831.37 |
| 6 | Misc | 809.96 |
| 7 | Racing | 732.04 |
| 8 | Fighting | 448.91 |
| 9 | Simulation | 392.20 |
| 10 | Puzzle | 244.95 |
# Plotting the 10 platforms with the highest number of game sales in the world
sns.set_style("whitegrid")
sns.barplot(x="Genre", y="Global_Sales", data=g_name_Global.head(10), palette="dark")
plt.xlabel("")
plt.ylabel("")
plt.title("The 10 Genres with the highest number of game sales in the world", fontsize=15, fontweight="bold")
plt.xticks(fontsize=10, fontweight="bold")
plt.yticks(fontsize=10, fontweight="bold")
sns.despine(left=True, bottom=True)
plt.tight_layout()
plt.xticks(rotation=60)
plt.show()
# Aggregate and sort the data based on the publisher sales in each region or globally
# NA_Sales
publ_NA = vgsales_df.groupby('Publisher').agg({'NA_Sales': 'sum'}).reset_index()
publ_NA = publ_NA.sort_values(by='NA_Sales', ascending=False)
# EU_Sales
publ_EU = vgsales_df.groupby('Publisher').agg({'EU_Sales': 'sum'}).reset_index()
publ_EU = publ_EU.sort_values(by='EU_Sales', ascending=False)
# JP_Sales
publ_JP = vgsales_df.groupby('Publisher').agg({'JP_Sales': 'sum'}).reset_index()
publ_JP = publ_JP.sort_values(by='JP_Sales', ascending=False)
# Other_Sales
publ_Other = vgsales_df.groupby('Publisher').agg({'Other_Sales': 'sum'}).reset_index()
publ_Other = publ_Other.sort_values(by='Other_Sales', ascending=False)
# Global_Sales
publ_Global = vgsales_df.groupby('Publisher').agg({'Global_Sales': 'sum'}).reset_index()
publ_Global = publ_Global.sort_values(by='Global_Sales', ascending=False)
# Plotting the 10 publishers in North America
plt.figure(figsize=(14, 6))
sns.barplot(x="Publisher", y="NA_Sales", data=publ_NA.head(10), palette="dark",
alpha=0.7)
plt.xlabel("")
plt.ylabel("Sales in North America (in millions)")
plt.title("Number of sales by Publisher in North America", fontsize=22, fontweight="bold")
plt.xticks(rotation=60)
sns.despine()
# Plotting the 10 publishers in Europe
plt.figure(figsize=(14, 6))
sns.barplot(x="Publisher", y="EU_Sales", data=publ_EU.head(10), palette="dark",
alpha=0.7)
plt.xlabel("")
plt.ylabel("Sales in Europe (in millions)")
plt.title("Number of sales by Publisher in Europe", fontsize=22, fontweight="bold")
plt.xticks(rotation=60)
sns.despine()
# Plotting the 10 publishers in Japan
plt.figure(figsize=(14, 6))
sns.barplot(x="Publisher", y="JP_Sales", data=publ_JP.head(10), palette="dark",
alpha=0.7)
plt.xlabel("")
plt.ylabel("Sales in Japan (in millions)")
plt.title("Number of sales by Publisher in Japan", fontsize=22, fontweight="bold")
plt.xticks(rotation=60)
sns.despine()
# Plotting the 10 publishers in the rest of the world
plt.figure(figsize=(14, 6))
sns.barplot(x="Publisher", y="Other_Sales", data=publ_Other.head(10), palette="dark",
alpha=0.7)
plt.xlabel("")
plt.ylabel("Sales in the rest of the world (in millions)")
plt.title("Number of sales by Publisher in rest of the world", fontsize=22, fontweight="bold")
plt.xticks(rotation=60)
sns.despine()
plt.show()
# Plotting the 10 publishers with the highest number of game sales in the world
sns.set_style("whitegrid")
sns.barplot(x="Publisher", y="Global_Sales", data=publ_Global.head(10), palette="dark")
plt.xlabel("")
plt.ylabel("")
plt.title("The 10 Publishers with the highest number of game sales Globally", fontsize=15, fontweight="bold")
plt.xticks(fontsize=10, fontweight="bold")
plt.yticks(fontsize=10, fontweight="bold")
sns.despine(left=True, bottom=True)
plt.tight_layout()
plt.xticks(rotation=60)
plt.show()
