In [1]:

    

# Great but no patience to read that!
# I've collected that data in a csv file


    

In [3]:

    

!cat top-grossing-games.csv


    

    




Game,Sales (billions)
Westward Journey series,3.9
Dungeon Fighter Online,4
Donkey Kong,4.4
Wii Fit,5
Lineage series,5.7
Wii Sports,6
CrossFire,6.8
World of Warcraft,8.5
Street Fighter II,10.6
Pac-Man,12.8
Space Invaders,13.9

In [4]:

    

# Let's read that data in
import pandas as pd


    

In [5]:

    

top_games = pd.read_csv('top-grossing-games.csv')


    

In [6]:

    

top_games.head()


    

    
Out[6]:






  

    

      

      
Game
      
Sales (billions)
    
  
  

    

      
0
      
Westward Journey series
      
3.9
    
    

      
1
      
Dungeon Fighter Online
      
4.0
    
    

      
2
      
Donkey Kong
      
4.4
    
    

      
3
      
Wii Fit
      
5.0
    
    

      
4
      
Lineage series
      
5.7
    
  

In [7]:

    

# let's see how this looks
# We will use Seaborn

import seaborn as sns


    

In [8]:

    

ax = sns.barplot(data=top_games, x='Sales (billions)', y='Game')


    

In [9]:

    

# doh. we don't have matplotlib inline
%matplotlib inline


    

In [10]:

    

ax = sns.barplot(data=top_games, x='Sales (billions)', y='Game')


    

In [11]:

    

# decent but needs some tweaks. Change the color palette. reorder the data.


    

In [12]:

    

data = top_games.sort('Sales (billions)', ascending=False)
ax = sns.barplot(data=data, x='Sales (billions)', y='Game', palette='Blues_d')


    

In [ ]:

    

# And we're done. Have a nice day!