In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import zipfile
import warnings
import math
import plotly
import plotly.plotly as py
import numpy as np
import copy
warnings.filterwarnings('ignore')
#pd.set_option('Display.float_format', lambda x: '%.2f'% x)
In [2]:
# data came from kaggle.com
zipfile.ZipFile('videogamesales.zip').extractall('.')
In [3]:
#open file
vgsales = pd.read_csv('vgsales.csv', index_col = 0)
#add a column to count the number of games
vgsales['Number of Games'] = 1
In [4]:
vgsales.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 16598 entries, 1 to 16600
Data columns (total 11 columns):
Name               16598 non-null object
Platform           16598 non-null object
Year               16327 non-null float64
Genre              16598 non-null object
Publisher          16540 non-null object
NA_Sales           16598 non-null float64
EU_Sales           16598 non-null float64
JP_Sales           16598 non-null float64
Other_Sales        16598 non-null float64
Global_Sales       16598 non-null float64
Number of Games    16598 non-null int64
dtypes: float64(6), int64(1), object(4)
memory usage: 1.5+ MB
In [5]:
#preview
vgsales.head(10)
Out[5]:
Name Platform Year Genre Publisher NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Number of Games
Rank
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 1
3 Mario Kart Wii Wii 2008.0 Racing Nintendo 15.85 12.88 3.79 3.31 35.82 1
4 Wii Sports Resort Wii 2009.0 Sports Nintendo 15.75 11.01 3.28 2.96 33.00 1
5 Pokemon Red/Pokemon Blue GB 1996.0 Role-Playing Nintendo 11.27 8.89 10.22 1.00 31.37 1
6 Tetris GB 1989.0 Puzzle Nintendo 23.20 2.26 4.22 0.58 30.26 1
7 New Super Mario Bros. DS 2006.0 Platform Nintendo 11.38 9.23 6.50 2.90 30.01 1
8 Wii Play Wii 2006.0 Misc Nintendo 14.03 9.20 2.93 2.85 29.02 1
9 New Super Mario Bros. Wii Wii 2009.0 Platform Nintendo 14.59 7.06 4.70 2.26 28.62 1
10 Duck Hunt NES 1984.0 Shooter Nintendo 26.93 0.63 0.28 0.47 28.31 1
In [6]:
#group by Year, set Year to an integer， and set_index Year
year_total = vgsales.groupby('Year').sum()
year_total = year_total.reset_index(drop=False)
year_total['Year'] = year_total['Year'].apply(int)
year_total = year_total.set_index('Year')
year_total.drop([2016,2017,2020], inplace=True)
In [7]:
#draw a line chart
year_total_line = year_total.plot(figsize=(15,10), fontsize=13)
plt.legend(loc='best', fontsize=13)
year_total_line.set_xlabel('Year', fontsize=13)
year_total_line.set_ylabel('Sales in Millions', fontsize=13)
plt.show()
In [8]:
#group by platform
Platform_total = vgsales.groupby('Platform').sum()
del Platform_total['Year'] #ignore the years
#sort by sales
Platform_sales_sort = Platform_total.sort_values(by='Global_Sales', ascending=False).head(10)
Platform_sales_sort
Out[8]:
NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Number of Games
Platform
PS2 583.84 339.29 139.20 193.44 1255.64 2161
X360 601.05 280.58 12.43 85.54 979.96 1265
PS3 392.26 343.71 79.99 141.93 957.84 1329
Wii 507.71 268.38 69.35 80.61 926.71 1325
DS 390.71 194.65 175.57 60.53 822.49 2163
PS 336.51 213.60 139.82 40.91 730.66 1196
GBA 187.54 75.25 47.33 7.73 318.50 822
PSP 108.99 68.25 76.79 42.19 296.28 1213
PS4 96.80 123.70 14.30 43.36 278.10 336
PC 93.28 139.68 0.17 24.86 258.82 960
In [9]:
#draw a bar chart
pd.Series(Platform_sales_sort['Global_Sales']).plot(kind='barh', figsize=(10,8), title='Global Sales of Games from 1980 to 2015') 
plt.show()
In [10]:
#sort by number
Platform_games_sort = Platform_total.sort_values(by='Number of Games', ascending=False).head(10)
Platform_games_sort
Out[10]:
NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Number of Games
Platform
DS 390.71 194.65 175.57 60.53 822.49 2163
PS2 583.84 339.29 139.20 193.44 1255.64 2161
PS3 392.26 343.71 79.99 141.93 957.84 1329
Wii 507.71 268.38 69.35 80.61 926.71 1325
X360 601.05 280.58 12.43 85.54 979.96 1265
PSP 108.99 68.25 76.79 42.19 296.28 1213
PS 336.51 213.60 139.82 40.91 730.66 1196
PC 93.28 139.68 0.17 24.86 258.82 960
XB 186.69 60.95 1.38 8.72 258.26 824
GBA 187.54 75.25 47.33 7.73 318.50 822
In [11]:
#draw a bar chart
pd.Series(Platform_games_sort['Number of Games']).plot(kind='barh', figsize=(10,8), title='Number of Games from 1980 to 2015') 
plt.show()
In [12]:
#group by Genre
Genre_total = vgsales.groupby('Genre').sum()
del Genre_total['Year'] #ignore the years
#sort by sales
Genre_sales_sort = Genre_total.sort_values(by='Global_Sales', ascending=False)
Genre_sales_sort
Out[12]:
NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Number of Games
Genre
Action 877.83 525.00 159.95 187.38 1751.18 3316
Sports 683.35 376.85 135.37 134.97 1330.93 2346
Shooter 582.60 313.27 38.28 102.69 1037.37 1310
Role-Playing 327.28 188.06 352.31 59.61 927.37 1488
Platform 447.05 201.63 130.77 51.59 831.37 886
Misc 410.24 215.98 107.76 75.32 809.96 1739
Racing 359.42 238.39 56.69 77.27 732.04 1249
Fighting 223.59 101.32 87.35 36.68 448.91 848
Simulation 183.31 113.38 63.70 31.52 392.20 867
Puzzle 123.78 50.78 57.31 12.55 244.95 582
Adventure 105.80 64.13 52.07 16.81 239.04 1286
Strategy 68.70 45.34 49.46 11.36 175.12 681
In [13]:
#draw a bar chart
pd.Series(Genre_sales_sort['Global_Sales']).plot(kind='barh', figsize=(10,8), title='Global Sales of Games from 1980 to 2015') 
plt.show()
In [14]:
#sort by number
Genre_games_sort = Genre_total.sort_values(by='Number of Games', ascending=False)
Genre_games_sort
Out[14]:
NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Number of Games
Genre
Action 877.83 525.00 159.95 187.38 1751.18 3316
Sports 683.35 376.85 135.37 134.97 1330.93 2346
Misc 410.24 215.98 107.76 75.32 809.96 1739
Role-Playing 327.28 188.06 352.31 59.61 927.37 1488
Shooter 582.60 313.27 38.28 102.69 1037.37 1310
Adventure 105.80 64.13 52.07 16.81 239.04 1286
Racing 359.42 238.39 56.69 77.27 732.04 1249
Platform 447.05 201.63 130.77 51.59 831.37 886
Simulation 183.31 113.38 63.70 31.52 392.20 867
Fighting 223.59 101.32 87.35 36.68 448.91 848
Strategy 68.70 45.34 49.46 11.36 175.12 681
Puzzle 123.78 50.78 57.31 12.55 244.95 582
In [15]:
#draw a bar chart
pd.Series(Genre_games_sort['Number of Games']).plot(kind='barh', figsize=(10,8), title='Number of Games from 1980 to 2015') 
plt.show()
In [16]:
#group by Publisher
Publisher_total = vgsales.groupby('Publisher').sum()
del Publisher_total['Year'] #ignore the years
#sort by sales
Publisher_sales_sort = Publisher_total.sort_values(by='Global_Sales', ascending=False).head(10)
Publisher_sales_sort
Out[16]:
NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Number of Games
Publisher
Nintendo 816.87 418.74 455.42 95.33 1786.56 703
Electronic Arts 595.07 371.27 14.04 129.77 1110.32 1351
Activision 429.70 215.53 6.54 75.34 727.46 975
Sony Computer Entertainment 265.22 187.72 74.10 80.45 607.50 683
Ubisoft 253.43 163.32 7.50 50.26 474.72 921
Take-Two Interactive 220.49 118.14 5.83 55.24 399.54 413
THQ 208.77 94.73 5.01 32.14 340.77 715
Konami Digital Entertainment 92.16 69.69 91.30 30.31 283.64 832
Sega 109.40 82.00 57.03 24.52 272.99 639
Namco Bandai Games 69.52 42.63 127.07 14.69 254.09 932
In [17]:
#draw a bar chart
pd.Series(Publisher_sales_sort['Global_Sales']).plot(kind='barh', figsize=(10,8), title='Global Sales of Games from 1980 to 2015') 
plt.show()
In [18]:
#sort by number
Publisher_games_sort = Publisher_total.sort_values(by='Number of Games', ascending=False).head(10)
Publisher_games_sort
Out[18]:
NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Number of Games
Publisher
Electronic Arts 595.07 371.27 14.04 129.77 1110.32 1351
Activision 429.70 215.53 6.54 75.34 727.46 975
Namco Bandai Games 69.52 42.63 127.07 14.69 254.09 932
Ubisoft 253.43 163.32 7.50 50.26 474.72 921
Konami Digital Entertainment 92.16 69.69 91.30 30.31 283.64 832
THQ 208.77 94.73 5.01 32.14 340.77 715
Nintendo 816.87 418.74 455.42 95.33 1786.56 703
Sony Computer Entertainment 265.22 187.72 74.10 80.45 607.50 683
Sega 109.40 82.00 57.03 24.52 272.99 639
Take-Two Interactive 220.49 118.14 5.83 55.24 399.54 413
In [19]:
#draw a bar chart
pd.Series(Publisher_games_sort['Number of Games']).plot(kind='barh', figsize=(10,8), title='Number of Games from 1980 to 2015') 
plt.show()
In [20]:
#select years from 2006 to 2015
sales06_15 = vgsales[vgsales.Year >= 2006]
In [21]:
#draw a bar chart to see the global sales and number of games in these 10 years
Genre_06_15 = sales06_15.groupby('Genre').sum()
del Genre_06_15['Year']
Genre_sales_06_15 = Genre_06_15.sort_values(by='Global_Sales', ascending=False).head(10)
pd.Series(Genre_sales_06_15['Global_Sales']).plot(kind='barh', figsize=(10,8), title='Global Sales of Games from 2006 to 2015') 
plt.show()
Genre_games_06_15 = Genre_06_15.sort_values(by='Number of Games', ascending=False).head(10)
pd.Series(Genre_games_06_15['Number of Games']).plot(kind='barh', figsize=(10,8), title='Number of Games from 2006 to 2015') 
plt.show()
In [22]:
#create data frame of each year
vgsale_2006 = vgsales[vgsales['Year'].isin([2006])]
vgsale_2007 = vgsales[vgsales['Year'].isin([2007])]
vgsale_2008 = vgsales[vgsales['Year'].isin([2008])]
vgsale_2009 = vgsales[vgsales['Year'].isin([2009])]
vgsale_2010 = vgsales[vgsales['Year'].isin([2010])]
vgsale_2011 = vgsales[vgsales['Year'].isin([2011])]
vgsale_2012 = vgsales[vgsales['Year'].isin([2012])]
vgsale_2013 = vgsales[vgsales['Year'].isin([2013])]
vgsale_2014 = vgsales[vgsales['Year'].isin([2014])]
vgsale_2015 = vgsales[vgsales['Year'].isin([2015])]
#create a list for easy to use
list_year = [vgsale_2006,vgsale_2007,vgsale_2008,vgsale_2009,vgsale_2010,vgsale_2011,vgsale_2012,vgsale_2013,vgsale_2014,vgsale_2015]
In [23]:
#turn years into integer
for i in list_year:
    i['Year'] = i['Year'].apply(int)
In [24]:
#create a list for easy to use
year06_15 = ['2006','2007','2008','2009','2010','2011','2012','2013','2014','2015']
Genre_list = sales06_15['Genre'].unique().tolist()
Genre_list
Out[24]:
['Sports',
 'Racing',
 'Platform',
 'Misc',
 'Action',
 'Role-Playing',
 'Shooter',
 'Fighting',
 'Simulation',
 'Adventure',
 'Puzzle',
 'Strategy']
In [25]:
#select each genre from each year
Sports_2006 = vgsale_2006.loc[vgsale_2006['Genre'] == 'Sports']
Sports_2007 = vgsale_2007.loc[vgsale_2007['Genre'] == 'Sports']
Sports_2008 = vgsale_2008.loc[vgsale_2008['Genre'] == 'Sports']
Sports_2009 = vgsale_2009.loc[vgsale_2009['Genre'] == 'Sports']
Sports_2010 = vgsale_2010.loc[vgsale_2010['Genre'] == 'Sports']
Sports_2011 = vgsale_2011.loc[vgsale_2011['Genre'] == 'Sports']
Sports_2012 = vgsale_2012.loc[vgsale_2012['Genre'] == 'Sports']
Sports_2013 = vgsale_2013.loc[vgsale_2013['Genre'] == 'Sports']
Sports_2014 = vgsale_2014.loc[vgsale_2014['Genre'] == 'Sports']
Sports_2015 = vgsale_2015.loc[vgsale_2015['Genre'] == 'Sports']
#create a list for easy to use
list_Sports = [Sports_2006,Sports_2007,Sports_2008,Sports_2009,Sports_2010,Sports_2011,Sports_2012,Sports_2013,Sports_2014,Sports_2015]
In [26]:
#combine the data frame of each genre from each year
Sports = pd.DataFrame()
for a in list_Sports:
    i = copy.deepcopy(a)
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum())) #sum the whole year sales
    Sports = pd.concat([Sports,i],axis=0)
Sports = Sports.loc['sum',:] #select the sums
Sports.index = year06_15 #set index by years
In [27]:
Racing_2006 = vgsale_2006.loc[vgsale_2006['Genre'] == 'Racing']
Racing_2007 = vgsale_2007.loc[vgsale_2007['Genre'] == 'Racing']
Racing_2008 = vgsale_2008.loc[vgsale_2008['Genre'] == 'Racing']
Racing_2009 = vgsale_2009.loc[vgsale_2009['Genre'] == 'Racing']
Racing_2010 = vgsale_2010.loc[vgsale_2010['Genre'] == 'Racing']
Racing_2011 = vgsale_2011.loc[vgsale_2011['Genre'] == 'Racing']
Racing_2012 = vgsale_2012.loc[vgsale_2012['Genre'] == 'Racing']
Racing_2013 = vgsale_2013.loc[vgsale_2013['Genre'] == 'Racing']
Racing_2014 = vgsale_2014.loc[vgsale_2014['Genre'] == 'Racing']
Racing_2015 = vgsale_2015.loc[vgsale_2015['Genre'] == 'Racing']
list_Racing = [Racing_2006,Racing_2007,Racing_2008,Racing_2009,Racing_2010,Racing_2011,Racing_2012,Racing_2013,Racing_2014,Racing_2015]
In [28]:
Racing = pd.DataFrame()
for a in list_Racing:
    i = copy.deepcopy(a)
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    Racing = pd.concat([Racing,i],axis=0)
Racing = Racing.loc['sum',:]
Racing.index = year06_15
In [29]:
Platform_2006 = vgsale_2006.loc[vgsale_2006['Genre'] == 'Platform']
Platform_2007 = vgsale_2007.loc[vgsale_2007['Genre'] == 'Platform']
Platform_2008 = vgsale_2008.loc[vgsale_2008['Genre'] == 'Platform']
Platform_2009 = vgsale_2009.loc[vgsale_2009['Genre'] == 'Platform']
Platform_2010 = vgsale_2010.loc[vgsale_2010['Genre'] == 'Platform']
Platform_2011 = vgsale_2011.loc[vgsale_2011['Genre'] == 'Platform']
Platform_2012 = vgsale_2012.loc[vgsale_2012['Genre'] == 'Platform']
Platform_2013 = vgsale_2013.loc[vgsale_2013['Genre'] == 'Platform']
Platform_2014 = vgsale_2014.loc[vgsale_2014['Genre'] == 'Platform']
Platform_2015 = vgsale_2015.loc[vgsale_2015['Genre'] == 'Platform']
list_Platform = [Platform_2006,Platform_2007,Platform_2008,Platform_2009,Platform_2010,Platform_2011,Platform_2012,Platform_2013,Platform_2014,Platform_2015]
In [30]:
Platform = pd.DataFrame()
for a in list_Platform:
    i = copy.deepcopy(a)
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    Platform = pd.concat([Platform,i],axis=0)
Platform = Platform.loc['sum',:]
Platform.index = year06_15
In [31]:
Misc_2006 = vgsale_2006.loc[vgsale_2006['Genre'] == 'Misc']
Misc_2007 = vgsale_2007.loc[vgsale_2007['Genre'] == 'Misc']
Misc_2008 = vgsale_2008.loc[vgsale_2008['Genre'] == 'Misc']
Misc_2009 = vgsale_2009.loc[vgsale_2009['Genre'] == 'Misc']
Misc_2010 = vgsale_2010.loc[vgsale_2010['Genre'] == 'Misc']
Misc_2011 = vgsale_2011.loc[vgsale_2011['Genre'] == 'Misc']
Misc_2012 = vgsale_2012.loc[vgsale_2012['Genre'] == 'Misc']
Misc_2013 = vgsale_2013.loc[vgsale_2013['Genre'] == 'Misc']
Misc_2014 = vgsale_2014.loc[vgsale_2014['Genre'] == 'Misc']
Misc_2015 = vgsale_2015.loc[vgsale_2015['Genre'] == 'Misc']
list_Misc = [Misc_2006,Misc_2007,Misc_2008,Misc_2009,Misc_2010,Misc_2011,Misc_2012,Misc_2013,Misc_2014,Misc_2015]
In [32]:
Misc = pd.DataFrame()
for a in list_Misc:
    i = copy.deepcopy(a)
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    Misc = pd.concat([Misc,i],axis=0)
Misc = Misc.loc['sum',:]
Misc.index = year06_15
In [33]:
Action_2006 = vgsale_2006.loc[vgsale_2006['Genre'] == 'Action']
Action_2007 = vgsale_2007.loc[vgsale_2007['Genre'] == 'Action']
Action_2008 = vgsale_2008.loc[vgsale_2008['Genre'] == 'Action']
Action_2009 = vgsale_2009.loc[vgsale_2009['Genre'] == 'Action']
Action_2010 = vgsale_2010.loc[vgsale_2010['Genre'] == 'Action']
Action_2011 = vgsale_2011.loc[vgsale_2011['Genre'] == 'Action']
Action_2012 = vgsale_2012.loc[vgsale_2012['Genre'] == 'Action']
Action_2013 = vgsale_2013.loc[vgsale_2013['Genre'] == 'Action']
Action_2014 = vgsale_2014.loc[vgsale_2014['Genre'] == 'Action']
Action_2015 = vgsale_2015.loc[vgsale_2015['Genre'] == 'Action']
list_Action = [Action_2006,Action_2007,Action_2008,Action_2009,Action_2010,Action_2011,Action_2012,Action_2013,Action_2014,Action_2015]
In [34]:
Action = pd.DataFrame()
for a in list_Action:
    i = copy.deepcopy(a)
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    Action = pd.concat([Action,i],axis=0)
Action = Action.loc['sum',:]
Action.index = year06_15
In [35]:
RolePlaying_2006 = vgsale_2006.loc[vgsale_2006['Genre'] == 'Role-Playing']
RolePlaying_2007 = vgsale_2007.loc[vgsale_2007['Genre'] == 'Role-Playing']
RolePlaying_2008 = vgsale_2008.loc[vgsale_2008['Genre'] == 'Role-Playing']
RolePlaying_2009 = vgsale_2009.loc[vgsale_2009['Genre'] == 'Role-Playing']
RolePlaying_2010 = vgsale_2010.loc[vgsale_2010['Genre'] == 'Role-Playing']
RolePlaying_2011 = vgsale_2011.loc[vgsale_2011['Genre'] == 'Role-Playing']
RolePlaying_2012 = vgsale_2012.loc[vgsale_2012['Genre'] == 'Role-Playing']
RolePlaying_2013 = vgsale_2013.loc[vgsale_2013['Genre'] == 'Role-Playing']
RolePlaying_2014 = vgsale_2014.loc[vgsale_2014['Genre'] == 'Role-Playing']
RolePlaying_2015 = vgsale_2015.loc[vgsale_2015['Genre'] == 'Role-Playing']
list_RolePlaying = [RolePlaying_2006,RolePlaying_2007,RolePlaying_2008,RolePlaying_2009,RolePlaying_2010,RolePlaying_2011,RolePlaying_2012,RolePlaying_2013,RolePlaying_2014,RolePlaying_2015]
In [36]:
RolePlaying = pd.DataFrame()
for a in list_RolePlaying:
    i = copy.deepcopy(a)
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    RolePlaying = pd.concat([RolePlaying,i],axis=0)
RolePlaying = RolePlaying.loc['sum',:]
RolePlaying.index = year06_15
In [37]:
Shooter_2006 = vgsale_2006.loc[vgsale_2006['Genre'] == 'Shooter']
Shooter_2007 = vgsale_2007.loc[vgsale_2007['Genre'] == 'Shooter']
Shooter_2008 = vgsale_2008.loc[vgsale_2008['Genre'] == 'Shooter']
Shooter_2009 = vgsale_2009.loc[vgsale_2009['Genre'] == 'Shooter']
Shooter_2010 = vgsale_2010.loc[vgsale_2010['Genre'] == 'Shooter']
Shooter_2011 = vgsale_2011.loc[vgsale_2011['Genre'] == 'Shooter']
Shooter_2012 = vgsale_2012.loc[vgsale_2012['Genre'] == 'Shooter']
Shooter_2013 = vgsale_2013.loc[vgsale_2013['Genre'] == 'Shooter']
Shooter_2014 = vgsale_2014.loc[vgsale_2014['Genre'] == 'Shooter']
Shooter_2015 = vgsale_2015.loc[vgsale_2015['Genre'] == 'Shooter']
list_Shooter = [Shooter_2006,Shooter_2007,Shooter_2008,Shooter_2009,Shooter_2010,Shooter_2011,Shooter_2012,Shooter_2013,Shooter_2014,Shooter_2015]
In [38]:
Shooter = pd.DataFrame()
for a in list_Shooter:
    i = copy.deepcopy(a)
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    Shooter = pd.concat([Shooter,i],axis=0)
Shooter = Shooter.loc['sum',:]
Shooter.index = year06_15
In [39]:
Fighting_2006 = vgsale_2006.loc[vgsale_2006['Genre'] == 'Fighting']
Fighting_2007 = vgsale_2007.loc[vgsale_2007['Genre'] == 'Fighting']
Fighting_2008 = vgsale_2008.loc[vgsale_2008['Genre'] == 'Fighting']
Fighting_2009 = vgsale_2009.loc[vgsale_2009['Genre'] == 'Fighting']
Fighting_2010 = vgsale_2010.loc[vgsale_2010['Genre'] == 'Fighting']
Fighting_2011 = vgsale_2011.loc[vgsale_2011['Genre'] == 'Fighting']
Fighting_2012 = vgsale_2012.loc[vgsale_2012['Genre'] == 'Fighting']
Fighting_2013 = vgsale_2013.loc[vgsale_2013['Genre'] == 'Fighting']
Fighting_2014 = vgsale_2014.loc[vgsale_2014['Genre'] == 'Fighting']
Fighting_2015 = vgsale_2015.loc[vgsale_2015['Genre'] == 'Fighting']
list_Fighting = [Fighting_2006,Fighting_2007,Fighting_2008,Fighting_2009,Fighting_2010,Fighting_2011,Fighting_2012,Fighting_2013,Fighting_2014,Fighting_2015]
In [40]:
Fighting = pd.DataFrame()
for a in list_Fighting:
    i = copy.deepcopy(a)
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    Fighting = pd.concat([Fighting,i],axis=0)
Fighting = Fighting.loc['sum',:]
Fighting.index = year06_15
In [41]:
Simulation_2006 = vgsale_2006.loc[vgsale_2006['Genre'] == 'Simulation']
Simulation_2007 = vgsale_2007.loc[vgsale_2007['Genre'] == 'Simulation']
Simulation_2008 = vgsale_2008.loc[vgsale_2008['Genre'] == 'Simulation']
Simulation_2009 = vgsale_2009.loc[vgsale_2009['Genre'] == 'Simulation']
Simulation_2010 = vgsale_2010.loc[vgsale_2010['Genre'] == 'Simulation']
Simulation_2011 = vgsale_2011.loc[vgsale_2011['Genre'] == 'Simulation']
Simulation_2012 = vgsale_2012.loc[vgsale_2012['Genre'] == 'Simulation']
Simulation_2013 = vgsale_2013.loc[vgsale_2013['Genre'] == 'Simulation']
Simulation_2014 = vgsale_2014.loc[vgsale_2014['Genre'] == 'Simulation']
Simulation_2015 = vgsale_2015.loc[vgsale_2015['Genre'] == 'Simulation']
list_Simulation = [Simulation_2006,Simulation_2007,Simulation_2008,Simulation_2009,Simulation_2010,Simulation_2011,Simulation_2012,Simulation_2013,Simulation_2014,Simulation_2015]
In [42]:
Simulation = pd.DataFrame()
for a in list_Simulation:
    i = copy.deepcopy(a)
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    Simulation = pd.concat([Simulation,i],axis=0)
Simulation = Simulation.loc['sum',:]
Simulation.index = year06_15
In [43]:
Adventure_2006 = vgsale_2006.loc[vgsale_2006['Genre'] == 'Adventure']
Adventure_2007 = vgsale_2007.loc[vgsale_2007['Genre'] == 'Adventure']
Adventure_2008 = vgsale_2008.loc[vgsale_2008['Genre'] == 'Adventure']
Adventure_2009 = vgsale_2009.loc[vgsale_2009['Genre'] == 'Adventure']
Adventure_2010 = vgsale_2010.loc[vgsale_2010['Genre'] == 'Adventure']
Adventure_2011 = vgsale_2011.loc[vgsale_2011['Genre'] == 'Adventure']
Adventure_2012 = vgsale_2012.loc[vgsale_2012['Genre'] == 'Adventure']
Adventure_2013 = vgsale_2013.loc[vgsale_2013['Genre'] == 'Adventure']
Adventure_2014 = vgsale_2014.loc[vgsale_2014['Genre'] == 'Adventure']
Adventure_2015 = vgsale_2015.loc[vgsale_2015['Genre'] == 'Adventure']
list_Adventure = [Adventure_2006,Adventure_2007,Adventure_2008,Adventure_2009,Adventure_2010,Adventure_2011,Adventure_2012,Adventure_2013,Adventure_2014,Adventure_2015]
In [44]:
Adventure = pd.DataFrame()
for a in list_Adventure:
    i = copy.deepcopy(a)
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    Adventure = pd.concat([Adventure,i],axis=0)
Adventure = Adventure.loc['sum',:]
Adventure.index = year06_15
In [45]:
Puzzle_2006 = vgsale_2006.loc[vgsale_2006['Genre'] == 'Puzzle']
Puzzle_2007 = vgsale_2007.loc[vgsale_2007['Genre'] == 'Puzzle']
Puzzle_2008 = vgsale_2008.loc[vgsale_2008['Genre'] == 'Puzzle']
Puzzle_2009 = vgsale_2009.loc[vgsale_2009['Genre'] == 'Puzzle']
Puzzle_2010 = vgsale_2010.loc[vgsale_2010['Genre'] == 'Puzzle']
Puzzle_2011 = vgsale_2011.loc[vgsale_2011['Genre'] == 'Puzzle']
Puzzle_2012 = vgsale_2012.loc[vgsale_2012['Genre'] == 'Puzzle']
Puzzle_2013 = vgsale_2013.loc[vgsale_2013['Genre'] == 'Puzzle']
Puzzle_2014 = vgsale_2014.loc[vgsale_2014['Genre'] == 'Puzzle']
Puzzle_2015 = vgsale_2015.loc[vgsale_2015['Genre'] == 'Puzzle']
list_Puzzle = [Puzzle_2006,Puzzle_2007,Puzzle_2008,Puzzle_2009,Puzzle_2010,Puzzle_2011,Puzzle_2012,Puzzle_2013,Puzzle_2014,Puzzle_2015]
In [46]:
Puzzle = pd.DataFrame()
for a in list_Puzzle:
    i = copy.deepcopy(a)
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    Puzzle = pd.concat([Puzzle,i],axis=0)
Puzzle = Puzzle.loc['sum',:]
Puzzle.index = year06_15
In [47]:
Strategy_2006 = vgsale_2006.loc[vgsale_2006['Genre'] == 'Strategy']
Strategy_2007 = vgsale_2007.loc[vgsale_2007['Genre'] == 'Strategy']
Strategy_2008 = vgsale_2008.loc[vgsale_2008['Genre'] == 'Strategy']
Strategy_2009 = vgsale_2009.loc[vgsale_2009['Genre'] == 'Strategy']
Strategy_2010 = vgsale_2010.loc[vgsale_2010['Genre'] == 'Strategy']
Strategy_2011 = vgsale_2011.loc[vgsale_2011['Genre'] == 'Strategy']
Strategy_2012 = vgsale_2012.loc[vgsale_2012['Genre'] == 'Strategy']
Strategy_2013 = vgsale_2013.loc[vgsale_2013['Genre'] == 'Strategy']
Strategy_2014 = vgsale_2014.loc[vgsale_2014['Genre'] == 'Strategy']
Strategy_2015 = vgsale_2015.loc[vgsale_2015['Genre'] == 'Strategy']
list_Strategy = [Strategy_2006,Strategy_2007,Strategy_2008,Strategy_2009,Strategy_2010,Strategy_2011,Strategy_2012,Strategy_2013,Strategy_2014,Strategy_2015]
In [48]:
Strategy = pd.DataFrame()
for a in list_Strategy:
    i = copy.deepcopy(a)
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    Strategy = pd.concat([Strategy,i],axis=0)
Strategy = Strategy.loc['sum',:]
Strategy.index = year06_15
In [49]:
#create dataframe of Global Sales of Genre from 2006 to 2015
Genre_Global_Sales = pd.DataFrame({'Sports':Sports.loc[:,'Global_Sales'],
 'Racing':Racing.loc[:,'Global_Sales'],
 'Platform':Platform.loc[:,'Global_Sales'],
 'Misc':Misc.loc[:,'Global_Sales'],
 'Action':Action.loc[:,'Global_Sales'],
 'Role-Playing':RolePlaying.loc[:,'Global_Sales'],
 'Shooter':Shooter.loc[:,'Global_Sales'],
 'Fighting':Fighting.loc[:,'Global_Sales'],
 'Simulation':Simulation.loc[:,'Global_Sales'],
 'Adventure':Adventure.loc[:,'Global_Sales'],
 'Puzzle':Puzzle.loc[:,'Global_Sales'],
 'Strategy':Strategy.loc[:,'Global_Sales']})
#draw a line chart
Genre_Global_Sales.plot(kind='line', figsize=(10,8), title='Global Sales of Genre from 2006 to 2015')
plt.xlabel('2006 to 2015')
plt.show()
In [50]:
#create dataframe of Global Number of Genre from 2006 to 2015
Genre_Global_Games = pd.DataFrame({'Sports':Sports.loc[:,'Number of Games'],
 'Racing':Racing.loc[:,'Number of Games'],
 'Platform':Platform.loc[:,'Number of Games'],
 'Misc':Misc.loc[:,'Number of Games'],
 'Action':Action.loc[:,'Number of Games'],
 'Role-Playing':RolePlaying.loc[:,'Number of Games'],
 'Shooter':Shooter.loc[:,'Number of Games'],
 'Fighting':Fighting.loc[:,'Number of Games'],
 'Simulation':Simulation.loc[:,'Number of Games'],
 'Adventure':Adventure.loc[:,'Number of Games'],
 'Puzzle':Puzzle.loc[:,'Number of Games'],
 'Strategy':Strategy.loc[:,'Number of Games']})
#draw a line chart
Genre_Global_Games.plot(kind='line', figsize=(10,8), title='Global Number of Genre from 2006 to 2015')
plt.xlabel('2006 to 2015')
plt.show()
In [51]:
#create dataframe of Global Growth of Genre from 2006 to 2015
Genre_Growth= pd.DataFrame({'Sports':Sports.loc[:,'Global_Sales']/Sports.loc[:,'Number of Games'],
 'Racing':Racing.loc[:,'Global_Sales']/Racing.loc[:,'Number of Games'],
 'Platform':Platform.loc[:,'Global_Sales']/Platform.loc[:,'Number of Games'],
 'Misc':Misc.loc[:,'Global_Sales']/Misc.loc[:,'Number of Games'],
 'Action':Action.loc[:,'Global_Sales']/Action.loc[:,'Number of Games'],
 'Role-Playing':RolePlaying.loc[:,'Global_Sales']/RolePlaying.loc[:,'Number of Games'],
 'Shooter':Shooter.loc[:,'Global_Sales']/Shooter.loc[:,'Number of Games'],
 'Fighting':Fighting.loc[:,'Global_Sales']/Fighting.loc[:,'Number of Games'],
 'Simulation':Simulation.loc[:,'Global_Sales']/Simulation.loc[:,'Number of Games'],
 'Adventure':Adventure.loc[:,'Global_Sales']/Adventure.loc[:,'Number of Games'],
 'Puzzle':Puzzle.loc[:,'Global_Sales']/Puzzle.loc[:,'Number of Games'],
 'Strategy':Strategy.loc[:,'Global_Sales']/Strategy.loc[:,'Number of Games']})
#draw a line chart
Genre_Growth.plot(kind='line', figsize=(10,8), title='Global Growth of Genre from 2006 to 2015')
plt.xlabel('2006 to 2015')
plt.show()
In [52]:
#preview the top 10 Global Sales and number of Publisher from 2006 to 2015
Publisher_06_15 = sales06_15.groupby('Publisher').sum()
del Publisher_06_15['Year']
Publisher_sales_06_15 = Publisher_06_15.sort_values(by='Global_Sales', ascending=False).head(10)
pd.Series(Publisher_sales_06_15['Global_Sales']).plot(kind='barh', figsize=(10,8), title='Global Sales of Games from 2006 to 2015') 
plt.show()
Publisher_games_06_15 = Publisher_06_15.sort_values(by='Number of Games', ascending=False).head(10)
pd.Series(Publisher_games_06_15['Number of Games']).plot(kind='barh', figsize=(10,8), title='Number of Games from 2006 to 2015') 
plt.show()
In [53]:
#create data frame of genre: Shooter for top 4 Publisher 
#Nintendo, Activision, Electronic Arts and Ubisoft
Shooter_Nintendo = pd.DataFrame()
for a in list_Shooter:
    i = copy.deepcopy(a)
    i = i.loc[i['Publisher'] == 'Nintendo']
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    Shooter_Nintendo = pd.concat([Shooter_Nintendo,i],axis=0)
Shooter_Nintendo = Shooter_Nintendo.loc['sum',:]
Shooter_Nintendo.index = year06_15
In [54]:
Shooter_Activision = pd.DataFrame()
for a in list_Shooter:
    i = copy.deepcopy(a)
    i = i.loc[i['Publisher'] == 'Activision']
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    Shooter_Activision = pd.concat([Shooter_Activision,i],axis=0)
Shooter_Activision = Shooter_Activision.loc['sum',:]
Shooter_Activision.index = year06_15
In [55]:
Shooter_ElectronicArts = pd.DataFrame()
for a in list_Shooter:
    i = copy.deepcopy(a)
    i = i.loc[i['Publisher'] == 'Electronic Arts']
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    Shooter_ElectronicArts = pd.concat([Shooter_ElectronicArts,i],axis=0)
Shooter_ElectronicArts = Shooter_ElectronicArts.loc['sum',:]
Shooter_ElectronicArts.index = year06_15
In [56]:
Shooter_Ubisoft = pd.DataFrame()
for a in list_Shooter:
    i = copy.deepcopy(a)
    i = i.loc[i['Publisher'] == 'Ubisoft']
    del i['Year'], i['Name'], i['Platform'], i['Genre'], i['Publisher']
    i.loc["sum"] = list(i.apply(lambda x: x.sum()))
    Shooter_Ubisoft = pd.concat([Shooter_Ubisoft,i],axis=0)
Shooter_Ubisoft = Shooter_Ubisoft.loc['sum',:]
Shooter_Ubisoft.index = year06_15
In [77]:
#draw a line chart by Number
Publisher_Number_Shooter= pd.DataFrame({'Nintendo':Shooter_Nintendo.loc[:,'Number of Games'],
 'Activision':Shooter_Activision.loc[:,'Number of Games'],
 'Electronic Arts':Shooter_ElectronicArts.loc[:,'Number of Games'],
 'Ubisoft':Shooter_Ubisoft.loc[:,'Number of Games']})
Publisher_Number_Shooter.plot(kind='line', figsize=(10,8), title='Global Number of Shooter from 2006 to 2015')
plt.xlabel('2006 to 2015')
plt.show()
In [78]:
#draw a line chart by Growth
Publisher_Growth_Shooter= pd.DataFrame({'Nintendo':Shooter_Nintendo.loc[:,'Global_Sales']/Shooter_Nintendo.loc[:,'Number of Games'],
 'Activision':Shooter_Activision.loc[:,'Global_Sales']/Shooter_Activision.loc[:,'Number of Games'],
 'Electronic Arts':Shooter_ElectronicArts.loc[:,'Global_Sales']/Shooter_ElectronicArts.loc[:,'Number of Games'],
 'Ubisoft':Shooter_Ubisoft.loc[:,'Global_Sales']/Shooter_Ubisoft.loc[:,'Number of Games']})
Publisher_Growth_Shooter.plot(kind='line', figsize=(10,8), title='Global Growth of Shooter from 2006 to 2015')
plt.xlabel('2006 to 2015')
plt.show()
In [59]:
#draw bar chart to see the sales and number of each Publisher  from 2006 to 2015
Nintendo_sales06_15 = sales06_15.loc[sales06_15['Publisher'] == 'Nintendo']
Nintendo_Genre_06_15 = Nintendo_sales06_15.groupby('Genre').sum()
del Nintendo_Genre_06_15['Year']
Nintendo_Genre_sales = Nintendo_Genre_06_15.sort_values(by='Global_Sales', ascending=False).head(5)
pd.Series(Nintendo_Genre_sales['Global_Sales']).plot(kind='barh', figsize=(10,8), title='Global Sales of Nintendo from 2006 to 2015') 
plt.show()
Nintendo_Genre_games = Nintendo_Genre_06_15.sort_values(by='Number of Games', ascending=False).head(5)
pd.Series(Nintendo_Genre_games['Number of Games']).plot(kind='barh', figsize=(10,8), title='Number of Games of Nintendo from 2006 to 2015') 
plt.show()
In [60]:
Activision_sales06_15 = sales06_15.loc[sales06_15['Publisher'] == 'Activision']
Activision_Genre_06_15 = Activision_sales06_15.groupby('Genre').sum()
del Activision_Genre_06_15['Year']
Activision_Genre_sales = Activision_Genre_06_15.sort_values(by='Global_Sales', ascending=False).head(5)
pd.Series(Activision_Genre_sales['Global_Sales']).plot(kind='barh', figsize=(10,8), title='Global Sales of Activision from 2006 to 2015') 
plt.show()
Activision_Genre_games = Activision_Genre_06_15.sort_values(by='Number of Games', ascending=False).head(5)
pd.Series(Activision_Genre_games['Number of Games']).plot(kind='barh', figsize=(10,8), title='Number of Games of Activision from 2006 to 2015') 
plt.show()
In [61]:
ElectronicArts_sales06_15 = sales06_15.loc[sales06_15['Publisher'] == 'Electronic Arts']
ElectronicArts_Genre_06_15 = ElectronicArts_sales06_15.groupby('Genre').sum()
del ElectronicArts_Genre_06_15['Year']
ElectronicArts_Genre_sales = ElectronicArts_Genre_06_15.sort_values(by='Global_Sales', ascending=False).head(5)
pd.Series(ElectronicArts_Genre_sales['Global_Sales']).plot(kind='barh', figsize=(10,8), title='Global Sales of Electronic Arts from 2006 to 2015') 
plt.show()
ElectronicArts_Genre_games = ElectronicArts_Genre_06_15.sort_values(by='Number of Games', ascending=False).head(5)
pd.Series(ElectronicArts_Genre_games['Number of Games']).plot(kind='barh', figsize=(10,8), title='Number of Games of Electronic Arts from 2006 to 2015') 
plt.show()
In [62]:
Ubisoft_sales06_15 = sales06_15.loc[sales06_15['Publisher'] == 'Ubisoft']
Ubisoft_Genre_06_15 = Ubisoft_sales06_15.groupby('Genre').sum()
del Ubisoft_Genre_06_15['Year']
Ubisoft_Genre_sales = Ubisoft_Genre_06_15.sort_values(by='Global_Sales', ascending=False).head(5)
pd.Series(Ubisoft_Genre_sales['Global_Sales']).plot(kind='barh', figsize=(10,8), title='Global Sales of Ubisoft from 2006 to 2015') 
plt.show()
Ubisoft_Genre_games = Ubisoft_Genre_06_15.sort_values(by='Number of Games', ascending=False).head(5)
pd.Series(Ubisoft_Genre_games['Number of Games']).plot(kind='barh', figsize=(10,8), title='Number of Games of Ubisoft from 2006 to 2015') 
plt.show()
In [63]:
#create data frame for top 4 Publisher
Nintendo = pd.DataFrame()
for i in list_year:
    df = copy.deepcopy(i)
    df = df.loc[df['Publisher'] == 'Nintendo']
    del df['Year'], df['Name'], df['Platform'], df['Genre'], df['Publisher']
    df.loc["sum"] = list(df.apply(lambda x: x.sum()))
    Nintendo = pd.concat([Nintendo,df],axis=0)
Nintendo = Nintendo.loc['sum',:]
Nintendo.index = year06_15
In [64]:
#draw line chart to see the sales and number by each area
Nintendo_sales = pd.DataFrame({'NA':Nintendo.loc[:,'NA_Sales'],
 'EU':Nintendo.loc[:,'EU_Sales'],
 'JP':Nintendo.loc[:,'JP_Sales'],
 'Others':Nintendo.loc[:,'Other_Sales']})
Nintendo_sales.plot(kind='line', figsize=(10,8), title='Sales of Nintendo from 2006 to 2015')
plt.xlabel('2006 to 2015')
plt.show()
In [65]:
Ubisoft = pd.DataFrame()
for i in list_year:
    df = copy.deepcopy(i)
    df = df.loc[df['Publisher'] == 'Ubisoft']
    del df['Year'], df['Name'], df['Platform'], df['Genre'], df['Publisher']
    df.loc["sum"] = list(df.apply(lambda x: x.sum()))
    Ubisoft = pd.concat([Ubisoft,df],axis=0)
Ubisoft = Ubisoft.loc['sum',:]
Ubisoft.index = year06_15
In [66]:
Ubisoft_sales = pd.DataFrame({'NA':Ubisoft.loc[:,'NA_Sales'],
 'EU':Ubisoft.loc[:,'EU_Sales'],
 'JP':Ubisoft.loc[:,'JP_Sales'],
 'Others':Ubisoft.loc[:,'Other_Sales']})
Ubisoft_sales.plot(kind='line', figsize=(10,8), title='Sales of Ubisoft from 2006 to 2015')
plt.xlabel('2006 to 2015')
plt.show()
In [67]:
ElectronicArts = pd.DataFrame()
for i in list_year:
    df = copy.deepcopy(i)
    df = df.loc[df['Publisher'] == 'Electronic Arts']
    del df['Year'], df['Name'], df['Platform'], df['Genre'], df['Publisher']
    df.loc["sum"] = list(df.apply(lambda x: x.sum()))
    ElectronicArts = pd.concat([ElectronicArts,df],axis=0)
ElectronicArts = ElectronicArts.loc['sum',:]
ElectronicArts.index = year06_15
In [68]:
ElectronicArts_sales = pd.DataFrame({'NA':ElectronicArts.loc[:,'NA_Sales'],
 'EU':ElectronicArts.loc[:,'EU_Sales'],
 'JP':ElectronicArts.loc[:,'JP_Sales'],
 'Others':ElectronicArts.loc[:,'Other_Sales']})
ElectronicArts_sales.plot(kind='line', figsize=(10,8), title='Sales of Electronic Arts from 2006 to 2015')
plt.xlabel('2006 to 2015')
plt.show()
In [69]:
Activision = pd.DataFrame()
for i in list_year:
    df = copy.deepcopy(i)
    df = df.loc[df['Publisher'] == 'Activision']
    del df['Year'], df['Name'], df['Platform'], df['Genre'], df['Publisher']
    df.loc["sum"] = list(df.apply(lambda x: x.sum()))
    Activision = pd.concat([Activision,df],axis=0)
Activision = Activision.loc['sum',:]
Activision.index = year06_15
In [70]:
Activision_sales = pd.DataFrame({'NA':Activision.loc[:,'NA_Sales'],
 'EU':Activision.loc[:,'EU_Sales'],
 'JP':Activision.loc[:,'JP_Sales'],
 'Others':Activision.loc[:,'Other_Sales']})
Activision_sales.plot(kind='line', figsize=(10,8), title='Sales of Activision from 2006 to 2015')
plt.xlabel('2006 to 2015')
plt.show()
In [71]:
#group by Platform to find the favorite platform from 2006 to 2015
Platform_06_15 = sales06_15.groupby('Platform').sum()
del Platform_06_15['Year']
Platform_sales_06_15 = Platform_06_15.sort_values(by='Global_Sales', ascending=False).head(10)
pd.Series(Platform_sales_06_15['Global_Sales']).plot(kind='barh', figsize=(10,8), title='Global Sales of Games from 2006 to 2015') 
plt.show()
Platform_games_06_15 = Platform_06_15.sort_values(by='Number of Games', ascending=False).head(10)
pd.Series(Platform_games_06_15['Number of Games']).plot(kind='barh', figsize=(10,8), title='Number of Games from 2006 to 2015') 
plt.show()
In [72]:
#create data frame for top 4 Platform
DS_sales06_15 = sales06_15.loc[sales06_15['Platform'] == 'DS']
DS_Genre_06_15 = DS_sales06_15.groupby('Genre').sum()
del DS_Genre_06_15['Year']
#draw bar chart to see the sales and number of Genre from each Platform
DS_Genre_sales = DS_Genre_06_15.sort_values(by='Global_Sales', ascending=False)
pd.Series(DS_Genre_sales['Global_Sales']).plot(kind='barh', figsize=(10,8), title='Global Sales of DS from 2006 to 2015') 
plt.show()
DS_Genre_games = DS_Genre_06_15.sort_values(by='Number of Games', ascending=False)
pd.Series(DS_Genre_games['Number of Games']).plot(kind='barh', figsize=(10,8), title='Number of Games of DS from 2006 to 2015') 
plt.show()
In [73]:
PS3_sales06_15 = sales06_15.loc[sales06_15['Platform'] == 'PS3']
PS3_Genre_06_15 = PS3_sales06_15.groupby('Genre').sum()
del PS3_Genre_06_15['Year']
PS3_Genre_sales = PS3_Genre_06_15.sort_values(by='Global_Sales', ascending=False)
pd.Series(PS3_Genre_sales['Global_Sales']).plot(kind='barh', figsize=(10,8), title='Global Sales of PS3 from 2006 to 2015') 
plt.show()
PS3_Genre_games = PS3_Genre_06_15.sort_values(by='Number of Games', ascending=False)
pd.Series(PS3_Genre_games['Number of Games']).plot(kind='barh', figsize=(10,8), title='Number of Games of PS3 from 2006 to 2015') 
plt.show()
In [74]:
Wii_sales06_15 = sales06_15.loc[sales06_15['Platform'] == 'Wii']
Wii_Genre_06_15 = Wii_sales06_15.groupby('Genre').sum()
del Wii_Genre_06_15['Year']
Wii_Genre_sales = Wii_Genre_06_15.sort_values(by='Global_Sales', ascending=False)
pd.Series(Wii_Genre_sales['Global_Sales']).plot(kind='barh', figsize=(10,8), title='Global Sales of Wii from 2006 to 2015') 
plt.show()
Wii_Genre_games = Wii_Genre_06_15.sort_values(by='Number of Games', ascending=False)
pd.Series(Wii_Genre_games['Number of Games']).plot(kind='barh', figsize=(10,8), title='Number of Games of Wii from 2006 to 2015') 
plt.show()
In [75]:
X360_sales06_15 = sales06_15.loc[sales06_15['Platform'] == 'X360']
X360_Genre_06_15 = X360_sales06_15.groupby('Genre').sum()
del X360_Genre_06_15['Year']
X360_Genre_sales = X360_Genre_06_15.sort_values(by='Global_Sales', ascending=False)
pd.Series(X360_Genre_sales['Global_Sales']).plot(kind='barh', figsize=(10,8), title='Global Sales of X360 from 2006 to 2015') 
plt.show()
X360_Genre_games = X360_Genre_06_15.sort_values(by='Number of Games', ascending=False)
pd.Series(X360_Genre_games['Number of Games']).plot(kind='barh', figsize=(10,8), title='Number of Games of X360 from 2006 to 2015') 
plt.show()