# 데이터셋 불러오기

import pandas as pd

df = pd.read_csv('/content/drive/MyDrive/vgames2.csv')
df.drop(['Unnamed: 0'],axis=1,inplace=True)

# 결측치 확인 및 제거

print(df.isnull().sum())
df[['Year','Genre','Publisher']].dropna(inplace=True)
df.reset_index(drop=True,inplace=True)

# 데이터 타입 확인 및 변환
print(df.dtypes)
df['NA_Sales'] = df['NA_Sales'].replace({"K":"*1e3", "M":"*1e6"}, regex=True).map(pd.eval).astype(float)
df['EU_Sales'] = df['EU_Sales'].replace({"K":"*1e3", "M":"*1e6"}, regex=True).map(pd.eval).astype(float)
df['JP_Sales'] = df['JP_Sales'].replace({"K":"*1e3", "M":"*1e6"}, regex=True).map(pd.eval).astype(float)
df['Other_Sales'] = df['Other_Sales'].replace({"K":"*1e3", "M":"*1e6"}, regex=True).map(pd.eval).astype(float)
df

!pip install plotly==4.10.0
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib as mpl
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly
from plotly.graph_objs import Layout

# 지역과 선호하는 게임 장르의 연관성

df_genre = df.drop(['Name','Platform','Year','Publisher'],axis=1)

df_genre2 = df_genre.groupby(by='Genre').sum().reset_index()
df_genre2 = df_genre2.loc[:, df_genre2.columns != 'Genre'].astype(float)


# 카이제곱 검정
from scipy import stats
from scipy.stats import chi2_contingency
import scipy as sp

chi2, p, dof, expected = stats.chi2_contingency(df_genre2)

print(f'게임 장르와 지역간의 카이제곱 검정 결과 P밸류가 {p}로 지역간의 장르 선호도에 차이가 있다.')

lab = ['Action',	'Adventure',	'Fighting',	'Misc',	'Platform',	'Puzzle',	'Racing',	'Role-Playing',	'Shooter',	'Simulation',	'Sports',	'Strategy']

fig3 = make_subplots(rows=2, cols=2,subplot_titles=("NA", "EU", "JP", "Other"),
                     specs=[[{"type": "domain"},{"type": "domain"}],[{"type": "domain"}, {"type": "domain"}]])

fig3.add_trace(go.Pie(values=df_genre2['NA_Sales'],labels = lab),
              row=1, col=1)
fig3.add_trace(go.Pie(values=df_genre2['EU_Sales'],labels = lab),
              row=1, col=2)
fig3.add_trace(go.Pie(values=df_genre2['JP_Sales'],labels = lab),
              row=2, col=1)
fig3.add_trace(go.Pie(values=df_genre2['Other_Sales'],labels = lab),
              row=2, col=2)
fig3.update_layout(height=1000, width=1200, title_text="Preference for game genre according to region")

fig3.show()

fig3.write_html("/content/sample_data/fig4.html")


df_genre

# 게임시장 규모

df_market = pd.DataFrame(data=[df_genre['NA_Sales'].sum(),df_genre['EU_Sales'].sum(),df_genre['JP_Sales'].sum(),df_genre['Other_Sales'].sum()])
df_market = df_market.T

sns.set(rc = {'figure.figsize':(15,10)})

df_market.columns = ['NA','EU','JP','Other']
df_market.index = ['Amount']
fig2 = px.bar(df_market,width=500, height=500,title='MARKET SIZE')
fig2.show()
fig2.write_html("/content/sample_data/fig2.html")

# 연도별 게임 트렌드

df['Year'] = df['Year'].dropna().astype(int)

df = df[df.Year > 100]
df.tail()

df_year = pd.crosstab(df.Genre,df.Year)
df_year = df_year.add_prefix('').reset_index().rename_axis(None, axis=1) 

df_year['1980s'] = df_year['1980']+df_year['1981']+df_year['1982']+df_year['1983']+df_year['1984']+df_year['1985']+df_year['1986']+df_year['1987']+df_year['1988']+df_year['1989']
df_year['1990s'] = df_year['1990']+df_year['1991']+df_year['1992']+df_year['1993']+df_year['1994']+df_year['1995']+df_year['1996']+df_year['1997']+df_year['1998']+df_year['1999']
df_year['2000s'] = df_year['2000']+df_year['2001']+df_year['2002']+df_year['2003']+df_year['2004']+df_year['2005']+df_year['2006']+df_year['2007']+df_year['2008']+df_year['2009']
df_year['2010s'] = df_year['2010']+df_year['2011']+df_year['2012']+df_year['2013']+df_year['2014']+df_year['2015']+df_year['2016']+df_year['2017']
df_year['2020s'] = df_year['2020']
df_year = df_year[['Genre','1980s','1990s','2000s','2010s','2020s']]

df_year = df_year.T
df_year.columns = ['Action',	'Adventure',	'Fighting', 'Misc',	'Platform',	'Puzzle',	'Racing',	'Role-Playing',	'Shooter',	'Simulation',	'Sports',	'Strategy']
df_year = df_year.drop(df_year.index[0])

df_year.plot.bar(stacked=True)

maxs=df_year.max(axis=1)
print("연도별 최대 출고 게임량")
print(maxs)
max_game = df_year.astype(int).idxmax(axis=1)
print('연도별 최대 출고 게임\n')
print(max_game)


df_year

# 출고량이 높은 게임 분석 및 시각화

df['Total_Sales'] = df['NA_Sales'] + df['EU_Sales'] + df['JP_Sales'] + df['Other_Sales']
na_max = df.loc[df.NA_Sales.idxmax(), 'Name']
eu_max = df.loc[df.EU_Sales.idxmax(), 'Name']
jp_max = df.loc[df.JP_Sales.idxmax(), 'Name']
other_max = df.loc[df.Other_Sales.idxmax(), 'Name']
total_max = df.loc[df.Total_Sales.idxmax(), 'Name']
print('북미 최대 출고 게임 :',na_max,'\n유럽 최대 출고 게임 :',eu_max,'\n일본 최대 출고 게임 :',jp_max,'\n기타지역 최대 출고 게임 :',other_max,'\n합계 최대 출고 게임 :',total_max)


# 출고량 상위 10위 시각화
df.reset_index(drop=True,inplace=True)

labels = df.iloc[[10673,12321,567,1935,1928,1777,8353,9622,7697,6314],[0]]
label = np.array(labels['Name'].tolist())
print(labels)
fig4 = px.pie(df, values=df['Total_Sales'].nlargest(10),names=label,title='TOP 10 GAMES',width=1000, height=1000)

fig4.show()
fig4.write_html("/content/sample_data/fig3.html")

# 휴대형 플랫폼과 거치형 플랫폼 출고량 분석

platform = df.drop_duplicates(['Platform'])
platform.reset_index(drop=True, inplace=True)
print('전체 플랫폼\n',platform['Platform'])
portable = ['DS','PSP','GBA','GB','PSV','WS','GG','3DS']
holder = ['Wii','PS3','PC','PS4','PS2','XB','X360','GC','2600','NES','SAT','N64','XOne','SNES','WiiU','DC','SCD','NG','TG16','3DO','PCFX']
print('휴대형 플랫폼\n',portable,'\n\n거치형 플랫폼\n',holder)

df_platform = df.groupby(by='Platform').sum().reset_index().drop(columns = ['Year','NA_Sales',	'EU_Sales',	'JP_Sales',	'Other_Sales'])
df_platform

df_platform_potable = pd.DataFrame(data=df_platform.iloc[[2,4,5,6,9,19,20,25]])
df_platform_potable.reset_index(drop=True,inplace=True)

df_platform_holder = df_platform.drop([2,4,5,6,9,19,20,25])
df_platform_holder.reset_index(drop=True,inplace=True)

platform_vs = [['Portable',df_platform_potable['Total_Sales'].sum()],['Holder',df_platform_holder['Total_Sales'].sum()]]

df_platform_vs = pd.DataFrame(platform_vs,columns = ['kind','Total_Sales'])
print(df_platform_vs)
fig5 = px.pie(df_platform_vs,values=df_platform_vs['Total_Sales'],names=df_platform_vs['kind'],width=600)
fig5.show()
fig5.write_html("/content/sample_data/fig5.html")


df_platform_potable

# 휴대형 플랫폼 내 출고량 분석

portable1= [['NINTENDO',df_platform_potable.iloc[0][1]+df_platform_potable.iloc[1][1]+df_platform_potable.iloc[2][1]+df_platform_potable.iloc[3][1]],
           ['SONY',df_platform_potable.iloc[5][1]+df_platform_potable.iloc[6][1]],
           ['OTHER',df_platform_potable.iloc[4][1]+df_platform_potable.iloc[7][1]]]
df_portable = pd.DataFrame(portable1,columns=['Company','Sales'])

fig6 = make_subplots(rows=1, cols=2,subplot_titles=("Analysis by Platform","Analysis by Company"),
                     specs=[[{"type": "domain"},{"type": "domain"}]])

fig6.add_trace(go.Pie(values=df_platform_potable['Total_Sales'],labels = df_platform_potable['Platform']),
              row=1, col=1)
fig6.add_trace(go.Pie(values=df_portable['Sales'],labels = df_portable['Company']),
              row=1, col=2)

fig6.show()
fig6.write_html("/content/sample_data/fig6.html")

df_platform_holder

# 거치형 플랫폼 내 출고량 분석

holder1 = [['SONY',df_platform_holder.iloc[10][1]+df_platform_holder.iloc[11][1]+df_platform_holder.iloc[12][1]+df_platform_holder.iloc[13][1]],
          ['NINTENDO',df_platform_holder.iloc[3][1]+df_platform_holder.iloc[5][1]+df_platform_holder.iloc[6][1]+df_platform_holder.iloc[16][1]+df_platform_holder.iloc[18][1]+df_platform_holder.iloc[19][1]],
          ['SEGA',df_platform_holder.iloc[2][1]+df_platform_holder.iloc[4][1]+df_platform_holder.iloc[14][1]+df_platform_holder.iloc[15][1]],
          ['MS',df_platform_holder.iloc[20][1]+df_platform_holder.iloc[21][1]+df_platform_holder.iloc[22][1]],
          ['Other',df_platform_holder.iloc[0][1]+df_platform_holder.iloc[1][1]+df_platform_holder.iloc[7][1]+df_platform_holder.iloc[8][1]+df_platform_holder.iloc[9][1]+df_platform_holder.iloc[17][1]]]

df_holder = pd.DataFrame(holder1, columns=['Company','Sales'])
df_holder

fig7 = px.pie(df_platform_holder,names=df_platform_holder['Platform'],values=df_platform_holder['Total_Sales'], height=600)
fig7.show()
fig7.write_html("/content/sample_data/fig7.html")

fig8 = px.pie(df_holder, names=df_holder['Company'],values=df_holder['Sales'], height=600)
fig8.show()
fig8.write_html("/content/sample_data/fig8.html")