kaggle in Korea(data둘러보기)

#How popular is kaggle in South Korea?

---

data 정제하기

df21_Ko = df21[df21['Q3'] == 'South Korea']
df21_Wo = df21[~(df21['Q3'] == 'South Korea')]
df21['region']=["Korea" if x == 'South Korea' else "World" for x in df21['Q3']]
df21['region'].value_counts()

df20_Ko = df20[df20['Q3'] == 'South Korea']
df20_Wo = df20[~(df20['Q3'] == 'South Korea')]
df20['region']=["Korea" if x == 'South Korea' else "World" for x in df20['Q3']]
df20['region'].value_counts()

df19_Ko = df19[df19['Q3'] == 'South Korea']
df19_Wo = df19[~(df19['Q3'] == 'South Korea')]
df19['region']=["Korea" if x == 'South Korea' else "World" for x in df19['Q3']]
df19['region'].value_counts()

df18_Ko = df18[df18['Q3'] == 'South Korea']
df18_Wo = df18[~(df18['Q3'] == 'South Korea')]
df18['region']=["Korea" if x == 'South Korea' else "World" for x in df18['Q3']]
df18['region'].value_counts()

df17_Ko = df17[df17['Country'] == 'South Korea']
df17_Wo = df17[~(df17['Country'] == 'South Korea')]
df17['region']=["Korea" if x == 'South Korea' else "World" for x in df17['Country']]
df17['region'].value_counts()

<<<<<<< HEAD

World 25615
Korea 359
Name: region, dtype: int64

World 19847
Korea 190
Name: region, dtype: int64

World 19536
Korea 182
Name: region, dtype: int64

World 23672
Korea 188
Name: region, dtype: int64

World 16522
Korea 194
Name: region, dtype: int64
=======

---

2021

• World 25615
• Korea 359
• Name: region, dtype: int64

2020

• World 19847
• Korea 190
• Name: region, dtype: int64

2019

• World 19536
• Korea 182
• Name: region, dtype: int64

2018

• World 23672
• Korea 188
• Name: region, dtype: int64

2017

• World 16522
• Korea 194
• Name: region, dtype: int64

---

trouble shooting

data 정제를 하다 보니 전체 data에서 korea가 1% 밖에 되지 않아 data set을 더 추가 하기로 했다.

##동아시아

Ref. East Asia

동아시아

• East Asia에는 대한민국, 일본, 중국, 타이완, 몽골, 북조선 총 6개의 국가가 속해 있다.
• 알 수 없지만, 18년도엔 타이완이 없다.

## East Asia에는 대한민국, 일본, 중국, 타이완, 몽골, 북조선 총 6개의 국가가 속해 있다. 
## 알 수 없지만, 18년도엔 타이완이 없다. 

EastAsia17 = ['China',"People 's Republic of China", 'Taiwan', 'South Korea', 'Japan']
EastAsia18= ['China', 'South Korea', 'Japan', 'Republic of Korea'] 
EastAsia19 = ['China','Taiwan', 'South Korea', 'Japan', 'Republic of Korea']
EastAsia20 = ['China','Taiwan', 'South Korea','Republic of Korea', 'Japan']
EastAsia21 = ['China','Taiwan', 'South Korea', 'Japan']


EastAsia = ['Republic of Korea','China','Taiwan', 'South Korea', 'Japan', "People 's Republic of China" ]

df21_Ea = df21[df21['Q3'].isin(EastAsia)]
df21_Wo = df21[~df21['Q3'].isin(EastAsia )]
df21['region']=["EastAsia" if x in EastAsia else "World" for x in df21['Q3']]

df20_Ea = df20[df20['Q3'].isin(EastAsia)]
df20_Wo = df20[~df20['Q3'].isin(EastAsia )]
df20['region']=["EastAsia" if x in EastAsia else "World" for x in df20['Q3']]

df19_Ea = df19[df19['Q3'].isin(EastAsia)]
df19_Wo = df19[~df19['Q3'].isin(EastAsia )]
df19['region']=["EastAsia" if x in EastAsia else "World" for x in df19['Q3']]

df18_Ea = df18[df18['Q3'].isin(EastAsia)]
df18_Wo = df18[~df18['Q3'].isin(EastAsia )]
df18['region']=["EastAsia" if x in EastAsia else "World" for x in df18['Q3']]

df17_Ea = df17[df17['Country'].isin(EastAsia)]
df17_Wo = df17[~df17['Country'].isin(EastAsia )]
df17['region']=["EastAsia" if x in EastAsia else "World" for x in df17['Country']]


#df21['region'].to_frame().value_counts().to_frame().rename(columns={'region': '21y', '' : 'count'})

21년도 를 .value_counts()로 뽑아 냈다.

1%대는 아니지만, 이제 10%대 data를 뽑아 냈다.

이것이 어떤 의미가 있을지 모르겠지만, 일단 주말동안 이 data로 궁금한 것을 Graph로 만들어 보자.

# % 계산을 위해 len() 을 통해 data 생성.

Ea21 = len(df21_Ea)
Wo21 = len(df21) - len(df21_Ea)

Ea20 = len(df20_Ea)
Wo20 = len(df20) - len(df20_Ea)

Ea19 = len(df19_Ea)
Wo19 = len(df19) - len(df19_Ea)

Ea18 = len(df18_Ea)
Wo18 = len(df18) -  len(df18_Ea)

Ea17 = len(df17_Ea)
Wo17 = len(df17) - len(df17_Ea)


def percent (a, b):
    result =a/(a+b)*100
    return result

def percentR (b, a):
    result =a/(a+b)*100
    return result

country = ['East Asia', 'Rest of the World']

years = ['2017', '2018', '2019', '2020', '2021']

fig = go.Figure(data=[
    go.Bar(name='Rest of the World', x=years, y=[percentR(Ea17, Wo17), percentR(Ea18, Wo18), percentR(Ea19, Wo19), 
                                                 percentR(Ea20, Wo20), percentR(Ea21, Wo21)]),
    go.Bar(name='East Asia', x=years, y=[percent(Ea17, Wo17), percent(Ea18, Wo18), percent(Ea19, Wo19), 
                                                 percent(Ea20, Wo20), percent(Ea21, Wo21)])
])

fig.update_layout(barmode='stack')
fig.show()

일단, plot은 뽑아 보았는데 이래도 되나 싶다 ^^ 하하

노동력을 더해서 대륙 별로 뽑던지 해야겠다 ㅂㄷㅂㄷ

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