게시 됨 업데이트 됨python - plotly한 시간안에 읽기 (약 7252 단어)

kaggle HeatMap

Kaggle 을 쓰는 East Asia의 사람들

  • Kaggle 정의
  • Kaggle 설문조사_개요
  • 그런데 우리는 EA에살아서 궁긍한 걸 찾아보자.

data import

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pylab as plt

import plotly.io as pio
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
pio.templates.default = "none"
# import plotly.offline as py
# py.offline.init_notebook_mode()

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

import warnings
warnings.filterwarnings("ignore")
/kaggle/input/kaggle-survey-2018/SurveySchema.csv
/kaggle/input/kaggle-survey-2018/freeFormResponses.csv
/kaggle/input/kaggle-survey-2018/multipleChoiceResponses.csv
/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv
/kaggle/input/kaggle-survey-2020/supplementary_data/kaggle_survey_2020_methodology.pdf
/kaggle/input/kaggle-survey-2020/supplementary_data/kaggle_survey_2020_answer_choices.pdf
/kaggle/input/kaggle-survey-2021/kaggle_survey_2021_responses.csv
/kaggle/input/kaggle-survey-2021/supplementary_data/kaggle_survey_2021_methodology.pdf
/kaggle/input/kaggle-survey-2021/supplementary_data/kaggle_survey_2021_answer_choices.pdf
/kaggle/input/kaggle-survey-2019/survey_schema.csv
/kaggle/input/kaggle-survey-2019/multiple_choice_responses.csv
/kaggle/input/kaggle-survey-2019/other_text_responses.csv
/kaggle/input/kaggle-survey-2019/questions_only.csv
/kaggle/input/kaggle-survey-2017/freeformResponses.csv
/kaggle/input/kaggle-survey-2017/schema.csv
/kaggle/input/kaggle-survey-2017/RespondentTypeREADME.txt
/kaggle/input/kaggle-survey-2017/multipleChoiceResponses.csv
/kaggle/input/kaggle-survey-2017/conversionRates.csv

1
2
3
4
5
6
df17= pd.read_csv("/kaggle/input/kaggle-survey-2017/multipleChoiceResponses.csv", encoding="ISO-8859-1")
df18= pd.read_csv("/kaggle/input/kaggle-survey-2018/multipleChoiceResponses.csv", )
df19= pd.read_csv("/kaggle/input/kaggle-survey-2019/multiple_choice_responses.csv", )
df20= pd.read_csv("/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv", )
df21= pd.read_csv("/kaggle/input/kaggle-survey-2021/kaggle_survey_2021_responses.csv", )

EastAsia VS World

1. data 전처리

1.1 EastAsia / World 나누기

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
#질문 제거하기, replace
df17= df17.iloc[1:, :].replace("People 's Republic of China",'China')
df18= df18.iloc[1:, :].replace('Republic of Korea','South Korea')
df19= df19.iloc[1:, :].replace('Republic of Korea','South Korea')
df20= df20.iloc[1:, :].replace('Republic of Korea','South Korea')
df21= df21.iloc[1:, :]


## 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" ]

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

#East Asia 뽑기
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'})
print('OK')
OK

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
# 나라별 data 뽑기
df21_Ch= df21_Ea[df21_Ea['Q3'] == 'China']
df21_Tw= df21_Ea[df21_Ea['Q3'] == 'South Korea']
df21_Ko= df21_Ea[df21_Ea['Q3'] == 'Taiwan']
df21_Jp= df21_Ea[df21_Ea['Q3'] == 'Japan']

df20_Ch= df20_Ea[df20_Ea['Q3'] == 'China']
df20_Tw= df20_Ea[df20_Ea['Q3'] == 'South Korea']
df20_Ko= df20_Ea[df20_Ea['Q3'] == 'Taiwan']
df20_Jp= df20_Ea[df20_Ea['Q3'] == 'Japan']

df19_Ch= df19_Ea[df19_Ea['Q3'] == 'China']
df19_Tw= df19_Ea[df19_Ea['Q3'] == 'South Korea']
df19_Ko= df19_Ea[df19_Ea['Q3'] == 'Taiwan']
df19_Jp= df19_Ea[df19_Ea['Q3'] == 'Japan']

df18_Ch= df18_Ea[df18_Ea['Q3'] == 'China']
df18_Tw= df18_Ea[df18_Ea['Q3'] == 'South Korea']
df18_Jp= df18_Ea[df18_Ea['Q3'] == 'Japan']

df17_Ch= df17_Ea[df17_Ea['Country'] == 'China']
df17_Tw= df17_Ea[df17_Ea['Country'] == 'South Korea']
df17_Ko= df17_Ea[df17_Ea['Country'] == 'Taiwan']
df17_Jp= df17_Ea[df17_Ea['Country'] == 'Japan']

2. Kaggle 사용자수 (W/Ea)

2.1 data 전처리

2.2 그래프 그리기

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
def world_map(locations,counts,title):
    data = [ dict(
            type = 'choropleth',
            locations = locations,
            z = counts,
            colorscale = 'Blues',
            locationmode = 'country names',
            autocolorscale = False,
            reversescale = True,
            marker = dict(
                line = dict(color = '#F7F7F7', width = 1.5)),
                colorbar = dict(autotick = True, legth = 3, len=0.75, title = 'respodents',
                               max = 1000, min = 0)
                )
           ]
    layout = dict(
        title = title,
        titlefont={'size': 28, 'family': 'san serif'},
        width=750, 
        height=475,
        paper_bgcolor='#F7F7F7',
        geo = dict(
            showframe = True,
            showcoastlines = True,
            fitbounds="locations",
            )
    )
    
    fig = dict(data=data, layout=layout)
    iplot(fig, validate=False, filename='world-map')
    
z = df21_Ea['Q3'].value_counts()
 
## 메서드 호출
world_map(locations=z.index, counts=z.values, title= '<b> EastAsia Countries (2021 survey) <b>')
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
# 수치 bar g = 사용자 수 비교.

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)

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

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

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

percent = [percent(Ea17, Wo17), percent(Ea18, Wo18), percent(Ea19, Wo19), 
                                                 percent(Ea20, Wo20), percent(Ea21, Wo21)]

# percentR = [percentR(Ea17, Wo17), percentR(Ea18, Wo18), percentR(Ea19, Wo19), 
#                                                  percentR(Ea20, Wo20), percentR(Ea21, Wo21)]
fig = go.Figure()
fig.add_trace(go.Bar(x=years, y=[len(df17), len(df18), len(df19), len(df20), len(df21)],
                base=[-len(df17), -len(df18), -len(df19), -len(df20), -len(df21)],
                marker_color='lightslategrey',
                name='World',
                textposition='outside',
                hovertemplate='<b>KaggleUser</b>: %{x}<br>'+
                                '<b>Count</b>: %{y}'
                
                ))
fig.add_trace(go.Bar(x=years, y=[Ea17, Ea18, Ea19, Ea20, Ea21],
                base=0,
                marker_color='pink',
                name='East Asia',
                text= percent,
                texttemplate='%{text}  %', 
                textposition='outside',
                hovertemplate='<b>KaggleUser</b>: %{x}<br>'+
                                '<b>Count</b>: %{y}',
                textfont_size=12                
                ))
fig.update_layout(width=600, height=700)
fig.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
from plotly.subplots import make_subplots
import plotly.graph_objects as go

total17 = (
    df17['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index()
)
total18 = (
    df18['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index()
)
total19 = (
    df19['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index()
)
total20 = (
    df20['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index()
)
total21 = (
    df21['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index()
)



# Create subplots: use 'domain' type for Pie subplot
fig = make_subplots(rows=1, cols=5, specs=[[{'type':'domain'}, {'type':'domain'}, {'type':'domain'}, {'type':'domain'}, {'type':'domain'}]],
                   subplot_titles=("2017", "2018", "2019", "2020", "2021"))
fig.add_trace(go.Pie(labels=total21['type'], values=total21['respodents'], name="2021", scalegroup='one'),
              1, 1)
fig.add_trace(go.Pie(labels=total20['type'], values=total20['respodents'], name="2020", scalegroup='one'),
              1, 2)
fig.add_trace(go.Pie(labels=total19['type'], values=total19['respodents'], name="2019", scalegroup='one'),
              1, 3)
fig.add_trace(go.Pie(labels=total18['type'], values=total18['respodents'], name="2018", scalegroup='one'),
              1, 4)
fig.add_trace(go.Pie(labels=total17['type'], values=total17['respodents'], name="2017", scalegroup='one'),
              1, 5)

# Use `hole` to create a donut-like pie chart
fig.update_traces(hole=.2, hoverinfo="label+percent+name")
fig.update_layout(
    title_text="<b>World vs EastAsia</b>",
    # Add annotations in the center of the donut pies.
   )
fig.show()

가설은 시간에따라서 응답자수가 증가 할 줄 알았는데 결과를 보니 오히려 감소하는 경향을 볼 수 있다.

East Asia 응답자는 2020년도(10.5%)에 가장 많았고 2121년도(7.15%)로 가장 적엇다.

0. Kaggle Gender (in Ea)

0.1 data 전처리

0.2 그래프 그리기

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63


df21_Ea=df21[df21['Q3'].isin(EastAsia21)]
Ea21= (
    df21_Ea['Q3'].value_counts().to_frame()
    .reset_index().rename(columns={'index':'Country', 'Q3':'21'}))


df20_Ea=df20[df20['Q3'].isin(EastAsia)]
Ea20= (
    df20_Ea['Q3'].replace('Republic of Korea','South Korea')
    .value_counts().to_frame().reset_index()
    .rename(columns={'index':'Country', 'Q3':'20'}))


df19_Ea=df19[df19['Q3'].isin(EastAsia)]
Ea19= (df19_Ea['Q3'].replace('Republic of Korea','South Korea')
       .value_counts().to_frame().reset_index()
       .rename(columns={'index':'Country', 'Q3':'19'}))


df18_Ea=df18[df18['Q3'].isin(EastAsia)]
Ea18= (df18_Ea['Q3'].replace('Republic of Korea','South Korea')
       .value_counts().to_frame().reset_index()
       .rename(columns={'index':'Country', 'Q3':'18'}))
Ea18.value_counts()
#df18 열에 taiwan = 0을 추가 해야 합니다. 


df17_Ea = df17[df17['Country'].isin(EastAsia)]
Ea17= (df17_Ea['Country'].replace("People 's Republic of China",'China')
       .value_counts().to_frame().reset_index()
       .rename(columns={'index':'Country', 'Country':'17'}))

#data를 합쳐서 하나의 dataframe으로 만들어 줌.

df5years = pd.merge(Ea17, Ea18, on='Country', how='outer')
df5year =pd.merge(Ea19,Ea20, on='Country', how='outer')
df5year=pd.merge(df5year, Ea21, on='Country', how='outer')

df5years = pd.merge(df5years, df5year, on='Country', how='outer')





fig = go.Figure(data=[
    go.Bar(name='2017', x=df5years['Country'], y=df5years['17']),
    go.Bar(name='2018', x=df5years['Country'], y=df5years['18']),
    go.Bar(name='2019', x=df5years['Country'], y=df5years['19']),
    go.Bar(name='2020', x=df5years['Country'], y=df5years['20']),
    go.Bar(name='2021', x=df5years['Country'], y=df5years['21'])
])

#Change the bar mode
fig.update_layout(barmode='stack', title='연도별 동아시아 Kaggle 사용자수'
                  )
fig.show()



# Text : percent

5년간 china의 Kaggle 응답자 수가 가장 많았다. taiwan이 가장 적지만, 2018년도에 어떤이유인지 모르겠지만, china로 결과 값이 흡수 된 것 같다.

south korea나 Taiwan의 응답자수는 china의 절반에 미치지 못한다.

  • 인구수 대비 몇 % 가 많다.

3. Kaggle Gender (W/Ea)

3.1 data 전처리

3.2 그래프 그리기

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
Gender_17 = (
    df17['GenderSelect']
    .replace(['A different identity', 'Prefer to self-describe', 'Non-binary, genderqueer, or gender non-conforming'], 'Others')
    .fillna('Others')
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'GenderSelect':'Gender'})
    .groupby('type')
    .sum()
    .reset_index()
)
Gender_18 = (
    df18['Q1']
    .replace(['Prefer not to say', 'Prefer to self-describe'], 'Others')
    .fillna('Others')
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'Q1':'Gender'})
    .groupby('type')
    .sum()
    .reset_index()
)
Gender_19 = (
    df19['Q2']
    .replace(['Prefer not to say','Prefer to self-describe'],'Others')
    .fillna('Others')
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'Q2':'Gender'})
    .groupby('type')
    .sum()
    .reset_index()
)
Gender_20 = (
    df20['Q2']
    .replace(['Prefer not to say', 'Prefer to self-describe', 'Nonbinary'], 'Others')
    .replace(['Man', 'Woman'], ['Male', 'Female'])
    .fillna('Others')
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'Q2':'Gender'})
    .groupby('type')
    .sum()
    .reset_index()
)
Gender_21 = (
    df21['Q2']
    .replace(['Prefer not to say', 'Prefer to self-describe', 'Nonbinary'], 'Others')
    .replace(['Man', 'Woman'], ['Male', 'Female'])
    .fillna('Others')
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'Q2':'Gender'})
    .groupby('type')
    .sum()
    .reset_index()
)

# Create subplots: use 'domain' type for Pie subplot
fig = make_subplots(rows=1, cols=5, specs=[[{'type':'domain'}, {'type':'domain'}, {'type':'domain'}, {'type':'domain'}, {'type':'domain'}]],
                   subplot_titles=("2017", "2018", "2019", "2020", "2021"))
fig.add_trace(go.Pie(labels=Gender_21['type'], values=Gender_21['Gender'], name="2021", scalegroup='one'),
              1, 5)
fig.add_trace(go.Pie(labels=Gender_20['type'], values=Gender_20['Gender'], name="2020", scalegroup='one'),
              1, 4)
fig.add_trace(go.Pie(labels=Gender_19['type'], values=Gender_19['Gender'], name="2019", scalegroup='one'),
              1, 3)
fig.add_trace(go.Pie(labels=Gender_18['type'], values=Gender_18['Gender'], name="2018", scalegroup='one'),
              1, 2)
fig.add_trace(go.Pie(labels=Gender_17['type'], values=Gender_17['Gender'], name="2017", scalegroup='one'),
              1, 1)

# Use `hole` to create a donut-like pie chart
fig.update_traces(hole=.2, hoverinfo="label+percent+name")
fig.update_layout(
    title_text="<b>World_Gender</b>",
    # Add annotations in the center of the donut pies.
   )
fig.show()

연도별, 지역별로 보았을때, 여성의 비율이 20% 미만으로 적은 것을 알 수 있다.

2019년 이전보다 2020년 이후가 증가 했다. (16%-> 19%)

0. Kaggle job (W/Ea)

0.1 data 전처리

0.2 그래프 그리기

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
#data 확인
Data_Analyst =['Data Analyst','Data Miner,Information technology','Data Miner',
               'Predictive Modeler','Information technology, networking, or system administration', 
              'A business discipline (accounting, economics, finance, etc.)', 'Business Analyst', 'Humanities',
                'Statistician', 'Mathematics or statistics', 'Medical or life sciences (biology, chemistry, medicine, etc.)', 
                'Physics or astronomy', 'Research Scientist', 'Researcher', 'Social sciences (anthropology, psychology, sociology, etc.)', 
                'Humanities (history, literature, philosophy, etc.)']
Data_Scientist =['Data Scientist', 'Environmental science or geology', 
                'Machine Learning Engineer', 'Scientist/Researcher']
Developer=['Developer Relations/Advocacy','Data Engineer','Engineer','Engineering (non-computer focused)',
           'Programmer','Software Engineer', 'Computer Scientist','Computer science (software engineering, etc.)', 
           'Fine arts or performing arts','Product Manager', 'Software Developer/Software Engineer',
           'Product/Project Manager','Program/Project Manager','DBA/Database Engineer']
Not_Employeed =['Currently not employed', 'Not employed', 'Student']
Others = ['I never declared a major', 'Other']


#연도별로 뽑은 나라별 직업.
df21job_Ea = df21_Ea.loc[:,['Q3','Q5']].reset_index().rename(columns={'index':'job', 'Q5':'2021'}).fillna('Other')
df20job_Ea = df20_Ea.loc[:,['Q3','Q5']].reset_index().rename(columns={'index':'job', 'Q5':'2020'}).fillna('Other')
df19job_Ea = df19_Ea.loc[:,['Q3','Q5']].reset_index().rename(columns={'index':'job', 'Q5':'2019'}).fillna('Other')
df18job_Ea = df18_Ea.loc[:,['Q3','Q5']].reset_index().rename(columns={'index':'job', 'Q5':'2018'}).fillna('Other')
df17job_Ea = df17_Ea.loc[:,['Country','CurrentJobTitleSelect']].reset_index().rename(columns={'index':'job', 'CurrentJobTitleSelect':'2017'}).fillna('Other')

# 연도별 job Grouping in east asia

df21job_Ea.value_counts('2021')
df21job_Ea['JOB']=["Data Analyst" if x in Data_Analyst
                   else "Data Scientist" if x in Data_Scientist # Data Scientist
                   else "Data Engineer" if x in Developer
                    else "NotEmployeed" if x in Not_Employeed
                   else "Others" 
                   for x in df21job_Ea['2021']]
df21job_Ea.value_counts('JOB')

df20job_Ea.value_counts('2020')
df20job_Ea['JOB']=["Data Analyst" if x in Data_Analyst
                   else "Data Scientist" if x in Data_Scientist 
                   else "Data Engineer" if x in Developer
                    else "NotEmployeed" if x in Not_Employeed
                   else "Other"
                   for x in df20job_Ea['2020']]
df20job_Ea[['2020','JOB']]

df19job_Ea.value_counts('2019')
df19job_Ea['JOB']=["Data Analyst" if x in Data_Analyst
                   else "Data Scientist" if x in Data_Scientist 
                   else "Data Engineer" if x in Developer
                    else "NotEmployeed" if x in Not_Employeed
                    else "Other"
                   for x in df19job_Ea['2019']]


#2019 data에   "Other" grouping이 제대로 이루어 졌는지 확인 
df19jobTest = df19job_Ea.loc[df19job_Ea.JOB == 'Other']
df19jobTest['2019'].value_counts()


df18job_Ea.value_counts('2018')
df18job_Ea['JOB']=["Data Analyst" if x in Data_Analyst
                   else "Data Scientist" if x in Data_Scientist 
                   else "Data Engineer" if x in Developer
                    else "NotEmployeed" if x in Not_Employeed
                    else "Other"
                   for x in df18job_Ea['2018']]

df18jobTest = df18job_Ea.loc[df18job_Ea.JOB == 'Other']
df18jobTest['2018'].value_counts()


df17job_Ea.value_counts('2017')
df17job_Ea['JOB']=["Data Analyst" if x in Data_Analyst
                   else "Data Scientist" if x in Data_Scientist 
                   else "Data Engineer" if x in Developer
                    else "NotEmployeed" if x in Not_Employeed
                    else "Other"
                   for x in df17job_Ea['2017']]

df17jobTest = df17job_Ea.loc[df17job_Ea.JOB == 'Other']
df17jobTest['2017'].value_counts()


df21jobTest = df21job_Ea.loc[df21job_Ea.JOB == 'Other']
df21jobTest['2021'].head()
df21job_Ea.value_counts('JOB')

#data frame 정리
dfjob21 =df21job_Ea.groupby(['Q3','JOB']).size().reset_index().rename(columns = {0:"Count"}).rename(columns={'Q3':'country', 'JOB':'2021'})
dfjob20 =df20job_Ea.groupby(['Q3','JOB']).size().reset_index().rename(columns = {0:"Count"}).rename(columns={'Q3':'country', 'JOB':'2020'})
dfjob19 =df19job_Ea.groupby(['Q3','JOB']).size().reset_index().rename(columns = {0:"Count"}).rename(columns={'Q3':'country', 'JOB':'2019'})
dfjob18 =df18job_Ea.groupby(['Q3','JOB']).size().reset_index().rename(columns = {0:"Count"}).rename(columns={'Q3':'country', 'JOB':'2018'})
dfjob17 =df17job_Ea.groupby(['Country','JOB']).size().reset_index().rename(columns = {0:"Count"}).rename(columns={'Country':'country', 'JOB':'2017'})

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
#21년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfjob21.groupby('country'):
   fig.add_trace(go.Bar(
       x = group['2021'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2021_나라별 직업 수',
                 width=700,
                 height=450
                 )
fig.show()

#20년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfjob20.groupby('country'):
   fig.add_trace(go.Bar(
       x = group['2020'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2020_나라별 직업 수',
                 width=700,
                 height=450)

fig.show()

#19년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfjob19.groupby('country'):
   fig.add_trace(go.Bar(
       x = group['2019'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2019_나라별 직업 수',
                 width=700,
                 height=450
                 )

fig.show()

#18년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfjob18.groupby('country'):
   fig.add_trace(go.Bar(
       x = group['2018'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2018_나라별 직업 수', width=700,
                 height=450
                 )
fig.show()


#17년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfjob17.groupby('country'):
   fig.add_trace(go.Bar(
       x = group['2017'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2017_나라별 직업 수',
                 width=700,
                 height=450
                 )
fig.show()

0. Kaggle age & Edu (W/Ea)

0.1 data 전처리

0.2 그래프 그리기

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
# 연도별 나이 
df21Age_Ea = df21_Ea.loc[:,['Q3','Q1']].reset_index().rename(columns={'Q3':'East_Asia', 'Q1':'2021'}).fillna('etc')
df20Age_Ea = df20_Ea.loc[:,['Q3','Q1']].reset_index().rename(columns={'Q3':'East_Asia', 'Q1':'2020'}).fillna('etc')
df19Age_Ea = df19_Ea.loc[:,['Q3','Q1']].reset_index().rename(columns={'Q3':'East_Asia', 'Q1':'2019'}).fillna('etc')
df18Age_Ea = df18_Ea.loc[:,['Q3','Q2']].reset_index().rename(columns={'Q3':'East_Asia', 'Q2':'2018'}).fillna('etc')
df17Age_Ea = df17_Ea.loc[:,['Country','Age']].reset_index().rename(columns={'Country':'East_Asia', 'Age':'2017'}).fillna('etc')

#data frame 정리
dfAge21 =df21Age_Ea.groupby(['East_Asia','2021']).size().reset_index().rename(columns = {0:"Count"})
dfAge20 =df20Age_Ea.groupby(['East_Asia','2020']).size().reset_index().rename(columns = {0:"Count"})
dfAge19 =df19Age_Ea.groupby(['East_Asia','2019']).size().reset_index().rename(columns = {0:"Count"})
dfAge18 =df18Age_Ea.groupby(['East_Asia','2018']).size().reset_index().rename(columns = {0:"Count"})
dfAge17 =(df17Age_Ea.groupby(['East_Asia','2017'])
          .size().reset_index().rename(columns = {0:"Count"}))

#2017data
# array([16.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0,
#        28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0,
#        39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 47.0, 50.0, 54.0, 100.0,
#        'etc', 46.0, 48.0, 49.0, 51.0, 52.0, 53.0, 55.0, 57.0, 58.0, 59.0,
#        62.0, 64.0, 65.0, 67.0, 68.0, 70.0, 17.0, 56.0, 60.0], dtype=object)


#21년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfAge21.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2021'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2021_나라별 연령')
fig.show()

#20년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfAge20.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2020'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2020_나라별 연령')

fig.show()

#19년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfAge19.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2019'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2019_나라별 연령')

fig.show()


#18년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfAge18.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2018'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2018_나라별 연령')

fig.show()


#17년도 Bar graph 그리기_ Scatter 로 변경
fig = go.Figure()

for country, group  in dfAge17.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2017'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2017_나라별 연령')

fig.show()


0.2 Kaggle age (Ea)

0.1.1 data 전처리

0.2.1 그래프 그리기

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
#data frame 정리

dfAge21_percent =df21Age_Ea.groupby(['East_Asia','2021']).size().reset_index().rename(columns = {0:"Count"})
dfAge21_percent['percent'] =((dfAge21_percent['Count'] / len(df21Age_Ea))*100).round(2)
dfAge21_percent['percent_str'] =((dfAge21_percent['Count'] / len(df21Age_Ea))*100).round(2).astype(str) + '%'
# dfAge21['percent'] = ((media['Count'] / len(df))*100).round(2).astype(str) + '%'
# dfAge_percent21=dfAge21.value_counts('East_Asia',normalize=True).mul(100).round(1).astype(str)
dfAge21_percent


# 나라별 연령대 비율 in East Asia
fig = go.Figure()
for country, group  in dfAge21_percent.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2021'], y = group['percent'], name = country, text=group['percent_str']
   ))
fig.update_layout(barmode="stack",
                 plot_bgcolor = "white",
                 title='2021_ 연령별 나라 비율 in East Asia')
fig.show()


# 연령별 나라 비율 in East Asia // 다중 pie 그래프로 바꾸기 
fig = go.Figure()

for country, group  in dfAge21_percent.groupby('2021'):
   fig.add_trace(go.Bar(
       x = group['East_Asia'], y =group['percent'], text=dfAge21_percent['percent_str']
   ))
fig.update_layout(barmode="stack",
                 plot_bgcolor = "white",
                 title='2021_ 나라별 연령 비율 in East Asia')
fig.show()

연도별 연령

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
# 나라별로 연도가 나왓으면 좋겠어요 .

z = df21Age_Ea.groupby(['East_Asia', '2021']).size().unstack().fillna(0).astype('int64')
z_data = z.apply(lambda x:np.round(x/x.sum(), 2), axis = 1).to_numpy() # convert to correlation matrix
x = z.columns.tolist()
y = z.index.tolist()

fig21 = ff.create_annotated_heatmap(z_data, x = x, y = y, colorscale = "sunset")

z = df20Age_Ea.groupby(['East_Asia', '2020']).size().unstack().fillna(0).astype('int64')
z_data = z.apply(lambda x:np.round(x/x.sum(), 2), axis = 1).to_numpy() # convert to correlation matrix
x = z.columns.tolist()
y = z.index.tolist()

fig20 = ff.create_annotated_heatmap(z_data, x = x, y = y, colorscale = "sunset")


z = df19Age_Ea.groupby(['East_Asia', '2019']).size().unstack().fillna(0).astype('int64')
z_data = z.apply(lambda x:np.round(x/x.sum(), 2), axis = 1).to_numpy() # convert to correlation matrix
x = z.columns.tolist()
y = z.index.tolist()

fig19 = ff.create_annotated_heatmap(z_data, x = x, y = y, colorscale = "sunset")


z = df18Age_Ea.groupby(['East_Asia', '2018']).size().unstack().fillna(0).astype('int64')
z_data = z.apply(lambda x:np.round(x/x.sum(), 2), axis = 1).to_numpy() # convert to correlation matrix
x = z.columns.tolist()
y = z.index.tolist()

fig18 = ff.create_annotated_heatmap(z_data, x = x, y = y, colorscale = "sunset")


z = df17Age_Ea.groupby(['East_Asia', '2017']).size().unstack().fillna(0).astype('int64')
z_data = z.apply(lambda x:np.round(x/x.sum(), 2), axis = 1).to_numpy() # convert to correlation matrix
x = z.columns.tolist()
y = z.index.tolist()

fig17 = ff.create_annotated_heatmap(z_data, x = x, y = y, colorscale = "sunset")



fig21.show()
fig20.show()
fig19.show()
fig18.show()

연령별 지역

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
# 연령-지역 %
dfKo_Age21= df21Age_Ea[df21Age_Ea['East_Asia']=='South Korea']
dfKo_Age21_per=dfKo_Age21['2021'].value_counts().to_frame().reset_index()
dfKo_Age21_per['South Korea']=((dfKo_Age21_per['2021'] / len(dfKo_Age21))*100).round(2)

dfTw_Age21= df21Age_Ea[df21Age_Ea['East_Asia']=='Taiwan']
dfTw_Age21_per=dfTw_Age21['2021'].value_counts().to_frame().reset_index()
dfTw_Age21_per['Taiwan']=((dfTw_Age21_per['2021'] / len(dfTw_Age21))*100).round(2)
dfTw_Age21_per

dfCh_Age21= df21Age_Ea[df21Age_Ea['East_Asia']=='China']
dfCh_Age21_per=dfCh_Age21['2021'].value_counts().to_frame().reset_index()
dfCh_Age21_per['China']=((dfCh_Age21_per['2021'] / len(dfCh_Age21))*100).round(2)
dfCh_Age21_per

df21Age_Ea.head()
dfJp_Age21= df21Age_Ea[df21Age_Ea['East_Asia']=='Japan']
dfJp_Age21_per=dfJp_Age21['2021'].value_counts().to_frame().reset_index()
dfJp_Age21_per['Japan']=((dfJp_Age21_per['2021'] / len(dfJp_Age21))*100).round(2)
dfJp_Age21_per



#g 그리기(heatMap)
merge1= pd.merge(dfKo_Age21_per,dfTw_Age21_per, on='index', how='outer')
merge2= pd.merge(dfCh_Age21_per,dfJp_Age21_per, on='index', how='outer')
merge= pd.merge(merge1,merge2, on='index', how='outer').fillna(0).sort_values(by=['index'],ascending=True)

merge.iloc[:,[2,4,6,8]]
merge.iloc[:,[2,4,6,8]].to_numpy()



fig = go.Figure(data=go.Heatmap(
                   z=merge.iloc[:,[2,4,6,8]].to_numpy(),
                   x=['South Korea','Taiwan','China','Japan'],
                   y=merge.sort_values(by=['index'],ascending=True)['index'].tolist(),
                   hoverongaps = False,
                   opacity=1.0, xgap=2.5, ygap=2.5, colorscale='orrd'),
                   )
fig.show()

0.2 Kaggle Edu (Ea)

0.1.1 data 전처리

0.2.1 그래프 그리기

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
# 연도별 학력
df21Edu_Ea = df21_Ea.loc[:,['Q3','Q4']].reset_index().rename(columns={'Q3':'East_Asia', 'Q4':'2021'}).fillna('etc')
df20Edu_Ea = df20_Ea.loc[:,['Q3','Q4']].reset_index().rename(columns={'Q3':'East_Asia', 'Q4':'2020'}).fillna('etc')
df19Edu_Ea = df19_Ea.loc[:,['Q3','Q4']].reset_index().rename(columns={'Q3':'East_Asia', 'Q4':'2019'}).fillna('etc')
df18Edu_Ea = df18_Ea.loc[:,['Q3','Q4']].reset_index().rename(columns={'Q3':'East_Asia', 'Q4':'2018'}).fillna('etc')
df17Edu_Ea = df17_Ea.loc[:,['Country','FormalEducation']].reset_index().rename(columns={'Country':'East_Asia', 'FormalEducation':'2017'}).fillna('etc')

df21Edu_Ea =df21Edu_Ea.replace({'I prefer not to answer':'etc'}).sort_values(by='2021', ascending=False)
df20Edu_Ea =df20Edu_Ea.replace({'I prefer not to answer':'etc'}).sort_values(by='2020', ascending=False)
df19Edu_Ea =df19Edu_Ea.replace({'I prefer not to answer':'etc'}).sort_values(by='2019', ascending=False)
df18Edu_Ea =df18Edu_Ea.replace({'I prefer not to answer':'etc'}).sort_values(by='2018', ascending=False)
df17Edu_Ea =df17Edu_Ea.replace({'I prefer not to answer':'etc'}).sort_values(by='2017', ascending=False)

#data frame 정리
dfEdu21 =df21Edu_Ea.groupby(['East_Asia','2021']).size().reset_index().rename(columns = {0:"Count"})
dfEdu20 =df20Edu_Ea.groupby(['East_Asia','2020']).size().reset_index().rename(columns = {0:"Count"})
dfEdu19 =df19Edu_Ea.groupby(['East_Asia','2019']).size().reset_index().rename(columns = {0:"Count"})
dfEdu18 =df18Edu_Ea.groupby(['East_Asia','2018']).size().reset_index().rename(columns = {0:"Count"})
dfEdu17 =(df17Edu_Ea.groupby(['East_Asia','2017'])
          .size().reset_index().rename(columns = {0:"Count"}))

# 비율 data 추가
##21
dfEdu21_percent =df21Edu_Ea.groupby(['East_Asia','2021']).size().reset_index().rename(columns = {0:"Count"})
dfEdu21_percent['percent'] =((dfEdu21_percent['Count'] / len(df21Edu_Ea))*100).round(2)
dfEdu21_percent['percent_str'] =((dfEdu21_percent['Count'] / len(df21Edu_Ea))*100).round(2).astype(str) + '%'
##20
dfEdu20_percent =df20Edu_Ea.groupby(['East_Asia','2020']).size().reset_index().rename(columns = {0:"Count"})
dfEdu20_percent['percent'] =((dfEdu20_percent['Count'] / len(df20Edu_Ea))*100).round(2)
dfEdu20_percent['percent_str'] =((dfEdu20_percent['Count'] / len(df20Edu_Ea))*100).round(2).astype(str) + '%'
##19
dfEdu19_percent =df19Edu_Ea.groupby(['East_Asia','2019']).size().reset_index().rename(columns = {0:"Count"})
dfEdu19_percent['percent'] =((dfEdu19_percent['Count'] / len(df19Edu_Ea))*100).round(2)
dfEdu19_percent['percent_str'] =((dfEdu19_percent['Count'] / len(df19Edu_Ea))*100).round(2).astype(str) + '%'
##18
dfEdu18_percent =df18Edu_Ea.groupby(['East_Asia','2018']).size().reset_index().rename(columns = {0:"Count"})
dfEdu18_percent['percent'] =((dfEdu18_percent['Count'] / len(df18Edu_Ea))*100).round(2)
dfEdu18_percent['percent_str'] =((dfEdu18_percent['Count'] / len(df18Edu_Ea))*100).round(2).astype(str) + '%'
##19
dfEdu17_percent =df17Edu_Ea.groupby(['East_Asia','2017']).size().reset_index().rename(columns = {0:"Count"})
dfEdu17_percent['percent'] =((dfEdu17_percent['Count'] / len(df17Edu_Ea))*100).round(2)
dfEdu17_percent['percent_str'] =((dfEdu17_percent['Count'] / len(df17Edu_Ea))*100).round(2).astype(str) + '%'


1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67

#21년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfEdu21_percent.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2021'], y = group['percent'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2021_나라별 학력')
fig.show()

#20년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfEdu20_percent.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2020'], y = group['percent'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2020_나라별 학력')

fig.show()

#19년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfEdu19_percent.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2019'], y = group['percent'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2019_나라별 학력')

fig.show()


#18년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfEdu18_percent.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2018'], y = group['percent'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2018_나라별 학력')

fig.show()


#17년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfEdu17_percent.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2017'], y = group['percent'], name = country
   ))
fig.update_layout(barmode="group", 
                 plot_bgcolor = "white",
                 title='2017_나라별 학력')

fig.show()

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67

#21년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfEdu21_percent.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2021'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="stack", 
                 plot_bgcolor = "white",
                 title='2021_나라별 학력 비율 ')
fig.show()

#20년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfEdu20_percent.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2020'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="stack", 
                 plot_bgcolor = "white",
                 title='2020_나라별 학력 비율 ')

fig.show()

#19년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfEdu19_percent.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2019'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="stack", 
                 plot_bgcolor = "white",
                 title='2019_나라별 학력 비율 ')

fig.show()


#18년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfEdu18_percent.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2018'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="stack", 
                 plot_bgcolor = "white",
                 title='2018_나라별 학력 비율  ')

fig.show()


#17년도 Bar graph 그리기
fig = go.Figure()

for country, group  in dfEdu17_percent.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2017'], y = group['Count'], name = country
   ))
fig.update_layout(barmode="stack", 
                 plot_bgcolor = "white",
                 title='2017_나라별 학력 비율 ')

fig.show()

1
2
3
4
5
6
7
8

z = df21_Ea.groupby(['Q4', 'Q1']).size().unstack().fillna(0).astype('int64')
z_data = z.apply(lambda x:np.round(x/x.sum(), 2), axis = 1).to_numpy() # convert to correlation matrix
x = z.columns.tolist()
y = z.index.tolist()

fig = ff.create_annotated_heatmap(z_data, x = x, y = y, colorscale = "sunset")
fig.show()

경력

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
#전체 코드
_3year = ['I have never written code', '< 1 years', '1-3 years']
_5year = ['3-5 years ','5-10 years']
_10year = ['10-20 years','20+ years']

df21_3year = df21['Q6'][df21['Q6'].isin(_3year)]
df21_5year = df21['Q6'][df21['Q6'].isin(_5year)]
df21_10year = df21['Q6'][df21['Q6'].isin(_10year)]

df21_3year.count()
df21_5year.count()
df21_10year.count()

years =['_3year','_5year', '_10year']
values =[df21_3year.count(),
         df21_5year.count(),
        df21_10year.count()]

fig = go.Figure(data=[
    go.Bar(name='21년 World kaggler들의 경력', x=years, y=values ,orientation='v'),])

fig.update_layout(title_text="<b>21년 World kaggler들의 경력</b>",title_font_size=35)

fig.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
#최종 합친 코드
_3year = ['I have never written code', '< 1 years', '1-3 years']
_5year = ['3-5 years ','5-10 years']
_10year = ['10-20 years','20+ years']

df21_Ea_3year = df21_Ea['Q3'][df21_Ea['Q6'].isin(_3year)].value_counts().to_frame().rename(columns = {'Q3':'3year'})
df21_Ea_5year = df21_Ea['Q3'][df21_Ea['Q6'].isin(_5year)].value_counts().to_frame().rename(columns = {'Q3':'5year'})
df21_Ea_10year = df21_Ea['Q3'][df21_Ea['Q6'].isin(_10year)].value_counts().to_frame().rename(columns = {'Q3':'10year'})

career=(df21_Ea_3year.join(df21_Ea_5year).join(df21_Ea_10year))
career

career.iloc[0,0:3] #China
career.iloc[1,0:3] #Japan
career.iloc[2,0:3] #South Korea
career.iloc[3,0:3] #Taiwan

fig = go.Figure(data=[
    go.Bar(name='China', x = years, 
                           y=career.iloc[0,0:3]),
    go.Bar(name='Japan', x = years, 
                             y=career.iloc[1,0:3]),
    go.Bar(name='South Korea', x= years, 
                            y=career.iloc[2,0:3]),
    go.Bar(name='Taiwan', x= years, 
                            y=career.iloc[3,0:3])
    ])

fig.update_layout(title_text="<b>21년 EastAisa kaggler들의 경력</b>",title_font_size=35)
fig.show()

연봉

1
2
3
4
5
6
7
8
9
10
11
12
13
14
#전체 코드

#마지막 행 삭제해줌
df21_=(df21['Q25'].value_counts().to_frame())
#df21_=df21_.drop(df21_.index[26])
#df21_

compensation = df21_['Q25'].index
fig = go.Figure(data=[
    go.Bar(name='21년 World kaggler들의 연봉', x=compensation, y=df21_['Q25'].to_numpy() ,orientation='v')])

fig.update_layout(title_text="<b>21년 World kaggler들의 연봉</b>",title_font_size=35)

fig.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
compensation = df21_['Q25'].index

fig = go.Figure(data=[
    go.Bar(name='China', x = compensation, 
                           y = df21_Ea['Q25'][df21_Ea['Q3'] =='Japan'].value_counts()),
    
    go.Bar(name='Japan', x = compensation, 
                             y=df21_Ea['Q25'][df21_Ea['Q3'] =='Taiwan'].value_counts()),
    
    go.Bar(name='South Korea', x = compensation, 
                            y=df21_Ea['Q25'][df21_Ea['Q3'] =='South Korea'].value_counts()),
    
    go.Bar(name='Taiwan', x = compensation, 
                            y=df21_Ea['Q25'][df21_Ea['Q3'] =='China'].value_counts())
    ])

fig.update_layout(title_text="<b>21년 EastAisa kaggler들의 연봉</b>",title_font_size=35)
fig.show()

언어

1
df21['Q7_Part_1'].value_counts()
Python    21860
Name: Q7_Part_1, dtype: int64

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
#코드 전체

df21_p = df21['Q7_Part_1'].value_counts().to_frame() #python
df21_r = df21['Q7_Part_2'].value_counts().to_frame() #r
df21_s = df21['Q7_Part_3'].value_counts().to_frame() #sql
df21_c = df21['Q7_Part_4'].value_counts().to_frame() #c
df21_cc = df21['Q7_Part_5'].value_counts().to_frame() #c++
df21_j = df21['Q7_Part_6'].value_counts().to_frame() #java
df21_js = df21['Q7_Part_7'].value_counts().to_frame() #javascript
df21_ju = df21['Q7_Part_8'].value_counts().to_frame() #julia
df21_sw = df21['Q7_Part_9'].value_counts().to_frame() #swift
df21_b = df21['Q7_Part_10'].value_counts().to_frame() #bash
df21_ma = df21['Q7_Part_11'].value_counts().to_frame() #matlab
df21_n = df21['Q7_Part_12'].value_counts().to_frame() #none

languages = ['Python','R','SQL','C','C++','Java','Javascript','Julia','Swift','Bash','MATLAB','None']

fig = go.Figure(data=[
    go.Bar(name='21년 World kaggler들이 사용하는 언어', x = languages, 
                                                     y = [df21_p.iloc[0,0],
                                                          df21_r.iloc[0,0],
                                                          df21_s.iloc[0,0],
                                                          df21_c.iloc[0,0],
                                                          df21_cc.iloc[0,0],
                                                          df21_j.iloc[0,0],
                                                          df21_js.iloc[0,0],
                                                          df21_ju.iloc[0,0],
                                                          df21_sw.iloc[0,0],
                                                          df21_b.iloc[0,0],
                                                          df21_ma.iloc[0,0],
                                                          df21_n.iloc[0,0]],orientation='v')
                    
                    ])

fig.update_layout(title_text="<b>21년 World kaggler들이 사용하는 언어</b>",title_font_size=35)

fig.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
df21_lan_ch_p=df21_Ea['Q7_Part_1'][df21_Ea['Q3']=='China'].value_counts().to_frame().rename(columns = {'Q7_Part_1':'cnt'})
df21_lan_ch_r=df21_Ea['Q7_Part_2'][df21_Ea['Q3']=='China'].value_counts().to_frame().rename(columns = {'Q7_Part_2':'cnt'})
df21_lan_ch_s=df21_Ea['Q7_Part_3'][df21_Ea['Q3']=='China'].value_counts().to_frame().rename(columns = {'Q7_Part_3':'cnt'})
df21_lan_ch_c=df21_Ea['Q7_Part_4'][df21_Ea['Q3']=='China'].value_counts().to_frame().rename(columns = {'Q7_Part_4':'cnt'})
df21_lan_ch_cc=df21_Ea['Q7_Part_5'][df21_Ea['Q3']=='China'].value_counts().to_frame().rename(columns = {'Q7_Part_5':'cnt'})
df21_lan_ch_j=df21_Ea['Q7_Part_6'][df21_Ea['Q3']=='China'].value_counts().to_frame().rename(columns = {'Q7_Part_6':'cnt'})
df21_lan_ch_js=df21_Ea['Q7_Part_7'][df21_Ea['Q3']=='China'].value_counts().to_frame().rename(columns = {'Q7_Part_7':'cnt'})
df21_lan_ch_ju=df21_Ea['Q7_Part_8'][df21_Ea['Q3']=='China'].value_counts().to_frame().rename(columns = {'Q7_Part_8':'cnt'})
df21_lan_ch_sw=df21_Ea['Q7_Part_9'][df21_Ea['Q3']=='China'].value_counts().to_frame().rename(columns = {'Q7_Part_9':'cnt'})
df21_lan_ch_b=df21_Ea['Q7_Part_10'][df21_Ea['Q3']=='China'].value_counts().to_frame().rename(columns = {'Q7_Part_10':'cnt'})
df21_lan_ch_ma=df21_Ea['Q7_Part_11'][df21_Ea['Q3']=='China'].value_counts().to_frame().rename(columns = {'Q7_Part_11':'cnt'})
df21_lan_ch_n=df21_Ea['Q7_Part_12'][df21_Ea['Q3']=='China'].value_counts().to_frame().rename(columns = {'Q7_Part_12':'cnt'})
ch_lan = pd.concat([df21_lan_ch_p,df21_lan_ch_r,df21_lan_ch_s,df21_lan_ch_c,df21_lan_ch_cc,df21_lan_ch_j,df21_lan_ch_js,df21_lan_ch_ju,df21_lan_ch_sw,df21_lan_ch_b,df21_lan_ch_ma,df21_lan_ch_n])


df21_lan_jp_p=df21_Ea['Q7_Part_1'][df21_Ea['Q3']=='Japan'].value_counts().to_frame().rename(columns = {'Q7_Part_1':'cnt'})
df21_lan_jp_r=df21_Ea['Q7_Part_2'][df21_Ea['Q3']=='Japan'].value_counts().to_frame().rename(columns = {'Q7_Part_2':'cnt'})
df21_lan_jp_s=df21_Ea['Q7_Part_3'][df21_Ea['Q3']=='Japan'].value_counts().to_frame().rename(columns = {'Q7_Part_3':'cnt'})
df21_lan_jp_c=df21_Ea['Q7_Part_4'][df21_Ea['Q3']=='Japan'].value_counts().to_frame().rename(columns = {'Q7_Part_4':'cnt'})
df21_lan_jp_cc=df21_Ea['Q7_Part_5'][df21_Ea['Q3']=='Japan'].value_counts().to_frame().rename(columns = {'Q7_Part_5':'cnt'})
df21_lan_jp_j=df21_Ea['Q7_Part_6'][df21_Ea['Q3']=='Japan'].value_counts().to_frame().rename(columns = {'Q7_Part_6':'cnt'})
df21_lan_jp_js=df21_Ea['Q7_Part_7'][df21_Ea['Q3']=='Japan'].value_counts().to_frame().rename(columns = {'Q7_Part_7':'cnt'})
df21_lan_jp_ju=df21_Ea['Q7_Part_8'][df21_Ea['Q3']=='Japan'].value_counts().to_frame().rename(columns = {'Q7_Part_8':'cnt'})
df21_lan_jp_sw=df21_Ea['Q7_Part_9'][df21_Ea['Q3']=='Japan'].value_counts().to_frame().rename(columns = {'Q7_Part_9':'cnt'})
df21_lan_jp_b=df21_Ea['Q7_Part_10'][df21_Ea['Q3']=='Japan'].value_counts().to_frame().rename(columns = {'Q7_Part_10':'cnt'})
df21_lan_jp_ma=df21_Ea['Q7_Part_11'][df21_Ea['Q3']=='Japan'].value_counts().to_frame().rename(columns = {'Q7_Part_11':'cnt'})
df21_lan_jp_n=df21_Ea['Q7_Part_12'][df21_Ea['Q3']=='Japan'].value_counts().to_frame().rename(columns = {'Q7_Part_12':'cnt'})
jp_lan = pd.concat([df21_lan_jp_p,df21_lan_jp_r,df21_lan_jp_s,df21_lan_jp_c,df21_lan_jp_cc,df21_lan_jp_j,df21_lan_jp_js,df21_lan_jp_ju,df21_lan_jp_sw,df21_lan_jp_b,df21_lan_jp_ma,df21_lan_jp_n])


df21_lan_tw_p=df21_Ea['Q7_Part_1'][df21_Ea['Q3']=='Taiwan'].value_counts().to_frame().rename(columns = {'Q7_Part_1':'cnt'})
df21_lan_tw_r=df21_Ea['Q7_Part_2'][df21_Ea['Q3']=='Taiwan'].value_counts().to_frame().rename(columns = {'Q7_Part_2':'cnt'})
df21_lan_tw_s=df21_Ea['Q7_Part_3'][df21_Ea['Q3']=='Taiwan'].value_counts().to_frame().rename(columns = {'Q7_Part_3':'cnt'})
df21_lan_tw_c=df21_Ea['Q7_Part_4'][df21_Ea['Q3']=='Taiwan'].value_counts().to_frame().rename(columns = {'Q7_Part_4':'cnt'})
df21_lan_tw_cc=df21_Ea['Q7_Part_5'][df21_Ea['Q3']=='Taiwan'].value_counts().to_frame().rename(columns = {'Q7_Part_5':'cnt'})
df21_lan_tw_j=df21_Ea['Q7_Part_6'][df21_Ea['Q3']=='Taiwan'].value_counts().to_frame().rename(columns = {'Q7_Part_6':'cnt'})
df21_lan_tw_js=df21_Ea['Q7_Part_7'][df21_Ea['Q3']=='Taiwan'].value_counts().to_frame().rename(columns = {'Q7_Part_7':'cnt'})
df21_lan_tw_ju=df21_Ea['Q7_Part_8'][df21_Ea['Q3']=='Taiwan'].value_counts().to_frame().rename(columns = {'Q7_Part_8':'cnt'})
df21_lan_tw_sw=df21_Ea['Q7_Part_9'][df21_Ea['Q3']=='Taiwan'].value_counts().to_frame().rename(columns = {'Q7_Part_9':'cnt'})
df21_lan_tw_b=df21_Ea['Q7_Part_10'][df21_Ea['Q3']=='Taiwan'].value_counts().to_frame().rename(columns = {'Q7_Part_10':'cnt'})
df21_lan_tw_ma=df21_Ea['Q7_Part_11'][df21_Ea['Q3']=='Taiwan'].value_counts().to_frame().rename(columns = {'Q7_Part_11':'cnt'})
df21_lan_tw_n=df21_Ea['Q7_Part_12'][df21_Ea['Q3']=='Taiwan'].value_counts().to_frame().rename(columns = {'Q7_Part_12':'cnt'})
tw_lan = pd.concat([df21_lan_tw_p,df21_lan_tw_r,df21_lan_tw_s,df21_lan_tw_c,df21_lan_tw_cc,df21_lan_tw_j,df21_lan_tw_js,df21_lan_tw_ju,df21_lan_tw_sw,df21_lan_tw_b,df21_lan_tw_ma,df21_lan_tw_n])


df21_lan_ko_p=df21_Ea['Q7_Part_1'][df21_Ea['Q3']=='South Korea'].value_counts().to_frame().rename(columns = {'Q7_Part_1':'cnt'})
df21_lan_ko_r=df21_Ea['Q7_Part_2'][df21_Ea['Q3']=='South Korea'].value_counts().to_frame().rename(columns = {'Q7_Part_2':'cnt'})
df21_lan_ko_s=df21_Ea['Q7_Part_3'][df21_Ea['Q3']=='South Korea'].value_counts().to_frame().rename(columns = {'Q7_Part_3':'cnt'})
df21_lan_ko_c=df21_Ea['Q7_Part_4'][df21_Ea['Q3']=='South Korea'].value_counts().to_frame().rename(columns = {'Q7_Part_4':'cnt'})
df21_lan_ko_cc=df21_Ea['Q7_Part_5'][df21_Ea['Q3']=='South Korea'].value_counts().to_frame().rename(columns = {'Q7_Part_5':'cnt'})
df21_lan_ko_j=df21_Ea['Q7_Part_6'][df21_Ea['Q3']=='South Korea'].value_counts().to_frame().rename(columns = {'Q7_Part_6':'cnt'})
df21_lan_ko_js=df21_Ea['Q7_Part_7'][df21_Ea['Q3']=='South Korea'].value_counts().to_frame().rename(columns = {'Q7_Part_7':'cnt'})
df21_lan_ko_ju=df21_Ea['Q7_Part_8'][df21_Ea['Q3']=='South Korea'].value_counts().to_frame().rename(columns = {'Q7_Part_8':'cnt'})
df21_lan_ko_sw=df21_Ea['Q7_Part_9'][df21_Ea['Q3']=='South Korea'].value_counts().to_frame().rename(columns = {'Q7_Part_9':'cnt'})
df21_lan_ko_b=df21_Ea['Q7_Part_10'][df21_Ea['Q3']=='South Korea'].value_counts().to_frame().rename(columns = {'Q7_Part_10':'cnt'})
df21_lan_ko_ma=df21_Ea['Q7_Part_11'][df21_Ea['Q3']=='South Korea'].value_counts().to_frame().rename(columns = {'Q7_Part_11':'cnt'})
df21_lan_ko_n=df21_Ea['Q7_Part_12'][df21_Ea['Q3']=='South Korea'].value_counts().to_frame().rename(columns = {'Q7_Part_12':'cnt'})
ko_lan = pd.concat([df21_lan_ko_p,df21_lan_ko_r,df21_lan_ko_s,df21_lan_ko_c,df21_lan_ko_cc,df21_lan_ko_j,df21_lan_ko_js,df21_lan_ko_ju,df21_lan_ko_sw,df21_lan_ko_b,df21_lan_ko_ma,df21_lan_ko_n])


ch_lan['cnt'].to_list()

languages = ['Python','R','SQL','C','C++','Java','Javascript','Julia','Swift','Bash','MATLAB','None']

fig = go.Figure(data=[
    go.Bar(name='China', x = languages, 
                         y = ch_lan['cnt'].tolist()),
    
    go.Bar(name='Japan', x = languages, 
                             y=jp_lan['cnt'].tolist()),
    
    go.Bar(name='South Korea', x = languages, 
                            y=ko_lan['cnt'].tolist()),
    
    go.Bar(name='Taiwan', x = languages, 
                            y=tw_lan['cnt'].tolist())
          ])

fig.update_layout(title_text="<b>21년 EastAisa kaggler들이 사용하는 언어</b>",title_font_size=35)
fig.show()

Thank you for reading!

 게시 됨 업데이트 됨python - plotly2분안에 읽기 (약 373 단어)

kaggle HeatMap

HeatMap

python문법의 plotly Library를 이용하여 Heatmap을 알아보자

  • HeatMap의 Gradation 색을 바꾸고 싶다면, colorscales 을 참고 해 보자.

HeatMap 1

1
2
3
4
5
6
7
8
import plotly.figure_factory as ff

z=[[1, 90, 30, 50, 1], [20, 1, 60, 80, 30], [30, 60, 1, 50, 20]]
x=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
y=['Morning', 'Afternoon', 'Evening']

fig = ff.create_annotated_heatmap(z, x = x, y = y, colorscale = "Viridis")
fig.show()

Input data를 맞춰 줘야한다.

  • x, y = List
  • z= 배열

heatmap

HeatMap 2

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
import plotly.graph_objects as go
from functools import reduce
from itertools import product

z=[[1, 90, 30, 50, 1], [20, 1, 60, 80, 30], [30, 60, 1, 50, 20]]
x=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
y=['Morning', 'Afternoon', 'Evening']

def get_anno_text(z_value):
    annotations=[]
    a, b = len(z_value), len(z_value[0])
    flat_z = reduce(lambda x,y: x+y, z_value) # z_value.flat if you deal with numpy
    coords = product(range(a), range(b))
    for pos, elem in zip(coords, flat_z):
        annotations.append({'font': {'color': '#FFFFFF'},
                    'showarrow': False,
                    'text': str(elem),
                    'x': pos[1],
                    'y': pos[0]})
    return annotations

fig = go.Figure(data=go.Heatmap(
                   z=z,
                   x=x,
                   y=y,
                   hoverongaps = True))

fig.update_layout(annotations = get_anno_text(z))
fig.show()

HeatMap2

HeatMap 3

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
import plotly.graph_objects as go

z = df.groupby(['Q4', 'Q1']).size().unstack().fillna(0).astype('int16')
# convert to correlation matrix
z2 = z.apply(lambda x:x/x.sum(), axis = 1)

x = z2.columns
y = z2.index

fig = go.Figure(data=go.Heatmap(
                   z=z2.to_numpy(),  #dataframe을 넘파이(배열)로 바꿔줌: List형태
                   x=x,
                   y=y,
                   type="heatmap",
                   colorscale = "Viridis",
                   hoverongaps = False))

fig.update_layout(
    title='Degree ~ Gender',
    xaxis_nticks=36)

fig.show()

HeatMap3

  • z2 = z.apply(lambda x:x/x.sum(), axis = 1)
  • 여기서 이 한줄로 인해 dataFrame이 Heatmap에 들어 갈 수 있는 상관관계G를 만들 수 있는 상태로 변환.
  • data 자료형을 맞춰줘야 한다. (x, y, z)

HeatMap Ref.

Ref.

 게시 됨 업데이트 됨python - plotly몇 초안에 읽기 (약 49 단어)

[object Object]

#Plotly Tutorial For Kaggle Survey Competitions



진도가 너무 더디게 나가서 Teacher’s code를 조금더 뜯어 본후

python for문이나 if문을 조금 더 잘 쓸 수 있을기를 바란다.

 게시 됨 업데이트 됨python - plotly4분안에 읽기 (약 674 단어)

Stacked Bar (kaggle in East-Asia)

World Vs East Asia



##python을 이용한 plotly Library로 plot 그리기

subplots 를 이용하여 다중 그래프를 그려 보자.

python Library Import

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pylab as plt

import plotly.io as pio
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
pio.templates.default = "none"
# import plotly.offline as py
# py.offline.init_notebook_mode()

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

import warnings
warnings.filterwarnings("ignore")

data import

1
2
3
4
5
6
df17= pd.read_csv("/kaggle/input/kaggle-survey-2017/multipleChoiceResponses.csv", encoding="ISO-8859-1")
df18= pd.read_csv("/kaggle/input/kaggle-survey-2018/multipleChoiceResponses.csv", )
df19= pd.read_csv("/kaggle/input/kaggle-survey-2019/multiple_choice_responses.csv", )
df20= pd.read_csv("/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv", )
df21= pd.read_csv("/kaggle/input/kaggle-survey-2021/kaggle_survey_2021_responses.csv", )

data frame 전처리

i) Q3를 기준으로 EastAsia에 속하는 나라만 연도별로 뽑아냅니다. 

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
df21_Ea=df21[df21['Q3'].isin(EastAsia21)]
Ea21= (
    df21_Ea['Q3'].value_counts().to_frame()
    .reset_index().rename(columns={'index':'Country', 'Q3':'21'}))


df20_Ea=df20[df20['Q3'].isin(EastAsia)]
Ea20= (
    df20_Ea['Q3'].replace('Republic of Korea','South Korea')
    .value_counts().to_frame().reset_index()
    .rename(columns={'index':'Country', 'Q3':'20'}))


df19_Ea=df19[df19['Q3'].isin(EastAsia)]
Ea19= (df19_Ea['Q3'].replace('Republic of Korea','South Korea')
       .value_counts().to_frame().reset_index()
       .rename(columns={'index':'Country', 'Q3':'19'}))


df18_Ea=df18[df18['Q3'].isin(EastAsia)]
Ea18= (df18_Ea['Q3'].replace('Republic of Korea','South Korea')
       .value_counts().to_frame().reset_index()
       .rename(columns={'index':'Country', 'Q3':'18'}))
Ea18.value_counts()
#df18 열에 taiwan = 0을 추가 해야 합니다. 


df17_Ea = df17[df17['Country'].isin(EastAsia)]
Ea17= (df17_Ea['Country'].replace("People 's Republic of China",'China')
       .value_counts().to_frame().reset_index()
       .rename(columns={'index':'Country', 'Country':'17'}))

ii) data를 합쳐서 하나의 dataframe으로 만들음.

이 과정에서 pd.merge()를 사용 해 주었기 때문에 18’ taiwan data가 Nan으로 추가 되었다.

pd.merge

1
2
3
4
5
6
7
8
9
fig = go.Figure(data=[
    go.Bar(name='2017', x=df5years['Country'], y=df5years['17']),
    go.Bar(name='2018', x=df5years['Country'], y=df5years['18']),
    go.Bar(name='2019', x=df5years['Country'], y=df5years['19']),
    go.Bar(name='2020', x=df5years['Country'], y=df5years['20']),
    go.Bar(name='2021', x=df5years['Country'], y=df5years['21'])
])

fig.show()

Q3barAsia

1
2
3
#Change the bar mode
fig.update_layout(barmode='stack', title='연도별 동아시아 Kaggle 사용자수'
                  )

Q3barAsia_stacked

  • stacked bar로 할까말까 고민중.
  • dictation 할 때 까지만 해도 bar 그래프 그리는 것이 뭐그리 어렵겠나? 했다.
  • 그냥 복사 붙여넣기로 만드려고 했는데
  • 그게 참 안되네 ㅂㄷㅂㄷ 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
df5years_ =df5years.transpose()
df5years_= df5years_.iloc[1:]

fig2 = go.Figure(data=[
    go.Bar(name='China', x=years, y=df5years_[0]),
    go.Bar(name='Japan', x=years, y=df5years_[1]),
    go.Bar(name='Taiwan', x=years, y=df5years_[2]),
    go.Bar(name='South Korea', x=years, y=df5years_[3]),
])





fig2.show()

Q3barAsia_stackedR

  • 축 reverse로 할까말까 고민중.
  • 어떤게 더 잘 보여 줄 수 있을까 … ㅜㅜ
 게시 됨 업데이트 됨python - plotly4분안에 읽기 (약 543 단어)

Subplots in python (kaggle in East-Asia)

World Vs East Asia



##python을 이용한 plotly Library로 plot 그리기

subplots 를 이용하여 다중 그래프를 그려 보자.

python Library Import

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pylab as plt

import plotly.io as pio
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
pio.templates.default = "none"
# import plotly.offline as py
# py.offline.init_notebook_mode()

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

import warnings
warnings.filterwarnings("ignore")

data import

1
2
3
4
5
6
df17= pd.read_csv("/kaggle/input/kaggle-survey-2017/multipleChoiceResponses.csv", encoding="ISO-8859-1")
df18= pd.read_csv("/kaggle/input/kaggle-survey-2018/multipleChoiceResponses.csv", )
df19= pd.read_csv("/kaggle/input/kaggle-survey-2019/multiple_choice_responses.csv", )
df20= pd.read_csv("/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv", )
df21= pd.read_csv("/kaggle/input/kaggle-survey-2021/kaggle_survey_2021_responses.csv", )

data frame 전처리

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
total17 = (
    df17['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index() 
)

total18 = (
    df18['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index() 
)

total19 = (
    df19['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index() 
)

total20 = (
    df20['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index() 
)

total21 = (
    df21['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index() 
)

plot 그리기

make_subplots 로 여러개의 그래프를 한 plot에 담아 봅시다.

  • pie그래프를 연도별로 담아 봅시다. 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
# Create subplots: use 'domain' type for Pie subplot


##subplots가 담길 행렬을 만들어 줍니다. rows와 cols를 맞춰서 제목도 달아 줍니다. 
fig = make_subplots(rows=1, cols=5, 
                    specs=[[{'type':'domain'}, {'type':'domain'}, {'type':'domain'}, {'type':'domain'}, {'type':'domain'}]],
                   subplot_titles=("2017", "2018", "2019", "2020", "2021"))

## scalegroup = one으로 설정 하게 되면, data의 크기에 따라 pie의 크기가 결정됩니다. 
fig.add_trace(go.Pie(labels=total21['type'],
                     values=total21['respodents'], name="2021", scalegroup='one'),
              1, 1)
fig.add_trace(go.Pie(labels=total20['type'], 
                     values=total20['respodents'], name="2020", scalegroup='one'),
              1, 2)
fig.add_trace(go.Pie(labels=total19['type'], 
                     values=total19['respodents'], name="2019", scalegroup='one'),
              1, 3)
fig.add_trace(go.Pie(labels=total18['type'], 
                     values=total18['respodents'], name="2018", scalegroup='one'),
              1, 4)
fig.add_trace(go.Pie(labels=total17['type'], 
                     values=total17['respodents'], name="2017", scalegroup='one'),
              1, 5)




# Use `hole` to create a donut-like pie chart
fig.update_traces(hole=.2, hoverinfo="label+percent+name")
fig.update_layout(
    title_text="<b>World vs EastAsia</b>",
    # Add annotations in the center of the donut pies.
   )
fig.show()

다중 파이 그래프

 게시 됨 업데이트 됨python - plotly3분안에 읽기 (약 384 단어)

kaggle in East-Asia(data둘러보기)

Kaggle in East Asia

0. introduction

1. World Vs East Asia

  1. Kgg 응답자 수
  2. Kgg 응답자 중 직업
    1.

2. East Asia

3. interestings

  • Tenure: 17y 경력? Q6(20y) Q8(18y) Q15(19y) Q6(20, 21y)
  • FormalEducation: 17y 학력, Q4 in 18y, 19y, 20y
  • 전공 Q5 in18y
  • compensation for year: Q10 in 18y,19y, Q24 in 20,
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
# 연도별 나이 
df21Age_Ea = df21_Ea.loc[:,['Q3','Q1']].reset_index().rename(columns={'Q3':'East_Asia', 'Q1':'2021'}).fillna('etc')
df20Age_Ea = df20_Ea.loc[:,['Q3','Q1']].reset_index().rename(columns={'Q3':'East_Asia', 'Q1':'2020'}).fillna('etc')
df19Age_Ea = df19_Ea.loc[:,['Q3','Q1']].reset_index().rename(columns={'Q3':'East_Asia', 'Q1':'2019'}).fillna('etc')
df18Age_Ea = df18_Ea.loc[:,['Q3','Q2']].reset_index().rename(columns={'Q3':'East_Asia', 'Q2':'2018'}).fillna('etc')
df17Age_Ea = df17_Ea.loc[:,['Country','Age']].reset_index().rename(columns={'Country':'East_Asia', 'Age':'2017'}).fillna('etc')


#data frame 정리

dfAge21_percent =df21Age_Ea.groupby(['East_Asia','2021']).size().reset_index().rename(columns = {0:"Count"})
dfAge21_percent['percent'] =((dfAge21_percent['Count'] / len(df21Age_Ea))*100).round(2)
dfAge21_percent['percent_str'] =((dfAge21_percent['Count'] / len(df21Age_Ea))*100).round(2).astype(str) + '%'
# dfAge21['percent'] = ((media['Count'] / len(df))*100).round(2).astype(str) + '%'
# dfAge_percent21=dfAge21.value_counts('East_Asia',normalize=True).mul(100).round(1).astype(str)
dfAge21_percent


# 나라별 연령대 비율 in East Asia
fig = go.Figure()
for country, group  in dfAge21_percent.groupby('East_Asia'):
   fig.add_trace(go.Bar(
       x = group['2021'], y = group['percent'], name = country, text=group['percent_str']
   ))
fig.update_layout(barmode="stack",
                 plot_bgcolor = "white",
                 title='2021_ 연령별 나라 비율 in East Asia')
fig.show()

Q1_EastAsia

1
2
3
4
5
6
7
8
9
10
11
12

# 연령별 나라 비율 in East Asia // 여기 name 어떻게 넣는거야 !!!! 
fig = go.Figure()

for country, group  in dfAge21_percent.groupby('2021'):
   fig.add_trace(go.Bar(
       x = group['East_Asia'], y =group['percent'], text=dfAge21_percent['percent_str']
   ))
fig.update_layout(barmode="stack",
                 plot_bgcolor = "white",
                 title='2021_ 나라별 연령 비율 in East Asia')
fig.show()

img_1.Q1_EastAsia

  • 못해먹겠다.

Metaplotly

 게시 됨 업데이트 됨python - plotly6분안에 읽기 (약 872 단어)

kaggle in Korea(data둘러보기)

#How popular is kaggle in South Korea?


data 정제하기

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
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
East Asia

Ref. East Asia

동아시아

  • East Asia에는 대한민국, 일본, 중국, 타이완, 몽골, 북조선 총 6개의 국가가 속해 있다.
  • 알 수 없지만, 18년도엔 타이완이 없다. 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
## 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.

region_df21

1%대는 아니지만, 이제 10%대 data를 뽑아 냈다.

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

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
# % 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

SrackFig1

072bc76c34c0f369e5fe7e814291408da6a83ffc

 게시 됨 업데이트 됨몇 초안에 읽기 (약 38 단어)

Make a gitHub Draft


Github blog에 초안(draft) 작성하기


YH's GitHub stats


ref.

 게시 됨 업데이트 됨python - plotly2분안에 읽기 (약 314 단어)

NLP_text_classification

##Kaggle _ API

  1. !pip Install Kaggle : Kaggle 설치
  2. google.colab에 kaggle.json files upload
    1. Saving kaggle.json to kaggle.json
    2. User uploaded file “kaggle.json” with length 66 bytes
    3. kaggle.json file에는 뭐가 들어있을까 너무 궁금하다.
  3. !kaggle competitions download -c nlp-getting-started
    1. 케글 대회 자료를 다운받기. (-c nlp-getting-started 이게 뭘까)
  4. data path 설정하기

##data 둘러보기

  1. data frame을 만들기 위해 Pandas와 numpy를 import후 각 file을 data set을 Load해 준다.
  2. data set 확인
    1. .head()로 대략적인 data set 확인
    2. .shape로 각 data set의 크기 확인
    3. .info()로 각 data frame의 정보 확인

##EDA
EDA

: 수집한 data를 다양한 각도에서 관찰하고 이해하는 과정

: 통계적 방법으로 자료를 직관적으로 바라보는 과정

  • data의 분포 및 값을 검토함으로써 데이터가 표현하는 현상을 더 잘 이해하고, 잠재적 문제를 발견 할 수 있다.
  • 문제를 발견하여 기존의 가설을 수정하거나 새로운 가설을 세울 수 있다.
  1. data visualiztion을 위해 matplotlib.pyplot과 seaborn 설치
    1. missing_colunms = [“keyword”, “location”]
    2. 각 data set에서 null인 columns를 가져온다.
  2. matplotlib.pyplot으로 bar plot 그리기
    MP_EDA

##Feature Engineering
##Mideling
##algorithm logistic regression

 게시 됨 업데이트 됨python - plotly5분안에 읽기 (약 813 단어)

to prepare kaggle Competition

#Kaggle Competition 준비하기

Kaggle Note 에서 작성됨.

  1. files and Library import
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pylab as plt

import plotly.io as pio
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
pio.templates.default = "none"
# import plotly.offline as py
# py.offline.init_notebook_mode()

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

import warnings
warnings.filterwarnings("ignore")

result

/kaggle/input/kaggle-survey-2018/SurveySchema.csv /kaggle/input/kaggle-survey-2018/freeFormResponses.csv /kaggle/input/kaggle-survey-2018/multipleChoiceResponses.csv /kaggle/input/kaggle-survey-2017/freeformResponses.csv /kaggle/input/kaggle-survey-2017/schema.csv /kaggle/input/kaggle-survey-2017/RespondentTypeREADME.txt /kaggle/input/kaggle-survey-2017/multipleChoiceResponses.csv /kaggle/input/kaggle-survey-2017/conversionRates.csv /kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv /kaggle/input/kaggle-survey-2020/supplementary_data/kaggle_survey_2020_methodology.pdf /kaggle/input/kaggle-survey-2020/supplementary_data/kaggle_survey_2020_answer_choices.pdf /kaggle/input/kaggle-survey-2021/kaggle_survey_2021_responses.csv /kaggle/input/kaggle-survey-2021/supplementary_data/kaggle_survey_2021_methodology.pdf /kaggle/input/kaggle-survey-2021/supplementary_data/kaggle_survey_2021_answer_choices.pdf /kaggle/input/kaggle-survey-2019/survey_schema.csv /kaggle/input/kaggle-survey-2019/multiple_choice_responses.csv /kaggle/input/kaggle-survey-2019/other_text_responses.csv /kaggle/input/kaggle-survey-2019/questions_only.csv

  1. dataframe create
1
2
3
4
5
df17= pd.read_csv("/kaggle/input/kaggle-survey-2017/multipleChoiceResponses.csv", encoding="ISO-8859-1")
df18= pd.read_csv("/kaggle/input/kaggle-survey-2018/multipleChoiceResponses.csv", )
df19= pd.read_csv("/kaggle/input/kaggle-survey-2019/multiple_choice_responses.csv", )
df20= pd.read_csv("/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv", )
df21= pd.read_csv("/kaggle/input/kaggle-survey-2021/kaggle_survey_2021_responses.csv", )
  1. data 확인하기
1
2
3
4
5
df21 = df21.iloc[1:, :]
#df21.value_counts()
#df21.count

df21.head()

df21.head

  1. data 1개씩 표로 만들어서 불러오기
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
country = (
    df21['Q3']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'country', 'Q3':'Count'})
    .sort_values(by=['country'], ascending=False)
     )  

Wage = (
    df21['Q25']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'Wage', 'Q25':'Count'})
    .sort_values(by=['Wage'], ascending=True)
     )  

Wage

Problem

나라별 임금을 확인 하기 위해
temp에 나라이름과 임금에 대해 넣었다.
C_ls : country list라는 List를 만들어 나라 별로 임금을 뽑을 수 있었다.

1
2
3
4
5
6
# India, Russia, China
temp = df21[['Q3','Q25']]

print(temp['Q3'].unique())

C_Ls = temp['Q3'].unique() #나라이름 뽑아주기

Unique함수

1
2
3
4
5

tempR = temp[temp['Q3'] == str(C_Ls[0])].value_counts()


# 이 경우에는 temp가 가공 할 수 없는 templet이기 때문에 plot을 그릴 수가 없다.

temp_noncalculable




trouble shooting

1
2
3
4
5
6
7
8
9
10
11
12
13
#선생님
temp= df21[['Q3','Q25']]
temp = temp.groupby(['Q3','Q25'])
        .size()
        .reset_index()
        .rename(columns = {0:"Count"})

C_Ls = temp['Q3'].unique()

for i in range(1,len(df21['Q3'])):
    temp2 = temp[temp['Q3'] == str(C_Ls[i])]
    fig = px.bar(temp2, x='Q25', y='Count',title=C_Ls[i])
    fig.show()

선생님의 도움을 받아 for문 안에 나라 이름을 넣어서

모든 나라의 임금을 확인 할 수 있었다.

모든 나라의 임금을 확인 했는데, 차별이 심한 나라를 찾기 힘들었다.

뭐때문에 이 graph를 그리려고 했는지 잃어버렸다.

  1. 한국에서 돈 잘 벌려면 어떤 직업, 어떤 … 을 해야 하는가
  2. 한국보다 평균임금이 더 많은 나라는?
  3. 한국 보다 평균임금이 더 많은 나라는 뭐가 다를까?
  4. 임금에 대한 것을 버려야 할까…

이런 재미있는 Issue에 대해 Note를 만들어 보려고 했는데 ㅎㅎ

코딩 실력이 안된당 흐흐 대회에서 잘 하려는 마음보다는 코드를 더 잘 읽고, 쓰는데 집중하자. ㅠㅠ

D-17 (대회 종료까지)

대회 종료 final version

팔로우

광고

링크

카테고리

최근 글

아카이브

태그

BarGraph4
Bargraph1
BioDataScientist1
BioData_Science1
Classifier1
Crawling1
DTS3
DataFrame1
DataScience1
Decision Tree1
Donut_Chart1
EDA4
HorizontalBar4
HyperParameter1
List1
Matplotlib1
Mow1
Numpy1
Pandas4
Plotly22
Python1
R4ds5
Rss1
Scatter3
ScatterLine3
Seaborn1
Series1
Stacked_Bar1
Study19
Subplots1
Test page,1
TextMining2
Token1
Treemap1
Tuple1
auto Machine Learning1
bioinformatics1
coding3
dataScience5
decisionTree2
draft1
ggplot21
github5
githubBlog1
google, colaboratory, github, upload1
googleColab1
green1
howtoGithub1
kaggle32
kaggleNote1
kaggle_Competition3
kaggle_dictation20
machineLeaning13
machineLearning2
make1
makeBlog3
makeBlog, makegithub1
makegithub1
markdown1
market1
method1
model selection1
multiplace1
namespace1
naver1
notion1
panel1
paperReview1
pipe1
plot12
prediction1
predictionModel2
pycaret1
python28
pythonFuction4
python_basic2
regression1
repository1
sklearn2
soil1
studyPython2
summary5
table1
table arrange1
timer1
title1
깃허브1

업데이트 소식 받기

follow.it

×