[데이터시각화] 4. Plotly, 판다스 백엔드

Author

고경수

Published

January 26, 2024

전북대학교 통계학과 최규빈 교수님 강의 정리내용입니다.

https://guebin.github.io/DV2023/

import

import numpy as np
import pandas as pd 
import plotly.io as pio
import matplotlib.pyplot as plt

기본적으로 판다스의 백엔드는 matplotlib으로 설정되어있음.

이를 매번 backend='plotly'와 같이 쳐줘도 되지만 노트북 전체에 백엔드를 설정할 수도 있음

pio.renderers.default = "notebook_connected"
pd.options.plotting.backend = "plotly" # 백엔드를 plotly로 설정
pio.templates.default = "plotly_white" # plotly의 템플릿 설정. 안해도 무방
print(pio.templates) # 템플릿의 종류
Templates configuration
-----------------------
    Default template: 'plotly_white'
    Available templates:
        ['ggplot2', 'seaborn', 'simple_white', 'plotly',
         'plotly_white', 'plotly_dark', 'presentation', 'xgridoff',
         'ygridoff', 'gridon', 'none']

Plotly

Plotly의 여러가지 플랏

A..plot.bar()

예제 1 - 성별 합격률 시각화

df = pd.read_csv("https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson.csv",index_col=0,header=[0,1]).reset_index().melt(id_vars='index').set_axis(['department','gender','result','count'],axis=1)
df
department gender result count
0 A male fail 314
1 B male fail 208
2 C male fail 204
3 D male fail 279
4 E male fail 137
5 F male fail 149
6 A male pass 511
7 B male pass 352
8 C male pass 121
9 D male pass 138
10 E male pass 54
11 F male pass 224
12 A female fail 19
13 B female fail 7
14 C female fail 391
15 D female fail 244
16 E female fail 299
17 F female fail 103
18 A female pass 89
19 B female pass 18
20 C female pass 202
21 D female pass 131
22 E female pass 94
23 F female pass 238 
df.pivot_table(index='gender',columns='result',values='count',aggfunc='sum')\
.assign(rate = lambda df:  df['pass']/(df['fail']+df['pass']))\
.assign(rate = lambda df:  np.round(df['rate'],2))\
.loc[:,'rate'].reset_index()\
.plot.bar(
    x='gender', y='rate',
    color='gender',
    text='rate',
    width=600
)

예제 2 - (성별,학과)별 지원자수 시각화

df = pd.read_csv("https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson.csv",index_col=0,header=[0,1]).reset_index().melt(id_vars='index').set_axis(['department','gender','result','count'],axis=1)
df
department gender result count
0 A male fail 314
1 B male fail 208
2 C male fail 204
3 D male fail 279
4 E male fail 137
5 F male fail 149
6 A male pass 511
7 B male pass 352
8 C male pass 121
9 D male pass 138
10 E male pass 54
11 F male pass 224
12 A female fail 19
13 B female fail 7
14 C female fail 391
15 D female fail 244
16 E female fail 299
17 F female fail 103
18 A female pass 89
19 B female pass 18
20 C female pass 202
21 D female pass 131
22 E female pass 94
23 F female pass 238 
df.groupby(['department','gender']).agg({'count':'sum'})\
.reset_index()\
.plot.bar(
    x='gender',y='count',
    color='gender',
    text='count',
    facet_col='department'
)

B..plot.line()

예제 1 - 휴대폰 판매량

df = pd.read_csv('https://raw.githubusercontent.com/guebin/2021DV/master/_notebooks/phone.csv')
df
Date Samsung Apple Huawei Xiaomi Oppo Mobicel Motorola LG Others Realme Google Nokia Lenovo OnePlus Sony Asus
0 2019-10 461 324 136 109 76 81 43 37 135 28 39 14 22 17 20 17
1 2019-11 461 358 167 141 86 61 29 36 141 27 29 20 23 10 19 27
2 2019-12 426 383 143 105 53 45 51 48 129 30 20 26 28 18 18 19
3 2020-01 677 494 212 187 110 79 65 49 158 23 13 19 19 22 27 22
4 2020-02 593 520 217 195 112 67 62 71 157 25 18 16 24 18 23 20
5 2020-03 637 537 246 187 92 66 59 67 145 21 16 24 18 31 22 14
6 2020-04 647 583 222 154 98 59 48 64 113 20 23 25 19 19 23 21
7 2020-05 629 518 192 176 91 87 50 66 150 43 27 15 18 19 19 13
8 2020-06 663 552 209 185 93 69 54 60 140 39 16 16 17 29 25 16
9 2020-07 599 471 214 193 89 78 65 59 130 40 27 25 21 18 18 12
10 2020-08 615 567 204 182 105 82 62 42 129 47 16 23 21 27 23 20
11 2020-09 621 481 230 220 102 88 56 49 143 54 14 15 17 15 19 15
12 2020-10 637 555 232 203 90 52 63 49 140 33 17 20 22 9 22 21 
df.melt(id_vars='Date')\
.set_axis(['날짜','회사','판매량'],axis=1)\
.plot.line(
    x='날짜',y='판매량',
    color='회사'
)

C..plot.scatter()

position_dict = {
    'GOALKEEPER':{'GK'},
    'DEFENDER':{'CB','RCB','LCB','RB','LB','RWB','LWB'},
    'MIDFIELDER':{'CM','RCM','LCM','CDM','RDM','LDM','CAM','RAM','LAM','RM','LM'},
    'FORWARD':{'ST','CF','RF','LF','RW','LW','RS','LS'},
    'SUB':{'SUB'},
    'RES':{'RES'}
}
df = pd.read_csv('https://raw.githubusercontent.com/guebin/DV2021/master/_notebooks/2021-10-25-FIFA22_official_data.csv')\
.loc[:,lambda df: df.isna().mean()<0.5].dropna()\
.assign(Position = lambda df: df.Position.str.split(">").str[-1].apply(lambda x: [k for k,v in position_dict.items() if x in v].pop()))\
.assign(Wage = lambda df: df.Wage.str[1:].str.replace('K','000').astype(int))
df
ID Name Age Photo Nationality Flag Overall Potential Club Club Logo ... SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes Best Position Best Overall Rating Release Clause DefensiveAwareness
0 212198 Bruno Fernandes 26 https://cdn.sofifa.com/players/212/198/22_60.png Portugal https://cdn.sofifa.com/flags/pt.png 88 89 Manchester United https://cdn.sofifa.com/teams/11/30.png ... 65.0 12.0 14.0 15.0 8.0 14.0 CAM 88.0 €206.9M 72.0
1 209658 L. Goretzka 26 https://cdn.sofifa.com/players/209/658/22_60.png Germany https://cdn.sofifa.com/flags/de.png 87 88 FC Bayern München https://cdn.sofifa.com/teams/21/30.png ... 77.0 13.0 8.0 15.0 11.0 9.0 CM 87.0 €160.4M 74.0
2 176580 L. Suárez 34 https://cdn.sofifa.com/players/176/580/22_60.png Uruguay https://cdn.sofifa.com/flags/uy.png 88 88 Atlético de Madrid https://cdn.sofifa.com/teams/240/30.png ... 38.0 27.0 25.0 31.0 33.0 37.0 ST 88.0 €91.2M 42.0
3 192985 K. De Bruyne 30 https://cdn.sofifa.com/players/192/985/22_60.png Belgium https://cdn.sofifa.com/flags/be.png 91 91 Manchester City https://cdn.sofifa.com/teams/10/30.png ... 53.0 15.0 13.0 5.0 10.0 13.0 CM 91.0 €232.2M 68.0
4 224334 M. Acuña 29 https://cdn.sofifa.com/players/224/334/22_60.png Argentina https://cdn.sofifa.com/flags/ar.png 84 84 Sevilla FC https://cdn.sofifa.com/teams/481/30.png ... 82.0 8.0 14.0 13.0 13.0 14.0 LB 84.0 €77.7M 80.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
16703 259718 F. Gebhardt 19 https://cdn.sofifa.com/players/259/718/22_60.png Germany https://cdn.sofifa.com/flags/de.png 52 66 FC Basel 1893 https://cdn.sofifa.com/teams/896/30.png ... 10.0 53.0 45.0 47.0 52.0 57.0 GK 52.0 €361K 6.0
16704 251433 B. Voll 20 https://cdn.sofifa.com/players/251/433/22_60.png Germany https://cdn.sofifa.com/flags/de.png 58 69 F.C. Hansa Rostock https://cdn.sofifa.com/teams/27/30.png ... 10.0 59.0 60.0 56.0 55.0 61.0 GK 58.0 €656K 5.0
16706 262846 �. Dobre 20 https://cdn.sofifa.com/players/262/846/22_60.png Romania https://cdn.sofifa.com/flags/ro.png 53 63 FC Academica Clinceni https://cdn.sofifa.com/teams/113391/30.png ... 12.0 57.0 52.0 53.0 48.0 58.0 GK 53.0 €279K 5.0
16707 241317 21 Xue Qinghao 19 https://cdn.sofifa.com/players/241/317/21_60.png China PR https://cdn.sofifa.com/flags/cn.png 47 60 Shanghai Shenhua FC https://cdn.sofifa.com/teams/110955/30.png ... 9.0 49.0 48.0 45.0 38.0 52.0 GK 47.0 €223K 21.0
16708 259646 A. Shaikh 18 https://cdn.sofifa.com/players/259/646/22_60.png India https://cdn.sofifa.com/flags/in.png 47 67 ATK Mohun Bagan FC https://cdn.sofifa.com/teams/113146/30.png ... 13.0 49.0 41.0 39.0 45.0 49.0 GK 47.0 €259K 7.0

14398 rows × 63 columns

df.columns
Index(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Flag', 'Overall',
       'Potential', 'Club', 'Club Logo', 'Value', 'Wage', 'Special',
       'Preferred Foot', 'International Reputation', 'Weak Foot',
       'Skill Moves', 'Work Rate', 'Body Type', 'Real Face', 'Position',
       'Jersey Number', 'Joined', 'Contract Valid Until', 'Height', 'Weight',
       'Crossing', 'Finishing', 'HeadingAccuracy', 'ShortPassing', 'Volleys',
       'Dribbling', 'Curve', 'FKAccuracy', 'LongPassing', 'BallControl',
       'Acceleration', 'SprintSpeed', 'Agility', 'Reactions', 'Balance',
       'ShotPower', 'Jumping', 'Stamina', 'Strength', 'LongShots',
       'Aggression', 'Interceptions', 'Positioning', 'Vision', 'Penalties',
       'Composure', 'StandingTackle', 'SlidingTackle', 'GKDiving',
       'GKHandling', 'GKKicking', 'GKPositioning', 'GKReflexes',
       'Best Position', 'Best Overall Rating', 'Release Clause',
       'DefensiveAwareness'],
      dtype='object')
df.query('Position =="FORWARD" or Position =="DEFENDER"')\
.plot.scatter(
    x='ShotPower',y='SlidingTackle',
    color='Position',
    size='Wage',
    opacity=0.5,
    width=600,
    hover_data=['Name','Age']
)

D..plot.box()

y1=[75,75,76,76,77,77,78,79,79,98] # A선생님에게 통계학을 배운 학생의 점수들
y2=[76,76,77,77,78,78,79,80,80,81] # B선생님에게 통계학을 배운 학생의 점수들 
df = pd.DataFrame({
    'score':y1+y2,
    'class':['A']*len(y1) + ['B']*len(y2)
})
df.plot.box(
    x='class',y='score',
    color='class',
    points='all',
    width=600
)

예제 2 - (년도, 시도)별 전기에너지 사용량

url = 'https://raw.githubusercontent.com/guebin/DV2022/main/posts/Energy/{}.csv'
prov = ['Seoul', 'Busan', 'Daegu', 'Incheon', 
        'Gwangju', 'Daejeon', 'Ulsan', 'Sejongsi', 
        'Gyeonggi-do', 'Gangwon-do', 'Chungcheongbuk-do', 
        'Chungcheongnam-do', 'Jeollabuk-do', 'Jeollanam-do', 
        'Gyeongsangbuk-do', 'Gyeongsangnam-do', 'Jeju-do']
df = pd.concat([pd.read_csv(url.format(p+y)).assign(년도=y, 시도=p) for p in prov for y in ['2018', '2019', '2020', '2021']]).reset_index(drop=True)\
.assign(년도 = lambda df: df.년도.astype(int))\
.set_index(['년도','시도','지역']).applymap(lambda x: int(str(x).replace(',','')))\
.reset_index()
df.head()
년도 시도 지역 건물동수 연면적 에너지사용량(TOE)/전기 에너지사용량(TOE)/도시가스 에너지사용량(TOE)/지역난방
0 2018 Seoul 종로구 17929 9141777 64818 82015 111
1 2018 Seoul 중구 10598 10056233 81672 75260 563
2 2018 Seoul 용산구 17201 10639652 52659 85220 12043
3 2018 Seoul 성동구 14180 11631770 60559 107416 0
4 2018 Seoul 광진구 21520 12054796 70609 130308 0 
df.plot.box(
    x='시도',y='에너지사용량(TOE)/전기',
    color='시도',
    facet_row='년도',
    hover_data=['지역','연면적'],
    height=1600
)

E..plot.hist()

df = pd.read_csv("https://raw.githubusercontent.com/guebin/DV2023/main/posts/titanic.csv")
df
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked logFare
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S 1.981001
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C 4.266662
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 2.070022
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S 3.972177
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S 2.085672
... ... ... ... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S 2.564949
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 B42 S 3.401197
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S 3.154870
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C 3.401197
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q 2.047693

891 rows × 13 columns

df.plot.hist(
    x='Age',
    color='Sex',
    facet_row='Sex',facet_col='Survived'
)

F..plot.area()

df = pd.read_csv('https://raw.githubusercontent.com/guebin/2021DV/master/_notebooks/phone.csv')
df
Date Samsung Apple Huawei Xiaomi Oppo Mobicel Motorola LG Others Realme Google Nokia Lenovo OnePlus Sony Asus
0 2019-10 461 324 136 109 76 81 43 37 135 28 39 14 22 17 20 17
1 2019-11 461 358 167 141 86 61 29 36 141 27 29 20 23 10 19 27
2 2019-12 426 383 143 105 53 45 51 48 129 30 20 26 28 18 18 19
3 2020-01 677 494 212 187 110 79 65 49 158 23 13 19 19 22 27 22
4 2020-02 593 520 217 195 112 67 62 71 157 25 18 16 24 18 23 20
5 2020-03 637 537 246 187 92 66 59 67 145 21 16 24 18 31 22 14
6 2020-04 647 583 222 154 98 59 48 64 113 20 23 25 19 19 23 21
7 2020-05 629 518 192 176 91 87 50 66 150 43 27 15 18 19 19 13
8 2020-06 663 552 209 185 93 69 54 60 140 39 16 16 17 29 25 16
9 2020-07 599 471 214 193 89 78 65 59 130 40 27 25 21 18 18 12
10 2020-08 615 567 204 182 105 82 62 42 129 47 16 23 21 27 23 20
11 2020-09 621 481 230 220 102 88 56 49 143 54 14 15 17 15 19 15
12 2020-10 637 555 232 203 90 52 63 49 140 33 17 20 22 9 22 21 
df.melt(id_vars='Date')\
.set_axis(['날짜','회사','판매량'],axis=1)\
.plot.area(
    x='날짜',y='판매량',
    color='회사',
    width=600
)

예제 2 - 에너지 사용량

url = 'https://raw.githubusercontent.com/guebin/DV2022/main/posts/Energy/{}.csv'
prov = ['Seoul', 'Busan', 'Daegu', 'Incheon', 
        'Gwangju', 'Daejeon', 'Ulsan', 'Sejongsi', 
        'Gyeonggi-do', 'Gangwon-do', 'Chungcheongbuk-do', 
        'Chungcheongnam-do', 'Jeollabuk-do', 'Jeollanam-do', 
        'Gyeongsangbuk-do', 'Gyeongsangnam-do', 'Jeju-do']
df = pd.concat([pd.read_csv(url.format(p+y)).assign(년도=y, 시도=p) for p in prov for y in ['2018', '2019', '2020', '2021']]).reset_index(drop=True)\
.assign(년도 = lambda df: df.년도.astype(int))\
.set_index(['년도','시도','지역']).applymap(lambda x: int(str(x).replace(',','')))\
.reset_index()
df.head()
년도 시도 지역 건물동수 연면적 에너지사용량(TOE)/전기 에너지사용량(TOE)/도시가스 에너지사용량(TOE)/지역난방
0 2018 Seoul 종로구 17929 9141777 64818 82015 111
1 2018 Seoul 중구 10598 10056233 81672 75260 563
2 2018 Seoul 용산구 17201 10639652 52659 85220 12043
3 2018 Seoul 성동구 14180 11631770 60559 107416 0
4 2018 Seoul 광진구 21520 12054796 70609 130308 0 
df.set_index(['년도','시도','지역','건물동수','연면적']).stack().reset_index()\
.rename({'level_5':'에너지종류', 0:'에너지사용량'},axis=1)\
.assign(에너지종류 = lambda df: df['에너지종류'].str.split('/').str[-1])\
.groupby(['년도','시도','에너지종류']).agg({'에너지사용량':'sum'})\
.stack().reset_index()\
.rename({0:'에너지사용량'},axis=1)\
.plot.area(
    x='년도',y='에너지사용량',
    color='시도',
    facet_col='에너지종류'
)

간단한 미세조정

fig = df.set_index(['년도','시도','지역','건물동수','연면적']).stack().reset_index()\
.rename({'level_5':'에너지종류', 0:'에너지사용량'},axis=1)\
.assign(에너지종류 = lambda df: df['에너지종류'].str.split('/').str[-1])\
.groupby(['년도','시도','에너지종류']).agg({'에너지사용량':'sum'})\
.stack().reset_index()\
.rename({0:'에너지사용량'},axis=1)\
.plot.area(
    x='년도',y='에너지사용량',
    color='시도',
    facet_col='에너지종류'
)
fig.update_layout(
    xaxis_domain=[0.0, 0.25],
    xaxis2_domain=[0.35, 0.60],
    xaxis3_domain=[0.70, 0.95]
)