Lesson 18: FIFA23 자료분석

Author

최규빈

Published

February 1, 2023

ref

https://guebin.github.io/DV2022/posts/2022-10-24-8wk-12.html#fifa23-데이터분석

https://guebin.github.io/DV2022/posts/2022-10-30-9wk-1-mid.html#자료분석-및-시각화-i-40점-fifa22자료

imports

# !pip install pandas_profiling
import pandas as pd
import numpy as np
import pandas_profiling
from plotnine import *
ModuleNotFoundError: No module named 'pandas_profiling'

FIFA23 데이터분석

FIFA23 data

- FIFA23라는 축구게임이 있음

- 게임에 실제 선수들이 나오면서 선수들의 능력치가 세밀하게 구현되어 있음

- 선수들 능력치에 대한 데이터셋은 캐글에 공개되어 있음 - https://www.kaggle.com/datasets/bryanb/fifa-player-stats-database?select=FIFA23_official_data.csv

데이터살펴보기

- 일단 살펴보기

df.loc[['Fernandes' in df.Name[i] for i in range(17660)],['Name','Overall']]
Name Overall
1 Bruno Fernandes 86
201 Gedson Fernandes 77
2061 E. Fernandes 71
2544 22 Rodrigo Fernandes 68
4523 Leandro Fernandes 61
5718 Joelson Fernandes 68
9707 S. Fernandes 60
10097 Rafael Fernandes 61
13090 21 D. Fernandes 53
14491 D. Fernandes 49
15616 22 S. Fernandes 48
15782 S. Fernandes 48
15964 D. Heuer Fernandes 73 
df=pd.read_csv('https://raw.githubusercontent.com/guebin/DV2022/master/posts/FIFA23_official_data.csv')
df.head()
ID Name Age Photo Nationality Flag Overall Potential Club Club Logo ... Real Face Position Joined Loaned From Contract Valid Until Height Weight Release Clause Kit Number Best Overall Rating
0 209658 L. Goretzka 27 https://cdn.sofifa.net/players/209/658/23_60.png Germany https://cdn.sofifa.net/flags/de.png 87 88 FC Bayern München https://cdn.sofifa.net/teams/21/30.png ... Yes <span class="pos pos28">SUB Jul 1, 2018 NaN 2026 189cm 82kg €157M 8.0 NaN
1 212198 Bruno Fernandes 27 https://cdn.sofifa.net/players/212/198/23_60.png Portugal https://cdn.sofifa.net/flags/pt.png 86 87 Manchester United https://cdn.sofifa.net/teams/11/30.png ... Yes <span class="pos pos15">LCM Jan 30, 2020 NaN 2026 179cm 69kg €155M 8.0 NaN
2 224334 M. Acuña 30 https://cdn.sofifa.net/players/224/334/23_60.png Argentina https://cdn.sofifa.net/flags/ar.png 85 85 Sevilla FC https://cdn.sofifa.net/teams/481/30.png ... No <span class="pos pos7">LB Sep 14, 2020 NaN 2024 172cm 69kg €97.7M 19.0 NaN
3 192985 K. De Bruyne 31 https://cdn.sofifa.net/players/192/985/23_60.png Belgium https://cdn.sofifa.net/flags/be.png 91 91 Manchester City https://cdn.sofifa.net/teams/10/30.png ... Yes <span class="pos pos13">RCM Aug 30, 2015 NaN 2025 181cm 70kg €198.9M 17.0 NaN
4 224232 N. Barella 25 https://cdn.sofifa.net/players/224/232/23_60.png Italy https://cdn.sofifa.net/flags/it.png 86 89 Inter https://cdn.sofifa.net/teams/44/30.png ... Yes <span class="pos pos13">RCM Sep 1, 2020 NaN 2026 172cm 68kg €154.4M 23.0 NaN

5 rows × 29 columns

트랜스포즈하여 보는 것이 편할때도 있음

df.T
0 1 2 3 4 5 6 7 8 9 ... 17650 17651 17652 17653 17654 17655 17656 17657 17658 17659
ID 209658 212198 224334 192985 224232 212622 197445 187961 208333 210514 ... 256879 269546 267647 253186 267461 269526 267946 270567 256624 256376
Name L. Goretzka Bruno Fernandes M. Acuña K. De Bruyne N. Barella J. Kimmich D. Alaba 22 Paulinho E. Can João Cancelo ... 22 G. Leijon Wu Fei 22 E. Grosz 22 S. Booth 22 L. Grimpe Deng Xiongtao 22 Lim Jun Sub A. Demir 21 S. Czajor 21 F. Jakobsson
Age 27 27 30 31 25 27 30 32 28 28 ... 19 32 18 20 17 19 17 25 18 20
Photo https://cdn.sofifa.net/players/209/658/23_60.png https://cdn.sofifa.net/players/212/198/23_60.png https://cdn.sofifa.net/players/224/334/23_60.png https://cdn.sofifa.net/players/192/985/23_60.png https://cdn.sofifa.net/players/224/232/23_60.png https://cdn.sofifa.net/players/212/622/23_60.png https://cdn.sofifa.net/players/197/445/23_60.png https://cdn.sofifa.net/players/187/961/22_60.png https://cdn.sofifa.net/players/208/333/23_60.png https://cdn.sofifa.net/players/210/514/23_60.png ... https://cdn.sofifa.net/players/256/879/22_60.png https://cdn.sofifa.net/players/269/546/23_60.png https://cdn.sofifa.net/players/267/647/22_60.png https://cdn.sofifa.net/players/253/186/22_60.png https://cdn.sofifa.net/players/267/461/22_60.png https://cdn.sofifa.net/players/269/526/23_60.png https://cdn.sofifa.net/players/267/946/22_60.png https://cdn.sofifa.net/players/270/567/23_60.png https://cdn.sofifa.net/players/256/624/21_60.png https://cdn.sofifa.net/players/256/376/21_60.png
Nationality Germany Portugal Argentina Belgium Italy Germany Austria Brazil Germany Portugal ... Sweden China PR Romania England Germany China PR Korea Republic Turkey Poland Sweden
Flag https://cdn.sofifa.net/flags/de.png https://cdn.sofifa.net/flags/pt.png https://cdn.sofifa.net/flags/ar.png https://cdn.sofifa.net/flags/be.png https://cdn.sofifa.net/flags/it.png https://cdn.sofifa.net/flags/de.png https://cdn.sofifa.net/flags/at.png https://cdn.sofifa.net/flags/br.png https://cdn.sofifa.net/flags/de.png https://cdn.sofifa.net/flags/pt.png ... https://cdn.sofifa.net/flags/se.png https://cdn.sofifa.net/flags/cn.png https://cdn.sofifa.net/flags/ro.png https://cdn.sofifa.net/flags/gb-eng.png https://cdn.sofifa.net/flags/de.png https://cdn.sofifa.net/flags/cn.png https://cdn.sofifa.net/flags/kr.png https://cdn.sofifa.net/flags/tr.png https://cdn.sofifa.net/flags/pl.png https://cdn.sofifa.net/flags/se.png
Overall 87 86 85 91 86 89 86 83 82 88 ... 52 51 52 51 54 48 48 51 50 50
Potential 88 87 85 91 89 90 86 83 82 88 ... 62 51 70 60 68 61 64 56 65 61
Club FC Bayern München Manchester United Sevilla FC Manchester City Inter FC Bayern München Real Madrid CF Al Ahli Borussia Dortmund Manchester City ... Örebro SK Wuhan Three Towns Gaz Metan Mediaş Crewe Alexandra RB Leipzig Meizhou Hakka Jeju United FC Ümraniyespor Fleetwood Town IFK Norrköping
Club Logo https://cdn.sofifa.net/teams/21/30.png https://cdn.sofifa.net/teams/11/30.png https://cdn.sofifa.net/teams/481/30.png https://cdn.sofifa.net/teams/10/30.png https://cdn.sofifa.net/teams/44/30.png https://cdn.sofifa.net/teams/21/30.png https://cdn.sofifa.net/teams/243/30.png https://cdn.sofifa.net/teams/112387/30.png https://cdn.sofifa.net/teams/22/30.png https://cdn.sofifa.net/teams/10/30.png ... https://cdn.sofifa.net/teams/705/30.png https://cdn.sofifa.net/teams/116361/30.png https://cdn.sofifa.net/teams/112637/30.png https://cdn.sofifa.net/teams/121/30.png https://cdn.sofifa.net/teams/112172/30.png https://cdn.sofifa.net/teams/114628/30.png https://cdn.sofifa.net/teams/1478/30.png https://cdn.sofifa.net/teams/113796/30.png https://cdn.sofifa.net/teams/112260/30.png https://cdn.sofifa.net/teams/702/30.png
Value €91M €78.5M €46.5M €107.5M €89.5M €105.5M €55.5M €28.5M €30.5M €82.5M ... €150K €30K €180K €110K €210K €100K €100K €70K €90K €90K
Wage €115K €190K €46K €350K €110K €130K €220K €61K €63K €250K ... €500 €2K €500 €850 €500 €500 €500 €2K €500 €500
Special 2312 2305 2303 2303 2296 2283 2277 2273 2271 2262 ... 779 777 775 768 767 762 761 759 758 749
Preferred Foot Right Right Left Right Right Right Left Right Right Right ... Right Right Right Right Right Right Right Right Right Left
International Reputation 4.0 3.0 2.0 4.0 3.0 4.0 4.0 3.0 3.0 3.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
Weak Foot 4.0 3.0 3.0 5.0 3.0 4.0 4.0 4.0 4.0 4.0 ... 3.0 2.0 2.0 2.0 3.0 3.0 2.0 2.0 2.0 2.0
Skill Moves 3.0 4.0 3.0 4.0 3.0 3.0 3.0 4.0 3.0 4.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
Work Rate High/ Medium High/ High High/ High High/ High High/ High High/ Medium Medium/ Medium High/ High Medium/ High High/ Medium ... Medium/ Medium Medium/ Medium Medium/ Medium Medium/ Medium Medium/ Medium Medium/ Medium Medium/ Medium Medium/ Medium Medium/ Medium Medium/ Medium
Body Type Unique Unique Stocky (170-185) Unique Normal (170-) Normal (170-185) Normal (170-185) Normal (170-185) Stocky (185+) Unique ... Normal (185+) Normal (185+) Lean (185+) Lean (185+) Lean (185+) Normal (185+) Lean (185+) Lean (185+) Normal (185+) Normal (185+)
Real Face Yes Yes No Yes Yes Yes Yes Yes Yes Yes ... No No No No No No No No No No
Position <span class="pos pos28">SUB <span class="pos pos15">LCM <span class="pos pos7">LB <span class="pos pos13">RCM <span class="pos pos13">RCM <span class="pos pos9">RDM <span class="pos pos6">LCB <span class="pos pos15">LCM <span class="pos pos28">SUB <span class="pos pos7">LB ... <span class="pos pos28">SUB <span class="pos pos28">SUB <span class="pos pos28">SUB <span class="pos pos29">RES <span class="pos pos29">RES <span class="pos pos29">RES <span class="pos pos29">RES <span class="pos pos29">RES <span class="pos pos29">RES <span class="pos pos29">RES
Joined Jul 1, 2018 Jan 30, 2020 Sep 14, 2020 Aug 30, 2015 Sep 1, 2020 Jul 1, 2015 Jul 1, 2021 Jul 22, 2021 Feb 18, 2020 Aug 7, 2019 ... Jun 14, 2020 Feb 15, 2019 Jul 1, 2020 Jul 1, 2019 Feb 7, 2022 Apr 11, 2022 Jan 1, 2022 Jun 6, 2021 Jan 1, 2020 Jan 8, 2020
Loaned From NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Contract Valid Until 2026 2026 2024 2025 2026 2025 2026 2024 2024 2027 ... 2022 2022 2022 2022 2023 2027 2026 2023 2021 2021
Height 189cm 179cm 172cm 181cm 172cm 177cm 180cm 183cm 186cm 182cm ... 188cm 186cm 190cm 195cm 186cm 190cm 195cm 190cm 187cm 186cm
Weight 82kg 69kg 69kg 70kg 68kg 75kg 78kg 80kg 86kg 74kg ... 81kg 78kg 70kg 80kg 78kg 78kg 84kg 82kg 79kg 78kg
Release Clause €157M €155M €97.7M €198.9M €154.4M €182M €113.8M €48.5M €51.9M €152.6M ... €218K €47K €356K €215K €488K €218K €188K €142K €214K €131K
Kit Number 8.0 8.0 19.0 17.0 23.0 6.0 4.0 15.0 23.0 7.0 ... 33.0 1.0 99.0 27.0 43.0 35.0 21.0 12.0 40.0 30.0
Best Overall Rating NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

29 rows × 17660 columns

- column이름조사

df.keys()
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',
       'Joined', 'Loaned From', 'Contract Valid Until', 'Height', 'Weight',
       'Release Clause', 'Kit Number', 'Best Overall Rating'],
      dtype='object')
  • 이름에 space가 있어서 좀 거슬림

- 각 column 별로 자료형조사

pd.DataFrame({'colname':df.keys(), 'dtype':[df[key].dtype for key in df.keys()]})
colname dtype
0 ID int64
1 Name object
2 Age int64
3 Photo object
4 Nationality object
5 Flag object
6 Overall int64
7 Potential int64
8 Club object
9 Club Logo object
10 Value object
11 Wage object
12 Special int64
13 Preferred Foot object
14 International Reputation float64
15 Weak Foot float64
16 Skill Moves float64
17 Work Rate object
18 Body Type object
19 Real Face object
20 Position object
21 Joined object
22 Loaned From object
23 Contract Valid Until object
24 Height object
25 Weight object
26 Release Clause object
27 Kit Number float64
28 Best Overall Rating object

- 결측치조사

pd.DataFrame({'colname':df.keys(), 
              'dtype':[df[key].dtype for key in df.keys()],
              'na':[df[key].isna().sum() for key in df.keys()]
             })
colname dtype na
0 ID int64 0
1 Name object 0
2 Age int64 0
3 Photo object 0
4 Nationality object 0
5 Flag object 0
6 Overall int64 0
7 Potential int64 0
8 Club object 211
9 Club Logo object 0
10 Value object 0
11 Wage object 0
12 Special int64 0
13 Preferred Foot object 0
14 International Reputation float64 0
15 Weak Foot float64 0
16 Skill Moves float64 0
17 Work Rate object 0
18 Body Type object 38
19 Real Face object 38
20 Position object 35
21 Joined object 1098
22 Loaned From object 16966
23 Contract Valid Until object 361
24 Height object 0
25 Weight object 0
26 Release Clause object 1151
27 Kit Number float64 35
28 Best Overall Rating object 17639

(퀴즈) 열의선택: 결측치가 10000개 이상인 열을 보고싶다면?

df.loc[:,[df[key].isna().sum()>10000 for key in df.keys()]]
Loaned From Best Overall Rating
0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
... ... ...
17655 NaN NaN
17656 NaN NaN
17657 NaN NaN
17658 NaN NaN
17659 NaN NaN

17660 rows × 2 columns

- .info()

df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 17660 entries, 0 to 17659
Data columns (total 29 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   ID                        17660 non-null  int64  
 1   Name                      17660 non-null  object 
 2   Age                       17660 non-null  int64  
 3   Photo                     17660 non-null  object 
 4   Nationality               17660 non-null  object 
 5   Flag                      17660 non-null  object 
 6   Overall                   17660 non-null  int64  
 7   Potential                 17660 non-null  int64  
 8   Club                      17449 non-null  object 
 9   Club Logo                 17660 non-null  object 
 10  Value                     17660 non-null  object 
 11  Wage                      17660 non-null  object 
 12  Special                   17660 non-null  int64  
 13  Preferred Foot            17660 non-null  object 
 14  International Reputation  17660 non-null  float64
 15  Weak Foot                 17660 non-null  float64
 16  Skill Moves               17660 non-null  float64
 17  Work Rate                 17660 non-null  object 
 18  Body Type                 17622 non-null  object 
 19  Real Face                 17622 non-null  object 
 20  Position                  17625 non-null  object 
 21  Joined                    16562 non-null  object 
 22  Loaned From               694 non-null    object 
 23  Contract Valid Until      17299 non-null  object 
 24  Height                    17660 non-null  object 
 25  Weight                    17660 non-null  object 
 26  Release Clause            16509 non-null  object 
 27  Kit Number                17625 non-null  float64
 28  Best Overall Rating       21 non-null     object 
dtypes: float64(4), int64(5), object(20)
memory usage: 3.9+ MB

- .describe(): 숫자들이 저장된 column에 대하여 기본통계량 조사

df.describe()
ID Age Overall Potential Special International Reputation Weak Foot Skill Moves Kit Number
count 17660.000000 17660.000000 17660.000000 17660.000000 17660.000000 17660.000000 17660.000000 17660.000000 17625.000000
mean 246319.424462 23.127746 63.369592 70.981200 1537.915855 1.106285 2.900340 2.297169 25.037957
std 31487.892861 4.639821 8.036268 6.529836 285.893809 0.407021 0.663523 0.754264 19.154116
min 16.000000 15.000000 43.000000 42.000000 749.000000 1.000000 1.000000 1.000000 1.000000
25% 240732.500000 20.000000 58.000000 67.000000 1387.000000 1.000000 3.000000 2.000000 11.000000
50% 257041.000000 22.000000 63.000000 71.000000 1548.000000 1.000000 3.000000 2.000000 22.000000
75% 263027.500000 26.000000 69.000000 75.000000 1727.000000 1.000000 3.000000 3.000000 32.000000
max 271340.000000 54.000000 91.000000 95.000000 2312.000000 5.000000 5.000000 5.000000 99.000000

- pandas_profiling.ProfileReport()을 이용한 전체적인 조사

# pandas_profiling.ProfileReport(df).to_file('fifa2023_reprot.html')

- 특정열을 중심으로 정렬하여 보기

df.sort_values(by='Overall',ascending=False).reset_index()
index ID Name Age Photo Nationality Flag Overall Potential Club ... Real Face Position Joined Loaned From Contract Valid Until Height Weight Release Clause Kit Number Best Overall Rating
0 41 188545 R. Lewandowski 33 https://cdn.sofifa.net/players/188/545/23_60.png Poland https://cdn.sofifa.net/flags/pl.png 91 91 FC Barcelona ... Yes <span class="pos pos25">ST Jul 18, 2022 NaN 2025 185cm 81kg €172.2M 9.0 NaN
1 124 165153 K. Benzema 34 https://cdn.sofifa.net/players/165/153/23_60.png France https://cdn.sofifa.net/flags/fr.png 91 91 Real Madrid CF ... Yes <span class="pos pos21">CF Jul 9, 2009 NaN 2023 185cm 81kg €131.2M 9.0 NaN
2 3 192985 K. De Bruyne 31 https://cdn.sofifa.net/players/192/985/23_60.png Belgium https://cdn.sofifa.net/flags/be.png 91 91 Manchester City ... Yes <span class="pos pos13">RCM Aug 30, 2015 NaN 2025 181cm 70kg €198.9M 17.0 NaN
3 56 158023 L. Messi 35 https://cdn.sofifa.net/players/158/023/23_60.png Argentina https://cdn.sofifa.net/flags/ar.png 91 91 Paris Saint-Germain ... Yes <span class="pos pos23">RW Aug 10, 2021 NaN 2023 169cm 67kg €99.9M 30.0 NaN
4 75 231747 K. Mbappé 23 https://cdn.sofifa.net/players/231/747/23_60.png France https://cdn.sofifa.net/flags/fr.png 91 95 Paris Saint-Germain ... Yes <span class="pos pos25">ST Jul 1, 2018 NaN 2025 182cm 73kg €366.7M 7.0 NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
17655 15513 266751 22 Jung Ho Yeon 20 https://cdn.sofifa.net/players/266/751/22_60.png Korea Republic https://cdn.sofifa.net/flags/kr.png 45 53 GwangJu FC ... No <span class="pos pos29">RES Jan 20, 2022 NaN 2026 180cm 73kg €145K 23.0 NaN
17656 16215 268279 22 J. Looschen 24 https://cdn.sofifa.net/players/268/279/22_60.png Germany https://cdn.sofifa.net/flags/de.png 44 47 SV Meppen ... No <span class="pos pos29">RES Mar 19, 2022 NaN 2026 178cm 78kg €92K 42.0 NaN
17657 16042 255283 20 Kim Yeong Geun 22 https://cdn.sofifa.net/players/255/283/20_60.png Korea Republic https://cdn.sofifa.net/flags/kr.png 44 49 Gyeongnam FC ... No <span class="pos pos29">RES Jan 9, 2020 NaN 2020 174cm 71kg €53K 43.0 NaN
17658 14634 269038 22 Zhang Wenxuan 16 https://cdn.sofifa.net/players/269/038/22_60.png China PR https://cdn.sofifa.net/flags/cn.png 44 59 Guangzhou FC ... No <span class="pos pos29">RES May 1, 2022 NaN 2022 175cm 70kg €239K 29.0 NaN
17659 17618 168933 07 I. Paskov 33 https://cdn.sofifa.net/players/168/933/07_60.png Bulgaria https://cdn.sofifa.net/flags/bg.png 43 42 NaN ... NaN <span class="pos pos28">SUB NaN NaN NaN 184cm 79kg NaN 24.0 NaN

17660 rows × 30 columns

- 특정열을 중심으로 그룹화하여 보기 (\(\star\))

df.Nationality.unique() # 데이터셋에 포함된 나라들 출력
array(['Germany', 'Portugal', 'Argentina', 'Belgium', 'Italy', 'Austria',
       'Brazil', 'Croatia', 'Serbia', 'Spain', 'Netherlands', 'France',
       'Colombia', 'England', 'Uruguay', 'Morocco', 'Egypt', 'Algeria',
       'Ukraine', 'United States', "Côte d'Ivoire", 'Poland', 'Chile',
       'Senegal', 'Central African Republic', 'Denmark', 'Nigeria',
       'Mexico', 'Turkey', 'Canada', 'Wales', 'Scotland', 'Romania',
       'Czech Republic', 'Ghana', 'Korea Republic',
       'Bosnia and Herzegovina', 'Mali', 'Slovakia', 'Armenia', 'Norway',
       'Switzerland', 'Cameroon', 'Peru', 'Jamaica', 'Zambia', 'Guinea',
       'Sweden', 'North Macedonia', 'Russia', 'Tunisia', 'Malta',
       'Angola', 'Republic of Ireland', 'Ecuador', 'Benin', 'Paraguay',
       'Montenegro', 'Australia', 'Comoros', 'Gabon', 'Iceland',
       'Slovenia', 'Japan', 'Israel', 'China PR', 'Venezuela', 'Liberia',
       'Greece', 'Bulgaria', 'Honduras', 'Saudi Arabia', 'Curacao',
       'Northern Ireland', 'Guinea Bissau', 'Kosovo', 'Hungary',
       'Finland', 'Costa Rica', 'Albania', 'Congo DR', 'Iran',
       'Mozambique', 'Suriname', 'Cape Verde Islands', 'Bolivia',
       'Madagascar', 'New Zealand', 'Burkina Faso', 'Dominican Republic',
       'Kazakhstan', 'Syria', 'Luxembourg', 'Kenya', 'Zimbabwe', 'Haiti',
       'Uzbekistan', 'South Africa', 'Cyprus', 'Qatar',
       'Equatorial Guinea', 'Libya', 'Thailand', 'Togo',
       'Trinidad and Tobago', 'Liechtenstein', 'Gambia', 'Georgia',
       'Philippines', 'Burundi', 'United Arab Emirates', 'Grenada',
       'Iraq', 'Panama', 'Malaysia', 'Moldova', 'Congo', 'India',
       'Jordan', 'Kuwait', 'Antigua and Barbuda', 'Cuba', 'Vietnam',
       'Korea DPR', 'Uganda', 'Lithuania', 'Estonia', 'Montserrat',
       'Sierra Leone', 'Afghanistan', 'New Caledonia', 'Belarus', 'Laos',
       'Saint Lucia', 'Bhutan', 'Guyana', 'Mauritania', 'Faroe Islands',
       'Namibia', 'Niger', 'Palestine', 'Sudan', 'Azerbaijan',
       'Hong Kong', 'Gibraltar', 'Tanzania', 'Latvia', 'Chinese Taipei',
       'Singapore', 'Lebanon', 'El Salvador', 'Indonesia', 'Guatemala',
       'Papua New Guinea', 'Puerto Rico', 'Malawi', 'South Sudan',
       'Ethiopia', 'San Marino', 'Andorra', 'Saint Kitts and Nevis'],
      dtype=object)
df.groupby(by='Nationality')[['Overall']].agg({np.mean,len}).sort_values(('Overall', 'mean'),ascending=False)
Overall
mean len
Nationality
Philippines 74.000000 1
Namibia 72.000000 1
Mozambique 72.000000 2
Kuwait 71.000000 1
Brazil 70.556586 539
... ... ...
San Marino 53.000000 1
China PR 52.230769 325
South Sudan 52.000000 5
India 51.994681 188
Saint Kitts and Nevis 51.000000 1

161 rows × 2 columns

  • groupby는 나중에 다시 설명 합니다.

데이터정리하기

- 칼럼이름변경

df.set_axis(pd.Index(map(lambda x: x.replace(' ','_'), df.columns)), axis=1)
ID Name Age Photo Nationality Flag Overall Potential Club Club_Logo ... Real_Face Position Joined Loaned_From Contract_Valid_Until Height Weight Release_Clause Kit_Number Best_Overall_Rating
0 209658 L. Goretzka 27 https://cdn.sofifa.net/players/209/658/23_60.png Germany https://cdn.sofifa.net/flags/de.png 87 88 FC Bayern München https://cdn.sofifa.net/teams/21/30.png ... Yes <span class="pos pos28">SUB Jul 1, 2018 NaN 2026 189cm 82kg €157M 8.0 NaN
1 212198 Bruno Fernandes 27 https://cdn.sofifa.net/players/212/198/23_60.png Portugal https://cdn.sofifa.net/flags/pt.png 86 87 Manchester United https://cdn.sofifa.net/teams/11/30.png ... Yes <span class="pos pos15">LCM Jan 30, 2020 NaN 2026 179cm 69kg €155M 8.0 NaN
2 224334 M. Acuña 30 https://cdn.sofifa.net/players/224/334/23_60.png Argentina https://cdn.sofifa.net/flags/ar.png 85 85 Sevilla FC https://cdn.sofifa.net/teams/481/30.png ... No <span class="pos pos7">LB Sep 14, 2020 NaN 2024 172cm 69kg €97.7M 19.0 NaN
3 192985 K. De Bruyne 31 https://cdn.sofifa.net/players/192/985/23_60.png Belgium https://cdn.sofifa.net/flags/be.png 91 91 Manchester City https://cdn.sofifa.net/teams/10/30.png ... Yes <span class="pos pos13">RCM Aug 30, 2015 NaN 2025 181cm 70kg €198.9M 17.0 NaN
4 224232 N. Barella 25 https://cdn.sofifa.net/players/224/232/23_60.png Italy https://cdn.sofifa.net/flags/it.png 86 89 Inter https://cdn.sofifa.net/teams/44/30.png ... Yes <span class="pos pos13">RCM Sep 1, 2020 NaN 2026 172cm 68kg €154.4M 23.0 NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
17655 269526 Deng Xiongtao 19 https://cdn.sofifa.net/players/269/526/23_60.png China PR https://cdn.sofifa.net/flags/cn.png 48 61 Meizhou Hakka https://cdn.sofifa.net/teams/114628/30.png ... No <span class="pos pos29">RES Apr 11, 2022 NaN 2027 190cm 78kg €218K 35.0 NaN
17656 267946 22 Lim Jun Sub 17 https://cdn.sofifa.net/players/267/946/22_60.png Korea Republic https://cdn.sofifa.net/flags/kr.png 48 64 Jeju United FC https://cdn.sofifa.net/teams/1478/30.png ... No <span class="pos pos29">RES Jan 1, 2022 NaN 2026 195cm 84kg €188K 21.0 NaN
17657 270567 A. Demir 25 https://cdn.sofifa.net/players/270/567/23_60.png Turkey https://cdn.sofifa.net/flags/tr.png 51 56 Ümraniyespor https://cdn.sofifa.net/teams/113796/30.png ... No <span class="pos pos29">RES Jun 6, 2021 NaN 2023 190cm 82kg €142K 12.0 NaN
17658 256624 21 S. Czajor 18 https://cdn.sofifa.net/players/256/624/21_60.png Poland https://cdn.sofifa.net/flags/pl.png 50 65 Fleetwood Town https://cdn.sofifa.net/teams/112260/30.png ... No <span class="pos pos29">RES Jan 1, 2020 NaN 2021 187cm 79kg €214K 40.0 NaN
17659 256376 21 F. Jakobsson 20 https://cdn.sofifa.net/players/256/376/21_60.png Sweden https://cdn.sofifa.net/flags/se.png 50 61 IFK Norrköping https://cdn.sofifa.net/teams/702/30.png ... No <span class="pos pos29">RES Jan 8, 2020 NaN 2021 186cm 78kg €131K 30.0 NaN

17660 rows × 29 columns

- 결측치제거

df.drop(columns=['Loaned From', 'Best Overall Rating']).dropna()
ID Name Age Photo Nationality Flag Overall Potential Club Club Logo ... Work Rate Body Type Real Face Position Joined Contract Valid Until Height Weight Release Clause Kit Number
0 209658 L. Goretzka 27 https://cdn.sofifa.net/players/209/658/23_60.png Germany https://cdn.sofifa.net/flags/de.png 87 88 FC Bayern München https://cdn.sofifa.net/teams/21/30.png ... High/ Medium Unique Yes <span class="pos pos28">SUB Jul 1, 2018 2026 189cm 82kg €157M 8.0
1 212198 Bruno Fernandes 27 https://cdn.sofifa.net/players/212/198/23_60.png Portugal https://cdn.sofifa.net/flags/pt.png 86 87 Manchester United https://cdn.sofifa.net/teams/11/30.png ... High/ High Unique Yes <span class="pos pos15">LCM Jan 30, 2020 2026 179cm 69kg €155M 8.0
2 224334 M. Acuña 30 https://cdn.sofifa.net/players/224/334/23_60.png Argentina https://cdn.sofifa.net/flags/ar.png 85 85 Sevilla FC https://cdn.sofifa.net/teams/481/30.png ... High/ High Stocky (170-185) No <span class="pos pos7">LB Sep 14, 2020 2024 172cm 69kg €97.7M 19.0
3 192985 K. De Bruyne 31 https://cdn.sofifa.net/players/192/985/23_60.png Belgium https://cdn.sofifa.net/flags/be.png 91 91 Manchester City https://cdn.sofifa.net/teams/10/30.png ... High/ High Unique Yes <span class="pos pos13">RCM Aug 30, 2015 2025 181cm 70kg €198.9M 17.0
4 224232 N. Barella 25 https://cdn.sofifa.net/players/224/232/23_60.png Italy https://cdn.sofifa.net/flags/it.png 86 89 Inter https://cdn.sofifa.net/teams/44/30.png ... High/ High Normal (170-) Yes <span class="pos pos13">RCM Sep 1, 2020 2026 172cm 68kg €154.4M 23.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
17655 269526 Deng Xiongtao 19 https://cdn.sofifa.net/players/269/526/23_60.png China PR https://cdn.sofifa.net/flags/cn.png 48 61 Meizhou Hakka https://cdn.sofifa.net/teams/114628/30.png ... Medium/ Medium Normal (185+) No <span class="pos pos29">RES Apr 11, 2022 2027 190cm 78kg €218K 35.0
17656 267946 22 Lim Jun Sub 17 https://cdn.sofifa.net/players/267/946/22_60.png Korea Republic https://cdn.sofifa.net/flags/kr.png 48 64 Jeju United FC https://cdn.sofifa.net/teams/1478/30.png ... Medium/ Medium Lean (185+) No <span class="pos pos29">RES Jan 1, 2022 2026 195cm 84kg €188K 21.0
17657 270567 A. Demir 25 https://cdn.sofifa.net/players/270/567/23_60.png Turkey https://cdn.sofifa.net/flags/tr.png 51 56 Ümraniyespor https://cdn.sofifa.net/teams/113796/30.png ... Medium/ Medium Lean (185+) No <span class="pos pos29">RES Jun 6, 2021 2023 190cm 82kg €142K 12.0
17658 256624 21 S. Czajor 18 https://cdn.sofifa.net/players/256/624/21_60.png Poland https://cdn.sofifa.net/flags/pl.png 50 65 Fleetwood Town https://cdn.sofifa.net/teams/112260/30.png ... Medium/ Medium Normal (185+) No <span class="pos pos29">RES Jan 1, 2020 2021 187cm 79kg €214K 40.0
17659 256376 21 F. Jakobsson 20 https://cdn.sofifa.net/players/256/376/21_60.png Sweden https://cdn.sofifa.net/flags/se.png 50 61 IFK Norrköping https://cdn.sofifa.net/teams/702/30.png ... Medium/ Medium Normal (185+) No <span class="pos pos29">RES Jan 8, 2020 2021 186cm 78kg €131K 30.0

16364 rows × 27 columns

- Height, Weight의 자료형을 float형으로 수정하기

df.assign(
    Height= list(map(lambda x: float(x[:-2]), df.Height)),
    Weight= list(map(lambda x: float(x[:-2]), df.Weight))
)
ID Name Age Photo Nationality Flag Overall Potential Club Club Logo ... Real Face Position Joined Loaned From Contract Valid Until Height Weight Release Clause Kit Number Best Overall Rating
0 209658 L. Goretzka 27 https://cdn.sofifa.net/players/209/658/23_60.png Germany https://cdn.sofifa.net/flags/de.png 87 88 FC Bayern München https://cdn.sofifa.net/teams/21/30.png ... Yes <span class="pos pos28">SUB Jul 1, 2018 NaN 2026 189.0 82.0 €157M 8.0 NaN
1 212198 Bruno Fernandes 27 https://cdn.sofifa.net/players/212/198/23_60.png Portugal https://cdn.sofifa.net/flags/pt.png 86 87 Manchester United https://cdn.sofifa.net/teams/11/30.png ... Yes <span class="pos pos15">LCM Jan 30, 2020 NaN 2026 179.0 69.0 €155M 8.0 NaN
2 224334 M. Acuña 30 https://cdn.sofifa.net/players/224/334/23_60.png Argentina https://cdn.sofifa.net/flags/ar.png 85 85 Sevilla FC https://cdn.sofifa.net/teams/481/30.png ... No <span class="pos pos7">LB Sep 14, 2020 NaN 2024 172.0 69.0 €97.7M 19.0 NaN
3 192985 K. De Bruyne 31 https://cdn.sofifa.net/players/192/985/23_60.png Belgium https://cdn.sofifa.net/flags/be.png 91 91 Manchester City https://cdn.sofifa.net/teams/10/30.png ... Yes <span class="pos pos13">RCM Aug 30, 2015 NaN 2025 181.0 70.0 €198.9M 17.0 NaN
4 224232 N. Barella 25 https://cdn.sofifa.net/players/224/232/23_60.png Italy https://cdn.sofifa.net/flags/it.png 86 89 Inter https://cdn.sofifa.net/teams/44/30.png ... Yes <span class="pos pos13">RCM Sep 1, 2020 NaN 2026 172.0 68.0 €154.4M 23.0 NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
17655 269526 Deng Xiongtao 19 https://cdn.sofifa.net/players/269/526/23_60.png China PR https://cdn.sofifa.net/flags/cn.png 48 61 Meizhou Hakka https://cdn.sofifa.net/teams/114628/30.png ... No <span class="pos pos29">RES Apr 11, 2022 NaN 2027 190.0 78.0 €218K 35.0 NaN
17656 267946 22 Lim Jun Sub 17 https://cdn.sofifa.net/players/267/946/22_60.png Korea Republic https://cdn.sofifa.net/flags/kr.png 48 64 Jeju United FC https://cdn.sofifa.net/teams/1478/30.png ... No <span class="pos pos29">RES Jan 1, 2022 NaN 2026 195.0 84.0 €188K 21.0 NaN
17657 270567 A. Demir 25 https://cdn.sofifa.net/players/270/567/23_60.png Turkey https://cdn.sofifa.net/flags/tr.png 51 56 Ümraniyespor https://cdn.sofifa.net/teams/113796/30.png ... No <span class="pos pos29">RES Jun 6, 2021 NaN 2023 190.0 82.0 €142K 12.0 NaN
17658 256624 21 S. Czajor 18 https://cdn.sofifa.net/players/256/624/21_60.png Poland https://cdn.sofifa.net/flags/pl.png 50 65 Fleetwood Town https://cdn.sofifa.net/teams/112260/30.png ... No <span class="pos pos29">RES Jan 1, 2020 NaN 2021 187.0 79.0 €214K 40.0 NaN
17659 256376 21 F. Jakobsson 20 https://cdn.sofifa.net/players/256/376/21_60.png Sweden https://cdn.sofifa.net/flags/se.png 50 61 IFK Norrköping https://cdn.sofifa.net/teams/702/30.png ... No <span class="pos pos29">RES Jan 8, 2020 NaN 2021 186.0 78.0 €131K 30.0 NaN

17660 rows × 29 columns

- Release Clause의 자료형을 float으로 수정하기

df['Release Clause']
0          €157M
1          €155M
2         €97.7M
3        €198.9M
4        €154.4M
          ...   
17655      €218K
17656      €188K
17657      €142K
17658      €214K
17659      €131K
Name: Release Clause, Length: 17660, dtype: object
_f = lambda x: float(x[1:-1])*1000 if x[-1]=='K' else float(x[1:-1])*1000000
_f('€157M')
157000000.0
_f('€131K')
131000.0

(시도1–실패)

list(map(_f,df['Release Clause']))
TypeError: 'float' object is not subscriptable

(시도1이 실패한 이유)

df['Release Clause'].isna().sum() # 이 column에는 1151개의 결측치가 존재
1151

(nan에 대한 예비학습)

df.loc[df['Release Clause'].isna(), 'Release Clause']
18       NaN
34       NaN
38       NaN
49       NaN
50       NaN
        ... 
17378    NaN
17386    NaN
17535    NaN
17590    NaN
17618    NaN
Name: Release Clause, Length: 1151, dtype: object
df.loc[18, 'Release Clause']
nan
pd.isna(df.loc[18, 'Release Clause'])
True
type(df.loc[18, 'Release Clause'])
float
df.loc[18, 'Release Clause'][-1]
TypeError: 'float' object is not subscriptable

(시도2–성공)

df.rename(columns={'Release Clause':'ReleaseClause'})\
.assign(ReleaseClause = list(map(lambda x: _f(x) if pd.isna(x)==False else x , df['Release Clause'])))\
.rename(columns={'ReleaseClause':'Release Clause'})
ID Name Age Photo Nationality Flag Overall Potential Club Club Logo ... Real Face Position Joined Loaned From Contract Valid Until Height Weight Release Clause Kit Number Best Overall Rating
0 209658 L. Goretzka 27 https://cdn.sofifa.net/players/209/658/23_60.png Germany https://cdn.sofifa.net/flags/de.png 87 88 FC Bayern München https://cdn.sofifa.net/teams/21/30.png ... Yes <span class="pos pos28">SUB Jul 1, 2018 NaN 2026 189cm 82kg 157000000.0 8.0 NaN
1 212198 Bruno Fernandes 27 https://cdn.sofifa.net/players/212/198/23_60.png Portugal https://cdn.sofifa.net/flags/pt.png 86 87 Manchester United https://cdn.sofifa.net/teams/11/30.png ... Yes <span class="pos pos15">LCM Jan 30, 2020 NaN 2026 179cm 69kg 155000000.0 8.0 NaN
2 224334 M. Acuña 30 https://cdn.sofifa.net/players/224/334/23_60.png Argentina https://cdn.sofifa.net/flags/ar.png 85 85 Sevilla FC https://cdn.sofifa.net/teams/481/30.png ... No <span class="pos pos7">LB Sep 14, 2020 NaN 2024 172cm 69kg 97700000.0 19.0 NaN
3 192985 K. De Bruyne 31 https://cdn.sofifa.net/players/192/985/23_60.png Belgium https://cdn.sofifa.net/flags/be.png 91 91 Manchester City https://cdn.sofifa.net/teams/10/30.png ... Yes <span class="pos pos13">RCM Aug 30, 2015 NaN 2025 181cm 70kg 198900000.0 17.0 NaN
4 224232 N. Barella 25 https://cdn.sofifa.net/players/224/232/23_60.png Italy https://cdn.sofifa.net/flags/it.png 86 89 Inter https://cdn.sofifa.net/teams/44/30.png ... Yes <span class="pos pos13">RCM Sep 1, 2020 NaN 2026 172cm 68kg 154400000.0 23.0 NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
17655 269526 Deng Xiongtao 19 https://cdn.sofifa.net/players/269/526/23_60.png China PR https://cdn.sofifa.net/flags/cn.png 48 61 Meizhou Hakka https://cdn.sofifa.net/teams/114628/30.png ... No <span class="pos pos29">RES Apr 11, 2022 NaN 2027 190cm 78kg 218000.0 35.0 NaN
17656 267946 22 Lim Jun Sub 17 https://cdn.sofifa.net/players/267/946/22_60.png Korea Republic https://cdn.sofifa.net/flags/kr.png 48 64 Jeju United FC https://cdn.sofifa.net/teams/1478/30.png ... No <span class="pos pos29">RES Jan 1, 2022 NaN 2026 195cm 84kg 188000.0 21.0 NaN
17657 270567 A. Demir 25 https://cdn.sofifa.net/players/270/567/23_60.png Turkey https://cdn.sofifa.net/flags/tr.png 51 56 Ümraniyespor https://cdn.sofifa.net/teams/113796/30.png ... No <span class="pos pos29">RES Jun 6, 2021 NaN 2023 190cm 82kg 142000.0 12.0 NaN
17658 256624 21 S. Czajor 18 https://cdn.sofifa.net/players/256/624/21_60.png Poland https://cdn.sofifa.net/flags/pl.png 50 65 Fleetwood Town https://cdn.sofifa.net/teams/112260/30.png ... No <span class="pos pos29">RES Jan 1, 2020 NaN 2021 187cm 79kg 214000.0 40.0 NaN
17659 256376 21 F. Jakobsson 20 https://cdn.sofifa.net/players/256/376/21_60.png Sweden https://cdn.sofifa.net/flags/se.png 50 61 IFK Norrköping https://cdn.sofifa.net/teams/702/30.png ... No <span class="pos pos29">RES Jan 8, 2020 NaN 2021 186cm 78kg 131000.0 30.0 NaN

17660 rows × 29 columns

(시도3–성공) 그냥 결측치를 제거하고 변형해도 무방..

df2 = df.drop(columns=['Loaned From', 'Best Overall Rating']).dropna()
df2['Release Clause'] = list(map(lambda x: _f(x) if pd.isna(x)==False else x , df2['Release Clause']))
df2
ID Name Age Photo Nationality Flag Overall Potential Club Club Logo ... Work Rate Body Type Real Face Position Joined Contract Valid Until Height Weight Release Clause Kit Number
0 209658 L. Goretzka 27 https://cdn.sofifa.net/players/209/658/23_60.png Germany https://cdn.sofifa.net/flags/de.png 87 88 FC Bayern München https://cdn.sofifa.net/teams/21/30.png ... High/ Medium Unique Yes <span class="pos pos28">SUB Jul 1, 2018 2026 189cm 82kg 157000000.0 8.0
1 212198 Bruno Fernandes 27 https://cdn.sofifa.net/players/212/198/23_60.png Portugal https://cdn.sofifa.net/flags/pt.png 86 87 Manchester United https://cdn.sofifa.net/teams/11/30.png ... High/ High Unique Yes <span class="pos pos15">LCM Jan 30, 2020 2026 179cm 69kg 155000000.0 8.0
2 224334 M. Acuña 30 https://cdn.sofifa.net/players/224/334/23_60.png Argentina https://cdn.sofifa.net/flags/ar.png 85 85 Sevilla FC https://cdn.sofifa.net/teams/481/30.png ... High/ High Stocky (170-185) No <span class="pos pos7">LB Sep 14, 2020 2024 172cm 69kg 97700000.0 19.0
3 192985 K. De Bruyne 31 https://cdn.sofifa.net/players/192/985/23_60.png Belgium https://cdn.sofifa.net/flags/be.png 91 91 Manchester City https://cdn.sofifa.net/teams/10/30.png ... High/ High Unique Yes <span class="pos pos13">RCM Aug 30, 2015 2025 181cm 70kg 198900000.0 17.0
4 224232 N. Barella 25 https://cdn.sofifa.net/players/224/232/23_60.png Italy https://cdn.sofifa.net/flags/it.png 86 89 Inter https://cdn.sofifa.net/teams/44/30.png ... High/ High Normal (170-) Yes <span class="pos pos13">RCM Sep 1, 2020 2026 172cm 68kg 154400000.0 23.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
17655 269526 Deng Xiongtao 19 https://cdn.sofifa.net/players/269/526/23_60.png China PR https://cdn.sofifa.net/flags/cn.png 48 61 Meizhou Hakka https://cdn.sofifa.net/teams/114628/30.png ... Medium/ Medium Normal (185+) No <span class="pos pos29">RES Apr 11, 2022 2027 190cm 78kg 218000.0 35.0
17656 267946 22 Lim Jun Sub 17 https://cdn.sofifa.net/players/267/946/22_60.png Korea Republic https://cdn.sofifa.net/flags/kr.png 48 64 Jeju United FC https://cdn.sofifa.net/teams/1478/30.png ... Medium/ Medium Lean (185+) No <span class="pos pos29">RES Jan 1, 2022 2026 195cm 84kg 188000.0 21.0
17657 270567 A. Demir 25 https://cdn.sofifa.net/players/270/567/23_60.png Turkey https://cdn.sofifa.net/flags/tr.png 51 56 Ümraniyespor https://cdn.sofifa.net/teams/113796/30.png ... Medium/ Medium Lean (185+) No <span class="pos pos29">RES Jun 6, 2021 2023 190cm 82kg 142000.0 12.0
17658 256624 21 S. Czajor 18 https://cdn.sofifa.net/players/256/624/21_60.png Poland https://cdn.sofifa.net/flags/pl.png 50 65 Fleetwood Town https://cdn.sofifa.net/teams/112260/30.png ... Medium/ Medium Normal (185+) No <span class="pos pos29">RES Jan 1, 2020 2021 187cm 79kg 214000.0 40.0
17659 256376 21 F. Jakobsson 20 https://cdn.sofifa.net/players/256/376/21_60.png Sweden https://cdn.sofifa.net/flags/se.png 50 61 IFK Norrköping https://cdn.sofifa.net/teams/702/30.png ... Medium/ Medium Normal (185+) No <span class="pos pos29">RES Jan 8, 2020 2021 186cm 78kg 131000.0 30.0

16364 rows × 27 columns

분석의 편의를 위하여 (1) colnames를 변경하고 (2) 결측치를 제거하고 (3) 몇 가지 전 처리를 추가로 진행한 뒤 df2를 만들어서 분석하는게 좋음

데이터분석+시각화

- Overall vs Potential

ggplot(data=df) + geom_point(aes(x='Overall',y='Potential'))

<ggplot: (8772275859669)>
  • 뭔가 Potential > Overall 인 관계가 성립하는 듯 하다. \(\to\) 우리가 생각하는 포텐셜의 의미는 사실 Potential2 = Potential - Overall 에 더 가깝다. \(\to\) Potential2 = Potential - Overall 인 변수를 새로 만들고 시각화 해보자.

- Potential2 = Potential - Overall 를 계산하여 새로운 열을 추가하자.

df.eval('Potential2 = Potential - Overall')
ID Name Age Photo Nationality Flag Overall Potential Club Club Logo ... Position Joined Loaned From Contract Valid Until Height Weight Release Clause Kit Number Best Overall Rating Potential2
0 209658 L. Goretzka 27 https://cdn.sofifa.net/players/209/658/23_60.png Germany https://cdn.sofifa.net/flags/de.png 87 88 FC Bayern München https://cdn.sofifa.net/teams/21/30.png ... <span class="pos pos28">SUB Jul 1, 2018 NaN 2026 189cm 82kg €157M 8.0 NaN 1
1 212198 Bruno Fernandes 27 https://cdn.sofifa.net/players/212/198/23_60.png Portugal https://cdn.sofifa.net/flags/pt.png 86 87 Manchester United https://cdn.sofifa.net/teams/11/30.png ... <span class="pos pos15">LCM Jan 30, 2020 NaN 2026 179cm 69kg €155M 8.0 NaN 1
2 224334 M. Acuña 30 https://cdn.sofifa.net/players/224/334/23_60.png Argentina https://cdn.sofifa.net/flags/ar.png 85 85 Sevilla FC https://cdn.sofifa.net/teams/481/30.png ... <span class="pos pos7">LB Sep 14, 2020 NaN 2024 172cm 69kg €97.7M 19.0 NaN 0
3 192985 K. De Bruyne 31 https://cdn.sofifa.net/players/192/985/23_60.png Belgium https://cdn.sofifa.net/flags/be.png 91 91 Manchester City https://cdn.sofifa.net/teams/10/30.png ... <span class="pos pos13">RCM Aug 30, 2015 NaN 2025 181cm 70kg €198.9M 17.0 NaN 0
4 224232 N. Barella 25 https://cdn.sofifa.net/players/224/232/23_60.png Italy https://cdn.sofifa.net/flags/it.png 86 89 Inter https://cdn.sofifa.net/teams/44/30.png ... <span class="pos pos13">RCM Sep 1, 2020 NaN 2026 172cm 68kg €154.4M 23.0 NaN 3
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
17655 269526 Deng Xiongtao 19 https://cdn.sofifa.net/players/269/526/23_60.png China PR https://cdn.sofifa.net/flags/cn.png 48 61 Meizhou Hakka https://cdn.sofifa.net/teams/114628/30.png ... <span class="pos pos29">RES Apr 11, 2022 NaN 2027 190cm 78kg €218K 35.0 NaN 13
17656 267946 22 Lim Jun Sub 17 https://cdn.sofifa.net/players/267/946/22_60.png Korea Republic https://cdn.sofifa.net/flags/kr.png 48 64 Jeju United FC https://cdn.sofifa.net/teams/1478/30.png ... <span class="pos pos29">RES Jan 1, 2022 NaN 2026 195cm 84kg €188K 21.0 NaN 16
17657 270567 A. Demir 25 https://cdn.sofifa.net/players/270/567/23_60.png Turkey https://cdn.sofifa.net/flags/tr.png 51 56 Ümraniyespor https://cdn.sofifa.net/teams/113796/30.png ... <span class="pos pos29">RES Jun 6, 2021 NaN 2023 190cm 82kg €142K 12.0 NaN 5
17658 256624 21 S. Czajor 18 https://cdn.sofifa.net/players/256/624/21_60.png Poland https://cdn.sofifa.net/flags/pl.png 50 65 Fleetwood Town https://cdn.sofifa.net/teams/112260/30.png ... <span class="pos pos29">RES Jan 1, 2020 NaN 2021 187cm 79kg €214K 40.0 NaN 15
17659 256376 21 F. Jakobsson 20 https://cdn.sofifa.net/players/256/376/21_60.png Sweden https://cdn.sofifa.net/flags/se.png 50 61 IFK Norrköping https://cdn.sofifa.net/teams/702/30.png ... <span class="pos pos29">RES Jan 8, 2020 NaN 2021 186cm 78kg €131K 30.0 NaN 11

17660 rows × 30 columns

- 수정된 데이터프레임으로 다시 시각화를 하자.

ggplot(data=df.eval('Potential2 = Potential - Overall'))\
+ geom_point(aes(x='Overall',y='Potential2'),alpha=0.01)

<ggplot: (8772307230189)>

- 일부점들이 겹치므로 position = ’jitter’를 사용하여 점들을 흩뿌리자.

ggplot(data=df.eval('Potential2 = Potential - Overall'))\
+ geom_point(aes(x='Overall',y='Potential2'),alpha=0.05,position='jitter')

<ggplot: (8772275596573)>

- 해석 - 해석1: Overall, Potential2는 음의 상관관계가 있다. - 해석2: 0근처에 데이터가 많음 \(\to\) 이미 은퇴한 선수들이 아닐까? - 해석3: Overall의 값이 작을수록 Potential2의 분산이 크다.

- 은퇴한 선수들은 제외하고 시각화하자.

ggplot(data=df.eval('Potential2 = Potential - Overall').query('Potential2 > 1'))\
+ geom_point(aes(x='Overall',y='Potential2'),alpha=0.05,position='jitter')

<ggplot: (8772275556513)>

- Overall에 따라서 구간을 나누고 그 구간에 대응하는 boxplot을 그리자.

df.eval('Potential2 = Potential - Overall').query('Potential2 > 1')\
.Overall.describe()
count    13644.000000
mean        61.415347
std          7.247821
min         44.000000
25%         56.000000
50%         61.000000
75%         66.000000
max         91.000000
Name: Overall, dtype: float64
def f(x):
    if x>66: 
        y='66<'
    elif x>61:
        y='61~66'
    elif x>56:
        y='56~61'
    else:
        y='<56' 
    return y
ggplot(data=df.eval('Potential2 = Potential - Overall').query('Potential2 > 1')\
.assign(Overall_grouped= lambda df: list(map(f,df.Overall))))\
    + geom_boxplot(aes(x='Overall_grouped',y='Potential2',color='Overall_grouped'))

<ggplot: (8772275730901)>

  • Overall_grouped = “<56” 에 대응하는 점들을 모두 뽑아서 mean(Overall)를 계산하고 그 값을 Overall_grouped = “<56” 에 대응하는 박스플랏의 x축위치로 설정

  • Overall_grouped = “56~61” 에 대응하는 점들을 모두 뽑아서 mean(Overall)를 계산하고 그 값을 Overall_grouped = “56~61” 에 대응하는 박스플랏의 x축위치로 설정

  • Overall_grouped = “61~66” 에 대응하는 점들을 모두 뽑아서 mean(Overall)를 계산하고 그 값을 Overall_grouped = “61~66” 에 대응하는 박스플랏의 x축위치로 설정

  • Overall_grouped = “66<” 에 대응하는 점들을 모두 뽑아서 mean(Overall)를 계산하고 그 값을 Overall_grouped = “66<” 에 대응하는 박스플랏의 x축위치로 설정

df.eval('Potential2 = Potential - Overall').query('Potential2 > 1')\
.assign(Overall_grouped= lambda df: list(map(f,df.Overall)))\
.query("Overall_grouped == '66<'").Overall.mean()
71.8127687727423

(방법1)

def g(x):
    if x=='66<': 
        y= 71.8127687727423
    elif x=='61~66':
        y= 63.773918342474104
    elif x=='56~61':
        y= 59.155840684309005
    else:
        y= 52.87743190661479
    return y
df.eval('Potential2 = Potential - Overall').query('Potential2 > 1')\
.assign(Overall_grouped= lambda df: list(map(f,df.Overall)))\
.assign(Overall_x= lambda df: list(map(g,df.Overall_grouped)))
ID Name Age Photo Nationality Flag Overall Potential Club Club Logo ... Loaned From Contract Valid Until Height Weight Release Clause Kit Number Best Overall Rating Potential2 Overall_grouped Overall_x
4 224232 N. Barella 25 https://cdn.sofifa.net/players/224/232/23_60.png Italy https://cdn.sofifa.net/flags/it.png 86 89 Inter https://cdn.sofifa.net/teams/44/30.png ... NaN 2026 172cm 68kg €154.4M 23.0 NaN 3 66< 71.812769
10 228251 L. Pellegrini 26 https://cdn.sofifa.net/players/228/251/23_60.png Italy https://cdn.sofifa.net/flags/it.png 84 87 Roma https://cdn.sofifa.net/teams/52/30.png ... NaN 2026 186cm 77kg €97.6M 7.0 NaN 3 66< 71.812769
13 225193 Merino 26 https://cdn.sofifa.net/players/225/193/23_60.png Spain https://cdn.sofifa.net/flags/es.png 83 86 Real Sociedad https://cdn.sofifa.net/teams/457/30.png ... NaN 2025 189cm 83kg €102.2M 8.0 NaN 3 66< 71.812769
17 228702 F. de Jong 25 https://cdn.sofifa.net/players/228/702/23_60.png Netherlands https://cdn.sofifa.net/flags/nl.png 87 92 FC Barcelona https://cdn.sofifa.net/teams/241/30.png ... NaN 2026 180cm 74kg €247.6M 21.0 NaN 5 66< 71.812769
21 231281 T. Alexander-Arnold 23 https://cdn.sofifa.net/players/231/281/23_60.png England https://cdn.sofifa.net/flags/gb-eng.png 87 90 Liverpool https://cdn.sofifa.net/teams/9/30.png ... NaN 2025 180cm 69kg €193.5M 66.0 NaN 3 66< 71.812769
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
17655 269526 Deng Xiongtao 19 https://cdn.sofifa.net/players/269/526/23_60.png China PR https://cdn.sofifa.net/flags/cn.png 48 61 Meizhou Hakka https://cdn.sofifa.net/teams/114628/30.png ... NaN 2027 190cm 78kg €218K 35.0 NaN 13 <56 52.877432
17656 267946 22 Lim Jun Sub 17 https://cdn.sofifa.net/players/267/946/22_60.png Korea Republic https://cdn.sofifa.net/flags/kr.png 48 64 Jeju United FC https://cdn.sofifa.net/teams/1478/30.png ... NaN 2026 195cm 84kg €188K 21.0 NaN 16 <56 52.877432
17657 270567 A. Demir 25 https://cdn.sofifa.net/players/270/567/23_60.png Turkey https://cdn.sofifa.net/flags/tr.png 51 56 Ümraniyespor https://cdn.sofifa.net/teams/113796/30.png ... NaN 2023 190cm 82kg €142K 12.0 NaN 5 <56 52.877432
17658 256624 21 S. Czajor 18 https://cdn.sofifa.net/players/256/624/21_60.png Poland https://cdn.sofifa.net/flags/pl.png 50 65 Fleetwood Town https://cdn.sofifa.net/teams/112260/30.png ... NaN 2021 187cm 79kg €214K 40.0 NaN 15 <56 52.877432
17659 256376 21 F. Jakobsson 20 https://cdn.sofifa.net/players/256/376/21_60.png Sweden https://cdn.sofifa.net/flags/se.png 50 61 IFK Norrköping https://cdn.sofifa.net/teams/702/30.png ... NaN 2021 186cm 78kg €131K 30.0 NaN 11 <56 52.877432

13644 rows × 32 columns

df2= df.eval('Potential2 = Potential - Overall').query('Potential2 > 1')\
.assign(Overall_grouped= lambda df: list(map(f,df.Overall)))\
.assign(Overall_x= lambda df: list(map(g,df.Overall_grouped)))
df2
ID Name Age Photo Nationality Flag Overall Potential Club Club Logo ... Loaned From Contract Valid Until Height Weight Release Clause Kit Number Best Overall Rating Potential2 Overall_grouped Overall_x
4 224232 N. Barella 25 https://cdn.sofifa.net/players/224/232/23_60.png Italy https://cdn.sofifa.net/flags/it.png 86 89 Inter https://cdn.sofifa.net/teams/44/30.png ... NaN 2026 172cm 68kg €154.4M 23.0 NaN 3 66< 71.812769
10 228251 L. Pellegrini 26 https://cdn.sofifa.net/players/228/251/23_60.png Italy https://cdn.sofifa.net/flags/it.png 84 87 Roma https://cdn.sofifa.net/teams/52/30.png ... NaN 2026 186cm 77kg €97.6M 7.0 NaN 3 66< 71.812769
13 225193 Merino 26 https://cdn.sofifa.net/players/225/193/23_60.png Spain https://cdn.sofifa.net/flags/es.png 83 86 Real Sociedad https://cdn.sofifa.net/teams/457/30.png ... NaN 2025 189cm 83kg €102.2M 8.0 NaN 3 66< 71.812769
17 228702 F. de Jong 25 https://cdn.sofifa.net/players/228/702/23_60.png Netherlands https://cdn.sofifa.net/flags/nl.png 87 92 FC Barcelona https://cdn.sofifa.net/teams/241/30.png ... NaN 2026 180cm 74kg €247.6M 21.0 NaN 5 66< 71.812769
21 231281 T. Alexander-Arnold 23 https://cdn.sofifa.net/players/231/281/23_60.png England https://cdn.sofifa.net/flags/gb-eng.png 87 90 Liverpool https://cdn.sofifa.net/teams/9/30.png ... NaN 2025 180cm 69kg €193.5M 66.0 NaN 3 66< 71.812769
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
17655 269526 Deng Xiongtao 19 https://cdn.sofifa.net/players/269/526/23_60.png China PR https://cdn.sofifa.net/flags/cn.png 48 61 Meizhou Hakka https://cdn.sofifa.net/teams/114628/30.png ... NaN 2027 190cm 78kg €218K 35.0 NaN 13 <56 52.877432
17656 267946 22 Lim Jun Sub 17 https://cdn.sofifa.net/players/267/946/22_60.png Korea Republic https://cdn.sofifa.net/flags/kr.png 48 64 Jeju United FC https://cdn.sofifa.net/teams/1478/30.png ... NaN 2026 195cm 84kg €188K 21.0 NaN 16 <56 52.877432
17657 270567 A. Demir 25 https://cdn.sofifa.net/players/270/567/23_60.png Turkey https://cdn.sofifa.net/flags/tr.png 51 56 Ümraniyespor https://cdn.sofifa.net/teams/113796/30.png ... NaN 2023 190cm 82kg €142K 12.0 NaN 5 <56 52.877432
17658 256624 21 S. Czajor 18 https://cdn.sofifa.net/players/256/624/21_60.png Poland https://cdn.sofifa.net/flags/pl.png 50 65 Fleetwood Town https://cdn.sofifa.net/teams/112260/30.png ... NaN 2021 187cm 79kg €214K 40.0 NaN 15 <56 52.877432
17659 256376 21 F. Jakobsson 20 https://cdn.sofifa.net/players/256/376/21_60.png Sweden https://cdn.sofifa.net/flags/se.png 50 61 IFK Norrköping https://cdn.sofifa.net/teams/702/30.png ... NaN 2021 186cm 78kg €131K 30.0 NaN 11 <56 52.877432

13644 rows × 32 columns

ggplot(data=df2)\
+geom_point(aes(x='Overall',y='Potential2',color='Overall_grouped'),position='jitter',alpha=0.05)\
+geom_boxplot(aes(x='Overall_x',y='Potential2',color='Overall_grouped'))

<ggplot: (8772275710537)>

(방법2)

_df = df.eval('Potential2 = Potential - Overall').query('Potential2 > 1')\
.assign(Overall_grouped= lambda df: list(map(f,df.Overall)))
df3=_df.groupby(by="Overall_grouped").agg({'Overall':np.mean}).reset_index()\
.rename(columns={'Overall':'Overall_x'}).merge(_df)
ggplot(data=df3)\
+geom_point(aes(x='Overall',y='Potential2',color='Overall_grouped'),position='jitter',alpha=0.05)\
+geom_boxplot(aes(x='Overall_x',y='Potential2',color='Overall_grouped'))

<ggplot: (8772275671993)>

FIFA22 데이터분석

데이터시각화 중간고사문제에서 발췌

아래의 코드를 활용하여 FIFA22의 자료를 불러온뒤 물음에 답하라.

df = pd.read_csv('https://raw.githubusercontent.com/guebin/DV2021/master/_notebooks/2021-10-25-FIFA22_official_data.csv')
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
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
16705 240558 18 L. Clayton 17 https://cdn.sofifa.com/players/240/558/18_60.png England https://cdn.sofifa.com/flags/gb-eng.png 53 70 Cheltenham Town https://cdn.sofifa.com/teams/1936/30.png ... 12.0 55.0 54.0 52.0 50.0 59.0 GK 52.0 €238K NaN
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
16709 178453 07 A. Censori 17 https://cdn.sofifa.com/players/178/453/07_60.png Italy https://cdn.sofifa.com/flags/it.png 28 38 Arezzo https://cdn.sofifa.com/teams/110907/30.png ... NaN 7.0 1.0 36.0 6.0 9.0 ST 36.0 NaN NaN

16710 rows × 65 columns

(1) 연령별로 선수들의 잠재력을 시각화하고 싶다. 여기에서 잠재력은 아래의 수식의 Potential2를 의미한다.

Potential2 = Potential - Overall

아래의 세부지침에 맞추어 연령별 Potential2의 산점도와 boxplot을 그려라. – (10점)

(세부지침)

step1: 결측치가 가장 많은 2개의 컬럼을 찾고 이를 제거하라.

step2: dropna()를 이용하여 결측치를 제거하라.

step3: Potential2 = Potential - Overall 를 이용하여 Potential2를 구하라.

step4: 구간 [0,20,22,26,100]를 설정하고 이를 기준으로 Age를 그룹화하라. (총 4개의 그룹으로 나누어져야 한다)

step5: 그룹화된 Age를 x축으로, Potential2를 y축으로, 색깔을 그룹화된 Age로 설정한 뒤 산점도와 박스플랏을 겹쳐그려라. - 산점도의 파라메터: alpha=0.5,size=0.1,position=‘jitter’ - 박스플랏의 파라메터: alpha=0.8

(풀이)

df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 16710 entries, 0 to 16709
Data columns (total 65 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   ID                        16710 non-null  int64  
 1   Name                      16710 non-null  object 
 2   Age                       16710 non-null  int64  
 3   Photo                     16710 non-null  object 
 4   Nationality               16710 non-null  object 
 5   Flag                      16710 non-null  object 
 6   Overall                   16710 non-null  int64  
 7   Potential                 16710 non-null  int64  
 8   Club                      16446 non-null  object 
 9   Club Logo                 16710 non-null  object 
 10  Value                     16710 non-null  object 
 11  Wage                      16710 non-null  object 
 12  Special                   16710 non-null  int64  
 13  Preferred Foot            16710 non-null  object 
 14  International Reputation  16710 non-null  float64
 15  Weak Foot                 16710 non-null  float64
 16  Skill Moves               16710 non-null  float64
 17  Work Rate                 16710 non-null  object 
 18  Body Type                 16681 non-null  object 
 19  Real Face                 16681 non-null  object 
 20  Position                  16684 non-null  object 
 21  Jersey Number             16684 non-null  float64
 22  Joined                    15198 non-null  object 
 23  Loaned From               1132 non-null   object 
 24  Contract Valid Until      16359 non-null  object 
 25  Height                    16710 non-null  object 
 26  Weight                    16710 non-null  object 
 27  Crossing                  16710 non-null  float64
 28  Finishing                 16710 non-null  float64
 29  HeadingAccuracy           16710 non-null  float64
 30  ShortPassing              16710 non-null  float64
 31  Volleys                   16673 non-null  float64
 32  Dribbling                 16710 non-null  float64
 33  Curve                     16673 non-null  float64
 34  FKAccuracy                16710 non-null  float64
 35  LongPassing               16710 non-null  float64
 36  BallControl               16710 non-null  float64
 37  Acceleration              16710 non-null  float64
 38  SprintSpeed               16710 non-null  float64
 39  Agility                   16673 non-null  float64
 40  Reactions                 16710 non-null  float64
 41  Balance                   16673 non-null  float64
 42  ShotPower                 16710 non-null  float64
 43  Jumping                   16673 non-null  float64
 44  Stamina                   16710 non-null  float64
 45  Strength                  16710 non-null  float64
 46  LongShots                 16710 non-null  float64
 47  Aggression                16710 non-null  float64
 48  Interceptions             16702 non-null  float64
 49  Positioning               16702 non-null  float64
 50  Vision                    16673 non-null  float64
 51  Penalties                 16710 non-null  float64
 52  Composure                 16459 non-null  float64
 53  Marking                   892 non-null    float64
 54  StandingTackle            16710 non-null  float64
 55  SlidingTackle             16673 non-null  float64
 56  GKDiving                  16710 non-null  float64
 57  GKHandling                16710 non-null  float64
 58  GKKicking                 16710 non-null  float64
 59  GKPositioning             16710 non-null  float64
 60  GKReflexes                16710 non-null  float64
 61  Best Position             16710 non-null  object 
 62  Best Overall Rating       16710 non-null  float64
 63  Release Clause            14961 non-null  object 
 64  DefensiveAwareness        15818 non-null  float64
dtypes: float64(40), int64(5), object(20)
memory usage: 8.3+ MB
  • ’Loaned From’와 ’Marking’이 가장 결측치가 많이 포함되어있음
data1= df.drop(columns=['Loaned From', 'Marking']).dropna()\
.eval('Potential2 = Potential- Overall')\
.assign(Age = lambda df: pd.cut(df['Age'],[0,20,22,26,100]))
data1
ID Name Age Photo Nationality Flag Overall Potential Club Club Logo ... GKDiving GKHandling GKKicking GKPositioning GKReflexes Best Position Best Overall Rating Release Clause DefensiveAwareness Potential2
0 212198 Bruno Fernandes (22, 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 ... 12.0 14.0 15.0 8.0 14.0 CAM 88.0 €206.9M 72.0 1
1 209658 L. Goretzka (22, 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 ... 13.0 8.0 15.0 11.0 9.0 CM 87.0 €160.4M 74.0 1
2 176580 L. Suárez (26, 100] 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 ... 27.0 25.0 31.0 33.0 37.0 ST 88.0 €91.2M 42.0 0
3 192985 K. De Bruyne (26, 100] 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 ... 15.0 13.0 5.0 10.0 13.0 CM 91.0 €232.2M 68.0 0
4 224334 M. Acuña (26, 100] 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 ... 8.0 14.0 13.0 13.0 14.0 LB 84.0 €77.7M 80.0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
16703 259718 F. Gebhardt (0, 20] 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 ... 53.0 45.0 47.0 52.0 57.0 GK 52.0 €361K 6.0 14
16704 251433 B. Voll (0, 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 ... 59.0 60.0 56.0 55.0 61.0 GK 58.0 €656K 5.0 11
16706 262846 �. Dobre (0, 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 ... 57.0 52.0 53.0 48.0 58.0 GK 53.0 €279K 5.0 10
16707 241317 21 Xue Qinghao (0, 20] 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 ... 49.0 48.0 45.0 38.0 52.0 GK 47.0 €223K 21.0 13
16708 259646 A. Shaikh (0, 20] 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 ... 49.0 41.0 39.0 45.0 49.0 GK 47.0 €259K 7.0 20

14398 rows × 64 columns

fig = ggplot(data=data1)
scatter = geom_point(aes(x='Age',y='Potential2',colour='Age'),alpha=0.5,size=0.1,position='jitter')
boxplot = geom_boxplot(aes(x='Age',y='Potential2',colour='Age'),alpha=0.8)
fig+scatter+boxplot

<ggplot: (8776319269061)>
  • 채점기준: Age의 Label을 사용하지 않아도 만점으로 인정함.

(2) 포지션별로 선수들의 능력치와 Wage를 시각화하고 싶다. 아래의 dictionary를 이용하여 Position을 재정의하라.

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'}
}
position_dict
{'GOALKEEPER': {'GK'},
 'DEFENDER': {'CB', 'LB', 'LCB', 'LWB', 'RB', 'RCB', 'RWB'},
 'MIDFIELDER': {'CAM',
  'CDM',
  'CM',
  'LAM',
  'LCM',
  'LDM',
  'LM',
  'RAM',
  'RCM',
  'RDM',
  'RM'},
 'FORWARD': {'CF', 'LF', 'LS', 'LW', 'RF', 'RS', 'RW', 'ST'},
 'SUB': {'SUB'},
 'RES': {'RES'}}

아래의 세부지침에 맞추어 포지션별 ShotPower와 SlidingTackle의 산점도를 그려라. – (30점)

세부지침

step1: 결측치가 가장 많은 2개의 컬럼을 찾고 이를 제거하라.

step2: dropna()를 이용하여 결측치를 제거하라.

step3: hint1과 position_dict을 참고하여 Position을 적절하게 변환하라. (변환된 값을 Position으로 저장할 것)

step4: hint2를 참고하여 Wage를 적절하게 변환하라. (변환된 값을 Wage에 저장할 것)

step5: Position==“DEFENDER” or Position==“FORWARD”에 해당하는 관측치를 고른 뒤 x축에 ShotPower, y축에 SlidingTackle 을 시각화하라. Position은 color로 구분하고 Wage는 size와 alpha로 구분하라.

hint1: Position column의 변환을 위한 코드

'<span class="pos pos18">CAM'.split('>')
['<span class="pos pos18"', 'CAM']

hint2: Wage column의 변환을 위한 함수

def f(x):
    if x[-1] == 'K' :
        y= float(x[1:-1])*1000
    elif x[-1] == 'M' : 
        y= float(x[1:-1])*1000000
    else:
        y= 0 
    return y

(풀이)

data2=df.drop(columns=['Loaned From', 'Marking']).dropna()\
.assign(Wage = lambda df: list(map(f,df['Wage'])))\
.assign(Position = lambda df: list(map(lambda x: x.split('>')[-1], df.Position)))\
.assign(Position = lambda df: 
        [key for x in df.Position for key in position_dict if x in position_dict[key]]
       )
data2
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

fig = ggplot(data=data2.query('Position=="DEFENDER" or Position=="FORWARD"'))
scatter = geom_point(aes(x='ShotPower',y='SlidingTackle',color='Position',size='Wage',alpha='Wage'))
fig+scatter

<ggplot: (8776325574701)>
  • 채점기준: df[‘Position’]이 아니라 df[‘Best Position’]을 이용하여 자료를 변형하고 시각화 하는 경우 부분점수 없이 0점임