[Pandas] Pandas 연습 문제 풀기 -3 🐼

 

 

 

 

 

 

데이터 EDA를 위한 Pandas 역량기르기 타~임!

 

 

 

 

 

 

필요한 라이브러리 임포트¶

In [2]:

import pandas as pd
import numpy as np
import os

In [3]:

os.getcwd()

Out[3]:

'C:\\sona\\KDT\\pandas\\Day_2'

In [4]:

os.listdir()

Out[4]:

['.ipynb_checkpoints',
 'auto-mpg.csv',
 'Euro_2012_stats_TEAM.csv',
 'Example-01.ipynb',
 'matzipData.csv',
 'Pandas-05.ipynb',
 'Pandas-06.ipynb',
 '남북한발전전력량.xlsx',
 '소상공인시장진흥공단_상가업소정보_의료기관.csv',
 '연습03.ipynb',
 '연습04.ipynb',
 '주가데이터.xlsx']

 

read_csv를 통해 데이터를 올바른 형태로 불러온 뒤 euro12 저장해주세요¶

파일: 유로2012에 참가한 전체 팀의 통계값파일인 './data/Euro_2012_stats_TEAM.csv'

In [7]:

df = pd.read_csv( './data/Euro_2012_stats_TEAM.csv')
df

Out[7]:

	Team	Goals	Shots on target	Shots off target	Shooting Accuracy	% Goals-to-shots	Total shots (inc. Blocked)	Hit Woodwork	Penalty goals	Penalties not scored	...	Saves made	Saves-to-shots ratio	Fouls Won	Fouls Conceded	Offsides	Yellow Cards	Red Cards	Subs on	Subs off	Players Used
0	Croatia	4	13	12	51.9%	16.0%	32	0	0	0	...	13	81.3%	41	62	2	9	0	9	9	16
1	Czech Republic	4	13	18	41.9%	12.9%	39	0	0	0	...	9	60.1%	53	73	8	7	0	11	11	19
2	Denmark	4	10	10	50.0%	20.0%	27	1	0	0	...	10	66.7%	25	38	8	4	0	7	7	15
3	England	5	11	18	50.0%	17.2%	40	0	0	0	...	22	88.1%	43	45	6	5	0	11	11	16
4	France	3	22	24	37.9%	6.5%	65	1	0	0	...	6	54.6%	36	51	5	6	0	11	11	19
5	Germany	10	32	32	47.8%	15.6%	80	2	1	0	...	10	62.6%	63	49	12	4	0	15	15	17
6	Greece	5	8	18	30.7%	19.2%	32	1	1	1	...	13	65.1%	67	48	12	9	1	12	12	20
7	Italy	6	34	45	43.0%	7.5%	110	2	0	0	...	20	74.1%	101	89	16	16	0	18	18	19
8	Netherlands	2	12	36	25.0%	4.1%	60	2	0	0	...	12	70.6%	35	30	3	5	0	7	7	15
9	Poland	2	15	23	39.4%	5.2%	48	0	0	0	...	6	66.7%	48	56	3	7	1	7	7	17
10	Portugal	6	22	42	34.3%	9.3%	82	6	0	0	...	10	71.5%	73	90	10	12	0	14	14	16
11	Republic of Ireland	1	7	12	36.8%	5.2%	28	0	0	0	...	17	65.4%	43	51	11	6	1	10	10	17
12	Russia	5	9	31	22.5%	12.5%	59	2	0	0	...	10	77.0%	34	43	4	6	0	7	7	16
13	Spain	12	42	33	55.9%	16.0%	100	0	1	0	...	15	93.8%	102	83	19	11	0	17	17	18
14	Sweden	5	17	19	47.2%	13.8%	39	3	0	0	...	8	61.6%	35	51	7	7	0	9	9	18
15	Ukraine	2	7	26	21.2%	6.0%	38	0	0	0	...	13	76.5%	48	31	4	5	0	9	9	18

16 rows × 35 columns

In [8]:

df.to_csv("./data/euro12.csv")

In [9]:

os.listdir('./data')

Out[9]:

['.ipynb_checkpoints',
 'auto-mpg.csv',
 'euro12.csv',
 'Euro_2012_stats_TEAM.csv',
 'matzipData.csv',
 '남북한발전전력량.xlsx',
 '소상공인시장진흥공단_상가업소정보_의료기관.csv',
 '주가데이터.xlsx']

In [18]:

euro12 = pd.read_csv("./data/euro12.csv", index_col=0)

 

Goal 칼럼만 조회해주세요¶

In [10]:

df["Goals"]

Out[10]:

0      4
1      4
2      4
3      5
4      3
5     10
6      5
7      6
8      2
9      2
10     6
11     1
12     5
13    12
14     5
15     2
Name: Goals, dtype: int64

 

몇 팀이 Euro2012에 참가했나요?¶

16

In [13]:

df["Team"].count()

Out[13]:

16

 

데이터셋은 몇 개의 열로 이루어져 있나요?¶

35

In [15]:

df.shape[1]

Out[15]:

35

 

euro12 데이터프레임에서 Team, Yellow Cards, Red Cards 칼럼만 따로 선택하고 이 결과를 discipline 이라는 변수에 저장하세요.¶

In [21]:

discipline = euro12[["Team","Yellow Cards","Red Cards"]]

In [22]:

discipline

Out[22]:

	Team	Yellow Cards	Red Cards
0	Croatia	9	0
1	Czech Republic	7	0
2	Denmark	4	0
3	England	5	0
4	France	6	0
5	Germany	4	0
6	Greece	9	1
7	Italy	16	0
8	Netherlands	5	0
9	Poland	7	1
10	Portugal	12	0
11	Republic of Ireland	6	1
12	Russia	6	0
13	Spain	11	0
14	Sweden	7	0
15	Ukraine	5	0

 

discpline칼럼을 정렬하세요.¶

정렬기준은 Red Cards기준 내림차순. Red Cards 수가 동일할 경우 Yellow Cards순으로 내림차순 입니다.

여러 칼럼을 기준으로 정렬하고자 한다면 ['Red Cards', 'Yellow Cards'] 형태로 정렬 기준을 입력해주면 됩니다.

In [25]:

discipline.sort_values(['Red Cards', 'Yellow Cards'], ascending=[False,False])

Out[25]:

	Team	Yellow Cards	Red Cards
6	Greece	9	1
9	Poland	7	1
11	Republic of Ireland	6	1
7	Italy	16	0
10	Portugal	12	0
13	Spain	11	0
0	Croatia	9	0
1	Czech Republic	7	0
14	Sweden	7	0
4	France	6	0
12	Russia	6	0
3	England	5	0
8	Netherlands	5	0
15	Ukraine	5	0
2	Denmark	4	0
5	Germany	4	0

 

팀당 평균 옐로카드 수를 구하세요¶

7.0

In [32]:

round(discipline["Yellow Cards"].mean())

Out[32]:

7

 

7골 이상 득점한 팀을 조회하세요¶

Germany, Spain

In [37]:

euro12[euro12["Goals"]>=7]

Out[37]:

	Team	Goals	Shots on target	Shots off target	Shooting Accuracy	% Goals-to-shots	Total shots (inc. Blocked)	Hit Woodwork	Penalty goals	Penalties not scored	...	Saves made	Saves-to-shots ratio	Fouls Won	Fouls Conceded	Offsides	Yellow Cards	Red Cards	Subs on	Subs off	Players Used
5	Germany	10	32	32	47.8%	15.6%	80	2	1	0	...	10	62.6%	63	49	12	4	0	15	15	17
13	Spain	12	42	33	55.9%	16.0%	100	0	1	0	...	15	93.8%	102	83	19	11	0	17	17	18

2 rows × 35 columns

 

팀명이 G로 시작하는 팀을 조회하세요¶

Germany, Greece

In [69]:

euro12[euro12["Team"].str.startswith("G")]

Out[69]:

	Team	Goals	Shots on target	Shots off target	Shooting Accuracy	% Goals-to-shots	Total shots (inc. Blocked)	Hit Woodwork	Penalty goals	Penalties not scored	...	Saves made	Saves-to-shots ratio	Fouls Won	Fouls Conceded	Offsides	Yellow Cards	Red Cards	Subs on	Subs off	Players Used
5	Germany	10	32	32	47.8%	15.6%	80	2	1	0	...	10	62.6%	63	49	12	4	0	15	15	17
6	Greece	5	8	18	30.7%	19.2%	32	1	1	1	...	13	65.1%	67	48	12	9	1	12	12	20

2 rows × 35 columns

 

처음부터 7번째 칼럼까지만 조회하세요¶

In [64]:

euro12.iloc[:,:7]

Out[64]:

	Team	Goals	Shots on target	Shots off target	Shooting Accuracy	% Goals-to-shots	Total shots (inc. Blocked)
0	Croatia	4	13	12	51.9%	16.0%	32
1	Czech Republic	4	13	18	41.9%	12.9%	39
2	Denmark	4	10	10	50.0%	20.0%	27
3	England	5	11	18	50.0%	17.2%	40
4	France	3	22	24	37.9%	6.5%	65
5	Germany	10	32	32	47.8%	15.6%	80
6	Greece	5	8	18	30.7%	19.2%	32
7	Italy	6	34	45	43.0%	7.5%	110
8	Netherlands	2	12	36	25.0%	4.1%	60
9	Poland	2	15	23	39.4%	5.2%	48
10	Portugal	6	22	42	34.3%	9.3%	82
11	Republic of Ireland	1	7	12	36.8%	5.2%	28
12	Russia	5	9	31	22.5%	12.5%	59
13	Spain	12	42	33	55.9%	16.0%	100
14	Sweden	5	17	19	47.2%	13.8%	39
15	Ukraine	2	7	26	21.2%	6.0%	38

 

끝에서 3개의 칼럼만 제외하고 euro12 데이터프레임을 조회하세요¶

In [71]:

euro12.iloc[:,:-3]

Out[71]:

	Team	Goals	Shots on target	Shots off target	Shooting Accuracy	% Goals-to-shots	Total shots (inc. Blocked)	Hit Woodwork	Penalty goals	Penalties not scored	...	Clean Sheets	Blocks	Goals conceded	Saves made	Saves-to-shots ratio	Fouls Won	Fouls Conceded	Offsides	Yellow Cards	Red Cards
0	Croatia	4	13	12	51.9%	16.0%	32	0	0	0	...	0	10	3	13	81.3%	41	62	2	9	0
1	Czech Republic	4	13	18	41.9%	12.9%	39	0	0	0	...	1	10	6	9	60.1%	53	73	8	7	0
2	Denmark	4	10	10	50.0%	20.0%	27	1	0	0	...	1	10	5	10	66.7%	25	38	8	4	0
3	England	5	11	18	50.0%	17.2%	40	0	0	0	...	2	29	3	22	88.1%	43	45	6	5	0
4	France	3	22	24	37.9%	6.5%	65	1	0	0	...	1	7	5	6	54.6%	36	51	5	6	0
5	Germany	10	32	32	47.8%	15.6%	80	2	1	0	...	1	11	6	10	62.6%	63	49	12	4	0
6	Greece	5	8	18	30.7%	19.2%	32	1	1	1	...	1	23	7	13	65.1%	67	48	12	9	1
7	Italy	6	34	45	43.0%	7.5%	110	2	0	0	...	2	18	7	20	74.1%	101	89	16	16	0
8	Netherlands	2	12	36	25.0%	4.1%	60	2	0	0	...	0	9	5	12	70.6%	35	30	3	5	0
9	Poland	2	15	23	39.4%	5.2%	48	0	0	0	...	0	8	3	6	66.7%	48	56	3	7	1
10	Portugal	6	22	42	34.3%	9.3%	82	6	0	0	...	2	11	4	10	71.5%	73	90	10	12	0
11	Republic of Ireland	1	7	12	36.8%	5.2%	28	0	0	0	...	0	23	9	17	65.4%	43	51	11	6	1
12	Russia	5	9	31	22.5%	12.5%	59	2	0	0	...	0	8	3	10	77.0%	34	43	4	6	0
13	Spain	12	42	33	55.9%	16.0%	100	0	1	0	...	5	8	1	15	93.8%	102	83	19	11	0
14	Sweden	5	17	19	47.2%	13.8%	39	3	0	0	...	1	12	5	8	61.6%	35	51	7	7	0
15	Ukraine	2	7	26	21.2%	6.0%	38	0	0	0	...	0	4	4	13	76.5%	48	31	4	5	0

16 rows × 32 columns

 

England, Italy, Russia의 Shooting Accuracy만 조회하세요¶

행은 3행(England, Italy, Russia) 열은 2열(Team, Shooting Accuracy)

In [80]:

euro12.loc[[3,7,12],["Team","Shooting Accuracy"]]

Out[80]:

	Team	Shooting Accuracy
3	England	50.0%
7	Italy	43.0%
12	Russia	22.5%

 

 

 

 

 

 

🐼 이제 조금씩 업그레이드 되어가는 지금!! EDA 고수들의 샘플들을 한번 찾아봐야징!