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개발일지/임시카테고리

pandas 판다스 기초 23 replace 응용 중요 루틴 및 dropna등 데이터 클리닝 여태까지 배운것 활용

by 다니엘의 개발 이야기 2022. 8. 19.
320x100

전체적으로 시험을 염두하고 강의한 섹션이였는데,

처음엔 모르다가도, 그래도 연결고리를 들으니 완전 마지막이나, 부분적으로 왜 그렇게 하는지

이해가 안간 것도 몇가지 있었으나 그 외의것은 아주 짜임새 있고 쓸모가 많다는

유용한 섹션이라는 생각이 들었다.

 

* Step은 중간에 안쓴것도 있다. 그냥 없는대로 흘려서 보자.

# Some Guidance and Hints
## Step 1: Getting Started

import pandas as pd
import numpy as np

summer = pd.read_csv('summer.csv')
wik_1996 = pd.read_csv('wik_1996.csv')
wik_1976 = pd.read_csv('wik_1976.csv')

summer.head()

'''

Year	City	Sport	Discipline	Athlete	Country	Gender	Event	Medal
0	1896	Athens	Aquatics	Swimming	HAJOS, Alfred	HUN	Men	100M Freestyle	Gold
1	1896	Athens	Aquatics	Swimming	HERSCHMANN, Otto	AUT	Men	100M Freestyle	Silver
2	1896	Athens	Aquatics	Swimming	DRIVAS, Dimitrios	GRE	Men	100M Freestyle For Sailors	Bronze
3	1896	Athens	Aquatics	Swimming	MALOKINIS, Ioannis	GRE	Men	100M Freestyle For Sailors	Gold
4	1896	Athens	Aquatics	Swimming	CHASAPIS, Spiridon	GRE	Men	100M Freestyle For Sailors	Silver
'''
summer.tail()

'''

Year	City	Sport	Discipline	Athlete	Country	Gender	Event	Medal
31160	2012	London	Wrestling	Wrestling Freestyle	JANIKOWSKI, Damian	POL	Men	Wg 84 KG	Bronze
31161	2012	London	Wrestling	Wrestling Freestyle	REZAEI, Ghasem Gholamreza	IRI	Men	Wg 96 KG	Gold
31162	2012	London	Wrestling	Wrestling Freestyle	TOTROV, Rustam	RUS	Men	Wg 96 KG	Silver
31163	2012	London	Wrestling	Wrestling Freestyle	ALEKSANYAN, Artur	ARM	Men	Wg 96 KG	Bronze
31164	2012	London	Wrestling	Wrestling Freestyle	LIDBERG, Jimmy	SWE	Men	Wg 96 KG	Bronze
'''
# 컬럼을 기준으로 결측값이 있는 행을 리턴
# 결측치 리턴
# 결측값 리턴
# Nan 리턴

summer.isna().any(axis = 1)

'''
0        False
1        False
2        False
3        False
4        False
         ...  
31160    False
31161    False
31162    False
31163    False
31164    False
Length: 31165, dtype: bool
'''
summer.loc[summer.isna().any(axis = 1)]

'''

Year	City	Sport	Discipline	Athlete	Country	Gender	Event	Medal
29603	2012	London	Athletics	Athletics	Pending	NaN	Women	1500M	Gold
31072	2012	London	Weightlifting	Weightlifting	Pending	NaN	Women	63KG	Gold
31091	2012	London	Weightlifting	Weightlifting	Pending	NaN	Men	94KG	Silver
31110	2012	London	Wrestling	Wrestling Freestyle	KUDUKHOV, Besik	NaN	Men	Wf 60 KG	Silver
'''
summer.dropna(inplace = True)
wik_1976.head()

'''
	Rank	NOC	Gold	Silver	Bronze	Total
0	1	Soviet Union (URS)	49	41	35	125
1	2	East Germany (GDR)	40	25	25	90
2	3	United States (USA)	34	35	25	94
3	4	West Germany (FRG)	10	12	17	39
4	5	Japan (JPN)	9	6	10	25
'''
wik_1996.head()

'''
	Rank	Nation	Gold	Silver	Bronze	Total
0	1	United States (USA)*	44	32	25	101
1	2	Russia (RUS)	26	21	16	63
2	3	Germany (GER)	20	18	27	65
3	4	China (CHN)	16	22	12	50
4	5	France (FRA)	15	7	15	37
'''
wik_1976.NOC.str.split('(', expand = True).iloc[:, 1].str.replace(')', '').str.replace('*','')
# 1. wik_1976 변수에 들어있는
# 2. NOC 컬럼 안의 값 중에서
# 3. 문자열 값이 있는데 (str)
# 4. 그 중에서 ( 라는 녀석을 찾아서
# 5. 없애주고, 그 없앤 ( 를 기준해서 양옆으로 각 한개씩으로 나눠라.
# 6. 나눈 후에는 DataFrame화 해줘라. ( expand = True)

wik_1976.NOC.str.split('(', expand = True)
# (를 기준으로 분할이 된 것 중에서 전체의 행, 그 중에서 1번 컬럼 인덱스를 가진 것을 리턴해줘

wik_1976.NOC.str.split('(', expand = True).iloc[:, 1]
# 그리고 다시 ) 라는 문자를 찾아서 아무것도 없는 것으로 변환해줘

wik_1976.NOC.str.split('(', expand = True).iloc[:,1].str.replace(')', '')
wik_1976.head()

'''
Rank	NOC	Gold	Silver	Bronze	Total
0	1	Soviet Union (URS)	49	41	35	125
1	2	East Germany (GDR)	40	25	25	90
2	3	United States (USA)	34	35	25	94
3	4	West Germany (FRG)	10	12	17	39
4	5	Japan (JPN)	9	6	10	25
'''
# 중간에  CAN* 라는게 껴있었어서 깔끔하게 해주기 위해 마지막 작업을 해주었다.
# 데이터 클리닝을 위해서 매우 중요한 루틴이다.

wik_1976.NOC.str.split('(', expand = True).iloc[:,1].str.replace(')', '').str.replace('*','')
wik_1976.head()

'''

Rank	NOC	Gold	Silver	Bronze	Total
0	1	Soviet Union (URS)	49	41	35	125
1	2	East Germany (GDR)	40	25	25	90
2	3	United States (USA)	34	35	25	94
3	4	West Germany (FRG)	10	12	17	39
4	5	Japan (JPN)	9	6	10	25
'''
wik_1976['Country'] = wik_1976.NOC.str.split('(', expand = True).iloc[:,1].str.replace(')', '').str.replace('*',"")
# Country, Gold, silver, bronze를 제외한 모든 컬럼 날려버리고
# Country를 기준 컬럼으로 잡는다.

# inplace = True같이 저장되는 형태의 변동사항이 아니기 때문에
# 변수에 지정해주는 식으로 이렇게 해주기

wik_1976 = wik_1976.drop(columns = ['Rank', 'NOC', 'Total']).set_index('Country')
wik_1976
'''
	Gold	Silver	Bronze
Country			
URS	49	41	35
GDR	40	25	25
USA	34	35	25
FRG	10	12	17
JPN	9	6	10
POL	7	6	13
BUL	6	9	7
CUB	6	4	3
ROU	4	9	14
HUN	4	5	13
FIN	4	2	0
SWE	4	1	0
GBR	3	5	5
ITA	2	7	4
FRA	2	3	4
YUG	2	3	3
TCH	2	2	4
NZL	2	1	1
KOR	1	1	4
SUI	1	1	2
JAM	1	1	0
PRK	1	1	0
NOR	1	1	0
DEN	1	0	2
MEX	1	0	1
TRI	1	0	0
CAN	0	5	6
BEL	0	3	3
NED	0	2	3
POR	0	2	0
ESP	0	2	0
AUS	0	1	4
IRI	0	1	1
MGL	0	1	0
VEN	0	1	0
BRA	0	0	2
AUT	0	0	1
BER	0	0	1
PAK	0	0	1
PUR	0	0	1
THA	0	0	1
41 NOCs	198	199	216
'''
wik_1996.Nation.str.split('(', expand = True)

'''

0	1
0	United States	USA)*
1	Russia	RUS)
2	Germany	GER)
3	China	CHN)
4	France	FRA)
...	...	...
75	Mozambique	MOZ)
76	Puerto Rico	PUR)
77	Tunisia	TUN)
78	Uganda	UGA)
79	Totals	79 nations)
80 rows × 2 columns
'''
wik_1996['Country'] = wik_1996.Nation.str.split('(', expand = True).iloc[:, 1].str.replace(')','').str.replace('*','')
wik_1996.head(1)

'''

Rank	Nation	Gold	Silver	Bronze	Total	Country
0	1	United States (USA)*	44	32	25	101	USA
'''
wik_1996 = wik_1996.drop(columns = ['Rank', 'Nation','Total']).set_index('Country')
wik_1996

'''
	Gold	Silver	Bronze
Country			
USA	44	32	25
RUS	26	21	16
GER	20	18	27
CHN	16	22	12
FRA	15	7	15
...	...	...	...
MOZ	0	0	1
PUR	0	0	1
TUN	0	0	1
UGA	0	0	1
79 nations	271	273	298
80 rows × 3 columns
'''
## Step 2: Craeting the Columns Event_Gender

summer['Event_Gender'] = summer['Gender']

summer.head()
'''
	Year	City	Sport	Discipline	Athlete	Country	Gender	Event	Medal	Event_Gender
0	1896	Athens	Aquatics	Swimming	HAJOS, Alfred	HUN	Men	100M Freestyle	Gold	Men
1	1896	Athens	Aquatics	Swimming	HERSCHMANN, Otto	AUT	Men	100M Freestyle	Silver	Men
2	1896	Athens	Aquatics	Swimming	DRIVAS, Dimitrios	GRE	Men	100M Freestyle For Sailors	Bronze	Men
3	1896	Athens	Aquatics	Swimming	MALOKINIS, Ioannis	GRE	Men	100M Freestyle For Sailors	Gold	Men
4	1896	Athens	Aquatics	Swimming	CHASAPIS, Spiridon	GRE	Men	100M Freestyle For Sailors	Silver	Men
'''
# 문제 1. The Event Column contains the string 'mixed'

# 매우 중요한 루틴
# 시작부터 놀랐다.
# 대문자가 섞여있기 때문에, 그 안에서 
summer.Event.str.lower()

'''
0                    100m freestyle
1                    100m freestyle
2        100m freestyle for sailors
3        100m freestyle for sailors
4        100m freestyle for sailors
                    ...            
31160                      wg 84 kg
31161                      wg 96 kg
31162                      wg 96 kg
31163                      wg 96 kg
31164                      wg 96 kg
Name: Event, Length: 31161, dtype: object
'''
# 지정한 컬럼 안에서 mixed라는 단어 찾기
summer.Event.str.lower().str.contains('mixed')
summer.Event.str.lower().str.contains('mixed').sum()
# 38

# scala로 sum 처리를 한다.
# 문제2. The Event Column contains the string 'pairs'

summer.Event.str.lower().str.contains('pairs').sum()
# 12
# 문제3. All 'Equestrian' Events have been mixed Events.

# Equestrian 이라는 게 Sport 컬럼안에 있는지 확인
(summer.Sport == 'Equestrian').sum()
# 939

# 뭐가 있긴 있다는 건데..
summer.Sport.str.lower()

'''
0         aquatics
1         aquatics
2         aquatics
3         aquatics
4         aquatics
           ...    
31160    wrestling
31161    wrestling
31162    wrestling
31163    wrestling
31164    wrestling
Name: Sport, Length: 31161, dtype: object
'''
summer.Sport.str.lower().str.contains('equestrian').sum()
# 939
# 계속 느끼지만 문제가 진짜 이상하다.
# 문제4. All 'Sailing' Events before 1988 have been mixed Events.

(summer.Sport == 'Sailing').sum()
# 1109
summer.Sport.str.lower().str.contains('sailing').sum()
# 1109
(summer.Sport.str.lower().str.contains('sailing') & summer.Year < 1988).sum()
# 31161
# 음... 이건 연산이 잘못작동되도록 만들어진 프로그램 같다.
# 전체의 것에 대하여 1988이전일리가..
# 결과값으로 나오는 수가 자꾸 이상한 것 같아서
# 1988년 이후의 연도도 있는지 확인해줬다.

summer.sort_values(by = 'Year', ascending= False)
(summer.Year < 1988).sum()
# 18051
# 먹긴 먹는거겠지?
# SQL처럼 AND라는 명령어는 Value Error를 초래하고
# &을 사용해줌으로 해결이 되었다.
((summer.Sport.str.lower().str.contains('sailing')) & (summer.Year < 1988)).sum()
# 755
mask1 = summer.Event.str.lower().str.contains('mixed')
mask2 = summer.Event.str.lower().str.contains('pairs')
mask3 = summer.Event.str.lower().str.contains('equestrain')
mask4 = ((summer.Sport.str.lower().str.contains('sailing')) & (summer.Year < 1988))
# | 는 and였는지 or 였는지 둘중 하나일 것이다.

summer.loc[mask1 | mask2 | mask3 | mask4, 'Event_Gender'] = 'X'
summer.Event_Gender.value_counts()

'''
Men      21968
Women     8388
X          805
Name: Event_Gender, dtype: int64
'''

 

# 문제5. The following medals (index labels) were awarded in Badminton mixed Double Events

summer['Event_Medals'] = summer.groupby(['Year', 'Sport', 'Discipline', 'Event', 'Event_Gender']).Medal.transform('count')

summer.Event_Medals.value_counts().sort_index()

'''
1        35
2       104
3      9216
4      1684
5        90
       ... 
73       73
74       74
76       76
82       82
116     116
Name: Event_Medals, Length: 66, dtype: int64
'''
summer.loc[summer.Event_Medals == 5]

'''

Year	City	Sport	Discipline	Athlete	Country	Gender	Event	Medal	Event_Gender	Event_Medals
1273	1908	London	Athletics	Athletics	ARCHIBALD, Edward Blake	CAN	Men	Pole Vault	Bronze	Men	5
1274	1908	London	Athletics	Athletics	JACOBS, Charles Sherman	USA	Men	Pole Vault	Bronze	Men	5
1275	1908	London	Athletics	Athletics	SÖDERSTRÖM, Bruno	SWE	Men	Pole Vault	Bronze	Men	5
1276	1908	London	Athletics	Athletics	COOKE, Edward Tiffin	USA	Men	Pole Vault	Gold	Men	5
1277	1908	London	Athletics	Athletics	GILBERT, Alfred Carleten	USA	Men	Pole Vault	Gold	Men	5
...	...	...	...	...	...	...	...	...	...	...	...
29728	2012	London	Athletics	Athletics	UKHOV, Ivan	RUS	Men	High Jump	Gold	Men	5
29729	2012	London	Athletics	Athletics	KYNARD, Erik	USA	Men	High Jump	Silver	Men	5
29730	2012	London	Athletics	Athletics	BARSHIM, Mutaz Essa	QAT	Men	High Jump	Bronze	Men	5
29731	2012	London	Athletics	Athletics	DROUIN, Derek	CAN	Men	High Jump	Bronze	Men	5
29732	2012	London	Athletics	Athletics	GRABARZ, Robert	GBR	Men	High Jump	Bronze	Men	5
90 rows × 11 columns
'''
# 인덱스 정렬
summer.Event_Medals.value_counts().sort_index()

'''
1        35
2       104
3      9216
4      1684
5        90
       ... 
73       73
74       74
76       76
82       82
116     116
Name: Event_Medals, Length: 66, dtype: int64
'''
summer.loc[summer.Event_Medals == 5]

'''
	Year	City	Sport	Discipline	Athlete	Country	Gender	Event	Medal	Event_Gender	Event_Medals
1273	1908	London	Athletics	Athletics	ARCHIBALD, Edward Blake	CAN	Men	Pole Vault	Bronze	Men	5
1274	1908	London	Athletics	Athletics	JACOBS, Charles Sherman	USA	Men	Pole Vault	Bronze	Men	5
1275	1908	London	Athletics	Athletics	SÖDERSTRÖM, Bruno	SWE	Men	Pole Vault	Bronze	Men	5
1276	1908	London	Athletics	Athletics	COOKE, Edward Tiffin	USA	Men	Pole Vault	Gold	Men	5
1277	1908	London	Athletics	Athletics	GILBERT, Alfred Carleten	USA	Men	Pole Vault	Gold	Men	5
...	...	...	...	...	...	...	...	...	...	...	...
29728	2012	London	Athletics	Athletics	UKHOV, Ivan	RUS	Men	High Jump	Gold	Men	5
29729	2012	London	Athletics	Athletics	KYNARD, Erik	USA	Men	High Jump	Silver	Men	5
29730	2012	London	Athletics	Athletics	BARSHIM, Mutaz Essa	QAT	Men	High Jump	Bronze	Men	5
29731	2012	London	Athletics	Athletics	DROUIN, Derek	CAN	Men	High Jump	Bronze	Men	5
29732	2012	London	Athletics	Athletics	GRABARZ, Robert	GBR	Men	High Jump	Bronze	Men	5
90 rows × 11 columns
'''
summer['Team'] = pd.Series(np.where(summer.Event_Medals > 5, 'Yes', 'No'))
summer.head()

'''
	Year	City	Sport	Discipline	Athlete	Country	Gender	Event	Medal	Event_Gender	Event_Medals	Team
0	1896	Athens	Aquatics	Swimming	HAJOS, Alfred	HUN	Men	100M Freestyle	Gold	Men	2	No
1	1896	Athens	Aquatics	Swimming	HERSCHMANN, Otto	AUT	Men	100M Freestyle	Silver	Men	2	No
2	1896	Athens	Aquatics	Swimming	DRIVAS, Dimitrios	GRE	Men	100M Freestyle For Sailors	Bronze	Men	3	No
3	1896	Athens	Aquatics	Swimming	MALOKINIS, Ioannis	GRE	Men	100M Freestyle For Sailors	Gold	Men	3	No
4	1896	Athens	Aquatics	Swimming	CHASAPIS, Spiridon	GRE	Men	100M Freestyle For Sailors	Silver	Men	3	No
'''
summer.Team.value_counts()

'''
summer.Team.value_counts()
1
summer.Team.value_counts()
Yes    20032
No     11125
Name: Team, dtype: int64
'''
## Step 5: Removing Duplicated Medals in Team Events

## The subset for determining Duplicates shall be formed by the Columns 'Year', 'Sport', 'Discipline', 'Country',
## 'Event', 'Event_Gender', 'Medal', Kepp one Medal!!

summer.reset_index(inplace= True)

summer.head()

'''

index	Year	City	Sport	Discipline	Athlete	Country	Gender	Event	Medal	Event_Gender	Event_Medals	Team
0	0	1896	Athens	Aquatics	Swimming	HAJOS, Alfred	HUN	Men	100M Freestyle	Gold	Men	2	No
1	1	1896	Athens	Aquatics	Swimming	HERSCHMANN, Otto	AUT	Men	100M Freestyle	Silver	Men	2	No
2	2	1896	Athens	Aquatics	Swimming	DRIVAS, Dimitrios	GRE	Men	100M Freestyle For Sailors	Bronze	Men	3	No
3	3	1896	Athens	Aquatics	Swimming	MALOKINIS, Ioannis	GRE	Men	100M Freestyle For Sailors	Gold	Men	3	No
4	4	1896	Athens	Aquatics	Swimming	CHASAPIS, Spiridon	GRE	Men	100M Freestyle For Sailors	Silver	Men	3	No
'''
singles = summer.loc[summer.Team == 'No'].copy()
singles.shape

# (11125, 13)
team = summer.loc[summer.Team == 'Yes'].copy()
team.shape

# (20032, 13)
# 여기가 좀 독특하다.
# 왜 drop_duplicates와
# subset으로 써줬을지 모르겠다.
# 공부하자.

team.drop_duplicates(subset = ['Year', 'Sport', 'Discipline', 'Country', 'Event', 'Event_Gender', 'Medal'], inplace = True)

# drop_duplicates는 중복값을 제거하기 위한 함수라고 하며
# subset은 중복값으로 확인하고 제거하기 위한 값을 받기위한 매개변수라고한다.
team.shape

# (3710, 13)
pd.concat([singles, team]).shape

# (14835, 13)
summer_new = pd.concat([singles, team])
summer_new.set_index('index', inplace = True)
summer.head()

'''

index	Year	City	Sport	Discipline	Athlete	Country	Gender	Event	Medal	Event_Gender	Event_Medals	Team
0	0	1896	Athens	Aquatics	Swimming	HAJOS, Alfred	HUN	Men	100M Freestyle	Gold	Men	2	No
1	1	1896	Athens	Aquatics	Swimming	HERSCHMANN, Otto	AUT	Men	100M Freestyle	Silver	Men	2	No
2	2	1896	Athens	Aquatics	Swimming	DRIVAS, Dimitrios	GRE	Men	100M Freestyle For Sailors	Bronze	Men	3	No
3	3	1896	Athens	Aquatics	Swimming	MALOKINIS, Ioannis	GRE	Men	100M Freestyle For Sailors	Gold	Men	3	No
4	4	1896	Athens	Aquatics	Swimming	CHASAPIS, Spiridon	GRE	Men	100M Freestyle For Sailors	Silver	Men	3	No
'''
summer_new.info()

'''
<class 'pandas.core.frame.DataFrame'>
Int64Index: 14835 entries, 0 to 31036
Data columns (total 12 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Year          14835 non-null  int64 
 1   City          14835 non-null  object
 2   Sport         14835 non-null  object
 3   Discipline    14835 non-null  object
 4   Athlete       14835 non-null  object
 5   Country       14835 non-null  object
 6   Gender        14835 non-null  object
 7   Event         14835 non-null  object
 8   Medal         14835 non-null  object
 9   Event_Gender  14835 non-null  object
 10  Event_Medals  14835 non-null  int64 
 11  Team          14835 non-null  object
dtypes: int64(2), object(10)
memory usage: 1.5+ MB
'''
## Step 6: Creating the official Medal Table for all Editions

# 이 부분은 전체적으로 이해가 안간다.
# 중간의 count() 까지는 그런대로 따라간다고 볼 수 있으나.
# unstack부터는 속수무책으로 이해가 안간다.

medal_tables = summer_new.groupby(['Year', 'Country', 'Medal']).Medal.count().unstack(fill_value = 0)[['Gold', 'Silver', 'Bronze']]

'''

Medal	Bronze	Gold	Silver
Year	Country			
1896	AUS	0	2	0
AUT	2	2	1
DEN	3	1	2
FRA	2	5	4
GBR	2	2	3
...	...	...	...	...
2012	UGA	0	1	0
UKR	9	6	4
USA	32	46	28
UZB	2	1	0
VEN	0	1	0
1157 rows × 3 columns
'''
## Step 7: Comparison with Wikipedia Medal Tables

medal_tables.head()

'''
	Medal	Gold	Silver	Bronze
Year	Country			
1896	AUS	2	0	0
AUT	2	1	2
DEN	1	2	3
FRA	5	4	2
GBR	2	3	2
'''
medal_tables.loc[1976]

'''

Medal	Gold	Silver	Bronze
Country			
AUS	0	1	4
AUT	0	0	1
BEL	0	3	3
BER	0	0	1
BRA	0	0	2
BUL	6	9	7
CAN	0	5	6
CUB	6	4	3
DEN	1	0	2
ESP	0	2	0
FIN	4	2	0
FRA	2	3	4
FRG	12	12	17
GBR	3	5	5
GDR	40	25	25
HUN	4	5	13
IRI	0	1	1
ITA	2	7	4
JAM	1	1	0
JPN	9	6	10
KOR	1	1	4
MEX	1	0	1
MGL	0	1	0
NED	0	2	3
NOR	1	1	0
NZL	2	1	1
PAK	0	0	1
POL	7	6	13
POR	0	2	0
PRK	1	1	0
PUR	0	0	1
ROU	4	9	14
SUI	1	2	2
SWE	4	1	0
TCH	2	2	4
THA	0	0	1
TRI	1	0	0
URS	49	41	35
USA	35	35	25
VEN	0	1	0
YUG	2	3	3
'''
agg_1976 = medal_tables.loc[1976].sort_values(['Gold', 'Silver', 'Bronze'], ascending = False).copy()

agg_1976.head()

'''

Medal	Gold	Silver	Bronze
Country			
URS	49	41	35
GDR	40	25	25
USA	35	35	25
FRG	12	12	17
JPN	9	6	10
'''
wik_1976.head()

'''

Gold	Silver	Bronze
Country			
URS	49	41	35
GDR	40	25	25
USA	34	35	25
FRG	10	12	17
JPN	9	6	10
'''
# 결측값 드랍
# 결측값 제거

div_76 = agg_1976.sub(wik_1976).abs().dropna()

'''
Medal	Gold	Silver	Bronze
Country			
41 NOCs	NaN	NaN	NaN
AUS	0.0	0.0	0.0
AUT	0.0	0.0	0.0
BEL	0.0	0.0	0.0
BER	0.0	0.0	0.0
BRA	0.0	0.0	0.0
BUL	0.0	0.0	0.0
CAN	0.0	0.0	0.0
CUB	0.0	0.0	0.0
DEN	0.0	0.0	0.0
ESP	0.0	0.0	0.0
FIN	0.0	0.0	0.0
FRA	0.0	0.0	0.0
FRG	2.0	0.0	0.0
GBR	0.0	0.0	0.0
GDR	0.0	0.0	0.0
HUN	0.0	0.0	0.0
IRI	0.0	0.0	0.0
ITA	0.0	0.0	0.0
JAM	0.0	0.0	0.0
JPN	0.0	0.0	0.0
KOR	0.0	0.0	0.0
MEX	0.0	0.0	0.0
MGL	0.0	0.0	0.0
NED	0.0	0.0	0.0
NOR	0.0	0.0	0.0
NZL	0.0	0.0	0.0
PAK	0.0	0.0	0.0
POL	0.0	0.0	0.0
POR	0.0	0.0	0.0
PRK	0.0	0.0	0.0
PUR	0.0	0.0	0.0
ROU	0.0	0.0	0.0
SUI	0.0	1.0	0.0
SWE	0.0	0.0	0.0
TCH	0.0	0.0	0.0
THA	0.0	0.0	0.0
TRI	0.0	0.0	0.0
URS	0.0	0.0	0.0
USA	1.0	0.0	0.0
VEN	0.0	0.0	0.0
YUG	0.0	0.0	0.0
'''
# 이중 sum
# 이런 표현방식은 참 신기하다.

score_76 = div_76.sum().sum()
# 4.0
agg_1996 = medal_tables.loc[1996].sort_values(['Gold', 'Silver', 'Bronze'], ascending = False).copy()

'''

Medal	Gold	Silver	Bronze
Country			
USA	44	36	27
RUS	26	21	16
GER	22	18	27
CHN	16	22	12
FRA	15	7	15
...	...	...	...
MGL	0	0	1
MOZ	0	0	1
PUR	0	0	1
TUN	0	0	1
UGA	0	0	1
79 rows × 3 columns
'''
agg_1996.head()

'''

Medal	Gold	Silver	Bronze
Country			
USA	44	36	27
RUS	26	21	16
GER	22	18	27
CHN	16	22	12
FRA	15	7	15
'''
wik_1996.head()

'''
	Gold	Silver	Bronze
Country			
USA	44	32	25
RUS	26	21	16
GER	20	18	27
CHN	16	22	12
FRA	15	7	15
'''
# 여기서 sub의 기능은 scala 방식으로 뺄셈을 해주는 것이다.

div_96 = agg_1996.sub(wik_1996).abs().dropna()
score_96 = div_96.sum().sum()
# 13.0
300x250

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