[Pandas] Pandas 연습 문제 풀기 - 2 🐼

 

 

 

 

데이터 EDA를 위한 Pandas 역량기르기 타~임!

 

 

 

 

 

* 문제풀이 깃허브: https://github.com/LIMSONA/KDT/blob/main/pandas/Day_1/%EC%97%B0%EC%8A%B502.ipynb

 

 

Step 1. 필요한 라이브러리 임포트¶

In [1]:

import pandas as pd
import numpy as np

 

Step 2. 데이터 확인¶

이번 데이터는 dictionary 형태로 주어져 있습니다

• regiment 연대
• company 군대

In [2]:

# Create an example dataframe about a fictional army
raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],
            'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'],
            'deaths': [523, 52, 25, 616, 43, 234, 523, 62, 62, 73, 37, 35],
            'battles': [5, 42, 2, 2, 4, 7, 8, 3, 4, 7, 8, 9],
            'size': [1045, 957, 1099, 1400, 1592, 1006, 987, 849, 973, 1005, 1099, 1523],
            'veterans': [1, 5, 62, 26, 73, 37, 949, 48, 48, 435, 63, 345],
            'readiness': [1, 2, 3, 3, 2, 1, 2, 3, 2, 1, 2, 3],
            'armored': [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1],
            'deserters': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],
            'origin': ['Arizona', 'California', 'Texas', 'Florida', 'Maine', 'Iowa', 'Alaska', 'Washington', 'Oregon', 'Wyoming', 'Louisana', 'Georgia']}

 

Step 3. 데이터 불러오기¶

raw_data를 pandas DataFrame형태로 임포트 후 army 변수에 대입해주세요

raw_data에 입력된 순서를 지키면서 데이터프레임을 생성해주세요. (regiment, company, deaths, ...)

In [13]:

army = pd.DataFrame(raw_data)
army

Out[13]:

	regiment	company	deaths	battles	size	veterans	readiness	armored	deserters	origin
0	Nighthawks	1st	523	5	1045	1	1	1	4	Arizona
1	Nighthawks	1st	52	42	957	5	2	0	24	California
2	Nighthawks	2nd	25	2	1099	62	3	1	31	Texas
3	Nighthawks	2nd	616	2	1400	26	3	1	2	Florida
4	Dragoons	1st	43	4	1592	73	2	0	3	Maine
5	Dragoons	1st	234	7	1006	37	1	1	4	Iowa
6	Dragoons	2nd	523	8	987	949	2	0	24	Alaska
7	Dragoons	2nd	62	3	849	48	3	1	31	Washington
8	Scouts	1st	62	4	973	48	2	0	2	Oregon
9	Scouts	1st	73	7	1005	435	1	0	3	Wyoming
10	Scouts	2nd	37	8	1099	63	2	1	2	Louisana
11	Scouts	2nd	35	9	1523	345	3	1	3	Georgia

 

Step 4. 'origin'칼럼을 인덱스로 설정후 저장해주세요¶

인덱스로 설정 후 army에 다시 대입해주셔야 합니다

In [14]:

army.set_index("origin", inplace=True)
army

Out[14]:

	regiment	company	deaths	battles	size	veterans	readiness	armored	deserters
origin
Arizona	Nighthawks	1st	523	5	1045	1	1	1	4
California	Nighthawks	1st	52	42	957	5	2	0	24
Texas	Nighthawks	2nd	25	2	1099	62	3	1	31
Florida	Nighthawks	2nd	616	2	1400	26	3	1	2
Maine	Dragoons	1st	43	4	1592	73	2	0	3
Iowa	Dragoons	1st	234	7	1006	37	1	1	4
Alaska	Dragoons	2nd	523	8	987	949	2	0	24
Washington	Dragoons	2nd	62	3	849	48	3	1	31
Oregon	Scouts	1st	62	4	973	48	2	0	2
Wyoming	Scouts	1st	73	7	1005	435	1	0	3
Louisana	Scouts	2nd	37	8	1099	63	2	1	2
Georgia	Scouts	2nd	35	9	1523	345	3	1	3

 

Step 5. veterans 칼럼만 조회해주세요¶

In [16]:

army["veterans"]

Out[16]:

origin
Arizona         1
California      5
Texas          62
Florida        26
Maine          73
Iowa           37
Alaska        949
Washington     48
Oregon         48
Wyoming       435
Louisana       63
Georgia       345
Name: veterans, dtype: int64

 

Step 6.'veterans'과 'deaths' 칼럼만 조회해주세요¶

In [17]:

army[["veterans","deaths"]]

Out[17]:

	veterans	deaths
origin
Arizona	1	523
California	5	52
Texas	62	25
Florida	26	616
Maine	73	43
Iowa	37	234
Alaska	949	523
Washington	48	62
Oregon	48	62
Wyoming	435	73
Louisana	63	37
Georgia	345	35

 

Step 7. 열(column)의 이름들을 모두 조회해주세요¶

Index(['regiment', 'company', 'deaths', 'battles', 'size', 'veterans', 'readiness', 'armored', 'deserters'], dtype='object')

In [19]:

army.columns

Out[19]:

Index(['regiment', 'company', 'deaths', 'battles', 'size', 'veterans',
       'readiness', 'armored', 'deserters'],
      dtype='object')

 

Step 8. Maine과 Alaska의 'deaths', 'size', 'deserters'를 조회해주세요¶

In [21]:

army.loc[["Maine","Alaska"],['deaths', 'size', 'deserters']]

Out[21]:

	deaths	size	deserters
origin
Maine	43	1592	3
Alaska	523	987	24

 

Step 9. 3행에서 7행까지, 그리고 3열에서 6열까지 조회해주세요¶

In [24]:

army.iloc[2:7,2:6]

Out[24]:

	deaths	battles	size	veterans
origin
Texas	25	2	1099	62
Florida	616	2	1400	26
Maine	43	4	1592	73
Iowa	234	7	1006	37
Alaska	523	8	987	949

 

Step 10. 행은 4행부터, 열은 모든 열을 포함해서 조회해주세요¶

In [27]:

army.iloc[3:,:]

Out[27]:

	regiment	company	deaths	battles	size	veterans	readiness	armored	deserters
origin
Florida	Nighthawks	2nd	616	2	1400	26	3	1	2
Maine	Dragoons	1st	43	4	1592	73	2	0	3
Iowa	Dragoons	1st	234	7	1006	37	1	1	4
Alaska	Dragoons	2nd	523	8	987	949	2	0	24
Washington	Dragoons	2nd	62	3	849	48	3	1	31
Oregon	Scouts	1st	62	4	973	48	2	0	2
Wyoming	Scouts	1st	73	7	1005	435	1	0	3
Louisana	Scouts	2nd	37	8	1099	63	2	1	2
Georgia	Scouts	2nd	35	9	1523	345	3	1	3

 

Step 11. 행은 4행까지, 열은 모든 열을 포함해서 조회해주세요¶

In [29]:

army.iloc[:4,:]

Out[29]:

	regiment	company	deaths	battles	size	veterans	readiness	armored	deserters
origin
Arizona	Nighthawks	1st	523	5	1045	1	1	1	4
California	Nighthawks	1st	52	42	957	5	2	0	24
Texas	Nighthawks	2nd	25	2	1099	62	3	1	31
Florida	Nighthawks	2nd	616	2	1400	26	3	1	2

 

Step 12. 행은 모든 행을 포함하고, 열은 3열부터 7열까지 조회해주세요¶

In [30]:

army.iloc[:,2:7]

Out[30]:

	deaths	battles	size	veterans	readiness
origin
Arizona	523	5	1045	1	1
California	52	42	957	5	2
Texas	25	2	1099	62	3
Florida	616	2	1400	26	3
Maine	43	4	1592	73	2
Iowa	234	7	1006	37	1
Alaska	523	8	987	949	2
Washington	62	3	849	48	3
Oregon	62	4	973	48	2
Wyoming	73	7	1005	435	1
Louisana	37	8	1099	63	2
Georgia	35	9	1523	345	3

 

Step 13. Texas와 Arizona의 데이터를 조회해주세요¶

In [31]:

army.loc[["Texas","Arizona"]]

Out[31]:

	regiment	company	deaths	battles	size	veterans	readiness	armored	deserters
origin
Texas	Nighthawks	2nd	25	2	1099	62	3	1	31
Arizona	Nighthawks	1st	523	5	1045	1	1	1	4

 

Step 14. Arizona행에서 세번째 칼럼에 기록되어있는 값을 구하세요¶

523

In [34]:

army.iloc[0,2]

Out[34]:

523

 

Step 15. deaths열의 세번째 기록되어 있는 값을 구하세요¶

25

In [35]:

army.iloc[2,2]

Out[35]:

25

 

 

 

 

 

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