[Pandas 기초]Section 04.Pandas Exercise

In [1]:

import pandas as pd

In [2]:

# 데이터 불러오기
df = pd.read_csv('/content/drive/MyDrive/Study/Pytorch/PYTORCH_NOTEBOOKS/00-Crash-Course-Topics/01-Crash-Course-Pandas/bank.csv')

In [3]:

# 5개의 row만 출력하기
df.head()

Out[3]:

	age	job	marital	education	default	balance	housing	loan	contact	day	month	duration	campaign	pdays	previous	poutcome	y
0	30	unemployed	married	primary	no	1787	no	no	cellular	19	oct	79	1	-1	0	unknown	no
1	33	services	married	secondary	no	4789	yes	yes	cellular	11	may	220	1	339	4	failure	no
2	35	management	single	tertiary	no	1350	yes	no	cellular	16	apr	185	1	330	1	failure	no
3	30	management	married	tertiary	no	1476	yes	yes	unknown	3	jun	199	4	-1	0	unknown	no
4	59	blue-collar	married	secondary	no	0	yes	no	unknown	5	may	226	1	-1	0	unknown	no

In [4]:

# 데이터의 age 칼럼 평균 구하기
df['age'].mean()

Out[4]:

41.17009511170095

In [5]:

# 가장 나이가 어린 사람의 혼인여부 찾기
df['marital'][df['age'].idxmin()]

Out[5]:

'single'

In [6]:

# 풀이
# 가장 나이가 어린 사람 찾기
df['age'].min()

Out[6]:

19

In [10]:

df[df['age']==19]

Out[10]:

	age	job	marital	education	default	balance	housing	loan	contact	day	month	duration	campaign	pdays	previous	poutcome	y
503	19	student	single	primary	no	103	no	no	cellular	10	jul	104	2	-1	0	unknown	yes
1900	19	student	single	unknown	no	0	no	no	cellular	11	feb	123	3	-1	0	unknown	no
2780	19	student	single	secondary	no	302	no	no	cellular	16	jul	205	1	-1	0	unknown	yes
3233	19	student	single	unknown	no	1169	no	no	cellular	6	feb	463	18	-1	0	unknown	no

가장 나이가 어린 사람 모두 single인 상태임.
그중 제일 첫 번째 사람의 index는 503.
따라서 'marital'칼럼에서 503번째 사람을 return하면 정답임.

In [11]:

df['marital'][503]

Out[11]:

'single'

503이라는 index는 위의 과정을 거쳐 찾을 수도 있지만, idxmin()이라는 메소드를 통해서도 찾을 수 있음.

In [12]:

df['marital'][df['age'].idxmin()]

Out[12]:

'single'

In [13]:

# 직업 카테고리의 유일 갯수 구하기
df['job'].nunique()

Out[13]:

12

In [14]:

# 각 직업군에 해당하는 인원이 몇명인지 구하기
df['job'].value_counts()

Out[14]:

management       969
blue-collar      946
technician       768
admin.           478
services         417
retired          230
self-employed    183
entrepreneur     168
unemployed       128
housemaid        112
student           84
unknown           38
Name: job, dtype: int64

In [16]:

# 데이터셋에서 결혼 한 사람의 비율 구하기
(len(df[df['marital']=='married'])/len(df))*100

Out[16]:

61.86684361866843

In [17]:

# 풀이
# 'marital'칼럼에서 'married' 즉 결혼한 인원을 조건으로 만듦
df['marital']=='married'

Out[17]:

0        True
1        True
2       False
3        True
4        True
        ...  
4516     True
4517     True
4518     True
4519     True
4520    False
Name: marital, Length: 4521, dtype: bool

조건식이 되었기 때문에 이를 df[]안에 넣어주면 조건에 해당되는 인원만 표기 가능

In [18]:

df[df['marital']=='married']

Out[18]:

	age	job	marital	education	default	balance	housing	loan	contact	day	month	duration	campaign	pdays	previous	poutcome	y
0	30	unemployed	married	primary	no	1787	no	no	cellular	19	oct	79	1	-1	0	unknown	no
1	33	services	married	secondary	no	4789	yes	yes	cellular	11	may	220	1	339	4	failure	no
3	30	management	married	tertiary	no	1476	yes	yes	unknown	3	jun	199	4	-1	0	unknown	no
4	59	blue-collar	married	secondary	no	0	yes	no	unknown	5	may	226	1	-1	0	unknown	no
6	36	self-employed	married	tertiary	no	307	yes	no	cellular	14	may	341	1	330	2	other	no
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4514	38	blue-collar	married	secondary	no	1205	yes	no	cellular	20	apr	45	4	153	1	failure	no
4516	33	services	married	secondary	no	-333	yes	no	cellular	30	jul	329	5	-1	0	unknown	no
4517	57	self-employed	married	tertiary	yes	-3313	yes	yes	unknown	9	may	153	1	-1	0	unknown	no
4518	57	technician	married	secondary	no	295	no	no	cellular	19	aug	151	11	-1	0	unknown	no
4519	28	blue-collar	married	secondary	no	1137	no	no	cellular	6	feb	129	4	211	3	other	no

2797 rows × 17 columns

총 2797개의 row를 가지게 되었으므로, 이를 len()을 활용하여 갯수로 변환함.

In [19]:

len(df[df['marital']=='married'])

Out[19]:

2797

데이터 셋의 총 인원 중 결혼한 인원의 갯수를 확인 하였으니, 이를 총 인원수로 나누어 비율을 계산함.

In [20]:

100*len(df[df['marital']=='married'])/len(df)

Out[20]:

61.86684361866843

In [22]:

# .apply를 활용하여 기혼 여부를 's' 또는 'm'으로 나타내는 칼럼 'marital code' 만들기
df['marital code']=df['marital'].apply(lambda status: status[0])
df

Out[22]:

	age	job	marital	education	default	balance	housing	loan	contact	day	month	duration	campaign	pdays	previous	poutcome	y	marital code
0	30	unemployed	married	primary	no	1787	no	no	cellular	19	oct	79	1	-1	0	unknown	no	m
1	33	services	married	secondary	no	4789	yes	yes	cellular	11	may	220	1	339	4	failure	no	m
2	35	management	single	tertiary	no	1350	yes	no	cellular	16	apr	185	1	330	1	failure	no	s
3	30	management	married	tertiary	no	1476	yes	yes	unknown	3	jun	199	4	-1	0	unknown	no	m
4	59	blue-collar	married	secondary	no	0	yes	no	unknown	5	may	226	1	-1	0	unknown	no	m
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4516	33	services	married	secondary	no	-333	yes	no	cellular	30	jul	329	5	-1	0	unknown	no	m
4517	57	self-employed	married	tertiary	yes	-3313	yes	yes	unknown	9	may	153	1	-1	0	unknown	no	m
4518	57	technician	married	secondary	no	295	no	no	cellular	19	aug	151	11	-1	0	unknown	no	m
4519	28	blue-collar	married	secondary	no	1137	no	no	cellular	6	feb	129	4	211	3	other	no	m
4520	44	entrepreneur	single	tertiary	no	1136	yes	yes	cellular	3	apr	345	2	249	7	other	no	s

4521 rows × 18 columns

In [23]:

# 'duration' 칼럼에서 가장 큰 값 찾기
df['duration'].max()

Out[23]:

3025

In [24]:

# 직업이 없는 사람들의 교육 현황 구하기
df[df['job']=='unemployed']['education'].value_counts()

Out[24]:

secondary    68
tertiary     32
primary      26
unknown       2
Name: education, dtype: int64

In [25]:

# 직업이 없는 사람들의 나이 평균 구하기
df[df['job']=='unemployed']['age'].mean()

Out[25]:

40.90625

In [ ]: