고객 데이터 분석 프로젝트

고객 데이터 분석

데이터 출처 : UCI Machine Learning Repository

[링크주소 및 다운로드]https://archive.ics.uci.edu/ml/datasets/bank+marketing

Moro, S., Cortez, P., & Rita, P. (2014). A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62, 22-31

<데이터 소개="">

  • 해외의 은행이 진행한 마케팅 데이터
  • 아웃바운드 텔레마케팅으로 마케팅 캠페인을 진행

bank client data:

1 - age (numeric)
2 - job : type of job (categorical: ‘admin.’,’blue-collar’,’entrepreneur’,’housemaid’,’management’,’retired’,’self-employed’,’services’,’student’,’technician’,’unemployed’,’unknown’)
3 - marital : marital status (categorical: ‘divorced’,’married’,’single’,’unknown’; note: ‘divorced’ means divorced or widowed)
4 - education (categorical: ‘basic.4y’,’basic.6y’,’basic.9y’,’high.school’,’illiterate’,’professional.course’,’university.degree’,’unknown’)
5 - default: has credit in default? (categorical: ‘no’,’yes’,’unknown’)
6 - housing: has housing loan? (categorical: ‘no’,’yes’,’unknown’)
7 - loan: has personal loan? (categorical: ‘no’,’yes’,’unknown’)

related with the last contact of the current campaign:

8 - contact: contact communication type (categorical: ‘cellular’,’telephone’)
9 - month: last contact month of year (categorical: ‘jan’, ‘feb’, ‘mar’, …, ‘nov’, ‘dec’)
10 - day_of_week: last contact day of the week (categorical: ‘mon’,’tue’,’wed’,’thu’,’fri’)
11 - duration: last contact duration, in seconds (numeric).

other attributes:

12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)
14 - previous: number of contacts performed before this campaign and for this client (numeric)
15 - poutcome: outcome of the previous marketing campaign (categorical: ‘failure’,’nonexistent’,’success’)

social and economic context attributes

16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)
17 - cons.price.idx: consumer price index - monthly indicator (numeric)
18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric)
19 - euribor3m: euribor 3 month rate - daily indicator (numeric)
20 - nr.employed: number of employees - quarterly indicator (numeric)

Output variable:

21 - y - has the client subscribed a term deposit? (binary: ‘yes’,’no’)

import pandas as pd
from pandas import Series
from pandas import DataFrame

import matplotlib.pyplot as plt

# matplotlib 한글 폰트 출력코드
# 출처 : 데이터공방( https://kiddwannabe.blog.me)

import matplotlib
from matplotlib import font_manager, rc
import platform

try : 
    if platform.system() == 'Windows':
    # 윈도우인 경우
        font_name = font_manager.FontProperties(fname="c:/Windows/Fonts/malgun.ttf").get_name()
        rc('font', family=font_name)
    else:    
    # Mac 인 경우
        rc('font', family='AppleGothic')
except : 
    pass
matplotlib.rcParams['axes.unicode_minus'] = False

데이터불러오기

파일명 : bank-additional-full.csv

#window 방법1 : \\
#engine='python' - 에러 반환시, 디렉토리 혹은 파일명에 한글이 있을 경우 추가
df=pd.read_csv('bankadditionalfull.csv')

#head()
df.head()
age;"job";"marital";"education";"default";"housing";"loan";"contact";"month";"day_of_week";"duration";"campaign";"pdays";"previous";"poutcome";"emp.var.rate";"cons.price.idx";"cons.conf.idx";"euribor3m";"nr.employed";"y"
0 56;"housemaid";"married";"basic.4y";"no";"no";...
1 57;"services";"married";"high.school";"unknown...
2 37;"services";"married";"high.school";"no";"ye...
3 40;"admin.";"married";"basic.6y";"no";"no";"no...
4 56;"services";"married";"high.school";"no";"no...
  • 엑셀로 데이터를 열었을 때의 화면
  • 콜론으로 구분된 데이터
    customer1
#window
#sep = ';'
df=pd.read_csv('bankadditionalfull.csv',
                 engine='python',sep=';')

df.head()
age job marital education default housing loan contact month day_of_week ... campaign pdays previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed y
0 56 housemaid married basic.4y no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
1 57 services married high.school unknown no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
2 37 services married high.school no yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
3 40 admin. married basic.6y no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
4 56 services married high.school no no yes telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no

5 rows × 21 columns

# 데이터탐색

  • 학습목표 :
    1. 데이터 탐색과정에서 사용되는 함수를 살펴보고 실전 사례를 통해 사용법을 익힌다.
#head - 데이터의 첫 5행, default : 5행
df.head()
age job marital education default housing loan contact month day_of_week ... campaign pdays previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed y
0 56 housemaid married basic.4y no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
1 57 services married high.school unknown no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
2 37 services married high.school no yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
3 40 admin. married basic.6y no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
4 56 services married high.school no no yes telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no

5 rows × 21 columns

df.head(10)
age job marital education default housing loan contact month day_of_week ... campaign pdays previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed y
0 56 housemaid married basic.4y no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
1 57 services married high.school unknown no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
2 37 services married high.school no yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
3 40 admin. married basic.6y no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
4 56 services married high.school no no yes telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
5 45 services married basic.9y unknown no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
6 59 admin. married professional.course no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
7 41 blue-collar married unknown unknown no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
8 24 technician single professional.course no yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
9 25 services single high.school no yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no

10 rows × 21 columns

#tail - 데이터의 끝 5행, default : 5행
df.tail()
age job marital education default housing loan contact month day_of_week ... campaign pdays previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed y
41183 73 retired married professional.course no yes no cellular nov fri ... 1 999 0 nonexistent -1.1 94.767 -50.8 1.028 4963.6 yes
41184 46 blue-collar married professional.course no no no cellular nov fri ... 1 999 0 nonexistent -1.1 94.767 -50.8 1.028 4963.6 no
41185 56 retired married university.degree no yes no cellular nov fri ... 2 999 0 nonexistent -1.1 94.767 -50.8 1.028 4963.6 no
41186 44 technician married professional.course no no no cellular nov fri ... 1 999 0 nonexistent -1.1 94.767 -50.8 1.028 4963.6 yes
41187 74 retired married professional.course no yes no cellular nov fri ... 3 999 1 failure -1.1 94.767 -50.8 1.028 4963.6 no

5 rows × 21 columns

df.tail(10)
age job marital education default housing loan contact month day_of_week ... campaign pdays previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed y
41178 62 retired married university.degree no no no cellular nov thu ... 2 6 3 success -1.1 94.767 -50.8 1.031 4963.6 yes
41179 64 retired divorced professional.course no yes no cellular nov fri ... 3 999 0 nonexistent -1.1 94.767 -50.8 1.028 4963.6 no
41180 36 admin. married university.degree no no no cellular nov fri ... 2 999 0 nonexistent -1.1 94.767 -50.8 1.028 4963.6 no
41181 37 admin. married university.degree no yes no cellular nov fri ... 1 999 0 nonexistent -1.1 94.767 -50.8 1.028 4963.6 yes
41182 29 unemployed single basic.4y no yes no cellular nov fri ... 1 9 1 success -1.1 94.767 -50.8 1.028 4963.6 no
41183 73 retired married professional.course no yes no cellular nov fri ... 1 999 0 nonexistent -1.1 94.767 -50.8 1.028 4963.6 yes
41184 46 blue-collar married professional.course no no no cellular nov fri ... 1 999 0 nonexistent -1.1 94.767 -50.8 1.028 4963.6 no
41185 56 retired married university.degree no yes no cellular nov fri ... 2 999 0 nonexistent -1.1 94.767 -50.8 1.028 4963.6 no
41186 44 technician married professional.course no no no cellular nov fri ... 1 999 0 nonexistent -1.1 94.767 -50.8 1.028 4963.6 yes
41187 74 retired married professional.course no yes no cellular nov fri ... 3 999 1 failure -1.1 94.767 -50.8 1.028 4963.6 no

10 rows × 21 columns

결측치 확인

# 결측치 확인
df.isnull()
age job marital education default housing loan contact month day_of_week ... campaign pdays previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed y
0 False False False False False False False False False False ... False False False False False False False False False False
1 False False False False False False False False False False ... False False False False False False False False False False
2 False False False False False False False False False False ... False False False False False False False False False False
3 False False False False False False False False False False ... False False False False False False False False False False
4 False False False False False False False False False False ... False False False False False False False False False False
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
41183 False False False False False False False False False False ... False False False False False False False False False False
41184 False False False False False False False False False False ... False False False False False False False False False False
41185 False False False False False False False False False False ... False False False False False False False False False False
41186 False False False False False False False False False False ... False False False False False False False False False False
41187 False False False False False False False False False False ... False False False False False False False False False False

41188 rows × 21 columns

# 결측치 확인 - 열단위
df.isnull().sum()

age               0
job               0
marital           0
education         0
default           0
housing           0
loan              0
contact           0
month             0
day_of_week       0
duration          0
campaign          0
pdays             0
previous          0
poutcome          0
emp.var.rate      0
cons.price.idx    0
cons.conf.idx     0
euribor3m         0
nr.employed       0
y                 0
dtype: int64

#shape - dataframe의 크기(행, 열의 수)
df.shape

(41188, 21)

#describe() - 열에 대한 기술통계량
#데이터의 수, 평균, 표준편차, 최소값, 1사분위수, 2사분위수, 3사분위수, 최대값
df.describe()
age duration campaign pdays previous emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed
count 41188.00000 41188.000000 41188.000000 41188.000000 41188.000000 41188.000000 41188.000000 41188.000000 41188.000000 41188.000000
mean 40.02406 258.285010 2.567593 962.475454 0.172963 0.081886 93.575664 -40.502600 3.621291 5167.035911
std 10.42125 259.279249 2.770014 186.910907 0.494901 1.570960 0.578840 4.628198 1.734447 72.251528
min 17.00000 0.000000 1.000000 0.000000 0.000000 -3.400000 92.201000 -50.800000 0.634000 4963.600000
25% 32.00000 102.000000 1.000000 999.000000 0.000000 -1.800000 93.075000 -42.700000 1.344000 5099.100000
50% 38.00000 180.000000 2.000000 999.000000 0.000000 1.100000 93.749000 -41.800000 4.857000 5191.000000
75% 47.00000 319.000000 3.000000 999.000000 0.000000 1.400000 93.994000 -36.400000 4.961000 5228.100000
max 98.00000 4918.000000 56.000000 999.000000 7.000000 1.400000 94.767000 -26.900000 5.045000 5228.100000 
#columns - 칼럼명 반환
df.columns

Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',
       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',
       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',
       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],
      dtype='object')

#unique() - 열의 고유값
#education
df['education'].unique()

array(['basic.4y', 'high.school', 'basic.6y', 'basic.9y',
       'professional.course', 'unknown', 'university.degree',
       'illiterate'], dtype=object)

#value_counts() - 열의 고유값 빈도
#education
df['education'].value_counts()

university.degree      12168
high.school             9515
basic.9y                6045
professional.course     5243
basic.4y                4176
basic.6y                2292
unknown                 1731
illiterate                18
Name: education, dtype: int64

#unique() - 열의 고유값
#marital
df['marital'].unique()

array(['married', 'single', 'divorced', 'unknown'], dtype=object)

#value_counts() - 열의 고유값 빈도
#marital
df['marital'].value_counts()

married     24928
single      11568
divorced     4612
unknown        80
Name: marital, dtype: int64

데이터 시각화

  • 학습목표 :
    1. 현업의 데이터를 사용하여 데이터 시각화를 실습한다.
    2. 데이터를 가공,처리하여 시각화를 진행한다.
df['age']

0        56
1        57
2        37
3        40
4        56
         ..
41183    73
41184    46
41185    56
41186    44
41187    74
Name: age, Length: 41188, dtype: int64

df['age'].plot()
plt.show()
#x축이 인덱스 순서

output_30_0

  • age 칼럼 선그래프 그리기(오름차순)
    1. 노출수칼럼을 수치 순서대로 오름차순 정렬
    2. 정렬된 데이터(시리즈)의 형태대로 인덱스 재생성
#오름차순 정렬
#age칼럼 
age=df['age'].sort_values()

#age변수출력
age

38274    17
37579    17
37539    17
37140    17
37558    17
         ..
40450    92
38921    94
27826    95
38455    98
38452    98
Name: age, Length: 41188, dtype: int64

#reset_index - 인덱스 재생성, 기존 인덱스를 데이터프레임의 열로 반환
age=age.reset_index()

#age 변수출력
age
index age
0 38274 17
1 37579 17
2 37539 17
3 37140 17
4 37558 17
... ... ...
41183 40450 92
41184 38921 94
41185 27826 95
41186 38455 98
41187 38452 98

41188 rows × 2 columns

#drop(axis=1) - 삭제(열 기준)
age=age.drop('index',axis=1)

#age 변수출력
age
age
0 17
1 17
2 17
3 17
4 17
... ...
41183 92
41184 94
41185 95
41186 98
41187 98

41188 rows × 1 columns

#plotting
age.plot()
plt.show()
#값의 오름차순별로 정렬한 그래프
#보통 '나이'를 20대,30대,40..대로 나누어 데이터를 확인함

output_38_0

#계급간 빈도를 나타내주는 히스토그램
df['age'].plot.hist()
plt.show()

output_39_0

#히스토그램
#bins - 계급구간(10,20,30...100)
#figsize=[15,8]
#xticks(fontsize=15)
#yticks(fontsize=15)
#plt.title('Histogram of df.age',fontsize=20)

df['age'].plot.hist(bins=range(10,101,10),figsize=[15,8])
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.title('Histogram of df.age',fontsize=20)
plt.show()

output_40_0

#시각화 예제2 : duration(전화통화시간) 선 그래프 시각화
(((df['duration'].sort_values()).reset_index()).drop('index',axis=1)).plot()
plt.show()
#1. 선그래프로 데이터의 패턴 분석
#2. 히스토그램으로 전화통화 시간별 빈도 분석

output_41_0

#히스토그램의 계급구간을 설정하기 위한 최소값, 최대값 파악
#describe()
df['duration'].describe()

count    41188.000000
mean       258.285010
std        259.279249
min          0.000000
25%        102.000000
50%        180.000000
75%        319.000000
max       4918.000000
Name: duration, dtype: float64

#bins=range(0,5001,100)
df['duration'].plot.hist(bins=range(0,5001,100))
plt.show()

output_43_0

#히스토그램
#bins - 계급구간(0,100,200...5000)
#figsize=[15,8]
#xticks(fontsize=15)
#yticks(fontsize=15)
#plt.title('Histogram of df.duration',fontsize=20)

df['duration'].plot.hist(bins=range(0,5001,100), figsize=[15,8])
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.title('Histogram of df.duration',fontsize=20)
plt.show()

output_44_0

막대그래프, 가로막대그래프

#선그래프
#marital
df['marital'].plot()
plt.show()
#no numeric data to plot

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-36-7df365c79815> in <module>
      1 #선그래프
      2 #marital
----> 3 df['marital'].plot()
      4 plt.show()
      5 #no numeric data to plot


~\anaconda3\lib\site-packages\pandas\plotting\_core.py in __call__(self, *args, **kwargs)
    953                     data.columns = label_name
    954 
--> 955         return plot_backend.plot(data, kind=kind, **kwargs)
    956 
    957     __call__.__doc__ = __doc__


~\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\__init__.py in plot(data, kind, **kwargs)
     59             kwargs["ax"] = getattr(ax, "left_ax", ax)
     60     plot_obj = PLOT_CLASSES[kind](data, **kwargs)
---> 61     plot_obj.generate()
     62     plot_obj.draw()
     63     return plot_obj.result


~\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\core.py in generate(self)
    276     def generate(self):
    277         self._args_adjust()
--> 278         self._compute_plot_data()
    279         self._setup_subplots()
    280         self._make_plot()


~\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\core.py in _compute_plot_data(self)
    439         # no non-numeric frames or series allowed
    440         if is_empty:
--> 441             raise TypeError("no numeric data to plot")
    442 
    443         self.data = numeric_data.apply(self._convert_to_ndarray)


TypeError: no numeric data to plot
  • 숫자데이터가 아니기 때문에 시각화 할 수 없다.
#히스토그램
df['marital'].plot.hist()
#no numeric data to plot

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-38-414b9b3e32cb> in <module>
      1 #히스토그램
----> 2 df['marital'].plot.hist()
      3 #no numeric data to plot


~\anaconda3\lib\site-packages\pandas\plotting\_core.py in hist(self, by, bins, **kwargs)
   1294             >>> ax = df.plot.hist(bins=12, alpha=0.5)
   1295         """
-> 1296         return self(kind="hist", by=by, bins=bins, **kwargs)
   1297 
   1298     def kde(self, bw_method=None, ind=None, **kwargs):


~\anaconda3\lib\site-packages\pandas\plotting\_core.py in __call__(self, *args, **kwargs)
    953                     data.columns = label_name
    954 
--> 955         return plot_backend.plot(data, kind=kind, **kwargs)
    956 
    957     __call__.__doc__ = __doc__


~\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\__init__.py in plot(data, kind, **kwargs)
     59             kwargs["ax"] = getattr(ax, "left_ax", ax)
     60     plot_obj = PLOT_CLASSES[kind](data, **kwargs)
---> 61     plot_obj.generate()
     62     plot_obj.draw()
     63     return plot_obj.result


~\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\core.py in generate(self)
    276     def generate(self):
    277         self._args_adjust()
--> 278         self._compute_plot_data()
    279         self._setup_subplots()
    280         self._make_plot()


~\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\core.py in _compute_plot_data(self)
    439         # no non-numeric frames or series allowed
    440         if is_empty:
--> 441             raise TypeError("no numeric data to plot")
    442 
    443         self.data = numeric_data.apply(self._convert_to_ndarray)


TypeError: no numeric data to plot
  • 숫자데이터가 아니기 때문에 시각화 할 수 없다.
#unique()함수를 사용한 age칼럼 고유값 확인 
df['age'].unique()
#선그래프를 그린 age칼럼 데이터는 수치데이터

array([56, 57, 37, 40, 45, 59, 41, 24, 25, 29, 35, 54, 46, 50, 39, 30, 55,
       49, 34, 52, 58, 32, 38, 44, 42, 60, 53, 47, 51, 48, 33, 31, 43, 36,
       28, 27, 26, 22, 23, 20, 21, 61, 19, 18, 70, 66, 76, 67, 73, 88, 95,
       77, 68, 75, 63, 80, 62, 65, 72, 82, 64, 71, 69, 78, 85, 79, 83, 81,
       74, 17, 87, 91, 86, 98, 94, 84, 92, 89], dtype=int64)

#unique()함수를 사용한 marital칼럼 고유값 확인 
df['marital'].unique()
#marital칼럼의 데이터는 문자

array(['married', 'single', 'divorced', 'unknown'], dtype=object)
  • 막대그래프를 통한 시각화
    1. value_counts
    2. 막대그래프 시각화
#1. value_counts() - 문자데이터는 value_counts() 함수를 사용하여 시각화 할 수 있다.
#marital
marital=df['marital'].value_counts()

#2. marital변수 막대그래프 시각화
marital.plot.bar()
plt.show()

output_54_0

#2-1. 가로막대그래프 시각화
marital.plot.barh()
plt.show()

output_55_0

df['education'].unique()

array(['basic.4y', 'high.school', 'basic.6y', 'basic.9y',
       'professional.course', 'unknown', 'university.degree',
       'illiterate'], dtype=object)

#education 가로막대그래프 한줄 코드
#value_counts(),plot.barh()
education=df['education'].value_counts()

education.plot.barh()
plt.show()

output_58_0

데이터 분석

분석주제 1 :

대출이 있는 사람이라면 은행 상품에 잘 가입하지 않을 것이다.

  • 학습목표 :
    1. 가설검증과정 코딩 실습하기
    2. groupby활용한 실습 진행하기
  • 분석을 위한 코딩과정 도식화
    1. 가입여부에 따라 가입한 그룹과 가입하지 않은 그룹으로 나눈다.
    2. 나뉜 데이터를 대출여부에 따라 나눈다.
    3. 가입한 그룹 중 대출이 있는 사람의 비중과, 가입하지 않은 그룹 중 대출이 있는 사람의 비중을 비교한다.



customer2

# 1. 가입여부에 따라 가입한 그룹과 가입하지 않은 그룹으로 나눈다.
#가입여부에 대한 칼럼 : 'y'
#unique()
df['y'].unique()
#groupby사용 - yes, no그룹으로 나뉘게 됨

array(['no', 'yes'], dtype=object)

# 1. 가입여부에 따라 가입한 그룹과 가입하지 않은 그룹으로 나눈다.
#groupby('y')
grouped=df.groupby('y')

# 1. 가입여부에 따라 가입한 그룹과 가입하지 않은 그룹으로 나눈다.
#get_group('yes') - y칼럼이 'yes'인 데이터프레임 추출 - 가입한 그룹만 추출
#get_group('no') - y칼럼이 'no'인 데이터프레임 추출 - 가입하지 않은 그룹만 추출
yes_group=grouped.get_group('yes')
no_group=grouped.get_group('no')

# 1. 가입여부에 따라 가입한 그룹과 가입하지 않은 그룹으로 나눈다.
#yes_group 출력
yes_group.head()
age job marital education default housing loan contact month day_of_week ... campaign pdays previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed y
75 41 blue-collar divorced basic.4y unknown yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 yes
83 49 entrepreneur married university.degree unknown yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 yes
88 49 technician married basic.9y no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 yes
129 41 technician married professional.course unknown yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 yes
139 45 blue-collar married basic.9y unknown yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 yes

5 rows × 21 columns

# 1-3. 가입여부에 따라 가입한 그룹과 가입하지 않은 그룹으로 나눈다.
#no_group 출력
no_group.head()
age job marital education default housing loan contact month day_of_week ... campaign pdays previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed y
0 56 housemaid married basic.4y no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
1 57 services married high.school unknown no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
2 37 services married high.school no yes no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
3 40 admin. married basic.6y no no no telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
4 56 services married high.school no no yes telephone may mon ... 1 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no

5 rows × 21 columns

#2. 나뉜 데이터(yes_group,no_group)를 대출여부(loan)에 따라 나눈다.
#value_counts
yes=yes_group['loan'].value_counts()

#yes변수 출력
yes
#yes_group의 대출여부 빈도 출력

no         3850
yes         683
unknown     107
Name: loan, dtype: int64

#2. 나뉜 데이터(yes_group,no_group)를 대출여부(loan)에 따라 나눈다.
#value_counts
no=no_group['loan'].value_counts()

#no변수 출력
no
#no_group의 대출여부 빈도 출력

no         30100
yes         5565
unknown      883
Name: loan, dtype: int64

#3. 가입한 그룹 내 대출이 있는 사람의 비중과, 가입하지 않은 그룹 내 대출이 있는 사람의 비중을 비교한다(yes_group).
#비중 : 시리즈 변수 각각의 value를 시리즈의 총합으로 나눔
#시리즈는 산술연산자(+,-,*,/,%,**,//)와 함께 사용가능
#series/series.sum()
yes=yes/yes.sum()

#yes 출력
yes

no         0.829741
yes        0.147198
unknown    0.023060
Name: loan, dtype: float64

#3. 가입한 그룹 내 대출이 있는 사람의 비중과, 가입하지 않은 그룹 내 대출이 있는 사람의 비중을 비교한다(no_group).
no=no/no.sum()

#no 출력
no

no         0.823574
yes        0.152266
unknown    0.024160
Name: loan, dtype: float64

#3. 가입한 그룹 중 대출이 있는 사람의 비중과, 가입하지 않은 그룹 중 대출이 있는 사람의 비중을 비교한다.
#concat : 시리즈 혹은 데이터프레임 결합(default-행방향 결합)
pd.concat([yes,no],axis=1)# column 합치기
#칼럼명이 모두 loan
loan loan
no 0.829741 0.823574
yes 0.147198 0.152266
unknown 0.023060 0.024160 
#series.name : 시리즈의 이름 설정
yes.name='y_yes'

#series.name : 시리즈의 이름 설정
no.name='y_no'

pd.concat([yes,no],axis=1)
#=> 가입한 그룹의 대출 비중이 가입하지 않은 그룹보다 0.005 더 적다.
y_yes y_no
no 0.829741 0.823574
yes 0.147198 0.152266
unknown 0.023060 0.024160

분석주제 2 :

같은 상품을 새로운 고객에게 마케팅 하려고한다.

연령과 상품가입여부, 직업을 함께 고려할때 마케팅 전략을 변화시켜야 할 그룹은?

  • 학습목표 :
    1. 가설검증과정 코딩 실습하기
    2. pivot_table활용한 실습 진행하기
  • 분석조건 : 세 개의 칼럼(age, job, y)을 함께 분석해야 함
  • pd.pivot_table(‘데이터프레임 변수’,values=집계 대상 칼럼(수치 데이터), index=행 인덱스가 될 칼럼명, columns=열 인덱스가 될 칼럼명, aggfunc=집계함수-sum,mean,min,max,std,var)

pivot_table 사용예제

#pd.pivot_table('데이터프레임 변수',values=집계 대상 칼럼, index=행 인덱스가 될 칼럼명, columns=열 인덱스가 될 칼럼명, aggfunc=sum)
pd.pivot_table(df,values='age',index='y',columns='job',aggfunc='mean')
job admin. blue-collar entrepreneur housemaid management retired self-employed services student technician unemployed unknown
y
no 38.219846 39.582057 41.703453 44.705451 42.309707 59.926128 40.176887 38.090236 26.396667 38.600033 39.844828 45.375427
yes 37.968935 39.200627 41.935484 52.650943 42.783537 68.253456 38.006711 36.077399 24.800000 37.746575 39.062500 47.054054 
#values,index,columns파라미터를 일일이 쓰지 않고 순서대로 입력하여 실행 가능
pd.pivot_table(df,'age','y','job',aggfunc='mean')
job admin. blue-collar entrepreneur housemaid management retired self-employed services student technician unemployed unknown
y
no 38.219846 39.582057 41.703453 44.705451 42.309707 59.926128 40.176887 38.090236 26.396667 38.600033 39.844828 45.375427
yes 37.968935 39.200627 41.935484 52.650943 42.783537 68.253456 38.006711 36.077399 24.800000 37.746575 39.062500 47.054054 
#멀티 인덱스(multi-index) - 행 인덱스
#['y','marital']
pd.pivot_table(df,'age',['y','marital'],'job',aggfunc='mean')
job admin. blue-collar entrepreneur housemaid management retired self-employed services student technician unemployed unknown
y marital
no divorced 43.098432 42.903704 44.042424 48.806897 46.123288 61.480469 42.871795 41.991984 34.500000 42.173484 42.140351 43.300000
married 40.148663 40.857804 42.477111 44.849218 43.634997 60.019048 42.349148 39.992951 30.484848 40.686245 41.636861 47.532110
single 33.858265 33.409255 35.472527 38.087379 34.070776 53.938272 33.783537 32.159921 26.062500 33.950697 33.536946 38.288136
unknown 34.666667 42.818182 35.500000 40.000000 51.000000 59.750000 39.400000 40.000000 30.000000 33.300000 47.200000 40.166667
yes divorced 44.878788 42.037736 44.857143 57.000000 46.692308 72.739130 41.875000 43.484848 35.666667 40.738462 47.900000 76.333333
married 41.386503 41.363420 43.090909 54.256757 44.756637 67.033435 41.036585 38.379518 31.250000 41.398438 41.941860 58.750000
single 32.404594 32.652174 35.666667 40.875000 33.285714 67.500000 31.921569 31.024194 24.481061 32.078853 32.062500 30.000000
unknown 42.500000 37.000000 31.000000 NaN NaN 66.000000 NaN NaN NaN 30.000000 NaN 40.666667 
#멀티 인덱스(multi-index) - 열 인덱스
#['job','contact']
pd.pivot_table(df,'age',['y','marital'],['job','contact'],aggfunc='mean')
job admin. blue-collar entrepreneur housemaid management ... services student technician unemployed unknown
contact cellular telephone cellular telephone cellular telephone cellular telephone cellular telephone ... cellular telephone cellular telephone cellular telephone cellular telephone cellular telephone
y marital
no divorced 43.143639 43.019185 42.906907 42.900585 43.301887 45.372881 48.292683 49.476190 44.786164 47.721805 ... 42.028269 41.944444 36.000000 27.000000 42.069034 42.435644 42.253731 41.978723 46.000000 40.600000
married 40.349554 39.800357 41.182477 40.494249 42.213628 42.802273 45.900966 43.342561 43.740678 43.452416 ... 40.483733 39.395833 32.150000 27.923077 40.283568 41.365495 41.452769 41.871369 48.349057 46.758929
single 33.515821 34.646707 32.872838 34.183554 36.181034 34.227273 36.827586 39.711111 33.747405 34.697987 ... 32.360927 31.863081 25.396509 27.742138 33.529248 35.008741 33.252101 33.940476 39.535714 37.161290
unknown 35.000000 31.000000 43.800000 42.000000 40.000000 31.000000 40.000000 NaN NaN 51.000000 ... 34.500000 42.750000 30.000000 NaN 30.285714 40.333333 46.500000 50.000000 48.333333 32.000000
yes divorced 45.028302 44.269231 41.500000 43.400000 45.333333 44.000000 59.857143 37.000000 46.297297 54.000000 ... 43.083333 44.555556 35.666667 NaN 40.701754 41.000000 51.000000 40.666667 76.333333 NaN
married 41.362832 41.540230 41.682432 40.608000 44.177419 40.500000 55.375000 50.777778 45.401099 42.090909 ... 38.634921 37.575000 30.333333 34.000000 41.299065 41.904762 42.746479 38.133333 58.181818 60.000000
single 32.117284 34.150000 31.934426 34.897436 35.117647 38.000000 40.769231 41.333333 32.763636 36.875000 ... 30.846154 31.950000 24.174468 26.965517 31.995902 32.657143 32.355556 27.666667 31.000000 28.000000
unknown 42.500000 NaN 37.000000 NaN 31.000000 NaN NaN NaN NaN NaN ... NaN NaN NaN NaN 30.000000 NaN NaN NaN 45.000000 32.000000

8 rows × 24 columns

#fill-value - 결측치 대체
#fill_value=0
pd.pivot_table(df,'age',['y','marital'],['job','contact'],aggfunc='mean',fill_value=0)
job admin. blue-collar entrepreneur housemaid management ... services student technician unemployed unknown
contact cellular telephone cellular telephone cellular telephone cellular telephone cellular telephone ... cellular telephone cellular telephone cellular telephone cellular telephone cellular telephone
y marital
no divorced 43.143639 43.019185 42.906907 42.900585 43.301887 45.372881 48.292683 49.476190 44.786164 47.721805 ... 42.028269 41.944444 36.000000 27.000000 42.069034 42.435644 42.253731 41.978723 46.000000 40.600000
married 40.349554 39.800357 41.182477 40.494249 42.213628 42.802273 45.900966 43.342561 43.740678 43.452416 ... 40.483733 39.395833 32.150000 27.923077 40.283568 41.365495 41.452769 41.871369 48.349057 46.758929
single 33.515821 34.646707 32.872838 34.183554 36.181034 34.227273 36.827586 39.711111 33.747405 34.697987 ... 32.360927 31.863081 25.396509 27.742138 33.529248 35.008741 33.252101 33.940476 39.535714 37.161290
unknown 35.000000 31.000000 43.800000 42.000000 40.000000 31.000000 40.000000 0.000000 0.000000 51.000000 ... 34.500000 42.750000 30.000000 0.000000 30.285714 40.333333 46.500000 50.000000 48.333333 32.000000
yes divorced 45.028302 44.269231 41.500000 43.400000 45.333333 44.000000 59.857143 37.000000 46.297297 54.000000 ... 43.083333 44.555556 35.666667 0.000000 40.701754 41.000000 51.000000 40.666667 76.333333 0.000000
married 41.362832 41.540230 41.682432 40.608000 44.177419 40.500000 55.375000 50.777778 45.401099 42.090909 ... 38.634921 37.575000 30.333333 34.000000 41.299065 41.904762 42.746479 38.133333 58.181818 60.000000
single 32.117284 34.150000 31.934426 34.897436 35.117647 38.000000 40.769231 41.333333 32.763636 36.875000 ... 30.846154 31.950000 24.174468 26.965517 31.995902 32.657143 32.355556 27.666667 31.000000 28.000000
unknown 42.500000 0.000000 37.000000 0.000000 31.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 30.000000 0.000000 0.000000 0.000000 45.000000 32.000000

8 rows × 24 columns

pivot_table를 사용한 주제2 분석

같은 상품을 새로운 고객에게 마케팅 하려고한다.

연령과 상품가입여부, 직업을 함께 고려할때 마케팅 전략을 변화시켜야 할 그룹은?

#pivot_table
pivot=pd.pivot_table(df,values='age',index='y',columns='job',aggfunc='mean')

#pivot 변수 출력
pivot
job admin. blue-collar entrepreneur housemaid management retired self-employed services student technician unemployed unknown
y
no 38.219846 39.582057 41.703453 44.705451 42.309707 59.926128 40.176887 38.090236 26.396667 38.600033 39.844828 45.375427
yes 37.968935 39.200627 41.935484 52.650943 42.783537 68.253456 38.006711 36.077399 24.800000 37.746575 39.062500 47.054054 
#yes행과 no행의 차 연산(loc인덱서 사용)
pivot.loc['yes']-pivot.loc['no']

job
admin.          -0.250911
blue-collar     -0.381430
entrepreneur     0.232030
housemaid        7.945493
management       0.473829
retired          8.327329
self-employed   -2.170175
services        -2.012836
student         -1.596667
technician      -0.853458
unemployed      -0.782328
unknown          1.678627
dtype: float64

#diff행 생성(yes행과 no행의 차)
pivot.loc['diff']=pivot.loc['yes']-pivot.loc['no']

#pivot 변수 출력
pivot
job admin. blue-collar entrepreneur housemaid management retired self-employed services student technician unemployed unknown
y
no 38.219846 39.582057 41.703453 44.705451 42.309707 59.926128 40.176887 38.090236 26.396667 38.600033 39.844828 45.375427
yes 37.968935 39.200627 41.935484 52.650943 42.783537 68.253456 38.006711 36.077399 24.800000 37.746575 39.062500 47.054054
diff -0.250911 -0.381430 0.232030 7.945493 0.473829 8.327329 -2.170175 -2.012836 -1.596667 -0.853458 -0.782328 1.678627 
#diff 기준으로 내림차순 정렬 
#sort_values() - default : 열 기준 오름차순 정렬
#axis=1,ascending=False : 행 기준 내림차순 정렬
#axis=1 안넣어주면 오류 발생
result=pivot.sort_values('diff',axis=1,ascending=False)

#result 출력
result
job retired housemaid unknown management entrepreneur admin. blue-collar unemployed technician student services self-employed
y
no 59.926128 44.705451 45.375427 42.309707 41.703453 38.219846 39.582057 39.844828 38.600033 26.396667 38.090236 40.176887
yes 68.253456 52.650943 47.054054 42.783537 41.935484 37.968935 39.200627 39.062500 37.746575 24.800000 36.077399 38.006711
diff 8.327329 7.945493 1.678627 0.473829 0.232030 -0.250911 -0.381430 -0.782328 -0.853458 -1.596667 -2.012836 -2.170175 
#result의 diff행 막대그래프 시각화
result.loc['diff'].plot.bar()
plt.show()

output_95_0

#result의 diff행 막대그래프 시각화
#figsize=[15,10]
#title('주제2 시각화',fontsize=20)
#x축 눈금 - fontsize=16,rotation=45
#y축 눈금 - fontsize=16)
#xlabel - 'job',fontsize=16
#ylabel - 'diff',fontsize=16

result.loc['diff'].plot.bar(figsize=[15,10])
plt.title('주제2 시각화',fontsize=20)
plt.xticks(fontsize=16,rotation=45)
plt.yticks(fontsize=16)
plt.xlabel('job',fontsize=16)
plt.ylabel('diff',fontsize=16)
plt.show()

output_96_0

  • 결론 - retired , housemaid 경우는 연령을 높여서 더욱 타겟팅 해야한다