E-Commerce 결제 내역 분석 EDA

 

 

 

In [38]:

!pip install nbconvert

 

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In [4]:

import pandas as pd

In [5]:

import matplotlib as mpl
import matplotlib.pyplot as plt
 
%config InlineBackend.figure_format = 'retina'
 
!apt -qq -y install fonts-nanum
 
import matplotlib.font_manager as fm
fontpath = '/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf'
font = fm.FontProperties(fname=fontpath, size=9)
plt.rc('font', family='NanumBarunGothic') 
mpl.font_manager._rebuild()

 

fonts-nanum is already the newest version (20170925-1).
0 upgraded, 0 newly installed, 0 to remove and 15 not upgraded.

In [6]:

df = pd.read_csv('/content/Download.CSV')

In [7]:

df.info()

 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 417 entries, 0 to 416
Data columns (total 41 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   날짜                 417 non-null    object 
 1   시간                 417 non-null    object 
 2   시간대                417 non-null    object 
 3   이름                 363 non-null    object 
 4   유형                 417 non-null    object 
 5   상태                 417 non-null    object 
 6   통화                 417 non-null    object 
 7   총액                 417 non-null    object 
 8   수수료                417 non-null    float64
 9   순액                 417 non-null    object 
 10  보내는 이메일 주소         399 non-null    object 
 11  받는 이메일 주소          381 non-null    object 
 12  거래 ID              417 non-null    object 
 13  배송 주소              352 non-null    object 
 14  주소 상태              363 non-null    object 
 15  상품 이름              363 non-null    object 
 16  상품 ID              0 non-null      float64
 17  배송 및 처리 금액         330 non-null    float64
 18  보험 금액              0 non-null      float64
 19  판매세                337 non-null    float64
 20  옵션 1 이름            0 non-null      float64
 21  옵션 1 값             0 non-null      float64
 22  옵션 2 이름            0 non-null      float64
 23  옵션 2 값             0 non-null      float64
 24  참조 납세 번호           70 non-null     object 
 25  인보이스 번호            363 non-null    object 
 26  맞춤형 번호             359 non-null    object 
 27  수량                 363 non-null    float64
 28  영수증 ID             3 non-null      float64
 29  잔액                 417 non-null    object 
 30  주소 입력란 1           352 non-null    object 
 31  주소 입력란 2/지역/동      171 non-null    object 
 32  시/읍                352 non-null    object 
 33  주/도/지역/군/준주/현/공화국  128 non-null    object 
 34  우편번호               203 non-null    object 
 35  국가                 352 non-null    object 
 36  연락처 전화번호           360 non-null    float64
 37  제목                 363 non-null    object 
 38  메모                 3 non-null      object 
 39  국가번호               352 non-null    object 
 40  잔액 영향              417 non-null    object 
dtypes: float64(12), object(29)
memory usage: 133.7+ KB

 

중요한 해당 컬럼만 따로 빼서 df로 활용하고자 함

'날짜','시간,'이름','유형','상태','통화' ,'총액' ,'국가번호'

---

시간대별 결제빈도 및 금액 파악 -> 해당 시간대에 반짝이벤트

날짜별 결제빈도 및 금액 파악 -> 급여날? 월초월중월말 어떤 날로 마케팅할지

국가별 평균결제금액 -> 파악 후 다음 상품 적정한 가격 선정

In [8]:

df = df.loc[:,['날짜','시간','이름','유형' ,'총액' ,'국가번호']]
df.head()

Out[8]:

	날짜	시간	이름	유형	총액	국가번호
0	2020.01.01	10:22:55	NADIA ALSHAIJI	웹사이트 결제	280.00	KW
1	2020.01.01	16:19:39	Reem al teneiji	웹사이트 결제	2,550.00	AE
2	2020.01.02	11:53:27	Bothaina Al-Thani	결제 환불	-180.00	NaN
3	2020.01.02	14:46:27	humaid bin hendi	웹사이트 결제	150.00	US
4	2020.01.03	13:27:49	NADIA ALSHAIJI	웹사이트 결제	140.00	KW

In [9]:

df.info()

 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 417 entries, 0 to 416
Data columns (total 6 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   날짜      417 non-null    object
 1   시간      417 non-null    object
 2   이름      363 non-null    object
 3   유형      417 non-null    object
 4   총액      417 non-null    object
 5   국가번호    352 non-null    object
dtypes: object(6)
memory usage: 19.7+ KB

In [10]:

# 유형 컬럼에서 '익스프레스 체크아웃 결제'만 선택

df = df[df['유형'] == '익스프레스 체크아웃 결제']

In [11]:

df.head()

Out[11]:

	날짜	시간	이름	유형	총액	국가번호
34	2020.03.27	15:44:15	Yousef alamid	익스프레스 체크아웃 결제	0.10	SA
37	2020.05.21	07:34:07	Fayeza Ahmed	익스프레스 체크아웃 결제	85.00	AE
38	2020.05.22	21:35:54	Wafa Al Mahrami	익스프레스 체크아웃 결제	85.00	OM
39	2020.05.23	01:49:32	Sarah ALshamari	익스프레스 체크아웃 결제	85.00	QA
40	2020.05.24	01:25:09	wadha alraqeeb	익스프레스 체크아웃 결제	85.00	KW

In [12]:

import sys
if 'google.colab' in sys.modules:
    !pip install category_encoders==2.*
    !pip install pandas-profiling==2.*
    !pip install plotly==4.*

 

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In [13]:

from pandas_profiling import ProfileReport

profile = ProfileReport(df, title="dataset")
profile

 

 

 

 

 

 

 

In [14]:

 

df['총액'] = pd.to_numeric(df['총액'])

In [15]:

df.info()

 

<class 'pandas.core.frame.DataFrame'>
Int64Index: 329 entries, 34 to 413
Data columns (total 6 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   날짜      329 non-null    object 
 1   시간      329 non-null    object 
 2   이름      329 non-null    object 
 3   유형      329 non-null    object 
 4   총액      329 non-null    float64
 5   국가번호    329 non-null    object 
dtypes: float64(1), object(5)
memory usage: 18.0+ KB

In [16]:

import seaborn as sns
import matplotlib.pyplot as plt

%matplotlib inline

sns.set_theme(style="whitegrid")
ax = sns.barplot(x="국가번호", y="총액", data=df)
ax.set(xlabel='country', ylabel='payment per person')

for p in ax.patches:
    ax.annotate(format(p.get_height(), '.1f'), 
                   (p.get_x() + p.get_width() / 2., p.get_height()), 
                   ha = 'center', va = 'center', 
                   xytext = (0, 9), 
                   textcoords = 'offset points')

plt.show()

 

 

최저평균가격은 사우디아라비아의 118.5불¶

바레인은 결제건이 많지 않으므로 그다음 카타르에 의미를 두면¶

최고 평균가격은 151.7불¶

GB는 예외국가이므로 제외¶

 

---

In [17]:

#시간 데이터 object 를 datetime 형식으로 변경

df['시간'] = pd.to_datetime(df['시간'].str.strip(), format='%H:%M:%S', errors='raise')

In [18]:

# 1시간 단위로 끊어서 총 결제금액을 합침

df2 = df.groupby(df.시간.dt.to_period('H')).agg('sum')

In [20]:

df2.head()

Out[20]:

	총액
시간
1900-01-01 00:00	2219.69
1900-01-01 01:00	2844.34
1900-01-01 02:00	2701.15
1900-01-01 03:00	3962.65
1900-01-01 04:00	2955.30

In [24]:

df2.reset_index()

Out[24]:

	시간	총액
0	1900-01-01 00:00	2219.69
1	1900-01-01 01:00	2844.34
2	1900-01-01 02:00	2701.15
3	1900-01-01 03:00	3962.65
4	1900-01-01 04:00	2955.30
5	1900-01-01 05:00	1919.26
6	1900-01-01 06:00	1431.84
7	1900-01-01 07:00	760.95
8	1900-01-01 08:00	435.19
9	1900-01-01 09:00	265.80
10	1900-01-01 10:00	1278.95
11	1900-01-01 11:00	1091.96
12	1900-01-01 12:00	474.82
13	1900-01-01 13:00	428.20
14	1900-01-01 14:00	599.10
15	1900-01-01 15:00	959.76
16	1900-01-01 16:00	1753.68
17	1900-01-01 17:00	2157.51
18	1900-01-01 18:00	3232.22
19	1900-01-01 19:00	2696.92
20	1900-01-01 20:00	1620.58
21	1900-01-01 21:00	2484.14
22	1900-01-01 22:00	3360.01
23	1900-01-01 23:00	2533.30

In [32]:

df2.info()

 

<class 'pandas.core.frame.DataFrame'>
PeriodIndex: 24 entries, 1900-01-01 00:00 to 1900-01-01 23:00
Freq: H
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   총액      24 non-null     float64
dtypes: float64(1)
memory usage: 384.0 bytes