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관리 메뉴

흰둥이는 코드를 짤 때 짖어 (왈!왈!왈!왈!왈!왈!왈!왈!왈!왈!왈!)

(Python) 가상 쇼핑몰 데이터 프로젝트 본문

파이썬 데이터 분석

(Python) 가상 쇼핑몰 데이터 프로젝트

흰둥아솜사탕 2023. 6. 8. 17:37

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In [ ]:

import pandas as pd

In [ ]:

retail = pd.read_csv('/content/drive/MyDrive/KDT/2. 데이터분석/OnlineRetail.csv')
retail

Out[ ]:

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
0	536365	85123A	WHITE HANGING HEART T-LIGHT HOLDER	6	12/1/2010 8:26	2.55	17850.0	United Kingdom
1	536365	71053	WHITE METAL LANTERN	6	12/1/2010 8:26	3.39	17850.0	United Kingdom
2	536365	84406B	CREAM CUPID HEARTS COAT HANGER	8	12/1/2010 8:26	2.75	17850.0	United Kingdom
3	536365	84029G	KNITTED UNION FLAG HOT WATER BOTTLE	6	12/1/2010 8:26	3.39	17850.0	United Kingdom
4	536365	84029E	RED WOOLLY HOTTIE WHITE HEART.	6	12/1/2010 8:26	3.39	17850.0	United Kingdom
...	...	...	...	...	...	...	...	...
541904	581587	22613	PACK OF 20 SPACEBOY NAPKINS	12	12/9/2011 12:50	0.85	12680.0	France
541905	581587	22899	CHILDREN'S APRON DOLLY GIRL	6	12/9/2011 12:50	2.10	12680.0	France
541906	581587	23254	CHILDRENS CUTLERY DOLLY GIRL	4	12/9/2011 12:50	4.15	12680.0	France
541907	581587	23255	CHILDRENS CUTLERY CIRCUS PARADE	4	12/9/2011 12:50	4.15	12680.0	France
541908	581587	22138	BAKING SET 9 PIECE RETROSPOT	3	12/9/2011 12:50	4.95	12680.0	France

541909 rows × 8 columns

In [ ]:

pd.options.display.max_rows = 6

In [ ]:

retail

Out[ ]:

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
0	536365	85123A	WHITE HANGING HEART T-LIGHT HOLDER	6	12/1/2010 8:26	2.55	17850.0	United Kingdom
1	536365	71053	WHITE METAL LANTERN	6	12/1/2010 8:26	3.39	17850.0	United Kingdom
2	536365	84406B	CREAM CUPID HEARTS COAT HANGER	8	12/1/2010 8:26	2.75	17850.0	United Kingdom
...	...	...	...	...	...	...	...	...
541906	581587	23254	CHILDRENS CUTLERY DOLLY GIRL	4	12/9/2011 12:50	4.15	12680.0	France
541907	581587	23255	CHILDRENS CUTLERY CIRCUS PARADE	4	12/9/2011 12:50	4.15	12680.0	France
541908	581587	22138	BAKING SET 9 PIECE RETROSPOT	3	12/9/2011 12:50	4.95	12680.0	France

541909 rows × 8 columns

In [ ]:

retail.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 541909 entries, 0 to 541908
Data columns (total 8 columns):
 #   Column       Non-Null Count   Dtype  
---  ------       --------------   -----  
 0   InvoiceNo    541909 non-null  object 
 1   StockCode    541909 non-null  object 
 2   Description  540455 non-null  object 
 3   Quantity     541909 non-null  int64  
 4   InvoiceDate  541909 non-null  object 
 5   UnitPrice    541909 non-null  float64
 6   CustomerID   406829 non-null  float64
 7   Country      541909 non-null  object 
dtypes: float64(2), int64(1), object(5)
memory usage: 33.1+ MB

InvoiceNo: 주문번호
StockCode: 상품코드
Description: 상품설명
Quantity: 주문수량
InvoiceDate: 주문날짜
UnitPrice: 상품가격
CustomerID: 고객 아이디
Country: 고객거주지역(국가)

In [ ]:

pd.options.display.max_rows = 10
retail.isnull().sum()

Out[ ]:

InvoiceNo           0
StockCode           0
Description      1454
Quantity            0
InvoiceDate         0
UnitPrice           0
CustomerID     135080
Country             0
dtype: int64

In [ ]:

# 각 필드당 null이 몇% 있는지 확인
retail.isnull().mean()

Out[ ]:

InvoiceNo      0.000000
StockCode      0.000000
Description    0.002683
Quantity       0.000000
InvoiceDate    0.000000
UnitPrice      0.000000
CustomerID     0.249267
Country        0.000000
dtype: float64

In [ ]:

# 비회원을 제거
retail = retail[pd.notnull(retail['CustomerID'])]

In [ ]:

len(retail)

Out[ ]:

In [ ]:

# 구입 수량이 0이하인 데이터 확인
retail[retail['Quantity'] <= 0]

Out[ ]:

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
141	C536379	D	Discount	-1	12/1/2010 9:41	27.50	14527.0	United Kingdom
154	C536383	35004C	SET OF 3 COLOURED FLYING DUCKS	-1	12/1/2010 9:49	4.65	15311.0	United Kingdom
235	C536391	22556	PLASTERS IN TIN CIRCUS PARADE	-12	12/1/2010 10:24	1.65	17548.0	United Kingdom
236	C536391	21984	PACK OF 12 PINK PAISLEY TISSUES	-24	12/1/2010 10:24	0.29	17548.0	United Kingdom
237	C536391	21983	PACK OF 12 BLUE PAISLEY TISSUES	-24	12/1/2010 10:24	0.29	17548.0	United Kingdom
...	...	...	...	...	...	...	...	...
540449	C581490	23144	ZINC T-LIGHT HOLDER STARS SMALL	-11	12/9/2011 9:57	0.83	14397.0	United Kingdom
541541	C581499	M	Manual	-1	12/9/2011 10:28	224.69	15498.0	United Kingdom
541715	C581568	21258	VICTORIAN SEWING BOX LARGE	-5	12/9/2011 11:57	10.95	15311.0	United Kingdom
541716	C581569	84978	HANGING HEART JAR T-LIGHT HOLDER	-1	12/9/2011 11:58	1.25	17315.0	United Kingdom
541717	C581569	20979	36 PENCILS TUBE RED RETROSPOT	-5	12/9/2011 11:58	1.25	17315.0	United Kingdom

8905 rows × 8 columns

In [ ]:

# 구입 가격이 0이하인 데이터 확인
retail[retail['UnitPrice'] <= 0]

Out[ ]:

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
9302	537197	22841	ROUND CAKE TIN VINTAGE GREEN	1	12/5/2010 14:02	0.0	12647.0	Germany
33576	539263	22580	ADVENT CALENDAR GINGHAM SACK	4	12/16/2010 14:36	0.0	16560.0	United Kingdom
40089	539722	22423	REGENCY CAKESTAND 3 TIER	10	12/21/2010 13:45	0.0	14911.0	EIRE
47068	540372	22090	PAPER BUNTING RETROSPOT	24	1/6/2011 16:41	0.0	13081.0	United Kingdom
47070	540372	22553	PLASTERS IN TIN SKULLS	24	1/6/2011 16:41	0.0	13081.0	United Kingdom
...	...	...	...	...	...	...	...	...
479079	577129	22464	HANGING METAL HEART LANTERN	4	11/17/2011 19:52	0.0	15602.0	United Kingdom
479546	577168	M	Manual	1	11/18/2011 10:42	0.0	12603.0	Germany
480649	577314	23407	SET OF 2 TRAYS HOME SWEET HOME	2	11/18/2011 13:23	0.0	12444.0	Norway
485985	577696	M	Manual	1	11/21/2011 11:57	0.0	16406.0	United Kingdom
502122	578841	84826	ASSTD DESIGN 3D PAPER STICKERS	12540	11/25/2011 15:57	0.0	13256.0	United Kingdom

40 rows × 8 columns

In [ ]:

# 구입 수량이 1이상인 데이터를 저장
retail = retail[retail['Quantity'] >= 1]

In [ ]:

# 구입 가격이 1이상인 데이터를 저장
retail = retail[retail['UnitPrice'] >= 1]

In [ ]:

len(retail)

Out[ ]:

In [ ]:

# 고객의 총 지출비용(CheckoutPrice) 파생변수 구하기
# 수량 * 가격
retail['CheckoutPrice'] = retail['UnitPrice'] * retail['Quantity']
retail

<ipython-input-15-1652791f7c6a>:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  retail['CheckoutPrice'] = retail['UnitPrice'] * retail['Quantity']

Out[ ]:

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country	CheckoutPrice
0	536365	85123A	WHITE HANGING HEART T-LIGHT HOLDER	6	12/1/2010 8:26	2.55	17850.0	United Kingdom	15.30
1	536365	71053	WHITE METAL LANTERN	6	12/1/2010 8:26	3.39	17850.0	United Kingdom	20.34
2	536365	84406B	CREAM CUPID HEARTS COAT HANGER	8	12/1/2010 8:26	2.75	17850.0	United Kingdom	22.00
3	536365	84029G	KNITTED UNION FLAG HOT WATER BOTTLE	6	12/1/2010 8:26	3.39	17850.0	United Kingdom	20.34
4	536365	84029E	RED WOOLLY HOTTIE WHITE HEART.	6	12/1/2010 8:26	3.39	17850.0	United Kingdom	20.34
...	...	...	...	...	...	...	...	...	...
541903	581587	23256	CHILDRENS CUTLERY SPACEBOY	4	12/9/2011 12:50	4.15	12680.0	France	16.60
541905	581587	22899	CHILDREN'S APRON DOLLY GIRL	6	12/9/2011 12:50	2.10	12680.0	France	12.60
541906	581587	23254	CHILDRENS CUTLERY DOLLY GIRL	4	12/9/2011 12:50	4.15	12680.0	France	16.60
541907	581587	23255	CHILDRENS CUTLERY CIRCUS PARADE	4	12/9/2011 12:50	4.15	12680.0	France	16.60
541908	581587	22138	BAKING SET 9 PIECE RETROSPOT	3	12/9/2011 12:50	4.95	12680.0	France	14.85

304911 rows × 9 columns

In [ ]:

retail.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 304911 entries, 0 to 541908
Data columns (total 9 columns):
 #   Column         Non-Null Count   Dtype  
---  ------         --------------   -----  
 0   InvoiceNo      304911 non-null  object 
 1   StockCode      304911 non-null  object 
 2   Description    304911 non-null  object 
 3   Quantity       304911 non-null  int64  
 4   InvoiceDate    304911 non-null  object 
 5   UnitPrice      304911 non-null  float64
 6   CustomerID     304911 non-null  float64
 7   Country        304911 non-null  object 
 8   CheckoutPrice  304911 non-null  float64
dtypes: float64(3), int64(1), object(5)
memory usage: 23.3+ MB

In [ ]:

retail.head()

Out[ ]:

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country	CheckoutPrice
0	536365	85123A	WHITE HANGING HEART T-LIGHT HOLDER	6	12/1/2010 8:26	2.55	17850.0	United Kingdom	15.30
1	536365	71053	WHITE METAL LANTERN	6	12/1/2010 8:26	3.39	17850.0	United Kingdom	20.34
2	536365	84406B	CREAM CUPID HEARTS COAT HANGER	8	12/1/2010 8:26	2.75	17850.0	United Kingdom	22.00
3	536365	84029G	KNITTED UNION FLAG HOT WATER BOTTLE	6	12/1/2010 8:26	3.39	17850.0	United Kingdom	20.34
4	536365	84029E	RED WOOLLY HOTTIE WHITE HEART.	6	12/1/2010 8:26	3.39	17850.0	United Kingdom	20.34

In [ ]:

retail['InvoiceDate'] = pd.to_datetime(retail['InvoiceDate'])

In [ ]:

retail.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 304911 entries, 0 to 541908
Data columns (total 9 columns):
 #   Column         Non-Null Count   Dtype         
---  ------         --------------   -----         
 0   InvoiceNo      304911 non-null  object        
 1   StockCode      304911 non-null  object        
 2   Description    304911 non-null  object        
 3   Quantity       304911 non-null  int64         
 4   InvoiceDate    304911 non-null  datetime64[ns]
 5   UnitPrice      304911 non-null  float64       
 6   CustomerID     304911 non-null  float64       
 7   Country        304911 non-null  object        
 8   CheckoutPrice  304911 non-null  float64       
dtypes: datetime64[ns](1), float64(3), int64(1), object(4)
memory usage: 23.3+ MB

In [ ]:

# 전체 매출
total_revenue = retail['CheckoutPrice'].sum()
total_revenue

Out[ ]:

7863916.5

In [ ]:

retail['Country'].value_counts()

Out[ ]:

United Kingdom    271313
Germany             6923
France              6335
EIRE                5654
Spain               1983
                   ...  
Lithuania             35
Brazil                31
Bahrain               17
Czech Republic        14
Saudi Arabia           8
Name: Country, Length: 37, dtype: int64

In [ ]:

pd.options.display.max_rows = 40

In [ ]:

retail['Country'].value_counts()

Out[ ]:

United Kingdom          271313
Germany                   6923
France                    6335
EIRE                      5654
Spain                     1983
Netherlands               1723
Belgium                   1536
Switzerland               1403
Portugal                  1130
Australia                  945
Norway                     836
Italy                      646
Channel Islands            618
Cyprus                     535
Finland                    508
Austria                    314
Sweden                     290
Denmark                    289
Poland                     275
Japan                      220
Unspecified                191
Israel                     177
Singapore                  167
Iceland                    139
USA                        131
Greece                     122
Malta                      102
Canada                     101
RSA                         54
European Community          52
United Arab Emirates        51
Lebanon                     43
Lithuania                   35
Brazil                      31
Bahrain                     17
Czech Republic              14
Saudi Arabia                 8
Name: Country, dtype: int64

In [ ]:

pd.options.display.max_rows = 10

In [ ]:

# 국가별 매출
rev_by_countries = retail.groupby('Country')['CheckoutPrice'].sum().sort_values()
rev_by_countries

Out[ ]:

Country
Saudi Arabia          140.88
Bahrain               548.40
Czech Republic        586.66
RSA                   981.97
Brazil               1123.20
                     ...    
France             183549.73
Germany            202050.01
EIRE               236665.02
Netherlands        245279.99
United Kingdom    6459137.29
Name: CheckoutPrice, Length: 37, dtype: float64

In [ ]:

plot = rev_by_countries.plot(kind='bar', figsize=(20, 10))
plot.set_xlabel('Country', fontsize=12)
plot.set_ylabel('Revenue', fontsize=12)
plot.set_title('Revenue By Country', fontsize=15)
plot.set_xticklabels(labels=rev_by_countries.index, rotation=45)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-35-d1c32f1ad393> in <cell line: 6>()
      4 plot.set_title('Revenue By Country', fontsize=15)
      5 plot.set_xticklabels(labels=rev_by_countries.index, rotation=45)
----> 6 plot.show()

AttributeError: 'Axes' object has no attribute 'show'

In [ ]:

rev_by_countries / total_revenue

Out[ ]:

Country
Saudi Arabia      0.000018
Bahrain           0.000070
Czech Republic    0.000075
RSA               0.000125
Brazil            0.000143
                    ...   
France            0.023341
Germany           0.025693
EIRE              0.030095
Netherlands       0.031191
United Kingdom    0.821364
Name: CheckoutPrice, Length: 37, dtype: float64

In [ ]:

# 월별 매출 구하기
retail['InvoiceDate'].sort_values(ascending=False)

Out[ ]:

541908   2011-12-09 12:50:00
541907   2011-12-09 12:50:00
541906   2011-12-09 12:50:00
541905   2011-12-09 12:50:00
541903   2011-12-09 12:50:00
                 ...        
3        2010-12-01 08:26:00
1        2010-12-01 08:26:00
5        2010-12-01 08:26:00
6        2010-12-01 08:26:00
0        2010-12-01 08:26:00
Name: InvoiceDate, Length: 304911, dtype: datetime64[ns]

In [ ]:

def extract_month(date):
    month = str(date.month) 
    if date.month < 10:
        month = '0' + month
    return str(date.year) + month

In [ ]:

rev_by_month = retail.set_index('InvoiceDate').groupby(extract_month)['CheckoutPrice'].sum()
rev_by_month

Out[ ]:

InvoiceDate
201012     509239.10
201101     516132.35
201102     388097.54
201103     521947.66
201104     403484.66
             ...    
201108     564748.05
201109     839985.55
201110     918071.63
201111    1028772.36
201112     474144.60
Name: CheckoutPrice, Length: 13, dtype: float64

In [ ]:

def plot_bar(df, xlabel, ylabel, title, rotation=45, titlesize=15, fontsize=12, figsize=(20, 10),):
    plot = df.plot(kind='bar', figsize=figsize)
    plot.set_xlabel(xlabel, fontsize=fontsize)
    plot.set_ylabel(ylabel, fontsize=fontsize)
    plot.set_title(title, fontsize=titlesize)
    plot.set_xticklabels(labels=df.index, rotation=rotation)

In [ ]:

plot_bar(rev_by_month, 'Month', 'Revenue', 'Revenue By Month')

In [ ]:

# 요일별 매출 구하기
def extract_dow(date):
    return date.dayofweek

In [ ]:

rev_by_dow = retail.set_index('InvoiceDate').groupby(lambda date: date.dayofweek)['CheckoutPrice'].sum()

In [ ]:

rev_by_dow

Out[ ]:

InvoiceDate
0    1207916.45
1    1509635.68
2    1386213.36
3    1739873.53
4    1329026.13
6     691251.35
Name: CheckoutPrice, dtype: float64

In [ ]:

import numpy as np

In [ ]:

DAY_OF_WEEK = np.array(['Mon', 'Tue', 'Wed', 'Thur', 'Fri', 'Sat', 'Sun'])
rev_by_dow.index = DAY_OF_WEEK[rev_by_dow.index]

In [ ]:

rev_by_dow

Out[ ]:

Mon     1207916.45
Tue     1509635.68
Wed     1386213.36
Thur    1739873.53
Fri     1329026.13
Sun      691251.35
Name: CheckoutPrice, dtype: float64

In [ ]:

plot_bar(rev_by_dow, 'DOW', 'Revenue', 'Revenue By DOW')

In [ ]:

# 시간대별 매출 구하기
rev_by_hour = retail.set_index('InvoiceDate').groupby(lambda date: date.hour)['CheckoutPrice'].sum()
rev_by_hour

Out[ ]:

InvoiceDate
6           4.25
7       28326.65
8      250730.92
9      757407.81
10    1110115.45
         ...    
16     418520.35
17     208414.41
18      94131.78
19      42280.07
20      17505.85
Name: CheckoutPrice, Length: 15, dtype: float64

In [ ]:

plot_bar(rev_by_hour, 'Hour', 'Revenue', 'Revenue By Hour')

In [ ]:

# 판매제품(StockCode) Top 10
# 기준: Quantity
# datas1 = retail.set_index('StockCode')['Quantity'].sort_values(ascending=False).head(10)
# datas1
top_selling = retail.groupby('StockCode')['Quantity'].sum().sort_values(ascending=False)[:10]
top_selling

Out[ ]:

StockCode
23843     80995
23166     77916
85099B    46181
85123A    36782
84879     35362
23084     27202
21915     20939
22178     20321
22386     20165
23203     19516
Name: Quantity, dtype: int64

In [ ]:

# 우수고객(CustomerID) Top 10
# 지불금액: CheckoutPrice
# datas2 = retail.set_index('CustomerID')['CheckoutPrice'].sort_values(ascending=False).head(10)
# datas2
vvip = retail.groupby('CustomerID')['CheckoutPrice'].sum().sort_values(ascending=False).head(10)
vvip

Out[ ]:

CustomerID
18102.0    257518.90
14646.0    241148.31
17450.0    192236.59
16446.0    168472.50
14911.0    125365.19
12415.0    110334.21
14156.0    107573.14
17511.0     77796.46
12346.0     77183.60
16029.0     76512.84
Name: CheckoutPrice, dtype: float64

데이터로부터 Insight

전체 매출의 약 82%가 UK에서 발생
11년도 가장 많은 매출이 발생한 달은 11월
매출은 꾸준히 성장(11년 12월 데이터는 9일까지만 포함)
토요일은 영업을 하지 않음
새벽 6시에 오픈, 오후 9시에 마감이 예상
일주일 중 목요일까지는 성장세를 보이고, 이후 하락

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