매출,가장 많이 팔린 아이템 확인하기
학습목표
- 아이템별 지표 확인하기
- 시간별 지역별 판매 지표 확인하기
import numpy as np
import pandas as pd
# seaborn
import seaborn as sns
COLORS = sns.color_palette()
%matplotlib inline
데이터 로딩
- 정제된 데이터 사용(retail.csv)
dtypes = {
'UnitPrice': np.float32,
'CustomerID': np.int32,
'Quantity': np.int32
}
retail = pd.read_csv('./OnlineRetailClean.csv', dtype=dtypes)
retail.head()
| Unnamed: 0 | InvoiceNo | StockCode | Description | Quantity | InvoiceDate | UnitPrice | CustomerID | Country | CheckoutPrice | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 536365 | 85123A | WHITE HANGING HEART T-LIGHT HOLDER | 6 | 12/1/2010 8:26 | 2.55 | 17850 | United Kingdom | 15.30 |
| 1 | 1 | 536365 | 71053 | WHITE METAL LANTERN | 6 | 12/1/2010 8:26 | 3.39 | 17850 | United Kingdom | 20.34 |
| 2 | 2 | 536365 | 84406B | CREAM CUPID HEARTS COAT HANGER | 8 | 12/1/2010 8:26 | 2.75 | 17850 | United Kingdom | 22.00 |
| 3 | 3 | 536365 | 84029G | KNITTED UNION FLAG HOT WATER BOTTLE | 6 | 12/1/2010 8:26 | 3.39 | 17850 | United Kingdom | 20.34 |
| 4 | 4 | 536365 | 84029E | RED WOOLLY HOTTIE WHITE HEART. | 6 | 12/1/2010 8:26 | 3.39 | 17850 | United Kingdom | 20.34 |
날짜 타입 데이터 변환
- 문자열로 로딩하는 것보다 date/datetime 타입으로 로딩하는 것이 분석에 용이
retail['InvoiceDate'] = pd.to_datetime(retail['InvoiceDate'], infer_datetime_format=True)# infer_datetime_format=True 날짜시간 포맷 추정해서 파싱하기
retail.info() #5 InvoiceDate 397884 non-null datetime64[ns] 바꿔짐
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 397884 entries, 0 to 397883
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Unnamed: 0 397884 non-null int64
1 InvoiceNo 397884 non-null int64
2 StockCode 397884 non-null object
3 Description 397884 non-null object
4 Quantity 397884 non-null int32
5 InvoiceDate 397884 non-null datetime64[ns]
6 UnitPrice 397884 non-null float32
7 CustomerID 397884 non-null int32
8 Country 397884 non-null object
9 CheckoutPrice 397884 non-null float64
dtypes: datetime64[ns](1), float32(1), float64(1), int32(2), int64(2), object(3)
memory usage: 25.8+ MB
해당 기간 동안의 매출
- 전체 매출
- 국가별 매출
- 월별 매출
- 요일별 매출
- 시간별 매출
전체 매출
total_revenue = retail['CheckoutPrice'].sum()
total_revenue
8911407.904
국가별 매출
rev_by_countries = retail.groupby('Country').sum()['CheckoutPrice'].sort_values()
rev_by_countries
Country
Saudi Arabia 1.459200e+02
Bahrain 5.484000e+02
Czech Republic 8.267400e+02
RSA 1.002310e+03
Brazil 1.143600e+03
European Community 1.300250e+03
Lithuania 1.661060e+03
Lebanon 1.693880e+03
United Arab Emirates 1.902280e+03
Unspecified 2.667070e+03
Malta 2.725590e+03
USA 3.580390e+03
Canada 3.666380e+03
Iceland 4.310000e+03
Greece 4.760520e+03
Israel 7.221690e+03
Poland 7.334650e+03
Austria 1.019868e+04
Cyprus 1.359038e+04
Italy 1.748324e+04
Denmark 1.895534e+04
Channel Islands 2.045044e+04
Singapore 2.127929e+04
Finland 2.254608e+04
Portugal 3.343989e+04
Norway 3.616544e+04
Japan 3.741637e+04
Sweden 3.837833e+04
Belgium 4.119634e+04
Switzerland 5.644395e+04
Spain 6.157711e+04
Australia 1.385213e+05
France 2.090240e+05
Germany 2.288671e+05
EIRE 2.655459e+05
Netherlands 2.854463e+05
United Kingdom 7.308392e+06
Name: CheckoutPrice, dtype: float64
plot = rev_by_countries.plot(kind='bar', color=COLORS[-1], figsize=(20, 10))
plot.set_xlabel('Country', fontsize=11)
plot.set_ylabel('Revenue', fontsize=11)
plot.set_title('Revenue by Country', fontsize=13)
plot.set_xticklabels(labels=rev_by_countries.index, rotation=45)
[Text(0, 0, 'Saudi Arabia'),
Text(1, 0, 'Bahrain'),
Text(2, 0, 'Czech Republic'),
Text(3, 0, 'RSA'),
Text(4, 0, 'Brazil'),
Text(5, 0, 'European Community'),
Text(6, 0, 'Lithuania'),
Text(7, 0, 'Lebanon'),
Text(8, 0, 'United Arab Emirates'),
Text(9, 0, 'Unspecified'),
Text(10, 0, 'Malta'),
Text(11, 0, 'USA'),
Text(12, 0, 'Canada'),
Text(13, 0, 'Iceland'),
Text(14, 0, 'Greece'),
Text(15, 0, 'Israel'),
Text(16, 0, 'Poland'),
Text(17, 0, 'Austria'),
Text(18, 0, 'Cyprus'),
Text(19, 0, 'Italy'),
Text(20, 0, 'Denmark'),
Text(21, 0, 'Channel Islands'),
Text(22, 0, 'Singapore'),
Text(23, 0, 'Finland'),
Text(24, 0, 'Portugal'),
Text(25, 0, 'Norway'),
Text(26, 0, 'Japan'),
Text(27, 0, 'Sweden'),
Text(28, 0, 'Belgium'),
Text(29, 0, 'Switzerland'),
Text(30, 0, 'Spain'),
Text(31, 0, 'Australia'),
Text(32, 0, 'France'),
Text(33, 0, 'Germany'),
Text(34, 0, 'EIRE'),
Text(35, 0, 'Netherlands'),
Text(36, 0, 'United Kingdom')]
rev_by_countries / total_revenue #비율 확인할 수 있다.
Country
Saudi Arabia 0.000016
Bahrain 0.000062
Czech Republic 0.000093
RSA 0.000112
Brazil 0.000128
European Community 0.000146
Lithuania 0.000186
Lebanon 0.000190
United Arab Emirates 0.000213
Unspecified 0.000299
Malta 0.000306
USA 0.000402
Canada 0.000411
Iceland 0.000484
Greece 0.000534
Israel 0.000810
Poland 0.000823
Austria 0.001144
Cyprus 0.001525
Italy 0.001962
Denmark 0.002127
Channel Islands 0.002295
Singapore 0.002388
Finland 0.002530
Portugal 0.003752
Norway 0.004058
Japan 0.004199
Sweden 0.004307
Belgium 0.004623
Switzerland 0.006334
Spain 0.006910
Australia 0.015544
France 0.023456
Germany 0.025682
EIRE 0.029798
Netherlands 0.032032
United Kingdom 0.820116
Name: CheckoutPrice, dtype: float64
그래프 유틸 함수
def plot_bar(df, xlabel, ylabel, title, color=COLORS[0], figsize=(20, 10), rotation=45):
plot = df.plot(kind='bar', color=color, figsize=figsize)
plot.set_xlabel(xlabel, fontsize=11)
plot.set_ylabel(ylabel, fontsize=11)
plot.set_title(title, fontsize=13)
plot.set_xticklabels(labels=df.index, rotation=rotation)
plot_bar(rev_by_countries, 'Country', 'Revenue', 'Revenue by Country')
월별 매출
retail['InvoiceDate'].sort_values(ascending=False)
397883 2011-12-09 12:50:00
397876 2011-12-09 12:50:00
397870 2011-12-09 12:50:00
397871 2011-12-09 12:50:00
397872 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: 397884, dtype: datetime64[ns]
def extract_month(date):
month = str(date.month)
if date.month < 10:
month = '0' + month
return str(date.year) + month
rev_by_month = retail.set_index('InvoiceDate').groupby(extract_month).sum()['CheckoutPrice']
rev_by_month
201012 572713.890
201101 569445.040
201102 447137.350
201103 595500.760
201104 469200.361
201105 678594.560
201106 661213.690
201107 600091.011
201108 645343.900
201109 952838.382
201110 1039318.790
201111 1161817.380
201112 518192.790
Name: CheckoutPrice, dtype: float64
plot_bar(rev_by_month, 'Month', 'Revenue', 'Revenue by Month')
요일별 매출
rev_by_dow = retail.set_index('InvoiceDate').groupby(lambda date:date.dayofweek).sum()['CheckoutPrice']
rev_by_dow
0 1367146.411
1 1700634.631
2 1588336.170
3 1976859.070
4 1485917.401
6 792514.221
Name: CheckoutPrice, dtype: float64
DAY_OF_WEEK = np.array(['Mon', 'Tue', 'Wed', 'Thur', 'Fri', 'Sat', 'Sun'])
rev_by_dow.index = DAY_OF_WEEK[rev_by_dow.index]
plot_bar(rev_by_dow, 'DOW', 'Revenue', 'Revenue by DOW')
시간별 매출
rev_by_hour = retail.set_index('InvoiceDate').groupby(lambda date:date.hour).sum()['CheckoutPrice']
plot_bar(rev_by_hour, 'hour', 'revenue', 'revenue by hour')
매출 데이터로부터 insight
- 전체 매출의 82%가 UK에서 발생
- 11년도의 가장 많은 주문이 발생한 달 11월(12월의 전체 데이터가 반영이 되진 않았음)
- 11, 12월의 판매량이 압도(블랙프라이데이, 사이버먼데이, 크리스마스 휴일)
- 일주일중 목요일까지는 성장세를 보이다가, 이후로 하락(토요일에는 주문X)
- 7시를 시작으로 주문이 시작되어 12시까지 증가세, 15시까지 하락을, 15시 이후 부터 급락)
제품별 metrics
- Top 10 판매 제품
- Top 10 매출 제품
top_selling = retail.groupby('StockCode').sum()['Quantity'].sort_values(ascending=False)[:3]
top_selling
StockCode
23843 80995
23166 77916
84077 54415
Name: Quantity, dtype: int32
top_revenue = retail.groupby('StockCode').sum()['CheckoutPrice'].sort_values(ascending=False)[:10]
top_revenue
StockCode
23843 168469.60
22423 142592.95
85123A 100603.50
85099B 85220.78
23166 81416.73
POST 77803.96
47566 68844.33
84879 56580.34
M 53779.93
23084 51346.20
Name: CheckoutPrice, dtype: float64
top 3 아이템의 월별 판매량 추이
retail.set_index('InvoiceDate').groupby(['StockCode', extract_month]).sum()[['Quantity', 'CheckoutPrice']].loc[top_selling.index]
| Quantity | CheckoutPrice | ||
|---|---|---|---|
| StockCode | |||
| 23843 | 201112 | 80995 | 168469.60 |
| 23166 | 201101 | 74215 | 77183.60 |
| 201105 | 792 | 869.04 | |
| 201106 | 391 | 458.51 | |
| 201107 | 718 | 826.94 | |
| 201108 | 405 | 486.09 | |
| 201109 | 342 | 397.26 | |
| 201110 | 235 | 283.67 | |
| 201111 | 631 | 708.11 | |
| 201112 | 187 | 203.51 | |
| 84077 | 201012 | 5139 | 1150.47 |
| 201101 | 1488 | 385.44 | |
| 201102 | 3457 | 795.17 | |
| 201103 | 3888 | 943.20 | |
| 201104 | 10224 | 2281.44 | |
| 201105 | 4944 | 1249.44 | |
| 201106 | 1920 | 533.76 | |
| 201107 | 3600 | 982.56 | |
| 201108 | 2256 | 654.24 | |
| 201109 | 3462 | 985.70 | |
| 201110 | 8174 | 1953.98 | |
| 201111 | 4500 | 1294.20 | |
| 201112 | 1363 | 376.65 |
monthly_top3 = retail.set_index('InvoiceDate').groupby(['StockCode', extract_month]).sum()[['Quantity', 'CheckoutPrice']].loc[top_selling.index]
plot_bar(monthly_top3['CheckoutPrice'], 'Product/Month', 'Revenue', 'Revenue of top 3 items')
