Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells30
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory115.4 B

Variable types

DateTime1
Numeric4
Categorical4
Text2
Unsupported1
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/be10b601-62d8-4f86-8bff-b291e43c6fd2

Alerts

일반일간결제일자 has constant value ""Constant
결제상품ID has constant value ""Constant
시도명 has constant value ""Constant
사용여부 has constant value ""Constant
결제금액 has constant value ""Constant
가맹점우편번호 is highly overall correlated with 위도High correlation
위도 is highly overall correlated with 가맹점우편번호High correlation
결제상품명 has 30 (100.0%) missing valuesMissing
가맹점번호 has unique valuesUnique
가맹점우편번호 has unique valuesUnique
읍면동명 has unique valuesUnique
경도 has unique valuesUnique
결제상품명 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 14:16:39.688907
Analysis finished2023-12-10 14:16:44.303383
Duration4.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일반일간결제일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2022-03-04 00:00:00
Maximum2022-03-04 00:00:00
2023-12-10T23:16:44.387727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:44.611771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

가맹점번호
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5549333 × 108
Minimum7.000005 × 108
Maximum7.9794892 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:16:44.909453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.000005 × 108
5-th percentile7.0000079 × 108
Q17.0000289 × 108
median7.9787108 × 108
Q37.9794371 × 108
95-th percentile7.9794846 × 108
Maximum7.9794892 × 108
Range97948417
Interquartile range (IQR)97940817

Descriptive statistics

Standard deviation49354863
Coefficient of variation (CV)0.065327993
Kurtosis-2.0620541
Mean7.5549333 × 108
Median Absolute Deviation (MAD)77384.5
Skewness-0.28344115
Sum2.26648 × 1010
Variance2.4359025 × 1015
MonotonicityNot monotonic
2023-12-10T23:16:45.360626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
797941680 1
 
3.3%
797943859 1
 
3.3%
700004954 1
 
3.3%
797948921 1
 
3.3%
700003608 1
 
3.3%
797948492 1
 
3.3%
700003501 1
 
3.3%
797948427 1
 
3.3%
700003049 1
 
3.3%
797946167 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700000504 1
3.3%
700000619 1
3.3%
700001005 1
3.3%
700001826 1
3.3%
700002298 1
3.3%
700002791 1
3.3%
700002849 1
3.3%
700002872 1
3.3%
700002953 1
3.3%
700003049 1
3.3%
ValueCountFrequency (%)
797948921 1
3.3%
797948492 1
3.3%
797948427 1
3.3%
797946167 1
3.3%
797944133 1
3.3%
797943956 1
3.3%
797943859 1
3.3%
797943786 1
3.3%
797943478 1
3.3%
797943102 1
3.3%

결제상품ID
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
999999999999999
30 

Length

Max length15
Median length15
Mean length15
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row999999999999999
2nd row999999999999999
3rd row999999999999999
4th row999999999999999
5th row999999999999999

Common Values

ValueCountFrequency (%)
999999999999999 30
100.0%

Length

2023-12-10T23:16:45.630945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:16:45.848722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 30
100.0%

가맹점업종명
Categorical

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
일반휴게음식
12 
레저업소
3 
유통업 영리
2 
사무통신
2 
의류
2 
Other values (8)
9 

Length

Max length6
Median length5.5
Mean length4.9666667
Min length2

Unique

Unique7 ?
Unique (%)23.3%

Sample

1st row자동차판매
2nd row수리서비스
3rd row일반휴게음식
4th row레저업소
5th row서적문구

Common Values

ValueCountFrequency (%)
일반휴게음식 12
40.0%
레저업소 3
 
10.0%
유통업 영리 2
 
6.7%
사무통신 2
 
6.7%
의류 2
 
6.7%
보건위생 2
 
6.7%
자동차판매 1
 
3.3%
수리서비스 1
 
3.3%
서적문구 1
 
3.3%
용역 서비스 1
 
3.3%
Other values (3) 3
 
10.0%

Length

2023-12-10T23:16:46.072708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반휴게음식 12
36.4%
레저업소 3
 
9.1%
유통업 2
 
6.1%
영리 2
 
6.1%
사무통신 2
 
6.1%
의류 2
 
6.1%
보건위생 2
 
6.1%
자동차판매 1
 
3.0%
수리서비스 1
 
3.0%
서적문구 1
 
3.0%
Other values (5) 5
15.2%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13421.8
Minimum10111
Maximum17848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:16:46.334781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10111
5-th percentile10370.95
Q111939
median13033
Q315252.5
95-th percentile17326.7
Maximum17848
Range7737
Interquartile range (IQR)3313.5

Descriptive statistics

Standard deviation2305.9847
Coefficient of variation (CV)0.1718089
Kurtosis-0.91627787
Mean13421.8
Median Absolute Deviation (MAD)1911
Skewness0.41345972
Sum402654
Variance5317565.3
MonotonicityNot monotonic
2023-12-10T23:16:46.605623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
13445 1
 
3.3%
12090 1
 
3.3%
10930 1
 
3.3%
13590 1
 
3.3%
12097 1
 
3.3%
17848 1
 
3.3%
15266 1
 
3.3%
15851 1
 
3.3%
13951 1
 
3.3%
11932 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10111 1
3.3%
10366 1
3.3%
10377 1
3.3%
10594 1
3.3%
10902 1
3.3%
10930 1
3.3%
11314 1
3.3%
11932 1
3.3%
11960 1
3.3%
12090 1
3.3%
ValueCountFrequency (%)
17848 1
3.3%
17738 1
3.3%
16824 1
3.3%
16456 1
3.3%
16284 1
3.3%
16269 1
3.3%
15851 1
3.3%
15266 1
3.3%
15212 1
3.3%
14106 1
3.3%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도
2nd row경기도
3rd row경기도
4th row경기도
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T23:16:46.856663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:16:47.010014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:16:47.284958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.2
Min length3

Characters and Unicode

Total characters156
Distinct characters37
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)30.0%

Sample

1st row성남시 수정구
2nd row안산시 단원구
3rd row구리시
4th row파주시
5th row고양시 덕양구
ValueCountFrequency (%)
성남시 4
 
8.9%
남양주시 3
 
6.7%
고양시 3
 
6.7%
수원시 3
 
6.7%
안양시 2
 
4.4%
구리시 2
 
4.4%
파주시 2
 
4.4%
안산시 2
 
4.4%
동안구 2
 
4.4%
일산서구 2
 
4.4%
Other values (15) 20
44.4%
2023-12-10T23:16:47.984704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
18.6%
구 17
 
10.9%
15
 
9.6%
양 10
 
6.4%
안 8
 
5.1%
남 7
 
4.5%
주 7
 
4.5%
수 6
 
3.8%
원 5
 
3.2%
산 4
 
2.6%
Other values (27) 48
30.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 141
90.4%
Space Separator 15
 
9.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
20.6%
구 17
 
12.1%
양 10
 
7.1%
안 8
 
5.7%
남 7
 
5.0%
주 7
 
5.0%
수 6
 
4.3%
원 5
 
3.5%
산 4
 
2.8%
성 4
 
2.8%
Other values (26) 44
31.2%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 141
90.4%
Common 15
 
9.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
20.6%
구 17
 
12.1%
양 10
 
7.1%
안 8
 
5.7%
남 7
 
5.0%
주 7
 
5.0%
수 6
 
4.3%
원 5
 
3.5%
산 4
 
2.8%
성 4
 
2.8%
Other values (26) 44
31.2%
Common
ValueCountFrequency (%)
15
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 141
90.4%
ASCII 15
 
9.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
20.6%
구 17
 
12.1%
양 10
 
7.1%
안 8
 
5.7%
남 7
 
5.0%
주 7
 
5.0%
수 6
 
4.3%
원 5
 
3.5%
산 4
 
2.8%
성 4
 
2.8%
Other values (26) 44
31.2%
ASCII
ValueCountFrequency (%)
15
100.0%

읍면동명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:16:48.328675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9666667
Min length2

Characters and Unicode

Total characters89
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row시흥동
2nd row선부동
3rd row토평동
4th row동패동
5th row동산동
ValueCountFrequency (%)
시흥동 1
 
3.3%
선부동 1
 
3.3%
서현동 1
 
3.3%
별내동 1
 
3.3%
동삭동 1
 
3.3%
와동 1
 
3.3%
당정동 1
 
3.3%
관양동 1
 
3.3%
수택동 1
 
3.3%
와부읍 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:16:48.859885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 31
34.8%
내 3
 
3.4%
수 3
 
3.4%
정 2
 
2.2%
부 2
 
2.2%
별 2
 
2.2%
면 2
 
2.2%
와 2
 
2.2%
화 2
 
2.2%
시 1
 
1.1%
Other values (39) 39
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 31
34.8%
내 3
 
3.4%
수 3
 
3.4%
정 2
 
2.2%
부 2
 
2.2%
별 2
 
2.2%
면 2
 
2.2%
와 2
 
2.2%
화 2
 
2.2%
시 1
 
1.1%
Other values (39) 39
43.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 31
34.8%
내 3
 
3.4%
수 3
 
3.4%
정 2
 
2.2%
부 2
 
2.2%
별 2
 
2.2%
면 2
 
2.2%
와 2
 
2.2%
화 2
 
2.2%
시 1
 
1.1%
Other values (39) 39
43.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 31
34.8%
내 3
 
3.4%
수 3
 
3.4%
정 2
 
2.2%
부 2
 
2.2%
별 2
 
2.2%
면 2
 
2.2%
와 2
 
2.2%
화 2
 
2.2%
시 1
 
1.1%
Other values (39) 39
43.8%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.467867
Minimum37.011
Maximum37.905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:16:49.068184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.011
5-th percentile37.1525
Q137.34175
median37.4245
Q337.642
95-th percentile37.73905
Maximum37.905
Range0.894
Interquartile range (IQR)0.30025

Descriptive statistics

Standard deviation0.20402193
Coefficient of variation (CV)0.0054452507
Kurtosis-0.046478992
Mean37.467867
Median Absolute Deviation (MAD)0.1455
Skewness-0.098925217
Sum1124.036
Variance0.041624947
MonotonicityNot monotonic
2023-12-10T23:16:49.334354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
37.376 2
 
6.7%
37.421 1
 
3.3%
37.347 1
 
3.3%
37.762 1
 
3.3%
37.389 1
 
3.3%
37.665 1
 
3.3%
37.011 1
 
3.3%
37.333 1
 
3.3%
37.344 1
 
3.3%
37.401 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
37.011 1
3.3%
37.058 1
3.3%
37.268 1
3.3%
37.289 1
3.3%
37.301 1
3.3%
37.311 1
3.3%
37.333 1
3.3%
37.341 1
3.3%
37.344 1
3.3%
37.347 1
3.3%
ValueCountFrequency (%)
37.905 1
3.3%
37.762 1
3.3%
37.711 1
3.3%
37.695 1
3.3%
37.676 1
3.3%
37.668 1
3.3%
37.665 1
3.3%
37.649 1
3.3%
37.621 1
3.3%
37.595 1
3.3%

경도
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.03817
Minimum126.722
Maximum127.645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:16:49.592021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.722
5-th percentile126.74385
Q1126.91225
median127.0665
Q3127.1265
95-th percentile127.3788
Maximum127.645
Range0.923
Interquartile range (IQR)0.21425

Descriptive statistics

Standard deviation0.20882216
Coefficient of variation (CV)0.001643775
Kurtosis1.4994022
Mean127.03817
Median Absolute Deviation (MAD)0.0875
Skewness0.74744063
Sum3811.145
Variance0.043606695
MonotonicityNot monotonic
2023-12-10T23:16:49.831812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
127.103 1
 
3.3%
127.128 1
 
3.3%
126.775 1
 
3.3%
127.122 1
 
3.3%
127.115 1
 
3.3%
127.1 1
 
3.3%
126.826 1
 
3.3%
126.949 1
 
3.3%
126.974 1
 
3.3%
127.145 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
126.722 1
3.3%
126.738 1
3.3%
126.751 1
3.3%
126.764 1
3.3%
126.775 1
3.3%
126.813 1
3.3%
126.826 1
3.3%
126.9 1
3.3%
126.949 1
3.3%
126.965 1
3.3%
ValueCountFrequency (%)
127.645 1
3.3%
127.476 1
3.3%
127.26 1
3.3%
127.217 1
3.3%
127.149 1
3.3%
127.145 1
3.3%
127.133 1
3.3%
127.128 1
3.3%
127.122 1
3.3%
127.116 1
3.3%

결제상품명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)100.0%
Memory size402.0 B

사용여부
Boolean

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
30 
ValueCountFrequency (%)
False 30
100.0%
2023-12-10T23:16:50.129634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
100.0%

Length

2023-12-10T23:16:50.438085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:16:50.645771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2023-12-10T23:16:42.912903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:40.301425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:41.244750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:42.164959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:43.064879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:40.472633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:41.551808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:42.430215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:43.243113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:40.772953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:41.822009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:42.615820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:43.426591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:40.988258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:41.989600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:42.759444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:16:50.771410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점업종명가맹점우편번호시군구명읍면동명위도경도
가맹점번호1.0000.4440.4170.0281.0000.0000.000
가맹점업종명0.4441.0000.0000.4231.0000.1830.000
가맹점우편번호0.4170.0001.0000.9841.0000.8520.673
시군구명0.0280.4230.9841.0001.0000.9710.957
읍면동명1.0001.0001.0001.0001.0001.0001.000
위도0.0000.1830.8520.9711.0001.0000.543
경도0.0000.0000.6730.9571.0000.5431.000
2023-12-10T23:16:51.396773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도가맹점업종명
가맹점번호1.0000.093-0.1490.1700.182
가맹점우편번호0.0931.000-0.9080.1550.000
위도-0.149-0.9081.000-0.1430.000
경도0.1700.155-0.1431.0000.000
가맹점업종명0.1820.0000.0000.0001.000

Missing values

2023-12-10T23:16:43.740426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:16:44.140086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
02022-03-04797941680999999999999999자동차판매13445경기도성남시 수정구시흥동37.421127.103<NA>N0
12022-03-04797799681999999999999999수리서비스15212경기도안산시 단원구선부동37.341126.813<NA>N0
22022-03-04797941998999999999999999일반휴게음식11960경기도구리시토평동37.585127.149<NA>N0
32022-03-04700000504999999999999999레저업소10902경기도파주시동패동37.711126.738<NA>N0
42022-03-04797942848999999999999999서적문구10594경기도고양시 덕양구동산동37.649126.9<NA>N0
52022-03-04700000619999999999999999일반휴게음식12571경기도양평군강상면37.489127.476<NA>N0
62022-03-04797942929999999999999999레저업소12636경기도여주시오학동37.311127.645<NA>N0
72022-03-04797800470999999999999999유통업 영리10377경기도고양시 일산서구대화동37.676126.751<NA>N0
82022-03-04797943030999999999999999사무통신14106경기도안양시 동안구호계동37.376126.965<NA>N0
92022-03-04700001005999999999999999용역 서비스16456경기도수원시 팔달구매교동37.268127.014<NA>N0
일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202022-03-04797944133999999999999999일반휴게음식12739경기도광주시송정동37.428127.26<NA>N0
212022-03-04700002953999999999999999음료식품12271경기도남양주시와부읍37.58127.217<NA>N0
222022-03-04797946167999999999999999보건위생11932경기도구리시수택동37.595127.145<NA>N0
232022-03-04700003049999999999999999일반휴게음식13951경기도안양시 동안구관양동37.401126.974<NA>N0
242022-03-04797948427999999999999999문화.취미15851경기도군포시당정동37.344126.949<NA>N0
252022-03-04700003501999999999999999일반휴게음식15266경기도안산시 단원구와동37.333126.826<NA>N0
262022-03-04797948492999999999999999일반휴게음식17848경기도평택시동삭동37.011127.1<NA>N0
272022-03-04700003608999999999999999일반휴게음식12097경기도남양주시별내동37.665127.115<NA>N0
282022-03-04797948921999999999999999일반휴게음식13590경기도성남시 분당구서현동37.389127.122<NA>N0
292022-03-04700004954999999999999999사무통신10930경기도파주시금촌동37.762126.775<NA>N0