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

Categorical5
Numeric4
Text2
Unsupported1
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/02d430a4-8dec-433f-b9d2-c74e49e2c7b1

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
가맹점번호 is highly overall correlated with 가맹점업종명High correlation
가맹점우편번호 is highly overall correlated with 위도High correlation
위도 is highly overall correlated with 가맹점우편번호High correlation
가맹점업종명 is highly overall correlated with 가맹점번호High correlation
결제상품명 has 30 (100.0%) missing valuesMissing
가맹점번호 has unique valuesUnique
가맹점우편번호 has unique valuesUnique
위도 has unique valuesUnique
결제상품명 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-17 15:45:27.705237
Analysis finished2024-04-17 15:45:29.538996
Duration1.83 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일반일간결제일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-01
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-12-01
2nd row2023-12-01
3rd row2023-12-01
4th row2023-12-01
5th row2023-12-01

Common Values

ValueCountFrequency (%)
2023-12-01 30
100.0%

Length

2024-04-18T00:45:29.590626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T00:45:29.659518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-12-01 30
100.0%

가맹점번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0663565 × 108
Minimum7.000005 × 108
Maximum7.9932926 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-18T00:45:29.748310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.000005 × 108
5-th percentile7.0000565 × 108
Q17.0000907 × 108
median7.0001557 × 108
Q37.0002226 × 108
95-th percentile7.5464148 × 108
Maximum7.9932926 × 108
Range99328760
Interquartile range (IQR)13193.25

Descriptive statistics

Standard deviation25196648
Coefficient of variation (CV)0.0356572
Kurtosis12.20663
Mean7.0663565 × 108
Median Absolute Deviation (MAD)6796.5
Skewness3.6599982
Sum2.1199069 × 1010
Variance6.3487108 × 1014
MonotonicityNot monotonic
2024-04-18T00:45:29.844597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700000504 1
 
3.3%
700015532 1
 
3.3%
700025525 1
 
3.3%
700025169 1
 
3.3%
700025046 1
 
3.3%
700023735 1
 
3.3%
700023232 1
 
3.3%
700022692 1
 
3.3%
700020965 1
 
3.3%
700020941 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700000504 1
3.3%
700005502 1
3.3%
700005828 1
3.3%
700006614 1
3.3%
700007588 1
3.3%
700008021 1
3.3%
700008566 1
3.3%
700008972 1
3.3%
700009352 1
3.3%
700011986 1
3.3%
ValueCountFrequency (%)
799329264 1
3.3%
799327263 1
3.3%
700025525 1
3.3%
700025169 1
3.3%
700025046 1
3.3%
700023735 1
3.3%
700023232 1
3.3%
700022692 1
3.3%
700020965 1
3.3%
700020941 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

2024-04-18T00:45:29.939887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T00:45:30.012957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 30
100.0%

가맹점업종명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
일반/휴게 음식
8 
학원
4 
음료/식품
3 
전자제품
3 
일반유통
2 
Other values (8)
10 

Length

Max length10
Median length8
Mean length5.3
Min length2

Unique

Unique6 ?
Unique (%)20.0%

Sample

1st row레저/스포츠 서비스
2nd row가구
3rd row일반유통
4th row일반/휴게 음식
5th row일반/휴게 음식

Common Values

ValueCountFrequency (%)
일반/휴게 음식 8
26.7%
학원 4
13.3%
음료/식품 3
 
10.0%
전자제품 3
 
10.0%
일반유통 2
 
6.7%
사무/통신 2
 
6.7%
용역서비스 2
 
6.7%
레저/스포츠 서비스 1
 
3.3%
가구 1
 
3.3%
미용/위생 1
 
3.3%
Other values (3) 3
 
10.0%

Length

2024-04-18T00:45:30.096355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반/휴게 8
20.5%
음식 8
20.5%
학원 4
10.3%
음료/식품 3
 
7.7%
전자제품 3
 
7.7%
일반유통 2
 
5.1%
사무/통신 2
 
5.1%
용역서비스 2
 
5.1%
레저/스포츠 1
 
2.6%
서비스 1
 
2.6%
Other values (5) 5
12.8%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14598.733
Minimum10125
Maximum18584
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-18T00:45:30.216953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10125
5-th percentile10528.45
Q112259.75
median14477.5
Q316971.25
95-th percentile18269.65
Maximum18584
Range8459
Interquartile range (IQR)4711.5

Descriptive statistics

Standard deviation2717.3455
Coefficient of variation (CV)0.18613571
Kurtosis-1.3978879
Mean14598.733
Median Absolute Deviation (MAD)2320.5
Skewness-0.055056146
Sum437962
Variance7383966.8
MonotonicityNot monotonic
2024-04-18T00:45:30.309845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
10902 1
 
3.3%
12139 1
 
3.3%
18105 1
 
3.3%
13807 1
 
3.3%
18136 1
 
3.3%
18584 1
 
3.3%
16571 1
 
3.3%
11813 1
 
3.3%
12798 1
 
3.3%
12175 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10125 1
3.3%
10285 1
3.3%
10826 1
3.3%
10902 1
3.3%
11813 1
3.3%
12139 1
3.3%
12175 1
3.3%
12256 1
3.3%
12271 1
3.3%
12774 1
3.3%
ValueCountFrequency (%)
18584 1
3.3%
18379 1
3.3%
18136 1
3.3%
18105 1
3.3%
17940 1
3.3%
17758 1
3.3%
17128 1
3.3%
16977 1
3.3%
16954 1
3.3%
16571 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

2024-04-18T00:45:30.397019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T00:45:30.465588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-18T00:45:30.582398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4.2333333
Min length3

Characters and Unicode

Total characters127
Distinct characters38
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

Unique10 ?
Unique (%)33.3%

Sample

1st row파주시
2nd row의왕시
3rd row광주시
4th row광주시
5th row용인시 처인구
ValueCountFrequency (%)
남양주시 4
 
10.5%
광주시 3
 
7.9%
용인시 3
 
7.9%
부천시 3
 
7.9%
수원시 3
 
7.9%
화성시 2
 
5.3%
오산시 2
 
5.3%
평택시 2
 
5.3%
파주시 2
 
5.3%
기흥구 2
 
5.3%
Other values (12) 12
31.6%
2024-04-18T00:45:30.819464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 30
23.6%
주 9
 
7.1%
구 8
 
6.3%
8
 
6.3%
양 7
 
5.5%
인 4
 
3.1%
남 4
 
3.1%
부 4
 
3.1%
천 4
 
3.1%
원 3
 
2.4%
Other values (28) 46
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 119
93.7%
Space Separator 8
 
6.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 30
25.2%
주 9
 
7.6%
구 8
 
6.7%
양 7
 
5.9%
인 4
 
3.4%
남 4
 
3.4%
부 4
 
3.4%
천 4
 
3.4%
원 3
 
2.5%
수 3
 
2.5%
Other values (27) 43
36.1%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 119
93.7%
Common 8
 
6.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 30
25.2%
주 9
 
7.6%
구 8
 
6.7%
양 7
 
5.9%
인 4
 
3.4%
남 4
 
3.4%
부 4
 
3.4%
천 4
 
3.4%
원 3
 
2.5%
수 3
 
2.5%
Other values (27) 43
36.1%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 119
93.7%
ASCII 8
 
6.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 30
25.2%
주 9
 
7.6%
구 8
 
6.7%
양 7
 
5.9%
인 4
 
3.4%
남 4
 
3.4%
부 4
 
3.4%
천 4
 
3.4%
원 3
 
2.5%
수 3
 
2.5%
Other values (27) 43
36.1%
ASCII
ValueCountFrequency (%)
8
100.0%

읍면동명
Text

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-18T00:45:30.958962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9
Min length2

Characters and Unicode

Total characters87
Distinct characters40
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

Unique27 ?
Unique (%)90.0%

Sample

1st row동패동
2nd row오전동
3rd row오포읍
4th row오포읍
5th row이동읍
ValueCountFrequency (%)
오포읍 3
 
10.0%
동패동 1
 
3.3%
진건읍 1
 
3.3%
중앙동 1
 
3.3%
오산동 1
 
3.3%
장안면 1
 
3.3%
권선동 1
 
3.3%
민락동 1
 
3.3%
화도읍 1
 
3.3%
구갈동 1
 
3.3%
Other values (18) 18
60.0%
2024-04-18T00:45:31.252342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 21
24.1%
읍 10
 
11.5%
오 5
 
5.7%
산 4
 
4.6%
포 3
 
3.4%
중 3
 
3.4%
영 2
 
2.3%
양 2
 
2.3%
안 2
 
2.3%
화 2
 
2.3%
Other values (30) 33
37.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
24.1%
읍 10
 
11.5%
오 5
 
5.7%
산 4
 
4.6%
포 3
 
3.4%
중 3
 
3.4%
영 2
 
2.3%
양 2
 
2.3%
안 2
 
2.3%
화 2
 
2.3%
Other values (30) 33
37.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 87
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
24.1%
읍 10
 
11.5%
오 5
 
5.7%
산 4
 
4.6%
포 3
 
3.4%
중 3
 
3.4%
영 2
 
2.3%
양 2
 
2.3%
안 2
 
2.3%
화 2
 
2.3%
Other values (30) 33
37.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 87
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 21
24.1%
읍 10
 
11.5%
오 5
 
5.7%
산 4
 
4.6%
포 3
 
3.4%
중 3
 
3.4%
영 2
 
2.3%
양 2
 
2.3%
안 2
 
2.3%
화 2
 
2.3%
Other values (30) 33
37.9%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.405233
Minimum36.983
Maximum37.849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-18T00:45:31.353436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.983
5-th percentile37.0746
Q137.26425
median37.3655
Q337.59575
95-th percentile37.7286
Maximum37.849
Range0.866
Interquartile range (IQR)0.3315

Descriptive statistics

Standard deviation0.22092169
Coefficient of variation (CV)0.0059061705
Kurtosis-0.76860432
Mean37.405233
Median Absolute Deviation (MAD)0.1665
Skewness0.10974809
Sum1122.157
Variance0.048806392
MonotonicityNot monotonic
2024-04-18T00:45:31.449923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
37.711 1
 
3.3%
37.64 1
 
3.3%
37.195 1
 
3.3%
37.428 1
 
3.3%
37.152 1
 
3.3%
37.071 1
 
3.3%
37.262 1
 
3.3%
37.743 1
 
3.3%
37.351 1
 
3.3%
37.657 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
36.983 1
3.3%
37.071 1
3.3%
37.079 1
3.3%
37.152 1
3.3%
37.194 1
3.3%
37.195 1
3.3%
37.234 1
3.3%
37.262 1
3.3%
37.271 1
3.3%
37.275 1
3.3%
ValueCountFrequency (%)
37.849 1
3.3%
37.743 1
3.3%
37.711 1
3.3%
37.687 1
3.3%
37.657 1
3.3%
37.64 1
3.3%
37.607 1
3.3%
37.601 1
3.3%
37.58 1
3.3%
37.528 1
3.3%

경도
Real number (ℝ)

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.02213
Minimum126.738
Maximum127.304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-18T00:45:31.539639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.738
5-th percentile126.76065
Q1126.887
median127.0395
Q3127.1495
95-th percentile127.25775
Maximum127.304
Range0.566
Interquartile range (IQR)0.2625

Descriptive statistics

Standard deviation0.16400709
Coefficient of variation (CV)0.0012911694
Kurtosis-0.92997438
Mean127.02213
Median Absolute Deviation (MAD)0.1205
Skewness-0.17759505
Sum3810.664
Variance0.026898326
MonotonicityNot monotonic
2024-04-18T00:45:31.630378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
127.068 2
 
6.7%
126.738 1
 
3.3%
127.163 1
 
3.3%
127.029 1
 
3.3%
126.99 1
 
3.3%
126.835 1
 
3.3%
127.026 1
 
3.3%
127.095 1
 
3.3%
127.255 1
 
3.3%
127.304 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
126.738 1
3.3%
126.753 1
3.3%
126.77 1
3.3%
126.772 1
3.3%
126.819 1
3.3%
126.835 1
3.3%
126.866 1
3.3%
126.874 1
3.3%
126.926 1
3.3%
126.966 1
3.3%
ValueCountFrequency (%)
127.304 1
3.3%
127.26 1
3.3%
127.255 1
3.3%
127.217 1
3.3%
127.212 1
3.3%
127.206 1
3.3%
127.163 1
3.3%
127.157 1
3.3%
127.127 1
3.3%
127.095 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%
2024-04-18T00:45:31.708735image/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

2024-04-18T00:45:31.787587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T00:45:31.866919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2024-04-18T00:45:29.026320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:28.037560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:28.554777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:28.791368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:29.089688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:28.361225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:28.616999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:28.850181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:29.148730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:28.419227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:28.674610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:28.907312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:29.219073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:28.482847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:28.731740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T00:45:28.966439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T00:45:31.913423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점업종명가맹점우편번호시군구명읍면동명위도경도
가맹점번호1.0001.0000.0001.0001.0000.0000.000
가맹점업종명1.0001.0000.2900.6240.7100.0000.521
가맹점우편번호0.0000.2901.0001.0001.0000.8280.792
시군구명1.0000.6241.0001.0001.0000.8130.639
읍면동명1.0000.7101.0001.0001.0001.0000.940
위도0.0000.0000.8280.8131.0001.0000.843
경도0.0000.5210.7920.6390.9400.8431.000
2024-04-18T00:45:32.021444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도가맹점업종명
가맹점번호1.0000.117-0.148-0.0290.779
가맹점우편번호0.1171.000-0.928-0.0050.000
위도-0.148-0.9281.000-0.1000.000
경도-0.029-0.005-0.1001.0000.180
가맹점업종명0.7790.0000.0000.1801.000

Missing values

2024-04-18T00:45:29.320771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T00:45:29.474438image/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
02023-12-01700000504999999999999999레저/스포츠 서비스10902경기도파주시동패동37.711126.738<NA>N0
12023-12-01799327263999999999999999가구16072경기도의왕시오전동37.355126.966<NA>N0
22023-12-01700005502999999999999999일반유통12774경기도광주시오포읍37.345127.212<NA>N0
32023-12-01700005828999999999999999일반/휴게 음식12797경기도광주시오포읍37.376127.26<NA>N0
42023-12-01700006614999999999999999일반/휴게 음식17128경기도용인시 처인구이동읍37.194127.206<NA>N0
52023-12-01700007588999999999999999일반/휴게 음식14407경기도부천시고강동37.528126.819<NA>N0
62023-12-01700008021999999999999999학원14596경기도부천시상동37.496126.753<NA>N0
72023-12-01799329264999999999999999미용/위생10125경기도김포시고촌읍37.601126.77<NA>N0
82023-12-01700008566999999999999999학원14548경기도부천시중동37.503126.772<NA>N0
92023-12-01700008972999999999999999일반/휴게 음식16954경기도용인시 기흥구영덕동37.275127.072<NA>N0
일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202023-12-01700019284999999999999999전자제품14056경기도안양시 동안구관양동37.401126.969<NA>N0
212023-12-01700019663999999999999999일반/휴게 음식16977경기도용인시 기흥구구갈동37.271127.127<NA>N0
222023-12-01700020941999999999999999일반/휴게 음식12175경기도남양주시화도읍37.657127.304<NA>N0
232023-12-01700020965999999999999999직물/침구류12798경기도광주시오포읍37.351127.255<NA>N0
242023-12-01700022692999999999999999일반/휴게 음식11813경기도의정부시민락동37.743127.095<NA>N0
252023-12-01700023232999999999999999전자제품16571경기도수원시 권선구권선동37.262127.026<NA>N0
262023-12-01700023735999999999999999용역서비스18584경기도화성시장안면37.071126.835<NA>N0
272023-12-01700025046999999999999999대형유통18136경기도오산시오산동37.152127.068<NA>N0
282023-12-01700025169999999999999999사무/통신13807경기도과천시중앙동37.428126.99<NA>N0
292023-12-01700025525999999999999999일반/휴게 음식18105경기도오산시양산동37.195127.029<NA>N0