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/467897e0-b326-45bd-9dd4-fd4c5e61fd2c

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
결제상품명 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 13:42:42.400931
Analysis finished2023-12-10 13:42:47.423674
Duration5.02 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
Minimum2020-09-01 00:00:00
Maximum2020-09-01 00:00:00
2023-12-10T22:42:47.554898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:47.778977image/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%
Mean7.0663023 × 108
Minimum7.0000006 × 108
Maximum7.9933305 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:42:48.036543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0000006 × 108
5-th percentile7.0000086 × 108
Q17.0000592 × 108
median7.0000966 × 108
Q37.0001234 × 108
95-th percentile7.5463971 × 108
Maximum7.9933305 × 108
Range99332988
Interquartile range (IQR)6417.25

Descriptive statistics

Standard deviation25199386
Coefficient of variation (CV)0.035661347
Kurtosis12.206632
Mean7.0663023 × 108
Median Absolute Deviation (MAD)2953
Skewness3.6599985
Sum2.1198907 × 1010
Variance6.3500904 × 1014
MonotonicityNot monotonic
2023-12-10T22:42:48.800114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700000058 1
 
3.3%
700009629 1
 
3.3%
700014829 1
 
3.3%
700014714 1
 
3.3%
700013912 1
 
3.3%
700013002 1
 
3.3%
700012447 1
 
3.3%
700012439 1
 
3.3%
700012032 1
 
3.3%
700011986 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700000058 1
3.3%
700000749 1
3.3%
700001005 1
3.3%
700003827 1
3.3%
700004564 1
3.3%
700004696 1
3.3%
700005366 1
3.3%
700005713 1
3.3%
700006541 1
3.3%
700007181 1
3.3%
ValueCountFrequency (%)
799333046 1
3.3%
799332803 1
3.3%
700014829 1
3.3%
700014714 1
3.3%
700013912 1
3.3%
700013002 1
3.3%
700012447 1
3.3%
700012439 1
3.3%
700012032 1
3.3%
700011986 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

2023-12-10T22:42:48.987948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:49.129435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 30
100.0%

가맹점업종명
Categorical

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
일반휴게음식
8 
학원
6 
보건위생
3 
용역 서비스
2 
유통업 영리
2 
Other values (9)
9 

Length

Max length8
Median length6
Mean length4.5
Min length2

Unique

Unique9 ?
Unique (%)30.0%

Sample

1st row일반휴게음식
2nd row일반휴게음식
3rd row가구
4th row용역 서비스
5th row유통업 영리

Common Values

ValueCountFrequency (%)
일반휴게음식 8
26.7%
학원 6
20.0%
보건위생 3
 
10.0%
용역 서비스 2
 
6.7%
유통업 영리 2
 
6.7%
가구 1
 
3.3%
기타의료기관 1
 
3.3%
레저업소 1
 
3.3%
신변잡화 1
 
3.3%
자동차정비 유지 1
 
3.3%
Other values (4) 4
13.3%

Length

2023-12-10T22:42:49.287784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반휴게음식 8
22.9%
학원 6
17.1%
보건위생 3
 
8.6%
용역 2
 
5.7%
서비스 2
 
5.7%
유통업 2
 
5.7%
영리 2
 
5.7%
유지 1
 
2.9%
사무통신 1
 
2.9%
의류 1
 
2.9%
Other values (7) 7
20.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14555.7
Minimum10030
Maximum18453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:42:49.556444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10030
5-th percentile10569.45
Q112321
median15341
Q316425.5
95-th percentile17881.85
Maximum18453
Range8423
Interquartile range (IQR)4104.5

Descriptive statistics

Standard deviation2488.005
Coefficient of variation (CV)0.17092995
Kurtosis-1.0277771
Mean14555.7
Median Absolute Deviation (MAD)1376.5
Skewness-0.45877232
Sum436671
Variance6190169
MonotonicityNot monotonic
2023-12-10T22:42:49.854940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
11409 1
 
3.3%
14912 1
 
3.3%
15517 1
 
3.3%
16334 1
 
3.3%
16284 1
 
3.3%
16835 1
 
3.3%
17559 1
 
3.3%
15002 1
 
3.3%
10826 1
 
3.3%
16512 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10030 1
3.3%
10362 1
3.3%
10823 1
3.3%
10826 1
3.3%
10915 1
3.3%
11409 1
3.3%
12164 1
3.3%
12253 1
3.3%
12525 1
3.3%
12798 1
3.3%
ValueCountFrequency (%)
18453 1
3.3%
18146 1
3.3%
17559 1
3.3%
16941 1
3.3%
16835 1
3.3%
16600 1
3.3%
16512 1
3.3%
16456 1
3.3%
16334 1
3.3%
16284 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

2023-12-10T22:42:50.046621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:50.196298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:42:50.448040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7.5
Mean length4.8333333
Min length3

Characters and Unicode

Total characters145
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

Unique12 ?
Unique (%)40.0%

Sample

1st row양주시
2nd row군포시
3rd row안산시 단원구
4th row수원시 팔달구
5th row양평군
ValueCountFrequency (%)
안산시 5
 
11.6%
수원시 5
 
11.6%
파주시 3
 
7.0%
상록구 3
 
7.0%
단원구 2
 
4.7%
남양주시 2
 
4.7%
시흥시 2
 
4.7%
부천시 2
 
4.7%
수지구 2
 
4.7%
용인시 2
 
4.7%
Other values (14) 15
34.9%
2023-12-10T22:42:51.096651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 31
21.4%
13
 
9.0%
구 13
 
9.0%
안 8
 
5.5%
수 7
 
4.8%
산 7
 
4.8%
원 7
 
4.8%
주 7
 
4.8%
양 5
 
3.4%
파 3
 
2.1%
Other values (28) 44
30.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 132
91.0%
Space Separator 13
 
9.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 31
23.5%
구 13
 
9.8%
안 8
 
6.1%
수 7
 
5.3%
산 7
 
5.3%
원 7
 
5.3%
주 7
 
5.3%
양 5
 
3.8%
파 3
 
2.3%
록 3
 
2.3%
Other values (27) 41
31.1%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 132
91.0%
Common 13
 
9.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 31
23.5%
구 13
 
9.8%
안 8
 
6.1%
수 7
 
5.3%
산 7
 
5.3%
원 7
 
5.3%
주 7
 
5.3%
양 5
 
3.8%
파 3
 
2.3%
록 3
 
2.3%
Other values (27) 41
31.1%
Common
ValueCountFrequency (%)
13
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 132
91.0%
ASCII 13
 
9.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 31
23.5%
구 13
 
9.8%
안 8
 
6.1%
수 7
 
5.3%
산 7
 
5.3%
원 7
 
5.3%
주 7
 
5.3%
양 5
 
3.8%
파 3
 
2.3%
록 3
 
2.3%
Other values (27) 41
31.1%
ASCII
ValueCountFrequency (%)
13
100.0%

읍면동명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:42:51.483195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8666667
Min length2

Characters and Unicode

Total characters86
Distinct characters50
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 row남면
2nd row당동
3rd row목내동
4th row매교동
5th row단월면
ValueCountFrequency (%)
남면 1
 
3.3%
당동 1
 
3.3%
정자동 1
 
3.3%
조원동 1
 
3.3%
풍덕천동 1
 
3.3%
공도읍 1
 
3.3%
장곡동 1
 
3.3%
법원읍 1
 
3.3%
하동 1
 
3.3%
부곡동 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:42:52.246512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 22
25.6%
읍 6
 
7.0%
원 4
 
4.7%
곡 3
 
3.5%
산 3
 
3.5%
도 2
 
2.3%
면 2
 
2.3%
정 2
 
2.3%
대 1
 
1.2%
야 1
 
1.2%
Other values (40) 40
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 86
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 22
25.6%
읍 6
 
7.0%
원 4
 
4.7%
곡 3
 
3.5%
산 3
 
3.5%
도 2
 
2.3%
면 2
 
2.3%
정 2
 
2.3%
대 1
 
1.2%
야 1
 
1.2%
Other values (40) 40
46.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 86
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 22
25.6%
읍 6
 
7.0%
원 4
 
4.7%
곡 3
 
3.5%
산 3
 
3.5%
도 2
 
2.3%
면 2
 
2.3%
정 2
 
2.3%
대 1
 
1.2%
야 1
 
1.2%
Other values (40) 40
46.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 86
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 22
25.6%
읍 6
 
7.0%
원 4
 
4.7%
곡 3
 
3.5%
산 3
 
3.5%
도 2
 
2.3%
면 2
 
2.3%
정 2
 
2.3%
대 1
 
1.2%
야 1
 
1.2%
Other values (40) 40
46.5%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.4348
Minimum37.001
Maximum37.875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:42:52.447773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.001
5-th percentile37.16465
Q137.2965
median37.3435
Q337.632
95-th percentile37.8534
Maximum37.875
Range0.874
Interquartile range (IQR)0.3355

Descriptive statistics

Standard deviation0.22405393
Coefficient of variation (CV)0.0059851778
Kurtosis-0.44987192
Mean37.4348
Median Absolute Deviation (MAD)0.091
Skewness0.52732872
Sum1123.044
Variance0.050200166
MonotonicityNot monotonic
2023-12-10T22:42:52.677694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
37.303 2
 
6.7%
37.875 1
 
3.3%
37.442 1
 
3.3%
37.295 1
 
3.3%
37.302 1
 
3.3%
37.323 1
 
3.3%
37.001 1
 
3.3%
37.382 1
 
3.3%
37.849 1
 
3.3%
37.292 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
37.001 1
3.3%
37.13 1
3.3%
37.207 1
3.3%
37.26 1
3.3%
37.268 1
3.3%
37.288 1
3.3%
37.292 1
3.3%
37.295 1
3.3%
37.301 1
3.3%
37.302 1
3.3%
ValueCountFrequency (%)
37.875 1
3.3%
37.857 1
3.3%
37.849 1
3.3%
37.751 1
3.3%
37.673 1
3.3%
37.669 1
3.3%
37.657 1
3.3%
37.64 1
3.3%
37.608 1
3.3%
37.518 1
3.3%

경도
Real number (ℝ)

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.9646
Minimum126.584
Maximum127.602
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:42:52.932222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.584
5-th percentile126.76925
Q1126.79675
median126.9695
Q3127.072
95-th percentile127.276
Maximum127.602
Range1.018
Interquartile range (IQR)0.27525

Descriptive statistics

Standard deviation0.20497848
Coefficient of variation (CV)0.0016144538
Kurtosis1.9219199
Mean126.9646
Median Absolute Deviation (MAD)0.1385
Skewness0.99672935
Sum3808.938
Variance0.042016179
MonotonicityNot monotonic
2023-12-10T22:42:53.419649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
126.995 2
 
6.7%
126.946 1
 
3.3%
126.866 1
 
3.3%
127.01 1
 
3.3%
127.096 1
 
3.3%
127.176 1
 
3.3%
126.782 1
 
3.3%
126.874 1
 
3.3%
127.068 1
 
3.3%
126.863 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
126.584 1
3.3%
126.767 1
3.3%
126.772 1
3.3%
126.775 1
3.3%
126.782 1
3.3%
126.783 1
3.3%
126.792 1
3.3%
126.794 1
3.3%
126.805 1
3.3%
126.819 1
3.3%
ValueCountFrequency (%)
127.602 1
3.3%
127.303 1
3.3%
127.243 1
3.3%
127.176 1
3.3%
127.151 1
3.3%
127.096 1
3.3%
127.077 1
3.3%
127.073 1
3.3%
127.069 1
3.3%
127.068 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%
2023-12-10T22:42:53.733898image/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

2023-12-10T22:42:53.977793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:54.168516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2023-12-10T22:42:45.574447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:43.453934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:44.367542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:44.876148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:45.747190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:43.674760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:44.497268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:45.049850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:45.975699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:43.965163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:44.614215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:45.201740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:46.188847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:44.212729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:44.752968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:45.401541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:42:54.326314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점업종명가맹점우편번호시군구명읍면동명위도경도
가맹점번호1.0000.5180.6051.0001.0000.8601.000
가맹점업종명0.5181.0000.6360.5331.0000.7220.591
가맹점우편번호0.6050.6361.0000.9821.0000.8200.780
시군구명1.0000.5330.9821.0001.0000.9540.927
읍면동명1.0001.0001.0001.0001.0001.0001.000
위도0.8600.7220.8200.9541.0001.0000.486
경도1.0000.5910.7800.9271.0000.4861.000
2023-12-10T22:42:54.509995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도가맹점업종명
가맹점번호1.0000.238-0.1970.1530.293
가맹점우편번호0.2381.000-0.9410.4310.232
위도-0.197-0.9411.000-0.3560.325
경도0.1530.431-0.3561.0000.221
가맹점업종명0.2930.2320.3250.2211.000

Missing values

2023-12-10T22:42:46.570315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:42:47.195926image/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
02020-09-01700000058999999999999999일반휴게음식11409경기도양주시남면37.875126.995<NA>N0
12020-09-01799332803999999999999999일반휴게음식15855경기도군포시당동37.354126.946<NA>N0
22020-09-01700000749999999999999999가구15599경기도안산시 단원구목내동37.303126.775<NA>N0
32020-09-01700001005999999999999999용역 서비스16456경기도수원시 팔달구매교동37.268127.014<NA>N0
42020-09-01700003827999999999999999유통업 영리12525경기도양평군단월면37.64127.602<NA>N0
52020-09-01700004564999999999999999일반휴게음식15622경기도안산시 상록구사동37.288126.851<NA>N0
62020-09-01700004696999999999999999기타의료기관14473경기도부천시원종동37.518126.805<NA>N0
72020-09-01799333046999999999999999레저업소12798경기도광주시오포읍37.364127.243<NA>N0
82020-09-01700005366999999999999999신변잡화10823경기도파주시문산읍37.857126.783<NA>N0
92020-09-01700005713999999999999999자동차정비 유지16600경기도수원시 권선구평동37.26126.993<NA>N0
일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202020-09-01700011027999999999999999유통업 영리14784경기도부천시옥길동37.468126.819<NA>N0
212020-09-01700011782999999999999999학원15303경기도안산시 상록구부곡동37.333126.863<NA>N0
222020-09-01700011986999999999999999학원16512경기도수원시 영통구하동37.292127.068<NA>N0
232020-09-01700012032999999999999999음료식품10826경기도파주시법원읍37.849126.874<NA>N0
242020-09-01700012439999999999999999학원15002경기도시흥시장곡동37.382126.782<NA>N0
252020-09-01700012447999999999999999학원17559경기도안성시공도읍37.001127.176<NA>N0
262020-09-01700013002999999999999999일반휴게음식16835경기도용인시 수지구풍덕천동37.323127.096<NA>N0
272020-09-01700013912999999999999999일반휴게음식16284경기도수원시 장안구조원동37.302127.01<NA>N0
282020-09-01700014714999999999999999일반휴게음식16334경기도수원시 장안구정자동37.295126.995<NA>N0
292020-09-01700014829999999999999999일반휴게음식15517경기도안산시 상록구일동37.303126.866<NA>N0