Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells78
Missing cells (%)14.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory156.4 B

Variable types

Categorical7
Text5
Numeric5
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/f3ba9dd8-d8e3-4bd5-bf92-aaeaff204545

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
위도 is highly overall correlated with 가맹점번호 and 7 other fieldsHigh correlation
가맹점우편번호 is highly overall correlated with 회원코드 and 9 other fieldsHigh correlation
성별코드 is highly overall correlated with 연령대코드 and 2 other fieldsHigh correlation
시도명 is highly overall correlated with 회원코드 and 9 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점번호 and 7 other fieldsHigh correlation
사용여부 is highly overall correlated with 가맹점번호 and 6 other fieldsHigh correlation
회원코드 is highly overall correlated with 가맹점우편번호 and 1 other fieldsHigh correlation
가맹점번호 is highly overall correlated with 결제금액 and 5 other fieldsHigh correlation
연령대코드 is highly overall correlated with 성별코드 and 5 other fieldsHigh correlation
결제상품ID is highly overall correlated with 가맹점우편번호 and 3 other fieldsHigh correlation
결제금액 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
가맹점우편번호 is highly imbalanced (64.1%)Imbalance
위도 is highly imbalanced (64.1%)Imbalance
경도 is highly imbalanced (64.1%)Imbalance
가맹점업종명 has 26 (86.7%) missing valuesMissing
시군구명 has 26 (86.7%) missing valuesMissing
읍면동명 has 26 (86.7%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
연령대코드 has 11 (36.7%) zerosZeros
결제금액 has 25 (83.3%) zerosZeros

Reproduction

Analysis started2024-03-13 11:48:08.951282
Analysis finished2024-03-13 11:48:13.227992
Duration4.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책주간결제시작일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-02-06
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-02-06
2nd row2023-02-06
3rd row2023-02-06
4th row2023-02-06
5th row2023-02-06

Common Values

ValueCountFrequency (%)
2023-02-06 30
100.0%

Length

2024-03-13T20:48:13.321919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:48:13.435086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-02-06 30
100.0%

정책주간결제종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-02-12
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-02-12
2nd row2023-02-12
3rd row2023-02-12
4th row2023-02-12
5th row2023-02-12

Common Values

ValueCountFrequency (%)
2023-02-12 30
100.0%

Length

2024-03-13T20:48:13.545375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:48:13.651604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-02-12 30
100.0%

카드번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:48:13.868798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length44
Mean length44
Min length44

Characters and Unicode

Total characters1320
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
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 rowzzwZ3yNjAU7Rqk9cmFYIQlIANUp9mq/1kX15jQabBxc=
2nd row++CCHlQIlMES0E92v22rANru5LMDUixxqGlBs05Kdbo=
3rd rowGzdO7x9W3YDDZ20G1Hfy2YvnmNXlf1yADJzswszln+4=
4th rowzzcfv/rbDNEmHlfXPhRFDX8hsCCYw+a/N2s1ph3I3Zo=
5th rowdOcNsudnGbd45Z+ATFccMkTNi4bBB8V+LivzjuvnJwI=
ValueCountFrequency (%)
zzwz3ynjau7rqk9cmfyiqlianup9mq/1kx15jqabbxc 1
 
3.3%
cchlqilmes0e92v22ranru5lmduixxqglbs05kdbo 1
 
3.3%
1z4ptssjss+dzxrtudm1clqbrozbtyfnxs2alambduu 1
 
3.3%
4kxklzejifhg3uswanu4+ybi2p8+qzowvm4kalgvqz8 1
 
3.3%
7xilqptaaked00wdy0tp5rpo/6/kpxny/rbluh1s7xq 1
 
3.3%
amj8hlvbbwzlvai7jlksj56296vgwgtk2dbnkrihbyu 1
 
3.3%
d3kzerbve8+y+m0v4qdfuniz34sbj1g1swuzl35he78 1
 
3.3%
zzumsgusei7jfilhk3ngerz9w675peehz9l1z5tljiy 1
 
3.3%
mchwzs/y7l6g4xkpe3gus6p4wpic+qw/ixk5+9nqg7y 1
 
3.3%
siaqbx61ur3kky4wauncymhhzhxllo2acsshlfgxwk4 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:48:14.367509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 35
 
2.7%
z 33
 
2.5%
N 33
 
2.5%
d 33
 
2.5%
i 33
 
2.5%
A 30
 
2.3%
3 30
 
2.3%
= 30
 
2.3%
v 26
 
2.0%
X 26
 
2.0%
Other values (55) 1011
76.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 553
41.9%
Uppercase Letter 494
37.4%
Decimal Number 201
 
15.2%
Math Symbol 56
 
4.2%
Other Punctuation 16
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 35
 
6.3%
z 33
 
6.0%
d 33
 
6.0%
i 33
 
6.0%
v 26
 
4.7%
k 25
 
4.5%
h 24
 
4.3%
s 23
 
4.2%
x 22
 
4.0%
b 21
 
3.8%
Other values (16) 278
50.3%
Uppercase Letter
ValueCountFrequency (%)
N 33
 
6.7%
A 30
 
6.1%
X 26
 
5.3%
Z 25
 
5.1%
B 23
 
4.7%
G 22
 
4.5%
K 22
 
4.5%
S 21
 
4.3%
E 20
 
4.0%
J 20
 
4.0%
Other values (16) 252
51.0%
Decimal Number
ValueCountFrequency (%)
3 30
14.9%
0 25
12.4%
4 25
12.4%
2 20
10.0%
1 19
9.5%
5 18
9.0%
6 17
8.5%
8 16
8.0%
7 16
8.0%
9 15
7.5%
Math Symbol
ValueCountFrequency (%)
= 30
53.6%
+ 26
46.4%
Other Punctuation
ValueCountFrequency (%)
/ 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1047
79.3%
Common 273
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 35
 
3.3%
z 33
 
3.2%
N 33
 
3.2%
d 33
 
3.2%
i 33
 
3.2%
A 30
 
2.9%
v 26
 
2.5%
X 26
 
2.5%
k 25
 
2.4%
Z 25
 
2.4%
Other values (42) 748
71.4%
Common
ValueCountFrequency (%)
3 30
11.0%
= 30
11.0%
+ 26
9.5%
0 25
9.2%
4 25
9.2%
2 20
7.3%
1 19
7.0%
5 18
 
6.6%
6 17
 
6.2%
/ 16
 
5.9%
Other values (3) 47
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 35
 
2.7%
z 33
 
2.5%
N 33
 
2.5%
d 33
 
2.5%
i 33
 
2.5%
A 30
 
2.3%
3 30
 
2.3%
= 30
 
2.3%
v 26
 
2.0%
X 26
 
2.0%
Other values (55) 1011
76.6%

회원코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0224683 × 109
Minimum3.0019092 × 109
Maximum3.0929495 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:48:14.569603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0019092 × 109
5-th percentile3.0042086 × 109
Q13.0167917 × 109
median3.0175523 × 109
Q33.0205493 × 109
95-th percentile3.0493785 × 109
Maximum3.0929495 × 109
Range91040336
Interquartile range (IQR)3757557.8

Descriptive statistics

Standard deviation17314028
Coefficient of variation (CV)0.0057284401
Kurtosis9.2869596
Mean3.0224683 × 109
Median Absolute Deviation (MAD)2149226.5
Skewness2.6809437
Sum9.0674048 × 1010
Variance2.9977558 × 1014
MonotonicityNot monotonic
2024-03-13T20:48:14.741196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3020173266 1
 
3.3%
3015801116 1
 
3.3%
3015731238 1
 
3.3%
3017808023 1
 
3.3%
3016796887 1
 
3.3%
3019147140 1
 
3.3%
3019613636 1
 
3.3%
3008447173 1
 
3.3%
3018457609 1
 
3.3%
3034257274 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3001909177 1
3.3%
3001942503 1
3.3%
3006978178 1
3.3%
3008447173 1
3.3%
3015315183 1
3.3%
3015731238 1
3.3%
3015801116 1
3.3%
3016790028 1
3.3%
3016796887 1
3.3%
3016870755 1
3.3%
ValueCountFrequency (%)
3092949513 1
3.3%
3056907676 1
3.3%
3040176168 1
3.3%
3038792285 1
3.3%
3034257274 1
3.3%
3030640198 1
3.3%
3024133173 1
3.3%
3020671694 1
3.3%
3020182120 1
3.3%
3020173266 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4700802 × 1014
Minimum7.1134974 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:48:14.883264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.1134974 × 108
5-th percentile7.4275561 × 108
Q11 × 1015
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.9999929 × 1014
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.5455739 × 1014
Coefficient of variation (CV)0.41859981
Kurtosis2.3233348
Mean8.4700802 × 1014
Median Absolute Deviation (MAD)0
Skewness-2.0096351
Sum2.541024 × 1016
Variance1.2571095 × 1029
MonotonicityNot monotonic
2024-03-13T20:48:15.040173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
999999999999999 25
83.3%
721751821 1
 
3.3%
711349738 1
 
3.3%
768426910 1
 
3.3%
796909805 1
 
3.3%
410237470052901 1
 
3.3%
ValueCountFrequency (%)
711349738 1
 
3.3%
721751821 1
 
3.3%
768426910 1
 
3.3%
796909805 1
 
3.3%
410237470052901 1
 
3.3%
999999999999999 25
83.3%
ValueCountFrequency (%)
999999999999999 25
83.3%
410237470052901 1
 
3.3%
796909805 1
 
3.3%
768426910 1
 
3.3%
721751821 1
 
3.3%
711349738 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
F
22 
M
8 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 22
73.3%
M 8
 
26.7%

Length

2024-03-13T20:48:15.203171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:48:15.366524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 22
73.3%
m 8
 
26.7%

연령대코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.666667
Minimum0
Maximum70
Zeros11
Zeros (%)36.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:48:15.483177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30
Q347.5
95-th percentile60
Maximum70
Range70
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation23.973165
Coefficient of variation (CV)0.89899369
Kurtosis-1.5073371
Mean26.666667
Median Absolute Deviation (MAD)25
Skewness0.11739316
Sum800
Variance574.71264
MonotonicityNot monotonic
2024-03-13T20:48:15.619911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 11
36.7%
40 6
20.0%
50 4
 
13.3%
60 3
 
10.0%
20 2
 
6.7%
30 2
 
6.7%
10 1
 
3.3%
70 1
 
3.3%
ValueCountFrequency (%)
0 11
36.7%
10 1
 
3.3%
20 2
 
6.7%
30 2
 
6.7%
40 6
20.0%
50 4
 
13.3%
60 3
 
10.0%
70 1
 
3.3%
ValueCountFrequency (%)
70 1
 
3.3%
60 3
 
10.0%
50 4
 
13.3%
40 6
20.0%
30 2
 
6.7%
20 2
 
6.7%
10 1
 
3.3%
0 11
36.7%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000011 × 1011
Minimum1.4000002 × 1011
Maximum1.4000129 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:48:15.763216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000004 × 1011
median1.4000006 × 1011
Q31.4000011 × 1011
95-th percentile1.4000012 × 1011
Maximum1.4000129 × 1011
Range1274000
Interquartile range (IQR)66500

Descriptive statistics

Standard deviation225876.95
Coefficient of variation (CV)1.6134055 × 10-6
Kurtosis28.321729
Mean1.4000011 × 1011
Median Absolute Deviation (MAD)34000
Skewness5.2543379
Sum4.2000033 × 1012
Variance5.1020395 × 1010
MonotonicityNot monotonic
2024-03-13T20:48:15.903593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
140000046000 3
 
10.0%
140000116000 3
 
10.0%
140000044000 2
 
6.7%
140000026000 1
 
3.3%
140000030000 1
 
3.3%
140000100000 1
 
3.3%
140000086000 1
 
3.3%
140000028000 1
 
3.3%
140000018000 1
 
3.3%
140000106000 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
140000016000 1
 
3.3%
140000018000 1
 
3.3%
140000024000 1
 
3.3%
140000026000 1
 
3.3%
140000028000 1
 
3.3%
140000030000 1
 
3.3%
140000034000 1
 
3.3%
140000044000 2
6.7%
140000046000 3
10.0%
140000048000 1
 
3.3%
ValueCountFrequency (%)
140001290000 1
 
3.3%
140000126000 1
 
3.3%
140000122000 1
 
3.3%
140000120000 1
 
3.3%
140000116000 3
10.0%
140000112000 1
 
3.3%
140000106000 1
 
3.3%
140000100000 1
 
3.3%
140000094000 1
 
3.3%
140000086000 1
 
3.3%

결제상품명
Text

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:48:16.120092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12.5
Mean length7.9333333
Min length4

Characters and Unicode

Total characters238
Distinct characters64
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)73.3%

Sample

1st row수원페이
2nd row안양사랑페이
3rd row포천사랑상품권
4th row용인와이페이
5th row용인와이페이_세로형
ValueCountFrequency (%)
용인와이페이 3
 
8.3%
오산화폐 3
 
8.3%
행복화성지역화폐 3
 
8.3%
오색전 2
 
5.6%
의정부사랑카드 1
 
2.8%
수원페이 1
 
2.8%
다온(통합 1
 
2.8%
안산사랑상품권 1
 
2.8%
과천토리(통합 1
 
2.8%
과천화폐 1
 
2.8%
Other values (19) 19
52.8%
2024-03-13T20:48:16.846712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 16
 
6.7%
화 12
 
5.0%
사 10
 
4.2%
페 10
 
4.2%
랑 10
 
4.2%
폐 9
 
3.8%
) 9
 
3.8%
통 9
 
3.8%
( 9
 
3.8%
합 8
 
3.4%
Other values (54) 136
57.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 210
88.2%
Close Punctuation 9
 
3.8%
Open Punctuation 9
 
3.8%
Space Separator 6
 
2.5%
Lowercase Letter 2
 
0.8%
Connector Punctuation 1
 
0.4%
Uppercase Letter 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 16
 
7.6%
화 12
 
5.7%
사 10
 
4.8%
페 10
 
4.8%
랑 10
 
4.8%
폐 9
 
4.3%
통 9
 
4.3%
합 8
 
3.8%
천 7
 
3.3%
오 6
 
2.9%
Other values (47) 113
53.8%
Lowercase Letter
ValueCountFrequency (%)
y 1
50.0%
a 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 208
87.4%
Common 25
 
10.5%
Latin 3
 
1.3%
Han 2
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 16
 
7.7%
화 12
 
5.8%
사 10
 
4.8%
페 10
 
4.8%
랑 10
 
4.8%
폐 9
 
4.3%
통 9
 
4.3%
합 8
 
3.8%
천 7
 
3.4%
오 6
 
2.9%
Other values (46) 111
53.4%
Common
ValueCountFrequency (%)
) 9
36.0%
( 9
36.0%
6
24.0%
_ 1
 
4.0%
Latin
ValueCountFrequency (%)
y 1
33.3%
a 1
33.3%
P 1
33.3%
Han
ValueCountFrequency (%)
愛 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 208
87.4%
ASCII 28
 
11.8%
CJK 2
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 16
 
7.7%
화 12
 
5.8%
사 10
 
4.8%
페 10
 
4.8%
랑 10
 
4.8%
폐 9
 
4.3%
통 9
 
4.3%
합 8
 
3.8%
천 7
 
3.4%
오 6
 
2.9%
Other values (46) 111
53.4%
ASCII
ValueCountFrequency (%)
) 9
32.1%
( 9
32.1%
6
21.4%
_ 1
 
3.6%
y 1
 
3.6%
a 1
 
3.6%
P 1
 
3.6%
CJK
ValueCountFrequency (%)
愛 2
100.0%

가맹점업종명
Text

MISSING 

Distinct3
Distinct (%)75.0%
Missing26
Missing (%)86.7%
Memory size372.0 B
2024-03-13T20:48:17.025851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.5
Min length4

Characters and Unicode

Total characters22
Distinct characters14
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

Unique2 ?
Unique (%)50.0%

Sample

1st row음료식품
2nd row유통업 영리
3rd row일반휴게음식
4th row유통업 영리
ValueCountFrequency (%)
유통업 2
33.3%
영리 2
33.3%
음료식품 1
16.7%
일반휴게음식 1
16.7%
2024-03-13T20:48:17.374002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
유 2
9.1%
통 2
9.1%
업 2
9.1%
2
9.1%
영 2
9.1%
리 2
9.1%
음 2
9.1%
식 2
9.1%
료 1
 
4.5%
품 1
 
4.5%
Other values (4) 4
18.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20
90.9%
Space Separator 2
 
9.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 2
10.0%
통 2
10.0%
업 2
10.0%
영 2
10.0%
리 2
10.0%
음 2
10.0%
식 2
10.0%
료 1
 
5.0%
품 1
 
5.0%
일 1
 
5.0%
Other values (3) 3
15.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20
90.9%
Common 2
 
9.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 2
10.0%
통 2
10.0%
업 2
10.0%
영 2
10.0%
리 2
10.0%
음 2
10.0%
식 2
10.0%
료 1
 
5.0%
품 1
 
5.0%
일 1
 
5.0%
Other values (3) 3
15.0%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20
90.9%
ASCII 2
 
9.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
유 2
10.0%
통 2
10.0%
업 2
10.0%
영 2
10.0%
리 2
10.0%
음 2
10.0%
식 2
10.0%
료 1
 
5.0%
품 1
 
5.0%
일 1
 
5.0%
Other values (3) 3
15.0%
ASCII
ValueCountFrequency (%)
2
100.0%

가맹점우편번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
26 
16903
 
1
16035
 
1
17331
 
1
10504
 
1

Length

Max length5
Median length4
Mean length4.1333333
Min length4

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row16903

Common Values

ValueCountFrequency (%)
<NA> 26
86.7%
16903 1
 
3.3%
16035 1
 
3.3%
17331 1
 
3.3%
10504 1
 
3.3%

Length

2024-03-13T20:48:17.544701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:48:17.699585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 26
86.7%
16903 1
 
3.3%
16035 1
 
3.3%
17331 1
 
3.3%
10504 1
 
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
26 
경기도
4 

Length

Max length4
Median length4
Mean length3.8666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row경기도

Common Values

ValueCountFrequency (%)
<NA> 26
86.7%
경기도 4
 
13.3%

Length

2024-03-13T20:48:17.838722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:48:17.956341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 26
86.7%
경기도 4
 
13.3%

시군구명
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing26
Missing (%)86.7%
Memory size372.0 B
2024-03-13T20:48:18.102411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5
Min length3

Characters and Unicode

Total characters20
Distinct characters14
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

Unique4 ?
Unique (%)100.0%

Sample

1st row용인시 기흥구
2nd row의왕시
3rd row이천시
4th row고양시 덕양구
ValueCountFrequency (%)
용인시 1
16.7%
기흥구 1
16.7%
의왕시 1
16.7%
이천시 1
16.7%
고양시 1
16.7%
덕양구 1
16.7%
2024-03-13T20:48:18.472235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 4
20.0%
2
10.0%
구 2
10.0%
양 2
10.0%
용 1
 
5.0%
인 1
 
5.0%
기 1
 
5.0%
흥 1
 
5.0%
의 1
 
5.0%
왕 1
 
5.0%
Other values (4) 4
20.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18
90.0%
Space Separator 2
 
10.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 4
22.2%
구 2
11.1%
양 2
11.1%
용 1
 
5.6%
인 1
 
5.6%
기 1
 
5.6%
흥 1
 
5.6%
의 1
 
5.6%
왕 1
 
5.6%
이 1
 
5.6%
Other values (3) 3
16.7%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18
90.0%
Common 2
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 4
22.2%
구 2
11.1%
양 2
11.1%
용 1
 
5.6%
인 1
 
5.6%
기 1
 
5.6%
흥 1
 
5.6%
의 1
 
5.6%
왕 1
 
5.6%
이 1
 
5.6%
Other values (3) 3
16.7%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18
90.0%
ASCII 2
 
10.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 4
22.2%
구 2
11.1%
양 2
11.1%
용 1
 
5.6%
인 1
 
5.6%
기 1
 
5.6%
흥 1
 
5.6%
의 1
 
5.6%
왕 1
 
5.6%
이 1
 
5.6%
Other values (3) 3
16.7%
ASCII
ValueCountFrequency (%)
2
100.0%

읍면동명
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing26
Missing (%)86.7%
Memory size372.0 B
2024-03-13T20:48:18.686545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters9
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

Unique4 ?
Unique (%)100.0%

Sample

1st row보정동
2nd row내손동
3rd row부발읍
4th row화정동
ValueCountFrequency (%)
보정동 1
25.0%
내손동 1
25.0%
부발읍 1
25.0%
화정동 1
25.0%
2024-03-13T20:48:18.966539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 3
25.0%
정 2
16.7%
보 1
 
8.3%
내 1
 
8.3%
손 1
 
8.3%
부 1
 
8.3%
발 1
 
8.3%
읍 1
 
8.3%
화 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3
25.0%
정 2
16.7%
보 1
 
8.3%
내 1
 
8.3%
손 1
 
8.3%
부 1
 
8.3%
발 1
 
8.3%
읍 1
 
8.3%
화 1
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3
25.0%
정 2
16.7%
보 1
 
8.3%
내 1
 
8.3%
손 1
 
8.3%
부 1
 
8.3%
발 1
 
8.3%
읍 1
 
8.3%
화 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 3
25.0%
정 2
16.7%
보 1
 
8.3%
내 1
 
8.3%
손 1
 
8.3%
부 1
 
8.3%
발 1
 
8.3%
읍 1
 
8.3%
화 1
 
8.3%

위도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
0.0
26 
37.314
 
1
37.388
 
1
37.251
 
1
37.628
 
1

Length

Max length6
Median length3
Mean length3.4
Min length3

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row37.314

Common Values

ValueCountFrequency (%)
0.0 26
86.7%
37.314 1
 
3.3%
37.388 1
 
3.3%
37.251 1
 
3.3%
37.628 1
 
3.3%

Length

2024-03-13T20:48:19.123240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:48:19.271976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 26
86.7%
37.314 1
 
3.3%
37.388 1
 
3.3%
37.251 1
 
3.3%
37.628 1
 
3.3%

경도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
0.0
26 
127.11
 
1
126.974
 
1
127.472
 
1
126.833
 
1

Length

Max length7
Median length3
Mean length3.5
Min length3

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row127.11

Common Values

ValueCountFrequency (%)
0.0 26
86.7%
127.11 1
 
3.3%
126.974 1
 
3.3%
127.472 1
 
3.3%
126.833 1
 
3.3%

Length

2024-03-13T20:48:19.403006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:48:19.522758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 26
86.7%
127.11 1
 
3.3%
126.974 1
 
3.3%
127.472 1
 
3.3%
126.833 1
 
3.3%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
25 
True
5 
ValueCountFrequency (%)
False 25
83.3%
True 5
 
16.7%
2024-03-13T20:48:19.650843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1954
Minimum0
Maximum24000
Zeros25
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:48:19.765341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile15210
Maximum24000
Range24000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5686.9388
Coefficient of variation (CV)2.9104088
Kurtosis9.3273522
Mean1954
Median Absolute Deviation (MAD)0
Skewness3.1370333
Sum58620
Variance32341273
MonotonicityNot monotonic
2024-03-13T20:48:19.884233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 25
83.3%
1520 1
 
3.3%
3300 1
 
3.3%
24000 1
 
3.3%
18000 1
 
3.3%
11800 1
 
3.3%
ValueCountFrequency (%)
0 25
83.3%
1520 1
 
3.3%
3300 1
 
3.3%
11800 1
 
3.3%
18000 1
 
3.3%
24000 1
 
3.3%
ValueCountFrequency (%)
24000 1
 
3.3%
18000 1
 
3.3%
11800 1
 
3.3%
3300 1
 
3.3%
1520 1
 
3.3%
0 25
83.3%

Interactions

2024-03-13T20:48:12.104657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:09.807101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:10.342337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:10.921386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:11.502535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:12.216113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:09.893087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:10.457446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:11.034002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:11.592617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:12.332929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:10.006576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:10.565018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:11.172636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:11.695549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:12.430914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:10.125407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:10.692862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:11.294831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:11.814299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:12.526288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:10.236857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:10.810783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:11.409363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:48:11.978871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:48:19.975245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시군구명읍면동명위도경도사용여부결제금액
카드번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
회원코드1.0001.0000.5800.0000.5561.0000.0890.8271.0001.0001.0000.6700.6700.4790.000
가맹점번호1.0000.5801.0000.1440.7080.3140.000NaNNaNNaNNaN0.6420.6421.0000.826
성별코드1.0000.0000.1441.0000.7690.0000.0871.0001.0001.0001.0000.3720.3720.2370.372
연령대코드1.0000.5560.7080.7691.0000.7350.0001.0001.0001.0001.0000.7700.7700.9250.770
결제상품ID1.0001.0000.3140.0000.7351.0001.0001.0001.0001.0001.0001.0001.0000.0000.000
결제상품명1.0000.0890.0000.0870.0001.0001.0001.0001.0001.0001.0001.0001.0000.5150.681
가맹점업종명1.0000.827NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.000
가맹점우편번호1.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.000
시군구명1.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.000
읍면동명1.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.000
위도1.0000.6700.6420.3720.7701.0001.0001.0001.0001.0001.0001.0001.0000.7220.988
경도1.0000.6700.6420.3720.7701.0001.0001.0001.0001.0001.0001.0001.0000.7220.988
사용여부1.0000.4791.0000.2370.9250.0000.515NaNNaNNaNNaN0.7220.7221.0000.722
결제금액1.0000.0000.8260.3720.7700.0000.6811.0001.0001.0001.0000.9880.9880.7221.000
2024-03-13T20:48:20.134089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도가맹점우편번호성별코드시도명경도사용여부
위도1.0001.0000.4241.0001.0000.809
가맹점우편번호1.0001.0001.0001.0001.0001.000
성별코드0.4241.0001.0001.0000.4240.148
시도명1.0001.0001.0001.0001.0001.000
경도1.0001.0000.4241.0001.0000.809
사용여부0.8091.0000.1481.0000.8091.000
2024-03-13T20:48:20.243562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID결제금액성별코드가맹점우편번호시도명위도경도사용여부
회원코드1.0000.237-0.3050.418-0.2910.0001.0001.0000.4380.4380.371
가맹점번호0.2371.000-0.1520.046-0.9840.3551.0001.0000.6240.6240.982
연령대코드-0.305-0.1521.000-0.1700.1800.5201.0001.0000.5730.5730.674
결제상품ID0.4180.046-0.1701.000-0.0610.0001.0001.0000.9450.9450.000
결제금액-0.291-0.9840.180-0.0611.0000.4241.0001.0000.8430.8430.809
성별코드0.0000.3550.5200.0000.4241.0001.0001.0000.4240.4240.148
가맹점우편번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시도명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도0.4380.6240.5730.9450.8430.4241.0001.0001.0001.0000.809
경도0.4380.6240.5730.9450.8430.4241.0001.0001.0001.0000.809
사용여부0.3710.9820.6740.0000.8090.1481.0001.0000.8090.8091.000

Missing values

2024-03-13T20:48:12.675052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:48:12.948185image/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.
2024-03-13T20:48:13.142496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
02023-02-062023-02-12zzwZ3yNjAU7Rqk9cmFYIQlIANUp9mq/1kX15jQabBxc=3020173266999999999999999F0140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
12023-02-062023-02-12++CCHlQIlMES0E92v22rANru5LMDUixxqGlBs05Kdbo=3017065277999999999999999F0140000034000안양사랑페이<NA><NA><NA><NA><NA>0.00.0N0
22023-02-062023-02-12GzdO7x9W3YDDZ20G1Hfy2YvnmNXlf1yADJzswszln+4=3015315183999999999999999F40140000052000포천사랑상품권<NA><NA><NA><NA><NA>0.00.0N0
32023-02-062023-02-12zzcfv/rbDNEmHlfXPhRFDX8hsCCYw+a/N2s1ph3I3Zo=3016790028999999999999999F50140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0N0
42023-02-062023-02-12dOcNsudnGbd45Z+ATFccMkTNi4bBB8V+LivzjuvnJwI=3092949513721751821F20140001290000용인와이페이_세로형음료식품16903경기도용인시 기흥구보정동37.314127.11Y1520
52023-02-062023-02-12zzvF+ov+PTx2n03hc8hg8yddlNqtHtDXeevtvGpuvRw=3017296498999999999999999F0140000016000가평사랑상품권<NA><NA><NA><NA><NA>0.00.0N0
62023-02-062023-02-124kXKyX+yIxFujrz7jT3ZMAvpwmktF3AdiNZvlOLbfB0=3001909177999999999999999F50140000112000군포愛머니<NA><NA><NA><NA><NA>0.00.0N0
72023-02-062023-02-12++BkinhRRutX4jldjGuQ3em29jd7nd6CnsSjeNp4yNk=3020182120999999999999999F0140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0N0
82023-02-062023-02-12oa0AUJwN0HMJer6NSg7BEEXZy5lbfdWbGKGNahiPmH0=3030640198999999999999999F10140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0N0
92023-02-062023-02-12dO3NhmbhnL3f5Srj28zTEJlyscljYiczRYQPk3KPOms=3040176168999999999999999F60140000094000하남하머니(통합)<NA><NA><NA><NA><NA>0.00.0N0
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202023-02-062023-02-12XrN9qYIoLe/63LR+bl+ltd1cRqWrmruv2Q5q0IzG2Zs=3017062184999999999999999F40140000060000이천사랑지역화폐(통합)<NA><NA><NA><NA><NA>0.00.0N0
212023-02-062023-02-12SIAqbx61UR3kKy4wAuNcYmHhzhXllO2AcsshlfgXwK4=3024133173999999999999999M60140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
222023-02-062023-02-12McHWzs/Y7L6g4XkPE3Gus6P4WpiC+qW/IxK5+9nQG7Y=3034257274999999999999999M30140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
232023-02-062023-02-12zzumsgUSeI7jFIlHK3ngerZ9w675PEehZ9l1z5TljiY=3018457609999999999999999F0140000106000군포愛머니(통합)<NA><NA><NA><NA><NA>0.00.0N0
242023-02-062023-02-12D3KzErBvE8+Y+m0v4qdfuNiz34SBJ1g1SWUzl35hE78=3008447173796909805M40140000018000고양페이카드유통업 영리10504경기도고양시 덕양구화정동37.628126.833Y18000
252023-02-062023-02-12AMJ8hLvbBWZlvAI7JlKSj56296vGWgTK2dbNkRihByU=3019613636999999999999999F60140000028000구리사랑카드<NA><NA><NA><NA><NA>0.00.0N0
262023-02-062023-02-127XilQPTAaKed00wdY0tp5Rpo/6/kPxnY/rBlUh1S7XQ=3019147140999999999999999M40140000086000과천화폐 과천토리(통합)<NA><NA><NA><NA><NA>0.00.0N0
272023-02-062023-02-124kXKlzeJifhg3uSwANU4+yBi2P8+QZoWvM4KalGvQZ8=3016796887410237470052901F20140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0Y11800
282023-02-062023-02-121z4ptssJSs+dZXrtUdM1CLqBRozBtYfNXS2aLAMBdUU=3017808023999999999999999M40140000100000안산사랑상품권 다온(통합)<NA><NA><NA><NA><NA>0.00.0N0
292023-02-062023-02-12/AatazZyzdg92iGZKMTa8Ykj+JblM4xMWOZXsi3i3jw=3015731238999999999999999F40140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0