Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells67
Missing cells (%)12.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory155.4 B

Variable types

DateTime2
Text4
Numeric7
Categorical4
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/fbc2b420-c35a-4c51-b687-af6fc565d965

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
가맹점업종명 is highly overall correlated with 결제금액 and 2 other fieldsHigh correlation
가맹점번호 is highly overall correlated with 가맹점우편번호 and 6 other fieldsHigh correlation
시도명 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
사용여부 is highly overall correlated with 가맹점우편번호 and 6 other fieldsHigh correlation
결제상품ID is highly overall correlated with 가맹점우편번호High correlation
가맹점우편번호 is highly overall correlated with 결제상품ID and 2 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 4 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
결제금액 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
가맹점우편번호 has 21 (70.0%) missing valuesMissing
시군구명 has 23 (76.7%) missing valuesMissing
읍면동명 has 23 (76.7%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
연령대코드 has 3 (10.0%) zerosZeros
위도 has 23 (76.7%) zerosZeros
경도 has 23 (76.7%) zerosZeros
결제금액 has 18 (60.0%) zerosZeros

Reproduction

Analysis started2024-03-13 11:45:10.176667
Analysis finished2024-03-13 11:45:21.550499
Duration11.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책주간결제시작일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2021-09-06 00:00:00
Maximum2021-09-06 00:00:00
2024-03-13T20:45:21.611632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:21.736616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

정책주간결제종료일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2021-09-12 00:00:00
Maximum2021-09-12 00:00:00
2024-03-13T20:45:21.908083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:22.060489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

카드번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:45:22.291333image/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 row++14qB6tOQKQ7Yf+A3/W454J/ud5RvUiS8KiNJH0Onw=
2nd rowzzKb6m0rUDEyjrjqTwds9FSKlWAMKmnwuxg0ZU2v/pw=
3rd rowAEj82ZtBseYObWvzQxyx/2REz82885IgYANWAAuUKn4=
4th row1f16GO2Zh1tFgyo+yHBe3nDtwMzy/Sqne24QY5um6es=
5th row++7EOn8rYoqEinU9XOqcfi+UkEG4gog6UBVkDRhCITA=
ValueCountFrequency (%)
14qb6toqkq7yf+a3/w454j/ud5rvuis8kinjh0onw 1
 
3.3%
zzkb6m0rudeyjrjqtwds9fsklwamkmnwuxg0zu2v/pw 1
 
3.3%
0f4udakzhrwidolu2mooyojt2cyixvie3cpqq291f0w 1
 
3.3%
0ialkrpfyvs66+psqk8n8d6y7kde/pw9vzujexc5h8q 1
 
3.3%
0/h+hqwp532by5satvxihjsmu2sikxza3sv/prqmnzk 1
 
3.3%
k72fx3mt9v7jqu5qa4kelr1as/zvfit+xlqcndbv0a 1
 
3.3%
slurg8lymu8ikuyiujm4lgcdc54w1lyq/u03zpaq58 1
 
3.3%
a6y++svinhdjegwkmvza79vlyldqnpxxrdw8godzhu 1
 
3.3%
r62ppmvutrhg/0h+2ehjgfonplq0r1jxtjcxjgt32o 1
 
3.3%
wd0+lpq1rbrvenapzcmdmzolfb0ysrwx1gxyjq5yty 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:45:22.694478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
+ 37
 
2.8%
= 30
 
2.3%
t 28
 
2.1%
Y 28
 
2.1%
Q 27
 
2.0%
0 27
 
2.0%
2 26
 
2.0%
A 26
 
2.0%
n 25
 
1.9%
D 24
 
1.8%
Other values (55) 1042
78.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 516
39.1%
Lowercase Letter 508
38.5%
Decimal Number 208
15.8%
Math Symbol 67
 
5.1%
Other Punctuation 21
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 28
 
5.5%
n 25
 
4.9%
e 24
 
4.7%
q 23
 
4.5%
u 23
 
4.5%
k 23
 
4.5%
g 23
 
4.5%
p 23
 
4.5%
i 22
 
4.3%
l 22
 
4.3%
Other values (16) 272
53.5%
Uppercase Letter
ValueCountFrequency (%)
Y 28
 
5.4%
Q 27
 
5.2%
A 26
 
5.0%
D 24
 
4.7%
K 24
 
4.7%
F 23
 
4.5%
O 23
 
4.5%
N 22
 
4.3%
X 22
 
4.3%
P 22
 
4.3%
Other values (16) 275
53.3%
Decimal Number
ValueCountFrequency (%)
0 27
13.0%
2 26
12.5%
6 23
11.1%
5 22
10.6%
1 21
10.1%
8 20
9.6%
4 19
9.1%
3 18
8.7%
9 17
8.2%
7 15
7.2%
Math Symbol
ValueCountFrequency (%)
+ 37
55.2%
= 30
44.8%
Other Punctuation
ValueCountFrequency (%)
/ 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1024
77.6%
Common 296
 
22.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 28
 
2.7%
Y 28
 
2.7%
Q 27
 
2.6%
A 26
 
2.5%
n 25
 
2.4%
D 24
 
2.3%
e 24
 
2.3%
K 24
 
2.3%
F 23
 
2.2%
q 23
 
2.2%
Other values (42) 772
75.4%
Common
ValueCountFrequency (%)
+ 37
12.5%
= 30
10.1%
0 27
9.1%
2 26
8.8%
6 23
7.8%
5 22
7.4%
1 21
7.1%
/ 21
7.1%
8 20
 
6.8%
4 19
 
6.4%
Other values (3) 50
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+ 37
 
2.8%
= 30
 
2.3%
t 28
 
2.1%
Y 28
 
2.1%
Q 27
 
2.0%
0 27
 
2.0%
2 26
 
2.0%
A 26
 
2.0%
n 25
 
1.9%
D 24
 
1.8%
Other values (55) 1042
78.9%

회원코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0187236 × 109
Minimum3.0020632 × 109
Maximum3.0507354 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:45:22.871774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0020632 × 109
5-th percentile3.0022051 × 109
Q13.0143369 × 109
median3.0175801 × 109
Q33.0191893 × 109
95-th percentile3.0432172 × 109
Maximum3.0507354 × 109
Range48672147
Interquartile range (IQR)4852474.2

Descriptive statistics

Standard deviation12052356
Coefficient of variation (CV)0.003992534
Kurtosis1.471495
Mean3.0187236 × 109
Median Absolute Deviation (MAD)2888435
Skewness1.0471076
Sum9.0561707 × 1010
Variance1.4525929 × 1014
MonotonicityNot monotonic
2024-03-13T20:45:23.022736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3017807231 1
 
3.3%
3031819338 1
 
3.3%
3017344765 1
 
3.3%
3002353950 1
 
3.3%
3017503176 1
 
3.3%
3022292107 1
 
3.3%
3016769683 1
 
3.3%
3017035205 1
 
3.3%
3011359122 1
 
3.3%
3035699217 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3002063235 1
3.3%
3002083387 1
3.3%
3002353950 1
3.3%
3002536387 1
3.3%
3003526100 1
3.3%
3010989113 1
3.3%
3011359122 1
3.3%
3013982103 1
3.3%
3015401134 1
3.3%
3016701103 1
3.3%
ValueCountFrequency (%)
3050735382 1
3.3%
3048260433 1
3.3%
3037053150 1
3.3%
3035699217 1
3.3%
3031819338 1
3.3%
3023514314 1
3.3%
3022292107 1
3.3%
3019510436 1
3.3%
3018226032 1
3.3%
3018191604 1
3.3%

가맹점번호
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
999999999999999
18 
726077022
 
1
724927230
 
1
410768850036401
 
1
725539095
 
1
Other values (8)
8 

Length

Max length15
Median length15
Mean length13
Min length9

Unique

Unique12 ?
Unique (%)40.0%

Sample

1st row726077022
2nd row999999999999999
3rd row999999999999999
4th row724927230
5th row410768850036401

Common Values

ValueCountFrequency (%)
999999999999999 18
60.0%
726077022 1
 
3.3%
724927230 1
 
3.3%
410768850036401 1
 
3.3%
725539095 1
 
3.3%
723291858 1
 
3.3%
725333793 1
 
3.3%
720482146 1
 
3.3%
721409952 1
 
3.3%
792221906 1
 
3.3%
Other values (3) 3
 
10.0%

Length

2024-03-13T20:45:23.171582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
999999999999999 18
60.0%
726077022 1
 
3.3%
724927230 1
 
3.3%
410768850036401 1
 
3.3%
725539095 1
 
3.3%
723291858 1
 
3.3%
725333793 1
 
3.3%
720482146 1
 
3.3%
721409952 1
 
3.3%
792221906 1
 
3.3%
Other values (3) 3
 
10.0%

성별코드
Categorical

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
F
18 
M
12 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 18
60.0%
M 12
40.0%

Length

2024-03-13T20:45:23.333261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:45:23.464522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 18
60.0%
m 12
40.0%

연령대코드
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.666667
Minimum0
Maximum60
Zeros3
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:45:23.580472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122.5
median30
Q340
95-th percentile50
Maximum60
Range60
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation15.521583
Coefficient of variation (CV)0.4751505
Kurtosis0.07227247
Mean32.666667
Median Absolute Deviation (MAD)10
Skewness-0.59736709
Sum980
Variance240.91954
MonotonicityNot monotonic
2024-03-13T20:45:23.709346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
30 8
26.7%
40 7
23.3%
50 6
20.0%
20 5
16.7%
0 3
 
10.0%
60 1
 
3.3%
ValueCountFrequency (%)
0 3
 
10.0%
20 5
16.7%
30 8
26.7%
40 7
23.3%
50 6
20.0%
60 1
 
3.3%
ValueCountFrequency (%)
60 1
 
3.3%
50 6
20.0%
40 7
23.3%
30 8
26.7%
20 5
16.7%
0 3
 
10.0%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000009 × 1011
Minimum1.4000002 × 1011
Maximum1.4000014 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:45:23.863604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000004 × 1011
median1.4000011 × 1011
Q31.4000012 × 1011
95-th percentile1.4000013 × 1011
Maximum1.4000014 × 1011
Range122000
Interquartile range (IQR)77500

Descriptive statistics

Standard deviation41921.711
Coefficient of variation (CV)2.9944061 × 10-7
Kurtosis-1.6156672
Mean1.4000009 × 1011
Median Absolute Deviation (MAD)18000
Skewness-0.42209513
Sum4.2000026 × 1012
Variance1.7574299 × 109
MonotonicityNot monotonic
2024-03-13T20:45:24.036642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
140000116000 4
13.3%
140000030000 4
13.3%
140000124000 3
10.0%
140000122000 3
10.0%
140000126000 2
 
6.7%
140000018000 2
 
6.7%
140000090000 2
 
6.7%
140000046000 2
 
6.7%
140000038000 1
 
3.3%
140000044000 1
 
3.3%
Other values (6) 6
20.0%
ValueCountFrequency (%)
140000018000 2
6.7%
140000030000 4
13.3%
140000038000 1
 
3.3%
140000044000 1
 
3.3%
140000046000 2
6.7%
140000058000 1
 
3.3%
140000060000 1
 
3.3%
140000090000 2
6.7%
140000112000 1
 
3.3%
140000114000 1
 
3.3%
ValueCountFrequency (%)
140000140000 1
 
3.3%
140000126000 2
6.7%
140000124000 3
10.0%
140000122000 3
10.0%
140000118000 1
 
3.3%
140000116000 4
13.3%
140000114000 1
 
3.3%
140000112000 1
 
3.3%
140000090000 2
6.7%
140000060000 1
 
3.3%

결제상품명
Text

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:45:24.363310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length7.4
Min length4

Characters and Unicode

Total characters222
Distinct characters63
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)26.7%

Sample

1st row행복화성지역화폐
2nd row고양페이카드
3rd row수원페이
4th row군포愛머니
5th row안산사랑상품권 다온
ValueCountFrequency (%)
행복화성지역화폐 4
 
11.1%
부천페이 4
 
11.1%
안산사랑상품권 3
 
8.3%
다온 3
 
8.3%
의정부사랑카드 3
 
8.3%
고양페이카드 2
 
5.6%
수원페이 2
 
5.6%
용인와이페이 2
 
5.6%
고양페이카드(통합 2
 
5.6%
이천사랑지역화폐(통합 1
 
2.8%
Other values (10) 10
27.8%
2024-03-13T20:45:24.757046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 16
 
7.2%
화 13
 
5.9%
페 12
 
5.4%
카 8
 
3.6%
드 8
 
3.6%
사 8
 
3.6%
랑 8
 
3.6%
폐 7
 
3.2%
부 7
 
3.2%
6
 
2.7%
Other values (53) 129
58.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 194
87.4%
Lowercase Letter 8
 
3.6%
Space Separator 6
 
2.7%
Close Punctuation 4
 
1.8%
Open Punctuation 4
 
1.8%
Uppercase Letter 4
 
1.8%
Dash Punctuation 1
 
0.5%
Connector Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 16
 
8.2%
화 13
 
6.7%
페 12
 
6.2%
카 8
 
4.1%
드 8
 
4.1%
사 8
 
4.1%
랑 8
 
4.1%
폐 7
 
3.6%
부 7
 
3.6%
지 6
 
3.1%
Other values (37) 101
52.1%
Lowercase Letter
ValueCountFrequency (%)
a 2
25.0%
y 1
12.5%
u 1
12.5%
o 1
12.5%
k 1
12.5%
n 1
12.5%
h 1
12.5%
Uppercase Letter
ValueCountFrequency (%)
N 1
25.0%
P 1
25.0%
Y 1
25.0%
T 1
25.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 193
86.9%
Common 16
 
7.2%
Latin 12
 
5.4%
Han 1
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 16
 
8.3%
화 13
 
6.7%
페 12
 
6.2%
카 8
 
4.1%
드 8
 
4.1%
사 8
 
4.1%
랑 8
 
4.1%
폐 7
 
3.6%
부 7
 
3.6%
지 6
 
3.1%
Other values (36) 100
51.8%
Latin
ValueCountFrequency (%)
a 2
16.7%
N 1
8.3%
y 1
8.3%
P 1
8.3%
u 1
8.3%
o 1
8.3%
Y 1
8.3%
k 1
8.3%
n 1
8.3%
h 1
8.3%
Common
ValueCountFrequency (%)
6
37.5%
) 4
25.0%
( 4
25.0%
- 1
 
6.2%
_ 1
 
6.2%
Han
ValueCountFrequency (%)
愛 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 193
86.9%
ASCII 28
 
12.6%
CJK 1
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 16
 
8.3%
화 13
 
6.7%
페 12
 
6.2%
카 8
 
4.1%
드 8
 
4.1%
사 8
 
4.1%
랑 8
 
4.1%
폐 7
 
3.6%
부 7
 
3.6%
지 6
 
3.1%
Other values (36) 100
51.8%
ASCII
ValueCountFrequency (%)
6
21.4%
) 4
14.3%
( 4
14.3%
a 2
 
7.1%
- 1
 
3.6%
_ 1
 
3.6%
N 1
 
3.6%
y 1
 
3.6%
P 1
 
3.6%
u 1
 
3.6%
Other values (6) 6
21.4%
CJK
ValueCountFrequency (%)
愛 1
100.0%

가맹점업종명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
21 
일반휴게음식
4 
유통업 영리
3 
학원
 
1
음료식품
 
1

Length

Max length6
Median length4
Mean length4.4
Min length2

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 21
70.0%
일반휴게음식 4
 
13.3%
유통업 영리 3
 
10.0%
학원 1
 
3.3%
음료식품 1
 
3.3%

Length

2024-03-13T20:45:24.954293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:45:25.115650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 21
63.6%
일반휴게음식 4
 
12.1%
유통업 3
 
9.1%
영리 3
 
9.1%
학원 1
 
3.0%
음료식품 1
 
3.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)100.0%
Missing21
Missing (%)70.0%
Infinite0
Infinite (%)0.0%
Mean15425.222
Minimum12155
Maximum18471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:45:25.246855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12155
5-th percentile12317
Q114467
median14544
Q317909
95-th percentile18406.6
Maximum18471
Range6316
Interquartile range (IQR)3442

Descriptive statistics

Standard deviation2381.8703
Coefficient of variation (CV)0.154414
Kurtosis-1.4316721
Mean15425.222
Median Absolute Deviation (MAD)1984
Skewness0.082012257
Sum138827
Variance5673305.9
MonotonicityNot monotonic
2024-03-13T20:45:25.380179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
18310 1
 
3.3%
15885 1
 
3.3%
12155 1
 
3.3%
14467 1
 
3.3%
17909 1
 
3.3%
18471 1
 
3.3%
14526 1
 
3.3%
14544 1
 
3.3%
12560 1
 
3.3%
(Missing) 21
70.0%
ValueCountFrequency (%)
12155 1
3.3%
12560 1
3.3%
14467 1
3.3%
14526 1
3.3%
14544 1
3.3%
15885 1
3.3%
17909 1
3.3%
18310 1
3.3%
18471 1
3.3%
ValueCountFrequency (%)
18471 1
3.3%
18310 1
3.3%
17909 1
3.3%
15885 1
3.3%
14544 1
3.3%
14526 1
3.3%
14467 1
3.3%
12560 1
3.3%
12155 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
21 
경기도
7 
NONE
 
2

Length

Max length4
Median length4
Mean length3.7666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 21
70.0%
경기도 7
 
23.3%
NONE 2
 
6.7%

Length

2024-03-13T20:45:25.542145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:45:25.682198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 21
70.0%
경기도 7
 
23.3%
none 2
 
6.7%

시군구명
Text

MISSING 

Distinct5
Distinct (%)71.4%
Missing23
Missing (%)76.7%
Memory size372.0 B
2024-03-13T20:45:25.825296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21
Distinct characters10
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 (%)57.1%

Sample

1st row군포시
2nd row부천시
3rd row평택시
4th row화성시
5th row부천시
ValueCountFrequency (%)
부천시 3
42.9%
군포시 1
 
14.3%
평택시 1
 
14.3%
화성시 1
 
14.3%
양평군 1
 
14.3%
2024-03-13T20:45:26.141782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 6
28.6%
부 3
14.3%
천 3
14.3%
군 2
 
9.5%
평 2
 
9.5%
포 1
 
4.8%
택 1
 
4.8%
화 1
 
4.8%
성 1
 
4.8%
양 1
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 6
28.6%
부 3
14.3%
천 3
14.3%
군 2
 
9.5%
평 2
 
9.5%
포 1
 
4.8%
택 1
 
4.8%
화 1
 
4.8%
성 1
 
4.8%
양 1
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 6
28.6%
부 3
14.3%
천 3
14.3%
군 2
 
9.5%
평 2
 
9.5%
포 1
 
4.8%
택 1
 
4.8%
화 1
 
4.8%
성 1
 
4.8%
양 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 6
28.6%
부 3
14.3%
천 3
14.3%
군 2
 
9.5%
평 2
 
9.5%
포 1
 
4.8%
택 1
 
4.8%
화 1
 
4.8%
성 1
 
4.8%
양 1
 
4.8%

읍면동명
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing23
Missing (%)76.7%
Memory size372.0 B
2024-03-13T20:45:26.340093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters21
Distinct characters14
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

Unique7 ?
Unique (%)100.0%

Sample

1st row도마교동
2nd row원종동
3rd row평택동
4th row영천동
5th row도당동
ValueCountFrequency (%)
도마교동 1
14.3%
원종동 1
14.3%
평택동 1
14.3%
영천동 1
14.3%
도당동 1
14.3%
상동 1
14.3%
양평읍 1
14.3%
2024-03-13T20:45:26.791577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 6
28.6%
도 2
 
9.5%
평 2
 
9.5%
마 1
 
4.8%
교 1
 
4.8%
원 1
 
4.8%
종 1
 
4.8%
택 1
 
4.8%
영 1
 
4.8%
천 1
 
4.8%
Other values (4) 4
19.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 6
28.6%
도 2
 
9.5%
평 2
 
9.5%
마 1
 
4.8%
교 1
 
4.8%
원 1
 
4.8%
종 1
 
4.8%
택 1
 
4.8%
영 1
 
4.8%
천 1
 
4.8%
Other values (4) 4
19.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 6
28.6%
도 2
 
9.5%
평 2
 
9.5%
마 1
 
4.8%
교 1
 
4.8%
원 1
 
4.8%
종 1
 
4.8%
택 1
 
4.8%
영 1
 
4.8%
천 1
 
4.8%
Other values (4) 4
19.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 6
28.6%
도 2
 
9.5%
평 2
 
9.5%
마 1
 
4.8%
교 1
 
4.8%
원 1
 
4.8%
종 1
 
4.8%
택 1
 
4.8%
영 1
 
4.8%
천 1
 
4.8%
Other values (4) 4
19.0%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7178
Minimum0
Maximum37.52
Zeros23
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:45:26.912787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile37.50565
Maximum37.52
Range37.52
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.072763
Coefficient of variation (CV)1.8436719
Kurtosis-0.2567574
Mean8.7178
Median Absolute Deviation (MAD)0
Skewness1.3284601
Sum261.534
Variance258.33371
MonotonicityNot monotonic
2024-03-13T20:45:27.059513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 23
76.7%
37.314 1
 
3.3%
37.52 1
 
3.3%
36.993 1
 
3.3%
37.206 1
 
3.3%
37.507 1
 
3.3%
37.504 1
 
3.3%
37.49 1
 
3.3%
ValueCountFrequency (%)
0.0 23
76.7%
36.993 1
 
3.3%
37.206 1
 
3.3%
37.314 1
 
3.3%
37.49 1
 
3.3%
37.504 1
 
3.3%
37.507 1
 
3.3%
37.52 1
 
3.3%
ValueCountFrequency (%)
37.52 1
 
3.3%
37.507 1
 
3.3%
37.504 1
 
3.3%
37.49 1
 
3.3%
37.314 1
 
3.3%
37.206 1
 
3.3%
36.993 1
 
3.3%
0.0 23
76.7%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.6319
Minimum0
Maximum127.508
Zeros23
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:45:27.253807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile127.09285
Maximum127.508
Range127.508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation54.630742
Coefficient of variation (CV)1.8436463
Kurtosis-0.25723476
Mean29.6319
Median Absolute Deviation (MAD)0
Skewness1.3283566
Sum888.957
Variance2984.518
MonotonicityNot monotonic
2024-03-13T20:45:27.381058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 23
76.7%
126.924 1
 
3.3%
126.808 1
 
3.3%
127.089 1
 
3.3%
127.096 1
 
3.3%
126.78 1
 
3.3%
126.752 1
 
3.3%
127.508 1
 
3.3%
ValueCountFrequency (%)
0.0 23
76.7%
126.752 1
 
3.3%
126.78 1
 
3.3%
126.808 1
 
3.3%
126.924 1
 
3.3%
127.089 1
 
3.3%
127.096 1
 
3.3%
127.508 1
 
3.3%
ValueCountFrequency (%)
127.508 1
 
3.3%
127.096 1
 
3.3%
127.089 1
 
3.3%
126.924 1
 
3.3%
126.808 1
 
3.3%
126.78 1
 
3.3%
126.752 1
 
3.3%
0.0 23
76.7%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
18 
True
12 
ValueCountFrequency (%)
False 18
60.0%
True 12
40.0%
2024-03-13T20:45:27.524970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9776.3333
Minimum0
Maximum150000
Zeros18
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:45:27.654469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39780
95-th percentile26661.5
Maximum150000
Range150000
Interquartile range (IQR)9780

Descriptive statistics

Standard deviation27722.184
Coefficient of variation (CV)2.8356422
Kurtosis24.459237
Mean9776.3333
Median Absolute Deviation (MAD)0
Skewness4.7719977
Sum293290
Variance7.6851947 × 108
MonotonicityNot monotonic
2024-03-13T20:45:27.787151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 18
60.0%
18000 2
 
6.7%
21420 1
 
3.3%
30950 1
 
3.3%
4500 1
 
3.3%
150000 1
 
3.3%
3500 1
 
3.3%
9900 1
 
3.3%
9420 1
 
3.3%
10000 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
0 18
60.0%
1200 1
 
3.3%
3500 1
 
3.3%
4500 1
 
3.3%
9420 1
 
3.3%
9900 1
 
3.3%
10000 1
 
3.3%
16400 1
 
3.3%
18000 2
 
6.7%
21420 1
 
3.3%
ValueCountFrequency (%)
150000 1
3.3%
30950 1
3.3%
21420 1
3.3%
18000 2
6.7%
16400 1
3.3%
10000 1
3.3%
9900 1
3.3%
9420 1
3.3%
4500 1
3.3%
3500 1
3.3%

Interactions

2024-03-13T20:45:20.008134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:14.168673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:15.787453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:16.483507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:17.373778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:18.262110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:19.090125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:20.364557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:14.538886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:15.889996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:16.617278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:17.496918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:18.369592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:19.223793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:20.458050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:14.767825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:16.005602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:16.722798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:17.660127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:18.486669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:19.326160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:20.562193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:15.290519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:16.103163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:16.839283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:17.787631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:18.602188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:19.452589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:20.681831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:15.464118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:16.207534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:16.940462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:17.901230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:18.743026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:19.602411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:20.781997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:15.587276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:16.287347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:17.060807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:18.033199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:18.845912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:19.774438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:20.881469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:15.677305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:16.387305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:17.195930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:18.139462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:18.971033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:45:19.892708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:45:28.223552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
카드번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
회원코드1.0001.0000.6680.4910.6040.7100.8980.0000.8570.2630.9771.0000.3720.3720.0000.905
가맹점번호1.0000.6681.0000.2920.0000.5710.3081.0001.0001.0001.0001.0001.0001.0001.0001.000
성별코드1.0000.4910.2921.0000.5740.0000.4800.0000.0000.0000.2491.0000.0000.0000.0000.000
연령대코드1.0000.6040.0000.5741.0000.3970.6130.0000.7750.0000.7961.0000.0000.0000.0000.000
결제상품ID1.0000.7100.5710.0000.3971.0001.0000.0001.0000.0001.0001.0000.7390.7390.5280.721
결제상품명1.0000.8980.3080.4800.6131.0001.0000.0001.0000.7131.0001.0000.8120.8120.4800.949
가맹점업종명1.0000.0001.0000.0000.0000.0000.0001.0000.0000.8050.0001.0000.8050.805NaN0.897
가맹점우편번호1.0000.8571.0000.0000.7751.0001.0000.0001.0000.0001.0001.0000.0000.000NaN0.600
시도명1.0000.2631.0000.0000.0000.0000.7130.8050.0001.000NaNNaN0.8330.833NaN0.805
시군구명1.0000.9771.0000.2490.7961.0001.0000.0001.000NaN1.0001.000NaNNaNNaN0.672
읍면동명1.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
위도1.0000.3721.0000.0000.0000.7390.8120.8050.0000.833NaNNaN1.0000.9890.7870.474
경도1.0000.3721.0000.0000.0000.7390.8120.8050.0000.833NaNNaN0.9891.0000.7870.474
사용여부1.0000.0001.0000.0000.0000.5280.480NaNNaNNaNNaNNaN0.7870.7871.0000.761
결제금액1.0000.9051.0000.0000.0000.7210.9490.8970.6000.8050.6721.0000.4740.4740.7611.000
2024-03-13T20:45:28.402596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별코드가맹점업종명가맹점번호시도명사용여부
성별코드1.0000.0000.1780.0000.000
가맹점업종명0.0001.0001.0000.4741.000
가맹점번호0.1781.0001.0001.0000.779
시도명0.0000.4741.0001.0001.000
사용여부0.0001.0000.7791.0001.000
2024-03-13T20:45:28.523355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드연령대코드결제상품ID가맹점우편번호위도경도결제금액가맹점번호성별코드가맹점업종명시도명사용여부
회원코드1.0000.237-0.2320.3500.1560.2560.1790.3080.3400.0000.0000.000
연령대코드0.2371.0000.097-0.2860.2190.2330.2710.0000.3790.0000.0000.000
결제상품ID-0.2320.0971.0000.502-0.428-0.368-0.1150.1970.0000.0000.0000.235
가맹점우편번호0.350-0.2860.5021.000-0.3100.159-0.1511.0000.0000.0000.0001.000
위도0.1560.219-0.428-0.3101.0000.9640.6390.7790.0000.4740.6190.576
경도0.2560.233-0.3680.1590.9641.0000.6400.7790.0000.4740.6190.576
결제금액0.1790.271-0.115-0.1510.6390.6401.0000.8090.0000.5410.4740.530
가맹점번호0.3080.0000.1971.0000.7790.7790.8091.0000.1781.0001.0000.779
성별코드0.3400.3790.0000.0000.0000.0000.0000.1781.0000.0000.0000.000
가맹점업종명0.0000.0000.0000.0000.4740.4740.5411.0000.0001.0000.4741.000
시도명0.0000.0000.0000.0000.6190.6190.4741.0000.0000.4741.0001.000
사용여부0.0000.0000.2351.0000.5760.5760.5300.7790.0001.0001.0001.000

Missing values

2024-03-13T20:45:21.036194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:45:21.301025image/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.
2024-03-13T20:45:21.461775image/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
02021-09-062021-09-12++14qB6tOQKQ7Yf+A3/W454J/ud5RvUiS8KiNJH0Onw=3017807231726077022F30140000116000행복화성지역화폐유통업 영리18310NONE<NA><NA>0.00.0Y21420
12021-09-062021-09-12zzKb6m0rUDEyjrjqTwds9FSKlWAMKmnwuxg0ZU2v/pw=3017656931999999999999999M40140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
22021-09-062021-09-12AEj82ZtBseYObWvzQxyx/2REz82885IgYANWAAuUKn4=3002063235999999999999999F20140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
32021-09-062021-09-121f16GO2Zh1tFgyo+yHBe3nDtwMzy/Sqne24QY5um6es=3037053150724927230M40140000112000군포愛머니유통업 영리15885경기도군포시도마교동37.314126.924Y30950
42021-09-062021-09-12++7EOn8rYoqEinU9XOqcfi+UkEG4gog6UBVkDRhCITA=3017757232410768850036401M40140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0Y4500
52021-09-062021-09-12/A6/7RzBjPKiauJcgKZP1iWkf4N2qyS20kxWiQZF+MY=3002083387999999999999999F0140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0N0
62021-09-062021-09-120IWV4x0IqZktenHbCGuUcOokFlrzMVXeqXJLc8rl898=3018226032999999999999999F30140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
72021-09-062021-09-12zzNCtTZNnPszoxRDPZXPxlpqBpB8QC3la1eF2rdOpVY=3015401134725539095F40140000114000Thank You Pay-N학원12155NONE<NA><NA>0.00.0Y150000
82021-09-062021-09-129Bvh+fmEqV1ZA76e5Geb+Zsa+5QdUx9XzJJBWUeVNF0=3018191604999999999999999F20140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
92021-09-062021-09-12KgR6AzRyIHsu44FGHtkYxNtF/TA3sVVzrv6BRXIQR1A=3002536387723291858M50140000030000부천페이유통업 영리14467경기도부천시원종동37.52126.808Y18000
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202021-09-062021-09-12+AliYqK0xgQ6K02o6+7WpmJ+q2cyt1K/0o5Nmu++tzU=3013982103999999999999999F30140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
212021-09-062021-09-12+WD0+lPq1rBRvENApzcmDmZOlfb0ySrWX1GxYJq5YtY=3010989113999999999999999M20140000090000고양페이카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
222021-09-062021-09-12+r62PPMVutRHg/0h+2eHJgFOnPlq0r1JXTJCXJgt32o=3035699217410407072618901M40140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0Y1200
232021-09-062021-09-12/A6y++SViNhDjeGwkMVzA79VLYldQnPxxrDw8GOdzhU=3011359122999999999999999F30140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
242021-09-062021-09-12/SLuRG8LYmU8IKuYIujM4lgcDC54w1lYQ/u03ZPaQ58=3017035205999999999999999F50140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
252021-09-062021-09-12/k72FX3mt9V7jqu5QA4kELr1As/ZvFit+XlqcndbV0A=3016769683999999999999999F50140000140000행복화성지역화폐_화이트<NA><NA><NA><NA><NA>0.00.0N0
262021-09-062021-09-120/h+hqWp532BY5SaTvXiHJSMu2sikXZa3sv/PRQmnZk=3022292107999999999999999M60140000044000오산화폐 오색전<NA><NA><NA><NA><NA>0.00.0N0
272021-09-062021-09-120IalKRPFyvs66+psqK8N8d6y7kde/pw9VZuJEXc5h8Q=3017503176999999999999999F0140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
282021-09-062021-09-120f4UDakZHRWidoLU2mOOYoJT2cYIxVIe3cpQQ291F0w=3002353950999999999999999F0140000090000고양페이카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
292021-09-062021-09-120zfh7yangPkCPCnPKoBczMOO5utvUr64rDreCuGVvDM=3017344765GG_X0F9ZGM30140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0Y16400