Overview

Dataset statistics

Number of variables15
Number of observations30
Missing cells8
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory131.4 B

Variable types

Categorical4
Text4
Numeric7

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/46b5c6d5-e019-44f2-ac17-7119ee1da35c

Alerts

지원금결제일자 has constant value ""Constant
가맹점우편번호 is highly overall correlated with 정책명High correlation
위도 is highly overall correlated with 시도명High correlation
경도 is highly overall correlated with 시도명High correlation
결제상품ID is highly overall correlated with 정책명High correlation
정책명 is highly overall correlated with 가맹점우편번호 and 1 other fieldsHigh correlation
시도명 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
시군구명 has 4 (13.3%) missing valuesMissing
읍면동명 has 4 (13.3%) missing valuesMissing
가맹점번호 has unique valuesUnique
결제금액 has unique valuesUnique
위도 has 4 (13.3%) zerosZeros
경도 has 4 (13.3%) zerosZeros

Reproduction

Analysis started2023-12-10 14:08:25.837569
Analysis finished2023-12-10 14:08:36.034231
Duration10.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지원금결제일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2020-05-18
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-05-18
2nd row2020-05-18
3rd row2020-05-18
4th row2020-05-18
5th row2020-05-18

Common Values

ValueCountFrequency (%)
2020-05-18 30
100.0%

Length

2023-12-10T23:08:36.138497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:08:36.286252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-05-18 30
100.0%

카드번호
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:08:36.717575image/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

Unique28 ?
Unique (%)93.3%

Sample

1st row+JvfJrdtXmahmCDR5Fmj9XWUos5+74M1+u4Yyy8rbfI=
2nd row+9yHNrTfBbWH17eSH6k7crQLcoIyrLrMtr7SmWCKgjo=
3rd row+MtV3VhxZSCFD+DSzQ+/iQYcdyvg/pJvCser+nezKC4=
4th row+a2amJc1WMpM3bSU+CcMKxjPiqagZFcVTB/vw8BNB2I=
5th row+OnclLq0kIa3GWtQAgD7veykFJvsi9tERJnrLGhMjgw=
ValueCountFrequency (%)
00gqcya6qv51jsinopqbricr6cvroff0vzqhlhzisla 2
 
6.7%
jvfjrdtxmahmcdr5fmj9xwuos5+74m1+u4yyy8rbfi 1
 
3.3%
yiur/lvdgqry/i57mijhhfh0qo5pkgi2sb5zxddd+s 1
 
3.3%
0reaio+9lngnjnuqjewt+lbyagtgrjcp/x5bqzk6pae 1
 
3.3%
0esrfrstr3kda+ygpe2crvdjyfrmrvgpcig6dyi7xbc 1
 
3.3%
0f8qt5wodmvct4haxwd8uwknn1ji1sczlyj5cimud/a 1
 
3.3%
0wt2ku4rcazfqi3ople7ltqzstxaxq1ru9ogathmbsc 1
 
3.3%
0dsceua2ymbh49iugchiymdpgr+qb52ia0nxm9hhuua 1
 
3.3%
0df2xcv4c4o3yoaficuabux5cgi1q/b4wqyk3tuoimo 1
 
3.3%
0a/ecupvi1bp4uyrkhccrtod648wc92lebonwqltkui 1
 
3.3%
Other values (19) 19
63.3%
2023-12-10T23:08:37.469375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
+ 36
 
2.7%
c 34
 
2.6%
0 32
 
2.4%
= 30
 
2.3%
r 29
 
2.2%
y 28
 
2.1%
C 28
 
2.1%
A 26
 
2.0%
G 26
 
2.0%
i 25
 
1.9%
Other values (55) 1026
77.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 535
40.5%
Uppercase Letter 479
36.3%
Decimal Number 215
16.3%
Math Symbol 66
 
5.0%
Other Punctuation 25
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 34
 
6.4%
r 29
 
5.4%
y 28
 
5.2%
i 25
 
4.7%
j 24
 
4.5%
v 23
 
4.3%
h 22
 
4.1%
l 21
 
3.9%
q 21
 
3.9%
s 21
 
3.9%
Other values (16) 287
53.6%
Uppercase Letter
ValueCountFrequency (%)
C 28
 
5.8%
A 26
 
5.4%
G 26
 
5.4%
F 24
 
5.0%
U 24
 
5.0%
D 23
 
4.8%
B 23
 
4.8%
J 22
 
4.6%
Y 21
 
4.4%
I 20
 
4.2%
Other values (16) 242
50.5%
Decimal Number
ValueCountFrequency (%)
0 32
14.9%
9 22
10.2%
1 22
10.2%
5 22
10.2%
3 21
9.8%
8 21
9.8%
2 20
9.3%
6 19
8.8%
4 18
8.4%
7 18
8.4%
Math Symbol
ValueCountFrequency (%)
+ 36
54.5%
= 30
45.5%
Other Punctuation
ValueCountFrequency (%)
/ 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1014
76.8%
Common 306
 
23.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 34
 
3.4%
r 29
 
2.9%
y 28
 
2.8%
C 28
 
2.8%
A 26
 
2.6%
G 26
 
2.6%
i 25
 
2.5%
F 24
 
2.4%
j 24
 
2.4%
U 24
 
2.4%
Other values (42) 746
73.6%
Common
ValueCountFrequency (%)
+ 36
11.8%
0 32
10.5%
= 30
9.8%
/ 25
8.2%
9 22
 
7.2%
1 22
 
7.2%
5 22
 
7.2%
3 21
 
6.9%
8 21
 
6.9%
2 20
 
6.5%
Other values (3) 55
18.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+ 36
 
2.7%
c 34
 
2.6%
0 32
 
2.4%
= 30
 
2.3%
r 29
 
2.2%
y 28
 
2.1%
C 28
 
2.1%
A 26
 
2.0%
G 26
 
2.0%
i 25
 
1.9%
Other values (55) 1026
77.7%

회원코드
Real number (ℝ)

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0152822 × 109
Minimum3.0023877 × 109
Maximum3.0225453 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:08:37.778374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0023877 × 109
5-th percentile3.0041298 × 109
Q13.0135799 × 109
median3.0169164 × 109
Q33.0182546 × 109
95-th percentile3.0200427 × 109
Maximum3.0225453 × 109
Range20157596
Interquartile range (IQR)4674751.2

Descriptive statistics

Standard deviation5023465
Coefficient of variation (CV)0.0016660016
Kurtosis1.4034975
Mean3.0152822 × 109
Median Absolute Deviation (MAD)1580255
Skewness-1.3908837
Sum9.0458465 × 1010
Variance2.52352 × 1013
MonotonicityNot monotonic
2023-12-10T23:08:38.157695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3016036222 2
 
6.7%
3002537417 1
 
3.3%
3017119748 1
 
3.3%
3016710130 1
 
3.3%
3017517591 1
 
3.3%
3018231079 1
 
3.3%
3012228105 1
 
3.3%
3013486101 1
 
3.3%
3018564956 1
 
3.3%
3016620447 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
3002387660 1
3.3%
3002537417 1
3.3%
3006076121 1
3.3%
3007098120 1
3.3%
3010979147 1
3.3%
3011844179 1
3.3%
3012228105 1
3.3%
3013486101 1
3.3%
3013861223 1
3.3%
3016036222 2
6.7%
ValueCountFrequency (%)
3022545256 1
3.3%
3020277533 1
3.3%
3019755637 1
3.3%
3019595690 1
3.3%
3018994137 1
3.3%
3018564956 1
3.3%
3018428289 1
3.3%
3018262484 1
3.3%
3018231079 1
3.3%
3018055534 1
3.3%

가맹점번호
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2985554 × 108
Minimum7.0133421 × 108
Maximum7.9693863 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:08:38.502337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0133421 × 108
5-th percentile7.029539 × 108
Q17.0718596 × 108
median7.1485012 × 108
Q37.3870798 × 108
95-th percentile7.960938 × 108
Maximum7.9693863 × 108
Range95604414
Interquartile range (IQR)31522013

Descriptive statistics

Standard deviation31864297
Coefficient of variation (CV)0.043658362
Kurtosis0.15670644
Mean7.2985554 × 108
Median Absolute Deviation (MAD)10138066
Skewness1.239758
Sum2.1895666 × 1010
Variance1.0153334 × 1015
MonotonicityNot monotonic
2023-12-10T23:08:38.738089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
706052701 1
 
3.3%
713986821 1
 
3.3%
703739841 1
 
3.3%
711790123 1
 
3.3%
720153034 1
 
3.3%
787485234 1
 
3.3%
703252826 1
 
3.3%
708912894 1
 
3.3%
715713420 1
 
3.3%
739001417 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
701334213 1
3.3%
702709318 1
3.3%
703252826 1
3.3%
703739841 1
3.3%
705684268 1
3.3%
706052701 1
3.3%
706260638 1
3.3%
706975016 1
3.3%
707818811 1
3.3%
708486882 1
3.3%
ValueCountFrequency (%)
796938627 1
3.3%
796881676 1
3.3%
795130841 1
3.3%
787485234 1
3.3%
786112421 1
3.3%
754044179 1
3.3%
753491711 1
3.3%
739001417 1
3.3%
737827661 1
3.3%
732710544 1
3.3%

정책명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
부천시 긴급재난지원금(행안부)
5 
광주시 긴급재난지원금(행안부)
4 
광명시 긴급재난지원금(행안부)
3 
용인시 긴급재난지원금(행안부)
3 
의왕시 긴급재난지원금(행안부)
2 
Other values (9)
13 

Length

Max length17
Median length16
Mean length16.066667
Min length16

Unique

Unique5 ?
Unique (%)16.7%

Sample

1st row평택시 긴급재난지원금(행안부)
2nd row부천시 긴급재난지원금(행안부)
3rd row광주시 긴급재난지원금(행안부)
4th row광명시 긴급재난지원금(행안부)
5th row의왕시 긴급재난지원금(행안부)

Common Values

ValueCountFrequency (%)
부천시 긴급재난지원금(행안부) 5
16.7%
광주시 긴급재난지원금(행안부) 4
13.3%
광명시 긴급재난지원금(행안부) 3
10.0%
용인시 긴급재난지원금(행안부) 3
10.0%
의왕시 긴급재난지원금(행안부) 2
 
6.7%
양주시 긴급재난지원금(행안부) 2
 
6.7%
안성시 긴급재난지원금(행안부) 2
 
6.7%
파주시 긴급재난지원금(행안부) 2
 
6.7%
동두천시 긴급재난지원금(행안부) 2
 
6.7%
평택시 긴급재난지원금(행안부) 1
 
3.3%
Other values (4) 4
13.3%

Length

2023-12-10T23:08:38.964264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
긴급재난지원금(행안부 30
50.0%
부천시 5
 
8.3%
광주시 4
 
6.7%
광명시 3
 
5.0%
용인시 3
 
5.0%
의왕시 2
 
3.3%
양주시 2
 
3.3%
안성시 2
 
3.3%
파주시 2
 
3.3%
동두천시 2
 
3.3%
Other values (5) 5
 
8.3%

가맹점업종명
Categorical

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
일반휴게음식
12 
유통업 영리
6 
음료식품
5 
연료판매점
3 
서적문구
 
1
Other values (3)
3 

Length

Max length6
Median length6
Mean length5.1666667
Min length2

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st row일반휴게음식
2nd row일반휴게음식
3rd row유통업 영리
4th row유통업 영리
5th row음료식품

Common Values

ValueCountFrequency (%)
일반휴게음식 12
40.0%
유통업 영리 6
20.0%
음료식품 5
16.7%
연료판매점 3
 
10.0%
서적문구 1
 
3.3%
약국 1
 
3.3%
레저업소 1
 
3.3%
학원 1
 
3.3%

Length

2023-12-10T23:08:39.182313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:08:39.377368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반휴게음식 12
33.3%
유통업 6
16.7%
영리 6
16.7%
음료식품 5
13.9%
연료판매점 3
 
8.3%
서적문구 1
 
2.8%
약국 1
 
2.8%
레저업소 1
 
2.8%
학원 1
 
2.8%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14343.533
Minimum10874
Maximum18337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:08:39.592713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10874
5-th percentile11073.8
Q112765
median14349
Q316083.75
95-th percentile17692.6
Maximum18337
Range7463
Interquartile range (IQR)3318.75

Descriptive statistics

Standard deviation2256.2221
Coefficient of variation (CV)0.15729891
Kurtosis-1.0589308
Mean14343.533
Median Absolute Deviation (MAD)1658.5
Skewness0.099399565
Sum430306
Variance5090538.3
MonotonicityNot monotonic
2023-12-10T23:08:39.784644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
14548 2
 
6.7%
10874 2
 
6.7%
17779 1
 
3.3%
14546 1
 
3.3%
16884 1
 
3.3%
15535 1
 
3.3%
16944 1
 
3.3%
11457 1
 
3.3%
14424 1
 
3.3%
11318 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
10874 2
6.7%
11318 1
3.3%
11327 1
3.3%
11456 1
3.3%
11457 1
3.3%
12736 1
3.3%
12758 1
3.3%
12786 1
3.3%
12797 1
3.3%
12938 1
3.3%
ValueCountFrequency (%)
18337 1
3.3%
17779 1
3.3%
17587 1
3.3%
17568 1
3.3%
16978 1
3.3%
16944 1
3.3%
16884 1
3.3%
16094 1
3.3%
16053 1
3.3%
15535 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
26 
NONE
4 

Length

Max length4
Median length3
Mean length3.1333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 26
86.7%
NONE 4
 
13.3%

Length

2023-12-10T23:08:39.993123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:08:40.152522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 26
86.7%
none 4
 
13.3%

시군구명
Text

MISSING 

Distinct16
Distinct (%)61.5%
Missing4
Missing (%)13.3%
Memory size372.0 B
2023-12-10T23:08:40.369539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.6923077
Min length3

Characters and Unicode

Total characters96
Distinct characters32
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 (%)38.5%

Sample

1st row평택시
2nd row부천시
3rd row광명시
4th row의왕시
5th row하남시
ValueCountFrequency (%)
부천시 5
16.7%
광명시 3
 
10.0%
용인시 3
 
10.0%
의왕시 2
 
6.7%
동두천시 2
 
6.7%
안성시 2
 
6.7%
파주시 2
 
6.7%
화성시 1
 
3.3%
평택시 1
 
3.3%
상록구 1
 
3.3%
Other values (8) 8
26.7%
2023-12-10T23:08:40.804884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 26
27.1%
천 8
 
8.3%
부 5
 
5.2%
구 4
 
4.2%
4
 
4.2%
인 4
 
4.2%
주 4
 
4.2%
광 4
 
4.2%
명 3
 
3.1%
안 3
 
3.1%
Other values (22) 31
32.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92
95.8%
Space Separator 4
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 26
28.3%
천 8
 
8.7%
부 5
 
5.4%
구 4
 
4.3%
인 4
 
4.3%
주 4
 
4.3%
광 4
 
4.3%
명 3
 
3.3%
안 3
 
3.3%
성 3
 
3.3%
Other values (21) 28
30.4%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 92
95.8%
Common 4
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 26
28.3%
천 8
 
8.7%
부 5
 
5.4%
구 4
 
4.3%
인 4
 
4.3%
주 4
 
4.3%
광 4
 
4.3%
명 3
 
3.3%
안 3
 
3.3%
성 3
 
3.3%
Other values (21) 28
30.4%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 92
95.8%
ASCII 4
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 26
28.3%
천 8
 
8.7%
부 5
 
5.4%
구 4
 
4.3%
인 4
 
4.3%
주 4
 
4.3%
광 4
 
4.3%
명 3
 
3.3%
안 3
 
3.3%
성 3
 
3.3%
Other values (21) 28
30.4%
ASCII
ValueCountFrequency (%)
4
100.0%

읍면동명
Text

MISSING 

Distinct20
Distinct (%)76.9%
Missing4
Missing (%)13.3%
Memory size372.0 B
2023-12-10T23:08:41.072997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8461538
Min length2

Characters and Unicode

Total characters74
Distinct characters33
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

Unique16 ?
Unique (%)61.5%

Sample

1st row서정동
2nd row중동
3rd row광명동
4th row오전동
5th row풍산동
ValueCountFrequency (%)
중동 4
 
15.4%
생연동 2
 
7.7%
동패동 2
 
7.7%
광명동 2
 
7.7%
삼동 1
 
3.8%
서정동 1
 
3.8%
연지동 1
 
3.8%
본오동 1
 
3.8%
상현동 1
 
3.8%
원종동 1
 
3.8%
Other values (10) 10
38.5%
2023-12-10T23:08:41.551808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 27
36.5%
중 4
 
5.4%
연 3
 
4.1%
현 3
 
4.1%
안 2
 
2.7%
광 2
 
2.7%
명 2
 
2.7%
생 2
 
2.7%
패 2
 
2.7%
오 2
 
2.7%
Other values (23) 25
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 74
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
36.5%
중 4
 
5.4%
연 3
 
4.1%
현 3
 
4.1%
안 2
 
2.7%
광 2
 
2.7%
명 2
 
2.7%
생 2
 
2.7%
패 2
 
2.7%
오 2
 
2.7%
Other values (23) 25
33.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 74
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
36.5%
중 4
 
5.4%
연 3
 
4.1%
현 3
 
4.1%
안 2
 
2.7%
광 2
 
2.7%
명 2
 
2.7%
생 2
 
2.7%
패 2
 
2.7%
오 2
 
2.7%
Other values (23) 25
33.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 74
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 27
36.5%
중 4
 
5.4%
연 3
 
4.1%
현 3
 
4.1%
안 2
 
2.7%
광 2
 
2.7%
명 2
 
2.7%
생 2
 
2.7%
패 2
 
2.7%
오 2
 
2.7%
Other values (23) 25
33.8%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.4546
Minimum0
Maximum37.908
Zeros4
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:08:41.758797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q137.23425
median37.4285
Q337.50275
95-th percentile37.87065
Maximum37.908
Range37.908
Interquartile range (IQR)0.2685

Descriptive statistics

Standard deviation12.949273
Coefficient of variation (CV)0.39899654
Kurtosis3.3823422
Mean32.4546
Median Absolute Deviation (MAD)0.129
Skewness-2.2711452
Sum973.638
Variance167.68367
MonotonicityNot monotonic
2023-12-10T23:08:41.999946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0 4
 
13.3%
37.059 1
 
3.3%
37.503 1
 
3.3%
37.33 1
 
3.3%
37.3 1
 
3.3%
37.299 1
 
3.3%
37.523 1
 
3.3%
37.899 1
 
3.3%
37.908 1
 
3.3%
37.271 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
0.0 4
13.3%
37.011 1
 
3.3%
37.023 1
 
3.3%
37.059 1
 
3.3%
37.222 1
 
3.3%
37.271 1
 
3.3%
37.299 1
 
3.3%
37.3 1
 
3.3%
37.318 1
 
3.3%
37.33 1
 
3.3%
ValueCountFrequency (%)
37.908 1
3.3%
37.899 1
3.3%
37.836 1
3.3%
37.725 1
3.3%
37.724 1
3.3%
37.547 1
3.3%
37.523 1
3.3%
37.503 1
3.3%
37.502 1
3.3%
37.501 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.0409
Minimum0
Maximum127.27
Zeros4
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:08:42.207243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1126.7655
median126.9115
Q3127.06475
95-th percentile127.25855
Maximum127.27
Range127.27
Interquartile range (IQR)0.29925

Descriptive statistics

Standard deviation43.899754
Coefficient of variation (CV)0.39894034
Kurtosis3.3858183
Mean110.0409
Median Absolute Deviation (MAD)0.1495
Skewness-2.2724552
Sum3301.227
Variance1927.1884
MonotonicityNot monotonic
2023-12-10T23:08:42.399169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0 4
 
13.3%
127.058 1
 
3.3%
126.762 1
 
3.3%
127.153 1
 
3.3%
126.871 1
 
3.3%
127.066 1
 
3.3%
126.805 1
 
3.3%
127.061 1
 
3.3%
127.051 1
 
3.3%
127.128 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
0.0 4
13.3%
126.718 1
 
3.3%
126.72 1
 
3.3%
126.762 1
 
3.3%
126.764 1
 
3.3%
126.77 1
 
3.3%
126.773 1
 
3.3%
126.805 1
 
3.3%
126.852 1
 
3.3%
126.857 1
 
3.3%
ValueCountFrequency (%)
127.27 1
3.3%
127.259 1
3.3%
127.258 1
3.3%
127.193 1
3.3%
127.153 1
3.3%
127.128 1
3.3%
127.068 1
3.3%
127.066 1
3.3%
127.061 1
3.3%
127.058 1
3.3%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000006 × 1011
Minimum1.4000002 × 1011
Maximum1.4000012 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:08:42.568220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000003 × 1011
median1.4000005 × 1011
Q31.4000009 × 1011
95-th percentile1.4000012 × 1011
Maximum1.4000012 × 1011
Range104000
Interquartile range (IQR)58500

Descriptive statistics

Standard deviation37835.944
Coefficient of variation (CV)2.7025662 × 10-7
Kurtosis-1.2994267
Mean1.4000006 × 1011
Median Absolute Deviation (MAD)24000
Skewness0.57984158
Sum4.2000018 × 1012
Variance1.4315586 × 109
MonotonicityNot monotonic
2023-12-10T23:08:42.752827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
140000030000 4
13.3%
140000024000 3
 
10.0%
140000020000 3
 
10.0%
140000046000 3
 
10.0%
140000120000 2
 
6.7%
140000026000 2
 
6.7%
140000110000 2
 
6.7%
140000058000 1
 
3.3%
140000124000 1
 
3.3%
140000036000 1
 
3.3%
Other values (8) 8
26.7%
ValueCountFrequency (%)
140000020000 3
10.0%
140000024000 3
10.0%
140000026000 2
6.7%
140000030000 4
13.3%
140000032000 1
 
3.3%
140000036000 1
 
3.3%
140000046000 3
10.0%
140000058000 1
 
3.3%
140000074000 1
 
3.3%
140000078000 1
 
3.3%
ValueCountFrequency (%)
140000124000 1
3.3%
140000120000 2
6.7%
140000118000 1
3.3%
140000116000 1
3.3%
140000110000 2
6.7%
140000086000 1
3.3%
140000084000 1
3.3%
140000080000 1
3.3%
140000078000 1
3.3%
140000074000 1
3.3%

결제상품명
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:08:43.018418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length7.2666667
Min length4

Characters and Unicode

Total characters218
Distinct characters51
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)36.7%

Sample

1st row평택사랑카드(정책)
2nd row부천페이(정책)
3rd row광주사랑카드
4th row광명사랑화폐
5th row의왕사랑상품권
ValueCountFrequency (%)
부천페이 4
 
11.8%
용인와이페이 3
 
8.8%
광주사랑카드 3
 
8.8%
광명사랑화폐 3
 
8.8%
의왕사랑상품권 2
 
5.9%
동두천사랑카드 2
 
5.9%
파주 2
 
5.9%
pay(파주페이 2
 
5.9%
행복화성지역화폐 1
 
2.9%
안산사랑상품권 1
 
2.9%
Other values (11) 11
32.4%
2023-12-10T23:08:43.471790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 17
 
7.8%
랑 17
 
7.8%
이 13
 
6.0%
드 11
 
5.0%
카 11
 
5.0%
주 10
 
4.6%
페 10
 
4.6%
천 9
 
4.1%
( 8
 
3.7%
) 8
 
3.7%
Other values (41) 104
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 192
88.1%
Open Punctuation 8
 
3.7%
Close Punctuation 8
 
3.7%
Space Separator 4
 
1.8%
Lowercase Letter 4
 
1.8%
Uppercase Letter 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 17
 
8.9%
랑 17
 
8.9%
이 13
 
6.8%
드 11
 
5.7%
카 11
 
5.7%
주 10
 
5.2%
페 10
 
5.2%
천 9
 
4.7%
광 7
 
3.6%
책 6
 
3.1%
Other values (35) 81
42.2%
Lowercase Letter
ValueCountFrequency (%)
y 2
50.0%
a 2
50.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 192
88.1%
Common 20
 
9.2%
Latin 6
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 17
 
8.9%
랑 17
 
8.9%
이 13
 
6.8%
드 11
 
5.7%
카 11
 
5.7%
주 10
 
5.2%
페 10
 
5.2%
천 9
 
4.7%
광 7
 
3.6%
책 6
 
3.1%
Other values (35) 81
42.2%
Common
ValueCountFrequency (%)
( 8
40.0%
) 8
40.0%
4
20.0%
Latin
ValueCountFrequency (%)
P 2
33.3%
y 2
33.3%
a 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 192
88.1%
ASCII 26
 
11.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 17
 
8.9%
랑 17
 
8.9%
이 13
 
6.8%
드 11
 
5.7%
카 11
 
5.7%
주 10
 
5.2%
페 10
 
5.2%
천 9
 
4.7%
광 7
 
3.6%
책 6
 
3.1%
Other values (35) 81
42.2%
ASCII
ValueCountFrequency (%)
( 8
30.8%
) 8
30.8%
4
15.4%
P 2
 
7.7%
y 2
 
7.7%
a 2
 
7.7%

결제금액
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44528
Minimum1320
Maximum470000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:08:44.129271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1320
5-th percentile1736
Q19125
median21500
Q346200
95-th percentile128942.5
Maximum470000
Range468680
Interquartile range (IQR)37075

Descriptive statistics

Standard deviation86509.725
Coefficient of variation (CV)1.9428163
Kurtosis21.611156
Mean44528
Median Absolute Deviation (MAD)14800
Skewness4.435494
Sum1335840
Variance7.4839326 × 109
MonotonicityNot monotonic
2023-12-10T23:08:44.381080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
42000 1
 
3.3%
1320 1
 
3.3%
47900 1
 
3.3%
47740 1
 
3.3%
15830 1
 
3.3%
470000 1
 
3.3%
47600 1
 
3.3%
26000 1
 
3.3%
15900 1
 
3.3%
68000 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1320 1
3.3%
1520 1
3.3%
2000 1
3.3%
4000 1
3.3%
6400 1
3.3%
7000 1
3.3%
7100 1
3.3%
9000 1
3.3%
9500 1
3.3%
11700 1
3.3%
ValueCountFrequency (%)
470000 1
3.3%
152050 1
3.3%
100700 1
3.3%
68000 1
3.3%
50000 1
3.3%
47900 1
3.3%
47740 1
3.3%
47600 1
3.3%
42000 1
3.3%
38500 1
3.3%

Interactions

2023-12-10T23:08:33.870250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:26.987738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:28.214915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:29.322238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:30.397804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:31.755054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:32.843464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:34.045147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:27.180122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:28.384135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:29.494179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:30.543493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:31.951957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:33.027331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:34.184470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:27.326098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:28.509958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:29.680586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:30.667120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:32.144082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:33.170268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:34.325059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:27.552058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:28.735722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:29.821099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:30.805227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:32.300571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:33.315344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:34.498802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:27.725437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:28.886389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:29.959674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:30.944042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:32.448630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:33.446737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:34.754382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:27.887949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:29.026996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:30.130893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:31.099664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:32.579542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:33.592265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:34.929357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:28.058263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:29.187806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:30.277766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:31.262644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:32.707459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:33.741757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:08:44.573798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카드번호회원코드가맹점번호정책명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품ID결제상품명결제금액
카드번호1.0001.0001.0001.0000.9671.0001.0001.0001.0001.0001.0001.0001.0001.000
회원코드1.0001.0000.6440.6820.0000.0000.2140.5640.8580.2140.2140.8830.8130.213
가맹점번호1.0000.6441.0000.0000.0000.0000.3930.7220.8630.3930.3930.0000.0000.000
정책명1.0000.6820.0001.0000.6290.9630.7511.0001.0000.7510.7510.9221.0000.614
가맹점업종명0.9670.0000.0000.6291.0000.0000.3240.7350.8320.3240.3240.7050.8830.649
가맹점우편번호1.0000.0000.0000.9630.0001.0000.6470.9570.9180.6470.6470.8641.0000.000
시도명1.0000.2140.3930.7510.3240.6471.000NaNNaN0.9730.9730.0000.8870.381
시군구명1.0000.5640.7221.0000.7350.957NaN1.0001.000NaNNaN0.9430.9940.000
읍면동명1.0000.8580.8631.0000.8320.918NaN1.0001.000NaNNaN0.9760.9860.815
위도1.0000.2140.3930.7510.3240.6470.973NaNNaN1.0000.9730.0000.8870.381
경도1.0000.2140.3930.7510.3240.6470.973NaNNaN0.9731.0000.0000.8870.381
결제상품ID1.0000.8830.0000.9220.7050.8640.0000.9430.9760.0000.0001.0001.0000.000
결제상품명1.0000.8130.0001.0000.8831.0000.8870.9940.9860.8870.8871.0001.0000.823
결제금액1.0000.2130.0000.6140.6490.0000.3810.0000.8150.3810.3810.0000.8231.000
2023-12-10T23:08:44.893191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정책명시도명가맹점업종명
정책명1.0000.4450.257
시도명0.4451.0000.197
가맹점업종명0.2570.1971.000
2023-12-10T23:08:45.096427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호가맹점우편번호위도경도결제상품ID결제금액정책명가맹점업종명시도명
회원코드1.000-0.0600.158-0.057-0.095-0.107-0.1800.1810.0660.195
가맹점번호-0.0601.000-0.128-0.281-0.071-0.3030.0270.0000.0000.386
가맹점우편번호0.158-0.1281.000-0.4600.407-0.1100.2390.7450.0000.422
위도-0.057-0.281-0.4601.0000.0560.242-0.3580.4450.1970.850
경도-0.095-0.0710.4070.0561.0000.1470.0450.4450.1970.850
결제상품ID-0.107-0.303-0.1100.2420.1471.0000.0290.6520.3160.000
결제금액-0.1800.0270.239-0.3580.0450.0291.0000.3040.4440.440
정책명0.1810.0000.7450.4450.4450.6520.3041.0000.2570.445
가맹점업종명0.0660.0000.0000.1970.1970.3160.4440.2571.0000.197
시도명0.1950.3860.4220.8500.8500.0000.4400.4450.1971.000

Missing values

2023-12-10T23:08:35.145238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:08:35.536558image/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.
2023-12-10T23:08:35.912364image/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

지원금결제일자카드번호회원코드가맹점번호정책명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품ID결제상품명결제금액
02020-05-18+JvfJrdtXmahmCDR5Fmj9XWUos5+74M1+u4Yyy8rbfI=3002537417706052701평택시 긴급재난지원금(행안부)일반휴게음식17779경기도평택시서정동37.059127.058140000058000평택사랑카드(정책)42000
12020-05-18+9yHNrTfBbWH17eSH6k7crQLcoIyrLrMtr7SmWCKgjo=3017119748702709318부천시 긴급재난지원금(행안부)일반휴게음식14608경기도부천시중동37.492126.764140000080000부천페이(정책)16580
22020-05-18+MtV3VhxZSCFD+DSzQ+/iQYcdyvg/pJvCser+nezKC4=3018262484732710544광주시 긴급재난지원금(행안부)유통업 영리12736NONE<NA><NA>0.00.0140000024000광주사랑카드11700
32020-05-18+a2amJc1WMpM3bSU+CcMKxjPiqagZFcVTB/vw8BNB2I=3017364561707818811광명시 긴급재난지원금(행안부)유통업 영리14221경기도광명시광명동37.48126.857140000020000광명사랑화폐1520
42020-05-18+OnclLq0kIa3GWtQAgD7veykFJvsi9tERJnrLGhMjgw=3020277533796938627의왕시 긴급재난지원금(행안부)음료식품16053경기도의왕시오전동37.358126.967140000026000의왕사랑상품권23000
52020-05-18+eGk3Ep3/E2hlwYsG4Dz+zzwlr2peWo1iNfeBZAGPyc=3013861223706975016하남시 긴급재난지원금(행안부)연료판매점12938경기도하남시풍산동37.547127.193140000118000하남하머니152050
62020-05-18+njT9u8BmQxNuN68PED4NxClxSj6xMkvuG+dbX4PHFc=3016122376786112421광주시 긴급재난지원금(행안부)일반휴게음식12758경기도광주시경안동37.409127.259140000024000광주사랑카드7100
72020-05-18+od7JtiJyjCjnqjYGgyM6SvA+N33xAXN7Yh289+30Fc=3010979147709015258광명시 긴급재난지원금(행안부)음료식품14274경기도광명시광명동37.473126.852140000020000광명사랑화폐100700
82020-05-18+qCrzOm82v0Qllj3cdfdvzdSXykNyByUtjn/+/GoTck=3002387660753491711양주시 긴급재난지원금(행안부)서적문구11456경기도양주시덕정동37.836127.068140000074000양주사랑카드(정책)2000
92020-05-18+uh8PZJ5zG+9XrPw6CWe3Uk+/8w8hpRGRwlWfjoZHkg=3017285407796881676부천시 긴급재난지원금(행안부)일반휴게음식14548경기도부천시중동37.502126.77140000030000부천페이38500
지원금결제일자카드번호회원코드가맹점번호정책명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품ID결제상품명결제금액
202020-05-1801Y21ELUTyTxq4nyAJkUHby9n89XDUvqCv7IzwF7r0s=3019755637701334213과천시 긴급재난지원금(행안부)유통업 영리13821경기도과천시과천동37.448127.009140000086000과천화폐 과천토리(정책)9000
212020-05-1808LBGN8CNfus5m0pXZbon+Qorm/jwnYlVYLGNIvd1Dk=3017730500720484325용인시 긴급재난지원금(행안부)일반휴게음식16978경기도용인시 기흥구구갈동37.271127.128140000046000용인와이페이20000
222020-05-180A/eCUpVI1bp4UYRkHCcRtoD648wc92LEBonwqLTKUI=3019595690739001417광주시 긴급재난지원금(행안부)일반휴게음식12786NONE<NA><NA>0.00.0140000024000광주사랑카드68000
232020-05-180DF2xcV4C4O3YoAFIcUABux5CGi1Q/b4wQYK3TUoIMo=3016620447715713420동두천시 긴급재난지원금(행안부)일반휴게음식11327경기도동두천시생연동37.908127.051140000110000동두천사랑카드15900
242020-05-180DsCEUa2YMbh49iugChiymDPGr+QB52Ia0NXm9hhuuA=3018564956708912894동두천시 긴급재난지원금(행안부)레저업소11318경기도동두천시생연동37.899127.061140000110000동두천사랑카드26000
252020-05-180Wt2kU4RCAZfqi3OPle7LtqzstXAXq1RU9OGaThMbsc=3013486101703252826부천시 긴급재난지원금(행안부)일반휴게음식14424경기도부천시원종동37.523126.805140000030000부천페이47600
262020-05-180F8qt5WOdMVCt4HAXwD8uwKNn1jI1SCZlYJ5cimud/A=3012228105787485234양주시 긴급재난지원금(행안부)학원11457NONE<NA><NA>0.00.0140000036000양주사랑카드470000
272020-05-180eSrFRsTr3kDA+YgPe2CrvdjYFRmrVgpcig6dyi7xbc=3018231079720153034용인시 긴급재난지원금(행안부)연료판매점16944경기도용인시 수지구상현동37.299127.066140000046000용인와이페이15830
282020-05-180reAio+9lnGnJnUqJEWT+LBYAgtgrjcp/x5BQZk6pAE=3017517591711790123안산시 긴급재난지원금(행안부)유통업 영리15535경기도안산시 상록구본오동37.3126.871140000124000안산사랑상품권 다온47740
292020-05-1815NOeeBfOGB6XsY5KPdgzm7KUi2BFJm/MnNwl6Ub81c=3016710130703739841용인시 긴급재난지원금(행안부)유통업 영리16884경기도용인시 처인구모현읍37.33127.153140000046000용인와이페이47900