Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells54
Missing cells (%)10.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory156.4 B

Variable types

DateTime2
Text4
Numeric7
Categorical4
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/9aff7826-add7-4cd8-9eea-4084516f5a8e

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
성별코드 is highly overall correlated with 시도명High correlation
사용여부 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
시도명 is highly overall correlated with 회원코드 and 10 other fieldsHigh correlation
가맹점업종명 is highly overall correlated with 회원코드 and 5 other fieldsHigh correlation
연령대코드 is highly overall correlated with 가맹점우편번호 and 1 other fieldsHigh correlation
회원코드 is highly overall correlated with 가맹점우편번호 and 2 other fieldsHigh correlation
가맹점번호 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
결제상품ID is highly overall correlated with 시도명High correlation
가맹점우편번호 is highly overall correlated with 회원코드 and 4 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점번호 and 6 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
결제금액 is highly overall correlated with 가맹점번호 and 3 other fieldsHigh correlation
가맹점우편번호 has 18 (60.0%) missing valuesMissing
시군구명 has 18 (60.0%) missing valuesMissing
읍면동명 has 18 (60.0%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
위도 has 18 (60.0%) zerosZeros
경도 has 18 (60.0%) zerosZeros
결제금액 has 18 (60.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:23:16.490375
Analysis finished2023-12-10 14:23:21.920653
Duration5.43 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
Minimum2021-01-04 00:00:00
Maximum2021-01-04 00:00:00
2023-12-10T23:23:21.959441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:22.047300image/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
Minimum2021-01-10 00:00:00
Maximum2021-01-10 00:00:00
2023-12-10T23:23:22.126791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:22.209076image/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
2023-12-10T23:23:22.417253image/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 rowwDs+wG3zXtyQXSBflxWiILoBpu9Hzmn4vDyqYtgeae4=
2nd rowkB3UauHCr0arA1KOp1ogrZZYCkB3jv0CTBnN25BT9cQ=
3rd rowSuR2QaNY6AVStTA7drn5MglPvG1nhZ9AY8j2pC14FpA=
4th rowwE+1gS79UIS7OKDT5cCAuJ/rBorTnJpgKTSfVfQaHKI=
5th rowt0p3PWL6oTQtGWJe5HZYyzRib/nErxO4NfEaOAvHNHE=
ValueCountFrequency (%)
wds+wg3zxtyqxsbflxwiilobpu9hzmn4vdyqytgeae4 1
 
3.3%
kb3uauhcr0ara1kop1ogrzzyckb3jv0ctbnn25bt9cq 1
 
3.3%
fxeu55o/u+fzczhxsyiyalulazwi/xnuacqkov8caua 1
 
3.3%
7yaruc1vqmrk3indbtg2qacprnjw+h6sqrv3+glgqlo 1
 
3.3%
e3kapyodnbnnvqis+pvf6fatz5qapmlapcfzdki/jdg 1
 
3.3%
1pjwaowbbb4i4tkko9flgu6dymoiovqzfadvr0bbh5g 1
 
3.3%
qehyxq/ca2hbdvtv3sgjwg1khjxrnnijd5qkmhxsojm 1
 
3.3%
4lpf09gbodouy/kh1qk/4qh2gg2bqcrmadgbb00a2i8 1
 
3.3%
t1ww4omqczgpubctelyjauka1dvljfnn52u45tddzvw 1
 
3.3%
6jlbf3bo+fbls7bde4hi0i9u/lf81pnl5ceblw6ksxu 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:23:22.752642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 32
 
2.4%
= 30
 
2.3%
q 30
 
2.3%
a 30
 
2.3%
j 29
 
2.2%
H 27
 
2.0%
k 26
 
2.0%
c 26
 
2.0%
C 26
 
2.0%
S 25
 
1.9%
Other values (55) 1039
78.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 537
40.7%
Lowercase Letter 525
39.8%
Decimal Number 189
 
14.3%
Math Symbol 47
 
3.6%
Other Punctuation 22
 
1.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 32
 
6.0%
H 27
 
5.0%
C 26
 
4.8%
S 25
 
4.7%
V 24
 
4.5%
J 24
 
4.5%
W 23
 
4.3%
A 22
 
4.1%
D 22
 
4.1%
T 22
 
4.1%
Other values (16) 290
54.0%
Lowercase Letter
ValueCountFrequency (%)
q 30
 
5.7%
a 30
 
5.7%
j 29
 
5.5%
k 26
 
5.0%
c 26
 
5.0%
t 25
 
4.8%
v 23
 
4.4%
g 21
 
4.0%
u 21
 
4.0%
o 21
 
4.0%
Other values (16) 273
52.0%
Decimal Number
ValueCountFrequency (%)
0 25
13.2%
1 23
12.2%
5 22
11.6%
4 22
11.6%
7 18
9.5%
6 17
9.0%
9 16
8.5%
3 16
8.5%
8 15
7.9%
2 15
7.9%
Math Symbol
ValueCountFrequency (%)
= 30
63.8%
+ 17
36.2%
Other Punctuation
ValueCountFrequency (%)
/ 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1062
80.5%
Common 258
 
19.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 32
 
3.0%
q 30
 
2.8%
a 30
 
2.8%
j 29
 
2.7%
H 27
 
2.5%
k 26
 
2.4%
c 26
 
2.4%
C 26
 
2.4%
S 25
 
2.4%
t 25
 
2.4%
Other values (42) 786
74.0%
Common
ValueCountFrequency (%)
= 30
11.6%
0 25
9.7%
1 23
8.9%
5 22
8.5%
/ 22
8.5%
4 22
8.5%
7 18
 
7.0%
+ 17
 
6.6%
6 17
 
6.6%
9 16
 
6.2%
Other values (3) 46
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 32
 
2.4%
= 30
 
2.3%
q 30
 
2.3%
a 30
 
2.3%
j 29
 
2.2%
H 27
 
2.0%
k 26
 
2.0%
c 26
 
2.0%
C 26
 
2.0%
S 25
 
1.9%
Other values (55) 1039
78.7%

회원코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0164611 × 109
Minimum3.00217 × 109
Maximum3.0312222 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:23:22.892792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.00217 × 109
5-th percentile3.0023481 × 109
Q13.0123002 × 109
median3.0176078 × 109
Q33.0205457 × 109
95-th percentile3.0250828 × 109
Maximum3.0312222 × 109
Range29052160
Interquartile range (IQR)8245564.2

Descriptive statistics

Standard deviation7386553.9
Coefficient of variation (CV)0.0024487482
Kurtosis-0.033381
Mean3.0164611 × 109
Median Absolute Deviation (MAD)3476344.5
Skewness-0.58833811
Sum9.0493834 × 1010
Variance5.4561179 × 1013
MonotonicityNot monotonic
2023-12-10T23:23:23.040501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3021480120 1
 
3.3%
3022418600 1
 
3.3%
3017315847 1
 
3.3%
3016877594 1
 
3.3%
3003263427 1
 
3.3%
3010343126 1
 
3.3%
3018079603 1
 
3.3%
3018889768 1
 
3.3%
3020688238 1
 
3.3%
3017665790 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3002170027 1
3.3%
3002228808 1
3.3%
3002493802 1
3.3%
3003263427 1
3.3%
3006608170 1
3.3%
3010343126 1
3.3%
3010700126 1
3.3%
3011142193 1
3.3%
3015774123 1
3.3%
3016795480 1
3.3%
ValueCountFrequency (%)
3031222187 1
3.3%
3025494133 1
3.3%
3024580145 1
3.3%
3024302148 1
3.3%
3023938334 1
3.3%
3022418600 1
3.3%
3021480120 1
3.3%
3020688238 1
3.3%
3020118245 1
3.3%
3019704249 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0000029 × 1014
Minimum7.0369593 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:23:23.152532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0369593 × 108
5-th percentile7.0634505 × 108
Q17.3564442 × 108
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.999993 × 1014
Interquartile range (IQR)9.9999926 × 1014

Descriptive statistics

Standard deviation4.9827251 × 1014
Coefficient of variation (CV)0.83045378
Kurtosis-1.9499559
Mean6.0000029 × 1014
Median Absolute Deviation (MAD)0
Skewness-0.43005695
Sum1.8000009 × 1016
Variance2.482755 × 1029
MonotonicityNot monotonic
2023-12-10T23:23:23.259576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
999999999999999 18
60.0%
798065217 1
 
3.3%
721870058 1
 
3.3%
708061299 1
 
3.3%
703695932 1
 
3.3%
714127424 1
 
3.3%
722815587 1
 
3.3%
774130901 1
 
3.3%
714320496 1
 
3.3%
711295807 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
703695932 1
3.3%
704940842 1
3.3%
708061299 1
3.3%
711295807 1
3.3%
714127424 1
3.3%
714320496 1
3.3%
721870058 1
3.3%
722815587 1
3.3%
774130901 1
3.3%
779366685 1
3.3%
ValueCountFrequency (%)
999999999999999 18
60.0%
798065217 1
 
3.3%
788317242 1
 
3.3%
779366685 1
 
3.3%
774130901 1
 
3.3%
722815587 1
 
3.3%
721870058 1
 
3.3%
714320496 1
 
3.3%
714127424 1
 
3.3%
711295807 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T23:23:23.377366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:23:23.470991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 18
60.0%
f 12
40.0%

연령대코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
20
10 
40
8 
50
6 
30
4 
60
2 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50
2nd row30
3rd row30
4th row40
5th row20

Common Values

ValueCountFrequency (%)
20 10
33.3%
40 8
26.7%
50 6
20.0%
30 4
 
13.3%
60 2
 
6.7%

Length

2023-12-10T23:23:23.573975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:23:23.700636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 10
33.3%
40 8
26.7%
50 6
20.0%
30 4
 
13.3%
60 2
 
6.7%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000008 × 1011
Minimum1.4000002 × 1011
Maximum1.4000013 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:23:23.829798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000003 × 1011
median1.4000009 × 1011
Q31.4000012 × 1011
95-th percentile1.4000012 × 1011
Maximum1.4000013 × 1011
Range108000
Interquartile range (IQR)85000

Descriptive statistics

Standard deviation42015.925
Coefficient of variation (CV)3.0011358 × 10-7
Kurtosis-1.7821762
Mean1.4000008 × 1011
Median Absolute Deviation (MAD)33000
Skewness-0.20955057
Sum4.2000023 × 1012
Variance1.7653379 × 109
MonotonicityNot monotonic
2023-12-10T23:23:23.964046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
140000124000 5
16.7%
140000116000 3
 
10.0%
140000034000 2
 
6.7%
140000030000 2
 
6.7%
140000024000 2
 
6.7%
140000100000 2
 
6.7%
140000048000 1
 
3.3%
140000020000 1
 
3.3%
140000122000 1
 
3.3%
140000092000 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
140000018000 1
3.3%
140000020000 1
3.3%
140000024000 2
6.7%
140000028000 1
3.3%
140000030000 2
6.7%
140000034000 2
6.7%
140000036000 1
3.3%
140000044000 1
3.3%
140000048000 1
3.3%
140000070000 1
3.3%
ValueCountFrequency (%)
140000126000 1
 
3.3%
140000124000 5
16.7%
140000122000 1
 
3.3%
140000120000 1
 
3.3%
140000116000 3
10.0%
140000114000 1
 
3.3%
140000100000 2
 
6.7%
140000092000 1
 
3.3%
140000090000 1
 
3.3%
140000074000 1
 
3.3%

결제상품명
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:23:24.134206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length8.4666667
Min length4

Characters and Unicode

Total characters254
Distinct characters55
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)46.7%

Sample

1st row안산사랑상품권 다온
2nd row안산사랑상품권 다온
3rd row안산사랑상품권 다온(통합)
4th row행복화성지역화폐
5th row여주사랑카드(통합)
ValueCountFrequency (%)
안산사랑상품권 7
17.1%
다온 5
 
12.2%
행복화성지역화폐 3
 
7.3%
부천페이 2
 
4.9%
광주사랑카드 2
 
4.9%
안양사랑페이 2
 
4.9%
다온(통합 2
 
4.9%
양주사랑카드 1
 
2.4%
pay-n 1
 
2.4%
you 1
 
2.4%
Other values (15) 15
36.6%
2023-12-10T23:23:24.416470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 18
 
7.1%
랑 18
 
7.1%
11
 
4.3%
화 11
 
4.3%
안 9
 
3.5%
카 9
 
3.5%
드 9
 
3.5%
이 9
 
3.5%
산 8
 
3.1%
페 8
 
3.1%
Other values (45) 144
56.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 213
83.9%
Space Separator 11
 
4.3%
Lowercase Letter 10
 
3.9%
Open Punctuation 7
 
2.8%
Close Punctuation 7
 
2.8%
Uppercase Letter 5
 
2.0%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 18
 
8.5%
랑 18
 
8.5%
화 11
 
5.2%
안 9
 
4.2%
카 9
 
4.2%
드 9
 
4.2%
이 9
 
4.2%
산 8
 
3.8%
페 8
 
3.8%
다 7
 
3.3%
Other values (30) 107
50.2%
Lowercase Letter
ValueCountFrequency (%)
a 3
30.0%
y 2
20.0%
u 1
 
10.0%
o 1
 
10.0%
k 1
 
10.0%
n 1
 
10.0%
h 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
P 2
40.0%
Y 1
20.0%
T 1
20.0%
N 1
20.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 213
83.9%
Common 26
 
10.2%
Latin 15
 
5.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 18
 
8.5%
랑 18
 
8.5%
화 11
 
5.2%
안 9
 
4.2%
카 9
 
4.2%
드 9
 
4.2%
이 9
 
4.2%
산 8
 
3.8%
페 8
 
3.8%
다 7
 
3.3%
Other values (30) 107
50.2%
Latin
ValueCountFrequency (%)
a 3
20.0%
y 2
13.3%
P 2
13.3%
u 1
 
6.7%
o 1
 
6.7%
Y 1
 
6.7%
k 1
 
6.7%
n 1
 
6.7%
h 1
 
6.7%
T 1
 
6.7%
Common
ValueCountFrequency (%)
11
42.3%
( 7
26.9%
) 7
26.9%
- 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 213
83.9%
ASCII 41
 
16.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 18
 
8.5%
랑 18
 
8.5%
화 11
 
5.2%
안 9
 
4.2%
카 9
 
4.2%
드 9
 
4.2%
이 9
 
4.2%
산 8
 
3.8%
페 8
 
3.8%
다 7
 
3.3%
Other values (30) 107
50.2%
ASCII
ValueCountFrequency (%)
11
26.8%
( 7
17.1%
) 7
17.1%
a 3
 
7.3%
y 2
 
4.9%
P 2
 
4.9%
- 1
 
2.4%
u 1
 
2.4%
o 1
 
2.4%
Y 1
 
2.4%
Other values (5) 5
12.2%

가맹점업종명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
18 
음료식품
4 
일반휴게음식
4 
유통업 영리
2 
문화.취미
 
1

Length

Max length6
Median length4
Mean length4.3666667
Min length2

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 18
60.0%
음료식품 4
 
13.3%
일반휴게음식 4
 
13.3%
유통업 영리 2
 
6.7%
문화.취미 1
 
3.3%
의원 1
 
3.3%

Length

2023-12-10T23:23:24.536634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:23:24.632250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 18
56.2%
음료식품 4
 
12.5%
일반휴게음식 4
 
12.5%
유통업 2
 
6.2%
영리 2
 
6.2%
문화.취미 1
 
3.1%
의원 1
 
3.1%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing18
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean15322.417
Minimum10820
Maximum18455
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:23:24.718826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10820
5-th percentile11165.95
Q113560.25
median15593.5
Q317512.25
95-th percentile18402.2
Maximum18455
Range7635
Interquartile range (IQR)3952

Descriptive statistics

Standard deviation2715.1108
Coefficient of variation (CV)0.1771986
Kurtosis-1.0410494
Mean15322.417
Median Absolute Deviation (MAD)2121
Skewness-0.54025678
Sum183869
Variance7371826.6
MonotonicityNot monotonic
2023-12-10T23:23:24.808916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
15632 1
 
3.3%
11932 1
 
3.3%
16687 1
 
3.3%
10820 1
 
3.3%
11449 1
 
3.3%
18455 1
 
3.3%
14103 1
 
3.3%
18119 1
 
3.3%
18359 1
 
3.3%
15448 1
 
3.3%
Other values (2) 2
 
6.7%
(Missing) 18
60.0%
ValueCountFrequency (%)
10820 1
3.3%
11449 1
3.3%
11932 1
3.3%
14103 1
3.3%
15448 1
3.3%
15555 1
3.3%
15632 1
3.3%
16687 1
3.3%
17310 1
3.3%
18119 1
3.3%
ValueCountFrequency (%)
18455 1
3.3%
18359 1
3.3%
18119 1
3.3%
17310 1
3.3%
16687 1
3.3%
15632 1
3.3%
15555 1
3.3%
15448 1
3.3%
14103 1
3.3%
11932 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
18 
경기도
12 

Length

Max length4
Median length4
Mean length3.6
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 18
60.0%
경기도 12
40.0%

Length

2023-12-10T23:23:24.929536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:23:25.015040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 18
60.0%
경기도 12
40.0%

시군구명
Text

MISSING 

Distinct10
Distinct (%)83.3%
Missing18
Missing (%)60.0%
Memory size372.0 B
2023-12-10T23:23:25.133344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4.6666667
Min length3

Characters and Unicode

Total characters56
Distinct characters22
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

Unique8 ?
Unique (%)66.7%

Sample

1st row안산시 상록구
2nd row구리시
3rd row수원시 영통구
4th row파주시
5th row양주시
ValueCountFrequency (%)
안산시 3
17.6%
상록구 2
11.8%
화성시 2
11.8%
구리시 1
 
5.9%
수원시 1
 
5.9%
영통구 1
 
5.9%
파주시 1
 
5.9%
양주시 1
 
5.9%
안양시 1
 
5.9%
동안구 1
 
5.9%
Other values (3) 3
17.6%
2023-12-10T23:23:25.445645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 12
21.4%
구 6
10.7%
안 5
 
8.9%
5
 
8.9%
산 4
 
7.1%
성 2
 
3.6%
양 2
 
3.6%
주 2
 
3.6%
원 2
 
3.6%
화 2
 
3.6%
Other values (12) 14
25.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51
91.1%
Space Separator 5
 
8.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 12
23.5%
구 6
11.8%
안 5
9.8%
산 4
 
7.8%
성 2
 
3.9%
양 2
 
3.9%
주 2
 
3.9%
원 2
 
3.9%
화 2
 
3.9%
록 2
 
3.9%
Other values (11) 12
23.5%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 51
91.1%
Common 5
 
8.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 12
23.5%
구 6
11.8%
안 5
9.8%
산 4
 
7.8%
성 2
 
3.9%
양 2
 
3.9%
주 2
 
3.9%
원 2
 
3.9%
화 2
 
3.9%
록 2
 
3.9%
Other values (11) 12
23.5%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 51
91.1%
ASCII 5
 
8.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 12
23.5%
구 6
11.8%
안 5
9.8%
산 4
 
7.8%
성 2
 
3.9%
양 2
 
3.9%
주 2
 
3.9%
원 2
 
3.9%
화 2
 
3.9%
록 2
 
3.9%
Other values (11) 12
23.5%
ASCII
ValueCountFrequency (%)
5
100.0%

읍면동명
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing18
Missing (%)60.0%
Memory size372.0 B
2023-12-10T23:23:25.694951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9166667
Min length2

Characters and Unicode

Total characters35
Distinct characters23
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

Unique12 ?
Unique (%)100.0%

Sample

1st row사동
2nd row수택동
3rd row망포동
4th row문산읍
5th row덕정동
ValueCountFrequency (%)
사동 1
8.3%
수택동 1
8.3%
망포동 1
8.3%
문산읍 1
8.3%
덕정동 1
8.3%
반송동 1
8.3%
평촌동 1
8.3%
청학동 1
8.3%
송산동 1
8.3%
초지동 1
8.3%
Other values (2) 2
16.7%
2023-12-10T23:23:26.057431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 10
28.6%
사 2
 
5.7%
산 2
 
5.7%
송 2
 
5.7%
평 1
 
2.9%
본 1
 
2.9%
면 1
 
2.9%
백 1
 
2.9%
지 1
 
2.9%
초 1
 
2.9%
Other values (13) 13
37.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 35
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10
28.6%
사 2
 
5.7%
산 2
 
5.7%
송 2
 
5.7%
평 1
 
2.9%
본 1
 
2.9%
면 1
 
2.9%
백 1
 
2.9%
지 1
 
2.9%
초 1
 
2.9%
Other values (13) 13
37.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 35
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 10
28.6%
사 2
 
5.7%
산 2
 
5.7%
송 2
 
5.7%
평 1
 
2.9%
본 1
 
2.9%
면 1
 
2.9%
백 1
 
2.9%
지 1
 
2.9%
초 1
 
2.9%
Other values (13) 13
37.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 35
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 10
28.6%
사 2
 
5.7%
산 2
 
5.7%
송 2
 
5.7%
평 1
 
2.9%
본 1
 
2.9%
면 1
 
2.9%
백 1
 
2.9%
지 1
 
2.9%
초 1
 
2.9%
Other values (13) 13
37.1%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.956233
Minimum0
Maximum37.86
Zeros18
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:23:26.189085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q337.2705
95-th percentile37.7337
Maximum37.86
Range37.86
Interquartile range (IQR)37.2705

Descriptive statistics

Standard deviation18.631319
Coefficient of variation (CV)1.2457227
Kurtosis-1.9494354
Mean14.956233
Median Absolute Deviation (MAD)0
Skewness0.43026747
Sum448.687
Variance347.12604
MonotonicityNot monotonic
2023-12-10T23:23:26.316530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 18
60.0%
37.281 1
 
3.3%
37.594 1
 
3.3%
37.239 1
 
3.3%
37.86 1
 
3.3%
37.848 1
 
3.3%
37.201 1
 
3.3%
37.385 1
 
3.3%
37.152 1
 
3.3%
37.208 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
0.0 18
60.0%
37.152 1
 
3.3%
37.201 1
 
3.3%
37.208 1
 
3.3%
37.239 1
 
3.3%
37.281 1
 
3.3%
37.291 1
 
3.3%
37.306 1
 
3.3%
37.322 1
 
3.3%
37.385 1
 
3.3%
ValueCountFrequency (%)
37.86 1
3.3%
37.848 1
3.3%
37.594 1
3.3%
37.385 1
3.3%
37.322 1
3.3%
37.306 1
3.3%
37.291 1
3.3%
37.281 1
3.3%
37.239 1
3.3%
37.208 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.804867
Minimum0
Maximum127.473
Zeros18
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:23:26.458413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3126.93775
95-th percentile127.11115
Maximum127.473
Range127.473
Interquartile range (IQR)126.93775

Descriptive statistics

Standard deviation63.286822
Coefficient of variation (CV)1.2456842
Kurtosis-1.9499297
Mean50.804867
Median Absolute Deviation (MAD)0
Skewness0.43006759
Sum1524.146
Variance4005.2219
MonotonicityNot monotonic
2023-12-10T23:23:26.654672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 18
60.0%
126.852 1
 
3.3%
127.144 1
 
3.3%
127.053 1
 
3.3%
126.778 1
 
3.3%
127.063 1
 
3.3%
127.071 1
 
3.3%
126.961 1
 
3.3%
127.057 1
 
3.3%
127.012 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
0.0 18
60.0%
126.778 1
 
3.3%
126.814 1
 
3.3%
126.852 1
 
3.3%
126.868 1
 
3.3%
126.961 1
 
3.3%
127.012 1
 
3.3%
127.053 1
 
3.3%
127.057 1
 
3.3%
127.063 1
 
3.3%
ValueCountFrequency (%)
127.473 1
3.3%
127.144 1
3.3%
127.071 1
3.3%
127.063 1
3.3%
127.057 1
3.3%
127.053 1
3.3%
127.012 1
3.3%
126.961 1
3.3%
126.868 1
3.3%
126.852 1
3.3%

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%
2023-12-10T23:23:26.842860image/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%
Mean7258.3333
Minimum0
Maximum80000
Zeros18
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:23:26.972927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37875
95-th percentile28070
Maximum80000
Range80000
Interquartile range (IQR)7875

Descriptive statistics

Standard deviation16002.349
Coefficient of variation (CV)2.2046865
Kurtosis15.217608
Mean7258.3333
Median Absolute Deviation (MAD)0
Skewness3.6354616
Sum217750
Variance2.5607519 × 108
MonotonicityNot monotonic
2023-12-10T23:23:27.137629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 18
60.0%
8000 2
 
6.7%
7500 1
 
3.3%
16750 1
 
3.3%
3500 1
 
3.3%
80000 1
 
3.3%
4500 1
 
3.3%
3900 1
 
3.3%
35000 1
 
3.3%
19600 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
0 18
60.0%
3500 1
 
3.3%
3900 1
 
3.3%
4500 1
 
3.3%
7500 1
 
3.3%
8000 2
 
6.7%
14000 1
 
3.3%
16750 1
 
3.3%
17000 1
 
3.3%
19600 1
 
3.3%
ValueCountFrequency (%)
80000 1
3.3%
35000 1
3.3%
19600 1
3.3%
17000 1
3.3%
16750 1
3.3%
14000 1
3.3%
8000 2
6.7%
7500 1
3.3%
4500 1
3.3%
3900 1
3.3%

Interactions

2023-12-10T23:23:20.645742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:17.100625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:17.586108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:18.124056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:18.800009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:19.461336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:20.089279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:20.730167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:17.168807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:17.656341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:18.224483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:18.888063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:19.550665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:20.166843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:20.816583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:17.240170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:17.726665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:18.367635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:18.986940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:19.630669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:20.243204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:20.905861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:17.310714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:17.801008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:18.447186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:19.065200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:19.729542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:20.326700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:21.009710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:17.381208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:17.879079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:18.525470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:19.149121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:19.821645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:20.408916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:21.094750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:17.447720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:17.959172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:18.607931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:19.248773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:19.906961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:20.489151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:21.173020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:17.511616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:18.033102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:18.687078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:19.344948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:19.986731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:23:20.558309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:23:27.277025image/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
회원코드1.0001.0000.0000.4300.0000.7410.9160.8630.4950.9261.0000.0000.0000.0000.878
가맹점번호1.0000.0001.0000.0000.1530.0000.000NaNNaNNaNNaN0.9930.9930.9930.720
성별코드1.0000.4300.0001.0000.1600.1810.4070.2590.6050.7171.0000.0000.0000.0000.000
연령대코드1.0000.0000.1530.1601.0000.2030.6420.0000.8221.0001.0000.0400.0400.0400.000
결제상품ID1.0000.7410.0000.1810.2031.0001.0000.6700.5250.8821.0000.0000.0000.0000.000
결제상품명1.0000.9160.0000.4070.6421.0001.0000.9101.0001.0001.0000.0000.0000.0000.854
가맹점업종명1.0000.863NaN0.2590.0000.6700.9101.0000.2150.6871.000NaNNaNNaN0.823
가맹점우편번호1.0000.495NaN0.6050.8220.5251.0000.2151.0001.0001.000NaNNaNNaN0.000
시군구명1.0000.926NaN0.7171.0000.8821.0000.6871.0001.0001.000NaNNaNNaN0.751
읍면동명1.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.000NaNNaNNaN1.000
위도1.0000.0000.9930.0000.0400.0000.000NaNNaNNaNNaN1.0000.9940.9940.434
경도1.0000.0000.9930.0000.0400.0000.000NaNNaNNaNNaN0.9941.0000.9940.434
사용여부1.0000.0000.9930.0000.0400.0000.000NaNNaNNaNNaN0.9940.9941.0000.434
결제금액1.0000.8780.7200.0000.0000.0000.8540.8230.0000.7511.0000.4340.4340.4341.000
2023-12-10T23:23:27.484932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별코드사용여부시도명가맹점업종명연령대코드
성별코드1.0000.0001.0000.2000.173
사용여부0.0001.0001.0001.0000.000
시도명1.0001.0001.0001.0001.000
가맹점업종명0.2001.0001.0001.0000.000
연령대코드0.1730.0001.0000.0001.000
2023-12-10T23:23:27.603226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호결제상품ID가맹점우편번호위도경도결제금액성별코드연령대코드가맹점업종명시도명사용여부
회원코드1.000-0.049-0.034-0.7830.1910.030-0.0070.3150.0000.5221.0000.000
가맹점번호-0.0491.000-0.1680.245-0.932-0.897-0.9480.0000.0001.0001.0000.928
결제상품ID-0.034-0.1681.0000.2350.1140.0810.1230.2030.1320.4461.0000.000
가맹점우편번호-0.7830.2450.2351.000-0.9090.350-0.1860.2450.6040.0001.0001.000
위도0.191-0.9320.114-0.9091.0000.9090.9290.0000.0001.0001.0000.928
경도0.030-0.8970.0810.3500.9091.0000.8890.0000.0001.0001.0000.928
결제금액-0.007-0.9480.123-0.1860.9290.8891.0000.0000.0000.4061.0000.496
성별코드0.3150.0000.2030.2450.0000.0000.0001.0000.1730.2001.0000.000
연령대코드0.0000.0000.1320.6040.0000.0000.0000.1731.0000.0001.0000.000
가맹점업종명0.5221.0000.4460.0001.0001.0000.4060.2000.0001.0001.0001.000
시도명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사용여부0.0000.9280.0001.0000.9280.9280.4960.0000.0001.0001.0001.000

Missing values

2023-12-10T23:23:21.303929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:23:21.726894image/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.
2023-12-10T23:23:21.855795image/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
02021-01-042021-01-10wDs+wG3zXtyQXSBflxWiILoBpu9Hzmn4vDyqYtgeae4=3021480120798065217M50140000124000안산사랑상품권 다온음료식품15632경기도안산시 상록구사동37.281126.852Y7500
12021-01-042021-01-10kB3UauHCr0arA1KOp1ogrZZYCkB3jv0CTBnN25BT9cQ=3017549879999999999999999F30140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
22021-01-042021-01-10SuR2QaNY6AVStTA7drn5MglPvG1nhZ9AY8j2pC14FpA=3024302148999999999999999M30140000100000안산사랑상품권 다온(통합)<NA><NA><NA><NA><NA>0.00.0N0
32021-01-042021-01-10wE+1gS79UIS7OKDT5cCAuJ/rBorTnJpgKTSfVfQaHKI=3016912354999999999999999F40140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
42021-01-042021-01-10t0p3PWL6oTQtGWJe5HZYyzRib/nErxO4NfEaOAvHNHE=3011142193999999999999999F20140000070000여주사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
52021-01-042021-01-10kBCFvGvXDyBj9eKjEQqHl+HPDF6dKTLCqmJPmo85Vqk=3025494133721870058M50140000028000구리사랑카드유통업 영리11932경기도구리시수택동37.594127.144Y16750
62021-01-042021-01-10w0YdQ0GcMvjwxLLxgj/ktkM8e67hNS85HI7Y5Gm1yMk=3010700126999999999999999M20140000074000양주사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
72021-01-042021-01-10Sul3/ESFSQ3CNDfhbZQUhPvXpLBJbs3mPZ4j47Hg7Ss=3017417096999999999999999M50140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
82021-01-042021-01-10xcBjVVckf+6NXnmNUfds5cKcnm3jHjI2DtFGpUVo4Yc=3020118245708061299F20140000126000수원페이음료식품16687경기도수원시 영통구망포동37.239127.053Y3500
92021-01-042021-01-10t0vYiRfGixTPksCD+RGJkFxSWqBug2EsjSYRPUQV8Mk=3031222187703695932F20140000120000파주 Pay(파주페이)문화.취미10820경기도파주시문산읍37.86126.778Y80000
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202021-01-042021-01-10xcGzrKBA/vj9XqavvCcbq/JhIO/dFtTcWS1x7Avh0to=3019704249999999999999999M50140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
212021-01-042021-01-106JlBf3BO+FBLS7bde4Hi0I9U/lf81pnl5cEBlW6KSXU=3002170027711295807F40140000092000행복화성지역화폐(통합)의원18359경기도화성시송산동37.208127.012Y19600
222021-01-042021-01-10t1WW4OmqCZGPuBCTeLYJAuKa1dvLJFnn52u45tDDzVw=3017665790999999999999999M20140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
232021-01-042021-01-104LPf09Gbodouy/Kh1qk/4QH2GG2bQCRmaDgbB00a2I8=3020688238999999999999999M50140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0N0
242021-01-042021-01-10qEhyXQ/ca2HBdVtV3sgJWG1KHjXRNniJd5qkmHxSOJM=3018889768999999999999999F30140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
252021-01-042021-01-101pjWaoWbbB4i4tKKo9fLGu6DYmoiOVQZFaDvR0bBH5g=3018079603999999999999999F20140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
262021-01-042021-01-10E3kaPYoDnbnNVqiS+Pvf6FATZ5qaPmlAPCFzdkI/JDg=3010343126704940842M30140000100000안산사랑상품권 다온(통합)일반휴게음식15448경기도안산시 단원구초지동37.306126.814Y17000
272021-01-042021-01-107yaruc1vqmrk3indBTg2qAcpRNjw+H6sqrv3+gLGqLo=3003263427999999999999999M40140000020000광명사랑화폐<NA><NA><NA><NA><NA>0.00.0N0
282021-01-042021-01-10Fxeu55o/u+FZCzHXsYIYaluLaZWI/xNUacQKOV8CaUA=3016877594779366685M60140000048000이천사랑지역화폐일반휴게음식17310경기도이천시백사면37.322127.473Y8000
292021-01-042021-01-10wErCSoX9xJJTI9Fgqc2a0AZpbeFEC+vnr3aG+DIBqOA=3017315847788317242M50140000124000안산사랑상품권 다온일반휴게음식15555경기도안산시 상록구본오동37.291126.868Y14000