Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells90
Missing cells (%)16.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory156.4 B

Variable types

DateTime2
Text5
Numeric8
Categorical2
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/edde92c9-ed15-4594-a3e4-527c5b02d83e

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
사용여부 is highly overall correlated with 가맹점번호 and 6 other fieldsHigh correlation
시도명 is highly overall correlated with 가맹점번호 and 2 other fieldsHigh correlation
가맹점번호 is highly overall correlated with 연령대코드 and 5 other fieldsHigh correlation
연령대코드 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
결제상품ID is highly overall correlated with 시도명High correlation
가맹점우편번호 is highly overall correlated with 연령대코드 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
결제금액 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
가맹점업종명 has 22 (73.3%) missing valuesMissing
가맹점우편번호 has 22 (73.3%) 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 12 (40.0%) zerosZeros
위도 has 23 (76.7%) zerosZeros
경도 has 23 (76.7%) zerosZeros
결제금액 has 20 (66.7%) zerosZeros

Reproduction

Analysis started2024-03-13 11:46:06.289076
Analysis finished2024-03-13 11:46:15.532537
Duration9.24 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
Minimum2023-04-03 00:00:00
Maximum2023-04-03 00:00:00
2024-03-13T20:46:15.576287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:15.677732image/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
Minimum2023-04-09 00:00:00
Maximum2023-04-09 00:00:00
2024-03-13T20:46:15.794427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:15.888033image/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
2024-03-13T20:46:16.181817image/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 rowzhXiW3bjb34ctyWwzIadhGk4YR0uSEfgYCKYQWOcBZY=
2nd row++ErVFJ+KRS9ueqvSV77NlQWJyu/BhpAKSPgHxZzWb8=
3rd rowxZ775iJ/bU9pLncBEulYzygQF79uvOF3Xm7v3Ddq5WQ=
4th rowzhOGwsPVIW9RxNnkp2T8c5DpEbBZuwY/RpDXiNczF1o=
5th rowzhM6+GGB/Eh8+H0l7yMHM0kyQZ4W6yKRpMtJ2d4XZbs=
ValueCountFrequency (%)
zhxiw3bjb34ctywwziadhgk4yr0usefgyckyqwocbzy 1
 
3.3%
ervfj+krs9ueqvsv77nlqwjyu/bhpakspghxzzwb8 1
 
3.3%
zfxq1qyiox/u8btimiwl+8dkmv47k1a+82wndmqa4wc 1
 
3.3%
zfyvtyybgk3lqnak1blisioai4uxuux5lrcv7cr0f88 1
 
3.3%
zfzupyhdhjmibmjx0srvluq/d7bi6wlyapqzcpivpvu 1
 
3.3%
zg33lqvnpr+ms5vqzs2nsy7tuh6dlwwlfc+cvbedeh0 1
 
3.3%
zgc1/p/4tu/zjydddxbaqncz5+se02mutsn7bmhdvci 1
 
3.3%
zgcbsqd9avwg2bjaqjfsrzv8altx2oysayfjpd9g/aq 1
 
3.3%
zgdelionvg/j1niqcdaquihpv+ngcmzqhnespbpul5w 1
 
3.3%
zgh1obpki0wlivdxncq4wu808tt4ghyyudu2vrxnxtw 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:46:16.599015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
z 41
 
3.1%
h 30
 
2.3%
= 30
 
2.3%
i 29
 
2.2%
u 28
 
2.1%
y 28
 
2.1%
g 27
 
2.0%
Q 27
 
2.0%
b 26
 
2.0%
w 26
 
2.0%
Other values (55) 1028
77.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 560
42.4%
Uppercase Letter 512
38.8%
Decimal Number 174
 
13.2%
Math Symbol 51
 
3.9%
Other Punctuation 23
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
z 41
 
7.3%
h 30
 
5.4%
i 29
 
5.2%
u 28
 
5.0%
y 28
 
5.0%
g 27
 
4.8%
b 26
 
4.6%
w 26
 
4.6%
d 25
 
4.5%
p 24
 
4.3%
Other values (16) 276
49.3%
Uppercase Letter
ValueCountFrequency (%)
Q 27
 
5.3%
D 25
 
4.9%
U 25
 
4.9%
I 25
 
4.9%
V 25
 
4.9%
Z 24
 
4.7%
K 24
 
4.7%
A 22
 
4.3%
B 21
 
4.1%
P 21
 
4.1%
Other values (16) 273
53.3%
Decimal Number
ValueCountFrequency (%)
8 22
12.6%
7 20
11.5%
3 20
11.5%
0 18
10.3%
2 18
10.3%
4 18
10.3%
9 17
9.8%
1 15
8.6%
5 14
8.0%
6 12
6.9%
Math Symbol
ValueCountFrequency (%)
= 30
58.8%
+ 21
41.2%
Other Punctuation
ValueCountFrequency (%)
/ 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1072
81.2%
Common 248
 
18.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
z 41
 
3.8%
h 30
 
2.8%
i 29
 
2.7%
u 28
 
2.6%
y 28
 
2.6%
g 27
 
2.5%
Q 27
 
2.5%
b 26
 
2.4%
w 26
 
2.4%
D 25
 
2.3%
Other values (42) 785
73.2%
Common
ValueCountFrequency (%)
= 30
12.1%
/ 23
9.3%
8 22
8.9%
+ 21
8.5%
7 20
8.1%
3 20
8.1%
0 18
7.3%
2 18
7.3%
4 18
7.3%
9 17
6.9%
Other values (3) 41
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
z 41
 
3.1%
h 30
 
2.3%
= 30
 
2.3%
i 29
 
2.2%
u 28
 
2.1%
y 28
 
2.1%
g 27
 
2.0%
Q 27
 
2.0%
b 26
 
2.0%
w 26
 
2.0%
Other values (55) 1028
77.9%

회원코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.026212 × 109
Minimum3.0026612 × 109
Maximum3.0789017 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:16.751179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0026612 × 109
5-th percentile3.0061256 × 109
Q13.0165252 × 109
median3.0195169 × 109
Q33.0340925 × 109
95-th percentile3.0667709 × 109
Maximum3.0789017 × 109
Range76240499
Interquartile range (IQR)17567364

Descriptive statistics

Standard deviation19452546
Coefficient of variation (CV)0.0064280182
Kurtosis1.1783598
Mean3.026212 × 109
Median Absolute Deviation (MAD)9550587.5
Skewness1.3325373
Sum9.078636 × 1010
Variance3.7840154 × 1014
MonotonicityNot monotonic
2024-03-13T20:46:16.907364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3017204091 1
 
3.3%
3070085551 1
 
3.3%
3018632677 1
 
3.3%
3006811157 1
 
3.3%
3029424286 1
 
3.3%
3022448184 1
 
3.3%
3012150124 1
 
3.3%
3039560412 1
 
3.3%
3019958159 1
 
3.3%
3019908538 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3002661237 1
3.3%
3005839559 1
3.3%
3006475206 1
3.3%
3006811157 1
3.3%
3009533120 1
3.3%
3010323111 1
3.3%
3012150124 1
3.3%
3016414211 1
3.3%
3016857968 1
3.3%
3017204091 1
3.3%
ValueCountFrequency (%)
3078901736 1
3.3%
3070085551 1
3.3%
3062719602 1
3.3%
3056766884 1
3.3%
3044651842 1
3.3%
3039560412 1
3.3%
3038792265 1
3.3%
3034653278 1
3.3%
3032410225 1
3.3%
3029424286 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9403063 × 1014
Minimum7.0775441 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:17.045975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0775441 × 108
5-th percentile7.1014089 × 108
Q11.0253428 × 1014
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.9999929 × 1014
Interquartile range (IQR)8.9746572 × 1014

Descriptive statistics

Standard deviation4.5053968 × 1014
Coefficient of variation (CV)0.64916397
Kurtosis-1.232649
Mean6.9403063 × 1014
Median Absolute Deviation (MAD)0
Skewness-0.86740415
Sum2.0820919 × 1016
Variance2.02986 × 1029
MonotonicityNot monotonic
2024-03-13T20:46:17.206183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
999999999999999 20
66.7%
712939339 1
 
3.3%
410134852260501 1
 
3.3%
707754406 1
 
3.3%
707851243 1
 
3.3%
749315922 1
 
3.3%
730501140 1
 
3.3%
724244772 1
 
3.3%
722055975 1
 
3.3%
731752579 1
 
3.3%
ValueCountFrequency (%)
707754406 1
3.3%
707851243 1
3.3%
712939339 1
3.3%
722055975 1
3.3%
724244772 1
3.3%
730501140 1
3.3%
731752579 1
3.3%
749315922 1
3.3%
410134852260501 1
3.3%
410778120102702 1
3.3%
ValueCountFrequency (%)
999999999999999 20
66.7%
410778120102702 1
 
3.3%
410134852260501 1
 
3.3%
749315922 1
 
3.3%
731752579 1
 
3.3%
730501140 1
 
3.3%
724244772 1
 
3.3%
722055975 1
 
3.3%
712939339 1
 
3.3%
707851243 1
 
3.3%

성별코드
Categorical

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2024-03-13T20:46:17.329513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:46:17.447065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 22
73.3%
m 8
 
26.7%

연령대코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.666667
Minimum0
Maximum70
Zeros12
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:17.550826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q350
95-th percentile50
Maximum70
Range70
Interquartile range (IQR)50

Descriptive statistics

Standard deviation23.116397
Coefficient of variation (CV)0.97674917
Kurtosis-1.4846924
Mean23.666667
Median Absolute Deviation (MAD)20
Skewness0.29029204
Sum710
Variance534.36782
MonotonicityNot monotonic
2024-03-13T20:46:17.674275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 12
40.0%
50 8
26.7%
20 4
 
13.3%
40 3
 
10.0%
70 1
 
3.3%
10 1
 
3.3%
30 1
 
3.3%
ValueCountFrequency (%)
0 12
40.0%
10 1
 
3.3%
20 4
 
13.3%
30 1
 
3.3%
40 3
 
10.0%
50 8
26.7%
70 1
 
3.3%
ValueCountFrequency (%)
70 1
 
3.3%
50 8
26.7%
40 3
 
10.0%
30 1
 
3.3%
20 4
 
13.3%
10 1
 
3.3%
0 12
40.0%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000014 × 1011
Minimum1.4000002 × 1011
Maximum1.400011 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:17.829080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000004 × 1011
median1.4000008 × 1011
Q31.4000012 × 1011
95-th percentile1.4000063 × 1011
Maximum1.400011 × 1011
Range1087000
Interquartile range (IQR)79500

Descriptive statistics

Standard deviation254955.02
Coefficient of variation (CV)1.8211055 × 10-6
Kurtosis11.562548
Mean1.4000014 × 1011
Median Absolute Deviation (MAD)42000
Skewness3.5134283
Sum4.2000042 × 1012
Variance6.5002064 × 1010
MonotonicityNot monotonic
2024-03-13T20:46:17.974353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
140000046000 2
 
6.7%
140000140000 2
 
6.7%
140000018000 2
 
6.7%
140000122000 2
 
6.7%
140000054000 1
 
3.3%
140000040000 1
 
3.3%
140000032000 1
 
3.3%
140000112000 1
 
3.3%
140000030000 1
 
3.3%
140000124000 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
140000018000 2
6.7%
140000020000 1
3.3%
140000024000 1
3.3%
140000028000 1
3.3%
140000030000 1
3.3%
140000032000 1
3.3%
140000040000 1
3.3%
140000044000 1
3.3%
140000046000 2
6.7%
140000048000 1
3.3%
ValueCountFrequency (%)
140001105000 1
3.3%
140001025000 1
3.3%
140000140000 2
6.7%
140000126000 1
3.3%
140000124000 1
3.3%
140000122000 2
6.7%
140000116000 1
3.3%
140000114000 1
3.3%
140000112000 1
3.3%
140000100000 1
3.3%

결제상품명
Text

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:46:18.224937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14.5
Mean length8.7
Min length4

Characters and Unicode

Total characters261
Distinct characters78
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

Unique22 ?
Unique (%)73.3%

Sample

1st row가평GP페이(통합)
2nd row행복화성지역화폐
3rd row구리사랑카드
4th row안산사랑상품권 다온(통합)
5th row고양페이카드(통합)
ValueCountFrequency (%)
행복화성지역화폐_화이트 2
 
5.1%
용인와이페이 2
 
5.1%
고양페이카드 2
 
5.1%
의정부사랑카드 2
 
5.1%
안산사랑상품권 2
 
5.1%
다온 1
 
2.6%
경기 1
 
2.6%
남양주시 1
 
2.6%
건강과일 1
 
2.6%
지원금 1
 
2.6%
Other values (24) 24
61.5%
2024-03-13T20:46:18.772400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 14
 
5.4%
랑 13
 
5.0%
사 13
 
5.0%
화 12
 
4.6%
드 12
 
4.6%
카 12
 
4.6%
) 9
 
3.4%
( 9
 
3.4%
페 9
 
3.4%
9
 
3.4%
Other values (68) 149
57.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 214
82.0%
Lowercase Letter 10
 
3.8%
Close Punctuation 9
 
3.4%
Open Punctuation 9
 
3.4%
Space Separator 9
 
3.4%
Uppercase Letter 7
 
2.7%
Connector Punctuation 2
 
0.8%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 14
 
6.5%
랑 13
 
6.1%
사 13
 
6.1%
화 12
 
5.6%
드 12
 
5.6%
카 12
 
5.6%
페 9
 
4.2%
주 7
 
3.3%
통 7
 
3.3%
합 7
 
3.3%
Other values (51) 108
50.5%
Lowercase Letter
ValueCountFrequency (%)
a 3
30.0%
y 2
20.0%
u 1
 
10.0%
o 1
 
10.0%
h 1
 
10.0%
n 1
 
10.0%
k 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
P 3
42.9%
T 1
 
14.3%
N 1
 
14.3%
Y 1
 
14.3%
G 1
 
14.3%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 213
81.6%
Common 30
 
11.5%
Latin 17
 
6.5%
Han 1
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 14
 
6.6%
랑 13
 
6.1%
사 13
 
6.1%
화 12
 
5.6%
드 12
 
5.6%
카 12
 
5.6%
페 9
 
4.2%
주 7
 
3.3%
통 7
 
3.3%
합 7
 
3.3%
Other values (50) 107
50.2%
Latin
ValueCountFrequency (%)
a 3
17.6%
P 3
17.6%
y 2
11.8%
u 1
 
5.9%
o 1
 
5.9%
T 1
 
5.9%
h 1
 
5.9%
n 1
 
5.9%
k 1
 
5.9%
N 1
 
5.9%
Other values (2) 2
11.8%
Common
ValueCountFrequency (%)
) 9
30.0%
( 9
30.0%
9
30.0%
_ 2
 
6.7%
- 1
 
3.3%
Han
ValueCountFrequency (%)
愛 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 213
81.6%
ASCII 47
 
18.0%
CJK 1
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 14
 
6.6%
랑 13
 
6.1%
사 13
 
6.1%
화 12
 
5.6%
드 12
 
5.6%
카 12
 
5.6%
페 9
 
4.2%
주 7
 
3.3%
통 7
 
3.3%
합 7
 
3.3%
Other values (50) 107
50.2%
ASCII
ValueCountFrequency (%)
) 9
19.1%
( 9
19.1%
9
19.1%
a 3
 
6.4%
P 3
 
6.4%
y 2
 
4.3%
_ 2
 
4.3%
u 1
 
2.1%
o 1
 
2.1%
T 1
 
2.1%
Other values (7) 7
14.9%
CJK
ValueCountFrequency (%)
愛 1
100.0%

가맹점업종명
Text

MISSING 

Distinct6
Distinct (%)75.0%
Missing22
Missing (%)73.3%
Memory size372.0 B
2024-03-13T20:46:18.968876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4
Min length2

Characters and Unicode

Total characters32
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)50.0%

Sample

1st row약국
2nd row학원
3rd row일반휴게음식
4th row의원
5th row음료식품
ValueCountFrequency (%)
일반휴게음식 2
22.2%
음료식품 2
22.2%
약국 1
11.1%
학원 1
11.1%
의원 1
11.1%
유통업 1
11.1%
영리 1
11.1%
2024-03-13T20:46:19.306967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
음 4
12.5%
식 4
12.5%
일 2
 
6.2%
휴 2
 
6.2%
게 2
 
6.2%
료 2
 
6.2%
품 2
 
6.2%
반 2
 
6.2%
원 2
 
6.2%
통 1
 
3.1%
Other values (9) 9
28.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31
96.9%
Space Separator 1
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
음 4
12.9%
식 4
12.9%
일 2
 
6.5%
휴 2
 
6.5%
게 2
 
6.5%
료 2
 
6.5%
품 2
 
6.5%
반 2
 
6.5%
원 2
 
6.5%
통 1
 
3.2%
Other values (8) 8
25.8%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31
96.9%
Common 1
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
음 4
12.9%
식 4
12.9%
일 2
 
6.5%
휴 2
 
6.5%
게 2
 
6.5%
료 2
 
6.5%
품 2
 
6.5%
반 2
 
6.5%
원 2
 
6.5%
통 1
 
3.2%
Other values (8) 8
25.8%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31
96.9%
ASCII 1
 
3.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
음 4
12.9%
식 4
12.9%
일 2
 
6.5%
휴 2
 
6.5%
게 2
 
6.5%
료 2
 
6.5%
품 2
 
6.5%
반 2
 
6.5%
원 2
 
6.5%
통 1
 
3.2%
Other values (8) 8
25.8%
ASCII
ValueCountFrequency (%)
1
100.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing22
Missing (%)73.3%
Infinite0
Infinite (%)0.0%
Mean14043.75
Minimum10927
Maximum17858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:19.440500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10927
5-th percentile11198.6
Q111886.75
median13552.5
Q316134.75
95-th percentile17538.1
Maximum17858
Range6931
Interquartile range (IQR)4248

Descriptive statistics

Standard deviation2605.2373
Coefficient of variation (CV)0.18550867
Kurtosis-1.6284319
Mean14043.75
Median Absolute Deviation (MAD)2081
Skewness0.34110816
Sum112350
Variance6787261.6
MonotonicityNot monotonic
2024-03-13T20:46:19.559544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
11948 1
 
3.3%
17858 1
 
3.3%
14316 1
 
3.3%
11703 1
 
3.3%
12789 1
 
3.3%
10927 1
 
3.3%
16944 1
 
3.3%
15865 1
 
3.3%
(Missing) 22
73.3%
ValueCountFrequency (%)
10927 1
3.3%
11703 1
3.3%
11948 1
3.3%
12789 1
3.3%
14316 1
3.3%
15865 1
3.3%
16944 1
3.3%
17858 1
3.3%
ValueCountFrequency (%)
17858 1
3.3%
16944 1
3.3%
15865 1
3.3%
14316 1
3.3%
12789 1
3.3%
11948 1
3.3%
11703 1
3.3%
10927 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
22 
경기도
7 
NONE
 
1

Length

Max length4
Median length4
Mean length3.7666667
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 22
73.3%
경기도 7
 
23.3%
NONE 1
 
3.3%

Length

2024-03-13T20:46:19.694782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:46:19.795527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 22
73.3%
경기도 7
 
23.3%
none 1
 
3.3%

시군구명
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing23
Missing (%)76.7%
Memory size372.0 B
2024-03-13T20:46:19.969430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.7142857
Min length3

Characters and Unicode

Total characters26
Distinct characters19
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

Unique7 ?
Unique (%)100.0%

Sample

1st row구리시
2nd row평택시
3rd row광명시
4th row의정부시
5th row파주시
ValueCountFrequency (%)
구리시 1
12.5%
평택시 1
12.5%
광명시 1
12.5%
의정부시 1
12.5%
파주시 1
12.5%
용인시 1
12.5%
수지구 1
12.5%
군포시 1
12.5%
2024-03-13T20:46:20.685611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 7
26.9%
구 2
 
7.7%
파 1
 
3.8%
군 1
 
3.8%
지 1
 
3.8%
수 1
 
3.8%
1
 
3.8%
인 1
 
3.8%
용 1
 
3.8%
주 1
 
3.8%
Other values (9) 9
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25
96.2%
Space Separator 1
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 7
28.0%
구 2
 
8.0%
파 1
 
4.0%
군 1
 
4.0%
지 1
 
4.0%
수 1
 
4.0%
인 1
 
4.0%
용 1
 
4.0%
주 1
 
4.0%
부 1
 
4.0%
Other values (8) 8
32.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25
96.2%
Common 1
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 7
28.0%
구 2
 
8.0%
파 1
 
4.0%
군 1
 
4.0%
지 1
 
4.0%
수 1
 
4.0%
인 1
 
4.0%
용 1
 
4.0%
주 1
 
4.0%
부 1
 
4.0%
Other values (8) 8
32.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25
96.2%
ASCII 1
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 7
28.0%
구 2
 
8.0%
파 1
 
4.0%
군 1
 
4.0%
지 1
 
4.0%
수 1
 
4.0%
인 1
 
4.0%
용 1
 
4.0%
주 1
 
4.0%
부 1
 
4.0%
Other values (8) 8
32.0%
ASCII
ValueCountFrequency (%)
1
100.0%

읍면동명
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing23
Missing (%)76.7%
Memory size372.0 B
2024-03-13T20:46:20.901529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21
Distinct characters15
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 row수택동
2nd row죽백동
3rd row소하동
4th row호원동
5th row금촌동
ValueCountFrequency (%)
수택동 1
14.3%
죽백동 1
14.3%
소하동 1
14.3%
호원동 1
14.3%
금촌동 1
14.3%
상현동 1
14.3%
산본동 1
14.3%
2024-03-13T20:46:21.293527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 7
33.3%
수 1
 
4.8%
택 1
 
4.8%
죽 1
 
4.8%
백 1
 
4.8%
소 1
 
4.8%
하 1
 
4.8%
호 1
 
4.8%
원 1
 
4.8%
금 1
 
4.8%
Other values (5) 5
23.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7
33.3%
수 1
 
4.8%
택 1
 
4.8%
죽 1
 
4.8%
백 1
 
4.8%
소 1
 
4.8%
하 1
 
4.8%
호 1
 
4.8%
원 1
 
4.8%
금 1
 
4.8%
Other values (5) 5
23.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7
33.3%
수 1
 
4.8%
택 1
 
4.8%
죽 1
 
4.8%
백 1
 
4.8%
소 1
 
4.8%
하 1
 
4.8%
호 1
 
4.8%
원 1
 
4.8%
금 1
 
4.8%
Other values (5) 5
23.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 7
33.3%
수 1
 
4.8%
택 1
 
4.8%
죽 1
 
4.8%
백 1
 
4.8%
소 1
 
4.8%
하 1
 
4.8%
호 1
 
4.8%
원 1
 
4.8%
금 1
 
4.8%
Other values (5) 5
23.8%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7398
Minimum0
Maximum37.76
Zeros23
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:21.427551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile37.66885
Maximum37.76
Range37.76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.113513
Coefficient of variation (CV)1.8436936
Kurtosis-0.25635134
Mean8.7398
Median Absolute Deviation (MAD)0
Skewness1.3285479
Sum262.194
Variance259.64531
MonotonicityNot monotonic
2024-03-13T20:46:21.579338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 23
76.7%
37.599 1
 
3.3%
37.005 1
 
3.3%
37.447 1
 
3.3%
37.726 1
 
3.3%
37.76 1
 
3.3%
37.298 1
 
3.3%
37.359 1
 
3.3%
ValueCountFrequency (%)
0.0 23
76.7%
37.005 1
 
3.3%
37.298 1
 
3.3%
37.359 1
 
3.3%
37.447 1
 
3.3%
37.599 1
 
3.3%
37.726 1
 
3.3%
37.76 1
 
3.3%
ValueCountFrequency (%)
37.76 1
 
3.3%
37.726 1
 
3.3%
37.599 1
 
3.3%
37.447 1
 
3.3%
37.359 1
 
3.3%
37.298 1
 
3.3%
37.005 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%
Mean29.631733
Minimum0
Maximum127.137
Zeros23
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:21.712973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile127.0943
Maximum127.137
Range127.137
Interquartile range (IQR)0

Descriptive statistics

Standard deviation54.630335
Coefficient of variation (CV)1.8436429
Kurtosis-0.25729815
Mean29.631733
Median Absolute Deviation (MAD)0
Skewness1.3283429
Sum888.952
Variance2984.4735
MonotonicityNot monotonic
2024-03-13T20:46:21.846363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 23
76.7%
127.137 1
 
3.3%
127.115 1
 
3.3%
126.883 1
 
3.3%
127.049 1
 
3.3%
126.768 1
 
3.3%
127.069 1
 
3.3%
126.931 1
 
3.3%
ValueCountFrequency (%)
0.0 23
76.7%
126.768 1
 
3.3%
126.883 1
 
3.3%
126.931 1
 
3.3%
127.049 1
 
3.3%
127.069 1
 
3.3%
127.115 1
 
3.3%
127.137 1
 
3.3%
ValueCountFrequency (%)
127.137 1
 
3.3%
127.115 1
 
3.3%
127.069 1
 
3.3%
127.049 1
 
3.3%
126.931 1
 
3.3%
126.883 1
 
3.3%
126.768 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
20 
True
10 
ValueCountFrequency (%)
False 20
66.7%
True 10
33.3%
2024-03-13T20:46:21.952380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9806.3333
Minimum0
Maximum140000
Zeros20
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:22.036610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36350
95-th percentile43980
Maximum140000
Range140000
Interquartile range (IQR)6350

Descriptive statistics

Standard deviation27537.328
Coefficient of variation (CV)2.8081167
Kurtosis18.450807
Mean9806.3333
Median Absolute Deviation (MAD)0
Skewness4.1370426
Sum294190
Variance7.5830444 × 108
MonotonicityNot monotonic
2024-03-13T20:46:22.147558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 20
66.7%
18000 2
 
6.7%
5900 1
 
3.3%
4500 1
 
3.3%
140000 1
 
3.3%
21100 1
 
3.3%
6500 1
 
3.3%
62700 1
 
3.3%
9450 1
 
3.3%
8040 1
 
3.3%
ValueCountFrequency (%)
0 20
66.7%
4500 1
 
3.3%
5900 1
 
3.3%
6500 1
 
3.3%
8040 1
 
3.3%
9450 1
 
3.3%
18000 2
 
6.7%
21100 1
 
3.3%
62700 1
 
3.3%
140000 1
 
3.3%
ValueCountFrequency (%)
140000 1
 
3.3%
62700 1
 
3.3%
21100 1
 
3.3%
18000 2
 
6.7%
9450 1
 
3.3%
8040 1
 
3.3%
6500 1
 
3.3%
5900 1
 
3.3%
4500 1
 
3.3%
0 20
66.7%

Interactions

2024-03-13T20:46:14.042962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:06.995356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:07.918192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:08.789118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:09.741097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:10.681645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:11.601214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:12.989076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:14.187340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:07.152655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:08.039103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:08.902644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:09.873492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:10.803133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:11.785471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:13.120836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:14.279072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:07.260889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:08.132780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:09.001820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:09.978253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:10.901106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:11.929831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:13.231545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:14.406915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:07.381072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:08.221143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:09.118860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:10.091661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:11.004121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:12.382842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:13.357877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:14.529086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:07.505354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:08.308852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:09.267873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:10.198838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:11.109240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:12.504902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:13.476404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:14.642958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:07.620928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:08.403358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:09.402627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:10.334499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:11.212615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:12.626177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:13.591539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:14.757840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:07.723808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:08.581158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:09.519762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:10.462745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:11.328970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:12.736672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:13.709248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:14.864139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:07.823246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:08.682612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:09.624338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:10.568105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:11.440441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:12.860454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:13.878030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:46:22.259124image/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
회원코드1.0001.0000.0000.4070.0000.9030.8840.9400.5110.0001.0001.0000.0000.0000.0000.000
가맹점번호1.0000.0001.0000.1150.8300.0000.000NaNNaNNaNNaNNaN0.6320.6321.0000.782
성별코드1.0000.4070.1151.0000.4860.0000.0000.7710.0000.0001.0001.0000.3540.3540.3580.502
연령대코드1.0000.0000.8300.4861.0000.0000.0001.0001.0000.0001.0001.0000.6520.6520.6980.327
결제상품ID1.0000.9030.0000.0000.0001.0001.000NaNNaNNaNNaNNaN0.0000.0000.0910.000
결제상품명1.0000.8840.0000.0000.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.8770.936
가맹점업종명1.0000.940NaN0.7711.000NaN1.0001.0000.4840.0001.0001.0000.0000.000NaN1.000
가맹점우편번호1.0000.511NaN0.0001.000NaN1.0000.4841.0001.0001.0001.0001.0001.000NaN0.913
시도명1.0000.000NaN0.0000.000NaN1.0000.0001.0001.000NaNNaN0.3960.396NaN0.000
시군구명1.0001.000NaN1.0001.000NaN1.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
읍면동명1.0001.000NaN1.0001.000NaN1.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
위도1.0000.0000.6320.3540.6520.0000.0000.0001.0000.396NaNNaN1.0000.9890.8790.944
경도1.0000.0000.6320.3540.6520.0000.0000.0001.0000.396NaNNaN0.9891.0000.8790.944
사용여부1.0000.0001.0000.3580.6980.0910.877NaNNaNNaNNaNNaN0.8790.8791.0000.786
결제금액1.0000.0000.7820.5020.3270.0000.9361.0000.9130.0001.0001.0000.9440.9440.7861.000
2024-03-13T20:46:22.435164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부성별코드시도명
사용여부1.0000.2311.000
성별코드0.2311.0000.000
시도명1.0000.0001.000
2024-03-13T20:46:22.552430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드시도명사용여부
회원코드1.0000.130-0.1290.3220.190-0.066-0.051-0.0750.3110.0000.000
가맹점번호0.1301.000-0.6360.196-0.476-0.831-0.850-0.9680.2701.0000.982
연령대코드-0.129-0.6361.000-0.382-0.5770.6170.6000.6210.4670.0000.682
결제상품ID0.3220.196-0.3821.000-0.190-0.117-0.146-0.0870.0001.0000.091
가맹점우편번호0.190-0.476-0.577-0.1901.000-0.7620.3570.0600.0000.4081.000
위도-0.066-0.8310.617-0.117-0.7621.0000.9670.8410.2280.2180.683
경도-0.051-0.8500.600-0.1460.3570.9671.0000.8310.2280.2180.683
결제금액-0.075-0.9680.621-0.0870.0600.8410.8311.0000.3240.0000.554
성별코드0.3110.2700.4670.0000.0000.2280.2280.3241.0000.0000.231
시도명0.0001.0000.0001.0000.4080.2180.2180.0000.0001.0001.000
사용여부0.0000.9820.6820.0911.0000.6830.6830.5540.2311.0001.000

Missing values

2024-03-13T20:46:15.011362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:46:15.268382image/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.
2024-03-13T20:46:15.441225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
02023-04-032023-04-09zhXiW3bjb34ctyWwzIadhGk4YR0uSEfgYCKYQWOcBZY=3017204091999999999999999F40140000054000가평GP페이(통합)<NA><NA><NA><NA><NA>0.00.0N0
12023-04-032023-04-09++ErVFJ+KRS9ueqvSV77NlQWJyu/BhpAKSPgHxZzWb8=3078901736999999999999999M50140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
22023-04-032023-04-09xZ775iJ/bU9pLncBEulYzygQF79uvOF3Xm7v3Ddq5WQ=3009533120712939339F50140000028000구리사랑카드약국11948경기도구리시수택동37.599127.137Y5900
32023-04-032023-04-09zhOGwsPVIW9RxNnkp2T8c5DpEbBZuwY/RpDXiNczF1o=3005839559410134852260501M20140000100000안산사랑상품권 다온(통합)<NA><NA><NA><NA><NA>0.00.0Y4500
42023-04-032023-04-09zhM6+GGB/Eh8+H0l7yMHM0kyQZ4W6yKRpMtJ2d4XZbs=3002661237999999999999999F0140000090000고양페이카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
52023-04-032023-04-09zhKHmPfZYCLHc3ijnibiIKbCOKMY1kza9tZiKue3uLU=3032410225999999999999999F0140000084000광주사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
62023-04-032023-04-09zhHU3IhbftRuMSSr6oaYfJMHhy5cxeOpKhx1pAM8m5g=3016414211999999999999999F0140000140000행복화성지역화폐_화이트<NA><NA><NA><NA><NA>0.00.0N0
72023-04-032023-04-09++EE7gZUnep98mNEcPWZGiT9lxAKvaK2DJOdJ8YeZ3w=3056766884707754406M40140000058000평택사랑카드(통합)학원17858경기도평택시죽백동37.005127.115Y140000
82023-04-032023-04-09zhDxSTPdzyvWrNIZ2apjtIyQSiW1n8U9qQ6pibVxirM=3017713801707851243F50140000020000광명사랑화폐일반휴게음식14316경기도광명시소하동37.447126.883Y18000
92023-04-032023-04-09zh7Ldu8kXyxNw//jDrjNdCha/N5zuk2iNaKueUdkNoQ=3019503157999999999999999F20140000114000Thank You Pay-N<NA><NA><NA><NA><NA>0.00.0N0
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202023-04-032023-04-09zgUDOr4vlUIVGKK2RWWdbax2UFLeuAQeEu3vhjTDHVU=3006475206999999999999999F20140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
212023-04-032023-04-09zgH1obPKi0WlIVdXncq4wu808TT4gHYyUdu2VRxNXTw=3010323111999999999999999F0140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
222023-04-032023-04-09zgDeLIoNvG/J1NIQCDaquIHpV+NgCmZQHnESPBpUl5w=3019908538722055975M50140000046000용인와이페이음료식품16944경기도용인시 수지구상현동37.298127.069Y9450
232023-04-032023-04-09zgCbsqD9AVWg2bJaQJfsrZV8aLtx2OySAyFjpD9G/AQ=3019958159999999999999999F0140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
242023-04-032023-04-09zgC1/P/4tu/ZJYddDxBaqncz5+SE02Mutsn7BmHDVCI=3039560412999999999999999F0140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
252023-04-032023-04-09zg33lQvnPR+ms5VqZs2nSy7tUH6DLwWlFC+cVBeDEh0=3012150124731752579F50140000112000군포愛머니일반휴게음식15865경기도군포시산본동37.359126.931Y18000
262023-04-032023-04-09zfzupYhDhjMIbMJX0srvlUQ/D7bI6wlyAPQzcPIVpVU=3022448184999999999999999M70140000032000안성사랑카드<NA><NA><NA><NA><NA>0.00.0N0
272023-04-032023-04-09zfyVtYybgk3lQNAk1blisIoaI4uxuUX5lRcV7Cr0f88=3029424286410778120102702F10140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0Y8040
282023-04-032023-04-09zfxq1QYiox/U8BtimiWL+8dKmv47k1A+82wNdmqA4wc=3006811157999999999999999M30140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
292023-04-032023-04-09zfumef6eHa4o6xigOQpG3T9vPQljdF2ET72xBAjm2LY=3018632677999999999999999F0140000040000여주사랑카드<NA><NA><NA><NA><NA>0.00.0N0