Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells47
Missing cells (%)8.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory156.4 B

Variable types

DateTime2
Text4
Numeric8
Categorical3
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/9a81eef5-8b1b-418e-addb-5ef21ae64b55

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
사용여부 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
시도명 is highly overall correlated with 가맹점번호 and 2 other fieldsHigh correlation
가맹점업종명 is highly overall correlated with 가맹점번호 and 1 other fieldsHigh correlation
가맹점번호 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
가맹점우편번호 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점번호 and 3 other fieldsHigh correlation
결제금액 is highly overall correlated with 가맹점번호 and 2 other fieldsHigh correlation
가맹점우편번호 has 15 (50.0%) missing valuesMissing
시군구명 has 16 (53.3%) missing valuesMissing
읍면동명 has 16 (53.3%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
위도 has 16 (53.3%) zerosZeros
경도 has 16 (53.3%) zerosZeros
결제금액 has 15 (50.0%) zerosZeros

Reproduction

Analysis started2024-03-13 12:00:40.938402
Analysis finished2024-03-13 12:00:49.064059
Duration8.13 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
Minimum2021-02-01 00:00:00
Maximum2021-02-01 00:00:00
2024-03-13T21:00:49.128093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:49.293703image/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
Minimum2021-02-07 00:00:00
Maximum2021-02-07 00:00:00
2024-03-13T21:00:49.392788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:49.485708image/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
2024-03-13T21:00:49.700397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length44
Mean length44
Min length44

Characters and Unicode

Total characters1320
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row++jTKAH72ANhj1szVLm+MK8pSM/gncabQ4t3Vnnm1XY=
2nd row+4hcOUie0Vec4IoWD5oHEvBdyYu2/hkrao9+fhCWk/M=
3rd row++3snq512aH2fWWTY3ZHknYZk+R2WhQ6vnL4mdQv7Fw=
4th row2y+5o672btx1T7yW+a4Gq2ZsSz855NLcMSSj91j/AXc=
5th row9AalQnClpjuhK5HxCJ4ryQF0QUa2KIY2xHH6AhpGYaI=
ValueCountFrequency (%)
jtkah72anhj1szvlm+mk8psm/gncabq4t3vnnm1xy 1
 
3.3%
4hcouie0vec4iowd5ohevbdyyu2/hkrao9+fhcwk/m 1
 
3.3%
exyzfthsk/bjxjz7ywvrnu6nv5uhczwjk1iou76vpvm 1
 
3.3%
9d62euo3d5rd4uvampqtsya5zirjgc0x2jtb7opvrds 1
 
3.3%
3w9b+2ktcre9kxb7cifarzigofvxleuqieatlutxsd4 1
 
3.3%
5g7lha30tieiwrm93wnwzfvj0wl7fk4crcjlvjkvii 1
 
3.3%
hiapz7oamqw6ogfu++kvuezqk7doicm1hkhkybn6hu8 1
 
3.3%
vqy7wvb2uf/pstflrmujmaiagkpmwr44wgzdh/wdmza 1
 
3.3%
ku1xqrhbty6/7wjbza/5hhtovm4drypoqbacuhngia0 1
 
3.3%
9grl2yqd0zjlo1yl4v3q/wpi+xf4uqrjx09k6gsby+m 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T21:00:50.370853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
+ 39
 
3.0%
7 30
 
2.3%
= 30
 
2.3%
V 28
 
2.1%
2 28
 
2.1%
Y 27
 
2.0%
4 27
 
2.0%
A 26
 
2.0%
M 26
 
2.0%
r 25
 
1.9%
Other values (55) 1034
78.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 514
38.9%
Uppercase Letter 495
37.5%
Decimal Number 224
17.0%
Math Symbol 69
 
5.2%
Other Punctuation 18
 
1.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
V 28
 
5.7%
Y 27
 
5.5%
A 26
 
5.3%
M 26
 
5.3%
U 24
 
4.8%
E 23
 
4.6%
H 22
 
4.4%
B 22
 
4.4%
X 21
 
4.2%
W 21
 
4.2%
Other values (16) 255
51.5%
Lowercase Letter
ValueCountFrequency (%)
r 25
 
4.9%
c 25
 
4.9%
q 24
 
4.7%
z 24
 
4.7%
p 23
 
4.5%
n 23
 
4.5%
h 23
 
4.5%
t 21
 
4.1%
l 21
 
4.1%
k 21
 
4.1%
Other values (16) 284
55.3%
Decimal Number
ValueCountFrequency (%)
7 30
13.4%
2 28
12.5%
4 27
12.1%
9 25
11.2%
1 23
10.3%
0 23
10.3%
6 21
9.4%
5 18
8.0%
3 18
8.0%
8 11
 
4.9%
Math Symbol
ValueCountFrequency (%)
+ 39
56.5%
= 30
43.5%
Other Punctuation
ValueCountFrequency (%)
/ 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1009
76.4%
Common 311
 
23.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
V 28
 
2.8%
Y 27
 
2.7%
A 26
 
2.6%
M 26
 
2.6%
r 25
 
2.5%
c 25
 
2.5%
U 24
 
2.4%
q 24
 
2.4%
z 24
 
2.4%
p 23
 
2.3%
Other values (42) 757
75.0%
Common
ValueCountFrequency (%)
+ 39
12.5%
7 30
9.6%
= 30
9.6%
2 28
9.0%
4 27
8.7%
9 25
8.0%
1 23
7.4%
0 23
7.4%
6 21
6.8%
/ 18
 
5.8%
Other values (3) 47
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+ 39
 
3.0%
7 30
 
2.3%
= 30
 
2.3%
V 28
 
2.1%
2 28
 
2.1%
Y 27
 
2.0%
4 27
 
2.0%
A 26
 
2.0%
M 26
 
2.0%
r 25
 
1.9%
Other values (55) 1034
78.3%

회원코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0196847 × 109
Minimum3.0020789 × 109
Maximum3.0385593 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:00:50.541163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0020789 × 109
5-th percentile3.002639 × 109
Q13.0168498 × 109
median3.0193407 × 109
Q33.0241846 × 109
95-th percentile3.035276 × 109
Maximum3.0385593 × 109
Range36480377
Interquartile range (IQR)7334831.5

Descriptive statistics

Standard deviation8973298.6
Coefficient of variation (CV)0.0029716012
Kurtosis0.37796739
Mean3.0196847 × 109
Median Absolute Deviation (MAD)3085447
Skewness-0.02032604
Sum9.0590542 × 1010
Variance8.0520088 × 1013
MonotonicityNot monotonic
2024-03-13T21:00:50.702724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3011471134 1
 
3.3%
3019459440 1
 
3.3%
3021974195 1
 
3.3%
3002856343 1
 
3.3%
3016962736 1
 
3.3%
3013993267 1
 
3.3%
3038559317 1
 
3.3%
3002461200 1
 
3.3%
3031536160 1
 
3.3%
3019313312 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3002078940 1
3.3%
3002461200 1
3.3%
3002856343 1
3.3%
3008014104 1
3.3%
3011471134 1
3.3%
3013993267 1
3.3%
3016809297 1
3.3%
3016812150 1
3.3%
3016962736 1
3.3%
3017485645 1
3.3%
ValueCountFrequency (%)
3038559317 1
3.3%
3036804193 1
3.3%
3033408197 1
3.3%
3031536160 1
3.3%
3029428174 1
3.3%
3024940156 1
3.3%
3024743123 1
3.3%
3024620136 1
3.3%
3022878104 1
3.3%
3021974195 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0000037 × 1014
Minimum7.0204338 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:00:50.830001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0204338 × 108
5-th percentile7.0998323 × 108
Q17.2168787 × 108
median5.000004 × 1014
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.999993 × 1014
Interquartile range (IQR)9.9999928 × 1014

Descriptive statistics

Standard deviation5.0854725 × 1014
Coefficient of variation (CV)1.0170937
Kurtosis-2.1481481
Mean5.0000037 × 1014
Median Absolute Deviation (MAD)4.999996 × 1014
Skewness-8.9970661 × 10-15
Sum1.5000011 × 1016
Variance2.586203 × 1029
MonotonicityNot monotonic
2024-03-13T21:00:50.955463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
999999999999999 15
50.0%
702043382 1
 
3.3%
709745620 1
 
3.3%
723081830 1
 
3.3%
786239327 1
 
3.3%
796056915 1
 
3.3%
720711611 1
 
3.3%
788020420 1
 
3.3%
790372437 1
 
3.3%
715316562 1
 
3.3%
Other values (6) 6
 
20.0%
ValueCountFrequency (%)
702043382 1
3.3%
709745620 1
3.3%
710273634 1
3.3%
713727936 1
3.3%
715316562 1
3.3%
720711611 1
3.3%
721037232 1
3.3%
721223213 1
3.3%
723081830 1
3.3%
786239327 1
3.3%
ValueCountFrequency (%)
999999999999999 15
50.0%
796056915 1
 
3.3%
792404685 1
 
3.3%
790372437 1
 
3.3%
788020420 1
 
3.3%
787681394 1
 
3.3%
786239327 1
 
3.3%
723081830 1
 
3.3%
721223213 1
 
3.3%
721037232 1
 
3.3%

성별코드
Categorical

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
M
17 
F
13 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 17
56.7%
F 13
43.3%

Length

2024-03-13T21:00:51.091309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:00:51.191924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 17
56.7%
f 13
43.3%

연령대코드
Real number (ℝ)

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.333333
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:00:51.303102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q132.5
median40
Q350
95-th percentile60
Maximum80
Range60
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation14.700066
Coefficient of variation (CV)0.3392323
Kurtosis-0.071032465
Mean43.333333
Median Absolute Deviation (MAD)10
Skewness0.21193792
Sum1300
Variance216.09195
MonotonicityNot monotonic
2024-03-13T21:00:51.444080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
40 9
30.0%
60 6
20.0%
50 6
20.0%
30 4
13.3%
20 4
13.3%
80 1
 
3.3%
ValueCountFrequency (%)
20 4
13.3%
30 4
13.3%
40 9
30.0%
50 6
20.0%
60 6
20.0%
80 1
 
3.3%
ValueCountFrequency (%)
80 1
 
3.3%
60 6
20.0%
50 6
20.0%
40 9
30.0%
30 4
13.3%
20 4
13.3%

결제상품ID
Real number (ℝ)

Distinct19
Distinct (%)63.3%
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
2024-03-13T21:00:51.582422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000004 × 1011
median1.4000009 × 1011
Q31.4000012 × 1011
95-th percentile1.4000012 × 1011
Maximum1.4000013 × 1011
Range108000
Interquartile range (IQR)82500

Descriptive statistics

Standard deviation41594.429
Coefficient of variation (CV)2.971029 × 10-7
Kurtosis-1.814386
Mean1.4000008 × 1011
Median Absolute Deviation (MAD)38000
Skewness-0.12089963
Sum4.2000023 × 1012
Variance1.7300966 × 109
MonotonicityNot monotonic
2024-03-13T21:00:51.734550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
140000030000 4
13.3%
140000124000 3
 
10.0%
140000116000 3
 
10.0%
140000018000 2
 
6.7%
140000122000 2
 
6.7%
140000100000 2
 
6.7%
140000044000 2
 
6.7%
140000038000 1
 
3.3%
140000082000 1
 
3.3%
140000120000 1
 
3.3%
Other values (9) 9
30.0%
ValueCountFrequency (%)
140000018000 2
6.7%
140000020000 1
 
3.3%
140000030000 4
13.3%
140000034000 1
 
3.3%
140000038000 1
 
3.3%
140000044000 2
6.7%
140000048000 1
 
3.3%
140000050000 1
 
3.3%
140000058000 1
 
3.3%
140000082000 1
 
3.3%
ValueCountFrequency (%)
140000126000 1
 
3.3%
140000124000 3
10.0%
140000122000 2
6.7%
140000120000 1
 
3.3%
140000118000 1
 
3.3%
140000116000 3
10.0%
140000112000 1
 
3.3%
140000100000 2
6.7%
140000090000 1
 
3.3%
140000082000 1
 
3.3%

결제상품명
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T21:00:51.915619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length7.5333333
Min length4

Characters and Unicode

Total characters226
Distinct characters55
Distinct categories6 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)40.0%

Sample

1st row고양페이카드(통합)
2nd row안양사랑페이
3rd row평택사랑카드(통합)
4th row수원페이
5th row부천페이
ValueCountFrequency (%)
안산사랑상품권 5
13.2%
부천페이 4
 
10.5%
다온 3
 
7.9%
행복화성지역화폐 3
 
7.9%
오산화폐 2
 
5.3%
오색전 2
 
5.3%
의정부사랑카드 2
 
5.3%
다온(통합 2
 
5.3%
고양페이카드 2
 
5.3%
이천사랑지역화폐 1
 
2.6%
Other values (12) 12
31.6%
2024-03-13T21:00:52.272221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 13
 
5.8%
랑 13
 
5.8%
이 11
 
4.9%
페 10
 
4.4%
화 10
 
4.4%
드 8
 
3.5%
카 8
 
3.5%
8
 
3.5%
폐 7
 
3.1%
산 7
 
3.1%
Other values (45) 131
58.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 203
89.8%
Space Separator 8
 
3.5%
Close Punctuation 6
 
2.7%
Open Punctuation 6
 
2.7%
Lowercase Letter 2
 
0.9%
Uppercase Letter 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 13
 
6.4%
랑 13
 
6.4%
이 11
 
5.4%
페 10
 
4.9%
화 10
 
4.9%
드 8
 
3.9%
카 8
 
3.9%
폐 7
 
3.4%
산 7
 
3.4%
통 6
 
3.0%
Other values (39) 110
54.2%
Lowercase Letter
ValueCountFrequency (%)
y 1
50.0%
a 1
50.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 202
89.4%
Common 20
 
8.8%
Latin 3
 
1.3%
Han 1
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 13
 
6.4%
랑 13
 
6.4%
이 11
 
5.4%
페 10
 
5.0%
화 10
 
5.0%
드 8
 
4.0%
카 8
 
4.0%
폐 7
 
3.5%
산 7
 
3.5%
통 6
 
3.0%
Other values (38) 109
54.0%
Common
ValueCountFrequency (%)
8
40.0%
) 6
30.0%
( 6
30.0%
Latin
ValueCountFrequency (%)
y 1
33.3%
a 1
33.3%
P 1
33.3%
Han
ValueCountFrequency (%)
愛 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 202
89.4%
ASCII 23
 
10.2%
CJK 1
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 13
 
6.4%
랑 13
 
6.4%
이 11
 
5.4%
페 10
 
5.0%
화 10
 
5.0%
드 8
 
4.0%
카 8
 
4.0%
폐 7
 
3.5%
산 7
 
3.5%
통 6
 
3.0%
Other values (38) 109
54.0%
ASCII
ValueCountFrequency (%)
8
34.8%
) 6
26.1%
( 6
26.1%
y 1
 
4.3%
a 1
 
4.3%
P 1
 
4.3%
CJK
ValueCountFrequency (%)
愛 1
100.0%

가맹점업종명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
15 
유통업 영리
6 
일반휴게음식
5 
음료식품
4 

Length

Max length6
Median length4
Mean length4.7333333
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 15
50.0%
유통업 영리 6
 
20.0%
일반휴게음식 5
 
16.7%
음료식품 4
 
13.3%

Length

2024-03-13T21:00:52.437648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:00:52.565186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 15
41.7%
유통업 6
 
16.7%
영리 6
 
16.7%
일반휴게음식 5
 
13.9%
음료식품 4
 
11.1%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)100.0%
Missing15
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean14464.6
Minimum10551
Maximum18110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:00:52.689588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10551
5-th percentile10795.3
Q112747.5
median14547
Q315568
95-th percentile17978.4
Maximum18110
Range7559
Interquartile range (IQR)2820.5

Descriptive statistics

Standard deviation2377.655
Coefficient of variation (CV)0.16437752
Kurtosis-0.78957358
Mean14464.6
Median Absolute Deviation (MAD)1612
Skewness-0.1179605
Sum216969
Variance5653243.4
MonotonicityNot monotonic
2024-03-13T21:00:52.857950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
17922 1
 
3.3%
14414 1
 
3.3%
14547 1
 
3.3%
10551 1
 
3.3%
17353 1
 
3.3%
15596 1
 
3.3%
14242 1
 
3.3%
15274 1
 
3.3%
12935 1
 
3.3%
18110 1
 
3.3%
Other values (5) 5
 
16.7%
(Missing) 15
50.0%
ValueCountFrequency (%)
10551 1
3.3%
10900 1
3.3%
11695 1
3.3%
12560 1
3.3%
12935 1
3.3%
14242 1
3.3%
14414 1
3.3%
14547 1
3.3%
15274 1
3.3%
15330 1
3.3%
ValueCountFrequency (%)
18110 1
3.3%
17922 1
3.3%
17353 1
3.3%
15596 1
3.3%
15540 1
3.3%
15330 1
3.3%
15274 1
3.3%
14547 1
3.3%
14414 1
3.3%
14242 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
15 
경기도
14 
NONE
 
1

Length

Max length4
Median length4
Mean length3.5333333
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 15
50.0%
경기도 14
46.7%
NONE 1
 
3.3%

Length

2024-03-13T21:00:53.005136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:00:53.102448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 15
50.0%
경기도 14
46.7%
none 1
 
3.3%

시군구명
Text

MISSING 

Distinct11
Distinct (%)78.6%
Missing16
Missing (%)53.3%
Memory size372.0 B
2024-03-13T21:00:53.234038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4.5
Min length3

Characters and Unicode

Total characters63
Distinct characters26
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

Unique9 ?
Unique (%)64.3%

Sample

1st row평택시
2nd row부천시
3rd row부천시
4th row고양시 덕양구
5th row이천시
ValueCountFrequency (%)
안산시 4
21.1%
상록구 3
15.8%
부천시 2
10.5%
평택시 1
 
5.3%
고양시 1
 
5.3%
덕양구 1
 
5.3%
이천시 1
 
5.3%
광명시 1
 
5.3%
하남시 1
 
5.3%
오산시 1
 
5.3%
Other values (3) 3
15.8%
2024-03-13T21:00:53.533168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 14
22.2%
구 5
 
7.9%
5
 
7.9%
산 5
 
7.9%
안 4
 
6.3%
부 3
 
4.8%
천 3
 
4.8%
록 3
 
4.8%
상 3
 
4.8%
양 2
 
3.2%
Other values (16) 16
25.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 58
92.1%
Space Separator 5
 
7.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 14
24.1%
구 5
 
8.6%
산 5
 
8.6%
안 4
 
6.9%
부 3
 
5.2%
천 3
 
5.2%
록 3
 
5.2%
상 3
 
5.2%
양 2
 
3.4%
남 1
 
1.7%
Other values (15) 15
25.9%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 58
92.1%
Common 5
 
7.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 14
24.1%
구 5
 
8.6%
산 5
 
8.6%
안 4
 
6.9%
부 3
 
5.2%
천 3
 
5.2%
록 3
 
5.2%
상 3
 
5.2%
양 2
 
3.4%
남 1
 
1.7%
Other values (15) 15
25.9%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 58
92.1%
ASCII 5
 
7.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 14
24.1%
구 5
 
8.6%
산 5
 
8.6%
안 4
 
6.9%
부 3
 
5.2%
천 3
 
5.2%
록 3
 
5.2%
상 3
 
5.2%
양 2
 
3.4%
남 1
 
1.7%
Other values (15) 15
25.9%
ASCII
ValueCountFrequency (%)
5
100.0%

읍면동명
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing16
Missing (%)53.3%
Memory size372.0 B
2024-03-13T21:00:53.719932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8571429
Min length2

Characters and Unicode

Total characters40
Distinct characters26
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

Unique14 ?
Unique (%)100.0%

Sample

1st row평택동
2nd row원종동
3rd row중동
4th row도내동
5th row증포동
ValueCountFrequency (%)
평택동 1
 
7.1%
원종동 1
 
7.1%
중동 1
 
7.1%
도내동 1
 
7.1%
증포동 1
 
7.1%
사동 1
 
7.1%
철산동 1
 
7.1%
월피동 1
 
7.1%
덕풍동 1
 
7.1%
수청동 1
 
7.1%
Other values (4) 4
28.6%
2024-03-13T21:00:54.029052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 15
37.5%
평 1
 
2.5%
피 1
 
2.5%
본 1
 
2.5%
부 1
 
2.5%
정 1
 
2.5%
의 1
 
2.5%
와 1
 
2.5%
목 1
 
2.5%
청 1
 
2.5%
Other values (16) 16
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 40
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 15
37.5%
평 1
 
2.5%
피 1
 
2.5%
본 1
 
2.5%
부 1
 
2.5%
정 1
 
2.5%
의 1
 
2.5%
와 1
 
2.5%
목 1
 
2.5%
청 1
 
2.5%
Other values (16) 16
40.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 40
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 15
37.5%
평 1
 
2.5%
피 1
 
2.5%
본 1
 
2.5%
부 1
 
2.5%
정 1
 
2.5%
의 1
 
2.5%
와 1
 
2.5%
목 1
 
2.5%
청 1
 
2.5%
Other values (16) 16
40.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 40
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 15
37.5%
평 1
 
2.5%
피 1
 
2.5%
본 1
 
2.5%
부 1
 
2.5%
정 1
 
2.5%
의 1
 
2.5%
와 1
 
2.5%
목 1
 
2.5%
청 1
 
2.5%
Other values (16) 16
40.0%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.461167
Minimum0
Maximum37.739
Zeros16
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:00:54.152260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q337.334
95-th percentile37.6813
Maximum37.739
Range37.739
Interquartile range (IQR)37.334

Descriptive statistics

Standard deviation18.986419
Coefficient of variation (CV)1.0873511
Kurtosis-2.1265916
Mean17.461167
Median Absolute Deviation (MAD)0
Skewness0.1409471
Sum523.835
Variance360.48412
MonotonicityNot monotonic
2024-03-13T21:00:54.281536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.0 16
53.3%
36.989 1
 
3.3%
37.528 1
 
3.3%
37.502 1
 
3.3%
37.634 1
 
3.3%
37.289 1
 
3.3%
37.283 1
 
3.3%
37.473 1
 
3.3%
37.335 1
 
3.3%
37.544 1
 
3.3%
Other values (5) 5
 
16.7%
ValueCountFrequency (%)
0.0 16
53.3%
36.989 1
 
3.3%
37.172 1
 
3.3%
37.283 1
 
3.3%
37.289 1
 
3.3%
37.296 1
 
3.3%
37.331 1
 
3.3%
37.335 1
 
3.3%
37.473 1
 
3.3%
37.502 1
 
3.3%
ValueCountFrequency (%)
37.739 1
3.3%
37.72 1
3.3%
37.634 1
3.3%
37.544 1
3.3%
37.528 1
3.3%
37.502 1
3.3%
37.473 1
3.3%
37.335 1
3.3%
37.331 1
3.3%
37.296 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.242367
Minimum0
Maximum127.451
Zeros16
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:00:54.400808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3126.86
95-th percentile127.15
Maximum127.451
Range127.451
Interquartile range (IQR)126.86

Descriptive statistics

Standard deviation64.415581
Coefficient of variation (CV)1.0873229
Kurtosis-2.1268887
Mean59.242367
Median Absolute Deviation (MAD)0
Skewness0.14078272
Sum1777.271
Variance4149.3671
MonotonicityNot monotonic
2024-03-13T21:00:54.506631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.0 16
53.3%
127.084 1
 
3.3%
126.81 1
 
3.3%
126.762 1
 
3.3%
126.871 1
 
3.3%
127.451 1
 
3.3%
126.827 1
 
3.3%
126.873 1
 
3.3%
126.854 1
 
3.3%
127.204 1
 
3.3%
Other values (5) 5
 
16.7%
ValueCountFrequency (%)
0.0 16
53.3%
126.745 1
 
3.3%
126.762 1
 
3.3%
126.81 1
 
3.3%
126.819 1
 
3.3%
126.827 1
 
3.3%
126.854 1
 
3.3%
126.862 1
 
3.3%
126.871 1
 
3.3%
126.873 1
 
3.3%
ValueCountFrequency (%)
127.451 1
3.3%
127.204 1
3.3%
127.084 1
3.3%
127.058 1
3.3%
127.051 1
3.3%
126.873 1
3.3%
126.871 1
3.3%
126.862 1
3.3%
126.854 1
3.3%
126.827 1
3.3%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
15 
True
15 
ValueCountFrequency (%)
False 15
50.0%
True 15
50.0%
2024-03-13T21:00:54.613395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15058.333
Minimum0
Maximum308000
Zeros15
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:00:54.713540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1575
Q38795
95-th percentile25480
Maximum308000
Range308000
Interquartile range (IQR)8795

Descriptive statistics

Standard deviation55807.602
Coefficient of variation (CV)3.7060942
Kurtosis28.871099
Mean15058.333
Median Absolute Deviation (MAD)1575
Skewness5.3318846
Sum451750
Variance3.1144885 × 109
MonotonicityNot monotonic
2024-03-13T21:00:54.815027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 15
50.0%
31600 1
 
3.3%
10600 1
 
3.3%
308000 1
 
3.3%
12000 1
 
3.3%
4500 1
 
3.3%
11370 1
 
3.3%
3150 1
 
3.3%
18000 1
 
3.3%
5200 1
 
3.3%
Other values (6) 6
 
20.0%
ValueCountFrequency (%)
0 15
50.0%
3150 1
 
3.3%
3500 1
 
3.3%
4500 1
 
3.3%
5200 1
 
3.3%
5500 1
 
3.3%
5550 1
 
3.3%
6080 1
 
3.3%
9700 1
 
3.3%
10600 1
 
3.3%
ValueCountFrequency (%)
308000 1
3.3%
31600 1
3.3%
18000 1
3.3%
17000 1
3.3%
12000 1
3.3%
11370 1
3.3%
10600 1
3.3%
9700 1
3.3%
6080 1
3.3%
5550 1
3.3%

Interactions

2024-03-13T21:00:47.353037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:41.679309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:42.323532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:43.025173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:44.239482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:45.035326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:45.818293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:46.635754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:47.459985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:41.764905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:42.409335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:43.124641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:44.357578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:45.134273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:45.914741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:46.721284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:47.576373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:41.839120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:42.493028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:43.227069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:44.443691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:45.214237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:46.053959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:46.810521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:47.693826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:41.920411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:42.578603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:43.668421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:44.532063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:45.334198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:46.172888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:46.888830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:47.840738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:41.998814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:42.661617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:43.827197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:44.608527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:45.434626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:46.265210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:46.977546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:48.018324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:42.081990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:42.744502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:43.956707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:44.709754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:45.526846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:46.352210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:47.079875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:48.156687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:42.161824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:42.836873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:44.050967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:44.812251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:45.621412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:46.449110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:47.186296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:48.321036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:42.241867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:42.929542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:44.141593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:44.930818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:45.709167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:46.548178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:47.263698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:00:54.924482image/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
회원코드1.0001.0000.0000.3100.3440.7950.5950.0000.0001.0000.6541.0000.1970.1970.0000.700
가맹점번호1.0000.0001.0000.0000.0000.0000.000NaNNaNNaNNaNNaN0.9750.9750.9940.000
성별코드1.0000.3100.0001.0000.3960.0000.0000.2570.6670.0000.5431.0000.0000.0000.0000.102
연령대코드1.0000.3440.0000.3961.0000.7350.4720.5150.5450.0000.9291.0000.0000.0000.0000.000
결제상품ID1.0000.7950.0000.0000.7351.0001.0000.1870.7510.0001.0001.0000.0000.0000.0000.000
결제상품명1.0000.5950.0000.0000.4721.0001.0000.7591.0001.0001.0001.0000.0000.0000.0000.572
가맹점업종명1.0000.000NaN0.2570.5150.1870.7591.0000.6360.0000.6551.0000.0000.000NaN0.361
가맹점우편번호1.0000.000NaN0.6670.5450.7511.0000.6361.0001.0001.0001.0001.0001.000NaN0.459
시도명1.0001.000NaN0.0000.0000.0001.0000.0001.0001.000NaNNaN0.5870.587NaN0.000
시군구명1.0000.654NaN0.5430.9291.0001.0000.6551.000NaN1.0001.000NaNNaNNaN0.000
읍면동명1.0001.000NaN1.0001.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
위도1.0000.1970.9750.0000.0000.0000.0000.0001.0000.587NaNNaN1.0000.9940.9770.076
경도1.0000.1970.9750.0000.0000.0000.0000.0001.0000.587NaNNaN0.9941.0000.9770.076
사용여부1.0000.0000.9940.0000.0000.0000.000NaNNaNNaNNaNNaN0.9770.9771.0000.043
결제금액1.0000.7000.0000.1020.0000.0000.5720.3610.4590.0000.0001.0000.0760.0760.0431.000
2024-03-13T21:00:55.133135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부성별코드시도명가맹점업종명
사용여부1.0000.0001.0001.000
성별코드0.0001.0000.0000.391
시도명1.0000.0001.0000.000
가맹점업종명1.0000.3910.0001.000
2024-03-13T21:00:55.262248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드가맹점업종명시도명사용여부
회원코드1.000-0.172-0.155-0.027-0.0290.3120.3050.1250.2410.0000.0000.000
가맹점번호-0.1721.000-0.2360.0270.396-0.806-0.763-0.8970.0001.0001.0000.931
연령대코드-0.155-0.2361.000-0.216-0.0770.053-0.0560.2170.2540.3910.0000.000
결제상품ID-0.0270.027-0.2161.0000.2310.0560.0530.1180.0000.2200.0000.000
가맹점우편번호-0.0290.396-0.0770.2311.000-0.7320.364-0.4210.4680.0000.6791.000
위도0.312-0.8060.0530.056-0.7321.0000.8440.8710.0000.0000.3940.864
경도0.305-0.763-0.0560.0530.3640.8441.0000.7920.0000.0000.3940.864
결제금액0.125-0.8970.2170.118-0.4210.8710.7921.0000.1590.0730.0000.051
성별코드0.2410.0000.2540.0000.4680.0000.0000.1591.0000.3910.0000.000
가맹점업종명0.0001.0000.3910.2200.0000.0000.0000.0730.3911.0000.0001.000
시도명0.0001.0000.0000.0000.6790.3940.3940.0000.0000.0001.0001.000
사용여부0.0000.9310.0000.0001.0000.8640.8640.0510.0001.0001.0001.000

Missing values

2024-03-13T21:00:48.513906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:00:48.792741image/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.
2024-03-13T21:00:48.960115image/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
02021-02-012021-02-07++jTKAH72ANhj1szVLm+MK8pSM/gncabQ4t3Vnnm1XY=3011471134999999999999999F30140000090000고양페이카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
12021-02-012021-02-07+4hcOUie0Vec4IoWD5oHEvBdyYu2/hkrao9+fhCWk/M=3036804193999999999999999F60140000034000안양사랑페이<NA><NA><NA><NA><NA>0.00.0N0
22021-02-012021-02-07++3snq512aH2fWWTY3ZHknYZk+R2WhQ6vnL4mdQv7Fw=3029428174715316562M50140000058000평택사랑카드(통합)음료식품17922경기도평택시평택동36.989127.084Y5200
32021-02-012021-02-072y+5o672btx1T7yW+a4Gq2ZsSz855NLcMSSj91j/AXc=3018956180999999999999999F40140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
42021-02-012021-02-079AalQnClpjuhK5HxCJ4ryQF0QUa2KIY2xHH6AhpGYaI=3002078940721037232M60140000030000부천페이일반휴게음식14414경기도부천시원종동37.528126.81Y6080
52021-02-012021-02-07++soivCrrbU4vXCcMySuMYlxbcCOEOABsN004IM7MDc=3024940156999999999999999F30140000100000안산사랑상품권 다온(통합)<NA><NA><NA><NA><NA>0.00.0N0
62021-02-012021-02-07+Asd3Aq22v4zYgtAgweHiBqlZgg7fm3zybhgPXVoO14=3018836235999999999999999M40140000050000평택사랑카드<NA><NA><NA><NA><NA>0.00.0N0
72021-02-012021-02-07+4wShqryvXtZu08GyCVgJB5cc6rmVrL3yiKaew0N+hk=3033408197710273634M60140000030000부천페이일반휴게음식14547경기도부천시중동37.502126.762Y17000
82021-02-012021-02-07KOsTkVW1z4Nl0W+HUgzOViK9axXOgLXYpVdwzwFnBnU=3021011448999999999999999M20140000112000군포愛머니<NA><NA><NA><NA><NA>0.00.0N0
92021-02-012021-02-07h9pxrPWa75pAS3LFMBjlT2/zXL4DpVz9h0quiC62G3Y=3016812150999999999999999F60140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202021-02-012021-02-07+Fmlt0J7igx5cFpK7Vt6ZW6fX3n6mYXLW9UIGHkka90=3021179541788020420M40140000044000오산화폐 오색전유통업 영리18110경기도오산시수청동37.172127.058Y3150
212021-02-012021-02-079Grl2YqD0zjlo1yl4v3q/wpI+xF4UQRJX09k6GSby+M=3017485645999999999999999F50140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
222021-02-012021-02-07KU1xqRhbtY6/7WJbzA/5HhtoVm4dRYpoqBacuHnGIA0=3019313312720711611M60140000120000파주 Pay(파주페이)유통업 영리10900경기도파주시목동동37.72126.745Y11370
232021-02-012021-02-07VqY7wVB2Uf/PsTfLrmujmAiAGKpmwr44wGzDH/WdmzA=3031536160796056915M20140000100000안산사랑상품권 다온(통합)유통업 영리15330경기도안산시 단원구와동37.331126.819Y4500
242021-02-012021-02-07hIAPZ7oAMQw6OgFU++KVUEZqK7DOIcM1hKHKYbN6hU8=3002461200999999999999999M20140000082000구리사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
252021-02-012021-02-07+5G7lhA30TIEiWrM93wnwZfVJ0wL7FK4CRCjLvjKViI=3038559317786239327F40140000122000의정부사랑카드음료식품11695경기도의정부시의정부동37.739127.051Y12000
262021-02-012021-02-073W9B+2ktCRe9kxB7cIfArZiGOFVxLEuqIeAtlUtxsD4=3013993267723081830F50140000124000안산사랑상품권 다온음료식품15540경기도안산시 상록구본오동37.296126.862Y308000
272021-02-012021-02-079D62eUo3d5rD4UvampqTSya5ZIRJGc0x2jTb7opvrDs=3016962736999999999999999F40140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0N0
282021-02-012021-02-07ExyzFthsk/bjXjz7YWvRnu6nv5UhCZWJk1iOu76VpVM=3002856343709745620M60140000038000양평통보유통업 영리12560NONE<NA><NA>0.00.0Y10600
292021-02-012021-02-07KPiYpU+B9r8yzGFEYenrJPMsUTkSyp12iUi3DPq9pEs=3021974195999999999999999F80140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0