Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells92
Missing cells (%)17.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory156.4 B

Variable types

Categorical5
Text4
Numeric8
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/b2eb3b42-f441-4e2b-bc23-76af42ff5d6e

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
사용여부 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
결제상품명 is highly overall correlated with 회원코드 and 3 other fieldsHigh correlation
성별코드 is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 회원코드 and 10 other fieldsHigh correlation
회원코드 is highly overall correlated with 결제상품명 and 1 other fieldsHigh correlation
가맹점번호 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
연령대코드 is highly overall correlated with 시도명High correlation
결제상품ID is highly overall correlated with 결제상품명 and 1 other fieldsHigh correlation
가맹점우편번호 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점번호 and 5 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
가맹점업종명 has 23 (76.7%) missing valuesMissing
가맹점우편번호 has 23 (76.7%) 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 4 (13.3%) zerosZeros
위도 has 23 (76.7%) zerosZeros
경도 has 23 (76.7%) zerosZeros
결제금액 has 23 (76.7%) zerosZeros

Reproduction

Analysis started2024-03-13 11:58:05.284356
Analysis finished2024-03-13 11:58:13.474499
Duration8.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책주간결제시작일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2022-03-07
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-03-07
2nd row2022-03-07
3rd row2022-03-07
4th row2022-03-07
5th row2022-03-07

Common Values

ValueCountFrequency (%)
2022-03-07 30
100.0%

Length

2024-03-13T20:58:13.553159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:58:13.669119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-03-07 30
100.0%

정책주간결제종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2022-03-13
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-03-13
2nd row2022-03-13
3rd row2022-03-13
4th row2022-03-13
5th row2022-03-13

Common Values

ValueCountFrequency (%)
2022-03-13 30
100.0%

Length

2024-03-13T20:58:13.808442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:58:13.929013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-03-13 30
100.0%

카드번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:58:14.138190image/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 rowVGS27kgiURkBTFgCvQFm3dX4DkHNJ1BZuOveFLEq8dg=
2nd rowzzSjty686hoUbUXL2HmP9NgXFhvgBfRB/2GImDCMfIc=
3rd rowRooEh5AjJ4mVTN/BKJlVc8C5dshWPQrnn+PYnF7Pq1M=
4th rowVGSaf2VPKVEwbK27qPD6xGbnMI2Nc5L8bPzx8F4fZeA=
5th rowVGWhzDFvtxXX20rzu4D4tidzkjueh77jKLm9C/7dvNA=
ValueCountFrequency (%)
vgs27kgiurkbtfgcvqfm3dx4dkhnj1bzuovefleq8dg 1
 
3.3%
zzsjty686houbuxl2hmp9ngxfhvgbfrb/2gimdcmfic 1
 
3.3%
vhjul5cuny9xaxcwmfqcmjd9sady2b6ql1j6gu5qdpo 1
 
3.3%
vgwuxoxzmxgpshi5cmarkglrux/t3hxvh6wdblbndvg 1
 
3.3%
vhs3nxear38zhw8aa5repwgjbnne0uhrjwidqcdhdyc 1
 
3.3%
vjnss1xnflinvrjxtn4jtgfhc9f0d85ourw0kgetjhi 1
 
3.3%
vhogxvanrlgbkg7caq5bpevijrfked/qupsq+tuluje 1
 
3.3%
vjjizxvmy0fav3lmyods+b5h0yqz5at42ja5oinkeys 1
 
3.3%
vh0husnuk1ghe0zzf3wax8p4cordjub8r7+w2kglpuw 1
 
3.3%
vjayrf4xln6tcgmktsyaflsz+muzscgc3+njnrxzpic 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:58:14.495807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
V 39
 
3.0%
= 30
 
2.3%
X 29
 
2.2%
4 27
 
2.0%
F 27
 
2.0%
5 26
 
2.0%
D 26
 
2.0%
8 26
 
2.0%
z 26
 
2.0%
U 25
 
1.9%
Other values (55) 1039
78.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 535
40.5%
Lowercase Letter 517
39.2%
Decimal Number 192
 
14.5%
Math Symbol 54
 
4.1%
Other Punctuation 22
 
1.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
V 39
 
7.3%
X 29
 
5.4%
F 27
 
5.0%
D 26
 
4.9%
U 25
 
4.7%
R 24
 
4.5%
G 23
 
4.3%
H 23
 
4.3%
M 23
 
4.3%
Y 22
 
4.1%
Other values (16) 274
51.2%
Lowercase Letter
ValueCountFrequency (%)
z 26
 
5.0%
c 25
 
4.8%
g 25
 
4.8%
h 24
 
4.6%
j 24
 
4.6%
u 23
 
4.4%
p 23
 
4.4%
s 22
 
4.3%
y 22
 
4.3%
a 21
 
4.1%
Other values (16) 282
54.5%
Decimal Number
ValueCountFrequency (%)
4 27
14.1%
5 26
13.5%
8 26
13.5%
7 21
10.9%
3 18
9.4%
0 18
9.4%
2 16
8.3%
9 15
7.8%
6 14
7.3%
1 11
5.7%
Math Symbol
ValueCountFrequency (%)
= 30
55.6%
+ 24
44.4%
Other Punctuation
ValueCountFrequency (%)
/ 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1052
79.7%
Common 268
 
20.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
V 39
 
3.7%
X 29
 
2.8%
F 27
 
2.6%
D 26
 
2.5%
z 26
 
2.5%
U 25
 
2.4%
c 25
 
2.4%
g 25
 
2.4%
R 24
 
2.3%
h 24
 
2.3%
Other values (42) 782
74.3%
Common
ValueCountFrequency (%)
= 30
11.2%
4 27
10.1%
5 26
9.7%
8 26
9.7%
+ 24
9.0%
/ 22
8.2%
7 21
7.8%
3 18
6.7%
0 18
6.7%
2 16
 
6.0%
Other values (3) 40
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
V 39
 
3.0%
= 30
 
2.3%
X 29
 
2.2%
4 27
 
2.0%
F 27
 
2.0%
5 26
 
2.0%
D 26
 
2.0%
8 26
 
2.0%
z 26
 
2.0%
U 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%
Mean3.0210853 × 109
Minimum3.0019161 × 109
Maximum3.0721076 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:58:14.616791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0019161 × 109
5-th percentile3.0022521 × 109
Q13.0166089 × 109
median3.01738 × 109
Q33.0189607 × 109
95-th percentile3.064444 × 109
Maximum3.0721076 × 109
Range70191464
Interquartile range (IQR)2351705.5

Descriptive statistics

Standard deviation17684515
Coefficient of variation (CV)0.0058536961
Kurtosis3.0330803
Mean3.0210853 × 109
Median Absolute Deviation (MAD)1439749.5
Skewness1.7652047
Sum9.0632559 × 1010
Variance3.1274208 × 1014
MonotonicityNot monotonic
2024-03-13T20:58:15.049950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3018537821 1
 
3.3%
3067970615 1
 
3.3%
3016518254 1
 
3.3%
3032834178 1
 
3.3%
3017415356 1
 
3.3%
3017560209 1
 
3.3%
3060133761 1
 
3.3%
3002482412 1
 
3.3%
3004377108 1
 
3.3%
3036315207 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3001916139 1
3.3%
3002118977 1
3.3%
3002414767 1
3.3%
3002482412 1
3.3%
3004377108 1
3.3%
3004426376 1
3.3%
3012582103 1
3.3%
3016518254 1
3.3%
3016881025 1
3.3%
3016909265 1
3.3%
ValueCountFrequency (%)
3072107603 1
3.3%
3067970615 1
3.3%
3060133761 1
3.3%
3036315207 1
3.3%
3032834178 1
3.3%
3031637167 1
3.3%
3021083444 1
3.3%
3019101596 1
3.3%
3018537821 1
3.3%
3018116413 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6666684 × 1014
Minimum7.0639884 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:58:15.172760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0639884 × 108
5-th percentile7.1637994 × 108
Q11 × 1015
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.9999929 × 1014
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.3018275 × 1014
Coefficient of variation (CV)0.56110781
Kurtosis-0.25732032
Mean7.6666684 × 1014
Median Absolute Deviation (MAD)0
Skewness-1.3283381
Sum2.3000005 × 1016
Variance1.850572 × 1029
MonotonicityNot monotonic
2024-03-13T20:58:15.351353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
999999999999999 23
76.7%
769920038 1
 
3.3%
706398836 1
 
3.3%
786689242 1
 
3.3%
740558362 1
 
3.3%
720985222 1
 
3.3%
712611976 1
 
3.3%
724136135 1
 
3.3%
ValueCountFrequency (%)
706398836 1
 
3.3%
712611976 1
 
3.3%
720985222 1
 
3.3%
724136135 1
 
3.3%
740558362 1
 
3.3%
769920038 1
 
3.3%
786689242 1
 
3.3%
999999999999999 23
76.7%
ValueCountFrequency (%)
999999999999999 23
76.7%
786689242 1
 
3.3%
769920038 1
 
3.3%
740558362 1
 
3.3%
724136135 1
 
3.3%
720985222 1
 
3.3%
712611976 1
 
3.3%
706398836 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2024-03-13T20:58:15.475121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:58:15.572618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 17
56.7%
m 13
43.3%

연령대코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37
Minimum0
Maximum70
Zeros4
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:58:15.666488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122.5
median40
Q350
95-th percentile60
Maximum70
Range70
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation20.703157
Coefficient of variation (CV)0.55954477
Kurtosis-0.72897971
Mean37
Median Absolute Deviation (MAD)10
Skewness-0.617526
Sum1110
Variance428.62069
MonotonicityNot monotonic
2024-03-13T20:58:15.789513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
50 9
30.0%
40 5
16.7%
0 4
13.3%
60 4
13.3%
30 3
 
10.0%
20 2
 
6.7%
10 2
 
6.7%
70 1
 
3.3%
ValueCountFrequency (%)
0 4
13.3%
10 2
 
6.7%
20 2
 
6.7%
30 3
 
10.0%
40 5
16.7%
50 9
30.0%
60 4
13.3%
70 1
 
3.3%
ValueCountFrequency (%)
70 1
 
3.3%
60 4
13.3%
50 9
30.0%
40 5
16.7%
30 3
 
10.0%
20 2
 
6.7%
10 2
 
6.7%
0 4
13.3%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000009 × 1011
Minimum1.4000002 × 1011
Maximum1.4000013 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:58:15.920641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000004 × 1011
Q11.4000005 × 1011
median1.4000012 × 1011
Q31.4000012 × 1011
95-th percentile1.4000013 × 1011
Maximum1.4000013 × 1011
Range108000
Interquartile range (IQR)76000

Descriptive statistics

Standard deviation39091.346
Coefficient of variation (CV)2.7922372 × 10-7
Kurtosis-1.8126385
Mean1.4000009 × 1011
Median Absolute Deviation (MAD)10000
Skewness-0.36272968
Sum4.2000026 × 1012
Variance1.5281333 × 109
MonotonicityNot monotonic
2024-03-13T20:58:16.093103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
140000046000 6
20.0%
140000126000 4
13.3%
140000122000 3
10.0%
140000116000 3
10.0%
140000124000 2
 
6.7%
140000120000 2
 
6.7%
140000118000 2
 
6.7%
140000040000 2
 
6.7%
140000044000 2
 
6.7%
140000018000 1
 
3.3%
Other values (3) 3
10.0%
ValueCountFrequency (%)
140000018000 1
 
3.3%
140000040000 2
 
6.7%
140000044000 2
 
6.7%
140000046000 6
20.0%
140000054000 1
 
3.3%
140000066000 1
 
3.3%
140000114000 1
 
3.3%
140000116000 3
10.0%
140000118000 2
 
6.7%
140000120000 2
 
6.7%
ValueCountFrequency (%)
140000126000 4
13.3%
140000124000 2
 
6.7%
140000122000 3
10.0%
140000120000 2
 
6.7%
140000118000 2
 
6.7%
140000116000 3
10.0%
140000114000 1
 
3.3%
140000066000 1
 
3.3%
140000054000 1
 
3.3%
140000046000 6
20.0%

결제상품명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
용인와이페이
6 
수원페이
4 
의정부사랑카드
3 
행복화성지역화폐
3 
안산사랑상품권 다온
2 
Other values (8)
12 

Length

Max length15
Median length11
Mean length7.4333333
Min length4

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st row의정부사랑카드
2nd row용인와이페이
3rd row용인와이페이
4th row수원페이
5th row용인와이페이

Common Values

ValueCountFrequency (%)
용인와이페이 6
20.0%
수원페이 4
13.3%
의정부사랑카드 3
10.0%
행복화성지역화폐 3
10.0%
안산사랑상품권 다온 2
 
6.7%
파주 Pay(파주페이) 2
 
6.7%
하남하머니 2
 
6.7%
여주사랑카드 2
 
6.7%
오산화폐 오색전 2
 
6.7%
고양페이카드 1
 
3.3%
Other values (3) 3
10.0%

Length

2024-03-13T20:58:16.284024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
용인와이페이 6
15.4%
수원페이 4
 
10.3%
의정부사랑카드 3
 
7.7%
행복화성지역화폐 3
 
7.7%
오산화폐 3
 
7.7%
하남하머니 2
 
5.1%
오색전 2
 
5.1%
여주사랑카드 2
 
5.1%
pay(파주페이 2
 
5.1%
파주 2
 
5.1%
Other values (8) 10
25.6%

가맹점업종명
Text

MISSING 

Distinct6
Distinct (%)85.7%
Missing23
Missing (%)76.7%
Memory size372.0 B
2024-03-13T20:58:16.436325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.2857143
Min length2

Characters and Unicode

Total characters23
Distinct characters17
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

Unique5 ?
Unique (%)71.4%

Sample

1st row약국
2nd row음료식품
3rd row학원
4th row수리서비스
5th row병원
ValueCountFrequency (%)
음료식품 2
28.6%
약국 1
14.3%
학원 1
14.3%
수리서비스 1
14.3%
병원 1
14.3%
서적문구 1
14.3%
2024-03-13T20:58:16.717487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
음 2
 
8.7%
식 2
 
8.7%
품 2
 
8.7%
원 2
 
8.7%
료 2
 
8.7%
서 2
 
8.7%
비 1
 
4.3%
문 1
 
4.3%
적 1
 
4.3%
병 1
 
4.3%
Other values (7) 7
30.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
음 2
 
8.7%
식 2
 
8.7%
품 2
 
8.7%
원 2
 
8.7%
료 2
 
8.7%
서 2
 
8.7%
비 1
 
4.3%
문 1
 
4.3%
적 1
 
4.3%
병 1
 
4.3%
Other values (7) 7
30.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
음 2
 
8.7%
식 2
 
8.7%
품 2
 
8.7%
원 2
 
8.7%
료 2
 
8.7%
서 2
 
8.7%
비 1
 
4.3%
문 1
 
4.3%
적 1
 
4.3%
병 1
 
4.3%
Other values (7) 7
30.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
음 2
 
8.7%
식 2
 
8.7%
품 2
 
8.7%
원 2
 
8.7%
료 2
 
8.7%
서 2
 
8.7%
비 1
 
4.3%
문 1
 
4.3%
적 1
 
4.3%
병 1
 
4.3%
Other values (7) 7
30.4%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing23
Missing (%)76.7%
Infinite0
Infinite (%)0.0%
Mean14593.429
Minimum10905
Maximum18431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:58:16.829187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10905
5-th percentile10908
Q111917
median15594
Q316695
95-th percentile17952.8
Maximum18431
Range7526
Interquartile range (IQR)4778

Descriptive statistics

Standard deviation3014.9816
Coefficient of variation (CV)0.20659858
Kurtosis-1.8194174
Mean14593.429
Median Absolute Deviation (MAD)2675
Skewness-0.24103174
Sum102154
Variance9090114
MonotonicityNot monotonic
2024-03-13T20:58:16.945916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
16837 1
 
3.3%
16553 1
 
3.3%
15594 1
 
3.3%
10915 1
 
3.3%
12919 1
 
3.3%
18431 1
 
3.3%
10905 1
 
3.3%
(Missing) 23
76.7%
ValueCountFrequency (%)
10905 1
3.3%
10915 1
3.3%
12919 1
3.3%
15594 1
3.3%
16553 1
3.3%
16837 1
3.3%
18431 1
3.3%
ValueCountFrequency (%)
18431 1
3.3%
16837 1
3.3%
16553 1
3.3%
15594 1
3.3%
12919 1
3.3%
10915 1
3.3%
10905 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
23 
경기도
7 

Length

Max length4
Median length4
Mean length3.7666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 23
76.7%
경기도 7
 
23.3%

Length

2024-03-13T20:58:17.087636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:58:17.217275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 23
76.7%
경기도 7
 
23.3%

시군구명
Text

MISSING 

Distinct6
Distinct (%)85.7%
Missing23
Missing (%)76.7%
Memory size372.0 B
2024-03-13T20:58:17.351806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4.7142857
Min length3

Characters and Unicode

Total characters33
Distinct characters20
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

Unique5 ?
Unique (%)71.4%

Sample

1st row용인시 수지구
2nd row수원시 권선구
3rd row안산시 상록구
4th row파주시
5th row하남시
ValueCountFrequency (%)
파주시 2
20.0%
용인시 1
10.0%
수지구 1
10.0%
수원시 1
10.0%
권선구 1
10.0%
안산시 1
10.0%
상록구 1
10.0%
하남시 1
10.0%
화성시 1
10.0%
2024-03-13T20:58:17.679920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 7
21.2%
3
 
9.1%
구 3
 
9.1%
파 2
 
6.1%
수 2
 
6.1%
주 2
 
6.1%
산 1
 
3.0%
화 1
 
3.0%
남 1
 
3.0%
하 1
 
3.0%
Other values (10) 10
30.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30
90.9%
Space Separator 3
 
9.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 7
23.3%
구 3
 
10.0%
파 2
 
6.7%
수 2
 
6.7%
주 2
 
6.7%
산 1
 
3.3%
화 1
 
3.3%
남 1
 
3.3%
하 1
 
3.3%
록 1
 
3.3%
Other values (9) 9
30.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30
90.9%
Common 3
 
9.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 7
23.3%
구 3
 
10.0%
파 2
 
6.7%
수 2
 
6.7%
주 2
 
6.7%
산 1
 
3.3%
화 1
 
3.3%
남 1
 
3.3%
하 1
 
3.3%
록 1
 
3.3%
Other values (9) 9
30.0%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30
90.9%
ASCII 3
 
9.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 7
23.3%
구 3
 
10.0%
파 2
 
6.7%
수 2
 
6.7%
주 2
 
6.7%
산 1
 
3.3%
화 1
 
3.3%
남 1
 
3.3%
하 1
 
3.3%
록 1
 
3.3%
Other values (9) 9
30.0%
ASCII
ValueCountFrequency (%)
3
100.0%

읍면동명
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing23
Missing (%)76.7%
Memory size372.0 B
2024-03-13T20:58:17.845709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8571429
Min length2

Characters and Unicode

Total characters20
Distinct characters12
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 row풍덕천동
2nd row권선동
3rd row사동
4th row금촌동
5th row풍산동
ValueCountFrequency (%)
풍덕천동 1
14.3%
권선동 1
14.3%
사동 1
14.3%
금촌동 1
14.3%
풍산동 1
14.3%
능동 1
14.3%
동패동 1
14.3%
2024-03-13T20:58:18.176965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 8
40.0%
풍 2
 
10.0%
덕 1
 
5.0%
천 1
 
5.0%
권 1
 
5.0%
선 1
 
5.0%
사 1
 
5.0%
금 1
 
5.0%
촌 1
 
5.0%
산 1
 
5.0%
Other values (2) 2
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8
40.0%
풍 2
 
10.0%
덕 1
 
5.0%
천 1
 
5.0%
권 1
 
5.0%
선 1
 
5.0%
사 1
 
5.0%
금 1
 
5.0%
촌 1
 
5.0%
산 1
 
5.0%
Other values (2) 2
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8
40.0%
풍 2
 
10.0%
덕 1
 
5.0%
천 1
 
5.0%
권 1
 
5.0%
선 1
 
5.0%
사 1
 
5.0%
금 1
 
5.0%
촌 1
 
5.0%
산 1
 
5.0%
Other values (2) 2
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 8
40.0%
풍 2
 
10.0%
덕 1
 
5.0%
천 1
 
5.0%
권 1
 
5.0%
선 1
 
5.0%
사 1
 
5.0%
금 1
 
5.0%
촌 1
 
5.0%
산 1
 
5.0%
Other values (2) 2
 
10.0%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7365333
Minimum0
Maximum37.751
Zeros23
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:58:18.325247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile37.64135
Maximum37.751
Range37.751
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.107367
Coefficient of variation (CV)1.8436794
Kurtosis-0.25661147
Mean8.7365333
Median Absolute Deviation (MAD)0
Skewness1.3284912
Sum262.096
Variance259.44726
MonotonicityNot monotonic
2024-03-13T20:58:18.472210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 23
76.7%
37.323 1
 
3.3%
37.25 1
 
3.3%
37.296 1
 
3.3%
37.751 1
 
3.3%
37.555 1
 
3.3%
37.209 1
 
3.3%
37.712 1
 
3.3%
ValueCountFrequency (%)
0.0 23
76.7%
37.209 1
 
3.3%
37.25 1
 
3.3%
37.296 1
 
3.3%
37.323 1
 
3.3%
37.555 1
 
3.3%
37.712 1
 
3.3%
37.751 1
 
3.3%
ValueCountFrequency (%)
37.751 1
 
3.3%
37.712 1
 
3.3%
37.555 1
 
3.3%
37.323 1
 
3.3%
37.296 1
 
3.3%
37.25 1
 
3.3%
37.209 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%
Mean29.623733
Minimum0
Maximum127.189
Zeros23
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:58:18.575659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile127.07925
Maximum127.189
Range127.189
Interquartile range (IQR)0

Descriptive statistics

Standard deviation54.61561
Coefficient of variation (CV)1.8436437
Kurtosis-0.25728255
Mean29.623733
Median Absolute Deviation (MAD)0
Skewness1.3283463
Sum888.712
Variance2982.8649
MonotonicityNot monotonic
2024-03-13T20:58:18.694090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 23
76.7%
127.095 1
 
3.3%
127.035 1
 
3.3%
126.821 1
 
3.3%
126.767 1
 
3.3%
127.189 1
 
3.3%
127.06 1
 
3.3%
126.745 1
 
3.3%
ValueCountFrequency (%)
0.0 23
76.7%
126.745 1
 
3.3%
126.767 1
 
3.3%
126.821 1
 
3.3%
127.035 1
 
3.3%
127.06 1
 
3.3%
127.095 1
 
3.3%
127.189 1
 
3.3%
ValueCountFrequency (%)
127.189 1
 
3.3%
127.095 1
 
3.3%
127.06 1
 
3.3%
127.035 1
 
3.3%
126.821 1
 
3.3%
126.767 1
 
3.3%
126.745 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
23 
True
7 
ValueCountFrequency (%)
False 23
76.7%
True 7
 
23.3%
2024-03-13T20:58:18.801527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52609
Minimum0
Maximum1194170
Zeros23
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:58:18.895586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile170350
Maximum1194170
Range1194170
Interquartile range (IQR)0

Descriptive statistics

Standard deviation220705.45
Coefficient of variation (CV)4.1952034
Kurtosis27.053622
Mean52609
Median Absolute Deviation (MAD)0
Skewness5.1263382
Sum1578270
Variance4.8710898 × 1010
MonotonicityNot monotonic
2024-03-13T20:58:19.010467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 23
76.7%
11200 1
 
3.3%
5900 1
 
3.3%
250000 1
 
3.3%
4000 1
 
3.3%
1194170 1
 
3.3%
73000 1
 
3.3%
40000 1
 
3.3%
ValueCountFrequency (%)
0 23
76.7%
4000 1
 
3.3%
5900 1
 
3.3%
11200 1
 
3.3%
40000 1
 
3.3%
73000 1
 
3.3%
250000 1
 
3.3%
1194170 1
 
3.3%
ValueCountFrequency (%)
1194170 1
 
3.3%
250000 1
 
3.3%
73000 1
 
3.3%
40000 1
 
3.3%
11200 1
 
3.3%
5900 1
 
3.3%
4000 1
 
3.3%
0 23
76.7%

Interactions

2024-03-13T20:58:12.096767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:05.992735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:06.759439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:07.592189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:08.400865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:09.585992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:10.456760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:11.208272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:12.192479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:06.092135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:06.865532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:07.709177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:08.839113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:09.679026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:10.549177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:11.299047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:12.278564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:06.196740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:06.951125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:07.814249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:08.925634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:09.789519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:10.644697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:11.380079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:12.373226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:06.299062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:07.085484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:07.921099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:09.030685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:09.919485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:10.734743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:11.515396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:12.473436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:06.396895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:07.198825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:08.005911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:09.112210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:10.035079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:10.818298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:11.636080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:12.584961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:06.500937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:07.296882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:08.116345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:09.257732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:10.145759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:10.907040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:11.748051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:12.692483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:06.590959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:07.388125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:08.217668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:09.381250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:10.257245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:10.999923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:11.864513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:12.779056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:06.672512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:07.470885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:08.310822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:09.488422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:10.360090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:11.110442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:11.983778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:58:19.110039image/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
회원코드1.0001.0000.0000.5090.2230.8860.8760.0001.0001.0001.0000.0000.0000.0000.611
가맹점번호1.0000.0001.0000.0000.2650.0000.346NaNNaNNaNNaN0.9850.9850.9850.297
성별코드1.0000.5090.0001.0000.3110.1930.2961.0000.0000.0001.0000.0000.0000.0000.102
연령대코드1.0000.2230.2650.3111.0000.6560.4550.0001.0000.0001.0000.4280.4280.4280.000
결제상품ID1.0000.8860.0000.1930.6561.0001.0001.0000.0001.0001.0000.0000.0000.0000.000
결제상품명1.0000.8760.3460.2960.4551.0001.0000.8971.0001.0001.0000.0000.0000.0000.548
가맹점업종명1.0000.000NaN1.0000.0001.0000.8971.0000.9420.8971.000NaNNaNNaN1.000
가맹점우편번호1.0001.000NaN0.0001.0000.0001.0000.9421.0001.0001.000NaNNaNNaN1.000
시군구명1.0001.000NaN0.0000.0001.0001.0000.8971.0001.0001.000NaNNaNNaN1.000
읍면동명1.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.000NaNNaNNaN1.000
위도1.0000.0000.9850.0000.4280.0000.000NaNNaNNaNNaN1.0000.9890.9890.261
경도1.0000.0000.9850.0000.4280.0000.000NaNNaNNaNNaN0.9891.0000.9890.261
사용여부1.0000.0000.9850.0000.4280.0000.000NaNNaNNaNNaN0.9890.9891.0000.261
결제금액1.0000.6110.2970.1020.0000.0000.5481.0001.0001.0001.0000.2610.2610.2611.000
2024-03-13T20:58:19.289226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부결제상품명성별코드시도명
사용여부1.0000.0000.0001.000
결제상품명0.0001.0000.1821.000
성별코드0.0000.1821.0001.000
시도명1.0001.0001.0001.000
2024-03-13T20:58:19.406787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드결제상품명시도명사용여부
회원코드1.000-0.0870.0730.018-0.0710.0970.1180.0670.2720.5421.0000.000
가맹점번호-0.0871.000-0.175-0.281-0.214-0.968-0.982-0.9760.0000.0001.0000.903
연령대코드0.073-0.1751.000-0.081-0.1340.2000.1940.1810.1870.1481.0000.272
결제상품ID0.018-0.281-0.0811.000-0.3780.2620.2430.2650.1910.8421.0000.000
가맹점우편번호-0.071-0.214-0.134-0.3781.000-0.8570.6070.0710.0000.8941.0001.000
위도0.097-0.9680.2000.262-0.8571.0000.9680.9720.0000.0001.0000.903
경도0.118-0.9820.1940.2430.6070.9681.0000.9870.0000.0001.0000.903
결제금액0.067-0.9760.1810.2650.0710.9720.9871.0000.1590.2711.0000.414
성별코드0.2720.0000.1870.1910.0000.0000.0000.1591.0000.1821.0000.000
결제상품명0.5420.0000.1480.8420.8940.0000.0000.2710.1821.0001.0000.000
시도명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사용여부0.0000.9030.2720.0001.0000.9030.9030.4140.0000.0001.0001.000

Missing values

2024-03-13T20:58:12.953159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:58:13.225168image/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.
2024-03-13T20:58:13.385854image/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
02022-03-072022-03-13VGS27kgiURkBTFgCvQFm3dX4DkHNJ1BZuOveFLEq8dg=3018537821999999999999999F0140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0N0
12022-03-072022-03-13zzSjty686hoUbUXL2HmP9NgXFhvgBfRB/2GImDCMfIc=3019101596769920038M50140000046000용인와이페이약국16837경기도용인시 수지구풍덕천동37.323127.095Y11200
22022-03-072022-03-13RooEh5AjJ4mVTN/BKJlVc8C5dshWPQrnn+PYnF7Pq1M=3002414767999999999999999F30140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0N0
32022-03-072022-03-13VGSaf2VPKVEwbK27qPD6xGbnMI2Nc5L8bPzx8F4fZeA=3018070890999999999999999F0140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
42022-03-072022-03-13VGWhzDFvtxXX20rzu4D4tidzkjueh77jKLm9C/7dvNA=3017049818999999999999999M40140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0N0
52022-03-072022-03-13VGuAU9kI98NBy+hyWY4uYcup40hE/UuiiJlwM4vsCf4=3012582103999999999999999F40140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
62022-03-072022-03-13VGZNFtEaiE/qg9Fg/88F4l/jw3PnIFU9Vm+VgVbHAY0=3016909265999999999999999F60140000114000Thank You Pay-N<NA><NA><NA><NA><NA>0.00.0N0
72022-03-072022-03-13zzWhJqzuKLd0FEAJso9aX+4v7183+HjX+7MFGFfYkX4=3021083444706398836F50140000126000수원페이음료식품16553경기도수원시 권선구권선동37.25127.035Y5900
82022-03-072022-03-13DXHR/x32Y4QpMEacpa5b7zDjUgczKCQuIw4R5QQsuzs=3017456514999999999999999F0140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
92022-03-072022-03-13P6RbDpOUKi950SoG7chDFlRl3ynhsf6jSFdSx4M5wnw=3016881025786689242M50140000124000안산사랑상품권 다온학원15594경기도안산시 상록구사동37.296126.821Y250000
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202022-03-072022-03-13VH0aCWED8vj8Yb53/xjUcKa/QvcyTYV6vBrR1GW7JwA=3031637167999999999999999F40140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0N0
212022-03-072022-03-13VJAYrF4xlN6tcgMkTSYaflSZ+mUzSCgC3+njNRXZPIc=3002118977999999999999999F20140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0N0
222022-03-072022-03-13VH0hUSNUK1GHe0zZf3WAX8p4corDJUb8r7+w2kgLPuw=3036315207999999999999999F50140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0N0
232022-03-072022-03-13VJJizXVmY0fAV3LMyods+B5H0Yqz5aT42JA5OinKeys=3004377108999999999999999M40140000118000하남하머니<NA><NA><NA><NA><NA>0.00.0N0
242022-03-072022-03-13VHOGXvaNRlgbkG7caQ5BpEViJRFkED/quPsq+TuLUJE=3002482412712611976F30140000116000행복화성지역화폐음료식품18431경기도화성시능동37.209127.06Y73000
252022-03-072022-03-13VJnsS1XnFlINVrJXtN4jtgFhc9f0d85oUrW0KgeTjHI=3060133761999999999999999M50140000066000오산화폐 오색전(통합)<NA><NA><NA><NA><NA>0.00.0N0
262022-03-072022-03-13VHS3NXeAR38zHw8aa5repWgjbnNE0UHrjwIDqcdhDyc=3017560209999999999999999F50140000044000오산화폐 오색전<NA><NA><NA><NA><NA>0.00.0N0
272022-03-072022-03-13VGwUxoXZMxgpShi5cMaRkGlRux/T3hXvh6WdBLbnDvg=3017415356999999999999999M30140000044000오산화폐 오색전<NA><NA><NA><NA><NA>0.00.0N0
282022-03-072022-03-13VHjUL5Cuny9xAXCwMfqCmjD9SaDY2b6Ql1j6Gu5Qdpo=3032834178999999999999999F10140000040000여주사랑카드<NA><NA><NA><NA><NA>0.00.0N0
292022-03-072022-03-13VKRF8luModS4zYB/+cYugs5o1sQUp/B7UJm8S574iX8=3016518254724136135M40140000120000파주 Pay(파주페이)서적문구10905경기도파주시동패동37.712126.745Y40000