Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells84
Missing cells (%)15.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory155.4 B

Variable types

DateTime2
Text5
Numeric7
Categorical3
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/1422ed60-1ad8-40cb-9238-76630e47dec8

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
사용여부 is highly overall correlated with 가맹점우편번호 and 5 other fieldsHigh correlation
가맹점번호 is highly overall correlated with 가맹점우편번호 and 5 other fieldsHigh correlation
시도명 is highly overall correlated with 가맹점우편번호 and 4 other fieldsHigh correlation
가맹점우편번호 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점우편번호 and 5 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
결제금액 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
가맹점업종명 has 20 (66.7%) missing valuesMissing
가맹점우편번호 has 20 (66.7%) missing valuesMissing
시군구명 has 22 (73.3%) missing valuesMissing
읍면동명 has 22 (73.3%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
연령대코드 has 1 (3.3%) zerosZeros
위도 has 22 (73.3%) zerosZeros
경도 has 22 (73.3%) zerosZeros
결제금액 has 18 (60.0%) zerosZeros

Reproduction

Analysis started2024-03-13 11:53:33.124998
Analysis finished2024-03-13 11:53:38.333308
Duration5.21 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
Minimum2022-05-02 00:00:00
Maximum2022-05-02 00:00:00
2024-03-13T20:53:38.382289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:38.489522image/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
Minimum2022-05-08 00:00:00
Maximum2022-05-08 00:00:00
2024-03-13T20:53:38.568991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:38.643552image/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
2024-03-13T20:53:38.842614image/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 rowwOqlQd+AX3lw5OWikJW4P9I3z2PnOP7gfe7oypxzUMs=
2nd rowzzFhJLPuQLMZXkssYiQWMjazftE7xdtzmGEIalQk3jw=
3rd rowMB5VQhTyX0+6QC9uHK8hmmyff+5b17HXsi8CLd5fPYs=
4th rowwP0ciKVO2DDxfn6KoeEQExlS+U3tGkcNof9kHoKx9jc=
5th rowwP2fFbqdmAvPAy50XQ5b4YLICA++EOs5/UQ2XTLjgsU=
ValueCountFrequency (%)
woqlqd+ax3lw5owikjw4p9i3z2pnop7gfe7oypxzums 1
 
3.3%
zzfhjlpuqlmzxkssyiqwmjazfte7xdtzmgeialqk3jw 1
 
3.3%
wqh07y7aafqmdolvzsqbe4yybjettw5lzkmmnbdevya 1
 
3.3%
wptxysk3gczcuswybtkjk5svfs29n+e3gmwz2wxzru4 1
 
3.3%
wqzwljma9rx3nrkqsoezqxwgomxp1bfevko3qq3yu5m 1
 
3.3%
wrs9p2yp8q/ltw8gousq6bkftzwgfw6w0fr5t1g9xrc 1
 
3.3%
wqzn79d3f+yhn1wqc0kezwsmfgv+f2rclx31fozecvc 1
 
3.3%
wryzzuwdjt9cgp54ottysvwf01syrwwvipapljf1wy0 1
 
3.3%
wqsy8nxqpal5t3vqktgfzmyw52v4poco5+hqx1r4+qc 1
 
3.3%
wrksbi2tarayyvsw1sdawrhsjxmac1tlmdbnmof6640 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:53:39.189022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 42
 
3.2%
Q 32
 
2.4%
s 31
 
2.3%
= 30
 
2.3%
K 29
 
2.2%
P 29
 
2.2%
z 27
 
2.0%
W 26
 
2.0%
f 25
 
1.9%
j 25
 
1.9%
Other values (55) 1024
77.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 530
40.2%
Uppercase Letter 523
39.6%
Decimal Number 204
 
15.5%
Math Symbol 54
 
4.1%
Other Punctuation 9
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 42
 
7.9%
s 31
 
5.8%
z 27
 
5.1%
f 25
 
4.7%
j 25
 
4.7%
y 24
 
4.5%
r 23
 
4.3%
c 22
 
4.2%
t 22
 
4.2%
b 22
 
4.2%
Other values (16) 267
50.4%
Uppercase Letter
ValueCountFrequency (%)
Q 32
 
6.1%
K 29
 
5.5%
P 29
 
5.5%
W 26
 
5.0%
Y 23
 
4.4%
Z 23
 
4.4%
A 23
 
4.4%
M 22
 
4.2%
T 21
 
4.0%
G 20
 
3.8%
Other values (16) 275
52.6%
Decimal Number
ValueCountFrequency (%)
8 23
11.3%
5 23
11.3%
7 21
10.3%
2 21
10.3%
4 21
10.3%
0 21
10.3%
3 21
10.3%
6 20
9.8%
9 18
8.8%
1 15
7.4%
Math Symbol
ValueCountFrequency (%)
= 30
55.6%
+ 24
44.4%
Other Punctuation
ValueCountFrequency (%)
/ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1053
79.8%
Common 267
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 42
 
4.0%
Q 32
 
3.0%
s 31
 
2.9%
K 29
 
2.8%
P 29
 
2.8%
z 27
 
2.6%
W 26
 
2.5%
f 25
 
2.4%
j 25
 
2.4%
y 24
 
2.3%
Other values (42) 763
72.5%
Common
ValueCountFrequency (%)
= 30
11.2%
+ 24
9.0%
8 23
8.6%
5 23
8.6%
7 21
7.9%
2 21
7.9%
4 21
7.9%
0 21
7.9%
3 21
7.9%
6 20
7.5%
Other values (3) 42
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 42
 
3.2%
Q 32
 
2.4%
s 31
 
2.3%
= 30
 
2.3%
K 29
 
2.2%
P 29
 
2.2%
z 27
 
2.0%
W 26
 
2.0%
f 25
 
1.9%
j 25
 
1.9%
Other values (55) 1024
77.6%

회원코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0204502 × 109
Minimum3.0023023 × 109
Maximum3.0485134 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:53:39.315655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0023023 × 109
5-th percentile3.0032448 × 109
Q13.0169711 × 109
median3.0184732 × 109
Q33.0207283 × 109
95-th percentile3.0426445 × 109
Maximum3.0485134 × 109
Range46211090
Interquartile range (IQR)3757152

Descriptive statistics

Standard deviation10507688
Coefficient of variation (CV)0.0034788483
Kurtosis1.9397653
Mean3.0204502 × 109
Median Absolute Deviation (MAD)2022642.5
Skewness1.0650915
Sum9.0613506 × 1010
Variance1.1041151 × 1014
MonotonicityNot monotonic
2024-03-13T20:53:39.471480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3017455123 1
 
3.3%
3018137715 1
 
3.3%
3020764412 1
 
3.3%
3016697100 1
 
3.3%
3019439456 1
 
3.3%
3017552904 1
 
3.3%
3020850152 1
 
3.3%
3017560790 1
 
3.3%
3003708421 1
 
3.3%
3016644515 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3002302288 1
3.3%
3002865490 1
3.3%
3003708421 1
3.3%
3014473223 1
3.3%
3016188128 1
3.3%
3016644515 1
3.3%
3016697100 1
3.3%
3016809800 1
3.3%
3017455123 1
3.3%
3017552904 1
3.3%
ValueCountFrequency (%)
3048513378 1
3.3%
3045367590 1
3.3%
3039316225 1
3.3%
3038048335 1
3.3%
3027871231 1
3.3%
3020850152 1
3.3%
3020823296 1
3.3%
3020764412 1
3.3%
3020619895 1
3.3%
3020569480 1
3.3%

가맹점번호
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
999999999999999
18 
798320253
 
1
701230824
 
1
793714316
 
1
719988390
 
1
Other values (8)
8 

Length

Max length15
Median length15
Mean length12.8
Min length9

Unique

Unique12 ?
Unique (%)40.0%

Sample

1st row999999999999999
2nd row999999999999999
3rd row999999999999999
4th row798320253
5th row999999999999999

Common Values

ValueCountFrequency (%)
999999999999999 18
60.0%
798320253 1
 
3.3%
701230824 1
 
3.3%
793714316 1
 
3.3%
719988390 1
 
3.3%
726598659 1
 
3.3%
709707054 1
 
3.3%
771450429 1
 
3.3%
713144508 1
 
3.3%
410130352982401 1
 
3.3%
Other values (3) 3
 
10.0%

Length

2024-03-13T20:53:39.590573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
999999999999999 18
60.0%
798320253 1
 
3.3%
701230824 1
 
3.3%
793714316 1
 
3.3%
719988390 1
 
3.3%
726598659 1
 
3.3%
709707054 1
 
3.3%
771450429 1
 
3.3%
713144508 1
 
3.3%
410130352982401 1
 
3.3%
Other values (3) 3
 
10.0%

성별코드
Categorical

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 rowM
4th rowF
5th rowF

Common Values

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

Length

2024-03-13T20:53:39.689846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:53:39.764722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 17
56.7%
m 13
43.3%

연령대코드
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.333333
Minimum0
Maximum60
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:53:39.832844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q130
median40
Q347.5
95-th percentile60
Maximum60
Range60
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation14.125871
Coefficient of variation (CV)0.37837154
Kurtosis0.37671635
Mean37.333333
Median Absolute Deviation (MAD)10
Skewness-0.2770632
Sum1120
Variance199.54023
MonotonicityNot monotonic
2024-03-13T20:53:39.916886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
40 9
30.0%
30 8
26.7%
50 4
13.3%
20 4
13.3%
60 4
13.3%
0 1
 
3.3%
ValueCountFrequency (%)
0 1
 
3.3%
20 4
13.3%
30 8
26.7%
40 9
30.0%
50 4
13.3%
60 4
13.3%
ValueCountFrequency (%)
60 4
13.3%
50 4
13.3%
40 9
30.0%
30 8
26.7%
20 4
13.3%
0 1
 
3.3%

결제상품ID
Real number (ℝ)

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000007 × 1011
Minimum1.4000002 × 1011
Maximum1.4000012 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:53:40.011954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000003 × 1011
median1.4000005 × 1011
Q31.400001 × 1011
95-th percentile1.4000012 × 1011
Maximum1.4000012 × 1011
Range106000
Interquartile range (IQR)71500

Descriptive statistics

Standard deviation38476.982
Coefficient of variation (CV)2.7483545 × 10-7
Kurtosis-1.7592251
Mean1.4000007 × 1011
Median Absolute Deviation (MAD)30000
Skewness0.1701378
Sum4.200002 × 1012
Variance1.4804782 × 109
MonotonicityNot monotonic
2024-03-13T20:53:40.117300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
140000030000 3
 
10.0%
140000116000 3
 
10.0%
140000046000 3
 
10.0%
140000024000 2
 
6.7%
140000032000 2
 
6.7%
140000084000 2
 
6.7%
140000102000 2
 
6.7%
140000018000 1
 
3.3%
140000020000 1
 
3.3%
140000048000 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
140000018000 1
 
3.3%
140000020000 1
 
3.3%
140000024000 2
6.7%
140000030000 3
10.0%
140000032000 2
6.7%
140000034000 1
 
3.3%
140000042000 1
 
3.3%
140000046000 3
10.0%
140000048000 1
 
3.3%
140000050000 1
 
3.3%
ValueCountFrequency (%)
140000124000 1
 
3.3%
140000122000 1
 
3.3%
140000118000 1
 
3.3%
140000116000 3
10.0%
140000112000 1
 
3.3%
140000104000 1
 
3.3%
140000102000 2
6.7%
140000100000 1
 
3.3%
140000096000 1
 
3.3%
140000084000 2
6.7%

결제상품명
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:53:40.316215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length7.6333333
Min length4

Characters and Unicode

Total characters229
Distinct characters62
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)43.3%

Sample

1st row고양페이카드
2nd row광주사랑카드(통합)
3rd row광주사랑카드(통합)
4th row광주사랑카드
5th row부천페이
ValueCountFrequency (%)
부천페이 3
 
8.6%
행복화성지역화폐 3
 
8.6%
용인와이페이 3
 
8.6%
안성사랑카드 2
 
5.7%
광주사랑카드(통합 2
 
5.7%
안산사랑상품권 2
 
5.7%
광주사랑카드 2
 
5.7%
수원페이(통합 2
 
5.7%
you 1
 
2.9%
고양페이카드 1
 
2.9%
Other values (14) 14
40.0%
2024-03-13T20:53:40.636730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 15
 
6.6%
사 14
 
6.1%
랑 14
 
6.1%
페 11
 
4.8%
드 9
 
3.9%
카 9
 
3.9%
화 8
 
3.5%
) 8
 
3.5%
( 8
 
3.5%
합 7
 
3.1%
Other values (52) 126
55.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 192
83.8%
Lowercase Letter 10
 
4.4%
Close Punctuation 8
 
3.5%
Open Punctuation 8
 
3.5%
Space Separator 5
 
2.2%
Uppercase Letter 5
 
2.2%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 15
 
7.8%
사 14
 
7.3%
랑 14
 
7.3%
페 11
 
5.7%
드 9
 
4.7%
카 9
 
4.7%
화 8
 
4.2%
합 7
 
3.6%
통 7
 
3.6%
주 6
 
3.1%
Other values (37) 92
47.9%
Lowercase Letter
ValueCountFrequency (%)
a 3
30.0%
y 2
20.0%
u 1
 
10.0%
o 1
 
10.0%
n 1
 
10.0%
k 1
 
10.0%
h 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
P 2
40.0%
Y 1
20.0%
N 1
20.0%
T 1
20.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 191
83.4%
Common 22
 
9.6%
Latin 15
 
6.6%
Han 1
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 15
 
7.9%
사 14
 
7.3%
랑 14
 
7.3%
페 11
 
5.8%
드 9
 
4.7%
카 9
 
4.7%
화 8
 
4.2%
합 7
 
3.7%
통 7
 
3.7%
주 6
 
3.1%
Other values (36) 91
47.6%
Latin
ValueCountFrequency (%)
a 3
20.0%
y 2
13.3%
P 2
13.3%
Y 1
 
6.7%
N 1
 
6.7%
u 1
 
6.7%
o 1
 
6.7%
n 1
 
6.7%
k 1
 
6.7%
h 1
 
6.7%
Common
ValueCountFrequency (%)
) 8
36.4%
( 8
36.4%
5
22.7%
- 1
 
4.5%
Han
ValueCountFrequency (%)
愛 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 191
83.4%
ASCII 37
 
16.2%
CJK 1
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 15
 
7.9%
사 14
 
7.3%
랑 14
 
7.3%
페 11
 
5.8%
드 9
 
4.7%
카 9
 
4.7%
화 8
 
4.2%
합 7
 
3.7%
통 7
 
3.7%
주 6
 
3.1%
Other values (36) 91
47.6%
ASCII
ValueCountFrequency (%)
) 8
21.6%
( 8
21.6%
5
13.5%
a 3
 
8.1%
y 2
 
5.4%
P 2
 
5.4%
Y 1
 
2.7%
N 1
 
2.7%
- 1
 
2.7%
u 1
 
2.7%
Other values (5) 5
13.5%
CJK
ValueCountFrequency (%)
愛 1
100.0%

가맹점업종명
Text

MISSING 

Distinct5
Distinct (%)50.0%
Missing20
Missing (%)66.7%
Memory size372.0 B
2024-03-13T20:53:40.789388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5
Min length2

Characters and Unicode

Total characters50
Distinct characters18
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

Unique3 ?
Unique (%)30.0%

Sample

1st row병원
2nd row일반휴게음식
3rd row유통업 영리
4th row유통업 영리
5th row의류
ValueCountFrequency (%)
유통업 4
28.6%
영리 4
28.6%
일반휴게음식 3
21.4%
병원 1
 
7.1%
의류 1
 
7.1%
음료식품 1
 
7.1%
2024-03-13T20:53:41.035621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
유 4
 
8.0%
통 4
 
8.0%
업 4
 
8.0%
4
 
8.0%
영 4
 
8.0%
리 4
 
8.0%
식 4
 
8.0%
음 4
 
8.0%
게 3
 
6.0%
휴 3
 
6.0%
Other values (8) 12
24.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46
92.0%
Space Separator 4
 
8.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 4
8.7%
통 4
8.7%
업 4
8.7%
영 4
8.7%
리 4
8.7%
식 4
8.7%
음 4
8.7%
게 3
 
6.5%
휴 3
 
6.5%
반 3
 
6.5%
Other values (7) 9
19.6%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46
92.0%
Common 4
 
8.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 4
8.7%
통 4
8.7%
업 4
8.7%
영 4
8.7%
리 4
8.7%
식 4
8.7%
음 4
8.7%
게 3
 
6.5%
휴 3
 
6.5%
반 3
 
6.5%
Other values (7) 9
19.6%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 46
92.0%
ASCII 4
 
8.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
유 4
8.7%
통 4
8.7%
업 4
8.7%
영 4
8.7%
리 4
8.7%
식 4
8.7%
음 4
8.7%
게 3
 
6.5%
휴 3
 
6.5%
반 3
 
6.5%
Other values (7) 9
19.6%
ASCII
ValueCountFrequency (%)
4
100.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing20
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean15014.9
Minimum11033
Maximum18484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:53:41.128589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11033
5-th percentile11351.15
Q112382.75
median15650
Q317296.75
95-th percentile18480.4
Maximum18484
Range7451
Interquartile range (IQR)4914

Descriptive statistics

Standard deviation2845.0443
Coefficient of variation (CV)0.1894814
Kurtosis-1.7232504
Mean15014.9
Median Absolute Deviation (MAD)2830
Skewness-0.16175831
Sum150149
Variance8094277.2
MonotonicityNot monotonic
2024-03-13T20:53:41.214225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
12784 1
 
3.3%
11033 1
 
3.3%
17552 1
 
3.3%
15444 1
 
3.3%
15856 1
 
3.3%
11740 1
 
3.3%
16531 1
 
3.3%
18484 1
 
3.3%
12249 1
 
3.3%
18476 1
 
3.3%
(Missing) 20
66.7%
ValueCountFrequency (%)
11033 1
3.3%
11740 1
3.3%
12249 1
3.3%
12784 1
3.3%
15444 1
3.3%
15856 1
3.3%
16531 1
3.3%
17552 1
3.3%
18476 1
3.3%
18484 1
3.3%
ValueCountFrequency (%)
18484 1
3.3%
18476 1
3.3%
17552 1
3.3%
16531 1
3.3%
15856 1
3.3%
15444 1
3.3%
12784 1
3.3%
12249 1
3.3%
11740 1
3.3%
11033 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
20 
경기도
8 
NONE
 
2

Length

Max length4
Median length4
Mean length3.7333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 20
66.7%
경기도 8
 
26.7%
NONE 2
 
6.7%

Length

2024-03-13T20:53:41.313936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:53:41.392409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
66.7%
경기도 8
 
26.7%
none 2
 
6.7%

시군구명
Text

MISSING 

Distinct7
Distinct (%)87.5%
Missing22
Missing (%)73.3%
Memory size372.0 B
2024-03-13T20:53:41.518972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4.125
Min length3

Characters and Unicode

Total characters33
Distinct characters21
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

Unique6 ?
Unique (%)75.0%

Sample

1st row광주시
2nd row연천군
3rd row안산시 단원구
4th row군포시
5th row의정부시
ValueCountFrequency (%)
화성시 2
20.0%
광주시 1
10.0%
연천군 1
10.0%
안산시 1
10.0%
단원구 1
10.0%
군포시 1
10.0%
의정부시 1
10.0%
수원시 1
10.0%
영통구 1
10.0%
2024-03-13T20:53:41.794715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 7
21.2%
화 2
 
6.1%
군 2
 
6.1%
구 2
 
6.1%
원 2
 
6.1%
성 2
 
6.1%
2
 
6.1%
천 1
 
3.0%
안 1
 
3.0%
산 1
 
3.0%
Other values (11) 11
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31
93.9%
Space Separator 2
 
6.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 7
22.6%
화 2
 
6.5%
군 2
 
6.5%
구 2
 
6.5%
원 2
 
6.5%
성 2
 
6.5%
천 1
 
3.2%
안 1
 
3.2%
산 1
 
3.2%
연 1
 
3.2%
Other values (10) 10
32.3%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31
93.9%
Common 2
 
6.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 7
22.6%
화 2
 
6.5%
군 2
 
6.5%
구 2
 
6.5%
원 2
 
6.5%
성 2
 
6.5%
천 1
 
3.2%
안 1
 
3.2%
산 1
 
3.2%
연 1
 
3.2%
Other values (10) 10
32.3%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31
93.9%
ASCII 2
 
6.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 7
22.6%
화 2
 
6.5%
군 2
 
6.5%
구 2
 
6.5%
원 2
 
6.5%
성 2
 
6.5%
천 1
 
3.2%
안 1
 
3.2%
산 1
 
3.2%
연 1
 
3.2%
Other values (10) 10
32.3%
ASCII
ValueCountFrequency (%)
2
100.0%

읍면동명
Text

MISSING 

Distinct8
Distinct (%)100.0%
Missing22
Missing (%)73.3%
Memory size372.0 B
2024-03-13T20:53:41.932814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.75
Min length2

Characters and Unicode

Total characters22
Distinct characters14
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

Unique8 ?
Unique (%)100.0%

Sample

1st row태전동
2nd row전곡읍
3rd row초지동
4th row당동
5th row신곡동
ValueCountFrequency (%)
태전동 1
12.5%
전곡읍 1
12.5%
초지동 1
12.5%
당동 1
12.5%
신곡동 1
12.5%
매탄동 1
12.5%
목동 1
12.5%
청계동 1
12.5%
2024-03-13T20:53:42.166288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 7
31.8%
전 2
 
9.1%
곡 2
 
9.1%
태 1
 
4.5%
읍 1
 
4.5%
초 1
 
4.5%
지 1
 
4.5%
당 1
 
4.5%
신 1
 
4.5%
매 1
 
4.5%
Other values (4) 4
18.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7
31.8%
전 2
 
9.1%
곡 2
 
9.1%
태 1
 
4.5%
읍 1
 
4.5%
초 1
 
4.5%
지 1
 
4.5%
당 1
 
4.5%
신 1
 
4.5%
매 1
 
4.5%
Other values (4) 4
18.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7
31.8%
전 2
 
9.1%
곡 2
 
9.1%
태 1
 
4.5%
읍 1
 
4.5%
초 1
 
4.5%
지 1
 
4.5%
당 1
 
4.5%
신 1
 
4.5%
매 1
 
4.5%
Other values (4) 4
18.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 7
31.8%
전 2
 
9.1%
곡 2
 
9.1%
태 1
 
4.5%
읍 1
 
4.5%
초 1
 
4.5%
지 1
 
4.5%
당 1
 
4.5%
신 1
 
4.5%
매 1
 
4.5%
Other values (4) 4
18.2%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9823667
Minimum0
Maximum38.026
Zeros22
Zeros (%)73.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:53:42.263198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q327.888
95-th percentile37.58425
Maximum38.026
Range38.026
Interquartile range (IQR)27.888

Descriptive statistics

Standard deviation16.837505
Coefficient of variation (CV)1.6867248
Kurtosis-0.82275988
Mean9.9823667
Median Absolute Deviation (MAD)0
Skewness1.1119323
Sum299.471
Variance283.50158
MonotonicityNot monotonic
2024-03-13T20:53:42.347293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.0 22
73.3%
37.389 1
 
3.3%
38.026 1
 
3.3%
37.31 1
 
3.3%
37.346 1
 
3.3%
37.744 1
 
3.3%
37.272 1
 
3.3%
37.184 1
 
3.3%
37.2 1
 
3.3%
ValueCountFrequency (%)
0.0 22
73.3%
37.184 1
 
3.3%
37.2 1
 
3.3%
37.272 1
 
3.3%
37.31 1
 
3.3%
37.346 1
 
3.3%
37.389 1
 
3.3%
37.744 1
 
3.3%
38.026 1
 
3.3%
ValueCountFrequency (%)
38.026 1
 
3.3%
37.744 1
 
3.3%
37.389 1
 
3.3%
37.346 1
 
3.3%
37.31 1
 
3.3%
37.272 1
 
3.3%
37.2 1
 
3.3%
37.184 1
 
3.3%
0.0 22
73.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.879633
Minimum0
Maximum127.23
Zeros22
Zeros (%)73.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:53:42.433975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q395.10975
95-th percentile127.1196
Maximum127.23
Range127.23
Interquartile range (IQR)95.10975

Descriptive statistics

Standard deviation57.143512
Coefficient of variation (CV)1.6866626
Kurtosis-0.82384669
Mean33.879633
Median Absolute Deviation (MAD)0
Skewness1.1116675
Sum1016.389
Variance3265.381
MonotonicityNot monotonic
2024-03-13T20:53:42.523052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.0 22
73.3%
127.23 1
 
3.3%
127.068 1
 
3.3%
126.813 1
 
3.3%
126.943 1
 
3.3%
127.056 1
 
3.3%
127.041 1
 
3.3%
127.125 1
 
3.3%
127.113 1
 
3.3%
ValueCountFrequency (%)
0.0 22
73.3%
126.813 1
 
3.3%
126.943 1
 
3.3%
127.041 1
 
3.3%
127.056 1
 
3.3%
127.068 1
 
3.3%
127.113 1
 
3.3%
127.125 1
 
3.3%
127.23 1
 
3.3%
ValueCountFrequency (%)
127.23 1
 
3.3%
127.125 1
 
3.3%
127.113 1
 
3.3%
127.068 1
 
3.3%
127.056 1
 
3.3%
127.041 1
 
3.3%
126.943 1
 
3.3%
126.813 1
 
3.3%
0.0 22
73.3%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
18 
True
12 
ValueCountFrequency (%)
False 18
60.0%
True 12
40.0%
2024-03-13T20:53:42.606469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8038.6667
Minimum0
Maximum60000
Zeros18
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:53:42.678146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37690
95-th percentile40635
Maximum60000
Range60000
Interquartile range (IQR)7690

Descriptive statistics

Standard deviation15131.936
Coefficient of variation (CV)1.8823937
Kurtosis4.9990252
Mean8038.6667
Median Absolute Deviation (MAD)0
Skewness2.2995141
Sum241160
Variance2.2897547 × 108
MonotonicityNot monotonic
2024-03-13T20:53:42.760888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 18
60.0%
47700 1
 
3.3%
3900 1
 
3.3%
20800 1
 
3.3%
7900 1
 
3.3%
60000 1
 
3.3%
19000 1
 
3.3%
7060 1
 
3.3%
25500 1
 
3.3%
5000 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
0 18
60.0%
3900 1
 
3.3%
4200 1
 
3.3%
5000 1
 
3.3%
7060 1
 
3.3%
7900 1
 
3.3%
8100 1
 
3.3%
19000 1
 
3.3%
20800 1
 
3.3%
25500 1
 
3.3%
ValueCountFrequency (%)
60000 1
3.3%
47700 1
3.3%
32000 1
3.3%
25500 1
3.3%
20800 1
3.3%
19000 1
3.3%
8100 1
3.3%
7900 1
3.3%
7060 1
3.3%
5000 1
3.3%

Interactions

2024-03-13T20:53:37.164512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:33.787766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:34.445383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:34.961195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:35.520273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:36.058786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:36.646343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:37.244931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:33.861823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:34.529011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:35.040249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:35.601112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:36.147775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:36.716609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:37.337298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:33.963959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:34.598791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:35.111866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:35.673840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:36.263051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:36.786678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:37.412968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:34.064356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:34.669029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:35.203920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:35.749223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:36.348699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:36.866802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:37.485299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:34.171770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:34.745534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:35.304473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:35.823862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:36.424729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:36.950921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:37.561783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:34.273852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:34.820424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:35.378657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:35.900778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:36.496698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:37.023912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:37.631459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:34.364314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:34.890009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:35.448426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:35.974023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:36.574110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:37.093325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:53:42.873133image/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
회원코드1.0001.0000.0000.3310.0000.7660.1490.0001.0000.0001.0001.0000.0000.0000.4330.000
가맹점번호1.0000.0001.0000.0000.0000.0000.7931.0001.0001.0001.0001.0001.0001.0001.0001.000
성별코드1.0000.3310.0001.0000.0000.4260.4650.0001.0000.0001.0001.0000.0000.0000.2300.000
연령대코드1.0000.0000.0000.0001.0000.3150.6760.2710.5290.0000.5211.0000.0740.0740.5630.000
결제상품ID1.0000.7660.0000.4260.3151.0001.0000.6891.0000.8081.0001.0000.0000.0000.0000.361
결제상품명1.0000.1490.7930.4650.6761.0001.0000.9021.0001.0001.0001.0000.5950.5950.0000.782
가맹점업종명1.0000.0001.0000.0000.2710.6890.9021.0000.8630.3840.8321.0000.3840.384NaN0.678
가맹점우편번호1.0001.0001.0001.0000.5291.0001.0000.8631.0001.0001.0001.0001.0001.000NaN0.513
시도명1.0000.0001.0000.0000.0000.8081.0000.3841.0001.000NaNNaN0.8470.847NaN0.000
시군구명1.0001.0001.0001.0000.5211.0001.0000.8321.000NaN1.0001.000NaNNaNNaN0.798
읍면동명1.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
위도1.0000.0001.0000.0000.0740.0000.5950.3841.0000.847NaNNaN1.0000.9900.8500.563
경도1.0000.0001.0000.0000.0740.0000.5950.3841.0000.847NaNNaN0.9901.0000.8500.563
사용여부1.0000.4331.0000.2300.5630.0000.000NaNNaNNaNNaNNaN0.8500.8501.0000.688
결제금액1.0000.0001.0000.0000.0000.3610.7820.6780.5130.0000.7981.0000.5630.5630.6881.000
2024-03-13T20:53:43.027426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부가맹점번호성별코드시도명
사용여부1.0000.7790.1441.000
가맹점번호0.7791.0000.0001.000
성별코드0.1440.0001.0000.000
시도명1.0001.0000.0001.000
2024-03-13T20:53:43.111619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드연령대코드결제상품ID가맹점우편번호위도경도결제금액가맹점번호성별코드시도명사용여부
회원코드1.000-0.138-0.2210.370-0.364-0.301-0.4460.0000.2110.0000.346
연령대코드-0.1381.0000.3970.3630.1020.1280.1410.0000.0000.0000.371
결제상품ID-0.2210.3971.0000.2070.2780.2860.2070.0000.2290.3850.000
가맹점우편번호0.3700.3630.2071.000-0.6440.1090.2001.0000.0000.5001.000
위도-0.3640.1020.278-0.6441.0000.9660.6860.7790.0000.6380.646
경도-0.3010.1280.2860.1090.9661.0000.6920.7790.0000.6380.646
결제금액-0.4460.1410.2070.2000.6860.6921.0000.8600.0000.0000.672
가맹점번호0.0000.0000.0001.0000.7790.7790.8601.0000.0001.0000.779
성별코드0.2110.0000.2290.0000.0000.0000.0000.0001.0000.0000.144
시도명0.0000.0000.3850.5000.6380.6380.0001.0000.0001.0001.000
사용여부0.3460.3710.0001.0000.6460.6460.6720.7790.1441.0001.000

Missing values

2024-03-13T20:53:37.740478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:53:38.152929image/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.
2024-03-13T20:53:38.275160image/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
02022-05-022022-05-08wOqlQd+AX3lw5OWikJW4P9I3z2PnOP7gfe7oypxzUMs=3017455123999999999999999F50140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
12022-05-022022-05-08zzFhJLPuQLMZXkssYiQWMjazftE7xdtzmGEIalQk3jw=3048513378999999999999999M30140000084000광주사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
22022-05-022022-05-08MB5VQhTyX0+6QC9uHK8hmmyff+5b17HXsi8CLd5fPYs=3045367590999999999999999M50140000084000광주사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
32022-05-022022-05-08wP0ciKVO2DDxfn6KoeEQExlS+U3tGkcNof9kHoKx9jc=3020422234798320253F30140000024000광주사랑카드병원12784경기도광주시태전동37.389127.23Y47700
42022-05-022022-05-08wP2fFbqdmAvPAy50XQ5b4YLICA++EOs5/UQ2XTLjgsU=3027871231999999999999999F20140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
52022-05-022022-05-08wPs86jN636P7oezwPAgIOB80NrqjcWHMZI07N+lCRYw=3018428440999999999999999F60140000118000하남하머니<NA><NA><NA><NA><NA>0.00.0N0
62022-05-022022-05-08wP7EBBa6y4Vy+wDQdOtn+VbwUxKiQa7LU3TKalXcbPc=3002865490701230824M30140000042000연천사랑상품권일반휴게음식11033경기도연천군전곡읍38.026127.068Y3900
72022-05-022022-05-08zzSN+ebz6TBibtnjdhfJ4cPytb9NeuUJqgjMR2wUPWA=3020569480999999999999999F0140000096000파주 Pay(파주페이)(통합)<NA><NA><NA><NA><NA>0.00.0N0
82022-05-022022-05-08P0pk7sZ7jZ4j5Q+p9b404wZrCqPhZM6tI546f1KojWw=3016188128999999999999999M40140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
92022-05-022022-05-08egPgzSYHVq6drqZFTj04cB19AKXcgEjrtPTz2b0eC+I=3020619895793714316F30140000032000안성사랑카드유통업 영리17552NONE<NA><NA>0.00.0Y20800
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202022-05-022022-05-08wQGd59JWgn1mP3KXQWbKQgZHjOMvrSejp6vm3XMy9Og=3038048335999999999999999M50140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0N0
212022-05-022022-05-08wRKSbI2TARAYyvsw1sdaWrHSJxMaC1tLMDBnmoF6640=3039316225999999999999999M20140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
222022-05-022022-05-08wQSY8nxQPaL5t3VqKTGfzMYW52V4pOcO5+HQx1r4+qc=3016644515713144508F40140000116000행복화성지역화폐일반휴게음식18484경기도화성시목동37.184127.125Y25500
232022-05-022022-05-08wRYZzuWDjt9cGp54oTtYsvWf01SyRwwViPAplJf1Wy0=3003708421410130352982401F40140000030000부천페이<NA><NA><NA><NA><NA>0.00.0Y5000
242022-05-022022-05-08wQZn79D3f+YHN1Wqc0kezWsmFGv+f2rClX31foZEcVc=3017560790738724044M40140000104000Thank You Pay-N(통합)음료식품12249NONE<NA><NA>0.00.0Y8100
252022-05-022022-05-08wRs9P2Yp8Q/lTW8gouSq6BKftzwGFW6w0Fr5t1G9xRc=3020850152999999999999999F30140000050000평택사랑카드<NA><NA><NA><NA><NA>0.00.0N0
262022-05-022022-05-08wQZwlJMA9rX3NRkQsoEzqxWGoMXp1bfEVKO3qQ3YU5M=3017552904999999999999999F40140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
272022-05-022022-05-08wPtxysK3GcZCuswybtkjk5SVfs29N+e3GMWz2wXZru4=3019439456999999999999999M20140000032000안성사랑카드<NA><NA><NA><NA><NA>0.00.0N0
282022-05-022022-05-08wQh07Y7aafqmDOlvZSQbE4yYbJettW5lzKmMNBDevYA=3016697100GG_604JXLF40140000048000이천사랑지역화폐<NA><NA><NA><NA><NA>0.00.0Y32000
292022-05-022022-05-08wSKuqRCARlJQCjLhA7yuhzK0rA7hLKT04GthpK5shgk=3020764412720903662F60140000116000행복화성지역화폐유통업 영리18476경기도화성시청계동37.2127.113Y4200