Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells53
Missing cells (%)9.8%
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/5fb3021f-37ee-44b6-9089-1567c38e2376

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
사용여부 is highly overall correlated with 연령대코드 and 6 other fieldsHigh correlation
시도명 is highly overall correlated with 결제금액 and 1 other fieldsHigh correlation
가맹점업종명 is highly overall correlated with 사용여부High correlation
연령대코드 is highly overall correlated with 사용여부High correlation
가맹점우편번호 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점우편번호 and 3 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
결제금액 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
성별코드 is highly overall correlated with 가맹점우편번호High correlation
가맹점우편번호 has 17 (56.7%) missing valuesMissing
시군구명 has 18 (60.0%) missing valuesMissing
읍면동명 has 18 (60.0%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
연령대코드 has 2 (6.7%) zerosZeros
위도 has 18 (60.0%) zerosZeros
경도 has 18 (60.0%) zerosZeros
결제금액 has 14 (46.7%) zerosZeros

Reproduction

Analysis started2024-03-13 11:53:16.460948
Analysis finished2024-03-13 11:53:23.917423
Duration7.46 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
Minimum2022-01-03 00:00:00
Maximum2022-01-03 00:00:00
2024-03-13T20:53:23.965530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:24.082293image/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
Minimum2022-01-09 00:00:00
Maximum2022-01-09 00:00:00
2024-03-13T20:53:24.187363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:24.291848image/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
2024-03-13T20:53:24.598291image/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++756kwM9COyPW+VBszQ6zDFSChks90NGL2l7CLW74E=
2nd row+wy5Quc03SXWZ6MhYzMZHRAQFF8Zmixo8KaZHDeQkto=
3rd row++BiPjfB+1ZKVMmPw2XLZYPxCY6y/QaiGChd/xEBPtw=
4th row/YnE7hc4675TtdHpluOmDFddeQeA1VB3O10/IC6ccMc=
5th row84YRMaCj4OofH4oS7R76XwN3Mne5gH4M+lZLLi1gQXY=
ValueCountFrequency (%)
756kwm9coypw+vbszq6zdfschks90ngl2l7clw74e 1
 
3.3%
wy5quc03sxwz6mhyzmzhraqff8zmixo8kazhdeqkto 1
 
3.3%
2vcvzek5j6s8hnmlx515pgjl80htrmtms2ozrf5ij+q 1
 
3.3%
1lsrznrf6rturybtrnatfhdjgzew6xgbz446cj6dgw 1
 
3.3%
n+u0n05lxl1bylkofznkzoejqsodedkgro9o1zbg6j0 1
 
3.3%
kokbggbb/d4dxt2srowgy9box2lqqdgavfthzwc+fpy 1
 
3.3%
iejavtpvtrcbeoukuivoqu3bqrnejubtxoxklelajnc 1
 
3.3%
gdc8smexbpns+xy28knl/1/lxtyipskeie4gjsgthzq 1
 
3.3%
epou2kxlc7pp/wk3qfpvn8vjhxpuyj3paz5amt8y74s 1
 
3.3%
cc0cek+rqst3cwycbz1zdf3rwxzxh6s7ya2ymhqk6ju 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:53:25.095812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
= 30
 
2.3%
Q 29
 
2.2%
J 28
 
2.1%
+ 28
 
2.1%
w 27
 
2.0%
6 27
 
2.0%
C 27
 
2.0%
y 27
 
2.0%
F 27
 
2.0%
d 27
 
2.0%
Other values (55) 1043
79.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 544
41.2%
Lowercase Letter 492
37.3%
Decimal Number 210
 
15.9%
Math Symbol 58
 
4.4%
Other Punctuation 16
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Q 29
 
5.3%
J 28
 
5.1%
C 27
 
5.0%
F 27
 
5.0%
L 26
 
4.8%
H 25
 
4.6%
R 25
 
4.6%
B 25
 
4.6%
Z 24
 
4.4%
A 23
 
4.2%
Other values (16) 285
52.4%
Lowercase Letter
ValueCountFrequency (%)
w 27
 
5.5%
y 27
 
5.5%
d 27
 
5.5%
o 27
 
5.5%
k 25
 
5.1%
p 24
 
4.9%
b 23
 
4.7%
c 21
 
4.3%
s 20
 
4.1%
g 20
 
4.1%
Other values (16) 251
51.0%
Decimal Number
ValueCountFrequency (%)
6 27
12.9%
5 25
11.9%
0 24
11.4%
4 22
10.5%
7 22
10.5%
8 21
10.0%
2 19
9.0%
9 18
8.6%
3 16
7.6%
1 16
7.6%
Math Symbol
ValueCountFrequency (%)
= 30
51.7%
+ 28
48.3%
Other Punctuation
ValueCountFrequency (%)
/ 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1036
78.5%
Common 284
 
21.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
Q 29
 
2.8%
J 28
 
2.7%
w 27
 
2.6%
C 27
 
2.6%
y 27
 
2.6%
F 27
 
2.6%
d 27
 
2.6%
o 27
 
2.6%
L 26
 
2.5%
k 25
 
2.4%
Other values (42) 766
73.9%
Common
ValueCountFrequency (%)
= 30
10.6%
+ 28
9.9%
6 27
9.5%
5 25
8.8%
0 24
8.5%
4 22
7.7%
7 22
7.7%
8 21
7.4%
2 19
 
6.7%
9 18
 
6.3%
Other values (3) 48
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
= 30
 
2.3%
Q 29
 
2.2%
J 28
 
2.1%
+ 28
 
2.1%
w 27
 
2.0%
6 27
 
2.0%
C 27
 
2.0%
y 27
 
2.0%
F 27
 
2.0%
d 27
 
2.0%
Other values (55) 1043
79.0%

회원코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0218609 × 109
Minimum3.0019713 × 109
Maximum3.0610537 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:53:25.270835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0019713 × 109
5-th percentile3.0045696 × 109
Q13.0171434 × 109
median3.0180717 × 109
Q33.0252085 × 109
95-th percentile3.0505051 × 109
Maximum3.0610537 × 109
Range59082403
Interquartile range (IQR)8065071.5

Descriptive statistics

Standard deviation13437885
Coefficient of variation (CV)0.0044468907
Kurtosis3.094585
Mean3.0218609 × 109
Median Absolute Deviation (MAD)3513127
Skewness1.5324715
Sum9.0655828 × 1010
Variance1.8057677 × 1014
MonotonicityNot monotonic
2024-03-13T20:53:25.423862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3024306341 1
 
3.3%
3021278409 1
 
3.3%
3019488492 1
 
3.3%
3018129807 1
 
3.3%
3039269232 1
 
3.3%
3019191108 1
 
3.3%
3061053667 1
 
3.3%
3017103262 1
 
3.3%
3008457129 1
 
3.3%
3011828161 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3001971264 1
3.3%
3002188214 1
3.3%
3007480114 1
3.3%
3008457129 1
3.3%
3011828161 1
3.3%
3014252155 1
3.3%
3016740179 1
3.3%
3017103262 1
3.3%
3017263752 1
3.3%
3017382405 1
3.3%
ValueCountFrequency (%)
3061053667 1
3.3%
3059698017 1
3.3%
3039269232 1
3.3%
3033645270 1
3.3%
3033120265 1
3.3%
3030343190 1
3.3%
3029744314 1
3.3%
3025509161 1
3.3%
3024306341 1
3.3%
3021278409 1
3.3%

가맹점번호
Text

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:53:25.604573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length12.2
Min length9

Characters and Unicode

Total characters366
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)53.3%

Sample

1st row757944100
2nd row719944674
3rd row723867592
4th row999999999999999
5th row999999999999999
ValueCountFrequency (%)
999999999999999 14
46.7%
757944100 1
 
3.3%
720557128 1
 
3.3%
726935147 1
 
3.3%
727683854 1
 
3.3%
725050029 1
 
3.3%
720173844 1
 
3.3%
786222841 1
 
3.3%
707691927 1
 
3.3%
790272937 1
 
3.3%
Other values (7) 7
23.3%
2024-03-13T20:53:25.978096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 226
61.7%
7 29
 
7.9%
2 22
 
6.0%
0 17
 
4.6%
4 16
 
4.4%
1 13
 
3.6%
5 12
 
3.3%
8 9
 
2.5%
6 8
 
2.2%
3 6
 
1.6%
Other values (7) 8
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 358
97.8%
Uppercase Letter 7
 
1.9%
Connector Punctuation 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 226
63.1%
7 29
 
8.1%
2 22
 
6.1%
0 17
 
4.7%
4 16
 
4.5%
1 13
 
3.6%
5 12
 
3.4%
8 9
 
2.5%
6 8
 
2.2%
3 6
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
G 2
28.6%
F 1
14.3%
H 1
14.3%
U 1
14.3%
O 1
14.3%
V 1
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 359
98.1%
Latin 7
 
1.9%

Most frequent character per script

Common
ValueCountFrequency (%)
9 226
63.0%
7 29
 
8.1%
2 22
 
6.1%
0 17
 
4.7%
4 16
 
4.5%
1 13
 
3.6%
5 12
 
3.3%
8 9
 
2.5%
6 8
 
2.2%
3 6
 
1.7%
Latin
ValueCountFrequency (%)
G 2
28.6%
F 1
14.3%
H 1
14.3%
U 1
14.3%
O 1
14.3%
V 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 226
61.7%
7 29
 
7.9%
2 22
 
6.0%
0 17
 
4.6%
4 16
 
4.4%
1 13
 
3.6%
5 12
 
3.3%
8 9
 
2.5%
6 8
 
2.2%
3 6
 
1.6%
Other values (7) 8
 
2.2%

성별코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
F
19 
M
11 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 19
63.3%
M 11
36.7%

Length

2024-03-13T20:53:26.142807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:53:26.278514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 19
63.3%
m 11
36.7%

연령대코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.666667
Minimum0
Maximum80
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:53:26.369884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q130
median40
Q350
95-th percentile65.5
Maximum80
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.564332
Coefficient of variation (CV)0.45424996
Kurtosis0.79445072
Mean38.666667
Median Absolute Deviation (MAD)10
Skewness-0.069151795
Sum1160
Variance308.50575
MonotonicityNot monotonic
2024-03-13T20:53:26.524781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
40 8
26.7%
30 7
23.3%
50 6
20.0%
20 3
 
10.0%
0 2
 
6.7%
60 2
 
6.7%
80 1
 
3.3%
70 1
 
3.3%
ValueCountFrequency (%)
0 2
 
6.7%
20 3
 
10.0%
30 7
23.3%
40 8
26.7%
50 6
20.0%
60 2
 
6.7%
70 1
 
3.3%
80 1
 
3.3%
ValueCountFrequency (%)
80 1
 
3.3%
70 1
 
3.3%
60 2
 
6.7%
50 6
20.0%
40 8
26.7%
30 7
23.3%
20 3
 
10.0%
0 2
 
6.7%

결제상품ID
Real number (ℝ)

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000008 × 1011
Minimum1.4000002 × 1011
Maximum1.4000016 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:53:26.692452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000003 × 1011
Q11.4000004 × 1011
median1.400001 × 1011
Q31.4000012 × 1011
95-th percentile1.4000013 × 1011
Maximum1.4000016 × 1011
Range140000
Interquartile range (IQR)77500

Descriptive statistics

Standard deviation41876.447
Coefficient of variation (CV)2.991173 × 10-7
Kurtosis-1.5262624
Mean1.4000008 × 1011
Median Absolute Deviation (MAD)30000
Skewness-0.088518595
Sum4.2000025 × 1012
Variance1.7536368 × 109
MonotonicityNot monotonic
2024-03-13T20:53:26.875629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
140000116000 3
 
10.0%
140000058000 3
 
10.0%
140000122000 3
 
10.0%
140000030000 2
 
6.7%
140000112000 2
 
6.7%
140000032000 2
 
6.7%
140000126000 2
 
6.7%
140000096000 1
 
3.3%
140000106000 1
 
3.3%
140000114000 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
140000020000 1
 
3.3%
140000022000 1
 
3.3%
140000030000 2
6.7%
140000032000 2
6.7%
140000036000 1
 
3.3%
140000038000 1
 
3.3%
140000040000 1
 
3.3%
140000046000 1
 
3.3%
140000058000 3
10.0%
140000066000 1
 
3.3%
ValueCountFrequency (%)
140000160000 1
 
3.3%
140000126000 2
6.7%
140000124000 1
 
3.3%
140000122000 3
10.0%
140000116000 3
10.0%
140000114000 1
 
3.3%
140000112000 2
6.7%
140000106000 1
 
3.3%
140000100000 1
 
3.3%
140000096000 1
 
3.3%

결제상품명
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:53:27.146147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length7.9333333
Min length4

Characters and Unicode

Total characters238
Distinct characters73
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 row파주 Pay(파주페이)(통합)
2nd row양평통보
3rd row양주사랑카드
4th row부천페이
5th row경기도 시장상권 진흥원
ValueCountFrequency (%)
행복화성지역화폐 3
 
7.7%
평택사랑카드(통합 3
 
7.7%
의정부사랑카드 3
 
7.7%
부천페이 2
 
5.1%
군포愛머니 2
 
5.1%
안성사랑카드 2
 
5.1%
수원페이 2
 
5.1%
안산사랑상품권 2
 
5.1%
군포愛머니(통합 1
 
2.6%
과천화폐 1
 
2.6%
Other values (18) 18
46.2%
2024-03-13T20:53:27.556368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 13
 
5.5%
랑 13
 
5.5%
드 10
 
4.2%
카 10
 
4.2%
화 9
 
3.8%
9
 
3.8%
( 8
 
3.4%
) 8
 
3.4%
통 8
 
3.4%
이 7
 
2.9%
Other values (63) 143
60.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 197
82.8%
Lowercase Letter 10
 
4.2%
Space Separator 9
 
3.8%
Open Punctuation 8
 
3.4%
Close Punctuation 8
 
3.4%
Uppercase Letter 5
 
2.1%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 13
 
6.6%
랑 13
 
6.6%
드 10
 
5.1%
카 10
 
5.1%
화 9
 
4.6%
통 8
 
4.1%
이 7
 
3.6%
합 7
 
3.6%
페 6
 
3.0%
폐 6
 
3.0%
Other values (48) 108
54.8%
Lowercase Letter
ValueCountFrequency (%)
a 3
30.0%
y 2
20.0%
u 1
 
10.0%
o 1
 
10.0%
k 1
 
10.0%
n 1
 
10.0%
h 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
P 2
40.0%
Y 1
20.0%
T 1
20.0%
N 1
20.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 194
81.5%
Common 26
 
10.9%
Latin 15
 
6.3%
Han 3
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 13
 
6.7%
랑 13
 
6.7%
드 10
 
5.2%
카 10
 
5.2%
화 9
 
4.6%
통 8
 
4.1%
이 7
 
3.6%
합 7
 
3.6%
페 6
 
3.1%
폐 6
 
3.1%
Other values (47) 105
54.1%
Latin
ValueCountFrequency (%)
a 3
20.0%
y 2
13.3%
P 2
13.3%
u 1
 
6.7%
o 1
 
6.7%
Y 1
 
6.7%
k 1
 
6.7%
n 1
 
6.7%
h 1
 
6.7%
T 1
 
6.7%
Common
ValueCountFrequency (%)
9
34.6%
( 8
30.8%
) 8
30.8%
- 1
 
3.8%
Han
ValueCountFrequency (%)
愛 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 194
81.5%
ASCII 41
 
17.2%
CJK 3
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 13
 
6.7%
랑 13
 
6.7%
드 10
 
5.2%
카 10
 
5.2%
화 9
 
4.6%
통 8
 
4.1%
이 7
 
3.6%
합 7
 
3.6%
페 6
 
3.1%
폐 6
 
3.1%
Other values (47) 105
54.1%
ASCII
ValueCountFrequency (%)
9
22.0%
( 8
19.5%
) 8
19.5%
a 3
 
7.3%
y 2
 
4.9%
P 2
 
4.9%
- 1
 
2.4%
u 1
 
2.4%
o 1
 
2.4%
Y 1
 
2.4%
Other values (5) 5
12.2%
CJK
ValueCountFrequency (%)
愛 3
100.0%

가맹점업종명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
17 
일반휴게음식
6 
유통업 영리
4 
음료식품
 
1
보건위생
 
1

Length

Max length6
Median length4
Mean length4.6
Min length2

Unique

Unique3 ?
Unique (%)10.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 17
56.7%
일반휴게음식 6
 
20.0%
유통업 영리 4
 
13.3%
음료식품 1
 
3.3%
보건위생 1
 
3.3%
의원 1
 
3.3%

Length

2024-03-13T20:53:27.710688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:53:27.853393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
50.0%
일반휴게음식 6
 
17.6%
유통업 4
 
11.8%
영리 4
 
11.8%
음료식품 1
 
2.9%
보건위생 1
 
2.9%
의원 1
 
2.9%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)100.0%
Missing17
Missing (%)56.7%
Infinite0
Infinite (%)0.0%
Mean14715.308
Minimum10863
Maximum17932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:53:27.996489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10863
5-th percentile11229
Q111772
median15459
Q317561
95-th percentile17893
Maximum17932
Range7069
Interquartile range (IQR)5789

Descriptive statistics

Standard deviation2727.8359
Coefficient of variation (CV)0.18537403
Kurtosis-1.7721226
Mean14715.308
Median Absolute Deviation (MAD)2408
Skewness-0.19453335
Sum191299
Variance7441088.7
MonotonicityNot monotonic
2024-03-13T20:53:28.116348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10863 1
 
3.3%
12514 1
 
3.3%
11473 1
 
3.3%
17867 1
 
3.3%
17561 1
 
3.3%
17932 1
 
3.3%
15874 1
 
3.3%
16284 1
 
3.3%
11772 1
 
3.3%
11631 1
 
3.3%
Other values (3) 3
 
10.0%
(Missing) 17
56.7%
ValueCountFrequency (%)
10863 1
3.3%
11473 1
3.3%
11631 1
3.3%
11772 1
3.3%
12514 1
3.3%
14501 1
3.3%
15459 1
3.3%
15874 1
3.3%
16284 1
3.3%
17561 1
3.3%
ValueCountFrequency (%)
17932 1
3.3%
17867 1
3.3%
17568 1
3.3%
17561 1
3.3%
16284 1
3.3%
15874 1
3.3%
15459 1
3.3%
14501 1
3.3%
12514 1
3.3%
11772 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
17 
경기도
12 
NONE
 
1

Length

Max length4
Median length4
Mean length3.6
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 17
56.7%
경기도 12
40.0%
NONE 1
 
3.3%

Length

2024-03-13T20:53:28.297863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:53:28.408142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
56.7%
경기도 12
40.0%
none 1
 
3.3%

시군구명
Text

MISSING 

Distinct9
Distinct (%)75.0%
Missing18
Missing (%)60.0%
Memory size372.0 B
2024-03-13T20:53:28.558344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.8333333
Min length3

Characters and Unicode

Total characters46
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

Unique6 ?
Unique (%)50.0%

Sample

1st row양평군
2nd row양주시
3rd row평택시
4th row안성시
5th row평택시
ValueCountFrequency (%)
평택시 2
14.3%
안성시 2
14.3%
의정부시 2
14.3%
양평군 1
7.1%
양주시 1
7.1%
군포시 1
7.1%
수원시 1
7.1%
장안구 1
7.1%
안산시 1
7.1%
단원구 1
7.1%
2024-03-13T20:53:28.924765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 11
23.9%
안 4
 
8.7%
평 3
 
6.5%
부 3
 
6.5%
구 2
 
4.3%
성 2
 
4.3%
의 2
 
4.3%
정 2
 
4.3%
양 2
 
4.3%
군 2
 
4.3%
Other values (10) 13
28.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44
95.7%
Space Separator 2
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 11
25.0%
안 4
 
9.1%
평 3
 
6.8%
부 3
 
6.8%
구 2
 
4.5%
성 2
 
4.5%
의 2
 
4.5%
정 2
 
4.5%
양 2
 
4.5%
군 2
 
4.5%
Other values (9) 11
25.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44
95.7%
Common 2
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 11
25.0%
안 4
 
9.1%
평 3
 
6.8%
부 3
 
6.8%
구 2
 
4.5%
성 2
 
4.5%
의 2
 
4.5%
정 2
 
4.5%
양 2
 
4.5%
군 2
 
4.5%
Other values (9) 11
25.0%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44
95.7%
ASCII 2
 
4.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 11
25.0%
안 4
 
9.1%
평 3
 
6.8%
부 3
 
6.8%
구 2
 
4.5%
성 2
 
4.5%
의 2
 
4.5%
정 2
 
4.5%
양 2
 
4.5%
군 2
 
4.5%
Other values (9) 11
25.0%
ASCII
ValueCountFrequency (%)
2
100.0%

읍면동명
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing18
Missing (%)60.0%
Memory size372.0 B
2024-03-13T20:53:29.097646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)100.0%

Sample

1st row용문면
2nd row옥정동
3rd row용이동
4th row공도읍
5th row안중읍
ValueCountFrequency (%)
용문면 1
8.3%
옥정동 1
8.3%
용이동 1
8.3%
공도읍 1
8.3%
안중읍 1
8.3%
부곡동 1
8.3%
조원동 1
8.3%
용현동 1
8.3%
호원동 1
8.3%
당왕동 1
8.3%
Other values (2) 2
16.7%
2024-03-13T20:53:29.505593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 9
25.0%
용 3
 
8.3%
정 2
 
5.6%
읍 2
 
5.6%
원 2
 
5.6%
곡 1
 
2.8%
잔 1
 
2.8%
고 1
 
2.8%
왕 1
 
2.8%
당 1
 
2.8%
Other values (13) 13
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 36
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9
25.0%
용 3
 
8.3%
정 2
 
5.6%
읍 2
 
5.6%
원 2
 
5.6%
곡 1
 
2.8%
잔 1
 
2.8%
고 1
 
2.8%
왕 1
 
2.8%
당 1
 
2.8%
Other values (13) 13
36.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 36
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9
25.0%
용 3
 
8.3%
정 2
 
5.6%
읍 2
 
5.6%
원 2
 
5.6%
곡 1
 
2.8%
잔 1
 
2.8%
고 1
 
2.8%
왕 1
 
2.8%
당 1
 
2.8%
Other values (13) 13
36.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 36
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 9
25.0%
용 3
 
8.3%
정 2
 
5.6%
읍 2
 
5.6%
원 2
 
5.6%
곡 1
 
2.8%
잔 1
 
2.8%
고 1
 
2.8%
왕 1
 
2.8%
당 1
 
2.8%
Other values (13) 13
36.1%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.942033
Minimum0
Maximum37.819
Zeros18
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:53:29.768768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q337.22975
95-th percentile37.73965
Maximum37.819
Range37.819
Interquartile range (IQR)37.22975

Descriptive statistics

Standard deviation18.614013
Coefficient of variation (CV)1.2457483
Kurtosis-1.949113
Mean14.942033
Median Absolute Deviation (MAD)0
Skewness0.43039959
Sum448.261
Variance346.48148
MonotonicityNot monotonic
2024-03-13T20:53:29.910157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 18
60.0%
37.497 1
 
3.3%
37.819 1
 
3.3%
36.998 1
 
3.3%
37.001 1
 
3.3%
36.984 1
 
3.3%
37.337 1
 
3.3%
37.301 1
 
3.3%
37.75 1
 
3.3%
37.727 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
0.0 18
60.0%
36.984 1
 
3.3%
36.998 1
 
3.3%
37.001 1
 
3.3%
37.016 1
 
3.3%
37.301 1
 
3.3%
37.313 1
 
3.3%
37.337 1
 
3.3%
37.497 1
 
3.3%
37.518 1
 
3.3%
ValueCountFrequency (%)
37.819 1
3.3%
37.75 1
3.3%
37.727 1
3.3%
37.518 1
3.3%
37.497 1
3.3%
37.337 1
3.3%
37.313 1
3.3%
37.301 1
3.3%
37.016 1
3.3%
37.001 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.828033
Minimum0
Maximum127.603
Zeros18
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:53:30.050613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3126.99
95-th percentile127.2186
Maximum127.603
Range127.603
Interquartile range (IQR)126.99

Descriptive statistics

Standard deviation63.315722
Coefficient of variation (CV)1.2456851
Kurtosis-1.9499193
Mean50.828033
Median Absolute Deviation (MAD)0
Skewness0.43007181
Sum1524.841
Variance4008.8807
MonotonicityNot monotonic
2024-03-13T20:53:30.195805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 18
60.0%
127.603 1
 
3.3%
127.094 1
 
3.3%
127.142 1
 
3.3%
127.168 1
 
3.3%
126.921 1
 
3.3%
126.933 1
 
3.3%
127.009 1
 
3.3%
127.08 1
 
3.3%
127.043 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
0.0 18
60.0%
126.765 1
 
3.3%
126.823 1
 
3.3%
126.921 1
 
3.3%
126.933 1
 
3.3%
127.009 1
 
3.3%
127.043 1
 
3.3%
127.08 1
 
3.3%
127.094 1
 
3.3%
127.142 1
 
3.3%
ValueCountFrequency (%)
127.603 1
3.3%
127.26 1
3.3%
127.168 1
3.3%
127.142 1
3.3%
127.094 1
3.3%
127.08 1
3.3%
127.043 1
3.3%
127.009 1
3.3%
126.933 1
3.3%
126.921 1
3.3%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
True
16 
False
14 
ValueCountFrequency (%)
True 16
53.3%
False 14
46.7%
2024-03-13T20:53:30.318265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8018.3333
Minimum0
Maximum32500
Zeros14
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:53:30.429423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2200
Q315000
95-th percentile30385
Maximum32500
Range32500
Interquartile range (IQR)15000

Descriptive statistics

Standard deviation10882.844
Coefficient of variation (CV)1.3572451
Kurtosis0.070464824
Mean8018.3333
Median Absolute Deviation (MAD)2200
Skewness1.1915136
Sum240550
Variance1.1843629 × 108
MonotonicityNot monotonic
2024-03-13T20:53:30.564072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 14
46.7%
12000 2
 
6.7%
6000 2
 
6.7%
4000 2
 
6.7%
21500 1
 
3.3%
32500 1
 
3.3%
30700 1
 
3.3%
16550 1
 
3.3%
1400 1
 
3.3%
28000 1
 
3.3%
Other values (4) 4
 
13.3%
ValueCountFrequency (%)
0 14
46.7%
1400 1
 
3.3%
3000 1
 
3.3%
4000 2
 
6.7%
6000 2
 
6.7%
12000 2
 
6.7%
16000 1
 
3.3%
16550 1
 
3.3%
16900 1
 
3.3%
21500 1
 
3.3%
ValueCountFrequency (%)
32500 1
3.3%
30700 1
3.3%
30000 1
3.3%
28000 1
3.3%
21500 1
3.3%
16900 1
3.3%
16550 1
3.3%
16000 1
3.3%
12000 2
6.7%
6000 2
6.7%

Interactions

2024-03-13T20:53:22.215695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:17.237113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:18.087886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:18.911964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:19.783571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:20.705487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:21.522917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:22.321525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:17.337389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:18.250623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:19.028137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:19.902554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:20.811948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:21.624629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:22.441864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:17.464800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:18.364716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:19.156349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:20.019390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:20.939924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:21.720600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:22.547919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:17.576613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:18.492663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:19.282242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:20.120348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:21.051538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:21.823548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:23.015233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:17.689543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:18.597607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:19.424934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:20.285887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:21.168946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:21.922798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:23.110812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:17.793289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:18.695210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:19.544015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:20.480086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:21.277492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:22.013409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:23.205953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:17.903488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:18.794138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:19.673131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:20.606799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:21.401461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:22.105623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:53:30.688977image/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
회원코드1.0001.0000.0000.4450.6270.8190.8750.0000.0000.3500.0001.0000.0000.0000.0000.000
가맹점번호1.0000.0001.0000.0000.0000.0000.6641.0001.0001.0001.0001.0001.0001.0001.0001.000
성별코드1.0000.4450.0001.0000.6260.3210.6620.0001.0000.0001.0001.0000.3090.3090.0810.000
연령대코드1.0000.6270.0000.6261.0000.3970.8450.0000.3180.3690.5871.0000.4370.4370.7530.000
결제상품ID1.0000.8190.0000.3210.3971.0001.0000.3100.7980.4801.0001.0000.0000.0000.6630.541
결제상품명1.0000.8750.6640.6620.8451.0001.0000.0001.0001.0001.0001.0000.4300.4300.7670.724
가맹점업종명1.0000.0001.0000.0000.0000.3100.0001.0000.0000.0000.0001.0000.0000.000NaN0.454
가맹점우편번호1.0000.0001.0001.0000.3180.7981.0000.0001.000NaN1.0001.000NaNNaNNaN0.000
시도명1.0000.3501.0000.0000.3690.4801.0000.000NaN1.000NaNNaN0.5620.562NaN1.000
시군구명1.0000.0001.0001.0000.5871.0001.0000.0001.000NaN1.0001.000NaNNaNNaN0.000
읍면동명1.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
위도1.0000.0001.0000.3090.4370.0000.4300.000NaN0.562NaNNaN1.0000.9940.8780.975
경도1.0000.0001.0000.3090.4370.0000.4300.000NaN0.562NaNNaN0.9941.0000.8780.975
사용여부1.0000.0001.0000.0810.7530.6630.767NaNNaNNaNNaNNaN0.8780.8781.0000.964
결제금액1.0000.0001.0000.0000.0000.5410.7240.4540.0001.0000.0001.0000.9750.9750.9641.000
2024-03-13T20:53:30.868556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부성별코드시도명가맹점업종명
사용여부1.0000.0381.0001.000
성별코드0.0381.0000.0000.000
시도명1.0000.0001.0000.000
가맹점업종명1.0000.0000.0001.000
2024-03-13T20:53:31.304007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드가맹점업종명시도명사용여부
회원코드1.0000.3860.095-0.2030.025-0.1220.0220.3190.0000.2130.000
연령대코드0.3861.000-0.158-0.3030.0910.0540.1630.4130.0000.1740.508
결제상품ID0.095-0.1581.000-0.190-0.126-0.204-0.2290.1260.0000.2130.426
가맹점우편번호-0.203-0.303-0.1901.000-0.5490.225-0.3030.5560.0000.0001.000
위도0.0250.091-0.126-0.5491.0000.9140.6730.1970.0000.3720.682
경도-0.1220.054-0.2040.2250.9141.0000.6710.1970.0000.3720.682
결제금액0.0220.163-0.229-0.3030.6730.6711.0000.0000.1440.7390.737
성별코드0.3190.4130.1260.5560.1970.1970.0001.0000.0000.0000.038
가맹점업종명0.0000.0000.0000.0000.0000.0000.1440.0001.0000.0001.000
시도명0.2130.1740.2130.0000.3720.3720.7390.0000.0001.0001.000
사용여부0.0000.5080.4261.0000.6820.6820.7370.0381.0001.0001.000

Missing values

2024-03-13T20:53:23.351443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:53:23.646708image/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.
2024-03-13T20:53:23.831026image/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
02022-01-032022-01-09++756kwM9COyPW+VBszQ6zDFSChks90NGL2l7CLW74E=3024306341757944100M50140000096000파주 Pay(파주페이)(통합)일반휴게음식10863NONE<NA><NA>0.00.0Y21500
12022-01-032022-01-09+wy5Quc03SXWZ6MhYzMZHRAQFF8Zmixo8KaZHDeQkto=3001971264719944674F40140000038000양평통보일반휴게음식12514경기도양평군용문면37.497127.603Y32500
22022-01-032022-01-09++BiPjfB+1ZKVMmPw2XLZYPxCY6y/QaiGChd/xEBPtw=3018010117723867592F40140000036000양주사랑카드음료식품11473경기도양주시옥정동37.819127.094Y30700
32022-01-032022-01-09/YnE7hc4675TtdHpluOmDFddeQeA1VB3O10/IC6ccMc=3018013691999999999999999M20140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
42022-01-032022-01-0984YRMaCj4OofH4oS7R76XwN3Mne5gH4M+lZLLi1gQXY=3002188214999999999999999F30140000160000경기도 시장상권 진흥원<NA><NA><NA><NA><NA>0.00.0N0
52022-01-032022-01-09Gd8QCHK5GPgkDeykQkdXqUOtC8aKaSXMHkrq0695Zdc=3029744314999999999999999M50140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
62022-01-032022-01-09++kYc5XxEdSnkLJ0UMYm49Fm9chJKWczbDCjJ0cdOHQ=3030343190410126290752901M40140000040000여주사랑카드<NA><NA><NA><NA><NA>0.00.0Y16550
72022-01-032022-01-09Dj+RXwS2QymtLIIMIBT642Ufb+GSxBhVVL6hA8Iq6Do=3033120265794547928F40140000058000평택사랑카드(통합)보건위생17867경기도평택시용이동36.998127.142Y12000
82022-01-032022-01-09LdJZ8p9DqO87Hvo7HRZD070vnoRLB2sHR25yj/5WrIA=3017802803999999999999999F50140000112000군포愛머니<NA><NA><NA><NA><NA>0.00.0N0
92022-01-032022-01-09RrytyK7FFwTSQv0huW8seKJrSwHlb30GJH7TkL5sC64=3014252155727420748F40140000032000안성사랑카드유통업 영리17561경기도안성시공도읍37.001127.168Y1400
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202022-01-032022-01-09A8juTqzDfxYJi7bKDRAeFRFzCTtYof9SK9bzjAGmEUA=3018315875786222841F50140000122000의정부사랑카드일반휴게음식11772경기도의정부시용현동37.75127.08Y16000
212022-01-032022-01-09CC0CEk+RQSt3CwycbZ1zDF3RwxZxh6s7yA2YmhQk6JU=3025509161720173844F30140000122000의정부사랑카드유통업 영리11631경기도의정부시호원동37.727127.043Y6000
222022-01-032022-01-09EPou2KXLc7PP/wK3QFpVN8vjhXpuyJ3paZ5Amt8y74s=3011828161725050029F30140000032000안성사랑카드일반휴게음식17568경기도안성시당왕동37.016127.26Y12000
232022-01-032022-01-09GdC8SMexbpNS+XY28kNl/1/LxtyipskEiE4gjSGtHzQ=3008457129999999999999999M20140000066000오산화폐 오색전(통합)<NA><NA><NA><NA><NA>0.00.0N0
242022-01-032022-01-09IejAvTPVtRcBEOUKuIvOQu3bQrnEjuBTXOXKlelAJnc=3017103262999999999999999F0140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
252022-01-032022-01-09KokBGgbB/D4dXT2SrOWgy9BoX2LQQdgAVFThzWc+fpY=3061053667727683854M50140000100000안산사랑상품권 다온(통합)유통업 영리15459경기도안산시 단원구고잔동37.313126.823Y4000
262022-01-032022-01-09N+U0n05lxl1bYLkOfZNkzoEjqSodeDKGRO9o1ZbG6J0=3019191108726935147M30140000030000부천페이일반휴게음식14501경기도부천시삼정동37.518126.765Y4000
272022-01-032022-01-09+1lSRzNrF6RTURyBtrNATFHdJgzew6XgbZ446CJ6Dgw=3039269232999999999999999M60140000022000과천화폐 과천토리<NA><NA><NA><NA><NA>0.00.0N0
282022-01-032022-01-092VCVzek5j6S8HNmLx515PgJL80HTRmTMS2OZRF5iJ+Q=3018129807410769350025101F30140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0Y3000
292022-01-032022-01-096JtLidC8woFhtyJc2CZq4HMLol/FgUkp65wHFHtksPo=3019488492999999999999999F60140000114000Thank You Pay-N<NA><NA><NA><NA><NA>0.00.0N0