Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells48
Missing cells (%)8.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory156.4 B

Variable types

DateTime2
Text4
Numeric8
Categorical3
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/d021d3ca-b291-4208-98d1-33b231d59345

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
사용여부 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
성별코드 is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 회원코드 and 10 other fieldsHigh correlation
가맹점업종명 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
회원코드 is highly overall correlated with 시도명High correlation
가맹점번호 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
연령대코드 is highly overall correlated with 시도명High correlation
결제상품ID is highly overall correlated with 시도명High correlation
가맹점우편번호 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점번호 and 6 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
가맹점우편번호 has 16 (53.3%) missing valuesMissing
시군구명 has 16 (53.3%) missing valuesMissing
읍면동명 has 16 (53.3%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
위도 has 16 (53.3%) zerosZeros
경도 has 16 (53.3%) zerosZeros
결제금액 has 16 (53.3%) zerosZeros

Reproduction

Analysis started2024-03-13 11:58:56.013083
Analysis finished2024-03-13 11:59:02.436857
Duration6.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책주간결제시작일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2021-01-04 00:00:00
Maximum2021-01-04 00:00:00
2024-03-13T20:59:02.471342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:02.546376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

정책주간결제종료일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2021-01-10 00:00:00
Maximum2021-01-10 00:00:00
2024-03-13T20:59:02.630152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:02.705126image/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
2024-03-13T20:59:02.961812image/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++BEchgJKwrJ/9+J64aVeohMz6VVTUxBJC8S+G/IhIg=
2nd rowi3JpH2pqR/EIBAcw+YI67UWpifC45pVvfxdA75SHQrU=
3rd rowmit/1qlARPN3f2X8szr5XQTD7V38d56V+1FrbcDY28w=
4th rowGNlBywZt3H9kg2J/wfIoRNp3rB/7NzAradBIbXZ7ecA=
5th row++CTBFzwSsAih3UgOlyyqRkTJHWDhIjtLeriPwPJlGA=
ValueCountFrequency (%)
bechgjkwrj/9+j64aveohmz6vvtuxbjc8s+g/ihig 1
 
3.3%
i3jph2pqr/eibacw+yi67uwpifc45pvvfxda75shqru 1
 
3.3%
belrp4j0i0zojvp/1plcj4qhbzf2+7oehzvvicsizws 1
 
3.3%
a4ufpkavaitudzlrjin9+sv/p4+xywky+gpfe0e3huc 1
 
3.3%
8wvyjicqfvv+djayx63+qzifczj8wn7ia0rcgyfmjpa 1
 
3.3%
6un4tdu0tighumj4ik7zq9tn4odbcv+7nlm1ogi1gps 1
 
3.3%
5imryl9w3c3nyjbd2qf+w4/fr3tg/7zu0ufb8zlew8y 1
 
3.3%
3krtlfwyntlm0dxcpbgm+8fvslfajiakuw/9oylddlk 1
 
3.3%
21lbkaycmx9ykjbhi/zc+g9ggm4pbf4l3myqn1ob1qc 1
 
3.3%
0qutfpawbcshcb7e7n7fc9j9j0oj3tq+en/5jorjdcu 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:59:03.269775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
+ 33
 
2.5%
A 32
 
2.4%
= 30
 
2.3%
j 29
 
2.2%
I 28
 
2.1%
l 28
 
2.1%
w 28
 
2.1%
4 26
 
2.0%
p 25
 
1.9%
3 25
 
1.9%
Other values (55) 1036
78.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 541
41.0%
Uppercase Letter 491
37.2%
Decimal Number 203
 
15.4%
Math Symbol 63
 
4.8%
Other Punctuation 22
 
1.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 32
 
6.5%
I 28
 
5.7%
U 25
 
5.1%
F 25
 
5.1%
C 24
 
4.9%
B 24
 
4.9%
N 21
 
4.3%
J 21
 
4.3%
Y 20
 
4.1%
R 20
 
4.1%
Other values (16) 251
51.1%
Lowercase Letter
ValueCountFrequency (%)
j 29
 
5.4%
l 28
 
5.2%
w 28
 
5.2%
p 25
 
4.6%
f 25
 
4.6%
z 24
 
4.4%
o 24
 
4.4%
i 23
 
4.3%
g 23
 
4.3%
t 22
 
4.1%
Other values (16) 290
53.6%
Decimal Number
ValueCountFrequency (%)
4 26
12.8%
3 25
12.3%
7 24
11.8%
0 23
11.3%
8 22
10.8%
9 21
10.3%
2 18
8.9%
1 17
8.4%
5 16
7.9%
6 11
5.4%
Math Symbol
ValueCountFrequency (%)
+ 33
52.4%
= 30
47.6%
Other Punctuation
ValueCountFrequency (%)
/ 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1032
78.2%
Common 288
 
21.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 32
 
3.1%
j 29
 
2.8%
I 28
 
2.7%
l 28
 
2.7%
w 28
 
2.7%
p 25
 
2.4%
U 25
 
2.4%
f 25
 
2.4%
F 25
 
2.4%
z 24
 
2.3%
Other values (42) 763
73.9%
Common
ValueCountFrequency (%)
+ 33
11.5%
= 30
10.4%
4 26
9.0%
3 25
8.7%
7 24
8.3%
0 23
8.0%
8 22
7.6%
/ 22
7.6%
9 21
7.3%
2 18
6.2%
Other values (3) 44
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+ 33
 
2.5%
A 32
 
2.4%
= 30
 
2.3%
j 29
 
2.2%
I 28
 
2.1%
l 28
 
2.1%
w 28
 
2.1%
4 26
 
2.0%
p 25
 
1.9%
3 25
 
1.9%
Other values (55) 1036
78.5%

회원코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0188093 × 109
Minimum3.0020489 × 109
Maximum3.0361602 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:03.380247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0020489 × 109
5-th percentile3.0044701 × 109
Q13.0166386 × 109
median3.0177096 × 109
Q33.0201762 × 109
95-th percentile3.0347636 × 109
Maximum3.0361602 × 109
Range34111235
Interquartile range (IQR)3537612.5

Descriptive statistics

Standard deviation8561060.8
Coefficient of variation (CV)0.0028359065
Kurtosis0.51079642
Mean3.0188093 × 109
Median Absolute Deviation (MAD)2292203.5
Skewness0.4231437
Sum9.0564279 × 1010
Variance7.3291762 × 1013
MonotonicityNot monotonic
2024-03-13T20:59:03.491044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3012385512 1
 
3.3%
3006864249 1
 
3.3%
3036160167 1
 
3.3%
3019288321 1
 
3.3%
3008976248 1
 
3.3%
3033395170 1
 
3.3%
3017103104 1
 
3.3%
3020072567 1
 
3.3%
3020336809 1
 
3.3%
3017136665 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3002048932 1
3.3%
3002511334 1
3.3%
3006864249 1
3.3%
3008976248 1
3.3%
3012385512 1
3.3%
3014295128 1
3.3%
3015488160 1
3.3%
3016627131 1
3.3%
3016672845 1
3.3%
3016780319 1
3.3%
ValueCountFrequency (%)
3036160167 1
3.3%
3034806270 1
3.3%
3034711444 1
3.3%
3033395170 1
3.3%
3032984244 1
3.3%
3021740158 1
3.3%
3020336809 1
3.3%
3020210707 1
3.3%
3020072567 1
3.3%
3019388361 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3333369 × 1014
Minimum7.0891451 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:03.871362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0891451 × 108
5-th percentile7.1353665 × 108
Q17.729142 × 108
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.9999929 × 1014
Interquartile range (IQR)9.9999923 × 1014

Descriptive statistics

Standard deviation5.0741588 × 1014
Coefficient of variation (CV)0.95140414
Kurtosis-2.1269133
Mean5.3333369 × 1014
Median Absolute Deviation (MAD)0
Skewness-0.14076918
Sum1.6000011 × 1016
Variance2.5747087 × 1029
MonotonicityNot monotonic
2024-03-13T20:59:03.991115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
999999999999999 16
53.3%
797982009 1
 
3.3%
762377199 1
 
3.3%
761012433 1
 
3.3%
781478069 1
 
3.3%
712381108 1
 
3.3%
714948989 1
 
3.3%
721361299 1
 
3.3%
777610263 1
 
3.3%
708914512 1
 
3.3%
Other values (5) 5
 
16.7%
ValueCountFrequency (%)
708914512 1
3.3%
712381108 1
3.3%
714948989 1
3.3%
721361299 1
3.3%
722481817 1
3.3%
761012433 1
3.3%
762377199 1
3.3%
771348840 1
3.3%
777610263 1
3.3%
781478069 1
3.3%
ValueCountFrequency (%)
999999999999999 16
53.3%
797982009 1
 
3.3%
796160546 1
 
3.3%
791208997 1
 
3.3%
788844326 1
 
3.3%
781478069 1
 
3.3%
777610263 1
 
3.3%
771348840 1
 
3.3%
762377199 1
 
3.3%
761012433 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
M
18 
F
12 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 18
60.0%
F 12
40.0%

Length

2024-03-13T20:59:04.102053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:59:04.178712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 18
60.0%
f 12
40.0%

연령대코드
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.333333
Minimum10
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:04.254097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q130
median40
Q350
95-th percentile61
Maximum70
Range60
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.879614
Coefficient of variation (CV)0.35287154
Kurtosis0.33131341
Mean39.333333
Median Absolute Deviation (MAD)10
Skewness0.12687641
Sum1180
Variance192.64368
MonotonicityNot monotonic
2024-03-13T20:59:04.332961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
40 10
33.3%
50 8
26.7%
30 5
16.7%
20 4
 
13.3%
70 2
 
6.7%
10 1
 
3.3%
ValueCountFrequency (%)
10 1
 
3.3%
20 4
 
13.3%
30 5
16.7%
40 10
33.3%
50 8
26.7%
70 2
 
6.7%
ValueCountFrequency (%)
70 2
 
6.7%
50 8
26.7%
40 10
33.3%
30 5
16.7%
20 4
 
13.3%
10 1
 
3.3%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000008 × 1011
Minimum1.4000002 × 1011
Maximum1.4000013 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:04.428044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000003 × 1011
Q11.4000004 × 1011
median1.4000008 × 1011
Q31.4000011 × 1011
95-th percentile1.4000013 × 1011
Maximum1.4000013 × 1011
Range102000
Interquartile range (IQR)72000

Descriptive statistics

Standard deviation37088.338
Coefficient of variation (CV)2.6491655 × 10-7
Kurtosis-1.7694035
Mean1.4000008 × 1011
Median Absolute Deviation (MAD)39000
Skewness-0.08036295
Sum4.2000023 × 1012
Variance1.3755448 × 109
MonotonicityNot monotonic
2024-03-13T20:59:04.532446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
140000046000 4
13.3%
140000114000 3
 
10.0%
140000126000 3
 
10.0%
140000102000 2
 
6.7%
140000032000 2
 
6.7%
140000112000 2
 
6.7%
140000036000 2
 
6.7%
140000094000 1
 
3.3%
140000030000 1
 
3.3%
140000024000 1
 
3.3%
Other values (9) 9
30.0%
ValueCountFrequency (%)
140000024000 1
 
3.3%
140000030000 1
 
3.3%
140000032000 2
6.7%
140000036000 2
6.7%
140000038000 1
 
3.3%
140000040000 1
 
3.3%
140000046000 4
13.3%
140000048000 1
 
3.3%
140000078000 1
 
3.3%
140000082000 1
 
3.3%
ValueCountFrequency (%)
140000126000 3
10.0%
140000124000 1
 
3.3%
140000116000 1
 
3.3%
140000114000 3
10.0%
140000112000 2
6.7%
140000104000 1
 
3.3%
140000102000 2
6.7%
140000094000 1
 
3.3%
140000088000 1
 
3.3%
140000082000 1
 
3.3%

결제상품명
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:59:04.695102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12.5
Mean length7.8333333
Min length4

Characters and Unicode

Total characters235
Distinct characters60
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

Unique12 ?
Unique (%)40.0%

Sample

1st row하남하머니(통합)
2nd rowThank You Pay-N
3rd row수원페이(통합)
4th row안성사랑카드
5th row용인와이페이
ValueCountFrequency (%)
용인와이페이 4
 
10.3%
thank 4
 
10.3%
you 4
 
10.3%
pay-n 3
 
7.7%
수원페이 3
 
7.7%
수원페이(통합 2
 
5.1%
안성사랑카드 2
 
5.1%
군포愛머니 2
 
5.1%
양주사랑카드 2
 
5.1%
이천사랑지역화폐 1
 
2.6%
Other values (12) 12
30.8%
2024-03-13T20:59:04.979029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 15
 
6.4%
사 11
 
4.7%
랑 11
 
4.7%
페 10
 
4.3%
9
 
3.8%
a 8
 
3.4%
드 8
 
3.4%
카 8
 
3.4%
통 8
 
3.4%
) 7
 
3.0%
Other values (50) 140
59.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 160
68.1%
Lowercase Letter 32
 
13.6%
Uppercase Letter 16
 
6.8%
Space Separator 9
 
3.8%
Close Punctuation 7
 
3.0%
Open Punctuation 7
 
3.0%
Dash Punctuation 4
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 15
 
9.4%
사 11
 
6.9%
랑 11
 
6.9%
페 10
 
6.2%
드 8
 
5.0%
카 8
 
5.0%
통 8
 
5.0%
합 7
 
4.4%
원 5
 
3.1%
수 5
 
3.1%
Other values (35) 72
45.0%
Lowercase Letter
ValueCountFrequency (%)
a 8
25.0%
k 4
12.5%
h 4
12.5%
n 4
12.5%
o 4
12.5%
u 4
12.5%
y 4
12.5%
Uppercase Letter
ValueCountFrequency (%)
N 4
25.0%
T 4
25.0%
Y 4
25.0%
P 4
25.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 158
67.2%
Latin 48
 
20.4%
Common 27
 
11.5%
Han 2
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 15
 
9.5%
사 11
 
7.0%
랑 11
 
7.0%
페 10
 
6.3%
드 8
 
5.1%
카 8
 
5.1%
통 8
 
5.1%
합 7
 
4.4%
원 5
 
3.2%
수 5
 
3.2%
Other values (34) 70
44.3%
Latin
ValueCountFrequency (%)
a 8
16.7%
N 4
8.3%
T 4
8.3%
k 4
8.3%
h 4
8.3%
n 4
8.3%
Y 4
8.3%
o 4
8.3%
u 4
8.3%
P 4
8.3%
Common
ValueCountFrequency (%)
9
33.3%
) 7
25.9%
( 7
25.9%
- 4
14.8%
Han
ValueCountFrequency (%)
愛 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 158
67.2%
ASCII 75
31.9%
CJK 2
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 15
 
9.5%
사 11
 
7.0%
랑 11
 
7.0%
페 10
 
6.3%
드 8
 
5.1%
카 8
 
5.1%
통 8
 
5.1%
합 7
 
4.4%
원 5
 
3.2%
수 5
 
3.2%
Other values (34) 70
44.3%
ASCII
ValueCountFrequency (%)
9
12.0%
a 8
 
10.7%
) 7
 
9.3%
( 7
 
9.3%
N 4
 
5.3%
T 4
 
5.3%
k 4
 
5.3%
- 4
 
5.3%
h 4
 
5.3%
n 4
 
5.3%
Other values (5) 20
26.7%
CJK
ValueCountFrequency (%)
愛 2
100.0%

가맹점업종명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
16 
일반휴게음식
5 
음료식품
3 
유통업 영리
3 
약국
 
1
Other values (2)
2 

Length

Max length6
Median length4
Mean length4.4
Min length2

Unique

Unique3 ?
Unique (%)10.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 16
53.3%
일반휴게음식 5
 
16.7%
음료식품 3
 
10.0%
유통업 영리 3
 
10.0%
약국 1
 
3.3%
의원 1
 
3.3%
보건위생 1
 
3.3%

Length

2024-03-13T20:59:05.156215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:59:05.267914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 16
48.5%
일반휴게음식 5
 
15.2%
음료식품 3
 
9.1%
유통업 3
 
9.1%
영리 3
 
9.1%
약국 1
 
3.0%
의원 1
 
3.0%
보건위생 1
 
3.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing16
Missing (%)53.3%
Infinite0
Infinite (%)0.0%
Mean15095.143
Minimum11494
Maximum18501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:05.369274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11494
5-th percentile11796.9
Q112609
median16130
Q317036.5
95-th percentile17853.6
Maximum18501
Range7007
Interquartile range (IQR)4427.5

Descriptive statistics

Standard deviation2452.9984
Coefficient of variation (CV)0.1625025
Kurtosis-1.6783325
Mean15095.143
Median Absolute Deviation (MAD)1631.5
Skewness-0.28952676
Sum211332
Variance6017201.2
MonotonicityNot monotonic
2024-03-13T20:59:05.465057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
16921 1
 
3.3%
12557 1
 
3.3%
17075 1
 
3.3%
14242 1
 
3.3%
11960 1
 
3.3%
12159 1
 
3.3%
18501 1
 
3.3%
17505 1
 
3.3%
11494 1
 
3.3%
16439 1
 
3.3%
Other values (4) 4
 
13.3%
(Missing) 16
53.3%
ValueCountFrequency (%)
11494 1
3.3%
11960 1
3.3%
12159 1
3.3%
12557 1
3.3%
12765 1
3.3%
14242 1
3.3%
15821 1
3.3%
16439 1
3.3%
16524 1
3.3%
16921 1
3.3%
ValueCountFrequency (%)
18501 1
3.3%
17505 1
3.3%
17369 1
3.3%
17075 1
3.3%
16921 1
3.3%
16524 1
3.3%
16439 1
3.3%
15821 1
3.3%
14242 1
3.3%
12765 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
16 
경기도
14 

Length

Max length4
Median length4
Mean length3.5333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 16
53.3%
경기도 14
46.7%

Length

2024-03-13T20:59:05.575110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:59:05.671220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 16
53.3%
경기도 14
46.7%

시군구명
Text

MISSING 

Distinct13
Distinct (%)92.9%
Missing16
Missing (%)53.3%
Memory size372.0 B
2024-03-13T20:59:05.808002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4.2142857
Min length3

Characters and Unicode

Total characters59
Distinct characters27
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

Unique12 ?
Unique (%)85.7%

Sample

1st row용인시 기흥구
2nd row양평군
3rd row용인시 기흥구
4th row광명시
5th row구리시
ValueCountFrequency (%)
용인시 2
 
11.1%
기흥구 2
 
11.1%
수원시 2
 
11.1%
양평군 1
 
5.6%
광명시 1
 
5.6%
구리시 1
 
5.6%
남양주시 1
 
5.6%
화성시 1
 
5.6%
안성시 1
 
5.6%
양주시 1
 
5.6%
Other values (5) 5
27.8%
2024-03-13T20:59:06.213393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 13
22.0%
구 5
 
8.5%
4
 
6.8%
양 3
 
5.1%
주 3
 
5.1%
용 2
 
3.4%
인 2
 
3.4%
수 2
 
3.4%
성 2
 
3.4%
광 2
 
3.4%
Other values (17) 21
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 55
93.2%
Space Separator 4
 
6.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 13
23.6%
구 5
 
9.1%
양 3
 
5.5%
주 3
 
5.5%
용 2
 
3.6%
인 2
 
3.6%
수 2
 
3.6%
성 2
 
3.6%
광 2
 
3.6%
군 2
 
3.6%
Other values (16) 19
34.5%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 55
93.2%
Common 4
 
6.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 13
23.6%
구 5
 
9.1%
양 3
 
5.5%
주 3
 
5.5%
용 2
 
3.6%
인 2
 
3.6%
수 2
 
3.6%
성 2
 
3.6%
광 2
 
3.6%
군 2
 
3.6%
Other values (16) 19
34.5%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 55
93.2%
ASCII 4
 
6.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 13
23.6%
구 5
 
9.1%
양 3
 
5.5%
주 3
 
5.5%
용 2
 
3.6%
인 2
 
3.6%
수 2
 
3.6%
성 2
 
3.6%
광 2
 
3.6%
군 2
 
3.6%
Other values (16) 19
34.5%
ASCII
ValueCountFrequency (%)
4
100.0%

읍면동명
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing16
Missing (%)53.3%
Memory size372.0 B
2024-03-13T20:59:06.433259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters42
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)100.0%

Sample

1st row마북동
2nd row양평읍
3rd row보라동
4th row철산동
5th row토평동
ValueCountFrequency (%)
마북동 1
 
7.1%
양평읍 1
 
7.1%
보라동 1
 
7.1%
철산동 1
 
7.1%
토평동 1
 
7.1%
화도읍 1
 
7.1%
산척동 1
 
7.1%
고삼면 1
 
7.1%
만송동 1
 
7.1%
화서동 1
 
7.1%
Other values (4) 4
28.6%
2024-03-13T20:59:06.805519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 11
26.2%
산 3
 
7.1%
평 2
 
4.8%
읍 2
 
4.8%
화 2
 
4.8%
마 1
 
2.4%
면 1
 
2.4%
매 1
 
2.4%
현 1
 
2.4%
목 1
 
2.4%
Other values (17) 17
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 11
26.2%
산 3
 
7.1%
평 2
 
4.8%
읍 2
 
4.8%
화 2
 
4.8%
마 1
 
2.4%
면 1
 
2.4%
매 1
 
2.4%
현 1
 
2.4%
목 1
 
2.4%
Other values (17) 17
40.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 11
26.2%
산 3
 
7.1%
평 2
 
4.8%
읍 2
 
4.8%
화 2
 
4.8%
마 1
 
2.4%
면 1
 
2.4%
매 1
 
2.4%
현 1
 
2.4%
목 1
 
2.4%
Other values (17) 17
40.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 11
26.2%
산 3
 
7.1%
평 2
 
4.8%
읍 2
 
4.8%
화 2
 
4.8%
마 1
 
2.4%
면 1
 
2.4%
매 1
 
2.4%
현 1
 
2.4%
목 1
 
2.4%
Other values (17) 17
40.5%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.4483
Minimum0
Maximum37.788
Zeros16
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:06.951082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q337.29625
95-th percentile37.62955
Maximum37.788
Range37.788
Interquartile range (IQR)37.29625

Descriptive statistics

Standard deviation18.972344
Coefficient of variation (CV)1.0873463
Kurtosis-2.1266409
Mean17.4483
Median Absolute Deviation (MAD)0
Skewness0.14091919
Sum523.449
Variance359.94983
MonotonicityNot monotonic
2024-03-13T20:59:07.092045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.0 16
53.3%
37.3 1
 
3.3%
37.49 1
 
3.3%
37.254 1
 
3.3%
37.473 1
 
3.3%
37.585 1
 
3.3%
37.666 1
 
3.3%
37.17 1
 
3.3%
37.092 1
 
3.3%
37.788 1
 
3.3%
Other values (5) 5
 
16.7%
ValueCountFrequency (%)
0.0 16
53.3%
37.092 1
 
3.3%
37.17 1
 
3.3%
37.254 1
 
3.3%
37.27 1
 
3.3%
37.28 1
 
3.3%
37.285 1
 
3.3%
37.3 1
 
3.3%
37.362 1
 
3.3%
37.434 1
 
3.3%
ValueCountFrequency (%)
37.788 1
3.3%
37.666 1
3.3%
37.585 1
3.3%
37.49 1
3.3%
37.473 1
3.3%
37.434 1
3.3%
37.362 1
3.3%
37.3 1
3.3%
37.285 1
3.3%
37.28 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.337767
Minimum0
Maximum127.495
Zeros16
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:07.272497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3127.1065
95-th percentile127.3801
Maximum127.495
Range127.495
Interquartile range (IQR)127.1065

Descriptive statistics

Standard deviation64.519284
Coefficient of variation (CV)1.0873224
Kurtosis-2.1268936
Mean59.337767
Median Absolute Deviation (MAD)0
Skewness0.14078002
Sum1780.133
Variance4162.738
MonotonicityNot monotonic
2024-03-13T20:59:07.418774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.0 16
53.3%
127.107 1
 
3.3%
127.495 1
 
3.3%
127.105 1
 
3.3%
126.873 1
 
3.3%
127.15 1
 
3.3%
127.302 1
 
3.3%
127.118 1
 
3.3%
127.259 1
 
3.3%
127.086 1
 
3.3%
Other values (5) 5
 
16.7%
ValueCountFrequency (%)
0.0 16
53.3%
126.873 1
 
3.3%
126.923 1
 
3.3%
127.006 1
 
3.3%
127.049 1
 
3.3%
127.086 1
 
3.3%
127.105 1
 
3.3%
127.107 1
 
3.3%
127.118 1
 
3.3%
127.15 1
 
3.3%
ValueCountFrequency (%)
127.495 1
3.3%
127.444 1
3.3%
127.302 1
3.3%
127.259 1
3.3%
127.216 1
3.3%
127.15 1
3.3%
127.118 1
3.3%
127.107 1
3.3%
127.105 1
3.3%
127.086 1
3.3%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
16 
True
14 
ValueCountFrequency (%)
False 16
53.3%
True 14
46.7%
2024-03-13T20:59:07.560757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17310.667
Minimum0
Maximum385000
Zeros16
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:07.670703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36075
95-th percentile28200
Maximum385000
Range385000
Interquartile range (IQR)6075

Descriptive statistics

Standard deviation69905.151
Coefficient of variation (CV)4.0382703
Kurtosis29.133616
Mean17310.667
Median Absolute Deviation (MAD)0
Skewness5.3667467
Sum519320
Variance4.8867302 × 109
MonotonicityNot monotonic
2024-03-13T20:59:07.792618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 16
53.3%
13000 1
 
3.3%
10200 1
 
3.3%
5800 1
 
3.3%
5100 1
 
3.3%
385000 1
 
3.3%
9020 1
 
3.3%
14000 1
 
3.3%
39000 1
 
3.3%
4600 1
 
3.3%
Other values (5) 5
 
16.7%
ValueCountFrequency (%)
0 16
53.3%
1200 1
 
3.3%
4600 1
 
3.3%
5100 1
 
3.3%
5300 1
 
3.3%
5800 1
 
3.3%
6000 1
 
3.3%
6100 1
 
3.3%
9020 1
 
3.3%
10200 1
 
3.3%
ValueCountFrequency (%)
385000 1
3.3%
39000 1
3.3%
15000 1
3.3%
14000 1
3.3%
13000 1
3.3%
10200 1
3.3%
9020 1
3.3%
6100 1
3.3%
6000 1
3.3%
5800 1
3.3%

Interactions

2024-03-13T20:59:01.375601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:56.685299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:57.299438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:57.898323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:58.593175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:59.429642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:00.047626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:00.763880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:01.452982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:56.761888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:57.377387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:57.967314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:58.687090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:59.509218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:00.124111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:00.841744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:01.526460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:56.839621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:57.445654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:58.039695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:58.764478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:59.583851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:00.206435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:00.920561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:01.602801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:56.915376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:57.516464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:58.110534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:59.072099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:59.652566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:00.291873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:00.994139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:01.682976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:56.996258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:57.587148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:58.193844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:59.136338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:59.726022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:00.368949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:01.066986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:01.765785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:57.068689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:57.668648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:58.263187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:59.204515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:59.811938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:00.444211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:01.138287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:01.858849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:57.144413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:57.750483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:58.366980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:59.279336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:59.894291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:00.544966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:01.214024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:01.940597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:57.219734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:57.821825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:58.501148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:59.353463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:58:59.968647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:00.661138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:01.290241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:59:07.898521image/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
회원코드1.0001.0000.0000.3470.0000.6800.0000.0000.3650.8871.0000.0000.0000.0000.745
가맹점번호1.0000.0001.0000.0000.0000.0000.000NaNNaNNaNNaN0.9940.9940.9940.093
성별코드1.0000.3470.0001.0000.0000.5690.6430.5410.0001.0001.0000.0000.0000.0000.047
연령대코드1.0000.0000.0000.0001.0000.1790.0000.3770.6530.8321.0000.0000.0000.0000.000
결제상품ID1.0000.6800.0000.5690.1791.0001.0000.5930.7261.0001.0000.0000.0000.0000.583
결제상품명1.0000.0000.0000.6430.0001.0001.0000.4751.0001.0001.0000.0000.0000.0000.779
가맹점업종명1.0000.000NaN0.5410.3770.5930.4751.0000.2610.4751.000NaNNaNNaN0.869
가맹점우편번호1.0000.365NaN0.0000.6530.7261.0000.2611.0001.0001.000NaNNaNNaN0.000
시군구명1.0000.887NaN1.0000.8321.0001.0000.4751.0001.0001.000NaNNaNNaN1.000
읍면동명1.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.000NaNNaNNaN1.000
위도1.0000.0000.9940.0000.0000.0000.000NaNNaNNaNNaN1.0000.9940.9940.076
경도1.0000.0000.9940.0000.0000.0000.000NaNNaNNaNNaN0.9941.0000.9940.076
사용여부1.0000.0000.9940.0000.0000.0000.000NaNNaNNaNNaN0.9940.9941.0000.076
결제금액1.0000.7450.0930.0470.0000.5830.7790.8690.0001.0001.0000.0760.0760.0761.000
2024-03-13T20:59:08.076582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부성별코드시도명가맹점업종명
사용여부1.0000.0001.0001.000
성별코드0.0001.0001.0000.277
시도명1.0001.0001.0001.000
가맹점업종명1.0000.2771.0001.000
2024-03-13T20:59:08.210073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드가맹점업종명시도명사용여부
회원코드1.000-0.017-0.2910.1160.0990.015-0.095-0.0020.3870.1101.0000.000
가맹점번호-0.0171.000-0.1590.0760.429-0.919-0.885-0.8910.0001.0001.0000.931
연령대코드-0.291-0.1591.000-0.475-0.1040.2040.2340.1400.0000.1621.0000.000
결제상품ID0.1160.076-0.4751.0000.128-0.139-0.202-0.1830.4000.3691.0000.000
가맹점우편번호0.0990.429-0.1040.1281.000-0.9560.0200.1430.1740.0001.0001.000
위도0.015-0.9190.204-0.139-0.9561.0000.8860.8600.0001.0001.0000.931
경도-0.095-0.8850.234-0.2020.0200.8861.0000.9560.0001.0001.0000.931
결제금액-0.002-0.8910.140-0.1830.1430.8600.9561.0000.0600.4641.0000.115
성별코드0.3870.0000.0000.4000.1740.0000.0000.0601.0000.2771.0000.000
가맹점업종명0.1101.0000.1620.3690.0001.0001.0000.4640.2771.0001.0001.000
시도명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사용여부0.0000.9310.0000.0001.0000.9310.9310.1150.0001.0001.0001.000

Missing values

2024-03-13T20:59:02.071868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:59:02.261081image/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.
2024-03-13T20:59:02.378754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
02021-01-042021-01-10++BEchgJKwrJ/9+J64aVeohMz6VVTUxBJC8S+G/IhIg=3012385512999999999999999M20140000094000하남하머니(통합)<NA><NA><NA><NA><NA>0.00.0N0
12021-01-042021-01-10i3JpH2pqR/EIBAcw+YI67UWpifC45pVvfxdA75SHQrU=3017879501999999999999999M50140000114000Thank You Pay-N<NA><NA><NA><NA><NA>0.00.0N0
22021-01-042021-01-10mit/1qlARPN3f2X8szr5XQTD7V38d56V+1FrbcDY28w=3034711444999999999999999M40140000102000수원페이(통합)<NA><NA><NA><NA><NA>0.00.0N0
32021-01-042021-01-10GNlBywZt3H9kg2J/wfIoRNp3rB/7NzAradBIbXZ7ecA=3017419278999999999999999M50140000032000안성사랑카드<NA><NA><NA><NA><NA>0.00.0N0
42021-01-042021-01-10++CTBFzwSsAih3UgOlyyqRkTJHWDhIjtLeriPwPJlGA=3018065814797982009M30140000046000용인와이페이음료식품16921경기도용인시 기흥구마북동37.3127.107Y13000
52021-01-042021-01-103kItsg2tZ/SkWSRfxaidfLM227+gblIeNeA+99mHtUA=3002048932762377199F50140000038000양평통보약국12557경기도양평군양평읍37.49127.495Y10200
62021-01-042021-01-10A4cKcbtggSxNmc9cgrUo8KYSY+PUMyT5ULqMyAaU5Is=3032984244761012433M40140000046000용인와이페이의원17075경기도용인시 기흥구보라동37.254127.105Y5800
72021-01-042021-01-10i3ZAjprnnqydwzo+hjJok/Hbys8nf4vewb8Fxj+vxYI=3016627131999999999999999M30140000112000군포愛머니<NA><NA><NA><NA><NA>0.00.0N0
82021-01-042021-01-10qJX8tA7ekB/9ECw5fENoIit9oB9lbMOLyaFztaSpSaw=3034806270999999999999999M30140000104000Thank You Pay-N(통합)<NA><NA><NA><NA><NA>0.00.0N0
92021-01-042021-01-10GReFNFT4U6ClsONxTYiwKiGc1A/NsFuMoUf9VuWD7cQ=3021740158781478069M40140000088000광명사랑화폐(통합)음료식품14242경기도광명시철산동37.473126.873Y5100
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202021-01-042021-01-10+ufCayTKVt+0LghzcxjH4+vbDGFUVC2vjFFkI2+tlo8=3016780319771348840F20140000126000수원페이유통업 영리16439경기도수원시 팔달구화서동37.285127.006Y5300
212021-01-042021-01-100QutFPaWBcShCB7E7n7fC9j9J0Oj3tQ+en/5JORjdCU=3016672845999999999999999F30140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
222021-01-042021-01-1021LBKAYCMx9YkjBhi/zc+G9GgM4pbF4l3MyqN1oB1qc=3017136665999999999999999M50140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0N0
232021-01-042021-01-103kRtlfwyNTlm0dxCpBgm+8fvslfAjIakUW/9oYldDLk=3020336809999999999999999F10140000114000Thank You Pay-N<NA><NA><NA><NA><NA>0.00.0N0
242021-01-042021-01-105Imryl9w3C3NyJBD2qf+w4/fr3tG/7zU0uFb8zlEw8Y=3020072567999999999999999F40140000078000안성사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
252021-01-042021-01-106uN4tdU0tiGHumj4IK7zQ9tn4oDBcv+7NLm1oGi1gps=3017103104796160546F50140000048000이천사랑지역화폐일반휴게음식17369경기도이천시창전동37.28127.444Y6000
262021-01-042021-01-108WvyjICqFVV+DjaYX63+QZiFczJ8wn7iA0rCGyFmjpA=3033395170791208997M70140000112000군포愛머니유통업 영리15821경기도군포시산본동37.362126.923Y1200
272021-01-042021-01-10A4ufpkavaiTudzlrjin9+sV/p4+xyWkY+gPFE0E3HUc=3008976248999999999999999M50140000036000양주사랑카드<NA><NA><NA><NA><NA>0.00.0N0
282021-01-042021-01-10BelRP4j0i0zojvp/1PLcj4QhbZF2+7oEhZVViCSIZws=3019288321788844326M40140000024000광주사랑카드일반휴게음식12765경기도광주시목현동37.434127.216Y15000
292021-01-042021-01-10DAPjibEzGjlfXk8EoQdo7ed/r1aiEDwRS0jBnqxmpZU=3036160167722481817M30140000102000수원페이(통합)음료식품16524경기도수원시 영통구매탄동37.27127.049Y6100