Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells76
Missing cells (%)14.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory156.4 B

Variable types

DateTime2
Text5
Numeric8
Categorical2
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/0737aeb1-f83b-47ba-8a35-9859db1b9f81

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 9 other fieldsHigh correlation
회원코드 is highly overall correlated with 시도명High correlation
가맹점번호 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
연령대코드 is highly overall correlated with 시도명High correlation
결제상품ID is highly overall correlated with 가맹점우편번호 and 1 other fieldsHigh correlation
가맹점우편번호 is highly overall correlated with 결제상품ID and 3 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
결제금액 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
가맹점업종명 has 19 (63.3%) missing valuesMissing
가맹점우편번호 has 19 (63.3%) missing valuesMissing
시군구명 has 19 (63.3%) missing valuesMissing
읍면동명 has 19 (63.3%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
연령대코드 has 3 (10.0%) zerosZeros
위도 has 19 (63.3%) zerosZeros
경도 has 19 (63.3%) zerosZeros
결제금액 has 17 (56.7%) zerosZeros

Reproduction

Analysis started2024-03-13 11:55:42.911474
Analysis finished2024-03-13 11:55:50.815008
Duration7.9 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
Minimum2021-07-05 00:00:00
Maximum2021-07-05 00:00:00
2024-03-13T20:55:50.853894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:50.939645image/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
Minimum2021-07-11 00:00:00
Maximum2021-07-11 00:00:00
2024-03-13T20:55:51.024242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:51.141187image/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
2024-03-13T20:55:51.364921image/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 row7suymp18rt64CutuE0+n88mG+vlxAYTjBQ/jV27UkyM=
2nd row7t5upHjnwvWHCvcZuUFF1Cmfs+sI9W85igHhFHirQK0=
3rd row7tEMJySKLoivjgyys+CSY9lXYvTxhbvNHbdARjdPwsE=
4th row7u3rvGGDKZAT171IbdwkAAzu2PQGPEaUJuAGHUzlgtI=
5th row7tEhHXdxSUD5TBhLZ0rrNFpc59+bVooobLedScAis+A=
ValueCountFrequency (%)
7suymp18rt64cutue0+n88mg+vlxaytjbq/jv27ukym 1
 
3.3%
7t5uphjnwvwhcvczuuff1cmfs+si9w85ighhfhirqk0 1
 
3.3%
m+pmli82a52rkubr+buet3gnkldc/nu40ivnxk6n7c4 1
 
3.3%
hymi1ytcappkugt+kkx97a2wawxujnwvh4tcqtwtsac 1
 
3.3%
cxcrzo+mv1+eyevlsyahsknmt29jzoem0ntqprsf1lo 1
 
3.3%
yr/au4efdt2fa8nxlhdjr6ga6/jbvfn3k9u47rqfgai 1
 
3.3%
u+j5v393tzbwfwr9z8ytdc9tvwztcrphcdysalopspg 1
 
3.3%
7uudl55lnfmjo+/r/jqjgjosnlivarnx0+62caf+tb8 1
 
3.3%
7wt8vglrpq/uzs14of4sw1ja/v2+g9xn2ixiy6nawxq 1
 
3.3%
7utbzwf4plknxhcf61cebdbxcb5nr34rd3tfld0f5lc 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:55:51.772016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 35
 
2.7%
u 32
 
2.4%
k 32
 
2.4%
= 30
 
2.3%
s 28
 
2.1%
2 27
 
2.0%
T 27
 
2.0%
w 26
 
2.0%
9 26
 
2.0%
t 24
 
1.8%
Other values (55) 1033
78.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 530
40.2%
Uppercase Letter 489
37.0%
Decimal Number 226
17.1%
Math Symbol 54
 
4.1%
Other Punctuation 21
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 32
 
6.0%
k 32
 
6.0%
s 28
 
5.3%
w 26
 
4.9%
t 24
 
4.5%
c 24
 
4.5%
p 23
 
4.3%
x 23
 
4.3%
d 22
 
4.2%
j 21
 
4.0%
Other values (16) 275
51.9%
Uppercase Letter
ValueCountFrequency (%)
T 27
 
5.5%
S 24
 
4.9%
K 24
 
4.9%
N 22
 
4.5%
U 22
 
4.5%
H 22
 
4.5%
F 22
 
4.5%
B 21
 
4.3%
W 21
 
4.3%
I 20
 
4.1%
Other values (16) 264
54.0%
Decimal Number
ValueCountFrequency (%)
7 35
15.5%
2 27
11.9%
9 26
11.5%
5 24
10.6%
8 21
9.3%
6 21
9.3%
0 20
8.8%
1 19
8.4%
4 17
7.5%
3 16
7.1%
Math Symbol
ValueCountFrequency (%)
= 30
55.6%
+ 24
44.4%
Other Punctuation
ValueCountFrequency (%)
/ 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1019
77.2%
Common 301
 
22.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 32
 
3.1%
k 32
 
3.1%
s 28
 
2.7%
T 27
 
2.6%
w 26
 
2.6%
t 24
 
2.4%
S 24
 
2.4%
K 24
 
2.4%
c 24
 
2.4%
p 23
 
2.3%
Other values (42) 755
74.1%
Common
ValueCountFrequency (%)
7 35
11.6%
= 30
10.0%
2 27
9.0%
9 26
8.6%
+ 24
8.0%
5 24
8.0%
8 21
7.0%
/ 21
7.0%
6 21
7.0%
0 20
 
6.6%
Other values (3) 52
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 35
 
2.7%
u 32
 
2.4%
k 32
 
2.4%
= 30
 
2.3%
s 28
 
2.1%
2 27
 
2.0%
T 27
 
2.0%
w 26
 
2.0%
9 26
 
2.0%
t 24
 
1.8%
Other values (55) 1033
78.3%

회원코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.016005 × 109
Minimum3.0022569 × 109
Maximum3.0321893 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:55:51.911098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0022569 × 109
5-th percentile3.002497 × 109
Q13.0131994 × 109
median3.0180717 × 109
Q33.0201726 × 109
95-th percentile3.0218957 × 109
Maximum3.0321893 × 109
Range29932412
Interquartile range (IQR)6973220.8

Descriptive statistics

Standard deviation7038454.8
Coefficient of variation (CV)0.0023337013
Kurtosis0.67108476
Mean3.016005 × 109
Median Absolute Deviation (MAD)2274467.5
Skewness-0.71038849
Sum9.0480151 × 1010
Variance4.9539845 × 1013
MonotonicityNot monotonic
2024-03-13T20:55:52.048063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3019803830 1
 
3.3%
3032189282 1
 
3.3%
3018766841 1
 
3.3%
3020331015 1
 
3.3%
3018730152 1
 
3.3%
3002465756 1
 
3.3%
3019175133 1
 
3.3%
3014031126 1
 
3.3%
3002535202 1
 
3.3%
3020361260 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3002256870 1
3.3%
3002465756 1
3.3%
3002535202 1
3.3%
3002580292 1
3.3%
3003706191 1
3.3%
3011378106 1
3.3%
3012375448 1
3.3%
3012922144 1
3.3%
3014031126 1
3.3%
3017288061 1
3.3%
ValueCountFrequency (%)
3032189282 1
3.3%
3022498118 1
3.3%
3021159347 1
3.3%
3020453848 1
3.3%
3020369502 1
3.3%
3020361260 1
3.3%
3020331015 1
3.3%
3020295537 1
3.3%
3019803830 1
3.3%
3019518366 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9403538 × 1014
Minimum7.0189624 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:55:52.205082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0189624 × 108
5-th percentile7.0885122 × 108
Q17.2582901 × 108
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.999993 × 1014
Interquartile range (IQR)9.9999927 × 1014

Descriptive statistics

Standard deviation4.824765 × 1014
Coefficient of variation (CV)0.81220162
Kurtosis-1.9018106
Mean5.9403538 × 1014
Median Absolute Deviation (MAD)0
Skewness-0.3884279
Sum1.7821061 × 1016
Variance2.3278357 × 1029
MonotonicityNot monotonic
2024-03-13T20:55:52.347123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
999999999999999 17
56.7%
410303176461501 1
 
3.3%
795710061 1
 
3.3%
711543704 1
 
3.3%
709182418 1
 
3.3%
725287450 1
 
3.3%
720409303 1
 
3.3%
708580241 1
 
3.3%
701896236 1
 
3.3%
727453704 1
 
3.3%
Other values (4) 4
 
13.3%
ValueCountFrequency (%)
701896236 1
3.3%
708580241 1
3.3%
709182418 1
3.3%
711543704 1
3.3%
720308888 1
3.3%
720409303 1
3.3%
721082543 1
3.3%
725287450 1
3.3%
727453704 1
3.3%
758228717 1
3.3%
ValueCountFrequency (%)
999999999999999 17
56.7%
410750130093501 1
 
3.3%
410303176461501 1
 
3.3%
795710061 1
 
3.3%
758228717 1
 
3.3%
727453704 1
 
3.3%
725287450 1
 
3.3%
721082543 1
 
3.3%
720409303 1
 
3.3%
720308888 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2024-03-13T20:55:52.478693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:55:52.569855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 19
63.3%
f 11
36.7%

연령대코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37
Minimum0
Maximum80
Zeros3
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:55:52.659991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median40
Q350
95-th percentile65.5
Maximum80
Range80
Interquartile range (IQR)30

Descriptive statistics

Standard deviation20.86905
Coefficient of variation (CV)0.56402837
Kurtosis-0.53567484
Mean37
Median Absolute Deviation (MAD)20
Skewness-0.10780297
Sum1110
Variance435.51724
MonotonicityNot monotonic
2024-03-13T20:55:52.800615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
40 7
23.3%
20 6
20.0%
50 5
16.7%
60 4
13.3%
0 3
10.0%
30 2
 
6.7%
10 1
 
3.3%
80 1
 
3.3%
70 1
 
3.3%
ValueCountFrequency (%)
0 3
10.0%
10 1
 
3.3%
20 6
20.0%
30 2
 
6.7%
40 7
23.3%
50 5
16.7%
60 4
13.3%
70 1
 
3.3%
80 1
 
3.3%
ValueCountFrequency (%)
80 1
 
3.3%
70 1
 
3.3%
60 4
13.3%
50 5
16.7%
40 7
23.3%
30 2
 
6.7%
20 6
20.0%
10 1
 
3.3%
0 3
10.0%

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
2024-03-13T20:55:52.978701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000003 × 1011
median1.400001 × 1011
Q31.4000012 × 1011
95-th percentile1.4000013 × 1011
Maximum1.4000013 × 1011
Range108000
Interquartile range (IQR)83000

Descriptive statistics

Standard deviation42367.033
Coefficient of variation (CV)3.026215 × 10-7
Kurtosis-1.7237471
Mean1.4000008 × 1011
Median Absolute Deviation (MAD)27000
Skewness-0.32730315
Sum4.2000024 × 1012
Variance1.7949655 × 109
MonotonicityNot monotonic
2024-03-13T20:55:53.102981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
140000126000 4
13.3%
140000024000 2
 
6.7%
140000096000 2
 
6.7%
140000046000 2
 
6.7%
140000018000 2
 
6.7%
140000030000 2
 
6.7%
140000112000 2
 
6.7%
140000106000 2
 
6.7%
140000116000 2
 
6.7%
140000022000 1
 
3.3%
Other values (9) 9
30.0%
ValueCountFrequency (%)
140000018000 2
6.7%
140000020000 1
3.3%
140000022000 1
3.3%
140000024000 2
6.7%
140000030000 2
6.7%
140000040000 1
3.3%
140000046000 2
6.7%
140000052000 1
3.3%
140000064000 1
3.3%
140000096000 2
6.7%
ValueCountFrequency (%)
140000126000 4
13.3%
140000124000 1
 
3.3%
140000122000 1
 
3.3%
140000116000 2
6.7%
140000114000 1
 
3.3%
140000112000 2
6.7%
140000110000 1
 
3.3%
140000106000 2
6.7%
140000102000 1
 
3.3%
140000096000 2
6.7%

결제상품명
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:55:53.314505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length10
Mean length7.3666667
Min length4

Characters and Unicode

Total characters221
Distinct characters63
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

Unique10 ?
Unique (%)33.3%

Sample

1st row부천페이
2nd row고양페이카드
3rd row행복화성지역화폐
4th row행복화성지역화폐
5th row군포愛머니(통합)
ValueCountFrequency (%)
수원페이 4
 
11.1%
군포愛머니(통합 2
 
5.6%
군포愛머니 2
 
5.6%
광주사랑카드 2
 
5.6%
고양페이카드 2
 
5.6%
용인와이페이 2
 
5.6%
파주 2
 
5.6%
pay(파주페이)(통합 2
 
5.6%
부천페이 2
 
5.6%
행복화성지역화폐 2
 
5.6%
Other values (14) 14
38.9%
2024-03-13T20:55:53.636905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 17
 
7.7%
페 14
 
6.3%
( 8
 
3.6%
) 8
 
3.6%
사 8
 
3.6%
랑 8
 
3.6%
드 7
 
3.2%
카 7
 
3.2%
주 7
 
3.2%
통 6
 
2.7%
Other values (53) 131
59.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 180
81.4%
Lowercase Letter 12
 
5.4%
Open Punctuation 8
 
3.6%
Close Punctuation 8
 
3.6%
Space Separator 6
 
2.7%
Uppercase Letter 6
 
2.7%
Dash Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 17
 
9.4%
페 14
 
7.8%
사 8
 
4.4%
랑 8
 
4.4%
드 7
 
3.9%
카 7
 
3.9%
주 7
 
3.9%
통 6
 
3.3%
합 6
 
3.3%
화 6
 
3.3%
Other values (38) 94
52.2%
Lowercase Letter
ValueCountFrequency (%)
a 4
33.3%
y 3
25.0%
k 1
 
8.3%
u 1
 
8.3%
o 1
 
8.3%
n 1
 
8.3%
h 1
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
P 3
50.0%
N 1
 
16.7%
Y 1
 
16.7%
T 1
 
16.7%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 176
79.6%
Common 23
 
10.4%
Latin 18
 
8.1%
Han 4
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 17
 
9.7%
페 14
 
8.0%
사 8
 
4.5%
랑 8
 
4.5%
드 7
 
4.0%
카 7
 
4.0%
주 7
 
4.0%
통 6
 
3.4%
합 6
 
3.4%
화 6
 
3.4%
Other values (37) 90
51.1%
Latin
ValueCountFrequency (%)
a 4
22.2%
P 3
16.7%
y 3
16.7%
k 1
 
5.6%
N 1
 
5.6%
u 1
 
5.6%
o 1
 
5.6%
Y 1
 
5.6%
n 1
 
5.6%
h 1
 
5.6%
Common
ValueCountFrequency (%)
( 8
34.8%
) 8
34.8%
6
26.1%
- 1
 
4.3%
Han
ValueCountFrequency (%)
愛 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 176
79.6%
ASCII 41
 
18.6%
CJK 4
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 17
 
9.7%
페 14
 
8.0%
사 8
 
4.5%
랑 8
 
4.5%
드 7
 
4.0%
카 7
 
4.0%
주 7
 
4.0%
통 6
 
3.4%
합 6
 
3.4%
화 6
 
3.4%
Other values (37) 90
51.1%
ASCII
ValueCountFrequency (%)
( 8
19.5%
) 8
19.5%
6
14.6%
a 4
9.8%
P 3
 
7.3%
y 3
 
7.3%
k 1
 
2.4%
N 1
 
2.4%
- 1
 
2.4%
u 1
 
2.4%
Other values (5) 5
12.2%
CJK
ValueCountFrequency (%)
愛 4
100.0%

가맹점업종명
Text

MISSING 

Distinct6
Distinct (%)54.5%
Missing19
Missing (%)63.3%
Memory size372.0 B
2024-03-13T20:55:53.827004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.0909091
Min length4

Characters and Unicode

Total characters56
Distinct characters23
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

Unique4 ?
Unique (%)36.4%

Sample

1st row일반휴게음식
2nd row일반휴게음식
3rd row음료식품
4th row음료식품
5th row일반휴게음식
ValueCountFrequency (%)
음료식품 4
30.8%
일반휴게음식 3
23.1%
유통업 2
15.4%
영리 1
 
7.7%
비영리 1
 
7.7%
보건위생 1
 
7.7%
연료판매점 1
 
7.7%
2024-03-13T20:55:54.113531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
음 7
12.5%
식 7
12.5%
료 5
 
8.9%
품 4
 
7.1%
일 3
 
5.4%
반 3
 
5.4%
휴 3
 
5.4%
게 3
 
5.4%
리 2
 
3.6%
영 2
 
3.6%
Other values (13) 17
30.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54
96.4%
Space Separator 2
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
음 7
13.0%
식 7
13.0%
료 5
 
9.3%
품 4
 
7.4%
일 3
 
5.6%
반 3
 
5.6%
휴 3
 
5.6%
게 3
 
5.6%
리 2
 
3.7%
영 2
 
3.7%
Other values (12) 15
27.8%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54
96.4%
Common 2
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
음 7
13.0%
식 7
13.0%
료 5
 
9.3%
품 4
 
7.4%
일 3
 
5.6%
반 3
 
5.6%
휴 3
 
5.6%
게 3
 
5.6%
리 2
 
3.7%
영 2
 
3.7%
Other values (12) 15
27.8%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54
96.4%
ASCII 2
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
음 7
13.0%
식 7
13.0%
료 5
 
9.3%
품 4
 
7.4%
일 3
 
5.6%
반 3
 
5.6%
휴 3
 
5.6%
게 3
 
5.6%
리 2
 
3.7%
영 2
 
3.7%
Other values (12) 15
27.8%
ASCII
ValueCountFrequency (%)
2
100.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)90.9%
Missing19
Missing (%)63.3%
Infinite0
Infinite (%)0.0%
Mean13993.636
Minimum10446
Maximum16531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:55:54.244728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10446
5-th percentile10905.5
Q112462
median14220
Q315865
95-th percentile16424
Maximum16531
Range6085
Interquartile range (IQR)3403

Descriptive statistics

Standard deviation2086.4297
Coefficient of variation (CV)0.14909847
Kurtosis-1.1358267
Mean13993.636
Median Absolute Deviation (MAD)1645
Skewness-0.37793066
Sum153930
Variance4353189.1
MonotonicityNot monotonic
2024-03-13T20:55:54.353935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
15865 2
 
6.7%
10446 1
 
3.3%
14590 1
 
3.3%
12285 1
 
3.3%
13807 1
 
3.3%
14220 1
 
3.3%
11365 1
 
3.3%
12639 1
 
3.3%
16317 1
 
3.3%
16531 1
 
3.3%
(Missing) 19
63.3%
ValueCountFrequency (%)
10446 1
3.3%
11365 1
3.3%
12285 1
3.3%
12639 1
3.3%
13807 1
3.3%
14220 1
3.3%
14590 1
3.3%
15865 2
6.7%
16317 1
3.3%
16531 1
3.3%
ValueCountFrequency (%)
16531 1
3.3%
16317 1
3.3%
15865 2
6.7%
14590 1
3.3%
14220 1
3.3%
13807 1
3.3%
12639 1
3.3%
12285 1
3.3%
11365 1
3.3%
10446 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
19 
경기도
11 

Length

Max length4
Median length4
Mean length3.6333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 19
63.3%
경기도 11
36.7%

Length

2024-03-13T20:55:54.483182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:55:54.582230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
63.3%
경기도 11
36.7%

시군구명
Text

MISSING 

Distinct10
Distinct (%)90.9%
Missing19
Missing (%)63.3%
Memory size372.0 B
2024-03-13T20:55:54.722281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.3636364
Min length3

Characters and Unicode

Total characters48
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)81.8%

Sample

1st row고양시 일산동구
2nd row군포시
3rd row부천시
4th row남양주시
5th row과천시
ValueCountFrequency (%)
군포시 2
14.3%
수원시 2
14.3%
고양시 1
7.1%
일산동구 1
7.1%
부천시 1
7.1%
남양주시 1
7.1%
과천시 1
7.1%
광명시 1
7.1%
동두천시 1
7.1%
여주시 1
7.1%
Other values (2) 2
14.3%
2024-03-13T20:55:55.107315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 11
22.9%
3
 
6.2%
구 3
 
6.2%
천 3
 
6.2%
군 2
 
4.2%
양 2
 
4.2%
동 2
 
4.2%
원 2
 
4.2%
포 2
 
4.2%
주 2
 
4.2%
Other values (15) 16
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 45
93.8%
Space Separator 3
 
6.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 11
24.4%
구 3
 
6.7%
천 3
 
6.7%
군 2
 
4.4%
양 2
 
4.4%
동 2
 
4.4%
원 2
 
4.4%
포 2
 
4.4%
주 2
 
4.4%
수 2
 
4.4%
Other values (14) 14
31.1%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 45
93.8%
Common 3
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 11
24.4%
구 3
 
6.7%
천 3
 
6.7%
군 2
 
4.4%
양 2
 
4.4%
동 2
 
4.4%
원 2
 
4.4%
포 2
 
4.4%
주 2
 
4.4%
수 2
 
4.4%
Other values (14) 14
31.1%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 45
93.8%
ASCII 3
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 11
24.4%
구 3
 
6.7%
천 3
 
6.7%
군 2
 
4.4%
양 2
 
4.4%
동 2
 
4.4%
원 2
 
4.4%
포 2
 
4.4%
주 2
 
4.4%
수 2
 
4.4%
Other values (14) 14
31.1%
ASCII
ValueCountFrequency (%)
3
100.0%

읍면동명
Text

MISSING 

Distinct10
Distinct (%)90.9%
Missing19
Missing (%)63.3%
Memory size372.0 B
2024-03-13T20:55:55.310958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9090909
Min length2

Characters and Unicode

Total characters32
Distinct characters19
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

Unique9 ?
Unique (%)81.8%

Sample

1st row백석동
2nd row산본동
3rd row상동
4th row다산동
5th row중앙동
ValueCountFrequency (%)
산본동 2
18.2%
백석동 1
9.1%
상동 1
9.1%
다산동 1
9.1%
중앙동 1
9.1%
광명동 1
9.1%
송내동 1
9.1%
오학동 1
9.1%
정자동 1
9.1%
매탄동 1
9.1%
2024-03-13T20:55:55.602219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 11
34.4%
산 3
 
9.4%
본 2
 
6.2%
송 1
 
3.1%
매 1
 
3.1%
자 1
 
3.1%
정 1
 
3.1%
학 1
 
3.1%
오 1
 
3.1%
내 1
 
3.1%
Other values (9) 9
28.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 11
34.4%
산 3
 
9.4%
본 2
 
6.2%
송 1
 
3.1%
매 1
 
3.1%
자 1
 
3.1%
정 1
 
3.1%
학 1
 
3.1%
오 1
 
3.1%
내 1
 
3.1%
Other values (9) 9
28.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 11
34.4%
산 3
 
9.4%
본 2
 
6.2%
송 1
 
3.1%
매 1
 
3.1%
자 1
 
3.1%
정 1
 
3.1%
학 1
 
3.1%
오 1
 
3.1%
내 1
 
3.1%
Other values (9) 9
28.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 11
34.4%
산 3
 
9.4%
본 2
 
6.2%
송 1
 
3.1%
매 1
 
3.1%
자 1
 
3.1%
정 1
 
3.1%
학 1
 
3.1%
오 1
 
3.1%
내 1
 
3.1%
Other values (9) 9
28.1%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.7381
Minimum0
Maximum37.887
Zeros19
Zeros (%)63.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:55:55.725684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q337.344
95-th percentile37.63375
Maximum37.887
Range37.887
Interquartile range (IQR)37.344

Descriptive statistics

Standard deviation18.364394
Coefficient of variation (CV)1.3367492
Kurtosis-1.7836706
Mean13.7381
Median Absolute Deviation (MAD)0
Skewness0.58305145
Sum412.143
Variance337.25096
MonotonicityNot monotonic
2024-03-13T20:55:55.872220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.0 19
63.3%
37.645 1
 
3.3%
37.36 1
 
3.3%
37.499 1
 
3.3%
37.62 1
 
3.3%
37.428 1
 
3.3%
37.479 1
 
3.3%
37.887 1
 
3.3%
37.302 1
 
3.3%
37.292 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
0.0 19
63.3%
37.273 1
 
3.3%
37.292 1
 
3.3%
37.302 1
 
3.3%
37.358 1
 
3.3%
37.36 1
 
3.3%
37.428 1
 
3.3%
37.479 1
 
3.3%
37.499 1
 
3.3%
37.62 1
 
3.3%
ValueCountFrequency (%)
37.887 1
3.3%
37.645 1
3.3%
37.62 1
3.3%
37.499 1
3.3%
37.479 1
3.3%
37.428 1
3.3%
37.36 1
3.3%
37.358 1
3.3%
37.302 1
3.3%
37.292 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.5715
Minimum0
Maximum127.644
Zeros19
Zeros (%)63.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:55:55.992429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3126.9115
95-th percentile127.1111
Maximum127.644
Range127.644
Interquartile range (IQR)126.9115

Descriptive statistics

Standard deviation62.253451
Coefficient of variation (CV)1.3367285
Kurtosis-1.7839577
Mean46.5715
Median Absolute Deviation (MAD)0
Skewness0.58294967
Sum1397.145
Variance3875.4922
MonotonicityNot monotonic
2024-03-13T20:55:56.106011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.0 19
63.3%
126.787 1
 
3.3%
126.93 1
 
3.3%
126.752 1
 
3.3%
127.157 1
 
3.3%
126.991 1
 
3.3%
126.856 1
 
3.3%
127.055 1
 
3.3%
127.644 1
 
3.3%
126.999 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
0.0 19
63.3%
126.752 1
 
3.3%
126.787 1
 
3.3%
126.856 1
 
3.3%
126.93 1
 
3.3%
126.931 1
 
3.3%
126.991 1
 
3.3%
126.999 1
 
3.3%
127.043 1
 
3.3%
127.055 1
 
3.3%
ValueCountFrequency (%)
127.644 1
3.3%
127.157 1
3.3%
127.055 1
3.3%
127.043 1
3.3%
126.999 1
3.3%
126.991 1
3.3%
126.931 1
3.3%
126.93 1
3.3%
126.856 1
3.3%
126.787 1
3.3%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
17 
True
13 
ValueCountFrequency (%)
False 17
56.7%
True 13
43.3%
2024-03-13T20:55:56.218204image/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%
Mean7848.6667
Minimum0
Maximum63000
Zeros17
Zeros (%)56.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:55:56.311521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39375
95-th percentile33700
Maximum63000
Range63000
Interquartile range (IQR)9375

Descriptive statistics

Standard deviation14492.767
Coefficient of variation (CV)1.846526
Kurtosis6.8723196
Mean7848.6667
Median Absolute Deviation (MAD)0
Skewness2.5044716
Sum235460
Variance2.1004029 × 108
MonotonicityNot monotonic
2024-03-13T20:55:56.431242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 17
56.7%
1200 1
 
3.3%
18000 1
 
3.3%
20000 1
 
3.3%
5700 1
 
3.3%
5000 1
 
3.3%
63000 1
 
3.3%
3000 1
 
3.3%
9000 1
 
3.3%
25160 1
 
3.3%
Other values (4) 4
 
13.3%
ValueCountFrequency (%)
0 17
56.7%
1200 1
 
3.3%
3000 1
 
3.3%
5000 1
 
3.3%
5700 1
 
3.3%
9000 1
 
3.3%
9500 1
 
3.3%
9900 1
 
3.3%
18000 1
 
3.3%
20000 1
 
3.3%
ValueCountFrequency (%)
63000 1
3.3%
40000 1
3.3%
26000 1
3.3%
25160 1
3.3%
20000 1
3.3%
18000 1
3.3%
9900 1
3.3%
9500 1
3.3%
9000 1
3.3%
5700 1
3.3%

Interactions

2024-03-13T20:55:49.187246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:43.578238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:44.612614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:45.381565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:46.213285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:46.965862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:47.692176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:48.442817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:49.272135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:43.666718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:44.713314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:45.484896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:46.312084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:47.056108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:47.786926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:48.542973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:49.369982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:43.747155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:44.813370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:45.572490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:46.393421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:47.145084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:47.876666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:48.629663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:49.485412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:43.868150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:44.909796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:45.708492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:46.486481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:47.237612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:47.967559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:48.722013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:49.580448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:44.266363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:45.000283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:45.817937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:46.576400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:47.354127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:48.049570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:48.804575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:49.665277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:44.347581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:45.087546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:45.900205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:46.660250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:47.434347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:48.130826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:48.880777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:49.776166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:44.442171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:45.185001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:45.978498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:46.754436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:47.531728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:48.219151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:48.999545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:50.139946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:44.523989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:45.283496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:46.077968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:46.867449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:47.610987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:48.333977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:55:49.092518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:55:56.533319image/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
회원코드1.0001.0000.3830.0000.3730.1100.4370.5340.3770.7640.7640.3720.3720.2990.000
가맹점번호1.0000.3831.0000.0000.0000.3510.000NaNNaNNaNNaN1.0001.0001.0000.586
성별코드1.0000.0000.0001.0000.4350.4410.1290.7160.9301.0001.0000.0000.0000.0000.000
연령대코드1.0000.3730.0000.4351.0000.6210.7130.8220.4050.8810.8810.4100.4100.2740.604
결제상품ID1.0000.1100.3510.4410.6211.0001.0000.0000.6951.0001.0000.0000.0000.4700.000
결제상품명1.0000.4370.0000.1290.7131.0001.0000.7801.0000.9500.9500.0000.0000.0000.000
가맹점업종명1.0000.534NaN0.7160.8220.0000.7801.0000.8320.7460.746NaNNaNNaN0.815
가맹점우편번호1.0000.377NaN0.9300.4050.6951.0000.8321.0001.0001.000NaNNaNNaN0.858
시군구명1.0000.764NaN1.0000.8811.0000.9500.7461.0001.0001.000NaNNaNNaN0.789
읍면동명1.0000.764NaN1.0000.8811.0000.9500.7461.0001.0001.000NaNNaNNaN0.789
위도1.0000.3721.0000.0000.4100.0000.000NaNNaNNaNNaN1.0000.9930.9470.601
경도1.0000.3721.0000.0000.4100.0000.000NaNNaNNaNNaN0.9931.0000.9470.601
사용여부1.0000.2991.0000.0000.2740.4700.000NaNNaNNaNNaN0.9470.9471.0000.623
결제금액1.0000.0000.5860.0000.6040.0000.0000.8150.8580.7890.7890.6010.6010.6231.000
2024-03-13T20:55:56.688660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부성별코드시도명
사용여부1.0000.0001.000
성별코드0.0001.0001.000
시도명1.0001.0001.000
2024-03-13T20:55:56.786049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드시도명사용여부
회원코드1.0000.2770.1760.0100.105-0.339-0.377-0.3690.0001.0000.301
가맹점번호0.2771.000-0.1610.146-0.059-0.912-0.878-0.8910.0001.0000.982
연령대코드0.176-0.1611.0000.0220.4660.0440.0910.1050.3671.0000.218
결제상품ID0.0100.1460.0221.0000.553-0.1550.013-0.1240.3321.0000.288
가맹점우편번호0.105-0.0590.4660.5531.000-0.815-0.1140.2870.4761.0001.000
위도-0.339-0.9120.044-0.155-0.8151.0000.9180.8180.0001.0000.792
경도-0.377-0.8780.0910.013-0.1140.9181.0000.8680.0001.0000.792
결제금액-0.369-0.8910.105-0.1240.2870.8180.8681.0000.0001.0000.605
성별코드0.0000.0000.3670.3320.4760.0000.0000.0001.0001.0000.000
시도명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사용여부0.3010.9820.2180.2881.0000.7920.7920.6050.0001.0001.000

Missing values

2024-03-13T20:55:50.277447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:55:50.512920image/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.
2024-03-13T20:55:50.744747image/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
02021-07-052021-07-117suymp18rt64CutuE0+n88mG+vlxAYTjBQ/jV27UkyM=3019803830410303176461501M60140000030000부천페이<NA><NA><NA><NA><NA>0.00.0Y1200
12021-07-052021-07-117t5upHjnwvWHCvcZuUFF1Cmfs+sI9W85igHhFHirQK0=3018038002795710061M20140000018000고양페이카드일반휴게음식10446경기도고양시 일산동구백석동37.645126.787Y18000
22021-07-052021-07-117tEMJySKLoivjgyys+CSY9lXYvTxhbvNHbdARjdPwsE=3021159347999999999999999M50140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
32021-07-052021-07-117u3rvGGDKZAT171IbdwkAAzu2PQGPEaUJuAGHUzlgtI=3017688672999999999999999F0140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
42021-07-052021-07-117tEhHXdxSUD5TBhLZ0rrNFpc59+bVooobLedScAis+A=3020453848711543704M20140000106000군포愛머니(통합)일반휴게음식15865경기도군포시산본동37.36126.93Y20000
52021-07-052021-07-11U+8ktpbXbxq9jBWK/VfK1zxgqf0JzPaueI0zW5KhoDA=3012922144709182418F50140000030000부천페이음료식품14590경기도부천시상동37.499126.752Y5700
62021-07-052021-07-11IsSqMC2dAyUG0Se/e3Rpesib13k0Flcslj+Et52kHKk=3011378106725287450M40140000114000Thank You Pay-N음료식품12285경기도남양주시다산동37.62127.157Y5000
72021-07-052021-07-11pHAJ/cSqzejf56KLN/xDQMl4K/66+WN2nfl92rPw0Ck=3003706191999999999999999F60140000112000군포愛머니<NA><NA><NA><NA><NA>0.00.0N0
82021-07-052021-07-117tNSVwp8mg8gBOOmIePxY17dnms6yGgfOWCOZI1N02c=3002256870720409303M40140000022000과천화폐 과천토리일반휴게음식13807경기도과천시중앙동37.428126.991Y63000
92021-07-052021-07-117uQjpnSakvkDBn9n2LnkikTgxwUK2tweWjYH0LCrc9I=3017924143999999999999999F20140000024000광주사랑카드<NA><NA><NA><NA><NA>0.00.0N0
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202021-07-052021-07-117uM64WtGtU7APFnksS6w9N6XBnSXJb7T78VJ8ko00MM=3012375448727453704M30140000040000여주사랑카드유통업 비영리12639경기도여주시오학동37.302127.644Y25160
212021-07-052021-07-117utBzwf4plKNxhcf61CebDBxcb5NR34rD3tFlD0F5Lc=3017502289410750130093501M40140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0Y9500
222021-07-052021-07-117wT8VGlrpq/uZS14OF4Sw1Ja/v2+g9XN2ixiy6NAwxQ=3020361260720308888F50140000126000수원페이음료식품16317경기도수원시 장안구정자동37.292126.999Y9900
232021-07-052021-07-117uudl55LNFmJO+/r/JqjGjOsNlIvarnX0+62caf+Tb8=3002535202758228717F70140000126000수원페이보건위생16531경기도수원시 영통구매탄동37.273127.043Y26000
242021-07-052021-07-11U+j5V393TzBwFWR9z8YtDC9TvWztCRphcDySALOpsPg=3014031126721082543M30140000112000군포愛머니연료판매점15865경기도군포시산본동37.358126.931Y40000
252021-07-052021-07-11YR/aU4eFDT2Fa8nxLHdjr6gA6/JBvFN3k9u47RqFgaI=3019175133999999999999999M40140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0N0
262021-07-052021-07-11cxcRzO+mV1+EyevlSYAHSknMT29JzOEM0NtqpRsF1lo=3002465756999999999999999M20140000096000파주 Pay(파주페이)(통합)<NA><NA><NA><NA><NA>0.00.0N0
272021-07-052021-07-11hYmI1yTcAppkUGT+KKx97a2WAwxUjNwvH4TcQTWtsac=3018730152999999999999999F0140000052000포천사랑상품권<NA><NA><NA><NA><NA>0.00.0N0
282021-07-052021-07-11m+PmlI82a52Rkubr+BUEt3gnkldC/nu40IVNXk6N7c4=3020331015999999999999999M40140000024000광주사랑카드<NA><NA><NA><NA><NA>0.00.0N0
292021-07-052021-07-11qRKIb4ZRuocPzqWBVNCd7ITLdevbQCDd3PgUuDiPj20=3018766841999999999999999F20140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0