Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells59
Missing cells (%)10.9%
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/d40473dd-0a8e-4236-810b-39959fe28b9f

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
사용여부 is highly overall correlated with 가맹점번호 and 6 other fieldsHigh correlation
시도명 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
가맹점업종명 is highly overall correlated with 가맹점번호 and 6 other fieldsHigh correlation
가맹점번호 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
연령대코드 is highly overall correlated with 가맹점업종명 and 1 other fieldsHigh correlation
결제상품ID is highly overall correlated with 가맹점우편번호High correlation
가맹점우편번호 is highly overall correlated with 결제상품ID and 2 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
성별코드 is highly overall correlated with 가맹점업종명High correlation
가맹점우편번호 has 19 (63.3%) missing valuesMissing
시군구명 has 20 (66.7%) missing valuesMissing
읍면동명 has 20 (66.7%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
연령대코드 has 1 (3.3%) zerosZeros
위도 has 20 (66.7%) zerosZeros
경도 has 20 (66.7%) zerosZeros
결제금액 has 18 (60.0%) zerosZeros

Reproduction

Analysis started2024-03-13 11:59:29.380899
Analysis finished2024-03-13 11:59:37.783965
Duration8.4 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
Minimum2021-11-01 00:00:00
Maximum2021-11-01 00:00:00
2024-03-13T20:59:37.839902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:37.957771image/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
Minimum2021-11-07 00:00:00
Maximum2021-11-07 00:00:00
2024-03-13T20:59:38.069978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:38.192571image/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
2024-03-13T20:59:38.445119image/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++13oaNfFPmEbF7lohC/xR9Hj9TCxKdte0VilopBfCM=
2nd rowdt1TdosjJva/EXr3RwvRYfOrp6Cn2DuJwVXqBPCkLJc=
3rd rowh4fXBiKUgKGJf9qXAfzgpq8NTig7k43lbYHyyHZ6BIQ=
4th row6y8d1K7OwZW2kpAW0UW3v5eWMRzdatb2r0HY+uBDdFs=
5th rowNlLfvpse6cncrjBzD0UebjVDZjuxDCd16/J6luO7DsE=
ValueCountFrequency (%)
13oanffpmebf7lohc/xr9hj9tcxkdte0vilopbfcm 1
 
3.3%
dt1tdosjjva/exr3rwvryforp6cn2dujwvxqbpckljc 1
 
3.3%
0uapshbec89wcqzgje8lcma8webccaa5z7ap6fqbfly 1
 
3.3%
pswf6rxmsj0umrydbmts68bvirzqqm/xthfcxjdxiq 1
 
3.3%
8lxfzgwenlgpd3xe7ipmuth+a4fk98ksx4wyk4qs0 1
 
3.3%
aqwzqzlbrrifjbvpky8gdxkd3jl6phudxkrcaebipw 1
 
3.3%
91xb+p3m9deciqtdirfrf+5gc4xjxhytajzlp8noo 1
 
3.3%
ejasdcmi2mg6mj1efnu+rq49vsvfzoenaf64jnrb73q 1
 
3.3%
aec9bvl/vm8f4y+hq4s2cgmsdy+bbkgb7th1nbf9q24 1
 
3.3%
w192fgp7nw8i1wkdogp7pewzirnxfnr2cpjrr/8frm8 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:59:38.883095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
f 34
 
2.6%
C 31
 
2.3%
d 31
 
2.3%
= 30
 
2.3%
B 29
 
2.2%
P 29
 
2.2%
J 28
 
2.1%
+ 26
 
2.0%
8 26
 
2.0%
r 26
 
2.0%
Other values (55) 1030
78.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 547
41.4%
Lowercase Letter 500
37.9%
Decimal Number 198
 
15.0%
Math Symbol 56
 
4.2%
Other Punctuation 19
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 34
 
6.8%
d 31
 
6.2%
r 26
 
5.2%
m 24
 
4.8%
e 24
 
4.8%
g 23
 
4.6%
x 22
 
4.4%
p 21
 
4.2%
s 20
 
4.0%
w 19
 
3.8%
Other values (16) 256
51.2%
Uppercase Letter
ValueCountFrequency (%)
C 31
 
5.7%
B 29
 
5.3%
P 29
 
5.3%
J 28
 
5.1%
W 25
 
4.6%
E 24
 
4.4%
L 24
 
4.4%
Z 24
 
4.4%
D 22
 
4.0%
S 22
 
4.0%
Other values (16) 289
52.8%
Decimal Number
ValueCountFrequency (%)
8 26
13.1%
2 23
11.6%
6 23
11.6%
4 22
11.1%
3 20
10.1%
9 20
10.1%
7 17
8.6%
1 17
8.6%
0 16
8.1%
5 14
7.1%
Math Symbol
ValueCountFrequency (%)
= 30
53.6%
+ 26
46.4%
Other Punctuation
ValueCountFrequency (%)
/ 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1047
79.3%
Common 273
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 34
 
3.2%
C 31
 
3.0%
d 31
 
3.0%
B 29
 
2.8%
P 29
 
2.8%
J 28
 
2.7%
r 26
 
2.5%
W 25
 
2.4%
E 24
 
2.3%
L 24
 
2.3%
Other values (42) 766
73.2%
Common
ValueCountFrequency (%)
= 30
11.0%
+ 26
9.5%
8 26
9.5%
2 23
8.4%
6 23
8.4%
4 22
8.1%
3 20
7.3%
9 20
7.3%
/ 19
7.0%
7 17
 
6.2%
Other values (3) 47
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 34
 
2.6%
C 31
 
2.3%
d 31
 
2.3%
= 30
 
2.3%
B 29
 
2.2%
P 29
 
2.2%
J 28
 
2.1%
+ 26
 
2.0%
8 26
 
2.0%
r 26
 
2.0%
Other values (55) 1030
78.0%

회원코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0235426 × 109
Minimum3.0025722 × 109
Maximum3.0598588 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:39.051311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0025722 × 109
5-th percentile3.007141 × 109
Q13.0168582 × 109
median3.0192918 × 109
Q33.0255021 × 109
95-th percentile3.0519802 × 109
Maximum3.0598588 × 109
Range57286630
Interquartile range (IQR)8643937.8

Descriptive statistics

Standard deviation13889008
Coefficient of variation (CV)0.0045936208
Kurtosis1.3456739
Mean3.0235426 × 109
Median Absolute Deviation (MAD)3251900.5
Skewness1.293789
Sum9.0706278 × 1010
Variance1.9290455 × 1014
MonotonicityNot monotonic
2024-03-13T20:59:39.185735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3007682214 1
 
3.3%
3013006118 1
 
3.3%
3020609966 1
 
3.3%
3016847276 1
 
3.3%
3018660687 1
 
3.3%
3016609360 1
 
3.3%
3019482700 1
 
3.3%
3058585508 1
 
3.3%
3030842289 1
 
3.3%
3016890937 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3002572153 1
3.3%
3006698246 1
3.3%
3007682214 1
3.3%
3013006118 1
3.3%
3013218128 1
3.3%
3016609360 1
3.3%
3016786334 1
3.3%
3016847276 1
3.3%
3016890937 1
3.3%
3016910799 1
3.3%
ValueCountFrequency (%)
3059858783 1
3.3%
3058585508 1
3.3%
3043907153 1
3.3%
3043402242 1
3.3%
3041701342 1
3.3%
3038099793 1
3.3%
3030842289 1
3.3%
3026163131 1
3.3%
3023519123 1
3.3%
3023113161 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1368505 × 1014
Minimum7.1407681 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:39.311112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.1407681 × 108
5-th percentile7.1455427 × 108
Q17.6217135 × 108
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.9999929 × 1014
Interquartile range (IQR)9.9999924 × 1014

Descriptive statistics

Standard deviation4.867296 × 1014
Coefficient of variation (CV)0.79312605
Kurtosis-1.853816
Mean6.1368505 × 1014
Median Absolute Deviation (MAD)0
Skewness-0.48475274
Sum1.8410552 × 1016
Variance2.3690571 × 1029
MonotonicityNot monotonic
2024-03-13T20:59:39.445167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
999999999999999 18
60.0%
714084787 1
 
3.3%
731144581 1
 
3.3%
714076808 1
 
3.3%
789687244 1
 
3.3%
715128080 1
 
3.3%
720727210 1
 
3.3%
798716648 1
 
3.3%
791100242 1
 
3.3%
410543470065201 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
714076808 1
3.3%
714084787 1
3.3%
715128080 1
3.3%
720727210 1
3.3%
725294117 1
3.3%
726436746 1
3.3%
731144581 1
3.3%
752999391 1
3.3%
789687244 1
3.3%
791100242 1
3.3%
ValueCountFrequency (%)
999999999999999 18
60.0%
410543470065201 1
 
3.3%
798716648 1
 
3.3%
791100242 1
 
3.3%
789687244 1
 
3.3%
752999391 1
 
3.3%
731144581 1
 
3.3%
726436746 1
 
3.3%
725294117 1
 
3.3%
720727210 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
F
16 
M
14 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 16
53.3%
M 14
46.7%

Length

2024-03-13T20:59:39.581712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:59:39.689387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 16
53.3%
m 14
46.7%

연령대코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.666667
Minimum0
Maximum60
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:39.785239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.5
Q130
median40
Q340
95-th percentile55.5
Maximum60
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.565507
Coefficient of variation (CV)0.38034133
Kurtosis0.6180503
Mean35.666667
Median Absolute Deviation (MAD)10
Skewness-0.46149199
Sum1070
Variance184.02299
MonotonicityNot monotonic
2024-03-13T20:59:39.897432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
40 9
30.0%
30 9
30.0%
50 5
16.7%
20 3
 
10.0%
60 2
 
6.7%
0 1
 
3.3%
10 1
 
3.3%
ValueCountFrequency (%)
0 1
 
3.3%
10 1
 
3.3%
20 3
 
10.0%
30 9
30.0%
40 9
30.0%
50 5
16.7%
60 2
 
6.7%
ValueCountFrequency (%)
60 2
 
6.7%
50 5
16.7%
40 9
30.0%
30 9
30.0%
20 3
 
10.0%
10 1
 
3.3%
0 1
 
3.3%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000008 × 1011
Minimum1.4000002 × 1011
Maximum1.4000017 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:40.016461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000005 × 1011
median1.4000009 × 1011
Q31.4000011 × 1011
95-th percentile1.4000012 × 1011
Maximum1.4000017 × 1011
Range148000
Interquartile range (IQR)68000

Descriptive statistics

Standard deviation41201.774
Coefficient of variation (CV)2.9429822 × 10-7
Kurtosis-1.1224971
Mean1.4000008 × 1011
Median Absolute Deviation (MAD)29000
Skewness-0.035141633
Sum4.2000024 × 1012
Variance1.6975862 × 109
MonotonicityNot monotonic
2024-03-13T20:59:40.148223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
140000018000 4
 
13.3%
140000116000 4
 
13.3%
140000046000 3
 
10.0%
140000114000 2
 
6.7%
140000112000 2
 
6.7%
140000096000 1
 
3.3%
140000064000 1
 
3.3%
140000122000 1
 
3.3%
140000106000 1
 
3.3%
140000028000 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
140000018000 4
13.3%
140000028000 1
 
3.3%
140000038000 1
 
3.3%
140000040000 1
 
3.3%
140000046000 3
10.0%
140000056000 1
 
3.3%
140000058000 1
 
3.3%
140000060000 1
 
3.3%
140000064000 1
 
3.3%
140000080000 1
 
3.3%
ValueCountFrequency (%)
140000166000 1
 
3.3%
140000124000 1
 
3.3%
140000122000 1
 
3.3%
140000116000 4
13.3%
140000114000 2
6.7%
140000112000 2
6.7%
140000106000 1
 
3.3%
140000104000 1
 
3.3%
140000102000 1
 
3.3%
140000096000 1
 
3.3%

결제상품명
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:59:40.351242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15.5
Mean length8.7666667
Min length4

Characters and Unicode

Total characters263
Distinct characters64
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

Unique15 ?
Unique (%)50.0%

Sample

1st row고양페이카드
2nd row행복화성지역화폐
3rd row평택사랑카드(통합)
4th row행복화성지역화폐
5th rowThank You Pay-N
ValueCountFrequency (%)
고양페이카드 4
 
10.5%
행복화성지역화폐 4
 
10.5%
용인와이페이 3
 
7.9%
thank 3
 
7.9%
you 3
 
7.9%
pay-n 2
 
5.3%
군포愛머니 2
 
5.3%
수원페이(통합 1
 
2.6%
여주사랑카드(여성청소년 1
 
2.6%
의정부사랑카드 1
 
2.6%
Other values (14) 14
36.8%
2024-03-13T20:59:40.666653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 16
 
6.1%
) 11
 
4.2%
페 11
 
4.2%
( 11
 
4.2%
통 10
 
3.8%
카 9
 
3.4%
드 9
 
3.4%
화 9
 
3.4%
합 9
 
3.4%
사 8
 
3.0%
Other values (54) 160
60.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 191
72.6%
Lowercase Letter 26
 
9.9%
Uppercase Letter 13
 
4.9%
Close Punctuation 11
 
4.2%
Open Punctuation 11
 
4.2%
Space Separator 8
 
3.0%
Dash Punctuation 3
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 16
 
8.4%
페 11
 
5.8%
통 10
 
5.2%
카 9
 
4.7%
드 9
 
4.7%
화 9
 
4.7%
합 9
 
4.7%
사 8
 
4.2%
랑 8
 
4.2%
양 5
 
2.6%
Other values (39) 97
50.8%
Lowercase Letter
ValueCountFrequency (%)
a 7
26.9%
y 4
15.4%
o 3
11.5%
h 3
11.5%
n 3
11.5%
k 3
11.5%
u 3
11.5%
Uppercase Letter
ValueCountFrequency (%)
P 4
30.8%
Y 3
23.1%
T 3
23.1%
N 3
23.1%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 188
71.5%
Latin 39
 
14.8%
Common 33
 
12.5%
Han 3
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 16
 
8.5%
페 11
 
5.9%
통 10
 
5.3%
카 9
 
4.8%
드 9
 
4.8%
화 9
 
4.8%
합 9
 
4.8%
사 8
 
4.3%
랑 8
 
4.3%
양 5
 
2.7%
Other values (38) 94
50.0%
Latin
ValueCountFrequency (%)
a 7
17.9%
P 4
10.3%
y 4
10.3%
o 3
7.7%
h 3
7.7%
n 3
7.7%
k 3
7.7%
Y 3
7.7%
T 3
7.7%
u 3
7.7%
Common
ValueCountFrequency (%)
) 11
33.3%
( 11
33.3%
8
24.2%
- 3
 
9.1%
Han
ValueCountFrequency (%)
愛 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 188
71.5%
ASCII 72
 
27.4%
CJK 3
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 16
 
8.5%
페 11
 
5.9%
통 10
 
5.3%
카 9
 
4.8%
드 9
 
4.8%
화 9
 
4.8%
합 9
 
4.8%
사 8
 
4.3%
랑 8
 
4.3%
양 5
 
2.7%
Other values (38) 94
50.0%
ASCII
ValueCountFrequency (%)
) 11
15.3%
( 11
15.3%
8
11.1%
a 7
9.7%
P 4
 
5.6%
y 4
 
5.6%
o 3
 
4.2%
h 3
 
4.2%
n 3
 
4.2%
k 3
 
4.2%
Other values (5) 15
20.8%
CJK
ValueCountFrequency (%)
愛 3
100.0%

가맹점업종명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
19 
일반휴게음식
6 
음료식품
4 
보건위생
 
1

Length

Max length6
Median length4
Mean length4.4
Min length4

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 19
63.3%
일반휴게음식 6
 
20.0%
음료식품 4
 
13.3%
보건위생 1
 
3.3%

Length

2024-03-13T20:59:40.800159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:59:40.908274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
63.3%
일반휴게음식 6
 
20.0%
음료식품 4
 
13.3%
보건위생 1
 
3.3%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)90.9%
Missing19
Missing (%)63.3%
Infinite0
Infinite (%)0.0%
Mean14790.818
Minimum10564
Maximum18616
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:41.059524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10564
5-th percentile10719
Q112032.5
median16930
Q317047
95-th percentile18546
Maximum18616
Range8052
Interquartile range (IQR)5014.5

Descriptive statistics

Standard deviation3208.8305
Coefficient of variation (CV)0.21694746
Kurtosis-2.0704576
Mean14790.818
Median Absolute Deviation (MAD)1686
Skewness-0.17613304
Sum162699
Variance10296593
MonotonicityNot monotonic
2024-03-13T20:59:41.235914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
16930 2
 
6.7%
18476 1
 
3.3%
12138 1
 
3.3%
12150 1
 
3.3%
17038 1
 
3.3%
17056 1
 
3.3%
10874 1
 
3.3%
11927 1
 
3.3%
18616 1
 
3.3%
10564 1
 
3.3%
(Missing) 19
63.3%
ValueCountFrequency (%)
10564 1
3.3%
10874 1
3.3%
11927 1
3.3%
12138 1
3.3%
12150 1
3.3%
16930 2
6.7%
17038 1
3.3%
17056 1
3.3%
18476 1
3.3%
18616 1
3.3%
ValueCountFrequency (%)
18616 1
3.3%
18476 1
3.3%
17056 1
3.3%
17038 1
3.3%
16930 2
6.7%
12150 1
3.3%
12138 1
3.3%
11927 1
3.3%
10874 1
3.3%
10564 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
19 
경기도
10 
NONE
 
1

Length

Max length4
Median length4
Mean length3.6666667
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 19
63.3%
경기도 10
33.3%
NONE 1
 
3.3%

Length

2024-03-13T20:59:41.646835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:59:41.732682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
63.3%
경기도 10
33.3%
none 1
 
3.3%

시군구명
Text

MISSING 

Distinct7
Distinct (%)70.0%
Missing20
Missing (%)66.7%
Memory size372.0 B
2024-03-13T20:59:41.849861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5.5
Mean length5.1
Min length3

Characters and Unicode

Total characters51
Distinct characters17
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 (%)40.0%

Sample

1st row화성시
2nd row남양주시
3rd row용인시 처인구
4th row용인시 처인구
5th row파주시
ValueCountFrequency (%)
용인시 4
26.7%
화성시 2
13.3%
처인구 2
13.3%
수지구 2
13.3%
남양주시 1
 
6.7%
파주시 1
 
6.7%
구리시 1
 
6.7%
고양시 1
 
6.7%
덕양구 1
 
6.7%
2024-03-13T20:59:42.258652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 10
19.6%
인 6
11.8%
구 6
11.8%
5
9.8%
용 4
 
7.8%
양 3
 
5.9%
화 2
 
3.9%
지 2
 
3.9%
주 2
 
3.9%
수 2
 
3.9%
Other values (7) 9
17.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46
90.2%
Space Separator 5
 
9.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 10
21.7%
인 6
13.0%
구 6
13.0%
용 4
 
8.7%
양 3
 
6.5%
화 2
 
4.3%
지 2
 
4.3%
주 2
 
4.3%
수 2
 
4.3%
성 2
 
4.3%
Other values (6) 7
15.2%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46
90.2%
Common 5
 
9.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 10
21.7%
인 6
13.0%
구 6
13.0%
용 4
 
8.7%
양 3
 
6.5%
화 2
 
4.3%
지 2
 
4.3%
주 2
 
4.3%
수 2
 
4.3%
성 2
 
4.3%
Other values (6) 7
15.2%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 46
90.2%
ASCII 5
 
9.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 10
21.7%
인 6
13.0%
구 6
13.0%
용 4
 
8.7%
양 3
 
6.5%
화 2
 
4.3%
지 2
 
4.3%
주 2
 
4.3%
수 2
 
4.3%
성 2
 
4.3%
Other values (6) 7
15.2%
ASCII
ValueCountFrequency (%)
5
100.0%

읍면동명
Text

MISSING 

Distinct9
Distinct (%)90.0%
Missing20
Missing (%)66.7%
Memory size372.0 B
2024-03-13T20:59:42.416710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
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

Unique8 ?
Unique (%)80.0%

Sample

1st row청계동
2nd row호평동
3rd row고림동
4th row역북동
5th row동패동
ValueCountFrequency (%)
상현동 2
20.0%
청계동 1
10.0%
호평동 1
10.0%
고림동 1
10.0%
역북동 1
10.0%
동패동 1
10.0%
수택동 1
10.0%
향남읍 1
10.0%
원흥동 1
10.0%
2024-03-13T20:59:42.762444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 10
33.3%
상 2
 
6.7%
현 2
 
6.7%
패 1
 
3.3%
원 1
 
3.3%
읍 1
 
3.3%
남 1
 
3.3%
향 1
 
3.3%
택 1
 
3.3%
수 1
 
3.3%
Other values (9) 9
30.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10
33.3%
상 2
 
6.7%
현 2
 
6.7%
패 1
 
3.3%
원 1
 
3.3%
읍 1
 
3.3%
남 1
 
3.3%
향 1
 
3.3%
택 1
 
3.3%
수 1
 
3.3%
Other values (9) 9
30.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 10
33.3%
상 2
 
6.7%
현 2
 
6.7%
패 1
 
3.3%
원 1
 
3.3%
읍 1
 
3.3%
남 1
 
3.3%
향 1
 
3.3%
택 1
 
3.3%
수 1
 
3.3%
Other values (9) 9
30.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 10
33.3%
상 2
 
6.7%
현 2
 
6.7%
패 1
 
3.3%
원 1
 
3.3%
읍 1
 
3.3%
남 1
 
3.3%
향 1
 
3.3%
택 1
 
3.3%
수 1
 
3.3%
Other values (9) 9
30.0%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.467533
Minimum0
Maximum37.725
Zeros20
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:42.888063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q337.22175
95-th percentile37.65185
Maximum37.725
Range37.725
Interquartile range (IQR)37.22175

Descriptive statistics

Standard deviation17.933624
Coefficient of variation (CV)1.438426
Kurtosis-1.5530332
Mean12.467533
Median Absolute Deviation (MAD)0
Skewness0.74504891
Sum374.026
Variance321.61488
MonotonicityNot monotonic
2024-03-13T20:59:43.005149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 20
66.7%
37.2 1
 
3.3%
37.655 1
 
3.3%
37.258 1
 
3.3%
37.229 1
 
3.3%
37.725 1
 
3.3%
37.306 1
 
3.3%
37.598 1
 
3.3%
37.1 1
 
3.3%
37.648 1
 
3.3%
ValueCountFrequency (%)
0.0 20
66.7%
37.1 1
 
3.3%
37.2 1
 
3.3%
37.229 1
 
3.3%
37.258 1
 
3.3%
37.306 1
 
3.3%
37.307 1
 
3.3%
37.598 1
 
3.3%
37.648 1
 
3.3%
37.655 1
 
3.3%
ValueCountFrequency (%)
37.725 1
3.3%
37.655 1
3.3%
37.648 1
3.3%
37.598 1
3.3%
37.307 1
3.3%
37.306 1
3.3%
37.258 1
3.3%
37.229 1
3.3%
37.2 1
3.3%
37.1 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.3521
Minimum0
Maximum127.245
Zeros20
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:43.126621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3126.89125
95-th percentile127.2058
Maximum127.245
Range127.245
Interquartile range (IQR)126.89125

Descriptive statistics

Standard deviation60.918908
Coefficient of variation (CV)1.4383917
Kurtosis-1.5535453
Mean42.3521
Median Absolute Deviation (MAD)0
Skewness0.74488869
Sum1270.563
Variance3711.1133
MonotonicityNot monotonic
2024-03-13T20:59:43.318700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0 20
66.7%
127.084 2
 
6.7%
127.113 1
 
3.3%
127.245 1
 
3.3%
127.222 1
 
3.3%
127.186 1
 
3.3%
126.718 1
 
3.3%
127.138 1
 
3.3%
126.896 1
 
3.3%
126.877 1
 
3.3%
ValueCountFrequency (%)
0.0 20
66.7%
126.718 1
 
3.3%
126.877 1
 
3.3%
126.896 1
 
3.3%
127.084 2
 
6.7%
127.113 1
 
3.3%
127.138 1
 
3.3%
127.186 1
 
3.3%
127.222 1
 
3.3%
127.245 1
 
3.3%
ValueCountFrequency (%)
127.245 1
 
3.3%
127.222 1
 
3.3%
127.186 1
 
3.3%
127.138 1
 
3.3%
127.113 1
 
3.3%
127.084 2
 
6.7%
126.896 1
 
3.3%
126.877 1
 
3.3%
126.718 1
 
3.3%
0.0 20
66.7%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
18 
True
12 
ValueCountFrequency (%)
False 18
60.0%
True 12
40.0%
2024-03-13T20:59:43.479892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3558.3333
Minimum0
Maximum20000
Zeros18
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:43.585976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35750
95-th percentile14257.5
Maximum20000
Range20000
Interquartile range (IQR)5750

Descriptive statistics

Standard deviation5460.0443
Coefficient of variation (CV)1.5344387
Kurtosis1.7718447
Mean3558.3333
Median Absolute Deviation (MAD)0
Skewness1.5543348
Sum106750
Variance29812083
MonotonicityNot monotonic
2024-03-13T20:59:43.718064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 18
60.0%
8800 1
 
3.3%
15000 1
 
3.3%
10800 1
 
3.3%
2100 1
 
3.3%
4800 1
 
3.3%
7800 1
 
3.3%
13350 1
 
3.3%
20000 1
 
3.3%
9000 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
0 18
60.0%
2100 1
 
3.3%
4100 1
 
3.3%
4800 1
 
3.3%
5000 1
 
3.3%
6000 1
 
3.3%
7800 1
 
3.3%
8800 1
 
3.3%
9000 1
 
3.3%
10800 1
 
3.3%
ValueCountFrequency (%)
20000 1
3.3%
15000 1
3.3%
13350 1
3.3%
10800 1
3.3%
9000 1
3.3%
8800 1
3.3%
7800 1
3.3%
6000 1
3.3%
5000 1
3.3%
4800 1
3.3%

Interactions

2024-03-13T20:59:36.286270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:30.156860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:30.950781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:31.705922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:32.542467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:33.400033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:34.278579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:35.505016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:36.399454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:30.242062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:31.034150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:31.814791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:32.666969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:33.510681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:34.405674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:35.594222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:36.544003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:30.333156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:31.142062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:31.907427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:32.764389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:33.636625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:34.812632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:35.680135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:36.659925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:30.431308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:31.228973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:31.989064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:32.882892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:33.752435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:34.936897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:35.784171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:36.775487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:30.525145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:31.320741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:32.116662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:32.987679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:33.853558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:35.034824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:35.904050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:36.873163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:30.640835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:31.413822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:32.249925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:33.095594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:33.949210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:35.128814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:36.003604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:36.996699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:30.746322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:31.507751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:32.342617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:33.208487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:34.065297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:35.237565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:36.103787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:37.117857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:30.856416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:31.605306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:32.444669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:33.297179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:34.161180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:35.385375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:36.196303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:59:43.845029image/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
회원코드1.0001.0000.3450.4060.6590.4750.9160.9290.7801.0000.7800.0000.0000.0000.0000.733
가맹점번호1.0000.3451.0000.0000.0000.0000.902NaNNaNNaNNaNNaN0.6580.6581.0000.848
성별코드1.0000.4060.0001.0000.0000.2690.0000.5340.0000.0000.0000.0000.0000.0000.0000.000
연령대코드1.0000.6590.0000.0001.0000.6230.8720.6450.0001.0000.0000.7160.2700.2700.0000.000
결제상품ID1.0000.4750.0000.2690.6231.0001.0000.0000.9220.0000.8690.6750.2470.2470.2100.000
결제상품명1.0000.9160.9020.0000.8721.0001.0000.0000.8850.0000.9910.8460.3570.3570.7180.000
가맹점업종명1.0000.929NaN0.5340.6450.0000.0001.0000.0001.0000.0001.0001.0001.000NaN0.751
가맹점우편번호1.0000.780NaN0.0000.0000.9220.8850.0001.0000.0001.0001.0000.0000.000NaN0.583
시도명1.0001.000NaN0.0001.0000.0000.0001.0000.0001.000NaNNaN0.5230.523NaN1.000
시군구명1.0000.780NaN0.0000.0000.8690.9910.0001.000NaN1.0001.000NaNNaNNaN0.858
읍면동명1.0000.000NaN0.0000.7160.6750.8461.0001.000NaN1.0001.000NaNNaNNaN0.860
위도1.0000.0000.6580.0000.2700.2470.3571.0000.0000.523NaNNaN1.0000.9930.9440.908
경도1.0000.0000.6580.0000.2700.2470.3571.0000.0000.523NaNNaN0.9931.0000.9440.908
사용여부1.0000.0001.0000.0000.0000.2100.718NaNNaNNaNNaNNaN0.9440.9441.0001.000
결제금액1.0000.7330.8480.0000.0000.0000.0000.7510.5831.0000.8580.8600.9080.9081.0001.000
2024-03-13T20:59:44.014066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부성별코드시도명가맹점업종명
사용여부1.0000.0001.0001.000
성별코드0.0001.0000.0000.749
시도명1.0000.0001.0000.943
가맹점업종명1.0000.7490.9431.000
2024-03-13T20:59:44.134526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드가맹점업종명시도명사용여부
회원코드1.0000.215-0.064-0.047-0.036-0.124-0.185-0.1530.0480.0000.0000.000
가맹점번호0.2151.000-0.207-0.006-0.105-0.867-0.868-0.8990.0001.0001.0000.982
연령대코드-0.064-0.2071.0000.060-0.3140.1590.1320.3590.0000.5010.8160.000
결제상품ID-0.047-0.0060.0601.0000.508-0.174-0.131-0.0120.1350.0000.0000.000
가맹점우편번호-0.036-0.105-0.3140.5081.000-0.6790.370-0.2640.0000.0590.0001.000
위도-0.124-0.8670.159-0.174-0.6791.0000.9400.7710.0000.9430.3370.785
경도-0.185-0.8680.132-0.1310.3700.9401.0000.7630.0000.9430.3370.785
결제금액-0.153-0.8990.359-0.012-0.2640.7710.7631.0000.0000.3390.5770.866
성별코드0.0480.0000.0000.1350.0000.0000.0000.0001.0000.7490.0000.000
가맹점업종명0.0001.0000.5010.0000.0590.9430.9430.3390.7491.0000.9431.000
시도명0.0001.0000.8160.0000.0000.3370.3370.5770.0000.9431.0001.000
사용여부0.0000.9820.0000.0001.0000.7850.7850.8660.0001.0001.0001.000

Missing values

2024-03-13T20:59:37.276787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:59:37.554645image/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.
2024-03-13T20:59:37.714663image/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
02021-11-012021-11-07++13oaNfFPmEbF7lohC/xR9Hj9TCxKdte0VilopBfCM=3007682214999999999999999M50140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
12021-11-012021-11-07dt1TdosjJva/EXr3RwvRYfOrp6Cn2DuJwVXqBPCkLJc=3016910799714084787F40140000116000행복화성지역화폐일반휴게음식18476경기도화성시청계동37.2127.113Y8800
22021-11-012021-11-07h4fXBiKUgKGJf9qXAfzgpq8NTig7k43lbYHyyHZ6BIQ=3041701342999999999999999F30140000058000평택사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
32021-11-012021-11-076y8d1K7OwZW2kpAW0UW3v5eWMRzdatb2r0HY+uBDdFs=3016786334999999999999999M40140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
42021-11-012021-11-07NlLfvpse6cncrjBzD0UebjVDZjuxDCd16/J6luO7DsE=3006698246731144581M50140000114000Thank You Pay-N보건위생12138NONE<NA><NA>0.00.0Y15000
52021-11-012021-11-07++1k2zXvZlhdZhLJ5YvB3Cgr//ZfKQH/ER2qCDJiaMo=3059858783999999999999999F50140000112000군포愛머니<NA><NA><NA><NA><NA>0.00.0N0
62021-11-012021-11-07+PW/FANZVHSNs2a7updRCGmI9nxHn7kfjufepWPWZe8=3023113161999999999999999F50140000056000포천사랑상품권(통합)<NA><NA><NA><NA><NA>0.00.0N0
72021-11-012021-11-07dtFOLCEGREv3fDDjgELTqw0sml/YCd4dxlAiVVWhiws=3019576960999999999999999M20140000040000여주사랑카드<NA><NA><NA><NA><NA>0.00.0N0
82021-11-012021-11-07bn3Samd/34N/QBfHmQSBGFSOt/a8q+DteUeDYtsSUs4=3002572153714076808F40140000114000Thank You Pay-N일반휴게음식12150경기도남양주시호평동37.655127.245Y10800
92021-11-012021-11-07sI/GF/ru4eZhmSmlExp2IJISatoYpLG2FMZoWfzKYYI=3026163131999999999999999F0140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202021-11-012021-11-07RrSrZDJLio6YL16Jhf+cSmJOC1jANituvskFheZH+uQ=3038099793999999999999999F10140000104000Thank You Pay-N(통합)<NA><NA><NA><NA><NA>0.00.0N0
212021-11-012021-11-07W192fGP7NW8I1wKdOgP7pEWzIrnXfnR2cpjRR/8FRm8=3019800238999999999999999M20140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
222021-11-012021-11-07aEC9bvl/vm8f4y+Hq4S2CGMSDy+BBKGb7tH1nBf9Q24=3016890937999999999999999M40140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
232021-11-012021-11-07eJAsdCMI2mg6Mj1EfNU+Rq49VsVfZOENAf64JNrB73Q=3030842289791100242F60140000028000구리사랑카드일반휴게음식11927경기도구리시수택동37.598127.138Y20000
242021-11-012021-11-07++91xb+p3m9dEcIQTdirfRF+5Gc4XjxHyTAJzlP8nOo=3058585508410543470065201M50140000106000군포愛머니(통합)<NA><NA><NA><NA><NA>0.00.0Y9000
252021-11-012021-11-07+AQWZQZlBrrifJbVPKy8gdxKd3jL6PHUdXKrCaeBIPw=3019482700999999999999999F30140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0N0
262021-11-012021-11-07++8LxFzGWeNLgPd3XE7IPmutH+a4FK98KSX4WYK4qS0=3016609360999999999999999M30140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
272021-11-012021-11-07+Pswf6rxmSJ0uMrydBmTs68bVIRzQqM/XTHFCxjdxiQ=3018660687726436746M30140000116000행복화성지역화폐음료식품18616경기도화성시향남읍37.1126.896Y5000
282021-11-012021-11-070UAPsHbec89WCqZgJe8LcMA8wEBCCAA5z7aP6FqbfLY=3016847276725294117F30140000018000고양페이카드일반휴게음식10564경기도고양시 덕양구원흥동37.648126.877Y4100
292021-11-012021-11-072cUeFrDfwYGCyoL33O2rNphkmbYPLLK4PAdw7I59CJ4=3020609966752999391M60140000064000용인와이페이(통합)음료식품16930경기도용인시 수지구상현동37.307127.084Y6000