Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells56
Missing cells (%)10.4%
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/2901f5a8-9a3b-4d1a-83e3-826e17b059fc

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 1 other fieldsHigh correlation
가맹점업종명 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
회원코드 is highly overall correlated with 연령대코드High correlation
가맹점번호 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
연령대코드 is highly overall correlated with 회원코드High correlation
결제상품ID is highly overall correlated with 가맹점업종명High correlation
가맹점우편번호 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점번호 and 4 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 가맹점우편번호 and 1 other fieldsHigh correlation
가맹점우편번호 has 18 (60.0%) missing valuesMissing
시군구명 has 19 (63.3%) missing valuesMissing
읍면동명 has 19 (63.3%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
위도 has 19 (63.3%) zerosZeros
경도 has 19 (63.3%) zerosZeros
결제금액 has 18 (60.0%) zerosZeros

Reproduction

Analysis started2024-03-13 11:59:46.022205
Analysis finished2024-03-13 11:59:53.885145
Duration7.86 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
Minimum2021-04-05 00:00:00
Maximum2021-04-05 00:00:00
2024-03-13T20:59:53.954424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:54.060357image/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
Minimum2021-04-11 00:00:00
Maximum2021-04-11 00:00:00
2024-03-13T20:59:54.152818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:54.236410image/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
2024-03-13T20:59:54.504367image/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++3z6TkpYP6zz803CqWXuz2n/yIh29HwSC5tA4GPaCI=
2nd row++g8fFDUN95Mq6Xl3nlTdLHa41SE+/8q0t6IZjYCHZg=
3rd rowN3DvrKllf/+7xNbi5YeT1kbAG6KLjKomJ687Apwt2/I=
4th row++4OKFp9sPmcX8VS7BTQzxIDYrS2g0NdVxCO2Er9RwY=
5th row++sJndfoOHNy9wdo/PlN//KCIfAn5OtGBuNTcCWDRQ4=
ValueCountFrequency (%)
3z6tkpyp6zz803cqwxuz2n/yih29hwsc5ta4gpaci 1
 
3.3%
g8ffdun95mq6xl3nltdlha41se+/8q0t6izjychzg 1
 
3.3%
1a1z5/y6iydese0ag+jhfbewzqpqlmwx4ri0aih7upe 1
 
3.3%
a5ijwo5keehsyxeiy8iknenzohaga5o3chaolo7trs 1
 
3.3%
w24n/wmlrvnfm5xe5jq3lkcmm9a+q5geszrqapjg 1
 
3.3%
wcr8lamgjfsoa7rqogkewtzkt4xjyvr7neknxnqorko 1
 
3.3%
ty+qi+/weuf2zoj+fox79fcyb7tisi9m2q+9e615mv8 1
 
3.3%
rec1snkrwxrj1aqj25gflw6+zxn2y42hgwgiwc2v6ro 1
 
3.3%
qnjzpllk7ittbudkbac594kzjh3rctobtmwpi+zpmjk 1
 
3.3%
ovtidaz6fspbhwstmciynncknsayzyfoqvqccua48ae 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:59:54.885427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 31
 
2.3%
= 30
 
2.3%
C 29
 
2.2%
+ 29
 
2.2%
w 28
 
2.1%
4 27
 
2.0%
c 27
 
2.0%
I 26
 
2.0%
X 25
 
1.9%
N 25
 
1.9%
Other values (55) 1043
79.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 525
39.8%
Lowercase Letter 501
38.0%
Decimal Number 212
16.1%
Math Symbol 59
 
4.5%
Other Punctuation 23
 
1.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 29
 
5.5%
I 26
 
5.0%
X 25
 
4.8%
N 25
 
4.8%
M 25
 
4.8%
R 25
 
4.8%
Z 24
 
4.6%
J 24
 
4.6%
Q 24
 
4.6%
E 24
 
4.6%
Other values (16) 274
52.2%
Lowercase Letter
ValueCountFrequency (%)
w 28
 
5.6%
c 27
 
5.4%
j 24
 
4.8%
a 23
 
4.6%
o 23
 
4.6%
l 23
 
4.6%
s 22
 
4.4%
i 21
 
4.2%
x 21
 
4.2%
f 20
 
4.0%
Other values (16) 269
53.7%
Decimal Number
ValueCountFrequency (%)
9 31
14.6%
4 27
12.7%
7 25
11.8%
5 24
11.3%
6 22
10.4%
2 21
9.9%
8 19
9.0%
3 18
8.5%
0 13
6.1%
1 12
 
5.7%
Math Symbol
ValueCountFrequency (%)
= 30
50.8%
+ 29
49.2%
Other Punctuation
ValueCountFrequency (%)
/ 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1026
77.7%
Common 294
 
22.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 29
 
2.8%
w 28
 
2.7%
c 27
 
2.6%
I 26
 
2.5%
X 25
 
2.4%
N 25
 
2.4%
M 25
 
2.4%
R 25
 
2.4%
Z 24
 
2.3%
J 24
 
2.3%
Other values (42) 768
74.9%
Common
ValueCountFrequency (%)
9 31
10.5%
= 30
10.2%
+ 29
9.9%
4 27
9.2%
7 25
8.5%
5 24
8.2%
/ 23
7.8%
6 22
7.5%
2 21
7.1%
8 19
6.5%
Other values (3) 43
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 31
 
2.3%
= 30
 
2.3%
C 29
 
2.2%
+ 29
 
2.2%
w 28
 
2.1%
4 27
 
2.0%
c 27
 
2.0%
I 26
 
2.0%
X 25
 
1.9%
N 25
 
1.9%
Other values (55) 1043
79.0%

회원코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0208844 × 109
Minimum3.0030757 × 109
Maximum3.0432742 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:55.043612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0030757 × 109
5-th percentile3.0087476 × 109
Q13.0168382 × 109
median3.017694 × 109
Q33.0221879 × 109
95-th percentile3.0376099 × 109
Maximum3.0432742 × 109
Range40198515
Interquartile range (IQR)5349730.5

Descriptive statistics

Standard deviation9107444
Coefficient of variation (CV)0.0030148271
Kurtosis0.6216061
Mean3.0208844 × 109
Median Absolute Deviation (MAD)2059572.5
Skewness0.8121413
Sum9.0626531 × 1010
Variance8.2945536 × 1013
MonotonicityNot monotonic
2024-03-13T20:59:55.186433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3018100840 1
 
3.3%
3007590295 1
 
3.3%
3017018947 1
 
3.3%
3017742583 1
 
3.3%
3017645327 1
 
3.3%
3034757263 1
 
3.3%
3018302262 1
 
3.3%
3033525152 1
 
3.3%
3019640363 1
 
3.3%
3003075732 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3003075732 1
3.3%
3007590295 1
3.3%
3010162126 1
3.3%
3014665177 1
3.3%
3015986191 1
3.3%
3016632679 1
3.3%
3016696572 1
3.3%
3016793443 1
3.3%
3016972398 1
3.3%
3017018947 1
3.3%
ValueCountFrequency (%)
3043274247 1
3.3%
3039609308 1
3.3%
3035166230 1
3.3%
3034757263 1
3.3%
3033525152 1
3.3%
3031526280 1
3.3%
3026958125 1
3.3%
3022353166 1
3.3%
3021692151 1
3.3%
3021681112 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0000029 × 1014
Minimum7.014656 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:55.341412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.014656 × 108
5-th percentile7.0388867 × 108
Q17.2307985 × 108
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.999993 × 1014
Interquartile range (IQR)9.9999928 × 1014

Descriptive statistics

Standard deviation4.9827252 × 1014
Coefficient of variation (CV)0.83045379
Kurtosis-1.9499559
Mean6.0000029 × 1014
Median Absolute Deviation (MAD)0
Skewness-0.43005695
Sum1.8000009 × 1016
Variance2.482755 × 1029
MonotonicityNot monotonic
2024-03-13T20:59:55.485204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
999999999999999 18
60.0%
723282655 1
 
3.3%
765309645 1
 
3.3%
715498708 1
 
3.3%
701969564 1
 
3.3%
779538020 1
 
3.3%
706234240 1
 
3.3%
714989074 1
 
3.3%
795598755 1
 
3.3%
723012243 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
701465602 1
3.3%
701969564 1
3.3%
706234240 1
3.3%
714989074 1
3.3%
715498708 1
3.3%
715514997 1
3.3%
719987995 1
3.3%
723012243 1
3.3%
723282655 1
3.3%
765309645 1
3.3%
ValueCountFrequency (%)
999999999999999 18
60.0%
795598755 1
 
3.3%
779538020 1
 
3.3%
765309645 1
 
3.3%
723282655 1
 
3.3%
723012243 1
 
3.3%
719987995 1
 
3.3%
715514997 1
 
3.3%
715498708 1
 
3.3%
714989074 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
F
20 
M
10 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 20
66.7%
M 10
33.3%

Length

2024-03-13T20:59:55.602287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:59:55.694242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 20
66.7%
m 10
33.3%

연령대코드
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.666667
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:55.786884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q130
median40
Q350
95-th percentile71
Maximum80
Range60
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.50148
Coefficient of variation (CV)0.37789649
Kurtosis-0.15532316
Mean43.666667
Median Absolute Deviation (MAD)10
Skewness0.35074763
Sum1310
Variance272.29885
MonotonicityNot monotonic
2024-03-13T20:59:55.906652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
40 7
23.3%
50 7
23.3%
60 5
16.7%
20 5
16.7%
30 4
13.3%
80 2
 
6.7%
ValueCountFrequency (%)
20 5
16.7%
30 4
13.3%
40 7
23.3%
50 7
23.3%
60 5
16.7%
80 2
 
6.7%
ValueCountFrequency (%)
80 2
 
6.7%
60 5
16.7%
50 7
23.3%
40 7
23.3%
30 4
13.3%
20 5
16.7%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000012 × 1011
Minimum1.4000003 × 1011
Maximum1.4000111 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:56.314745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000003 × 1011
5-th percentile1.4000003 × 1011
Q11.4000005 × 1011
median1.4000011 × 1011
Q31.4000012 × 1011
95-th percentile1.4000015 × 1011
Maximum1.4000111 × 1011
Range1077000
Interquartile range (IQR)75500

Descriptive statistics

Standard deviation190571.4
Coefficient of variation (CV)1.3612231 × 10-6
Kurtosis27.11318
Mean1.4000012 × 1011
Median Absolute Deviation (MAD)31000
Skewness5.0897735
Sum4.2000036 × 1012
Variance3.6317459 × 1010
MonotonicityNot monotonic
2024-03-13T20:59:56.427809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
140000046000 6
20.0%
140000126000 3
 
10.0%
140000030000 3
 
10.0%
140000044000 2
 
6.7%
140000112000 2
 
6.7%
140000116000 2
 
6.7%
140000050000 1
 
3.3%
140000120000 1
 
3.3%
140000122000 1
 
3.3%
140000140000 1
 
3.3%
Other values (8) 8
26.7%
ValueCountFrequency (%)
140000030000 3
10.0%
140000044000 2
 
6.7%
140000046000 6
20.0%
140000050000 1
 
3.3%
140000078000 1
 
3.3%
140000098000 1
 
3.3%
140000104000 1
 
3.3%
140000110000 1
 
3.3%
140000112000 2
 
6.7%
140000114000 1
 
3.3%
ValueCountFrequency (%)
140001107000 1
 
3.3%
140000166000 1
 
3.3%
140000140000 1
 
3.3%
140000126000 3
10.0%
140000124000 1
 
3.3%
140000122000 1
 
3.3%
140000120000 1
 
3.3%
140000116000 2
6.7%
140000114000 1
 
3.3%
140000112000 2
6.7%

결제상품명
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:59:56.597690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13
Mean length7.8333333
Min length4

Characters and Unicode

Total characters235
Distinct characters70
Distinct categories8 ?
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 row수원페이
2nd row행복화성지역화폐
3rd row행복화성지역화폐
4th row용인와이페이
5th row부천페이
ValueCountFrequency (%)
용인와이페이 6
15.8%
수원페이 3
 
7.9%
부천페이 3
 
7.9%
오산화폐 2
 
5.3%
오색전 2
 
5.3%
군포愛머니 2
 
5.3%
행복화성지역화폐 2
 
5.3%
thank 2
 
5.3%
you 2
 
5.3%
경기지역화폐(시흥시 1
 
2.6%
Other values (13) 13
34.2%
2024-03-13T20:59:56.976371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 20
 
8.5%
페 13
 
5.5%
화 10
 
4.3%
8
 
3.4%
사 7
 
3.0%
랑 7
 
3.0%
용 6
 
2.6%
인 6
 
2.6%
( 6
 
2.6%
) 6
 
2.6%
Other values (60) 146
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 185
78.7%
Lowercase Letter 18
 
7.7%
Uppercase Letter 9
 
3.8%
Space Separator 8
 
3.4%
Open Punctuation 6
 
2.6%
Close Punctuation 6
 
2.6%
Dash Punctuation 2
 
0.9%
Connector Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 20
 
10.8%
페 13
 
7.0%
화 10
 
5.4%
사 7
 
3.8%
랑 7
 
3.8%
용 6
 
3.2%
인 6
 
3.2%
폐 6
 
3.2%
카 6
 
3.2%
드 6
 
3.2%
Other values (44) 98
53.0%
Lowercase Letter
ValueCountFrequency (%)
a 5
27.8%
y 3
16.7%
h 2
 
11.1%
n 2
 
11.1%
k 2
 
11.1%
o 2
 
11.1%
u 2
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
P 3
33.3%
T 2
22.2%
Y 2
22.2%
N 2
22.2%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 183
77.9%
Latin 27
 
11.5%
Common 23
 
9.8%
Han 2
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 20
 
10.9%
페 13
 
7.1%
화 10
 
5.5%
사 7
 
3.8%
랑 7
 
3.8%
용 6
 
3.3%
인 6
 
3.3%
폐 6
 
3.3%
카 6
 
3.3%
드 6
 
3.3%
Other values (43) 96
52.5%
Latin
ValueCountFrequency (%)
a 5
18.5%
P 3
11.1%
y 3
11.1%
T 2
 
7.4%
h 2
 
7.4%
n 2
 
7.4%
k 2
 
7.4%
o 2
 
7.4%
u 2
 
7.4%
Y 2
 
7.4%
Common
ValueCountFrequency (%)
8
34.8%
( 6
26.1%
) 6
26.1%
- 2
 
8.7%
_ 1
 
4.3%
Han
ValueCountFrequency (%)
愛 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 183
77.9%
ASCII 50
 
21.3%
CJK 2
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 20
 
10.9%
페 13
 
7.1%
화 10
 
5.5%
사 7
 
3.8%
랑 7
 
3.8%
용 6
 
3.3%
인 6
 
3.3%
폐 6
 
3.3%
카 6
 
3.3%
드 6
 
3.3%
Other values (43) 96
52.5%
ASCII
ValueCountFrequency (%)
8
16.0%
( 6
12.0%
) 6
12.0%
a 5
10.0%
P 3
 
6.0%
y 3
 
6.0%
T 2
 
4.0%
h 2
 
4.0%
- 2
 
4.0%
n 2
 
4.0%
Other values (6) 11
22.0%
CJK
ValueCountFrequency (%)
愛 2
100.0%

가맹점업종명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
18 
일반휴게음식
4 
음료식품
4 
유통업 영리
2 
전기제품
 
1

Length

Max length6
Median length4
Mean length4.3333333
Min length2

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 18
60.0%
일반휴게음식 4
 
13.3%
음료식품 4
 
13.3%
유통업 영리 2
 
6.7%
전기제품 1
 
3.3%
의원 1
 
3.3%

Length

2024-03-13T20:59:57.149236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:59:57.283146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 18
56.2%
일반휴게음식 4
 
12.5%
음료식품 4
 
12.5%
유통업 2
 
6.2%
영리 2
 
6.2%
전기제품 1
 
3.1%
의원 1
 
3.1%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing18
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean15948.417
Minimum11692
Maximum18143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:57.396827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11692
5-th percentile11746.45
Q114895.5
median16930.5
Q317256.5
95-th percentile18139.7
Maximum18143
Range6451
Interquartile range (IQR)2361

Descriptive statistics

Standard deviation2245.1922
Coefficient of variation (CV)0.14077837
Kurtosis0.26180431
Mean15948.417
Median Absolute Deviation (MAD)1016
Skewness-1.1626736
Sum191381
Variance5040887.9
MonotonicityNot monotonic
2024-03-13T20:59:57.496778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
16295 1
 
3.3%
14618 1
 
3.3%
18143 1
 
3.3%
11692 1
 
3.3%
16889 1
 
3.3%
14988 1
 
3.3%
17010 1
 
3.3%
17756 1
 
3.3%
16972 1
 
3.3%
18137 1
 
3.3%
Other values (2) 2
 
6.7%
(Missing) 18
60.0%
ValueCountFrequency (%)
11692 1
3.3%
11791 1
3.3%
14618 1
3.3%
14988 1
3.3%
16295 1
3.3%
16889 1
3.3%
16972 1
3.3%
17010 1
3.3%
17090 1
3.3%
17756 1
3.3%
ValueCountFrequency (%)
18143 1
3.3%
18137 1
3.3%
17756 1
3.3%
17090 1
3.3%
17010 1
3.3%
16972 1
3.3%
16889 1
3.3%
16295 1
3.3%
14988 1
3.3%
14618 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
18 
경기도
11 
NONE
 
1

Length

Max length4
Median length4
Mean length3.6333333
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 18
60.0%
경기도 11
36.7%
NONE 1
 
3.3%

Length

2024-03-13T20:59:57.625252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:59:57.752450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 18
60.0%
경기도 11
36.7%
none 1
 
3.3%

시군구명
Text

MISSING 

Distinct9
Distinct (%)81.8%
Missing19
Missing (%)63.3%
Memory size372.0 B
2024-03-13T20:59:57.883383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.9090909
Min length3

Characters and Unicode

Total characters54
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)63.6%

Sample

1st row수원시 장안구
2nd row부천시
3rd row오산시
4th row의정부시
5th row용인시 수지구
ValueCountFrequency (%)
용인시 4
25.0%
오산시 2
12.5%
기흥구 2
12.5%
수원시 1
 
6.2%
장안구 1
 
6.2%
부천시 1
 
6.2%
의정부시 1
 
6.2%
수지구 1
 
6.2%
시흥시 1
 
6.2%
평택시 1
 
6.2%
2024-03-13T20:59:58.184006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 12
22.2%
인 5
9.3%
5
9.3%
구 5
9.3%
용 4
 
7.4%
흥 3
 
5.6%
오 2
 
3.7%
기 2
 
3.7%
수 2
 
3.7%
산 2
 
3.7%
Other values (11) 12
22.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49
90.7%
Space Separator 5
 
9.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 12
24.5%
인 5
10.2%
구 5
10.2%
용 4
 
8.2%
흥 3
 
6.1%
오 2
 
4.1%
기 2
 
4.1%
수 2
 
4.1%
산 2
 
4.1%
부 2
 
4.1%
Other values (10) 10
20.4%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49
90.7%
Common 5
 
9.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 12
24.5%
인 5
10.2%
구 5
10.2%
용 4
 
8.2%
흥 3
 
6.1%
오 2
 
4.1%
기 2
 
4.1%
수 2
 
4.1%
산 2
 
4.1%
부 2
 
4.1%
Other values (10) 10
20.4%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49
90.7%
ASCII 5
 
9.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 12
24.5%
인 5
10.2%
구 5
10.2%
용 4
 
8.2%
흥 3
 
6.1%
오 2
 
4.1%
기 2
 
4.1%
수 2
 
4.1%
산 2
 
4.1%
부 2
 
4.1%
Other values (10) 10
20.4%
ASCII
ValueCountFrequency (%)
5
100.0%

읍면동명
Text

MISSING 

Distinct11
Distinct (%)100.0%
Missing19
Missing (%)63.3%
Memory size372.0 B
2024-03-13T20:59:58.354033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8181818
Min length2

Characters and Unicode

Total characters31
Distinct characters18
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

Unique11 ?
Unique (%)100.0%

Sample

1st row조원동
2nd row상동
3rd row원동
4th row의정부동
5th row죽전동
ValueCountFrequency (%)
조원동 1
9.1%
상동 1
9.1%
원동 1
9.1%
의정부동 1
9.1%
죽전동 1
9.1%
조남동 1
9.1%
중동 1
9.1%
지산동 1
9.1%
구갈동 1
9.1%
오산동 1
9.1%
2024-03-13T20:59:58.687115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 11
35.5%
조 2
 
6.5%
산 2
 
6.5%
원 2
 
6.5%
중 1
 
3.2%
삼 1
 
3.2%
오 1
 
3.2%
갈 1
 
3.2%
구 1
 
3.2%
지 1
 
3.2%
Other values (8) 8
25.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 11
35.5%
조 2
 
6.5%
산 2
 
6.5%
원 2
 
6.5%
중 1
 
3.2%
삼 1
 
3.2%
오 1
 
3.2%
갈 1
 
3.2%
구 1
 
3.2%
지 1
 
3.2%
Other values (8) 8
25.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 11
35.5%
조 2
 
6.5%
산 2
 
6.5%
원 2
 
6.5%
중 1
 
3.2%
삼 1
 
3.2%
오 1
 
3.2%
갈 1
 
3.2%
구 1
 
3.2%
지 1
 
3.2%
Other values (8) 8
25.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 11
35.5%
조 2
 
6.5%
산 2
 
6.5%
원 2
 
6.5%
중 1
 
3.2%
삼 1
 
3.2%
오 1
 
3.2%
갈 1
 
3.2%
구 1
 
3.2%
지 1
 
3.2%
Other values (8) 8
25.8%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.679633
Minimum0
Maximum37.741
Zeros19
Zeros (%)63.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:58.811373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q337.21625
95-th percentile37.44025
Maximum37.741
Range37.741
Interquartile range (IQR)37.21625

Descriptive statistics

Standard deviation18.28623
Coefficient of variation (CV)1.3367485
Kurtosis-1.7836795
Mean13.679633
Median Absolute Deviation (MAD)0
Skewness0.58304825
Sum410.389
Variance334.38619
MonotonicityNot monotonic
2024-03-13T20:59:58.970381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.0 19
63.3%
37.302 1
 
3.3%
37.492 1
 
3.3%
37.138 1
 
3.3%
37.741 1
 
3.3%
37.325 1
 
3.3%
37.377 1
 
3.3%
37.267 1
 
3.3%
37.081 1
 
3.3%
37.281 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
0.0 19
63.3%
37.081 1
 
3.3%
37.138 1
 
3.3%
37.145 1
 
3.3%
37.24 1
 
3.3%
37.267 1
 
3.3%
37.281 1
 
3.3%
37.302 1
 
3.3%
37.325 1
 
3.3%
37.377 1
 
3.3%
ValueCountFrequency (%)
37.741 1
3.3%
37.492 1
3.3%
37.377 1
3.3%
37.325 1
3.3%
37.302 1
3.3%
37.281 1
3.3%
37.267 1
3.3%
37.24 1
3.3%
37.145 1
3.3%
37.138 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.580767
Minimum0
Maximum127.166
Zeros19
Zeros (%)63.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:59.127067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3127.037
95-th percentile127.14095
Maximum127.166
Range127.166
Interquartile range (IQR)127.037

Descriptive statistics

Standard deviation62.265721
Coefficient of variation (CV)1.336726
Kurtosis-1.7839927
Mean46.580767
Median Absolute Deviation (MAD)0
Skewness0.58293728
Sum1397.423
Variance3877.02
MonotonicityNot monotonic
2024-03-13T20:59:59.248067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.0 19
63.3%
127.007 1
 
3.3%
126.764 1
 
3.3%
127.073 1
 
3.3%
127.047 1
 
3.3%
127.125 1
 
3.3%
126.851 1
 
3.3%
127.154 1
 
3.3%
127.058 1
 
3.3%
127.111 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
0.0 19
63.3%
126.764 1
 
3.3%
126.851 1
 
3.3%
127.007 1
 
3.3%
127.047 1
 
3.3%
127.058 1
 
3.3%
127.067 1
 
3.3%
127.073 1
 
3.3%
127.111 1
 
3.3%
127.125 1
 
3.3%
ValueCountFrequency (%)
127.166 1
3.3%
127.154 1
3.3%
127.125 1
3.3%
127.111 1
3.3%
127.073 1
3.3%
127.067 1
3.3%
127.058 1
3.3%
127.047 1
3.3%
127.007 1
3.3%
126.851 1
3.3%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
18 
True
12 
ValueCountFrequency (%)
False 18
60.0%
True 12
40.0%
2024-03-13T20:59:59.357285image/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%
Mean6080
Minimum0
Maximum38000
Zeros18
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:59:59.442052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34900
95-th percentile31445
Maximum38000
Range38000
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation11120.76
Coefficient of variation (CV)1.8290724
Kurtosis2.7279447
Mean6080
Median Absolute Deviation (MAD)0
Skewness1.9620426
Sum182400
Variance1.2367131 × 108
MonotonicityNot monotonic
2024-03-13T20:59:59.552128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 18
60.0%
4600 1
 
3.3%
24000 1
 
3.3%
7700 1
 
3.3%
22500 1
 
3.3%
38000 1
 
3.3%
35900 1
 
3.3%
4300 1
 
3.3%
7500 1
 
3.3%
3000 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
0 18
60.0%
3000 1
 
3.3%
3900 1
 
3.3%
4300 1
 
3.3%
4600 1
 
3.3%
5000 1
 
3.3%
7500 1
 
3.3%
7700 1
 
3.3%
22500 1
 
3.3%
24000 1
 
3.3%
ValueCountFrequency (%)
38000 1
3.3%
35900 1
3.3%
26000 1
3.3%
24000 1
3.3%
22500 1
3.3%
7700 1
3.3%
7500 1
3.3%
5000 1
3.3%
4600 1
3.3%
4300 1
3.3%

Interactions

2024-03-13T20:59:52.367979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:46.712116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:47.413181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:48.126899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:48.853087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:49.583284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:50.700826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:51.483128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:52.484339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:46.813514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:47.510629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:48.208773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:48.953537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:49.711177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:50.818131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:51.580819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:52.600814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:46.893686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:47.590938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:48.285677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:49.033557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:50.160049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:50.909983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:51.674525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:52.702931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:46.979892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:47.669234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:48.361138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:49.138108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:50.246520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:51.009780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:51.777590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:52.826513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:47.063957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:47.767632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:48.439278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:49.221187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:50.338791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:51.099255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:51.895896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:52.953008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:47.141629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:47.881835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:48.534521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:49.313865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:50.421273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:51.191610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:51.998439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:53.063920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:47.234748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:47.965943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:48.662721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:49.405643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:50.507131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:51.293685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:52.102234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:53.161598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:47.325895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:48.048562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:48.765686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:49.492935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:50.597990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:51.384084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:59:52.227083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:59:59.648900image/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
회원코드1.0001.0000.1420.2560.3530.5080.4290.8230.6820.0000.8371.0000.0000.0000.1250.273
가맹점번호1.0000.1421.0000.0860.4300.1140.301NaNNaNNaNNaNNaN0.9710.9710.9930.990
성별코드1.0000.2560.0861.0000.5310.0800.0000.6161.0000.0001.0001.0000.0000.0000.1850.384
연령대코드1.0000.3530.4300.5311.0000.0000.4560.7850.4280.0000.7501.0000.0000.0000.3770.407
결제상품ID1.0000.5080.1140.0800.0001.0001.0001.0001.0000.0001.0001.0000.1130.1130.1110.604
결제상품명1.0000.4290.3010.0000.4561.0001.0000.8201.0001.0001.0001.0000.4320.4320.4820.691
가맹점업종명1.0000.823NaN0.6160.7851.0000.8201.0000.6950.0000.7761.0000.0000.000NaN0.274
가맹점우편번호1.0000.682NaN1.0000.4281.0001.0000.6951.0001.0001.0001.0001.0001.000NaN0.235
시도명1.0000.000NaN0.0000.0000.0001.0000.0001.0001.000NaNNaN0.5450.545NaN0.354
시군구명1.0000.837NaN1.0000.7501.0001.0000.7761.000NaN1.0001.000NaNNaNNaN0.597
읍면동명1.0001.000NaN1.0001.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
위도1.0000.0000.9710.0000.0000.1130.4320.0001.0000.545NaNNaN1.0000.9930.9750.978
경도1.0000.0000.9710.0000.0000.1130.4320.0001.0000.545NaNNaN0.9931.0000.9750.978
사용여부1.0000.1250.9930.1850.3770.1110.482NaNNaNNaNNaNNaN0.9750.9751.0000.992
결제금액1.0000.2730.9900.3840.4070.6040.6910.2740.2350.3540.5971.0000.9780.9780.9921.000
2024-03-13T20:59:59.815588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부성별코드시도명가맹점업종명
사용여부1.0000.1131.0001.000
성별코드0.1131.0000.0000.608
시도명1.0000.0001.0000.000
가맹점업종명1.0000.6080.0001.000
2024-03-13T20:59:59.933652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드가맹점업종명시도명사용여부
회원코드1.000-0.2020.533-0.119-0.4060.1570.0030.2680.1970.4060.0000.000
가맹점번호-0.2021.000-0.1690.260-0.035-0.873-0.871-0.9020.1131.0001.0000.928
연령대코드0.533-0.1691.000-0.171-0.2410.1820.0520.1270.3480.3550.0000.240
결제상품ID-0.1190.260-0.1711.000-0.496-0.289-0.402-0.2170.1100.8370.0000.163
가맹점우편번호-0.406-0.035-0.241-0.4961.000-0.5870.629-0.5170.5200.5010.0001.000
위도0.157-0.8730.182-0.289-0.5871.0000.9030.8680.0000.0000.3570.856
경도0.003-0.8710.052-0.4020.6290.9031.0000.8090.0000.0000.3570.856
결제금액0.268-0.9020.127-0.217-0.5170.8680.8091.0000.2450.0000.0000.849
성별코드0.1970.1130.3480.1100.5200.0000.0000.2451.0000.6080.0000.113
가맹점업종명0.4061.0000.3550.8370.5010.0000.0000.0000.6081.0000.0001.000
시도명0.0001.0000.0000.0000.0000.3570.3570.0000.0000.0001.0001.000
사용여부0.0000.9280.2400.1631.0000.8560.8560.8490.1131.0001.0001.000

Missing values

2024-03-13T20:59:53.327980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:59:53.623665image/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.
2024-03-13T20:59:53.795211image/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
02021-04-052021-04-11++3z6TkpYP6zz803CqWXuz2n/yIh29HwSC5tA4GPaCI=3018100840723282655F40140000126000수원페이일반휴게음식16295경기도수원시 장안구조원동37.302127.007Y4600
12021-04-052021-04-11++g8fFDUN95Mq6Xl3nlTdLHa41SE+/8q0t6IZjYCHZg=3021692151999999999999999M60140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
22021-04-052021-04-11N3DvrKllf/+7xNbi5YeT1kbAG6KLjKomJ687Apwt2/I=3017487917999999999999999F20140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
32021-04-052021-04-11++4OKFp9sPmcX8VS7BTQzxIDYrS2g0NdVxCO2Er9RwY=3019866692999999999999999M50140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0N0
42021-04-052021-04-11++sJndfoOHNy9wdo/PlN//KCIfAn5OtGBuNTcCWDRQ4=3031526280765309645F60140000030000부천페이전기제품14618경기도부천시상동37.492126.764Y24000
52021-04-052021-04-114JsL7eKS5uvLwiAi3CxXKgAvSs8p9rNPXjQSZUWxyhQ=3039609308999999999999999M60140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
62021-04-052021-04-11AbKog/3DXI5k54cI+ewquoLvusdyFQff/M9Ca8hqqy0=3016793443715498708F40140000044000오산화폐 오색전음료식품18143경기도오산시원동37.138127.073Y7700
72021-04-052021-04-11GjydAMJM9L8LYci63JjlDMJtYrbE29eWJuB6Dj3IjcM=3017206126999999999999999F20140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
82021-04-052021-04-11N3JpRZHQaPdSS4fT5iwhwja0H1noCs3oVqrrbfX2iaw=3026958125999999999999999F60140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0N0
92021-04-052021-04-11TH69CEzecUiN0Kb4kDDM2MMEcQVx0JXvYkE9G3HQUx4=3022353166701969564M80140000098000의정부사랑카드(통합)유통업 영리11692경기도의정부시의정부동37.741127.047Y22500
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202021-04-052021-04-11ldf7PwXIqUwFEM7RL1l5j4+Ufou9rwNnKaWxY8tW9jk=3016696572999999999999999F20140000140000행복화성지역화폐_화이트<NA><NA><NA><NA><NA>0.00.0N0
212021-04-052021-04-11ovtIdaZ6fsPbhWstMCIYnncKNsayzyfoQVQCCUa48AE=3017118331723012243F40140000046000용인와이페이일반휴게음식16972경기도용인시 기흥구구갈동37.281127.111Y3000
222021-04-052021-04-11qNJzPLLK7iTtBuDkBAC594KZJh3RcTObTMwpi+ZPmjk=3003075732999999999999999F50140000112000군포愛머니<NA><NA><NA><NA><NA>0.00.0N0
232021-04-052021-04-11rec1snKrwxRj1aQJ25gfLW6+ZXn2y42HgWGiWc2v6Ro=3019640363999999999999999F20140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
242021-04-052021-04-11tY+QI+/WeuF2Zoj+FoX79FcYb7TIsI9M2Q+9E615mV8=3033525152999999999999999M50140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
252021-04-052021-04-11wcR8lAMGjFSoa7RqoGkewtzkt4xjyVR7neKNXNQORKo=3018302262701465602F60140000044000오산화폐 오색전유통업 영리18137경기도오산시오산동37.145127.067Y3900
262021-04-052021-04-11+//w24n/wMlRvNfM5Xe5jQ3lKcmm9A+Q5gesZRqapJg=3034757263719987995F40140000122000의정부사랑카드음료식품11791NONE<NA><NA>0.00.0Y26000
272021-04-052021-04-11/a5IjWo5KEeHsYxeiY8IkNENZoHAgA5O3CHaoLo7tRs=3017645327999999999999999M50140000120000파주 Pay(파주페이)<NA><NA><NA><NA><NA>0.00.0N0
282021-04-052021-04-111A1z5/y6Iydese0AG+JHFBewzQPqlmwx4RI0aiH7UPE=3017742583715514997F40140000046000용인와이페이음료식품17090경기도용인시 처인구삼가동37.24127.166Y5000
292021-04-052021-04-112mw6rLv/OCObd59cScwi+TB+SM2CpigiGxpa6Zck944=3017018947999999999999999M40140000112000군포愛머니<NA><NA><NA><NA><NA>0.00.0N0