Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells94
Missing cells (%)17.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory156.4 B

Variable types

Categorical4
Text5
Numeric8
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/7fe09c86-7e3a-473f-afac-49cf0740612a

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
사용여부 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
시도명 is highly overall correlated with 가맹점번호 and 2 other fieldsHigh correlation
회원코드 is highly overall correlated with 가맹점우편번호High correlation
가맹점번호 is highly overall correlated with 가맹점우편번호 and 5 other fieldsHigh correlation
연령대코드 is highly overall correlated with 가맹점우편번호High correlation
결제상품ID is highly overall correlated with 시도명High correlation
가맹점우편번호 is highly overall correlated with 회원코드 and 4 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점번호 and 3 other fieldsHigh correlation
결제금액 is highly overall correlated with 가맹점번호 and 3 other fieldsHigh correlation
가맹점업종명 has 23 (76.7%) missing valuesMissing
가맹점우편번호 has 23 (76.7%) missing valuesMissing
시군구명 has 24 (80.0%) missing valuesMissing
읍면동명 has 24 (80.0%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
연령대코드 has 7 (23.3%) zerosZeros
위도 has 24 (80.0%) zerosZeros
경도 has 24 (80.0%) zerosZeros
결제금액 has 22 (73.3%) zerosZeros

Reproduction

Analysis started2024-03-13 12:01:12.467941
Analysis finished2024-03-13 12:01:20.421185
Duration7.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책주간결제시작일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-07-03
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-03
2nd row2023-07-03
3rd row2023-07-03
4th row2023-07-03
5th row2023-07-03

Common Values

ValueCountFrequency (%)
2023-07-03 30
100.0%

Length

2024-03-13T21:01:20.491452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:01:20.606564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-03 30
100.0%

정책주간결제종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-07-09
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-09
2nd row2023-07-09
3rd row2023-07-09
4th row2023-07-09
5th row2023-07-09

Common Values

ValueCountFrequency (%)
2023-07-09 30
100.0%

Length

2024-03-13T21:01:20.708726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:01:20.797858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-09 30
100.0%

카드번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T21:01:21.007012image/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 rowDEy/5YuiPGGMkf6XpZV/gll6G1MhtCaqMfztpXZen+I=
2nd rowzzhP0kTwRVfq9YdMjc3sHtp8byc12Djh69vWXPS8ySU=
3rd row/kkCt/gP+y+D3R7hUfFLISnmKctX1y/fFtUFdTYNnQo=
4th rowJ3FeSIr0tDxo79qxvv0SNuIrbcLXgJFlF4MZlf1epQ8=
5th rowfSqhZkNo0IyNO9uzDZSnzKP5XZsOJ63p98VwcF2/M9M=
ValueCountFrequency (%)
dey/5yuipggmkf6xpzv/gll6g1mhtcaqmfztpxzen+i 1
 
3.3%
zzhp0ktwrvfq9ydmjc3shtp8byc12djh69vwxps8ysu 1
 
3.3%
eojlnazxoel4vq9x40xgtgz5ccy44v03vnqxemycije 1
 
3.3%
dfbnhx3/vnt3tncrgm9klgt1qostssbcj7uvhytaajo 1
 
3.3%
9btcoxphgnuic+b0wmnxvoaa+3jfrnwf4k1f0crn3tm 1
 
3.3%
6or1vvsqxhnuvgnqw+/p5cu5sorlhzhcq9lqz5fkvrg 1
 
3.3%
3cvjbg0cctsameb+muaa1owp0ges3wj5ug/autmslve 1
 
3.3%
df5rny0cidtsk7pwrtgndicnm9qbu0912gbhi/e2si8 1
 
3.3%
wb3axsfwxtrm8xo4lvkqljnmgcjo+jliccfudrz40zo 1
 
3.3%
qx6nluaiekkioscspfw6sspwowjs2yrccxgs8fai9ow 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T21:01:21.420398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 30
 
2.3%
= 30
 
2.3%
N 30
 
2.3%
f 29
 
2.2%
C 28
 
2.1%
G 26
 
2.0%
0 26
 
2.0%
r 25
 
1.9%
O 25
 
1.9%
F 25
 
1.9%
Other values (55) 1046
79.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 545
41.3%
Lowercase Letter 510
38.6%
Decimal Number 190
 
14.4%
Math Symbol 52
 
3.9%
Other Punctuation 23
 
1.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 30
 
5.5%
N 30
 
5.5%
C 28
 
5.1%
G 26
 
4.8%
O 25
 
4.6%
F 25
 
4.6%
I 25
 
4.6%
Q 25
 
4.6%
M 24
 
4.4%
X 24
 
4.4%
Other values (16) 283
51.9%
Lowercase Letter
ValueCountFrequency (%)
f 29
 
5.7%
r 25
 
4.9%
b 25
 
4.9%
g 24
 
4.7%
t 24
 
4.7%
q 23
 
4.5%
a 22
 
4.3%
o 22
 
4.3%
c 22
 
4.3%
s 21
 
4.1%
Other values (16) 273
53.5%
Decimal Number
ValueCountFrequency (%)
0 26
13.7%
3 23
12.1%
9 21
11.1%
4 21
11.1%
2 19
10.0%
6 19
10.0%
8 19
10.0%
1 16
8.4%
5 15
7.9%
7 11
5.8%
Math Symbol
ValueCountFrequency (%)
= 30
57.7%
+ 22
42.3%
Other Punctuation
ValueCountFrequency (%)
/ 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1055
79.9%
Common 265
 
20.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 30
 
2.8%
N 30
 
2.8%
f 29
 
2.7%
C 28
 
2.7%
G 26
 
2.5%
r 25
 
2.4%
O 25
 
2.4%
F 25
 
2.4%
I 25
 
2.4%
b 25
 
2.4%
Other values (42) 787
74.6%
Common
ValueCountFrequency (%)
= 30
11.3%
0 26
9.8%
3 23
8.7%
/ 23
8.7%
+ 22
8.3%
9 21
7.9%
4 21
7.9%
2 19
7.2%
6 19
7.2%
8 19
7.2%
Other values (3) 42
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 30
 
2.3%
= 30
 
2.3%
N 30
 
2.3%
f 29
 
2.2%
C 28
 
2.1%
G 26
 
2.0%
0 26
 
2.0%
r 25
 
1.9%
O 25
 
1.9%
F 25
 
1.9%
Other values (55) 1046
79.2%

회원코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0270626 × 109
Minimum3.0021894 × 109
Maximum3.1003229 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:21.572985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0021894 × 109
5-th percentile3.0075148 × 109
Q13.017289 × 109
median3.0204078 × 109
Q33.031594 × 109
95-th percentile3.0604771 × 109
Maximum3.1003229 × 109
Range98133500
Interquartile range (IQR)14305025

Descriptive statistics

Standard deviation19185032
Coefficient of variation (CV)0.0063378377
Kurtosis6.7585157
Mean3.0270626 × 109
Median Absolute Deviation (MAD)5823314
Skewness2.2575509
Sum9.0811879 × 1010
Variance3.6806545 × 1014
MonotonicityNot monotonic
2024-03-13T21:01:21.699188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3026366191 1
 
3.3%
3019216739 1
 
3.3%
3017173844 1
 
3.3%
3029022248 1
 
3.3%
3017459943 1
 
3.3%
3035667328 1
 
3.3%
3019327689 1
 
3.3%
3012678145 1
 
3.3%
3003290304 1
 
3.3%
3044179227 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3002189401 1
3.3%
3003290304 1
3.3%
3012678145 1
3.3%
3013406147 1
3.3%
3016709003 1
3.3%
3017168134 1
3.3%
3017173844 1
3.3%
3017231960 1
3.3%
3017459943 1
3.3%
3017888905 1
3.3%
ValueCountFrequency (%)
3100322901 1
3.3%
3061291521 1
3.3%
3059481691 1
3.3%
3044179227 1
3.3%
3037082196 1
3.3%
3035667328 1
3.3%
3032259285 1
3.3%
3031631193 1
3.3%
3031482343 1
3.3%
3029022248 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4701287 × 1014
Minimum7.0245279 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:21.871124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0245279 × 108
5-th percentile7.1206151 × 108
Q15.5778579 × 1014
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.999993 × 1014
Interquartile range (IQR)4.4221421 × 1014

Descriptive statistics

Standard deviation4.3261697 × 1014
Coefficient of variation (CV)0.5791292
Kurtosis-0.57289608
Mean7.4701287 × 1014
Median Absolute Deviation (MAD)0
Skewness-1.1863452
Sum2.2410386 × 1016
Variance1.8715744 × 1029
MonotonicityNot monotonic
2024-03-13T21:01:22.049133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
999999999999999 22
73.3%
712472624 1
 
3.3%
720994044 1
 
3.3%
733774562 1
 
3.3%
728482684 1
 
3.3%
702452793 1
 
3.3%
731144436 1
 
3.3%
410381050195601 1
 
3.3%
711725137 1
 
3.3%
ValueCountFrequency (%)
702452793 1
 
3.3%
711725137 1
 
3.3%
712472624 1
 
3.3%
720994044 1
 
3.3%
728482684 1
 
3.3%
731144436 1
 
3.3%
733774562 1
 
3.3%
410381050195601 1
 
3.3%
999999999999999 22
73.3%
ValueCountFrequency (%)
999999999999999 22
73.3%
410381050195601 1
 
3.3%
733774562 1
 
3.3%
731144436 1
 
3.3%
728482684 1
 
3.3%
720994044 1
 
3.3%
712472624 1
 
3.3%
711725137 1
 
3.3%
702452793 1
 
3.3%

성별코드
Categorical

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
F
23 
M
7 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 23
76.7%
M 7
 
23.3%

Length

2024-03-13T21:01:22.181108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:01:22.272000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 23
76.7%
m 7
 
23.3%

연령대코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31
Minimum0
Maximum70
Zeros7
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:22.357976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median35
Q347.5
95-th percentile60
Maximum70
Range70
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation20.902071
Coefficient of variation (CV)0.67426034
Kurtosis-0.90739622
Mean31
Median Absolute Deviation (MAD)15
Skewness-0.26313452
Sum930
Variance436.89655
MonotonicityNot monotonic
2024-03-13T21:01:22.479736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
40 7
23.3%
0 7
23.3%
50 5
16.7%
30 5
16.7%
20 3
10.0%
60 2
 
6.7%
70 1
 
3.3%
ValueCountFrequency (%)
0 7
23.3%
20 3
10.0%
30 5
16.7%
40 7
23.3%
50 5
16.7%
60 2
 
6.7%
70 1
 
3.3%
ValueCountFrequency (%)
70 1
 
3.3%
60 2
 
6.7%
50 5
16.7%
40 7
23.3%
30 5
16.7%
20 3
10.0%
0 7
23.3%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000007 × 1011
Minimum1.4000002 × 1011
Maximum1.4000016 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:22.595302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000003 × 1011
median1.4000006 × 1011
Q31.4000011 × 1011
95-th percentile1.4000013 × 1011
Maximum1.4000016 × 1011
Range146000
Interquartile range (IQR)78000

Descriptive statistics

Standard deviation40838.566
Coefficient of variation (CV)2.917039 × 10-7
Kurtosis-0.92000775
Mean1.4000007 × 1011
Median Absolute Deviation (MAD)32000
Skewness0.39429648
Sum4.2000022 × 1012
Variance1.6677885 × 109
MonotonicityNot monotonic
2024-03-13T21:01:22.728683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
140000030000 4
 
13.3%
140000058000 3
 
10.0%
140000018000 3
 
10.0%
140000044000 2
 
6.7%
140000122000 2
 
6.7%
140000124000 2
 
6.7%
140000126000 1
 
3.3%
140000048000 1
 
3.3%
140000090000 1
 
3.3%
140000164000 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
140000018000 3
10.0%
140000028000 1
 
3.3%
140000030000 4
13.3%
140000044000 2
6.7%
140000048000 1
 
3.3%
140000058000 3
10.0%
140000060000 1
 
3.3%
140000064000 1
 
3.3%
140000072000 1
 
3.3%
140000074000 1
 
3.3%
ValueCountFrequency (%)
140000164000 1
3.3%
140000126000 1
3.3%
140000124000 2
6.7%
140000122000 2
6.7%
140000116000 1
3.3%
140000114000 1
3.3%
140000104000 1
3.3%
140000100000 1
3.3%
140000090000 1
3.3%
140000080000 1
3.3%

결제상품명
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T21:01:22.950043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length8.6
Min length4

Characters and Unicode

Total characters258
Distinct characters58
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)46.7%

Sample

1st row구리사랑카드
2nd row고양페이카드
3rd row오산화폐 오색전
4th row이천사랑지역화폐(통합)
5th row용인와이페이(통합)
ValueCountFrequency (%)
부천페이 4
 
10.0%
평택사랑카드(통합 3
 
7.5%
오산화폐 3
 
7.5%
고양페이카드 3
 
7.5%
안산사랑상품권 3
 
7.5%
다온 2
 
5.0%
you 2
 
5.0%
thank 2
 
5.0%
의정부사랑카드 2
 
5.0%
오색전 2
 
5.0%
Other values (14) 14
35.0%
2024-03-13T21:01:23.344519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 14
 
5.4%
통 13
 
5.0%
) 12
 
4.7%
사 12
 
4.7%
랑 12
 
4.7%
( 12
 
4.7%
합 11
 
4.3%
페 11
 
4.3%
카 11
 
4.3%
드 11
 
4.3%
Other values (48) 139
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 198
76.7%
Lowercase Letter 16
 
6.2%
Close Punctuation 12
 
4.7%
Open Punctuation 12
 
4.7%
Space Separator 10
 
3.9%
Uppercase Letter 8
 
3.1%
Dash Punctuation 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 14
 
7.1%
통 13
 
6.6%
사 12
 
6.1%
랑 12
 
6.1%
합 11
 
5.6%
페 11
 
5.6%
카 11
 
5.6%
드 11
 
5.6%
천 7
 
3.5%
화 7
 
3.5%
Other values (33) 89
44.9%
Lowercase Letter
ValueCountFrequency (%)
a 4
25.0%
y 2
12.5%
u 2
12.5%
o 2
12.5%
k 2
12.5%
n 2
12.5%
h 2
12.5%
Uppercase Letter
ValueCountFrequency (%)
N 2
25.0%
P 2
25.0%
Y 2
25.0%
T 2
25.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 198
76.7%
Common 36
 
14.0%
Latin 24
 
9.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 14
 
7.1%
통 13
 
6.6%
사 12
 
6.1%
랑 12
 
6.1%
합 11
 
5.6%
페 11
 
5.6%
카 11
 
5.6%
드 11
 
5.6%
천 7
 
3.5%
화 7
 
3.5%
Other values (33) 89
44.9%
Latin
ValueCountFrequency (%)
a 4
16.7%
N 2
8.3%
y 2
8.3%
P 2
8.3%
u 2
8.3%
o 2
8.3%
Y 2
8.3%
k 2
8.3%
n 2
8.3%
h 2
8.3%
Common
ValueCountFrequency (%)
) 12
33.3%
( 12
33.3%
10
27.8%
- 2
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 198
76.7%
ASCII 60
 
23.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 14
 
7.1%
통 13
 
6.6%
사 12
 
6.1%
랑 12
 
6.1%
합 11
 
5.6%
페 11
 
5.6%
카 11
 
5.6%
드 11
 
5.6%
천 7
 
3.5%
화 7
 
3.5%
Other values (33) 89
44.9%
ASCII
ValueCountFrequency (%)
) 12
20.0%
( 12
20.0%
10
16.7%
a 4
 
6.7%
N 2
 
3.3%
- 2
 
3.3%
y 2
 
3.3%
P 2
 
3.3%
u 2
 
3.3%
o 2
 
3.3%
Other values (5) 10
16.7%

가맹점업종명
Text

MISSING 

Distinct4
Distinct (%)57.1%
Missing23
Missing (%)76.7%
Memory size372.0 B
2024-03-13T21:01:23.502964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length6
Min length4

Characters and Unicode

Total characters42
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)28.6%

Sample

1st row일반/휴게 음식
2nd row일반/휴게 음식
3rd row일반/휴게 음식
4th row미용/위생
5th row일반유통
ValueCountFrequency (%)
일반/휴게 3
30.0%
음식 3
30.0%
일반유통 2
20.0%
미용/위생 1
 
10.0%
음료/식품 1
 
10.0%
2024-03-13T21:01:23.817047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
일 5
11.9%
반 5
11.9%
/ 5
11.9%
음 4
9.5%
식 4
9.5%
휴 3
7.1%
게 3
7.1%
3
7.1%
유 2
 
4.8%
통 2
 
4.8%
Other values (6) 6
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34
81.0%
Other Punctuation 5
 
11.9%
Space Separator 3
 
7.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 5
14.7%
반 5
14.7%
음 4
11.8%
식 4
11.8%
휴 3
8.8%
게 3
8.8%
유 2
 
5.9%
통 2
 
5.9%
미 1
 
2.9%
용 1
 
2.9%
Other values (4) 4
11.8%
Other Punctuation
ValueCountFrequency (%)
/ 5
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34
81.0%
Common 8
 
19.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 5
14.7%
반 5
14.7%
음 4
11.8%
식 4
11.8%
휴 3
8.8%
게 3
8.8%
유 2
 
5.9%
통 2
 
5.9%
미 1
 
2.9%
용 1
 
2.9%
Other values (4) 4
11.8%
Common
ValueCountFrequency (%)
/ 5
62.5%
3
37.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34
81.0%
ASCII 8
 
19.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
일 5
14.7%
반 5
14.7%
음 4
11.8%
식 4
11.8%
휴 3
8.8%
게 3
8.8%
유 2
 
5.9%
통 2
 
5.9%
미 1
 
2.9%
용 1
 
2.9%
Other values (4) 4
11.8%
ASCII
ValueCountFrequency (%)
/ 5
62.5%
3
37.5%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing23
Missing (%)76.7%
Infinite0
Infinite (%)0.0%
Mean15222
Minimum11637
Maximum18136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:23.966389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11637
5-th percentile11724.3
Q113200.5
median14548
Q317916
95-th percentile18131.2
Maximum18136
Range6499
Interquartile range (IQR)4715.5

Descriptive statistics

Standard deviation2822.7598
Coefficient of variation (CV)0.18543948
Kurtosis-1.9856578
Mean15222
Median Absolute Deviation (MAD)2911
Skewness-0.1869659
Sum106554
Variance7967973
MonotonicityNot monotonic
2024-03-13T21:01:24.097477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
11928 1
 
3.3%
18120 1
 
3.3%
11637 1
 
3.3%
14548 1
 
3.3%
17712 1
 
3.3%
14473 1
 
3.3%
18136 1
 
3.3%
(Missing) 23
76.7%
ValueCountFrequency (%)
11637 1
3.3%
11928 1
3.3%
14473 1
3.3%
14548 1
3.3%
17712 1
3.3%
18120 1
3.3%
18136 1
3.3%
ValueCountFrequency (%)
18136 1
3.3%
18120 1
3.3%
17712 1
3.3%
14548 1
3.3%
14473 1
3.3%
11928 1
3.3%
11637 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
23 
경기도
6 
NONE
 
1

Length

Max length4
Median length4
Mean length3.8
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 23
76.7%
경기도 6
 
20.0%
NONE 1
 
3.3%

Length

2024-03-13T21:01:24.279146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:01:24.401052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 23
76.7%
경기도 6
 
20.0%
none 1
 
3.3%

시군구명
Text

MISSING 

Distinct4
Distinct (%)66.7%
Missing24
Missing (%)80.0%
Memory size372.0 B
2024-03-13T21:01:24.530215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1666667
Min length3

Characters and Unicode

Total characters19
Distinct characters9
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

Unique2 ?
Unique (%)33.3%

Sample

1st row구리시
2nd row오산시
3rd row의정부시
4th row부천시
5th row부천시
ValueCountFrequency (%)
오산시 2
33.3%
부천시 2
33.3%
구리시 1
16.7%
의정부시 1
16.7%
2024-03-13T21:01:24.787546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 6
31.6%
부 3
15.8%
오 2
 
10.5%
산 2
 
10.5%
천 2
 
10.5%
구 1
 
5.3%
리 1
 
5.3%
의 1
 
5.3%
정 1
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 6
31.6%
부 3
15.8%
오 2
 
10.5%
산 2
 
10.5%
천 2
 
10.5%
구 1
 
5.3%
리 1
 
5.3%
의 1
 
5.3%
정 1
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 6
31.6%
부 3
15.8%
오 2
 
10.5%
산 2
 
10.5%
천 2
 
10.5%
구 1
 
5.3%
리 1
 
5.3%
의 1
 
5.3%
정 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 6
31.6%
부 3
15.8%
오 2
 
10.5%
산 2
 
10.5%
천 2
 
10.5%
구 1
 
5.3%
리 1
 
5.3%
의 1
 
5.3%
정 1
 
5.3%

읍면동명
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing24
Missing (%)80.0%
Memory size372.0 B
2024-03-13T21:01:24.947425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.6666667
Min length2

Characters and Unicode

Total characters16
Distinct characters10
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

Unique6 ?
Unique (%)100.0%

Sample

1st row수택동
2nd row궐동
3rd row호원동
4th row중동
5th row원종동
ValueCountFrequency (%)
수택동 1
16.7%
궐동 1
16.7%
호원동 1
16.7%
중동 1
16.7%
원종동 1
16.7%
오산동 1
16.7%
2024-03-13T21:01:25.249721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 6
37.5%
원 2
 
12.5%
수 1
 
6.2%
택 1
 
6.2%
궐 1
 
6.2%
호 1
 
6.2%
중 1
 
6.2%
종 1
 
6.2%
오 1
 
6.2%
산 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 6
37.5%
원 2
 
12.5%
수 1
 
6.2%
택 1
 
6.2%
궐 1
 
6.2%
호 1
 
6.2%
중 1
 
6.2%
종 1
 
6.2%
오 1
 
6.2%
산 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 6
37.5%
원 2
 
12.5%
수 1
 
6.2%
택 1
 
6.2%
궐 1
 
6.2%
호 1
 
6.2%
중 1
 
6.2%
종 1
 
6.2%
오 1
 
6.2%
산 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 6
37.5%
원 2
 
12.5%
수 1
 
6.2%
택 1
 
6.2%
궐 1
 
6.2%
호 1
 
6.2%
중 1
 
6.2%
종 1
 
6.2%
오 1
 
6.2%
산 1
 
6.2%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4882333
Minimum0
Maximum37.724
Zeros24
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:25.358002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile37.5608
Maximum37.724
Range37.724
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.232808
Coefficient of variation (CV)2.0342326
Kurtosis0.52830562
Mean7.4882333
Median Absolute Deviation (MAD)0
Skewness1.5802933
Sum224.647
Variance232.03844
MonotonicityNot monotonic
2024-03-13T21:01:25.467564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 24
80.0%
37.595 1
 
3.3%
37.157 1
 
3.3%
37.724 1
 
3.3%
37.501 1
 
3.3%
37.519 1
 
3.3%
37.151 1
 
3.3%
ValueCountFrequency (%)
0.0 24
80.0%
37.151 1
 
3.3%
37.157 1
 
3.3%
37.501 1
 
3.3%
37.519 1
 
3.3%
37.595 1
 
3.3%
37.724 1
 
3.3%
ValueCountFrequency (%)
37.724 1
 
3.3%
37.595 1
 
3.3%
37.519 1
 
3.3%
37.501 1
 
3.3%
37.157 1
 
3.3%
37.151 1
 
3.3%
0.0 24
80.0%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.396667
Minimum0
Maximum127.149
Zeros24
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:25.624051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile127.06405
Maximum127.149
Range127.149
Interquartile range (IQR)0

Descriptive statistics

Standard deviation51.661698
Coefficient of variation (CV)2.0341921
Kurtosis0.52747927
Mean25.396667
Median Absolute Deviation (MAD)0
Skewness1.5801362
Sum761.9
Variance2668.9311
MonotonicityNot monotonic
2024-03-13T21:01:25.753347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 24
80.0%
127.149 1
 
3.3%
127.058 1
 
3.3%
127.045 1
 
3.3%
126.774 1
 
3.3%
126.805 1
 
3.3%
127.069 1
 
3.3%
ValueCountFrequency (%)
0.0 24
80.0%
126.774 1
 
3.3%
126.805 1
 
3.3%
127.045 1
 
3.3%
127.058 1
 
3.3%
127.069 1
 
3.3%
127.149 1
 
3.3%
ValueCountFrequency (%)
127.149 1
 
3.3%
127.069 1
 
3.3%
127.058 1
 
3.3%
127.045 1
 
3.3%
126.805 1
 
3.3%
126.774 1
 
3.3%
0.0 24
80.0%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
22 
True
8 
ValueCountFrequency (%)
False 22
73.3%
True 8
 
26.7%
2024-03-13T21:01:25.894061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8730.6667
Minimum0
Maximum122000
Zeros22
Zeros (%)73.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:26.004902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32250
95-th percentile45875
Maximum122000
Range122000
Interquartile range (IQR)2250

Descriptive statistics

Standard deviation24924.254
Coefficient of variation (CV)2.8547939
Kurtosis15.877016
Mean8730.6667
Median Absolute Deviation (MAD)0
Skewness3.8656073
Sum261920
Variance6.2121842 × 108
MonotonicityNot monotonic
2024-03-13T21:01:26.142418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 22
73.3%
10500 1
 
3.3%
16500 1
 
3.3%
65000 1
 
3.3%
122000 1
 
3.3%
15900 1
 
3.3%
6520 1
 
3.3%
3000 1
 
3.3%
22500 1
 
3.3%
ValueCountFrequency (%)
0 22
73.3%
3000 1
 
3.3%
6520 1
 
3.3%
10500 1
 
3.3%
15900 1
 
3.3%
16500 1
 
3.3%
22500 1
 
3.3%
65000 1
 
3.3%
122000 1
 
3.3%
ValueCountFrequency (%)
122000 1
 
3.3%
65000 1
 
3.3%
22500 1
 
3.3%
16500 1
 
3.3%
15900 1
 
3.3%
10500 1
 
3.3%
6520 1
 
3.3%
3000 1
 
3.3%
0 22
73.3%

Interactions

2024-03-13T21:01:18.885004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:13.189674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:14.211410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:15.016453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:15.839027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:16.587906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:17.410331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:18.207369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:18.967045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:13.270959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:14.310851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:15.121763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:15.925736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:16.691343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:17.548720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:18.302308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:19.352809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:13.379197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:14.399810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:15.248840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:16.006972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:16.779998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:17.642194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:18.388924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:19.431828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:13.474450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:14.508089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:15.345987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:16.105355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:16.876411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:17.741175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:18.482683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:19.513663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:13.567851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:14.600607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:15.460728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:16.214010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:16.976347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:17.846547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:18.567137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:19.616268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:13.940647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:14.692328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:15.561215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:16.311696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:17.061826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:17.943601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:18.646429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:19.768353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:14.039001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:14.810457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:15.662181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:16.411695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:17.186351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:18.031253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:18.727913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:19.853944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:14.125464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:14.911130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:15.756082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:16.499993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:17.286555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:18.118271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:18.803631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:01:26.234482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
카드번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
회원코드1.0001.0000.0000.0000.5530.0000.8500.2390.3950.0000.0001.0000.0000.0000.3900.000
가맹점번호1.0000.0001.0000.0000.0000.6280.000NaNNaNNaNNaNNaN1.0001.0001.0000.479
성별코드1.0000.0000.0001.0000.2720.0000.0000.0000.1930.0000.0001.0000.0000.0000.0000.000
연령대코드1.0000.5530.0000.2721.0000.0000.0000.8711.0000.0000.8951.0000.1010.1010.3370.434
결제상품ID1.0000.0000.6280.0000.0001.0001.0000.0000.8271.0001.0001.0000.6200.6200.6220.382
결제상품명1.0000.8500.0000.0000.0001.0001.0000.0001.0001.0001.0001.0000.4850.4850.4170.000
가맹점업종명1.0000.239NaN0.0000.8710.0000.0001.0000.0000.0000.0001.0000.0000.000NaN0.493
가맹점우편번호1.0000.395NaN0.1931.0000.8271.0000.0001.0000.0001.0001.0000.0000.000NaN0.923
시도명1.0000.000NaN0.0000.0001.0001.0000.0000.0001.000NaNNaN0.2930.293NaN0.000
시군구명1.0000.000NaN0.0000.8951.0001.0000.0001.000NaN1.0001.000NaNNaNNaN0.952
읍면동명1.0001.000NaN1.0001.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
위도1.0000.0001.0000.0000.1010.6200.4850.0000.0000.293NaNNaN1.0000.9860.9080.850
경도1.0000.0001.0000.0000.1010.6200.4850.0000.0000.293NaNNaN0.9861.0000.9080.850
사용여부1.0000.3901.0000.0000.3370.6220.417NaNNaNNaNNaNNaN0.9080.9081.0000.895
결제금액1.0000.0000.4790.0000.4340.3820.0000.4930.9230.0000.9521.0000.8500.8500.8951.000
2024-03-13T21:01:26.430016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부성별코드시도명
사용여부1.0000.0001.000
성별코드0.0001.0000.000
시도명1.0000.0001.000
2024-03-13T21:01:26.845102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드시도명사용여부
회원코드1.000-0.1650.078-0.1190.5000.1620.1760.1340.0000.0000.217
가맹점번호-0.1651.000-0.3360.381-0.643-0.800-0.820-0.9750.0001.0000.982
연령대코드0.078-0.3361.0000.038-0.6170.3380.3310.3140.2520.0000.318
결제상품ID-0.1190.3810.0381.0000.109-0.281-0.308-0.3390.0000.7750.338
가맹점우편번호0.500-0.643-0.6170.1091.000-0.8930.0000.1070.3160.0001.000
위도0.162-0.8000.338-0.281-0.8931.0000.9830.8560.0000.0910.724
경도0.176-0.8200.331-0.3080.0000.9831.0000.8530.0000.0910.724
결제금액0.134-0.9750.314-0.3390.1070.8560.8531.0000.0000.0000.680
성별코드0.0000.0000.2520.0000.3160.0000.0000.0001.0000.0000.000
시도명0.0001.0000.0000.7750.0000.0910.0910.0000.0001.0001.000
사용여부0.2170.9820.3180.3381.0000.7240.7240.6800.0001.0001.000

Missing values

2024-03-13T21:01:19.982661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:01:20.208493image/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.
2024-03-13T21:01:20.348913image/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
02023-07-032023-07-09DEy/5YuiPGGMkf6XpZV/gll6G1MhtCaqMfztpXZen+I=3026366191712472624F50140000028000구리사랑카드일반/휴게 음식11928경기도구리시수택동37.595127.149Y10500
12023-07-032023-07-09zzhP0kTwRVfq9YdMjc3sHtp8byc12Djh69vWXPS8ySU=3023598397999999999999999M30140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
22023-07-032023-07-09/kkCt/gP+y+D3R7hUfFLISnmKctX1y/fFtUFdTYNnQo=3018418395720994044F40140000044000오산화폐 오색전일반/휴게 음식18120경기도오산시궐동37.157127.058Y16500
32023-07-032023-07-09J3FeSIr0tDxo79qxvv0SNuIrbcLXgJFlF4MZlf1epQ8=3061291521999999999999999F20140000060000이천사랑지역화폐(통합)<NA><NA><NA><NA><NA>0.00.0N0
42023-07-032023-07-09fSqhZkNo0IyNO9uzDZSnzKP5XZsOJ63p98VwcF2/M9M=3059481691999999999999999F60140000064000용인와이페이(통합)<NA><NA><NA><NA><NA>0.00.0N0
52023-07-032023-07-09DF1gEEYYOpoDEwR0wn2980jfboMqXa7vQk1APbb+PYQ=3016709003999999999999999F0140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0N0
62023-07-032023-07-09DFTQXb6IhTbAxfuYSIJgvxt+7mxyGzxMurNsTNz1bo8=3017231960733774562F50140000122000의정부사랑카드일반/휴게 음식11637경기도의정부시호원동37.724127.045Y65000
72023-07-032023-07-09zziKAQbqvFqKfiQNv3pzyhzMJJe8rNvvqeAXO/Zs+pc=3026096109999999999999999F70140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
82023-07-032023-07-09UCajIE6Cg0t+365qv8N0BUe0taZGYIc/khvZyaOiEzk=3017168134999999999999999F0140000100000안산사랑상품권 다온(통합)<NA><NA><NA><NA><NA>0.00.0N0
92023-07-032023-07-09fTGjtYVMkdJaODfQO2qlcVu+TERNQ+y4a9213G5W4Sg=3037082196728482684F20140000030000부천페이미용/위생14548경기도부천시중동37.501126.774Y122000
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202023-07-032023-07-09l/6SoBnbIaPB7yD+PvIbWfGBIbfAbli5Y5U2vQS/SY8=3032259285999999999999999M40140000126000수원페이<NA><NA><NA><NA><NA>0.00.0N0
212023-07-032023-07-09qX6nLuAiEKkIoSCSpFw6SspwOWjS2yrccXGS8FAi9Ow=3020348925999999999999999F40140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
222023-07-032023-07-09wB3AxSFWXTrm8Xo4LvKQlJNMgCjO+jlICCfUDRZ40Zo=3044179227999999999999999F0140000058000평택사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
232023-07-032023-07-09DF5RNy0CIDTsK7pwRtgndICNM9qbu0912gbHI/e2si8=3003290304999999999999999F50140000072000양평통보(통합)<NA><NA><NA><NA><NA>0.00.0N0
242023-07-032023-07-093cvjbG0CCtsAmEb+MuAa1oWP0gES3WJ5UG/aUtMsLVE=3012678145999999999999999F0140000074000양주사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
252023-07-032023-07-096OR1VVSQXHnuVGnqW+/P5Cu5sOrLHzhCQ9LQZ5FKvRg=3019327689999999999999999F30140000080000부천페이(통합)<NA><NA><NA><NA><NA>0.00.0N0
262023-07-032023-07-099BTCOxpHGnuiC+b0wMNxVoaA+3jfRnWf4K1F0CrN3TM=3035667328711725137F40140000044000오산화폐 오색전음료/식품18136경기도오산시오산동37.151127.069Y22500
272023-07-032023-07-09DFbNhX3/vnT3tncrGM9kLgt1qOstSsBcj7uVHyTaAJo=3017459943999999999999999F50140000164000오산화폐 오색전(교통)<NA><NA><NA><NA><NA>0.00.0N0
282023-07-032023-07-09EoJlnaZXOel4vQ9X40xgtGz5Ccy44V03VNQxEmYCijE=3029022248999999999999999F20140000090000고양페이카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
292023-07-032023-07-09GE3Nj8YlHwUeWaqmBoWgO9jUo5Pu+1ZIJoB1IPnbc0E=3017173844999999999999999M40140000048000이천사랑지역화폐<NA><NA><NA><NA><NA>0.00.0N0