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/cada1a4f-59d0-4972-9cf3-01c9f9ac6985

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
가맹점번호 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
결제상품ID is highly overall correlated with 시도명High correlation
가맹점우편번호 is highly overall correlated with 위도 and 1 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
위도 has 24 (80.0%) zerosZeros
경도 has 24 (80.0%) zerosZeros
결제금액 has 22 (73.3%) zerosZeros

Reproduction

Analysis started2024-03-13 11:46:24.434714
Analysis finished2024-03-13 11:46:33.700593
Duration9.27 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
2022-09-05
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-09-05
2nd row2022-09-05
3rd row2022-09-05
4th row2022-09-05
5th row2022-09-05

Common Values

ValueCountFrequency (%)
2022-09-05 30
100.0%

Length

2024-03-13T20:46:33.770419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:46:33.890223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-09-05 30
100.0%

정책주간결제종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2022-09-11
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-09-11
2nd row2022-09-11
3rd row2022-09-11
4th row2022-09-11
5th row2022-09-11

Common Values

ValueCountFrequency (%)
2022-09-11 30
100.0%

Length

2024-03-13T20:46:34.025855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:46:34.150031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-09-11 30
100.0%

카드번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:46:34.442082image/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+++46thHq/RWJnrLXPkSakZvIKEh9HNUYUcmxD6eRwU=
2nd rowBVa3nuGpQOBL27muhuN3AGBzf702aHzpVDIX8gvj/Kw=
3rd row++1k2zXvZlhdZhLJ5YvB3Cgr//ZfKQH/ER2qCDJiaMo=
4th row++2rmfHYFDQq5etO3Tlme5XexnmLHGkSqO/FSCAtfR8=
5th row++7S8t/P1lQvSeVwxj6ZqmOoPM8MTzPol3/q2q6yMI0=
ValueCountFrequency (%)
46thhq/rwjnrlxpksakzvikeh9hnuyucmxd6erwu 1
 
3.3%
bva3nugpqobl27muhun3agbzf702ahzpvdix8gvj/kw 1
 
3.3%
y+gtwyzs+c1hlcnnzabzwlrbjenn5bebelnl/b5t8 1
 
3.3%
xuoanukgq23n5bpmcu1xxbtruv64tokehujagebyu 1
 
3.3%
v7tcq2ri/cgxnguwbug7o6uawu7txeqsyh4t+4bzg 1
 
3.3%
sjndfoohny9wdo/pln//kcifan5otgbuntccwdrq4 1
 
3.3%
s68ddqhwbev4z+nvx+wtz8nhpig5szyohjlkzomec 1
 
3.3%
qqtapylfoyjiw6h1smbgcyiynnxiihzkrngyob660 1
 
3.3%
ojzet7hsywjpqd41ui/fo/hs3pqxlqxqjzg9aqjww 1
 
3.3%
ontt1ad82egyiga+syjdckelpd5e1ny19qd4n05im 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:46:34.861710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
+ 74
 
5.6%
/ 31
 
2.3%
q 30
 
2.3%
= 30
 
2.3%
t 29
 
2.2%
Z 29
 
2.2%
E 27
 
2.0%
N 24
 
1.8%
x 24
 
1.8%
H 23
 
1.7%
Other values (55) 999
75.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 515
39.0%
Lowercase Letter 504
38.2%
Decimal Number 166
 
12.6%
Math Symbol 104
 
7.9%
Other Punctuation 31
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
q 30
 
6.0%
t 29
 
5.8%
x 24
 
4.8%
o 23
 
4.6%
j 23
 
4.6%
w 22
 
4.4%
h 21
 
4.2%
b 21
 
4.2%
c 21
 
4.2%
l 21
 
4.2%
Other values (16) 269
53.4%
Uppercase Letter
ValueCountFrequency (%)
Z 29
 
5.6%
E 27
 
5.2%
N 24
 
4.7%
H 23
 
4.5%
J 23
 
4.5%
X 23
 
4.5%
O 22
 
4.3%
Q 22
 
4.3%
G 21
 
4.1%
C 21
 
4.1%
Other values (16) 280
54.4%
Decimal Number
ValueCountFrequency (%)
1 19
11.4%
5 19
11.4%
2 18
10.8%
8 17
10.2%
7 17
10.2%
9 17
10.2%
6 16
9.6%
3 16
9.6%
4 16
9.6%
0 11
6.6%
Math Symbol
ValueCountFrequency (%)
+ 74
71.2%
= 30
28.8%
Other Punctuation
ValueCountFrequency (%)
/ 31
100.0%

Most occurring scripts

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

Most frequent character per script

Latin
ValueCountFrequency (%)
q 30
 
2.9%
t 29
 
2.8%
Z 29
 
2.8%
E 27
 
2.6%
N 24
 
2.4%
x 24
 
2.4%
H 23
 
2.3%
J 23
 
2.3%
X 23
 
2.3%
o 23
 
2.3%
Other values (42) 764
75.0%
Common
ValueCountFrequency (%)
+ 74
24.6%
/ 31
10.3%
= 30
10.0%
1 19
 
6.3%
5 19
 
6.3%
2 18
 
6.0%
8 17
 
5.6%
7 17
 
5.6%
9 17
 
5.6%
6 16
 
5.3%
Other values (3) 43
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+ 74
 
5.6%
/ 31
 
2.3%
q 30
 
2.3%
= 30
 
2.3%
t 29
 
2.2%
Z 29
 
2.2%
E 27
 
2.0%
N 24
 
1.8%
x 24
 
1.8%
H 23
 
1.7%
Other values (55) 999
75.7%

회원코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0234848 × 109
Minimum3.0019362 × 109
Maximum3.0598588 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:35.007821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0019362 × 109
5-th percentile3.0061882 × 109
Q13.017003 × 109
median3.0184383 × 109
Q33.0315722 × 109
95-th percentile3.044764 × 109
Maximum3.0598588 × 109
Range57922623
Interquartile range (IQR)14569164

Descriptive statistics

Standard deviation12994362
Coefficient of variation (CV)0.0042978097
Kurtosis0.90582195
Mean3.0234848 × 109
Median Absolute Deviation (MAD)7297161
Skewness0.87458065
Sum9.0704545 × 1010
Variance1.6885345 × 1014
MonotonicityNot monotonic
2024-03-13T20:46:35.169149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3004674502 1
 
3.3%
3029505449 1
 
3.3%
3020074692 1
 
3.3%
3036082449 1
 
3.3%
3018090963 1
 
3.3%
3020661102 1
 
3.3%
3031526280 1
 
3.3%
3008606121 1
 
3.3%
3018245033 1
 
3.3%
3016977515 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3001936160 1
3.3%
3004674502 1
3.3%
3008038347 1
3.3%
3008606121 1
3.3%
3013676253 1
3.3%
3015804220 1
3.3%
3016750301 1
3.3%
3016977515 1
3.3%
3017079536 1
3.3%
3017218707 1
3.3%
ValueCountFrequency (%)
3059858783 1
3.3%
3049374372 1
3.3%
3039129190 1
3.3%
3037724845 1
3.3%
3036082449 1
3.3%
3036034197 1
3.3%
3035117222 1
3.3%
3031587485 1
3.3%
3031526280 1
3.3%
3029505449 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4702956 × 1014
Minimum7.1269965 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:35.318107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.1269965 × 108
5-th percentile7.2230841 × 108
Q15.581613 × 1014
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.9999929 × 1014
Interquartile range (IQR)4.418387 × 1014

Descriptive statistics

Standard deviation4.3260354 × 1014
Coefficient of variation (CV)0.57909828
Kurtosis-0.57243504
Mean7.4702956 × 1014
Median Absolute Deviation (MAD)0
Skewness-1.1865021
Sum2.2410887 × 1016
Variance1.8714582 × 1029
MonotonicityNot monotonic
2024-03-13T20:46:35.470063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
999999999999999 22
73.3%
712699652 1
 
3.3%
719117330 1
 
3.3%
410881730022801 1
 
3.3%
726208616 1
 
3.3%
727044751 1
 
3.3%
728697536 1
 
3.3%
728902926 1
 
3.3%
795044819 1
 
3.3%
ValueCountFrequency (%)
712699652 1
 
3.3%
719117330 1
 
3.3%
726208616 1
 
3.3%
727044751 1
 
3.3%
728697536 1
 
3.3%
728902926 1
 
3.3%
795044819 1
 
3.3%
410881730022801 1
 
3.3%
999999999999999 22
73.3%
ValueCountFrequency (%)
999999999999999 22
73.3%
410881730022801 1
 
3.3%
795044819 1
 
3.3%
728902926 1
 
3.3%
728697536 1
 
3.3%
727044751 1
 
3.3%
726208616 1
 
3.3%
719117330 1
 
3.3%
712699652 1
 
3.3%

성별코드
Categorical

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 rowM
3rd rowF
4th rowF
5th rowM

Common Values

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

Length

2024-03-13T20:46:35.622682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:46:35.755222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 16
53.3%
m 14
46.7%

연령대코드
Real number (ℝ)

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.666667
Minimum20
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:35.861314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q130
median50
Q350
95-th percentile60
Maximum70
Range50
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.767361
Coefficient of variation (CV)0.31528308
Kurtosis-0.92816603
Mean43.666667
Median Absolute Deviation (MAD)10
Skewness-0.21155744
Sum1310
Variance189.54023
MonotonicityNot monotonic
2024-03-13T20:46:36.002519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
50 11
36.7%
30 7
23.3%
60 5
16.7%
40 3
 
10.0%
20 3
 
10.0%
70 1
 
3.3%
ValueCountFrequency (%)
20 3
 
10.0%
30 7
23.3%
40 3
 
10.0%
50 11
36.7%
60 5
16.7%
70 1
 
3.3%
ValueCountFrequency (%)
70 1
 
3.3%
60 5
16.7%
50 11
36.7%
40 3
 
10.0%
30 7
23.3%
20 3
 
10.0%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000008 × 1011
Minimum1.4000002 × 1011
Maximum1.4000013 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:36.148184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000004 × 1011
median1.400001 × 1011
Q31.4000011 × 1011
95-th percentile1.4000012 × 1011
Maximum1.4000013 × 1011
Range110000
Interquartile range (IQR)77000

Descriptive statistics

Standard deviation39370.039
Coefficient of variation (CV)2.8121441 × 10-7
Kurtosis-1.5710801
Mean1.4000008 × 1011
Median Absolute Deviation (MAD)28000
Skewness-0.36432185
Sum4.2000024 × 1012
Variance1.55 × 109
MonotonicityNot monotonic
2024-03-13T20:46:36.273756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
140000116000 3
 
10.0%
140000124000 3
 
10.0%
140000046000 2
 
6.7%
140000100000 2
 
6.7%
140000102000 2
 
6.7%
140000114000 2
 
6.7%
140000030000 2
 
6.7%
140000024000 2
 
6.7%
140000110000 1
 
3.3%
140000126000 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
140000016000 1
3.3%
140000018000 1
3.3%
140000024000 2
6.7%
140000030000 2
6.7%
140000032000 1
3.3%
140000034000 1
3.3%
140000046000 2
6.7%
140000056000 1
3.3%
140000064000 1
3.3%
140000068000 1
3.3%
ValueCountFrequency (%)
140000126000 1
 
3.3%
140000124000 3
10.0%
140000116000 3
10.0%
140000114000 2
6.7%
140000112000 1
 
3.3%
140000110000 1
 
3.3%
140000102000 2
6.7%
140000100000 2
6.7%
140000092000 1
 
3.3%
140000090000 1
 
3.3%

결제상품명
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:46:36.536511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length8.5
Min length4

Characters and Unicode

Total characters255
Distinct characters58
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)40.0%

Sample

1st row용인와이페이
2nd row포천사랑상품권(통합)
3rd row군포愛머니
4th row수원페이(통합)
5th row수원페이(통합)
ValueCountFrequency (%)
안산사랑상품권 5
 
12.8%
행복화성지역화폐 3
 
7.7%
다온 3
 
7.7%
용인와이페이 2
 
5.1%
수원페이(통합 2
 
5.1%
thank 2
 
5.1%
you 2
 
5.1%
pay-n 2
 
5.1%
부천페이 2
 
5.1%
다온(통합 2
 
5.1%
Other values (13) 14
35.9%
2024-03-13T20:46:36.977972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 14
 
5.5%
사 13
 
5.1%
랑 13
 
5.1%
페 11
 
4.3%
) 9
 
3.5%
9
 
3.5%
합 9
 
3.5%
통 9
 
3.5%
( 9
 
3.5%
상 8
 
3.1%
Other values (48) 151
59.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 202
79.2%
Lowercase Letter 16
 
6.3%
Close Punctuation 9
 
3.5%
Space Separator 9
 
3.5%
Open Punctuation 9
 
3.5%
Uppercase Letter 8
 
3.1%
Dash Punctuation 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 14
 
6.9%
사 13
 
6.4%
랑 13
 
6.4%
페 11
 
5.4%
합 9
 
4.5%
통 9
 
4.5%
상 8
 
4.0%
품 8
 
4.0%
권 8
 
4.0%
화 8
 
4.0%
Other values (33) 101
50.0%
Lowercase Letter
ValueCountFrequency (%)
a 4
25.0%
h 2
12.5%
u 2
12.5%
o 2
12.5%
k 2
12.5%
y 2
12.5%
n 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 (%)
) 9
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 201
78.8%
Common 29
 
11.4%
Latin 24
 
9.4%
Han 1
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 14
 
7.0%
사 13
 
6.5%
랑 13
 
6.5%
페 11
 
5.5%
합 9
 
4.5%
통 9
 
4.5%
상 8
 
4.0%
품 8
 
4.0%
권 8
 
4.0%
화 8
 
4.0%
Other values (32) 100
49.8%
Latin
ValueCountFrequency (%)
a 4
16.7%
N 2
8.3%
h 2
8.3%
P 2
8.3%
u 2
8.3%
o 2
8.3%
Y 2
8.3%
k 2
8.3%
y 2
8.3%
n 2
8.3%
Common
ValueCountFrequency (%)
) 9
31.0%
9
31.0%
( 9
31.0%
- 2
 
6.9%
Han
ValueCountFrequency (%)
愛 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 201
78.8%
ASCII 53
 
20.8%
CJK 1
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 14
 
7.0%
사 13
 
6.5%
랑 13
 
6.5%
페 11
 
5.5%
합 9
 
4.5%
통 9
 
4.5%
상 8
 
4.0%
품 8
 
4.0%
권 8
 
4.0%
화 8
 
4.0%
Other values (32) 100
49.8%
ASCII
ValueCountFrequency (%)
) 9
17.0%
9
17.0%
( 9
17.0%
a 4
 
7.5%
N 2
 
3.8%
h 2
 
3.8%
- 2
 
3.8%
P 2
 
3.8%
u 2
 
3.8%
o 2
 
3.8%
Other values (5) 10
18.9%
CJK
ValueCountFrequency (%)
愛 1
100.0%

가맹점업종명
Text

MISSING 

Distinct5
Distinct (%)71.4%
Missing23
Missing (%)76.7%
Memory size372.0 B
2024-03-13T20:46:37.179868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.5714286
Min length2

Characters and Unicode

Total characters32
Distinct characters20
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

Unique3 ?
Unique (%)42.9%

Sample

1st row일반휴게음식
2nd row신변잡화
3rd row유통업 영리
4th row음료식품
5th row의원
ValueCountFrequency (%)
유통업 2
22.2%
영리 2
22.2%
음료식품 2
22.2%
일반휴게음식 1
11.1%
신변잡화 1
11.1%
의원 1
11.1%
2024-03-13T20:46:37.953916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
음 3
 
9.4%
식 3
 
9.4%
유 2
 
6.2%
업 2
 
6.2%
2
 
6.2%
영 2
 
6.2%
리 2
 
6.2%
료 2
 
6.2%
품 2
 
6.2%
통 2
 
6.2%
Other values (10) 10
31.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30
93.8%
Space Separator 2
 
6.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
음 3
 
10.0%
식 3
 
10.0%
유 2
 
6.7%
업 2
 
6.7%
영 2
 
6.7%
리 2
 
6.7%
료 2
 
6.7%
품 2
 
6.7%
통 2
 
6.7%
변 1
 
3.3%
Other values (9) 9
30.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30
93.8%
Common 2
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
음 3
 
10.0%
식 3
 
10.0%
유 2
 
6.7%
업 2
 
6.7%
영 2
 
6.7%
리 2
 
6.7%
료 2
 
6.7%
품 2
 
6.7%
통 2
 
6.7%
변 1
 
3.3%
Other values (9) 9
30.0%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30
93.8%
ASCII 2
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
음 3
 
10.0%
식 3
 
10.0%
유 2
 
6.7%
업 2
 
6.7%
영 2
 
6.7%
리 2
 
6.7%
료 2
 
6.7%
품 2
 
6.7%
통 2
 
6.7%
변 1
 
3.3%
Other values (9) 9
30.0%
ASCII
ValueCountFrequency (%)
2
100.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing23
Missing (%)76.7%
Infinite0
Infinite (%)0.0%
Mean13885.571
Minimum11028
Maximum17590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:38.085004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11028
5-th percentile11111.1
Q111701
median14072
Q315553.5
95-th percentile17262.7
Maximum17590
Range6562
Interquartile range (IQR)3852.5

Descriptive statistics

Standard deviation2553.8699
Coefficient of variation (CV)0.18392257
Kurtosis-1.4891829
Mean13885.571
Median Absolute Deviation (MAD)2427
Skewness0.31977362
Sum97199
Variance6522251.6
MonotonicityNot monotonic
2024-03-13T20:46:38.199179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
14072 1
 
3.3%
11028 1
 
3.3%
17590 1
 
3.3%
12097 1
 
3.3%
16499 1
 
3.3%
14608 1
 
3.3%
11305 1
 
3.3%
(Missing) 23
76.7%
ValueCountFrequency (%)
11028 1
3.3%
11305 1
3.3%
12097 1
3.3%
14072 1
3.3%
14608 1
3.3%
16499 1
3.3%
17590 1
3.3%
ValueCountFrequency (%)
17590 1
3.3%
16499 1
3.3%
14608 1
3.3%
14072 1
3.3%
12097 1
3.3%
11305 1
3.3%
11028 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 row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

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

Length

2024-03-13T20:46:38.348497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:46:38.468535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 23
76.7%
경기도 6
 
20.0%
none 1
 
3.3%

시군구명
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing24
Missing (%)80.0%
Memory size372.0 B
2024-03-13T20:46:38.669806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5.5
Mean length4
Min length3

Characters and Unicode

Total characters24
Distinct characters14
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

Unique6 ?
Unique (%)100.0%

Sample

1st row안양시 동안구
2nd row연천군
3rd row안성시
4th row남양주시
5th row부천시
ValueCountFrequency (%)
안양시 1
14.3%
동안구 1
14.3%
연천군 1
14.3%
안성시 1
14.3%
남양주시 1
14.3%
부천시 1
14.3%
동두천시 1
14.3%
2024-03-13T20:46:39.089711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 5
20.8%
안 3
12.5%
천 3
12.5%
양 2
 
8.3%
동 2
 
8.3%
1
 
4.2%
구 1
 
4.2%
연 1
 
4.2%
군 1
 
4.2%
성 1
 
4.2%
Other values (4) 4
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23
95.8%
Space Separator 1
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 5
21.7%
안 3
13.0%
천 3
13.0%
양 2
 
8.7%
동 2
 
8.7%
구 1
 
4.3%
연 1
 
4.3%
군 1
 
4.3%
성 1
 
4.3%
남 1
 
4.3%
Other values (3) 3
13.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23
95.8%
Common 1
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 5
21.7%
안 3
13.0%
천 3
13.0%
양 2
 
8.7%
동 2
 
8.7%
구 1
 
4.3%
연 1
 
4.3%
군 1
 
4.3%
성 1
 
4.3%
남 1
 
4.3%
Other values (3) 3
13.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23
95.8%
ASCII 1
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 5
21.7%
안 3
13.0%
천 3
13.0%
양 2
 
8.7%
동 2
 
8.7%
구 1
 
4.3%
연 1
 
4.3%
군 1
 
4.3%
성 1
 
4.3%
남 1
 
4.3%
Other values (3) 3
13.0%
ASCII
ValueCountFrequency (%)
1
100.0%

읍면동명
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing24
Missing (%)80.0%
Memory size372.0 B
2024-03-13T20:46:39.262153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters18
Distinct characters13
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 row호계동
2nd row전곡읍
3rd row숭인동
4th row별내동
5th row중동
ValueCountFrequency (%)
호계동 1
16.7%
전곡읍 1
16.7%
숭인동 1
16.7%
별내동 1
16.7%
중동 1
16.7%
동두천동 1
16.7%
2024-03-13T20:46:39.598694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 6
33.3%
호 1
 
5.6%
계 1
 
5.6%
전 1
 
5.6%
곡 1
 
5.6%
읍 1
 
5.6%
숭 1
 
5.6%
인 1
 
5.6%
별 1
 
5.6%
내 1
 
5.6%
Other values (3) 3
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 6
33.3%
호 1
 
5.6%
계 1
 
5.6%
전 1
 
5.6%
곡 1
 
5.6%
읍 1
 
5.6%
숭 1
 
5.6%
인 1
 
5.6%
별 1
 
5.6%
내 1
 
5.6%
Other values (3) 3
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 6
33.3%
호 1
 
5.6%
계 1
 
5.6%
전 1
 
5.6%
곡 1
 
5.6%
읍 1
 
5.6%
숭 1
 
5.6%
인 1
 
5.6%
별 1
 
5.6%
내 1
 
5.6%
Other values (3) 3
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 6
33.3%
호 1
 
5.6%
계 1
 
5.6%
전 1
 
5.6%
곡 1
 
5.6%
읍 1
 
5.6%
숭 1
 
5.6%
인 1
 
5.6%
별 1
 
5.6%
내 1
 
5.6%
Other values (3) 3
16.7%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5170333
Minimum0
Maximum38.025
Zeros24
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:39.743657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile37.8091
Maximum38.025
Range38.025
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.29186
Coefficient of variation (CV)2.0342946
Kurtosis0.5295689
Mean7.5170333
Median Absolute Deviation (MAD)0
Skewness1.5805334
Sum225.511
Variance233.84098
MonotonicityNot monotonic
2024-03-13T20:46:39.864380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 24
80.0%
37.391 1
 
3.3%
38.025 1
 
3.3%
37.01 1
 
3.3%
37.665 1
 
3.3%
37.493 1
 
3.3%
37.927 1
 
3.3%
ValueCountFrequency (%)
0.0 24
80.0%
37.01 1
 
3.3%
37.391 1
 
3.3%
37.493 1
 
3.3%
37.665 1
 
3.3%
37.927 1
 
3.3%
38.025 1
 
3.3%
ValueCountFrequency (%)
38.025 1
 
3.3%
37.927 1
 
3.3%
37.665 1
 
3.3%
37.493 1
 
3.3%
37.391 1
 
3.3%
37.01 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%
Mean25.407667
Minimum0
Maximum127.274
Zeros24
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:40.026053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile127.09505
Maximum127.274
Range127.274
Interquartile range (IQR)0

Descriptive statistics

Standard deviation51.684083
Coefficient of variation (CV)2.0341924
Kurtosis0.5274861
Mean25.407667
Median Absolute Deviation (MAD)0
Skewness1.5801375
Sum762.23
Variance2671.2444
MonotonicityNot monotonic
2024-03-13T20:46:40.171311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 24
80.0%
126.954 1
 
3.3%
127.067 1
 
3.3%
127.274 1
 
3.3%
127.118 1
 
3.3%
126.764 1
 
3.3%
127.053 1
 
3.3%
ValueCountFrequency (%)
0.0 24
80.0%
126.764 1
 
3.3%
126.954 1
 
3.3%
127.053 1
 
3.3%
127.067 1
 
3.3%
127.118 1
 
3.3%
127.274 1
 
3.3%
ValueCountFrequency (%)
127.274 1
 
3.3%
127.118 1
 
3.3%
127.067 1
 
3.3%
127.053 1
 
3.3%
126.954 1
 
3.3%
126.764 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%
2024-03-13T20:46:40.287414image/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%
Mean2081.3333
Minimum0
Maximum16050
Zeros22
Zeros (%)73.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:46:40.370900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32625
95-th percentile10660
Maximum16050
Range16050
Interquartile range (IQR)2625

Descriptive statistics

Standard deviation4110.7351
Coefficient of variation (CV)1.9750489
Kurtosis4.1388205
Mean2081.3333
Median Absolute Deviation (MAD)0
Skewness2.130051
Sum62440
Variance16898143
MonotonicityNot monotonic
2024-03-13T20:46:40.480083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 22
73.3%
11200 1
 
3.3%
6000 1
 
3.3%
16050 1
 
3.3%
4890 1
 
3.3%
7200 1
 
3.3%
3600 1
 
3.3%
10000 1
 
3.3%
3500 1
 
3.3%
ValueCountFrequency (%)
0 22
73.3%
3500 1
 
3.3%
3600 1
 
3.3%
4890 1
 
3.3%
6000 1
 
3.3%
7200 1
 
3.3%
10000 1
 
3.3%
11200 1
 
3.3%
16050 1
 
3.3%
ValueCountFrequency (%)
16050 1
 
3.3%
11200 1
 
3.3%
10000 1
 
3.3%
7200 1
 
3.3%
6000 1
 
3.3%
4890 1
 
3.3%
3600 1
 
3.3%
3500 1
 
3.3%
0 22
73.3%

Interactions

2024-03-13T20:46:32.158490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:25.220985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:26.109862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:27.221316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:28.196858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:29.095142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:30.360599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:31.232484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:32.268382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:25.338012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:26.228963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:27.354760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:28.311897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:29.234043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:30.501539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:31.342297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:32.404473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:25.448135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:26.345689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:27.540139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:28.426581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:29.363945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:30.633569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:31.456463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:32.508723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:25.542047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:26.458526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:27.663723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:28.527339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:29.481646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:30.728852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:31.572915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:32.622216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:25.654545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:26.595144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:27.760324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:28.633006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:29.924591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:30.827827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:31.680616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:32.715721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:25.761569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:26.785882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:27.856849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:28.746449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:30.029279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:30.920747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:31.778768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:32.823060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:25.871496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:26.943250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:27.970971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:28.857268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:30.125528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:31.010405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:31.915917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:32.942361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:25.978973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:27.094862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:28.077145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:28.968877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:30.234902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:31.093574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:46:32.039707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:46:40.593203image/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
회원코드1.0001.0000.0000.0000.2830.7750.7260.4651.0000.0001.0001.0000.0000.0000.1370.000
가맹점번호1.0000.0001.0000.0000.7070.0000.000NaNNaNNaNNaNNaN0.6170.6171.0001.000
성별코드1.0000.0000.0001.0000.0000.1380.2040.4651.0000.0001.0001.0000.0000.0000.0000.000
연령대코드1.0000.2830.7070.0001.0000.6520.7980.0001.0000.0001.0001.0000.5430.5430.6350.000
결제상품ID1.0000.7750.0000.1380.6521.0001.0000.6621.0001.0001.0001.0000.5850.5850.3510.000
결제상품명1.0000.7260.0000.2040.7981.0001.0001.0001.0001.0001.0001.0000.8000.8000.5950.714
가맹점업종명1.0000.465NaN0.4650.0000.6621.0001.0001.0001.0001.0001.0001.0001.000NaN0.203
가맹점우편번호1.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.000
시도명1.0000.000NaN0.0000.0001.0001.0001.0001.0001.000NaNNaN0.2930.293NaN0.000
시군구명1.0001.000NaN1.0001.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
읍면동명1.0001.000NaN1.0001.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
위도1.0000.0000.6170.0000.5430.5850.8001.0001.0000.293NaNNaN1.0000.9860.9080.995
경도1.0000.0000.6170.0000.5430.5850.8001.0001.0000.293NaNNaN0.9861.0000.9080.995
사용여부1.0000.1371.0000.0000.6350.3510.595NaNNaNNaNNaNNaN0.9080.9081.0001.000
결제금액1.0000.0001.0000.0000.0000.0000.7140.2031.0000.0001.0001.0000.9950.9951.0001.000
2024-03-13T20:46:40.764814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부성별코드시도명
사용여부1.0000.0001.000
성별코드0.0001.0000.000
시도명1.0000.0001.000
2024-03-13T20:46:40.863883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드시도명사용여부
회원코드1.0000.0890.173-0.0720.2140.0400.053-0.1040.0000.0000.152
가맹점번호0.0891.000-0.442-0.0350.036-0.854-0.863-0.9700.0001.0000.982
연령대코드0.173-0.4421.000-0.153-0.2890.4640.4320.3780.0000.0000.424
결제상품ID-0.072-0.035-0.1531.000-0.250-0.136-0.1490.0720.1780.7750.317
가맹점우편번호0.2140.036-0.289-0.2501.000-0.929-0.1430.0000.4470.4471.000
위도0.040-0.8540.464-0.136-0.9291.0000.9830.7920.0000.0910.724
경도0.053-0.8630.432-0.149-0.1430.9831.0000.7900.0000.0910.724
결제금액-0.104-0.9700.3780.0720.0000.7920.7901.0000.0000.0000.926
성별코드0.0000.0000.0000.1780.4470.0000.0000.0001.0000.0000.000
시도명0.0001.0000.0000.7750.4470.0910.0910.0000.0001.0001.000
사용여부0.1520.9820.4240.3171.0000.7240.7240.9260.0001.0001.000

Missing values

2024-03-13T20:46:33.127653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:46:33.408605image/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.
2024-03-13T20:46:33.616662image/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
02022-09-052022-09-11+++46thHq/RWJnrLXPkSakZvIKEh9HNUYUcmxD6eRwU=3004674502999999999999999M50140000046000용인와이페이<NA><NA><NA><NA><NA>0.00.0N0
12022-09-052022-09-11BVa3nuGpQOBL27muhuN3AGBzf702aHzpVDIX8gvj/Kw=3037724845999999999999999M40140000056000포천사랑상품권(통합)<NA><NA><NA><NA><NA>0.00.0N0
22022-09-052022-09-11++1k2zXvZlhdZhLJ5YvB3Cgr//ZfKQH/ER2qCDJiaMo=3059858783999999999999999F50140000112000군포愛머니<NA><NA><NA><NA><NA>0.00.0N0
32022-09-052022-09-11++2rmfHYFDQq5etO3Tlme5XexnmLHGkSqO/FSCAtfR8=3036034197999999999999999F50140000102000수원페이(통합)<NA><NA><NA><NA><NA>0.00.0N0
42022-09-052022-09-11++7S8t/P1lQvSeVwxj6ZqmOoPM8MTzPol3/q2q6yMI0=3035117222999999999999999M50140000102000수원페이(통합)<NA><NA><NA><NA><NA>0.00.0N0
52022-09-052022-09-11++9CNLujpE8Oz4qCHl2tSiblJ8Mp6/m8jXLLZREul+M=3017563300712699652M50140000034000안양사랑페이일반휴게음식14072경기도안양시 동안구호계동37.391126.954Y11200
62022-09-052022-09-11++BrIrqyt6GdoxWCspxQATO913wEh1trwGZbJlVqrPs=3017874831999999999999999M30140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
72022-09-052022-09-11BVb8mVftKKVtTwr9Vt5dO2kvGyN9cUJrWFJTHCOorQw=3015804220999999999999999M50140000064000용인와이페이(통합)<NA><NA><NA><NA><NA>0.00.0N0
82022-09-052022-09-11++DaOcETDJjYwiJr7Oz3zxR+TL0DAW+TyhHVV38Pxjk=3028667175999999999999999F40140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
92022-09-052022-09-11++FfP1a7+SQqCkK+eERX9XvzRDquizbhYMZbkUOv7DM=3008038347999999999999999M30140000092000행복화성지역화폐(통합)<NA><NA><NA><NA><NA>0.00.0N0
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202022-09-052022-09-11++l4zBWLvdYGdlIi3t7300vW2ZymGJLzIQokFuwqBkk=3017699271727044751F50140000114000Thank You Pay-N음료식품12097경기도남양주시별내동37.665127.118Y7200
212022-09-052022-09-11++oNtt1AD82EGyiGa+syJdCKElpd5E1nY19qD4n05iM=3017218707728697536M60140000126000수원페이의원16499NONE<NA><NA>0.00.0Y3600
222022-09-052022-09-11++ojZeT7hsywjPQd41uI/Fo/hS3pQXlqXQJZG9aqJww=3016977515999999999999999M20140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
232022-09-052022-09-11++qqtApYLfoYjiW6h1sMbGcyIyNnxIiHzKRngYoB660=3018245033999999999999999F30140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
242022-09-052022-09-11++s68dDQhWbEv4Z+nVX+wTZ8nhpIg5SZyoHJLKZoMEc=3008606121999999999999999F50140000114000Thank You Pay-N<NA><NA><NA><NA><NA>0.00.0N0
252022-09-052022-09-11++sJndfoOHNy9wdo/PlN//KCIfAn5OtGBuNTcCWDRQ4=3031526280728902926F60140000030000부천페이음료식품14608경기도부천시중동37.493126.764Y10000
262022-09-052022-09-11++v7tcq2RI/CgXNgUWBUg7o6uAWu7txEqsyh4t+4bZg=3020661102999999999999999F50140000100000안산사랑상품권 다온(통합)<NA><NA><NA><NA><NA>0.00.0N0
272022-09-052022-09-11++xUOaNUKgQ23N5bpmCU1xXBtRUv64tokEHUJagEbYU=3018090963999999999999999F20140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
282022-09-052022-09-11++y+GTWYzS+C1HlCNNZAbzWLRBjEnN5beBeLnL/B5T8=3036082449795044819F60140000110000동두천사랑카드유통업 영리11305경기도동두천시동두천동37.927127.053Y3500
292022-09-052022-09-11++y1yeEHVlc8S47PbH+sjoEGZSurm9ec20hnH0avxFM=3020074692999999999999999M30140000024000광주사랑카드<NA><NA><NA><NA><NA>0.00.0N0