Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells88
Missing cells (%)16.3%
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/0f0edd37-af20-4e75-a770-14a9c21c6e71

Alerts

정책주간결제시작일자 has constant value ""Constant
정책주간결제종료일자 has constant value ""Constant
사용여부 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
성별코드 is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 회원코드 and 9 other fieldsHigh correlation
회원코드 is highly overall correlated with 시도명High correlation
가맹점번호 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
연령대코드 is highly overall correlated with 시도명High correlation
결제상품ID is highly overall correlated with 시도명High correlation
가맹점우편번호 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
결제금액 is highly overall correlated with 가맹점번호 and 5 other fieldsHigh correlation
가맹점업종명 has 22 (73.3%) missing valuesMissing
가맹점우편번호 has 22 (73.3%) missing valuesMissing
시군구명 has 22 (73.3%) missing valuesMissing
읍면동명 has 22 (73.3%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
연령대코드 has 3 (10.0%) zerosZeros
위도 has 22 (73.3%) zerosZeros
경도 has 22 (73.3%) zerosZeros
결제금액 has 22 (73.3%) zerosZeros

Reproduction

Analysis started2024-03-13 11:57:49.552224
Analysis finished2024-03-13 11:57:57.235997
Duration7.68 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
2022-04-04
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-04-04
2nd row2022-04-04
3rd row2022-04-04
4th row2022-04-04
5th row2022-04-04

Common Values

ValueCountFrequency (%)
2022-04-04 30
100.0%

Length

2024-03-13T20:57:57.301881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:57:57.391814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-04-04 30
100.0%

정책주간결제종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2022-04-10
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-04-10
2nd row2022-04-10
3rd row2022-04-10
4th row2022-04-10
5th row2022-04-10

Common Values

ValueCountFrequency (%)
2022-04-10 30
100.0%

Length

2024-03-13T20:57:57.491392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:57:57.589323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-04-10 30
100.0%

카드번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:57:57.792179image/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 rowAdxA+j5yFlHgRvbRnc/05YtpOwvqvcnIKjdEV6lQWZU=
2nd rowAe/N9yQGf0Ktc3Oj5ScinhAV0OJNXiVn3O9Is2m/XLQ=
3rd rowAdzCPxUX56P2mWCw4lZBdShitrOsy6e1QxpFkHu2rHI=
4th rowAe6nUK1SSUW4bsyUfxjkKW3rj02+VYwIzJHq4wQfUqA=
5th rowAeHsWf9uZHDxRQtR1ipZ3zxZ+d4c4idzJOegBXtqqpo=
ValueCountFrequency (%)
adxa+j5yflhgrvbrnc/05ytpowvqvcnikjdev6lqwzu 1
 
3.3%
ae/n9yqgf0ktc3oj5scinhav0ojnxivn3o9is2m/xlq 1
 
3.3%
ahcvn8nfg0w9xqav1ie3jiyamjfvenptb9iamyusj5e 1
 
3.3%
akrmyiufrd8bilwjs+ejvjshb3lawnuf04jbyrt9uko 1
 
3.3%
ahaybxlooodfinxsu7tuuk8dlap2zb4dnzlivynmwv0 1
 
3.3%
af4nokixrl05lqaqrggjlytf2xpvrbbay4vk+9oobxo 1
 
3.3%
ah2v7ezq9x65kdjyw64rgm1tpbk4mdslbwogz6zbo6e 1
 
3.3%
akmfp2ub4bvnvv7sthq0ojtsftpsch46iuzcmmrhmvu 1
 
3.3%
agpjr72fqe4bmsz6fzc4slq9wv6n5u2bgrxkbp9bipo 1
 
3.3%
akd0ygmsazat4wcz+sbzezpxrpk2zhul8qa7msw6cc4 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:57:58.226155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 51
 
3.9%
= 30
 
2.3%
e 28
 
2.1%
S 28
 
2.1%
4 28
 
2.1%
2 27
 
2.0%
g 26
 
2.0%
z 26
 
2.0%
J 25
 
1.9%
V 25
 
1.9%
Other values (55) 1026
77.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 530
40.2%
Uppercase Letter 524
39.7%
Decimal Number 213
16.1%
Math Symbol 44
 
3.3%
Other Punctuation 9
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 51
 
9.7%
S 28
 
5.3%
J 25
 
4.8%
V 25
 
4.8%
D 24
 
4.6%
I 23
 
4.4%
W 23
 
4.4%
R 23
 
4.4%
L 22
 
4.2%
U 22
 
4.2%
Other values (16) 258
49.2%
Lowercase Letter
ValueCountFrequency (%)
e 28
 
5.3%
g 26
 
4.9%
z 26
 
4.9%
o 24
 
4.5%
x 24
 
4.5%
c 23
 
4.3%
k 22
 
4.2%
s 22
 
4.2%
b 22
 
4.2%
f 21
 
4.0%
Other values (16) 292
55.1%
Decimal Number
ValueCountFrequency (%)
4 28
13.1%
2 27
12.7%
0 24
11.3%
6 24
11.3%
7 23
10.8%
5 20
9.4%
1 19
8.9%
9 18
8.5%
3 16
7.5%
8 14
6.6%
Math Symbol
ValueCountFrequency (%)
= 30
68.2%
+ 14
31.8%
Other Punctuation
ValueCountFrequency (%)
/ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1054
79.8%
Common 266
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 51
 
4.8%
e 28
 
2.7%
S 28
 
2.7%
g 26
 
2.5%
z 26
 
2.5%
J 25
 
2.4%
V 25
 
2.4%
D 24
 
2.3%
o 24
 
2.3%
x 24
 
2.3%
Other values (42) 773
73.3%
Common
ValueCountFrequency (%)
= 30
11.3%
4 28
10.5%
2 27
10.2%
0 24
9.0%
6 24
9.0%
7 23
8.6%
5 20
7.5%
1 19
7.1%
9 18
6.8%
3 16
6.0%
Other values (3) 37
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 51
 
3.9%
= 30
 
2.3%
e 28
 
2.1%
S 28
 
2.1%
4 28
 
2.1%
2 27
 
2.0%
g 26
 
2.0%
z 26
 
2.0%
J 25
 
1.9%
V 25
 
1.9%
Other values (55) 1026
77.7%

회원코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0227982 × 109
Minimum3.0021148 × 109
Maximum3.0482636 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:58.400603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0021148 × 109
5-th percentile3.012157 × 109
Q13.0173432 × 109
median3.019568 × 109
Q33.0290937 × 109
95-th percentile3.0381913 × 109
Maximum3.0482636 × 109
Range46148798
Interquartile range (IQR)11750544

Descriptive statistics

Standard deviation9719704
Coefficient of variation (CV)0.0032154657
Kurtosis0.58826361
Mean3.0227982 × 109
Median Absolute Deviation (MAD)4885010
Skewness0.63800454
Sum9.0683946 × 1010
Variance9.4472646 × 1013
MonotonicityNot monotonic
2024-03-13T20:57:58.542095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3035870154 1
 
3.3%
3014777110 1
 
3.3%
3026593197 1
 
3.3%
3021539218 1
 
3.3%
3019984308 1
 
3.3%
3019506293 1
 
3.3%
3017280175 1
 
3.3%
3024547130 1
 
3.3%
3019629756 1
 
3.3%
3039083291 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3002114754 1
3.3%
3011143152 1
3.3%
3013396130 1
3.3%
3014053130 1
3.3%
3014777110 1
3.3%
3016513305 1
3.3%
3016533588 1
3.3%
3017280175 1
3.3%
3017532221 1
3.3%
3017782182 1
3.3%
ValueCountFrequency (%)
3048263552 1
3.3%
3039083291 1
3.3%
3037101162 1
3.3%
3035870154 1
3.3%
3034553155 1
3.3%
3032686262 1
3.3%
3031566242 1
3.3%
3029927241 1
3.3%
3026593197 1
3.3%
3024968238 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3333353 × 1014
Minimum7.0374854 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:58.677756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0374854 × 108
5-th percentile7.1450449 × 108
Q12.5000059 × 1014
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.999993 × 1014
Interquartile range (IQR)7.4999941 × 1014

Descriptive statistics

Standard deviation4.4977612 × 1014
Coefficient of variation (CV)0.61333091
Kurtosis-0.82386364
Mean7.3333353 × 1014
Median Absolute Deviation (MAD)0
Skewness-1.1116634
Sum2.2000006 × 1016
Variance2.0229856 × 1029
MonotonicityNot monotonic
2024-03-13T20:57:58.790771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
999999999999999 22
73.3%
787693712 1
 
3.3%
713914463 1
 
3.3%
720075574 1
 
3.3%
715494538 1
 
3.3%
716579474 1
 
3.3%
751852674 1
 
3.3%
703748539 1
 
3.3%
715225639 1
 
3.3%
ValueCountFrequency (%)
703748539 1
 
3.3%
713914463 1
 
3.3%
715225639 1
 
3.3%
715494538 1
 
3.3%
716579474 1
 
3.3%
720075574 1
 
3.3%
751852674 1
 
3.3%
787693712 1
 
3.3%
999999999999999 22
73.3%
ValueCountFrequency (%)
999999999999999 22
73.3%
787693712 1
 
3.3%
751852674 1
 
3.3%
720075574 1
 
3.3%
716579474 1
 
3.3%
715494538 1
 
3.3%
715225639 1
 
3.3%
713914463 1
 
3.3%
703748539 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
F
18 
M
12 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 18
60.0%
M 12
40.0%

Length

2024-03-13T20:57:58.903631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:57:58.997256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 18
60.0%
m 12
40.0%

연령대코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.666667
Minimum0
Maximum70
Zeros3
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:59.126843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122.5
median40
Q350
95-th percentile60
Maximum70
Range70
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation18.134238
Coefficient of variation (CV)0.50843657
Kurtosis-0.25464033
Mean35.666667
Median Absolute Deviation (MAD)10
Skewness-0.33815712
Sum1070
Variance328.85057
MonotonicityNot monotonic
2024-03-13T20:57:59.254399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
30 6
20.0%
50 6
20.0%
40 6
20.0%
20 5
16.7%
60 3
10.0%
0 3
10.0%
70 1
 
3.3%
ValueCountFrequency (%)
0 3
10.0%
20 5
16.7%
30 6
20.0%
40 6
20.0%
50 6
20.0%
60 3
10.0%
70 1
 
3.3%
ValueCountFrequency (%)
70 1
 
3.3%
60 3
10.0%
50 6
20.0%
40 6
20.0%
30 6
20.0%
20 5
16.7%
0 3
10.0%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000011 × 1011
Minimum1.4000002 × 1011
Maximum1.4000123 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:59.675058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000004 × 1011
median1.4000008 × 1011
Q31.4000012 × 1011
95-th percentile1.4000014 × 1011
Maximum1.4000123 × 1011
Range1209000
Interquartile range (IQR)77500

Descriptive statistics

Standard deviation213724.36
Coefficient of variation (CV)1.5266013 × 10-6
Kurtosis27.801244
Mean1.4000011 × 1011
Median Absolute Deviation (MAD)36000
Skewness5.1832989
Sum4.2000034 × 1012
Variance4.5678102 × 1010
MonotonicityNot monotonic
2024-03-13T20:57:59.794305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
140000102000 2
 
6.7%
140000030000 2
 
6.7%
140000116000 2
 
6.7%
140000120000 2
 
6.7%
140000048000 2
 
6.7%
140000032000 2
 
6.7%
140000140000 2
 
6.7%
140000078000 1
 
3.3%
140000088000 1
 
3.3%
140000124000 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
140000018000 1
3.3%
140000020000 1
3.3%
140000024000 1
3.3%
140000030000 2
6.7%
140000032000 2
6.7%
140000036000 1
3.3%
140000044000 1
3.3%
140000048000 2
6.7%
140000052000 1
3.3%
140000062000 1
3.3%
ValueCountFrequency (%)
140001227000 1
3.3%
140000140000 2
6.7%
140000124000 1
3.3%
140000120000 2
6.7%
140000116000 2
6.7%
140000114000 1
3.3%
140000108000 1
3.3%
140000102000 2
6.7%
140000100000 1
3.3%
140000092000 1
3.3%

결제상품명
Text

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:57:59.996269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length9.0333333
Min length4

Characters and Unicode

Total characters271
Distinct characters65
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)53.3%

Sample

1st row안성사랑카드(통합)
2nd row행복화성지역화폐_화이트
3rd row부천페이
4th row안산사랑상품권 다온(통합)
5th row광명사랑화폐
ValueCountFrequency (%)
안성사랑카드 2
 
5.4%
행복화성지역화폐_화이트 2
 
5.4%
행복화성지역화폐 2
 
5.4%
부천페이 2
 
5.4%
수원페이(통합 2
 
5.4%
안산사랑상품권 2
 
5.4%
이천사랑지역화폐 2
 
5.4%
pay(파주페이 2
 
5.4%
파주 2
 
5.4%
행복화성지역화폐(통합 1
 
2.7%
Other values (18) 18
48.6%
2024-03-13T20:58:00.450929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
화 17
 
6.3%
사 15
 
5.5%
랑 15
 
5.5%
( 12
 
4.4%
) 12
 
4.4%
이 11
 
4.1%
폐 10
 
3.7%
통 9
 
3.3%
성 9
 
3.3%
합 9
 
3.3%
Other values (55) 152
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 219
80.8%
Open Punctuation 12
 
4.4%
Close Punctuation 12
 
4.4%
Lowercase Letter 12
 
4.4%
Space Separator 7
 
2.6%
Uppercase Letter 6
 
2.2%
Connector Punctuation 2
 
0.7%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
화 17
 
7.8%
사 15
 
6.8%
랑 15
 
6.8%
이 11
 
5.0%
폐 10
 
4.6%
통 9
 
4.1%
성 9
 
4.1%
합 9
 
4.1%
페 7
 
3.2%
역 7
 
3.2%
Other values (39) 110
50.2%
Lowercase Letter
ValueCountFrequency (%)
a 4
33.3%
y 3
25.0%
n 1
 
8.3%
h 1
 
8.3%
k 1
 
8.3%
u 1
 
8.3%
o 1
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
P 3
50.0%
T 1
 
16.7%
Y 1
 
16.7%
N 1
 
16.7%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 219
80.8%
Common 34
 
12.5%
Latin 18
 
6.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
화 17
 
7.8%
사 15
 
6.8%
랑 15
 
6.8%
이 11
 
5.0%
폐 10
 
4.6%
통 9
 
4.1%
성 9
 
4.1%
합 9
 
4.1%
페 7
 
3.2%
역 7
 
3.2%
Other values (39) 110
50.2%
Latin
ValueCountFrequency (%)
a 4
22.2%
y 3
16.7%
P 3
16.7%
n 1
 
5.6%
T 1
 
5.6%
h 1
 
5.6%
k 1
 
5.6%
Y 1
 
5.6%
u 1
 
5.6%
o 1
 
5.6%
Common
ValueCountFrequency (%)
( 12
35.3%
) 12
35.3%
7
20.6%
_ 2
 
5.9%
- 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 219
80.8%
ASCII 52
 
19.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
화 17
 
7.8%
사 15
 
6.8%
랑 15
 
6.8%
이 11
 
5.0%
폐 10
 
4.6%
통 9
 
4.1%
성 9
 
4.1%
합 9
 
4.1%
페 7
 
3.2%
역 7
 
3.2%
Other values (39) 110
50.2%
ASCII
ValueCountFrequency (%)
( 12
23.1%
) 12
23.1%
7
13.5%
a 4
 
7.7%
y 3
 
5.8%
P 3
 
5.8%
_ 2
 
3.8%
n 1
 
1.9%
T 1
 
1.9%
h 1
 
1.9%
Other values (6) 6
11.5%

가맹점업종명
Text

MISSING 

Distinct4
Distinct (%)50.0%
Missing22
Missing (%)73.3%
Memory size372.0 B
2024-03-13T20:58:00.618365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.25
Min length2

Characters and Unicode

Total characters42
Distinct characters16
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

Unique2 ?
Unique (%)25.0%

Sample

1st row유통업 영리
2nd row일반휴게음식
3rd row일반휴게음식
4th row유통업 영리
5th row일반휴게음식
ValueCountFrequency (%)
유통업 3
27.3%
영리 3
27.3%
일반휴게음식 3
27.3%
음료식품 1
 
9.1%
의류 1
 
9.1%
2024-03-13T20:58:00.910996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
음 4
 
9.5%
식 4
 
9.5%
유 3
 
7.1%
통 3
 
7.1%
업 3
 
7.1%
3
 
7.1%
영 3
 
7.1%
리 3
 
7.1%
일 3
 
7.1%
반 3
 
7.1%
Other values (6) 10
23.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39
92.9%
Space Separator 3
 
7.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
음 4
10.3%
식 4
10.3%
유 3
7.7%
통 3
7.7%
업 3
7.7%
영 3
7.7%
리 3
7.7%
일 3
7.7%
반 3
7.7%
휴 3
7.7%
Other values (5) 7
17.9%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39
92.9%
Common 3
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
음 4
10.3%
식 4
10.3%
유 3
7.7%
통 3
7.7%
업 3
7.7%
영 3
7.7%
리 3
7.7%
일 3
7.7%
반 3
7.7%
휴 3
7.7%
Other values (5) 7
17.9%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39
92.9%
ASCII 3
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
음 4
10.3%
식 4
10.3%
유 3
7.7%
통 3
7.7%
업 3
7.7%
영 3
7.7%
리 3
7.7%
일 3
7.7%
반 3
7.7%
휴 3
7.7%
Other values (5) 7
17.9%
ASCII
ValueCountFrequency (%)
3
100.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing22
Missing (%)73.3%
Infinite0
Infinite (%)0.0%
Mean15481.125
Minimum10346
Maximum18412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:58:01.025766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10346
5-th percentile11260.2
Q113902.25
median16088
Q317729.5
95-th percentile18320.65
Maximum18412
Range8066
Interquartile range (IQR)3827.25

Descriptive statistics

Standard deviation2919.6838
Coefficient of variation (CV)0.18859636
Kurtosis-0.64087179
Mean15481.125
Median Absolute Deviation (MAD)1967
Skewness-0.69940327
Sum123849
Variance8524553.3
MonotonicityNot monotonic
2024-03-13T20:58:01.135212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
14598 1
 
3.3%
14217 1
 
3.3%
10346 1
 
3.3%
18412 1
 
3.3%
17578 1
 
3.3%
12958 1
 
3.3%
17589 1
 
3.3%
18151 1
 
3.3%
(Missing) 22
73.3%
ValueCountFrequency (%)
10346 1
3.3%
12958 1
3.3%
14217 1
3.3%
14598 1
3.3%
17578 1
3.3%
17589 1
3.3%
18151 1
3.3%
18412 1
3.3%
ValueCountFrequency (%)
18412 1
3.3%
18151 1
3.3%
17589 1
3.3%
17578 1
3.3%
14598 1
3.3%
14217 1
3.3%
12958 1
3.3%
10346 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
22 
경기도
8 

Length

Max length4
Median length4
Mean length3.7333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 22
73.3%
경기도 8
 
26.7%

Length

2024-03-13T20:58:01.303562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:58:01.434983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 22
73.3%
경기도 8
 
26.7%

시군구명
Text

MISSING 

Distinct7
Distinct (%)87.5%
Missing22
Missing (%)73.3%
Memory size372.0 B
2024-03-13T20:58:01.575419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.625
Min length3

Characters and Unicode

Total characters29
Distinct characters18
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 (%)75.0%

Sample

1st row부천시
2nd row광명시
3rd row고양시 일산서구
4th row화성시
5th row안성시
ValueCountFrequency (%)
안성시 2
22.2%
부천시 1
11.1%
광명시 1
11.1%
고양시 1
11.1%
일산서구 1
11.1%
화성시 1
11.1%
하남시 1
11.1%
오산시 1
11.1%
2024-03-13T20:58:01.844725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 8
27.6%
성 3
 
10.3%
안 2
 
6.9%
산 2
 
6.9%
일 1
 
3.4%
남 1
 
3.4%
하 1
 
3.4%
화 1
 
3.4%
구 1
 
3.4%
서 1
 
3.4%
Other values (8) 8
27.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28
96.6%
Space Separator 1
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 8
28.6%
성 3
 
10.7%
안 2
 
7.1%
산 2
 
7.1%
일 1
 
3.6%
남 1
 
3.6%
하 1
 
3.6%
화 1
 
3.6%
구 1
 
3.6%
서 1
 
3.6%
Other values (7) 7
25.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28
96.6%
Common 1
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 8
28.6%
성 3
 
10.7%
안 2
 
7.1%
산 2
 
7.1%
일 1
 
3.6%
남 1
 
3.6%
하 1
 
3.6%
화 1
 
3.6%
구 1
 
3.6%
서 1
 
3.6%
Other values (7) 7
25.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28
96.6%
ASCII 1
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 8
28.6%
성 3
 
10.7%
안 2
 
7.1%
산 2
 
7.1%
일 1
 
3.6%
남 1
 
3.6%
하 1
 
3.6%
화 1
 
3.6%
구 1
 
3.6%
서 1
 
3.6%
Other values (7) 7
25.0%
ASCII
ValueCountFrequency (%)
1
100.0%

읍면동명
Text

MISSING 

Distinct8
Distinct (%)100.0%
Missing22
Missing (%)73.3%
Memory size372.0 B
2024-03-13T20:58:02.022260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.875
Min length2

Characters and Unicode

Total characters23
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)100.0%

Sample

1st row상동
2nd row광명동
3rd row일산동
4th row병점동
5th row석정동
ValueCountFrequency (%)
상동 1
12.5%
광명동 1
12.5%
일산동 1
12.5%
병점동 1
12.5%
석정동 1
12.5%
신장동 1
12.5%
대천동 1
12.5%
청호동 1
12.5%
2024-03-13T20:58:02.321165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 8
34.8%
상 1
 
4.3%
광 1
 
4.3%
명 1
 
4.3%
일 1
 
4.3%
산 1
 
4.3%
병 1
 
4.3%
점 1
 
4.3%
석 1
 
4.3%
정 1
 
4.3%
Other values (6) 6
26.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8
34.8%
상 1
 
4.3%
광 1
 
4.3%
명 1
 
4.3%
일 1
 
4.3%
산 1
 
4.3%
병 1
 
4.3%
점 1
 
4.3%
석 1
 
4.3%
정 1
 
4.3%
Other values (6) 6
26.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8
34.8%
상 1
 
4.3%
광 1
 
4.3%
명 1
 
4.3%
일 1
 
4.3%
산 1
 
4.3%
병 1
 
4.3%
점 1
 
4.3%
석 1
 
4.3%
정 1
 
4.3%
Other values (6) 6
26.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 8
34.8%
상 1
 
4.3%
광 1
 
4.3%
명 1
 
4.3%
일 1
 
4.3%
산 1
 
4.3%
병 1
 
4.3%
점 1
 
4.3%
석 1
 
4.3%
정 1
 
4.3%
Other values (6) 6
26.1%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9515
Minimum0
Maximum37.688
Zeros22
Zeros (%)73.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:58:02.447256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q327.75525
95-th percentile37.51595
Maximum37.688
Range37.688
Interquartile range (IQR)27.75525

Descriptive statistics

Standard deviation16.785306
Coefficient of variation (CV)1.6867112
Kurtosis-0.82300669
Mean9.9515
Median Absolute Deviation (MAD)0
Skewness1.1118732
Sum298.545
Variance281.74651
MonotonicityNot monotonic
2024-03-13T20:58:02.629095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.0 22
73.3%
37.489 1
 
3.3%
37.478 1
 
3.3%
37.688 1
 
3.3%
37.204 1
 
3.3%
37.01 1
 
3.3%
37.538 1
 
3.3%
37.007 1
 
3.3%
37.131 1
 
3.3%
ValueCountFrequency (%)
0.0 22
73.3%
37.007 1
 
3.3%
37.01 1
 
3.3%
37.131 1
 
3.3%
37.204 1
 
3.3%
37.478 1
 
3.3%
37.489 1
 
3.3%
37.538 1
 
3.3%
37.688 1
 
3.3%
ValueCountFrequency (%)
37.688 1
 
3.3%
37.538 1
 
3.3%
37.489 1
 
3.3%
37.478 1
 
3.3%
37.204 1
 
3.3%
37.131 1
 
3.3%
37.01 1
 
3.3%
37.007 1
 
3.3%
0.0 22
73.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.874567
Minimum0
Maximum127.271
Zeros22
Zeros (%)73.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:58:02.769166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q395.06325
95-th percentile127.2356
Maximum127.271
Range127.271
Interquartile range (IQR)95.06325

Descriptive statistics

Standard deviation57.135029
Coefficient of variation (CV)1.6866645
Kurtosis-0.82381461
Mean33.874567
Median Absolute Deviation (MAD)0
Skewness1.1116754
Sum1016.237
Variance3264.4116
MonotonicityNot monotonic
2024-03-13T20:58:02.874224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.0 22
73.3%
126.751 1
 
3.3%
126.858 1
 
3.3%
126.773 1
 
3.3%
127.035 1
 
3.3%
127.259 1
 
3.3%
127.207 1
 
3.3%
127.271 1
 
3.3%
127.083 1
 
3.3%
ValueCountFrequency (%)
0.0 22
73.3%
126.751 1
 
3.3%
126.773 1
 
3.3%
126.858 1
 
3.3%
127.035 1
 
3.3%
127.083 1
 
3.3%
127.207 1
 
3.3%
127.259 1
 
3.3%
127.271 1
 
3.3%
ValueCountFrequency (%)
127.271 1
 
3.3%
127.259 1
 
3.3%
127.207 1
 
3.3%
127.083 1
 
3.3%
127.035 1
 
3.3%
126.858 1
 
3.3%
126.773 1
 
3.3%
126.751 1
 
3.3%
0.0 22
73.3%

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%
2024-03-13T20:58:02.990179image/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%
Mean3352
Minimum0
Maximum22000
Zeros22
Zeros (%)73.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:58:03.094702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34125
95-th percentile17397
Maximum22000
Range22000
Interquartile range (IQR)4125

Descriptive statistics

Standard deviation6447.1017
Coefficient of variation (CV)1.9233597
Kurtosis2.2583523
Mean3352
Median Absolute Deviation (MAD)0
Skewness1.8527529
Sum100560
Variance41565120
MonotonicityNot monotonic
2024-03-13T20:58:03.204435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 22
73.3%
5500 1
 
3.3%
6000 1
 
3.3%
16000 1
 
3.3%
18000 1
 
3.3%
7900 1
 
3.3%
8500 1
 
3.3%
22000 1
 
3.3%
16660 1
 
3.3%
ValueCountFrequency (%)
0 22
73.3%
5500 1
 
3.3%
6000 1
 
3.3%
7900 1
 
3.3%
8500 1
 
3.3%
16000 1
 
3.3%
16660 1
 
3.3%
18000 1
 
3.3%
22000 1
 
3.3%
ValueCountFrequency (%)
22000 1
 
3.3%
18000 1
 
3.3%
16660 1
 
3.3%
16000 1
 
3.3%
8500 1
 
3.3%
7900 1
 
3.3%
6000 1
 
3.3%
5500 1
 
3.3%
0 22
73.3%

Interactions

2024-03-13T20:57:56.059993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:50.191631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:51.039893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:51.846545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:52.644712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:53.416477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:54.532565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:55.309832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:56.137560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:50.276966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:51.124291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:51.937492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:52.737538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:53.829408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:54.620287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:55.395273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:56.231138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:50.371577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:51.213933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:52.047751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:52.828821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:53.923877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:54.713742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:55.478505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:56.314619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:50.491754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:51.317978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:52.143165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:52.929753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:54.043968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:54.815904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:55.581660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:56.400199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:50.592605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:51.450147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:52.244724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:53.034646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:54.151523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:54.944103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:55.675387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:56.483264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:50.731446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:51.576112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:52.348914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:53.153345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:54.250621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:55.066220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:55.758705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:56.558808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:50.845126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:51.667467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:52.448613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:53.247265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:54.343093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:55.149795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:55.883219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:56.633783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:50.952040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:51.766726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:52.550338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:53.333272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:54.427407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:55.228128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:55.986940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:58:03.317912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시군구명읍면동명위도경도사용여부결제금액
카드번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
회원코드1.0001.0000.6110.4360.4350.0000.9390.0000.3640.8531.0000.5130.5130.5130.494
가맹점번호1.0000.6111.0000.0000.4180.1790.535NaNNaNNaNNaN0.9880.9880.9881.000
성별코드1.0000.4360.0001.0000.3680.1940.0000.0000.0000.0001.0000.0000.0000.0000.363
연령대코드1.0000.4350.4180.3681.0000.0000.9040.4530.7251.0001.0000.4360.4360.4360.000
결제상품ID1.0000.0000.1790.1940.0001.0001.0001.0001.0001.0001.0000.1560.1560.1560.606
결제상품명1.0000.9390.5350.0000.9041.0001.0000.4601.0001.0001.0000.6780.6780.6780.000
가맹점업종명1.0000.000NaN0.0000.4531.0000.4601.0000.7080.4601.000NaNNaNNaN0.397
가맹점우편번호1.0000.364NaN0.0000.7251.0001.0000.7081.0001.0001.000NaNNaNNaN0.000
시군구명1.0000.853NaN0.0001.0001.0001.0000.4601.0001.0001.000NaNNaNNaN0.657
읍면동명1.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.000NaNNaNNaN1.000
위도1.0000.5130.9880.0000.4360.1560.678NaNNaNNaNNaN1.0000.9900.9901.000
경도1.0000.5130.9880.0000.4360.1560.678NaNNaNNaNNaN0.9901.0000.9901.000
사용여부1.0000.5130.9880.0000.4360.1560.678NaNNaNNaNNaN0.9900.9901.0001.000
결제금액1.0000.4941.0000.3630.0000.6060.0000.3970.0000.6571.0001.0001.0001.0001.000
2024-03-13T20:58:03.500750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부성별코드시도명
사용여부1.0000.0001.000
성별코드0.0001.0001.000
시도명1.0001.0001.000
2024-03-13T20:58:03.612668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드시도명사용여부
회원코드1.0000.0480.2500.0790.048-0.0290.023-0.0250.3731.0000.412
가맹점번호0.0481.000-0.4720.376-0.476-0.948-0.984-0.9860.0001.0000.912
연령대코드0.250-0.4721.000-0.4690.1640.4700.4730.4750.3481.0000.417
결제상품ID0.0790.376-0.4691.0000.491-0.339-0.320-0.3230.3181.0000.221
가맹점우편번호0.048-0.4760.1640.4911.000-0.7620.3570.5240.0001.0001.000
위도-0.029-0.9480.470-0.339-0.7621.0000.9470.9560.0001.0000.912
경도0.023-0.9840.473-0.3200.3570.9471.0000.9850.0001.0000.912
결제금액-0.025-0.9860.475-0.3230.5240.9560.9851.0000.2301.0000.926
성별코드0.3730.0000.3480.3180.0000.0000.0000.2301.0001.0000.000
시도명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사용여부0.4120.9120.4170.2211.0000.9120.9120.9260.0001.0001.000

Missing values

2024-03-13T20:57:56.756725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:57:56.988804image/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.
2024-03-13T20:57:57.146301image/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
02022-04-042022-04-10AdxA+j5yFlHgRvbRnc/05YtpOwvqvcnIKjdEV6lQWZU=3035870154999999999999999F30140000078000안성사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
12022-04-042022-04-10Ae/N9yQGf0Ktc3Oj5ScinhAV0OJNXiVn3O9Is2m/XLQ=3016513305999999999999999M30140000140000행복화성지역화폐_화이트<NA><NA><NA><NA><NA>0.00.0N0
22022-04-042022-04-10AdzCPxUX56P2mWCw4lZBdShitrOsy6e1QxpFkHu2rHI=3032686262787693712F60140000030000부천페이유통업 영리14598경기도부천시상동37.489126.751Y5500
32022-04-042022-04-10Ae6nUK1SSUW4bsyUfxjkKW3rj02+VYwIzJHq4wQfUqA=3023434177999999999999999F50140000100000안산사랑상품권 다온(통합)<NA><NA><NA><NA><NA>0.00.0N0
42022-04-042022-04-10AeHsWf9uZHDxRQtR1ipZ3zxZ+d4c4idzJOegBXtqqpo=3011143152713914463M40140000020000광명사랑화폐일반휴게음식14217경기도광명시광명동37.478126.858Y6000
52022-04-042022-04-10Af/v1+MKuAOleu8LNRtQG1uwGh87g2zyZ9tKRoa17Mo=3024968238999999999999999F50140000036000양주사랑카드<NA><NA><NA><NA><NA>0.00.0N0
62022-04-042022-04-10AeMSI53LbTU7hlr+xKFlvZ3oyJmfvYbLgsJD0pbYOS4=3018607147999999999999999F30140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
72022-04-042022-04-10Af6GRL61zR9olW4bCodS519dclywPCFIr3+dMIv3mhg=3037101162999999999999999F70140000108000동두천사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
82022-04-042022-04-10AehHNhNVxZKeouVm9pD+KNeIZnK4t3p8sO2Xh1mE7so=3017849305999999999999999F50140000024000광주사랑카드<NA><NA><NA><NA><NA>0.00.0N0
92022-04-042022-04-10Ae2jgCDn4H/gs4W7fR2AD1twhTSW7cUsXYgL1VmmUpE=3014053130720075574F50140000018000고양페이카드일반휴게음식10346경기도고양시 일산서구일산동37.688126.773Y16000
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202022-04-042022-04-10AgaKedGaktpu0fqApJm05rmHqcDlZsnLtroxDHxTYqc=3031566242751852674M40140001227000하남하머니(여성청소년)음료식품12958경기도하남시신장동37.538127.207Y8500
212022-04-042022-04-10AkD0YgmSazAT4wcZ+SbzezPXrPK2ZHul8qA7mSW6cc4=3017864364999999999999999F0140000120000파주 Pay(파주페이)<NA><NA><NA><NA><NA>0.00.0N0
222022-04-042022-04-10AgpJR72FqE4bMsz6fzc4SLQ9WV6n5U2BgRXKBP9bIpo=3039083291999999999999999M40140000062000의왕사랑상품권(통합)<NA><NA><NA><NA><NA>0.00.0N0
232022-04-042022-04-10AkMfP2Ub4bvnVv7SThQ0oJTsFtPscH46iUzCMMrhMVU=3019629756703748539F50140000032000안성사랑카드의류17589경기도안성시대천동37.007127.271Y22000
242022-04-042022-04-10Ah2v7ezq9x65KdjyW64RgM1TPBk4mdSLBWogZ6zbo6E=3024547130715225639F60140000044000오산화폐 오색전유통업 영리18151경기도오산시청호동37.131127.083Y16660
252022-04-042022-04-10Af4nOkIxrL05LQAQrggJLyTf2xPVRBBAy4VK+9OoBxo=3017280175999999999999999F0140000120000파주 Pay(파주페이)<NA><NA><NA><NA><NA>0.00.0N0
262022-04-042022-04-10AhAYBxLOOoDfInxSU7TUuk8dLaP2ZB4dnzliVynMWv0=3019506293999999999999999M20140000114000Thank You Pay-N<NA><NA><NA><NA><NA>0.00.0N0
272022-04-042022-04-10AkrmyiUFrd8biLWJS+EJVJSHB3LaWNuf04JbYrt9Uko=3019984308999999999999999F0140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
282022-04-042022-04-10AhCVN8NFg0W9xqav1IE3JiyamjfvENpTB9IAmYuSj5E=3021539218999999999999999F30140000088000광명사랑화폐(통합)<NA><NA><NA><NA><NA>0.00.0N0
292022-04-042022-04-10AkzXtBO80yFBlE6DtReH29ZYwDz5kNDSR24VOx8zScI=3026593197999999999999999M60140000102000수원페이(통합)<NA><NA><NA><NA><NA>0.00.0N0