Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells90
Missing cells (%)16.7%
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/6a5b0627-bdcb-4074-993d-40c462c66472

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
연령대코드 is highly overall correlated with 성별코드High correlation
결제상품ID is highly overall correlated with 가맹점우편번호High correlation
가맹점우편번호 is highly overall correlated with 결제상품ID and 2 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점번호 and 3 other fieldsHigh correlation
결제금액 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
성별코드 is highly overall correlated with 연령대코드High correlation
가맹점업종명 has 22 (73.3%) missing valuesMissing
가맹점우편번호 has 22 (73.3%) missing valuesMissing
시군구명 has 23 (76.7%) missing valuesMissing
읍면동명 has 23 (76.7%) missing valuesMissing
카드번호 has unique valuesUnique
회원코드 has unique valuesUnique
연령대코드 has 9 (30.0%) zerosZeros
위도 has 23 (76.7%) zerosZeros
경도 has 23 (76.7%) zerosZeros
결제금액 has 21 (70.0%) zerosZeros

Reproduction

Analysis started2024-03-13 12:00:56.961554
Analysis finished2024-03-13 12:01:04.846265
Duration7.88 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
2023-03-06
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-03-06
2nd row2023-03-06
3rd row2023-03-06
4th row2023-03-06
5th row2023-03-06

Common Values

ValueCountFrequency (%)
2023-03-06 30
100.0%

Length

2024-03-13T21:01:04.941387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:01:05.042739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-03-06 30
100.0%

정책주간결제종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-03-12
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-03-12
2nd row2023-03-12
3rd row2023-03-12
4th row2023-03-12
5th row2023-03-12

Common Values

ValueCountFrequency (%)
2023-03-12 30
100.0%

Length

2024-03-13T21:01:05.134488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:01:05.220737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-03-12 30
100.0%

카드번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T21:01:05.431418image/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 rowyYASP+ZSRyn1ESZZFaihh0G3rR60tSN/Pa/PZ6EHBV0=
2nd row2zEZykEUU2UezqpmQx08Mgq8RmtXkru5Y+CPT8zRzbk=
3rd rowwTNG9WVREjHaBU1nmQJjjZCs7SyOhz9bs0fb7tzF6kA=
4th rowyY/039t9otFh1Q7IZ2eLtnO7jk8g0NTnNujLOUxseqs=
5th rowyXxxTux08yLc5+FBg+Z20KMflE9I05m+Ct8kdhRo2Kw=
ValueCountFrequency (%)
yyasp+zsryn1eszzfaihh0g3rr60tsn/pa/pz6ehbv0 1
 
3.3%
2zezykeuu2uezqpmqx08mgq8rmtxkru5y+cpt8zrzbk 1
 
3.3%
yxbq6/wdrgmlz4v8ke/tah1ubw4yzqe1qkkpie8utdu 1
 
3.3%
yxgzt6nrdd+wv1nogjso44sr3sox1bvramsvnczrjyc 1
 
3.3%
yxgcfameamajdnht8enaj8fkcd+2c+f5an4ujurwx2s 1
 
3.3%
yxnfhhvztkpr/tntw3ucn4kj29plmqej5lxsm1sye5i 1
 
3.3%
yxp5nsybfyxqhw47pp4yyxrhxvmeojiumffg2eytulm 1
 
3.3%
yxv3u9kaxhjpozcnnomw9wajoyexdssiqzk1mu0f27o 1
 
3.3%
yxwghvbjcdu+f6neq5lkyb2txyqaxw+gsyosjrdzxwe 1
 
3.3%
yxx+f13ne0lmu1fhcaid62dkly75ayf7eucvu58jwue 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T21:01:05.815010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
y 49
 
3.7%
X 43
 
3.3%
U 35
 
2.7%
f 30
 
2.3%
= 30
 
2.3%
Z 28
 
2.1%
s 27
 
2.0%
7 27
 
2.0%
t 27
 
2.0%
n 27
 
2.0%
Other values (55) 997
75.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 527
39.9%
Lowercase Letter 521
39.5%
Decimal Number 201
 
15.2%
Math Symbol 54
 
4.1%
Other Punctuation 17
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y 49
 
9.4%
f 30
 
5.8%
s 27
 
5.2%
t 27
 
5.2%
n 27
 
5.2%
k 26
 
5.0%
b 22
 
4.2%
r 22
 
4.2%
e 21
 
4.0%
m 19
 
3.6%
Other values (16) 251
48.2%
Uppercase Letter
ValueCountFrequency (%)
X 43
 
8.2%
U 35
 
6.6%
Z 28
 
5.3%
V 25
 
4.7%
B 25
 
4.7%
E 24
 
4.6%
M 23
 
4.4%
Y 22
 
4.2%
P 22
 
4.2%
S 22
 
4.2%
Other values (16) 258
49.0%
Decimal Number
ValueCountFrequency (%)
7 27
13.4%
2 22
10.9%
8 22
10.9%
4 22
10.9%
9 21
10.4%
5 20
10.0%
1 19
9.5%
0 19
9.5%
6 17
8.5%
3 12
6.0%
Math Symbol
ValueCountFrequency (%)
= 30
55.6%
+ 24
44.4%
Other Punctuation
ValueCountFrequency (%)
/ 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1048
79.4%
Common 272
 
20.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
y 49
 
4.7%
X 43
 
4.1%
U 35
 
3.3%
f 30
 
2.9%
Z 28
 
2.7%
s 27
 
2.6%
t 27
 
2.6%
n 27
 
2.6%
k 26
 
2.5%
V 25
 
2.4%
Other values (42) 731
69.8%
Common
ValueCountFrequency (%)
= 30
11.0%
7 27
9.9%
+ 24
8.8%
2 22
8.1%
8 22
8.1%
4 22
8.1%
9 21
7.7%
5 20
7.4%
1 19
7.0%
0 19
7.0%
Other values (3) 46
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
y 49
 
3.7%
X 43
 
3.3%
U 35
 
2.7%
f 30
 
2.3%
= 30
 
2.3%
Z 28
 
2.1%
s 27
 
2.0%
7 27
 
2.0%
t 27
 
2.0%
n 27
 
2.0%
Other values (55) 997
75.5%

회원코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0207489 × 109
Minimum3.0019277 × 109
Maximum3.0614889 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:05.967562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0019277 × 109
5-th percentile3.0030892 × 109
Q13.0106091 × 109
median3.0181503 × 109
Q33.0214715 × 109
95-th percentile3.0588341 × 109
Maximum3.0614889 × 109
Range59561212
Interquartile range (IQR)10862368

Descriptive statistics

Standard deviation15791218
Coefficient of variation (CV)0.005227584
Kurtosis1.8311912
Mean3.0207489 × 109
Median Absolute Deviation (MAD)6587644.5
Skewness1.4411242
Sum9.0622466 × 1010
Variance2.4936258 × 1014
MonotonicityNot monotonic
2024-03-13T21:01:06.108319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3024075432 1
 
3.3%
3016419264 1
 
3.3%
3032222150 1
 
3.3%
3004994165 1
 
3.3%
3013959212 1
 
3.3%
3061488870 1
 
3.3%
3021795328 1
 
3.3%
3018539008 1
 
3.3%
3017398571 1
 
3.3%
3037296241 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3001927658 1
3.3%
3002634199 1
3.3%
3003645270 1
3.3%
3004994165 1
3.3%
3005736145 1
3.3%
3006525101 1
3.3%
3009084138 1
3.3%
3010512115 1
3.3%
3010900143 1
3.3%
3013959212 1
3.3%
ValueCountFrequency (%)
3061488870 1
3.3%
3060549536 1
3.3%
3056737549 1
3.3%
3037296241 1
3.3%
3033735399 1
3.3%
3032222150 1
3.3%
3024075432 1
3.3%
3021795328 1
3.3%
3020499974 1
3.3%
3020387274 1
3.3%

가맹점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0000022 × 1014
Minimum7.0340378 × 108
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:06.291669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0340378 × 108
5-th percentile7.10568 × 108
Q17.5758574 × 108
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.999993 × 1014
Interquartile range (IQR)9.9999924 × 1014

Descriptive statistics

Standard deviation4.6609126 × 1014
Coefficient of variation (CV)0.66584445
Kurtosis-1.2421265
Mean7.0000022 × 1014
Median Absolute Deviation (MAD)0
Skewness-0.91950043
Sum2.1000007 × 1016
Variance2.1724106 × 1029
MonotonicityNot monotonic
2024-03-13T21:01:06.446696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
999999999999999 21
70.0%
709319675 1
 
3.3%
703403776 1
 
3.3%
750528651 1
 
3.3%
728536352 1
 
3.3%
713991713 1
 
3.3%
712093737 1
 
3.3%
722866170 1
 
3.3%
712389235 1
 
3.3%
778757021 1
 
3.3%
ValueCountFrequency (%)
703403776 1
 
3.3%
709319675 1
 
3.3%
712093737 1
 
3.3%
712389235 1
 
3.3%
713991713 1
 
3.3%
722866170 1
 
3.3%
728536352 1
 
3.3%
750528651 1
 
3.3%
778757021 1
 
3.3%
999999999999999 21
70.0%
ValueCountFrequency (%)
999999999999999 21
70.0%
778757021 1
 
3.3%
750528651 1
 
3.3%
728536352 1
 
3.3%
722866170 1
 
3.3%
713991713 1
 
3.3%
712389235 1
 
3.3%
712093737 1
 
3.3%
709319675 1
 
3.3%
703403776 1
 
3.3%

성별코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
F
21 
M
9 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 21
70.0%
M 9
30.0%

Length

2024-03-13T21:01:06.578537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:01:06.672490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 21
70.0%
m 9
30.0%

연령대코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.333333
Minimum0
Maximum60
Zeros9
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:06.760019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30
Q340
95-th percentile55.5
Maximum60
Range60
Interquartile range (IQR)40

Descriptive statistics

Standard deviation20.758602
Coefficient of variation (CV)0.78830133
Kurtosis-1.3615305
Mean26.333333
Median Absolute Deviation (MAD)20
Skewness-0.041540621
Sum790
Variance430.91954
MonotonicityNot monotonic
2024-03-13T21:01:06.853404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 9
30.0%
20 5
16.7%
50 5
16.7%
40 5
16.7%
30 4
13.3%
60 2
 
6.7%
ValueCountFrequency (%)
0 9
30.0%
20 5
16.7%
30 4
13.3%
40 5
16.7%
50 5
16.7%
60 2
 
6.7%
ValueCountFrequency (%)
60 2
 
6.7%
50 5
16.7%
40 5
16.7%
30 4
13.3%
20 5
16.7%
0 9
30.0%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4000008 × 1011
Minimum1.4000002 × 1011
Maximum1.4000012 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:07.003461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000002 × 1011
Q11.4000004 × 1011
median1.4000008 × 1011
Q31.4000011 × 1011
95-th percentile1.4000012 × 1011
Maximum1.4000012 × 1011
Range106000
Interquartile range (IQR)69500

Descriptive statistics

Standard deviation37310.81
Coefficient of variation (CV)2.6650564 × 10-7
Kurtosis-1.586813
Mean1.4000008 × 1011
Median Absolute Deviation (MAD)32000
Skewness-0.16255988
Sum4.2000023 × 1012
Variance1.3920966 × 109
MonotonicityNot monotonic
2024-03-13T21:01:07.130285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
140000116000 3
 
10.0%
140000046000 2
 
6.7%
140000030000 2
 
6.7%
140000114000 2
 
6.7%
140000018000 2
 
6.7%
140000096000 2
 
6.7%
140000122000 2
 
6.7%
140000044000 1
 
3.3%
140000112000 1
 
3.3%
140000118000 1
 
3.3%
Other values (12) 12
40.0%
ValueCountFrequency (%)
140000018000 2
6.7%
140000022000 1
3.3%
140000030000 2
6.7%
140000034000 1
3.3%
140000042000 1
3.3%
140000044000 1
3.3%
140000046000 2
6.7%
140000052000 1
3.3%
140000054000 1
3.3%
140000058000 1
3.3%
ValueCountFrequency (%)
140000124000 1
 
3.3%
140000122000 2
6.7%
140000118000 1
 
3.3%
140000116000 3
10.0%
140000114000 2
6.7%
140000112000 1
 
3.3%
140000104000 1
 
3.3%
140000096000 2
6.7%
140000090000 1
 
3.3%
140000088000 1
 
3.3%

결제상품명
Text

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T21:01:07.316833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length8.8666667
Min length4

Characters and Unicode

Total characters266
Distinct characters68
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)50.0%

Sample

1st row오산화폐 오색전
2nd row가평GP페이(통합)
3rd row용인와이페이
4th row부천페이
5th rowThank You Pay-N(통합)
ValueCountFrequency (%)
행복화성지역화폐 3
 
7.3%
thank 3
 
7.3%
you 3
 
7.3%
pay(파주페이)(통합 2
 
4.9%
부천페이 2
 
4.9%
의정부사랑카드 2
 
4.9%
용인와이페이 2
 
4.9%
파주 2
 
4.9%
고양페이카드 2
 
4.9%
pay-n 2
 
4.9%
Other values (18) 18
43.9%
2024-03-13T21:01:07.683487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 16
 
6.0%
페 13
 
4.9%
) 11
 
4.1%
11
 
4.1%
( 11
 
4.1%
화 9
 
3.4%
합 9
 
3.4%
통 9
 
3.4%
사 8
 
3.0%
랑 8
 
3.0%
Other values (58) 161
60.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 186
69.9%
Lowercase Letter 28
 
10.5%
Uppercase Letter 16
 
6.0%
Close Punctuation 11
 
4.1%
Space Separator 11
 
4.1%
Open Punctuation 11
 
4.1%
Dash Punctuation 3
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 16
 
8.6%
페 13
 
7.0%
화 9
 
4.8%
합 9
 
4.8%
통 9
 
4.8%
사 8
 
4.3%
랑 8
 
4.3%
천 7
 
3.8%
폐 6
 
3.2%
카 6
 
3.2%
Other values (42) 95
51.1%
Lowercase Letter
ValueCountFrequency (%)
a 8
28.6%
y 5
17.9%
h 3
 
10.7%
u 3
 
10.7%
o 3
 
10.7%
k 3
 
10.7%
n 3
 
10.7%
Uppercase Letter
ValueCountFrequency (%)
P 6
37.5%
Y 3
18.8%
N 3
18.8%
T 3
18.8%
G 1
 
6.2%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 185
69.5%
Latin 44
 
16.5%
Common 36
 
13.5%
Han 1
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 16
 
8.6%
페 13
 
7.0%
화 9
 
4.9%
합 9
 
4.9%
통 9
 
4.9%
사 8
 
4.3%
랑 8
 
4.3%
천 7
 
3.8%
폐 6
 
3.2%
카 6
 
3.2%
Other values (41) 94
50.8%
Latin
ValueCountFrequency (%)
a 8
18.2%
P 6
13.6%
y 5
11.4%
h 3
 
6.8%
u 3
 
6.8%
o 3
 
6.8%
Y 3
 
6.8%
k 3
 
6.8%
n 3
 
6.8%
N 3
 
6.8%
Other values (2) 4
9.1%
Common
ValueCountFrequency (%)
) 11
30.6%
11
30.6%
( 11
30.6%
- 3
 
8.3%
Han
ValueCountFrequency (%)
愛 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 185
69.5%
ASCII 80
30.1%
CJK 1
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 16
 
8.6%
페 13
 
7.0%
화 9
 
4.9%
합 9
 
4.9%
통 9
 
4.9%
사 8
 
4.3%
랑 8
 
4.3%
천 7
 
3.8%
폐 6
 
3.2%
카 6
 
3.2%
Other values (41) 94
50.8%
ASCII
ValueCountFrequency (%)
) 11
13.8%
11
13.8%
( 11
13.8%
a 8
10.0%
P 6
 
7.5%
y 5
 
6.2%
h 3
 
3.8%
- 3
 
3.8%
u 3
 
3.8%
o 3
 
3.8%
Other values (6) 16
20.0%
CJK
ValueCountFrequency (%)
愛 1
100.0%

가맹점업종명
Text

MISSING 

Distinct6
Distinct (%)75.0%
Missing22
Missing (%)73.3%
Memory size372.0 B
2024-03-13T21:01:07.876761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4
Min length2

Characters and Unicode

Total characters32
Distinct characters22
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

Unique5 ?
Unique (%)62.5%

Sample

1st row음료식품
2nd row의원
3rd row유통업 영리
4th row음료식품
5th row음료식품
ValueCountFrequency (%)
음료식품 3
33.3%
의원 1
 
11.1%
유통업 1
 
11.1%
영리 1
 
11.1%
일반휴게음식 1
 
11.1%
서적문구 1
 
11.1%
약국 1
 
11.1%
2024-03-13T21:01:08.139534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
음 4
 
12.5%
식 4
 
12.5%
품 3
 
9.4%
료 3
 
9.4%
반 1
 
3.1%
약 1
 
3.1%
구 1
 
3.1%
문 1
 
3.1%
적 1
 
3.1%
서 1
 
3.1%
Other values (12) 12
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31
96.9%
Space Separator 1
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
음 4
 
12.9%
식 4
 
12.9%
품 3
 
9.7%
료 3
 
9.7%
반 1
 
3.2%
약 1
 
3.2%
구 1
 
3.2%
문 1
 
3.2%
적 1
 
3.2%
서 1
 
3.2%
Other values (11) 11
35.5%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31
96.9%
Common 1
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
음 4
 
12.9%
식 4
 
12.9%
품 3
 
9.7%
료 3
 
9.7%
반 1
 
3.2%
약 1
 
3.2%
구 1
 
3.2%
문 1
 
3.2%
적 1
 
3.2%
서 1
 
3.2%
Other values (11) 11
35.5%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31
96.9%
ASCII 1
 
3.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
음 4
 
12.9%
식 4
 
12.9%
품 3
 
9.7%
료 3
 
9.7%
반 1
 
3.2%
약 1
 
3.2%
구 1
 
3.2%
문 1
 
3.2%
적 1
 
3.2%
서 1
 
3.2%
Other values (11) 11
35.5%
ASCII
ValueCountFrequency (%)
1
100.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing22
Missing (%)73.3%
Infinite0
Infinite (%)0.0%
Mean14535.875
Minimum10900
Maximum18133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:08.257323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10900
5-th percentile10991.35
Q112104.5
median14820.5
Q316998.75
95-th percentile17754.65
Maximum18133
Range7233
Interquartile range (IQR)4894.25

Descriptive statistics

Standard deviation2839.4145
Coefficient of variation (CV)0.1953384
Kurtosis-1.8900438
Mean14535.875
Median Absolute Deviation (MAD)2316.5
Skewness-0.14311265
Sum116287
Variance8062275
MonotonicityNot monotonic
2024-03-13T21:01:08.403128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
18133 1
 
3.3%
12419 1
 
3.3%
16981 1
 
3.3%
10900 1
 
3.3%
15804 1
 
3.3%
11161 1
 
3.3%
13837 1
 
3.3%
17052 1
 
3.3%
(Missing) 22
73.3%
ValueCountFrequency (%)
10900 1
3.3%
11161 1
3.3%
12419 1
3.3%
13837 1
3.3%
15804 1
3.3%
16981 1
3.3%
17052 1
3.3%
18133 1
3.3%
ValueCountFrequency (%)
18133 1
3.3%
17052 1
3.3%
16981 1
3.3%
15804 1
3.3%
13837 1
3.3%
12419 1
3.3%
11161 1
3.3%
10900 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
22 
경기도
7 
NONE
 
1

Length

Max length4
Median length4
Mean length3.7666667
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 22
73.3%
경기도 7
 
23.3%
NONE 1
 
3.3%

Length

2024-03-13T21:01:08.580558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:01:08.674599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 22
73.3%
경기도 7
 
23.3%
none 1
 
3.3%

시군구명
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing23
Missing (%)76.7%
Memory size372.0 B
2024-03-13T21:01:08.810958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.5714286
Min length3

Characters and Unicode

Total characters25
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

Unique7 ?
Unique (%)100.0%

Sample

1st row오산시
2nd row가평군
3rd row파주시
4th row군포시
5th row포천시
ValueCountFrequency (%)
오산시 1
12.5%
가평군 1
12.5%
파주시 1
12.5%
군포시 1
12.5%
포천시 1
12.5%
과천시 1
12.5%
용인시 1
12.5%
처인구 1
12.5%
2024-03-13T21:01:09.141816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 6
24.0%
군 2
 
8.0%
포 2
 
8.0%
천 2
 
8.0%
인 2
 
8.0%
오 1
 
4.0%
산 1
 
4.0%
가 1
 
4.0%
평 1
 
4.0%
파 1
 
4.0%
Other values (6) 6
24.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24
96.0%
Space Separator 1
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 6
25.0%
군 2
 
8.3%
포 2
 
8.3%
천 2
 
8.3%
인 2
 
8.3%
오 1
 
4.2%
산 1
 
4.2%
가 1
 
4.2%
평 1
 
4.2%
파 1
 
4.2%
Other values (5) 5
20.8%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24
96.0%
Common 1
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 6
25.0%
군 2
 
8.3%
포 2
 
8.3%
천 2
 
8.3%
인 2
 
8.3%
오 1
 
4.2%
산 1
 
4.2%
가 1
 
4.2%
평 1
 
4.2%
파 1
 
4.2%
Other values (5) 5
20.8%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24
96.0%
ASCII 1
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 6
25.0%
군 2
 
8.3%
포 2
 
8.3%
천 2
 
8.3%
인 2
 
8.3%
오 1
 
4.2%
산 1
 
4.2%
가 1
 
4.2%
평 1
 
4.2%
파 1
 
4.2%
Other values (5) 5
20.8%
ASCII
ValueCountFrequency (%)
1
100.0%

읍면동명
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing23
Missing (%)76.7%
Memory size372.0 B
2024-03-13T21:01:09.312644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1428571
Min length3

Characters and Unicode

Total characters22
Distinct characters15
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

Unique7 ?
Unique (%)100.0%

Sample

1st row오산동
2nd row가평읍
3rd row목동동
4th row산본동
5th row선단동
ValueCountFrequency (%)
오산동 1
14.3%
가평읍 1
14.3%
목동동 1
14.3%
산본동 1
14.3%
선단동 1
14.3%
별양동 1
14.3%
김량장동 1
14.3%
2024-03-13T21:01:09.623867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 7
31.8%
산 2
 
9.1%
오 1
 
4.5%
가 1
 
4.5%
평 1
 
4.5%
읍 1
 
4.5%
목 1
 
4.5%
본 1
 
4.5%
선 1
 
4.5%
단 1
 
4.5%
Other values (5) 5
22.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7
31.8%
산 2
 
9.1%
오 1
 
4.5%
가 1
 
4.5%
평 1
 
4.5%
읍 1
 
4.5%
목 1
 
4.5%
본 1
 
4.5%
선 1
 
4.5%
단 1
 
4.5%
Other values (5) 5
22.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7
31.8%
산 2
 
9.1%
오 1
 
4.5%
가 1
 
4.5%
평 1
 
4.5%
읍 1
 
4.5%
목 1
 
4.5%
본 1
 
4.5%
선 1
 
4.5%
단 1
 
4.5%
Other values (5) 5
22.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 7
31.8%
산 2
 
9.1%
오 1
 
4.5%
가 1
 
4.5%
평 1
 
4.5%
읍 1
 
4.5%
목 1
 
4.5%
본 1
 
4.5%
선 1
 
4.5%
단 1
 
4.5%
Other values (5) 5
22.7%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7527667
Minimum0
Maximum37.854
Zeros23
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:09.743201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile37.7796
Maximum37.854
Range37.854
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.137496
Coefficient of variation (CV)1.8437023
Kurtosis-0.25618404
Mean8.7527667
Median Absolute Deviation (MAD)0
Skewness1.3285837
Sum262.583
Variance260.41878
MonotonicityNot monotonic
2024-03-13T21:01:09.841203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 23
76.7%
37.149 1
 
3.3%
37.83 1
 
3.3%
37.718 1
 
3.3%
37.368 1
 
3.3%
37.854 1
 
3.3%
37.428 1
 
3.3%
37.236 1
 
3.3%
ValueCountFrequency (%)
0.0 23
76.7%
37.149 1
 
3.3%
37.236 1
 
3.3%
37.368 1
 
3.3%
37.428 1
 
3.3%
37.718 1
 
3.3%
37.83 1
 
3.3%
37.854 1
 
3.3%
ValueCountFrequency (%)
37.854 1
 
3.3%
37.83 1
 
3.3%
37.718 1
 
3.3%
37.428 1
 
3.3%
37.368 1
 
3.3%
37.236 1
 
3.3%
37.149 1
 
3.3%
0.0 23
76.7%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.653867
Minimum0
Maximum127.513
Zeros23
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:09.948256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile127.18635
Maximum127.513
Range127.513
Interquartile range (IQR)0

Descriptive statistics

Standard deviation54.671218
Coefficient of variation (CV)1.8436455
Kurtosis-0.25724897
Mean29.653867
Median Absolute Deviation (MAD)0
Skewness1.3283535
Sum889.616
Variance2988.9421
MonotonicityNot monotonic
2024-03-13T21:01:10.052710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 23
76.7%
127.076 1
 
3.3%
127.513 1
 
3.3%
126.738 1
 
3.3%
126.927 1
 
3.3%
127.166 1
 
3.3%
126.993 1
 
3.3%
127.203 1
 
3.3%
ValueCountFrequency (%)
0.0 23
76.7%
126.738 1
 
3.3%
126.927 1
 
3.3%
126.993 1
 
3.3%
127.076 1
 
3.3%
127.166 1
 
3.3%
127.203 1
 
3.3%
127.513 1
 
3.3%
ValueCountFrequency (%)
127.513 1
 
3.3%
127.203 1
 
3.3%
127.166 1
 
3.3%
127.076 1
 
3.3%
126.993 1
 
3.3%
126.927 1
 
3.3%
126.738 1
 
3.3%
0.0 23
76.7%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
21 
True
9 
ValueCountFrequency (%)
False 21
70.0%
True 9
30.0%
2024-03-13T21:01:10.153452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4147
Minimum0
Maximum40400
Zeros21
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T21:01:10.242254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33250
95-th percentile24892.5
Maximum40400
Range40400
Interquartile range (IQR)3250

Descriptive statistics

Standard deviation9624.6699
Coefficient of variation (CV)2.3208753
Kurtosis8.7988377
Mean4147
Median Absolute Deviation (MAD)0
Skewness2.982748
Sum124410
Variance92634270
MonotonicityNot monotonic
2024-03-13T21:01:10.353154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 21
70.0%
14800 1
 
3.3%
5500 1
 
3.3%
9360 1
 
3.3%
8000 1
 
3.3%
7200 1
 
3.3%
33150 1
 
3.3%
3500 1
 
3.3%
40400 1
 
3.3%
2500 1
 
3.3%
ValueCountFrequency (%)
0 21
70.0%
2500 1
 
3.3%
3500 1
 
3.3%
5500 1
 
3.3%
7200 1
 
3.3%
8000 1
 
3.3%
9360 1
 
3.3%
14800 1
 
3.3%
33150 1
 
3.3%
40400 1
 
3.3%
ValueCountFrequency (%)
40400 1
 
3.3%
33150 1
 
3.3%
14800 1
 
3.3%
9360 1
 
3.3%
8000 1
 
3.3%
7200 1
 
3.3%
5500 1
 
3.3%
3500 1
 
3.3%
2500 1
 
3.3%
0 21
70.0%

Interactions

2024-03-13T21:01:03.275277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:57.625653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:58.385926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:59.440866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:00.230821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:01.026758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:01.858116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:02.576684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:03.356316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:57.720644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:58.469957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:59.560533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:00.330286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:01.133908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:01.961825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:02.671290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:03.443210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:57.815894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:58.588599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:59.661147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:00.423110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:01.232258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:02.067224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:02.785289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:03.526606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:57.903085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:58.665596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:59.741266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:00.520650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:01.319568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:02.155662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:02.872649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:03.613016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:57.988130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:58.754718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:59.845860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:00.614588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:01.403305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:02.234208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:02.959409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:03.704129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:58.101110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:58.861810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:59.958001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:00.725229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:01.501266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:02.316447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:03.036079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:03.800628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:58.198901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:59.230402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:00.056451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:00.838528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:01.607100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:02.400912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:03.115887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:03.921557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:58.291203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:00:59.315120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:00.141217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:00.940169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:01.743580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:02.487006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:01:03.191595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:01:10.475734image/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
회원코드1.0001.0000.5190.4790.4200.0000.5650.0000.8790.3021.0001.0000.4850.4850.5710.000
가맹점번호1.0000.5191.0000.0000.5620.5250.739NaNNaNNaNNaNNaN0.9050.9050.9900.943
성별코드1.0000.4790.0001.0000.8200.4210.5690.3160.0000.0001.0001.0000.0000.0000.0000.000
연령대코드1.0000.4200.5620.8201.0000.3200.9070.8480.5110.4351.0001.0000.5360.5360.6150.551
결제상품ID1.0000.0000.5250.4210.3201.0001.0000.0001.0000.0001.0001.0000.6460.6460.5820.633
결제상품명1.0000.5650.7390.5690.9071.0001.0000.6381.0000.0001.0001.0000.7550.7550.8280.869
가맹점업종명1.0000.000NaN0.3160.8480.0000.6381.0000.8971.0001.0001.0001.0001.000NaN0.718
가맹점우편번호1.0000.879NaN0.0000.5111.0001.0000.8971.0000.0001.0001.0000.0000.000NaN0.956
시도명1.0000.302NaN0.0000.4350.0000.0001.0000.0001.000NaNNaN0.3960.396NaN1.000
시군구명1.0001.000NaN1.0001.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
읍면동명1.0001.000NaN1.0001.0001.0001.0001.0001.000NaN1.0001.000NaNNaNNaN1.000
위도1.0000.4850.9050.0000.5360.6460.7551.0000.0000.396NaNNaN1.0000.9890.9220.857
경도1.0000.4850.9050.0000.5360.6460.7551.0000.0000.396NaNNaN0.9891.0000.9220.857
사용여부1.0000.5710.9900.0000.6150.5820.828NaNNaNNaNNaNNaN0.9220.9221.0000.953
결제금액1.0000.0000.9430.0000.5510.6330.8690.7180.9561.0001.0001.0000.8570.8570.9531.000
2024-03-13T21:01:10.708433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부성별코드시도명
사용여부1.0000.0001.000
성별코드0.0001.0000.000
시도명1.0000.0001.000
2024-03-13T21:01:10.828446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회원코드가맹점번호연령대코드결제상품ID가맹점우편번호위도경도결제금액성별코드시도명사용여부
회원코드1.0000.236-0.083-0.2040.119-0.086-0.059-0.2720.3290.4080.329
가맹점번호0.2361.000-0.2300.2010.048-0.849-0.852-0.9760.0001.0000.918
연령대코드-0.083-0.2301.0000.0270.1240.0810.1030.2040.5750.0000.408
결제상품ID-0.2040.2010.0271.000-0.515-0.223-0.251-0.2110.1840.0000.247
가맹점우편번호0.1190.0480.124-0.5151.000-0.8570.0480.1670.0000.0001.000
위도-0.086-0.8490.081-0.223-0.8571.0000.9810.8060.0000.2180.747
경도-0.059-0.8520.103-0.2510.0480.9811.0000.7960.0000.2180.747
결제금액-0.272-0.9760.204-0.2110.1670.8060.7961.0000.0000.5770.746
성별코드0.3290.0000.5750.1840.0000.0000.0000.0001.0000.0000.000
시도명0.4081.0000.0000.0000.0000.2180.2180.5770.0001.0001.000
사용여부0.3290.9180.4080.2471.0000.7470.7470.7460.0001.0001.000

Missing values

2024-03-13T21:01:04.114091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:01:04.619639image/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.
2024-03-13T21:01:04.756464image/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
02023-03-062023-03-12yYASP+ZSRyn1ESZZFaihh0G3rR60tSN/Pa/PZ6EHBV0=3024075432709319675F20140000044000오산화폐 오색전음료식품18133경기도오산시오산동37.149127.076Y14800
12023-03-062023-03-122zEZykEUU2UezqpmQx08Mgq8RmtXkru5Y+CPT8zRzbk=3018291979703403776F50140000054000가평GP페이(통합)의원12419경기도가평군가평읍37.83127.513Y5500
22023-03-062023-03-12wTNG9WVREjHaBU1nmQJjjZCs7SyOhz9bs0fb7tzF6kA=3006525101750528651F50140000046000용인와이페이유통업 영리16981NONE<NA><NA>0.00.0Y9360
32023-03-062023-03-12yY/039t9otFh1Q7IZ2eLtnO7jk8g0NTnNujLOUxseqs=3020387274999999999999999F0140000030000부천페이<NA><NA><NA><NA><NA>0.00.0N0
42023-03-062023-03-12yXxxTux08yLc5+FBg+Z20KMflE9I05m+Ct8kdhRo2Kw=3010512115999999999999999F20140000104000Thank You Pay-N(통합)<NA><NA><NA><NA><NA>0.00.0N0
52023-03-062023-03-12yXxteV1BsSwwtFpNxrnxEJWwAm6c/tF58+Jywx+FJGY=3010900143999999999999999F50140000114000Thank You Pay-N<NA><NA><NA><NA><NA>0.00.0N0
62023-03-062023-03-12yXwlg9WYtdfCtUyaBU7FkZmJk413YHepWvRACJZUmms=3060549536999999999999999F0140000090000고양페이카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
72023-03-062023-03-122zCXHaSpDXPUFxsnKsBLvSKqvj9feLQVGReVSyk5e+c=3020188338999999999999999F0140000124000안산사랑상품권 다온<NA><NA><NA><NA><NA>0.00.0N0
82023-03-062023-03-12yXrZ7pbqBggfWG6BnRksc/QYP0xpVRnFMUnSpDDI4vM=3056737549999999999999999F0140000058000평택사랑카드(통합)<NA><NA><NA><NA><NA>0.00.0N0
92023-03-062023-03-12yXqsbZbUqfLrzBN9kCuL+7jDtrLJB4sY7fYwVaL9J+o=3019633844728536352M20140000096000파주 Pay(파주페이)(통합)음료식품10900경기도파주시목동동37.718126.738Y8000
정책주간결제시작일자정책주간결제종료일자카드번호회원코드가맹점번호성별코드연령대코드결제상품ID결제상품명가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도사용여부결제금액
202023-03-062023-03-12yXXyEM8CIRMWSFldNbiRn1VQ9FB3bqb9/qFCAccv8yU=3001927658999999999999999F0140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
212023-03-062023-03-12yXX+f13ne0lMu1FhCAID62dkly75ayf7EUCvu58JWUE=3003645270712389235M20140000022000과천화폐 과천토리서적문구13837경기도과천시별양동37.428126.993Y40400
222023-03-062023-03-12yXWGhVbJcDu+f6Neq5Lkyb2tXyqAXW+GsyosjrdZXwE=3037296241999999999999999F40140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0
232023-03-062023-03-12yXV3U9kAxhJPozcNnOmW9wAJoYExdSSIQzK1Mu0F27o=3017398571999999999999999F0140000116000행복화성지역화폐<NA><NA><NA><NA><NA>0.00.0N0
242023-03-062023-03-12yXP5nSYBFyXqhw47PP4yYxRhXvMeojIUmffg2EyTUlM=3018539008778757021M40140000046000용인와이페이약국17052경기도용인시 처인구김량장동37.236127.203Y2500
252023-03-062023-03-12yXNfhHVZtKPr/TNtW3UCn4kj29PlMQej5lxSm1sYe5I=3021795328999999999999999M60140000064000용인와이페이(통합)<NA><NA><NA><NA><NA>0.00.0N0
262023-03-062023-03-12yXGcfameaMajdnhT8enaj8fkCD+2c+F5aN4ujURwX2s=3061488870999999999999999F40140000096000파주 Pay(파주페이)(통합)<NA><NA><NA><NA><NA>0.00.0N0
272023-03-062023-03-12yXGZt6NrdD+Wv1noGJsO44sR3sox1BVRamsVnCZRJyc=3013959212999999999999999M60140000122000의정부사랑카드<NA><NA><NA><NA><NA>0.00.0N0
282023-03-062023-03-12yXBQ6/wdrGMLz4V8kE/TAh1Ubw4YZQE1qKKPiE8uTDU=3004994165999999999999999F30140000118000하남하머니<NA><NA><NA><NA><NA>0.00.0N0
292023-03-062023-03-12yXADZzEsvcuCPtttbn7a+87+Zz9J+ghB0cm5irVZVM4=3032222150999999999999999M40140000018000고양페이카드<NA><NA><NA><NA><NA>0.00.0N0