Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells30
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory115.4 B

Variable types

DateTime1
Numeric4
Categorical3
Text3
Unsupported1
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/8f577e32-4fbc-4133-898a-bdf103d25ea4

Alerts

일반일간결제일자 has constant value ""Constant
결제상품ID has constant value ""Constant
시도명 has constant value ""Constant
사용여부 has constant value ""Constant
결제금액 has constant value ""Constant
가맹점우편번호 is highly overall correlated with 위도High correlation
위도 is highly overall correlated with 가맹점우편번호High correlation
결제상품명 has 30 (100.0%) missing valuesMissing
가맹점번호 has unique valuesUnique
가맹점우편번호 has unique valuesUnique
위도 has unique valuesUnique
결제상품명 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 13:56:59.787039
Analysis finished2023-12-10 13:57:04.089334
Duration4.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일반일간결제일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-03-01 00:00:00
Maximum2023-03-01 00:00:00
2023-12-10T22:57:04.156563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:04.330054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

가맹점번호
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1000235 × 108
Minimum7.0000068 × 108
Maximum7.2000372 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:57:04.487092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0000068 × 108
5-th percentile7.000048 × 108
Q17.0001203 × 108
median7.1000126 × 108
Q37.1999253 × 108
95-th percentile7.2000206 × 108
Maximum7.2000372 × 108
Range20003039
Interquartile range (IQR)19980500

Descriptive statistics

Standard deviation10162121
Coefficient of variation (CV)0.014312799
Kurtosis-2.1481465
Mean7.1000235 × 108
Median Absolute Deviation (MAD)9991055
Skewness2.5082975 × 10-7
Sum2.130007 × 1010
Variance1.032687 × 1014
MonotonicityNot monotonic
2023-12-10T22:57:04.703386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
719983024 1
 
3.3%
719994123 1
 
3.3%
700019493 1
 
3.3%
720003715 1
 
3.3%
700018504 1
 
3.3%
720002760 1
 
3.3%
700016480 1
 
3.3%
720001202 1
 
3.3%
700014497 1
 
3.3%
720000192 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700000676 1
3.3%
700003305 1
3.3%
700006622 1
3.3%
700007464 1
3.3%
700008372 1
3.3%
700008631 1
3.3%
700009454 1
3.3%
700011686 1
3.3%
700013077 1
3.3%
700013346 1
3.3%
ValueCountFrequency (%)
720003715 1
3.3%
720002760 1
3.3%
720001202 1
3.3%
720000192 1
3.3%
719999811 1
3.3%
719996929 1
3.3%
719994123 1
3.3%
719992755 1
3.3%
719991872 1
3.3%
719990055 1
3.3%

결제상품ID
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
999999999999999
30 

Length

Max length15
Median length15
Mean length15
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row999999999999999
2nd row999999999999999
3rd row999999999999999
4th row999999999999999
5th row999999999999999

Common Values

ValueCountFrequency (%)
999999999999999 30
100.0%

Length

2023-12-10T22:57:04.934682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:57:05.148479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 30
100.0%

가맹점업종명
Text

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:57:05.348766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.6
Min length2

Characters and Unicode

Total characters138
Distinct characters46
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

Unique9 ?
Unique (%)30.0%

Sample

1st row광학제품
2nd row일반휴게음식
3rd row일반휴게음식
4th row레저업소
5th row서적문구
ValueCountFrequency (%)
일반휴게음식 10
30.3%
레저업소 3
 
9.1%
신변잡화 2
 
6.1%
유통업 2
 
6.1%
영리 2
 
6.1%
건축자재 2
 
6.1%
레져용품 2
 
6.1%
용역 1
 
3.0%
전기제품 1
 
3.0%
가구 1
 
3.0%
Other values (7) 7
21.2%
2023-12-10T22:57:06.003342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
일 10
 
7.2%
휴 10
 
7.2%
게 10
 
7.2%
음 10
 
7.2%
식 10
 
7.2%
반 10
 
7.2%
레 5
 
3.6%
업 5
 
3.6%
품 4
 
2.9%
저 3
 
2.2%
Other values (36) 61
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 135
97.8%
Space Separator 3
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 10
 
7.4%
휴 10
 
7.4%
게 10
 
7.4%
음 10
 
7.4%
식 10
 
7.4%
반 10
 
7.4%
레 5
 
3.7%
업 5
 
3.7%
품 4
 
3.0%
저 3
 
2.2%
Other values (35) 58
43.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 135
97.8%
Common 3
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 10
 
7.4%
휴 10
 
7.4%
게 10
 
7.4%
음 10
 
7.4%
식 10
 
7.4%
반 10
 
7.4%
레 5
 
3.7%
업 5
 
3.7%
품 4
 
3.0%
저 3
 
2.2%
Other values (35) 58
43.0%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 135
97.8%
ASCII 3
 
2.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
일 10
 
7.4%
휴 10
 
7.4%
게 10
 
7.4%
음 10
 
7.4%
식 10
 
7.4%
반 10
 
7.4%
레 5
 
3.7%
업 5
 
3.7%
품 4
 
3.0%
저 3
 
2.2%
Other values (35) 58
43.0%
ASCII
ValueCountFrequency (%)
3
100.0%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14844.633
Minimum10106
Maximum18139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:57:06.250793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10106
5-th percentile10590.6
Q113031.25
median15232
Q316888.75
95-th percentile17886.2
Maximum18139
Range8033
Interquartile range (IQR)3857.5

Descriptive statistics

Standard deviation2494.6604
Coefficient of variation (CV)0.16805133
Kurtosis-1.0953945
Mean14844.633
Median Absolute Deviation (MAD)1852.5
Skewness-0.43765212
Sum445339
Variance6223330.4
MonotonicityNot monotonic
2023-12-10T22:57:06.500157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
14103 1
 
3.3%
10317 1
 
3.3%
18139 1
 
3.3%
15884 1
 
3.3%
16253 1
 
3.3%
16422 1
 
3.3%
14102 1
 
3.3%
16442 1
 
3.3%
17420 1
 
3.3%
10106 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10106 1
3.3%
10317 1
3.3%
10925 1
3.3%
11413 1
3.3%
11675 1
3.3%
11923 1
3.3%
12700 1
3.3%
12985 1
3.3%
13170 1
3.3%
13642 1
3.3%
ValueCountFrequency (%)
18139 1
3.3%
17969 1
3.3%
17785 1
3.3%
17777 1
3.3%
17420 1
3.3%
17117 1
3.3%
17052 1
3.3%
16914 1
3.3%
16813 1
3.3%
16802 1
3.3%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T22:57:06.737636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:57:06.952727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:57:07.211975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.0666667
Min length3

Characters and Unicode

Total characters152
Distinct characters41
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

Unique16 ?
Unique (%)53.3%

Sample

1st row안양시 동안구
2nd row성남시 수정구
3rd row수원시 영통구
4th row부천시
5th row평택시
ValueCountFrequency (%)
용인시 5
 
11.1%
수원시 4
 
8.9%
평택시 3
 
6.7%
팔달구 3
 
6.7%
안양시 3
 
6.7%
수지구 2
 
4.4%
처인구 2
 
4.4%
성남시 2
 
4.4%
부천시 2
 
4.4%
동안구 2
 
4.4%
Other values (17) 17
37.8%
2023-12-10T22:57:07.850167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 30
19.7%
구 16
 
10.5%
15
 
9.9%
수 7
 
4.6%
인 7
 
4.6%
안 6
 
3.9%
양 5
 
3.3%
원 5
 
3.3%
용 5
 
3.3%
부 3
 
2.0%
Other values (31) 53
34.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 137
90.1%
Space Separator 15
 
9.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 30
21.9%
구 16
 
11.7%
수 7
 
5.1%
인 7
 
5.1%
안 6
 
4.4%
양 5
 
3.6%
원 5
 
3.6%
용 5
 
3.6%
부 3
 
2.2%
주 3
 
2.2%
Other values (30) 50
36.5%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 137
90.1%
Common 15
 
9.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 30
21.9%
구 16
 
11.7%
수 7
 
5.1%
인 7
 
5.1%
안 6
 
4.4%
양 5
 
3.6%
원 5
 
3.6%
용 5
 
3.6%
부 3
 
2.2%
주 3
 
2.2%
Other values (30) 50
36.5%
Common
ValueCountFrequency (%)
15
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 137
90.1%
ASCII 15
 
9.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 30
21.9%
구 16
 
11.7%
수 7
 
5.1%
인 7
 
5.1%
안 6
 
4.4%
양 5
 
3.6%
원 5
 
3.6%
용 5
 
3.6%
부 3
 
2.2%
주 3
 
2.2%
Other values (30) 50
36.5%
ASCII
ValueCountFrequency (%)
15
100.0%

읍면동명
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:57:08.170855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0333333
Min length2

Characters and Unicode

Total characters91
Distinct characters49
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

Unique28 ?
Unique (%)93.3%

Sample

1st row평촌동
2nd row창곡동
3rd row하동
4th row고강동
5th row현덕면
ValueCountFrequency (%)
화서동 2
 
6.7%
평촌동 1
 
3.3%
김량장동 1
 
3.3%
도마교동 1
 
3.3%
북수동 1
 
3.3%
관양동 1
 
3.3%
장호원읍 1
 
3.3%
북변동 1
 
3.3%
인창동 1
 
3.3%
남사면 1
 
3.3%
Other values (19) 19
63.3%
2023-12-10T22:57:08.784434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 26
28.6%
면 4
 
4.4%
남 3
 
3.3%
서 3
 
3.3%
화 2
 
2.2%
북 2
 
2.2%
원 2
 
2.2%
촌 2
 
2.2%
금 2
 
2.2%
덕 2
 
2.2%
Other values (39) 43
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 26
28.6%
면 4
 
4.4%
남 3
 
3.3%
서 3
 
3.3%
화 2
 
2.2%
북 2
 
2.2%
원 2
 
2.2%
촌 2
 
2.2%
금 2
 
2.2%
덕 2
 
2.2%
Other values (39) 43
47.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 26
28.6%
면 4
 
4.4%
남 3
 
3.3%
서 3
 
3.3%
화 2
 
2.2%
북 2
 
2.2%
원 2
 
2.2%
촌 2
 
2.2%
금 2
 
2.2%
덕 2
 
2.2%
Other values (39) 43
47.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 26
28.6%
면 4
 
4.4%
남 3
 
3.3%
서 3
 
3.3%
화 2
 
2.2%
북 2
 
2.2%
원 2
 
2.2%
촌 2
 
2.2%
금 2
 
2.2%
덕 2
 
2.2%
Other values (39) 43
47.3%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.385833
Minimum36.956
Maximum37.865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:57:09.071025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.956
5-th percentile37.0555
Q137.279
median37.364
Q337.5205
95-th percentile37.7524
Maximum37.865
Range0.909
Interquartile range (IQR)0.2415

Descriptive statistics

Standard deviation0.22281569
Coefficient of variation (CV)0.0059598964
Kurtosis-0.34231949
Mean37.385833
Median Absolute Deviation (MAD)0.131
Skewness0.20790192
Sum1121.575
Variance0.049646833
MonotonicityNot monotonic
2023-12-10T22:57:09.348504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
37.385 1
 
3.3%
37.685 1
 
3.3%
37.146 1
 
3.3%
37.315 1
 
3.3%
37.282 1
 
3.3%
37.286 1
 
3.3%
37.404 1
 
3.3%
37.278 1
 
3.3%
37.115 1
 
3.3%
37.625 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
36.956 1
3.3%
37.051 1
3.3%
37.061 1
3.3%
37.115 1
3.3%
37.146 1
3.3%
37.153 1
3.3%
37.236 1
3.3%
37.278 1
3.3%
37.282 1
3.3%
37.286 1
3.3%
ValueCountFrequency (%)
37.865 1
3.3%
37.756 1
3.3%
37.748 1
3.3%
37.685 1
3.3%
37.625 1
3.3%
37.602 1
3.3%
37.545 1
3.3%
37.528 1
3.3%
37.498 1
3.3%
37.476 1
3.3%

경도
Real number (ℝ)

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.03233
Minimum126.704
Maximum127.631
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:57:09.700848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.704
5-th percentile126.77425
Q1126.95775
median127.045
Q3127.142
95-th percentile127.19555
Maximum127.631
Range0.927
Interquartile range (IQR)0.18425

Descriptive statistics

Standard deviation0.17555102
Coefficient of variation (CV)0.0013819397
Kurtosis3.6651574
Mean127.03233
Median Absolute Deviation (MAD)0.0945
Skewness0.95975978
Sum3810.97
Variance0.030818161
MonotonicityNot monotonic
2023-12-10T22:57:10.589713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
127.057 2
 
6.7%
126.963 1
 
3.3%
126.985 1
 
3.3%
127.075 1
 
3.3%
126.916 1
 
3.3%
127.016 1
 
3.3%
126.979 1
 
3.3%
126.956 1
 
3.3%
127.001 1
 
3.3%
127.631 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
126.704 1
3.3%
126.772 1
3.3%
126.777 1
3.3%
126.808 1
3.3%
126.822 1
3.3%
126.916 1
3.3%
126.918 1
3.3%
126.956 1
3.3%
126.963 1
3.3%
126.97 1
3.3%
ValueCountFrequency (%)
127.631 1
3.3%
127.205 1
3.3%
127.184 1
3.3%
127.176 1
3.3%
127.171 1
3.3%
127.159 1
3.3%
127.15 1
3.3%
127.145 1
3.3%
127.133 1
3.3%
127.075 1
3.3%

결제상품명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)100.0%
Memory size402.0 B

사용여부
Boolean

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
30 
ValueCountFrequency (%)
False 30
100.0%
2023-12-10T22:57:10.917394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
100.0%

Length

2023-12-10T22:57:11.324523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:57:11.583154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2023-12-10T22:57:02.519258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:00.387827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:01.289739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:01.924299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:02.711599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:00.720722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:01.465671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:02.073282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:02.897817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:00.901092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:01.613191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:02.217352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:03.131903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:01.078079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:01.744636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:57:02.349545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:57:11.814634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점업종명가맹점우편번호시군구명읍면동명위도경도
가맹점번호1.0000.4090.0000.0001.0000.0000.000
가맹점업종명0.4091.0000.7760.0000.9800.7050.000
가맹점우편번호0.0000.7761.0001.0001.0000.8490.636
시군구명0.0000.0001.0001.0001.0000.9090.934
읍면동명1.0000.9801.0001.0001.0001.0000.959
위도0.0000.7050.8490.9091.0001.0000.384
경도0.0000.0000.6360.9340.9590.3841.000
2023-12-10T22:57:12.101716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도
가맹점번호1.0000.017-0.134-0.189
가맹점우편번호0.0171.000-0.9390.358
위도-0.134-0.9391.000-0.348
경도-0.1890.358-0.3481.000

Missing values

2023-12-10T22:57:03.586366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:57:03.988908image/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.

Sample

일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
02023-03-01719983024999999999999999광학제품14103경기도안양시 동안구평촌동37.385126.963<NA>N0
12023-03-01700000676999999999999999일반휴게음식13642경기도성남시 수정구창곡동37.473127.15<NA>N0
22023-03-01719985950999999999999999일반휴게음식16512경기도수원시 영통구하동37.293127.071<NA>N0
32023-03-01700003305999999999999999레저업소14406경기도부천시고강동37.528126.822<NA>N0
42023-03-01719986218999999999999999서적문구17969경기도평택시현덕면36.956126.97<NA>N0
52023-03-01700006622999999999999999신변잡화10925경기도파주시금촌동37.756126.772<NA>N0
62023-03-01719987338999999999999999일반휴게음식12700경기도광주시남한산성면37.476127.184<NA>N0
72023-03-01700007464999999999999999일반휴게음식16914경기도용인시 기흥구청덕동37.291127.145<NA>N0
82023-03-01719988965999999999999999보건위생14580경기도부천시중동37.498126.777<NA>N0
92023-03-01700008372999999999999999유통업 영리17785경기도평택시이충동37.051127.057<NA>N0
일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202023-03-01719999811999999999999999레저업소17117경기도용인시 처인구남사면37.153127.171<NA>N0
212023-03-01700013849999999999999999전기제품11923경기도구리시인창동37.602127.133<NA>N0
222023-03-01720000192999999999999999일반휴게음식10106경기도김포시북변동37.625126.704<NA>N0
232023-03-01700014497999999999999999레저업소17420경기도이천시장호원읍37.115127.631<NA>N0
242023-03-01720001202999999999999999신변잡화16442경기도수원시 팔달구화서동37.278127.001<NA>N0
252023-03-01700016480999999999999999유통업 영리14102경기도안양시 동안구관양동37.404126.956<NA>N0
262023-03-01720002760999999999999999일반휴게음식16422경기도수원시 팔달구화서동37.286126.979<NA>N0
272023-03-01700018504999999999999999학원16253경기도수원시 팔달구북수동37.282127.016<NA>N0
282023-03-01720003715999999999999999일반휴게음식15884경기도군포시도마교동37.315126.916<NA>N0
292023-03-01700019493999999999999999일반휴게음식18139경기도오산시원동37.146127.075<NA>N0