Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells44
Missing cells (%)11.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory115.4 B

Variable types

Categorical5
Numeric4
Text2
Unsupported1
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/77994767-7ecc-453f-8cf4-81539a9794dd

Alerts

일반일간결제일자 has constant value ""Constant
결제상품ID has constant value ""Constant
사용여부 has constant value ""Constant
결제금액 has constant value ""Constant
시도명 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
가맹점업종명 is highly overall correlated with 시도명High correlation
가맹점번호 is highly overall correlated with 시도명High correlation
가맹점우편번호 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점우편번호 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 시도명High correlation
시군구명 has 7 (23.3%) missing valuesMissing
읍면동명 has 7 (23.3%) missing valuesMissing
결제상품명 has 30 (100.0%) missing valuesMissing
가맹점번호 has unique valuesUnique
가맹점우편번호 has unique valuesUnique
결제상품명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
위도 has 7 (23.3%) zerosZeros
경도 has 7 (23.3%) zerosZeros

Reproduction

Analysis started2024-03-13 11:50:53.220231
Analysis finished2024-03-13 11:50:55.855760
Duration2.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일반일간결제일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2020-07-01
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-07-01
2nd row2020-07-01
3rd row2020-07-01
4th row2020-07-01
5th row2020-07-01

Common Values

ValueCountFrequency (%)
2020-07-01 30
100.0%

Length

2024-03-13T20:50:55.937319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:50:56.052573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-07-01 30
100.0%

가맹점번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4123913 × 108
Minimum7.0107474 × 108
Maximum7.9820828 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:50:56.199398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0107474 × 108
5-th percentile7.0193735 × 108
Q17.2068451 × 108
median7.300921 × 108
Q37.7472192 × 108
95-th percentile7.9606657 × 108
Maximum7.9820828 × 108
Range97133541
Interquartile range (IQR)54037408

Descriptive statistics

Standard deviation32805544
Coefficient of variation (CV)0.044257707
Kurtosis-1.0336729
Mean7.4123913 × 108
Median Absolute Deviation (MAD)9407752.5
Skewness0.71653893
Sum2.2237174 × 1010
Variance1.0762037 × 1015
MonotonicityNot monotonic
2024-03-13T20:50:56.342448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
720684284 1
 
3.3%
785064389 1
 
3.3%
774721693 1
 
3.3%
735267656 1
 
3.3%
733485593 1
 
3.3%
731818535 1
 
3.3%
730091782 1
 
3.3%
721288030 1
 
3.3%
720684365 1
 
3.3%
720732039 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
701074739 1
3.3%
701075010 1
3.3%
702991329 1
3.3%
703214764 1
3.3%
720684284 1
3.3%
720684333 1
3.3%
720684365 1
3.3%
720684501 1
3.3%
720684535 1
3.3%
720732039 1
3.3%
ValueCountFrequency (%)
798208280 1
3.3%
798208231 1
3.3%
793448986 1
3.3%
792870857 1
3.3%
792869772 1
3.3%
785064389 1
3.3%
782085110 1
3.3%
774721993 1
3.3%
774721693 1
3.3%
735267656 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

2024-03-13T20:50:56.483519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:50:56.607281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 30
100.0%

가맹점업종명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
보건위생
5 
일반휴게음식
4 
학원
4 
용역 서비스
3 
레저업소
3 
Other values (9)
11 

Length

Max length8
Median length6
Mean length4.1
Min length2

Unique

Unique7 ?
Unique (%)23.3%

Sample

1st row용역 서비스
2nd row일반휴게음식
3rd row일반휴게음식
4th row보건위생
5th row음료식품

Common Values

ValueCountFrequency (%)
보건위생 5
16.7%
일반휴게음식 4
13.3%
학원 4
13.3%
용역 서비스 3
10.0%
레저업소 3
10.0%
음료식품 2
 
6.7%
여행 2
 
6.7%
연료판매점 1
 
3.3%
건축자재 1
 
3.3%
레져용품 1
 
3.3%
Other values (4) 4
13.3%

Length

2024-03-13T20:50:56.765016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
보건위생 5
14.7%
일반휴게음식 4
11.8%
학원 4
11.8%
용역 3
8.8%
서비스 3
8.8%
레저업소 3
8.8%
음료식품 2
 
5.9%
여행 2
 
5.9%
연료판매점 1
 
2.9%
건축자재 1
 
2.9%
Other values (6) 6
17.6%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13920.1
Minimum10303
Maximum18593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:50:56.948736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10303
5-th percentile10387.05
Q111371.25
median14360.5
Q315557.25
95-th percentile18028
Maximum18593
Range8290
Interquartile range (IQR)4186

Descriptive statistics

Standard deviation2643.2996
Coefficient of variation (CV)0.18989085
Kurtosis-1.2055879
Mean13920.1
Median Absolute Deviation (MAD)2179
Skewness0.0073297684
Sum417603
Variance6987032.6
MonotonicityNot monotonic
2024-03-13T20:50:57.108470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
14468 1
 
3.3%
10414 1
 
3.3%
13574 1
 
3.3%
13504 1
 
3.3%
10446 1
 
3.3%
18136 1
 
3.3%
10303 1
 
3.3%
18593 1
 
3.3%
14253 1
 
3.3%
16930 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10303 1
3.3%
10374 1
3.3%
10403 1
3.3%
10414 1
3.3%
10446 1
3.3%
10526 1
3.3%
10564 1
3.3%
11342 1
3.3%
11459 1
3.3%
12167 1
3.3%
ValueCountFrequency (%)
18593 1
3.3%
18136 1
3.3%
17896 1
3.3%
17006 1
3.3%
16930 1
3.3%
16495 1
3.3%
16300 1
3.3%
15589 1
3.3%
15462 1
3.3%
15301 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
23 
<NA>
7 

Length

Max length4
Median length3
Mean length3.2333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 23
76.7%
<NA> 7
 
23.3%

Length

2024-03-13T20:50:57.273200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:50:57.397191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 23
76.7%
na 7
 
23.3%

시군구명
Text

MISSING 

Distinct18
Distinct (%)78.3%
Missing7
Missing (%)23.3%
Memory size372.0 B
2024-03-13T20:50:57.592725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.0434783
Min length3

Characters and Unicode

Total characters139
Distinct characters37
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

Unique14 ?
Unique (%)60.9%

Sample

1st row부천시
2nd row안산시 상록구
3rd row고양시 일산서구
4th row고양시 덕양구
5th row동두천시
ValueCountFrequency (%)
고양시 6
15.4%
안산시 3
 
7.7%
일산동구 3
 
7.7%
용인시 2
 
5.1%
성남시 2
 
5.1%
분당구 2
 
5.1%
덕양구 2
 
5.1%
상록구 2
 
5.1%
수원시 2
 
5.1%
동안구 1
 
2.6%
Other values (14) 14
35.9%
2024-03-13T20:50:57.985679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 23
16.5%
16
 
11.5%
구 16
 
11.5%
양 11
 
7.9%
산 7
 
5.0%
고 6
 
4.3%
안 6
 
4.3%
동 5
 
3.6%
일 4
 
2.9%
성 3
 
2.2%
Other values (27) 42
30.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 123
88.5%
Space Separator 16
 
11.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 23
18.7%
구 16
13.0%
양 11
 
8.9%
산 7
 
5.7%
고 6
 
4.9%
안 6
 
4.9%
동 5
 
4.1%
일 4
 
3.3%
성 3
 
2.4%
남 3
 
2.4%
Other values (26) 39
31.7%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 123
88.5%
Common 16
 
11.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 23
18.7%
구 16
13.0%
양 11
 
8.9%
산 7
 
5.7%
고 6
 
4.9%
안 6
 
4.9%
동 5
 
4.1%
일 4
 
3.3%
성 3
 
2.4%
남 3
 
2.4%
Other values (26) 39
31.7%
Common
ValueCountFrequency (%)
16
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 123
88.5%
ASCII 16
 
11.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 23
18.7%
구 16
13.0%
양 11
 
8.9%
산 7
 
5.7%
고 6
 
4.9%
안 6
 
4.9%
동 5
 
4.1%
일 4
 
3.3%
성 3
 
2.4%
남 3
 
2.4%
Other values (26) 39
31.7%
ASCII
ValueCountFrequency (%)
16
100.0%

읍면동명
Text

MISSING 

Distinct22
Distinct (%)95.7%
Missing7
Missing (%)23.3%
Memory size372.0 B
2024-03-13T20:50:58.206076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.826087
Min length2

Characters and Unicode

Total characters65
Distinct characters38
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

Unique21 ?
Unique (%)91.3%

Sample

1st row고강동
2nd row사동
3rd row일산동
4th row원흥동
5th row생연동
ValueCountFrequency (%)
장항동 2
 
8.7%
비전동 1
 
4.3%
고강동 1
 
4.3%
화도읍 1
 
4.3%
야탑동 1
 
4.3%
풍동 1
 
4.3%
향남읍 1
 
4.3%
철산동 1
 
4.3%
상현동 1
 
4.3%
송죽동 1
 
4.3%
Other values (12) 12
52.2%
2024-03-13T20:50:58.680173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 21
32.3%
읍 3
 
4.6%
고 3
 
4.6%
장 2
 
3.1%
항 2
 
3.1%
산 2
 
3.1%
향 1
 
1.5%
송 1
 
1.5%
죽 1
 
1.5%
상 1
 
1.5%
Other values (28) 28
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
32.3%
읍 3
 
4.6%
고 3
 
4.6%
장 2
 
3.1%
항 2
 
3.1%
산 2
 
3.1%
향 1
 
1.5%
송 1
 
1.5%
죽 1
 
1.5%
상 1
 
1.5%
Other values (28) 28
43.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
32.3%
읍 3
 
4.6%
고 3
 
4.6%
장 2
 
3.1%
항 2
 
3.1%
산 2
 
3.1%
향 1
 
1.5%
송 1
 
1.5%
죽 1
 
1.5%
상 1
 
1.5%
Other values (28) 28
43.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 21
32.3%
읍 3
 
4.6%
고 3
 
4.6%
장 2
 
3.1%
항 2
 
3.1%
산 2
 
3.1%
향 1
 
1.5%
송 1
 
1.5%
죽 1
 
1.5%
상 1
 
1.5%
Other values (28) 28
43.1%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.7226
Minimum0
Maximum37.897
Zeros7
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:50:58.951209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q137.02975
median37.3275
Q337.6405
95-th percentile37.74895
Maximum37.897
Range37.897
Interquartile range (IQR)0.61075

Descriptive statistics

Standard deviation16.117665
Coefficient of variation (CV)0.56114923
Kurtosis-0.25790137
Mean28.7226
Median Absolute Deviation (MAD)0.322
Skewness-1.3277939
Sum861.678
Variance259.77912
MonotonicityNot monotonic
2024-03-13T20:50:59.193325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.0 7
23.3%
37.52 1
 
3.3%
37.365 1
 
3.3%
37.383 1
 
3.3%
37.411 1
 
3.3%
37.672 1
 
3.3%
37.131 1
 
3.3%
37.473 1
 
3.3%
37.306 1
 
3.3%
37.305 1
 
3.3%
Other values (14) 14
46.7%
ValueCountFrequency (%)
0.0 7
23.3%
36.996 1
 
3.3%
37.131 1
 
3.3%
37.27 1
 
3.3%
37.287 1
 
3.3%
37.295 1
 
3.3%
37.305 1
 
3.3%
37.306 1
 
3.3%
37.314 1
 
3.3%
37.341 1
 
3.3%
ValueCountFrequency (%)
37.897 1
3.3%
37.807 1
3.3%
37.678 1
3.3%
37.672 1
3.3%
37.657 1
3.3%
37.656 1
3.3%
37.65 1
3.3%
37.649 1
3.3%
37.615 1
3.3%
37.52 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.3325
Minimum0
Maximum127.292
Zeros7
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:50:59.372201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1126.7665
median126.8535
Q3127.044
95-th percentile127.1512
Maximum127.292
Range127.292
Interquartile range (IQR)0.2775

Descriptive statistics

Standard deviation54.614237
Coefficient of variation (CV)0.56110997
Kurtosis-0.25734342
Mean97.3325
Median Absolute Deviation (MAD)0.1895
Skewness-1.3283165
Sum2919.975
Variance2982.7148
MonotonicityNot monotonic
2024-03-13T20:50:59.539696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.0 7
23.3%
126.812 1
 
3.3%
126.964 1
 
3.3%
127.149 1
 
3.3%
127.134 1
 
3.3%
126.802 1
 
3.3%
126.909 1
 
3.3%
126.865 1
 
3.3%
127.084 1
 
3.3%
127.001 1
 
3.3%
Other values (14) 14
46.7%
ValueCountFrequency (%)
0.0 7
23.3%
126.765 1
 
3.3%
126.771 1
 
3.3%
126.777 1
 
3.3%
126.802 1
 
3.3%
126.812 1
 
3.3%
126.831 1
 
3.3%
126.835 1
 
3.3%
126.842 1
 
3.3%
126.865 1
 
3.3%
ValueCountFrequency (%)
127.292 1
3.3%
127.153 1
3.3%
127.149 1
3.3%
127.134 1
3.3%
127.092 1
3.3%
127.084 1
3.3%
127.07 1
3.3%
127.045 1
3.3%
127.041 1
3.3%
127.001 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%
2024-03-13T20:50:59.664945image/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

2024-03-13T20:50:59.769044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:50:59.884257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2024-03-13T20:50:54.915136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:53.624377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:54.039489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:54.433820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:55.012109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:53.727568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:54.168607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:54.535730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:55.102751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:53.819293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:54.265645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:54.638317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:55.208141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:53.923386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:54.350549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:50:54.757429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:50:59.989332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점업종명가맹점우편번호시군구명읍면동명위도경도
가맹점번호1.0000.0000.0000.0000.0000.6090.609
가맹점업종명0.0001.0000.1050.7880.9850.7460.746
가맹점우편번호0.0000.1051.0001.0001.0000.6260.626
시군구명0.0000.7881.0001.0001.000NaNNaN
읍면동명0.0000.9851.0001.0001.000NaNNaN
위도0.6090.7460.626NaNNaN1.0000.989
경도0.6090.7460.626NaNNaN0.9891.000
2024-03-13T20:51:00.471133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명가맹점업종명
시도명1.0001.000
가맹점업종명1.0001.000
2024-03-13T20:51:00.586767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도가맹점업종명시도명
가맹점번호1.0000.082-0.1150.1740.0001.000
가맹점우편번호0.0821.000-0.6400.1990.0001.000
위도-0.115-0.6401.0000.4000.4421.000
경도0.1740.1990.4001.0000.4421.000
가맹점업종명0.0000.0000.4420.4421.0001.000
시도명1.0001.0001.0001.0001.0001.000

Missing values

2024-03-13T20:50:55.361320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:50:55.665954image/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.
2024-03-13T20:50:55.789969image/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

일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
02020-07-01720684284999999999999999용역 서비스14468경기도부천시고강동37.52126.812<NA>N0
12020-07-01798208231999999999999999일반휴게음식15589경기도안산시 상록구사동37.295126.842<NA>N0
22020-07-01721182319999999999999999일반휴게음식10374경기도고양시 일산서구일산동37.678126.765<NA>N0
32020-07-01721065312999999999999999보건위생10564경기도고양시 덕양구원흥동37.649126.873<NA>N0
42020-07-01720684501999999999999999음료식품11342경기도동두천시생연동37.897127.045<NA>N0
52020-07-01720684333999999999999999보건위생15462경기도안산시 단원구고잔동37.314126.831<NA>N0
62020-07-01733485358999999999999999연료판매점16495경기도수원시 영통구이의동37.287127.041<NA>N0
72020-07-01798208280999999999999999건축자재11459경기도양주시고읍동37.807127.07<NA>N0
82020-07-01792870857999999999999999레져용품15301경기도안산시 상록구부곡동37.341126.868<NA>N0
92020-07-01701075010999999999999999학원10526경기도고양시 덕양구행신동37.615126.835<NA>N0
일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202020-07-01702991329999999999999999레저업소10403경기도고양시 일산동구장항동37.656126.771<NA>N0
212020-07-01720684535999999999999999레저업소16300경기도수원시 장안구송죽동37.305127.001<NA>N0
222020-07-01720732039999999999999999용역 서비스16930경기도용인시 수지구상현동37.306127.084<NA>N0
232020-07-01720684365999999999999999학원14253경기도광명시철산동37.473126.865<NA>N0
242020-07-01721288030999999999999999음료식품18593경기도화성시향남읍37.131126.909<NA>N0
252020-07-01730091782999999999999999여행10303경기도고양시 일산동구풍동37.672126.802<NA>N0
262020-07-01731818535999999999999999보건위생18136<NA><NA><NA>0.00.0<NA>N0
272020-07-01733485593999999999999999자동차정비 유지10446<NA><NA><NA>0.00.0<NA>N0
282020-07-01735267656999999999999999일반휴게음식13504경기도성남시 분당구야탑동37.411127.134<NA>N0
292020-07-01774721693999999999999999레저업소13574경기도성남시 분당구율동37.383127.149<NA>N0