Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells36
Missing cells (%)9.2%
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/1be0fba8-09cd-470f-b799-5a8e75865d4b

Alerts

정책일간결제일자 has constant value ""Constant
결제상품ID 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 가맹점우편번호 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
시도명 is highly imbalanced (53.1%)Imbalance
시군구명 has 3 (10.0%) missing valuesMissing
읍면동명 has 3 (10.0%) missing valuesMissing
결제상품명 has 30 (100.0%) missing valuesMissing
가맹점번호 has unique valuesUnique
결제상품명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
위도 has 3 (10.0%) zerosZeros
경도 has 3 (10.0%) zerosZeros

Reproduction

Analysis started2024-03-13 11:57:02.836677
Analysis finished2024-03-13 11:57:05.136787
Duration2.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책일간결제일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-11-02
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-11-02
2nd row2023-11-02
3rd row2023-11-02
4th row2023-11-02
5th row2023-11-02

Common Values

ValueCountFrequency (%)
2023-11-02 30
100.0%

Length

2024-03-13T20:57:05.537071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:57:05.627607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-11-02 30
100.0%

가맹점번호
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0664007 × 108
Minimum7.000033 × 108
Maximum7.9932976 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:05.744600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.000033 × 108
5-th percentile7.000047 × 108
Q17.0001199 × 108
median7.0001724 × 108
Q37.0003027 × 108
95-th percentile7.5464727 × 108
Maximum7.9932976 × 108
Range99326454
Interquartile range (IQR)18276.5

Descriptive statistics

Standard deviation25195640
Coefficient of variation (CV)0.035655549
Kurtosis12.206627
Mean7.0664007 × 108
Median Absolute Deviation (MAD)10192.5
Skewness3.6599976
Sum2.1199202 × 1010
Variance6.3482027 × 1014
MonotonicityNot monotonic
2024-03-13T20:57:05.883491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700003305 1
 
3.3%
700016723 1
 
3.3%
700037256 1
 
3.3%
700036965 1
 
3.3%
700036827 1
 
3.3%
700034607 1
 
3.3%
700032922 1
 
3.3%
700030395 1
 
3.3%
700029894 1
 
3.3%
700029537 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700003305 1
3.3%
700004564 1
3.3%
700004856 1
3.3%
700005536 1
3.3%
700006719 1
3.3%
700008794 1
3.3%
700011721 1
3.3%
700011986 1
3.3%
700012015 1
3.3%
700014359 1
3.3%
ValueCountFrequency (%)
799329759 1
3.3%
799328195 1
3.3%
700037256 1
3.3%
700036965 1
3.3%
700036827 1
3.3%
700034607 1
3.3%
700032922 1
3.3%
700030395 1
3.3%
700029894 1
3.3%
700029537 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

2024-03-13T20:57:06.063274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:57:06.173788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 30
100.0%

가맹점업종명
Categorical

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
음료/식품
5 
일반/휴게 음식
4 
일반유통
4 
미용/위생
3 
레저/스포츠 서비스
2 
Other values (9)
12 

Length

Max length10
Median length9
Mean length5.5333333
Min length2

Unique

Unique6 ?
Unique (%)20.0%

Sample

1st row레저/스포츠 서비스
2nd row학원
3rd row일반/휴게 음식
4th row건축자재
5th row미용/위생

Common Values

ValueCountFrequency (%)
음료/식품 5
16.7%
일반/휴게 음식 4
13.3%
일반유통 4
13.3%
미용/위생 3
10.0%
레저/스포츠 서비스 2
 
6.7%
학원 2
 
6.7%
건축자재 2
 
6.7%
신변잡화 2
 
6.7%
서적/문구/학습자재 1
 
3.3%
숙박업 1
 
3.3%
Other values (4) 4
13.3%

Length

2024-03-13T20:57:06.286067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
음료/식품 5
13.5%
음식 4
10.8%
일반유통 4
10.8%
일반/휴게 4
10.8%
미용/위생 3
8.1%
학원 2
 
5.4%
신변잡화 2
 
5.4%
건축자재 2
 
5.4%
서비스 2
 
5.4%
레저/스포츠 2
 
5.4%
Other values (7) 7
18.9%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14234.133
Minimum10010
Maximum18442
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:06.405452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10010
5-th percentile10030.85
Q112118.75
median14538
Q316240.25
95-th percentile18046.55
Maximum18442
Range8432
Interquartile range (IQR)4121.5

Descriptive statistics

Standard deviation2683.9631
Coefficient of variation (CV)0.18855824
Kurtosis-1.0371664
Mean14234.133
Median Absolute Deviation (MAD)2128
Skewness-0.27513052
Sum427024
Variance7203658.1
MonotonicityNot monotonic
2024-03-13T20:57:06.522450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
15382 2
 
6.7%
14406 1
 
3.3%
17814 1
 
3.3%
14103 1
 
3.3%
11430 1
 
3.3%
12073 1
 
3.3%
10049 1
 
3.3%
16385 1
 
3.3%
15806 1
 
3.3%
10401 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
10010 1
3.3%
10016 1
3.3%
10049 1
3.3%
10077 1
3.3%
10401 1
3.3%
10495 1
3.3%
11430 1
3.3%
12073 1
3.3%
12256 1
3.3%
13345 1
3.3%
ValueCountFrequency (%)
18442 1
3.3%
18137 1
3.3%
17936 1
3.3%
17814 1
3.3%
16977 1
3.3%
16835 1
3.3%
16512 1
3.3%
16385 1
3.3%
15806 1
3.3%
15622 1
3.3%

시도명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
27 
NONE
3 

Length

Max length4
Median length3
Mean length3.1
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 27
90.0%
NONE 3
 
10.0%

Length

2024-03-13T20:57:06.700825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:57:06.820642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 27
90.0%
none 3
 
10.0%

시군구명
Text

MISSING 

Distinct19
Distinct (%)70.4%
Missing3
Missing (%)10.0%
Memory size372.0 B
2024-03-13T20:57:06.995250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.7407407
Min length3

Characters and Unicode

Total characters128
Distinct characters38
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

Unique13 ?
Unique (%)48.1%

Sample

1st row부천시
2nd row김포시
3rd row안산시 상록구
4th row화성시
5th row오산시
ValueCountFrequency (%)
부천시 3
 
7.9%
김포시 3
 
7.9%
안산시 3
 
7.9%
단원구 2
 
5.3%
시흥시 2
 
5.3%
남양주시 2
 
5.3%
평택시 2
 
5.3%
성남시 2
 
5.3%
고양시 2
 
5.3%
수원시 2
 
5.3%
Other values (15) 15
39.5%
2024-03-13T20:57:07.365205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
22.7%
11
 
8.6%
구 11
 
8.6%
양 7
 
5.5%
안 5
 
3.9%
산 5
 
3.9%
포 4
 
3.1%
원 4
 
3.1%
남 4
 
3.1%
부 3
 
2.3%
Other values (28) 45
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 117
91.4%
Space Separator 11
 
8.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
24.8%
구 11
 
9.4%
양 7
 
6.0%
안 5
 
4.3%
산 5
 
4.3%
포 4
 
3.4%
원 4
 
3.4%
남 4
 
3.4%
부 3
 
2.6%
성 3
 
2.6%
Other values (27) 42
35.9%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 117
91.4%
Common 11
 
8.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
24.8%
구 11
 
9.4%
양 7
 
6.0%
안 5
 
4.3%
산 5
 
4.3%
포 4
 
3.4%
원 4
 
3.4%
남 4
 
3.4%
부 3
 
2.6%
성 3
 
2.6%
Other values (27) 42
35.9%
Common
ValueCountFrequency (%)
11
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 117
91.4%
ASCII 11
 
8.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
24.8%
구 11
 
9.4%
양 7
 
6.0%
안 5
 
4.3%
산 5
 
4.3%
포 4
 
3.4%
원 4
 
3.4%
남 4
 
3.4%
부 3
 
2.6%
성 3
 
2.6%
Other values (27) 42
35.9%
ASCII
ValueCountFrequency (%)
11
100.0%

읍면동명
Text

MISSING 

Distinct25
Distinct (%)92.6%
Missing3
Missing (%)10.0%
Memory size372.0 B
2024-03-13T20:57:07.582669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8518519
Min length2

Characters and Unicode

Total characters77
Distinct characters40
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

Unique23 ?
Unique (%)85.2%

Sample

1st row고강동
2nd row운양동
3rd row사동
4th row반송동
5th row오산동
ValueCountFrequency (%)
중동 2
 
7.4%
원곡동 2
 
7.4%
구갈동 1
 
3.7%
고강동 1
 
3.7%
포승읍 1
 
3.7%
은현면 1
 
3.7%
진접읍 1
 
3.7%
양촌읍 1
 
3.7%
금곡동 1
 
3.7%
산본동 1
 
3.7%
Other values (15) 15
55.6%
2024-03-13T20:57:07.931424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 21
27.3%
읍 5
 
6.5%
중 3
 
3.9%
산 3
 
3.9%
곡 3
 
3.9%
신 3
 
3.9%
촌 2
 
2.6%
현 2
 
2.6%
진 2
 
2.6%
양 2
 
2.6%
Other values (30) 31
40.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
27.3%
읍 5
 
6.5%
중 3
 
3.9%
산 3
 
3.9%
곡 3
 
3.9%
신 3
 
3.9%
촌 2
 
2.6%
현 2
 
2.6%
진 2
 
2.6%
양 2
 
2.6%
Other values (30) 31
40.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
27.3%
읍 5
 
6.5%
중 3
 
3.9%
산 3
 
3.9%
곡 3
 
3.9%
신 3
 
3.9%
촌 2
 
2.6%
현 2
 
2.6%
진 2
 
2.6%
양 2
 
2.6%
Other values (30) 31
40.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 21
27.3%
읍 5
 
6.5%
중 3
 
3.9%
산 3
 
3.9%
곡 3
 
3.9%
신 3
 
3.9%
촌 2
 
2.6%
현 2
 
2.6%
진 2
 
2.6%
양 2
 
2.6%
Other values (30) 31
40.3%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.6793
Minimum0
Maximum37.856
Zeros3
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:08.077428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q137.2715
median37.3765
Q337.58575
95-th percentile37.6972
Maximum37.856
Range37.856
Interquartile range (IQR)0.31425

Descriptive statistics

Standard deviation11.420169
Coefficient of variation (CV)0.33908571
Kurtosis6.3021673
Mean33.6793
Median Absolute Deviation (MAD)0.1675
Skewness-2.8074453
Sum1010.379
Variance130.42027
MonotonicityNot monotonic
2024-03-13T20:57:08.236302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 3
 
10.0%
37.528 1
 
3.3%
37.352 1
 
3.3%
37.383 1
 
3.3%
37.856 1
 
3.3%
37.699 1
 
3.3%
37.605 1
 
3.3%
37.273 1
 
3.3%
37.37 1
 
3.3%
37.661 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.0 3
10.0%
36.984 1
 
3.3%
36.987 1
 
3.3%
37.138 1
 
3.3%
37.193 1
 
3.3%
37.271 1
 
3.3%
37.273 1
 
3.3%
37.288 1
 
3.3%
37.292 1
 
3.3%
37.326 1
 
3.3%
ValueCountFrequency (%)
37.856 1
3.3%
37.699 1
3.3%
37.695 1
3.3%
37.661 1
3.3%
37.646 1
3.3%
37.624 1
3.3%
37.607 1
3.3%
37.605 1
3.3%
37.528 1
3.3%
37.505 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.2129
Minimum0
Maximum127.178
Zeros3
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:57:08.352298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1126.76325
median126.843
Q3127.05775
95-th percentile127.15025
Maximum127.178
Range127.178
Interquartile range (IQR)0.2945

Descriptive statistics

Standard deviation38.722145
Coefficient of variation (CV)0.33903478
Kurtosis6.3077242
Mean114.2129
Median Absolute Deviation (MAD)0.141
Skewness-2.8090263
Sum3426.387
Variance1499.4045
MonotonicityNot monotonic
2024-03-13T20:57:08.462499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 3
 
10.0%
126.822 1
 
3.3%
126.723 1
 
3.3%
126.964 1
 
3.3%
127.03 1
 
3.3%
127.178 1
 
3.3%
126.593 1
 
3.3%
126.941 1
 
3.3%
126.938 1
 
3.3%
126.767 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.0 3
10.0%
126.593 1
 
3.3%
126.594 1
 
3.3%
126.682 1
 
3.3%
126.723 1
 
3.3%
126.762 1
 
3.3%
126.767 1
 
3.3%
126.776 1
 
3.3%
126.78 1
 
3.3%
126.8 1
 
3.3%
ValueCountFrequency (%)
127.178 1
3.3%
127.157 1
3.3%
127.142 1
3.3%
127.127 1
3.3%
127.113 1
3.3%
127.073 1
3.3%
127.068 1
3.3%
127.067 1
3.3%
127.03 1
3.3%
126.964 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%
2024-03-13T20:57:08.556053image/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

2024-03-13T20:57:08.645350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:57:08.730567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2024-03-13T20:57:04.327625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:03.208442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:03.611214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:03.948358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:04.453171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:03.296613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:03.693869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:04.040319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:04.560915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:03.375081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:03.762331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:04.132609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:04.647295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:03.502403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:03.859279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:57:04.223624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:57:08.804943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도
가맹점번호1.0000.2090.0000.0000.0001.0000.0000.000
가맹점업종명0.2091.0000.5220.4940.0000.8480.4940.494
가맹점우편번호0.0000.5221.0000.0001.0001.0000.0000.000
시도명0.0000.4940.0001.000NaNNaN0.9550.955
시군구명0.0000.0001.000NaN1.0001.000NaNNaN
읍면동명1.0000.8481.000NaN1.0001.000NaNNaN
위도0.0000.4940.0000.955NaNNaN1.0000.955
경도0.0000.4940.0000.955NaNNaN0.9551.000
2024-03-13T20:57:08.963595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명가맹점업종명
시도명1.0000.270
가맹점업종명0.2701.000
2024-03-13T20:57:09.051024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도가맹점업종명시도명
가맹점번호1.000-0.3780.3360.0920.1290.000
가맹점우편번호-0.3781.000-0.7110.2870.1530.000
위도0.336-0.7111.0000.0840.2700.807
경도0.0920.2870.0841.0000.2700.807
가맹점업종명0.1290.1530.2700.2701.0000.270
시도명0.0000.0000.8070.8070.2701.000

Missing values

2024-03-13T20:57:04.770882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:57:04.961787image/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.
2024-03-13T20:57:05.083516image/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

정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
02023-11-02700003305999999999999999레저/스포츠 서비스14406경기도부천시고강동37.528126.822<NA>N0
12023-11-02799328195999999999999999학원10077경기도김포시운양동37.646126.682<NA>N0
22023-11-02700004564999999999999999일반/휴게 음식15622경기도안산시 상록구사동37.288126.851<NA>N0
32023-11-02700004856999999999999999건축자재14539NONE<NA><NA>0.00.0<NA>N0
42023-11-02700005536999999999999999미용/위생18442경기도화성시반송동37.193127.073<NA>N0
52023-11-02700006719999999999999999음료/식품18137경기도오산시오산동37.138127.067<NA>N0
62023-11-02700008794999999999999999서적/문구/학습자재10016경기도김포시통진읍37.695126.594<NA>N0
72023-11-02799329759999999999999999신변잡화13535경기도성남시 분당구백현동37.386127.113<NA>N0
82023-11-02700011721999999999999999숙박업15382경기도안산시 단원구원곡동37.326126.801<NA>N0
92023-11-02700011986999999999999999학원16512경기도수원시 영통구하동37.292127.068<NA>N0
정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202023-11-02700025972999999999999999자동차 정비/유지16835NONE<NA><NA>0.00.0<NA>N0
212023-11-02700027104999999999999999미용/위생13345경기도성남시 수정구신흥동37.438127.142<NA>N0
222023-11-02700029537999999999999999일반/휴게 음식14948경기도시흥시신천동37.441126.78<NA>N0
232023-11-02700029894999999999999999일반/휴게 음식10401경기도고양시 일산동구장항동37.661126.767<NA>N0
242023-11-02700030395999999999999999음료/식품15806경기도군포시산본동37.37126.938<NA>N0
252023-11-02700032922999999999999999레저/스포츠 서비스16385경기도수원시 권선구금곡동37.273126.941<NA>N0
262023-11-02700034607999999999999999일반유통10049경기도김포시양촌읍37.605126.593<NA>N0
272023-11-02700036827999999999999999음료/식품12073경기도남양주시진접읍37.699127.178<NA>N0
282023-11-02700036965999999999999999주유/충전소11430경기도양주시은현면37.856127.03<NA>N0
292023-11-02700037256999999999999999음료/식품14103경기도안양시 동안구평촌동37.383126.964<NA>N0