Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells32
Missing cells (%)8.2%
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/6ce7bc30-9d27-4449-94fc-3c7c2f47088d

Alerts

일반일간결제일자 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 가맹점업종명High correlation
가맹점업종명 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
시도명 is highly overall correlated with 가맹점업종명High correlation
시도명 is highly imbalanced (78.9%)Imbalance
시군구명 has 1 (3.3%) missing valuesMissing
읍면동명 has 1 (3.3%) missing valuesMissing
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
위도 has 1 (3.3%) zerosZeros
경도 has 1 (3.3%) zerosZeros

Reproduction

Analysis started2024-03-13 11:52:49.273754
Analysis finished2024-03-13 11:52:52.422061
Duration3.15 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
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

2024-03-13T20:52:52.496766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:52:52.628857image/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%
Mean7.066471 × 108
Minimum7.000032 × 108
Maximum7.9933666 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:52:52.765458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.000032 × 108
5-th percentile7.0000549 × 108
Q17.0001626 × 108
median7.0003201 × 108
Q37.0004054 × 108
95-th percentile7.54655 × 108
Maximum7.9933666 × 108
Range99333465
Interquartile range (IQR)24284.5

Descriptive statistics

Standard deviation25195636
Coefficient of variation (CV)0.035655189
Kurtosis12.206624
Mean7.066471 × 108
Median Absolute Deviation (MAD)13133.5
Skewness3.6599969
Sum2.1199413 × 1010
Variance6.3482007 × 1014
MonotonicityNot monotonic
2024-03-13T20:52:52.928527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700003195 1
 
3.3%
700030952 1
 
3.3%
700045735 1
 
3.3%
700045224 1
 
3.3%
700045061 1
 
3.3%
700042453 1
 
3.3%
700041758 1
 
3.3%
700040981 1
 
3.3%
700039224 1
 
3.3%
700037240 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700003195 1
3.3%
700005244 1
3.3%
700005788 1
3.3%
700009863 1
3.3%
700010217 1
3.3%
700010494 1
3.3%
700015784 1
3.3%
700016212 1
3.3%
700016393 1
3.3%
700017632 1
3.3%
ValueCountFrequency (%)
799336660 1
3.3%
799335306 1
3.3%
700045735 1
3.3%
700045224 1
3.3%
700045061 1
3.3%
700042453 1
3.3%
700041758 1
3.3%
700040981 1
3.3%
700039224 1
3.3%
700037240 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

2024-03-13T20:52:53.085362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:52:53.238049image/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
일반유통
5 
일반/휴게 음식
4 
학원
4 
미용/위생
4 
레저/스포츠 서비스
3 
Other values (9)
10 

Length

Max length10
Median length6
Mean length5.3666667
Min length2

Unique

Unique8 ?
Unique (%)26.7%

Sample

1st row일반/휴게 음식
2nd row일반유통
3rd row전자제품
4th row레저/스포츠 서비스
5th row학원

Common Values

ValueCountFrequency (%)
일반유통 5
16.7%
일반/휴게 음식 4
13.3%
학원 4
13.3%
미용/위생 4
13.3%
레저/스포츠 서비스 3
10.0%
직물/침구류 2
 
6.7%
전자제품 1
 
3.3%
서적/문구/학습자재 1
 
3.3%
사무/통신 1
 
3.3%
주유/충전소 1
 
3.3%
Other values (4) 4
13.3%

Length

2024-03-13T20:52:53.397522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반유통 5
13.5%
일반/휴게 4
10.8%
음식 4
10.8%
학원 4
10.8%
미용/위생 4
10.8%
레저/스포츠 3
8.1%
서비스 3
8.1%
직물/침구류 2
 
5.4%
전자제품 1
 
2.7%
서적/문구/학습자재 1
 
2.7%
Other values (6) 6
16.2%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14344.233
Minimum10079
Maximum18600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:52:53.573960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10079
5-th percentile10581.75
Q112210.25
median14485
Q316671.75
95-th percentile18365.25
Maximum18600
Range8521
Interquartile range (IQR)4461.5

Descriptive statistics

Standard deviation2645.3646
Coefficient of variation (CV)0.18442008
Kurtosis-1.236305
Mean14344.233
Median Absolute Deviation (MAD)2254.5
Skewness0.081248605
Sum430327
Variance6997953.9
MonotonicityNot monotonic
2024-03-13T20:52:53.722981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
12614 1
 
3.3%
12190 1
 
3.3%
17720 1
 
3.3%
16336 1
 
3.3%
17904 1
 
3.3%
11932 1
 
3.3%
14451 1
 
3.3%
16620 1
 
3.3%
14710 1
 
3.3%
13525 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10079 1
3.3%
10386 1
3.3%
10821 1
3.3%
10855 1
3.3%
11161 1
3.3%
11806 1
3.3%
11932 1
3.3%
12190 1
3.3%
12271 1
3.3%
12614 1
3.3%
ValueCountFrequency (%)
18600 1
3.3%
18453 1
3.3%
18258 1
3.3%
17904 1
3.3%
17720 1
3.3%
17052 1
3.3%
16898 1
3.3%
16689 1
3.3%
16620 1
3.3%
16336 1
3.3%

시도명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
29 
NONE
 
1

Length

Max length4
Median length3
Mean length3.0333333
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 29
96.7%
NONE 1
 
3.3%

Length

2024-03-13T20:52:53.876186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:52:53.996538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 29
96.7%
none 1
 
3.3%

시군구명
Text

MISSING 

Distinct21
Distinct (%)72.4%
Missing1
Missing (%)3.3%
Memory size372.0 B
2024-03-13T20:52:54.195240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.2413793
Min length3

Characters and Unicode

Total characters123
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 (%)55.2%

Sample

1st row여주시
2nd row하남시
3rd row파주시
4th row용인시 처인구
5th row화성시
ValueCountFrequency (%)
부천시 4
 
10.8%
수원시 3
 
8.1%
화성시 3
 
8.1%
파주시 2
 
5.4%
평택시 2
 
5.4%
남양주시 2
 
5.4%
용인시 2
 
5.4%
여주시 1
 
2.7%
시흥시 1
 
2.7%
권선구 1
 
2.7%
Other values (16) 16
43.2%
2024-03-13T20:52:54.581013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 30
24.4%
구 8
 
6.5%
8
 
6.5%
주 6
 
4.9%
천 6
 
4.9%
부 5
 
4.1%
성 4
 
3.3%
원 4
 
3.3%
남 4
 
3.3%
화 3
 
2.4%
Other values (31) 45
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 115
93.5%
Space Separator 8
 
6.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 30
26.1%
구 8
 
7.0%
주 6
 
5.2%
천 6
 
5.2%
부 5
 
4.3%
성 4
 
3.5%
원 4
 
3.5%
남 4
 
3.5%
화 3
 
2.6%
수 3
 
2.6%
Other values (30) 42
36.5%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 115
93.5%
Common 8
 
6.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 30
26.1%
구 8
 
7.0%
주 6
 
5.2%
천 6
 
5.2%
부 5
 
4.3%
성 4
 
3.5%
원 4
 
3.5%
남 4
 
3.5%
화 3
 
2.6%
수 3
 
2.6%
Other values (30) 42
36.5%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 115
93.5%
ASCII 8
 
6.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 30
26.1%
구 8
 
7.0%
주 6
 
5.2%
천 6
 
5.2%
부 5
 
4.3%
성 4
 
3.5%
원 4
 
3.5%
남 4
 
3.5%
화 3
 
2.6%
수 3
 
2.6%
Other values (30) 42
36.5%
ASCII
ValueCountFrequency (%)
8
100.0%

읍면동명
Text

MISSING 

Distinct29
Distinct (%)100.0%
Missing1
Missing (%)3.3%
Memory size372.0 B
2024-03-13T20:52:54.870110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0344828
Min length2

Characters and Unicode

Total characters88
Distinct characters52
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

Unique29 ?
Unique (%)100.0%

Sample

1st row북내면
2nd row덕풍동
3rd row금촌동
4th row김량장동
5th row반송동
ValueCountFrequency (%)
북내면 1
 
3.4%
고잔동 1
 
3.4%
정자동 1
 
3.4%
합정동 1
 
3.4%
삼정동 1
 
3.4%
서둔동 1
 
3.4%
심곡본동 1
 
3.4%
삼평동 1
 
3.4%
주엽동 1
 
3.4%
문산읍 1
 
3.4%
Other values (19) 19
65.5%
2024-03-13T20:52:55.305621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 22
25.0%
읍 6
 
6.8%
정 4
 
4.5%
도 2
 
2.3%
중 2
 
2.3%
삼 2
 
2.3%
장 2
 
2.3%
남 2
 
2.3%
산 2
 
2.3%
곡 2
 
2.3%
Other values (42) 42
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 22
25.0%
읍 6
 
6.8%
정 4
 
4.5%
도 2
 
2.3%
중 2
 
2.3%
삼 2
 
2.3%
장 2
 
2.3%
남 2
 
2.3%
산 2
 
2.3%
곡 2
 
2.3%
Other values (42) 42
47.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 22
25.0%
읍 6
 
6.8%
정 4
 
4.5%
도 2
 
2.3%
중 2
 
2.3%
삼 2
 
2.3%
장 2
 
2.3%
남 2
 
2.3%
산 2
 
2.3%
곡 2
 
2.3%
Other values (42) 42
47.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 22
25.0%
읍 6
 
6.8%
정 4
 
4.5%
도 2
 
2.3%
중 2
 
2.3%
삼 2
 
2.3%
장 2
 
2.3%
남 2
 
2.3%
산 2
 
2.3%
곡 2
 
2.3%
Other values (42) 42
47.7%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.186233
Minimum0
Maximum37.858
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:52:55.503807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.0305
Q137.24825
median37.4085
Q337.5705
95-th percentile37.81285
Maximum37.858
Range37.858
Interquartile range (IQR)0.32225

Descriptive statistics

Standard deviation6.838115
Coefficient of variation (CV)0.18897007
Kurtosis29.929602
Mean36.186233
Median Absolute Deviation (MAD)0.169
Skewness-5.4678884
Sum1085.587
Variance46.759817
MonotonicityNot monotonic
2024-03-13T20:52:55.699793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
37.332 1
 
3.3%
37.652 1
 
3.3%
37.08 1
 
3.3%
37.309 1
 
3.3%
36.99 1
 
3.3%
0.0 1
 
3.3%
37.521 1
 
3.3%
37.267 1
 
3.3%
37.483 1
 
3.3%
37.397 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.0 1
3.3%
36.99 1
3.3%
37.08 1
3.3%
37.131 1
3.3%
37.207 1
3.3%
37.212 1
3.3%
37.235 1
3.3%
37.242 1
3.3%
37.267 1
3.3%
37.309 1
3.3%
ValueCountFrequency (%)
37.858 1
3.3%
37.852 1
3.3%
37.765 1
3.3%
37.741 1
3.3%
37.669 1
3.3%
37.652 1
3.3%
37.646 1
3.3%
37.58 1
3.3%
37.542 1
3.3%
37.521 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.7719
Minimum0
Maximum127.694
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:52:55.865014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile126.7135
Q1126.77975
median126.992
Q3127.1145
95-th percentile127.3215
Maximum127.694
Range127.694
Interquartile range (IQR)0.33475

Descriptive statistics

Standard deviation23.189008
Coefficient of variation (CV)0.18887879
Kurtosis29.99371
Mean122.7719
Median Absolute Deviation (MAD)0.207
Skewness-5.4763912
Sum3683.157
Variance537.73009
MonotonicityNot monotonic
2024-03-13T20:52:56.015518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
126.77 2
 
6.7%
126.772 2
 
6.7%
126.785 2
 
6.7%
127.694 1
 
3.3%
126.841 1
 
3.3%
127.064 1
 
3.3%
126.984 1
 
3.3%
127.102 1
 
3.3%
0.0 1
 
3.3%
126.994 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
0.0 1
3.3%
126.673 1
3.3%
126.763 1
3.3%
126.77 2
6.7%
126.772 2
6.7%
126.778 1
3.3%
126.785 2
6.7%
126.824 1
3.3%
126.841 1
3.3%
126.922 1
3.3%
ValueCountFrequency (%)
127.694 1
3.3%
127.326 1
3.3%
127.316 1
3.3%
127.217 1
3.3%
127.202 1
3.3%
127.201 1
3.3%
127.162 1
3.3%
127.115 1
3.3%
127.113 1
3.3%
127.102 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%
2024-03-13T20:52:56.138157image/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

2024-03-13T20:52:56.270014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:52:56.380225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2024-03-13T20:52:51.417979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:49.743787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:50.504552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:50.932029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:51.524743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:50.204803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:50.617921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:51.075386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:51.619556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:50.297585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:50.714646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:51.182664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:51.721827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:50.394994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:50.818275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:51.303864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:52:56.445986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도
가맹점번호1.0000.0000.1170.0000.0001.0000.0000.000
가맹점업종명0.0001.0000.3441.0000.0001.0001.0001.000
가맹점우편번호0.1170.3441.0000.0000.9991.0000.0000.000
시도명0.0001.0000.0001.000NaNNaN0.6550.655
시군구명0.0000.0000.999NaN1.0001.000NaNNaN
읍면동명1.0001.0001.000NaN1.0001.000NaNNaN
위도0.0001.0000.0000.655NaNNaN1.0000.655
경도0.0001.0000.0000.655NaNNaN0.6551.000
2024-03-13T20:52:56.590270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명가맹점업종명
시도명1.0000.756
가맹점업종명0.7561.000
2024-03-13T20:52:56.694896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도가맹점업종명시도명
가맹점번호1.0000.149-0.198-0.1090.0000.000
가맹점우편번호0.1491.000-0.8020.1460.0000.000
위도-0.198-0.8021.000-0.0840.7560.454
경도-0.1090.146-0.0841.0000.7560.454
가맹점업종명0.0000.0000.7560.7561.0000.756
시도명0.0000.0000.4540.4540.7561.000

Missing values

2024-03-13T20:52:51.908600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:52:52.206567image/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.
2024-03-13T20:52:52.367018image/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
02023-11-02700003195999999999999999일반/휴게 음식12614경기도여주시북내면37.332127.694<NA>N0
12023-11-02799335306999999999999999일반유통12971경기도하남시덕풍동37.542127.201<NA>N0
22023-11-02700005244999999999999999전자제품10855경기도파주시금촌동37.765126.77<NA>N0
32023-11-02700005788999999999999999레저/스포츠 서비스17052경기도용인시 처인구김량장동37.235127.202<NA>N0
42023-11-02700009863999999999999999학원18453경기도화성시반송동37.207127.073<NA>N0
52023-11-02700010217999999999999999학원10079경기도김포시장기동37.646126.673<NA>N0
62023-11-02700010494999999999999999일반/휴게 음식18258경기도화성시남양읍37.212126.824<NA>N0
72023-11-02799336660999999999999999미용/위생18600경기도화성시향남읍37.131126.922<NA>N0
82023-11-02700015784999999999999999서적/문구/학습자재14548경기도부천시중동37.501126.772<NA>N0
92023-11-02700016212999999999999999학원16898경기도용인시 기흥구보정동37.319127.115<NA>N0
일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202023-11-02700035714999999999999999대형유통10821경기도파주시문산읍37.858126.785<NA>N0
212023-11-02700036957999999999999999레저/스포츠 서비스10386경기도고양시 일산서구주엽동37.669126.763<NA>N0
222023-11-02700037240999999999999999일반/휴게 음식13525경기도성남시 분당구삼평동37.397127.113<NA>N0
232023-11-02700039224999999999999999미용/위생14710경기도부천시심곡본동37.483126.785<NA>N0
242023-11-02700040981999999999999999건축자재16620경기도수원시 권선구서둔동37.267126.994<NA>N0
252023-11-02700041758999999999999999직물/침구류14451경기도부천시삼정동37.521126.772<NA>N0
262023-11-02700042453999999999999999신변잡화11932NONE<NA><NA>0.00.0<NA>N0
272023-11-02700045061999999999999999레저/스포츠 서비스17904경기도평택시합정동36.99127.102<NA>N0
282023-11-02700045224999999999999999일반/휴게 음식16336경기도수원시 장안구정자동37.309126.984<NA>N0
292023-11-02700045735999999999999999의류17720경기도평택시독곡동37.08127.064<NA>N0