Overview

Dataset statistics

Number of variables11
Number of observations71
Missing cells14
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory95.9 B

Variable types

Numeric6
Categorical3
Text2

Dataset

Description서울특별시 성동구 내에 위치한 거리가게 현황 정보입니다. 허가여부, 관리번호, 형태, 취급품목, 면적, 보도폭 등의 정보를 포함하고 있습니다.
Author서울특별시 성동구
URLhttps://www.data.go.kr/data/15064067/fileData.do

Alerts

형태 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
허가여부 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
연번 is highly overall correlated with 허가여부 and 1 other fieldsHigh correlation
가로 is highly overall correlated with 면적High correlation
세로 is highly overall correlated with 면적 and 1 other fieldsHigh correlation
면적 is highly overall correlated with 가로 and 3 other fieldsHigh correlation
보도폭 is highly overall correlated with 유효보도폭 and 1 other fieldsHigh correlation
유효보도폭 is highly overall correlated with 보도폭High correlation
보도폭 has 14 (19.7%) missing valuesMissing
연번 has unique valuesUnique
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:11:45.733898
Analysis finished2023-12-12 19:11:50.798015
Duration5.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36
Minimum1
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-13T04:11:50.907139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.5
Q118.5
median36
Q353.5
95-th percentile67.5
Maximum71
Range70
Interquartile range (IQR)35

Descriptive statistics

Standard deviation20.639767
Coefficient of variation (CV)0.57332687
Kurtosis-1.2
Mean36
Median Absolute Deviation (MAD)18
Skewness0
Sum2556
Variance426
MonotonicityStrictly increasing
2023-12-13T04:11:51.057067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
2 1
 
1.4%
53 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
46 1
 
1.4%
Other values (61) 61
85.9%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
71 1
1.4%
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%
64 1
1.4%
63 1
1.4%
62 1
1.4%

허가여부
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size700.0 B
미허가
59 
기허가
12 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미허가 59
83.1%
기허가 12
 
16.9%

Length

2023-12-13T04:11:51.243598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:11:51.399105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미허가 59
83.1%
기허가 12
 
16.9%

관리번호
Text

UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size700.0 B
2023-12-13T04:11:51.731794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.8309859
Min length4

Characters and Unicode

Total characters343
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)100.0%

Sample

1st row거가-1
2nd row거가-2
3rd row거가-4
4th row거가-5
5th row거가-6
ValueCountFrequency (%)
거가-1 1
 
1.4%
거가-72 1
 
1.4%
거가-68 1
 
1.4%
거가-67 1
 
1.4%
거가-64 1
 
1.4%
거가-63 1
 
1.4%
거가-62 1
 
1.4%
거가-60 1
 
1.4%
거가-58 1
 
1.4%
거가-49 1
 
1.4%
Other values (61) 61
85.9%
2023-12-13T04:11:52.257094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 71
20.7%
거 59
17.2%
가 59
17.2%
4 18
 
5.2%
5 17
 
5.0%
1 16
 
4.7%
3 16
 
4.7%
7 13
 
3.8%
6 12
 
3.5%
2 12
 
3.5%
Other values (8) 50
14.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 149
43.4%
Decimal Number 123
35.9%
Dash Punctuation 71
20.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 18
14.6%
5 17
13.8%
1 16
13.0%
3 16
13.0%
7 13
10.6%
6 12
9.8%
2 12
9.8%
9 7
 
5.7%
8 7
 
5.7%
0 5
 
4.1%
Other Letter
ValueCountFrequency (%)
거 59
39.6%
가 59
39.6%
디 7
 
4.7%
자 7
 
4.7%
인 7
 
4.7%
시 5
 
3.4%
범 5
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 194
56.6%
Hangul 149
43.4%

Most frequent character per script

Common
ValueCountFrequency (%)
- 71
36.6%
4 18
 
9.3%
5 17
 
8.8%
1 16
 
8.2%
3 16
 
8.2%
7 13
 
6.7%
6 12
 
6.2%
2 12
 
6.2%
9 7
 
3.6%
8 7
 
3.6%
Hangul
ValueCountFrequency (%)
거 59
39.6%
가 59
39.6%
디 7
 
4.7%
자 7
 
4.7%
인 7
 
4.7%
시 5
 
3.4%
범 5
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194
56.6%
Hangul 149
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 71
36.6%
4 18
 
9.3%
5 17
 
8.8%
1 16
 
8.2%
3 16
 
8.2%
7 13
 
6.7%
6 12
 
6.2%
2 12
 
6.2%
9 7
 
3.6%
8 7
 
3.6%
Hangul
ValueCountFrequency (%)
거 59
39.6%
가 59
39.6%
디 7
 
4.7%
자 7
 
4.7%
인 7
 
4.7%
시 5
 
3.4%
범 5
 
3.4%

형태
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size700.0 B
좌판
35 
손수레
19 
규격노점
12 
포장마차
4 
차량
 
1

Length

Max length4
Median length2
Mean length2.7183099
Min length2

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row손수레
2nd row손수레
3rd row손수레
4th row손수레
5th row좌판

Common Values

ValueCountFrequency (%)
좌판 35
49.3%
손수레 19
26.8%
규격노점 12
 
16.9%
포장마차 4
 
5.6%
차량 1
 
1.4%

Length

2023-12-13T04:11:52.469766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:11:52.618263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
좌판 35
49.3%
손수레 19
26.8%
규격노점 12
 
16.9%
포장마차 4
 
5.6%
차량 1
 
1.4%

취급품목
Categorical

Distinct5
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size700.0 B
농수산물
33 
음식
30 
의류
5 
잡화
 
2
액세서리
 
1

Length

Max length4
Median length2
Mean length2.9577465
Min length2

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row음식
2nd row음식
3rd row잡화
4th row음식
5th row농수산물

Common Values

ValueCountFrequency (%)
농수산물 33
46.5%
음식 30
42.3%
의류 5
 
7.0%
잡화 2
 
2.8%
액세서리 1
 
1.4%

Length

2023-12-13T04:11:52.808699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:11:53.000603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농수산물 33
46.5%
음식 30
42.3%
의류 5
 
7.0%
잡화 2
 
2.8%
액세서리 1
 
1.4%

인접지 지번
Text

Distinct45
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Memory size700.0 B
2023-12-13T04:11:53.278988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length32
Mean length26.633803
Min length17

Characters and Unicode

Total characters1891
Distinct characters105
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)46.5%

Sample

1st row서울특별시 성동구 도선동 35-2 공원 앞
2nd row서울특별시 성동구 도선동 39-2 편의점 옆
3rd row서울특별시 성동구 도선동 46 외환은행
4th row서울특별시 성동구 마장동 773번지 3호 제일은행앞
5th row서울특별시 성동구 마장동521번지12호 우시장앞
ValueCountFrequency (%)
서울특별시 71
20.4%
성동구 71
20.4%
금호동3가 24
 
6.9%
행당동 8
 
2.3%
앞 8
 
2.3%
421번지(삼일약국 8
 
2.3%
옥수동 5
 
1.4%
413번지 5
 
1.4%
용답동 5
 
1.4%
656-308(뚝섬역 5
 
1.4%
Other values (83) 138
39.7%
2023-12-13T04:11:53.794491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
282
 
14.9%
동 137
 
7.2%
성 94
 
5.0%
구 83
 
4.4%
3 74
 
3.9%
시 72
 
3.8%
서 71
 
3.8%
울 71
 
3.8%
별 71
 
3.8%
특 71
 
3.8%
Other values (95) 865
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1186
62.7%
Decimal Number 338
 
17.9%
Space Separator 282
 
14.9%
Dash Punctuation 29
 
1.5%
Open Punctuation 28
 
1.5%
Close Punctuation 28
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 137
 
11.6%
성 94
 
7.9%
구 83
 
7.0%
시 72
 
6.1%
서 71
 
6.0%
울 71
 
6.0%
별 71
 
6.0%
특 71
 
6.0%
번 53
 
4.5%
가 44
 
3.7%
Other values (81) 419
35.3%
Decimal Number
ValueCountFrequency (%)
3 74
21.9%
1 64
18.9%
2 51
15.1%
6 34
10.1%
4 31
9.2%
5 24
 
7.1%
8 22
 
6.5%
9 15
 
4.4%
0 13
 
3.8%
7 10
 
3.0%
Space Separator
ValueCountFrequency (%)
282
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1186
62.7%
Common 705
37.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 137
 
11.6%
성 94
 
7.9%
구 83
 
7.0%
시 72
 
6.1%
서 71
 
6.0%
울 71
 
6.0%
별 71
 
6.0%
특 71
 
6.0%
번 53
 
4.5%
가 44
 
3.7%
Other values (81) 419
35.3%
Common
ValueCountFrequency (%)
282
40.0%
3 74
 
10.5%
1 64
 
9.1%
2 51
 
7.2%
6 34
 
4.8%
4 31
 
4.4%
- 29
 
4.1%
( 28
 
4.0%
) 28
 
4.0%
5 24
 
3.4%
Other values (4) 60
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1186
62.7%
ASCII 705
37.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
282
40.0%
3 74
 
10.5%
1 64
 
9.1%
2 51
 
7.2%
6 34
 
4.8%
4 31
 
4.4%
- 29
 
4.1%
( 28
 
4.0%
) 28
 
4.0%
5 24
 
3.4%
Other values (4) 60
 
8.5%
Hangul
ValueCountFrequency (%)
동 137
 
11.6%
성 94
 
7.9%
구 83
 
7.0%
시 72
 
6.1%
서 71
 
6.0%
울 71
 
6.0%
별 71
 
6.0%
특 71
 
6.0%
번 53
 
4.5%
가 44
 
3.7%
Other values (81) 419
35.3%

가로
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6704225
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-13T04:11:53.963682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q12
median2.2
Q33
95-th percentile4.85
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1023863
Coefficient of variation (CV)0.41281343
Kurtosis0.78110561
Mean2.6704225
Median Absolute Deviation (MAD)0.4
Skewness1.1278206
Sum189.6
Variance1.2152555
MonotonicityNot monotonic
2023-12-13T04:11:54.118309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2.0 26
36.6%
3.0 9
 
12.7%
2.5 5
 
7.0%
1.5 3
 
4.2%
4.2 3
 
4.2%
2.2 3
 
4.2%
1.0 3
 
4.2%
2.6 3
 
4.2%
4.0 3
 
4.2%
1.7 2
 
2.8%
Other values (10) 11
15.5%
ValueCountFrequency (%)
1.0 3
 
4.2%
1.5 3
 
4.2%
1.7 2
 
2.8%
2.0 26
36.6%
2.1 1
 
1.4%
2.2 3
 
4.2%
2.5 5
 
7.0%
2.6 3
 
4.2%
3.0 9
 
12.7%
3.4 1
 
1.4%
ValueCountFrequency (%)
6.0 1
 
1.4%
5.5 1
 
1.4%
5.4 1
 
1.4%
5.0 1
 
1.4%
4.7 1
 
1.4%
4.4 2
2.8%
4.2 3
4.2%
4.0 3
4.2%
3.8 1
 
1.4%
3.5 1
 
1.4%

세로
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6732394
Minimum1
Maximum4.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-13T04:11:54.229796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5
median1.5
Q31.6
95-th percentile2.9
Maximum4.2
Range3.2
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.62334529
Coefficient of variation (CV)0.37253801
Kurtosis5.9102138
Mean1.6732394
Median Absolute Deviation (MAD)0.1
Skewness2.2438694
Sum118.8
Variance0.38855936
MonotonicityNot monotonic
2023-12-13T04:11:54.355301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1.5 33
46.5%
1.6 11
 
15.5%
1.0 9
 
12.7%
2.5 4
 
5.6%
1.2 3
 
4.2%
2.0 3
 
4.2%
2.2 1
 
1.4%
1.4 1
 
1.4%
2.1 1
 
1.4%
3.4 1
 
1.4%
Other values (4) 4
 
5.6%
ValueCountFrequency (%)
1.0 9
 
12.7%
1.2 3
 
4.2%
1.4 1
 
1.4%
1.5 33
46.5%
1.6 11
 
15.5%
2.0 3
 
4.2%
2.1 1
 
1.4%
2.2 1
 
1.4%
2.5 4
 
5.6%
2.8 1
 
1.4%
ValueCountFrequency (%)
4.2 1
 
1.4%
4.0 1
 
1.4%
3.4 1
 
1.4%
3.0 1
 
1.4%
2.8 1
 
1.4%
2.5 4
 
5.6%
2.2 1
 
1.4%
2.1 1
 
1.4%
2.0 3
 
4.2%
1.6 11
15.5%

면적
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.773662
Minimum1.5
Maximum22.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-13T04:11:54.488372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile1.7
Q13
median3.5
Q34.8
95-th percentile11.85
Maximum22.68
Range21.18
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation3.7819984
Coefficient of variation (CV)0.79226356
Kurtosis8.2531891
Mean4.773662
Median Absolute Deviation (MAD)1
Skewness2.6728015
Sum338.93
Variance14.303512
MonotonicityNot monotonic
2023-12-13T04:11:54.623901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3.0 18
25.4%
4.5 5
 
7.0%
3.75 3
 
4.2%
4.8 3
 
4.2%
3.52 3
 
4.2%
3.2 3
 
4.2%
1.5 3
 
4.2%
2.0 3
 
4.2%
6.0 3
 
4.2%
10.5 3
 
4.2%
Other values (24) 24
33.8%
ValueCountFrequency (%)
1.5 3
 
4.2%
1.6 1
 
1.4%
1.8 1
 
1.4%
2.0 3
 
4.2%
2.04 1
 
1.4%
2.4 1
 
1.4%
2.5 1
 
1.4%
2.55 1
 
1.4%
2.6 1
 
1.4%
3.0 18
25.4%
ValueCountFrequency (%)
22.68 1
 
1.4%
16.8 1
 
1.4%
15.98 1
 
1.4%
13.2 1
 
1.4%
10.5 3
4.2%
9.5 1
 
1.4%
8.25 1
 
1.4%
8.0 1
 
1.4%
7.48 1
 
1.4%
6.4 1
 
1.4%

보도폭
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)17.5%
Missing14
Missing (%)19.7%
Infinite0
Infinite (%)0.0%
Mean3.8736842
Minimum1.5
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-13T04:11:54.746911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2
Q13
median4
Q35
95-th percentile5
Maximum8
Range6.5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.174814
Coefficient of variation (CV)0.30328079
Kurtosis1.2226921
Mean3.8736842
Median Absolute Deviation (MAD)1
Skewness0.48755869
Sum220.8
Variance1.380188
MonotonicityNot monotonic
2023-12-13T04:11:54.858294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3.0 22
31.0%
5.0 18
25.4%
4.0 5
 
7.0%
4.5 4
 
5.6%
2.0 2
 
2.8%
1.5 2
 
2.8%
3.7 1
 
1.4%
4.6 1
 
1.4%
8.0 1
 
1.4%
3.5 1
 
1.4%
(Missing) 14
19.7%
ValueCountFrequency (%)
1.5 2
 
2.8%
2.0 2
 
2.8%
3.0 22
31.0%
3.5 1
 
1.4%
3.7 1
 
1.4%
4.0 5
 
7.0%
4.5 4
 
5.6%
4.6 1
 
1.4%
5.0 18
25.4%
8.0 1
 
1.4%
ValueCountFrequency (%)
8.0 1
 
1.4%
5.0 18
25.4%
4.6 1
 
1.4%
4.5 4
 
5.6%
4.0 5
 
7.0%
3.7 1
 
1.4%
3.5 1
 
1.4%
3.0 22
31.0%
2.0 2
 
2.8%
1.5 2
 
2.8%

유효보도폭
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4366197
Minimum-4
Maximum6.5
Zeros0
Zeros (%)0.0%
Negative14
Negative (%)19.7%
Memory size771.0 B
2023-12-13T04:11:54.968795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile-1.6
Q10.9
median1.5
Q32.85
95-th percentile3.5
Maximum6.5
Range10.5
Interquartile range (IQR)1.95

Descriptive statistics

Standard deviation1.9477635
Coefficient of variation (CV)1.3557962
Kurtosis0.62790222
Mean1.4366197
Median Absolute Deviation (MAD)1
Skewness-0.69934247
Sum102
Variance3.7937827
MonotonicityNot monotonic
2023-12-13T04:11:55.370871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1.5 16
22.5%
3.5 10
14.1%
2.5 7
9.9%
1.4 7
9.9%
-1.5 4
 
5.6%
-1.6 4
 
5.6%
3.0 4
 
5.6%
-1.0 3
 
4.2%
3.6 2
 
2.8%
0.8 2
 
2.8%
Other values (11) 12
16.9%
ValueCountFrequency (%)
-4.0 1
 
1.4%
-3.4 1
 
1.4%
-3.0 1
 
1.4%
-1.6 4
5.6%
-1.5 4
5.6%
-1.0 3
4.2%
0.5 2
 
2.8%
0.8 2
 
2.8%
1.0 1
 
1.4%
1.4 7
9.9%
ValueCountFrequency (%)
6.5 1
 
1.4%
3.6 2
 
2.8%
3.5 10
14.1%
3.0 4
 
5.6%
2.9 1
 
1.4%
2.8 1
 
1.4%
2.5 7
9.9%
2.3 1
 
1.4%
2.2 1
 
1.4%
2.0 1
 
1.4%

Interactions

2023-12-13T04:11:49.935890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:46.326880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:47.324588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:48.004329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:48.702272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:49.305234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:50.023757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:46.468655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:47.456058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:48.140098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:48.811271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:49.406275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:50.128224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:46.579382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:47.569499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:48.259190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:48.898587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:49.535562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:50.236957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:46.685030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:47.675791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:48.375787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:48.995295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:49.662633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:50.314590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:46.791325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:47.782158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:48.483099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:49.094982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:49.757478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:50.406003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:46.905290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:47.895436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:48.599957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:49.214807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:49.848937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:11:55.453629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번허가여부관리번호형태취급품목인접지 지번가로세로면적보도폭유효보도폭
연번1.0000.9911.0000.8780.3810.9850.5860.4030.5130.7390.652
허가여부0.9911.0001.0001.0000.3731.0000.3020.4770.5280.7590.449
관리번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
형태0.8781.0001.0001.0000.6920.9980.6490.7290.7390.6030.651
취급품목0.3810.3731.0000.6921.0000.8570.5870.0000.0000.4510.170
인접지 지번0.9851.0001.0000.9980.8571.0000.9100.9960.9891.0000.991
가로0.5860.3021.0000.6490.5870.9101.0000.7850.8550.4530.545
세로0.4030.4771.0000.7290.0000.9960.7851.0000.9350.7340.655
면적0.5130.5281.0000.7390.0000.9890.8550.9351.0000.0000.592
보도폭0.7390.7591.0000.6030.4511.0000.4530.7340.0001.0000.881
유효보도폭0.6520.4491.0000.6510.1700.9910.5450.6550.5920.8811.000
2023-12-13T04:11:55.566799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
형태취급품목허가여부
형태1.0000.3230.978
취급품목0.3231.0000.444
허가여부0.9780.4441.000
2023-12-13T04:11:55.651209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번가로세로면적보도폭유효보도폭허가여부형태취급품목
연번1.000-0.0360.2400.0660.101-0.0470.8590.5360.203
가로-0.0361.0000.2930.8810.2970.0120.3600.2990.300
세로0.2400.2931.0000.6250.322-0.1790.3420.5490.000
면적0.0660.8810.6251.0000.355-0.0420.5020.5280.000
보도폭0.1010.2970.3220.3551.0000.8390.5440.4240.321
유효보도폭-0.0470.012-0.179-0.0420.8391.0000.4770.4920.140
허가여부0.8590.3600.3420.5020.5440.4771.0000.9780.444
형태0.5360.2990.5490.5280.4240.4920.9781.0000.323
취급품목0.2030.3000.0000.0000.3210.1400.4440.3231.000

Missing values

2023-12-13T04:11:50.534140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:11:50.718577image/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

연번허가여부관리번호형태취급품목인접지 지번가로세로면적보도폭유효보도폭
01미허가거가-1손수레음식서울특별시 성동구 도선동 35-2 공원 앞1.71.22.043.72.5
12미허가거가-2손수레음식서울특별시 성동구 도선동 39-2 편의점 옆2.61.02.6<NA>-1.0
23미허가거가-4손수레잡화서울특별시 성동구 도선동 46 외환은행2.51.02.54.63.6
34미허가거가-5손수레음식서울특별시 성동구 마장동 773번지 3호 제일은행앞3.01.54.58.06.5
45미허가거가-6좌판농수산물서울특별시 성동구 마장동521번지12호 우시장앞3.42.27.485.02.8
56미허가거가-7좌판의류서울특별시 성동구 마장동521번지11호 국민은행앞3.02.06.05.03.0
67미허가거가-9손수레농수산물서울특별시 성동구 마장동478번지39호2.61.43.645.03.6
78미허가거가-10손수레음식서울특별시 성동구 마장동521번지11호 국민은행앞2.52.15.255.02.9
89미허가거가-12손수레액세서리서울특별시 성동구 행당동 286-2(맥도날드)앞3.51.03.54.53.5
910미허가거가-13좌판의류서울특별시 성동구 행당동 286-2(맥도날드)앞4.41.04.44.53.5
연번허가여부관리번호형태취급품목인접지 지번가로세로면적보도폭유효보도폭
6162기허가디자인-3규격노점음식서울특별시 성동구 성수1가2동 656-308(뚝섬역 8번출구)2.01.53.05.03.5
6263기허가디자인-4규격노점음식서울특별시 성동구 성수1가2동 656-308(뚝섬역 8번출구)2.01.53.05.03.5
6364기허가디자인-5규격노점음식서울특별시 성동구 성수1가2동 656-308(뚝섬역 8번출구)2.01.53.05.03.5
6465기허가디자인-7규격노점음식서울특별시 성동구 행당동 17번지(한양대역 3번출구)2.01.53.05.03.5
6566기허가디자인-8규격노점음식서울특별시 성동구 성수2가 314-6(성수역 4번출구)5.44.222.685.00.8
6667기허가시범-1규격노점음식서울특별시 성동구 성수2가 315-1 (성수역 3번출구)4.22.510.55.02.5
6768기허가시범-2규격노점음식서울특별시 성동구 성수2가 315-1 (성수역 3번출구)4.22.510.55.02.5
6869기허가시범-3규격노점음식서울특별시 성동구 성수2가 315-1 (성수역 3번출구)4.22.510.55.02.5
6970기허가시범-4규격노점잡화서울특별시 성동구 금호4가 561-5 맥주이야기 앞2.01.53.04.02.5
7071기허가시범-5규격노점음식서울특별시 성동구 금호4가 561-5 김소아과 앞2.01.53.04.02.5