Overview

Dataset statistics

Number of variables15
Number of observations85
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.6 KiB
Average record size in memory127.6 B

Variable types

Categorical9
Text2
Numeric4

Dataset

Description부산광역시남구_현수막지정게시대현황_20220317
Author부산광역시 남구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3081661

Alerts

관리기관명 has constant value ""Constant
관리기관전화번호 has constant value ""Constant
구군명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
규격 is highly overall correlated with 부착면수 and 2 other fieldsHigh correlation
특징 is highly overall correlated with 부착면수 and 1 other fieldsHigh correlation
부착면수 is highly overall correlated with 점용료 and 3 other fieldsHigh correlation
점용료 is highly overall correlated with 부착면수 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 행정동High correlation
경도 is highly overall correlated with 행정동High correlation
행정동 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
부착제한일 is highly overall correlated with 행정동High correlation
민원수수료 is highly overall correlated with 부착면수 and 1 other fieldsHigh correlation
특징 is highly imbalanced (78.0%)Imbalance
규격 is highly imbalanced (51.4%)Imbalance
부착제한일 is highly imbalanced (83.9%)Imbalance
위치 has unique valuesUnique
점용료 has 37 (43.5%) zerosZeros

Reproduction

Analysis started2023-12-10 16:57:03.769011
Analysis finished2023-12-10 16:57:07.460317
Duration3.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size812.0 B
부산광역시 남구청
85 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 남구청
2nd row부산광역시 남구청
3rd row부산광역시 남구청
4th row부산광역시 남구청
5th row부산광역시 남구청

Common Values

ValueCountFrequency (%)
부산광역시 남구청 85
100.0%

Length

2023-12-11T01:57:07.543534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:57:07.668666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 85
50.0%
남구청 85
50.0%

행정동
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size812.0 B
대연제3동
15 
용당동
11 
대연제6동
9 
대연제1동
6 
문현제3동
5 
Other values (12)
39 

Length

Max length5
Median length5
Mean length4.6470588
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row감만제1동
2nd row감만제1동
3rd row감만제1동
4th row감만제1동
5th row감만제2동

Common Values

ValueCountFrequency (%)
대연제3동 15
17.6%
용당동 11
12.9%
대연제6동 9
10.6%
대연제1동 6
 
7.1%
문현제3동 5
 
5.9%
용호제1동 5
 
5.9%
우암동 4
 
4.7%
문현제2동 4
 
4.7%
문현제4동 4
 
4.7%
용호제3동 4
 
4.7%
Other values (7) 18
21.2%

Length

2023-12-11T01:57:07.862108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대연제3동 15
17.6%
용당동 11
12.9%
대연제6동 9
10.6%
대연제1동 6
 
7.1%
문현제3동 5
 
5.9%
용호제1동 5
 
5.9%
감만제1동 4
 
4.7%
용호제3동 4
 
4.7%
문현제4동 4
 
4.7%
문현제2동 4
 
4.7%
Other values (7) 18
21.2%

위치
Text

UNIQUE 

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size812.0 B
2023-12-11T01:57:08.163668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length23
Mean length16.305882
Min length4

Characters and Unicode

Total characters1386
Distinct characters214
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)100.0%

Sample

1st row감만삼거리
2nd row수영돼지국밥 앞
3rd row우암로 54(감만동 버스종점 우측) 앞
4th row무민사로 40 앞
5th row홈플러스 감만점 건너편
ValueCountFrequency (%)
앞 25
 
9.5%
맞은편 12
 
4.5%
사거리 8
 
3.0%
대연동 5
 
1.9%
용호지구대 5
 
1.9%
옆 5
 
1.9%
황령터널 4
 
1.5%
구청 4
 
1.5%
앞(제2게시대 4
 
1.5%
앞(제1게시대 4
 
1.5%
Other values (141) 188
71.2%
2023-12-11T01:57:08.633644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
180
 
13.0%
대 67
 
4.8%
) 45
 
3.2%
( 44
 
3.2%
시 42
 
3.0%
앞 39
 
2.8%
게 36
 
2.6%
제 31
 
2.2%
동 25
 
1.8%
구 25
 
1.8%
Other values (204) 852
61.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 995
71.8%
Space Separator 180
 
13.0%
Decimal Number 90
 
6.5%
Close Punctuation 45
 
3.2%
Open Punctuation 44
 
3.2%
Uppercase Letter 17
 
1.2%
Other Punctuation 10
 
0.7%
Dash Punctuation 5
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 67
 
6.7%
시 42
 
4.2%
앞 39
 
3.9%
게 36
 
3.6%
제 31
 
3.1%
동 25
 
2.5%
구 25
 
2.5%
로 22
 
2.2%
은 22
 
2.2%
리 21
 
2.1%
Other values (181) 665
66.8%
Decimal Number
ValueCountFrequency (%)
1 22
24.4%
2 21
23.3%
3 15
16.7%
4 12
13.3%
5 6
 
6.7%
9 4
 
4.4%
6 4
 
4.4%
7 3
 
3.3%
0 3
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
E 4
23.5%
L 3
17.6%
D 3
17.6%
S 2
11.8%
K 2
11.8%
V 1
 
5.9%
I 1
 
5.9%
W 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 7
70.0%
* 3
30.0%
Space Separator
ValueCountFrequency (%)
180
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 44
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 995
71.8%
Common 374
 
27.0%
Latin 17
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 67
 
6.7%
시 42
 
4.2%
앞 39
 
3.9%
게 36
 
3.6%
제 31
 
3.1%
동 25
 
2.5%
구 25
 
2.5%
로 22
 
2.2%
은 22
 
2.2%
리 21
 
2.1%
Other values (181) 665
66.8%
Common
ValueCountFrequency (%)
180
48.1%
) 45
 
12.0%
( 44
 
11.8%
1 22
 
5.9%
2 21
 
5.6%
3 15
 
4.0%
4 12
 
3.2%
. 7
 
1.9%
5 6
 
1.6%
- 5
 
1.3%
Other values (5) 17
 
4.5%
Latin
ValueCountFrequency (%)
E 4
23.5%
L 3
17.6%
D 3
17.6%
S 2
11.8%
K 2
11.8%
V 1
 
5.9%
I 1
 
5.9%
W 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 995
71.8%
ASCII 391
 
28.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
180
46.0%
) 45
 
11.5%
( 44
 
11.3%
1 22
 
5.6%
2 21
 
5.4%
3 15
 
3.8%
4 12
 
3.1%
. 7
 
1.8%
5 6
 
1.5%
- 5
 
1.3%
Other values (13) 34
 
8.7%
Hangul
ValueCountFrequency (%)
대 67
 
6.7%
시 42
 
4.2%
앞 39
 
3.9%
게 36
 
3.6%
제 31
 
3.1%
동 25
 
2.5%
구 25
 
2.5%
로 22
 
2.2%
은 22
 
2.2%
리 21
 
2.1%
Other values (181) 665
66.8%

주소
Text

Distinct62
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Memory size812.0 B
2023-12-11T01:57:08.928794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length250
Median length249
Mean length160.22353
Min length12

Characters and Unicode

Total characters13619
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)51.8%

Sample

1st row부산광역시 남구 감만동 75-56
2nd row부산광역시 남구 감만동 84-21
3rd row부산광역시 남구 우암로 54
4th row부산광역시 남구 무민사로 40
5th row부산광역시 남구 감만동 51-8
ValueCountFrequency (%)
부산광역시 85
25.1%
남구 83
24.6%
대연동 32
 
9.5%
용호동 12
 
3.6%
문현동 12
 
3.6%
용당동 9
 
2.7%
172-18 5
 
1.5%
우암동 4
 
1.2%
1268-1 4
 
1.2%
383-1 3
 
0.9%
Other values (68) 89
26.3%
2023-12-11T01:57:09.367335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12374
90.9%
산 86
 
0.6%
부 85
 
0.6%
광 85
 
0.6%
역 85
 
0.6%
시 85
 
0.6%
남 83
 
0.6%
구 83
 
0.6%
1 81
 
0.6%
동 72
 
0.5%
Other values (42) 500
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 12374
90.9%
Other Letter 858
 
6.3%
Decimal Number 329
 
2.4%
Dash Punctuation 58
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 86
10.0%
부 85
9.9%
광 85
9.9%
역 85
9.9%
시 85
9.9%
남 83
9.7%
구 83
9.7%
동 72
8.4%
대 33
 
3.8%
연 32
 
3.7%
Other values (30) 129
15.0%
Decimal Number
ValueCountFrequency (%)
1 81
24.6%
2 42
12.8%
8 36
10.9%
3 32
 
9.7%
4 28
 
8.5%
5 26
 
7.9%
7 26
 
7.9%
9 22
 
6.7%
6 21
 
6.4%
0 15
 
4.6%
Space Separator
ValueCountFrequency (%)
12374
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12761
93.7%
Hangul 858
 
6.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 86
10.0%
부 85
9.9%
광 85
9.9%
역 85
9.9%
시 85
9.9%
남 83
9.7%
구 83
9.7%
동 72
8.4%
대 33
 
3.8%
연 32
 
3.7%
Other values (30) 129
15.0%
Common
ValueCountFrequency (%)
12374
97.0%
1 81
 
0.6%
- 58
 
0.5%
2 42
 
0.3%
8 36
 
0.3%
3 32
 
0.3%
4 28
 
0.2%
5 26
 
0.2%
7 26
 
0.2%
9 22
 
0.2%
Other values (2) 36
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12761
93.7%
Hangul 858
 
6.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12374
97.0%
1 81
 
0.6%
- 58
 
0.5%
2 42
 
0.3%
8 36
 
0.3%
3 32
 
0.3%
4 28
 
0.2%
5 26
 
0.2%
7 26
 
0.2%
9 22
 
0.2%
Other values (2) 36
 
0.3%
Hangul
ValueCountFrequency (%)
산 86
10.0%
부 85
9.9%
광 85
9.9%
역 85
9.9%
시 85
9.9%
남 83
9.7%
구 83
9.7%
동 72
8.4%
대 33
 
3.8%
연 32
 
3.7%
Other values (30) 129
15.0%

특징
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size812.0 B
탱탱이 걸이식(좌측고정)
82 
LED 전자게시대
 
3

Length

Max length13
Median length13
Mean length12.858824
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row탱탱이 걸이식(좌측고정)
2nd row탱탱이 걸이식(좌측고정)
3rd row탱탱이 걸이식(좌측고정)
4th row탱탱이 걸이식(좌측고정)
5th row탱탱이 걸이식(좌측고정)

Common Values

ValueCountFrequency (%)
탱탱이 걸이식(좌측고정) 82
96.5%
LED 전자게시대 3
 
3.5%

Length

2023-12-11T01:57:09.546681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:57:09.669465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
탱탱이 82
48.2%
걸이식(좌측고정 82
48.2%
led 3
 
1.8%
전자게시대 3
 
1.8%

규격
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size812.0 B
6.0*0.7
61 
7.0*0.9
17 
6.3*1.2
 
3
6.5*0.9
 
2
7.0*0.7
 
1

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st row7.0*0.9
2nd row6.0*0.7
3rd row6.0*0.7
4th row6.0*0.7
5th row6.0*0.7

Common Values

ValueCountFrequency (%)
6.0*0.7 61
71.8%
7.0*0.9 17
 
20.0%
6.3*1.2 3
 
3.5%
6.5*0.9 2
 
2.4%
7.0*0.7 1
 
1.2%
5.8*0.7 1
 
1.2%

Length

2023-12-11T01:57:09.823272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:57:09.982992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6.0*0.7 61
71.8%
7.0*0.9 17
 
20.0%
6.3*1.2 3
 
3.5%
6.5*0.9 2
 
2.4%
7.0*0.7 1
 
1.2%
5.8*0.7 1
 
1.2%

부착면수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9176471
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-11T01:57:10.117702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0366392
Coefficient of variation (CV)0.69804166
Kurtosis3.0692156
Mean2.9176471
Median Absolute Deviation (MAD)1
Skewness1.5775768
Sum248
Variance4.1478992
MonotonicityNot monotonic
2023-12-11T01:57:10.228961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 27
31.8%
1 22
25.9%
5 19
22.4%
3 11
12.9%
10 3
 
3.5%
4 2
 
2.4%
6 1
 
1.2%
ValueCountFrequency (%)
1 22
25.9%
2 27
31.8%
3 11
12.9%
4 2
 
2.4%
5 19
22.4%
6 1
 
1.2%
10 3
 
3.5%
ValueCountFrequency (%)
10 3
 
3.5%
6 1
 
1.2%
5 19
22.4%
4 2
 
2.4%
3 11
12.9%
2 27
31.8%
1 22
25.9%

부착제한일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size812.0 B
10
83 
0
 
2

Length

Max length2
Median length2
Mean length1.9764706
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 83
97.6%
0 2
 
2.4%

Length

2023-12-11T01:57:10.362392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:57:10.506137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 83
97.6%
0 2
 
2.4%

민원수수료
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size812.0 B
10000
48 
0
37 

Length

Max length5
Median length5
Mean length3.2588235
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10000
2nd row10000
3rd row10000
4th row10000
5th row10000

Common Values

ValueCountFrequency (%)
10000 48
56.5%
0 37
43.5%

Length

2023-12-11T01:57:10.643331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:57:10.772619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10000 48
56.5%
0 37
43.5%

점용료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean844.35294
Minimum0
Maximum1890
Zeros37
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-11T01:57:10.870373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1260
Q31260
95-th percentile1890
Maximum1890
Range1890
Interquartile range (IQR)1260

Descriptive statistics

Standard deviation778.74318
Coefficient of variation (CV)0.92229581
Kurtosis-1.7080091
Mean844.35294
Median Absolute Deviation (MAD)630
Skewness-0.0035404602
Sum71770
Variance606440.95
MonotonicityNot monotonic
2023-12-11T01:57:11.013913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 37
43.5%
1260 28
32.9%
1890 16
18.8%
1755 2
 
2.4%
1470 1
 
1.2%
1270 1
 
1.2%
ValueCountFrequency (%)
0 37
43.5%
1260 28
32.9%
1270 1
 
1.2%
1470 1
 
1.2%
1755 2
 
2.4%
1890 16
18.8%
ValueCountFrequency (%)
1890 16
18.8%
1755 2
 
2.4%
1470 1
 
1.2%
1270 1
 
1.2%
1260 28
32.9%
0 37
43.5%

관리기관전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size812.0 B
051-607-4626
85 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row051-607-4626
2nd row051-607-4626
3rd row051-607-4626
4th row051-607-4626
5th row051-607-4626

Common Values

ValueCountFrequency (%)
051-607-4626 85
100.0%

Length

2023-12-11T01:57:11.174927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:57:11.281667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
051-607-4626 85
100.0%

구군명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size812.0 B
부산광역시 남구
85 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 남구
2nd row부산광역시 남구
3rd row부산광역시 남구
4th row부산광역시 남구
5th row부산광역시 남구

Common Values

ValueCountFrequency (%)
부산광역시 남구 85
100.0%

Length

2023-12-11T01:57:11.430961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:57:11.583374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 85
50.0%
남구 85
50.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size812.0 B
2022-03-17
85 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-03-17
2nd row2022-03-17
3rd row2022-03-17
4th row2022-03-17
5th row2022-03-17

Common Values

ValueCountFrequency (%)
2022-03-17 85
100.0%

Length

2023-12-11T01:57:11.735922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:57:11.851741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-03-17 85
100.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.130471
Minimum35.1
Maximum35.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-11T01:57:11.962044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.1
5-th percentile35.11
Q135.12
median35.13
Q335.14
95-th percentile35.15
Maximum35.17
Range0.07
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.011741855
Coefficient of variation (CV)0.00033423562
Kurtosis1.0352372
Mean35.130471
Median Absolute Deviation (MAD)0.01
Skewness0.087669981
Sum2986.09
Variance0.00013787115
MonotonicityNot monotonic
2023-12-11T01:57:12.122595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
35.13 30
35.3%
35.14 22
25.9%
35.12 20
23.5%
35.15 6
 
7.1%
35.11 4
 
4.7%
35.1 2
 
2.4%
35.17 1
 
1.2%
ValueCountFrequency (%)
35.1 2
 
2.4%
35.11 4
 
4.7%
35.12 20
23.5%
35.13 30
35.3%
35.14 22
25.9%
35.15 6
 
7.1%
35.17 1
 
1.2%
ValueCountFrequency (%)
35.17 1
 
1.2%
35.15 6
 
7.1%
35.14 22
25.9%
35.13 30
35.3%
35.12 20
23.5%
35.11 4
 
4.7%
35.1 2
 
2.4%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.09092
Minimum129.06418
Maximum129.11576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-11T01:57:12.359411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.06418
5-th percentile129.067
Q1129.0815
median129.09206
Q3129.10107
95-th percentile129.11396
Maximum129.11576
Range0.0515817
Interquartile range (IQR)0.0195701

Descriptive statistics

Standard deviation0.014483498
Coefficient of variation (CV)0.0001121961
Kurtosis-0.96502983
Mean129.09092
Median Absolute Deviation (MAD)0.010256
Skewness-0.16939262
Sum10972.728
Variance0.00020977171
MonotonicityNot monotonic
2023-12-11T01:57:12.576439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.08443 4
 
4.7%
129.1143634 4
 
4.7%
129.100254 3
 
3.5%
129.0973949 3
 
3.5%
129.0978175 2
 
2.4%
129.0931872 2
 
2.4%
129.0903983 2
 
2.4%
129.0815124 2
 
2.4%
129.1020668 2
 
2.4%
129.092056 2
 
2.4%
Other values (51) 59
69.4%
ValueCountFrequency (%)
129.0641817 1
1.2%
129.0647 2
2.4%
129.066676 1
1.2%
129.0669 1
1.2%
129.0674 1
1.2%
129.067675 1
1.2%
129.068023 2
2.4%
129.0699938 1
1.2%
129.0711 1
1.2%
129.072471 1
1.2%
ValueCountFrequency (%)
129.1157634 1
 
1.2%
129.1143634 4
4.7%
129.112343 1
 
1.2%
129.1119 1
 
1.2%
129.1116617 1
 
1.2%
129.110065 1
 
1.2%
129.109865 1
 
1.2%
129.109179 1
 
1.2%
129.109106 1
 
1.2%
129.106744 2
2.4%

Interactions

2023-12-11T01:57:06.423404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:04.717258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:05.279402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:05.893463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:06.519789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:04.850666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:05.415515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:06.024633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:06.621630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:04.999056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:05.593740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:06.149853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:06.745115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:05.162168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:05.756525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:57:06.293065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:57:12.714589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동위치주소특징규격부착면수부착제한일민원수수료점용료위도경도
행정동1.0001.0000.9980.1620.0000.6010.6660.4730.1920.8350.905
위치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
주소0.9981.0001.0000.1820.1610.8651.0000.7680.3010.9960.999
특징0.1621.0000.1821.0001.0001.0000.0000.1710.1690.0000.000
규격0.0001.0000.1611.0001.0000.8610.0000.5820.9130.3610.231
부착면수0.6011.0000.8651.0000.8611.0000.0000.5740.8800.2340.413
부착제한일0.6661.0001.0000.0000.0000.0001.0000.0000.0000.0000.485
민원수수료0.4731.0000.7680.1710.5820.5740.0001.0001.0000.0000.000
점용료0.1921.0000.3010.1690.9130.8800.0001.0001.0000.0000.160
위도0.8351.0000.9960.0000.3610.2340.0000.0000.0001.0000.408
경도0.9051.0000.9990.0000.2310.4130.4850.0000.1600.4081.000
2023-12-11T01:57:12.870470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동규격부착제한일민원수수료특징
행정동1.0000.0000.5490.3840.122
규격0.0001.0000.0000.4120.976
부착제한일0.5490.0001.0000.0000.000
민원수수료0.3840.4120.0001.0000.109
특징0.1220.9760.0000.1091.000
2023-12-11T01:57:13.011383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부착면수점용료위도경도행정동특징규격부착제한일민원수수료
부착면수1.0000.5240.139-0.0610.2980.9690.7230.0000.598
점용료0.5241.000-0.011-0.0930.0860.1090.7910.0000.988
위도0.139-0.0111.000-0.1690.5370.0000.2210.0000.000
경도-0.061-0.093-0.1691.0000.6280.0000.1090.3120.000
행정동0.2980.0860.5370.6281.0000.1220.0000.5490.384
특징0.9690.1090.0000.0000.1221.0000.9760.0000.109
규격0.7230.7910.2210.1090.0000.9761.0000.0000.412
부착제한일0.0000.0000.0000.3120.5490.0000.0001.0000.000
민원수수료0.5980.9880.0000.0000.3840.1090.4120.0001.000

Missing values

2023-12-11T01:57:07.151532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:57:07.376022image/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

관리기관명행정동위치주소특징규격부착면수부착제한일민원수수료점용료관리기관전화번호구군명데이터기준일자위도경도
0부산광역시 남구청감만제1동감만삼거리부산광역시 남구 감만동 75-56탱탱이 걸이식(좌측고정)7.0*0.9510100001890051-607-4626부산광역시 남구2022-03-1735.12129.07396
1부산광역시 남구청감만제1동수영돼지국밥 앞부산광역시 남구 감만동 84-21탱탱이 걸이식(좌측고정)6.0*0.7210100001260051-607-4626부산광역시 남구2022-03-1735.12129.080185
2부산광역시 남구청감만제1동우암로 54(감만동 버스종점 우측) 앞부산광역시 남구 우암로 54탱탱이 걸이식(좌측고정)6.0*0.7210100001260051-607-4626부산광역시 남구2022-03-1735.11129.0815
3부산광역시 남구청감만제1동무민사로 40 앞부산광역시 남구 무민사로 40탱탱이 걸이식(좌측고정)6.0*0.7210100001260051-607-4626부산광역시 남구2022-03-1735.11129.0818
4부산광역시 남구청감만제2동홈플러스 감만점 건너편부산광역시 남구 감만동 51-8탱탱이 걸이식(좌측고정)6.0*0.7210100001260051-607-4626부산광역시 남구2022-03-1735.12129.080734
5부산광역시 남구청감만제2동남광시장 입구부산광역시 남구 석포로 66탱탱이 걸이식(좌측고정)6.0*0.741000051-607-4626부산광역시 남구2022-03-1735.12129.084558
6부산광역시 남구청대연제1동대연동 사거리 부산은행 앞(제1게시대)부산광역시 남구 대연동 1740-8탱탱이 걸이식(좌측고정)6.0*0.7210100001260051-607-4626부산광역시 남구2022-03-1735.13129.092227
7부산광역시 남구청대연제1동대연동 사거리 부산은행 앞(제2게시대)부산광역시 남구 대연동 1740-8탱탱이 걸이식(좌측고정)6.0*0.721000051-607-4626부산광역시 남구2022-03-1735.13129.092227
8부산광역시 남구청대연제1동못골지하철역 3번출구(제1게시대)부산광역시 남구 수영로 158 앞탱탱이 걸이식(좌측고정)6.0*0.721000051-607-4626부산광역시 남구2022-03-1735.13129.083952
9부산광역시 남구청대연제1동못골지하철역 3번출구(제2게시대)부산광역시 남구 수영로 158 앞탱탱이 걸이식(좌측고정)6.0*0.711000051-607-4626부산광역시 남구2022-03-1735.13129.083952
관리기관명행정동위치주소특징규격부착면수부착제한일민원수수료점용료관리기관전화번호구군명데이터기준일자위도경도
75부산광역시 남구청용호제3동용호지구대 맞은편 제1게시대(좌측)부산광역시 남구 용호동 172-18탱탱이 걸이식(좌측고정)6.0*0.7310100001260051-607-4626부산광역시 남구2022-03-1735.13129.114363
76부산광역시 남구청용호제3동용호지구대 맞은편 제2게시대(우측-용호복지관 쪽)-맨 오른쪽)부산광역시 남구 용호동 172-18탱탱이 걸이식(좌측고정)6.0*0.7310100001260051-607-4626부산광역시 남구2022-03-1735.13129.114363
77부산광역시 남구청용호제3동용호지구대 맞은편 제3게시대(광안대교 쪽)부산광역시 남구 용호동 172-18탱탱이 걸이식(좌측고정)6.0*0.7310100001260051-607-4626부산광역시 남구2022-03-1735.13129.114363
78부산광역시 남구청용호제3동용호지구대 맞은편 제4게시대(광안대교 쪽)-맨 왼쪽부산광역시 남구 용호동 172-18탱탱이 걸이식(좌측고정)6.0*0.731000051-607-4626부산광역시 남구2022-03-1735.13129.114363
79부산광역시 남구청용호제4동백운포 체육공원 주차장부산광역시 남구 용호동 895-3탱탱이 걸이식(좌측고정)6.0*0.711000051-607-4626부산광역시 남구2022-03-1735.1129.109865
80부산광역시 남구청용호제4동성모병원 주차장 입구 옆부산광역시 남구 용호동 538-41탱탱이 걸이식(좌측고정)6.0*0.711000051-607-4626부산광역시 남구2022-03-1735.11129.109179
81부산광역시 남구청우암동뉴서울아파트 앞부산광역시 남구 우암동 53-1탱탱이 걸이식(좌측고정)7.0*0.9510100001890051-607-4626부산광역시 남구2022-03-1735.12129.079691
82부산광역시 남구청우암동새마을금고(구.부산은행) 맞은편(제1게시대)부산광역시 남구 우암동 184-250탱탱이 걸이식(좌측고정)7.0*0.9510100001890051-607-4626부산광역시 남구2022-03-1735.12129.07396
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