Overview

Dataset statistics

Number of variables15
Number of observations93
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.6 KiB
Average record size in memory127.4 B

Variable types

Categorical8
Text2
Numeric4
DateTime1

Dataset

Description부산광역시남구_현수막지정게시대현황_20220609
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 특징 and 2 other fieldsHigh correlation
점용료 is highly overall correlated with 규격 and 1 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 (69.8%)Imbalance
규격 is highly imbalanced (52.7%)Imbalance
부착제한일 is highly imbalanced (85.0%)Imbalance
위치 has unique valuesUnique
점용료 has 41 (44.1%) zerosZeros

Reproduction

Analysis started2023-12-10 16:56:49.084529
Analysis finished2023-12-10 16:56:53.090377
Duration4.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size876.0 B
부산광역시 남구청
93 

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 93
100.0%

Length

2023-12-11T01:56:53.490006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:56:53.612952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 93
50.0%
남구청 93
50.0%

행정동
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size876.0 B
대연제3동
15 
대연제6동
12 
용당동
11 
대연제4동
7 
대연제1동
6 
Other values (12)
42 

Length

Max length5
Median length5
Mean length4.6774194
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대연제3동 15
16.1%
대연제6동 12
12.9%
용당동 11
11.8%
대연제4동 7
 
7.5%
대연제1동 6
 
6.5%
용호제1동 6
 
6.5%
문현제3동 5
 
5.4%
문현제2동 4
 
4.3%
우암동 4
 
4.3%
문현제4동 4
 
4.3%
Other values (7) 19
20.4%

Length

2023-12-11T01:56:53.788261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대연제3동 15
16.1%
대연제6동 12
12.9%
용당동 11
11.8%
대연제4동 7
 
7.5%
대연제1동 6
 
6.5%
용호제1동 6
 
6.5%
문현제3동 5
 
5.4%
용호제3동 4
 
4.3%
감만제1동 4
 
4.3%
우암동 4
 
4.3%
Other values (7) 19
20.4%

위치
Text

UNIQUE 

Distinct93
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size876.0 B
2023-12-11T01:56:54.110773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length25
Mean length16.806452
Min length4

Characters and Unicode

Total characters1563
Distinct characters216
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

Unique93 ?
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 33
 
10.9%
맞은편 12
 
4.0%
사거리 9
 
3.0%
옆 5
 
1.7%
용호지구대 5
 
1.7%
대연동 5
 
1.7%
황령터널 4
 
1.3%
앞(제2게시대 4
 
1.3%
앞(제1게시대 4
 
1.3%
부산은행 4
 
1.3%
Other values (155) 217
71.9%
2023-12-11T01:56:54.747424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
210
 
13.4%
대 76
 
4.9%
시 51
 
3.3%
앞 47
 
3.0%
) 47
 
3.0%
( 46
 
2.9%
게 44
 
2.8%
제 31
 
2.0%
1 28
 
1.8%
로 27
 
1.7%
Other values (206) 956
61.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1112
71.1%
Space Separator 210
 
13.4%
Decimal Number 104
 
6.7%
Close Punctuation 47
 
3.0%
Open Punctuation 46
 
2.9%
Uppercase Letter 23
 
1.5%
Other Punctuation 16
 
1.0%
Dash Punctuation 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 76
 
6.8%
시 51
 
4.6%
앞 47
 
4.2%
게 44
 
4.0%
제 31
 
2.8%
로 27
 
2.4%
동 26
 
2.3%
구 25
 
2.2%
은 22
 
2.0%
리 22
 
2.0%
Other values (183) 741
66.6%
Decimal Number
ValueCountFrequency (%)
1 28
26.9%
2 23
22.1%
3 17
16.3%
4 12
11.5%
5 6
 
5.8%
6 6
 
5.8%
0 5
 
4.8%
9 4
 
3.8%
7 3
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
E 6
26.1%
L 5
21.7%
D 5
21.7%
K 2
 
8.7%
S 2
 
8.7%
V 1
 
4.3%
W 1
 
4.3%
I 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 11
68.8%
* 5
31.2%
Space Separator
ValueCountFrequency (%)
210
100.0%
Close Punctuation
ValueCountFrequency (%)
) 47
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1112
71.1%
Common 428
 
27.4%
Latin 23
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 76
 
6.8%
시 51
 
4.6%
앞 47
 
4.2%
게 44
 
4.0%
제 31
 
2.8%
로 27
 
2.4%
동 26
 
2.3%
구 25
 
2.2%
은 22
 
2.0%
리 22
 
2.0%
Other values (183) 741
66.6%
Common
ValueCountFrequency (%)
210
49.1%
) 47
 
11.0%
( 46
 
10.7%
1 28
 
6.5%
2 23
 
5.4%
3 17
 
4.0%
4 12
 
2.8%
. 11
 
2.6%
5 6
 
1.4%
6 6
 
1.4%
Other values (5) 22
 
5.1%
Latin
ValueCountFrequency (%)
E 6
26.1%
L 5
21.7%
D 5
21.7%
K 2
 
8.7%
S 2
 
8.7%
V 1
 
4.3%
W 1
 
4.3%
I 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1112
71.1%
ASCII 451
28.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
210
46.6%
) 47
 
10.4%
( 46
 
10.2%
1 28
 
6.2%
2 23
 
5.1%
3 17
 
3.8%
4 12
 
2.7%
. 11
 
2.4%
5 6
 
1.3%
6 6
 
1.3%
Other values (13) 45
 
10.0%
Hangul
ValueCountFrequency (%)
대 76
 
6.8%
시 51
 
4.6%
앞 47
 
4.2%
게 44
 
4.0%
제 31
 
2.8%
로 27
 
2.4%
동 26
 
2.3%
구 25
 
2.2%
은 22
 
2.0%
리 22
 
2.0%
Other values (183) 741
66.6%

주소
Text

Distinct67
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Memory size876.0 B
2023-12-11T01:56:55.173510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length250
Median length249
Mean length147.96774
Min length12

Characters and Unicode

Total characters13761
Distinct characters54
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

Unique46 ?
Unique (%)49.5%

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 93
25.1%
남구 91
24.6%
대연동 33
 
8.9%
용호동 13
 
3.5%
문현동 12
 
3.2%
용당동 9
 
2.4%
172-18 5
 
1.4%
우암동 4
 
1.1%
1268-1 4
 
1.1%
감만동 3
 
0.8%
Other values (74) 103
27.8%
2023-12-11T01:56:55.832239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12398
90.1%
산 94
 
0.7%
부 93
 
0.7%
광 93
 
0.7%
역 93
 
0.7%
시 93
 
0.7%
남 91
 
0.7%
구 91
 
0.7%
1 89
 
0.6%
동 74
 
0.5%
Other values (44) 552
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 12398
90.1%
Other Letter 942
 
6.8%
Decimal Number 361
 
2.6%
Dash Punctuation 60
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 94
10.0%
부 93
9.9%
광 93
9.9%
역 93
9.9%
시 93
9.9%
남 91
9.7%
구 91
9.7%
동 74
7.9%
대 34
 
3.6%
연 33
 
3.5%
Other values (32) 153
16.2%
Decimal Number
ValueCountFrequency (%)
1 89
24.7%
2 47
13.0%
8 38
10.5%
3 34
 
9.4%
4 28
 
7.8%
7 27
 
7.5%
5 27
 
7.5%
6 26
 
7.2%
9 24
 
6.6%
0 21
 
5.8%
Space Separator
ValueCountFrequency (%)
12398
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12819
93.2%
Hangul 942
 
6.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 94
10.0%
부 93
9.9%
광 93
9.9%
역 93
9.9%
시 93
9.9%
남 91
9.7%
구 91
9.7%
동 74
7.9%
대 34
 
3.6%
연 33
 
3.5%
Other values (32) 153
16.2%
Common
ValueCountFrequency (%)
12398
96.7%
1 89
 
0.7%
- 60
 
0.5%
2 47
 
0.4%
8 38
 
0.3%
3 34
 
0.3%
4 28
 
0.2%
7 27
 
0.2%
5 27
 
0.2%
6 26
 
0.2%
Other values (2) 45
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12819
93.2%
Hangul 942
 
6.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12398
96.7%
1 89
 
0.7%
- 60
 
0.5%
2 47
 
0.4%
8 38
 
0.3%
3 34
 
0.3%
4 28
 
0.2%
7 27
 
0.2%
5 27
 
0.2%
6 26
 
0.2%
Other values (2) 45
 
0.4%
Hangul
ValueCountFrequency (%)
산 94
10.0%
부 93
9.9%
광 93
9.9%
역 93
9.9%
시 93
9.9%
남 91
9.7%
구 91
9.7%
동 74
7.9%
대 34
 
3.6%
연 33
 
3.5%
Other values (32) 153
16.2%

특징
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size876.0 B
탱탱이 걸이식(좌측고정)
88 
LED 전자게시대
 
5

Length

Max length13
Median length13
Mean length12.784946
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) 88
94.6%
LED 전자게시대 5
 
5.4%

Length

2023-12-11T01:56:56.064371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:56:56.236839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
탱탱이 88
47.3%
걸이식(좌측고정 88
47.3%
led 5
 
2.7%
전자게시대 5
 
2.7%

규격
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size876.0 B
6.0*0.7
67 
7.0*0.9
17 
6.3*1.2
 
3
6.5*0.9
 
2
6.3*1.0
 
2
Other values (2)
 
2

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique2 ?
Unique (%)2.2%

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 67
72.0%
7.0*0.9 17
 
18.3%
6.3*1.2 3
 
3.2%
6.5*0.9 2
 
2.2%
6.3*1.0 2
 
2.2%
7.0*0.7 1
 
1.1%
5.8*0.7 1
 
1.1%

Length

2023-12-11T01:56:56.411620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:56:56.568435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6.0*0.7 67
72.0%
7.0*0.9 17
 
18.3%
6.3*1.2 3
 
3.2%
6.5*0.9 2
 
2.2%
6.3*1.0 2
 
2.2%
7.0*0.7 1
 
1.1%
5.8*0.7 1
 
1.1%

부착면수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9462366
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-11T01:56:56.720324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile7.6
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2619998
Coefficient of variation (CV)0.76775907
Kurtosis2.7622774
Mean2.9462366
Median Absolute Deviation (MAD)1
Skewness1.6517391
Sum274
Variance5.1166433
MonotonicityNot monotonic
2023-12-11T01:56:56.876776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 28
30.1%
2 27
29.0%
5 19
20.4%
3 11
 
11.8%
10 5
 
5.4%
4 2
 
2.2%
6 1
 
1.1%
ValueCountFrequency (%)
1 28
30.1%
2 27
29.0%
3 11
 
11.8%
4 2
 
2.2%
5 19
20.4%
6 1
 
1.1%
10 5
 
5.4%
ValueCountFrequency (%)
10 5
 
5.4%
6 1
 
1.1%
5 19
20.4%
4 2
 
2.2%
3 11
 
11.8%
2 27
29.0%
1 28
30.1%

부착제한일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size876.0 B
10
91 
0
 
2

Length

Max length2
Median length2
Mean length1.9784946
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
10 91
97.8%
0 2
 
2.2%

Length

2023-12-11T01:56:57.045007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:56:57.248644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 91
97.8%
0 2
 
2.2%

민원수수료
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size876.0 B
10000
52 
0
41 

Length

Max length5
Median length5
Mean length3.2365591
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
10000 52
55.9%
0 41
44.1%

Length

2023-12-11T01:56:57.434365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:56:57.621234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10000 52
55.9%
0 41
44.1%

점용료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean825.91398
Minimum0
Maximum1890
Zeros41
Zeros (%)44.1%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-11T01:56:57.759807image/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 deviation769.33199
Coefficient of variation (CV)0.93149167
Kurtosis-1.6951755
Mean825.91398
Median Absolute Deviation (MAD)630
Skewness0.018417059
Sum76810
Variance591871.71
MonotonicityNot monotonic
2023-12-11T01:56:57.944960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 41
44.1%
1260 32
34.4%
1890 16
 
17.2%
1755 2
 
2.2%
1470 1
 
1.1%
1270 1
 
1.1%
ValueCountFrequency (%)
0 41
44.1%
1260 32
34.4%
1270 1
 
1.1%
1470 1
 
1.1%
1755 2
 
2.2%
1890 16
 
17.2%
ValueCountFrequency (%)
1890 16
 
17.2%
1755 2
 
2.2%
1470 1
 
1.1%
1270 1
 
1.1%
1260 32
34.4%
0 41
44.1%

관리기관전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size876.0 B
051-607-4626
93 

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 93
100.0%

Length

2023-12-11T01:56:58.148067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:56:58.325724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
051-607-4626 93
100.0%

구군명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size876.0 B
부산광역시 남구
93 

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 93
100.0%

Length

2023-12-11T01:56:58.486315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:56:58.638040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 93
50.0%
남구 93
50.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size876.0 B
Minimum2022-06-09 00:00:00
Maximum2022-06-09 00:00:00
2023-12-11T01:56:58.769226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:58.932618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.130108
Minimum35.1
Maximum35.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-11T01:56:59.117957image/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 deviation0.011372605
Coefficient of variation (CV)0.00032372815
Kurtosis1.2137789
Mean35.130108
Median Absolute Deviation (MAD)0.01
Skewness0.15981619
Sum3267.1
Variance0.00012933614
MonotonicityNot monotonic
2023-12-11T01:56:59.312067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
35.13 35
37.6%
35.12 23
24.7%
35.14 22
23.7%
35.15 6
 
6.5%
35.11 4
 
4.3%
35.1 2
 
2.2%
35.17 1
 
1.1%
ValueCountFrequency (%)
35.1 2
 
2.2%
35.11 4
 
4.3%
35.12 23
24.7%
35.13 35
37.6%
35.14 22
23.7%
35.15 6
 
6.5%
35.17 1
 
1.1%
ValueCountFrequency (%)
35.17 1
 
1.1%
35.15 6
 
6.5%
35.14 22
23.7%
35.13 35
37.6%
35.12 23
24.7%
35.11 4
 
4.3%
35.1 2
 
2.2%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.09079
Minimum129.06418
Maximum129.11576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size969.0 B
2023-12-11T01:56:59.579631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.06418
5-th percentile129.0672
Q1129.08151
median129.092
Q3129.10025
95-th percentile129.11315
Maximum129.11576
Range0.0515817
Interquartile range (IQR)0.0187416

Descriptive statistics

Standard deviation0.01399904
Coefficient of variation (CV)0.00010844336
Kurtosis-0.85587946
Mean129.09079
Median Absolute Deviation (MAD)0.0100025
Skewness-0.1319752
Sum12005.444
Variance0.00019597311
MonotonicityNot monotonic
2023-12-11T01:56:59.878884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.08443 4
 
4.3%
129.1143634 4
 
4.3%
129.0973949 3
 
3.2%
129.100254 3
 
3.2%
129.0647 2
 
2.2%
129.0739595 2
 
2.2%
129.0815124 2
 
2.2%
129.098535 2
 
2.2%
129.092056 2
 
2.2%
129.0931872 2
 
2.2%
Other values (56) 67
72.0%
ValueCountFrequency (%)
129.0641817 1
1.1%
129.0647 2
2.2%
129.066676 1
1.1%
129.0669 1
1.1%
129.0674 1
1.1%
129.067675 1
1.1%
129.068023 2
2.2%
129.0699938 1
1.1%
129.0711 1
1.1%
129.072471 1
1.1%
ValueCountFrequency (%)
129.1157634 1
 
1.1%
129.1143634 4
4.3%
129.112343 1
 
1.1%
129.1119 1
 
1.1%
129.1116617 1
 
1.1%
129.110065 1
 
1.1%
129.109865 1
 
1.1%
129.109179 1
 
1.1%
129.109106 1
 
1.1%
129.106744 2
2.2%

Interactions

2023-12-11T01:56:52.123435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:50.175796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:50.849129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:51.488818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:52.233060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:50.388154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:50.994991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:51.602217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:52.366829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:50.541714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:51.150472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:51.768051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:52.491168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:50.693015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:51.331613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:56:51.924441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:57:00.063403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동위치주소특징규격부착면수부착제한일민원수수료점용료위도경도
행정동1.0001.0000.9980.2300.0000.5910.6770.5570.4310.8240.911
위치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
주소0.9981.0001.0000.7610.7780.9061.0000.8000.5210.9960.998
특징0.2301.0000.7611.0001.0001.0000.0000.3020.3040.0000.000
규격0.0001.0000.7781.0001.0000.9500.0000.3910.8680.4930.160
부착면수0.5911.0000.9061.0000.9501.0000.0000.5230.8700.2600.422
부착제한일0.6771.0001.0000.0000.0000.0001.0000.0000.0000.0770.503
민원수수료0.5571.0000.8000.3020.3910.5230.0001.0001.0000.0000.000
점용료0.4311.0000.5210.3040.8680.8700.0001.0001.0000.0000.265
위도0.8241.0000.9960.0000.4930.2600.0770.0000.0001.0000.403
경도0.9111.0000.9980.0000.1600.4220.5030.0000.2650.4031.000
2023-12-11T01:57:00.341427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동규격부착제한일민원수수료특징
행정동1.0000.0000.5640.4590.184
규격0.0001.0000.0000.4060.972
부착제한일0.5640.0001.0000.0000.000
민원수수료0.4590.4060.0001.0000.195
특징0.1840.9720.0000.1951.000
2023-12-11T01:57:00.533959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부착면수점용료위도경도행정동특징규격부착제한일민원수수료
부착면수1.0000.4320.139-0.0350.2930.9720.6580.0000.545
점용료0.4321.000-0.011-0.0870.2300.2000.7900.0000.989
위도0.139-0.0111.000-0.1640.5250.0000.1880.0760.000
경도-0.035-0.087-0.1641.0000.6450.0000.0660.3290.000
행정동0.2930.2300.5250.6451.0000.1840.0000.5640.459
특징0.9720.2000.0000.0000.1841.0000.9720.0000.195
규격0.6580.7900.1880.0660.0000.9721.0000.0000.406
부착제한일0.0000.0000.0760.3290.5640.0000.0001.0000.000
민원수수료0.5450.9890.0000.0000.4590.1950.4060.0001.000

Missing values

2023-12-11T01:56:52.674240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:56:52.970797image/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 남구2022-06-0935.12129.07396
1부산광역시 남구청감만제1동수영돼지국밥 앞부산광역시 남구 감만동 84-21탱탱이 걸이식(좌측고정)6.0*0.7210100001260051-607-4626부산광역시 남구2022-06-0935.12129.080185
2부산광역시 남구청감만제1동우암로 54(감만동 버스종점 우측) 앞부산광역시 남구 우암로 54탱탱이 걸이식(좌측고정)6.0*0.7210100001260051-607-4626부산광역시 남구2022-06-0935.11129.0815
3부산광역시 남구청감만제1동무민사로 40 앞부산광역시 남구 무민사로 40탱탱이 걸이식(좌측고정)6.0*0.7210100001260051-607-4626부산광역시 남구2022-06-0935.11129.0818
4부산광역시 남구청감만제2동홈플러스 감만점 건너편부산광역시 남구 감만동 51-8탱탱이 걸이식(좌측고정)6.0*0.7210100001260051-607-4626부산광역시 남구2022-06-0935.12129.080734
5부산광역시 남구청감만제2동남광시장 입구부산광역시 남구 석포로 66탱탱이 걸이식(좌측고정)6.0*0.741000051-607-4626부산광역시 남구2022-06-0935.12129.084558
6부산광역시 남구청대연제1동대연동 사거리 부산은행 앞(제1게시대)부산광역시 남구 대연동 1740-8탱탱이 걸이식(좌측고정)6.0*0.7210100001260051-607-4626부산광역시 남구2022-06-0935.13129.092227
7부산광역시 남구청대연제1동대연동 사거리 부산은행 앞(제2게시대)부산광역시 남구 대연동 1740-8탱탱이 걸이식(좌측고정)6.0*0.721000051-607-4626부산광역시 남구2022-06-0935.13129.092227
8부산광역시 남구청대연제1동못골지하철역 3번출구(제1게시대)부산광역시 남구 수영로 158 앞탱탱이 걸이식(좌측고정)6.0*0.721000051-607-4626부산광역시 남구2022-06-0935.13129.083952
9부산광역시 남구청대연제1동못골지하철역 3번출구(제2게시대)부산광역시 남구 수영로 158 앞탱탱이 걸이식(좌측고정)6.0*0.711000051-607-4626부산광역시 남구2022-06-0935.13129.083952
관리기관명행정동위치주소특징규격부착면수부착제한일민원수수료점용료관리기관전화번호구군명데이터기준일자위도경도
83부산광역시 남구청우암동새마을금고(구.부산은행) 맞은편(제2게시대)부산광역시 남구 우암동 184-250탱탱이 걸이식(좌측고정)7.0*0.9510100001890051-607-4626부산광역시 남구2022-06-0935.12129.07396
84부산광역시 남구청우암동남부중앙새마을금고 본점 맞은편 LED 전자게시대 (6.34*1.2)부산광역시 남구 우암동 232LED 전자게시대6.3*1.2101000051-607-4626부산광역시 남구2022-06-0935.12129.0711
85부산광역시 남구청대연제6동수영로변 대연롯데캐슬레전드 아파트 앞 LED전자게시대(6.3*1.0)부산광역시 남구 대연동 1600-602LED 전자게시대6.3*1.0101000051-607-4626부산광역시 남구2022-06-0935.13129.0822
86부산광역시 남구청용호제1동용호동 남부운전면허시험장 앞 사거리 부근 LED 전자게시대(6.3*1.0)부산광역시 남구 용호동 176-50LED 전자게시대6.3*1.0101000051-607-4626부산광역시 남구2022-06-0935.12129.1064
87부산광역시 남구청대연제4동유엔로 부산공고 담장 앞 좌측게시대부산광역시 남구 수영로196번길 80탱탱이 걸이식(좌측고정)6.0*0.7110100001260051-607-4626부산광역시 남구2022-06-0935.13129.0876
88부산광역시 남구청대연제4동유엔로 부산공고 담장 앞 우측게시대부산광역시 남구 수영로196번길 80탱탱이 걸이식(좌측고정)6.0*0.7110100001260051-607-4626부산광역시 남구2022-06-0935.13129.0876
89부산광역시 남구청대연제4동석포로 112 푸드엔 앞 좌측게시대부산광역시 남구 석포로 112탱탱이 걸이식(좌측고정)6.0*0.7110100001260051-607-4626부산광역시 남구2022-06-0935.12129.0908
90부산광역시 남구청대연제4동석포로 112 푸드엔 앞 우측게시대부산광역시 남구 석포로 112탱탱이 걸이식(좌측고정)6.0*0.7110100001260051-607-4626부산광역시 남구2022-06-0935.12129.0908
91부산광역시 남구청대연제6동보건소 앞 좌측게시대부산광역시 남구 못골로 23탱탱이 걸이식(좌측고정)6.0*0.711000051-607-4626부산광역시 남구2022-06-0935.13129.0849
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