Overview

Dataset statistics

Number of variables5
Number of observations48
Missing cells14
Missing cells (%)5.8%
Duplicate rows1
Duplicate rows (%)2.1%
Total size in memory2.0 KiB
Average record size in memory43.7 B

Variable types

Numeric1
Categorical3
Text1

Dataset

Description공공데이터의 제공 및 이용활성화에 관한 법률 제21조 및 같은법 시행령 제16조에 에 따라, 해운대구 벽보게시판 설치위치 현황등록
Author부산광역시 해운대구
URLhttps://www.data.go.kr/data/3075795/fileData.do

Alerts

Dataset has 1 (2.1%) duplicate rowsDuplicates
행정동명 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
시군구명 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
시도명 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
연번 is highly overall correlated with 시도명 and 2 other fieldsHigh correlation
연번 has 7 (14.6%) missing valuesMissing
설치위치 has 7 (14.6%) missing valuesMissing

Reproduction

Analysis started2024-03-14 19:36:17.756734
Analysis finished2024-03-14 19:36:19.431274
Duration1.67 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)100.0%
Missing7
Missing (%)14.6%
Infinite0
Infinite (%)0.0%
Mean21
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-15T04:36:19.664952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21
Q331
95-th percentile39
Maximum41
Range40
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.979149
Coefficient of variation (CV)0.57043565
Kurtosis-1.2
Mean21
Median Absolute Deviation (MAD)10
Skewness0
Sum861
Variance143.5
MonotonicityStrictly increasing
2024-03-15T04:36:20.405046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
32 1
 
2.1%
24 1
 
2.1%
25 1
 
2.1%
26 1
 
2.1%
27 1
 
2.1%
28 1
 
2.1%
29 1
 
2.1%
30 1
 
2.1%
31 1
 
2.1%
33 1
 
2.1%
Other values (31) 31
64.6%
(Missing) 7
 
14.6%
ValueCountFrequency (%)
1 1
2.1%
2 1
2.1%
3 1
2.1%
4 1
2.1%
5 1
2.1%
6 1
2.1%
7 1
2.1%
8 1
2.1%
9 1
2.1%
10 1
2.1%
ValueCountFrequency (%)
41 1
2.1%
40 1
2.1%
39 1
2.1%
38 1
2.1%
37 1
2.1%
36 1
2.1%
35 1
2.1%
34 1
2.1%
33 1
2.1%
32 1
2.1%

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size512.0 B
부산광역시
41 
<NA>
7 

Length

Max length5
Median length5
Mean length4.8541667
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 41
85.4%
<NA> 7
 
14.6%

Length

2024-03-15T04:36:20.855873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:36:21.202431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 41
85.4%
na 7
 
14.6%

시군구명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size512.0 B
해운대구
41 
<NA>
7 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해운대구 41
85.4%
<NA> 7
 
14.6%

Length

2024-03-15T04:36:21.617870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:36:21.941019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해운대구 41
85.4%
na 7
 
14.6%

행정동명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Memory size512.0 B
<NA>
7 
중1동
6 
좌1동
6 
좌4동
6 
우3동
5 
Other values (9)
18 

Length

Max length4
Median length3
Mean length3.3125
Min length3

Unique

Unique5 ?
Unique (%)10.4%

Sample

1st row우1동
2nd row우1동
3rd row우1동
4th row우2동
5th row우3동

Common Values

ValueCountFrequency (%)
<NA> 7
14.6%
중1동 6
12.5%
좌1동 6
12.5%
좌4동 6
12.5%
우3동 5
10.4%
재송2동 5
10.4%
우1동 3
6.2%
좌2동 3
6.2%
좌3동 2
 
4.2%
우2동 1
 
2.1%
Other values (4) 4
8.3%

Length

2024-03-15T04:36:22.310421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 7
14.6%
중1동 6
12.5%
좌1동 6
12.5%
좌4동 6
12.5%
우3동 5
10.4%
재송2동 5
10.4%
우1동 3
6.2%
좌2동 3
6.2%
좌3동 2
 
4.2%
우2동 1
 
2.1%
Other values (4) 4
8.3%

설치위치
Text

MISSING 

Distinct41
Distinct (%)100.0%
Missing7
Missing (%)14.6%
Memory size512.0 B
2024-03-15T04:36:23.289389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length21
Mean length13.512195
Min length6

Characters and Unicode

Total characters554
Distinct characters141
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)100.0%

Sample

1st row경동마리나아파트 맞은편 신호대
2nd row아우디 해운대전시장 맞은편
3rd row운촌삼거리 신호대
4th row아르피나 입구
5th row오션타워 입구 사거리
ValueCountFrequency (%)
맞은편 14
 
11.3%
앞 12
 
9.7%
입구 10
 
8.1%
옆 5
 
4.0%
사거리 5
 
4.0%
버스정류소 4
 
3.2%
삼거리 3
 
2.4%
삼익아파트 3
 
2.4%
정문 2
 
1.6%
대우마리나아파트 2
 
1.6%
Other values (60) 64
51.6%
2024-03-15T04:36:24.411318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83
 
15.0%
은 19
 
3.4%
맞 17
 
3.1%
리 16
 
2.9%
편 16
 
2.9%
대 14
 
2.5%
아 14
 
2.5%
구 14
 
2.5%
파 12
 
2.2%
입 12
 
2.2%
Other values (131) 337
60.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 446
80.5%
Space Separator 83
 
15.0%
Uppercase Letter 8
 
1.4%
Open Punctuation 7
 
1.3%
Close Punctuation 7
 
1.3%
Decimal Number 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
은 19
 
4.3%
맞 17
 
3.8%
리 16
 
3.6%
편 16
 
3.6%
대 14
 
3.1%
아 14
 
3.1%
구 14
 
3.1%
파 12
 
2.7%
입 12
 
2.7%
앞 12
 
2.7%
Other values (119) 300
67.3%
Uppercase Letter
ValueCountFrequency (%)
C 2
25.0%
K 1
12.5%
I 1
12.5%
T 1
12.5%
S 1
12.5%
G 1
12.5%
N 1
12.5%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
1 1
33.3%
Space Separator
ValueCountFrequency (%)
83
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 446
80.5%
Common 100
 
18.1%
Latin 8
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
은 19
 
4.3%
맞 17
 
3.8%
리 16
 
3.6%
편 16
 
3.6%
대 14
 
3.1%
아 14
 
3.1%
구 14
 
3.1%
파 12
 
2.7%
입 12
 
2.7%
앞 12
 
2.7%
Other values (119) 300
67.3%
Latin
ValueCountFrequency (%)
C 2
25.0%
K 1
12.5%
I 1
12.5%
T 1
12.5%
S 1
12.5%
G 1
12.5%
N 1
12.5%
Common
ValueCountFrequency (%)
83
83.0%
( 7
 
7.0%
) 7
 
7.0%
2 2
 
2.0%
1 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 446
80.5%
ASCII 108
 
19.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
83
76.9%
( 7
 
6.5%
) 7
 
6.5%
2 2
 
1.9%
C 2
 
1.9%
K 1
 
0.9%
I 1
 
0.9%
T 1
 
0.9%
S 1
 
0.9%
G 1
 
0.9%
Other values (2) 2
 
1.9%
Hangul
ValueCountFrequency (%)
은 19
 
4.3%
맞 17
 
3.8%
리 16
 
3.6%
편 16
 
3.6%
대 14
 
3.1%
아 14
 
3.1%
구 14
 
3.1%
파 12
 
2.7%
입 12
 
2.7%
앞 12
 
2.7%
Other values (119) 300
67.3%

Interactions

2024-03-15T04:36:18.043757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T04:36:24.666966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동명설치위치
연번1.0000.9261.000
행정동명0.9261.0001.000
설치위치1.0001.0001.000
2024-03-15T04:36:24.924483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명시군구명시도명
행정동명1.0001.0001.000
시군구명1.0001.0001.000
시도명1.0001.0001.000
2024-03-15T04:36:25.089611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시도명시군구명행정동명
연번1.0001.0001.0000.704
시도명1.0001.0001.0001.000
시군구명1.0001.0001.0001.000
행정동명0.7041.0001.0001.000

Missing values

2024-03-15T04:36:18.403250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T04:36:18.804847image/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.
2024-03-15T04:36:19.243551image/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

연번시도명시군구명행정동명설치위치
01부산광역시해운대구우1동경동마리나아파트 맞은편 신호대
12부산광역시해운대구우1동아우디 해운대전시장 맞은편
23부산광역시해운대구우1동운촌삼거리 신호대
34부산광역시해운대구우2동아르피나 입구
45부산광역시해운대구우3동오션타워 입구 사거리
56부산광역시해운대구우3동구 홈플러스 앞 버스정류소
67부산광역시해운대구우3동경남마리나아파트 소방서 옆
78부산광역시해운대구우3동대우마리나아파트 입구 우리은행 앞
89부산광역시해운대구우3동대우마리나아파트 입구(구 홈플러스 맞은편)
910부산광역시해운대구중1동베니키아온천 사거리
연번시도명시군구명행정동명설치위치
3839부산광역시해운대구재송2동해운대경찰서 밑 신호대(재송역 맞은편)
3940부산광역시해운대구재송2동삼익아파트 삼거리 맞은편
4041부산광역시해운대구재송2동삼익아파트 입구 화단 앞
41<NA><NA><NA><NA><NA>
42<NA><NA><NA><NA><NA>
43<NA><NA><NA><NA><NA>
44<NA><NA><NA><NA><NA>
45<NA><NA><NA><NA><NA>
46<NA><NA><NA><NA><NA>
47<NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

연번시도명시군구명행정동명설치위치# duplicates
0<NA><NA><NA><NA><NA>7