Overview

Dataset statistics

Number of variables4
Number of observations63
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory35.1 B

Variable types

Numeric1
Categorical1
Text2

Dataset

Description부산광역시수영구_동민자율게시대현황_20230502
Author부산광역시 수영구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15055475

Alerts

연번 is highly overall correlated with 동High correlation
동 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
설치주소 has unique valuesUnique
설치장소 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:30:19.856970
Analysis finished2023-12-10 16:30:20.378429
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-11T01:30:20.459704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.1
Q116.5
median32
Q347.5
95-th percentile59.9
Maximum63
Range62
Interquartile range (IQR)31

Descriptive statistics

Standard deviation18.330303
Coefficient of variation (CV)0.57282196
Kurtosis-1.2
Mean32
Median Absolute Deviation (MAD)16
Skewness0
Sum2016
Variance336
MonotonicityStrictly increasing
2023-12-11T01:30:20.638453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.6%
2 1
 
1.6%
35 1
 
1.6%
36 1
 
1.6%
37 1
 
1.6%
38 1
 
1.6%
39 1
 
1.6%
40 1
 
1.6%
41 1
 
1.6%
42 1
 
1.6%
Other values (53) 53
84.1%
ValueCountFrequency (%)
1 1
1.6%
2 1
1.6%
3 1
1.6%
4 1
1.6%
5 1
1.6%
6 1
1.6%
7 1
1.6%
8 1
1.6%
9 1
1.6%
10 1
1.6%
ValueCountFrequency (%)
63 1
1.6%
62 1
1.6%
61 1
1.6%
60 1
1.6%
59 1
1.6%
58 1
1.6%
57 1
1.6%
56 1
1.6%
55 1
1.6%
54 1
1.6%

동
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size636.0 B
광안4동
10 
광안1동
9 
민락동
9 
망미1동
8 
망미2동
7 
Other values (5)
20 

Length

Max length4
Median length4
Mean length3.7936508
Min length3

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row남천1동
2nd row남천1동
3rd row남천1동
4th row남천1동
5th row남천1동

Common Values

ValueCountFrequency (%)
광안4동 10
15.9%
광안1동 9
14.3%
민락동 9
14.3%
망미1동 8
12.7%
망미2동 7
11.1%
남천1동 6
9.5%
광안2동 5
7.9%
광안3동 4
 
6.3%
수영동 4
 
6.3%
남천2동 1
 
1.6%

Length

2023-12-11T01:30:20.794911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:30:20.947072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광안4동 10
15.9%
광안1동 9
14.3%
민락동 9
14.3%
망미1동 8
12.7%
망미2동 7
11.1%
남천1동 6
9.5%
광안2동 5
7.9%
광안3동 4
 
6.3%
수영동 4
 
6.3%
남천2동 1
 
1.6%

설치주소
Text

UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
2023-12-11T01:30:21.319282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length20.015873
Min length15

Characters and Unicode

Total characters1261
Distinct characters53
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

Unique63 ?
Unique (%)100.0%

Sample

1st row부산광역시 수영구 남천서로 7
2nd row부산광역시 수영구 수영로 479
3rd row부산광역시 수영구 수영로487번길 20
4th row부산광역시 수영구 수영로384번길 7
5th row부산광역시 수영구 수영로408번길 65
ValueCountFrequency (%)
부산광역시 63
25.1%
수영구 62
24.7%
7 4
 
1.6%
43 3
 
1.2%
25 3
 
1.2%
연수로379번길 2
 
0.8%
5 2
 
0.8%
69 2
 
0.8%
망미번영로55번길 2
 
0.8%
18 2
 
0.8%
Other values (97) 106
42.2%
2023-12-11T01:30:21.841305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
188
14.9%
수 84
 
6.7%
영 80
 
6.3%
광 76
 
6.0%
구 64
 
5.1%
부 63
 
5.0%
역 63
 
5.0%
시 63
 
5.0%
산 63
 
5.0%
로 62
 
4.9%
Other values (43) 455
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 818
64.9%
Decimal Number 249
 
19.7%
Space Separator 188
 
14.9%
Dash Punctuation 6
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 84
10.3%
영 80
9.8%
광 76
9.3%
구 64
7.8%
부 63
7.7%
역 63
7.7%
시 63
7.7%
산 63
7.7%
로 62
7.6%
번 54
 
6.6%
Other values (31) 146
17.8%
Decimal Number
ValueCountFrequency (%)
1 33
13.3%
2 31
12.4%
5 29
11.6%
3 28
11.2%
4 25
10.0%
8 23
9.2%
7 23
9.2%
9 22
8.8%
6 18
7.2%
0 17
6.8%
Space Separator
ValueCountFrequency (%)
188
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 818
64.9%
Common 443
35.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 84
10.3%
영 80
9.8%
광 76
9.3%
구 64
7.8%
부 63
7.7%
역 63
7.7%
시 63
7.7%
산 63
7.7%
로 62
7.6%
번 54
 
6.6%
Other values (31) 146
17.8%
Common
ValueCountFrequency (%)
188
42.4%
1 33
 
7.4%
2 31
 
7.0%
5 29
 
6.5%
3 28
 
6.3%
4 25
 
5.6%
8 23
 
5.2%
7 23
 
5.2%
9 22
 
5.0%
6 18
 
4.1%
Other values (2) 23
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 818
64.9%
ASCII 443
35.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
188
42.4%
1 33
 
7.4%
2 31
 
7.0%
5 29
 
6.5%
3 28
 
6.3%
4 25
 
5.6%
8 23
 
5.2%
7 23
 
5.2%
9 22
 
5.0%
6 18
 
4.1%
Other values (2) 23
 
5.2%
Hangul
ValueCountFrequency (%)
수 84
10.3%
영 80
9.8%
광 76
9.3%
구 64
7.8%
부 63
7.7%
역 63
7.7%
시 63
7.7%
산 63
7.7%
로 62
7.6%
번 54
 
6.6%
Other values (31) 146
17.8%

설치장소
Text

UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
2023-12-11T01:30:22.142025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length19
Mean length10.793651
Min length6

Characters and Unicode

Total characters680
Distinct characters170
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

Unique63 ?
Unique (%)100.0%

Sample

1st row남천초등학교 맞은편
2nd row화목맨션 뒤편
3rd row소라맨션 2동 뒤편
4th row남천파크 2동 뒤편
5th row우성보라아파트 102동 옆 담벽
ValueCountFrequency (%)
앞 16
 
10.1%
옆 12
 
7.6%
뒤편 10
 
6.3%
담벽 9
 
5.7%
맞은편 6
 
3.8%
입구 5
 
3.2%
2동 2
 
1.3%
건너편 2
 
1.3%
뒤 2
 
1.3%
102동 2
 
1.3%
Other values (88) 92
58.2%
2023-12-11T01:30:22.593044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
95
 
14.0%
동 23
 
3.4%
편 23
 
3.4%
앞 21
 
3.1%
트 15
 
2.2%
아 14
 
2.1%
뒤 14
 
2.1%
파 14
 
2.1%
옆 12
 
1.8%
담 10
 
1.5%
Other values (160) 439
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 545
80.1%
Space Separator 95
 
14.0%
Decimal Number 22
 
3.2%
Uppercase Letter 5
 
0.7%
Open Punctuation 4
 
0.6%
Other Punctuation 4
 
0.6%
Close Punctuation 4
 
0.6%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
 
4.2%
편 23
 
4.2%
앞 21
 
3.9%
트 15
 
2.8%
아 14
 
2.6%
뒤 14
 
2.6%
파 14
 
2.6%
옆 12
 
2.2%
담 10
 
1.8%
이 9
 
1.7%
Other values (144) 390
71.6%
Decimal Number
ValueCountFrequency (%)
2 8
36.4%
1 6
27.3%
0 5
22.7%
6 1
 
4.5%
5 1
 
4.5%
9 1
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
E 1
20.0%
B 1
20.0%
S 1
20.0%
I 1
20.0%
G 1
20.0%
Space Separator
ValueCountFrequency (%)
95
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 545
80.1%
Common 130
 
19.1%
Latin 5
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
 
4.2%
편 23
 
4.2%
앞 21
 
3.9%
트 15
 
2.8%
아 14
 
2.6%
뒤 14
 
2.6%
파 14
 
2.6%
옆 12
 
2.2%
담 10
 
1.8%
이 9
 
1.7%
Other values (144) 390
71.6%
Common
ValueCountFrequency (%)
95
73.1%
2 8
 
6.2%
1 6
 
4.6%
0 5
 
3.8%
( 4
 
3.1%
/ 4
 
3.1%
) 4
 
3.1%
- 1
 
0.8%
6 1
 
0.8%
5 1
 
0.8%
Latin
ValueCountFrequency (%)
E 1
20.0%
B 1
20.0%
S 1
20.0%
I 1
20.0%
G 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 545
80.1%
ASCII 135
 
19.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
95
70.4%
2 8
 
5.9%
1 6
 
4.4%
0 5
 
3.7%
( 4
 
3.0%
/ 4
 
3.0%
) 4
 
3.0%
- 1
 
0.7%
6 1
 
0.7%
E 1
 
0.7%
Other values (6) 6
 
4.4%
Hangul
ValueCountFrequency (%)
동 23
 
4.2%
편 23
 
4.2%
앞 21
 
3.9%
트 15
 
2.8%
아 14
 
2.6%
뒤 14
 
2.6%
파 14
 
2.6%
옆 12
 
2.2%
담 10
 
1.8%
이 9
 
1.7%
Other values (144) 390
71.6%

Interactions

2023-12-11T01:30:20.103206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:30:22.686569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동설치주소설치장소
연번1.0000.9741.0001.000
동0.9741.0001.0001.000
설치주소1.0001.0001.0001.000
설치장소1.0001.0001.0001.000
2023-12-11T01:30:22.770144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동
연번1.0000.725
동0.7251.000

Missing values

2023-12-11T01:30:20.226854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:30:20.335598image/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

연번동설치주소설치장소
01남천1동부산광역시 수영구 남천서로 7남천초등학교 맞은편
12남천1동부산광역시 수영구 수영로 479화목맨션 뒤편
23남천1동부산광역시 수영구 수영로487번길 20소라맨션 2동 뒤편
34남천1동부산광역시 수영구 수영로384번길 7남천파크 2동 뒤편
45남천1동부산광역시 수영구 수영로408번길 65우성보라아파트 102동 옆 담벽
56남천1동부산광역시 수영구 광남로 2남천동 하이마트 앞
67남천2동부산광역시 수영구 광안해변로 100삼익B상가202동 앞
78광안1동부산광역시 수영구 무학로49번길 21부덕그린 뒤편
89광안1동부산광역시 수영구 광일로29번가길 43부산 나눔의 교회 앞
910광안1동부산광역시 수영구 광일로29번길 51광안성당 앞
연번동설치주소설치장소
5354망미2동부산광역시 수영구 구락로 150고려제강 앞
5455민락동부산광역시 수영구 무학로63번길 91민락아파트 다동 옆
5556민락동부산광역시 수영구 민락로 38동방주차장 앞(동방오거리)
5657민락동부산광역시 수영구 민락로14번길 46민락시장 안쪽
5758민락동부산광역시 수영구 민락본동로31번길 33오성여객 옆
5859민락동부산광역시 수영구 광안해변로277번길 36-12이진빌라1동 담벽
5960민락동부산광역시 수영구 감포로8번길 39진흥목화 뒤 담벽
6061민락동부산광역시 수영구 무학로64번길 35이화맨션 뒤편
6162민락동부산광역시 수영구 광안해변로 299씨랜드회센터 맞은편/진로비치아파트 102동 뒤편
6263민락동부산광역시 수영구 민락본동로 15송현다미랑아파트 뒤 담벽