Overview

Dataset statistics

Number of variables12
Number of observations346
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.6 KiB
Average record size in memory99.4 B

Variable types

Categorical6
Text4
Numeric2

Dataset

Description부산광역시 해운대구에 설치되어 있는 도로변 제설함 배치 현황입니다. 염화칼슘, 친환경제설제, 모래주머니 등이 들어있으며 누구나 사용 가능합니다.(관리번호, 설치장소, 위도, 경도 등)
Author부산광역시 해운대구
URLhttps://www.data.go.kr/data/3075738/fileData.do

Alerts

수량 has constant value ""Constant
관리기관(부서) has constant value ""Constant
연락처 has constant value ""Constant
구군명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 행정동명High correlation
경도 is highly overall correlated with 행정동명High correlation
행정동명 is highly overall correlated with 위도 and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-03-15 02:27:47.935294
Analysis finished2024-03-15 02:27:50.456811
Duration2.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
재송2동
63 
중2동
51 
재송1동
36 
반송2동
35 
송정동
25 
Other values (10)
136 

Length

Max length4
Median length4
Mean length3.6416185
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재송2동 63
18.2%
중2동 51
14.7%
재송1동 36
10.4%
반송2동 35
10.1%
송정동 25
 
7.2%
반여2동 23
 
6.6%
반송1동 22
 
6.4%
반여3동 20
 
5.8%
우2동 14
 
4.0%
좌4동 13
 
3.8%
Other values (5) 44
12.7%

Length

2024-03-15T11:27:50.674294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
재송2동 63
18.2%
중2동 51
14.7%
재송1동 36
10.4%
반송2동 35
10.1%
송정동 25
 
7.2%
반여2동 23
 
6.6%
반송1동 22
 
6.4%
반여3동 20
 
5.8%
우2동 14
 
4.0%
좌4동 13
 
3.8%
Other values (5) 44
12.7%

관리번호
Text

Distinct345
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-03-15T11:27:52.079288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.2745665
Min length5

Characters and Unicode

Total characters2171
Distinct characters20
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

Unique344 ?
Unique (%)99.4%

Sample

1st row우1동-1
2nd row우1동-2
3rd row우1동-3
4th row우1동-4
5th row우1동-5
ValueCountFrequency (%)
반송1동-5 2
 
0.6%
반송2동-17 1
 
0.3%
반송2동-25 1
 
0.3%
반송2동-24 1
 
0.3%
반송2동-23 1
 
0.3%
반송2동-22 1
 
0.3%
반송2동-21 1
 
0.3%
반송2동-20 1
 
0.3%
반송2동-19 1
 
0.3%
반송2동-30 1
 
0.3%
Other values (335) 335
96.8%
2024-03-15T11:27:53.992614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 346
15.9%
- 346
15.9%
2 287
13.2%
1 225
10.4%
송 181
8.3%
반 123
 
5.7%
재 99
 
4.6%
3 91
 
4.2%
4 79
 
3.6%
여 66
 
3.0%
Other values (10) 328
15.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 939
43.3%
Decimal Number 886
40.8%
Dash Punctuation 346
 
15.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 287
32.4%
1 225
25.4%
3 91
 
10.3%
4 79
 
8.9%
5 47
 
5.3%
6 36
 
4.1%
7 31
 
3.5%
9 30
 
3.4%
0 30
 
3.4%
8 30
 
3.4%
Other Letter
ValueCountFrequency (%)
동 346
36.8%
송 181
19.3%
반 123
 
13.1%
재 99
 
10.5%
여 66
 
7.0%
중 53
 
5.6%
우 26
 
2.8%
정 25
 
2.7%
좌 20
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1232
56.7%
Hangul 939
43.3%

Most frequent character per script

Common
ValueCountFrequency (%)
- 346
28.1%
2 287
23.3%
1 225
18.3%
3 91
 
7.4%
4 79
 
6.4%
5 47
 
3.8%
6 36
 
2.9%
7 31
 
2.5%
9 30
 
2.4%
0 30
 
2.4%
Hangul
ValueCountFrequency (%)
동 346
36.8%
송 181
19.3%
반 123
 
13.1%
재 99
 
10.5%
여 66
 
7.0%
중 53
 
5.6%
우 26
 
2.8%
정 25
 
2.7%
좌 20
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1232
56.7%
Hangul 939
43.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 346
36.8%
송 181
19.3%
반 123
 
13.1%
재 99
 
10.5%
여 66
 
7.0%
중 53
 
5.6%
우 26
 
2.8%
정 25
 
2.7%
좌 20
 
2.1%
ASCII
ValueCountFrequency (%)
- 346
28.1%
2 287
23.3%
1 225
18.3%
3 91
 
7.4%
4 79
 
6.4%
5 47
 
3.8%
6 36
 
2.9%
7 31
 
2.5%
9 30
 
2.4%
0 30
 
2.4%

설치장소
Text

Distinct336
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-03-15T11:27:54.943225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length23.5
Mean length12.387283
Min length2

Characters and Unicode

Total characters4286
Distinct characters318
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique327 ?
Unique (%)94.5%

Sample

1st row법륜사전봇대
2nd row해운대여고 정문 앞
3rd row해광사 앞
4th row해동초 입구(후문쪽)
5th row신동비치 입구
ValueCountFrequency (%)
앞 85
 
9.2%
맞은편 44
 
4.7%
옆 29
 
3.1%
입구 26
 
2.8%
인근 26
 
2.8%
정문 14
 
1.5%
센텀롯데캐슬2차 9
 
1.0%
재반로282번길 8
 
0.9%
주변 7
 
0.8%
남성선파크 7
 
0.8%
Other values (465) 673
72.5%
2024-03-15T11:27:56.401553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
586
 
13.7%
1 136
 
3.2%
( 105
 
2.4%
) 104
 
2.4%
동 102
 
2.4%
2 101
 
2.4%
앞 92
 
2.1%
로 81
 
1.9%
반 69
 
1.6%
근 68
 
1.6%
Other values (308) 2842
66.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2906
67.8%
Space Separator 586
 
13.7%
Decimal Number 537
 
12.5%
Open Punctuation 105
 
2.4%
Close Punctuation 104
 
2.4%
Dash Punctuation 22
 
0.5%
Uppercase Letter 15
 
0.3%
Math Symbol 4
 
0.1%
Lowercase Letter 4
 
0.1%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 102
 
3.5%
앞 92
 
3.2%
로 81
 
2.8%
반 69
 
2.4%
근 68
 
2.3%
인 62
 
2.1%
맞 59
 
2.0%
정 57
 
2.0%
길 57
 
2.0%
편 54
 
1.9%
Other values (282) 2205
75.9%
Decimal Number
ValueCountFrequency (%)
1 136
25.3%
2 101
18.8%
3 47
 
8.8%
8 44
 
8.2%
0 42
 
7.8%
7 39
 
7.3%
9 38
 
7.1%
4 34
 
6.3%
6 29
 
5.4%
5 27
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
S 3
20.0%
G 3
20.0%
A 3
20.0%
C 2
13.3%
B 1
 
6.7%
J 1
 
6.7%
I 1
 
6.7%
E 1
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
t 2
50.0%
k 2
50.0%
Space Separator
ValueCountFrequency (%)
586
100.0%
Open Punctuation
ValueCountFrequency (%)
( 105
100.0%
Close Punctuation
ValueCountFrequency (%)
) 104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2906
67.8%
Common 1361
31.8%
Latin 19
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 102
 
3.5%
앞 92
 
3.2%
로 81
 
2.8%
반 69
 
2.4%
근 68
 
2.3%
인 62
 
2.1%
맞 59
 
2.0%
정 57
 
2.0%
길 57
 
2.0%
편 54
 
1.9%
Other values (282) 2205
75.9%
Common
ValueCountFrequency (%)
586
43.1%
1 136
 
10.0%
( 105
 
7.7%
) 104
 
7.6%
2 101
 
7.4%
3 47
 
3.5%
8 44
 
3.2%
0 42
 
3.1%
7 39
 
2.9%
9 38
 
2.8%
Other values (6) 119
 
8.7%
Latin
ValueCountFrequency (%)
S 3
15.8%
G 3
15.8%
A 3
15.8%
t 2
10.5%
k 2
10.5%
C 2
10.5%
B 1
 
5.3%
J 1
 
5.3%
I 1
 
5.3%
E 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2906
67.8%
ASCII 1380
32.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
586
42.5%
1 136
 
9.9%
( 105
 
7.6%
) 104
 
7.5%
2 101
 
7.3%
3 47
 
3.4%
8 44
 
3.2%
0 42
 
3.0%
7 39
 
2.8%
9 38
 
2.8%
Other values (16) 138
 
10.0%
Hangul
ValueCountFrequency (%)
동 102
 
3.5%
앞 92
 
3.2%
로 81
 
2.8%
반 69
 
2.4%
근 68
 
2.3%
인 62
 
2.1%
맞 59
 
2.0%
정 57
 
2.0%
길 57
 
2.0%
편 54
 
1.9%
Other values (282) 2205
75.9%

소재지도로명주소
Text

Distinct190
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-03-15T11:27:57.285899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length19.783237
Min length14

Characters and Unicode

Total characters6845
Distinct characters55
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

Unique140 ?
Unique (%)40.5%

Sample

1st row부산광역시 해운대구 우동2로60번길
2nd row부산광역시 해운대구 우동1로85번길 71
3rd row부산광역시 해운대구 우동1로85번길 64
4th row부산광역시 해운대구 우동1로
5th row부산광역시 해운대구 우동2로
ValueCountFrequency (%)
부산광역시 346
26.3%
해운대구 346
26.3%
재반로 47
 
3.6%
신반송로 32
 
2.4%
달맞이길 28
 
2.1%
재송2로 19
 
1.4%
반송순환로 15
 
1.1%
장산로 12
 
0.9%
좌동순환로 12
 
0.9%
200 11
 
0.8%
Other values (194) 447
34.0%
2024-03-15T11:27:58.612116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
979
 
14.3%
운 389
 
5.7%
해 386
 
5.6%
대 386
 
5.6%
산 358
 
5.2%
광 352
 
5.1%
부 346
 
5.1%
역 346
 
5.1%
시 346
 
5.1%
구 346
 
5.1%
Other values (45) 2611
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4735
69.2%
Decimal Number 1090
 
15.9%
Space Separator 979
 
14.3%
Dash Punctuation 41
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
운 389
 
8.2%
해 386
 
8.2%
대 386
 
8.2%
산 358
 
7.6%
광 352
 
7.4%
부 346
 
7.3%
역 346
 
7.3%
시 346
 
7.3%
구 346
 
7.3%
로 298
 
6.3%
Other values (33) 1182
25.0%
Decimal Number
ValueCountFrequency (%)
1 259
23.8%
2 157
14.4%
3 101
 
9.3%
4 99
 
9.1%
6 99
 
9.1%
8 87
 
8.0%
0 84
 
7.7%
7 76
 
7.0%
9 75
 
6.9%
5 53
 
4.9%
Space Separator
ValueCountFrequency (%)
979
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4735
69.2%
Common 2110
30.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
운 389
 
8.2%
해 386
 
8.2%
대 386
 
8.2%
산 358
 
7.6%
광 352
 
7.4%
부 346
 
7.3%
역 346
 
7.3%
시 346
 
7.3%
구 346
 
7.3%
로 298
 
6.3%
Other values (33) 1182
25.0%
Common
ValueCountFrequency (%)
979
46.4%
1 259
 
12.3%
2 157
 
7.4%
3 101
 
4.8%
4 99
 
4.7%
6 99
 
4.7%
8 87
 
4.1%
0 84
 
4.0%
7 76
 
3.6%
9 75
 
3.6%
Other values (2) 94
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4735
69.2%
ASCII 2110
30.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
979
46.4%
1 259
 
12.3%
2 157
 
7.4%
3 101
 
4.8%
4 99
 
4.7%
6 99
 
4.7%
8 87
 
4.1%
0 84
 
4.0%
7 76
 
3.6%
9 75
 
3.6%
Other values (2) 94
 
4.5%
Hangul
ValueCountFrequency (%)
운 389
 
8.2%
해 386
 
8.2%
대 386
 
8.2%
산 358
 
7.6%
광 352
 
7.4%
부 346
 
7.3%
역 346
 
7.3%
시 346
 
7.3%
구 346
 
7.3%
로 298
 
6.3%
Other values (33) 1182
25.0%

소재지지번주소
Text

Distinct238
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-03-15T11:28:00.330229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length20.118497
Min length17

Characters and Unicode

Total characters6961
Distinct characters36
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

Unique186 ?
Unique (%)53.8%

Sample

1st row부산광역시 해운대구 우동 391-28
2nd row부산광역시 해운대구 우동 355-29
3rd row부산광역시 해운대구 우동 355-18
4th row부산광역시 해운대구 우동 345-1
5th row부산광역시 해운대구 우동 345-41
ValueCountFrequency (%)
부산광역시 346
24.8%
해운대구 346
24.8%
재송동 103
 
7.4%
반여동 62
 
4.4%
반송동 57
 
4.1%
중동 50
 
3.6%
우동 26
 
1.9%
송정동 25
 
1.8%
좌동 20
 
1.4%
산 14
 
1.0%
Other values (235) 347
24.9%
2024-03-15T11:28:02.539826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1055
15.2%
1 492
 
7.1%
산 383
 
5.5%
운 346
 
5.0%
구 346
 
5.0%
대 346
 
5.0%
부 346
 
5.0%
해 346
 
5.0%
시 346
 
5.0%
역 346
 
5.0%
Other values (26) 2609
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4095
58.8%
Decimal Number 1586
 
22.8%
Space Separator 1055
 
15.2%
Dash Punctuation 225
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 383
9.4%
운 346
8.4%
구 346
8.4%
대 346
8.4%
부 346
8.4%
해 346
8.4%
시 346
8.4%
역 346
8.4%
광 346
8.4%
동 345
8.4%
Other values (14) 599
14.6%
Decimal Number
ValueCountFrequency (%)
1 492
31.0%
5 167
 
10.5%
2 153
 
9.6%
3 125
 
7.9%
8 122
 
7.7%
7 121
 
7.6%
0 109
 
6.9%
9 102
 
6.4%
4 98
 
6.2%
6 97
 
6.1%
Space Separator
ValueCountFrequency (%)
1055
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 225
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4095
58.8%
Common 2866
41.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 383
9.4%
운 346
8.4%
구 346
8.4%
대 346
8.4%
부 346
8.4%
해 346
8.4%
시 346
8.4%
역 346
8.4%
광 346
8.4%
동 345
8.4%
Other values (14) 599
14.6%
Common
ValueCountFrequency (%)
1055
36.8%
1 492
17.2%
- 225
 
7.9%
5 167
 
5.8%
2 153
 
5.3%
3 125
 
4.4%
8 122
 
4.3%
7 121
 
4.2%
0 109
 
3.8%
9 102
 
3.6%
Other values (2) 195
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4095
58.8%
ASCII 2866
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1055
36.8%
1 492
17.2%
- 225
 
7.9%
5 167
 
5.8%
2 153
 
5.3%
3 125
 
4.4%
8 122
 
4.3%
7 121
 
4.2%
0 109
 
3.8%
9 102
 
3.6%
Other values (2) 195
 
6.8%
Hangul
ValueCountFrequency (%)
산 383
9.4%
운 346
8.4%
구 346
8.4%
대 346
8.4%
부 346
8.4%
해 346
8.4%
시 346
8.4%
역 346
8.4%
광 346
8.4%
동 345
8.4%
Other values (14) 599
14.6%

수량
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
1
346 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 346
100.0%

Length

2024-03-15T11:28:02.845058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T11:28:03.029534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 346
100.0%

관리기관(부서)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
해운대구청(재난안전과)
346 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row해운대구청(재난안전과)
2nd row해운대구청(재난안전과)
3rd row해운대구청(재난안전과)
4th row해운대구청(재난안전과)
5th row해운대구청(재난안전과)

Common Values

ValueCountFrequency (%)
해운대구청(재난안전과) 346
100.0%

Length

2024-03-15T11:28:03.341501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T11:28:03.657100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해운대구청(재난안전과 346
100.0%

연락처
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
051-749-4646
346 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row051-749-4646
2nd row051-749-4646
3rd row051-749-4646
4th row051-749-4646
5th row051-749-4646

Common Values

ValueCountFrequency (%)
051-749-4646 346
100.0%

Length

2024-03-15T11:28:04.002038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T11:28:04.259122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
051-749-4646 346
100.0%

구군명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
해운대구
346 

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 346
100.0%

Length

2024-03-15T11:28:04.438272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T11:28:04.608302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해운대구 346
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-01-10
346 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-10
2nd row2024-01-10
3rd row2024-01-10
4th row2024-01-10
5th row2024-01-10

Common Values

ValueCountFrequency (%)
2024-01-10 346
100.0%

Length

2024-03-15T11:28:04.905609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T11:28:05.134056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-01-10 346
100.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct236
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.189619
Minimum35.156298
Maximum35.238621
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-03-15T11:28:05.496067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.156298
5-th percentile35.160837
Q135.170656
median35.189939
Q335.200132
95-th percentile35.229422
Maximum35.238621
Range0.08232326
Interquartile range (IQR)0.029476258

Descriptive statistics

Standard deviation0.021017625
Coefficient of variation (CV)0.00059726775
Kurtosis-0.59210949
Mean35.189619
Median Absolute Deviation (MAD)0.013580845
Skewness0.43797466
Sum12175.608
Variance0.00044174055
MonotonicityNot monotonic
2024-03-15T11:28:05.947369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.19732548 9
 
2.6%
35.2302889 9
 
2.6%
35.16024024 8
 
2.3%
35.22875217 6
 
1.7%
35.1608372 6
 
1.7%
35.17553721 6
 
1.7%
35.18993864 6
 
1.7%
35.1927958 5
 
1.4%
35.16294748 5
 
1.4%
35.20263558 4
 
1.2%
Other values (226) 282
81.5%
ValueCountFrequency (%)
35.15629763 1
 
0.3%
35.15658568 1
 
0.3%
35.15750173 1
 
0.3%
35.15768146 3
 
0.9%
35.15823583 1
 
0.3%
35.1589186 1
 
0.3%
35.16024024 8
2.3%
35.16037143 1
 
0.3%
35.1608372 6
1.7%
35.16087347 1
 
0.3%
ValueCountFrequency (%)
35.23862089 1
 
0.3%
35.23709073 1
 
0.3%
35.23223692 1
 
0.3%
35.23102745 1
 
0.3%
35.2302889 9
2.6%
35.23028419 1
 
0.3%
35.23026317 1
 
0.3%
35.23018606 1
 
0.3%
35.22942229 3
 
0.9%
35.2293148 1
 
0.3%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct236
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.14943
Minimum129.11135
Maximum129.20195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-03-15T11:28:06.402353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.11135
5-th percentile129.12319
Q1129.12849
median129.14201
Q3129.16894
95-th percentile129.19396
Maximum129.20195
Range0.0905931
Interquartile range (IQR)0.040449025

Descriptive statistics

Standard deviation0.024053024
Coefficient of variation (CV)0.00018624181
Kurtosis-1.0426287
Mean129.14943
Median Absolute Deviation (MAD)0.01581715
Skewness0.5317876
Sum44685.704
Variance0.00057854797
MonotonicityNot monotonic
2024-03-15T11:28:06.860227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.1234791 9
 
2.6%
129.158665 9
 
2.6%
129.1755731 8
 
2.3%
129.1609486 6
 
1.7%
129.1777699 6
 
1.7%
129.1962056 6
 
1.7%
129.1292457 6
 
1.7%
129.1296321 5
 
1.4%
129.1787775 5
 
1.4%
129.1315036 4
 
1.2%
Other values (226) 282
81.5%
ValueCountFrequency (%)
129.1113527 1
0.3%
129.1121914 1
0.3%
129.1123998 1
0.3%
129.1153348 1
0.3%
129.1156702 1
0.3%
129.1158787 2
0.6%
129.1158833 1
0.3%
129.1160371 1
0.3%
129.1162505 1
0.3%
129.1167053 1
0.3%
ValueCountFrequency (%)
129.2019458 2
 
0.6%
129.1979265 1
 
0.3%
129.1971153 2
 
0.6%
129.1967199 1
 
0.3%
129.1965547 1
 
0.3%
129.1962056 6
1.7%
129.1945053 2
 
0.6%
129.1944078 1
 
0.3%
129.1943453 1
 
0.3%
129.1940937 1
 
0.3%

Interactions

2024-03-15T11:27:49.102955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:27:48.753797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:27:49.249030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:27:48.960141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T11:28:07.122960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명위도경도
행정동명1.0000.9650.962
위도0.9651.0000.947
경도0.9620.9471.000
2024-03-15T11:28:07.389237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도행정동명
위도1.000-0.4150.790
경도-0.4151.0000.780
행정동명0.7900.7801.000

Missing values

2024-03-15T11:27:49.584799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T11:27:50.250067image/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

행정동명관리번호설치장소소재지도로명주소소재지지번주소수량관리기관(부서)연락처구군명데이터기준일자위도경도
0우1동우1동-1법륜사전봇대부산광역시 해운대구 우동2로60번길부산광역시 해운대구 우동 391-281해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.169466129.157034
1우1동우1동-2해운대여고 정문 앞부산광역시 해운대구 우동1로85번길 71부산광역시 해운대구 우동 355-291해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.169635129.154363
2우1동우1동-3해광사 앞부산광역시 해운대구 우동1로85번길 64부산광역시 해운대구 우동 355-181해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.169402129.154074
3우1동우1동-4해동초 입구(후문쪽)부산광역시 해운대구 우동1로부산광역시 해운대구 우동 345-11해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.170305129.155447
4우1동우1동-5신동비치 입구부산광역시 해운대구 우동2로부산광역시 해운대구 우동 345-411해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.172293129.156776
5우1동우1동-6협성프라임 102동 입구부산광역시 해운대구 우동2로부산광역시 해운대구 우동 345-41해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.171203129.156157
6우1동우1동-7관광고 입구부산광역시 해운대구 우동2로부산광역시 해운대구 우동 산 87-11해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.169692129.15833
7우1동우1동-8해운대 엔스타 아파트부산광역시 해운대구 해운대로483번가길부산광역시 해운대구 우동 956-911해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.16452129.146293
8우1동우1동-9부경해물찜 앞부산광역시 해운대구 해운대로483번길부산광역시 해운대구 우동 942-131해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.16388129.146706
9우1동우1동-10신일교회 앞부산광역시 해운대구 해운대로483번길부산광역시 해운대구 우동 963-61해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.163583129.144672
행정동명관리번호설치장소소재지도로명주소소재지지번주소수량관리기관(부서)연락처구군명데이터기준일자위도경도
336재송2동재송2동-54코오롱 아파트 정문 앞부산광역시 해운대구 재반로84번길 80-2부산광역시 해운대구 재송동 1123-371해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.187378129.129434
337재송2동재송2동-55재송여중 정문 앞부산광역시 해운대구 재반로84번길 148부산광역시 해운대구 재송동 11571해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.189843129.131081
338재송2동재송2동-56재송동 아름다운 요양병원 주변 (1)부산광역시 해운대구 해운대로61번길부산광역시 해운대구 재송동 179-11해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.192999129.123195
339재송2동재송2동-57재송동 아름다운 요양병원 주변 (2)부산광역시 해운대구 해운대로61번길부산광역시 해운대구 재송동 179-11해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.192999129.123195
340재송2동재송2동-58재송동 아름다운 요양병원 주변 (3)부산광역시 해운대구 해운대로61번길부산광역시 해운대구 재송동 179-11해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.192999129.123195
341재송2동재송2동-59재송2로 209 앞부산광역시 해운대구 재송2로 209부산광역시 해운대구 재송동 1158-591해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.190213129.131756
342재송2동재송2동-60최가네가야밀면 앞부산광역시 해운대구 재반로 142부산광역시 해운대구 재송동 1144-51해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.19055129.127742
343재송2동재송2동-61재반로 130 앞부산광역시 해운대구 재반로 130부산광역시 해운대구 재송동 1131-111해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.18981129.127027
344재송2동재송2동-62한보사랑아파트 1동 앞부산광역시 해운대구 재송2로 96부산광역시 해운대구 재송동 1184-11해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.19249129.134128
345재송2동재송2동-63한보사랑아파트 1-2동 사이부산광역시 해운대구 재송2로 91부산광역시 해운대구 재송동 1182-11해운대구청(재난안전과)051-749-4646해운대구2024-01-1035.193593129.134306