Overview

Dataset statistics

Number of variables7
Number of observations47
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory59.8 B

Variable types

Numeric1
Categorical2
Text3
DateTime1

Dataset

Description부산광역시 수영구 소재 폭염 대비 그늘막 현황에 대한 데이터로, 그늘막의 행정동, 관리번호, 설치장소, 설치위치, 설치일시, 그늘막 유형 등의 항목을 제공합니다.
Author부산광역시 수영구
URLhttps://www.data.go.kr/data/15088918/fileData.do

Alerts

연번 is highly overall correlated with 행정동 구분High correlation
행정동 구분 is highly overall correlated with 연번High correlation
그늘막 유형 is highly imbalanced (51.1%)Imbalance
연번 has unique valuesUnique
관리번호 has unique valuesUnique
설치장소 has unique valuesUnique

Reproduction

Analysis started2023-12-23 07:33:22.220068
Analysis finished2023-12-23 07:33:32.007779
Duration9.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-23T07:33:32.503342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3
Q112.5
median24
Q335.5
95-th percentile44.7
Maximum47
Range46
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.711309
Coefficient of variation (CV)0.57130455
Kurtosis-1.2
Mean24
Median Absolute Deviation (MAD)12
Skewness0
Sum1128
Variance188
MonotonicityStrictly increasing
2023-12-23T07:33:33.529360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 1
 
2.1%
2 1
 
2.1%
27 1
 
2.1%
28 1
 
2.1%
29 1
 
2.1%
30 1
 
2.1%
31 1
 
2.1%
32 1
 
2.1%
33 1
 
2.1%
34 1
 
2.1%
Other values (37) 37
78.7%
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 (%)
47 1
2.1%
46 1
2.1%
45 1
2.1%
44 1
2.1%
43 1
2.1%
42 1
2.1%
41 1
2.1%
40 1
2.1%
39 1
2.1%
38 1
2.1%

행정동 구분
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size508.0 B
광안제2동
10 
민락동
8 
남천제1동
7 
수영동
7 
망미제1동
4 
Other values (5)
11 

Length

Max length5
Median length5
Mean length4.3617021
Min length3

Unique

Unique2 ?
Unique (%)4.3%

Sample

1st row남천제1동
2nd row남천제1동
3rd row남천제1동
4th row남천제1동
5th row남천제1동

Common Values

ValueCountFrequency (%)
광안제2동 10
21.3%
민락동 8
17.0%
남천제1동 7
14.9%
수영동 7
14.9%
망미제1동 4
 
8.5%
망미제2동 4
 
8.5%
광안제1동 3
 
6.4%
남천제2동 2
 
4.3%
광안제3동 1
 
2.1%
광안제4동 1
 
2.1%

Length

2023-12-23T07:33:34.291837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:33:34.924510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광안제2동 10
21.3%
민락동 8
17.0%
남천제1동 7
14.9%
수영동 7
14.9%
망미제1동 4
 
8.5%
망미제2동 4
 
8.5%
광안제1동 3
 
6.4%
남천제2동 2
 
4.3%
광안제3동 1
 
2.1%
광안제4동 1
 
2.1%

관리번호
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-23T07:33:35.832740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.0212766
Min length5

Characters and Unicode

Total characters283
Distinct characters17
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

Unique47 ?
Unique (%)100.0%

Sample

1st row수영구-1
2nd row수영구-2
3rd row수영구-3
4th row수영구-4
5th row수영구-32
ValueCountFrequency (%)
수영구-1 1
 
2.1%
수영구-15 1
 
2.1%
수영구-스마트3 1
 
2.1%
수영구-17 1
 
2.1%
수영구-18 1
 
2.1%
수영구-19 1
 
2.1%
수영구-20 1
 
2.1%
수영구-21 1
 
2.1%
수영구-22 1
 
2.1%
수영구-23 1
 
2.1%
Other values (37) 37
78.7%
2023-12-23T07:33:37.815611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 47
16.6%
영 47
16.6%
구 47
16.6%
- 47
16.6%
2 16
 
5.7%
1 16
 
5.7%
3 15
 
5.3%
4 8
 
2.8%
스 5
 
1.8%
마 5
 
1.8%
Other values (7) 30
10.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 156
55.1%
Decimal Number 80
28.3%
Dash Punctuation 47
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 16
20.0%
1 16
20.0%
3 15
18.8%
4 8
10.0%
5 5
 
6.2%
0 4
 
5.0%
8 4
 
5.0%
6 4
 
5.0%
7 4
 
5.0%
9 4
 
5.0%
Other Letter
ValueCountFrequency (%)
수 47
30.1%
영 47
30.1%
구 47
30.1%
스 5
 
3.2%
마 5
 
3.2%
트 5
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 156
55.1%
Common 127
44.9%

Most frequent character per script

Common
ValueCountFrequency (%)
- 47
37.0%
2 16
 
12.6%
1 16
 
12.6%
3 15
 
11.8%
4 8
 
6.3%
5 5
 
3.9%
0 4
 
3.1%
8 4
 
3.1%
6 4
 
3.1%
7 4
 
3.1%
Hangul
ValueCountFrequency (%)
수 47
30.1%
영 47
30.1%
구 47
30.1%
스 5
 
3.2%
마 5
 
3.2%
트 5
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 156
55.1%
ASCII 127
44.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 47
30.1%
영 47
30.1%
구 47
30.1%
스 5
 
3.2%
마 5
 
3.2%
트 5
 
3.2%
ASCII
ValueCountFrequency (%)
- 47
37.0%
2 16
 
12.6%
1 16
 
12.6%
3 15
 
11.8%
4 8
 
6.3%
5 5
 
3.9%
0 4
 
3.1%
8 4
 
3.1%
6 4
 
3.1%
7 4
 
3.1%

설치장소
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-23T07:33:38.742785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length10.212766
Min length5

Characters and Unicode

Total characters480
Distinct characters158
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

Unique47 ?
Unique (%)100.0%

Sample

1st rowKBS 삼거리 앞
2nd row사단법인 인구보건복지협회 앞
3rd row대남교차로
4th row남천마리나 앞
5th row남천역 4번출구 앞
ValueCountFrequency (%)
앞 37
31.4%
횡단보도 5
 
4.2%
103동 3
 
2.5%
맞은편 2
 
1.7%
코스트코 2
 
1.7%
센텀비스타동원2차 1
 
0.8%
출구 1
 
0.8%
미니스톱 1
 
0.8%
대우아이빌 1
 
0.8%
비치비키니 1
 
0.8%
Other values (64) 64
54.2%
2023-12-23T07:33:40.543503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
72
 
15.0%
앞 37
 
7.7%
스 13
 
2.7%
1 10
 
2.1%
동 9
 
1.9%
영 8
 
1.7%
수 8
 
1.7%
트 8
 
1.7%
구 8
 
1.7%
파 7
 
1.5%
Other values (148) 300
62.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 373
77.7%
Space Separator 72
 
15.0%
Decimal Number 24
 
5.0%
Uppercase Letter 9
 
1.9%
Dash Punctuation 1
 
0.2%
Lowercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 37
 
9.9%
스 13
 
3.5%
동 9
 
2.4%
영 8
 
2.1%
수 8
 
2.1%
트 8
 
2.1%
구 8
 
2.1%
파 7
 
1.9%
단 6
 
1.6%
보 6
 
1.6%
Other values (133) 263
70.5%
Uppercase Letter
ValueCountFrequency (%)
K 2
22.2%
S 2
22.2%
T 1
11.1%
I 1
11.1%
B 1
11.1%
L 1
11.1%
G 1
11.1%
Decimal Number
ValueCountFrequency (%)
1 10
41.7%
0 6
25.0%
2 3
 
12.5%
3 3
 
12.5%
4 2
 
8.3%
Space Separator
ValueCountFrequency (%)
72
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 373
77.7%
Common 97
 
20.2%
Latin 10
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 37
 
9.9%
스 13
 
3.5%
동 9
 
2.4%
영 8
 
2.1%
수 8
 
2.1%
트 8
 
2.1%
구 8
 
2.1%
파 7
 
1.9%
단 6
 
1.6%
보 6
 
1.6%
Other values (133) 263
70.5%
Latin
ValueCountFrequency (%)
K 2
20.0%
S 2
20.0%
T 1
10.0%
I 1
10.0%
B 1
10.0%
e 1
10.0%
L 1
10.0%
G 1
10.0%
Common
ValueCountFrequency (%)
72
74.2%
1 10
 
10.3%
0 6
 
6.2%
2 3
 
3.1%
3 3
 
3.1%
4 2
 
2.1%
- 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 373
77.7%
ASCII 107
 
22.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72
67.3%
1 10
 
9.3%
0 6
 
5.6%
2 3
 
2.8%
3 3
 
2.8%
K 2
 
1.9%
4 2
 
1.9%
S 2
 
1.9%
T 1
 
0.9%
I 1
 
0.9%
Other values (5) 5
 
4.7%
Hangul
ValueCountFrequency (%)
앞 37
 
9.9%
스 13
 
3.5%
동 9
 
2.4%
영 8
 
2.1%
수 8
 
2.1%
트 8
 
2.1%
구 8
 
2.1%
파 7
 
1.9%
단 6
 
1.6%
보 6
 
1.6%
Other values (133) 263
70.5%

설치위치
Text

Distinct43
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-23T07:33:41.526047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length40
Mean length26.595745
Min length11

Characters and Unicode

Total characters1250
Distinct characters77
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

Unique39 ?
Unique (%)83.0%

Sample

1st row부산광역시 수영구 광남로10번길 2 (남천동)
2nd row부산광역시 수영구 수영로 425 (남천동)
3rd row부산광역시 수영구 수영로 371 (남천동)
4th row부산광역시 수영구 광안해변로 44 (남천동)
5th row부산광역시 수영구 수영로 381 (남천동)
ValueCountFrequency (%)
수영구 45
18.5%
부산광역시 44
18.1%
수영로 15
 
6.2%
광안동 15
 
6.2%
광안해변로 14
 
5.8%
수영동 8
 
3.3%
남천동 8
 
3.3%
망미동 6
 
2.5%
민락동 6
 
2.5%
연수로 5
 
2.1%
Other values (65) 77
31.7%
2023-12-23T07:33:43.554598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
240
19.2%
수 80
 
6.4%
광 78
 
6.2%
영 75
 
6.0%
부 47
 
3.8%
구 47
 
3.8%
산 47
 
3.8%
로 47
 
3.8%
역 45
 
3.6%
동 44
 
3.5%
Other values (67) 500
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 753
60.2%
Space Separator 240
 
19.2%
Decimal Number 158
 
12.6%
Open Punctuation 43
 
3.4%
Close Punctuation 43
 
3.4%
Other Punctuation 9
 
0.7%
Dash Punctuation 3
 
0.2%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 80
10.6%
광 78
 
10.4%
영 75
 
10.0%
부 47
 
6.2%
구 47
 
6.2%
산 47
 
6.2%
로 47
 
6.2%
역 45
 
6.0%
동 44
 
5.8%
시 44
 
5.8%
Other values (51) 199
26.4%
Decimal Number
ValueCountFrequency (%)
1 30
19.0%
2 24
15.2%
7 21
13.3%
4 17
10.8%
3 16
10.1%
5 15
9.5%
0 11
 
7.0%
6 11
 
7.0%
9 8
 
5.1%
8 5
 
3.2%
Space Separator
ValueCountFrequency (%)
240
100.0%
Open Punctuation
ValueCountFrequency (%)
( 43
100.0%
Close Punctuation
ValueCountFrequency (%)
) 43
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 753
60.2%
Common 496
39.7%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 80
10.6%
광 78
 
10.4%
영 75
 
10.0%
부 47
 
6.2%
구 47
 
6.2%
산 47
 
6.2%
로 47
 
6.2%
역 45
 
6.0%
동 44
 
5.8%
시 44
 
5.8%
Other values (51) 199
26.4%
Common
ValueCountFrequency (%)
240
48.4%
( 43
 
8.7%
) 43
 
8.7%
1 30
 
6.0%
2 24
 
4.8%
7 21
 
4.2%
4 17
 
3.4%
3 16
 
3.2%
5 15
 
3.0%
0 11
 
2.2%
Other values (5) 36
 
7.3%
Latin
ValueCountFrequency (%)
e 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 753
60.2%
ASCII 497
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240
48.3%
( 43
 
8.7%
) 43
 
8.7%
1 30
 
6.0%
2 24
 
4.8%
7 21
 
4.2%
4 17
 
3.4%
3 16
 
3.2%
5 15
 
3.0%
0 11
 
2.2%
Other values (6) 37
 
7.4%
Hangul
ValueCountFrequency (%)
수 80
10.6%
광 78
 
10.4%
영 75
 
10.0%
부 47
 
6.2%
구 47
 
6.2%
산 47
 
6.2%
로 47
 
6.2%
역 45
 
6.0%
동 44
 
5.8%
시 44
 
5.8%
Other values (51) 199
26.4%

설치일시
Date

Distinct9
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
Minimum2018-07-17 00:00:00
Maximum2022-10-12 00:00:00
2023-12-23T07:33:44.215630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:33:44.855471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)

그늘막 유형
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size508.0 B
고정형
42 
스마트형
5 

Length

Max length4
Median length3
Mean length3.106383
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고정형 42
89.4%
스마트형 5
 
10.6%

Length

2023-12-23T07:33:45.480694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:33:46.304219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고정형 42
89.4%
스마트형 5
 
10.6%

Interactions

2023-12-23T07:33:30.114734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T07:33:46.702324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동 구분관리번호설치장소설치위치설치일시그늘막 유형
연번1.0000.9591.0001.0000.8780.5460.351
행정동 구분0.9591.0001.0001.0000.9870.0000.000
관리번호1.0001.0001.0001.0001.0001.0001.000
설치장소1.0001.0001.0001.0001.0001.0001.000
설치위치0.8780.9871.0001.0001.0000.9421.000
설치일시0.5460.0001.0001.0000.9421.0000.780
그늘막 유형0.3510.0001.0001.0001.0000.7801.000
2023-12-23T07:33:47.292102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 구분그늘막 유형
행정동 구분1.0000.000
그늘막 유형0.0001.000
2023-12-23T07:33:47.899967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동 구분그늘막 유형
연번1.0000.6860.236
행정동 구분0.6861.0000.000
그늘막 유형0.2360.0001.000

Missing values

2023-12-23T07:33:30.734231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T07:33:31.626233image/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

연번행정동 구분관리번호설치장소설치위치설치일시그늘막 유형
01남천제1동수영구-1KBS 삼거리 앞부산광역시 수영구 광남로10번길 2 (남천동)2018-07-17고정형
12남천제1동수영구-2사단법인 인구보건복지협회 앞부산광역시 수영구 수영로 425 (남천동)2018-07-17고정형
23남천제1동수영구-3대남교차로부산광역시 수영구 수영로 371 (남천동)2018-07-17고정형
34남천제1동수영구-4남천마리나 앞부산광역시 수영구 광안해변로 44 (남천동)2018-07-17고정형
45남천제1동수영구-32남천역 4번출구 앞부산광역시 수영구 수영로 381 (남천동)2020-08-27고정형
56남천제1동수영구-38LG베트스샵 맞은편부산광역시 수영구 황령대로 473번길 15, 건너편 횡단보도2022-08-26고정형
67남천제1동수영구-스마트1농협 남천동지점 앞부산광역시 수영구 광남로 35 (남천동)2019-05-07스마트형
78남천제2동수영구-5협진태양아파트 앞부산광역시 수영구 광안해변로 141 (남천동, 협진태양아파트)2018-07-17고정형
89남천제2동수영구-6협진태양 맞은편부산광역시 수영구 광안해변로 141 (남천동, 협진태양아파트)2018-07-17고정형
910수영동수영구-7더샵센텀포레 104동 앞부산광역시 수영구 수영로 775 (수영동)2018-07-17고정형
연번행정동 구분관리번호설치장소설치위치설치일시그늘막 유형
3738광안제3동수영구-25타이어뱅크 앞부산광역시 수영구 망미번영로38번길 3 (광안동)2018-07-17고정형
3839광안제4동수영구-26부산IT전문학교 앞부산광역시 수영구 수영로 525 (광안동)2018-07-17고정형
3940민락동수영구-27부산광역시 수영구민체육센터부산광역시 수영구 광남로257번길 12 (민락동)2019-07-24고정형
4041민락동수영구-28광안참사랑요양병원 앞부산광역시 수영구 광안해변로 233 (민락동)2018-07-17고정형
4142민락동수영구-29현가네 콩나물해장국 앞부산광역시 수영구 광안해변로 243 (민락동)2018-07-17고정형
4243민락동수영구-30커피스미스 앞부산광역시 수영구 광안해변로255번길 5 (민락동)2018-07-17고정형
4344민락동수영구-31새벽집 앞부산광역시 수영구 광안해변로 267 (민락동)2018-07-17고정형
4445민락동수영구-35민락역 1번출구 앞부산광역시 수영구 수영로 776 (민락동, 부산 더샵 센텀포레)2020-08-27고정형
4546민락동수영구-40센텀비스타동원2차 앞 교차로무학로63번길 1422022-10-12고정형
4647민락동수영구-41수영교 사거리수영로 776, 부산더샵센텀포레 104동 앞2022-10-12고정형