Overview

Dataset statistics

Number of variables10
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory85.4 B

Variable types

Numeric1
Categorical8
Text1

Dataset

Description부산광역시_금정구_인명피해우려지역정보_20230922
Author부산광역시 금정구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15025807

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
세분류 is highly overall correlated with 지역(도로)명 and 2 other fieldsHigh correlation
분 류 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
분 야 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
순번 is highly overall correlated with 분 야 and 1 other fieldsHigh correlation
지역(도로)명 is highly overall correlated with 분 야 and 2 other fieldsHigh correlation
구분 is highly imbalanced (53.1%)Imbalance
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 17:08:03.086976
Analysis finished2023-12-10 17:08:04.431169
Duration1.34 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.366667
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T02:08:04.562835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.45
Q19.25
median16.5
Q323.75
95-th percentile29.55
Maximum31
Range30
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation9.0114231
Coefficient of variation (CV)0.55059612
Kurtosis-1.1377732
Mean16.366667
Median Absolute Deviation (MAD)7.5
Skewness-0.063661998
Sum491
Variance81.205747
MonotonicityStrictly increasing
2023-12-11T02:08:04.787252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1
 
3.3%
18 1
 
3.3%
31 1
 
3.3%
30 1
 
3.3%
29 1
 
3.3%
28 1
 
3.3%
27 1
 
3.3%
26 1
 
3.3%
25 1
 
3.3%
24 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1 1
3.3%
2 1
3.3%
3 1
3.3%
4 1
3.3%
6 1
3.3%
7 1
3.3%
8 1
3.3%
9 1
3.3%
10 1
3.3%
11 1
3.3%
ValueCountFrequency (%)
31 1
3.3%
30 1
3.3%
29 1
3.3%
28 1
3.3%
27 1
3.3%
26 1
3.3%
25 1
3.3%
24 1
3.3%
23 1
3.3%
22 1
3.3%

구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
기존
27 
신규
3 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기존 27
90.0%
신규 3
 
10.0%

Length

2023-12-11T02:08:05.021054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:08:05.207680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기존 27
90.0%
신규 3
 
10.0%

지역(도로)명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
산사태취약지역
20 
구서IC 지하통로
 
1
오륜동 지하보행 통로
 
1
오륜동 지하통로
 
1
교량(舊(구) 신천교)
 
1
Other values (6)
6 

Length

Max length16
Median length7
Mean length8.1666667
Min length5

Unique

Unique10 ?
Unique (%)33.3%

Sample

1st row구서IC 지하통로
2nd row오륜동 지하보행 통로
3rd row오륜동 지하통로
4th row교량(舊(구) 신천교)
5th row하정마을 입구 지하차도

Common Values

ValueCountFrequency (%)
산사태취약지역 20
66.7%
구서IC 지하통로 1
 
3.3%
오륜동 지하보행 통로 1
 
3.3%
오륜동 지하통로 1
 
3.3%
교량(舊(구) 신천교) 1
 
3.3%
하정마을 입구 지하차도 1
 
3.3%
금사1지구 1
 
3.3%
경부고속도로 하부통로(양산1) 1
 
3.3%
구서지하차도 1
 
3.3%
경부고속도로 하부통로(양산2) 1
 
3.3%

Length

2023-12-11T02:08:05.411971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
산사태취약지역 20
50.0%
지하차도 2
 
5.0%
지하통로 2
 
5.0%
오륜동 2
 
5.0%
경부고속도로 2
 
5.0%
하부통로(양산2 1
 
2.5%
구서지하차도 1
 
2.5%
하부통로(양산1 1
 
2.5%
금사1지구 1
 
2.5%
하정마을 1
 
2.5%
Other values (7) 7
 
17.5%

상세지역_구간
Text

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-11T02:08:05.762807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length8.8333333
Min length6

Characters and Unicode

Total characters265
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)86.7%

Sample

1st row구서동 102-3
2nd row오륜동 665-3
3rd row오륜동 665-3
4th row선동 626-15
5th row선동 900-11
ValueCountFrequency (%)
구서동 8
 
13.3%
선동 5
 
8.3%
오륜동 2
 
3.3%
금성동 2
 
3.3%
청룡동 2
 
3.3%
산2-1 2
 
3.3%
부곡동 2
 
3.3%
665-3 2
 
3.3%
남산동 2
 
3.3%
산69-2 2
 
3.3%
Other values (29) 31
51.7%
2023-12-11T02:08:06.330083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 31
11.7%
30
 
11.3%
1 29
 
10.9%
- 24
 
9.1%
산 17
 
6.4%
2 14
 
5.3%
5 13
 
4.9%
6 10
 
3.8%
3 10
 
3.8%
구 10
 
3.8%
Other values (25) 77
29.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107
40.4%
Other Letter 100
37.7%
Space Separator 30
 
11.3%
Dash Punctuation 24
 
9.1%
Close Punctuation 2
 
0.8%
Open Punctuation 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 31
31.0%
산 17
17.0%
구 10
 
10.0%
서 8
 
8.0%
선 5
 
5.0%
금 3
 
3.0%
장 2
 
2.0%
전 2
 
2.0%
두 2
 
2.0%
곡 2
 
2.0%
Other values (11) 18
18.0%
Decimal Number
ValueCountFrequency (%)
1 29
27.1%
2 14
13.1%
5 13
12.1%
6 10
 
9.3%
3 10
 
9.3%
0 9
 
8.4%
9 8
 
7.5%
8 6
 
5.6%
7 5
 
4.7%
4 3
 
2.8%
Space Separator
ValueCountFrequency (%)
30
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 165
62.3%
Hangul 100
37.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 31
31.0%
산 17
17.0%
구 10
 
10.0%
서 8
 
8.0%
선 5
 
5.0%
금 3
 
3.0%
장 2
 
2.0%
전 2
 
2.0%
두 2
 
2.0%
곡 2
 
2.0%
Other values (11) 18
18.0%
Common
ValueCountFrequency (%)
30
18.2%
1 29
17.6%
- 24
14.5%
2 14
8.5%
5 13
7.9%
6 10
 
6.1%
3 10
 
6.1%
0 9
 
5.5%
9 8
 
4.8%
8 6
 
3.6%
Other values (4) 12
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165
62.3%
Hangul 100
37.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 31
31.0%
산 17
17.0%
구 10
 
10.0%
서 8
 
8.0%
선 5
 
5.0%
금 3
 
3.0%
장 2
 
2.0%
전 2
 
2.0%
두 2
 
2.0%
곡 2
 
2.0%
Other values (11) 18
18.0%
ASCII
ValueCountFrequency (%)
30
18.2%
1 29
17.6%
- 24
14.5%
2 14
8.5%
5 13
7.9%
6 10
 
6.1%
3 10
 
6.1%
0 9
 
5.5%
9 8
 
4.8%
8 6
 
3.6%
Other values (4) 12
 
7.3%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
부산
30 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산 30
100.0%

Length

2023-12-11T02:08:06.631455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:08:07.244447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산 30
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
금정구
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
금정구 30
100.0%

Length

2023-12-11T02:08:07.405674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:08:07.555092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금정구 30
100.0%

읍면동
Categorical

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
선두구동
7 
구서1동
4 
구서2동
4 
부곡3동
3 
청룡노포동
3 
Other values (5)
9 

Length

Max length5
Median length4
Mean length4.0333333
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row구서1동
2nd row부곡3동
3rd row부곡3동
4th row선두구동
5th row선두구동

Common Values

ValueCountFrequency (%)
선두구동 7
23.3%
구서1동 4
13.3%
구서2동 4
13.3%
부곡3동 3
10.0%
청룡노포동 3
10.0%
금사회동동 2
 
6.7%
금성동 2
 
6.7%
남산동 2
 
6.7%
장전1동 2
 
6.7%
부곡2동 1
 
3.3%

Length

2023-12-11T02:08:07.708974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:08:07.903165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
선두구동 7
23.3%
구서1동 4
13.3%
구서2동 4
13.3%
부곡3동 3
10.0%
청룡노포동 3
10.0%
금사회동동 2
 
6.7%
금성동 2
 
6.7%
남산동 2
 
6.7%
장전1동 2
 
6.7%
부곡2동 1
 
3.3%

분 야
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
인명피해 우려지역
25 
침수우려도로 등
5 

Length

Max length9
Median length9
Mean length8.8333333
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row침수우려도로 등
2nd row침수우려도로 등
3rd row침수우려도로 등
4th row침수우려도로 등
5th row침수우려도로 등

Common Values

ValueCountFrequency (%)
인명피해 우려지역 25
83.3%
침수우려도로 등 5
 
16.7%

Length

2023-12-11T02:08:08.133160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:08:08.315950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인명피해 25
41.7%
우려지역 25
41.7%
침수우려도로 5
 
8.3%
등 5
 
8.3%

분 류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
산사태
20 
도로
5 
지하차도
4 
자연재해위험개선지구
 
1

Length

Max length10
Median length3
Mean length3.2
Min length2

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
산사태 20
66.7%
도로 5
 
16.7%
지하차도 4
 
13.3%
자연재해위험개선지구 1
 
3.3%

Length

2023-12-11T02:08:08.515857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:08:08.710177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
산사태 20
66.7%
도로 5
 
16.7%
지하차도 4
 
13.3%
자연재해위험개선지구 1
 
3.3%

세분류
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
산사태취약지구
20 
저지대 도로
4 
침수위험 지하차도
4 
위험교량
 
1
침수위험
 
1

Length

Max length9
Median length7
Mean length6.9333333
Min length4

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row저지대 도로
2nd row저지대 도로
3rd row저지대 도로
4th row위험교량
5th row저지대 도로

Common Values

ValueCountFrequency (%)
산사태취약지구 20
66.7%
저지대 도로 4
 
13.3%
침수위험 지하차도 4
 
13.3%
위험교량 1
 
3.3%
침수위험 1
 
3.3%

Length

2023-12-11T02:08:08.942250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:08:09.140315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
산사태취약지구 20
52.6%
침수위험 5
 
13.2%
저지대 4
 
10.5%
도로 4
 
10.5%
지하차도 4
 
10.5%
위험교량 1
 
2.6%

Interactions

2023-12-11T02:08:03.742272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:08:09.293920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분지역(도로)명상세지역_구간읍면동분 야분 류세분류
순번1.0000.0000.4741.0000.9091.0000.9190.961
구분0.0001.0000.0001.0000.3550.0000.7140.394
지역(도로)명0.4740.0001.0000.0000.0001.0001.0001.000
상세지역_구간1.0001.0000.0001.0001.0001.0001.0001.000
읍면동0.9090.3550.0001.0001.0000.2040.5230.396
분 야1.0000.0001.0001.0000.2041.0001.0001.000
분 류0.9190.7141.0001.0000.5231.0001.0001.000
세분류0.9610.3941.0001.0000.3961.0001.0001.000
2023-12-11T02:08:09.587352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동구분세분류지역(도로)명분 류분 야
읍면동1.0000.2070.1130.0000.2800.084
구분0.2071.0000.4500.0000.4880.000
세분류0.1130.4501.0000.8720.9810.945
지역(도로)명0.0000.0000.8721.0000.8550.824
분 류0.2800.4880.9810.8551.0000.964
분 야0.0840.0000.9450.8240.9641.000
2023-12-11T02:08:09.777809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분지역(도로)명읍면동분 야분 류세분류
순번1.0000.0000.0280.3870.7680.6330.483
구분0.0001.0000.0000.2070.0000.4880.450
지역(도로)명0.0280.0001.0000.0000.8240.8550.872
읍면동0.3870.2070.0001.0000.0840.2800.113
분 야0.7680.0000.8240.0841.0000.9640.945
분 류0.6330.4880.8550.2800.9641.0000.981
세분류0.4830.4500.8720.1130.9450.9811.000

Missing values

2023-12-11T02:08:03.997670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:08:04.320740image/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

순번구분지역(도로)명상세지역_구간시도명시군구명읍면동분 야분 류세분류
01기존구서IC 지하통로구서동 102-3부산금정구구서1동침수우려도로 등도로저지대 도로
12기존오륜동 지하보행 통로오륜동 665-3부산금정구부곡3동침수우려도로 등도로저지대 도로
23기존오륜동 지하통로오륜동 665-3부산금정구부곡3동침수우려도로 등도로저지대 도로
34기존교량(舊(구) 신천교)선동 626-15부산금정구선두구동침수우려도로 등도로위험교량
46기존하정마을 입구 지하차도선동 900-11부산금정구선두구동침수우려도로 등도로저지대 도로
57기존산사태취약지역구서동 1055부산금정구구서1동인명피해 우려지역산사태산사태취약지구
68기존산사태취약지역구서동 1012-6부산금정구구서2동인명피해 우려지역산사태산사태취약지구
79기존산사태취약지역구서동 산11-2(1)부산금정구구서2동인명피해 우려지역산사태산사태취약지구
810신규산사태취약지역구서동 산11-2(2)부산금정구구서2동인명피해 우려지역산사태산사태취약지구
911기존산사태취약지역구서동 산134부산금정구구서2동인명피해 우려지역산사태산사태취약지구
순번구분지역(도로)명상세지역_구간시도명시군구명읍면동분 야분 류세분류
2022기존산사태취약지역장전동 75-2부산금정구장전1동인명피해 우려지역산사태산사태취약지구
2123기존산사태취약지역장전동 89-2부산금정구장전1동인명피해 우려지역산사태산사태취약지구
2224기존산사태취약지역노포동 산69-2부산금정구청룡노포동인명피해 우려지역산사태산사태취약지구
2325기존산사태취약지역청룡동 산2-1부산금정구청룡노포동인명피해 우려지역산사태산사태취약지구
2426기존산사태취약지역청룡동 산2-1부산금정구청룡노포동인명피해 우려지역산사태산사태취약지구
2527신규금사1지구금사동 154부산금정구금사회동동인명피해 우려지역자연재해위험개선지구침수위험
2628기존경부고속도로 하부통로(양산1)구서동 997-7부산금정구구서1동인명피해 우려지역지하차도침수위험 지하차도
2729기존구서지하차도구서동 481-1부산금정구구서1동인명피해 우려지역지하차도침수위험 지하차도
2830기존경부고속도로 하부통로(양산2)선동 산33-1부산금정구선두구동인명피해 우려지역지하차도침수위험 지하차도
2931기존노포기지창 지하차도두구동 1520-82부산금정구선두구동인명피해 우려지역지하차도침수위험 지하차도