Overview

Dataset statistics

Number of variables9
Number of observations111
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.3 KiB
Average record size in memory76.2 B

Variable types

Numeric3
Categorical6

Dataset

Description해수욕장의 연안사고 위험구역 인명구조함 위치 정보(시도, 시군구, 읍면동, 동리, 경도, 위도 등)를 포함한 데이터이다.
Author해양경찰청
URLhttps://www.data.go.kr/data/15091237/fileData.do

Alerts

장소 has constant value ""Constant
시군구 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
시도 is highly overall correlated with 연번 and 6 other fieldsHigh correlation
읍면동 is highly overall correlated with 연번 and 6 other fieldsHigh correlation
동리 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
연번 is highly overall correlated with 경도 and 4 other fieldsHigh correlation
경도 is highly overall correlated with 연번 and 6 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 4 other fieldsHigh correlation
구역구분 is highly overall correlated with 경도 and 3 other fieldsHigh correlation
동리 is highly imbalanced (72.7%)Imbalance
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:34:30.920287
Analysis finished2023-12-12 03:34:33.230939
Duration2.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct111
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.58559
Minimum36
Maximum841
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T12:34:33.342119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile62.5
Q1146
median249
Q3715
95-th percentile814.5
Maximum841
Range805
Interquartile range (IQR)569

Descriptive statistics

Standard deviation262.09753
Coefficient of variation (CV)0.73501999
Kurtosis-1.0463587
Mean356.58559
Median Absolute Deviation (MAD)113
Skewness0.74602724
Sum39581
Variance68695.118
MonotonicityStrictly increasing
2023-12-12T12:34:33.589679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 1
 
0.9%
37 1
 
0.9%
702 1
 
0.9%
701 1
 
0.9%
426 1
 
0.9%
425 1
 
0.9%
424 1
 
0.9%
387 1
 
0.9%
386 1
 
0.9%
384 1
 
0.9%
Other values (101) 101
91.0%
ValueCountFrequency (%)
36 1
0.9%
37 1
0.9%
38 1
0.9%
39 1
0.9%
56 1
0.9%
62 1
0.9%
63 1
0.9%
75 1
0.9%
76 1
0.9%
81 1
0.9%
ValueCountFrequency (%)
841 1
0.9%
821 1
0.9%
820 1
0.9%
819 1
0.9%
818 1
0.9%
815 1
0.9%
814 1
0.9%
792 1
0.9%
773 1
0.9%
772 1
0.9%

시도
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size1020.0 B
충청남도
37 
제주특별자치도
15 
인천광역시
14 
경상남도
13 
울산광역시
12 
Other values (4)
20 

Length

Max length7
Median length4
Mean length4.6756757
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 37
33.3%
제주특별자치도 15
13.5%
인천광역시 14
 
12.6%
경상남도 13
 
11.7%
울산광역시 12
 
10.8%
전라북도 8
 
7.2%
전라남도 6
 
5.4%
부산광역시 4
 
3.6%
경상북도 2
 
1.8%

Length

2023-12-12T12:34:33.861695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:34:34.071156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 37
33.3%
제주특별자치도 15
13.5%
인천광역시 14
 
12.6%
경상남도 13
 
11.7%
울산광역시 12
 
10.8%
전라북도 8
 
7.2%
전라남도 6
 
5.4%
부산광역시 4
 
3.6%
경상북도 2
 
1.8%

시군구
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size1020.0 B
태안군
26 
제주시
14 
동구
12 
거제시
11 
보령시
11 
Other values (13)
37 

Length

Max length4
Median length3
Mean length3.009009
Min length3

Unique

Unique3 ?
Unique (%)2.7%

Sample

1st row사하구
2nd row사하구
3rd row사하구
4th row사하구
5th row옹진군

Common Values

ValueCountFrequency (%)
태안군 26
23.4%
제주시 14
12.6%
동구 12
10.8%
거제시 11
9.9%
보령시 11
9.9%
옹진군 7
 
6.3%
부안군 4
 
3.6%
중구 4
 
3.6%
군산시 4
 
3.6%
사하구 4
 
3.6%
Other values (8) 14
12.6%

Length

2023-12-12T12:34:34.258681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
태안군 26
23.4%
제주시 14
12.6%
동구 12
10.8%
거제시 11
9.9%
보령시 11
9.9%
옹진군 7
 
6.3%
군산시 4
 
3.6%
사하구 4
 
3.6%
중구 4
 
3.6%
부안군 4
 
3.6%
Other values (8) 14
12.6%

읍면동
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Memory size1020.0 B
소원면
12 
일운면
11 
원북면
8 
일산동
7 
웅천읍
 
6
Other values (29)
67 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique13 ?
Unique (%)11.7%

Sample

1st row 다대
2nd row 다대
3rd row 다대
4th row 다대
5th row연평면

Common Values

ValueCountFrequency (%)
소원면 12
 
10.8%
일운면 11
 
9.9%
원북면 8
 
7.2%
일산동 7
 
6.3%
웅천읍 6
 
5.4%
영흥면 6
 
5.4%
조천읍 5
 
4.5%
고남면 5
 
4.5%
변산면 4
 
3.6%
다대 4
 
3.6%
Other values (24) 43
38.7%

Length

2023-12-12T12:34:34.421837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
소원면 12
 
10.8%
일운면 11
 
9.9%
원북면 8
 
7.2%
일산동 7
 
6.3%
웅천읍 6
 
5.4%
영흥면 6
 
5.4%
조천읍 5
 
4.5%
고남면 5
 
4.5%
변산면 4
 
3.6%
주전동 4
 
3.6%
Other values (24) 43
38.7%

동리
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1020.0 B
<NA>
103 
장곡리
 
6
신두리
 
2

Length

Max length4
Median length4
Mean length3.9279279
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 103
92.8%
장곡리 6
 
5.4%
신두리 2
 
1.8%

Length

2023-12-12T12:34:34.570334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:34:34.719934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 103
92.8%
장곡리 6
 
5.4%
신두리 2
 
1.8%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.728325
Minimum33.240508
Maximum37.668412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T12:34:34.891623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.240508
5-th percentile33.503117
Q134.813507
median35.739699
Q336.79054
95-th percentile37.455554
Maximum37.668412
Range4.4279037
Interquartile range (IQR)1.9770331

Descriptive statistics

Standard deviation1.2187263
Coefficient of variation (CV)0.034110928
Kurtosis-0.76039912
Mean35.728325
Median Absolute Deviation (MAD)0.9300802
Skewness-0.42381342
Sum3965.8441
Variance1.4852939
MonotonicityNot monotonic
2023-12-12T12:34:35.081712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.62944444 2
 
1.8%
35.04850497 1
 
0.9%
35.8164694 1
 
0.9%
35.7391693 1
 
0.9%
35.7396986 1
 
0.9%
34.5794195 1
 
0.9%
34.5807989 1
 
0.9%
34.5813849 1
 
0.9%
34.9154602 1
 
0.9%
34.9157061 1
 
0.9%
Other values (100) 100
90.1%
ValueCountFrequency (%)
33.2405083 1
0.9%
33.49735094 1
0.9%
33.4975515 1
0.9%
33.497677 1
0.9%
33.4998422 1
0.9%
33.50286139 1
0.9%
33.50337339 1
0.9%
33.52615997 1
0.9%
33.543016 1
0.9%
33.54348697 1
0.9%
ValueCountFrequency (%)
37.66841197 1
0.9%
37.65179818 1
0.9%
37.65146531 1
0.9%
37.4577303 1
0.9%
37.4573 1
0.9%
37.4557073 1
0.9%
37.4554002 1
0.9%
37.28260397 1
0.9%
37.28197 1
0.9%
37.28194737 1
0.9%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct109
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.17193
Minimum125.68573
Maximum129.48394
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T12:34:35.285033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.68573
5-th percentile126.14381
Q1126.36089
median126.51706
Q3128.68944
95-th percentile129.45347
Maximum129.48394
Range3.7982061
Interquartile range (IQR)2.3285508

Descriptive statistics

Standard deviation1.2222039
Coefficient of variation (CV)0.0096106416
Kurtosis-0.82867958
Mean127.17193
Median Absolute Deviation (MAD)0.3277614
Skewness0.95020608
Sum14116.084
Variance1.4937823
MonotonicityNot monotonic
2023-12-12T12:34:35.516118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.4702778 3
 
2.7%
128.960299 1
 
0.9%
126.4101502 1
 
0.9%
129.4839371 1
 
0.9%
129.4836363 1
 
0.9%
127.486239 1
 
0.9%
127.4871035 1
 
0.9%
127.4873398 1
 
0.9%
126.057904 1
 
0.9%
126.0578102 1
 
0.9%
Other values (99) 99
89.2%
ValueCountFrequency (%)
125.685731 1
0.9%
126.0578102 1
0.9%
126.057904 1
0.9%
126.1333071 1
0.9%
126.1333995 1
0.9%
126.1431929 1
0.9%
126.1444346 1
0.9%
126.1450209 1
0.9%
126.1457533 1
0.9%
126.1464073 1
0.9%
ValueCountFrequency (%)
129.4839371 1
0.9%
129.4836363 1
0.9%
129.457693 1
0.9%
129.45573 1
0.9%
129.45478 1
0.9%
129.453983 1
0.9%
129.452967 1
0.9%
129.443732 1
0.9%
129.431625 1
0.9%
129.431586 1
0.9%

구역구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1020.0 B
사망사고 발생구역
42 
연안사고 위험구역
35 
연안사고 다발구역
34 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사망사고 발생구역
2nd row사망사고 발생구역
3rd row사망사고 발생구역
4th row사망사고 발생구역
5th row연안사고 위험구역

Common Values

ValueCountFrequency (%)
사망사고 발생구역 42
37.8%
연안사고 위험구역 35
31.5%
연안사고 다발구역 34
30.6%

Length

2023-12-12T12:34:35.694153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:34:35.831689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
연안사고 69
31.1%
사망사고 42
18.9%
발생구역 42
18.9%
위험구역 35
15.8%
다발구역 34
15.3%

장소
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1020.0 B
해수욕장
111 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해수욕장 111
100.0%

Length

2023-12-12T12:34:35.989103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:34:36.117077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해수욕장 111
100.0%

Interactions

2023-12-12T12:34:32.507192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:31.451021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:32.160714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:32.663578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:31.568273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:32.286409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:32.805915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:31.998147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:32.376797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:34:36.224105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시도시군구읍면동동리경도위도구역구분
연번1.0000.9590.9820.996NaN0.8650.8240.574
시도0.9591.0001.0001.000NaN0.9370.9090.818
시군구0.9821.0001.0001.0000.0000.9770.9760.844
읍면동0.9961.0001.0001.0001.0000.9981.0000.903
동리NaNNaN0.0001.0001.0000.813NaN0.000
경도0.8650.9370.9770.9980.8131.0000.9490.666
위도0.8240.9090.9761.000NaN0.9491.0000.583
구역구분0.5740.8180.8440.9030.0000.6660.5831.000
2023-12-12T12:34:36.423486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구시도읍면동동리구역구분
시군구1.0000.9550.9100.0000.548
시도0.9551.0000.8691.0000.512
읍면동0.9100.8691.0000.9130.629
동리0.0001.0000.9131.0000.000
구역구분0.5480.5120.6290.0001.000
2023-12-12T12:34:36.587125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번경도위도시도시군구읍면동동리구역구분
연번1.000-0.7470.1640.9020.8810.8341.0000.455
경도-0.7471.000-0.6130.8080.8560.8500.5930.531
위도0.164-0.6131.0000.7420.8510.8651.0000.439
시도0.9020.8080.7421.0000.9550.8691.0000.512
시군구0.8810.8560.8510.9551.0000.9100.0000.548
읍면동0.8340.8500.8650.8690.9101.0000.9130.629
동리1.0000.5931.0001.0000.0000.9131.0000.000
구역구분0.4550.5310.4390.5120.5480.6290.0001.000

Missing values

2023-12-12T12:34:32.994498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:34:33.166839image/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

연번시도시군구읍면동동리경도위도구역구분장소
036부산광역시사하구다대<NA>35.048505128.960299사망사고 발생구역해수욕장
137부산광역시사하구다대<NA>35.046212128.963779사망사고 발생구역해수욕장
238부산광역시사하구다대<NA>35.045334128.965866사망사고 발생구역해수욕장
339부산광역시사하구다대<NA>35.043551128.967913사망사고 발생구역해수욕장
456인천광역시옹진군연평면<NA>37.668412125.685731연안사고 위험구역해수욕장
562인천광역시강화군삼산면<NA>37.651798126.332542연안사고 위험구역해수욕장
663인천광역시강화군삼산면<NA>37.651465126.333188연안사고 위험구역해수욕장
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