Overview

Dataset statistics

Number of variables9
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory75.1 B

Variable types

DateTime2
Categorical4
Text3

Dataset

Description2018년 한 해 동안 설악산, 지리산국립공원에서 발생된 낙상, 실족 등 안전사고에 대한 위치, 위도, 경도, 발생사유 등이 기재되어 있습니다.
Author국립공원공단
URLhttps://www.data.go.kr/data/15065479/fileData.do

Alerts

발생사유 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 발생사유 and 1 other fieldsHigh correlation
유형 is highly overall correlated with 사무소 and 2 other fieldsHigh correlation
사무소 is highly overall correlated with 유형High correlation
구분 is highly imbalanced (72.9%)Imbalance
유형 is highly imbalanced (79.9%)Imbalance

Reproduction

Analysis started2023-12-12 05:36:55.965584
Analysis finished2023-12-12 05:36:56.729297
Duration0.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

날짜
Date

Distinct35
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Memory size476.0 B
Minimum2018-04-07 00:00:00
Maximum2018-12-23 00:00:00
2023-12-12T14:36:56.793910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:36:56.918038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)

시간
Date

Distinct37
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
Minimum2023-12-12 06:00:00
Maximum2023-12-12 18:20:00
2023-12-12T14:36:57.064565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:36:57.194582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)

사무소
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
설악산
32 
지리산경남
6 
지리산전남
 
3
지리산전북
 
2

Length

Max length5
Median length3
Mean length3.5116279
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row설악산
2nd row지리산경남
3rd row설악산
4th row설악산
5th row지리산경남

Common Values

ValueCountFrequency (%)
설악산 32
74.4%
지리산경남 6
 
14.0%
지리산전남 3
 
7.0%
지리산전북 2
 
4.7%

Length

2023-12-12T14:36:57.329575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:36:57.695964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
설악산 32
74.4%
지리산경남 6
 
14.0%
지리산전남 3
 
7.0%
지리산전북 2
 
4.7%

위치
Text

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T14:36:57.907757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length7.7674419
Min length3

Characters and Unicode

Total characters334
Distinct characters118
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

Unique41 ?
Unique (%)95.3%

Sample

1st row수렴동 대피소
2nd row장터목 대피소
3rd row갱기폭포우골
4th row백담사~영시암 방향 1.2km 지점
5th row전망대 인근
ValueCountFrequency (%)
일원 14
 
15.7%
인근 5
 
5.6%
대피소 4
 
4.5%
수렴동 3
 
3.4%
지점 2
 
2.2%
상단 2
 
2.2%
설악골 2
 
2.2%
하단 2
 
2.2%
천불동 2
 
2.2%
오세암 2
 
2.2%
Other values (50) 51
57.3%
2023-12-12T14:36:58.288871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46
 
13.8%
원 14
 
4.2%
일 14
 
4.2%
봉 10
 
3.0%
대 8
 
2.4%
소 8
 
2.4%
골 8
 
2.4%
암 7
 
2.1%
인 6
 
1.8%
단 6
 
1.8%
Other values (108) 207
62.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 268
80.2%
Space Separator 46
 
13.8%
Decimal Number 7
 
2.1%
Math Symbol 5
 
1.5%
Lowercase Letter 3
 
0.9%
Close Punctuation 2
 
0.6%
Open Punctuation 2
 
0.6%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 14
 
5.2%
일 14
 
5.2%
봉 10
 
3.7%
대 8
 
3.0%
소 8
 
3.0%
골 8
 
3.0%
암 7
 
2.6%
인 6
 
2.2%
단 6
 
2.2%
동 6
 
2.2%
Other values (95) 181
67.5%
Decimal Number
ValueCountFrequency (%)
0 2
28.6%
6 1
14.3%
7 1
14.3%
1 1
14.3%
2 1
14.3%
4 1
14.3%
Lowercase Letter
ValueCountFrequency (%)
m 2
66.7%
k 1
33.3%
Space Separator
ValueCountFrequency (%)
46
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 268
80.2%
Common 63
 
18.9%
Latin 3
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 14
 
5.2%
일 14
 
5.2%
봉 10
 
3.7%
대 8
 
3.0%
소 8
 
3.0%
골 8
 
3.0%
암 7
 
2.6%
인 6
 
2.2%
단 6
 
2.2%
동 6
 
2.2%
Other values (95) 181
67.5%
Common
ValueCountFrequency (%)
46
73.0%
~ 5
 
7.9%
) 2
 
3.2%
( 2
 
3.2%
0 2
 
3.2%
6 1
 
1.6%
7 1
 
1.6%
1 1
 
1.6%
. 1
 
1.6%
2 1
 
1.6%
Latin
ValueCountFrequency (%)
m 2
66.7%
k 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 268
80.2%
ASCII 66
 
19.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46
69.7%
~ 5
 
7.6%
) 2
 
3.0%
( 2
 
3.0%
0 2
 
3.0%
m 2
 
3.0%
6 1
 
1.5%
7 1
 
1.5%
1 1
 
1.5%
. 1
 
1.5%
Other values (3) 3
 
4.5%
Hangul
ValueCountFrequency (%)
원 14
 
5.2%
일 14
 
5.2%
봉 10
 
3.7%
대 8
 
3.0%
소 8
 
3.0%
골 8
 
3.0%
암 7
 
2.6%
인 6
 
2.2%
단 6
 
2.2%
동 6
 
2.2%
Other values (95) 181
67.5%

위도
Text

Distinct41
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T14:36:58.515233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.55814
Min length6

Characters and Unicode

Total characters454
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)90.7%

Sample

1st row38.14611111
2nd row35.33216667
3rd row38.1366667
4th row38.15333333
5th row35.32222222
ValueCountFrequency (%)
38.12055556 2
 
4.7%
38.13968162 2
 
4.7%
38.16218 1
 
2.3%
38.25611111 1
 
2.3%
35.232 1
 
2.3%
38.11857222 1
 
2.3%
38.14611111 1
 
2.3%
38.18527778 1
 
2.3%
38.17416667 1
 
2.3%
38.124824 1
 
2.3%
Other values (31) 31
72.1%
2023-12-12T14:36:58.990621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 78
17.2%
8 60
13.2%
1 52
11.5%
2 52
11.5%
. 47
10.4%
5 40
8.8%
7 34
7.5%
4 34
7.5%
6 31
 
6.8%
9 17
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 407
89.6%
Other Punctuation 47
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 78
19.2%
8 60
14.7%
1 52
12.8%
2 52
12.8%
5 40
9.8%
7 34
8.4%
4 34
8.4%
6 31
 
7.6%
9 17
 
4.2%
0 9
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 454
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 78
17.2%
8 60
13.2%
1 52
11.5%
2 52
11.5%
. 47
10.4%
5 40
8.8%
7 34
7.5%
4 34
7.5%
6 31
 
6.8%
9 17
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 454
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 78
17.2%
8 60
13.2%
1 52
11.5%
2 52
11.5%
. 47
10.4%
5 40
8.8%
7 34
7.5%
4 34
7.5%
6 31
 
6.8%
9 17
 
3.7%

경도
Text

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T14:36:59.271284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.581395
Min length6

Characters and Unicode

Total characters455
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)95.3%

Sample

1st row128.4147222
2nd row127.7165944
3rd row128.3136111
4th row128.3880556
5th row127.6719444
ValueCountFrequency (%)
128.4746926 2
 
4.7%
128.3044444 1
 
2.3%
127.2831 1
 
2.3%
127.5105556 1
 
2.3%
128.47 1
 
2.3%
128.336178 1
 
2.3%
128.4102778 1
 
2.3%
128.8222222 1
 
2.3%
128.4644444 1
 
2.3%
128.4657306 1
 
2.3%
Other values (32) 32
74.4%
2023-12-12T14:36:59.764132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 84
18.5%
1 64
14.1%
8 55
12.1%
7 52
11.4%
4 49
10.8%
. 47
10.3%
5 30
 
6.6%
6 28
 
6.2%
3 18
 
4.0%
0 15
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 408
89.7%
Other Punctuation 47
 
10.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 84
20.6%
1 64
15.7%
8 55
13.5%
7 52
12.7%
4 49
12.0%
5 30
 
7.4%
6 28
 
6.9%
3 18
 
4.4%
0 15
 
3.7%
9 13
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 455
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 84
18.5%
1 64
14.1%
8 55
12.1%
7 52
11.4%
4 49
10.8%
. 47
10.3%
5 30
 
6.6%
6 28
 
6.2%
3 18
 
4.0%
0 15
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 84
18.5%
1 64
14.1%
8 55
12.1%
7 52
11.4%
4 49
10.8%
. 47
10.3%
5 30
 
6.6%
6 28
 
6.2%
3 18
 
4.0%
0 15
 
3.3%

발생사유
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
부주의(낙상)
16 
부주의
8 
부주의(실족)
5 
부주의(미끄러짐)
4 
부주의(추락)
3 
Other values (5)
7 

Length

Max length9
Median length7
Mean length5.8604651
Min length2

Unique

Unique4 ?
Unique (%)9.3%

Sample

1st row부주의(미끄러짐)
2nd row부주의(접질림)
3rd row부주의(실족)
4th row부주의(미끄러짐)
5th row부주의(낙상)

Common Values

ValueCountFrequency (%)
부주의(낙상) 16
37.2%
부주의 8
18.6%
부주의(실족) 5
 
11.6%
부주의(미끄러짐) 4
 
9.3%
부주의(추락) 3
 
7.0%
추락 3
 
7.0%
부주의(접질림) 1
 
2.3%
심정지 1
 
2.3%
익수 1
 
2.3%
무리한산행 1
 
2.3%

Length

2023-12-12T14:36:59.941677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:37:00.127557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부주의(낙상 16
37.2%
부주의 8
18.6%
부주의(실족 5
 
11.6%
부주의(미끄러짐 4
 
9.3%
부주의(추락 3
 
7.0%
추락 3
 
7.0%
부주의(접질림 1
 
2.3%
심정지 1
 
2.3%
익수 1
 
2.3%
무리한산행 1
 
2.3%

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
부상
41 
사망
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부상 41
95.3%
사망 2
 
4.7%

Length

2023-12-12T14:37:00.325461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:37:00.462398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부상 41
95.3%
사망 2
 
4.7%

유형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
골절상처
41 
심장돌연사
 
1
익사
 
1

Length

Max length5
Median length4
Mean length3.9767442
Min length2

Unique

Unique2 ?
Unique (%)4.7%

Sample

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

Common Values

ValueCountFrequency (%)
골절상처 41
95.3%
심장돌연사 1
 
2.3%
익사 1
 
2.3%

Length

2023-12-12T14:37:00.615516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:37:00.746718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
골절상처 41
95.3%
심장돌연사 1
 
2.3%
익사 1
 
2.3%

Correlations

2023-12-12T14:37:00.849779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
날짜시간사무소위치위도경도발생사유구분유형
날짜1.0000.8830.9190.9730.9780.9910.9541.0001.000
시간0.8831.0000.4860.9890.9340.9780.0000.0000.000
사무소0.9190.4861.0001.0001.0001.0000.6530.7020.514
위치0.9730.9891.0001.0000.9850.9950.9671.0001.000
위도0.9780.9341.0000.9851.0001.0000.9871.0001.000
경도0.9910.9781.0000.9951.0001.0001.0001.0001.000
발생사유0.9540.0000.6530.9670.9871.0001.0001.0001.000
구분1.0000.0000.7021.0001.0001.0001.0001.0001.000
유형1.0000.0000.5141.0001.0001.0001.0001.0001.000
2023-12-12T14:37:00.996491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생사유사무소구분유형
발생사유1.0000.4120.8970.908
사무소0.4121.0000.4850.508
구분0.8970.4851.0000.988
유형0.9080.5080.9881.000
2023-12-12T14:37:01.109147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사무소발생사유구분유형
사무소1.0000.4120.4850.508
발생사유0.4121.0000.8970.908
구분0.4850.8971.0000.988
유형0.5080.9080.9881.000

Missing values

2023-12-12T14:36:56.527011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:36:56.681274image/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

날짜시간사무소위치위도경도발생사유구분유형
02018-04-0709:45설악산수렴동 대피소38.14611111128.4147222부주의(미끄러짐)부상골절상처
12018-04-2814:30지리산경남장터목 대피소35.33216667127.7165944부주의(접질림)부상골절상처
22018-04-2816:40설악산갱기폭포우골38.1366667128.3136111부주의(실족)부상골절상처
32018-05-1209:20설악산백담사~영시암 방향 1.2km 지점38.15333333128.3880556부주의(미끄러짐)부상골절상처
42018-05-1415:15지리산경남전망대 인근35.32222222127.6719444부주의(낙상)부상골절상처
52018-05-2708:15설악산수렴동 대피소38.14638889128.4141667부주의(낙상)부상골절상처
62018-05-2909:10설악산천불동 천당폭포38.13968162128.4746926부주의(낙상)부상골절상처
72018-05-3012:10설악산주전골 금강문 일원38.08277778128.4305556부주의(낙상)부상골절상처
82018-06-0412:30지리산전남노고단대피소 갈림길35.29.75.61127.51.60.05부주의부상골절상처
92018-06-0915:00지리산경남장터목~중산리35.32944444127.7177778부주의부상골절상처
날짜시간사무소위치위도경도발생사유구분유형
332018-10-2110:20설악산오세암38.15194444128.4202778부주의(낙상)부상골절상처
342018-10-2113:20설악산천불동 일원38.13968162128.4746926부주의(낙상)부상골절상처
352018-10-2113:27설악산백운폭포 일원38.21694444128.7088889부주의부상골절상처
362018-10-2115:09설악산금강굴 일원38.16559849128.4620173부주의(낙상)부상골절상처
372018-10-2312:40설악산육담폭포 일원38.16250448128.501914부주의(미끄러짐)부상골절상처
382018-10-2810:51설악산토왕골 음벽길38.16218128.49424부주의(낙상)부상골절상처
392018-10-2811:00설악산중청대피소~끝청 방향 600m38.12055556128.4602778부주의(실족)부상골절상처
402018-11-2216:40설악산흔들바위 일원38.11149646128.2831746부주의(낙상)부상골절상처
412018-12-1613:38지리산전북구룡7곡 사랑다리 인근35.232127.2831부주의부상골절상처
422018-12-2315:00지리산전남묘봉치 상단35.33.31.42127.51.59.87부주의부상골절상처