Overview

Dataset statistics

Number of variables12
Number of observations29
Missing cells10
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory102.6 B

Variable types

Numeric2
Categorical5
DateTime1
Text4

Dataset

Description지리산, 설악산국립공원에서 2020년에 발생한 안전사고에 대해 사고 위치, 위도, 경도, 사고 유형 등의 데이터를 포함하고 있습니다.
Author국립공원공단
URLhttps://www.data.go.kr/data/15090305/fileData.do

Alerts

유형 is highly overall correlated with 월 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 유형 and 1 other fieldsHigh correlation
월 is highly overall correlated with 유형High correlation
사고원인 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
유형 is highly imbalanced (56.4%)Imbalance
좌표(위도) has 5 (17.2%) missing valuesMissing
좌표(경도) has 5 (17.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 02:52:44.160482
Analysis finished2023-12-12 02:52:45.468711
Duration1.31 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

월
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4482759
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T11:52:45.518867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.4
Q15
median7
Q310
95-th percentile10
Maximum11
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0657813
Coefficient of variation (CV)0.47544201
Kurtosis-0.99918861
Mean6.4482759
Median Absolute Deviation (MAD)3
Skewness-0.31354949
Sum187
Variance9.3990148
MonotonicityIncreasing
2023-12-12T11:52:45.621777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 7
24.1%
7 5
17.2%
5 4
13.8%
2 3
10.3%
8 3
10.3%
1 2
 
6.9%
6 2
 
6.9%
3 1
 
3.4%
4 1
 
3.4%
11 1
 
3.4%
ValueCountFrequency (%)
1 2
 
6.9%
2 3
10.3%
3 1
 
3.4%
4 1
 
3.4%
5 4
13.8%
6 2
 
6.9%
7 5
17.2%
8 3
10.3%
10 7
24.1%
11 1
 
3.4%
ValueCountFrequency (%)
11 1
 
3.4%
10 7
24.1%
8 3
10.3%
7 5
17.2%
6 2
 
6.9%
5 4
13.8%
4 1
 
3.4%
3 1
 
3.4%
2 3
10.3%
1 2
 
6.9%

일
Real number (ℝ)

Distinct17
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.068966
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T11:52:45.743759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q18
median13
Q321
95-th percentile24.6
Maximum27
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.3820779
Coefficient of variation (CV)0.52470652
Kurtosis-1.0765262
Mean14.068966
Median Absolute Deviation (MAD)5
Skewness0.043891102
Sum408
Variance54.495074
MonotonicityNot monotonic
2023-12-12T11:52:45.850288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
9 3
10.3%
8 3
10.3%
21 3
10.3%
1 2
 
6.9%
14 2
 
6.9%
13 2
 
6.9%
18 2
 
6.9%
7 2
 
6.9%
23 2
 
6.9%
24 1
 
3.4%
Other values (7) 7
24.1%
ValueCountFrequency (%)
1 2
6.9%
5 1
 
3.4%
7 2
6.9%
8 3
10.3%
9 3
10.3%
10 1
 
3.4%
12 1
 
3.4%
13 2
6.9%
14 2
6.9%
17 1
 
3.4%
ValueCountFrequency (%)
27 1
 
3.4%
25 1
 
3.4%
24 1
 
3.4%
23 2
6.9%
22 1
 
3.4%
21 3
10.3%
18 2
6.9%
17 1
 
3.4%
14 2
6.9%
13 2
6.9%

요일
Categorical

Distinct6
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size364.0 B
토
9 
금
6 
목
5 
일
5 
화
3 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.4%

Sample

1st row목
2nd row화
3rd row토
4th row금
5th row일

Common Values

ValueCountFrequency (%)
토 9
31.0%
금 6
20.7%
목 5
17.2%
일 5
17.2%
화 3
 
10.3%
월 1
 
3.4%

Length

2023-12-12T11:52:45.958150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:52:46.081558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
토 9
31.0%
금 6
20.7%
목 5
17.2%
일 5
17.2%
화 3
 
10.3%
월 1
 
3.4%

사무소
Categorical

Distinct3
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size364.0 B
설악
21 
경남
5 
전북
3 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row설악
2nd row전북
3rd row설악
4th row경남
5th row설악

Common Values

ValueCountFrequency (%)
설악 21
72.4%
경남 5
 
17.2%
전북 3
 
10.3%

Length

2023-12-12T11:52:46.221732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:52:46.343668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
설악 21
72.4%
경남 5
 
17.2%
전북 3
 
10.3%

시 간
Date

Distinct25
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Memory size364.0 B
Minimum2023-12-12 07:35:00
Maximum2023-12-12 16:30:00
2023-12-12T11:52:46.443537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:52:46.576238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)

위 치
Text

Distinct26
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-12T11:52:46.804824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length10.172414
Min length5

Characters and Unicode

Total characters295
Distinct characters111
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)82.8%

Sample

1st row오색~대청봉구간 2쉼터 일원
2nd row화개재일원
3rd row토왕성폭포 일원
4th row통천문 일원
5th row흔들바위 일원
ValueCountFrequency (%)
일원 14
 
20.9%
서북능선 3
 
4.5%
화개재일원 2
 
3.0%
소토왕골 2
 
3.0%
금강굴 1
 
1.5%
구간 1
 
1.5%
암벽 1
 
1.5%
한편의시를 1
 
1.5%
위한길 1
 
1.5%
6p 1
 
1.5%
Other values (40) 40
59.7%
2023-12-12T11:52:47.196946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39
 
13.2%
원 19
 
6.4%
일 18
 
6.1%
0 8
 
2.7%
소 7
 
2.4%
선 6
 
2.0%
대 6
 
2.0%
구 5
 
1.7%
폭 5
 
1.7%
포 5
 
1.7%
Other values (101) 177
60.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 216
73.2%
Space Separator 39
 
13.2%
Decimal Number 19
 
6.4%
Dash Punctuation 4
 
1.4%
Open Punctuation 4
 
1.4%
Math Symbol 4
 
1.4%
Close Punctuation 4
 
1.4%
Lowercase Letter 3
 
1.0%
Initial Punctuation 1
 
0.3%
Final Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 19
 
8.8%
일 18
 
8.3%
소 7
 
3.2%
선 6
 
2.8%
대 6
 
2.8%
구 5
 
2.3%
폭 5
 
2.3%
포 5
 
2.3%
간 4
 
1.9%
지 4
 
1.9%
Other values (84) 137
63.4%
Decimal Number
ValueCountFrequency (%)
0 8
42.1%
2 4
21.1%
3 2
 
10.5%
6 2
 
10.5%
7 1
 
5.3%
1 1
 
5.3%
4 1
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
p 1
33.3%
m 1
33.3%
k 1
33.3%
Space Separator
ValueCountFrequency (%)
39
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 216
73.2%
Common 76
 
25.8%
Latin 3
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 19
 
8.8%
일 18
 
8.3%
소 7
 
3.2%
선 6
 
2.8%
대 6
 
2.8%
구 5
 
2.3%
폭 5
 
2.3%
포 5
 
2.3%
간 4
 
1.9%
지 4
 
1.9%
Other values (84) 137
63.4%
Common
ValueCountFrequency (%)
39
51.3%
0 8
 
10.5%
- 4
 
5.3%
( 4
 
5.3%
~ 4
 
5.3%
2 4
 
5.3%
) 4
 
5.3%
3 2
 
2.6%
6 2
 
2.6%
7 1
 
1.3%
Other values (4) 4
 
5.3%
Latin
ValueCountFrequency (%)
p 1
33.3%
m 1
33.3%
k 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 216
73.2%
ASCII 77
 
26.1%
Punctuation 2
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39
50.6%
0 8
 
10.4%
- 4
 
5.2%
( 4
 
5.2%
~ 4
 
5.2%
2 4
 
5.2%
) 4
 
5.2%
3 2
 
2.6%
6 2
 
2.6%
p 1
 
1.3%
Other values (5) 5
 
6.5%
Hangul
ValueCountFrequency (%)
원 19
 
8.8%
일 18
 
8.3%
소 7
 
3.2%
선 6
 
2.8%
대 6
 
2.8%
구 5
 
2.3%
폭 5
 
2.3%
포 5
 
2.3%
간 4
 
1.9%
지 4
 
1.9%
Other values (84) 137
63.4%
Punctuation
ValueCountFrequency (%)
‘ 1
50.0%
’ 1
50.0%

좌표(위도)
Text

MISSING 

Distinct24
Distinct (%)100.0%
Missing5
Missing (%)17.2%
Memory size364.0 B
2023-12-12T11:52:47.447256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.5
Min length6

Characters and Unicode

Total characters228
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

Unique24 ?
Unique (%)100.0%

Sample

1st row38.13519444
2nd row35.30919832
3rd row38.15516389
4th row35.33662222
5th row38.11.18.10
ValueCountFrequency (%)
38.9.30.6122 1
 
4.2%
38.15516389 1
 
4.2%
38.9.13.997 1
 
4.2%
38.439184 1
 
4.2%
38.16430178 1
 
4.2%
38.64084 1
 
4.2%
38.11.59 1
 
4.2%
38.115128 1
 
4.2%
38.114483 1
 
4.2%
37.082836 1
 
4.2%
Other values (14) 14
58.3%
2023-12-12T11:52:47.803798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 40
17.5%
3 39
17.1%
1 30
13.2%
8 28
12.3%
5 19
8.3%
9 16
 
7.0%
4 16
 
7.0%
0 13
 
5.7%
6 11
 
4.8%
2 10
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 188
82.5%
Other Punctuation 40
 
17.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 39
20.7%
1 30
16.0%
8 28
14.9%
5 19
10.1%
9 16
8.5%
4 16
8.5%
0 13
 
6.9%
6 11
 
5.9%
2 10
 
5.3%
7 6
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 228
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 40
17.5%
3 39
17.1%
1 30
13.2%
8 28
12.3%
5 19
8.3%
9 16
 
7.0%
4 16
 
7.0%
0 13
 
5.7%
6 11
 
4.8%
2 10
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 40
17.5%
3 39
17.1%
1 30
13.2%
8 28
12.3%
5 19
8.3%
9 16
 
7.0%
4 16
 
7.0%
0 13
 
5.7%
6 11
 
4.8%
2 10
 
4.4%

좌표(경도)
Text

MISSING 

Distinct24
Distinct (%)100.0%
Missing5
Missing (%)17.2%
Memory size364.0 B
2023-12-12T11:52:48.072403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11.5
Mean length10.375
Min length8

Characters and Unicode

Total characters249
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

Unique24 ?
Unique (%)100.0%

Sample

1st row128.4639528
2nd row127.5842828
3rd row128.4950417
4th row127.7292111
5th row128.28.31.93
ValueCountFrequency (%)
128.29.48.05 1
 
4.2%
128.4950417 1
 
4.2%
128.27.24.539 1
 
4.2%
128.261721 1
 
4.2%
128.4658553 1
 
4.2%
128.244119 1
 
4.2%
128.44.99 1
 
4.2%
128.441758 1
 
4.2%
128.404319 1
 
4.2%
128.432083 1
 
4.2%
Other values (14) 14
58.3%
2023-12-12T11:52:48.436551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 45
18.1%
. 40
16.1%
1 38
15.3%
8 33
13.3%
4 23
9.2%
7 16
 
6.4%
9 13
 
5.2%
5 13
 
5.2%
3 12
 
4.8%
0 8
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 209
83.9%
Other Punctuation 40
 
16.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 45
21.5%
1 38
18.2%
8 33
15.8%
4 23
11.0%
7 16
 
7.7%
9 13
 
6.2%
5 13
 
6.2%
3 12
 
5.7%
0 8
 
3.8%
6 8
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 249
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 45
18.1%
. 40
16.1%
1 38
15.3%
8 33
13.3%
4 23
9.2%
7 16
 
6.4%
9 13
 
5.2%
5 13
 
5.2%
3 12
 
4.8%
0 8
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 249
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 45
18.1%
. 40
16.1%
1 38
15.3%
8 33
13.3%
4 23
9.2%
7 16
 
6.4%
9 13
 
5.2%
5 13
 
5.2%
3 12
 
4.8%
0 8
 
3.2%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
부상
25 
사망
4 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부상 25
86.2%
사망 4
 
13.8%

Length

2023-12-12T11:52:48.601905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:52:48.708790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부상 25
86.2%
사망 4
 
13.8%

유형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size364.0 B
골절상처
25 
심장돌연사
3 
추락사
 
1

Length

Max length5
Median length4
Mean length4.0689655
Min length3

Unique

Unique1 ?
Unique (%)3.4%

Sample

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

Common Values

ValueCountFrequency (%)
골절상처 25
86.2%
심장돌연사 3
 
10.3%
추락사 1
 
3.4%

Length

2023-12-12T11:52:48.865243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:52:48.982637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
골절상처 25
86.2%
심장돌연사 3
 
10.3%
추락사 1
 
3.4%

구체적 상태
Text

Distinct20
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-12T11:52:49.137363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length5.9310345
Min length2

Characters and Unicode

Total characters172
Distinct characters44
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

Unique16 ?
Unique (%)55.2%

Sample

1st row심정지
2nd row발목골절
3rd row안면부 열상
4th row발목골절
5th row심정지
ValueCountFrequency (%)
골절 14
25.0%
발목골절 7
 
12.5%
심정지 3
 
5.4%
발목 3
 
5.4%
타박상 2
 
3.6%
왼쪽 2
 
3.6%
오른쪽 2
 
3.6%
팔 2
 
3.6%
다리골절 2
 
3.6%
전신 1
 
1.8%
Other values (18) 18
32.1%
2023-12-12T11:52:49.480866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27
15.7%
골 27
15.7%
절 24
14.0%
목 11
 
6.4%
발 10
 
5.8%
팔 5
 
2.9%
정 4
 
2.3%
다 4
 
2.3%
리 4
 
2.3%
, 4
 
2.3%
Other values (34) 52
30.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 141
82.0%
Space Separator 27
 
15.7%
Other Punctuation 4
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
골 27
19.1%
절 24
17.0%
목 11
 
7.8%
발 10
 
7.1%
팔 5
 
3.5%
정 4
 
2.8%
다 4
 
2.8%
리 4
 
2.8%
쪽 4
 
2.8%
지 3
 
2.1%
Other values (32) 45
31.9%
Space Separator
ValueCountFrequency (%)
27
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 141
82.0%
Common 31
 
18.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
골 27
19.1%
절 24
17.0%
목 11
 
7.8%
발 10
 
7.1%
팔 5
 
3.5%
정 4
 
2.8%
다 4
 
2.8%
리 4
 
2.8%
쪽 4
 
2.8%
지 3
 
2.1%
Other values (32) 45
31.9%
Common
ValueCountFrequency (%)
27
87.1%
, 4
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 141
82.0%
ASCII 31
 
18.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27
87.1%
, 4
 
12.9%
Hangul
ValueCountFrequency (%)
골 27
19.1%
절 24
17.0%
목 11
 
7.8%
발 10
 
7.1%
팔 5
 
3.5%
정 4
 
2.8%
다 4
 
2.8%
리 4
 
2.8%
쪽 4
 
2.8%
지 3
 
2.1%
Other values (32) 45
31.9%

사고원인
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Memory size364.0 B
실족
8 
낙상
5 
미끄러짐
4 
심정지
2 
추락
2 
Other values (8)
8 

Length

Max length9
Median length2
Mean length3.3103448
Min length2

Unique

Unique8 ?
Unique (%)27.6%

Sample

1st row심정지
2nd row부주의(미끄러짐)
3rd row낙빙
4th row미끄러짐
5th row심정지

Common Values

ValueCountFrequency (%)
실족 8
27.6%
낙상 5
17.2%
미끄러짐 4
13.8%
심정지 2
 
6.9%
추락 2
 
6.9%
부주의(미끄러짐) 1
 
3.4%
낙빙 1
 
3.4%
무리한산행 1
 
3.4%
낙석 1
 
3.4%
추락(10m) 1
 
3.4%
Other values (3) 3
 
10.3%

Length

2023-12-12T11:52:49.669163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
실족 8
26.7%
낙상 5
16.7%
미끄러짐 4
13.3%
심정지 2
 
6.7%
추락 2
 
6.7%
부주의(미끄러짐 1
 
3.3%
낙빙 1
 
3.3%
무리한산행 1
 
3.3%
낙석 1
 
3.3%
추락(10m 1
 
3.3%
Other values (4) 4
13.3%

Interactions

2023-12-12T11:52:44.960501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:52:44.773777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:52:45.047869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:52:44.864470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:52:49.782554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월일요일사무소시 간위 치좌표(위도)좌표(경도)구분유형구체적 상태사고원인
월1.0000.6030.0000.4700.6580.9711.0001.0000.5700.8020.0000.503
일0.6031.0000.7070.2010.9550.9791.0001.0000.0000.0000.0000.736
요일0.0000.7071.0000.4370.8200.8481.0001.0000.0000.0000.0000.000
사무소0.4700.2010.4371.0000.0001.0001.0001.0000.0000.0000.0000.548
시 간0.6580.9550.8200.0001.0000.8711.0001.0000.0000.0000.0000.803
위 치0.9710.9790.8481.0000.8711.0001.0001.0001.0001.0000.9100.977
좌표(위도)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
좌표(경도)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
구분0.5700.0000.0000.0000.0001.0001.0001.0001.0001.0001.0000.880
유형0.8020.0000.0000.0000.0001.0001.0001.0001.0001.0001.0000.859
구체적 상태0.0000.0000.0000.0000.0000.9101.0001.0001.0001.0001.0000.422
사고원인0.5030.7360.0000.5480.8030.9771.0001.0000.8800.8590.4221.000
2023-12-12T11:52:49.922805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사무소유형요일구분사고원인
사무소1.0000.0000.1710.0000.266
유형0.0001.0000.0000.9810.579
요일0.1710.0001.0000.0000.000
구분0.0000.9810.0001.0000.665
사고원인0.2660.5790.0000.6651.000
2023-12-12T11:52:50.049571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월일요일사무소구분유형사고원인
월1.000-0.0910.0000.1840.3930.6140.135
일-0.0911.0000.3840.0000.0000.0000.298
요일0.0000.3841.0000.1710.0000.0000.000
사무소0.1840.0000.1711.0000.0000.0000.266
구분0.3930.0000.0000.0001.0000.9810.665
유형0.6140.0000.0000.0000.9811.0000.579
사고원인0.1350.2980.0000.2660.6650.5791.000

Missing values

2023-12-12T11:52:45.162942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:52:45.316622image/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.
2023-12-12T11:52:45.423764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

월일요일사무소시 간위 치좌표(위도)좌표(경도)구분유형구체적 상태사고원인
019목설악11:08오색~대청봉구간 2쉼터 일원38.13519444128.4639528사망심장돌연사심정지심정지
1121화전북14:23화개재일원35.30919832127.5842828부상골절상처발목골절부주의(미끄러짐)
228토설악11:39토왕성폭포 일원38.15516389128.4950417부상골절상처안면부 열상낙빙
3221금경남13:08통천문 일원35.33662222127.7292111부상골절상처발목골절미끄러짐
4223일설악13:15흔들바위 일원38.11.18.10128.28.31.93사망심장돌연사심정지심정지
537토설악16:30권금성 안락암 일원38.09.50128.29.17사망추락사사망추락
6418토경남10:01로타리대피소 상단 1km35.19.53.6127.44.02.8부상골절상처다리골절실족
751금경남11:20천왕샘 인근<NA><NA>사망심장돌연사심정지무리한산행
857목전북11:40팔랑치 일원35.41.14127.56.43부상골절상처왼쪽 발목 골절실족
9524일전북14:30화개재일원35.30919127.58428부상골절상처정강이 골절실족
월일요일사무소시 간위 치좌표(위도)좌표(경도)구분유형구체적 상태사고원인
19814금설악13:30소토왕골 한편의시를 위한길 6p 하단부<NA><NA>부상골절상처발목골절추락(3m)
20814금경남16:30와불산 정상 바위35.394127.7428부상골절상처늑골 및 슬개골 골절추락(50m)
21108목설악11:40공룡능선 03-02지점37.082836128.432083부상골절상처발목골절실족
22109금설악10:00서북능선 일원38.114483128.404319부상골절상처다리골절실족
23109금설악07:35서북능선 일원38.115128128.441758부상골절상처팔 골절실족
241010토설악11:07서북능선 일원38.11.59128.44.99부상골절상처코뼈 골절, 손목, 무릎 타박상낙상
251017토설악11:30한계령 삼거리 일원38.64084128.244119부상골절상처발목 골절낙상
261022목설악14:30비선대 인근(적벽 등반)38.16430178128.4658553부상골절상처뒷꿈치 골절개인 부주의
271027화설악15:00선녀탕 일원(07-03)38.439184128.261721부상골절상처오른쪽 발목 골절미끄러짐
28118일설악13:35금강굴 일원(02-02)38.165611128.461969부상골절상처팔 골절낙상