Overview

Dataset statistics

Number of variables4
Number of observations6336
Missing cells9506
Missing cells (%)37.5%
Duplicate rows134
Duplicate rows (%)2.1%
Total size in memory198.1 KiB
Average record size in memory32.0 B

Variable types

DateTime1
Text1
Categorical2

Dataset

Description한국서부발전 재난대응 비상발령 현황입니다.제공항목은 일자,제목,목적지명,수신지명 데이터를 제공합니다.
Author한국서부발전(주)
URLhttps://www.data.go.kr/data/15123086/fileData.do

Alerts

Dataset has 134 (2.1%) duplicate rowsDuplicates
수신지명 is highly overall correlated with 목적지명High correlation
목적지명 is highly overall correlated with 수신지명High correlation
목적지명 is highly imbalanced (70.0%)Imbalance
수신지명 is highly imbalanced (71.5%)Imbalance
일자 has 4753 (75.0%) missing valuesMissing
제목 has 4753 (75.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 16:58:04.786694
Analysis finished2023-12-12 16:58:05.480072
Duration0.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Date

MISSING 

Distinct187
Distinct (%)11.8%
Missing4753
Missing (%)75.0%
Memory size49.6 KiB
Minimum2015-05-30 00:00:00
Maximum2015-12-31 00:00:00
2023-12-13T01:58:05.585115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:05.803029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

제목
Text

MISSING 

Distinct436
Distinct (%)27.5%
Missing4753
Missing (%)75.0%
Memory size49.6 KiB
2023-12-13T01:58:06.275553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length37
Mean length17.540745
Min length1

Characters and Unicode

Total characters27767
Distinct characters405
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique371 ?
Unique (%)23.4%

Sample

1st row강북구 미아동 단독주택 화재
2nd row포천시 신북면 심곡리 산52-2번지 산불방생
3rd rowFw: 사내면 명월리 산불화재 진압완료
4th row포천시 신북면 심곡리 산52-2번지 산불완진
5th row강남구 역삼동 금화빌딩 1층 음식점 화재(최종)
ValueCountFrequency (%)
대치 669
 
13.7%
강풍주의보·풍랑주의보 611
 
12.5%
해제·풍랑주의보 593
 
12.2%
발표 325
 
6.7%
해제 203
 
4.2%
풍랑주의보 171
 
3.5%
호우주의보 109
 
2.2%
대설주의보 70
 
1.4%
화재 63
 
1.3%
지역 50
 
1.0%
Other values (903) 2007
41.2%
2023-12-13T01:58:06.913356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3291
 
11.9%
보 2714
 
9.8%
주 2520
 
9.1%
의 2482
 
8.9%
풍 2184
 
7.9%
랑 1448
 
5.2%
· 1396
 
5.0%
대 922
 
3.3%
제 864
 
3.1%
해 862
 
3.1%
Other values (395) 9084
32.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22125
79.7%
Space Separator 3291
 
11.9%
Other Punctuation 1415
 
5.1%
Decimal Number 343
 
1.2%
Open Punctuation 193
 
0.7%
Close Punctuation 193
 
0.7%
Lowercase Letter 159
 
0.6%
Uppercase Letter 29
 
0.1%
Dash Punctuation 18
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
보 2714
12.3%
주 2520
 
11.4%
의 2482
 
11.2%
풍 2184
 
9.9%
랑 1448
 
6.5%
대 922
 
4.2%
제 864
 
3.9%
해 862
 
3.9%
강 755
 
3.4%
치 739
 
3.3%
Other values (358) 6635
30.0%
Decimal Number
ValueCountFrequency (%)
1 104
30.3%
2 42
12.2%
8 38
 
11.1%
4 29
 
8.5%
5 29
 
8.5%
3 27
 
7.9%
0 23
 
6.7%
9 19
 
5.5%
6 17
 
5.0%
7 15
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
F 17
58.6%
T 4
 
13.8%
A 1
 
3.4%
P 1
 
3.4%
S 1
 
3.4%
X 1
 
3.4%
G 1
 
3.4%
K 1
 
3.4%
U 1
 
3.4%
C 1
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
k 68
42.8%
m 68
42.8%
w 17
 
10.7%
e 2
 
1.3%
t 2
 
1.3%
s 2
 
1.3%
Other Punctuation
ValueCountFrequency (%)
· 1396
98.7%
: 17
 
1.2%
, 1
 
0.1%
. 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 119
61.7%
[ 74
38.3%
Close Punctuation
ValueCountFrequency (%)
) 119
61.7%
] 74
38.3%
Space Separator
ValueCountFrequency (%)
3291
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Math Symbol
ValueCountFrequency (%)
→ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22125
79.7%
Common 5454
 
19.6%
Latin 188
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
보 2714
12.3%
주 2520
 
11.4%
의 2482
 
11.2%
풍 2184
 
9.9%
랑 1448
 
6.5%
대 922
 
4.2%
제 864
 
3.9%
해 862
 
3.9%
강 755
 
3.4%
치 739
 
3.3%
Other values (358) 6635
30.0%
Common
ValueCountFrequency (%)
3291
60.3%
· 1396
25.6%
( 119
 
2.2%
) 119
 
2.2%
1 104
 
1.9%
[ 74
 
1.4%
] 74
 
1.4%
2 42
 
0.8%
8 38
 
0.7%
4 29
 
0.5%
Other values (11) 168
 
3.1%
Latin
ValueCountFrequency (%)
k 68
36.2%
m 68
36.2%
w 17
 
9.0%
F 17
 
9.0%
T 4
 
2.1%
e 2
 
1.1%
t 2
 
1.1%
s 2
 
1.1%
A 1
 
0.5%
P 1
 
0.5%
Other values (6) 6
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22123
79.7%
ASCII 4245
 
15.3%
None 1396
 
5.0%
Compat Jamo 2
 
< 0.1%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3291
77.5%
( 119
 
2.8%
) 119
 
2.8%
1 104
 
2.4%
[ 74
 
1.7%
] 74
 
1.7%
k 68
 
1.6%
m 68
 
1.6%
2 42
 
1.0%
8 38
 
0.9%
Other values (25) 248
 
5.8%
Hangul
ValueCountFrequency (%)
보 2714
12.3%
주 2520
 
11.4%
의 2482
 
11.2%
풍 2184
 
9.9%
랑 1448
 
6.5%
대 922
 
4.2%
제 864
 
3.9%
해 862
 
3.9%
강 755
 
3.4%
치 739
 
3.3%
Other values (356) 6633
30.0%
None
ValueCountFrequency (%)
· 1396
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅇ 1
50.0%
ㅌ 1
50.0%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

목적지명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size49.6 KiB
<NA>
4754 
국민안전처
1283 
서울특별시
 
167
경기도
 
67
강원도
 
28
Other values (7)
 
37

Length

Max length5
Median length4
Mean length4.2130682
Min length3

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row서울특별시
2nd row경기도
3rd row강원도
4th row경기도
5th row서울특별시

Common Values

ValueCountFrequency (%)
<NA> 4754
75.0%
국민안전처 1283
 
20.2%
서울특별시 167
 
2.6%
경기도 67
 
1.1%
강원도 28
 
0.4%
충청남도 20
 
0.3%
인천광역시 6
 
0.1%
은평구 5
 
0.1%
용산구 3
 
< 0.1%
송파구 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2023-12-13T01:58:07.073733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4754
75.0%
국민안전처 1283
 
20.2%
서울특별시 167
 
2.6%
경기도 67
 
1.1%
강원도 28
 
0.4%
충청남도 20
 
0.3%
인천광역시 6
 
0.1%
은평구 5
 
0.1%
용산구 3
 
< 0.1%
송파구 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

수신지명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size49.6 KiB
<NA>
4754 
기상청
1203 
서울특별시 ...
 
119
국민안전처
 
68
서울 ...
 
48
Other values (13)
 
144

Length

Max length9
Median length4
Mean length4.0001578
Min length3

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row서울특별시 ...
2nd row경기도 ...
3rd row강원도 ...
4th row경기도 ...
5th row서울특별시 ...

Common Values

ValueCountFrequency (%)
<NA> 4754
75.0%
기상청 1203
 
19.0%
서울특별시 ... 119
 
1.9%
국민안전처 68
 
1.1%
서울 ... 48
 
0.8%
경기 ... 34
 
0.5%
경기도 ... 33
 
0.5%
강원도 ... 25
 
0.4%
충청남도 ... 20
 
0.3%
국민안전처 ... 12
 
0.2%
Other values (8) 20
 
0.3%

Length

2023-12-13T01:58:07.207525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4754
71.5%
기상청 1203
 
18.1%
310
 
4.7%
서울특별시 119
 
1.8%
국민안전처 80
 
1.2%
서울 48
 
0.7%
경기 34
 
0.5%
경기도 33
 
0.5%
강원도 25
 
0.4%
충청남도 20
 
0.3%
Other values (8) 20
 
0.3%

Correlations

2023-12-13T01:58:07.296683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
목적지명수신지명
목적지명1.0001.000
수신지명1.0001.000
2023-12-13T01:58:07.392873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수신지명목적지명
수신지명1.0000.998
목적지명0.9981.000
2023-12-13T01:58:07.482452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
목적지명수신지명
목적지명1.0000.998
수신지명0.9981.000

Missing values

2023-12-13T01:58:05.163082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:58:05.284302image/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.
2023-12-13T01:58:05.414230image/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

일자제목목적지명수신지명
02015-05-30강북구 미아동 단독주택 화재서울특별시서울특별시 ...
12015-06-01포천시 신북면 심곡리 산52-2번지 산불방생경기도경기도 ...
22015-06-01Fw: 사내면 명월리 산불화재 진압완료강원도강원도 ...
32015-06-01포천시 신북면 심곡리 산52-2번지 산불완진경기도경기도 ...
42015-06-02강남구 역삼동 금화빌딩 1층 음식점 화재(최종)서울특별시서울특별시 ...
52015-06-02[지진정보]경북 울진군 동남동쪽 57km 해역국민안전처국민안전처
62015-06-03Fw: 인제군 상남면 김부리 산불발생강원도강원도 ...
72015-06-06한강시민공원 잠원지구 오엔바엔다이닝 음식점 화재서울특별시서울특별시 ...
82015-06-07산불발생강원도강원도 ...
92015-06-07[지진정보]제주 제주시 북서쪽 45km 해역국민안전처국민안전처
일자제목목적지명수신지명
6326<NA><NA><NA><NA>
6327<NA><NA><NA><NA>
6328<NA><NA><NA><NA>
6329<NA><NA><NA><NA>
6330<NA><NA><NA><NA>
6331<NA><NA><NA><NA>
6332<NA><NA><NA><NA>
6333<NA><NA><NA><NA>
6334<NA><NA><NA><NA>
6335<NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

일자제목목적지명수신지명# duplicates
133<NA><NA><NA><NA>4753
892015-11-19강풍주의보·풍랑주의보 해제·풍랑주의보 대치국민안전처기상청570
1302015-12-22[지진정보]전북 익산시 북쪽 8km 지역국민안전처국민안전처31
1042015-11-26대설주의보 발표국민안전처기상청15
1142015-12-03대설주의보 발표국민안전처기상청12
1022015-11-26대설경보 대치국민안전처기상청10
1242015-12-16대설주의보 발표국민안전처기상청9
72015-07-23호우주의보 발표국민안전처기상청8
1272015-12-16풍랑주의보 발표국민안전처기상청7
02015-06-22[지진정보]충남 공주시 남동쪽 15km 지역국민안전처국민안전처6