Overview

Dataset statistics

Number of variables8
Number of observations23
Missing cells21
Missing cells (%)11.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory73.7 B

Variable types

Numeric1
DateTime1
Text3
Categorical3

Dataset

Description2022년 12월 31일 기준 인천광역시 화학사고 출동현황 자료로서 도시가스 누출 등 생활안전사고 및 단순 의심에 의한 안전조치 사항을 제외합니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15104258&srcSe=7661IVAWM27C61E190

Alerts

인명피해_중상 has constant value ""Constant
인명피해_사망 is highly imbalanced (74.2%)Imbalance
재산피해 has 21 (91.3%) missing valuesMissing
연번 has unique valuesUnique
사고장소 has unique valuesUnique

Reproduction

Analysis started2024-01-28 05:13:36.823872
Analysis finished2024-01-28 05:13:37.310426
Duration0.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-28T14:13:37.363479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.1
Q16.5
median12
Q317.5
95-th percentile21.9
Maximum23
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.78233
Coefficient of variation (CV)0.56519417
Kurtosis-1.2
Mean12
Median Absolute Deviation (MAD)6
Skewness0
Sum276
Variance46
MonotonicityStrictly increasing
2024-01-28T14:13:37.458856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 1
 
4.3%
2 1
 
4.3%
23 1
 
4.3%
22 1
 
4.3%
21 1
 
4.3%
20 1
 
4.3%
19 1
 
4.3%
18 1
 
4.3%
17 1
 
4.3%
16 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1 1
4.3%
2 1
4.3%
3 1
4.3%
4 1
4.3%
5 1
4.3%
6 1
4.3%
7 1
4.3%
8 1
4.3%
9 1
4.3%
10 1
4.3%
ValueCountFrequency (%)
23 1
4.3%
22 1
4.3%
21 1
4.3%
20 1
4.3%
19 1
4.3%
18 1
4.3%
17 1
4.3%
16 1
4.3%
15 1
4.3%
14 1
4.3%

발생일자
Date

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
Minimum2022-03-07 00:00:00
Maximum2022-12-22 00:00:00
2024-01-28T14:13:37.553246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:13:37.653054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

사고장소
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-01-28T14:13:37.821434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.26087
Min length9

Characters and Unicode

Total characters305
Distinct characters61
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row미추홀구 용현동 253
2nd row연수구 송도동 399-11
3rd row서구 정서진로 410
4th row남동구 고잔동 695-12
5th row남동구 고잔동 648-8
ValueCountFrequency (%)
남동구 9
 
12.9%
서구 5
 
7.1%
고잔동 4
 
5.7%
구월동 2
 
2.9%
중구 2
 
2.9%
강화군 2
 
2.9%
계양구 2
 
2.9%
가좌동 2
 
2.9%
연수구 2
 
2.9%
작적동 1
 
1.4%
Other values (39) 39
55.7%
2024-01-28T14:13:38.110812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47
15.4%
동 27
 
8.9%
구 23
 
7.5%
1 21
 
6.9%
- 15
 
4.9%
2 14
 
4.6%
9 12
 
3.9%
남 11
 
3.6%
0 9
 
3.0%
6 9
 
3.0%
Other values (51) 117
38.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 146
47.9%
Decimal Number 97
31.8%
Space Separator 47
 
15.4%
Dash Punctuation 15
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
18.5%
구 23
15.8%
남 11
 
7.5%
서 6
 
4.1%
잔 5
 
3.4%
고 5
 
3.4%
가 4
 
2.7%
로 3
 
2.1%
화 3
 
2.1%
강 3
 
2.1%
Other values (39) 56
38.4%
Decimal Number
ValueCountFrequency (%)
1 21
21.6%
2 14
14.4%
9 12
12.4%
0 9
9.3%
6 9
9.3%
3 8
 
8.2%
5 8
 
8.2%
7 7
 
7.2%
8 5
 
5.2%
4 4
 
4.1%
Space Separator
ValueCountFrequency (%)
47
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 159
52.1%
Hangul 146
47.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
18.5%
구 23
15.8%
남 11
 
7.5%
서 6
 
4.1%
잔 5
 
3.4%
고 5
 
3.4%
가 4
 
2.7%
로 3
 
2.1%
화 3
 
2.1%
강 3
 
2.1%
Other values (39) 56
38.4%
Common
ValueCountFrequency (%)
47
29.6%
1 21
13.2%
- 15
 
9.4%
2 14
 
8.8%
9 12
 
7.5%
0 9
 
5.7%
6 9
 
5.7%
3 8
 
5.0%
5 8
 
5.0%
7 7
 
4.4%
Other values (2) 9
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159
52.1%
Hangul 146
47.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47
29.6%
1 21
13.2%
- 15
 
9.4%
2 14
 
8.8%
9 12
 
7.5%
0 9
 
5.7%
6 9
 
5.7%
3 8
 
5.0%
5 8
 
5.0%
7 7
 
4.4%
Other values (2) 9
 
5.7%
Hangul
ValueCountFrequency (%)
동 27
18.5%
구 23
15.8%
남 11
 
7.5%
서 6
 
4.1%
잔 5
 
3.4%
고 5
 
3.4%
가 4
 
2.7%
로 3
 
2.1%
화 3
 
2.1%
강 3
 
2.1%
Other values (39) 56
38.4%

사고물질
Text

Distinct17
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-01-28T14:13:38.272776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length4.9130435
Min length2

Characters and Unicode

Total characters113
Distinct characters50
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)65.2%

Sample

1st row수은(Hg)
2nd row에폭시
3rd row디페닐카르바지드 용액
4th row부탄
5th row포름알데히드
ValueCountFrequency (%)
부탄 4
16.7%
염화수소(산 4
16.7%
수은(hg 1
 
4.2%
질소 1
 
4.2%
기타(부취제 1
 
4.2%
lpg 1
 
4.2%
메탄 1
 
4.2%
산소(고압가스 1
 
4.2%
수산화칼륨 1
 
4.2%
질소(액화 1
 
4.2%
Other values (8) 8
33.3%
2024-01-28T14:13:38.584277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 10
 
8.8%
) 10
 
8.8%
소 9
 
8.0%
화 7
 
6.2%
수 7
 
6.2%
산 7
 
6.2%
부 5
 
4.4%
탄 5
 
4.4%
염 4
 
3.5%
액 3
 
2.7%
Other values (40) 46
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 86
76.1%
Open Punctuation 10
 
8.8%
Close Punctuation 10
 
8.8%
Uppercase Letter 4
 
3.5%
Space Separator 2
 
1.8%
Lowercase Letter 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 9
 
10.5%
화 7
 
8.1%
수 7
 
8.1%
산 7
 
8.1%
부 5
 
5.8%
탄 5
 
5.8%
염 4
 
4.7%
액 3
 
3.5%
질 2
 
2.3%
드 2
 
2.3%
Other values (32) 35
40.7%
Uppercase Letter
ValueCountFrequency (%)
L 1
25.0%
P 1
25.0%
G 1
25.0%
H 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 86
76.1%
Common 22
 
19.5%
Latin 5
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 9
 
10.5%
화 7
 
8.1%
수 7
 
8.1%
산 7
 
8.1%
부 5
 
5.8%
탄 5
 
5.8%
염 4
 
4.7%
액 3
 
3.5%
질 2
 
2.3%
드 2
 
2.3%
Other values (32) 35
40.7%
Latin
ValueCountFrequency (%)
L 1
20.0%
P 1
20.0%
G 1
20.0%
H 1
20.0%
g 1
20.0%
Common
ValueCountFrequency (%)
( 10
45.5%
) 10
45.5%
2
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 86
76.1%
ASCII 27
 
23.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 10
37.0%
) 10
37.0%
2
 
7.4%
L 1
 
3.7%
P 1
 
3.7%
G 1
 
3.7%
H 1
 
3.7%
g 1
 
3.7%
Hangul
ValueCountFrequency (%)
소 9
 
10.5%
화 7
 
8.1%
수 7
 
8.1%
산 7
 
8.1%
부 5
 
5.8%
탄 5
 
5.8%
염 4
 
4.7%
액 3
 
3.5%
질 2
 
2.3%
드 2
 
2.3%
Other values (32) 35
40.7%

인명피해_사망
Categorical

IMBALANCE 

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
22 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22
95.7%
1 1
 
4.3%

Length

2024-01-28T14:13:38.743662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:13:38.846528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22
95.7%
1 1
 
4.3%

인명피해_중상
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
23 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23
100.0%

Length

2024-01-28T14:13:38.939019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:13:39.014145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 23
100.0%

인명피해_경상
Categorical

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
17 
1
4 
2
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row0
2nd row2
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 17
73.9%
1 4
 
17.4%
2 1
 
4.3%
3 1
 
4.3%

Length

2024-01-28T14:13:39.086677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:13:39.169309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17
73.9%
1 4
 
17.4%
2 1
 
4.3%
3 1
 
4.3%

재산피해
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing21
Missing (%)91.3%
Memory size316.0 B
2024-01-28T14:13:39.263060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5.5
Mean length5.5
Min length5

Characters and Unicode

Total characters11
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row3100만원
2nd row120만원
ValueCountFrequency (%)
3100만원 1
50.0%
120만원 1
50.0%
2024-01-28T14:13:39.480577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3
27.3%
1 2
18.2%
만 2
18.2%
원 2
18.2%
3 1
 
9.1%
2 1
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7
63.6%
Other Letter 4
36.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3
42.9%
1 2
28.6%
3 1
 
14.3%
2 1
 
14.3%
Other Letter
ValueCountFrequency (%)
만 2
50.0%
원 2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7
63.6%
Hangul 4
36.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3
42.9%
1 2
28.6%
3 1
 
14.3%
2 1
 
14.3%
Hangul
ValueCountFrequency (%)
만 2
50.0%
원 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7
63.6%
Hangul 4
36.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3
42.9%
1 2
28.6%
3 1
 
14.3%
2 1
 
14.3%
Hangul
ValueCountFrequency (%)
만 2
50.0%
원 2
50.0%

Interactions

2024-01-28T14:13:37.074533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T14:13:39.559642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번발생일자사고장소사고물질인명피해_사망인명피해_경상재산피해
연번1.0000.9901.0000.4710.4880.0000.000
발생일자0.9901.0001.0000.9371.0000.9830.000
사고장소1.0001.0001.0001.0001.0001.0000.000
사고물질0.4710.9371.0001.0001.0000.0000.000
인명피해_사망0.4881.0001.0001.0001.0000.000NaN
인명피해_경상0.0000.9831.0000.0000.0001.000NaN
재산피해0.0000.0000.0000.000NaNNaN1.000
2024-01-28T14:13:39.657529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인명피해_사망인명피해_경상
인명피해_사망1.0000.000
인명피해_경상0.0001.000
2024-01-28T14:13:39.735386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번인명피해_사망인명피해_경상
연번1.0000.2670.000
인명피해_사망0.2671.0000.000
인명피해_경상0.0000.0001.000

Missing values

2024-01-28T14:13:37.163514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T14:13:37.267122image/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

연번발생일자사고장소사고물질인명피해_사망인명피해_중상인명피해_경상재산피해
012022-03-07미추홀구 용현동 253수은(Hg)000<NA>
122022-03-27연수구 송도동 399-11에폭시002<NA>
232022-04-05서구 정서진로 410디페닐카르바지드 용액001<NA>
342022-06-11남동구 고잔동 695-12부탄001<NA>
452022-06-13남동구 고잔동 648-8포름알데히드0003100만원
562022-06-26계양구 작전동 211-18산소(액화)000<NA>
672022-06-28남동구 남촌동 622-2수소000120만원
782022-07-05남동구 고잔동 975-1염화수소(산)000<NA>
892022-07-05남동구 논현고잔로 173염화수소(산)000<NA>
9102022-07-15남동구 고잔동 198-3염화수소(산)000<NA>
연번발생일자사고장소사고물질인명피해_사망인명피해_중상인명피해_경상재산피해
13142022-08-21서구 가좌동 173-256질소000<NA>
14152022-09-09남동구 구월동 1270-11부탄000<NA>
15162022-09-13서구 가좌동 150-76염화수소(산)000<NA>
16172022-09-13연수구 송도바이오대로 300수산화칼륨001<NA>
17182022-09-23중구 항동7가10산소(고압가스)100<NA>
18192022-10-04계양구 작적동 915-1메탄000<NA>
19202022-10-09강화군 하점면 장정리 990LPG000<NA>
20212022-11-17서구 오류동 1604기타(부취제)000<NA>
21222022-11-30강화군 강화읍 신문리 699부탄000<NA>
22232022-12-22중구 신흥동3가 60-18휘발유001<NA>