Overview

Dataset statistics

Number of variables6
Number of observations180
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 KiB
Average record size in memory49.7 B

Variable types

Numeric1
Categorical2
Text2
DateTime1

Dataset

Description화재의 예방 및 안전관리에 관한 법률에 따른 소방안전 특별관리시설물에 대한 인천광역시 2023년 화재안전조사대상 현황을 시설물 및 관할소방서별로 구분한 데이터입니다.
URLhttps://www.data.go.kr/data/15105819/fileData.do

Alerts

데이터 기준일 has constant value ""Constant
연번 is highly overall correlated with 특별관리시설 구분High correlation
특별관리시설 구분 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:16:22.951820
Analysis finished2023-12-12 19:16:23.611589
Duration0.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.5
Minimum1
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T04:16:23.709453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.95
Q145.75
median90.5
Q3135.25
95-th percentile171.05
Maximum180
Range179
Interquartile range (IQR)89.5

Descriptive statistics

Standard deviation52.105662
Coefficient of variation (CV)0.57575317
Kurtosis-1.2
Mean90.5
Median Absolute Deviation (MAD)45
Skewness0
Sum16290
Variance2715
MonotonicityStrictly increasing
2023-12-13T04:16:23.887491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
115 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
120 1
 
0.6%
121 1
 
0.6%
122 1
 
0.6%
123 1
 
0.6%
124 1
 
0.6%
Other values (170) 170
94.4%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
180 1
0.6%
179 1
0.6%
178 1
0.6%
177 1
0.6%
176 1
0.6%
175 1
0.6%
174 1
0.6%
173 1
0.6%
172 1
0.6%
171 1
0.6%

특별관리시설 구분
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
도시철도시설
92 
지하연계복합건축물
22 
초고층건축물
19 
가스공급시설
16 
천연가스 공급망시설
15 
Other values (2)
16 

Length

Max length10
Median length6
Mean length6.6277778
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도시철도시설 92
51.1%
지하연계복합건축물 22
 
12.2%
초고층건축물 19
 
10.6%
가스공급시설 16
 
8.9%
천연가스 공급망시설 15
 
8.3%
영화상영관 13
 
7.2%
국가산업단지 3
 
1.7%

Length

2023-12-13T04:16:24.060297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:16:24.197843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도시철도시설 92
47.2%
지하연계복합건축물 22
 
11.3%
초고층건축물 19
 
9.7%
가스공급시설 16
 
8.2%
천연가스 15
 
7.7%
공급망시설 15
 
7.7%
영화상영관 13
 
6.7%
국가산업단지 3
 
1.5%

대상명
Text

Distinct179
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T04:16:24.474254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length18
Mean length7.7666667
Min length3

Characters and Unicode

Total characters1398
Distinct characters218
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)98.9%

Sample

1st rowCGV연수역
2nd rowCGV연수
3rd row메가박스 논현
4th rowCGV계양
5th row메가박스 송도
ValueCountFrequency (%)
청라 6
 
2.7%
cng충전소 4
 
1.8%
롯데시네마 4
 
1.8%
인천그린에너지㈜ 2
 
0.9%
㈜삼천리 2
 
0.9%
인천교통공사 2
 
0.9%
인천지역본부 2
 
0.9%
송도 2
 
0.9%
원인재역 2
 
0.9%
메가박스 2
 
0.9%
Other values (187) 191
87.2%
2023-12-13T04:16:24.961890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 97
 
6.9%
) 49
 
3.5%
( 48
 
3.4%
41
 
2.9%
인 36
 
2.6%
동 34
 
2.4%
천 31
 
2.2%
G 29
 
2.1%
공 21
 
1.5%
1 21
 
1.5%
Other values (208) 991
70.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1099
78.6%
Uppercase Letter 90
 
6.4%
Decimal Number 64
 
4.6%
Close Punctuation 49
 
3.5%
Open Punctuation 48
 
3.4%
Space Separator 41
 
2.9%
Other Symbol 6
 
0.4%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 97
 
8.8%
인 36
 
3.3%
동 34
 
3.1%
천 31
 
2.8%
공 21
 
1.9%
청 18
 
1.6%
스 18
 
1.6%
국 18
 
1.6%
지 17
 
1.5%
트 17
 
1.5%
Other values (186) 792
72.1%
Decimal Number
ValueCountFrequency (%)
1 21
32.8%
2 12
18.8%
0 10
15.6%
3 7
 
10.9%
4 4
 
6.2%
5 3
 
4.7%
6 3
 
4.7%
7 2
 
3.1%
9 2
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
G 29
32.2%
C 21
23.3%
N 13
14.4%
S 11
 
12.2%
V 11
 
12.2%
B 2
 
2.2%
A 2
 
2.2%
E 1
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Space Separator
ValueCountFrequency (%)
41
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 6
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1105
79.0%
Common 203
 
14.5%
Latin 90
 
6.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 97
 
8.8%
인 36
 
3.3%
동 34
 
3.1%
천 31
 
2.8%
공 21
 
1.9%
청 18
 
1.6%
스 18
 
1.6%
국 18
 
1.6%
지 17
 
1.5%
트 17
 
1.5%
Other values (187) 798
72.2%
Common
ValueCountFrequency (%)
) 49
24.1%
( 48
23.6%
41
20.2%
1 21
10.3%
2 12
 
5.9%
0 10
 
4.9%
3 7
 
3.4%
4 4
 
2.0%
5 3
 
1.5%
6 3
 
1.5%
Other values (3) 5
 
2.5%
Latin
ValueCountFrequency (%)
G 29
32.2%
C 21
23.3%
N 13
14.4%
S 11
 
12.2%
V 11
 
12.2%
B 2
 
2.2%
A 2
 
2.2%
E 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1099
78.6%
ASCII 293
 
21.0%
None 6
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 97
 
8.8%
인 36
 
3.3%
동 34
 
3.1%
천 31
 
2.8%
공 21
 
1.9%
청 18
 
1.6%
스 18
 
1.6%
국 18
 
1.6%
지 17
 
1.5%
트 17
 
1.5%
Other values (186) 792
72.1%
ASCII
ValueCountFrequency (%)
) 49
16.7%
( 48
16.4%
41
14.0%
G 29
9.9%
1 21
7.2%
C 21
7.2%
N 13
 
4.4%
2 12
 
4.1%
S 11
 
3.8%
V 11
 
3.8%
Other values (11) 37
12.6%
None
ValueCountFrequency (%)
㈜ 6
100.0%

주소
Text

Distinct160
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T04:16:25.434366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length13.194444
Min length7

Characters and Unicode

Total characters2375
Distinct characters171
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

Unique145 ?
Unique (%)80.6%

Sample

1st row연수구 벚꽃로 106
2nd row연수구 청능대로 210
3rd row남동구 논고개로 61
4th row계양구 장제로 738
5th row연수구 송도과학로16번길 33-4
ValueCountFrequency (%)
서구 43
 
7.8%
연수구 36
 
6.5%
남동구 24
 
4.4%
부평구 24
 
4.4%
중구 21
 
3.8%
미추홀구 20
 
3.6%
계양구 9
 
1.6%
경인로 8
 
1.5%
송도동 7
 
1.3%
372 5
 
0.9%
Other values (273) 354
64.2%
2023-12-13T04:16:26.079545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
378
 
15.9%
구 185
 
7.8%
로 116
 
4.9%
1 104
 
4.4%
동 102
 
4.3%
2 101
 
4.3%
4 75
 
3.2%
5 67
 
2.8%
7 63
 
2.7%
- 61
 
2.6%
Other values (161) 1123
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1267
53.3%
Decimal Number 650
27.4%
Space Separator 378
 
15.9%
Dash Punctuation 61
 
2.6%
Open Punctuation 7
 
0.3%
Close Punctuation 7
 
0.3%
Other Punctuation 4
 
0.2%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 185
 
14.6%
로 116
 
9.2%
동 102
 
8.1%
서 51
 
4.0%
수 45
 
3.6%
연 42
 
3.3%
번 33
 
2.6%
부 32
 
2.5%
길 31
 
2.4%
대 31
 
2.4%
Other values (144) 599
47.3%
Decimal Number
ValueCountFrequency (%)
1 104
16.0%
2 101
15.5%
4 75
11.5%
5 67
10.3%
7 63
9.7%
3 61
9.4%
9 53
8.2%
6 50
7.7%
8 40
 
6.2%
0 36
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 2
50.0%
/ 2
50.0%
Space Separator
ValueCountFrequency (%)
378
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 61
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1267
53.3%
Common 1107
46.6%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 185
 
14.6%
로 116
 
9.2%
동 102
 
8.1%
서 51
 
4.0%
수 45
 
3.6%
연 42
 
3.3%
번 33
 
2.6%
부 32
 
2.5%
길 31
 
2.4%
대 31
 
2.4%
Other values (144) 599
47.3%
Common
ValueCountFrequency (%)
378
34.1%
1 104
 
9.4%
2 101
 
9.1%
4 75
 
6.8%
5 67
 
6.1%
7 63
 
5.7%
- 61
 
5.5%
3 61
 
5.5%
9 53
 
4.8%
6 50
 
4.5%
Other values (6) 94
 
8.5%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1267
53.3%
ASCII 1108
46.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
378
34.1%
1 104
 
9.4%
2 101
 
9.1%
4 75
 
6.8%
5 67
 
6.0%
7 63
 
5.7%
- 61
 
5.5%
3 61
 
5.5%
9 53
 
4.8%
6 50
 
4.5%
Other values (7) 95
 
8.6%
Hangul
ValueCountFrequency (%)
구 185
 
14.6%
로 116
 
9.2%
동 102
 
8.1%
서 51
 
4.0%
수 45
 
3.6%
연 42
 
3.3%
번 33
 
2.6%
부 32
 
2.5%
길 31
 
2.4%
대 31
 
2.4%
Other values (144) 599
47.3%

관할소방서
Categorical

Distinct12
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
서부
33 
부평
24 
송도
23 
공단
21 
미추홀
20 
Other values (7)
59 

Length

Max length9
Median length2
Mean length2.15
Min length2

Unique

Unique2 ?
Unique (%)1.1%

Sample

1st row공단
2nd row공단
3rd row공단
4th row계양
5th row송도

Common Values

ValueCountFrequency (%)
서부 33
18.3%
부평 24
13.3%
송도 23
12.8%
공단 21
11.7%
미추홀 20
11.1%
남동 17
9.4%
영종 14
7.8%
계양 9
 
5.0%
검단 9
 
5.0%
중부 8
 
4.4%
Other values (2) 2
 
1.1%

Length

2023-12-13T04:16:26.268650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서부 33
18.3%
부평 24
13.3%
송도 23
12.8%
공단 21
11.7%
미추홀 20
11.1%
남동 17
9.4%
영종 14
7.8%
계양 9
 
5.0%
검단 9
 
5.0%
중부 8
 
4.4%
Other values (2) 2
 
1.1%

데이터 기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2023-01-01 00:00:00
Maximum2023-01-01 00:00:00
2023-12-13T04:16:26.396077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:16:26.530550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T04:16:23.284774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:16:26.629569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번특별관리시설 구분관할소방서
연번1.0000.8900.722
특별관리시설 구분0.8901.0000.551
관할소방서0.7220.5511.000
2023-12-13T04:16:26.738012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특별관리시설 구분관할소방서
특별관리시설 구분1.0000.303
관할소방서0.3031.000
2023-12-13T04:16:26.822890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번특별관리시설 구분관할소방서
연번1.0000.7230.405
특별관리시설 구분0.7231.0000.303
관할소방서0.4050.3031.000

Missing values

2023-12-13T04:16:23.430201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:16:23.566195image/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

연번특별관리시설 구분대상명주소관할소방서데이터 기준일
01영화상영관CGV연수역연수구 벚꽃로 106공단2023-01-01
12영화상영관CGV연수연수구 청능대로 210공단2023-01-01
23영화상영관메가박스 논현남동구 논고개로 61공단2023-01-01
34영화상영관CGV계양계양구 장제로 738계양2023-01-01
45영화상영관메가박스 송도연수구 송도과학로16번길 33-4송도2023-01-01
56영화상영관롯데시네마 아시아드경기장서구 봉수대로 806서부2023-01-01
67영화상영관CGV청라서구 청라루비로 76서부2023-01-01
78영화상영관CGV인천남동구 예술로 198남동2023-01-01
89영화상영관CGV부평부평구 마장로 489부평2023-01-01
910영화상영관롯데시네마 부평부평구 대정로 66부평2023-01-01
연번특별관리시설 구분대상명주소관할소방서데이터 기준일
170171가스공급시설시영운수서구 보도진로18번길 24-1서부2023-01-01
171172가스공급시설세운산업(주) 장수CNG충전소남동구 장수동 411남동2023-01-01
172173가스공급시설서창공영차고지 CNG충전소남동구 서창동 729남동2023-01-01
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