Overview

Dataset statistics

Number of variables5
Number of observations51
Missing cells3
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory43.6 B

Variable types

Numeric1
Text2
Categorical2

Dataset

Description소방청에서 공식적으로 생산한 자료를 토대로 119특수구조단 바인더에서 보유하고 있는 화학 및 생화학테러의 테러대비 소방활동대책 계획입니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15081328/fileData.do

Alerts

연번 is highly overall correlated with 용도 and 1 other fieldsHigh correlation
용도 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
중요도등급 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 has 1 (2.0%) missing valuesMissing
테러취약 시설명 has 1 (2.0%) missing valuesMissing
소 재 지 has 1 (2.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 02:19:59.253442
Analysis finished2023-12-12 02:19:59.971057
Duration0.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)100.0%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean25.5
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T11:20:00.072456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.45
Q113.25
median25.5
Q337.75
95-th percentile47.55
Maximum50
Range49
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation14.57738
Coefficient of variation (CV)0.57166195
Kurtosis-1.2
Mean25.5
Median Absolute Deviation (MAD)12.5
Skewness0
Sum1275
Variance212.5
MonotonicityStrictly increasing
2023-12-12T11:20:00.256102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
2.0%
39 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (40) 40
78.4%
ValueCountFrequency (%)
1 1
2.0%
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

테러취약 시설명
Text

MISSING 

Distinct50
Distinct (%)100.0%
Missing1
Missing (%)2.0%
Memory size540.0 B
2023-12-12T11:20:00.522405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length6.22
Min length3

Characters and Unicode

Total characters311
Distinct characters113
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row대전예술의전당
2nd row국립중앙과학관
3rd row월드컵경기장
4th row롯데시네마 대전둔산
5th row한화이글스파크
ValueCountFrequency (%)
홈플러스 4
 
6.6%
대전역 2
 
3.3%
대전예술의전당 1
 
1.6%
중앙로지하상가 1
 
1.6%
중구청역 1
 
1.6%
서대전네거리역 1
 
1.6%
오룡역 1
 
1.6%
충남대학교병원 1
 
1.6%
학교법인 1
 
1.6%
을지학원 1
 
1.6%
Other values (47) 47
77.0%
2023-12-12T11:20:00.921681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 25
 
8.0%
대 17
 
5.5%
전 14
 
4.5%
11
 
3.5%
점 9
 
2.9%
스 8
 
2.6%
학 8
 
2.6%
이 7
 
2.3%
지 6
 
1.9%
원 6
 
1.9%
Other values (103) 200
64.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 288
92.6%
Space Separator 11
 
3.5%
Uppercase Letter 6
 
1.9%
Open Punctuation 3
 
1.0%
Close Punctuation 3
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 25
 
8.7%
대 17
 
5.9%
전 14
 
4.9%
점 9
 
3.1%
스 8
 
2.8%
학 8
 
2.8%
이 7
 
2.4%
지 6
 
2.1%
원 6
 
2.1%
중 5
 
1.7%
Other values (97) 183
63.5%
Uppercase Letter
ValueCountFrequency (%)
K 2
33.3%
T 2
33.3%
X 2
33.3%
Space Separator
ValueCountFrequency (%)
11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 288
92.6%
Common 17
 
5.5%
Latin 6
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 25
 
8.7%
대 17
 
5.9%
전 14
 
4.9%
점 9
 
3.1%
스 8
 
2.8%
학 8
 
2.8%
이 7
 
2.4%
지 6
 
2.1%
원 6
 
2.1%
중 5
 
1.7%
Other values (97) 183
63.5%
Common
ValueCountFrequency (%)
11
64.7%
( 3
 
17.6%
) 3
 
17.6%
Latin
ValueCountFrequency (%)
K 2
33.3%
T 2
33.3%
X 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 288
92.6%
ASCII 23
 
7.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 25
 
8.7%
대 17
 
5.9%
전 14
 
4.9%
점 9
 
3.1%
스 8
 
2.8%
학 8
 
2.8%
이 7
 
2.4%
지 6
 
2.1%
원 6
 
2.1%
중 5
 
1.7%
Other values (97) 183
63.5%
ASCII
ValueCountFrequency (%)
11
47.8%
( 3
 
13.0%
) 3
 
13.0%
K 2
 
8.7%
T 2
 
8.7%
X 2
 
8.7%

용도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size540.0 B
운수시설
26 
판매시설
15 
문화및집회시설
6 
의료시설
3 
<NA>
 
1

Length

Max length7
Median length4
Mean length4.3529412
Min length4

Unique

Unique1 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
운수시설 26
51.0%
판매시설 15
29.4%
문화및집회시설 6
 
11.8%
의료시설 3
 
5.9%
<NA> 1
 
2.0%

Length

2023-12-12T11:20:01.069970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:20:01.168503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운수시설 26
51.0%
판매시설 15
29.4%
문화및집회시설 6
 
11.8%
의료시설 3
 
5.9%
na 1
 
2.0%

중요도등급
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size540.0 B
C
28 
C
7 
A
6 
B
4 
B
3 
Other values (2)
3 

Length

Max length4
Median length1
Mean length1.2941176
Min length1

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
C 28
54.9%
C 7
 
13.7%
A 6
 
11.8%
B 4
 
7.8%
B 3
 
5.9%
A 2
 
3.9%
<NA> 1
 
2.0%

Length

2023-12-12T11:20:01.277229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:20:01.388798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 35
68.6%
a 8
 
15.7%
b 7
 
13.7%
na 1
 
2.0%

소 재 지
Text

MISSING 

Distinct50
Distinct (%)100.0%
Missing1
Missing (%)2.0%
Memory size540.0 B
2023-12-12T11:20:01.630993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length14.88
Min length5

Characters and Unicode

Total characters744
Distinct characters84
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row서구 둔산대로 135 (만년동)
2nd row유성구 대덕대로 481
3rd row유성구 월드컵대로 32
4th row서구 월평동 243번지
5th row중구 대종로 373
ValueCountFrequency (%)
지하 20
 
11.7%
서구 19
 
11.1%
중구 12
 
7.0%
동구 10
 
5.8%
유성구 9
 
5.3%
한밭대로 6
 
3.5%
중앙로 4
 
2.3%
계룡로 4
 
2.3%
계백로 2
 
1.2%
대덕대로 2
 
1.2%
Other values (78) 83
48.5%
2023-12-12T11:20:02.022655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
122
 
16.4%
구 52
 
7.0%
로 46
 
6.2%
동 45
 
6.0%
( 29
 
3.9%
) 29
 
3.9%
1 26
 
3.5%
지 24
 
3.2%
서 21
 
2.8%
하 21
 
2.8%
Other values (74) 329
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 420
56.5%
Decimal Number 140
 
18.8%
Space Separator 122
 
16.4%
Open Punctuation 29
 
3.9%
Close Punctuation 29
 
3.9%
Other Punctuation 3
 
0.4%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 52
 
12.4%
로 46
 
11.0%
동 45
 
10.7%
지 24
 
5.7%
서 21
 
5.0%
하 21
 
5.0%
중 21
 
5.0%
대 19
 
4.5%
유 12
 
2.9%
성 11
 
2.6%
Other values (59) 148
35.2%
Decimal Number
ValueCountFrequency (%)
1 26
18.6%
5 16
11.4%
3 15
10.7%
7 15
10.7%
8 14
10.0%
2 14
10.0%
4 13
9.3%
9 9
 
6.4%
6 9
 
6.4%
0 9
 
6.4%
Space Separator
ValueCountFrequency (%)
122
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 420
56.5%
Common 324
43.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 52
 
12.4%
로 46
 
11.0%
동 45
 
10.7%
지 24
 
5.7%
서 21
 
5.0%
하 21
 
5.0%
중 21
 
5.0%
대 19
 
4.5%
유 12
 
2.9%
성 11
 
2.6%
Other values (59) 148
35.2%
Common
ValueCountFrequency (%)
122
37.7%
( 29
 
9.0%
) 29
 
9.0%
1 26
 
8.0%
5 16
 
4.9%
3 15
 
4.6%
7 15
 
4.6%
8 14
 
4.3%
2 14
 
4.3%
4 13
 
4.0%
Other values (5) 31
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 420
56.5%
ASCII 324
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
122
37.7%
( 29
 
9.0%
) 29
 
9.0%
1 26
 
8.0%
5 16
 
4.9%
3 15
 
4.6%
7 15
 
4.6%
8 14
 
4.3%
2 14
 
4.3%
4 13
 
4.0%
Other values (5) 31
 
9.6%
Hangul
ValueCountFrequency (%)
구 52
 
12.4%
로 46
 
11.0%
동 45
 
10.7%
지 24
 
5.7%
서 21
 
5.0%
하 21
 
5.0%
중 21
 
5.0%
대 19
 
4.5%
유 12
 
2.9%
성 11
 
2.6%
Other values (59) 148
35.2%

Interactions

2023-12-12T11:19:59.542772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:20:02.130961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번테러취약 시설명용도중요도등급소 재 지
연번1.0001.0000.9460.7851.000
테러취약 시설명1.0001.0001.0001.0001.000
용도0.9461.0001.0000.7231.000
중요도등급0.7851.0000.7231.0001.000
소 재 지1.0001.0001.0001.0001.000
2023-12-12T11:20:02.228213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도중요도등급
용도1.0000.543
중요도등급0.5431.000
2023-12-12T11:20:02.382169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번용도중요도등급
연번1.0000.8080.537
용도0.8081.0000.543
중요도등급0.5370.5431.000

Missing values

2023-12-12T11:19:59.660615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:19:59.774311image/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.
2023-12-12T11:19:59.892282image/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

연번테러취약 시설명용도중요도등급소 재 지
01대전예술의전당문화및집회시설A서구 둔산대로 135 (만년동)
12국립중앙과학관문화및집회시설A유성구 대덕대로 481
23월드컵경기장문화및집회시설A유성구 월드컵대로 32
34롯데시네마 대전둔산문화및집회시설B서구 월평동 243번지
45한화이글스파크문화및집회시설B중구 대종로 373
56한국마사회 대전지사문화및집회시설C서구 월평중로 21
67복합터미널운수시설A동구 동서대로 1689
78대전역(KTX)운수시설A동구 정동
89서대전역(KTX)운수시설B중구 오류동 74-3
910판암역운수시설C동구 옥천로 지하 162(판암동)
연번테러취약 시설명용도중요도등급소 재 지
4142홈플러스 가오점판매시설C동구 은어송로 72
4243홈플러스 동대전점판매시설C동구 한밭대로 1282
4344중앙시장판매시설C동구 대전로 783
4445대전역 지하상가판매시설C동구 중앙로 218
4546세이브존아이엔씨판매시설C서구 둔산로 201(둔산동)
4647이마트둔산점판매시설C서구 둔산북로 41
4748이마트 트레이더스판매시설C서구 한밭대로 580
4849코스트코대전점판매시설C중구 오류로 41
4950홈플러스 문화점판매시설C중구 계백로 1690
50<NA><NA><NA><NA><NA>