Overview

Dataset statistics

Number of variables7
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory62.7 B

Variable types

Numeric1
Categorical4
Text2

Dataset

Description광주 소방헬기 도심지역 이착륙장 현황(관리번호, 위치 등) 등 정보를 제공합니다.- 소방서 별, 관리번호, 위치 등 자료 등 자료입니다.
Author광주광역시
URLhttps://www.data.go.kr/data/15046186/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
상태 is highly overall correlated with 비고High correlation
비고 is highly overall correlated with 상태High correlation
연번 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
관리번호 has unique valuesUnique
위치 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:47:11.095836
Analysis finished2023-12-12 18:47:11.924194
Duration0.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  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
2023-12-13T03:47:12.015067image/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
2023-12-13T03:47:12.391335image/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%

구분
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Memory size316.0 B
(광산구)
7 
(북구)
5 
(동구)
3 
(남구)
2 
동부소방서
1 
Other values (5)
5 

Length

Max length5
Median length5
Mean length4.5217391
Min length4

Unique

Unique6 ?
Unique (%)26.1%

Sample

1st row동부소방서
2nd row(동구)
3rd row(동구)
4th row(동구)
5th row서부소방서

Common Values

ValueCountFrequency (%)
(광산구) 7
30.4%
(북구) 5
21.7%
(동구) 3
13.0%
(남구) 2
 
8.7%
동부소방서 1
 
4.3%
서부소방서 1
 
4.3%
(서구) 1
 
4.3%
남부소방서 1
 
4.3%
북부소방서 1
 
4.3%
광산소방서 1
 
4.3%

Length

2023-12-13T03:47:12.658785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:47:12.870583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광산구 7
30.4%
북구 5
21.7%
동구 3
13.0%
남구 2
 
8.7%
동부소방서 1
 
4.3%
서부소방서 1
 
4.3%
서구 1
 
4.3%
남부소방서 1
 
4.3%
북부소방서 1
 
4.3%
광산소방서 1
 
4.3%

관리번호
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-13T03:47:13.180139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters46
Distinct characters13
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

Unique23 ?
Unique (%)100.0%

Sample

1st row동1
2nd row동2
3rd row동3
4th row동4
5th row서1
ValueCountFrequency (%)
동1 1
 
4.3%
북4 1
 
4.3%
광7 1
 
4.3%
광6 1
 
4.3%
광5 1
 
4.3%
광4 1
 
4.3%
광3 1
 
4.3%
광2 1
 
4.3%
광1 1
 
4.3%
북6 1
 
4.3%
Other values (13) 13
56.5%
2023-12-13T03:47:14.193126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
광 8
17.4%
북 6
13.0%
1 5
10.9%
2 5
10.9%
동 4
8.7%
3 4
8.7%
4 3
 
6.5%
남 3
 
6.5%
서 2
 
4.3%
5 2
 
4.3%
Other values (3) 4
8.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23
50.0%
Decimal Number 23
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
21.7%
2 5
21.7%
3 4
17.4%
4 3
13.0%
5 2
 
8.7%
6 2
 
8.7%
7 1
 
4.3%
8 1
 
4.3%
Other Letter
ValueCountFrequency (%)
광 8
34.8%
북 6
26.1%
동 4
17.4%
남 3
 
13.0%
서 2
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23
50.0%
Common 23
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
21.7%
2 5
21.7%
3 4
17.4%
4 3
13.0%
5 2
 
8.7%
6 2
 
8.7%
7 1
 
4.3%
8 1
 
4.3%
Hangul
ValueCountFrequency (%)
광 8
34.8%
북 6
26.1%
동 4
17.4%
남 3
 
13.0%
서 2
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23
50.0%
ASCII 23
50.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
광 8
34.8%
북 6
26.1%
동 4
17.4%
남 3
 
13.0%
서 2
 
8.7%
ASCII
ValueCountFrequency (%)
1 5
21.7%
2 5
21.7%
3 4
17.4%
4 3
13.0%
5 2
 
8.7%
6 2
 
8.7%
7 1
 
4.3%
8 1
 
4.3%

위치
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-13T03:47:14.564002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length14.434783
Min length10

Characters and Unicode

Total characters332
Distinct characters102
Distinct categories5 ?
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 row운림동 증심사 관리사무소 주차장
2nd row지산동 지산유원지 정류장 건너편 공지
3rd row월남동 녹동마을 입구(주유소) 공지
4th row용현동 용현정수장 공지
5th row화정4동 월드컵경기장 주차장
ValueCountFrequency (%)
운동장 8
 
10.5%
주차장 5
 
6.6%
공지 5
 
6.6%
잔디밭 3
 
3.9%
건너편 2
 
2.6%
수련원 1
 
1.3%
운정동 1
 
1.3%
광주시립묘지 1
 
1.3%
우치동 1
 
1.3%
평동공단 1
 
1.3%
Other values (48) 48
63.2%
2023-12-13T03:47:15.260922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53
 
16.0%
동 35
 
10.5%
장 18
 
5.4%
주 12
 
3.6%
지 10
 
3.0%
운 10
 
3.0%
공 9
 
2.7%
교 9
 
2.7%
학 8
 
2.4%
원 6
 
1.8%
Other values (92) 162
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 273
82.2%
Space Separator 53
 
16.0%
Open Punctuation 2
 
0.6%
Close Punctuation 2
 
0.6%
Decimal Number 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 35
 
12.8%
장 18
 
6.6%
주 12
 
4.4%
지 10
 
3.7%
운 10
 
3.7%
공 9
 
3.3%
교 9
 
3.3%
학 8
 
2.9%
원 6
 
2.2%
소 5
 
1.8%
Other values (88) 151
55.3%
Space Separator
ValueCountFrequency (%)
53
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Decimal Number
ValueCountFrequency (%)
4 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 273
82.2%
Common 59
 
17.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 35
 
12.8%
장 18
 
6.6%
주 12
 
4.4%
지 10
 
3.7%
운 10
 
3.7%
공 9
 
3.3%
교 9
 
3.3%
학 8
 
2.9%
원 6
 
2.2%
소 5
 
1.8%
Other values (88) 151
55.3%
Common
ValueCountFrequency (%)
53
89.8%
( 2
 
3.4%
) 2
 
3.4%
4 2
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 273
82.2%
ASCII 59
 
17.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53
89.8%
( 2
 
3.4%
) 2
 
3.4%
4 2
 
3.4%
Hangul
ValueCountFrequency (%)
동 35
 
12.8%
장 18
 
6.6%
주 12
 
4.4%
지 10
 
3.7%
운 10
 
3.7%
공 9
 
3.3%
교 9
 
3.3%
학 8
 
2.9%
원 6
 
2.2%
소 5
 
1.8%
Other values (88) 151
55.3%

상태
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
△
12 
○
11 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row○
2nd row△
3rd row○
4th row△
5th row○

Common Values

ValueCountFrequency (%)
△ 12
52.2%
○ 11
47.8%

Length

2023-12-13T03:47:15.486528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:47:15.631006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
△ 12
52.2%
○ 11
47.8%

비고
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
11 
먼지주의
11 
공간협소
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row<NA>
2nd row먼지주의
3rd row<NA>
4th row먼지주의
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 11
47.8%
먼지주의 11
47.8%
공간협소 1
 
4.3%

Length

2023-12-13T03:47:15.793455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:47:15.937910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 11
47.8%
먼지주의 11
47.8%
공간협소 1
 
4.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-10-01
23 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-10-01
2nd row2023-10-01
3rd row2023-10-01
4th row2023-10-01
5th row2023-10-01

Common Values

ValueCountFrequency (%)
2023-10-01 23
100.0%

Length

2023-12-13T03:47:16.067374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:47:16.189093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-10-01 23
100.0%

Interactions

2023-12-13T03:47:11.514137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:47:16.307268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분관리번호위치상태비고
연번1.0000.9281.0001.0000.4880.000
구분0.9281.0001.0001.0000.0000.000
관리번호1.0001.0001.0001.0001.0001.000
위치1.0001.0001.0001.0001.0001.000
상태0.4880.0001.0001.0001.000NaN
비고0.0000.0001.0001.000NaN1.000
2023-12-13T03:47:16.487177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분상태비고
구분1.0000.0000.000
상태0.0001.0001.000
비고0.0001.0001.000
2023-12-13T03:47:16.635330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분상태비고
연번1.0000.5060.4390.000
구분0.5061.0000.0000.000
상태0.4390.0001.0001.000
비고0.0000.0001.0001.000

Missing values

2023-12-13T03:47:11.693150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:47:11.859331image/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

연번구분관리번호위치상태비고데이터기준일자
01동부소방서동1운림동 증심사 관리사무소 주차장○<NA>2023-10-01
12(동구)동2지산동 지산유원지 정류장 건너편 공지△먼지주의2023-10-01
23(동구)동3월남동 녹동마을 입구(주유소) 공지○<NA>2023-10-01
34(동구)동4용현동 용현정수장 공지△먼지주의2023-10-01
45서부소방서서1화정4동 월드컵경기장 주차장○<NA>2023-10-01
56(서구)서2매월동 대동고등학교 운동장△먼지주의2023-10-01
67남부소방서남1효덕동 광주대학교 운동장△먼지주의2023-10-01
78(남구)남2송하동 인성고등학교 운동장△먼지주의2023-10-01
89(남구)남3대촌동 대촌중학교 운동장△먼지주의2023-10-01
910북부소방서북1충효동 충효초등학교 운동장△먼지주의2023-10-01
연번구분관리번호위치상태비고데이터기준일자
1314(북구)북5우치동 우치공원 주차장○<NA>2023-10-01
1415(북구)북6매곡동 국립광주박물관 주차장○<NA>2023-10-01
1516광산소방서광1소촌동 광주소방학교 훈련탑 잔디밭○<NA>2023-10-01
1617(광산구)광2평동 평동공단 공지△먼지주의2023-10-01
1718(광산구)광3명화동 제병합동훈련장○<NA>2023-10-01
1819(광산구)광4삼거동 금북중학교 운동장○<NA>2023-10-01
1920(광산구)광5본량동 본량중학교앞 잔디밭○<NA>2023-10-01
2021(광산구)광6임곡동 용진교 건너편 공지△먼지주의2023-10-01
2122(광산구)광7하남동 광주전자(주) 헬기패드장○<NA>2023-10-01
2223(광산구)광8쌍암동 체육공원 운동장△먼지주의2023-10-01