Overview

Dataset statistics

Number of variables6
Number of observations67
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory52.9 B

Variable types

Numeric3
Text2
Categorical1

Dataset

Description대전광역시 중구에 위치한 안전지킴이집 정보입니다.This is information about the safety guard house located in Jung-gu, Daejeon.
Author대전광역시 중구
URLhttps://www.data.go.kr/data/15126561/fileData.do

Alerts

연번 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
명칭 has unique valuesUnique
도로명주소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2024-03-15 00:22:33.575368
Analysis finished2024-03-15 00:22:37.353086
Duration3.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34
Minimum1
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size731.0 B
2024-03-15T09:22:37.571052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.3
Q117.5
median34
Q350.5
95-th percentile63.7
Maximum67
Range66
Interquartile range (IQR)33

Descriptive statistics

Standard deviation19.485037
Coefficient of variation (CV)0.57308932
Kurtosis-1.2
Mean34
Median Absolute Deviation (MAD)17
Skewness0
Sum2278
Variance379.66667
MonotonicityStrictly increasing
2024-03-15T09:22:38.110728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
44 1
 
1.5%
50 1
 
1.5%
49 1
 
1.5%
48 1
 
1.5%
47 1
 
1.5%
46 1
 
1.5%
45 1
 
1.5%
43 1
 
1.5%
2 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
67 1
1.5%
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%
58 1
1.5%

명칭
Text

UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size664.0 B
2024-03-15T09:22:39.107950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length8.6567164
Min length3

Characters and Unicode

Total characters580
Distinct characters140
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

Unique67 ?
Unique (%)100.0%

Sample

1st row영진마트
2nd row석교문구
3rd row똘똘이문구
4th row야쿠르트(대흥점)
5th rowCU편의점(문창대로점)
ValueCountFrequency (%)
gs25 8
 
8.7%
cu 7
 
7.6%
이마트24 2
 
2.2%
세븐일레븐 2
 
2.2%
영진마트 1
 
1.1%
유천중앙점 1
 
1.1%
용두드림점 1
 
1.1%
세븐일레븐대전문화우리점 1
 
1.1%
태평점 1
 
1.1%
서대전역사거리점 1
 
1.1%
Other values (67) 67
72.8%
2024-03-15T09:22:40.370219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 46
 
7.9%
25
 
4.3%
대 21
 
3.6%
문 19
 
3.3%
븐 16
 
2.8%
전 15
 
2.6%
화 15
 
2.6%
( 14
 
2.4%
) 14
 
2.4%
2 14
 
2.4%
Other values (130) 381
65.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 451
77.8%
Uppercase Letter 48
 
8.3%
Decimal Number 28
 
4.8%
Space Separator 25
 
4.3%
Open Punctuation 14
 
2.4%
Close Punctuation 14
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 46
 
10.2%
대 21
 
4.7%
문 19
 
4.2%
븐 16
 
3.5%
전 15
 
3.3%
화 15
 
3.3%
트 14
 
3.1%
이 12
 
2.7%
리 11
 
2.4%
마 11
 
2.4%
Other values (120) 271
60.1%
Uppercase Letter
ValueCountFrequency (%)
G 12
25.0%
S 12
25.0%
C 12
25.0%
U 12
25.0%
Decimal Number
ValueCountFrequency (%)
2 14
50.0%
5 11
39.3%
4 3
 
10.7%
Space Separator
ValueCountFrequency (%)
25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 451
77.8%
Common 81
 
14.0%
Latin 48
 
8.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 46
 
10.2%
대 21
 
4.7%
문 19
 
4.2%
븐 16
 
3.5%
전 15
 
3.3%
화 15
 
3.3%
트 14
 
3.1%
이 12
 
2.7%
리 11
 
2.4%
마 11
 
2.4%
Other values (120) 271
60.1%
Common
ValueCountFrequency (%)
25
30.9%
( 14
17.3%
) 14
17.3%
2 14
17.3%
5 11
13.6%
4 3
 
3.7%
Latin
ValueCountFrequency (%)
G 12
25.0%
S 12
25.0%
C 12
25.0%
U 12
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 451
77.8%
ASCII 129
 
22.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 46
 
10.2%
대 21
 
4.7%
문 19
 
4.2%
븐 16
 
3.5%
전 15
 
3.3%
화 15
 
3.3%
트 14
 
3.1%
이 12
 
2.7%
리 11
 
2.4%
마 11
 
2.4%
Other values (120) 271
60.1%
ASCII
ValueCountFrequency (%)
25
19.4%
( 14
10.9%
) 14
10.9%
2 14
10.9%
G 12
9.3%
S 12
9.3%
C 12
9.3%
U 12
9.3%
5 11
8.5%
4 3
 
2.3%

도로명주소
Text

UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size664.0 B
2024-03-15T09:22:41.315066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length14.835821
Min length8

Characters and Unicode

Total characters994
Distinct characters87
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

Unique67 ?
Unique (%)100.0%

Sample

1st row중구 보문로 67
2nd row중구 대종로240번길 9
3rd row중구 문창로50번길 3
4th row문창로69번길 22
5th row중구 대전천서로 345
ValueCountFrequency (%)
중구 66
28.6%
대전광역시 26
 
11.3%
대종로 6
 
2.6%
선화서로 4
 
1.7%
8 3
 
1.3%
대둔산로 3
 
1.3%
송리로 3
 
1.3%
계백로1615번길 3
 
1.3%
목동로 2
 
0.9%
15 2
 
0.9%
Other values (103) 113
48.9%
2024-03-15T09:22:42.758129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
164
 
16.5%
중 68
 
6.8%
로 67
 
6.7%
구 66
 
6.6%
1 52
 
5.2%
대 45
 
4.5%
전 27
 
2.7%
광 26
 
2.6%
5 26
 
2.6%
역 26
 
2.6%
Other values (77) 427
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 554
55.7%
Decimal Number 249
25.1%
Space Separator 164
 
16.5%
Open Punctuation 8
 
0.8%
Close Punctuation 8
 
0.8%
Dash Punctuation 8
 
0.8%
Other Punctuation 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 68
12.3%
로 67
12.1%
구 66
11.9%
대 45
 
8.1%
전 27
 
4.9%
광 26
 
4.7%
역 26
 
4.7%
시 26
 
4.7%
번 21
 
3.8%
길 21
 
3.8%
Other values (61) 161
29.1%
Decimal Number
ValueCountFrequency (%)
1 52
20.9%
5 26
10.4%
0 24
9.6%
3 24
9.6%
2 24
9.6%
6 23
9.2%
4 21
8.4%
7 20
 
8.0%
9 18
 
7.2%
8 17
 
6.8%
Other Punctuation
ValueCountFrequency (%)
@ 2
66.7%
, 1
33.3%
Space Separator
ValueCountFrequency (%)
164
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 554
55.7%
Common 440
44.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 68
12.3%
로 67
12.1%
구 66
11.9%
대 45
 
8.1%
전 27
 
4.9%
광 26
 
4.7%
역 26
 
4.7%
시 26
 
4.7%
번 21
 
3.8%
길 21
 
3.8%
Other values (61) 161
29.1%
Common
ValueCountFrequency (%)
164
37.3%
1 52
 
11.8%
5 26
 
5.9%
0 24
 
5.5%
3 24
 
5.5%
2 24
 
5.5%
6 23
 
5.2%
4 21
 
4.8%
7 20
 
4.5%
9 18
 
4.1%
Other values (6) 44
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 554
55.7%
ASCII 440
44.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
164
37.3%
1 52
 
11.8%
5 26
 
5.9%
0 24
 
5.5%
3 24
 
5.5%
2 24
 
5.5%
6 23
 
5.2%
4 21
 
4.8%
7 20
 
4.5%
9 18
 
4.1%
Other values (6) 44
 
10.0%
Hangul
ValueCountFrequency (%)
중 68
12.3%
로 67
12.1%
구 66
11.9%
대 45
 
8.1%
전 27
 
4.9%
광 26
 
4.7%
역 26
 
4.7%
시 26
 
4.7%
번 21
 
3.8%
길 21
 
3.8%
Other values (61) 161
29.1%

위도
Real number (ℝ)

UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.319211
Minimum36.266946
Maximum36.341437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size731.0 B
2024-03-15T09:22:43.174285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.266946
5-th percentile36.302107
Q136.31179
median36.321036
Q336.326382
95-th percentile36.33624
Maximum36.341437
Range0.074491
Interquartile range (IQR)0.01459209

Descriptive statistics

Standard deviation0.011935909
Coefficient of variation (CV)0.000328639
Kurtosis4.4063628
Mean36.319211
Median Absolute Deviation (MAD)0.006835
Skewness-1.1694952
Sum2433.3871
Variance0.00014246593
MonotonicityNot monotonic
2024-03-15T09:22:43.628737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.313094 1
 
1.5%
36.32276997 1
 
1.5%
36.3168012 1
 
1.5%
36.32359506 1
 
1.5%
36.31157525 1
 
1.5%
36.32667366 1
 
1.5%
36.32128845 1
 
1.5%
36.32666867 1
 
1.5%
36.31713813 1
 
1.5%
36.310189 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
36.266946 1
1.5%
36.296202 1
1.5%
36.299899 1
1.5%
36.301853 1
1.5%
36.30269926 1
1.5%
36.306713 1
1.5%
36.30696 1
1.5%
36.30726 1
1.5%
36.308294 1
1.5%
36.30842 1
1.5%
ValueCountFrequency (%)
36.341437 1
1.5%
36.340968 1
1.5%
36.3378801 1
1.5%
36.33656 1
1.5%
36.3354935 1
1.5%
36.33498 1
1.5%
36.334928 1
1.5%
36.334689 1
1.5%
36.33356264 1
1.5%
36.330706 1
1.5%

경도
Real number (ℝ)

UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.41036
Minimum127.38553
Maximum127.4478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size731.0 B
2024-03-15T09:22:44.112505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.38553
5-th percentile127.39028
Q1127.39871
median127.40832
Q3127.41948
95-th percentile127.43513
Maximum127.4478
Range0.062273
Interquartile range (IQR)0.02077185

Descriptive statistics

Standard deviation0.015048615
Coefficient of variation (CV)0.00011811139
Kurtosis-0.60577671
Mean127.41036
Median Absolute Deviation (MAD)0.010376
Skewness0.43960445
Sum8536.4943
Variance0.00022646081
MonotonicityNot monotonic
2024-03-15T09:22:44.677145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.433631 1
 
1.5%
127.4341267 1
 
1.5%
127.3978176 1
 
1.5%
127.4145889 1
 
1.5%
127.411215 1
 
1.5%
127.392788 1
 
1.5%
127.4055112 1
 
1.5%
127.4290253 1
 
1.5%
127.4264966 1
 
1.5%
127.441172 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
127.385531 1
1.5%
127.385767 1
1.5%
127.3859672 1
1.5%
127.389827 1
1.5%
127.391335 1
1.5%
127.3925464 1
1.5%
127.392689 1
1.5%
127.392788 1
1.5%
127.393475 1
1.5%
127.394134 1
1.5%
ValueCountFrequency (%)
127.447804 1
1.5%
127.441172 1
1.5%
127.435516 1
1.5%
127.4354541 1
1.5%
127.434376 1
1.5%
127.4341267 1
1.5%
127.433631 1
1.5%
127.432444 1
1.5%
127.4317303 1
1.5%
127.4311501 1
1.5%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size664.0 B
아동안전지킴이집
41 
여성안전지킴이집
26 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아동안전지킴이집 41
61.2%
여성안전지킴이집 26
38.8%

Length

2024-03-15T09:22:44.936242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:22:45.117079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아동안전지킴이집 41
61.2%
여성안전지킴이집 26
38.8%

Interactions

2024-03-15T09:22:35.784863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:22:34.048903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:22:34.840930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:22:36.105122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:22:34.310563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:22:35.160635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:22:36.465049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:22:34.578540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:22:35.474046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T09:22:45.236400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번명칭도로명주소위도경도구분
연번1.0001.0001.0000.5230.6600.999
명칭1.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.000
위도0.5231.0001.0001.0000.6740.263
경도0.6601.0001.0000.6741.0000.000
구분0.9991.0001.0000.2630.0001.000
2024-03-15T09:22:45.425928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도구분
연번1.0000.291-0.0270.907
위도0.2911.0000.2950.184
경도-0.0270.2951.0000.000
구분0.9070.1840.0001.000

Missing values

2024-03-15T09:22:36.819000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T09:22:37.213442image/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

연번명칭도로명주소위도경도구분
01영진마트중구 보문로 6736.313094127.433631아동안전지킴이집
12석교문구중구 대종로240번길 936.310189127.441172아동안전지킴이집
23똘똘이문구중구 문창로50번길 336.317906127.434376아동안전지킴이집
34야쿠르트(대흥점)문창로69번길 2236.31904127.432444아동안전지킴이집
45CU편의점(문창대로점)중구 대전천서로 34536.321142127.435516아동안전지킴이집
56모암로가게중구 모암로 3436.301853127.447804아동안전지킴이집
67프라임센트럴마트중구 서문로 9636.317599127.41123아동안전지킴이집
78김가네김밥집중구 문화로 27336.318057127.414658아동안전지킴이집
89새생명약국중구 대흥로 7236.323638127.42059아동안전지킴이집
910길조온누리약국중구 계룡로 852(삼성@ 상가 101호)36.324177127.406666아동안전지킴이집
연번명칭도로명주소위도경도구분
5758CU 대전대흥사랑점대전광역시 중구 대종로 42636.322999127.429406여성안전지킴이집
5859이마트24 대전부사점대전광역시 중구 보문로 8936.313307127.43115여성안전지킴이집
5960이마트24 대전부사신일점대전광역시 중구 대종로 295번길 1536.312005127.435454여성안전지킴이집
6061미니스톱 대전세이점대전광역시 중구 계백로 170936.321891127.409039여성안전지킴이집
6162CU 유천현대점대전광역시 중구 수침로 4136.322845127.399681여성안전지킴이집
6263GS25 문화삼거리점대전광역시 중구 천근로 836.310676127.401449여성안전지킴이집
6364GS25 문화주공점대전광역시 중구 송리로 4036.309336127.401434여성안전지킴이집
6465CU 산성타운점대전광역시 중구 대둔산로 37336.302699127.385967여성안전지킴이집
6566세븐일레븐 선화드림점대전광역시 중구 대종로 60536.335493127.416888여성안전지킴이집
6667CU 대전선화원룸점대전광역시 중구 선화서로 5236.328283127.418034여성안전지킴이집