Overview

Dataset statistics

Number of variables7
Number of observations48
Missing cells28
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory58.8 B

Variable types

Categorical3
Text3
DateTime1

Dataset

Description밀양시 업소별 반려동물과 함께 출입가능한 업소의 구분, 장소, 주소, 내방조건, 관리기관명, 관리기관전화 데이터가 포함되어 있습니다.
Author경상남도 밀양시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15101152

Alerts

관리기관명 has constant value ""Constant
관리기관전화 has constant value ""Constant
데이터기준일자 has constant value ""Constant
내방조건 has 28 (58.3%) missing valuesMissing
장소 has unique valuesUnique

Reproduction

Analysis started2024-03-13 00:11:46.316750
Analysis finished2024-03-13 00:11:46.669732
Duration0.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct6
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size516.0 B
야영장/캠핑장
13 
카페
10 
공원/기타관광지
8 
펜션
7 
사찰/유적지
5 

Length

Max length8
Median length7
Mean length4.9791667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
야영장/캠핑장 13
27.1%
카페 10
20.8%
공원/기타관광지 8
16.7%
펜션 7
14.6%
사찰/유적지 5
 
10.4%
자연경관 5
 
10.4%

Length

2024-03-13T09:11:46.721637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:11:46.803687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
야영장/캠핑장 13
27.1%
카페 10
20.8%
공원/기타관광지 8
16.7%
펜션 7
14.6%
사찰/유적지 5
 
10.4%
자연경관 5
 
10.4%

장소
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-03-13T09:11:46.968981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length10
Mean length6.3125
Min length3

Characters and Unicode

Total characters303
Distinct characters144
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

Unique48 ?
Unique (%)100.0%

Sample

1st row초록발자국펜션
2nd row쿠쿠숲펜션
3rd row소나무펜션
4th row별아래펜션
5th row밀양테마펜션
ValueCountFrequency (%)
초록발자국펜션 1
 
1.9%
달빛쌈지공원 1
 
1.9%
사과향기캠핑장 1
 
1.9%
밀양아리랑대공원 1
 
1.9%
밀양유원지오토캠핑장 1
 
1.9%
밀양아리랑 1
 
1.9%
오토캠핑장 1
 
1.9%
패밀리오토캠핑장 1
 
1.9%
가온빌리지 1
 
1.9%
명례강변공원 1
 
1.9%
Other values (44) 44
81.5%
2024-03-13T09:11:47.247300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
핑 13
 
4.3%
캠 12
 
4.0%
장 12
 
4.0%
밀 10
 
3.3%
양 10
 
3.3%
원 7
 
2.3%
오 7
 
2.3%
펜 6
 
2.0%
션 6
 
2.0%
공 6
 
2.0%
Other values (134) 214
70.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 274
90.4%
Lowercase Letter 11
 
3.6%
Decimal Number 7
 
2.3%
Space Separator 6
 
2.0%
Uppercase Letter 2
 
0.7%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
핑 13
 
4.7%
캠 12
 
4.4%
장 12
 
4.4%
밀 10
 
3.6%
양 10
 
3.6%
원 7
 
2.6%
오 7
 
2.6%
펜 6
 
2.2%
션 6
 
2.2%
공 6
 
2.2%
Other values (113) 185
67.5%
Lowercase Letter
ValueCountFrequency (%)
l 1
9.1%
e 1
9.1%
t 1
9.1%
a 1
9.1%
y 1
9.1%
d 1
9.1%
n 1
9.1%
u 1
9.1%
o 1
9.1%
r 1
9.1%
Decimal Number
ValueCountFrequency (%)
9 2
28.6%
1 2
28.6%
2 1
14.3%
8 1
14.3%
3 1
14.3%
Space Separator
ValueCountFrequency (%)
6
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 274
90.4%
Common 16
 
5.3%
Latin 13
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
핑 13
 
4.7%
캠 12
 
4.4%
장 12
 
4.4%
밀 10
 
3.6%
양 10
 
3.6%
원 7
 
2.6%
오 7
 
2.6%
펜 6
 
2.2%
션 6
 
2.2%
공 6
 
2.2%
Other values (113) 185
67.5%
Latin
ValueCountFrequency (%)
P 2
15.4%
l 1
7.7%
e 1
7.7%
t 1
7.7%
a 1
7.7%
y 1
7.7%
d 1
7.7%
n 1
7.7%
u 1
7.7%
o 1
7.7%
Other values (2) 2
15.4%
Common
ValueCountFrequency (%)
6
37.5%
9 2
 
12.5%
1 2
 
12.5%
( 1
 
6.2%
) 1
 
6.2%
, 1
 
6.2%
2 1
 
6.2%
8 1
 
6.2%
3 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 274
90.4%
ASCII 29
 
9.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
핑 13
 
4.7%
캠 12
 
4.4%
장 12
 
4.4%
밀 10
 
3.6%
양 10
 
3.6%
원 7
 
2.6%
오 7
 
2.6%
펜 6
 
2.2%
션 6
 
2.2%
공 6
 
2.2%
Other values (113) 185
67.5%
ASCII
ValueCountFrequency (%)
6
20.7%
P 2
 
6.9%
9 2
 
6.9%
1 2
 
6.9%
( 1
 
3.4%
) 1
 
3.4%
l 1
 
3.4%
e 1
 
3.4%
, 1
 
3.4%
2 1
 
3.4%
Other values (11) 11
37.9%

주소
Text

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-03-13T09:11:47.476400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length13.833333
Min length6

Characters and Unicode

Total characters664
Distinct characters86
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

Unique46 ?
Unique (%)95.8%

Sample

1st row경상남도 밀양시 단장면 바드리길 55
2nd row경상남도 밀양시 단장면 동화2길 36-16
3rd row경상남도 밀양시 고례4안길 105
4th row경상남도 밀양시 단장면 바드리길 9
5th row경상남도 밀양시 상동면 고정리 160
ValueCountFrequency (%)
경상남도 10
 
6.5%
밀양시 10
 
6.5%
산내면 9
 
5.8%
산외면 7
 
4.5%
단장면 7
 
4.5%
부북면 4
 
2.6%
삼문동 3
 
1.9%
하남읍 3
 
1.9%
밀양대로 3
 
1.9%
삼랑진읍 3
 
1.9%
Other values (86) 96
61.9%
2024-03-13T09:11:47.799064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
107
 
16.1%
면 33
 
5.0%
1 31
 
4.7%
3 26
 
3.9%
- 25
 
3.8%
2 23
 
3.5%
산 20
 
3.0%
4 20
 
3.0%
리 18
 
2.7%
양 17
 
2.6%
Other values (76) 344
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 347
52.3%
Decimal Number 185
27.9%
Space Separator 107
 
16.1%
Dash Punctuation 25
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 33
 
9.5%
산 20
 
5.8%
리 18
 
5.2%
양 17
 
4.9%
남 14
 
4.0%
길 14
 
4.0%
밀 14
 
4.0%
내 13
 
3.7%
동 13
 
3.7%
로 12
 
3.5%
Other values (64) 179
51.6%
Decimal Number
ValueCountFrequency (%)
1 31
16.8%
3 26
14.1%
2 23
12.4%
4 20
10.8%
7 16
8.6%
5 16
8.6%
6 14
7.6%
0 14
7.6%
8 13
7.0%
9 12
 
6.5%
Space Separator
ValueCountFrequency (%)
107
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 347
52.3%
Common 317
47.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 33
 
9.5%
산 20
 
5.8%
리 18
 
5.2%
양 17
 
4.9%
남 14
 
4.0%
길 14
 
4.0%
밀 14
 
4.0%
내 13
 
3.7%
동 13
 
3.7%
로 12
 
3.5%
Other values (64) 179
51.6%
Common
ValueCountFrequency (%)
107
33.8%
1 31
 
9.8%
3 26
 
8.2%
- 25
 
7.9%
2 23
 
7.3%
4 20
 
6.3%
7 16
 
5.0%
5 16
 
5.0%
6 14
 
4.4%
0 14
 
4.4%
Other values (2) 25
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 347
52.3%
ASCII 317
47.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
107
33.8%
1 31
 
9.8%
3 26
 
8.2%
- 25
 
7.9%
2 23
 
7.3%
4 20
 
6.3%
7 16
 
5.0%
5 16
 
5.0%
6 14
 
4.4%
0 14
 
4.4%
Other values (2) 25
 
7.9%
Hangul
ValueCountFrequency (%)
면 33
 
9.5%
산 20
 
5.8%
리 18
 
5.2%
양 17
 
4.9%
남 14
 
4.0%
길 14
 
4.0%
밀 14
 
4.0%
내 13
 
3.7%
동 13
 
3.7%
로 12
 
3.5%
Other values (64) 179
51.6%

내방조건
Text

MISSING 

Distinct11
Distinct (%)55.0%
Missing28
Missing (%)58.3%
Memory size516.0 B
2024-03-13T09:11:47.932240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length11.2
Min length5

Characters and Unicode

Total characters224
Distinct characters42
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

Unique9 ?
Unique (%)45.0%

Sample

1st row대형견 불가능
2nd row평일: 대형견가능, 주말: 대형견 불가능
3rd row15kg 이상 불가능
4th row6kg 이상 불가능, 6~8kg 유선상 문의
5th row15kg 이상 불가능
ValueCountFrequency (%)
불가능 19
31.7%
대형견 10
16.7%
이상 7
 
11.7%
15kg 5
 
8.3%
가능 2
 
3.3%
맹견 2
 
3.3%
주말 1
 
1.7%
6kg 1
 
1.7%
6~8kg 1
 
1.7%
유선상 1
 
1.7%
Other values (11) 11
18.3%
2024-03-13T09:11:48.153108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
17.9%
능 22
 
9.8%
가 22
 
9.8%
불 19
 
8.5%
견 14
 
6.2%
형 12
 
5.4%
대 11
 
4.9%
g 8
 
3.6%
상 8
 
3.6%
k 8
 
3.6%
Other values (32) 60
26.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 141
62.9%
Space Separator 40
 
17.9%
Lowercase Letter 16
 
7.1%
Decimal Number 15
 
6.7%
Other Punctuation 9
 
4.0%
Math Symbol 1
 
0.4%
Close Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
능 22
15.6%
가 22
15.6%
불 19
13.5%
견 14
9.9%
형 12
8.5%
대 11
7.8%
상 8
 
5.7%
이 7
 
5.0%
실 3
 
2.1%
외 2
 
1.4%
Other values (19) 21
14.9%
Decimal Number
ValueCountFrequency (%)
1 6
40.0%
5 5
33.3%
6 2
 
13.3%
0 1
 
6.7%
8 1
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
g 8
50.0%
k 8
50.0%
Other Punctuation
ValueCountFrequency (%)
, 5
55.6%
: 4
44.4%
Space Separator
ValueCountFrequency (%)
40
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 141
62.9%
Common 67
29.9%
Latin 16
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
능 22
15.6%
가 22
15.6%
불 19
13.5%
견 14
9.9%
형 12
8.5%
대 11
7.8%
상 8
 
5.7%
이 7
 
5.0%
실 3
 
2.1%
외 2
 
1.4%
Other values (19) 21
14.9%
Common
ValueCountFrequency (%)
40
59.7%
1 6
 
9.0%
5 5
 
7.5%
, 5
 
7.5%
: 4
 
6.0%
6 2
 
3.0%
~ 1
 
1.5%
0 1
 
1.5%
) 1
 
1.5%
( 1
 
1.5%
Latin
ValueCountFrequency (%)
g 8
50.0%
k 8
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 141
62.9%
ASCII 83
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40
48.2%
g 8
 
9.6%
k 8
 
9.6%
1 6
 
7.2%
5 5
 
6.0%
, 5
 
6.0%
: 4
 
4.8%
6 2
 
2.4%
~ 1
 
1.2%
0 1
 
1.2%
Other values (3) 3
 
3.6%
Hangul
ValueCountFrequency (%)
능 22
15.6%
가 22
15.6%
불 19
13.5%
견 14
9.9%
형 12
8.5%
대 11
7.8%
상 8
 
5.7%
이 7
 
5.0%
실 3
 
2.1%
외 2
 
1.4%
Other values (19) 21
14.9%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
밀양시청 축산과
48 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row밀양시청 축산과
2nd row밀양시청 축산과
3rd row밀양시청 축산과
4th row밀양시청 축산과
5th row밀양시청 축산과

Common Values

ValueCountFrequency (%)
밀양시청 축산과 48
100.0%

Length

2024-03-13T09:11:48.249898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:11:48.316556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
밀양시청 48
50.0%
축산과 48
50.0%

관리기관전화
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
055-359-7171
48 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row055-359-7171
2nd row055-359-7171
3rd row055-359-7171
4th row055-359-7171
5th row055-359-7171

Common Values

ValueCountFrequency (%)
055-359-7171 48
100.0%

Length

2024-03-13T09:11:48.402248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:11:48.493346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
055-359-7171 48
100.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
Minimum2022-05-30 00:00:00
Maximum2022-05-30 00:00:00
2024-03-13T09:11:48.560629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:11:48.628841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2024-03-13T09:11:48.683014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분장소주소내방조건
구분1.0001.0001.0000.716
장소1.0001.0001.0001.000
주소1.0001.0001.0001.000
내방조건0.7161.0001.0001.000

Missing values

2024-03-13T09:11:46.552480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T09:11:46.636760image/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

구분장소주소내방조건관리기관명관리기관전화데이터기준일자
0펜션초록발자국펜션경상남도 밀양시 단장면 바드리길 55대형견 불가능밀양시청 축산과055-359-71712022-05-30
1펜션쿠쿠숲펜션경상남도 밀양시 단장면 동화2길 36-16<NA>밀양시청 축산과055-359-71712022-05-30
2펜션소나무펜션경상남도 밀양시 고례4안길 105평일: 대형견가능, 주말: 대형견 불가능밀양시청 축산과055-359-71712022-05-30
3펜션별아래펜션경상남도 밀양시 단장면 바드리길 915kg 이상 불가능밀양시청 축산과055-359-71712022-05-30
4펜션밀양테마펜션경상남도 밀양시 상동면 고정리 1606kg 이상 불가능, 6~8kg 유선상 문의밀양시청 축산과055-359-71712022-05-30
5펜션밀양멍멍아노올자펜션경상남도 밀양시 상동면 고정리 169<NA>밀양시청 축산과055-359-71712022-05-30
6펜션핑코하우스경상남도 밀양시 산내면 가인4길 23-615kg 이상 불가능밀양시청 축산과055-359-71712022-05-30
7카페코모네펫하우스경상남도 밀양시 삼문동 735-215kg 이상 불가능밀양시청 축산과055-359-71712022-05-30
8카페테이블382경상남도 밀양시 삼문동 382-1<NA>밀양시청 축산과055-359-71712022-05-30
9카페마리옹경상남도 밀양시 부북면 위양리 448-1<NA>밀양시청 축산과055-359-71712022-05-30
구분장소주소내방조건관리기관명관리기관전화데이터기준일자
38사찰/유적지수산제역사공원하남읍 수산리 927<NA>밀양시청 축산과055-359-71712022-05-30
39사찰/유적지사명대사생가지무안면 고라2길 17-5<NA>밀양시청 축산과055-359-71712022-05-30
40사찰/유적지아랑각내일동 40<NA>밀양시청 축산과055-359-71712022-05-30
41사찰/유적지만어사삼랑진읍 만어로 776<NA>밀양시청 축산과055-359-71712022-05-30
42사찰/유적지표충사단장면 표충로 1338<NA>밀양시청 축산과055-359-71712022-05-30
43자연경관밀양댐단장면 고례리 1759-2<NA>밀양시청 축산과055-359-71712022-05-30
44자연경관쇠점골(오천평반석)산내면 삼양리 31-5<NA>밀양시청 축산과055-359-71712022-05-30
45자연경관기회송림산외면 남기리 1096-1<NA>밀양시청 축산과055-359-71712022-05-30
46자연경관위양못부북면 위양리 279-2<NA>밀양시청 축산과055-359-71712022-05-30
47자연경관시례호박소산내면 삼양리 31-5<NA>밀양시청 축산과055-359-71712022-05-30