Overview

Dataset statistics

Number of variables8
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory67.8 B

Variable types

Numeric1
Categorical4
Text2
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
순번 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 순번High correlation
순번 has unique valuesUnique
장소 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2024-03-13 00:11:42.706492
Analysis finished2024-03-13 00:11:43.169428
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.5
Minimum1
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-03-13T09:11:43.228096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.35
Q112.75
median24.5
Q336.25
95-th percentile45.65
Maximum48
Range47
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation14
Coefficient of variation (CV)0.57142857
Kurtosis-1.2
Mean24.5
Median Absolute Deviation (MAD)12
Skewness0
Sum1176
Variance196
MonotonicityStrictly increasing
2024-03-13T09:11:43.353563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 1
 
2.1%
26 1
 
2.1%
28 1
 
2.1%
29 1
 
2.1%
30 1
 
2.1%
31 1
 
2.1%
32 1
 
2.1%
33 1
 
2.1%
34 1
 
2.1%
35 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
1 1
2.1%
2 1
2.1%
3 1
2.1%
4 1
2.1%
5 1
2.1%
6 1
2.1%
7 1
2.1%
8 1
2.1%
9 1
2.1%
10 1
2.1%
ValueCountFrequency (%)
48 1
2.1%
47 1
2.1%
46 1
2.1%
45 1
2.1%
44 1
2.1%
43 1
2.1%
42 1
2.1%
41 1
2.1%
40 1
2.1%
39 1
2.1%

구분
Categorical

HIGH CORRELATION 

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

2024-03-13T09:11:43.453267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:11:43.534315image/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
2024-03-13T09:11:43.718595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length6.5833333
Min length3

Characters and Unicode

Total characters316
Distinct characters147
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%
2024-03-13T09:11:44.039766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
핑 13
 
4.1%
캠 12
 
3.8%
장 11
 
3.5%
양 10
 
3.2%
밀 10
 
3.2%
원 8
 
2.5%
오 7
 
2.2%
공 7
 
2.2%
6
 
1.9%
사 6
 
1.9%
Other values (137) 226
71.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 283
89.6%
Lowercase Letter 11
 
3.5%
Decimal Number 7
 
2.2%
Space Separator 6
 
1.9%
Open Punctuation 3
 
0.9%
Close Punctuation 3
 
0.9%
Uppercase Letter 2
 
0.6%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
핑 13
 
4.6%
캠 12
 
4.2%
장 11
 
3.9%
양 10
 
3.5%
밀 10
 
3.5%
원 8
 
2.8%
오 7
 
2.5%
공 7
 
2.5%
사 6
 
2.1%
토 6
 
2.1%
Other values (116) 193
68.2%
Lowercase Letter
ValueCountFrequency (%)
t 1
9.1%
e 1
9.1%
l 1
9.1%
a 1
9.1%
y 1
9.1%
g 1
9.1%
d 1
9.1%
n 1
9.1%
u 1
9.1%
o 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%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 283
89.6%
Common 20
 
6.3%
Latin 13
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
핑 13
 
4.6%
캠 12
 
4.2%
장 11
 
3.9%
양 10
 
3.5%
밀 10
 
3.5%
원 8
 
2.8%
오 7
 
2.5%
공 7
 
2.5%
사 6
 
2.1%
토 6
 
2.1%
Other values (116) 193
68.2%
Latin
ValueCountFrequency (%)
P 2
15.4%
t 1
7.7%
e 1
7.7%
l 1
7.7%
a 1
7.7%
y 1
7.7%
g 1
7.7%
d 1
7.7%
n 1
7.7%
u 1
7.7%
Other values (2) 2
15.4%
Common
ValueCountFrequency (%)
6
30.0%
( 3
15.0%
) 3
15.0%
9 2
 
10.0%
1 2
 
10.0%
, 1
 
5.0%
2 1
 
5.0%
8 1
 
5.0%
3 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 283
89.6%
ASCII 33
 
10.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
핑 13
 
4.6%
캠 12
 
4.2%
장 11
 
3.9%
양 10
 
3.5%
밀 10
 
3.5%
원 8
 
2.8%
오 7
 
2.5%
공 7
 
2.5%
사 6
 
2.1%
토 6
 
2.1%
Other values (116) 193
68.2%
ASCII
ValueCountFrequency (%)
6
18.2%
( 3
 
9.1%
) 3
 
9.1%
P 2
 
6.1%
9 2
 
6.1%
1 2
 
6.1%
t 1
 
3.0%
e 1
 
3.0%
, 1
 
3.0%
2 1
 
3.0%
Other values (11) 11
33.3%

주소
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-03-13T09:11:44.274990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length21.020833
Min length15

Characters and Unicode

Total characters1009
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

Unique48 ?
Unique (%)100.0%

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 48
20.8%
밀양시 48
20.8%
산내면 9
 
3.9%
산외면 7
 
3.0%
단장면 7
 
3.0%
부북면 4
 
1.7%
삼랑진읍 3
 
1.3%
삼문동 3
 
1.3%
하남읍 3
 
1.3%
밀양대로 3
 
1.3%
Other values (87) 96
41.6%
2024-03-13T09:11:44.584913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
183
18.1%
양 55
 
5.5%
남 52
 
5.2%
밀 52
 
5.2%
상 50
 
5.0%
경 48
 
4.8%
도 48
 
4.8%
시 48
 
4.8%
면 33
 
3.3%
1 30
 
3.0%
Other values (76) 410
40.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 613
60.8%
Decimal Number 187
 
18.5%
Space Separator 183
 
18.1%
Dash Punctuation 26
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 55
 
9.0%
남 52
 
8.5%
밀 52
 
8.5%
상 50
 
8.2%
경 48
 
7.8%
도 48
 
7.8%
시 48
 
7.8%
면 33
 
5.4%
산 20
 
3.3%
리 18
 
2.9%
Other values (64) 189
30.8%
Decimal Number
ValueCountFrequency (%)
1 30
16.0%
3 26
13.9%
2 25
13.4%
4 20
10.7%
5 17
9.1%
7 16
8.6%
8 14
7.5%
0 14
7.5%
6 13
7.0%
9 12
 
6.4%
Space Separator
ValueCountFrequency (%)
183
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 613
60.8%
Common 396
39.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 55
 
9.0%
남 52
 
8.5%
밀 52
 
8.5%
상 50
 
8.2%
경 48
 
7.8%
도 48
 
7.8%
시 48
 
7.8%
면 33
 
5.4%
산 20
 
3.3%
리 18
 
2.9%
Other values (64) 189
30.8%
Common
ValueCountFrequency (%)
183
46.2%
1 30
 
7.6%
- 26
 
6.6%
3 26
 
6.6%
2 25
 
6.3%
4 20
 
5.1%
5 17
 
4.3%
7 16
 
4.0%
8 14
 
3.5%
0 14
 
3.5%
Other values (2) 25
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 613
60.8%
ASCII 396
39.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
183
46.2%
1 30
 
7.6%
- 26
 
6.6%
3 26
 
6.6%
2 25
 
6.3%
4 20
 
5.1%
5 17
 
4.3%
7 16
 
4.0%
8 14
 
3.5%
0 14
 
3.5%
Other values (2) 25
 
6.3%
Hangul
ValueCountFrequency (%)
양 55
 
9.0%
남 52
 
8.5%
밀 52
 
8.5%
상 50
 
8.2%
경 48
 
7.8%
도 48
 
7.8%
시 48
 
7.8%
면 33
 
5.4%
산 20
 
3.3%
리 18
 
2.9%
Other values (64) 189
30.8%

내방조건
Categorical

Distinct9
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size516.0 B
<NA>
22 
대형견 불가능
10 
반려견 안전수칙 준수, 목줄착용시(권장) 동반가능
6 
15kg 이상 불가능
 
2
야외만 가능
 
2
Other values (4)
6 

Length

Max length32
Median length28
Mean length9.1041667
Min length3

Unique

Unique2 ?
Unique (%)4.2%

Sample

1st row대형견 불가능
2nd row반려견 안전수칙 준수, 목줄착용시(권장) 동반가능
3rd row대형견 불가능
4th row대형견 불가능
5th row대형견 불가능

Common Values

ValueCountFrequency (%)
<NA> 22
45.8%
대형견 불가능 10
20.8%
반려견 안전수칙 준수, 목줄착용시(권장) 동반가능 6
 
12.5%
15kg 이상 불가능 2
 
4.2%
야외만 가능 2
 
4.2%
불가능 2
 
4.2%
10kg 이상 불가능 2
 
4.2%
실내 : 소형견만 안아주면 가능, 실외 : 목줄착용시 가능 1
 
2.1%
실내 : 불가능, 실외 : 가능 1
 
2.1%

Length

2024-03-13T09:11:44.692999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:11:44.778370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 22
21.0%
불가능 17
16.2%
대형견 10
9.5%
반려견 6
 
5.7%
안전수칙 6
 
5.7%
준수 6
 
5.7%
목줄착용시(권장 6
 
5.7%
동반가능 6
 
5.7%
가능 5
 
4.8%
4
 
3.8%
Other values (9) 17
16.2%

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

2024-03-13T09:11:44.878966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:11:44.944569image/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

2024-03-13T09:11:45.016637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:11:45.097046image/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
Minimum2023-08-29 00:00:00
Maximum2023-08-29 00:00:00
2024-03-13T09:11:45.168098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:11:45.253465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-13T09:11:42.950680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T09:11:45.308613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분장소주소내방조건
순번1.0000.9711.0001.0000.587
구분0.9711.0001.0001.0000.596
장소1.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.000
내방조건0.5870.5961.0001.0001.000
2024-03-13T09:11:45.382614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
내방조건구분
내방조건1.0000.400
구분0.4001.000
2024-03-13T09:11:45.446509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분내방조건
순번1.0000.8750.337
구분0.8751.0000.400
내방조건0.3370.4001.000

Missing values

2024-03-13T09:11:43.035491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T09:11:43.133842image/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

순번구분장소주소내방조건관리기관명관리기관전화데이터기준일자
01펜션초록발자국펜션경상남도 밀양시 단장면 바드리길 55대형견 불가능밀양시청 축산과055-359-71712023-08-29
12펜션쿠쿠숲펜션경상남도 밀양시 단장면 동화2길 36-16반려견 안전수칙 준수, 목줄착용시(권장) 동반가능밀양시청 축산과055-359-71712023-08-29
23펜션소나무펜션경상남도 밀양시 고례4안길 105대형견 불가능밀양시청 축산과055-359-71712023-08-29
34펜션별아래펜션경상남도 밀양시 단장면 바드리길 9대형견 불가능밀양시청 축산과055-359-71712023-08-29
45펜션밀양테마펜션경상남도 밀양시 상동면 고정리 160대형견 불가능밀양시청 축산과055-359-71712023-08-29
56펜션밀양멍멍아노올자펜션경상남도 밀양시 상동면 고정리 169-3대형견 불가능밀양시청 축산과055-359-71712023-08-29
67펜션핑코하우스경상남도 밀양시 산내면 가인4길 23-615kg 이상 불가능밀양시청 축산과055-359-71712023-08-29
78카페코모네펫하우스경상남도 밀양시 삼문동 735-215kg 이상 불가능밀양시청 축산과055-359-71712023-08-29
89카페테이블382경상남도 밀양시 삼문동 382-1<NA>밀양시청 축산과055-359-71712023-08-29
910카페마리옹경상남도 밀양시 부북면 위양리 448-1야외만 가능밀양시청 축산과055-359-71712023-08-29
순번구분장소주소내방조건관리기관명관리기관전화데이터기준일자
3839사찰/유적지수산제역사공원경상남도 밀양시 하남읍 수산리 927<NA>밀양시청 축산과055-359-71712023-08-29
3940사찰/유적지사명대사생가지경상남도 밀양시 무안면 고라2길 17-5<NA>밀양시청 축산과055-359-71712023-08-29
4041사찰/유적지아랑각경상남도 밀양시 내일동 40<NA>밀양시청 축산과055-359-71712023-08-29
4142사찰/유적지만어사경상남도 밀양시 삼랑진읍 만어로 776<NA>밀양시청 축산과055-359-71712023-08-29
4243사찰/유적지표충사경상남도 밀양시 단장면 표충로 1338<NA>밀양시청 축산과055-359-71712023-08-29
4344자연경관밀양댐경상남도 밀양시 단장면 고례리 1759-2<NA>밀양시청 축산과055-359-71712023-08-29
4445자연경관쇠점골(오천평반석)경상남도 밀양시 산내면 삼양리 185-2<NA>밀양시청 축산과055-359-71712023-08-29
4546자연경관기회송림경상남도 밀양시 산외면 남기리 1096-1<NA>밀양시청 축산과055-359-71712023-08-29
4647자연경관위양못경상남도 밀양시 부북면 위양리 279-2<NA>밀양시청 축산과055-359-71712023-08-29
4748자연경관시례호박소경상남도 밀양시 산내면 삼양리 31-5<NA>밀양시청 축산과055-359-71712023-08-29