Overview

Dataset statistics

Number of variables14
Number of observations30
Missing cells10
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory124.4 B

Variable types

Text4
DateTime1
Numeric8
Categorical1

Dataset

Description제주특별자치도 서귀포시 장애인복지시설 현황에 관한 데이터로 시설명, 소재지, 규모, 이용인원 등 현황 정보를 제공합니다.
URLhttps://www.data.go.kr/data/3044429/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
부지 시설규모(제곱미터) is highly overall correlated with 건물 시설규모(제곱미터) and 1 other fieldsHigh correlation
건물 시설규모(제곱미터) is highly overall correlated with 부지 시설규모(제곱미터) and 4 other fieldsHigh correlation
수용(이용)인원 정원 is highly overall correlated with 부지 시설규모(제곱미터) and 4 other fieldsHigh correlation
현원(1일이용인원) is highly overall correlated with 건물 시설규모(제곱미터) and 3 other fieldsHigh correlation
종사자 정원 is highly overall correlated with 건물 시설규모(제곱미터) and 3 other fieldsHigh correlation
종사자 현원 is highly overall correlated with 건물 시설규모(제곱미터) and 3 other fieldsHigh correlation
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
부지 시설규모(제곱미터) has 9 (30.0%) missing valuesMissing
연락처(사무실) has 1 (3.3%) missing valuesMissing
시설명 has unique valuesUnique
설립개원일 has unique valuesUnique
현원(1일이용인원) has 2 (6.7%) zerosZeros
종사자 현원 has 3 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-12 14:22:07.011396
Analysis finished2023-12-12 14:22:15.421472
Duration8.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T23:22:15.596689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length13.5
Mean length6.9
Min length3

Characters and Unicode

Total characters207
Distinct characters84
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

Unique30 ?
Unique (%)100.0%

Sample

1st row평화의마을
2nd row정혜재활원
3rd row어울림터
4th row우리집
5th row서귀포작은예수의 집
ValueCountFrequency (%)
행복나눔 2
 
5.9%
평화의마을 1
 
2.9%
장애인단기거주시설 1
 
2.9%
청각언어장애인주간보호시설 1
 
2.9%
돌담정낭 1
 
2.9%
명유원 1
 
2.9%
해인주간활동센터 1
 
2.9%
파란나라장애인주간활동센터 1
 
2.9%
서귀포해오름주간활동센터 1
 
2.9%
성자현 1
 
2.9%
Other values (23) 23
67.6%
2023-12-12T23:22:16.095222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 11
 
5.3%
간 9
 
4.3%
터 8
 
3.9%
집 6
 
2.9%
활 6
 
2.9%
인 6
 
2.9%
원 6
 
2.9%
시 6
 
2.9%
센 6
 
2.9%
서 6
 
2.9%
Other values (74) 137
66.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 202
97.6%
Space Separator 5
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 11
 
5.4%
간 9
 
4.5%
터 8
 
4.0%
집 6
 
3.0%
활 6
 
3.0%
인 6
 
3.0%
원 6
 
3.0%
시 6
 
3.0%
센 6
 
3.0%
서 6
 
3.0%
Other values (73) 132
65.3%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 202
97.6%
Common 5
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 11
 
5.4%
간 9
 
4.5%
터 8
 
4.0%
집 6
 
3.0%
활 6
 
3.0%
인 6
 
3.0%
원 6
 
3.0%
시 6
 
3.0%
센 6
 
3.0%
서 6
 
3.0%
Other values (73) 132
65.3%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 202
97.6%
ASCII 5
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 11
 
5.4%
간 9
 
4.5%
터 8
 
4.0%
집 6
 
3.0%
활 6
 
3.0%
인 6
 
3.0%
원 6
 
3.0%
시 6
 
3.0%
센 6
 
3.0%
서 6
 
3.0%
Other values (73) 132
65.3%
ASCII
ValueCountFrequency (%)
5
100.0%

소재지
Text

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T23:22:16.414613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length27
Mean length24.233333
Min length19

Characters and Unicode

Total characters727
Distinct characters71
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

Unique26 ?
Unique (%)86.7%

Sample

1st row제주특별자치도 서귀포시 대정읍 중산간서로 2195-12
2nd row제주특별자치도 서귀포시 인정오름로 85번길41
3rd row제주특별자치도 서귀포시 인정오름로26
4th row제주특별자치도 서귀포시 대정읍 중산간서로 2195-12
5th row제주특별자치도 서귀포시 속골로 24
ValueCountFrequency (%)
제주특별자치도 30
22.9%
서귀포시 29
22.1%
대정읍 6
 
4.6%
남원읍 4
 
3.1%
예래로 3
 
2.3%
2195-12 2
 
1.5%
33-1 2
 
1.5%
영서중로 2
 
1.5%
24 2
 
1.5%
중산간서로 2
 
1.5%
Other values (48) 49
37.4%
2023-12-12T23:22:16.966330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
107
 
14.7%
서 37
 
5.1%
도 31
 
4.3%
1 31
 
4.3%
제 30
 
4.1%
주 30
 
4.1%
특 30
 
4.1%
별 30
 
4.1%
자 30
 
4.1%
치 30
 
4.1%
Other values (61) 341
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 504
69.3%
Space Separator 107
 
14.7%
Decimal Number 103
 
14.2%
Dash Punctuation 13
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 37
 
7.3%
도 31
 
6.2%
제 30
 
6.0%
주 30
 
6.0%
특 30
 
6.0%
별 30
 
6.0%
자 30
 
6.0%
치 30
 
6.0%
로 30
 
6.0%
포 29
 
5.8%
Other values (49) 197
39.1%
Decimal Number
ValueCountFrequency (%)
1 31
30.1%
2 14
13.6%
4 11
 
10.7%
3 11
 
10.7%
9 10
 
9.7%
6 8
 
7.8%
5 6
 
5.8%
7 5
 
4.9%
0 4
 
3.9%
8 3
 
2.9%
Space Separator
ValueCountFrequency (%)
107
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 504
69.3%
Common 223
30.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 37
 
7.3%
도 31
 
6.2%
제 30
 
6.0%
주 30
 
6.0%
특 30
 
6.0%
별 30
 
6.0%
자 30
 
6.0%
치 30
 
6.0%
로 30
 
6.0%
포 29
 
5.8%
Other values (49) 197
39.1%
Common
ValueCountFrequency (%)
107
48.0%
1 31
 
13.9%
2 14
 
6.3%
- 13
 
5.8%
4 11
 
4.9%
3 11
 
4.9%
9 10
 
4.5%
6 8
 
3.6%
5 6
 
2.7%
7 5
 
2.2%
Other values (2) 7
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 504
69.3%
ASCII 223
30.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
107
48.0%
1 31
 
13.9%
2 14
 
6.3%
- 13
 
5.8%
4 11
 
4.9%
3 11
 
4.9%
9 10
 
4.5%
6 8
 
3.6%
5 6
 
2.7%
7 5
 
2.2%
Other values (2) 7
 
3.1%
Hangul
ValueCountFrequency (%)
서 37
 
7.3%
도 31
 
6.2%
제 30
 
6.0%
주 30
 
6.0%
특 30
 
6.0%
별 30
 
6.0%
자 30
 
6.0%
치 30
 
6.0%
로 30
 
6.0%
포 29
 
5.8%
Other values (49) 197
39.1%

지번주소
Text

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T23:22:17.277489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length24.1
Min length21

Characters and Unicode

Total characters723
Distinct characters61
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

Unique24 ?
Unique (%)80.0%

Sample

1st row제주특별자치도 서귀포시 대정읍 구억리 308
2nd row제주특별자치도 서귀포시 토평동 2895
3rd row제주특별자치도 서귀포시 토평동 1916
4th row제주특별자치도 서귀포시 대정읍 구억리 308
5th row제주특별자치도 서귀포시 호근동 1586-4
ValueCountFrequency (%)
제주특별자치도 30
22.6%
서귀포시 30
22.6%
대정읍 6
 
4.5%
동홍동 4
 
3.0%
남원읍 4
 
3.0%
상예동 3
 
2.3%
토평동 3
 
2.3%
2895 2
 
1.5%
648-18번지 2
 
1.5%
호근동 2
 
1.5%
Other values (41) 47
35.3%
2023-12-12T23:22:17.908339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
103
 
14.2%
서 33
 
4.6%
1 33
 
4.6%
포 31
 
4.3%
제 30
 
4.1%
주 30
 
4.1%
특 30
 
4.1%
별 30
 
4.1%
자 30
 
4.1%
치 30
 
4.1%
Other values (51) 343
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 465
64.3%
Decimal Number 132
 
18.3%
Space Separator 103
 
14.2%
Dash Punctuation 23
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 33
 
7.1%
포 31
 
6.7%
제 30
 
6.5%
주 30
 
6.5%
특 30
 
6.5%
별 30
 
6.5%
자 30
 
6.5%
치 30
 
6.5%
도 30
 
6.5%
귀 30
 
6.5%
Other values (39) 161
34.6%
Decimal Number
ValueCountFrequency (%)
1 33
25.0%
2 19
14.4%
6 14
10.6%
8 14
10.6%
3 13
 
9.8%
7 11
 
8.3%
4 10
 
7.6%
0 7
 
5.3%
5 6
 
4.5%
9 5
 
3.8%
Space Separator
ValueCountFrequency (%)
103
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 465
64.3%
Common 258
35.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 33
 
7.1%
포 31
 
6.7%
제 30
 
6.5%
주 30
 
6.5%
특 30
 
6.5%
별 30
 
6.5%
자 30
 
6.5%
치 30
 
6.5%
도 30
 
6.5%
귀 30
 
6.5%
Other values (39) 161
34.6%
Common
ValueCountFrequency (%)
103
39.9%
1 33
 
12.8%
- 23
 
8.9%
2 19
 
7.4%
6 14
 
5.4%
8 14
 
5.4%
3 13
 
5.0%
7 11
 
4.3%
4 10
 
3.9%
0 7
 
2.7%
Other values (2) 11
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 465
64.3%
ASCII 258
35.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
103
39.9%
1 33
 
12.8%
- 23
 
8.9%
2 19
 
7.4%
6 14
 
5.4%
8 14
 
5.4%
3 13
 
5.0%
7 11
 
4.3%
4 10
 
3.9%
0 7
 
2.7%
Other values (2) 11
 
4.3%
Hangul
ValueCountFrequency (%)
서 33
 
7.1%
포 31
 
6.7%
제 30
 
6.5%
주 30
 
6.5%
특 30
 
6.5%
별 30
 
6.5%
자 30
 
6.5%
치 30
 
6.5%
도 30
 
6.5%
귀 30
 
6.5%
Other values (39) 161
34.6%

설립개원일
Date

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2001-09-19 00:00:00
Maximum2023-03-29 00:00:00
2023-12-12T23:22:18.067542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:18.230143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

부지 시설규모(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)95.2%
Missing9
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean1650.0476
Minimum95
Maximum7190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T23:22:18.377704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile112
Q1744
median1029
Q31800
95-th percentile3888
Maximum7190
Range7095
Interquartile range (IQR)1056

Descriptive statistics

Standard deviation1651.4494
Coefficient of variation (CV)1.0008495
Kurtosis5.5616123
Mean1650.0476
Median Absolute Deviation (MAD)597
Skewness2.1192543
Sum34651
Variance2727285
MonotonicityNot monotonic
2023-12-12T23:22:18.536829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2966 2
 
6.7%
3113 1
 
3.3%
992 1
 
3.3%
342 1
 
3.3%
1556 1
 
3.3%
1029 1
 
3.3%
744 1
 
3.3%
947 1
 
3.3%
1555 1
 
3.3%
95 1
 
3.3%
Other values (10) 10
33.3%
(Missing) 9
30.0%
ValueCountFrequency (%)
95 1
3.3%
112 1
3.3%
310 1
3.3%
342 1
3.3%
610 1
3.3%
744 1
3.3%
764 1
3.3%
774 1
3.3%
947 1
3.3%
992 1
3.3%
ValueCountFrequency (%)
7190 1
3.3%
3888 1
3.3%
3113 1
3.3%
2966 2
6.7%
1800 1
3.3%
1626 1
3.3%
1556 1
3.3%
1555 1
3.3%
1272 1
3.3%
1029 1
3.3%

건물 시설규모(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean383.13333
Minimum62
Maximum1502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T23:22:18.709044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile72.25
Q184
median177
Q3395.5
95-th percentile1418.2
Maximum1502
Range1440
Interquartile range (IQR)311.5

Descriptive statistics

Standard deviation452.83953
Coefficient of variation (CV)1.1819372
Kurtosis1.2464529
Mean383.13333
Median Absolute Deviation (MAD)101.5
Skewness1.600803
Sum11494
Variance205063.64
MonotonicityNot monotonic
2023-12-12T23:22:18.886042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
75 2
 
6.7%
84 2
 
6.7%
939 1
 
3.3%
1465 1
 
3.3%
397 1
 
3.3%
76 1
 
3.3%
83 1
 
3.3%
100 1
 
3.3%
404 1
 
3.3%
226 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
62 1
3.3%
70 1
3.3%
75 2
6.7%
76 1
3.3%
82 1
3.3%
83 1
3.3%
84 2
6.7%
95 1
3.3%
100 1
3.3%
108 1
3.3%
ValueCountFrequency (%)
1502 1
3.3%
1465 1
3.3%
1361 1
3.3%
1196 1
3.3%
939 1
3.3%
851 1
3.3%
404 1
3.3%
397 1
3.3%
391 1
3.3%
371 1
3.3%

수용(이용)인원 정원
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.133333
Minimum4
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T23:22:19.044456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14
median18
Q320
95-th percentile42.2
Maximum50
Range46
Interquartile range (IQR)16

Descriptive statistics

Standard deviation14.245951
Coefficient of variation (CV)0.83147575
Kurtosis-0.3263759
Mean17.133333
Median Absolute Deviation (MAD)14
Skewness0.86182611
Sum514
Variance202.94713
MonotonicityNot monotonic
2023-12-12T23:22:19.176786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 12
40.0%
18 5
16.7%
20 4
 
13.3%
40 3
 
10.0%
30 2
 
6.7%
44 1
 
3.3%
12 1
 
3.3%
10 1
 
3.3%
50 1
 
3.3%
ValueCountFrequency (%)
4 12
40.0%
10 1
 
3.3%
12 1
 
3.3%
18 5
16.7%
20 4
 
13.3%
30 2
 
6.7%
40 3
 
10.0%
44 1
 
3.3%
50 1
 
3.3%
ValueCountFrequency (%)
50 1
 
3.3%
44 1
 
3.3%
40 3
 
10.0%
30 2
 
6.7%
20 4
 
13.3%
18 5
16.7%
12 1
 
3.3%
10 1
 
3.3%
4 12
40.0%

현원(1일이용인원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.833333
Minimum0
Maximum50
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T23:22:19.318948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.35
Q14
median12.5
Q320
95-th percentile40.55
Maximum50
Range50
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.575036
Coefficient of variation (CV)0.91517098
Kurtosis0.34569187
Mean14.833333
Median Absolute Deviation (MAD)8.5
Skewness1.0225437
Sum445
Variance184.28161
MonotonicityNot monotonic
2023-12-12T23:22:19.446669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4 6
20.0%
3 4
13.3%
18 4
13.3%
20 3
10.0%
0 2
 
6.7%
27 1
 
3.3%
24 1
 
3.3%
50 1
 
3.3%
5 1
 
3.3%
14 1
 
3.3%
Other values (6) 6
20.0%
ValueCountFrequency (%)
0 2
 
6.7%
3 4
13.3%
4 6
20.0%
5 1
 
3.3%
6 1
 
3.3%
11 1
 
3.3%
14 1
 
3.3%
18 4
13.3%
20 3
10.0%
22 1
 
3.3%
ValueCountFrequency (%)
50 1
 
3.3%
41 1
 
3.3%
40 1
 
3.3%
37 1
 
3.3%
27 1
 
3.3%
24 1
 
3.3%
22 1
 
3.3%
20 3
10.0%
18 4
13.3%
14 1
 
3.3%

종사자 정원
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T23:22:19.595011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median8
Q39
95-th percentile32.95
Maximum38
Range37
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.8146618
Coefficient of variation (CV)1.1546661
Kurtosis3.9780959
Mean8.5
Median Absolute Deviation (MAD)6.5
Skewness2.0175119
Sum255
Variance96.327586
MonotonicityNot monotonic
2023-12-12T23:22:19.729562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 11
36.7%
9 5
16.7%
8 5
16.7%
11 3
 
10.0%
38 1
 
3.3%
2 1
 
3.3%
6 1
 
3.3%
37 1
 
3.3%
15 1
 
3.3%
28 1
 
3.3%
ValueCountFrequency (%)
1 11
36.7%
2 1
 
3.3%
6 1
 
3.3%
8 5
16.7%
9 5
16.7%
11 3
 
10.0%
15 1
 
3.3%
28 1
 
3.3%
37 1
 
3.3%
38 1
 
3.3%
ValueCountFrequency (%)
38 1
 
3.3%
37 1
 
3.3%
28 1
 
3.3%
15 1
 
3.3%
11 3
 
10.0%
9 5
16.7%
8 5
16.7%
6 1
 
3.3%
2 1
 
3.3%
1 11
36.7%

종사자 현원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5333333
Minimum0
Maximum38
Zeros3
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T23:22:19.858505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q38
95-th percentile30.7
Maximum38
Range38
Interquartile range (IQR)7

Descriptive statistics

Standard deviation9.6444851
Coefficient of variation (CV)1.2802414
Kurtosis5.0085829
Mean7.5333333
Median Absolute Deviation (MAD)5
Skewness2.2322324
Sum226
Variance93.016092
MonotonicityNot monotonic
2023-12-12T23:22:20.025889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 8
26.7%
8 5
16.7%
6 3
 
10.0%
0 3
 
10.0%
7 3
 
10.0%
11 2
 
6.7%
2 2
 
6.7%
38 1
 
3.3%
37 1
 
3.3%
15 1
 
3.3%
ValueCountFrequency (%)
0 3
 
10.0%
1 8
26.7%
2 2
 
6.7%
6 3
 
10.0%
7 3
 
10.0%
8 5
16.7%
11 2
 
6.7%
15 1
 
3.3%
23 1
 
3.3%
37 1
 
3.3%
ValueCountFrequency (%)
38 1
 
3.3%
37 1
 
3.3%
23 1
 
3.3%
15 1
 
3.3%
11 2
 
6.7%
8 5
16.7%
7 3
 
10.0%
6 3
 
10.0%
2 2
 
6.7%
1 8
26.7%

연락처(사무실)
Text

MISSING 

Distinct23
Distinct (%)79.3%
Missing1
Missing (%)3.3%
Memory size372.0 B
2023-12-12T23:22:20.241789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.034483
Min length12

Characters and Unicode

Total characters349
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)69.0%

Sample

1st row064-794-6277
2nd row064-732-5004
3rd row064-732-0295
4th row064-794-6277
5th row064-739-3633
ValueCountFrequency (%)
064-764-3385 4
 
13.8%
064-794-6277 3
 
10.3%
064-732-5004 2
 
6.9%
064-763-2357 1
 
3.4%
064-792-1471 1
 
3.4%
070-4064-3305 1
 
3.4%
064-794-7724 1
 
3.4%
064-762-5006 1
 
3.4%
064-792-7576 1
 
3.4%
064-767-1001 1
 
3.4%
Other values (13) 13
44.8%
2023-12-12T23:22:20.629258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 58
16.6%
7 50
14.3%
6 48
13.8%
4 47
13.5%
0 45
12.9%
3 30
8.6%
2 17
 
4.9%
5 16
 
4.6%
8 15
 
4.3%
9 14
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 291
83.4%
Dash Punctuation 58
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 50
17.2%
6 48
16.5%
4 47
16.2%
0 45
15.5%
3 30
10.3%
2 17
 
5.8%
5 16
 
5.5%
8 15
 
5.2%
9 14
 
4.8%
1 9
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 349
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 58
16.6%
7 50
14.3%
6 48
13.8%
4 47
13.5%
0 45
12.9%
3 30
8.6%
2 17
 
4.9%
5 16
 
4.6%
8 15
 
4.3%
9 14
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 58
16.6%
7 50
14.3%
6 48
13.8%
4 47
13.5%
0 45
12.9%
3 30
8.6%
2 17
 
4.9%
5 16
 
4.6%
8 15
 
4.3%
9 14
 
4.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.268275
Minimum3.2213987
Maximum33.36263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T23:22:20.777212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.2213987
5-th percentile33.225277
Q133.251767
median33.259812
Q333.284677
95-th percentile33.316607
Maximum33.36263
Range30.141232
Interquartile range (IQR)0.03290965

Descriptive statistics

Standard deviation5.4861533
Coefficient of variation (CV)0.17001694
Kurtosis29.998204
Mean32.268275
Median Absolute Deviation (MAD)0.018566525
Skewness-5.4769874
Sum968.04826
Variance30.097878
MonotonicityNot monotonic
2023-12-12T23:22:20.899357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
33.25380786 3
 
10.0%
33.27912524 2
 
6.7%
33.28467665 2
 
6.7%
33.26251917 1
 
3.3%
33.36263045 1
 
3.3%
33.27496248 1
 
3.3%
3.221398663 1
 
3.3%
33.22672404 1
 
3.3%
33.33181082 1
 
3.3%
33.27989114 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
3.221398663 1
 
3.3%
33.22409235 1
 
3.3%
33.22672404 1
 
3.3%
33.24199219 1
 
3.3%
33.24413749 1
 
3.3%
33.24751935 1
 
3.3%
33.251561 1
 
3.3%
33.251758 1
 
3.3%
33.251794 1
 
3.3%
33.25380786 3
10.0%
ValueCountFrequency (%)
33.36263045 1
3.3%
33.33181082 1
3.3%
33.29802521 1
3.3%
33.29089776 1
3.3%
33.28819764 1
3.3%
33.28776816 1
3.3%
33.28725276 1
3.3%
33.28467665 2
6.7%
33.27989114 1
3.3%
33.27912524 2
6.7%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.5138
Minimum126.24707
Maximum126.84177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T23:22:21.036476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.24707
5-th percentile126.25804
Q1126.39537
median126.56349
Q3126.58333
95-th percentile126.77579
Maximum126.84177
Range0.5947078
Interquartile range (IQR)0.18795125

Descriptive statistics

Standard deviation0.16256951
Coefficient of variation (CV)0.0012849943
Kurtosis-0.53250197
Mean126.5138
Median Absolute Deviation (MAD)0.1095671
Skewness0.018362232
Sum3795.4141
Variance0.026428845
MonotonicityNot monotonic
2023-12-12T23:22:21.212704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
126.5715586 3
 
10.0%
126.2922622 2
 
6.7%
126.5814767 2
 
6.7%
126.4169408 1
 
3.3%
126.8417749 1
 
3.3%
126.3138921 1
 
3.3%
126.2613406 1
 
3.3%
126.2553354 1
 
3.3%
126.83004 1
 
3.3%
126.6675513 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
126.2470671 1
3.3%
126.2553354 1
3.3%
126.2613406 1
3.3%
126.2922622 2
6.7%
126.3138921 1
3.3%
126.395237 1
3.3%
126.39535 1
3.3%
126.395448 1
3.3%
126.4169408 1
3.3%
126.4484676 1
3.3%
ValueCountFrequency (%)
126.8417749 1
3.3%
126.83004 1
3.3%
126.7094846 1
3.3%
126.6988304 1
3.3%
126.6675513 1
3.3%
126.6651194 1
3.3%
126.5978298 1
3.3%
126.5839421 1
3.3%
126.5814767 2
6.7%
126.5775729 1
3.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-06-14
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-14
2nd row2023-06-14
3rd row2023-06-14
4th row2023-06-14
5th row2023-06-14

Common Values

ValueCountFrequency (%)
2023-06-14 30
100.0%

Length

2023-12-12T23:22:21.327920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:22:21.433094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-14 30
100.0%

Interactions

2023-12-12T23:22:13.745347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:07.489354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:08.418998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:09.628242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:10.373594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:11.220864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:12.055964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:12.854407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:13.848982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:07.596534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:08.881610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:09.773704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:10.471393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:11.338448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:12.155917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:12.952433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:13.959379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:07.713412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:08.977537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:09.856304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:10.575588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:11.456418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:12.266580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:13.059082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:14.063005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:07.834221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:09.069367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:09.938526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:10.673125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:11.562329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:12.356190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:13.156985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:14.145936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:07.933965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:09.166861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:10.023135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:10.817136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:11.664351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:12.443681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:13.285033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:14.252929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:08.044503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:09.279792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:10.108689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:10.915004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:11.757025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:12.531078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:13.408460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:14.358978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:08.149851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:09.418822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:10.205656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:11.023661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:11.856126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:12.628879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:13.564844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:14.464121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:08.290389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:09.513003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:10.296347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:11.125781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:11.960941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:12.721758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:22:13.659636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:22:21.512216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명소재지지번주소설립개원일부지 시설규모(제곱미터)건물 시설규모(제곱미터)수용(이용)인원 정원현원(1일이용인원)종사자 정원종사자 현원연락처(사무실)위도경도
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0000.6980.9800.8750.9770.9750.9771.0001.000
지번주소1.0001.0001.0001.0000.7430.8600.0000.0000.0000.0000.9591.0001.000
설립개원일1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
부지 시설규모(제곱미터)1.0001.0000.7431.0001.0000.6660.8380.6480.6390.7540.969NaN0.222
건물 시설규모(제곱미터)1.0000.6980.8601.0000.6661.0000.8710.8310.6470.6330.8680.0000.469
수용(이용)인원 정원1.0000.9800.0001.0000.8380.8711.0000.9180.8210.8130.8430.0000.000
현원(1일이용인원)1.0000.8750.0001.0000.6480.8310.9181.0000.8450.8230.8080.0000.641
종사자 정원1.0000.9770.0001.0000.6390.6470.8210.8451.0000.9970.9110.0000.000
종사자 현원1.0000.9750.0001.0000.7540.6330.8130.8230.9971.0000.9030.0000.000
연락처(사무실)1.0000.9770.9591.0000.9690.8680.8430.8080.9110.9031.0001.0000.978
위도1.0001.0001.0001.000NaN0.0000.0000.0000.0000.0001.0001.0000.000
경도1.0001.0001.0001.0000.2220.4690.0000.6410.0000.0000.9780.0001.000
2023-12-12T23:22:21.668775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부지 시설규모(제곱미터)건물 시설규모(제곱미터)수용(이용)인원 정원현원(1일이용인원)종사자 정원종사자 현원위도경도
부지 시설규모(제곱미터)1.0000.5640.5170.4650.4560.3570.330-0.019
건물 시설규모(제곱미터)0.5641.0000.7820.6660.8600.8030.4340.309
수용(이용)인원 정원0.5170.7821.0000.8690.9050.8550.2100.083
현원(1일이용인원)0.4650.6660.8691.0000.8020.8560.1930.229
종사자 정원0.4560.8600.9050.8021.0000.9440.3000.236
종사자 현원0.3570.8030.8550.8560.9441.0000.2410.278
위도0.3300.4340.2100.1930.3000.2411.0000.775
경도-0.0190.3090.0830.2290.2360.2780.7751.000

Missing values

2023-12-12T23:22:14.623146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:22:14.876597image/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.
2023-12-12T23:22:15.353156image/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

시설명소재지지번주소설립개원일부지 시설규모(제곱미터)건물 시설규모(제곱미터)수용(이용)인원 정원현원(1일이용인원)종사자 정원종사자 현원연락처(사무실)위도경도데이터기준일자
0평화의마을제주특별자치도 서귀포시 대정읍 중산간서로 2195-12제주특별자치도 서귀포시 대정읍 구억리 3082001-09-1929669394027118064-794-627733.279125126.2922622023-06-14
1정혜재활원제주특별자치도 서귀포시 인정오름로 85번길41제주특별자치도 서귀포시 토평동 28952002-04-257190136144413838064-732-500433.284677126.5814772023-06-14
2어울림터제주특별자치도 서귀포시 인정오름로26제주특별자치도 서귀포시 토평동 19162002-07-03311385130221111064-732-029533.287253126.5839422023-06-14
3우리집제주특별자치도 서귀포시 대정읍 중산간서로 2195-12제주특별자치도 서귀포시 대정읍 구억리 3082004-04-0629661084311064-794-627733.279125126.2922622023-06-14
4서귀포작은예수의 집제주특별자치도 서귀포시 속골로 24제주특별자치도 서귀포시 호근동 1586-42005-07-2961035412111111064-739-363333.241992126.5296282023-06-14
5살레시오의집제주특별자치도 서귀포시 남원읍 서의로 63제주특별자치도 서귀포시 남원읍 남원리 8232005-08-0418002154622064-764-441933.298025126.7094852023-06-14
6서귀포주간보호센터제주특별자치도 서귀포시 남원읍 위미 상원북로 24번길 104제주특별자치도 서귀포시 남원읍 위미리 2184-52008-12-181272164202096064-764-338533.288198126.6651192023-06-14
7서귀포시각장애인주간보호시설제주특별자치도 서귀포시 월평로 10제주특별자치도 서귀포시 월평동 476-12009-03-06774391202066064-738-111133.244137126.4593822023-06-14
8행복둥지제주특별자치도 서귀포시 예래로 119-3제주특별자치도 서귀포시 상예동 762-12009-10-22<NA>844410064-764-338533.251794126.3954482023-06-14
9행복이네제주특별자치도 서귀포시 대정읍 신영로16제주특별자치도 서귀포시 대정읍 하모리 1187-22009-10-231121124310064-794-627733.224092126.2470672023-06-14
시설명소재지지번주소설립개원일부지 시설규모(제곱미터)건물 시설규모(제곱미터)수용(이용)인원 정원현원(1일이용인원)종사자 정원종사자 현원연락처(사무실)위도경도데이터기준일자
20파란나라장애인주간활동센터제주특별자치도 서귀포시 동홍북로51번길 24제주특별자치도 서귀포시 동홍동 667-12019-05-13947188181887064-763-235733.260395126.5753722023-06-14
21장애인단기거주시설 행복나눔제주특별자치도 서귀포시 남원읍 위미대성로 19-9제주특별자치도 서귀포시 남원읍 위미리 2083-22019-10-0474422610598064-764-678833.279891126.6675512023-06-14
22서귀포해오름주간활동센터제주특별자치도 서귀포시 표선면 표선중앙로 9-8제주특별자치도 서귀포시 표선면 표선리 311-62019-10-301029404181887064-787-235833.331811126.830042023-06-14
23슬기네집제주특별자치도 서귀포시 동홍중앙로 33-1제주특별자치도 서귀포시 동홍동 648-18번지2020-08-11<NA>754411064-767-100133.253808126.5715592023-06-14
24아이꿈터제주특별자치도 대정읍 동일하모로 227-13제주특별자치도 서귀포시 대정읍 하모리 1570-22020-12-08<NA>100505098064-792-757633.226724126.2553352023-06-14
25온유네집제주특별자치도 서귀포시 동홍중앙로 33-1제주특별자치도 서귀포시 동홍동 648-18번지2021-07-13<NA>754411064-762-500633.253808126.5715592023-06-14
26해명원제주특별자치도 서귀포시 대정읍 영서중로 67-1제주특별자치도 서귀포시 대정읍 상모로 3111-42021-07-26<NA>834411064-794-772433.253808126.5715592023-06-14
27해주원제주특별자치도 서귀포시 대정읍 영서중로 71제주특별자치도 서귀포시 대정읍 상모로 3111-72022-05-20<NA>764311070-4064-33053.221399126.2613412023-06-14
28서귀포서부주간활동센터제주특별자치도 서귀포시 안덕면 화순서서로 326-7제주특별자치도 서귀포시 안덕면 화순서서로 326-72023-02-21155639720092064-792-147133.274962126.3138922023-06-14
29바굼지제주특별자치도 서귀포시 성산읍 신풍하동로 49-16제주특별자치도 서귀포시 성산읍 신풍리 4922023-03-29342844010<NA>33.36263126.8417752023-06-14