Overview

Dataset statistics

Number of variables7
Number of observations789
Missing cells17
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.0 KiB
Average record size in memory57.2 B

Variable types

Numeric1
Categorical2
Text4

Dataset

Description전국 지방자치단체에서 설립한 지방 출자출연기관(789개)의 주소지 현황을 공공데이터 포탈에 게시함으로써 이와 같은 자료를 필요로 하는 국민들에게 알리고자 합니다.
Author행정안전부
URLhttps://www.data.go.kr/data/15065372/fileData.do

Alerts

연번 is highly overall correlated with 시도High correlation
시도 is highly overall correlated with 연번High correlation
연락처 has 16 (2.0%) missing valuesMissing
연번 has unique valuesUnique
기관명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:52:18.090684
Analysis finished2023-12-12 05:52:19.147036
Duration1.06 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct789
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean395
Minimum1
Maximum789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-12-12T14:52:19.235261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile40.4
Q1198
median395
Q3592
95-th percentile749.6
Maximum789
Range788
Interquartile range (IQR)394

Descriptive statistics

Standard deviation227.90897
Coefficient of variation (CV)0.57698474
Kurtosis-1.2
Mean395
Median Absolute Deviation (MAD)197
Skewness0
Sum311655
Variance51942.5
MonotonicityStrictly increasing
2023-12-12T14:52:19.435004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
520 1
 
0.1%
522 1
 
0.1%
523 1
 
0.1%
524 1
 
0.1%
525 1
 
0.1%
526 1
 
0.1%
527 1
 
0.1%
528 1
 
0.1%
529 1
 
0.1%
Other values (779) 779
98.7%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
789 1
0.1%
788 1
0.1%
787 1
0.1%
786 1
0.1%
785 1
0.1%
784 1
0.1%
783 1
0.1%
782 1
0.1%
781 1
0.1%
780 1
0.1%

기관종류
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
출연기관
691 
출자기관
98 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
출연기관 691
87.6%
출자기관 98
 
12.4%

Length

2023-12-12T14:52:19.617450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:52:19.731807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
출연기관 691
87.6%
출자기관 98
 
12.4%

시도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
경기도
139 
강원도
76 
전라남도
72 
경상북도
68 
경상남도
66 
Other values (12)
368 

Length

Max length7
Median length5
Mean length4.035488
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 139
17.6%
강원도 76
9.6%
전라남도 72
9.1%
경상북도 68
8.6%
경상남도 66
8.4%
서울특별시 65
8.2%
충청남도 61
7.7%
전라북도 58
7.4%
충청북도 40
 
5.1%
부산광역시 31
 
3.9%
Other values (7) 113
14.3%

Length

2023-12-12T14:52:19.865279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 139
17.6%
강원도 76
9.6%
전라남도 72
9.1%
경상북도 68
8.6%
경상남도 66
8.4%
서울특별시 65
8.2%
충청남도 61
7.7%
전라북도 58
7.4%
충청북도 40
 
5.1%
부산광역시 31
 
3.9%
Other values (7) 113
14.3%

시군구
Text

Distinct203
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2023-12-12T14:52:20.194879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.468948
Min length2

Characters and Unicode

Total characters2737
Distinct characters133
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)6.3%

Sample

1st row서울특별시
2nd row서울특별시
3rd row서울특별시
4th row서울특별시
5th row서울특별시
ValueCountFrequency (%)
강원도 26
 
3.3%
경상북도 24
 
3.0%
전라남도 23
 
2.9%
경기도 22
 
2.8%
서울특별시 20
 
2.5%
부산광역시 19
 
2.4%
충청남도 19
 
2.4%
경상남도 16
 
2.0%
광주광역시 16
 
2.0%
전라북도 14
 
1.8%
Other values (193) 590
74.8%
2023-12-12T14:52:20.702816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 389
 
14.2%
도 180
 
6.6%
군 160
 
5.8%
광 111
 
4.1%
구 105
 
3.8%
주 87
 
3.2%
역 82
 
3.0%
남 78
 
2.8%
천 72
 
2.6%
경 72
 
2.6%
Other values (123) 1401
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2737
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 389
 
14.2%
도 180
 
6.6%
군 160
 
5.8%
광 111
 
4.1%
구 105
 
3.8%
주 87
 
3.2%
역 82
 
3.0%
남 78
 
2.8%
천 72
 
2.6%
경 72
 
2.6%
Other values (123) 1401
51.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2737
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 389
 
14.2%
도 180
 
6.6%
군 160
 
5.8%
광 111
 
4.1%
구 105
 
3.8%
주 87
 
3.2%
역 82
 
3.0%
남 78
 
2.8%
천 72
 
2.6%
경 72
 
2.6%
Other values (123) 1401
51.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2737
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 389
 
14.2%
도 180
 
6.6%
군 160
 
5.8%
광 111
 
4.1%
구 105
 
3.8%
주 87
 
3.2%
역 82
 
3.0%
남 78
 
2.8%
천 72
 
2.6%
경 72
 
2.6%
Other values (123) 1401
51.2%

기관명
Text

UNIQUE 

Distinct789
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2023-12-12T14:52:20.992246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length18
Mean length9.6058302
Min length2

Characters and Unicode

Total characters7579
Distinct characters351
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique789 ?
Unique (%)100.0%

Sample

1st row서울의료원
2nd row서울연구원
3rd row서울산업진흥원
4th row서울신용보증재단
5th row세종문화회관
ValueCountFrequency (%)
재단법인 145
 
14.7%
주식회사 19
 
1.9%
사회서비스원 7
 
0.7%
경상남도 4
 
0.4%
여성가족재단 3
 
0.3%
광주광역시 3
 
0.3%
서울특별시 2
 
0.2%
경상북도 2
 
0.2%
인재평생교육진흥원 2
 
0.2%
충청북도 2
 
0.2%
Other values (798) 799
80.9%
2023-12-12T14:52:21.426810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
재 558
 
7.4%
단 518
 
6.8%
원 253
 
3.3%
인 244
 
3.2%
199
 
2.6%
주 187
 
2.5%
화 174
 
2.3%
시 161
 
2.1%
법 156
 
2.1%
회 151
 
2.0%
Other values (341) 4978
65.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7205
95.1%
Space Separator 199
 
2.6%
Open Punctuation 76
 
1.0%
Close Punctuation 76
 
1.0%
Decimal Number 15
 
0.2%
Uppercase Letter 6
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
재 558
 
7.7%
단 518
 
7.2%
원 253
 
3.5%
인 244
 
3.4%
주 187
 
2.6%
화 174
 
2.4%
시 161
 
2.2%
법 156
 
2.2%
회 151
 
2.1%
산 151
 
2.1%
Other values (326) 4652
64.6%
Decimal Number
ValueCountFrequency (%)
0 5
33.3%
1 3
20.0%
4 2
 
13.3%
2 2
 
13.3%
3 1
 
6.7%
8 1
 
6.7%
5 1
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
C 3
50.0%
T 1
 
16.7%
I 1
 
16.7%
F 1
 
16.7%
Space Separator
ValueCountFrequency (%)
199
100.0%
Open Punctuation
ValueCountFrequency (%)
( 76
100.0%
Close Punctuation
ValueCountFrequency (%)
) 76
100.0%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7205
95.1%
Common 368
 
4.9%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
재 558
 
7.7%
단 518
 
7.2%
원 253
 
3.5%
인 244
 
3.4%
주 187
 
2.6%
화 174
 
2.4%
시 161
 
2.2%
법 156
 
2.2%
회 151
 
2.1%
산 151
 
2.1%
Other values (326) 4652
64.6%
Common
ValueCountFrequency (%)
199
54.1%
( 76
 
20.7%
) 76
 
20.7%
0 5
 
1.4%
1 3
 
0.8%
4 2
 
0.5%
· 2
 
0.5%
2 2
 
0.5%
3 1
 
0.3%
8 1
 
0.3%
Latin
ValueCountFrequency (%)
C 3
50.0%
T 1
 
16.7%
I 1
 
16.7%
F 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7205
95.1%
ASCII 372
 
4.9%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
재 558
 
7.7%
단 518
 
7.2%
원 253
 
3.5%
인 244
 
3.4%
주 187
 
2.6%
화 174
 
2.4%
시 161
 
2.2%
법 156
 
2.2%
회 151
 
2.1%
산 151
 
2.1%
Other values (326) 4652
64.6%
ASCII
ValueCountFrequency (%)
199
53.5%
( 76
 
20.4%
) 76
 
20.4%
0 5
 
1.3%
1 3
 
0.8%
C 3
 
0.8%
4 2
 
0.5%
2 2
 
0.5%
3 1
 
0.3%
T 1
 
0.3%
Other values (4) 4
 
1.1%
None
ValueCountFrequency (%)
· 2
100.0%

주소
Text

Distinct783
Distinct (%)99.4%
Missing1
Missing (%)0.1%
Memory size6.3 KiB
2023-12-12T14:52:21.774643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length40
Mean length23.625635
Min length4

Characters and Unicode

Total characters18617
Distinct characters437
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique778 ?
Unique (%)98.7%

Sample

1st row서울특별시 중랑구 신내로 156
2nd row서울특별시 서초구 남부순환로 340길 57
3rd row서울특별시 마포구 월드컵북로 400(상암동)
4th row서울특별시 마포구 마포대로 163 (공덕동, 서울신용보증재단빌딩)
5th row서울특별시 세종대로 175
ValueCountFrequency (%)
경기도 127
 
3.2%
강원도 73
 
1.8%
서울특별시 47
 
1.2%
전라북도 40
 
1.0%
2층 40
 
1.0%
경상북도 38
 
0.9%
경상남도 35
 
0.9%
전라남도 35
 
0.9%
충청남도 34
 
0.8%
전남 29
 
0.7%
Other values (1940) 3512
87.6%
2023-12-12T14:52:22.277737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3245
 
17.4%
로 672
 
3.6%
시 669
 
3.6%
1 592
 
3.2%
도 468
 
2.5%
2 445
 
2.4%
구 404
 
2.2%
3 340
 
1.8%
동 328
 
1.8%
경 311
 
1.7%
Other values (427) 11143
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11605
62.3%
Space Separator 3245
 
17.4%
Decimal Number 2890
 
15.5%
Close Punctuation 252
 
1.4%
Open Punctuation 252
 
1.4%
Other Punctuation 235
 
1.3%
Dash Punctuation 101
 
0.5%
Uppercase Letter 32
 
0.2%
Math Symbol 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 672
 
5.8%
시 669
 
5.8%
도 468
 
4.0%
구 404
 
3.5%
동 328
 
2.8%
경 311
 
2.7%
남 279
 
2.4%
산 276
 
2.4%
길 230
 
2.0%
군 218
 
1.9%
Other values (391) 7750
66.8%
Uppercase Letter
ValueCountFrequency (%)
B 8
25.0%
A 5
15.6%
C 4
12.5%
F 2
 
6.2%
K 2
 
6.2%
D 2
 
6.2%
T 2
 
6.2%
M 1
 
3.1%
H 1
 
3.1%
J 1
 
3.1%
Other values (4) 4
12.5%
Decimal Number
ValueCountFrequency (%)
1 592
20.5%
2 445
15.4%
3 340
11.8%
5 283
9.8%
0 263
9.1%
4 239
8.3%
7 203
 
7.0%
6 186
 
6.4%
8 174
 
6.0%
9 165
 
5.7%
Other Punctuation
ValueCountFrequency (%)
, 227
96.6%
. 5
 
2.1%
/ 2
 
0.9%
· 1
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
t 1
50.0%
Space Separator
ValueCountFrequency (%)
3245
100.0%
Close Punctuation
ValueCountFrequency (%)
) 252
100.0%
Open Punctuation
ValueCountFrequency (%)
( 252
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 101
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11605
62.3%
Common 6978
37.5%
Latin 34
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 672
 
5.8%
시 669
 
5.8%
도 468
 
4.0%
구 404
 
3.5%
동 328
 
2.8%
경 311
 
2.7%
남 279
 
2.4%
산 276
 
2.4%
길 230
 
2.0%
군 218
 
1.9%
Other values (391) 7750
66.8%
Common
ValueCountFrequency (%)
3245
46.5%
1 592
 
8.5%
2 445
 
6.4%
3 340
 
4.9%
5 283
 
4.1%
0 263
 
3.8%
) 252
 
3.6%
( 252
 
3.6%
4 239
 
3.4%
, 227
 
3.3%
Other values (10) 840
 
12.0%
Latin
ValueCountFrequency (%)
B 8
23.5%
A 5
14.7%
C 4
11.8%
F 2
 
5.9%
K 2
 
5.9%
D 2
 
5.9%
T 2
 
5.9%
M 1
 
2.9%
H 1
 
2.9%
J 1
 
2.9%
Other values (6) 6
17.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11605
62.3%
ASCII 7011
37.7%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3245
46.3%
1 592
 
8.4%
2 445
 
6.3%
3 340
 
4.8%
5 283
 
4.0%
0 263
 
3.8%
) 252
 
3.6%
( 252
 
3.6%
4 239
 
3.4%
, 227
 
3.2%
Other values (25) 873
 
12.5%
Hangul
ValueCountFrequency (%)
로 672
 
5.8%
시 669
 
5.8%
도 468
 
4.0%
구 404
 
3.5%
동 328
 
2.8%
경 311
 
2.7%
남 279
 
2.4%
산 276
 
2.4%
길 230
 
2.0%
군 218
 
1.9%
Other values (391) 7750
66.8%
None
ValueCountFrequency (%)
· 1
100.0%

연락처
Text

MISSING 

Distinct770
Distinct (%)99.6%
Missing16
Missing (%)2.0%
Memory size6.3 KiB
2023-12-12T14:52:22.505881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length12
Mean length12.037516
Min length9

Characters and Unicode

Total characters9305
Distinct characters17
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

Unique767 ?
Unique (%)99.2%

Sample

1st row02-2276-7084
2nd row02-2149-1020
3rd row02-2222-3851
4th row02-2174-5131
5th row02-399-1516
ValueCountFrequency (%)
054-773-5639 2
 
0.3%
02-729-5842 2
 
0.3%
031-851-0922 2
 
0.3%
064-726-9971 1
 
0.1%
041-635-6997 1
 
0.1%
063-859-5152 1
 
0.1%
063-281-4158 1
 
0.1%
02-2276-7084 1
 
0.1%
063-281-5082 1
 
0.1%
063-210-6511 1
 
0.1%
Other values (765) 765
98.3%
2023-12-12T14:52:22.893842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1539
16.5%
0 1482
15.9%
3 1005
10.8%
2 849
9.1%
1 847
9.1%
5 818
8.8%
6 677
7.3%
4 664
7.1%
7 529
 
5.7%
8 485
 
5.2%
Other values (7) 410
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7751
83.3%
Dash Punctuation 1539
 
16.5%
Space Separator 6
 
0.1%
Other Punctuation 5
 
0.1%
Other Letter 2
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1482
19.1%
3 1005
13.0%
2 849
11.0%
1 847
10.9%
5 818
10.6%
6 677
8.7%
4 664
8.6%
7 529
 
6.8%
8 485
 
6.3%
9 395
 
5.1%
Other Letter
ValueCountFrequency (%)
내 1
50.0%
선 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 1539
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9303
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1539
16.5%
0 1482
15.9%
3 1005
10.8%
2 849
9.1%
1 847
9.1%
5 818
8.8%
6 677
7.3%
4 664
7.1%
7 529
 
5.7%
8 485
 
5.2%
Other values (5) 408
 
4.4%
Hangul
ValueCountFrequency (%)
내 1
50.0%
선 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9303
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1539
16.5%
0 1482
15.9%
3 1005
10.8%
2 849
9.1%
1 847
9.1%
5 818
8.8%
6 677
7.3%
4 664
7.1%
7 529
 
5.7%
8 485
 
5.2%
Other values (5) 408
 
4.4%
Hangul
ValueCountFrequency (%)
내 1
50.0%
선 1
50.0%

Interactions

2023-12-12T14:52:18.692726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:52:22.993671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번기관종류시도
연번1.0000.1580.966
기관종류0.1581.0000.177
시도0.9660.1771.000
2023-12-12T14:52:23.094032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도기관종류
시도1.0000.157
기관종류0.1571.000
2023-12-12T14:52:23.188875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번기관종류시도
연번1.0000.1210.842
기관종류0.1211.0000.157
시도0.8420.1571.000

Missing values

2023-12-12T14:52:18.849778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:52:18.963734image/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.
2023-12-12T14:52:19.089003image/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

연번기관종류시도시군구기관명주소연락처
01출연기관서울특별시서울특별시서울의료원서울특별시 중랑구 신내로 15602-2276-7084
12출연기관서울특별시서울특별시서울연구원서울특별시 서초구 남부순환로 340길 5702-2149-1020
23출연기관서울특별시서울특별시서울산업진흥원서울특별시 마포구 월드컵북로 400(상암동)02-2222-3851
34출연기관서울특별시서울특별시서울신용보증재단서울특별시 마포구 마포대로 163 (공덕동, 서울신용보증재단빌딩)02-2174-5131
45출연기관서울특별시서울특별시세종문화회관서울특별시 세종대로 17502-399-1516
56출연기관서울특별시서울특별시서울시여성가족재단서울특별시 동작구 여의대방로54길 18(대방동)02-810-5082
67출연기관서울특별시서울특별시서울시복지재단(우)04147 서울시 마포구 백범로31길21, 서울시복지재단02-6353-0265
78출연기관서울특별시서울특별시서울문화재단서울특별시 동대문구 청계천로 51702-3290-7148
89출연기관서울특별시서울특별시서울시립교향악단서울특별시 종로구 세종대로 17502-3700-6356
910출연기관서울특별시서울특별시서울디자인재단서울특별시 종로구 율곡로 283(종로6가)02-2096-0095
연번기관종류시도시군구기관명주소연락처
779780출연기관제주특별자치도제주특별자치도제주연구원제주특별자치도 제주시 아연로 253064-729-0545
780781출연기관제주특별자치도제주특별자치도제주테크노파크제주특별자치도 제주시 중앙로217 제주벤처마루 9층064-720-2318
781782출연기관제주특별자치도제주특별자치도제주특별자치도경제통상진흥원제주특별자치도 제주시 연삼로 473064-805-3375
782783출연기관제주특별자치도제주특별자치도제주신용보증재단제주특별자치도 제주시 연북로 33, 4층064-750-4842
783784출연기관제주특별자치도제주특별자치도제주문화예술재단제주특별자치도 제주시 동광로 51064-800-9113
784785출연기관제주특별자치도제주특별자치도제주여성가족연구원제주특별자치도 제주시 연오로89 2,3층064-720-4923
785786출연기관제주특별자치도제주특별자치도제주평생교육장학진흥원제주특별자치도 제주시 연삼로 473064-726-9971
786787출연기관제주특별자치도제주특별자치도제주4·3평화재단제주특별자치도 제주시 명림로 430064-723-4377
787788출연기관제주특별자치도제주특별자치도제주한의약연구원제주시 첨단로 213-3 (영평동) 스마트빌딩 220호064-702-1224
788789출연기관제주특별자치도제주특별자치도제주영상·문화산업진흥원제주특별자치도 제주시 신산로 82064-735-0631