Overview

Dataset statistics

Number of variables8
Number of observations810
Missing cells4
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.5 KiB
Average record size in memory65.2 B

Variable types

Numeric1
Categorical3
Text4

Dataset

Description전북특별자치도교육청 교육기관 현황에 대한 데이터로 지역, 학교급, 설립구분, 학교명, 주소, 홈페이지 주소 등의 항목을 제공합니다.
Author전북특별자치도교육청
URLhttps://www.data.go.kr/data/3068876/fileData.do

Alerts

순 is highly overall correlated with 지역High correlation
지역 is highly overall correlated with 순High correlation
설립 is highly imbalanced (66.7%)Imbalance
순 has unique valuesUnique

Reproduction

Analysis started2024-03-14 14:45:00.724022
Analysis finished2024-03-14 14:45:02.758495
Duration2.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct810
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405.5
Minimum1
Maximum810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-03-14T23:45:02.988131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41.45
Q1203.25
median405.5
Q3607.75
95-th percentile769.55
Maximum810
Range809
Interquartile range (IQR)404.5

Descriptive statistics

Standard deviation233.97115
Coefficient of variation (CV)0.57699421
Kurtosis-1.2
Mean405.5
Median Absolute Deviation (MAD)202.5
Skewness0
Sum328455
Variance54742.5
MonotonicityStrictly increasing
2024-03-14T23:45:03.454747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
545 1
 
0.1%
535 1
 
0.1%
536 1
 
0.1%
537 1
 
0.1%
538 1
 
0.1%
539 1
 
0.1%
540 1
 
0.1%
541 1
 
0.1%
542 1
 
0.1%
Other values (800) 800
98.8%
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 (%)
810 1
0.1%
809 1
0.1%
808 1
0.1%
807 1
0.1%
806 1
0.1%
805 1
0.1%
804 1
0.1%
803 1
0.1%
802 1
0.1%
801 1
0.1%

지역
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
전주
158 
익산
110 
군산
94 
정읍
69 
김제
60 
Other values (9)
319 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주 158
19.5%
익산 110
13.6%
군산 94
11.6%
정읍 69
8.5%
김제 60
 
7.4%
완주 54
 
6.7%
남원 52
 
6.4%
부안 44
 
5.4%
고창 42
 
5.2%
진안 29
 
3.6%
Other values (4) 98
12.1%

Length

2024-03-14T23:45:03.877215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주 158
19.5%
익산 110
13.6%
군산 94
11.6%
정읍 69
8.5%
김제 60
 
7.4%
완주 54
 
6.7%
남원 52
 
6.4%
부안 44
 
5.4%
고창 42
 
5.2%
진안 29
 
3.6%
Other values (4) 98
12.1%

급별
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
초등
426 
중등
211 
고등
133 
유치원
 
30
특수
 
10

Length

Max length3
Median length2
Mean length2.037037
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고등
2nd row중등
3rd row고등
4th row고등
5th row고등

Common Values

ValueCountFrequency (%)
초등 426
52.6%
중등 211
26.0%
고등 133
 
16.4%
유치원 30
 
3.7%
특수 10
 
1.2%

Length

2024-03-14T23:45:04.244672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:45:04.580080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
초등 426
52.6%
중등 211
26.0%
고등 133
 
16.4%
유치원 30
 
3.7%
특수 10
 
1.2%

설립
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
공립
684 
사립
121 
국립
 
4
설립
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row사립
2nd row공립
3rd row공립
4th row사립
5th row사립

Common Values

ValueCountFrequency (%)
공립 684
84.4%
사립 121
 
14.9%
국립 4
 
0.5%
설립 1
 
0.1%

Length

2024-03-14T23:45:04.947208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:45:05.269310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공립 684
84.4%
사립 121
 
14.9%
국립 4
 
0.5%
설립 1
 
0.1%

학교명
Text

Distinct807
Distinct (%)99.9%
Missing2
Missing (%)0.2%
Memory size6.5 KiB
2024-03-14T23:45:06.149763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length6.7029703
Min length4

Characters and Unicode

Total characters5416
Distinct characters237
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique806 ?
Unique (%)99.8%

Sample

1st row상산고등학교
2nd row서전주중학교
3rd row양현고등학교
4th row완산고등학교
5th row완산여자고등학교
ValueCountFrequency (%)
부안초등학교 2
 
0.2%
서영여자고등학교 1
 
0.1%
왕신여자중학교 1
 
0.1%
소성초등학교 1
 
0.1%
배영중학교 1
 
0.1%
백암초등학교 1
 
0.1%
보성초등학교 1
 
0.1%
북면초등학교 1
 
0.1%
산외중학교 1
 
0.1%
산외초등학교 1
 
0.1%
Other values (797) 797
98.6%
2024-03-14T23:45:07.527332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 798
14.7%
학 797
14.7%
등 564
 
10.4%
초 428
 
7.9%
중 232
 
4.3%
전 170
 
3.1%
주 159
 
2.9%
고 150
 
2.8%
산 141
 
2.6%
원 68
 
1.3%
Other values (227) 1909
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5408
99.9%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 798
14.8%
학 797
14.7%
등 564
 
10.4%
초 428
 
7.9%
중 232
 
4.3%
전 170
 
3.1%
주 159
 
2.9%
고 150
 
2.8%
산 141
 
2.6%
원 68
 
1.3%
Other values (225) 1901
35.2%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5408
99.9%
Common 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 798
14.8%
학 797
14.7%
등 564
 
10.4%
초 428
 
7.9%
중 232
 
4.3%
전 170
 
3.1%
주 159
 
2.9%
고 150
 
2.8%
산 141
 
2.6%
원 68
 
1.3%
Other values (225) 1901
35.2%
Common
ValueCountFrequency (%)
) 4
50.0%
( 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5408
99.9%
ASCII 8
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 798
14.8%
학 797
14.7%
등 564
 
10.4%
초 428
 
7.9%
중 232
 
4.3%
전 170
 
3.1%
주 159
 
2.9%
고 150
 
2.8%
산 141
 
2.6%
원 68
 
1.3%
Other values (225) 1901
35.2%
ASCII
ValueCountFrequency (%)
) 4
50.0%
( 4
50.0%

주소
Text

Distinct761
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
2024-03-14T23:45:09.083170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length14.517284
Min length7

Characters and Unicode

Total characters11759
Distinct characters272
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

Unique715 ?
Unique (%)88.3%

Sample

1st row전주시 완산구 거마평로 130
2nd row전주시 완산구 거마평로 205
3rd row전주시 덕진구 틀못2길 9
4th row전주시 완산구 능안자구길 55
5th row전주시 완산구 덕적골1길 60
ValueCountFrequency (%)
전주시 154
 
5.1%
익산시 109
 
3.6%
군산시 93
 
3.1%
완산구 85
 
2.8%
덕진구 73
 
2.4%
정읍시 66
 
2.2%
김제시 59
 
1.9%
완주군 52
 
1.7%
남원시 50
 
1.7%
부안군 43
 
1.4%
Other values (1193) 2245
74.1%
2024-03-14T23:45:11.075051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2219
 
18.9%
1 537
 
4.6%
시 535
 
4.5%
로 508
 
4.3%
산 427
 
3.6%
면 372
 
3.2%
군 357
 
3.0%
길 340
 
2.9%
2 325
 
2.8%
3 296
 
2.5%
Other values (262) 5843
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6990
59.4%
Decimal Number 2402
 
20.4%
Space Separator 2219
 
18.9%
Dash Punctuation 148
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 535
 
7.7%
로 508
 
7.3%
산 427
 
6.1%
면 372
 
5.3%
군 357
 
5.1%
길 340
 
4.9%
주 252
 
3.6%
구 193
 
2.8%
전 171
 
2.4%
읍 163
 
2.3%
Other values (250) 3672
52.5%
Decimal Number
ValueCountFrequency (%)
1 537
22.4%
2 325
13.5%
3 296
12.3%
4 201
 
8.4%
5 191
 
8.0%
9 184
 
7.7%
7 183
 
7.6%
6 177
 
7.4%
0 161
 
6.7%
8 147
 
6.1%
Space Separator
ValueCountFrequency (%)
2219
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6990
59.4%
Common 4769
40.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 535
 
7.7%
로 508
 
7.3%
산 427
 
6.1%
면 372
 
5.3%
군 357
 
5.1%
길 340
 
4.9%
주 252
 
3.6%
구 193
 
2.8%
전 171
 
2.4%
읍 163
 
2.3%
Other values (250) 3672
52.5%
Common
ValueCountFrequency (%)
2219
46.5%
1 537
 
11.3%
2 325
 
6.8%
3 296
 
6.2%
4 201
 
4.2%
5 191
 
4.0%
9 184
 
3.9%
7 183
 
3.8%
6 177
 
3.7%
0 161
 
3.4%
Other values (2) 295
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6990
59.4%
ASCII 4769
40.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2219
46.5%
1 537
 
11.3%
2 325
 
6.8%
3 296
 
6.2%
4 201
 
4.2%
5 191
 
4.0%
9 184
 
3.9%
7 183
 
3.8%
6 177
 
3.7%
0 161
 
3.4%
Other values (2) 295
 
6.2%
Hangul
ValueCountFrequency (%)
시 535
 
7.7%
로 508
 
7.3%
산 427
 
6.1%
면 372
 
5.3%
군 357
 
5.1%
길 340
 
4.9%
주 252
 
3.6%
구 193
 
2.8%
전 171
 
2.4%
읍 163
 
2.3%
Other values (250) 3672
52.5%

연락처
Text

Distinct804
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
2024-03-14T23:45:12.084402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters9720
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

Unique798 ?
Unique (%)98.5%

Sample

1st row063-239-5300
2nd row063-221-1359
3rd row063-249-6101
4th row063-220-9202
5th row063-227-4452
ValueCountFrequency (%)
063-582-9822 2
 
0.2%
063-432-4072 2
 
0.2%
063-433-6373 2
 
0.2%
063-324-4417 2
 
0.2%
063-652-4305 2
 
0.2%
063-636-5763 2
 
0.2%
063-536-0146 1
 
0.1%
063-534-3274 1
 
0.1%
063-533-4124 1
 
0.1%
063-533-4123 1
 
0.1%
Other values (794) 794
98.0%
2024-03-14T23:45:13.462832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1620
16.7%
0 1470
15.1%
3 1439
14.8%
6 1393
14.3%
2 875
9.0%
5 717
7.4%
4 632
 
6.5%
1 576
 
5.9%
8 425
 
4.4%
7 345
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8100
83.3%
Dash Punctuation 1620
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1470
18.1%
3 1439
17.8%
6 1393
17.2%
2 875
10.8%
5 717
8.9%
4 632
7.8%
1 576
 
7.1%
8 425
 
5.2%
7 345
 
4.3%
9 228
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 1620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9720
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1620
16.7%
0 1470
15.1%
3 1439
14.8%
6 1393
14.3%
2 875
9.0%
5 717
7.4%
4 632
 
6.5%
1 576
 
5.9%
8 425
 
4.4%
7 345
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1620
16.7%
0 1470
15.1%
3 1439
14.8%
6 1393
14.3%
2 875
9.0%
5 717
7.4%
4 632
 
6.5%
1 576
 
5.9%
8 425
 
4.4%
7 345
 
3.5%

홈페이지
Text

Distinct791
Distinct (%)97.9%
Missing2
Missing (%)0.2%
Memory size6.5 KiB
2024-03-14T23:45:14.341944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length37
Mean length30.902228
Min length20

Characters and Unicode

Total characters24969
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique775 ?
Unique (%)95.9%

Sample

1st rowhttps://school.jbedu.kr/sangsan
2nd rowhttps://school.jbedu.kr/seojeonju
3rd rowhttps://school.jbedu.kr/yanghyeon-h
4th rowhttps://school.jbedu.kr/wansango
5th rowhttps://school.jbedu.kr/wansan-h
ValueCountFrequency (%)
https://school.jbedu.kr/ancheon 3
 
0.4%
https://school.jbedu.kr/mupung-mh 2
 
0.2%
https://school.jbedu.kr/sinsido 2
 
0.2%
https://school.jbedu.kr/kurim 2
 
0.2%
https://school.jbedu.kr/sanseo 2
 
0.2%
https://school.jbedu.kr/seonyudo 2
 
0.2%
https://school.jbedu.kr/jb-unam 2
 
0.2%
https://school.jbedu.kr/jbsports 2
 
0.2%
https://school.jbedu.kr/inwol-mh 2
 
0.2%
https://school.jbedu.kr/wido 2
 
0.2%
Other values (781) 787
97.4%
2024-03-14T23:45:15.365861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 2423
 
9.7%
o 2102
 
8.4%
s 2011
 
8.1%
h 1850
 
7.4%
t 1648
 
6.6%
. 1616
 
6.5%
e 1127
 
4.5%
j 1120
 
4.5%
u 1112
 
4.5%
b 1004
 
4.0%
Other values (24) 8956
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19882
79.6%
Other Punctuation 4847
 
19.4%
Dash Punctuation 213
 
0.9%
Decimal Number 27
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2102
 
10.6%
s 2011
 
10.1%
h 1850
 
9.3%
t 1648
 
8.3%
e 1127
 
5.7%
j 1120
 
5.6%
u 1112
 
5.6%
b 1004
 
5.0%
k 981
 
4.9%
d 925
 
4.7%
Other values (13) 6002
30.2%
Decimal Number
ValueCountFrequency (%)
0 8
29.6%
1 7
25.9%
4 5
18.5%
2 3
 
11.1%
3 2
 
7.4%
6 1
 
3.7%
5 1
 
3.7%
Other Punctuation
ValueCountFrequency (%)
/ 2423
50.0%
. 1616
33.3%
: 808
 
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 213
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19882
79.6%
Common 5087
 
20.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2102
 
10.6%
s 2011
 
10.1%
h 1850
 
9.3%
t 1648
 
8.3%
e 1127
 
5.7%
j 1120
 
5.6%
u 1112
 
5.6%
b 1004
 
5.0%
k 981
 
4.9%
d 925
 
4.7%
Other values (13) 6002
30.2%
Common
ValueCountFrequency (%)
/ 2423
47.6%
. 1616
31.8%
: 808
 
15.9%
- 213
 
4.2%
0 8
 
0.2%
1 7
 
0.1%
4 5
 
0.1%
2 3
 
0.1%
3 2
 
< 0.1%
6 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24969
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 2423
 
9.7%
o 2102
 
8.4%
s 2011
 
8.1%
h 1850
 
7.4%
t 1648
 
6.6%
. 1616
 
6.5%
e 1127
 
4.5%
j 1120
 
4.5%
u 1112
 
4.5%
b 1004
 
4.0%
Other values (24) 8956
35.9%

Interactions

2024-03-14T23:45:01.453061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T23:45:15.627542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순지역급별설립
순1.0000.9560.0440.118
지역0.9561.0000.0000.000
급별0.0440.0001.0000.366
설립0.1180.0000.3661.000
2024-03-14T23:45:15.881105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역급별설립
지역1.0000.0000.000
급별0.0001.0000.306
설립0.0000.3061.000
2024-03-14T23:45:16.121998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순지역급별설립
순1.0000.8160.0180.070
지역0.8161.0000.0000.000
급별0.0180.0001.0000.306
설립0.0700.0000.3061.000

Missing values

2024-03-14T23:45:01.889744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T23:45:02.310796image/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.
2024-03-14T23:45:02.611845image/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

순지역급별설립학교명주소연락처홈페이지
01전주고등사립상산고등학교전주시 완산구 거마평로 130063-239-5300https://school.jbedu.kr/sangsan
12전주중등공립서전주중학교전주시 완산구 거마평로 205063-221-1359https://school.jbedu.kr/seojeonju
23전주고등공립양현고등학교전주시 덕진구 틀못2길 9063-249-6101https://school.jbedu.kr/yanghyeon-h
34전주고등사립완산고등학교전주시 완산구 능안자구길 55063-220-9202https://school.jbedu.kr/wansango
45전주고등사립완산여자고등학교전주시 완산구 덕적골1길 60063-227-4452https://school.jbedu.kr/wansan-h
56전주중등사립완산중학교전주시 완산구 덕적골1길 60063-227-0741https://school.jbedu.kr/wansan-m
67전주고등사립우석고등학교전주시 덕진구 쪽구름2길 22063-211-0991https://school.jbedu.kr/woosuk
78전주고등사립유일여자고등학교전주시 덕진구 안덕원로 301063-245-3314https://school.jbedu.kr/yuil
89전주고등공립전라고등학교전주시 덕진구 솔내7길 25063-251-4902https://school.jbedu.kr/jeolla-h
910전주중등공립전라중학교전주시 덕진구 들사평1길 39063-252-2758https://school.jbedu.kr/jeolla
순지역급별설립학교명주소연락처홈페이지
800801무주초등공립부당초등학교무주군 부남면 부남로 1785063-322-0144https://school.jbedu.kr/budang
801802무주고등공립설천고등학교무주군 설천면 하평지길 19063-324-7507https://school.jbedu.kr/seolcheon-mh
802803무주중등공립설천중학교무주군 설천면 하평지길 19063-324-7033https://school.jbedu.kr/seolcheon-mh
803804무주초등공립설천초등학교무주군 설천면 무설로 1599063-324-7005https://school.jbedu.kr/sulchon
804805무주고등공립안성고등학교무주군 안성면 구량천로 147063-323-0274https://school.jbedu.kr/ansung-mh
805806무주중등공립안성중학교무주군 안성면 구량천로 149063-323-0115https://school.jbedu.kr/ansung-mh
806807무주초등공립안성초등학교무주군 안성면 칠연로 58063-323-2013https://school.jbedu.kr/ansung
807808무주중등공립적상중학교무주군 적상면 여용로 269063-324-6011https://school.jbedu.kr/jeoksang-m
808809무주초등공립적상초등학교무주군 적상면 삼가로 16063-324-6007https://school.jbedu.kr/jeoksang
809810무주고등사립푸른꿈고등학교무주군안성면진성로23129063-323-2058https://school.jbedu.kr/purunkum