Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory878.9 KiB
Average record size in memory90.0 B

Variable types

Categorical4
Text5
Numeric1

Dataset

Description문화체육관광부에서 제공하는 국가도서관통계시스템 내 전국학교도서관정보에 대한 주요 통계데이터를 제공하여 국민들의 문화체육관광 통계데이터 활성화 제고
Author문화체육관광부
URLhttps://www.data.go.kr/data/15072352/fileData.do

Alerts

평가년도 has constant value ""Constant
도서관구분 has constant value ""Constant
사서수 is highly imbalanced (61.0%)Imbalance
도서예산 has 123 (1.2%) zerosZeros

Reproduction

Analysis started2023-10-09 17:34:02.654648
Analysis finished2023-10-09 17:34:05.815627
Duration3.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

평가년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 10000
100.0%

Length

2023-10-10T02:34:05.962291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T02:34:06.170578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 10000
100.0%

도서관구분
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
LIBTYPE000004
10000 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLIBTYPE000004
2nd rowLIBTYPE000004
3rd rowLIBTYPE000004
4th rowLIBTYPE000004
5th rowLIBTYPE000004

Common Values

ValueCountFrequency (%)
LIBTYPE000004 10000
100.0%

Length

2023-10-10T02:34:06.424510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T02:34:06.649964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
libtype000004 10000
100.0%

도서관명
Text

Distinct8840
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-10-10T02:34:07.282004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length6.6398
Min length5

Characters and Unicode

Total characters66398
Distinct characters453
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8003 ?
Unique (%)80.0%

Sample

1st row장수초등학교
2nd row남강중학교
3rd row토월초등학교
4th row거학초등학교
5th row광주양동초등학교
ValueCountFrequency (%)
금성초등학교 10
 
0.1%
삼성초등학교 8
 
0.1%
옥산초등학교 7
 
0.1%
송정초등학교 6
 
0.1%
남양초등학교 6
 
0.1%
동산초등학교 6
 
0.1%
교동초등학교 6
 
0.1%
성산초등학교 6
 
0.1%
대덕초등학교 5
 
< 0.1%
반곡초등학교 5
 
< 0.1%
Other values (8831) 9936
99.4%
2023-10-10T02:34:08.454373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 10326
15.6%
학 10285
15.5%
등 7308
 
11.0%
초 5373
 
8.1%
중 2949
 
4.4%
고 2105
 
3.2%
대 902
 
1.4%
천 883
 
1.3%
산 881
 
1.3%
서 876
 
1.3%
Other values (443) 24510
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66373
> 99.9%
Lowercase Letter 14
 
< 0.1%
Uppercase Letter 10
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 10326
15.6%
학 10285
15.5%
등 7308
 
11.0%
초 5373
 
8.1%
중 2949
 
4.4%
고 2105
 
3.2%
대 902
 
1.4%
천 883
 
1.3%
산 881
 
1.3%
서 876
 
1.3%
Other values (427) 24485
36.9%
Lowercase Letter
ValueCountFrequency (%)
s 4
28.6%
e 2
14.3%
i 2
14.3%
n 2
14.3%
g 1
 
7.1%
l 1
 
7.1%
h 1
 
7.1%
u 1
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
P 2
20.0%
T 2
20.0%
I 2
20.0%
E 1
10.0%
B 1
10.0%
O 1
10.0%
K 1
10.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66373
> 99.9%
Latin 24
 
< 0.1%
Common 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 10326
15.6%
학 10285
15.5%
등 7308
 
11.0%
초 5373
 
8.1%
중 2949
 
4.4%
고 2105
 
3.2%
대 902
 
1.4%
천 883
 
1.3%
산 881
 
1.3%
서 876
 
1.3%
Other values (427) 24485
36.9%
Latin
ValueCountFrequency (%)
s 4
16.7%
e 2
 
8.3%
i 2
 
8.3%
P 2
 
8.3%
n 2
 
8.3%
T 2
 
8.3%
I 2
 
8.3%
E 1
 
4.2%
g 1
 
4.2%
l 1
 
4.2%
Other values (5) 5
20.8%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66373
> 99.9%
ASCII 25
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 10326
15.6%
학 10285
15.5%
등 7308
 
11.0%
초 5373
 
8.1%
중 2949
 
4.4%
고 2105
 
3.2%
대 902
 
1.4%
천 883
 
1.3%
산 881
 
1.3%
서 876
 
1.3%
Other values (427) 24485
36.9%
ASCII
ValueCountFrequency (%)
s 4
16.0%
e 2
 
8.0%
i 2
 
8.0%
P 2
 
8.0%
n 2
 
8.0%
T 2
 
8.0%
I 2
 
8.0%
E 1
 
4.0%
g 1
 
4.0%
l 1
 
4.0%
Other values (6) 6
24.0%

행정구역
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기
2078 
서울
1103 
경남
803 
경북
797 
전남
736 
Other values (12)
4483 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경북
2nd row서울
3rd row경기
4th row부산
5th row광주

Common Values

ValueCountFrequency (%)
경기 2078
20.8%
서울 1103
11.0%
경남 803
 
8.0%
경북 797
 
8.0%
전남 736
 
7.4%
전북 638
 
6.4%
충남 602
 
6.0%
강원 547
 
5.5%
부산 526
 
5.3%
인천 446
 
4.5%
Other values (7) 1724
17.2%

Length

2023-10-10T02:34:08.807534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 2078
20.8%
서울 1103
11.0%
경남 803
 
8.0%
경북 797
 
8.0%
전남 736
 
7.4%
전북 638
 
6.4%
충남 602
 
6.0%
강원 547
 
5.5%
부산 526
 
5.3%
인천 446
 
4.5%
Other values (7) 1724
17.2%

시군구
Text

Distinct207
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-10-10T02:34:09.480461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.959
Min length2

Characters and Unicode

Total characters29590
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

Unique0 ?
Unique (%)0.0%

Sample

1st row영주시
2nd row관악구
3rd row용인시
4th row연제구
5th row서구
ValueCountFrequency (%)
서구 228
 
2.3%
북구 227
 
2.3%
창원시 187
 
1.9%
수원시 170
 
1.7%
중구 170
 
1.7%
용인시 156
 
1.6%
남구 154
 
1.5%
청주시 151
 
1.5%
동구 151
 
1.5%
고양시 144
 
1.4%
Other values (197) 8262
82.6%
2023-10-10T02:34:10.523401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 5098
 
17.2%
구 3166
 
10.7%
군 2130
 
7.2%
주 1246
 
4.2%
천 904
 
3.1%
산 814
 
2.8%
성 814
 
2.8%
양 771
 
2.6%
남 655
 
2.2%
서 614
 
2.1%
Other values (123) 13378
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29590
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 5098
 
17.2%
구 3166
 
10.7%
군 2130
 
7.2%
주 1246
 
4.2%
천 904
 
3.1%
산 814
 
2.8%
성 814
 
2.8%
양 771
 
2.6%
남 655
 
2.2%
서 614
 
2.1%
Other values (123) 13378
45.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29590
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 5098
 
17.2%
구 3166
 
10.7%
군 2130
 
7.2%
주 1246
 
4.2%
천 904
 
3.1%
산 814
 
2.8%
성 814
 
2.8%
양 771
 
2.6%
남 655
 
2.2%
서 614
 
2.1%
Other values (123) 13378
45.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29590
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 5098
 
17.2%
구 3166
 
10.7%
군 2130
 
7.2%
주 1246
 
4.2%
천 904
 
3.1%
산 814
 
2.8%
성 814
 
2.8%
양 771
 
2.6%
남 655
 
2.2%
서 614
 
2.1%
Other values (123) 13378
45.2%

장서수
Text

Distinct8031
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-10-10T02:34:11.220343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.76
Min length2

Characters and Unicode

Total characters57600
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

Unique6494 ?
Unique (%)64.9%

Sample

1st row7915
2nd row14863
3rd row20574
4th row24948
5th row15789
ValueCountFrequency (%)
15267 7
 
0.1%
17212 6
 
0.1%
13985 5
 
0.1%
11565 5
 
0.1%
21783 4
 
< 0.1%
19484 4
 
< 0.1%
16489 4
 
< 0.1%
15028 4
 
< 0.1%
9645 4
 
< 0.1%
14021 4
 
< 0.1%
Other values (8020) 9788
99.5%
2023-10-10T02:34:12.301712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10165
17.6%
1 9202
16.0%
2 6475
11.2%
9 4087
7.1%
0 4084
7.1%
8 4064
 
7.1%
3 4047
 
7.0%
5 3928
 
6.8%
7 3882
 
6.7%
6 3872
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47435
82.4%
Space Separator 10165
 
17.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9202
19.4%
2 6475
13.7%
9 4087
8.6%
0 4084
8.6%
8 4064
8.6%
3 4047
8.5%
5 3928
8.3%
7 3882
8.2%
6 3872
8.2%
4 3794
8.0%
Space Separator
ValueCountFrequency (%)
10165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
10165
17.6%
1 9202
16.0%
2 6475
11.2%
9 4087
7.1%
0 4084
7.1%
8 4064
 
7.1%
3 4047
 
7.0%
5 3928
 
6.8%
7 3882
 
6.7%
6 3872
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10165
17.6%
1 9202
16.0%
2 6475
11.2%
9 4087
7.1%
0 4084
7.1%
8 4064
 
7.1%
3 4047
 
7.0%
5 3928
 
6.8%
7 3882
 
6.7%
6 3872
 
6.7%

사서수
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
8000 
1
1898 
2
 
101
<NA>
 
1

Length

Max length4
Median length2
Mean length2.0002
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
8000
80.0%
1 1898
 
19.0%
2 101
 
1.0%
<NA> 1
 
< 0.1%

Length

2023-10-10T02:34:12.651896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T02:34:13.245149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1898
94.9%
2 101
 
5.1%
na 1
 
< 0.1%

대출자수
Text

Distinct3072
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-10-10T02:34:14.011801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.0505
Min length2

Characters and Unicode

Total characters40505
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

Unique1426 ?
Unique (%)14.3%

Sample

1st row1
2nd row168
3rd row661
4th row1477
5th row375
ValueCountFrequency (%)
1 56
 
0.6%
2 31
 
0.3%
30 28
 
0.3%
120 23
 
0.2%
20 22
 
0.2%
100 22
 
0.2%
6 21
 
0.2%
164 20
 
0.2%
50 20
 
0.2%
14 20
 
0.2%
Other values (3061) 9027
97.2%
2023-10-10T02:34:15.081586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10710
26.4%
1 5093
12.6%
2 3743
 
9.2%
3 3256
 
8.0%
4 2909
 
7.2%
5 2688
 
6.6%
0 2611
 
6.4%
6 2454
 
6.1%
7 2448
 
6.0%
8 2389
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29795
73.6%
Space Separator 10710
 
26.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5093
17.1%
2 3743
12.6%
3 3256
10.9%
4 2909
9.8%
5 2688
9.0%
0 2611
8.8%
6 2454
8.2%
7 2448
8.2%
8 2389
8.0%
9 2204
7.4%
Space Separator
ValueCountFrequency (%)
10710
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40505
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
10710
26.4%
1 5093
12.6%
2 3743
 
9.2%
3 3256
 
8.0%
4 2909
 
7.2%
5 2688
 
6.6%
0 2611
 
6.4%
6 2454
 
6.1%
7 2448
 
6.0%
8 2389
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10710
26.4%
1 5093
12.6%
2 3743
 
9.2%
3 3256
 
8.0%
4 2909
 
7.2%
5 2688
 
6.6%
0 2611
 
6.4%
6 2454
 
6.1%
7 2448
 
6.0%
8 2389
 
5.9%

대출권수
Text

Distinct4303
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-10-10T02:34:15.988487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.3398
Min length2

Characters and Unicode

Total characters43398
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

Unique2306 ?
Unique (%)23.1%

Sample

1st row2
2nd row189
3rd row694
4th row3191
5th row434
ValueCountFrequency (%)
1 30
 
0.3%
3 25
 
0.3%
2 21
 
0.2%
4 18
 
0.2%
150 17
 
0.2%
200 16
 
0.2%
100 14
 
0.2%
603 14
 
0.2%
71 14
 
0.2%
300 14
 
0.2%
Other values (4292) 9110
98.0%
2023-10-10T02:34:17.100242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10707
24.7%
1 5260
12.1%
2 4227
 
9.7%
3 3529
 
8.1%
4 3176
 
7.3%
5 3024
 
7.0%
0 2900
 
6.7%
6 2735
 
6.3%
8 2673
 
6.2%
7 2658
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32691
75.3%
Space Separator 10707
 
24.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5260
16.1%
2 4227
12.9%
3 3529
10.8%
4 3176
9.7%
5 3024
9.3%
0 2900
8.9%
6 2735
8.4%
8 2673
8.2%
7 2658
8.1%
9 2509
7.7%
Space Separator
ValueCountFrequency (%)
10707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43398
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
10707
24.7%
1 5260
12.1%
2 4227
 
9.7%
3 3529
 
8.1%
4 3176
 
7.3%
5 3024
 
7.0%
0 2900
 
6.7%
6 2735
 
6.3%
8 2673
 
6.2%
7 2658
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43398
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10707
24.7%
1 5260
12.1%
2 4227
 
9.7%
3 3529
 
8.1%
4 3176
 
7.3%
5 3024
 
7.0%
0 2900
 
6.7%
6 2735
 
6.3%
8 2673
 
6.2%
7 2658
 
6.1%

도서예산
Real number (ℝ)

ZEROS 

Distinct2379
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9360963.9
Minimum0
Maximum56980000
Zeros123
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-10T02:34:17.531361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2600000
Q15196000
median8972500
Q312353250
95-th percentile18594300
Maximum56980000
Range56980000
Interquartile range (IQR)7157250

Descriptive statistics

Standard deviation5127146.9
Coefficient of variation (CV)0.5477157
Kurtosis2.091181
Mean9360963.9
Median Absolute Deviation (MAD)3615000
Skewness0.91823787
Sum9.3609639 × 1010
Variance2.6287635 × 1013
MonotonicityNot monotonic
2023-10-10T02:34:17.890536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000000 488
 
4.9%
12000000 335
 
3.4%
5000000 316
 
3.2%
8000000 291
 
2.9%
6000000 269
 
2.7%
3000000 239
 
2.4%
9000000 227
 
2.3%
4000000 221
 
2.2%
15000000 212
 
2.1%
7000000 187
 
1.9%
Other values (2369) 7215
72.2%
ValueCountFrequency (%)
0 123
1.2%
100000 2
 
< 0.1%
160000 1
 
< 0.1%
180000 1
 
< 0.1%
200000 2
 
< 0.1%
250000 1
 
< 0.1%
300000 4
 
< 0.1%
450000 1
 
< 0.1%
500000 5
 
0.1%
600000 4
 
< 0.1%
ValueCountFrequency (%)
56980000 1
< 0.1%
44610000 1
< 0.1%
40000000 1
< 0.1%
39471000 1
< 0.1%
38000000 1
< 0.1%
36200000 1
< 0.1%
36000000 2
< 0.1%
35280000 1
< 0.1%
34924000 1
< 0.1%
34000000 2
< 0.1%

Interactions

2023-10-10T02:34:04.858534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-10T02:34:18.095452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역사서수도서예산
행정구역1.0000.3450.459
사서수0.3451.0000.429
도서예산0.4590.4291.000
2023-10-10T02:34:18.289061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사서수행정구역
사서수1.0000.198
행정구역0.1981.000
2023-10-10T02:34:18.458702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도서예산행정구역사서수
도서예산1.0000.2020.210
행정구역0.2021.0000.198
사서수0.2100.1981.000

Missing values

2023-10-10T02:34:05.278562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-10T02:34:05.670308image/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

평가년도도서관구분도서관명행정구역시군구장서수사서수대출자수대출권수도서예산
104032021LIBTYPE000004장수초등학교경북영주시7915123950000
11702021LIBTYPE000004남강중학교서울관악구148631681898000000
49822021LIBTYPE000004토월초등학교경기용인시2057466169412000000
16482021LIBTYPE000004거학초등학교부산연제구249481477319114000000
30962021LIBTYPE000004광주양동초등학교광주서구157893754346300000
93492021LIBTYPE000004노안남초등학교전남나주시89806549794800000
99572021LIBTYPE000004영광고등학교경북영주시135503025939000000
29242021LIBTYPE000004운남고등학교광주광산구2135611077184910700000
58552021LIBTYPE000004용두초등학교경기고양시125481153116308000000
90792021LIBTYPE000004전남외국어고등학교전남나주시1705664079599240000
평가년도도서관구분도서관명행정구역시군구장서수사서수대출자수대출권수도서예산
82802021LIBTYPE000004전주영생고등학교전북전주시1766949351910390000
71452021LIBTYPE000004산남초등학교충북청주시21396997346612600000
79602021LIBTYPE000004논산중앙초등학교충남논산시312181125013118515870000
88432021LIBTYPE000004적상초등학교전북무주군125901161643810000
68302021LIBTYPE000004내성초등학교강원영월군121841242747984600000
16432021LIBTYPE000004서동초등학교부산금정구17196449376996750000
113192021LIBTYPE000004삼천포중앙여자중학교경남사천시13050195522319800000
52052021LIBTYPE000004안중초등학교경기평택시20251166933059900000
117922021LIBTYPE000004영전초등학교경남합천군7255321606330000
74252021LIBTYPE000004청안초등학교충북괴산군123391803584700000