Overview

Dataset statistics

Number of variables9
Number of observations32
Missing cells5
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory81.1 B

Variable types

Categorical2
Numeric4
Text3

Dataset

Description문화체육관광부에서 제공하는 국가도서관통계시스템 내 전국장애인도서관에 대한 주요 통계정보를 제공하여 국민들의 문화체육관광 통계데이터 활성화 제고
Author문화체육관광부
URLhttps://www.data.go.kr/data/15072350/fileData.do

Alerts

평가년도 has constant value ""Constant
도서관구분 has constant value ""Constant
도서관코드 is highly overall correlated with 대출권수High correlation
대출권수 is highly overall correlated with 도서관코드 and 1 other fieldsHigh correlation
도서예산 is highly overall correlated with 대출권수High correlation
도서관코드 has 4 (12.5%) missing valuesMissing
대출권수 has 1 (3.1%) missing valuesMissing
도서관명 has unique valuesUnique
장서수 has unique valuesUnique
장서수 has 1 (3.1%) zerosZeros
대출권수 has 4 (12.5%) zerosZeros
도서예산 has 6 (18.8%) zerosZeros

Reproduction

Analysis started2023-12-12 13:13:56.893502
Analysis finished2023-12-12 13:13:59.152180
Duration2.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

평가년도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
2020
32 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 32
100.0%

Length

2023-12-12T22:13:59.212411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:13:59.300006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 32
100.0%

도서관구분
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
LIBTYPE000002004
32 

Length

Max length16
Median length16
Mean length16
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLIBTYPE000002004
2nd rowLIBTYPE000002004
3rd rowLIBTYPE000002004
4th rowLIBTYPE000002004
5th rowLIBTYPE000002004

Common Values

ValueCountFrequency (%)
LIBTYPE000002004 32
100.0%

Length

2023-12-12T22:13:59.405492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:13:59.501969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
libtype000002004 32
100.0%

도서관코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)100.0%
Missing4
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean2.0297142 × 109
Minimum2.020422 × 109
Maximum2.030449 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T22:13:59.938386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.020422 × 109
5-th percentile2.0239344 × 109
Q12.030411 × 109
median2.0304235 × 109
Q32.0304433 × 109
95-th percentile2.0304487 × 109
Maximum2.030449 × 109
Range10026993
Interquartile range (IQR)32237.25

Descriptive statistics

Standard deviation2621004.7
Coefficient of variation (CV)0.0012913171
Kurtosis11.182577
Mean2.0297142 × 109
Median Absolute Deviation (MAD)12498
Skewness-3.5194332
Sum5.6831998 × 1010
Variance6.8696654 × 1012
MonotonicityNot monotonic
2023-12-12T22:14:00.115492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2030411009 1
 
3.1%
2030411018 1
 
3.1%
2030411020 1
 
3.1%
2030411016 1
 
3.1%
2030411014 1
 
3.1%
2030411013 1
 
3.1%
2030444001 1
 
3.1%
2030449002 1
 
3.1%
2030449001 1
 
3.1%
2030445003 1
 
3.1%
Other values (18) 18
56.2%
(Missing) 4
 
12.5%
ValueCountFrequency (%)
2020422009 1
3.1%
2020447001 1
3.1%
2030411002 1
3.1%
2030411004 1
3.1%
2030411007 1
3.1%
2030411009 1
3.1%
2030411013 1
3.1%
2030411014 1
3.1%
2030411016 1
3.1%
2030411018 1
3.1%
ValueCountFrequency (%)
2030449002 1
3.1%
2030449001 1
3.1%
2030448001 1
3.1%
2030447001 1
3.1%
2030446002 1
3.1%
2030445003 1
3.1%
2030444001 1
3.1%
2030443001 1
3.1%
2030442002 1
3.1%
2030441004 1
3.1%

도서관명
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T22:14:00.409343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length9.09375
Min length6

Characters and Unicode

Total characters291
Distinct characters68
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

Unique32 ?
Unique (%)100.0%

Sample

1st row강서점자도서관
2nd row강원점자도서관
3rd row경기북부시각장애인점자도서관
4th row경남점자정보도서관
5th row경북점자도서관
ValueCountFrequency (%)
강서점자도서관 1
 
2.9%
충청남도점자도서관 1
 
2.9%
울산광역시점자도서관 1
 
2.9%
전남점자도서관 1
 
2.9%
전라남도시각장애인점자도서관 1
 
2.9%
전라북도점자도서관 1
 
2.9%
제주도문화정보점자도서관 1
 
2.9%
제주점자도서관 1
 
2.9%
하상시각장애인도서관 1
 
2.9%
강원점자도서관 1
 
2.9%
Other values (24) 24
70.6%
2023-12-12T22:14:00.816282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 36
 
12.4%
서 35
 
12.0%
관 32
 
11.0%
점 28
 
9.6%
자 28
 
9.6%
시 7
 
2.4%
대 5
 
1.7%
인 5
 
1.7%
애 5
 
1.7%
장 5
 
1.7%
Other values (58) 105
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 289
99.3%
Space Separator 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 36
 
12.5%
서 35
 
12.1%
관 32
 
11.1%
점 28
 
9.7%
자 28
 
9.7%
시 7
 
2.4%
대 5
 
1.7%
인 5
 
1.7%
애 5
 
1.7%
장 5
 
1.7%
Other values (57) 103
35.6%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 289
99.3%
Common 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 36
 
12.5%
서 35
 
12.1%
관 32
 
11.1%
점 28
 
9.7%
자 28
 
9.7%
시 7
 
2.4%
대 5
 
1.7%
인 5
 
1.7%
애 5
 
1.7%
장 5
 
1.7%
Other values (57) 103
35.6%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 289
99.3%
ASCII 2
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 36
 
12.5%
서 35
 
12.1%
관 32
 
11.1%
점 28
 
9.7%
자 28
 
9.7%
시 7
 
2.4%
대 5
 
1.7%
인 5
 
1.7%
애 5
 
1.7%
장 5
 
1.7%
Other values (57) 103
35.6%
ASCII
ValueCountFrequency (%)
2
100.0%

행정구역
Text

Distinct16
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T22:14:01.006776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters64
Distinct characters19
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

Unique10 ?
Unique (%)31.2%

Sample

1st row서울
2nd row강원
3rd row경기
4th row경남
5th row경북
ValueCountFrequency (%)
서울 12
37.5%
경기 2
 
6.2%
경북 2
 
6.2%
대구 2
 
6.2%
전남 2
 
6.2%
제주 2
 
6.2%
강원 1
 
3.1%
경남 1
 
3.1%
광주 1
 
3.1%
대전 1
 
3.1%
Other values (6) 6
18.8%
2023-12-12T22:14:01.344719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
울 13
20.3%
서 12
18.8%
경 5
 
7.8%
북 4
 
6.2%
전 4
 
6.2%
남 4
 
6.2%
주 3
 
4.7%
대 3
 
4.7%
산 2
 
3.1%
충 2
 
3.1%
Other values (9) 12
18.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
울 13
20.3%
서 12
18.8%
경 5
 
7.8%
북 4
 
6.2%
전 4
 
6.2%
남 4
 
6.2%
주 3
 
4.7%
대 3
 
4.7%
산 2
 
3.1%
충 2
 
3.1%
Other values (9) 12
18.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
울 13
20.3%
서 12
18.8%
경 5
 
7.8%
북 4
 
6.2%
전 4
 
6.2%
남 4
 
6.2%
주 3
 
4.7%
대 3
 
4.7%
산 2
 
3.1%
충 2
 
3.1%
Other values (9) 12
18.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
울 13
20.3%
서 12
18.8%
경 5
 
7.8%
북 4
 
6.2%
전 4
 
6.2%
남 4
 
6.2%
주 3
 
4.7%
대 3
 
4.7%
산 2
 
3.1%
충 2
 
3.1%
Other values (9) 12
18.8%

시군구
Text

Distinct27
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T22:14:01.581351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9375
Min length2

Characters and Unicode

Total characters94
Distinct characters39
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

Unique22 ?
Unique (%)68.8%

Sample

1st row강서구
2nd row춘천시
3rd row의정부시
4th row창원시
5th row포항시
ValueCountFrequency (%)
강동구 2
 
6.2%
서대문구 2
 
6.2%
남구 2
 
6.2%
제주시 2
 
6.2%
목포시 2
 
6.2%
사상구 1
 
3.1%
강서구 1
 
3.1%
성남시 1
 
3.1%
종로구 1
 
3.1%
강남구 1
 
3.1%
Other values (17) 17
53.1%
2023-12-12T22:14:01.989606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 18
19.1%
시 13
 
13.8%
주 5
 
5.3%
강 4
 
4.3%
서 4
 
4.3%
남 4
 
4.3%
포 4
 
4.3%
대 3
 
3.2%
동 3
 
3.2%
문 2
 
2.1%
Other values (29) 34
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 94
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 18
19.1%
시 13
 
13.8%
주 5
 
5.3%
강 4
 
4.3%
서 4
 
4.3%
남 4
 
4.3%
포 4
 
4.3%
대 3
 
3.2%
동 3
 
3.2%
문 2
 
2.1%
Other values (29) 34
36.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 94
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 18
19.1%
시 13
 
13.8%
주 5
 
5.3%
강 4
 
4.3%
서 4
 
4.3%
남 4
 
4.3%
포 4
 
4.3%
대 3
 
3.2%
동 3
 
3.2%
문 2
 
2.1%
Other values (29) 34
36.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 94
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 18
19.1%
시 13
 
13.8%
주 5
 
5.3%
강 4
 
4.3%
서 4
 
4.3%
남 4
 
4.3%
포 4
 
4.3%
대 3
 
3.2%
동 3
 
3.2%
문 2
 
2.1%
Other values (29) 34
36.2%

장서수
Real number (ℝ)

UNIQUE  ZEROS 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6610.0625
Minimum0
Maximum29148
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T22:14:02.148237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile112.45
Q11913.5
median4589.5
Q39721.5
95-th percentile17422.75
Maximum29148
Range29148
Interquartile range (IQR)7808

Descriptive statistics

Standard deviation6594.2226
Coefficient of variation (CV)0.99760366
Kurtosis3.0529429
Mean6610.0625
Median Absolute Deviation (MAD)3424.5
Skewness1.5805128
Sum211522
Variance43483771
MonotonicityNot monotonic
2023-12-12T22:14:02.333605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2636 1
 
3.1%
17326 1
 
3.1%
5000 1
 
3.1%
7453 1
 
3.1%
7233 1
 
3.1%
29148 1
 
3.1%
890 1
 
3.1%
14273 1
 
3.1%
2364 1
 
3.1%
3842 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0 1
3.1%
3 1
3.1%
202 1
3.1%
469 1
3.1%
890 1
3.1%
985 1
3.1%
1345 1
3.1%
1888 1
3.1%
1922 1
3.1%
2268 1
3.1%
ValueCountFrequency (%)
29148 1
3.1%
17541 1
3.1%
17326 1
3.1%
14975 1
3.1%
14273 1
3.1%
11620 1
3.1%
11495 1
3.1%
10044 1
3.1%
9614 1
3.1%
8408 1
3.1%

대출권수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct28
Distinct (%)90.3%
Missing1
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean7612.4516
Minimum0
Maximum60150
Zeros4
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T22:14:02.478005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1300.5
median2805
Q37283
95-th percentile37649.5
Maximum60150
Range60150
Interquartile range (IQR)6982.5

Descriptive statistics

Standard deviation13838.742
Coefficient of variation (CV)1.8179087
Kurtosis8.3713092
Mean7612.4516
Median Absolute Deviation (MAD)2772
Skewness2.885829
Sum235986
Variance1.9151079 × 108
MonotonicityNot monotonic
2023-12-12T22:14:02.653146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 4
 
12.5%
131 1
 
3.1%
5570 1
 
3.1%
695 1
 
3.1%
16282 1
 
3.1%
888 1
 
3.1%
3876 1
 
3.1%
27051 1
 
3.1%
2805 1
 
3.1%
5503 1
 
3.1%
Other values (18) 18
56.2%
ValueCountFrequency (%)
0 4
12.5%
5 1
 
3.1%
30 1
 
3.1%
33 1
 
3.1%
131 1
 
3.1%
470 1
 
3.1%
695 1
 
3.1%
888 1
 
3.1%
1456 1
 
3.1%
1517 1
 
3.1%
ValueCountFrequency (%)
60150 1
3.1%
48248 1
3.1%
27051 1
3.1%
16282 1
3.1%
12360 1
3.1%
12203 1
3.1%
7573 1
3.1%
7564 1
3.1%
7002 1
3.1%
5570 1
3.1%

도서예산
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8086625
Minimum0
Maximum25469000
Zeros6
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T22:14:02.813428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11150000
median6016500
Q312102000
95-th percentile24516000
Maximum25469000
Range25469000
Interquartile range (IQR)10952000

Descriptive statistics

Standard deviation8403560.7
Coefficient of variation (CV)1.0391926
Kurtosis-0.48138682
Mean8086625
Median Absolute Deviation (MAD)5125000
Skewness0.92498082
Sum2.58772 × 108
Variance7.0619832 × 1013
MonotonicityNot monotonic
2023-12-12T22:14:02.967933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 6
 
18.8%
2000000 2
 
6.2%
20000000 2
 
6.2%
1000000 1
 
3.1%
6033000 1
 
3.1%
1500000 1
 
3.1%
14658000 1
 
3.1%
25000000 1
 
3.1%
7670000 1
 
3.1%
6290000 1
 
3.1%
Other values (15) 15
46.9%
ValueCountFrequency (%)
0 6
18.8%
406000 1
 
3.1%
1000000 1
 
3.1%
1200000 1
 
3.1%
1500000 1
 
3.1%
2000000 2
 
6.2%
3600000 1
 
3.1%
4000000 1
 
3.1%
5700000 1
 
3.1%
6000000 1
 
3.1%
ValueCountFrequency (%)
25469000 1
3.1%
25000000 1
3.1%
24120000 1
3.1%
21276000 1
3.1%
20000000 2
6.2%
18000000 1
3.1%
14658000 1
3.1%
11250000 1
3.1%
11000000 1
3.1%
7670000 1
3.1%

Interactions

2023-12-12T22:13:58.408686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:57.192613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:57.614717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:58.009600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:58.517372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:57.301079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:57.718112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:58.097402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:58.640867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:57.408373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:57.815344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:58.172418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:58.736549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:57.524633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:57.913066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:13:58.269759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:14:03.094130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도서관코드도서관명행정구역시군구장서수대출권수도서예산
도서관코드1.0001.0000.0001.0000.0000.0000.000
도서관명1.0001.0001.0001.0001.0001.0001.000
행정구역0.0001.0001.0000.9770.0000.9680.487
시군구1.0001.0000.9771.0000.1590.7970.850
장서수0.0001.0000.0000.1591.0000.0000.000
대출권수0.0001.0000.9680.7970.0001.0000.384
도서예산0.0001.0000.4870.8500.0000.3841.000
2023-12-12T22:14:03.226119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도서관코드장서수대출권수도서예산
도서관코드1.0000.1940.5190.474
장서수0.1941.0000.1960.273
대출권수0.5190.1961.0000.664
도서예산0.4740.2730.6641.000

Missing values

2023-12-12T22:13:58.872653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:13:59.012392image/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.
2023-12-12T22:13:59.104871image/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

평가년도도서관구분도서관코드도서관명행정구역시군구장서수대출권수도서예산
02020LIBTYPE0000020042030411002강서점자도서관서울강서구26361311000000
12020LIBTYPE0000020042030442002강원점자도서관강원춘천시114951236020000000
22020LIBTYPE0000020042030441001경기북부시각장애인점자도서관경기의정부시2841215111000000
32020LIBTYPE0000020042030448001경남점자정보도서관경남창원시13451220320000000
42020LIBTYPE0000020042030447001경북점자도서관경북포항시20226603600000
52020LIBTYPE0000020042030424001광주점자도서관광주광주192275732000000
62020LIBTYPE0000020042020422009달구벌점자도서관대구대구27624700
72020LIBTYPE0000020042020447001대구대학교점자도서관경북경산시985300
82020LIBTYPE000002004<NA>대구점자도서관대구달서구469756424120000
92020LIBTYPE0000020042030425001대전점자도서관대전중구1754129847500000
평가년도도서관구분도서관코드도서관명행정구역시군구장서수대출권수도서예산
222020LIBTYPE0000020042030445003전라북도점자도서관전북전주시8408346721276000
232020LIBTYPE0000020042030449001제주도문화정보점자도서관제주제주시10044550318000000
242020LIBTYPE0000020042030449002제주점자도서관제주제주시384228052000000
252020LIBTYPE0000020042030444001충청남도점자도서관충남천안시2364270514000000
262020LIBTYPE000002004<NA>하상시각장애인도서관서울강남구1427338766290000
272020LIBTYPE0000020042030411013한국시각장애인복지재단 점자도서관서울강동구8908887670000
282020LIBTYPE0000020042030411014한국점자도서관서울강동구291481628225000000
292020LIBTYPE0000020042030411016한국학생점자도서관서울종로구7233<NA>14658000
302020LIBTYPE0000020042030411020서대문농아복지관 수어영상도서관서울서대문구74536951500000
312020LIBTYPE0000020042030411018소리샘도서관서울동작구500000