Overview

Dataset statistics

Number of variables11
Number of observations1236
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory114.8 KiB
Average record size in memory95.1 B

Variable types

Categorical3
Numeric6
Text2

Dataset

Description문화체육관광부에서 제공하는 국가도서관통계시스템 내 전국공공도서관에 대한 총괄 정보를 제공하여 국민들이 공공도서관에 대한 정보를 수집할 수 있게 함
Author문화체육관광부
URLhttps://www.data.go.kr/data/15072611/fileData.do

Alerts

평가년도 has constant value ""Constant
장서수 is highly overall correlated with 사서수 and 2 other fieldsHigh correlation
사서수 is highly overall correlated with 장서수 and 2 other fieldsHigh correlation
대출자수 is highly overall correlated with 대출권수High correlation
대출권수 is highly overall correlated with 장서수 and 3 other fieldsHigh correlation
도서예산 is highly overall correlated with 장서수 and 2 other fieldsHigh correlation
도서관구분 is highly imbalanced (94.5%)Imbalance
도서관코드 has unique valuesUnique
사서수 has 49 (4.0%) zerosZeros
대출자수 has 14 (1.1%) zerosZeros
대출권수 has 14 (1.1%) zerosZeros

Reproduction

Analysis started2023-12-12 23:09:34.830142
Analysis finished2023-12-12 23:09:39.317662
Duration4.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

평가년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.8 KiB
2022
1236 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 1236
100.0%

Length

2023-12-13T08:09:39.377728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:09:39.485021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 1236
100.0%

도서관구분
Categorical

IMBALANCE 

Distinct29
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size9.8 KiB
LIBTYPE000002
1208 
LIBTYPE000007
 
1
LIBTYPE000017
 
1
LIBTYPE000018
 
1
LIBTYPE000019
 
1
Other values (24)
 
24

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique28 ?
Unique (%)2.3%

Sample

1st rowLIBTYPE000002
2nd rowLIBTYPE000002
3rd rowLIBTYPE000002
4th rowLIBTYPE000002
5th rowLIBTYPE000002

Common Values

ValueCountFrequency (%)
LIBTYPE000002 1208
97.7%
LIBTYPE000007 1
 
0.1%
LIBTYPE000017 1
 
0.1%
LIBTYPE000018 1
 
0.1%
LIBTYPE000019 1
 
0.1%
LIBTYPE000008 1
 
0.1%
LIBTYPE000030 1
 
0.1%
LIBTYPE000020 1
 
0.1%
LIBTYPE000005 1
 
0.1%
LIBTYPE000009 1
 
0.1%
Other values (19) 19
 
1.5%

Length

2023-12-13T08:09:39.577368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
libtype000002 1208
97.7%
libtype000006 1
 
0.1%
libtype000016 1
 
0.1%
libtype000015 1
 
0.1%
libtype000014 1
 
0.1%
libtype000013 1
 
0.1%
libtype000012 1
 
0.1%
libtype000025 1
 
0.1%
libtype000004 1
 
0.1%
libtype000022 1
 
0.1%
Other values (19) 19
 
1.5%

도서관코드
Real number (ℝ)

UNIQUE 

Distinct1236
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8825455 × 109
Minimum2.020125 × 109
Maximum2.060111 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2023-12-13T08:09:39.700353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.020125 × 109
5-th percentile2.050121 × 109
Q12.060111 × 109
median2.0601411 × 109
Q32.060145 × 109
95-th percentile2.060848 × 109
Maximum2.060111 × 1010
Range1.8580985 × 1010
Interquartile range (IQR)33990.5

Descriptive statistics

Standard deviation3.8252623 × 109
Coefficient of variation (CV)1.3270432
Kurtosis17.595145
Mean2.8825455 × 109
Median Absolute Deviation (MAD)17091
Skewness4.4234171
Sum3.5628263 × 1012
Variance1.4632632 × 1019
MonotonicityNot monotonic
2023-12-13T08:09:39.861521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2060122013 1
 
0.1%
2060141098 1
 
0.1%
2060141205 1
 
0.1%
2060141025 1
 
0.1%
2060141027 1
 
0.1%
2060141028 1
 
0.1%
2060141161 1
 
0.1%
2060141190 1
 
0.1%
2060141138 1
 
0.1%
2060141118 1
 
0.1%
Other values (1226) 1226
99.2%
ValueCountFrequency (%)
2020125001 1
0.1%
2020141001 1
0.1%
2030111001 1
0.1%
2030111003 1
0.1%
2030111004 1
0.1%
2030111007 1
0.1%
2030111008 1
0.1%
2030111009 1
0.1%
2030121002 1
0.1%
2030121003 1
0.1%
ValueCountFrequency (%)
20601110060 1
0.1%
20601110059 1
0.1%
20601110058 1
0.1%
20601110057 1
0.1%
20601110056 1
0.1%
20601110055 1
0.1%
20601110054 1
0.1%
20601110053 1
0.1%
20601110052 1
0.1%
20601110051 1
0.1%

도서관명
Text

Distinct1231
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size9.8 KiB
2023-12-13T08:09:40.099350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length20
Mean length8.7686084
Min length5

Characters and Unicode

Total characters10838
Distinct characters378
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

Unique1226 ?
Unique (%)99.2%

Sample

1st row2.28민주운동기념회관(도서관)
2nd row4.19 혁명기념 도서관
3rd row가락몰도서관
4th row가람도서관
5th row가수원도서관
ValueCountFrequency (%)
경상남도교육청 27
 
1.9%
경상북도교육청 26
 
1.8%
남양주시 11
 
0.8%
도서관 8
 
0.6%
천안시도서관본부 7
 
0.5%
중앙도서관 6
 
0.4%
군포시 6
 
0.4%
양산시립 6
 
0.4%
어린이도서관 5
 
0.3%
인천광역시 5
 
0.3%
Other values (1277) 1326
92.5%
2023-12-13T08:09:40.469647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 1340
 
12.4%
서 1297
 
12.0%
관 1273
 
11.7%
시 419
 
3.9%
립 307
 
2.8%
교 220
 
2.0%
육 210
 
1.9%
청 208
 
1.9%
197
 
1.8%
산 157
 
1.4%
Other values (368) 5210
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10612
97.9%
Space Separator 197
 
1.8%
Decimal Number 17
 
0.2%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%
Other Punctuation 3
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 1340
 
12.6%
서 1297
 
12.2%
관 1273
 
12.0%
시 419
 
3.9%
립 307
 
2.9%
교 220
 
2.1%
육 210
 
2.0%
청 208
 
2.0%
산 157
 
1.5%
남 150
 
1.4%
Other values (357) 5031
47.4%
Decimal Number
ValueCountFrequency (%)
2 5
29.4%
3 4
23.5%
1 3
17.6%
4 2
 
11.8%
8 2
 
11.8%
9 1
 
5.9%
Space Separator
ValueCountFrequency (%)
197
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10612
97.9%
Common 226
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 1340
 
12.6%
서 1297
 
12.2%
관 1273
 
12.0%
시 419
 
3.9%
립 307
 
2.9%
교 220
 
2.1%
육 210
 
2.0%
청 208
 
2.0%
산 157
 
1.5%
남 150
 
1.4%
Other values (357) 5031
47.4%
Common
ValueCountFrequency (%)
197
87.2%
2 5
 
2.2%
( 4
 
1.8%
) 4
 
1.8%
3 4
 
1.8%
1 3
 
1.3%
. 3
 
1.3%
4 2
 
0.9%
8 2
 
0.9%
- 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10612
97.9%
ASCII 226
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 1340
 
12.6%
서 1297
 
12.2%
관 1273
 
12.0%
시 419
 
3.9%
립 307
 
2.9%
교 220
 
2.1%
육 210
 
2.0%
청 208
 
2.0%
산 157
 
1.5%
남 150
 
1.4%
Other values (357) 5031
47.4%
ASCII
ValueCountFrequency (%)
197
87.2%
2 5
 
2.2%
( 4
 
1.8%
) 4
 
1.8%
3 4
 
1.8%
1 3
 
1.3%
. 3
 
1.3%
4 2
 
0.9%
8 2
 
0.9%
- 1
 
0.4%

행정구역
Categorical

Distinct17
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size9.8 KiB
경기
309 
서울
199 
경남
79 
전남
73 
경북
71 
Other values (12)
505 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구
2nd row서울
3rd row서울
4th row경기
5th row대전

Common Values

ValueCountFrequency (%)
경기 309
25.0%
서울 199
16.1%
경남 79
 
6.4%
전남 73
 
5.9%
경북 71
 
5.7%
전북 66
 
5.3%
충남 62
 
5.0%
강원 61
 
4.9%
인천 58
 
4.7%
충북 54
 
4.4%
Other values (7) 204
16.5%

Length

2023-12-13T08:09:40.617507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 309
25.0%
서울 199
16.1%
경남 79
 
6.4%
전남 73
 
5.9%
경북 71
 
5.7%
전북 66
 
5.3%
충남 62
 
5.0%
강원 61
 
4.9%
인천 58
 
4.7%
충북 54
 
4.4%
Other values (7) 204
16.5%

시군구
Text

Distinct210
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size9.8 KiB
2023-12-13T08:09:40.957183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.97411
Min length2

Characters and Unicode

Total characters3676
Distinct characters141
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

Unique19 ?
Unique (%)1.5%

Sample

1st row중구
2nd row종로구
3rd row송파구
4th row파주시
5th row서구
ValueCountFrequency (%)
수원시 27
 
2.2%
서구 26
 
2.1%
북구 26
 
2.1%
동구 23
 
1.9%
중구 21
 
1.7%
고양시 20
 
1.6%
파주시 19
 
1.5%
청주시 19
 
1.5%
용인시 19
 
1.5%
화성시 19
 
1.5%
Other values (200) 1017
82.3%
2023-12-13T08:09:41.400262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 636
 
17.3%
구 430
 
11.7%
군 219
 
6.0%
주 153
 
4.2%
천 113
 
3.1%
성 112
 
3.0%
양 102
 
2.8%
산 89
 
2.4%
남 82
 
2.2%
서 77
 
2.1%
Other values (131) 1663
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3659
99.5%
Decimal Number 14
 
0.4%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 636
 
17.4%
구 430
 
11.8%
군 219
 
6.0%
주 153
 
4.2%
천 113
 
3.1%
성 112
 
3.1%
양 102
 
2.8%
산 89
 
2.4%
남 82
 
2.2%
서 77
 
2.1%
Other values (123) 1646
45.0%
Decimal Number
ValueCountFrequency (%)
3 3
21.4%
6 3
21.4%
7 2
14.3%
2 2
14.3%
8 2
14.3%
1 1
 
7.1%
9 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3659
99.5%
Common 17
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 636
 
17.4%
구 430
 
11.8%
군 219
 
6.0%
주 153
 
4.2%
천 113
 
3.1%
성 112
 
3.1%
양 102
 
2.8%
산 89
 
2.4%
남 82
 
2.2%
서 77
 
2.1%
Other values (123) 1646
45.0%
Common
ValueCountFrequency (%)
3 3
17.6%
. 3
17.6%
6 3
17.6%
7 2
11.8%
2 2
11.8%
8 2
11.8%
1 1
 
5.9%
9 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3659
99.5%
ASCII 17
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 636
 
17.4%
구 430
 
11.8%
군 219
 
6.0%
주 153
 
4.2%
천 113
 
3.1%
성 112
 
3.1%
양 102
 
2.8%
산 89
 
2.4%
남 82
 
2.2%
서 77
 
2.1%
Other values (123) 1646
45.0%
ASCII
ValueCountFrequency (%)
3 3
17.6%
. 3
17.6%
6 3
17.6%
7 2
11.8%
2 2
11.8%
8 2
11.8%
1 1
 
5.9%
9 1
 
5.9%

장서수
Real number (ℝ)

HIGH CORRELATION 

Distinct1232
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99193.38
Minimum0
Maximum900474
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2023-12-13T08:09:41.606786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18594.5
Q145511
median74359.5
Q3122711.25
95-th percentile265899.25
Maximum900474
Range900474
Interquartile range (IQR)77200.25

Descriptive statistics

Standard deviation86166.36
Coefficient of variation (CV)0.86867047
Kurtosis12.918999
Mean99193.38
Median Absolute Deviation (MAD)34442
Skewness2.7082841
Sum1.2260302 × 108
Variance7.4246416 × 109
MonotonicityNot monotonic
2023-12-13T08:09:41.758542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93263 2
 
0.2%
46322 2
 
0.2%
25626 2
 
0.2%
30965 2
 
0.2%
51037 1
 
0.1%
53600 1
 
0.1%
47107 1
 
0.1%
141116 1
 
0.1%
111437 1
 
0.1%
128661 1
 
0.1%
Other values (1222) 1222
98.9%
ValueCountFrequency (%)
0 1
0.1%
3396 1
0.1%
3567 1
0.1%
4574 1
0.1%
6120 1
0.1%
6697 1
0.1%
6721 1
0.1%
8163 1
0.1%
8501 1
0.1%
8752 1
0.1%
ValueCountFrequency (%)
900474 1
0.1%
734787 1
0.1%
697002 1
0.1%
547865 1
0.1%
525070 1
0.1%
520954 1
0.1%
510043 1
0.1%
501337 1
0.1%
470947 1
0.1%
452649 1
0.1%

사서수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6569579
Minimum0
Maximum37
Zeros49
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2023-12-13T08:09:41.883670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q36
95-th percentile14
Maximum37
Range37
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.3756827
Coefficient of variation (CV)0.93960109
Kurtosis8.4816147
Mean4.6569579
Median Absolute Deviation (MAD)1.5
Skewness2.409322
Sum5756
Variance19.146599
MonotonicityNot monotonic
2023-12-13T08:09:42.034779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
3.0 236
19.1%
2.0 180
14.6%
4.0 155
12.5%
1.0 144
11.7%
5.0 83
 
6.7%
6.0 65
 
5.3%
0.0 49
 
4.0%
7.0 47
 
3.8%
8.0 38
 
3.1%
9.0 27
 
2.2%
Other values (31) 212
17.2%
ValueCountFrequency (%)
0.0 49
 
4.0%
0.5 8
 
0.6%
1.0 144
11.7%
1.5 18
 
1.5%
2.0 180
14.6%
2.5 16
 
1.3%
3.0 236
19.1%
3.5 14
 
1.1%
4.0 155
12.5%
4.5 9
 
0.7%
ValueCountFrequency (%)
37.0 1
 
0.1%
35.0 1
 
0.1%
31.0 2
 
0.2%
24.0 1
 
0.1%
23.0 3
 
0.2%
22.0 1
 
0.1%
21.0 6
0.5%
20.0 2
 
0.2%
19.0 3
 
0.2%
18.0 10
0.8%

대출자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1187
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13325.395
Minimum0
Maximum488856
Zeros14
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2023-12-13T08:09:42.214765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile488.75
Q12551.25
median6616.5
Q313621
95-th percentile46387
Maximum488856
Range488856
Interquartile range (IQR)11069.75

Descriptive statistics

Standard deviation25788.66
Coefficient of variation (CV)1.9353018
Kurtosis140.6318
Mean13325.395
Median Absolute Deviation (MAD)4806
Skewness9.3118404
Sum16470188
Variance6.6505499 × 108
MonotonicityNot monotonic
2023-12-13T08:09:42.426602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
1.1%
1794 3
 
0.2%
1192 2
 
0.2%
978 2
 
0.2%
357 2
 
0.2%
5782 2
 
0.2%
11769 2
 
0.2%
4956 2
 
0.2%
140 2
 
0.2%
6803 2
 
0.2%
Other values (1177) 1203
97.3%
ValueCountFrequency (%)
0 14
1.1%
50 1
 
0.1%
78 1
 
0.1%
108 1
 
0.1%
111 1
 
0.1%
128 1
 
0.1%
137 1
 
0.1%
139 1
 
0.1%
140 2
 
0.2%
145 1
 
0.1%
ValueCountFrequency (%)
488856 1
0.1%
407723 1
0.1%
151367 1
0.1%
151251 1
0.1%
142071 1
0.1%
128788 1
0.1%
124230 1
0.1%
117356 1
0.1%
117312 1
0.1%
116760 1
0.1%

대출권수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1221
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111823.82
Minimum0
Maximum1250038
Zeros14
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2023-12-13T08:09:42.570995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4901.5
Q127798
median76537.5
Q3160333
95-th percentile334695
Maximum1250038
Range1250038
Interquartile range (IQR)132535

Descriptive statistics

Standard deviation117979.7
Coefficient of variation (CV)1.0550498
Kurtosis10.208854
Mean111823.82
Median Absolute Deviation (MAD)55402.5
Skewness2.3515152
Sum1.3821424 × 108
Variance1.3919209 × 1010
MonotonicityNot monotonic
2023-12-13T08:09:42.722311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
1.1%
163958 2
 
0.2%
19748 2
 
0.2%
87820 1
 
0.1%
408768 1
 
0.1%
317046 1
 
0.1%
100613 1
 
0.1%
493994 1
 
0.1%
457530 1
 
0.1%
164711 1
 
0.1%
Other values (1211) 1211
98.0%
ValueCountFrequency (%)
0 14
1.1%
102 1
 
0.1%
122 1
 
0.1%
194 1
 
0.1%
278 1
 
0.1%
295 1
 
0.1%
501 1
 
0.1%
648 1
 
0.1%
998 1
 
0.1%
1145 1
 
0.1%
ValueCountFrequency (%)
1250038 1
0.1%
725327 1
0.1%
687978 1
0.1%
674461 1
0.1%
647028 1
0.1%
642332 1
0.1%
616051 1
0.1%
591741 1
0.1%
586357 1
0.1%
568780 1
0.1%

도서예산
Real number (ℝ)

HIGH CORRELATION 

Distinct1233
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1104180.8
Minimum0
Maximum47945212
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2023-12-13T08:09:42.889284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile137170.75
Q1378873.5
median675299
Q31209385
95-th percentile3386472
Maximum47945212
Range47945212
Interquartile range (IQR)830511.5

Descriptive statistics

Standard deviation1844490.5
Coefficient of variation (CV)1.6704605
Kurtosis340.11098
Mean1104180.8
Median Absolute Deviation (MAD)354709.5
Skewness14.439367
Sum1.3647675 × 109
Variance3.4021451 × 1012
MonotonicityNot monotonic
2023-12-13T08:09:43.017284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
669230 3
 
0.2%
0 2
 
0.2%
1498060 1
 
0.1%
2128492 1
 
0.1%
1188666 1
 
0.1%
299453 1
 
0.1%
1242282 1
 
0.1%
991683 1
 
0.1%
1083901 1
 
0.1%
1471695 1
 
0.1%
Other values (1223) 1223
98.9%
ValueCountFrequency (%)
0 2
0.2%
6400 1
0.1%
34050 1
0.1%
38680 1
0.1%
44000 1
0.1%
44881 1
0.1%
45631 1
0.1%
46152 1
0.1%
46200 1
0.1%
50632 1
0.1%
ValueCountFrequency (%)
47945212 1
0.1%
11716420 1
0.1%
11162645 1
0.1%
9947999 1
0.1%
9914506 1
0.1%
9437115 1
0.1%
8939901 1
0.1%
8710992 1
0.1%
8062725 1
0.1%
8039304 1
0.1%

Interactions

2023-12-13T08:09:38.413334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:35.505692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:36.023320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:36.524591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:37.058497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:37.863903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:38.551420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:35.610571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:36.105702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:36.622201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:37.169891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:37.946359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:38.642372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:35.685848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:36.177845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:36.697951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:37.260722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:38.037902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:38.746794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:35.774982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:36.259104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:36.790559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:37.363792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:38.134107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:38.831542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:35.857711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:36.358419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:36.872152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:37.438204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:38.222739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:38.940578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:35.939032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:36.441404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:36.972728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:37.531215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:09:38.304347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:09:43.100795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도서관구분도서관코드행정구역장서수사서수대출자수대출권수도서예산
도서관구분1.0000.0000.0000.0000.0000.0000.0000.000
도서관코드0.0001.0000.5340.1860.0360.0050.1350.000
행정구역0.0000.5341.0000.2560.2890.1690.2430.000
장서수0.0000.1860.2561.0000.7530.1370.4420.659
사서수0.0000.0360.2890.7531.0000.1950.4180.578
대출자수0.0000.0050.1690.1370.1951.0000.2970.000
대출권수0.0000.1350.2430.4420.4180.2971.0000.264
도서예산0.0000.0000.0000.6590.5780.0000.2641.000
2023-12-13T08:09:43.212047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도서관구분행정구역
도서관구분1.0000.000
행정구역0.0001.000
2023-12-13T08:09:43.308573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도서관코드장서수사서수대출자수대출권수도서예산도서관구분행정구역
도서관코드1.000-0.269-0.2990.039-0.030-0.2210.0000.479
장서수-0.2691.0000.5930.4100.5670.7350.0000.102
사서수-0.2990.5931.0000.4160.5900.7390.0000.119
대출자수0.0390.4100.4161.0000.7320.4310.0000.080
대출권수-0.0300.5670.5900.7321.0000.6410.0000.111
도서예산-0.2210.7350.7390.4310.6411.0000.0000.000
도서관구분0.0000.0000.0000.0000.0000.0001.0000.000
행정구역0.4790.1020.1190.0800.1110.0000.0001.000

Missing values

2023-12-13T08:09:39.104456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:09:39.261798image/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

평가년도도서관구분도서관코드도서관명행정구역시군구장서수사서수대출자수대출권수도서예산
02022LIBTYPE00000220601220132.28민주운동기념회관(도서관)대구중구510372.099514710640298
12022LIBTYPE00000220301110014.19 혁명기념 도서관서울종로구626293.040860592222667
22022LIBTYPE0000022030111008가락몰도서관서울송파구352604.0838181609241180
32022LIBTYPE0000022060141162가람도서관경기파주시559227.018535100189848278
42022LIBTYPE0000022060125008가수원도서관대전서구1630163.075001912621586132
52022LIBTYPE0000022060111042가온도서관서울중구832768.0540572458805392
62022LIBTYPE0000022060141215가재울도서관경기의정부시289043.5637839772362065
72022LIBTYPE0000022060141100가평군 설악도서관경기가평군756752.0116124834577271
82022LIBTYPE0000022060141083가평군 조종도서관경기가평군748372.0609922847547411
92022LIBTYPE0000022060141143가평군 청평도서관경기가평군791352.0175131359569944
평가년도도서관구분도서관코드도서관명행정구역시군구장서수사서수대출자수대출권수도서예산
12262022LIBTYPE0000022050142019화천교육도서관강원화천군526813.0542110461494360
12272022LIBTYPE0000022060842001화천어린이도서관강원화천군272941.095833276298397
12282022LIBTYPE0000022030841002화홍어린이도서관경기수원시118860.0723216634050
12292022LIBTYPE0000022050142014횡성교육도서관강원횡성군961833.070989875378007
12302022LIBTYPE0000022060142030횡성군립도서관강원횡성군739152.0211136387901899
12312022LIBTYPE0000022060123005효성도서관인천계양구478763.0391857996507765
12322022LIBTYPE0000022060811015휘경어린이도서관서울동대문구355744.0299164771264734
12332022LIBTYPE00000220601110056휘경행복도서관서울동대문구137693.0680318582324713
12342022LIBTYPE0000022060146026흑산자산문화도서관전남신안군89440.0012288500
12352022LIBTYPE0000022030141004희망샘도서관경기수원시501434.0618732133247927