Overview

Dataset statistics

Number of variables9
Number of observations200
Missing cells24
Missing cells (%)1.3%
Duplicate rows9
Duplicate rows (%)4.5%
Total size in memory15.0 KiB
Average record size in memory76.7 B

Variable types

Numeric4
Text3
Categorical2

Dataset

Description경기도 안양시 안양시립도서관 대출베스트정보 입니다. 순위, 서명, 저자, 출판사, ISBN, ISBN부가기호, KDC, 대출건수 등의 자료를 제공합니다.
Author경기도 안양시
URLhttps://www.data.go.kr/data/15069923/fileData.do

Alerts

Dataset has 9 (4.5%) duplicate rowsDuplicates
ISBN is highly overall correlated with 출판사 and 1 other fieldsHigh correlation
ISBN부가기호 is highly overall correlated with 출판사High correlation
KDC is highly overall correlated with 출판사 and 1 other fieldsHigh correlation
출판사 is highly overall correlated with ISBN and 2 other fieldsHigh correlation
출판년도 is highly overall correlated with ISBN and 1 other fieldsHigh correlation
ISBN부가기호 has 12 (6.0%) missing valuesMissing
KDC has 12 (6.0%) missing valuesMissing

Reproduction

Analysis started2024-04-19 05:51:20.044492
Analysis finished2024-04-19 05:51:22.838074
Duration2.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순위
Real number (ℝ)

Distinct90
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.41
Minimum1
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-19T14:51:22.919824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.95
Q150.75
median98
Q3150
95-th percentile189
Maximum197
Range196
Interquartile range (IQR)99.25

Descriptive statistics

Standard deviation57.365021
Coefficient of variation (CV)0.57705483
Kurtosis-1.1993673
Mean99.41
Median Absolute Deviation (MAD)48.5
Skewness0.0074712054
Sum19882
Variance3290.7456
MonotonicityIncreasing
2024-04-19T14:51:23.078538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182 7
 
3.5%
163 5
 
2.5%
128 5
 
2.5%
136 5
 
2.5%
98 5
 
2.5%
85 5
 
2.5%
150 5
 
2.5%
155 5
 
2.5%
171 5
 
2.5%
113 5
 
2.5%
Other values (80) 148
74.0%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
197 4
2.0%
196 1
 
0.5%
193 3
1.5%
189 4
2.0%
182 7
3.5%
180 2
 
1.0%
178 2
 
1.0%
176 2
 
1.0%
171 5
2.5%
168 3
1.5%

서명
Text

Distinct79
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-04-19T14:51:23.376901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length37
Mean length19.435
Min length4

Characters and Unicode

Total characters3887
Distinct characters314
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)29.5%

Sample

1st row불편한 편의점 :김호연 장편소설
2nd row체리새우 :황영미 장편소설
3rd row연이와 버들 도령
4th row불편한 편의점 :김호연 장편소설
5th row너무 잘하려고 애쓰지 마라 :나태주 시집
ValueCountFrequency (%)
그리스 25
 
2.7%
신화 25
 
2.7%
로마 25
 
2.7%
수학도둑 24
 
2.6%
코믹 23
 
2.5%
흔한남매 23
 
2.5%
go 22
 
2.4%
설민석의 22
 
2.4%
대모험 22
 
2.4%
한국사 19
 
2.0%
Other values (234) 704
75.4%
2024-04-19T14:51:23.765901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
767
 
19.7%
리 88
 
2.3%
학 83
 
2.1%
: 83
 
2.1%
한 76
 
2.0%
화 69
 
1.8%
험 65
 
1.7%
이 64
 
1.6%
수 57
 
1.5%
의 56
 
1.4%
Other values (304) 2479
63.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2630
67.7%
Space Separator 767
 
19.7%
Lowercase Letter 182
 
4.7%
Other Punctuation 142
 
3.7%
Uppercase Letter 44
 
1.1%
Close Punctuation 38
 
1.0%
Open Punctuation 38
 
1.0%
Decimal Number 29
 
0.7%
Dash Punctuation 13
 
0.3%
Math Symbol 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 88
 
3.3%
학 83
 
3.2%
한 76
 
2.9%
화 69
 
2.6%
험 65
 
2.5%
이 64
 
2.4%
수 57
 
2.2%
의 56
 
2.1%
사 51
 
1.9%
스 49
 
1.9%
Other values (251) 1972
75.0%
Lowercase Letter
ValueCountFrequency (%)
o 40
22.0%
g 36
19.8%
u 16
 
8.8%
s 12
 
6.6%
i 9
 
4.9%
e 8
 
4.4%
b 8
 
4.4%
n 7
 
3.8%
a 7
 
3.8%
r 7
 
3.8%
Other values (12) 32
17.6%
Uppercase Letter
ValueCountFrequency (%)
N 12
27.3%
G 11
25.0%
E 7
15.9%
S 5
11.4%
K 3
 
6.8%
T 2
 
4.5%
I 1
 
2.3%
M 1
 
2.3%
B 1
 
2.3%
O 1
 
2.3%
Decimal Number
ValueCountFrequency (%)
1 15
51.7%
5 3
 
10.3%
8 3
 
10.3%
7 2
 
6.9%
4 2
 
6.9%
2 1
 
3.4%
6 1
 
3.4%
3 1
 
3.4%
9 1
 
3.4%
Other Punctuation
ValueCountFrequency (%)
: 83
58.5%
· 26
 
18.3%
. 12
 
8.5%
& 7
 
4.9%
! 7
 
4.9%
, 6
 
4.2%
/ 1
 
0.7%
Space Separator
ValueCountFrequency (%)
767
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Math Symbol
ValueCountFrequency (%)
= 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2630
67.7%
Common 1031
 
26.5%
Latin 226
 
5.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 88
 
3.3%
학 83
 
3.2%
한 76
 
2.9%
화 69
 
2.6%
험 65
 
2.5%
이 64
 
2.4%
수 57
 
2.2%
의 56
 
2.1%
사 51
 
1.9%
스 49
 
1.9%
Other values (251) 1972
75.0%
Latin
ValueCountFrequency (%)
o 40
17.7%
g 36
15.9%
u 16
 
7.1%
N 12
 
5.3%
s 12
 
5.3%
G 11
 
4.9%
i 9
 
4.0%
e 8
 
3.5%
b 8
 
3.5%
n 7
 
3.1%
Other values (22) 67
29.6%
Common
ValueCountFrequency (%)
767
74.4%
: 83
 
8.1%
) 38
 
3.7%
( 38
 
3.7%
· 26
 
2.5%
1 15
 
1.5%
- 13
 
1.3%
. 12
 
1.2%
& 7
 
0.7%
! 7
 
0.7%
Other values (11) 25
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2630
67.7%
ASCII 1231
31.7%
None 26
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
767
62.3%
: 83
 
6.7%
o 40
 
3.2%
) 38
 
3.1%
( 38
 
3.1%
g 36
 
2.9%
u 16
 
1.3%
1 15
 
1.2%
- 13
 
1.1%
N 12
 
1.0%
Other values (42) 173
 
14.1%
Hangul
ValueCountFrequency (%)
리 88
 
3.3%
학 83
 
3.2%
한 76
 
2.9%
화 69
 
2.6%
험 65
 
2.5%
이 64
 
2.4%
수 57
 
2.2%
의 56
 
2.1%
사 51
 
1.9%
스 49
 
1.9%
Other values (251) 1972
75.0%
None
ValueCountFrequency (%)
· 26
100.0%

저자
Text

Distinct67
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-04-19T14:51:23.972894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length52
Mean length16.37
Min length2

Characters and Unicode

Total characters3274
Distinct characters153
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

Unique40 ?
Unique (%)20.0%

Sample

1st row지은이: 김호연
2nd row지은이: 황영미
3rd row백희나
4th row지은이: 김호연
5th row지은이: 나태주
ValueCountFrequency (%)
그림 115
 
14.9%
글 107
 
13.8%
원작 32
 
4.1%
지은이 27
 
3.5%
흔한남매 26
 
3.4%
최우빈 25
 
3.2%
박시연 25
 
3.2%
송도수 24
 
3.1%
설민석,그림 22
 
2.8%
서정 20
 
2.6%
Other values (97) 351
45.3%
2024-04-19T14:51:24.307115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
574
 
17.5%
: 269
 
8.2%
그 160
 
4.9%
림 154
 
4.7%
글 123
 
3.8%
; 123
 
3.8%
정 93
 
2.8%
김 84
 
2.6%
, 83
 
2.5%
이 57
 
1.7%
Other values (143) 1554
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2109
64.4%
Space Separator 574
 
17.5%
Other Punctuation 496
 
15.1%
Close Punctuation 45
 
1.4%
Open Punctuation 45
 
1.4%
Lowercase Letter 4
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
그 160
 
7.6%
림 154
 
7.3%
글 123
 
5.8%
정 93
 
4.4%
김 84
 
4.0%
이 57
 
2.7%
수 52
 
2.5%
한 50
 
2.4%
희 46
 
2.2%
시 43
 
2.0%
Other values (132) 1247
59.1%
Other Punctuation
ValueCountFrequency (%)
: 269
54.2%
; 123
24.8%
, 83
 
16.7%
· 19
 
3.8%
. 2
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
c 2
50.0%
o 2
50.0%
Space Separator
ValueCountFrequency (%)
574
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Math Symbol
ValueCountFrequency (%)
| 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2109
64.4%
Common 1161
35.5%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
그 160
 
7.6%
림 154
 
7.3%
글 123
 
5.8%
정 93
 
4.4%
김 84
 
4.0%
이 57
 
2.7%
수 52
 
2.5%
한 50
 
2.4%
희 46
 
2.2%
시 43
 
2.0%
Other values (132) 1247
59.1%
Common
ValueCountFrequency (%)
574
49.4%
: 269
23.2%
; 123
 
10.6%
, 83
 
7.1%
) 45
 
3.9%
( 45
 
3.9%
· 19
 
1.6%
. 2
 
0.2%
| 1
 
0.1%
Latin
ValueCountFrequency (%)
c 2
50.0%
o 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2109
64.4%
ASCII 1146
35.0%
None 19
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
574
50.1%
: 269
23.5%
; 123
 
10.7%
, 83
 
7.2%
) 45
 
3.9%
( 45
 
3.9%
c 2
 
0.2%
o 2
 
0.2%
. 2
 
0.2%
| 1
 
0.1%
Hangul
ValueCountFrequency (%)
그 160
 
7.6%
림 154
 
7.3%
글 123
 
5.8%
정 93
 
4.4%
김 84
 
4.0%
이 57
 
2.7%
수 52
 
2.5%
한 50
 
2.4%
희 46
 
2.2%
시 43
 
2.0%
Other values (132) 1247
59.1%
None
ValueCountFrequency (%)
· 19
100.0%

출판사
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
미래엔
43 
북이십일
38 
서울문화사
24 
휴먼큐브
16 
아울북
11 
Other values (31)
68 

Length

Max length15
Median length12
Mean length4.035
Min length2

Unique

Unique15 ?
Unique (%)7.5%

Sample

1st row나무옆의자
2nd row문학동네
3rd row책읽는곰
4th row나무옆의자
5th row열림원

Common Values

ValueCountFrequency (%)
미래엔 43
21.5%
북이십일 38
19.0%
서울문화사 24
12.0%
휴먼큐브 16
 
8.0%
아울북 11
 
5.5%
비룡소 7
 
3.5%
창비 6
 
3.0%
길벗스쿨 5
 
2.5%
주니어김영사 4
 
2.0%
문학동네 4
 
2.0%
Other values (26) 42
21.0%

Length

2024-04-19T14:51:24.443999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미래엔 43
21.1%
북이십일 38
18.6%
서울문화사 24
11.8%
휴먼큐브 16
 
7.8%
아울북 11
 
5.4%
비룡소 7
 
3.4%
창비 6
 
2.9%
길벗스쿨 5
 
2.5%
문학동네 4
 
2.0%
주니어김영사 4
 
2.0%
Other values (28) 46
22.5%

출판년도
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2017
43 
2021
32 
2019
28 
2022
21 
2020
18 
Other values (11)
58 

Length

Max length9
Median length4
Mean length4.375
Min length4

Unique

Unique4 ?
Unique (%)2.0%

Sample

1st row2021
2nd row2019
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2017 43
21.5%
2021 32
16.0%
2019 28
14.0%
2022 21
10.5%
2020 18
9.0%
2016 13
 
6.5%
2018 12
 
6.0%
2018-2020 12
 
6.0%
2005 11
 
5.5%
2012 2
 
1.0%
Other values (6) 8
 
4.0%

Length

2024-04-19T14:51:24.559705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017 43
21.5%
2021 32
16.0%
2019 28
14.0%
2022 21
10.5%
2020 18
9.0%
2016 13
 
6.5%
2018 12
 
6.0%
2018-2020 12
 
6.0%
2005 11
 
5.5%
2012 2
 
1.0%
Other values (6) 8
 
4.0%

ISBN
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7901347 × 1012
Minimum9.7889 × 1012
Maximum9.7912 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-19T14:51:24.666240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.7889 × 1012
5-th percentile9.78893 × 1012
Q19.78895 × 1012
median9.79116 × 1012
Q39.79116 × 1012
95-th percentile9.79119 × 1012
Maximum9.7912 × 1012
Range2.3 × 109
Interquartile range (IQR)2.21 × 109

Descriptive statistics

Standard deviation1.1109618 × 109
Coefficient of variation (CV)0.00011347768
Kurtosis-1.9994746
Mean9.7901347 × 1012
Median Absolute Deviation (MAD)35000000
Skewness-0.14122571
Sum1.9580269 × 1015
Variance1.2342361 × 1018
MonotonicityNot monotonic
2024-04-19T14:51:25.083010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
9788950000000 60
30.0%
9791160000000 58
29.0%
9791170000000 18
 
9.0%
9791190000000 18
 
9.0%
9788930000000 17
 
8.5%
9788940000000 10
 
5.0%
9791200000000 7
 
3.5%
9791130000000 5
 
2.5%
9788970000000 2
 
1.0%
9788990000000 2
 
1.0%
Other values (3) 3
 
1.5%
ValueCountFrequency (%)
9788900000000 1
 
0.5%
9788930000000 17
 
8.5%
9788940000000 10
 
5.0%
9788950000000 60
30.0%
9788960000000 1
 
0.5%
9788970000000 2
 
1.0%
9788990000000 2
 
1.0%
9791130000000 5
 
2.5%
9791140000000 1
 
0.5%
9791160000000 58
29.0%
ValueCountFrequency (%)
9791200000000 7
 
3.5%
9791190000000 18
 
9.0%
9791170000000 18
 
9.0%
9791160000000 58
29.0%
9791140000000 1
 
0.5%
9791130000000 5
 
2.5%
9788990000000 2
 
1.0%
9788970000000 2
 
1.0%
9788960000000 1
 
0.5%
9788950000000 60
30.0%

ISBN부가기호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)12.8%
Missing12
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean67521.117
Minimum3320
Maximum77900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-19T14:51:25.194894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3320
5-th percentile3810
Q174552.5
median74830
Q377470
95-th percentile77810
Maximum77900
Range74580
Interquartile range (IQR)2917.5

Descriptive statistics

Standard deviation21729.827
Coefficient of variation (CV)0.32182268
Kurtosis4.7178666
Mean67521.117
Median Absolute Deviation (MAD)2570
Skewness-2.5411072
Sum12693970
Variance4.7218537 × 108
MonotonicityNot monotonic
2024-04-19T14:51:25.341408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
74800 31
15.5%
67410 19
9.5%
74910 18
9.0%
77810 16
8.0%
74830 15
 
7.5%
3810 13
 
6.5%
77720 12
 
6.0%
74900 11
 
5.5%
77400 7
 
3.5%
74810 7
 
3.5%
Other values (14) 39
19.5%
(Missing) 12
 
6.0%
ValueCountFrequency (%)
3320 2
 
1.0%
3400 1
 
0.5%
3810 13
6.5%
3830 1
 
0.5%
4840 2
 
1.0%
43810 1
 
0.5%
67400 2
 
1.0%
67410 19
9.5%
73030 1
 
0.5%
73710 1
 
0.5%
ValueCountFrequency (%)
77900 4
 
2.0%
77830 3
 
1.5%
77810 16
8.0%
77720 12
6.0%
77700 3
 
1.5%
77490 7
 
3.5%
77470 7
 
3.5%
77400 7
 
3.5%
74910 18
9.0%
74900 11
5.5%

KDC
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)16.0%
Missing12
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean656.55596
Minimum219
Maximum980.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-19T14:51:25.500093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum219
5-th percentile219
Q1410
median810
Q3833.6
95-th percentile980.2
Maximum980.2
Range761.2
Interquartile range (IQR)423.6

Descriptive statistics

Standard deviation251.2502
Coefficient of variation (CV)0.38267903
Kurtosis-1.1835556
Mean656.55596
Median Absolute Deviation (MAD)101
Skewness-0.57184448
Sum123432.52
Variance63126.661
MonotonicityNot monotonic
2024-04-19T14:51:25.627219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
813.7 30
15.0%
219.0 25
12.5%
911.0 19
9.5%
410.0 19
9.5%
833.6 15
7.5%
711.4 12
 
6.0%
980.2 11
 
5.5%
810.0 10
 
5.0%
472.0 7
 
3.5%
400.0 7
 
3.5%
Other values (20) 33
16.5%
(Missing) 12
 
6.0%
ValueCountFrequency (%)
219.0 25
12.5%
325.04 1
 
0.5%
325.211 1
 
0.5%
326.0 1
 
0.5%
350.0 1
 
0.5%
400.0 7
 
3.5%
408.0 2
 
1.0%
410.0 19
9.5%
472.0 7
 
3.5%
477.0 1
 
0.5%
ValueCountFrequency (%)
980.2 11
 
5.5%
911.0 19
9.5%
909.0 4
 
2.0%
843.6 2
 
1.0%
833.8 1
 
0.5%
833.6 15
7.5%
830.0 3
 
1.5%
813.8 1
 
0.5%
813.7 30
15.0%
813.62 3
 
1.5%

대출건수
Text

Distinct90
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-04-19T14:51:25.875462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.01
Min length3

Characters and Unicode

Total characters602
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

Unique43 ?
Unique (%)21.5%

Sample

1st row1,077
2nd row571
3rd row477
4th row463
5th row395
ValueCountFrequency (%)
190 7
 
3.5%
194 5
 
2.5%
214 5
 
2.5%
196 5
 
2.5%
198 5
 
2.5%
199 5
 
2.5%
210 5
 
2.5%
203 5
 
2.5%
205 5
 
2.5%
219 5
 
2.5%
Other values (80) 148
74.0%
2024-04-19T14:51:26.262284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 166
27.6%
1 100
16.6%
9 66
 
11.0%
0 56
 
9.3%
3 44
 
7.3%
4 40
 
6.6%
8 36
 
6.0%
5 34
 
5.6%
6 32
 
5.3%
7 27
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 601
99.8%
Other Punctuation 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 166
27.6%
1 100
16.6%
9 66
 
11.0%
0 56
 
9.3%
3 44
 
7.3%
4 40
 
6.7%
8 36
 
6.0%
5 34
 
5.7%
6 32
 
5.3%
7 27
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 602
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 166
27.6%
1 100
16.6%
9 66
 
11.0%
0 56
 
9.3%
3 44
 
7.3%
4 40
 
6.6%
8 36
 
6.0%
5 34
 
5.6%
6 32
 
5.3%
7 27
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 602
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 166
27.6%
1 100
16.6%
9 66
 
11.0%
0 56
 
9.3%
3 44
 
7.3%
4 40
 
6.6%
8 36
 
6.0%
5 34
 
5.6%
6 32
 
5.3%
7 27
 
4.5%

Interactions

2024-04-19T14:51:22.142268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:20.931907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:21.386559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:21.761517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:22.238974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:21.039820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:21.485939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:21.862099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:22.335570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:21.160857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:21.571916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:21.947523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:22.435468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:21.289280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:21.672184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:51:22.046456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-19T14:51:26.381508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순위서명저자출판사출판년도ISBNISBN부가기호KDC대출건수
순위1.0000.6270.6160.5760.4440.1330.4950.3251.000
서명0.6271.0001.0000.9990.9990.9831.0001.0000.860
저자0.6161.0001.0000.9990.9870.9781.0000.9920.923
출판사0.5760.9990.9991.0000.8900.9360.9610.9170.923
출판년도0.4440.9990.9870.8901.0000.5710.7170.8590.682
ISBN0.1330.9830.9780.9360.5711.0000.0000.4210.000
ISBN부가기호0.4951.0001.0000.9610.7170.0001.0000.8310.901
KDC0.3251.0000.9920.9170.8590.4210.8311.0000.000
대출건수1.0000.8600.9230.9230.6820.0000.9010.0001.000
2024-04-19T14:51:26.498379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출판사출판년도
출판사1.0000.471
출판년도0.4711.000
2024-04-19T14:51:26.578621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순위ISBNISBN부가기호KDC출판사출판년도
순위1.000-0.016-0.180-0.1160.2220.179
ISBN-0.0161.0000.2450.3070.7920.504
ISBN부가기호-0.1800.2451.0000.2220.7160.491
KDC-0.1160.3070.2221.0000.6250.583
출판사0.2220.7920.7160.6251.0000.471
출판년도0.1790.5040.4910.5830.4711.000

Missing values

2024-04-19T14:51:22.564081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T14:51:22.687304image/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.
2024-04-19T14:51:22.786987image/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

순위서명저자출판사출판년도ISBNISBN부가기호KDC대출건수
01불편한 편의점 :김호연 장편소설지은이: 김호연나무옆의자202197911600000003810813.71,077
12체리새우 :황영미 장편소설지은이: 황영미문학동네201997889500000003810813.7571
23연이와 버들 도령백희나책읽는곰2022979116000000077810813.7477
34불편한 편의점 :김호연 장편소설지은이: 김호연나무옆의자202297911600000003810813.7463
45너무 잘하려고 애쓰지 마라 :나태주 시집지은이: 나태주열림원202297911700000003810811.62395
56클로버 :나혜림 장편소설지은이: 나혜림창비2022978894000000043810813.7357
67아버지의 해방일지 :정지아 장편소설지은이: 정지아창비202297889400000003810813.62341
78(추리 천재) 엉덩이 탐정트롤 글·그림 ;김정화 옮김미래엔2016979116000000074830833.6326
89마법천자문 :손오공의 한자 대탐험저자: 스튜디오시리얼,정수영아울북2005978895000000077720711.4301
910마법천자문 :손오공의 한자 대탐험저자: 스튜디오시리얼,정수영아울북2005978895000000077720711.4299
순위서명저자출판사출판년도ISBNISBN부가기호KDC대출건수
190189수학도둑송도수 글서울문화사<NA>979116000000067410<NA>189
191189(코믹 메이플스토리) 수학도둑 :언제 어디에서나 원리를 응용하여 문제를 격파하는 수학의 해결사!글: 송도수 ;그림: 서정 엔터테인먼트서울문화사2021979116000000067410410.0189
192193슈퍼 거북유설화 글·그림책읽는곰2014978899000000077810813.7188
193193아쿠아리움에서 살아남기 =Survival in aquarium글: 곰돌이 co. ;그림: 한현동미래엔2019979116000000077470477.0188
194193(코믹 메이플스토리) 수학도둑 :언제 어디에서나 원리를 응용하여 문제를 격파하는 수학의 해결사!글: 송도수 ;그림: 서정 엔터테인먼트서울문화사2021979116000000067410410.0188
195196그리스 로마 신화글: 박시연 ;그림: 최우빈북이십일2017978895000000074800219.0187
196197(코믹 메이플스토리) 수학도둑 :지식·능력·경험을 융합하여 정리하는 No.1 수학학습만화글: 송도수 ;그림: 서정 엔터테인먼트서울문화사2018-2020978893000000067410410.0186
197197(코믹 메이플스토리) 수학도둑 :창의사고력 강화, 수리논술력을 키워 주는 스토리텔링 수학의 선구자!글: 송도수 ;그림: 서정 엔터테인먼트서울문화사2015-2017978893000000067410410.0186
198197코믹 메이플 스토리 수학도둑 46 - 국내 최초 수학논술만화송도수 (글), 서정 엔터테인먼트 (그림), 여운방 (감수)서울문화사2015978893000000067410410.0186
199197이파라파냐무냐무 :이지은 그림책글·그림: 이지은사계절(사계절출판사)2020979116000000077810813.7186

Duplicate rows

Most frequently occurring

순위서명저자출판사출판년도ISBNISBN부가기호KDC대출건수# duplicates
185그리스 로마 신화글: 박시연 ;그림: 최우빈북이십일2017978895000000074800219.02193
043마법천자문 :손오공의 한자 대탐험저자: 스튜디오시리얼,정수영아울북2005978895000000077720711.42462
2113흔한남매원작: 흔한남매 ;그림: 유난희미래엔2019979116000000077810810.02102
3121Go go 카카오 프렌즈 :세계 역사 문화 체험 학습만화글: 김미영 ;그림: 김정한북이십일2018978895000000074900980.22082
4146(코믹 메이플스토리) 수학도둑 :지식·능력·경험을 융합하여 정리하는 No.1 수학학습만화글: 송도수 ;그림: 서정 엔터테인먼트서울문화사2018-2020979116000000067410410.02002
5150그리스 로마 신화글: 박시연 ;그림: 최우빈북이십일2017978895000000074800219.01992
6163그리스 로마 신화글: 박시연 ;그림: 최우빈북이십일2017978895000000074800219.01962
7171Go go 카카오 프렌즈 :세계 역사 문화 체험 학습만화글: 김미영 ;그림: 김정한북이십일2018978895000000074900980.21942
8180놓지 마 과학!글·그림: 신태훈,나승훈위즈덤하우스 미디어그룹2016979119000000067400408.01912