Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells11
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.1 KiB
Average record size in memory93.3 B

Variable types

Numeric3
Text6
Categorical2

Alerts

pub_date is highly overall correlated with seq and 2 other fieldsHigh correlation
vol is highly overall correlated with seq and 3 other fieldsHigh correlation
seq is highly overall correlated with vol and 1 other fieldsHigh correlation
isbn13 is highly overall correlated with vol and 1 other fieldsHigh correlation
loan_count is highly overall correlated with volHigh correlation
vol is highly imbalanced (86.0%)Imbalance
pub_date is highly imbalanced (68.8%)Imbalance
img_url has 8 (8.0%) missing valuesMissing
isbn has 3 (3.0%) missing valuesMissing
seq has unique valuesUnique
control_no has unique valuesUnique
isbn13 has unique valuesUnique
title has unique valuesUnique
author has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:09:13.866388
Analysis finished2023-12-10 10:09:17.826386
Duration3.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

seq
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303079.59
Minimum116165
Maximum6324460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:17.972327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum116165
5-th percentile116185.65
Q1116840.5
median116996
Q3117053.5
95-th percentile117217.05
Maximum6324460
Range6208295
Interquartile range (IQR)213

Descriptive statistics

Standard deviation1064185.2
Coefficient of variation (CV)3.5112402
Kurtosis29.89777
Mean303079.59
Median Absolute Deviation (MAD)120.5
Skewness5.5946488
Sum30307959
Variance1.1324902 × 1012
MonotonicityNot monotonic
2023-12-10T19:09:18.222422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116165 1
 
1.0%
117029 1
 
1.0%
117046 1
 
1.0%
117044 1
 
1.0%
117040 1
 
1.0%
117036 1
 
1.0%
117035 1
 
1.0%
117034 1
 
1.0%
117033 1
 
1.0%
117032 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
116165 1
1.0%
116171 1
1.0%
116175 1
1.0%
116176 1
1.0%
116179 1
1.0%
116186 1
1.0%
116188 1
1.0%
116192 1
1.0%
116194 1
1.0%
116196 1
1.0%
ValueCountFrequency (%)
6324460 1
1.0%
6323726 1
1.0%
6323696 1
1.0%
117220 1
1.0%
117218 1
1.0%
117217 1
1.0%
117216 1
1.0%
117215 1
1.0%
117214 1
1.0%
117141 1
1.0%

control_no
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:09:18.640881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1200
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowKMO200135104
2nd rowKMO202151164
3rd rowKMO200135087
4th rowKMO200135089
5th rowKMO200135106
ValueCountFrequency (%)
kmo200135104 1
 
1.0%
kmo200513385 1
 
1.0%
kmo200135969 1
 
1.0%
kmo200135941 1
 
1.0%
kmo200135878 1
 
1.0%
kmo200135877 1
 
1.0%
kmo200135920 1
 
1.0%
kmo200135866 1
 
1.0%
kmo200135885 1
 
1.0%
kmo200135883 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:09:19.234470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 232
19.3%
1 143
11.9%
2 123
10.2%
5 109
9.1%
K 100
8.3%
3 98
8.2%
M 94
7.8%
O 94
7.8%
7 49
 
4.1%
9 47
 
3.9%
Other values (5) 111
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 900
75.0%
Uppercase Letter 300
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 232
25.8%
1 143
15.9%
2 123
13.7%
5 109
12.1%
3 98
10.9%
7 49
 
5.4%
9 47
 
5.2%
8 47
 
5.2%
6 38
 
4.2%
4 14
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
K 100
33.3%
M 94
31.3%
O 94
31.3%
J 6
 
2.0%
U 6
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 900
75.0%
Latin 300
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 232
25.8%
1 143
15.9%
2 123
13.7%
5 109
12.1%
3 98
10.9%
7 49
 
5.4%
9 47
 
5.2%
8 47
 
5.2%
6 38
 
4.2%
4 14
 
1.6%
Latin
ValueCountFrequency (%)
K 100
33.3%
M 94
31.3%
O 94
31.3%
J 6
 
2.0%
U 6
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 232
19.3%
1 143
11.9%
2 123
10.2%
5 109
9.1%
K 100
8.3%
3 98
8.2%
M 94
7.8%
O 94
7.8%
7 49
 
4.1%
9 47
 
3.9%
Other values (5) 111
9.2%

isbn13
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.789018 × 1012
Minimum9.7889105 × 1012
Maximum9.7911909 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:19.593818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.7889105 × 1012
5-th percentile9.7889356 × 1012
Q19.7889729 × 1012
median9.7889816 × 1012
Q39.788988 × 1012
95-th percentile9.7889897 × 1012
Maximum9.7911909 × 1012
Range2.2804034 × 109
Interquartile range (IQR)15058561

Descriptive statistics

Standard deviation3.1002338 × 108
Coefficient of variation (CV)3.1670529 × 10-5
Kurtosis47.06025
Mean9.789018 × 1012
Median Absolute Deviation (MAD)7202453
Skewness6.9223082
Sum9.789018 × 1014
Variance9.6114495 × 1016
MonotonicityNot monotonic
2023-12-10T19:09:19.841245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9788975486456 1
 
1.0%
9788946761285 1
 
1.0%
9788987905648 1
 
1.0%
9788982814297 1
 
1.0%
9788980382248 1
 
1.0%
9788989701057 1
 
1.0%
9788989701040 1
 
1.0%
9788984971011 1
 
1.0%
9788934001850 1
 
1.0%
9788989701095 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
9788910451761 1
1.0%
9788919012338 1
1.0%
9788932105611 1
1.0%
9788932105666 1
1.0%
9788934001850 1
1.0%
9788935652853 1
1.0%
9788935652914 1
1.0%
9788935653478 1
1.0%
9788935653485 1
1.0%
9788935653492 1
1.0%
ValueCountFrequency (%)
9791190855204 1
1.0%
9791155551646 1
1.0%
9788995040973 1
1.0%
9788995040942 1
1.0%
9788989778028 1
1.0%
9788989701095 1
1.0%
9788989701088 1
1.0%
9788989701057 1
1.0%
9788989701040 1
1.0%
9788989560043 1
1.0%

title
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:09:20.303659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length27
Mean length15.87
Min length2

Characters and Unicode

Total characters1587
Distinct characters389
Distinct categories10 ?
Distinct scripts5 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row(L=MS²학문의 기원)學習學
2nd row그의 마지막 목소리가 듣고 싶었다 :이동순 수필집
3rd row건축기술 실무이야기:건축구조·시공/신기술·신공법
4th row(일상으로 본)조선시대 이야기
5th row투자신탁해설
ValueCountFrequency (%)
이야기 3
 
0.8%
a 3
 
0.8%
심리 2
 
0.6%
실제 2
 
0.6%
너를 2
 
0.6%
원칙 2
 
0.6%
동화 2
 
0.6%
되는 2
 
0.6%
수필집 2
 
0.6%
영혼의 2
 
0.6%
Other values (322) 333
93.8%
2023-12-10T19:09:21.101920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
255
 
16.1%
이 33
 
2.1%
의 33
 
2.1%
기 25
 
1.6%
: 23
 
1.4%
는 22
 
1.4%
사 22
 
1.4%
한 18
 
1.1%
자 17
 
1.1%
가 17
 
1.1%
Other values (379) 1122
70.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1071
67.5%
Space Separator 255
 
16.1%
Lowercase Letter 132
 
8.3%
Other Punctuation 44
 
2.8%
Uppercase Letter 33
 
2.1%
Close Punctuation 16
 
1.0%
Open Punctuation 16
 
1.0%
Decimal Number 12
 
0.8%
Math Symbol 7
 
0.4%
Other Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 33
 
3.1%
의 33
 
3.1%
기 25
 
2.3%
는 22
 
2.1%
사 22
 
2.1%
한 18
 
1.7%
자 17
 
1.6%
가 17
 
1.6%
학 15
 
1.4%
에 15
 
1.4%
Other values (323) 854
79.7%
Lowercase Letter
ValueCountFrequency (%)
o 16
12.1%
r 14
10.6%
a 12
 
9.1%
i 10
 
7.6%
n 9
 
6.8%
d 9
 
6.8%
e 9
 
6.8%
t 8
 
6.1%
p 6
 
4.5%
c 5
 
3.8%
Other values (12) 34
25.8%
Uppercase Letter
ValueCountFrequency (%)
A 6
18.2%
M 4
12.1%
T 3
9.1%
L 3
9.1%
V 3
9.1%
P 2
 
6.1%
I 2
 
6.1%
N 2
 
6.1%
H 2
 
6.1%
S 2
 
6.1%
Other values (4) 4
12.1%
Other Punctuation
ValueCountFrequency (%)
: 23
52.3%
, 7
 
15.9%
. 5
 
11.4%
? 2
 
4.5%
· 2
 
4.5%
! 2
 
4.5%
/ 2
 
4.5%
& 1
 
2.3%
Decimal Number
ValueCountFrequency (%)
1 3
25.0%
0 3
25.0%
3 2
16.7%
6 2
16.7%
8 1
 
8.3%
5 1
 
8.3%
Math Symbol
ValueCountFrequency (%)
= 6
85.7%
+ 1
 
14.3%
Space Separator
ValueCountFrequency (%)
255
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Other Number
ValueCountFrequency (%)
² 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1040
65.5%
Common 351
 
22.1%
Latin 163
 
10.3%
Han 31
 
2.0%
Greek 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 33
 
3.2%
의 33
 
3.2%
기 25
 
2.4%
는 22
 
2.1%
사 22
 
2.1%
한 18
 
1.7%
자 17
 
1.6%
가 17
 
1.6%
학 15
 
1.4%
에 15
 
1.4%
Other values (295) 823
79.1%
Latin
ValueCountFrequency (%)
o 16
 
9.8%
r 14
 
8.6%
a 12
 
7.4%
i 10
 
6.1%
n 9
 
5.5%
d 9
 
5.5%
e 9
 
5.5%
t 8
 
4.9%
p 6
 
3.7%
A 6
 
3.7%
Other values (25) 64
39.3%
Han
ValueCountFrequency (%)
書 2
 
6.5%
識 2
 
6.5%
學 2
 
6.5%
千 1
 
3.2%
字 1
 
3.2%
文 1
 
3.2%
習 1
 
3.2%
換 1
 
3.2%
集 1
 
3.2%
花 1
 
3.2%
Other values (18) 18
58.1%
Common
ValueCountFrequency (%)
255
72.6%
: 23
 
6.6%
) 16
 
4.6%
( 16
 
4.6%
, 7
 
2.0%
= 6
 
1.7%
. 5
 
1.4%
1 3
 
0.9%
0 3
 
0.9%
? 2
 
0.6%
Other values (10) 15
 
4.3%
Greek
ValueCountFrequency (%)
β 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1040
65.5%
ASCII 511
32.2%
CJK 31
 
2.0%
None 5
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
255
49.9%
: 23
 
4.5%
) 16
 
3.1%
o 16
 
3.1%
( 16
 
3.1%
r 14
 
2.7%
a 12
 
2.3%
i 10
 
2.0%
n 9
 
1.8%
d 9
 
1.8%
Other values (43) 131
25.6%
Hangul
ValueCountFrequency (%)
이 33
 
3.2%
의 33
 
3.2%
기 25
 
2.4%
는 22
 
2.1%
사 22
 
2.1%
한 18
 
1.7%
자 17
 
1.6%
가 17
 
1.6%
학 15
 
1.4%
에 15
 
1.4%
Other values (295) 823
79.1%
CJK
ValueCountFrequency (%)
書 2
 
6.5%
識 2
 
6.5%
學 2
 
6.5%
千 1
 
3.2%
字 1
 
3.2%
文 1
 
3.2%
習 1
 
3.2%
換 1
 
3.2%
集 1
 
3.2%
花 1
 
3.2%
Other values (18) 18
58.1%
None
ValueCountFrequency (%)
β 2
40.0%
· 2
40.0%
² 1
20.0%

author
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:09:21.569422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length24
Mean length10.52
Min length5

Characters and Unicode

Total characters1052
Distinct characters264
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row韓駿相 著
2nd row지은이: 이동순
3rd row삼성물산 건설부문 저
4th row정연식 지음
5th row박삼철 저
ValueCountFrequency (%)
지음 43
 
15.9%
옮김 24
 
8.9%
저 9
 
3.3%
엮음 3
 
1.1%
著 3
 
1.1%
지은이 3
 
1.1%
이케다 2
 
0.7%
공저 2
 
0.7%
譯 2
 
0.7%
안근찬 2
 
0.7%
Other values (173) 178
65.7%
2023-12-10T19:09:22.301521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
171
 
16.3%
지 75
 
7.1%
음 71
 
6.7%
김 45
 
4.3%
; 38
 
3.6%
이 28
 
2.7%
옮 26
 
2.5%
저 14
 
1.3%
사 12
 
1.1%
정 12
 
1.1%
Other values (254) 560
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 788
74.9%
Space Separator 171
 
16.3%
Other Punctuation 56
 
5.3%
Lowercase Letter 20
 
1.9%
Uppercase Letter 9
 
0.9%
Close Punctuation 4
 
0.4%
Open Punctuation 4
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 75
 
9.5%
음 71
 
9.0%
김 45
 
5.7%
이 28
 
3.6%
옮 26
 
3.3%
저 14
 
1.8%
사 12
 
1.5%
정 12
 
1.5%
리 9
 
1.1%
경 8
 
1.0%
Other values (224) 488
61.9%
Lowercase Letter
ValueCountFrequency (%)
n 3
15.0%
r 3
15.0%
e 3
15.0%
o 2
10.0%
s 2
10.0%
h 1
 
5.0%
d 1
 
5.0%
l 1
 
5.0%
a 1
 
5.0%
w 1
 
5.0%
Other values (2) 2
10.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
11.1%
J 1
11.1%
A 1
11.1%
D 1
11.1%
O 1
11.1%
E 1
11.1%
R 1
11.1%
Z 1
11.1%
M 1
11.1%
Other Punctuation
ValueCountFrequency (%)
; 38
67.9%
. 11
 
19.6%
: 4
 
7.1%
, 1
 
1.8%
/ 1
 
1.8%
· 1
 
1.8%
Space Separator
ValueCountFrequency (%)
171
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 760
72.2%
Common 235
 
22.3%
Latin 29
 
2.8%
Han 28
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 75
 
9.9%
음 71
 
9.3%
김 45
 
5.9%
이 28
 
3.7%
옮 26
 
3.4%
저 14
 
1.8%
사 12
 
1.6%
정 12
 
1.6%
리 9
 
1.2%
경 8
 
1.1%
Other values (203) 460
60.5%
Han
ValueCountFrequency (%)
著 5
17.9%
編 2
 
7.1%
譯 2
 
7.1%
會 2
 
7.1%
哉 1
 
3.6%
永 1
 
3.6%
韓 1
 
3.6%
禹 1
 
3.6%
金 1
 
3.6%
恩 1
 
3.6%
Other values (11) 11
39.3%
Latin
ValueCountFrequency (%)
n 3
 
10.3%
r 3
 
10.3%
e 3
 
10.3%
o 2
 
6.9%
s 2
 
6.9%
C 1
 
3.4%
h 1
 
3.4%
J 1
 
3.4%
A 1
 
3.4%
d 1
 
3.4%
Other values (11) 11
37.9%
Common
ValueCountFrequency (%)
171
72.8%
; 38
 
16.2%
. 11
 
4.7%
: 4
 
1.7%
] 4
 
1.7%
[ 4
 
1.7%
, 1
 
0.4%
/ 1
 
0.4%
· 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 760
72.2%
ASCII 263
 
25.0%
CJK 27
 
2.6%
None 1
 
0.1%
CJK Compat Ideographs 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
171
65.0%
; 38
 
14.4%
. 11
 
4.2%
: 4
 
1.5%
] 4
 
1.5%
[ 4
 
1.5%
n 3
 
1.1%
r 3
 
1.1%
e 3
 
1.1%
o 2
 
0.8%
Other values (19) 20
 
7.6%
Hangul
ValueCountFrequency (%)
지 75
 
9.9%
음 71
 
9.3%
김 45
 
5.9%
이 28
 
3.7%
옮 26
 
3.4%
저 14
 
1.8%
사 12
 
1.6%
정 12
 
1.6%
리 9
 
1.2%
경 8
 
1.1%
Other values (203) 460
60.5%
CJK
ValueCountFrequency (%)
著 5
18.5%
編 2
 
7.4%
譯 2
 
7.4%
會 2
 
7.4%
哉 1
 
3.7%
永 1
 
3.7%
韓 1
 
3.7%
禹 1
 
3.7%
金 1
 
3.7%
恩 1
 
3.7%
Other values (10) 10
37.0%
None
ValueCountFrequency (%)
· 1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
李 1
100.0%

publisher
Text

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:09:22.736271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length3.83
Min length2

Characters and Unicode

Total characters383
Distinct characters115
Distinct categories5 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)41.0%

Sample

1st row학지사
2nd row이지출판
3rd row공간예술사
4th row청년사
5th row삼우사
ValueCountFrequency (%)
무한 7
 
6.4%
학지사 6
 
5.5%
문학동네 6
 
5.5%
한길사 5
 
4.6%
태동출판사 5
 
4.6%
책읽는 4
 
3.7%
사람들 4
 
3.7%
청년사 3
 
2.8%
청년정신 3
 
2.8%
삼우사 2
 
1.8%
Other values (55) 64
58.7%
2023-12-10T19:09:23.398196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 51
 
13.3%
문 16
 
4.2%
학 16
 
4.2%
출 15
 
3.9%
동 15
 
3.9%
판 15
 
3.9%
한 13
 
3.4%
9
 
2.3%
지 9
 
2.3%
무 8
 
2.1%
Other values (105) 216
56.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 368
96.1%
Space Separator 9
 
2.3%
Lowercase Letter 4
 
1.0%
Other Punctuation 1
 
0.3%
Uppercase Letter 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 51
 
13.9%
문 16
 
4.3%
학 16
 
4.3%
출 15
 
4.1%
동 15
 
4.1%
판 15
 
4.1%
한 13
 
3.5%
지 9
 
2.4%
무 8
 
2.2%
청 8
 
2.2%
Other values (99) 202
54.9%
Lowercase Letter
ValueCountFrequency (%)
o 2
50.0%
s 1
25.0%
k 1
25.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Other Punctuation
ValueCountFrequency (%)
@ 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 358
93.5%
Common 10
 
2.6%
Han 10
 
2.6%
Latin 5
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 51
 
14.2%
문 16
 
4.5%
학 16
 
4.5%
출 15
 
4.2%
동 15
 
4.2%
판 15
 
4.2%
한 13
 
3.6%
지 9
 
2.5%
무 8
 
2.2%
청 8
 
2.2%
Other values (92) 192
53.6%
Han
ValueCountFrequency (%)
社 3
30.0%
文 2
20.0%
東 1
 
10.0%
選 1
 
10.0%
博 1
 
10.0%
英 1
 
10.0%
學 1
 
10.0%
Latin
ValueCountFrequency (%)
o 2
40.0%
s 1
20.0%
k 1
20.0%
B 1
20.0%
Common
ValueCountFrequency (%)
9
90.0%
@ 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 358
93.5%
ASCII 15
 
3.9%
CJK 10
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 51
 
14.2%
문 16
 
4.5%
학 16
 
4.5%
출 15
 
4.2%
동 15
 
4.2%
판 15
 
4.2%
한 13
 
3.6%
지 9
 
2.5%
무 8
 
2.2%
청 8
 
2.2%
Other values (92) 192
53.6%
ASCII
ValueCountFrequency (%)
9
60.0%
o 2
 
13.3%
s 1
 
6.7%
@ 1
 
6.7%
k 1
 
6.7%
B 1
 
6.7%
CJK
ValueCountFrequency (%)
社 3
30.0%
文 2
20.0%
東 1
 
10.0%
選 1
 
10.0%
博 1
 
10.0%
英 1
 
10.0%
學 1
 
10.0%

vol
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
97 
1
 
2
4
 
1

Length

Max length4
Median length4
Mean length3.91
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 97
97.0%
1 2
 
2.0%
4 1
 
1.0%

Length

2023-12-10T19:09:23.625967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:23.800328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 97
97.0%
1 2
 
2.0%
4 1
 
1.0%

pub_date
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2001
86 
2021
 
4
2000
 
4
2004
 
2
2005
 
1
Other values (3)
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row2001
2nd row2021
3rd row2001
4th row2001
5th row2001

Common Values

ValueCountFrequency (%)
2001 86
86.0%
2021 4
 
4.0%
2000 4
 
4.0%
2004 2
 
2.0%
2005 1
 
1.0%
1996 1
 
1.0%
2016 1
 
1.0%
[200 1
 
1.0%

Length

2023-12-10T19:09:23.973824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:24.290003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2001 86
86.0%
2021 4
 
4.0%
2000 4
 
4.0%
2004 2
 
2.0%
2005 1
 
1.0%
1996 1
 
1.0%
2016 1
 
1.0%
200 1
 
1.0%

img_url
Text

MISSING 

Distinct92
Distinct (%)100.0%
Missing8
Missing (%)8.0%
Memory size932.0 B
2023-12-10T19:09:24.766777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length85
Median length62
Mean length65.173913
Min length61

Characters and Unicode

Total characters5996
Distinct characters41
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)100.0%

Sample

1st rowhttp://image.aladin.co.kr/product/30/12/cover/8975486451_1.gif
2nd rowhttps://image.aladin.co.kr/product/28038/42/cover/k522734136_1.jpg
3rd rowhttp://image.aladin.co.kr/product/28/98/cover/8977990408_1.gif
4th rowhttp://image.aladin.co.kr/product/30/6/cover/8986254913_1.gif
5th rowhttps://bookthumb-phinf.pstatic.net/cover/211/770/21177004.jpg?type=m1&udate=20211109
ValueCountFrequency (%)
http://image.aladin.co.kr/product/29/95/cover/8986254905_1.gif 1
 
1.1%
http://image.aladin.co.kr/product/30/5/cover/8982814345_1.jpg 1
 
1.1%
http://image.aladin.co.kr/product/28/19/cover/898497076x_1.gif 1
 
1.1%
http://image.aladin.co.kr/product/30/54/cover/8980382243_1.gif 1
 
1.1%
http://image.aladin.co.kr/product/29/92/cover/8989701058_1.gif 1
 
1.1%
http://image.aladin.co.kr/product/29/73/cover/898970104x_1.gif 1
 
1.1%
http://image.aladin.co.kr/product/28/73/cover/8984971014_1.gif 1
 
1.1%
http://image.aladin.co.kr/product/30/38/cover/8934001852_1.gif 1
 
1.1%
http://image.aladin.co.kr/product/30/23/cover/8989701090_1.gif 1
 
1.1%
http://image.aladin.co.kr/product/30/23/cover/8989701082_1.gif 1
 
1.1%
Other values (82) 82
89.1%
2023-12-10T19:09:25.712894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 632
 
10.5%
. 356
 
5.9%
t 336
 
5.6%
o 276
 
4.6%
c 267
 
4.5%
a 264
 
4.4%
r 252
 
4.2%
i 243
 
4.1%
p 241
 
4.0%
e 208
 
3.5%
Other values (31) 2921
48.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3308
55.2%
Decimal Number 1472
24.5%
Other Punctuation 1104
 
18.4%
Connector Punctuation 76
 
1.3%
Math Symbol 24
 
0.4%
Dash Punctuation 12
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 336
 
10.2%
o 276
 
8.3%
c 267
 
8.1%
a 264
 
8.0%
r 252
 
7.6%
i 243
 
7.3%
p 241
 
7.3%
e 208
 
6.3%
d 172
 
5.2%
g 172
 
5.2%
Other values (13) 877
26.5%
Decimal Number
ValueCountFrequency (%)
8 207
14.1%
1 205
13.9%
0 199
13.5%
9 196
13.3%
2 176
12.0%
3 130
8.8%
4 102
6.9%
7 97
6.6%
6 83
5.6%
5 77
 
5.2%
Other Punctuation
ValueCountFrequency (%)
/ 632
57.2%
. 356
32.2%
: 92
 
8.3%
? 12
 
1.1%
& 12
 
1.1%
Connector Punctuation
ValueCountFrequency (%)
_ 76
100.0%
Math Symbol
ValueCountFrequency (%)
= 24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3308
55.2%
Common 2688
44.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 336
 
10.2%
o 276
 
8.3%
c 267
 
8.1%
a 264
 
8.0%
r 252
 
7.6%
i 243
 
7.3%
p 241
 
7.3%
e 208
 
6.3%
d 172
 
5.2%
g 172
 
5.2%
Other values (13) 877
26.5%
Common
ValueCountFrequency (%)
/ 632
23.5%
. 356
13.2%
8 207
 
7.7%
1 205
 
7.6%
0 199
 
7.4%
9 196
 
7.3%
2 176
 
6.5%
3 130
 
4.8%
4 102
 
3.8%
7 97
 
3.6%
Other values (8) 388
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 632
 
10.5%
. 356
 
5.9%
t 336
 
5.6%
o 276
 
4.6%
c 267
 
4.5%
a 264
 
4.4%
r 252
 
4.2%
i 243
 
4.1%
p 241
 
4.0%
e 208
 
3.5%
Other values (31) 2921
48.7%

loan_count
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4315.47
Minimum1
Maximum247341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:26.299759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20.85
Q1249
median625.5
Q31336.25
95-th percentile4255.35
Maximum247341
Range247340
Interquartile range (IQR)1087.25

Descriptive statistics

Standard deviation25631.874
Coefficient of variation (CV)5.9395324
Kurtosis84.145939
Mean4315.47
Median Absolute Deviation (MAD)458
Skewness8.970128
Sum431547
Variance6.5699297 × 108
MonotonicityNot monotonic
2023-12-10T19:09:26.562883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3
 
3.0%
1322 1
 
1.0%
1888 1
 
1.0%
990 1
 
1.0%
2197 1
 
1.0%
467 1
 
1.0%
272 1
 
1.0%
500 1
 
1.0%
1361 1
 
1.0%
420 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
1 3
3.0%
16 1
 
1.0%
18 1
 
1.0%
21 1
 
1.0%
23 1
 
1.0%
32 1
 
1.0%
34 1
 
1.0%
35 1
 
1.0%
64 1
 
1.0%
82 1
 
1.0%
ValueCountFrequency (%)
247341 1
1.0%
71489 1
1.0%
19458 1
1.0%
9053 1
1.0%
4395 1
1.0%
4248 1
1.0%
4182 1
1.0%
3456 1
1.0%
2707 1
1.0%
2563 1
1.0%

isbn
Text

MISSING 

Distinct97
Distinct (%)100.0%
Missing3
Missing (%)3.0%
Memory size932.0 B
2023-12-10T19:09:27.004606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length10.319588
Min length10

Characters and Unicode

Total characters1001
Distinct characters16
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)100.0%

Sample

1st row8975486451
2nd row8977990408
3rd row8972783498(세트
4th row8986254913
5th row8976820525(세트
ValueCountFrequency (%)
8988296281 1
 
1.0%
8995040971 1
 
1.0%
8987905640 1
 
1.0%
8982814299 1
 
1.0%
8980382243 1
 
1.0%
8989701058 1
 
1.0%
898970104x 1
 
1.0%
8984971014 1
 
1.0%
8934001852 1
 
1.0%
8989701090 1
 
1.0%
Other values (87) 87
89.7%
2023-12-10T19:09:27.707304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 213
21.3%
9 171
17.1%
0 88
8.8%
7 85
 
8.5%
4 80
 
8.0%
3 70
 
7.0%
2 66
 
6.6%
5 65
 
6.5%
6 61
 
6.1%
1 59
 
5.9%
Other values (6) 43
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 958
95.7%
Other Letter 18
 
1.8%
Uppercase Letter 13
 
1.3%
Open Punctuation 10
 
1.0%
Other Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 213
22.2%
9 171
17.8%
0 88
9.2%
7 85
 
8.9%
4 80
 
8.4%
3 70
 
7.3%
2 66
 
6.9%
5 65
 
6.8%
6 61
 
6.4%
1 59
 
6.2%
Other Letter
ValueCountFrequency (%)
세 9
50.0%
트 9
50.0%
Uppercase Letter
ValueCountFrequency (%)
X 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Other Punctuation
ValueCountFrequency (%)
: 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 970
96.9%
Hangul 18
 
1.8%
Latin 13
 
1.3%

Most frequent character per script

Common
ValueCountFrequency (%)
8 213
22.0%
9 171
17.6%
0 88
9.1%
7 85
 
8.8%
4 80
 
8.2%
3 70
 
7.2%
2 66
 
6.8%
5 65
 
6.7%
6 61
 
6.3%
1 59
 
6.1%
Other values (3) 12
 
1.2%
Hangul
ValueCountFrequency (%)
세 9
50.0%
트 9
50.0%
Latin
ValueCountFrequency (%)
X 13
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 983
98.2%
Hangul 18
 
1.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 213
21.7%
9 171
17.4%
0 88
9.0%
7 85
 
8.6%
4 80
 
8.1%
3 70
 
7.1%
2 66
 
6.7%
5 65
 
6.6%
6 61
 
6.2%
1 59
 
6.0%
Other values (4) 25
 
2.5%
Hangul
ValueCountFrequency (%)
세 9
50.0%
트 9
50.0%

Interactions

2023-12-10T19:09:16.758783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:15.730070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:16.206294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:16.911157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:15.908448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:16.425081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:17.071089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:16.051825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:16.622192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:09:27.951961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seqcontrol_noisbn13titleauthorpublishervolpub_dateimg_urlloan_countisbn
seq1.0001.0000.8071.0001.0001.000NaN0.9711.0000.000NaN
control_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
isbn130.8071.0001.0001.0001.0001.000NaN0.8511.0000.000NaN
title1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
author1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
publisher1.0001.0001.0001.0001.0001.0001.0000.9831.0001.0001.000
volNaN1.000NaN1.0001.0001.0001.000NaN0.000NaN1.000
pub_date0.9711.0000.8511.0001.0000.983NaN1.0001.0000.3481.000
img_url1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.000
loan_count0.0001.0000.0001.0001.0001.000NaN0.3481.0001.0001.000
isbnNaN1.000NaN1.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T19:09:28.187734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
pub_datevol
pub_date1.0001.000
vol1.0001.000
2023-12-10T19:09:28.327344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seqisbn13loan_countvolpub_date
seq1.0000.0710.0081.0000.824
isbn130.0711.0000.0021.0000.651
loan_count0.0080.0021.0001.0000.229
vol1.0001.0001.0001.0001.000
pub_date0.8240.6510.2291.0001.000

Missing values

2023-12-10T19:09:17.268119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:09:17.550339image/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.
2023-12-10T19:09:17.734699image/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

seqcontrol_noisbn13titleauthorpublishervolpub_dateimg_urlloan_countisbn
0116165KMO2001351049788975486456(L=MS²학문의 기원)學習學韓駿相 著학지사<NA>2001http://image.aladin.co.kr/product/30/12/cover/8975486451_1.gif3648975486451
16323696KMO2021511649791155551646그의 마지막 목소리가 듣고 싶었다 :이동순 수필집지은이: 이동순이지출판<NA>2021https://image.aladin.co.kr/product/28038/42/cover/k522734136_1.jpg1<NA>
2116171KMO2001350879788977990401건축기술 실무이야기:건축구조·시공/신기술·신공법삼성물산 건설부문 저공간예술사<NA>2001http://image.aladin.co.kr/product/28/98/cover/8977990408_1.gif12228977990408
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