Overview

Dataset statistics

Number of variables18
Number of observations100
Missing cells512
Missing cells (%)28.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.0 KiB
Average record size in memory153.3 B

Variable types

Numeric4
Unsupported4
Text7
DateTime1
Boolean2

Alerts

is_coll_aladin has constant value ""Constant
is_coll_naver has constant value ""Constant
add_code is highly overall correlated with priceHigh correlation
price is highly overall correlated with add_codeHigh correlation
vol has 100 (100.0%) missing valuesMissing
pub_date has 100 (100.0%) missing valuesMissing
add_code has 8 (8.0%) missing valuesMissing
price has 23 (23.0%) missing valuesMissing
img_url has 24 (24.0%) missing valuesMissing
description has 30 (30.0%) missing valuesMissing
kdc_class_no has 100 (100.0%) missing valuesMissing
pub_date_2 has 23 (23.0%) missing valuesMissing
isbn_origin has 100 (100.0%) missing valuesMissing
master_seq has unique valuesUnique
isbn13 has unique valuesUnique
vol is an unsupported type, check if it needs cleaning or further analysisUnsupported
pub_date is an unsupported type, check if it needs cleaning or further analysisUnsupported
kdc_class_no is an unsupported type, check if it needs cleaning or further analysisUnsupported
isbn_origin is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 09:50:52.202328
Analysis finished2023-12-10 09:50:59.863622
Duration7.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

master_seq
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6330215.4
Minimum6329484
Maximum6352227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:00.570697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6329484
5-th percentile6329490
Q16329510.8
median6329536.5
Q36329561.2
95-th percentile6329581
Maximum6352227
Range22743
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation3890.4608
Coefficient of variation (CV)0.00061458585
Kurtosis29.89432
Mean6330215.4
Median Absolute Deviation (MAD)25.5
Skewness5.5941806
Sum6.3302154 × 108
Variance15135685
MonotonicityNot monotonic
2023-12-10T18:51:01.289983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6329484 1
 
1.0%
6329548 1
 
1.0%
6329558 1
 
1.0%
6329557 1
 
1.0%
6329556 1
 
1.0%
6329555 1
 
1.0%
6329554 1
 
1.0%
6329553 1
 
1.0%
6329552 1
 
1.0%
6329551 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
6329484 1
1.0%
6329486 1
1.0%
6329487 1
1.0%
6329488 1
1.0%
6329489 1
1.0%
6329490 1
1.0%
6329492 1
1.0%
6329493 1
1.0%
6329494 1
1.0%
6329495 1
1.0%
ValueCountFrequency (%)
6352227 1
1.0%
6352226 1
1.0%
6352225 1
1.0%
6329583 1
1.0%
6329582 1
1.0%
6329581 1
1.0%
6329580 1
1.0%
6329579 1
1.0%
6329578 1
1.0%
6329577 1
1.0%

isbn13
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7135717 × 1012
Minimum2.0900001 × 1012
Maximum9.7911975 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:02.099915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0900001 × 1012
5-th percentile9.7889321 × 1012
Q19.7889872 × 1012
median9.7911383 × 1012
Q39.791167 × 1012
95-th percentile9.7911955 × 1012
Maximum9.7911975 × 1012
Range7.7011974 × 1012
Interquartile range (IQR)2.1798086 × 109

Descriptive statistics

Standard deviation7.7005833 × 1011
Coefficient of variation (CV)0.079276538
Kurtosis99.999681
Mean9.7135717 × 1012
Median Absolute Deviation (MAD)28736162
Skewness-9.9999763
Sum9.7135717 × 1014
Variance5.9298983 × 1023
MonotonicityNot monotonic
2023-12-10T18:51:02.929203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2090000107555 1
 
1.0%
9791167043122 1
 
1.0%
9791195435395 1
 
1.0%
9791158246914 1
 
1.0%
9791191503258 1
 
1.0%
9788931815993 1
 
1.0%
9791166471490 1
 
1.0%
9791166471483 1
 
1.0%
9791138305945 1
 
1.0%
9791158246891 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2090000107555 1
1.0%
9788930438865 1
1.0%
9788931815993 1
1.0%
9788932117997 1
1.0%
9788932118017 1
1.0%
9788932118024 1
1.0%
9788942593446 1
1.0%
9788949949833 1
1.0%
9788949949857 1
1.0%
9788949949888 1
1.0%
ValueCountFrequency (%)
9791197488801 1
1.0%
9791197453212 1
1.0%
9791196416850 1
1.0%
9791195807192 1
1.0%
9791195750863 1
1.0%
9791195435395 1
1.0%
9791191857436 1
1.0%
9791191506051 1
1.0%
9791191503302 1
1.0%
9791191503258 1
1.0%

vol
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

title
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:03.645839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length117
Median length43
Mean length25.08
Min length4

Characters and Unicode

Total characters2508
Distinct characters396
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)96.0%

Sample

1st row2022 인생일력
2nd row감정 쓰기 연습 - 더 나다운 나를 찾는
3rd row이상기후 재난과 중점 대응 방향 :
4th row경주 구황동 원지 종합정비계획
5th row도우미 여우 센코 씨 7 - S코믹스
ValueCountFrequency (%)
64
 
10.5%
2022 15
 
2.5%
2021 10
 
1.6%
및 9
 
1.5%
대비 8
 
1.3%
공무원 6
 
1.0%
수록 5
 
0.8%
보상법규 4
 
0.7%
핵심요약집 4
 
0.7%
시험대비 4
 
0.7%
Other values (408) 479
78.8%
2023-12-10T18:51:04.596659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
508
 
20.3%
2 92
 
3.7%
기 51
 
2.0%
- 45
 
1.8%
0 37
 
1.5%
사 28
 
1.1%
정 27
 
1.1%
대 27
 
1.1%
1 25
 
1.0%
의 25
 
1.0%
Other values (386) 1643
65.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1517
60.5%
Space Separator 508
 
20.3%
Decimal Number 185
 
7.4%
Uppercase Letter 78
 
3.1%
Lowercase Letter 73
 
2.9%
Other Punctuation 51
 
2.0%
Dash Punctuation 45
 
1.8%
Close Punctuation 21
 
0.8%
Open Punctuation 21
 
0.8%
Math Symbol 9
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 51
 
3.4%
사 28
 
1.8%
정 27
 
1.8%
대 27
 
1.8%
의 25
 
1.6%
가 22
 
1.5%
법 21
 
1.4%
시 21
 
1.4%
제 20
 
1.3%
이 19
 
1.3%
Other values (323) 1256
82.8%
Lowercase Letter
ValueCountFrequency (%)
o 10
13.7%
a 8
11.0%
i 8
11.0%
l 6
 
8.2%
n 6
 
8.2%
t 5
 
6.8%
r 4
 
5.5%
e 4
 
5.5%
s 3
 
4.1%
m 3
 
4.1%
Other values (10) 16
21.9%
Uppercase Letter
ValueCountFrequency (%)
C 13
16.7%
S 13
16.7%
N 8
10.3%
T 8
10.3%
M 5
 
6.4%
O 4
 
5.1%
P 4
 
5.1%
H 3
 
3.8%
A 3
 
3.8%
I 2
 
2.6%
Other values (9) 15
19.2%
Decimal Number
ValueCountFrequency (%)
2 92
49.7%
0 37
20.0%
1 25
 
13.5%
9 7
 
3.8%
7 6
 
3.2%
4 6
 
3.2%
3 5
 
2.7%
5 5
 
2.7%
8 1
 
0.5%
6 1
 
0.5%
Other Punctuation
ValueCountFrequency (%)
: 14
27.5%
/ 13
25.5%
, 10
19.6%
· 7
13.7%
! 4
 
7.8%
& 2
 
3.9%
. 1
 
2.0%
Math Symbol
ValueCountFrequency (%)
= 4
44.4%
+ 3
33.3%
~ 2
22.2%
Space Separator
ValueCountFrequency (%)
508
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1515
60.4%
Common 840
33.5%
Latin 151
 
6.0%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 51
 
3.4%
사 28
 
1.8%
정 27
 
1.8%
대 27
 
1.8%
의 25
 
1.7%
가 22
 
1.5%
법 21
 
1.4%
시 21
 
1.4%
제 20
 
1.3%
이 19
 
1.3%
Other values (321) 1254
82.8%
Latin
ValueCountFrequency (%)
C 13
 
8.6%
S 13
 
8.6%
o 10
 
6.6%
N 8
 
5.3%
T 8
 
5.3%
a 8
 
5.3%
i 8
 
5.3%
l 6
 
4.0%
n 6
 
4.0%
t 5
 
3.3%
Other values (29) 66
43.7%
Common
ValueCountFrequency (%)
508
60.5%
2 92
 
11.0%
- 45
 
5.4%
0 37
 
4.4%
1 25
 
3.0%
) 21
 
2.5%
( 21
 
2.5%
: 14
 
1.7%
/ 13
 
1.5%
, 10
 
1.2%
Other values (14) 54
 
6.4%
Han
ValueCountFrequency (%)
岩 1
50.0%
礪 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1515
60.4%
ASCII 984
39.2%
None 7
 
0.3%
CJK 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
508
51.6%
2 92
 
9.3%
- 45
 
4.6%
0 37
 
3.8%
1 25
 
2.5%
) 21
 
2.1%
( 21
 
2.1%
: 14
 
1.4%
/ 13
 
1.3%
C 13
 
1.3%
Other values (52) 195
 
19.8%
Hangul
ValueCountFrequency (%)
기 51
 
3.4%
사 28
 
1.8%
정 27
 
1.8%
대 27
 
1.8%
의 25
 
1.7%
가 22
 
1.5%
법 21
 
1.4%
시 21
 
1.4%
제 20
 
1.3%
이 19
 
1.3%
Other values (321) 1254
82.8%
None
ValueCountFrequency (%)
· 7
100.0%
CJK
ValueCountFrequency (%)
岩 1
50.0%
礪 1
50.0%

author
Text

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:05.399130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length163
Median length34
Mean length14.3
Min length3

Characters and Unicode

Total characters1430
Distinct characters248
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)84.0%

Sample

1st row민음사 편집부 (지은이)
2nd row홍보라 (지은이)
3rd row국립재난안전연구원 [편]
4th row[연구기관]: 신라문화유산연구원
5th row리무코로 (지은이), 나민형 (옮긴이)
ValueCountFrequency (%)
지은이 72
 
24.4%
옮긴이 14
 
4.7%
저 5
 
1.7%
취업적성연구소 4
 
1.4%
편집부 3
 
1.0%
강희영 2
 
0.7%
저자 2
 
0.7%
편저 2
 
0.7%
편저자 2
 
0.7%
박혜준 2
 
0.7%
Other values (178) 187
63.4%
2023-12-10T18:51:06.433340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
195
 
13.6%
이 100
 
7.0%
) 89
 
6.2%
( 89
 
6.2%
은 78
 
5.5%
지 74
 
5.2%
, 54
 
3.8%
김 23
 
1.6%
긴 14
 
1.0%
정 14
 
1.0%
Other values (238) 700
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 863
60.3%
Space Separator 195
 
13.6%
Lowercase Letter 100
 
7.0%
Close Punctuation 91
 
6.4%
Open Punctuation 91
 
6.4%
Other Punctuation 62
 
4.3%
Uppercase Letter 27
 
1.9%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 100
 
11.6%
은 78
 
9.0%
지 74
 
8.6%
김 23
 
2.7%
긴 14
 
1.6%
정 14
 
1.6%
박 14
 
1.6%
옮 14
 
1.6%
연 13
 
1.5%
소 12
 
1.4%
Other values (198) 507
58.7%
Lowercase Letter
ValueCountFrequency (%)
n 14
14.0%
a 12
12.0%
e 11
11.0%
o 11
11.0%
r 10
10.0%
l 9
9.0%
i 6
 
6.0%
s 5
 
5.0%
k 4
 
4.0%
t 3
 
3.0%
Other values (7) 15
15.0%
Uppercase Letter
ValueCountFrequency (%)
F 3
11.1%
H 3
11.1%
C 2
 
7.4%
L 2
 
7.4%
J 2
 
7.4%
B 2
 
7.4%
E 2
 
7.4%
A 2
 
7.4%
N 2
 
7.4%
M 2
 
7.4%
Other values (5) 5
18.5%
Close Punctuation
ValueCountFrequency (%)
) 89
97.8%
] 2
 
2.2%
Open Punctuation
ValueCountFrequency (%)
( 89
97.8%
[ 2
 
2.2%
Other Punctuation
ValueCountFrequency (%)
, 54
87.1%
: 8
 
12.9%
Space Separator
ValueCountFrequency (%)
195
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 854
59.7%
Common 440
30.8%
Latin 127
 
8.9%
Han 9
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 100
 
11.7%
은 78
 
9.1%
지 74
 
8.7%
김 23
 
2.7%
긴 14
 
1.6%
정 14
 
1.6%
박 14
 
1.6%
옮 14
 
1.6%
연 13
 
1.5%
소 12
 
1.4%
Other values (190) 498
58.3%
Latin
ValueCountFrequency (%)
n 14
 
11.0%
a 12
 
9.4%
e 11
 
8.7%
o 11
 
8.7%
r 10
 
7.9%
l 9
 
7.1%
i 6
 
4.7%
s 5
 
3.9%
k 4
 
3.1%
t 3
 
2.4%
Other values (22) 42
33.1%
Common
ValueCountFrequency (%)
195
44.3%
) 89
20.2%
( 89
20.2%
, 54
 
12.3%
: 8
 
1.8%
[ 2
 
0.5%
] 2
 
0.5%
- 1
 
0.2%
Han
ValueCountFrequency (%)
李 2
22.2%
堅 1
11.1%
兪 1
11.1%
孔 1
11.1%
龍 1
11.1%
迪 1
11.1%
華 1
11.1%
海 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 854
59.7%
ASCII 567
39.7%
CJK 9
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
195
34.4%
) 89
15.7%
( 89
15.7%
, 54
 
9.5%
n 14
 
2.5%
a 12
 
2.1%
e 11
 
1.9%
o 11
 
1.9%
r 10
 
1.8%
l 9
 
1.6%
Other values (30) 73
 
12.9%
Hangul
ValueCountFrequency (%)
이 100
 
11.7%
은 78
 
9.1%
지 74
 
8.7%
김 23
 
2.7%
긴 14
 
1.6%
정 14
 
1.6%
박 14
 
1.6%
옮 14
 
1.6%
연 13
 
1.5%
소 12
 
1.4%
Other values (190) 498
58.3%
CJK
ValueCountFrequency (%)
李 2
22.2%
堅 1
11.1%
兪 1
11.1%
孔 1
11.1%
龍 1
11.1%
迪 1
11.1%
華 1
11.1%
海 1
11.1%

publisher
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:06.934332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length5.24
Min length2

Characters and Unicode

Total characters524
Distinct characters130
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)30.0%

Sample

1st row민음사
2nd row새로운제안
3rd row국립재난안전연구원
4th row신라문화유산연구원
5th row㈜소미미디어
ValueCountFrequency (%)
시대고시기획 7
 
6.9%
박영사 7
 
6.9%
박문각 6
 
5.9%
대한기독교서회 5
 
4.9%
서원각 5
 
4.9%
학산문화사(만화 5
 
4.9%
현문사 4
 
3.9%
기문사 4
 
3.9%
경인문화사 3
 
2.9%
학연 3
 
2.9%
Other values (42) 53
52.0%
2023-12-10T18:51:07.764893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 39
 
7.4%
문 34
 
6.5%
박 20
 
3.8%
각 17
 
3.2%
기 17
 
3.2%
화 16
 
3.1%
시 15
 
2.9%
( 14
 
2.7%
대 14
 
2.7%
서 14
 
2.7%
Other values (120) 324
61.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 473
90.3%
Open Punctuation 14
 
2.7%
Close Punctuation 14
 
2.7%
Lowercase Letter 9
 
1.7%
Uppercase Letter 7
 
1.3%
Other Symbol 2
 
0.4%
Space Separator 2
 
0.4%
Other Punctuation 2
 
0.4%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 39
 
8.2%
문 34
 
7.2%
박 20
 
4.2%
각 17
 
3.6%
기 17
 
3.6%
화 16
 
3.4%
시 15
 
3.2%
대 14
 
3.0%
서 14
 
3.0%
판 14
 
3.0%
Other values (100) 273
57.7%
Uppercase Letter
ValueCountFrequency (%)
C 1
14.3%
D 1
14.3%
S 1
14.3%
G 1
14.3%
P 1
14.3%
A 1
14.3%
K 1
14.3%
Lowercase Letter
ValueCountFrequency (%)
l 3
33.3%
e 2
22.2%
b 1
 
11.1%
n 1
 
11.1%
d 1
 
11.1%
o 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
& 1
50.0%
, 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 475
90.6%
Common 33
 
6.3%
Latin 16
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 39
 
8.2%
문 34
 
7.2%
박 20
 
4.2%
각 17
 
3.6%
기 17
 
3.6%
화 16
 
3.4%
시 15
 
3.2%
대 14
 
2.9%
서 14
 
2.9%
판 14
 
2.9%
Other values (101) 275
57.9%
Latin
ValueCountFrequency (%)
l 3
18.8%
e 2
12.5%
C 1
 
6.2%
D 1
 
6.2%
S 1
 
6.2%
b 1
 
6.2%
n 1
 
6.2%
d 1
 
6.2%
o 1
 
6.2%
G 1
 
6.2%
Other values (3) 3
18.8%
Common
ValueCountFrequency (%)
( 14
42.4%
) 14
42.4%
2
 
6.1%
& 1
 
3.0%
3 1
 
3.0%
, 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 473
90.3%
ASCII 49
 
9.4%
None 2
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 39
 
8.2%
문 34
 
7.2%
박 20
 
4.2%
각 17
 
3.6%
기 17
 
3.6%
화 16
 
3.4%
시 15
 
3.2%
대 14
 
3.0%
서 14
 
3.0%
판 14
 
3.0%
Other values (100) 273
57.7%
ASCII
ValueCountFrequency (%)
( 14
28.6%
) 14
28.6%
l 3
 
6.1%
2
 
4.1%
e 2
 
4.1%
C 1
 
2.0%
& 1
 
2.0%
D 1
 
2.0%
S 1
 
2.0%
3 1
 
2.0%
Other values (9) 9
18.4%
None
ValueCountFrequency (%)
㈜ 2
100.0%

pub_date
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

add_code
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)44.6%
Missing8
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean44854.783
Minimum650
Maximum94360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:08.042388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum650
5-th percentile3268.5
Q17785
median13515
Q393360
95-th percentile93578
Maximum94360
Range93710
Interquartile range (IQR)85575

Descriptive statistics

Standard deviation42064.204
Coefficient of variation (CV)0.93778638
Kurtosis-1.9283665
Mean44854.783
Median Absolute Deviation (MAD)9790
Skewness0.29239076
Sum4126640
Variance1.7693973 × 109
MonotonicityNot monotonic
2023-12-10T18:51:08.312253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
13320 8
 
8.0%
93360 8
 
8.0%
93510 8
 
8.0%
4230 6
 
6.0%
7650 5
 
5.0%
13360 5
 
5.0%
7830 4
 
4.0%
93320 4
 
4.0%
93560 3
 
3.0%
13350 2
 
2.0%
Other values (31) 39
39.0%
(Missing) 8
 
8.0%
ValueCountFrequency (%)
650 1
 
1.0%
810 1
 
1.0%
3200 1
 
1.0%
3230 2
 
2.0%
3300 1
 
1.0%
3650 1
 
1.0%
3800 1
 
1.0%
3810 2
 
2.0%
4230 6
6.0%
4810 2
 
2.0%
ValueCountFrequency (%)
94360 1
 
1.0%
93910 1
 
1.0%
93720 1
 
1.0%
93620 1
 
1.0%
93600 1
 
1.0%
93560 3
 
3.0%
93550 2
 
2.0%
93530 2
 
2.0%
93510 8
8.0%
93370 2
 
2.0%

price
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)49.4%
Missing23
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean26161.039
Minimum4800
Maximum120000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:08.600635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4800
5-th percentile5000
Q113000
median22000
Q335000
95-th percentile60800
Maximum120000
Range115200
Interquartile range (IQR)22000

Descriptive statistics

Standard deviation19539.004
Coefficient of variation (CV)0.74687416
Kurtosis6.6114943
Mean26161.039
Median Absolute Deviation (MAD)10000
Skewness2.0742005
Sum2014400
Variance3.8177267 × 108
MonotonicityNot monotonic
2023-12-10T18:51:08.900756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
5000 6
 
6.0%
25000 5
 
5.0%
15000 5
 
5.0%
12000 4
 
4.0%
13000 4
 
4.0%
30000 3
 
3.0%
20000 3
 
3.0%
37000 3
 
3.0%
27000 3
 
3.0%
18000 3
 
3.0%
Other values (28) 38
38.0%
(Missing) 23
23.0%
ValueCountFrequency (%)
4800 1
 
1.0%
5000 6
6.0%
7000 2
 
2.0%
8000 1
 
1.0%
10000 1
 
1.0%
10800 1
 
1.0%
11000 1
 
1.0%
12000 4
4.0%
12800 1
 
1.0%
13000 4
4.0%
ValueCountFrequency (%)
120000 1
1.0%
77000 1
1.0%
76000 1
1.0%
72000 1
1.0%
58000 1
1.0%
50000 2
2.0%
47000 1
1.0%
45000 2
2.0%
43000 2
2.0%
40000 1
1.0%

img_url
Text

MISSING 

Distinct76
Distinct (%)100.0%
Missing24
Missing (%)24.0%
Memory size932.0 B
2023-12-10T18:51:09.504348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length85
Median length66
Mean length66.907895
Min length65

Characters and Unicode

Total characters5085
Distinct characters40
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

Unique76 ?
Unique (%)100.0%

Sample

1st rowhttps://image.aladin.co.kr/product/28228/16/cover/k312835474_1.jpg
2nd rowhttps://image.aladin.co.kr/product/28347/19/cover/8955336233_1.jpg
3rd rowhttps://image.aladin.co.kr/product/27257/59/cover/k962732690_1.jpg
4th rowhttps://image.aladin.co.kr/product/27324/66/cover/k852732224_1.jpg
5th rowhttps://image.aladin.co.kr/product/27226/63/cover/k722732399_1.jpg
ValueCountFrequency (%)
https://image.aladin.co.kr/product/27349/72/cover/k252732424_1.jpg 1
 
1.3%
https://image.aladin.co.kr/product/28138/3/cover/k322734652_1.jpg 1
 
1.3%
https://image.aladin.co.kr/product/28165/42/cover/8932117993_1.jpg 1
 
1.3%
https://image.aladin.co.kr/product/27496/2/cover/k632733997_1.jpg 1
 
1.3%
https://image.aladin.co.kr/product/28165/43/cover/8932118019_1.jpg 1
 
1.3%
https://image.aladin.co.kr/product/28095/85/cover/k562734344_1.jpg 1
 
1.3%
https://image.aladin.co.kr/product/28048/96/cover/k422734430_1.jpg 1
 
1.3%
https://image.aladin.co.kr/product/28128/75/cover/k382734259_1.jpg 1
 
1.3%
https://image.aladin.co.kr/product/28135/83/cover/k912734557_1.jpg 1
 
1.3%
https://image.aladin.co.kr/product/28158/40/cover/8959169072_1.jpg 1
 
1.3%
Other values (66) 66
86.8%
2023-12-10T18:51:10.437613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 528
 
10.4%
. 300
 
5.9%
t 248
 
4.9%
p 236
 
4.6%
o 228
 
4.5%
2 227
 
4.5%
a 224
 
4.4%
c 224
 
4.4%
r 220
 
4.3%
1 195
 
3.8%
Other values (30) 2455
48.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2762
54.3%
Decimal Number 1327
26.1%
Other Punctuation 912
 
17.9%
Connector Punctuation 72
 
1.4%
Math Symbol 8
 
0.2%
Dash Punctuation 4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 248
 
9.0%
p 236
 
8.5%
o 228
 
8.3%
a 224
 
8.1%
c 224
 
8.1%
r 220
 
8.0%
e 160
 
5.8%
i 152
 
5.5%
g 148
 
5.4%
d 148
 
5.4%
Other values (12) 774
28.0%
Decimal Number
ValueCountFrequency (%)
2 227
17.1%
1 195
14.7%
3 151
11.4%
7 150
11.3%
8 142
10.7%
4 123
9.3%
9 102
7.7%
5 89
 
6.7%
0 75
 
5.7%
6 73
 
5.5%
Other Punctuation
ValueCountFrequency (%)
/ 528
57.9%
. 300
32.9%
: 76
 
8.3%
? 4
 
0.4%
& 4
 
0.4%
Connector Punctuation
ValueCountFrequency (%)
_ 72
100.0%
Math Symbol
ValueCountFrequency (%)
= 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2762
54.3%
Common 2323
45.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 248
 
9.0%
p 236
 
8.5%
o 228
 
8.3%
a 224
 
8.1%
c 224
 
8.1%
r 220
 
8.0%
e 160
 
5.8%
i 152
 
5.5%
g 148
 
5.4%
d 148
 
5.4%
Other values (12) 774
28.0%
Common
ValueCountFrequency (%)
/ 528
22.7%
. 300
12.9%
2 227
9.8%
1 195
 
8.4%
3 151
 
6.5%
7 150
 
6.5%
8 142
 
6.1%
4 123
 
5.3%
9 102
 
4.4%
5 89
 
3.8%
Other values (8) 316
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5085
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 528
 
10.4%
. 300
 
5.9%
t 248
 
4.9%
p 236
 
4.6%
o 228
 
4.5%
2 227
 
4.5%
a 224
 
4.4%
c 224
 
4.4%
r 220
 
4.3%
1 195
 
3.8%
Other values (30) 2455
48.3%

description
Text

MISSING 

Distinct69
Distinct (%)98.6%
Missing30
Missing (%)30.0%
Memory size932.0 B
2023-12-10T18:51:11.262881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length208
Median length154.5
Mean length135.55714
Min length29

Characters and Unicode

Total characters9489
Distinct characters612
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)97.1%

Sample

1st row2018년부터 여러분과 함께해 온 인생일력. 민음사의 동양 고전 60여 권을 한 권의 일력에 고이 담았다. 조선 사람들의 골똘한 생각과 빼어난 시상을 손에 잡힐 듯 생생하게 전해줍니다. 때로 하루를 지탱하는 건 하나의 문장! 인생일력과 함께 오늘을 시작하자.
2nd row일상이 무너지지 않으려면 평소에 조금씩 꾸준히 문제를 직면하는 연습이 필요하다. 그 시작이 ‘글쓰기’가 되었으면 좋겠다고, 저자는 말한다. 여기서 저자가 말하는 글쓰기는 ‘자기감정을 시각화하는 것’을 의미한다. 우리가 알고 있는 글쓰기와는 조금 다르다. 상상력이나 문장력 같은 건 필요 없다. 얼마나 솔직하게 자신과 대화할 수 있는지가 가장 중요하다. 이 과정이 직면이다.
3rd row나카노를 흠모하는 후배이자 너구리 소녀 후쿠다 씨, 마침내 센코 씨와 맞닥뜨리다. 당황하는 나카노를 곁눈질하며 센코 씨는 후쿠다 씨에 대해 충격적인 한 마디를 던지는데. 나카노를 둘러싼 두 복슬복슬 소녀들의 불꽃 튀는 신경전의 결과는?!
4th row누구를 위해 나아가는가, 피와 철로 얼룩진 전장의 끝으로. 공식 코믹 앤솔로지 제4탄! 모든 지휘관에게 보내는 인형들의 이야기.
5th row부끄럼쟁이에 스스로 살짝 자신감이 없는 여고생 미하루와 반에서 인기인이지만 사랑에 소극적인 겁쟁이 남자 후지. 미하루에게 한눈에 반한 후지는 그녀에게 다가가기 위해 평소에 하지 않았던 독서를 하게 되고, 친구에게 연애 상담도 하게 되는데….
ValueCountFrequency (%)
수 31
 
1.4%
및 27
 
1.2%
있다 21
 
1.0%
대한 18
 
0.8%
위한 18
 
0.8%
이 15
 
0.7%
있도록 12
 
0.6%
있는 11
 
0.5%
책이다 10
 
0.5%
내용을 8
 
0.4%
Other values (1566) 1993
92.1%
2023-12-10T18:51:12.524830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2094
 
22.1%
다 180
 
1.9%
. 176
 
1.9%
이 175
 
1.8%
하 162
 
1.7%
한 145
 
1.5%
의 139
 
1.5%
을 137
 
1.4%
에 130
 
1.4%
는 120
 
1.3%
Other values (602) 6031
63.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6654
70.1%
Space Separator 2094
 
22.1%
Other Punctuation 335
 
3.5%
Decimal Number 167
 
1.8%
Uppercase Letter 79
 
0.8%
Lowercase Letter 71
 
0.7%
Open Punctuation 30
 
0.3%
Close Punctuation 30
 
0.3%
Final Punctuation 11
 
0.1%
Initial Punctuation 11
 
0.1%
Other values (3) 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
다 180
 
2.7%
이 175
 
2.6%
하 162
 
2.4%
한 145
 
2.2%
의 139
 
2.1%
을 137
 
2.1%
에 130
 
2.0%
는 120
 
1.8%
기 101
 
1.5%
고 92
 
1.4%
Other values (525) 5273
79.2%
Lowercase Letter
ValueCountFrequency (%)
t 12
16.9%
o 8
11.3%
r 6
8.5%
l 5
7.0%
i 5
7.0%
u 5
7.0%
a 5
7.0%
n 5
7.0%
e 5
7.0%
g 4
 
5.6%
Other values (9) 11
15.5%
Uppercase Letter
ValueCountFrequency (%)
P 13
16.5%
A 10
12.7%
M 9
11.4%
S 6
7.6%
T 6
7.6%
R 6
7.6%
I 4
 
5.1%
C 4
 
5.1%
K 4
 
5.1%
B 3
 
3.8%
Other values (8) 14
17.7%
Other Punctuation
ValueCountFrequency (%)
. 176
52.5%
, 98
29.3%
; 11
 
3.3%
' 10
 
3.0%
& 10
 
3.0%
! 9
 
2.7%
… 7
 
2.1%
/ 6
 
1.8%
? 5
 
1.5%
· 2
 
0.6%
Decimal Number
ValueCountFrequency (%)
2 50
29.9%
0 38
22.8%
1 32
19.2%
9 9
 
5.4%
5 9
 
5.4%
7 7
 
4.2%
6 6
 
3.6%
3 6
 
3.6%
4 6
 
3.6%
8 4
 
2.4%
Open Punctuation
ValueCountFrequency (%)
( 17
56.7%
[ 6
 
20.0%
『 4
 
13.3%
《 2
 
6.7%
〈 1
 
3.3%
Close Punctuation
ValueCountFrequency (%)
) 17
56.7%
] 6
 
20.0%
』 4
 
13.3%
》 2
 
6.7%
〉 1
 
3.3%
Final Punctuation
ValueCountFrequency (%)
’ 9
81.8%
” 2
 
18.2%
Initial Punctuation
ValueCountFrequency (%)
‘ 9
81.8%
“ 2
 
18.2%
Math Symbol
ValueCountFrequency (%)
+ 2
66.7%
~ 1
33.3%
Space Separator
ValueCountFrequency (%)
2094
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Symbol
ValueCountFrequency (%)
♪ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6648
70.1%
Common 2685
28.3%
Latin 150
 
1.6%
Han 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
다 180
 
2.7%
이 175
 
2.6%
하 162
 
2.4%
한 145
 
2.2%
의 139
 
2.1%
을 137
 
2.1%
에 130
 
2.0%
는 120
 
1.8%
기 101
 
1.5%
고 92
 
1.4%
Other values (519) 5267
79.2%
Common
ValueCountFrequency (%)
2094
78.0%
. 176
 
6.6%
, 98
 
3.6%
2 50
 
1.9%
0 38
 
1.4%
1 32
 
1.2%
( 17
 
0.6%
) 17
 
0.6%
; 11
 
0.4%
' 10
 
0.4%
Other values (30) 142
 
5.3%
Latin
ValueCountFrequency (%)
P 13
 
8.7%
t 12
 
8.0%
A 10
 
6.7%
M 9
 
6.0%
o 8
 
5.3%
r 6
 
4.0%
S 6
 
4.0%
T 6
 
4.0%
R 6
 
4.0%
l 5
 
3.3%
Other values (27) 69
46.0%
Han
ValueCountFrequency (%)
文 1
16.7%
脈 1
16.7%
理 1
16.7%
法 1
16.7%
略 1
16.7%
史 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6647
70.0%
ASCII 2789
29.4%
Punctuation 29
 
0.3%
None 16
 
0.2%
CJK 5
 
0.1%
Compat Jamo 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%
Misc Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2094
75.1%
. 176
 
6.3%
, 98
 
3.5%
2 50
 
1.8%
0 38
 
1.4%
1 32
 
1.1%
( 17
 
0.6%
) 17
 
0.6%
P 13
 
0.5%
t 12
 
0.4%
Other values (54) 242
 
8.7%
Hangul
ValueCountFrequency (%)
다 180
 
2.7%
이 175
 
2.6%
하 162
 
2.4%
한 145
 
2.2%
의 139
 
2.1%
을 137
 
2.1%
에 130
 
2.0%
는 120
 
1.8%
기 101
 
1.5%
고 92
 
1.4%
Other values (518) 5266
79.2%
Punctuation
ValueCountFrequency (%)
’ 9
31.0%
‘ 9
31.0%
… 7
24.1%
” 2
 
6.9%
“ 2
 
6.9%
None
ValueCountFrequency (%)
』 4
25.0%
『 4
25.0%
· 2
12.5%
》 2
12.5%
《 2
12.5%
〉 1
 
6.2%
〈 1
 
6.2%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%
CJK
ValueCountFrequency (%)
文 1
20.0%
脈 1
20.0%
理 1
20.0%
法 1
20.0%
史 1
20.0%
CJK Compat Ideographs
ValueCountFrequency (%)
略 1
100.0%
Misc Symbols
ValueCountFrequency (%)
♪ 1
100.0%

kdc_class_no
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

title_replace
Text

Distinct97
Distinct (%)98.0%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T18:51:13.015602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length81
Median length39
Mean length18.717172
Min length4

Characters and Unicode

Total characters1853
Distinct characters365
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)96.0%

Sample

1st row감정쓰기연습더나다운나를찾는
2nd row이상기후재난과중점대응방향
3rd row경주구황동원지종합정비계획
4th row도우미여우센코씨7s코믹스
5th row소녀전선코믹앤솔로지4slcomic
ValueCountFrequency (%)
시뮬레이션을적용한통합간호실습 2
 
2.0%
simulation실습지침서 2
 
2.0%
20229급공무원재난관리론기출문제정복하기2015년2021년기출문제수록,2022년9급공무원방재안전직시험대비 1
 
1.0%
지구에서사는법 1
 
1.0%
영국거버넌스체제변동연구 1
 
1.0%
자녀의대입합격을위한부모의무릎기도문30가지주제30일간기도서 1
 
1.0%
2022유튜브무료동영상이있는winq환경기능사필기실기단기완성2021년cbt최근기출복원문제수록실기작업형올컬러및핵심요약집빨간키수록한권으로필기실기완벽대비 1
 
1.0%
2022compact변시진도별상법사례연습 1
 
1.0%
성녀소화데레사자서전작은꽃,작은붓,작은길의영성 1
 
1.0%
대입정책과국가균형발전문재인정부를넘어서 1
 
1.0%
Other values (87) 87
87.9%
2023-12-10T18:51:13.953090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 89
 
4.8%
기 51
 
2.8%
0 36
 
1.9%
사 28
 
1.5%
정 27
 
1.5%
대 27
 
1.5%
1 25
 
1.3%
의 25
 
1.3%
가 22
 
1.2%
시 21
 
1.1%
Other values (355) 1502
81.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1511
81.5%
Decimal Number 181
 
9.8%
Lowercase Letter 151
 
8.1%
Other Punctuation 10
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 51
 
3.4%
사 28
 
1.9%
정 27
 
1.8%
대 27
 
1.8%
의 25
 
1.7%
가 22
 
1.5%
시 21
 
1.4%
법 21
 
1.4%
제 20
 
1.3%
직 19
 
1.3%
Other values (321) 1250
82.7%
Lowercase Letter
ValueCountFrequency (%)
s 16
10.6%
c 16
10.6%
o 14
 
9.3%
n 14
 
9.3%
t 13
 
8.6%
a 11
 
7.3%
i 10
 
6.6%
m 8
 
5.3%
l 7
 
4.6%
e 6
 
4.0%
Other values (13) 36
23.8%
Decimal Number
ValueCountFrequency (%)
2 89
49.2%
0 36
19.9%
1 25
 
13.8%
9 7
 
3.9%
7 6
 
3.3%
4 6
 
3.3%
3 5
 
2.8%
5 5
 
2.8%
8 1
 
0.6%
6 1
 
0.6%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1511
81.5%
Common 191
 
10.3%
Latin 151
 
8.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 51
 
3.4%
사 28
 
1.9%
정 27
 
1.8%
대 27
 
1.8%
의 25
 
1.7%
가 22
 
1.5%
시 21
 
1.4%
법 21
 
1.4%
제 20
 
1.3%
직 19
 
1.3%
Other values (321) 1250
82.7%
Latin
ValueCountFrequency (%)
s 16
10.6%
c 16
10.6%
o 14
 
9.3%
n 14
 
9.3%
t 13
 
8.6%
a 11
 
7.3%
i 10
 
6.6%
m 8
 
5.3%
l 7
 
4.6%
e 6
 
4.0%
Other values (13) 36
23.8%
Common
ValueCountFrequency (%)
2 89
46.6%
0 36
18.8%
1 25
 
13.1%
, 10
 
5.2%
9 7
 
3.7%
7 6
 
3.1%
4 6
 
3.1%
3 5
 
2.6%
5 5
 
2.6%
8 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1511
81.5%
ASCII 342
 
18.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 89
26.0%
0 36
 
10.5%
1 25
 
7.3%
s 16
 
4.7%
c 16
 
4.7%
o 14
 
4.1%
n 14
 
4.1%
t 13
 
3.8%
a 11
 
3.2%
i 10
 
2.9%
Other values (24) 98
28.7%
Hangul
ValueCountFrequency (%)
기 51
 
3.4%
사 28
 
1.9%
정 27
 
1.8%
대 27
 
1.8%
의 25
 
1.7%
가 22
 
1.5%
시 21
 
1.4%
법 21
 
1.4%
제 20
 
1.3%
직 19
 
1.3%
Other values (321) 1250
82.7%

author_replace
Text

Distinct90
Distinct (%)90.9%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T18:51:14.570047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length135
Median length26
Mean length10.363636
Min length3

Characters and Unicode

Total characters1026
Distinct characters223
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)83.8%

Sample

1st row홍보라지은이
2nd row국립재난안전연구원편
3rd row연구기관신라문화유산연구원
4th row리무코로지은이,나민형옮긴이
5th rowdna미디어코믹스편집부지은이,김장준옮긴이
ValueCountFrequency (%)
취업적성연구소지은이 4
 
4.0%
강희영저 2
 
2.0%
김택민지은이 2
 
2.0%
이관형지은이 2
 
2.0%
미즈노미나미지은이 2
 
2.0%
임민숙 2
 
2.0%
강정훈,박혜준지은이 2
 
2.0%
nas지은이 1
 
1.0%
김민지은이 1
 
1.0%
성녀소화데레사지은이,안응렬옮긴이 1
 
1.0%
Other values (80) 80
80.8%
2023-12-10T18:51:15.289217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 99
 
9.6%
은 77
 
7.5%
지 73
 
7.1%
, 54
 
5.3%
김 23
 
2.2%
n 16
 
1.6%
긴 14
 
1.4%
정 14
 
1.4%
a 14
 
1.4%
옮 14
 
1.4%
Other values (213) 628
61.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 845
82.4%
Lowercase Letter 127
 
12.4%
Other Punctuation 54
 
5.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 99
 
11.7%
은 77
 
9.1%
지 73
 
8.6%
김 23
 
2.7%
긴 14
 
1.7%
정 14
 
1.7%
옮 14
 
1.7%
박 14
 
1.7%
연 13
 
1.5%
소 12
 
1.4%
Other values (190) 492
58.2%
Lowercase Letter
ValueCountFrequency (%)
n 16
12.6%
a 14
11.0%
e 13
10.2%
l 11
 
8.7%
o 11
 
8.7%
r 10
 
7.9%
i 6
 
4.7%
f 6
 
4.7%
s 6
 
4.7%
h 5
 
3.9%
Other values (12) 29
22.8%
Other Punctuation
ValueCountFrequency (%)
, 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 845
82.4%
Latin 127
 
12.4%
Common 54
 
5.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 99
 
11.7%
은 77
 
9.1%
지 73
 
8.6%
김 23
 
2.7%
긴 14
 
1.7%
정 14
 
1.7%
옮 14
 
1.7%
박 14
 
1.7%
연 13
 
1.5%
소 12
 
1.4%
Other values (190) 492
58.2%
Latin
ValueCountFrequency (%)
n 16
12.6%
a 14
11.0%
e 13
10.2%
l 11
 
8.7%
o 11
 
8.7%
r 10
 
7.9%
i 6
 
4.7%
f 6
 
4.7%
s 6
 
4.7%
h 5
 
3.9%
Other values (12) 29
22.8%
Common
ValueCountFrequency (%)
, 54
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 845
82.4%
ASCII 181
 
17.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 99
 
11.7%
은 77
 
9.1%
지 73
 
8.6%
김 23
 
2.7%
긴 14
 
1.7%
정 14
 
1.7%
옮 14
 
1.7%
박 14
 
1.7%
연 13
 
1.5%
소 12
 
1.4%
Other values (190) 492
58.2%
ASCII
ValueCountFrequency (%)
, 54
29.8%
n 16
 
8.8%
a 14
 
7.7%
e 13
 
7.2%
l 11
 
6.1%
o 11
 
6.1%
r 10
 
5.5%
i 6
 
3.3%
f 6
 
3.3%
s 6
 
3.3%
Other values (13) 34
18.8%

pub_date_2
Date

MISSING 

Distinct46
Distinct (%)59.7%
Missing23
Missing (%)23.0%
Memory size932.0 B
Minimum2020-09-01 00:00:00
Maximum2022-01-10 00:00:00
2023-12-10T18:51:15.657273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:15.923288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)

is_coll_aladin
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing1
Missing (%)1.0%
Memory size332.0 B
True
99 
(Missing)
 
1
ValueCountFrequency (%)
True 99
99.0%
(Missing) 1
 
1.0%
2023-12-10T18:51:16.089357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

is_coll_naver
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing1
Missing (%)1.0%
Memory size332.0 B
True
99 
(Missing)
 
1
ValueCountFrequency (%)
True 99
99.0%
(Missing) 1
 
1.0%
2023-12-10T18:51:16.224854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

isbn_origin
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

Interactions

2023-12-10T18:50:57.947865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:55.387807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:56.382849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:57.313758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:58.091893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:55.672669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:56.632715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:57.477567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:58.288862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:56.066492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:56.871014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:57.646087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:58.435166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:56.224039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:57.061995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:57.789829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:51:16.383317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
master_seqisbn13titleauthorpublisheradd_codepriceimg_urldescriptiontitle_replaceauthor_replacepub_date_2
master_seq1.000NaN1.0001.0001.000NaN0.0001.0001.0001.0001.0001.000
isbn13NaN1.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
title1.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
author1.000NaN1.0001.0001.0001.0000.5861.0001.0001.0001.0000.992
publisher1.000NaN1.0001.0001.0000.9800.1591.0001.0001.0001.0000.975
add_codeNaNNaN1.0001.0000.9801.0000.4661.0001.0001.0001.0000.834
price0.000NaN1.0000.5860.1590.4661.0001.0001.0001.0000.6130.000
img_url1.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
description1.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
title_replace1.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
author_replace1.000NaN1.0001.0001.0001.0000.6131.0001.0001.0001.0000.991
pub_date_21.000NaN1.0000.9920.9750.8340.0001.0001.0001.0000.9911.000
2023-12-10T18:51:16.737960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
master_seqisbn13add_codeprice
master_seq1.0000.0710.0520.008
isbn130.0711.0000.0960.121
add_code0.0520.0961.0000.543
price0.0080.1210.5431.000

Missing values

2023-12-10T18:50:58.822924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:50:59.262844image/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.
2023-12-10T18:50:59.614753image/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

master_seqisbn13voltitleauthorpublisherpub_dateadd_codepriceimg_urldescriptionkdc_class_notitle_replaceauthor_replacepub_date_2is_coll_aladinis_coll_naverisbn_origin
063294842090000107555<NA>2022 인생일력민음사 편집부 (지은이)민음사<NA><NA>18000https://image.aladin.co.kr/product/28228/16/cover/k312835474_1.jpg2018년부터 여러분과 함께해 온 인생일력. 민음사의 동양 고전 60여 권을 한 권의 일력에 고이 담았다. 조선 사람들의 골똘한 생각과 빼어난 시상을 손에 잡힐 듯 생생하게 전해줍니다. 때로 하루를 지탱하는 건 하나의 문장! 인생일력과 함께 오늘을 시작하자.<NA><NA><NA>2021-12-08<NA><NA><NA>
163522259788955336238<NA>감정 쓰기 연습 - 더 나다운 나를 찾는홍보라 (지은이)새로운제안<NA><NA>15000https://image.aladin.co.kr/product/28347/19/cover/8955336233_1.jpg일상이 무너지지 않으려면 평소에 조금씩 꾸준히 문제를 직면하는 연습이 필요하다. 그 시작이 ‘글쓰기’가 되었으면 좋겠다고, 저자는 말한다. 여기서 저자가 말하는 글쓰기는 ‘자기감정을 시각화하는 것’을 의미한다. 우리가 알고 있는 글쓰기와는 조금 다르다. 상상력이나 문장력 같은 건 필요 없다. 얼마나 솔직하게 자신과 대화할 수 있는지가 가장 중요하다. 이 과정이 직면이다.<NA>감정쓰기연습더나다운나를찾는홍보라지은이2021-11-23YY<NA>
263294869788990564931<NA>이상기후 재난과 중점 대응 방향 :국립재난안전연구원 [편]국립재난안전연구원<NA><NA><NA><NA><NA><NA>이상기후재난과중점대응방향국립재난안전연구원편<NA>YY<NA>
363294879791197488801<NA>경주 구황동 원지 종합정비계획[연구기관]: 신라문화유산연구원신라문화유산연구원<NA>13900<NA><NA><NA><NA>경주구황동원지종합정비계획연구기관신라문화유산연구원<NA>YY<NA>
463294889791166118869<NA>도우미 여우 센코 씨 7 - S코믹스리무코로 (지은이), 나민형 (옮긴이)㈜소미미디어<NA>78305000https://image.aladin.co.kr/product/27257/59/cover/k962732690_1.jpg나카노를 흠모하는 후배이자 너구리 소녀 후쿠다 씨, 마침내 센코 씨와 맞닥뜨리다. 당황하는 나카노를 곁눈질하며 센코 씨는 후쿠다 씨에 대해 충격적인 한 마디를 던지는데. 나카노를 둘러싼 두 복슬복슬 소녀들의 불꽃 튀는 신경전의 결과는?!<NA>도우미여우센코씨7s코믹스리무코로지은이,나민형옮긴이2021-06-04YY<NA>
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663294909791166114632<NA>청춘 퍼즐 1 - S코믹스우타카타 (지은이), 정우주 (옮긴이)㈜소미미디어<NA>783010800https://image.aladin.co.kr/product/27226/63/cover/k722732399_1.jpg부끄럼쟁이에 스스로 살짝 자신감이 없는 여고생 미하루와 반에서 인기인이지만 사랑에 소극적인 겁쟁이 남자 후지. 미하루에게 한눈에 반한 후지는 그녀에게 다가가기 위해 평소에 하지 않았던 독서를 하게 되고, 친구에게 연애 상담도 하게 되는데….<NA>청춘퍼즐1s코믹스우타카타지은이,정우주옮긴이2021-06-03YY<NA>
763522269791195750863<NA>행동하는 심리학공정식, 강태신, 김현정, 김미영, 현문정, 신혜정 (지은이)(주)한국심리과학센터(KAPS)<NA><NA>33000https://image.aladin.co.kr/product/28380/44/cover/k402835604_2.jpg심리학은 인간이 행복하게 사는 데 필요한 정보와 기술을 제공해준다. 그래서 심리학은 과거지향적이라기 보다는 미래지향적이며, 인간의 복지를 최우선으로 생각하는 학문이다. 이미 미국과 유럽에서 심리학은 가장 취업이 잘되는 학문으로 자리를 잡아가고 있다. 이는 우리나라에서도 마찬가지이다....<NA>행동하는심리학공정식,강태신,김현정,김미영,현문정,신혜정지은이2021-11-15YY<NA>
863294929791138001823<NA>니코는 괴로워 4 - 픽시하우스알데히드 (지은이), 이하니 (옮긴이)영상출판미디어(주)<NA>78307000https://image.aladin.co.kr/product/27387/69/cover/k912732458_1.jpg수영장, 여름 축제, 그리고 알바. 밀어닥치는 시련에 니코는 맞설 수 있을까. 웃픈 여름 이야기가 가득한 니트 코미디.<NA>니코는괴로워4픽시하우스알데히드지은이,이하니옮긴이2021-07-05YY<NA>
963294939791134883003<NA>우리 동네 치바 군은 3오카모토 토카사 (지은이)학산문화사(만화)<NA>76505000https://image.aladin.co.kr/product/27349/71/cover/k192732424_1.jpg오노데라 마치는 예전에 다녔던 모교에서 학생들을 가르치는 28살 고교 교사. 그녀의 반에 치바 유토가 전학 온다. 그 미남 고교생은 마치의 고교 시절 첫사랑(짝사랑) 상대였던 치바 유이치의 동생으로, 고교시절의 그를 쏙 빼닮았는데……<NA>우리동네치바군은3오카모토토카사지은이2021-06-15YY<NA>
master_seqisbn13voltitleauthorpublisherpub_dateadd_codepriceimg_urldescriptionkdc_class_notitle_replaceauthor_replacepub_date_2is_coll_aladinis_coll_naverisbn_origin
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9463295789789946228761<NA>자력갱생 행진곡편집: 리광철문학예술출판사<NA><NA><NA><NA><NA><NA>자력갱생행진곡편집리광철<NA>YY<NA>
9563295799791167042453<NA>2021 최성진 샘의 핵심요약집 2차 부동산공법 (제32회 공인중개사 대비)최성진박문각<NA>1332012000https://bookthumb-phinf.pstatic.net/cover/209/182/20918273.jpg?type=m1&udate=20210908공인중개사 시험을 대비한 부동산공법 핵심 요약집이다.<NA>2021최성진샘의핵심요약집2차부동산공법제32회공인중개사대비최성진20210825YY<NA>
9663295809791167042408<NA>최신판 아담 교정관계법령 - 9급·7급 교정직, 7급 보호직 및 승진시험 대비이준 (지은이), 이언담 (감수)박문각<NA>1336035000https://image.aladin.co.kr/product/27803/85/cover/k762734165_1.jpg9급·7급 교정직, 7급 보호직 및 승진시험을 대비하기 위한 법령집이다. 법 개정 및 시행이후 70% 이상이 법령에서 출제가 되고 있다. 그만큼 법령의 중요성이 강조되고 있다. 하지만 이론서에 모든 법령을 반영한다는 것이 현실적으로 힘들어 법령집을 출간하게 되었다.<NA>최신판아담교정관계법령9급7급교정직,7급보호직및승진시험대비이준지은이,이언담감수2021-08-15YY<NA>
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9863295829791125737230<NA>2022 9급 공무원 재난관리론 기출문제 정복하기 - 2015년~2021년 기출문제 수록, 2022년 9급 공무원 방재안전직 시험대비공무원시험연구소 (지은이)서원각<NA>1335013000https://image.aladin.co.kr/product/28060/85/cover/k122734634_1.jpg2022년 9급 방재안전직 공무원 시험을 대비하기 위한 기출문제집이다. 2015년부터 2021년까지의 최다 기출문제를 수록하여 재난관리론 과목의 문제 유형과 출제 패턴을 파악할 수 있다. 또한 방대한 양의 기출문제를 풀어봄으로써 실전에 철저하게 대비할 수 있다.<NA>20229급공무원재난관리론기출문제정복하기2015년2021년기출문제수록,2022년9급공무원방재안전직시험대비공무원시험연구소지은이2022-01-07YY<NA>
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