Overview

Dataset statistics

Number of variables13
Number of observations1058
Missing cells727
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory114.8 KiB
Average record size in memory111.1 B

Variable types

Numeric6
Text5
Categorical2

Dataset

Description제주국제자유도시개발센터가 제주시 첨단로 330에서 운영하는 세미양빌딩 내 문화공간 "낭"의 2018년 도서 목록입니다. 도서명, 출판사, 분야, 가격,ISBN 등이 있습니다.
Author제주국제자유도시개발센터
URLhttps://www.data.go.kr/data/15084277/fileData.do

Alerts

종수 has constant value ""Constant
연번 is highly overall correlated with 단가 and 3 other fieldsHigh correlation
단가 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
금 액 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
견적금액 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
비고 is highly overall correlated with 연번High correlation
저자 2 has 710 (67.1%) missing valuesMissing
ISBN has 15 (1.4%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:39:13.458413
Analysis finished2023-12-12 02:39:19.577101
Duration6.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1058
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529.5
Minimum1
Maximum1058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-12-12T11:39:19.682593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile53.85
Q1265.25
median529.5
Q3793.75
95-th percentile1005.15
Maximum1058
Range1057
Interquartile range (IQR)528.5

Descriptive statistics

Standard deviation305.5626
Coefficient of variation (CV)0.57707761
Kurtosis-1.2
Mean529.5
Median Absolute Deviation (MAD)264.5
Skewness0
Sum560211
Variance93368.5
MonotonicityStrictly increasing
2023-12-12T11:39:19.805289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
712 1
 
0.1%
698 1
 
0.1%
699 1
 
0.1%
700 1
 
0.1%
701 1
 
0.1%
702 1
 
0.1%
703 1
 
0.1%
704 1
 
0.1%
705 1
 
0.1%
Other values (1048) 1048
99.1%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1058 1
0.1%
1057 1
0.1%
1056 1
0.1%
1055 1
0.1%
1054 1
0.1%
1053 1
0.1%
1052 1
0.1%
1051 1
0.1%
1050 1
0.1%
1049 1
0.1%
Distinct1056
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
2023-12-12T11:39:20.166166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length26
Mean length11.819471
Min length1

Characters and Unicode

Total characters12505
Distinct characters793
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

Unique1054 ?
Unique (%)99.6%

Sample

1st row숨기 대장 카멜레온
2nd row주주 할머니의 주스 가게
3rd row큰 건 내거야
4th row하양이의 나들이
5th row같은 것끼리 짝짝!
ValueCountFrequency (%)
초등 41
 
1.2%
25
 
0.7%
과학 24
 
0.7%
단행본 20
 
0.6%
내가 20
 
0.6%
우리 18
 
0.5%
이야기 17
 
0.5%
좋아하는 16
 
0.5%
아이 16
 
0.5%
엄마 14
 
0.4%
Other values (2194) 3197
93.8%
2023-12-12T11:39:20.702397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2476
 
19.8%
313
 
2.5%
221
 
1.8%
194
 
1.6%
182
 
1.5%
155
 
1.2%
146
 
1.2%
134
 
1.1%
128
 
1.0%
114
 
0.9%
Other values (783) 8442
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9174
73.4%
Space Separator 2476
 
19.8%
Decimal Number 358
 
2.9%
Other Punctuation 187
 
1.5%
Open Punctuation 107
 
0.9%
Close Punctuation 107
 
0.9%
Uppercase Letter 53
 
0.4%
Math Symbol 23
 
0.2%
Lowercase Letter 18
 
0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
313
 
3.4%
221
 
2.4%
194
 
2.1%
182
 
2.0%
155
 
1.7%
146
 
1.6%
134
 
1.5%
128
 
1.4%
114
 
1.2%
112
 
1.2%
Other values (733) 7475
81.5%
Uppercase Letter
ValueCountFrequency (%)
W 7
13.2%
O 5
 
9.4%
Y 5
 
9.4%
P 4
 
7.5%
H 4
 
7.5%
N 4
 
7.5%
L 3
 
5.7%
A 3
 
5.7%
E 3
 
5.7%
C 2
 
3.8%
Other values (9) 13
24.5%
Decimal Number
ValueCountFrequency (%)
1 97
27.1%
0 65
18.2%
2 43
12.0%
4 42
11.7%
5 27
 
7.5%
3 26
 
7.3%
6 19
 
5.3%
8 18
 
5.0%
9 11
 
3.1%
7 10
 
2.8%
Lowercase Letter
ValueCountFrequency (%)
w 4
22.2%
h 3
16.7%
y 3
16.7%
e 3
16.7%
o 2
11.1%
a 1
 
5.6%
l 1
 
5.6%
p 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 61
32.6%
? 60
32.1%
! 49
26.2%
: 11
 
5.9%
. 2
 
1.1%
% 2
 
1.1%
& 2
 
1.1%
Math Symbol
ValueCountFrequency (%)
~ 22
95.7%
+ 1
 
4.3%
Space Separator
ValueCountFrequency (%)
2476
100.0%
Open Punctuation
ValueCountFrequency (%)
( 107
100.0%
Close Punctuation
ValueCountFrequency (%)
) 107
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9174
73.4%
Common 3260
 
26.1%
Latin 71
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
313
 
3.4%
221
 
2.4%
194
 
2.1%
182
 
2.0%
155
 
1.7%
146
 
1.6%
134
 
1.5%
128
 
1.4%
114
 
1.2%
112
 
1.2%
Other values (733) 7475
81.5%
Latin
ValueCountFrequency (%)
W 7
 
9.9%
O 5
 
7.0%
Y 5
 
7.0%
w 4
 
5.6%
P 4
 
5.6%
H 4
 
5.6%
N 4
 
5.6%
h 3
 
4.2%
y 3
 
4.2%
L 3
 
4.2%
Other values (17) 29
40.8%
Common
ValueCountFrequency (%)
2476
76.0%
( 107
 
3.3%
) 107
 
3.3%
1 97
 
3.0%
0 65
 
2.0%
, 61
 
1.9%
? 60
 
1.8%
! 49
 
1.5%
2 43
 
1.3%
4 42
 
1.3%
Other values (13) 153
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9171
73.3%
ASCII 3331
 
26.6%
Compat Jamo 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2476
74.3%
( 107
 
3.2%
) 107
 
3.2%
1 97
 
2.9%
0 65
 
2.0%
, 61
 
1.8%
? 60
 
1.8%
! 49
 
1.5%
2 43
 
1.3%
4 42
 
1.3%
Other values (40) 224
 
6.7%
Hangul
ValueCountFrequency (%)
313
 
3.4%
221
 
2.4%
194
 
2.1%
182
 
2.0%
155
 
1.7%
146
 
1.6%
134
 
1.5%
128
 
1.4%
114
 
1.2%
112
 
1.2%
Other values (730) 7472
81.5%
Compat Jamo
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Distinct778
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
2023-12-12T11:39:21.042613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length3
Mean length4.4338374
Min length2

Characters and Unicode

Total characters4691
Distinct characters423
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique638 ?
Unique (%)60.3%

Sample

1st row문주영
2nd row정혜란
3rd row정순
4th row신지명
5th row김자영
ValueCountFrequency (%)
편집부 29
 
2.0%
22
 
1.5%
앤서니브라운 13
 
0.9%
11
 
0.8%
한규호 9
 
0.6%
이선아 8
 
0.5%
이혜옥 8
 
0.5%
이신애 7
 
0.5%
서지원 7
 
0.5%
최숙희 7
 
0.5%
Other values (1028) 1337
91.7%
2023-12-12T11:39:21.859370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
433
 
9.2%
195
 
4.2%
111
 
2.4%
106
 
2.3%
104
 
2.2%
78
 
1.7%
76
 
1.6%
69
 
1.5%
63
 
1.3%
58
 
1.2%
Other values (413) 3398
72.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4221
90.0%
Space Separator 436
 
9.3%
Uppercase Letter 16
 
0.3%
Lowercase Letter 9
 
0.2%
Other Punctuation 8
 
0.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
195
 
4.6%
111
 
2.6%
106
 
2.5%
104
 
2.5%
78
 
1.8%
76
 
1.8%
69
 
1.6%
63
 
1.5%
58
 
1.4%
53
 
1.3%
Other values (389) 3308
78.4%
Uppercase Letter
ValueCountFrequency (%)
C 3
18.8%
K 2
12.5%
B 2
12.5%
S 1
 
6.2%
X 1
 
6.2%
A 1
 
6.2%
L 1
 
6.2%
G 1
 
6.2%
U 1
 
6.2%
P 1
 
6.2%
Other values (2) 2
12.5%
Lowercase Letter
ValueCountFrequency (%)
r 2
22.2%
a 2
22.2%
o 1
11.1%
k 1
11.1%
e 1
11.1%
i 1
11.1%
l 1
11.1%
Space Separator
ValueCountFrequency (%)
433
99.3%
  3
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 7
87.5%
, 1
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4221
90.0%
Common 445
 
9.5%
Latin 25
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
195
 
4.6%
111
 
2.6%
106
 
2.5%
104
 
2.5%
78
 
1.8%
76
 
1.8%
69
 
1.6%
63
 
1.5%
58
 
1.4%
53
 
1.3%
Other values (389) 3308
78.4%
Latin
ValueCountFrequency (%)
C 3
 
12.0%
K 2
 
8.0%
B 2
 
8.0%
r 2
 
8.0%
a 2
 
8.0%
S 1
 
4.0%
X 1
 
4.0%
o 1
 
4.0%
A 1
 
4.0%
L 1
 
4.0%
Other values (9) 9
36.0%
Common
ValueCountFrequency (%)
433
97.3%
. 7
 
1.6%
  3
 
0.7%
, 1
 
0.2%
- 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4221
90.0%
ASCII 467
 
10.0%
None 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
433
92.7%
. 7
 
1.5%
C 3
 
0.6%
K 2
 
0.4%
B 2
 
0.4%
r 2
 
0.4%
a 2
 
0.4%
S 1
 
0.2%
X 1
 
0.2%
o 1
 
0.2%
Other values (13) 13
 
2.8%
Hangul
ValueCountFrequency (%)
195
 
4.6%
111
 
2.6%
106
 
2.5%
104
 
2.5%
78
 
1.8%
76
 
1.8%
69
 
1.6%
63
 
1.5%
58
 
1.4%
53
 
1.3%
Other values (389) 3308
78.4%
None
ValueCountFrequency (%)
  3
100.0%

저자 2
Text

MISSING 

Distinct254
Distinct (%)73.0%
Missing710
Missing (%)67.1%
Memory size8.4 KiB
2023-12-12T11:39:22.208459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length3
Mean length3.3706897
Min length2

Characters and Unicode

Total characters1173
Distinct characters213
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)59.8%

Sample

1st row한은미
2nd row이순성
3rd row이명숙
4th row정혜원
5th row박지영
ValueCountFrequency (%)
김현경 8
 
2.1%
박지영 7
 
1.8%
정혜원 7
 
1.8%
김현희 6
 
1.6%
조선학 5
 
1.3%
박혜수 5
 
1.3%
김희성 4
 
1.0%
이순영 4
 
1.0%
김난주 4
 
1.0%
김숙 4
 
1.0%
Other values (276) 330
85.9%
2023-12-12T11:39:22.713862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
 
7.2%
67
 
5.7%
55
 
4.7%
40
 
3.4%
40
 
3.4%
39
 
3.3%
37
 
3.2%
35
 
3.0%
32
 
2.7%
29
 
2.5%
Other values (203) 714
60.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1119
95.4%
Space Separator 37
 
3.2%
Other Punctuation 11
 
0.9%
Uppercase Letter 4
 
0.3%
Math Symbol 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
85
 
7.6%
67
 
6.0%
55
 
4.9%
40
 
3.6%
40
 
3.6%
39
 
3.5%
35
 
3.1%
32
 
2.9%
29
 
2.6%
27
 
2.4%
Other values (194) 670
59.9%
Uppercase Letter
ValueCountFrequency (%)
T 1
25.0%
X 1
25.0%
L 1
25.0%
R 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 10
90.9%
. 1
 
9.1%
Math Symbol
ValueCountFrequency (%)
> 1
50.0%
< 1
50.0%
Space Separator
ValueCountFrequency (%)
37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1119
95.4%
Common 50
 
4.3%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
85
 
7.6%
67
 
6.0%
55
 
4.9%
40
 
3.6%
40
 
3.6%
39
 
3.5%
35
 
3.1%
32
 
2.9%
29
 
2.6%
27
 
2.4%
Other values (194) 670
59.9%
Common
ValueCountFrequency (%)
37
74.0%
, 10
 
20.0%
> 1
 
2.0%
< 1
 
2.0%
. 1
 
2.0%
Latin
ValueCountFrequency (%)
T 1
25.0%
X 1
25.0%
L 1
25.0%
R 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1119
95.4%
ASCII 54
 
4.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
85
 
7.6%
67
 
6.0%
55
 
4.9%
40
 
3.6%
40
 
3.6%
39
 
3.5%
35
 
3.1%
32
 
2.9%
29
 
2.6%
27
 
2.4%
Other values (194) 670
59.9%
ASCII
ValueCountFrequency (%)
37
68.5%
, 10
 
18.5%
> 1
 
1.9%
T 1
 
1.9%
< 1
 
1.9%
X 1
 
1.9%
L 1
 
1.9%
. 1
 
1.9%
R 1
 
1.9%

ISBN
Text

MISSING 

Distinct1030
Distinct (%)98.8%
Missing15
Missing (%)1.4%
Memory size8.4 KiB
2023-12-12T11:39:23.027157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length16.975072
Min length8

Characters and Unicode

Total characters17705
Distinct characters16
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1020 ?
Unique (%)97.8%

Sample

1st row979-11-253-0550-7(50)
2nd row979-11-253-0552-1
3rd row979-11-253-0713-6
4th row979-11-253-0786-0
5th row979-11-253-0818-8
ValueCountFrequency (%)
8.81e+12 4
 
0.4%
979-11-5948-244-1 3
 
0.3%
979-11-253-1045-7 2
 
0.2%
978-89-98172-03-9 2
 
0.2%
979-11-5948-549-7 2
 
0.2%
979-11-253-0458-6 2
 
0.2%
979-11-253-0460-9 2
 
0.2%
979-11-253-0459-3 2
 
0.2%
979-11-5948-275-5 2
 
0.2%
979-11-5948-264-9 2
 
0.2%
Other values (1020) 1020
97.8%
2023-12-12T11:39:23.396342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 4150
23.4%
9 2882
16.3%
8 1898
10.7%
1 1797
10.1%
7 1746
9.9%
5 1084
 
6.1%
0 942
 
5.3%
2 876
 
4.9%
3 817
 
4.6%
6 773
 
4.4%
Other values (6) 740
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13537
76.5%
Dash Punctuation 4150
 
23.4%
Other Punctuation 4
 
< 0.1%
Uppercase Letter 4
 
< 0.1%
Math Symbol 4
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 2882
21.3%
8 1898
14.0%
1 1797
13.3%
7 1746
12.9%
5 1084
 
8.0%
0 942
 
7.0%
2 876
 
6.5%
3 817
 
6.0%
6 773
 
5.7%
4 722
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 4150
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17701
> 99.9%
Latin 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 4150
23.4%
9 2882
16.3%
8 1898
10.7%
1 1797
10.2%
7 1746
9.9%
5 1084
 
6.1%
0 942
 
5.3%
2 876
 
4.9%
3 817
 
4.6%
6 773
 
4.4%
Other values (5) 736
 
4.2%
Latin
ValueCountFrequency (%)
E 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 4150
23.4%
9 2882
16.3%
8 1898
10.7%
1 1797
10.1%
7 1746
9.9%
5 1084
 
6.1%
0 942
 
5.3%
2 876
 
4.9%
3 817
 
4.6%
6 773
 
4.4%
Other values (6) 740
 
4.2%
Distinct337
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
2023-12-12T11:39:23.682846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.4045369
Min length1

Characters and Unicode

Total characters4660
Distinct characters363
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

Unique194 ?
Unique (%)18.3%

Sample

1st rowNEKIDS
2nd rowNEKIDS
3rd rowNEKIDS
4th rowNEKIDS
5th rowNEKIDS
ValueCountFrequency (%)
nekids 192
 
18.0%
그린키즈 38
 
3.6%
길벗어린이 21
 
2.0%
책읽는곰 20
 
1.9%
웅진주니어 20
 
1.9%
동아출판 20
 
1.9%
비타북스 18
 
1.7%
비룡소 18
 
1.7%
시공주니어 17
 
1.6%
호박꽃 15
 
1.4%
Other values (331) 686
64.4%
2023-12-12T11:39:24.089308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 207
 
4.4%
S 195
 
4.2%
I 194
 
4.2%
D 193
 
4.1%
N 192
 
4.1%
E 192
 
4.1%
155
 
3.3%
135
 
2.9%
116
 
2.5%
112
 
2.4%
Other values (353) 2969
63.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3396
72.9%
Uppercase Letter 1215
 
26.1%
Lowercase Letter 26
 
0.6%
Decimal Number 9
 
0.2%
Space Separator 7
 
0.2%
Other Punctuation 7
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
155
 
4.6%
135
 
4.0%
116
 
3.4%
112
 
3.3%
81
 
2.4%
79
 
2.3%
72
 
2.1%
66
 
1.9%
65
 
1.9%
63
 
1.9%
Other values (322) 2452
72.2%
Uppercase Letter
ValueCountFrequency (%)
K 207
17.0%
S 195
16.0%
I 194
16.0%
D 193
15.9%
N 192
15.8%
E 192
15.8%
H 15
 
1.2%
R 14
 
1.2%
B 5
 
0.4%
M 5
 
0.4%
Other values (2) 3
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
o 7
26.9%
e 3
11.5%
k 3
11.5%
r 2
 
7.7%
i 2
 
7.7%
b 2
 
7.7%
u 2
 
7.7%
t 2
 
7.7%
c 2
 
7.7%
m 1
 
3.8%
Decimal Number
ValueCountFrequency (%)
0 2
22.2%
1 2
22.2%
2 2
22.2%
8 1
11.1%
3 1
11.1%
4 1
11.1%
Other Punctuation
ValueCountFrequency (%)
& 5
71.4%
. 2
 
28.6%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3396
72.9%
Latin 1241
 
26.6%
Common 23
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
155
 
4.6%
135
 
4.0%
116
 
3.4%
112
 
3.3%
81
 
2.4%
79
 
2.3%
72
 
2.1%
66
 
1.9%
65
 
1.9%
63
 
1.9%
Other values (322) 2452
72.2%
Latin
ValueCountFrequency (%)
K 207
16.7%
S 195
15.7%
I 194
15.6%
D 193
15.6%
N 192
15.5%
E 192
15.5%
H 15
 
1.2%
R 14
 
1.1%
o 7
 
0.6%
B 5
 
0.4%
Other values (12) 27
 
2.2%
Common
ValueCountFrequency (%)
7
30.4%
& 5
21.7%
. 2
 
8.7%
0 2
 
8.7%
1 2
 
8.7%
2 2
 
8.7%
8 1
 
4.3%
3 1
 
4.3%
4 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3396
72.9%
ASCII 1264
 
27.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 207
16.4%
S 195
15.4%
I 194
15.3%
D 193
15.3%
N 192
15.2%
E 192
15.2%
H 15
 
1.2%
R 14
 
1.1%
o 7
 
0.6%
7
 
0.6%
Other values (21) 48
 
3.8%
Hangul
ValueCountFrequency (%)
155
 
4.6%
135
 
4.0%
116
 
3.4%
112
 
3.3%
81
 
2.4%
79
 
2.3%
72
 
2.1%
66
 
1.9%
65
 
1.9%
63
 
1.9%
Other values (322) 2452
72.2%

발행연도
Real number (ℝ)

Distinct23
Distinct (%)2.2%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2016.1364
Minimum1994
Maximum2104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-12-12T11:39:24.254263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1994
5-th percentile2011
Q12015
median2017
Q32018
95-th percentile2018
Maximum2104
Range110
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.8921922
Coefficient of variation (CV)0.0019305203
Kurtosis249.67264
Mean2016.1364
Median Absolute Deviation (MAD)1
Skewness9.6375975
Sum2129040
Variance15.14916
MonotonicityNot monotonic
2023-12-12T11:39:24.387029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2018 321
30.3%
2017 320
30.2%
2015 187
17.7%
2016 96
 
9.1%
2014 34
 
3.2%
2013 23
 
2.2%
2012 15
 
1.4%
2011 10
 
0.9%
2009 8
 
0.8%
2010 8
 
0.8%
Other values (13) 34
 
3.2%
ValueCountFrequency (%)
1994 1
 
0.1%
1996 2
 
0.2%
1997 1
 
0.1%
2000 1
 
0.1%
2002 2
 
0.2%
2003 1
 
0.1%
2004 7
0.7%
2005 4
0.4%
2006 5
0.5%
2007 2
 
0.2%
ValueCountFrequency (%)
2104 1
 
0.1%
2019 2
 
0.2%
2018 321
30.3%
2017 320
30.2%
2016 96
 
9.1%
2015 187
17.7%
2014 34
 
3.2%
2013 23
 
2.2%
2012 15
 
1.4%
2011 10
 
0.9%

종수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
1
1058 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1058
100.0%

Length

2023-12-12T11:39:24.507905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:39:24.642076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1058
100.0%

책수
Real number (ℝ)

Distinct23
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9933837
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-12-12T11:39:24.745876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum93
Range92
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.0576055
Coefficient of variation (CV)3.5405152
Kurtosis89.732364
Mean1.9933837
Median Absolute Deviation (MAD)0
Skewness9.0814451
Sum2109
Variance49.809795
MonotonicityNot monotonic
2023-12-12T11:39:24.888890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 1001
94.6%
4 14
 
1.3%
5 7
 
0.7%
3 5
 
0.5%
6 5
 
0.5%
40 4
 
0.4%
12 3
 
0.3%
60 2
 
0.2%
2 2
 
0.2%
7 2
 
0.2%
Other values (13) 13
 
1.2%
ValueCountFrequency (%)
1 1001
94.6%
2 2
 
0.2%
3 5
 
0.5%
4 14
 
1.3%
5 7
 
0.7%
6 5
 
0.5%
7 2
 
0.2%
8 1
 
0.1%
10 1
 
0.1%
12 3
 
0.3%
ValueCountFrequency (%)
93 1
0.1%
87 1
0.1%
84 1
0.1%
64 1
0.1%
61 1
0.1%
60 2
0.2%
55 1
0.1%
48 1
0.1%
43 1
0.1%
41 1
0.1%

단가
Real number (ℝ)

HIGH CORRELATION 

Distinct107
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20713.8
Minimum6000
Maximum818300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-12-12T11:39:25.053143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6000
5-th percentile7500
Q110000
median12000
Q315000
95-th percentile35150
Maximum818300
Range812300
Interquartile range (IQR)5000

Descriptive statistics

Standard deviation55489.968
Coefficient of variation (CV)2.6788889
Kurtosis88.375504
Mean20713.8
Median Absolute Deviation (MAD)2100
Skewness8.8485278
Sum21915200
Variance3.0791365 × 109
MonotonicityNot monotonic
2023-12-12T11:39:25.269075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 178
16.8%
9500 104
 
9.8%
12000 94
 
8.9%
11000 76
 
7.2%
13000 66
 
6.2%
15000 54
 
5.1%
7000 42
 
4.0%
14000 32
 
3.0%
13800 30
 
2.8%
14800 26
 
2.5%
Other values (97) 356
33.6%
ValueCountFrequency (%)
6000 1
 
0.1%
6500 7
 
0.7%
7000 42
4.0%
7500 4
 
0.4%
7900 4
 
0.4%
8000 6
 
0.6%
8500 15
 
1.4%
8800 3
 
0.3%
9000 13
 
1.2%
9200 1
 
0.1%
ValueCountFrequency (%)
818300 1
 
0.1%
605000 1
 
0.1%
579500 1
 
0.1%
512000 2
0.2%
480000 1
 
0.1%
420000 1
 
0.1%
413000 1
 
0.1%
392000 1
 
0.1%
360000 3
0.3%
333200 1
 
0.1%

금 액
Real number (ℝ)

HIGH CORRELATION 

Distinct107
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20713.8
Minimum6000
Maximum818300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-12-12T11:39:25.468880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6000
5-th percentile7500
Q110000
median12000
Q315000
95-th percentile35150
Maximum818300
Range812300
Interquartile range (IQR)5000

Descriptive statistics

Standard deviation55489.968
Coefficient of variation (CV)2.6788889
Kurtosis88.375504
Mean20713.8
Median Absolute Deviation (MAD)2100
Skewness8.8485278
Sum21915200
Variance3.0791365 × 109
MonotonicityNot monotonic
2023-12-12T11:39:25.626983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 178
16.8%
9500 104
 
9.8%
12000 94
 
8.9%
11000 76
 
7.2%
13000 66
 
6.2%
15000 54
 
5.1%
7000 42
 
4.0%
14000 32
 
3.0%
13800 30
 
2.8%
14800 26
 
2.5%
Other values (97) 356
33.6%
ValueCountFrequency (%)
6000 1
 
0.1%
6500 7
 
0.7%
7000 42
4.0%
7500 4
 
0.4%
7900 4
 
0.4%
8000 6
 
0.6%
8500 15
 
1.4%
8800 3
 
0.3%
9000 13
 
1.2%
9200 1
 
0.1%
ValueCountFrequency (%)
818300 1
 
0.1%
605000 1
 
0.1%
579500 1
 
0.1%
512000 2
0.2%
480000 1
 
0.1%
420000 1
 
0.1%
413000 1
 
0.1%
392000 1
 
0.1%
360000 3
0.3%
333200 1
 
0.1%

견적금액
Real number (ℝ)

HIGH CORRELATION 

Distinct107
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18642.42
Minimum5400
Maximum736470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-12-12T11:39:25.803673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5400
5-th percentile6750
Q19000
median10800
Q313500
95-th percentile31635
Maximum736470
Range731070
Interquartile range (IQR)4500

Descriptive statistics

Standard deviation49940.971
Coefficient of variation (CV)2.6788889
Kurtosis88.375504
Mean18642.42
Median Absolute Deviation (MAD)1890
Skewness8.8485278
Sum19723680
Variance2.4941006 × 109
MonotonicityNot monotonic
2023-12-12T11:39:25.985983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9000 178
16.8%
8550 104
 
9.8%
10800 94
 
8.9%
9900 76
 
7.2%
11700 66
 
6.2%
13500 54
 
5.1%
6300 42
 
4.0%
12600 32
 
3.0%
12420 30
 
2.8%
13320 26
 
2.5%
Other values (97) 356
33.6%
ValueCountFrequency (%)
5400 1
 
0.1%
5850 7
 
0.7%
6300 42
4.0%
6750 4
 
0.4%
7110 4
 
0.4%
7200 6
 
0.6%
7650 15
 
1.4%
7920 3
 
0.3%
8100 13
 
1.2%
8280 1
 
0.1%
ValueCountFrequency (%)
736470 1
 
0.1%
544500 1
 
0.1%
521550 1
 
0.1%
460800 2
0.2%
432000 1
 
0.1%
378000 1
 
0.1%
371700 1
 
0.1%
352800 1
 
0.1%
324000 3
0.3%
299880 1
 
0.1%

비고
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
유아 그림책
100 
유아 누리과정
100 
유아과학/환경
95 
육아
 
63
자녀교육
 
49
Other values (24)
651 

Length

Max length12
Median length9
Mean length5.9404537
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row상상수프동화(수학)
2nd row상상수프동화(수학)
3rd row상상수프동화(수학)
4th row상상수프동화(수학)
5th row상상수프동화(수학)

Common Values

ValueCountFrequency (%)
유아 그림책 100
 
9.5%
유아 누리과정 100
 
9.5%
유아과학/환경 95
 
9.0%
육아 63
 
6.0%
자녀교육 49
 
4.6%
상상수프동화(수학) 48
 
4.5%
상상수프동화(인성) 48
 
4.5%
상상수프동화(창의) 48
 
4.5%
상상수프동화(숲) 48
 
4.5%
어린이동화 43
 
4.1%
Other values (19) 416
39.3%

Length

2023-12-12T11:39:26.173225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
유아 200
 
15.2%
누리과정 100
 
7.6%
그림책 100
 
7.6%
유아과학/환경 95
 
7.2%
육아 63
 
4.8%
어린이 54
 
4.1%
자녀교육 49
 
3.7%
자기계발 49
 
3.7%
상상수프동화(숲 48
 
3.7%
상상수프동화(창의 48
 
3.7%
Other values (20) 506
38.6%

Interactions

2023-12-12T11:39:18.570531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:14.709511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:15.469605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:16.332063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:17.021894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:17.873837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:18.666409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:14.829376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:15.598017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:16.437271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:17.146535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:18.004597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:18.768119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:14.928849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:15.755382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:16.554992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:17.292462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:18.120849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:18.862111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:15.041683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:15.905589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:16.666461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:17.418133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:18.226681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:18.961300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:15.180112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:16.055367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:16.770033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:17.554394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:18.345196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:19.080217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:15.332518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:16.198903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:16.900549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:17.705838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:39:18.458553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:39:26.276166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번발행연도책수단가금 액견적금액비고
연번1.0000.5620.1560.1650.1650.1650.989
발행연도0.5621.0000.1140.0000.0000.0000.603
책수0.1560.1141.0000.8600.8600.8600.806
단가0.1650.0000.8601.0001.0001.0000.796
금 액0.1650.0000.8601.0001.0001.0000.796
견적금액0.1650.0000.8601.0001.0001.0000.796
비고0.9890.6030.8060.7960.7960.7961.000
2023-12-12T11:39:26.419578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번발행연도책수단가금 액견적금액비고
연번1.0000.4680.0350.6480.6480.6480.908
발행연도0.4681.0000.0270.3240.3240.3240.361
책수0.0350.0271.0000.3880.3880.3880.492
단가0.6480.3240.3881.0001.0001.0000.447
금 액0.6480.3240.3881.0001.0001.0000.447
견적금액0.6480.3240.3881.0001.0001.0000.447
비고0.9080.3610.4920.4470.4470.4471.000

Missing values

2023-12-12T11:39:19.244527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:39:19.418890image/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.
2023-12-12T11:39:19.523913image/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

연번도서명저자 1저자 2ISBN발행자(출판사)발행연도종수책수단가금 액견적금액비고
01숨기 대장 카멜레온문주영<NA>979-11-253-0550-7(50)NEKIDS201511950095008550상상수프동화(수학)
12주주 할머니의 주스 가게정혜란<NA>979-11-253-0552-1NEKIDS201511950095008550상상수프동화(수학)
23큰 건 내거야정순<NA>979-11-253-0713-6NEKIDS201611950095008550상상수프동화(수학)
34하양이의 나들이신지명<NA>979-11-253-0786-0NEKIDS20151110000100009000상상수프동화(수학)
45같은 것끼리 짝짝!김자영<NA>979-11-253-0818-8NEKIDS201511950095008550상상수프동화(수학)
56싹둑싹둑 색종이 놀이김정란<NA>979-11-253-0835-5(6)NEKIDS20151110000100009000상상수프동화(수학)
67꾸리의 방귀 재주신지명<NA>979-11-253-0887-4NEKIDS201511950095008550상상수프동화(수학)
78밤하늘에 톡톡톡서동선<NA>979-11-253-0951-2NEKIDS20151110000100009000상상수프동화(수학)
89나나와 뭉치조미희<NA>979-11-253-0977-2NEKIDS201511950095008550상상수프동화(수학)
910도도 공주의 생일 케이크조미희<NA>979-11-253-1067-9(10)NEKIDS20151110000100009000상상수프동화(수학)
연번도서명저자 1저자 2ISBN발행자(출판사)발행연도종수책수단가금 액견적금액비고
10481049새는 날아가면서 뒤돌아 보지 않는다류시화<NA>979-11-86900-22-2더숲201811140001400012600에세이
10491050숨결이 바람될 때폴 칼라니티이종인978-89-6596-195-6흐름출판201711140001400012600에세이
10501051늘 그렇듯, 네가 좋으면 나도 좋아김재우조유리979-11-6165-157-6넥서스201711150001500013500에세이
10511052웰컴 나래바박나래<NA>978-89-546-4958-2싱긋201811145001450013050에세이
10521053참 소중한 너라서김지훈<NA>979-11-964842-2-4RHK201811148001480013320에세이
10531054사랑까지 딱 한 걸음심승현<NA>978-89-5913-586-8예담201811138001380012420에세이
1054105582년생 김지영조남주<NA>978-89-374-3867-7민음사201711130001300011700종합베스트
10551056일취월장고영성신영준979-11-294-2950-6로크미디어201811220002200019800종합베스트
10561057그대 눈동자에 건배히가시노게이고양윤옥978-89-7275-842-6현대문학201811140001400012600종합베스트
10571058김영철, 타일러의 진짜 미국식 영어김영철타일러979-11-6220-151-0위즈덤하우스201811148001480013320종합베스트