Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells10000
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory673.8 KiB
Average record size in memory69.0 B

Variable types

Text2
Unsupported1
Categorical2
Numeric2

Dataset

Description한국학중앙연구원 해외한국학지원사업 연구성과의 목차 정보
Author한국학중앙연구원
URLhttps://www.data.go.kr/data/15049070/fileData.do

Alerts

PAGE_NO_PAPER has constant value ""Constant
PAGE_NO_PDF has constant value ""Constant
LEVEL is highly overall correlated with CONTENTS_ORDERHigh correlation
CONTENTS_ORDER is highly overall correlated with LEVELHigh correlation
CONTENTS_ENG has 10000 (100.0%) missing valuesMissing
CONTENTS_ENG is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 05:41:26.107300
Analysis finished2023-12-12 05:41:28.445338
Duration2.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

CATALOG_ID
Text

Distinct1980
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T14:41:28.635592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length8.59
Min length5

Characters and Unicode

Total characters85900
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique459 ?
Unique (%)4.6%

Sample

1st row07C06_0062
2nd row06C10
3rd row07C15
4th row08C08_0009
5th row07P01_0005
ValueCountFrequency (%)
09c02 78
 
0.8%
09c05 72
 
0.7%
06c17 63
 
0.6%
07c06 62
 
0.6%
10r41 48
 
0.5%
08c09 48
 
0.5%
06c15 44
 
0.4%
07c09 42
 
0.4%
07c15 42
 
0.4%
09c12 40
 
0.4%
Other values (1970) 9461
94.6%
2023-12-12T14:41:29.026709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32813
38.2%
1 8998
 
10.5%
_ 7174
 
8.4%
C 6549
 
7.6%
6 4837
 
5.6%
7 4361
 
5.1%
9 3945
 
4.6%
2 3741
 
4.4%
8 2724
 
3.2%
5 2607
 
3.0%
Other values (8) 8151
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68696
80.0%
Uppercase Letter 9998
 
11.6%
Connector Punctuation 7174
 
8.4%
Lowercase Letter 32
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32813
47.8%
1 8998
 
13.1%
6 4837
 
7.0%
7 4361
 
6.3%
9 3945
 
5.7%
2 3741
 
5.4%
8 2724
 
4.0%
5 2607
 
3.8%
3 2425
 
3.5%
4 2245
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
C 6549
65.5%
R 1813
 
18.1%
P 1589
 
15.9%
S 47
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
a 17
53.1%
b 13
40.6%
t 2
 
6.2%
Connector Punctuation
ValueCountFrequency (%)
_ 7174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 75870
88.3%
Latin 10030
 
11.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32813
43.2%
1 8998
 
11.9%
_ 7174
 
9.5%
6 4837
 
6.4%
7 4361
 
5.7%
9 3945
 
5.2%
2 3741
 
4.9%
8 2724
 
3.6%
5 2607
 
3.4%
3 2425
 
3.2%
Latin
ValueCountFrequency (%)
C 6549
65.3%
R 1813
 
18.1%
P 1589
 
15.8%
S 47
 
0.5%
a 17
 
0.2%
b 13
 
0.1%
t 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32813
38.2%
1 8998
 
10.5%
_ 7174
 
8.4%
C 6549
 
7.6%
6 4837
 
5.6%
7 4361
 
5.1%
9 3945
 
4.6%
2 3741
 
4.4%
8 2724
 
3.2%
5 2607
 
3.0%
Other values (8) 8151
 
9.5%

CONTENTS_ORI
Text

Distinct7878
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T14:41:29.374464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length213
Median length146
Mean length29.8417
Min length1

Characters and Unicode

Total characters298417
Distinct characters2981
Distinct categories20 ?
Distinct scripts11 ?
Distinct blocks22 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7029 ?
Unique (%)70.3%

Sample

1st row4. 『朝鮮策略』의 原文 校勘
2nd rowThe LeftPeriphery Structure of Korean in Minimalist Syntax
3rd row한국에서의 우즈베키스탄 노동자와 아내
4th row2-lc) Income Distribution and Poverty
5th rowⅡ. Centгal Asia before 15th centuгy
ValueCountFrequency (%)
the 1587
 
3.2%
of 1326
 
2.7%
and 1114
 
2.3%
in 967
 
2.0%
korean 737
 
1.5%
1 650
 
1.3%
2 648
 
1.3%
3 554
 
1.1%
4 411
 
0.8%
411
 
0.8%
Other values (14820) 40831
82.9%
2023-12-12T14:41:30.416601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39289
 
13.2%
e 14843
 
5.0%
n 12878
 
4.3%
o 12021
 
4.0%
i 11484
 
3.8%
a 11253
 
3.8%
t 10399
 
3.5%
r 8966
 
3.0%
s 7972
 
2.7%
c 4900
 
1.6%
Other values (2971) 164412
55.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 144266
48.3%
Other Letter 65641
22.0%
Space Separator 39293
 
13.2%
Uppercase Letter 29238
 
9.8%
Other Punctuation 7383
 
2.5%
Decimal Number 7293
 
2.4%
Dash Punctuation 1374
 
0.5%
Close Punctuation 1230
 
0.4%
Open Punctuation 1014
 
0.3%
Final Punctuation 658
 
0.2%
Other values (10) 1027
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 2644
 
4.0%
한 1191
 
1.8%
문 935
 
1.4%
국 913
 
1.4%
어 825
 
1.3%
的 821
 
1.3%
과 805
 
1.2%
에 696
 
1.1%
대 695
 
1.1%
사 632
 
1.0%
Other values (2600) 55484
84.5%
Lowercase Letter
ValueCountFrequency (%)
e 14843
 
10.3%
n 12878
 
8.9%
o 12021
 
8.3%
i 11484
 
8.0%
a 11253
 
7.8%
t 10399
 
7.2%
r 8966
 
6.2%
s 7972
 
5.5%
c 4900
 
3.4%
l 4851
 
3.4%
Other values (126) 44699
31.0%
Uppercase Letter
ValueCountFrequency (%)
C 1870
 
6.4%
I 1785
 
6.1%
T 1758
 
6.0%
S 1737
 
5.9%
A 1624
 
5.6%
K 1570
 
5.4%
N 1397
 
4.8%
R 1330
 
4.5%
E 1254
 
4.3%
P 1127
 
3.9%
Other values (112) 13786
47.2%
Other Punctuation
ValueCountFrequency (%)
. 4416
59.8%
: 945
 
12.8%
, 503
 
6.8%
' 462
 
6.3%
、 459
 
6.2%
? 186
 
2.5%
· 99
 
1.3%
/ 92
 
1.2%
， 83
 
1.1%
。 22
 
0.3%
Other values (14) 116
 
1.6%
Decimal Number
ValueCountFrequency (%)
1 1680
23.0%
2 1680
23.0%
3 1173
16.1%
4 776
10.6%
5 500
 
6.9%
0 435
 
6.0%
9 409
 
5.6%
6 280
 
3.8%
7 178
 
2.4%
8 177
 
2.4%
Other values (5) 5
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 823
66.9%
』 148
 
12.0%
》 117
 
9.5%
」 67
 
5.4%
〉 28
 
2.3%
] 27
 
2.2%
） 10
 
0.8%
】 5
 
0.4%
} 3
 
0.2%
〛 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 613
60.5%
『 149
 
14.7%
《 115
 
11.3%
「 65
 
6.4%
[ 26
 
2.6%
〈 24
 
2.4%
（ 10
 
1.0%
【 5
 
0.5%
{ 3
 
0.3%
⟪ 3
 
0.3%
Nonspacing Mark
ValueCountFrequency (%)
ี 13
25.0%
ิ 12
23.1%
ั 9
17.3%
้ 6
11.5%
ุ 4
 
7.7%
̣ 2
 
3.8%
ู 2
 
3.8%
่ 1
 
1.9%
ื 1
 
1.9%
์ 1
 
1.9%
Math Symbol
ValueCountFrequency (%)
< 67
31.0%
> 65
30.1%
~ 25
 
11.6%
≪ 19
 
8.8%
≫ 19
 
8.8%
+ 10
 
4.6%
= 5
 
2.3%
− 3
 
1.4%
∅ 2
 
0.9%
| 1
 
0.5%
Letter Number
ValueCountFrequency (%)
Ⅱ 48
25.5%
Ⅲ 45
23.9%
Ⅰ 44
23.4%
Ⅳ 31
16.5%
Ⅴ 15
 
8.0%
Ⅵ 5
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 1343
97.7%
– 23
 
1.7%
－ 4
 
0.3%
― 2
 
0.1%
‐ 2
 
0.1%
Initial Punctuation
ValueCountFrequency (%)
“ 280
55.0%
‘ 189
37.1%
« 39
 
7.7%
‛ 1
 
0.2%
Final Punctuation
ValueCountFrequency (%)
’ 336
51.1%
” 285
43.3%
» 37
 
5.6%
Other Symbol
ValueCountFrequency (%)
┌ 6
75.0%
™ 1
 
12.5%
┐ 1
 
12.5%
Space Separator
ValueCountFrequency (%)
39289
> 99.9%
  4
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
` 17
94.4%
˳ 1
 
5.6%
Other Number
ValueCountFrequency (%)
① 3
75.0%
② 1
 
25.0%
Private Use
ValueCountFrequency (%)
1
50.0%
1
50.0%
Modifier Letter
ValueCountFrequency (%)
ー 20
100.0%
Control
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 152314
51.0%
Common 59030
 
19.8%
Hangul 41974
 
14.1%
Han 22111
 
7.4%
Cyrillic 21376
 
7.2%
Hiragana 1045
 
0.4%
Thai 384
 
0.1%
Katakana 176
 
0.1%
Greek 3
 
< 0.1%
Inherited 2
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
的 821
 
3.7%
朝 410
 
1.9%
国 400
 
1.8%
中 312
 
1.4%
日 282
 
1.3%
第 274
 
1.2%
一 273
 
1.2%
与 249
 
1.1%
鲜 249
 
1.1%
文 239
 
1.1%
Other values (1801) 18602
84.1%
Hangul
ValueCountFrequency (%)
의 2644
 
6.3%
한 1191
 
2.8%
문 935
 
2.2%
국 913
 
2.2%
어 825
 
2.0%
과 805
 
1.9%
에 696
 
1.7%
대 695
 
1.7%
사 632
 
1.5%
학 591
 
1.4%
Other values (663) 32047
76.3%
Latin
ValueCountFrequency (%)
e 14843
 
9.7%
n 12878
 
8.5%
o 12021
 
7.9%
i 11484
 
7.5%
a 11253
 
7.4%
t 10399
 
6.8%
r 8966
 
5.9%
s 7972
 
5.2%
c 4900
 
3.2%
l 4851
 
3.2%
Other values (184) 52747
34.6%
Common
ValueCountFrequency (%)
39289
66.6%
. 4416
 
7.5%
1 1680
 
2.8%
2 1680
 
2.8%
- 1343
 
2.3%
3 1173
 
2.0%
: 945
 
1.6%
) 823
 
1.4%
4 776
 
1.3%
( 613
 
1.0%
Other values (84) 6292
 
10.7%
Cyrillic
ValueCountFrequency (%)
о 1799
 
8.4%
и 1462
 
6.8%
е 1429
 
6.7%
а 1088
 
5.1%
н 1047
 
4.9%
с 946
 
4.4%
р 936
 
4.4%
т 789
 
3.7%
к 731
 
3.4%
в 687
 
3.2%
Other values (58) 10462
48.9%
Hiragana
ValueCountFrequency (%)
の 295
28.2%
と 139
13.3%
に 104
 
10.0%
る 45
 
4.3%
お 37
 
3.5%
か 30
 
2.9%
て 29
 
2.8%
め 29
 
2.8%
を 22
 
2.1%
け 22
 
2.1%
Other values (37) 293
28.0%
Thai
ValueCountFrequency (%)
า 40
 
10.4%
ร 28
 
7.3%
ก 26
 
6.8%
ย 19
 
4.9%
เ 18
 
4.7%
บ 18
 
4.7%
น 18
 
4.7%
ท 15
 
3.9%
ี 13
 
3.4%
ิ 12
 
3.1%
Other values (35) 177
46.1%
Katakana
ValueCountFrequency (%)
ア 43
24.4%
ジ 24
13.6%
ダ 10
 
5.7%
ン 8
 
4.5%
リ 7
 
4.0%
ツ 6
 
3.4%
ム 5
 
2.8%
シ 5
 
2.8%
テ 5
 
2.8%
ス 4
 
2.3%
Other values (33) 59
33.5%
Greek
ValueCountFrequency (%)
γ 1
33.3%
π 1
33.3%
ω 1
33.3%
Unknown
ValueCountFrequency (%)
1
50.0%
1
50.0%
Inherited
ValueCountFrequency (%)
̣ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206933
69.3%
Hangul 41916
 
14.0%
CJK 22055
 
7.4%
Cyrillic 21376
 
7.2%
None 2498
 
0.8%
Punctuation 1127
 
0.4%
Hiragana 1045
 
0.4%
Latin Ext Additional 509
 
0.2%
Thai 384
 
0.1%
Katakana 210
 
0.1%
Other values (12) 364
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39289
19.0%
e 14843
 
7.2%
n 12878
 
6.2%
o 12021
 
5.8%
i 11484
 
5.5%
a 11253
 
5.4%
t 10399
 
5.0%
r 8966
 
4.3%
s 7972
 
3.9%
c 4900
 
2.4%
Other values (81) 72928
35.2%
Hangul
ValueCountFrequency (%)
의 2644
 
6.3%
한 1191
 
2.8%
문 935
 
2.2%
국 913
 
2.2%
어 825
 
2.0%
과 805
 
1.9%
에 696
 
1.7%
대 695
 
1.7%
사 632
 
1.5%
학 591
 
1.4%
Other values (643) 31989
76.3%
Cyrillic
ValueCountFrequency (%)
о 1799
 
8.4%
и 1462
 
6.8%
е 1429
 
6.7%
а 1088
 
5.1%
н 1047
 
4.9%
с 946
 
4.4%
р 936
 
4.4%
т 789
 
3.7%
к 731
 
3.4%
в 687
 
3.2%
Other values (58) 10462
48.9%
CJK
ValueCountFrequency (%)
的 821
 
3.7%
朝 410
 
1.9%
国 400
 
1.8%
中 312
 
1.4%
日 282
 
1.3%
第 274
 
1.2%
一 273
 
1.2%
与 249
 
1.1%
鲜 249
 
1.1%
文 239
 
1.1%
Other values (1775) 18546
84.1%
None
ValueCountFrequency (%)
、 459
18.4%
ŏ 160
 
6.4%
『 149
 
6.0%
』 148
 
5.9%
é 146
 
5.8%
》 117
 
4.7%
《 115
 
4.6%
· 99
 
4.0%
， 83
 
3.3%
À 79
 
3.2%
Other values (92) 943
37.8%
Punctuation
ValueCountFrequency (%)
’ 336
29.8%
” 285
25.3%
“ 280
24.8%
‘ 189
16.8%
– 23
 
2.0%
• 5
 
0.4%
․ 2
 
0.2%
― 2
 
0.2%
… 2
 
0.2%
‐ 2
 
0.2%
Hiragana
ValueCountFrequency (%)
の 295
28.2%
と 139
13.3%
に 104
 
10.0%
る 45
 
4.3%
お 37
 
3.5%
か 30
 
2.9%
て 29
 
2.8%
め 29
 
2.8%
を 22
 
2.1%
け 22
 
2.1%
Other values (37) 293
28.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 48
25.5%
Ⅲ 45
23.9%
Ⅰ 44
23.4%
Ⅳ 31
16.5%
Ⅴ 15
 
8.0%
Ⅵ 5
 
2.7%
Katakana
ValueCountFrequency (%)
ア 43
20.5%
ジ 24
 
11.4%
ー 20
 
9.5%
・ 14
 
6.7%
ダ 10
 
4.8%
ン 8
 
3.8%
リ 7
 
3.3%
ツ 6
 
2.9%
ム 5
 
2.4%
シ 5
 
2.4%
Other values (35) 68
32.4%
Latin Ext Additional
ValueCountFrequency (%)
Ệ 41
 
8.1%
Ố 37
 
7.3%
ố 28
 
5.5%
ệ 19
 
3.7%
ể 18
 
3.5%
Ủ 17
 
3.3%
ớ 16
 
3.1%
Ể 15
 
2.9%
Ọ 14
 
2.8%
Ả 13
 
2.6%
Other values (58) 291
57.2%
Thai
ValueCountFrequency (%)
า 40
 
10.4%
ร 28
 
7.3%
ก 26
 
6.8%
ย 19
 
4.9%
เ 18
 
4.7%
บ 18
 
4.7%
น 18
 
4.7%
ท 15
 
3.9%
ี 13
 
3.4%
ิ 12
 
3.1%
Other values (35) 177
46.1%
Math Operators
ValueCountFrequency (%)
≪ 19
44.2%
≫ 19
44.2%
− 3
 
7.0%
∅ 2
 
4.7%
CJK Compat Ideographs
ValueCountFrequency (%)
李 11
19.6%
陸 6
 
10.7%
類 5
 
8.9%
樂 3
 
5.4%
理 3
 
5.4%
兩 2
 
3.6%
論 2
 
3.6%
金 2
 
3.6%
栗 2
 
3.6%
烈 2
 
3.6%
Other values (16) 18
32.1%
Box Drawing
ValueCountFrequency (%)
┌ 6
85.7%
┐ 1
 
14.3%
Compat Jamo
ValueCountFrequency (%)
ㄱ 6
 
10.5%
ㅋ 5
 
8.8%
ㅍ 4
 
7.0%
ㅅ 4
 
7.0%
ㅡ 4
 
7.0%
ㄴ 4
 
7.0%
ㅂ 3
 
5.3%
ㅈ 3
 
5.3%
ㅊ 3
 
5.3%
ㅇ 3
 
5.3%
Other values (9) 18
31.6%
Enclosed Alphanum
ValueCountFrequency (%)
① 3
75.0%
② 1
 
25.0%
Diacriticals
ValueCountFrequency (%)
̣ 2
100.0%
IPA Ext
ValueCountFrequency (%)
ɨ 2
100.0%
PUA
ValueCountFrequency (%)
1
50.0%
1
50.0%
Letterlike Symbols
ValueCountFrequency (%)
™ 1
100.0%
Jamo
ValueCountFrequency (%)
ᅳ 1
100.0%
Modifier Letters
ValueCountFrequency (%)
˳ 1
100.0%

CONTENTS_ENG
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

PAGE_NO_PAPER
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-12T14:41:30.597676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:41:30.726007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

PAGE_NO_PDF
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-12T14:41:30.840784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:41:30.949408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

LEVEL
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1741
Minimum0
Maximum5
Zeros28
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:41:31.068376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.69212506
Coefficient of variation (CV)0.31835015
Kurtosis-0.1134089
Mean2.1741
Median Absolute Deviation (MAD)0
Skewness-0.024598174
Sum21741
Variance0.47903709
MonotonicityNot monotonic
2023-12-12T14:41:31.215309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 5405
54.0%
3 2993
29.9%
1 1451
 
14.5%
4 114
 
1.1%
0 28
 
0.3%
5 9
 
0.1%
ValueCountFrequency (%)
0 28
 
0.3%
1 1451
 
14.5%
2 5405
54.0%
3 2993
29.9%
4 114
 
1.1%
5 9
 
0.1%
ValueCountFrequency (%)
5 9
 
0.1%
4 114
 
1.1%
3 2993
29.9%
2 5405
54.0%
1 1451
 
14.5%
0 28
 
0.3%

CONTENTS_ORDER
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8278
Minimum1
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:41:31.370636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q310
95-th percentile37
Maximum119
Range118
Interquartile range (IQR)7

Descriptive statistics

Standard deviation13.645745
Coefficient of variation (CV)1.3884842
Kurtosis15.965966
Mean9.8278
Median Absolute Deviation (MAD)3
Skewness3.5319073
Sum98278
Variance186.20637
MonotonicityNot monotonic
2023-12-12T14:41:31.543170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1347
13.5%
2 1005
10.1%
4 928
 
9.3%
3 891
 
8.9%
5 870
 
8.7%
6 730
 
7.3%
7 601
 
6.0%
8 478
 
4.8%
9 377
 
3.8%
10 286
 
2.9%
Other values (106) 2487
24.9%
ValueCountFrequency (%)
1 1347
13.5%
2 1005
10.1%
3 891
8.9%
4 928
9.3%
5 870
8.7%
6 730
7.3%
7 601
6.0%
8 478
 
4.8%
9 377
 
3.8%
10 286
 
2.9%
ValueCountFrequency (%)
119 1
 
< 0.1%
117 1
 
< 0.1%
115 1
 
< 0.1%
114 2
< 0.1%
113 2
< 0.1%
112 2
< 0.1%
111 2
< 0.1%
110 1
 
< 0.1%
109 2
< 0.1%
108 3
< 0.1%

Interactions

2023-12-12T14:41:27.972739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:41:27.722330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:41:28.114011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:41:27.847416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:41:31.656091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LEVELCONTENTS_ORDER
LEVEL1.0000.306
CONTENTS_ORDER0.3061.000
2023-12-12T14:41:31.741695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LEVELCONTENTS_ORDER
LEVEL1.0000.651
CONTENTS_ORDER0.6511.000

Missing values

2023-12-12T14:41:28.251857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:41:28.367259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CATALOG_IDCONTENTS_ORICONTENTS_ENGPAGE_NO_PAPERPAGE_NO_PDFLEVELCONTENTS_ORDER
696907C06_00624. 『朝鮮策略』의 原文 校勘<NA>0025
1091506C10The LeftPeriphery Structure of Korean in Minimalist Syntax<NA>0029
379007C15한국에서의 우즈베키스탄 노동자와 아내<NA>00360
1511108C08_00092-lc) Income Distribution and Poverty<NA>0047
1583407P01_0005Ⅱ. Centгal Asia before 15th centuгy<NA>0023
247707R38第一节图们江流域的地理位置<NA>00317
1199506C04_0013四、明朝在壬辰战争结束后对明鲜、鲜曰关系的反应<NA>0025
1115707C17_00054.3 AUN-Hankuk University of Foreign Studies<NA>00311
783110R33Trustworthiness<NA>00320
603107P01Феномен этнического предпринимательства как следствие миграционных процессов u адаптации национальных меньшинств в иноэтнuчной среде: на при мере корейской дuаспорыг.Новосuбuрска<NA>00338
CATALOG_IDCONTENTS_ORICONTENTS_ENGPAGE_NO_PAPERPAGE_NO_PDFLEVELCONTENTS_ORDER
1523107C18_00092.2.2.2 각주를 통하여<NA>00514
141306C06_00185)朝-清-日仲介貿易と制限的貿易課税政策<NA>0039
601007P01시베리아 지역 고려인 학자 롤-모델 연구를 통한 새로운 고려인 문제 연구방법<NA>00317
373211R22三、朝鲜族的民族通婚现状<NA>0025
1231408C12_0014From nominalizer to stance marker in the history of Okinawan<NA>0011
1002907C07_00055. 사용하여도 무방하게 된 복수 어휘<NA>00214
304006R25第三节 隋丽关系演变及对东北亚政局的影响<NA>00314
733509C01_0028Japan and Korea as a Source of Media and Cultural Capital<NA>0011
1357708C19_0029A “stretch”<NA>0022
962107P03_0006La dialectique traditionnelle de « Won » et « Han » : le fondement psychologique des Coréens<NA>0036