Overview

Dataset statistics

Number of variables31
Number of observations245
Missing cells2714
Missing cells (%)35.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory61.4 KiB
Average record size in memory256.5 B

Variable types

Text11
Categorical12
Unsupported7
DateTime1

Dataset

Description역사관련 사이트 메타데이터 기반 통합 검색을 위하여 한국역사정보통합시스템이 제공 중인 역사 자료 메타데이터 중 지도 자료
Author교육부 국사편찬위원회
URLhttps://www.data.go.kr/data/15051038/fileData.do

Alerts

SUBJECT_KHON1 has constant value ""Constant
UNIT has constant value ""Constant
DATE_CREATED has constant value ""Constant
MDCENTER is highly imbalanced (90.5%)Imbalance
SUBJECT_KHON is highly imbalanced (90.5%)Imbalance
DBINFO is highly imbalanced (90.5%)Imbalance
SUBJECT_KHON2 is highly imbalanced (90.5%)Imbalance
SUBJECT_KHDP is highly imbalanced (90.5%)Imbalance
FORMAT_MEDIUM is highly imbalanced (90.5%)Imbalance
DATE_ISSUED is highly imbalanced (90.5%)Imbalance
DATE_MODIFIED is highly imbalanced (90.5%)Imbalance
CREATORSORT is highly imbalanced (93.3%)Imbalance
ALTERNATIVE has 194 (79.2%) missing valuesMissing
DOCSENDER has 245 (100.0%) missing valuesMissing
EDITOR has 242 (98.8%) missing valuesMissing
AUTHOR has 242 (98.8%) missing valuesMissing
TYPE has 245 (100.0%) missing valuesMissing
PUBLISHER has 3 (1.2%) missing valuesMissing
TABLEOFCONTENTS has 245 (100.0%) missing valuesMissing
ISPARTOF_ID has 245 (100.0%) missing valuesMissing
ISPARTOF has 245 (100.0%) missing valuesMissing
REQUIRES has 245 (100.0%) missing valuesMissing
DATEEVENT has 245 (100.0%) missing valuesMissing
DOCCREATED has 242 (98.8%) missing valuesMissing
DOCISSUED has 39 (15.9%) missing valuesMissing
DATESORT has 37 (15.1%) missing valuesMissing
URI_KHON has unique valuesUnique
URI_KHDP has unique valuesUnique
URL has unique valuesUnique
DOCSENDER is an unsupported type, check if it needs cleaning or further analysisUnsupported
TYPE is an unsupported type, check if it needs cleaning or further analysisUnsupported
TABLEOFCONTENTS is an unsupported type, check if it needs cleaning or further analysisUnsupported
ISPARTOF_ID is an unsupported type, check if it needs cleaning or further analysisUnsupported
ISPARTOF is an unsupported type, check if it needs cleaning or further analysisUnsupported
REQUIRES is an unsupported type, check if it needs cleaning or further analysisUnsupported
DATEEVENT is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 02:30:54.760537
Analysis finished2023-12-12 02:30:56.057099
Duration1.3 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

URI_KHON
Text

UNIQUE 

Distinct245
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T11:30:56.206832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length18.987755
Min length18

Characters and Unicode

Total characters4652
Distinct characters22
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique245 ?
Unique (%)100.0%

Sample

1st rowKH.GASA.R000000024
2nd rowKH.GASA.R000000192
3rd rowKH.GASA.R000000193
4th rowKH.NAHF.om_001_0010
5th rowKH.NAHF.om_001_0020
ValueCountFrequency (%)
kh.gasa.r000000024 1
 
0.4%
kh.nahf.om_002_0130 1
 
0.4%
kh.nahf.om_003_0270 1
 
0.4%
kh.nahf.om_003_0280 1
 
0.4%
kh.nahf.om_003_0290 1
 
0.4%
kh.nahf.om_003_0300 1
 
0.4%
kh.nahf.om_003_0310 1
 
0.4%
kh.nahf.om_003_0320 1
 
0.4%
kh.nahf.om_003_0330 1
 
0.4%
kh.nahf.om_003_0340 1
 
0.4%
Other values (235) 235
95.9%
2023-12-12T11:30:56.645895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1051
22.6%
. 490
10.5%
H 487
10.5%
_ 484
10.4%
A 248
 
5.3%
K 245
 
5.3%
N 242
 
5.2%
m 242
 
5.2%
o 242
 
5.2%
F 242
 
5.2%
Other values (12) 679
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1721
37.0%
Uppercase Letter 1473
31.7%
Other Punctuation 490
 
10.5%
Connector Punctuation 484
 
10.4%
Lowercase Letter 484
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1051
61.1%
1 195
 
11.3%
3 105
 
6.1%
5 81
 
4.7%
2 79
 
4.6%
4 73
 
4.2%
6 35
 
2.0%
9 34
 
2.0%
7 34
 
2.0%
8 34
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
H 487
33.1%
A 248
16.8%
K 245
16.6%
N 242
16.4%
F 242
16.4%
R 3
 
0.2%
S 3
 
0.2%
G 3
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
m 242
50.0%
o 242
50.0%
Other Punctuation
ValueCountFrequency (%)
. 490
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2695
57.9%
Latin 1957
42.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1051
39.0%
. 490
18.2%
_ 484
18.0%
1 195
 
7.2%
3 105
 
3.9%
5 81
 
3.0%
2 79
 
2.9%
4 73
 
2.7%
6 35
 
1.3%
9 34
 
1.3%
Other values (2) 68
 
2.5%
Latin
ValueCountFrequency (%)
H 487
24.9%
A 248
12.7%
K 245
12.5%
N 242
12.4%
m 242
12.4%
o 242
12.4%
F 242
12.4%
R 3
 
0.2%
S 3
 
0.2%
G 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1051
22.6%
. 490
10.5%
H 487
10.5%
_ 484
10.4%
A 248
 
5.3%
K 245
 
5.3%
N 242
 
5.2%
m 242
 
5.2%
o 242
 
5.2%
F 242
 
5.2%
Other values (12) 679
14.6%

MDCENTER
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
NAHF
242 
GASA
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGASA
2nd rowGASA
3rd rowGASA
4th rowNAHF
5th rowNAHF

Common Values

ValueCountFrequency (%)
NAHF 242
98.8%
GASA 3
 
1.2%

Length

2023-12-12T11:30:56.828939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:30:56.992358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nahf 242
98.8%
gasa 3
 
1.2%

SUBJECT_KHON
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
KH.10.51.000
242 
KH.10.50.000
 
3

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKH.10.50.000
2nd rowKH.10.50.000
3rd rowKH.10.50.000
4th rowKH.10.51.000
5th rowKH.10.51.000

Common Values

ValueCountFrequency (%)
KH.10.51.000 242
98.8%
KH.10.50.000 3
 
1.2%

Length

2023-12-12T11:30:57.114598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:30:57.240284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kh.10.51.000 242
98.8%
kh.10.50.000 3
 
1.2%

DBINFO
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
독도동해관련고지도
242 
한국가사문학
 
3

Length

Max length9
Median length9
Mean length8.9632653
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row한국가사문학
2nd row한국가사문학
3rd row한국가사문학
4th row독도동해관련고지도
5th row독도동해관련고지도

Common Values

ValueCountFrequency (%)
독도동해관련고지도 242
98.8%
한국가사문학 3
 
1.2%

Length

2023-12-12T11:30:57.376731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:30:57.486897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
독도동해관련고지도 242
98.8%
한국가사문학 3
 
1.2%

URI_KHDP
Text

UNIQUE 

Distinct245
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T11:30:57.807258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.987755
Min length10

Characters and Unicode

Total characters2692
Distinct characters14
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

Unique245 ?
Unique (%)100.0%

Sample

1st rowR000000024
2nd rowR000000192
3rd rowR000000193
4th rowom_001_0010
5th rowom_001_0020
ValueCountFrequency (%)
r000000024 1
 
0.4%
om_002_0130 1
 
0.4%
om_003_0270 1
 
0.4%
om_003_0280 1
 
0.4%
om_003_0290 1
 
0.4%
om_003_0300 1
 
0.4%
om_003_0310 1
 
0.4%
om_003_0320 1
 
0.4%
om_003_0330 1
 
0.4%
om_003_0340 1
 
0.4%
Other values (235) 235
95.9%
2023-12-12T11:30:58.295887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1051
39.0%
_ 484
18.0%
o 242
 
9.0%
m 242
 
9.0%
1 195
 
7.2%
3 105
 
3.9%
5 81
 
3.0%
2 79
 
2.9%
4 73
 
2.7%
6 35
 
1.3%
Other values (4) 105
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1721
63.9%
Connector Punctuation 484
 
18.0%
Lowercase Letter 484
 
18.0%
Uppercase Letter 3
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1051
61.1%
1 195
 
11.3%
3 105
 
6.1%
5 81
 
4.7%
2 79
 
4.6%
4 73
 
4.2%
6 35
 
2.0%
9 34
 
2.0%
7 34
 
2.0%
8 34
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
o 242
50.0%
m 242
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 484
100.0%
Uppercase Letter
ValueCountFrequency (%)
R 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2205
81.9%
Latin 487
 
18.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1051
47.7%
_ 484
22.0%
1 195
 
8.8%
3 105
 
4.8%
5 81
 
3.7%
2 79
 
3.6%
4 73
 
3.3%
6 35
 
1.6%
9 34
 
1.5%
7 34
 
1.5%
Latin
ValueCountFrequency (%)
o 242
49.7%
m 242
49.7%
R 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1051
39.0%
_ 484
18.0%
o 242
 
9.0%
m 242
 
9.0%
1 195
 
7.2%
3 105
 
3.9%
5 81
 
3.0%
2 79
 
2.9%
4 73
 
2.7%
6 35
 
1.3%
Other values (4) 105
 
3.9%

MAINTITLE
Text

Distinct197
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T11:30:58.609324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length200
Median length113
Mean length38.477551
Min length4

Characters and Unicode

Total characters9427
Distinct characters375
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique181 ?
Unique (%)73.9%

Sample

1st row면앙정30영 혈포효무(穴浦曉霧)
2nd row담양부 지도
3rd row창평현 지도
4th row조선여지도/朝鮮輿地圖
5th row아시아 지역구분도/Asie divisée en principaux Etats, Empires & Royaumes
ValueCountFrequency (%)
de 58
 
3.9%
et 30
 
2.0%
of 29
 
2.0%
아시아 27
 
1.8%
la 25
 
1.7%
the 24
 
1.6%
강원도/江原道 23
 
1.6%
des 19
 
1.3%
les 18
 
1.2%
japon 18
 
1.2%
Other values (669) 1200
81.6%
2023-12-12T11:30:59.087059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1226
 
13.0%
e 669
 
7.1%
a 440
 
4.7%
i 426
 
4.5%
s 376
 
4.0%
r 341
 
3.6%
t 316
 
3.4%
n 270
 
2.9%
o 251
 
2.7%
/ 238
 
2.5%
Other values (365) 4874
51.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4409
46.8%
Other Letter 2151
22.8%
Space Separator 1226
 
13.0%
Uppercase Letter 1201
 
12.7%
Other Punctuation 394
 
4.2%
Decimal Number 20
 
0.2%
Dash Punctuation 15
 
0.2%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Final Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 210
 
9.8%
아 143
 
6.6%
지 110
 
5.1%
국 89
 
4.1%
시 78
 
3.6%
일 60
 
2.8%
圖 52
 
2.4%
본 51
 
2.4%
중 39
 
1.8%
한 37
 
1.7%
Other values (287) 1282
59.6%
Lowercase Letter
ValueCountFrequency (%)
e 669
15.2%
a 440
10.0%
i 426
9.7%
s 376
 
8.5%
r 341
 
7.7%
t 316
 
7.2%
n 270
 
6.1%
o 251
 
5.7%
d 201
 
4.6%
l 178
 
4.0%
Other values (22) 941
21.3%
Uppercase Letter
ValueCountFrequency (%)
A 191
15.9%
E 119
 
9.9%
C 106
 
8.8%
I 90
 
7.5%
N 74
 
6.2%
R 74
 
6.2%
T 63
 
5.2%
P 61
 
5.1%
S 57
 
4.7%
L 53
 
4.4%
Other values (18) 313
26.1%
Other Punctuation
ValueCountFrequency (%)
/ 238
60.4%
, 75
 
19.0%
' 58
 
14.7%
. 10
 
2.5%
; 6
 
1.5%
& 6
 
1.5%
: 1
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 6
30.0%
7 6
30.0%
8 4
20.0%
0 2
 
10.0%
3 2
 
10.0%
Space Separator
ValueCountFrequency (%)
1226
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 3
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5610
59.5%
Hangul 1694
 
18.0%
Common 1666
 
17.7%
Han 457
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 210
 
12.4%
아 143
 
8.4%
지 110
 
6.5%
국 89
 
5.3%
시 78
 
4.6%
일 60
 
3.5%
본 51
 
3.0%
중 39
 
2.3%
한 37
 
2.2%
의 33
 
1.9%
Other values (189) 844
49.8%
Han
ValueCountFrequency (%)
圖 52
 
11.4%
道 27
 
5.9%
全 24
 
5.3%
江 23
 
5.0%
原 23
 
5.0%
國 23
 
5.0%
大 22
 
4.8%
日 20
 
4.4%
地 20
 
4.4%
本 13
 
2.8%
Other values (88) 210
46.0%
Latin
ValueCountFrequency (%)
e 669
 
11.9%
a 440
 
7.8%
i 426
 
7.6%
s 376
 
6.7%
r 341
 
6.1%
t 316
 
5.6%
n 270
 
4.8%
o 251
 
4.5%
d 201
 
3.6%
A 191
 
3.4%
Other values (50) 2129
38.0%
Common
ValueCountFrequency (%)
1226
73.6%
/ 238
 
14.3%
, 75
 
4.5%
' 58
 
3.5%
- 15
 
0.9%
. 10
 
0.6%
; 6
 
0.4%
& 6
 
0.4%
1 6
 
0.4%
7 6
 
0.4%
Other values (8) 20
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7214
76.5%
Hangul 1694
 
18.0%
CJK 457
 
4.8%
None 57
 
0.6%
Punctuation 5
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1226
17.0%
e 669
 
9.3%
a 440
 
6.1%
i 426
 
5.9%
s 376
 
5.2%
r 341
 
4.7%
t 316
 
4.4%
n 270
 
3.7%
o 251
 
3.5%
/ 238
 
3.3%
Other values (57) 2661
36.9%
Hangul
ValueCountFrequency (%)
도 210
 
12.4%
아 143
 
8.4%
지 110
 
6.5%
국 89
 
5.3%
시 78
 
4.6%
일 60
 
3.5%
본 51
 
3.0%
중 39
 
2.3%
한 37
 
2.2%
의 33
 
1.9%
Other values (189) 844
49.8%
CJK
ValueCountFrequency (%)
圖 52
 
11.4%
道 27
 
5.9%
全 24
 
5.3%
江 23
 
5.0%
原 23
 
5.0%
國 23
 
5.0%
大 22
 
4.8%
日 20
 
4.4%
地 20
 
4.4%
本 13
 
2.8%
Other values (88) 210
46.0%
None
ValueCountFrequency (%)
é 37
64.9%
è 6
 
10.5%
à 3
 
5.3%
É 3
 
5.3%
ü 3
 
5.3%
Ö 2
 
3.5%
î 1
 
1.8%
ù 1
 
1.8%
Ü 1
 
1.8%
Punctuation
ValueCountFrequency (%)
’ 3
60.0%
‘ 2
40.0%

ALTERNATIVE
Text

MISSING 

Distinct26
Distinct (%)51.0%
Missing194
Missing (%)79.2%
Memory size2.0 KiB
2023-12-12T11:30:59.342727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length10.039216
Min length4

Characters and Unicode

Total characters512
Distinct characters93
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)31.4%

Sample

1st row穴浦曉霧
2nd row담양부 지도
3rd row창평현 지도
4th row강호대절용해내장(江戶大節用海內蔵)
5th row분방상밀대일본지도(分邦詳密 大日本地圖)
ValueCountFrequency (%)
여지도(輿地圖 7
 
13.0%
팔도지도(八道地圖 6
 
11.1%
조선지도(朝鮮地圖 4
 
7.4%
동국여지도(東國輿地圖 4
 
7.4%
지도(地圖 3
 
5.6%
해동지도(海東地圖 3
 
5.6%
청구팔역도(靑邱八域圖 2
 
3.7%
지도 2
 
3.7%
동국여도(東國輿圖 2
 
3.7%
동국여지승람(東國輿地勝覽 2
 
3.7%
Other values (18) 19
35.2%
2023-12-12T11:30:59.715941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 54
 
10.5%
) 48
 
9.4%
( 47
 
9.2%
圖 43
 
8.4%
지 40
 
7.8%
地 37
 
7.2%
여 17
 
3.3%
輿 17
 
3.3%
東 14
 
2.7%
동 14
 
2.7%
Other values (83) 181
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 410
80.1%
Close Punctuation 48
 
9.4%
Open Punctuation 47
 
9.2%
Decimal Number 4
 
0.8%
Space Separator 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 54
 
13.2%
圖 43
 
10.5%
지 40
 
9.8%
地 37
 
9.0%
여 17
 
4.1%
輿 17
 
4.1%
東 14
 
3.4%
동 14
 
3.4%
팔 10
 
2.4%
八 10
 
2.4%
Other values (76) 154
37.6%
Decimal Number
ValueCountFrequency (%)
7 1
25.0%
2 1
25.0%
1 1
25.0%
8 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 47
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 210
41.0%
Han 200
39.1%
Common 102
19.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 54
25.7%
지 40
19.0%
여 17
 
8.1%
동 14
 
6.7%
팔 10
 
4.8%
국 9
 
4.3%
해 6
 
2.9%
선 6
 
2.9%
조 6
 
2.9%
역 3
 
1.4%
Other values (33) 45
21.4%
Han
ValueCountFrequency (%)
圖 43
21.5%
地 37
18.5%
輿 17
 
8.5%
東 14
 
7.0%
八 10
 
5.0%
國 9
 
4.5%
道 8
 
4.0%
海 6
 
3.0%
朝 6
 
3.0%
鮮 6
 
3.0%
Other values (33) 44
22.0%
Common
ValueCountFrequency (%)
) 48
47.1%
( 47
46.1%
3
 
2.9%
7 1
 
1.0%
2 1
 
1.0%
1 1
 
1.0%
8 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 210
41.0%
CJK 200
39.1%
ASCII 102
19.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 54
25.7%
지 40
19.0%
여 17
 
8.1%
동 14
 
6.7%
팔 10
 
4.8%
국 9
 
4.3%
해 6
 
2.9%
선 6
 
2.9%
조 6
 
2.9%
역 3
 
1.4%
Other values (33) 45
21.4%
ASCII
ValueCountFrequency (%)
) 48
47.1%
( 47
46.1%
3
 
2.9%
7 1
 
1.0%
2 1
 
1.0%
1 1
 
1.0%
8 1
 
1.0%
CJK
ValueCountFrequency (%)
圖 43
21.5%
地 37
18.5%
輿 17
 
8.5%
東 14
 
7.0%
八 10
 
5.0%
國 9
 
4.5%
道 8
 
4.0%
海 6
 
3.0%
朝 6
 
3.0%
鮮 6
 
3.0%
Other values (33) 44
22.0%

DOCSENDER
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing245
Missing (%)100.0%
Memory size2.3 KiB

EDITOR
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing242
Missing (%)98.8%
Memory size2.0 KiB
2023-12-12T11:30:59.894760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length4
Min length2

Characters and Unicode

Total characters12
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st row박준규, 최한선
2nd row미상
3rd row미상
ValueCountFrequency (%)
미상 2
50.0%
박준규 1
25.0%
최한선 1
25.0%
2023-12-12T11:31:00.210434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
미 2
16.7%
상 2
16.7%
박 1
8.3%
준 1
8.3%
규 1
8.3%
, 1
8.3%
1
8.3%
최 1
8.3%
한 1
8.3%
선 1
8.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10
83.3%
Other Punctuation 1
 
8.3%
Space Separator 1
 
8.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
미 2
20.0%
상 2
20.0%
박 1
10.0%
준 1
10.0%
규 1
10.0%
최 1
10.0%
한 1
10.0%
선 1
10.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10
83.3%
Common 2
 
16.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
미 2
20.0%
상 2
20.0%
박 1
10.0%
준 1
10.0%
규 1
10.0%
최 1
10.0%
한 1
10.0%
선 1
10.0%
Common
ValueCountFrequency (%)
, 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10
83.3%
ASCII 2
 
16.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
미 2
20.0%
상 2
20.0%
박 1
10.0%
준 1
10.0%
규 1
10.0%
최 1
10.0%
한 1
10.0%
선 1
10.0%
ASCII
ValueCountFrequency (%)
, 1
50.0%
1
50.0%

AUTHOR
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing242
Missing (%)98.8%
Memory size2.0 KiB
2023-12-12T11:31:00.327193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st row박행보
2nd row이해섭
3rd row이해섭
ValueCountFrequency (%)
이해섭 2
66.7%
박행보 1
33.3%
2023-12-12T11:31:00.594903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 2
22.2%
해 2
22.2%
섭 2
22.2%
박 1
11.1%
행 1
11.1%
보 1
11.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 2
22.2%
해 2
22.2%
섭 2
22.2%
박 1
11.1%
행 1
11.1%
보 1
11.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 2
22.2%
해 2
22.2%
섭 2
22.2%
박 1
11.1%
행 1
11.1%
보 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 2
22.2%
해 2
22.2%
섭 2
22.2%
박 1
11.1%
행 1
11.1%
보 1
11.1%

SUBJECT_KHON1
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
KH.10
245 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKH.10
2nd rowKH.10
3rd rowKH.10
4th rowKH.10
5th rowKH.10

Common Values

ValueCountFrequency (%)
KH.10 245
100.0%

Length

2023-12-12T11:31:00.726351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:31:00.819685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kh.10 245
100.0%

SUBJECT_KHON2
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
KH.10.51
242 
KH.10.50
 
3

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKH.10.50
2nd rowKH.10.50
3rd rowKH.10.50
4th rowKH.10.51
5th rowKH.10.51

Common Values

ValueCountFrequency (%)
KH.10.51 242
98.8%
KH.10.50 3
 
1.2%

Length

2023-12-12T11:31:00.921558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:31:01.009035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kh.10.51 242
98.8%
kh.10.50 3
 
1.2%

SUBJECT_KHDP
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
om
242 
DF02
 
3

Length

Max length4
Median length2
Mean length2.0244898
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDF02
2nd rowDF02
3rd rowDF02
4th rowom
5th rowom

Common Values

ValueCountFrequency (%)
om 242
98.8%
DF02 3
 
1.2%

Length

2023-12-12T11:31:01.116760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:31:01.248561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
om 242
98.8%
df02 3
 
1.2%

TYPE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing245
Missing (%)100.0%
Memory size2.3 KiB

UNIT
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2
245 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 245
100.0%

Length

2023-12-12T11:31:01.343473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:31:01.426108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 245
100.0%

PUBLISHER
Text

MISSING 

Distinct157
Distinct (%)64.9%
Missing3
Missing (%)1.2%
Memory size2.0 KiB
2023-12-12T11:31:01.663483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length46
Mean length21.553719
Min length2

Characters and Unicode

Total characters5216
Distinct characters417
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)52.9%

Sample

1st row시미즈 미츠노리(淸水光憲)/일본
2nd row샤를르 프랑스와 드라마르셔(Charles Françlos. Delamarche)/프랑스
3rd row보곤디(R. de Vaugondy)/프랑스
4th row구리하라 노부아키(栗原信晁)/일본
5th row보곤디(Robert de Vaugondy)/프랑스
ValueCountFrequency (%)
한국 46
 
6.8%
de 18
 
2.7%
존 10
 
1.5%
필립 10
 
1.5%
vaugondy)/프랑스 9
 
1.3%
보곤디(robert 8
 
1.2%
니콜라 7
 
1.0%
자크 7
 
1.0%
장 6
 
0.9%
드 6
 
0.9%
Other values (376) 546
81.1%
2023-12-12T11:31:02.337642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
431
 
8.3%
e 247
 
4.7%
/ 194
 
3.7%
) 187
 
3.6%
( 187
 
3.6%
a 182
 
3.5%
i 162
 
3.1%
o 159
 
3.0%
n 158
 
3.0%
l 130
 
2.5%
Other values (407) 3179
60.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1907
36.6%
Other Letter 1889
36.2%
Space Separator 431
 
8.3%
Uppercase Letter 376
 
7.2%
Other Punctuation 232
 
4.4%
Close Punctuation 187
 
3.6%
Open Punctuation 187
 
3.6%
Decimal Number 4
 
0.1%
Dash Punctuation 2
 
< 0.1%
Initial Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 121
 
6.4%
국 103
 
5.5%
프 73
 
3.9%
랑 69
 
3.7%
일 58
 
3.1%
한 54
 
2.9%
리 52
 
2.8%
영 42
 
2.2%
본 42
 
2.2%
르 40
 
2.1%
Other values (340) 1235
65.4%
Lowercase Letter
ValueCountFrequency (%)
e 247
13.0%
a 182
9.5%
i 162
 
8.5%
o 159
 
8.3%
n 158
 
8.3%
l 130
 
6.8%
r 124
 
6.5%
s 101
 
5.3%
u 96
 
5.0%
t 95
 
5.0%
Other values (18) 453
23.8%
Uppercase Letter
ValueCountFrequency (%)
J 47
12.5%
B 37
 
9.8%
A 32
 
8.5%
T 24
 
6.4%
D 22
 
5.9%
R 21
 
5.6%
L 20
 
5.3%
C 20
 
5.3%
G 19
 
5.1%
P 18
 
4.8%
Other values (14) 116
30.9%
Other Punctuation
ValueCountFrequency (%)
/ 194
83.6%
. 28
 
12.1%
; 3
 
1.3%
& 3
 
1.3%
' 3
 
1.3%
· 1
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 1
25.0%
1 1
25.0%
8 1
25.0%
4 1
25.0%
Space Separator
ValueCountFrequency (%)
431
100.0%
Close Punctuation
ValueCountFrequency (%)
) 187
100.0%
Open Punctuation
ValueCountFrequency (%)
( 187
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2283
43.8%
Hangul 1728
33.1%
Common 1044
20.0%
Han 161
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 121
 
7.0%
국 103
 
6.0%
프 73
 
4.2%
랑 69
 
4.0%
일 58
 
3.4%
한 54
 
3.1%
리 52
 
3.0%
영 42
 
2.4%
본 42
 
2.4%
르 40
 
2.3%
Other values (228) 1074
62.2%
Han
ValueCountFrequency (%)
藤 7
 
4.3%
助 6
 
3.7%
之 6
 
3.7%
三 4
 
2.5%
原 4
 
2.5%
伊 3
 
1.9%
內 3
 
1.9%
田 3
 
1.9%
野 3
 
1.9%
郞 2
 
1.2%
Other values (102) 120
74.5%
Latin
ValueCountFrequency (%)
e 247
 
10.8%
a 182
 
8.0%
i 162
 
7.1%
o 159
 
7.0%
n 158
 
6.9%
l 130
 
5.7%
r 124
 
5.4%
s 101
 
4.4%
u 96
 
4.2%
t 95
 
4.2%
Other values (42) 829
36.3%
Common
ValueCountFrequency (%)
431
41.3%
/ 194
18.6%
) 187
17.9%
( 187
17.9%
. 28
 
2.7%
; 3
 
0.3%
& 3
 
0.3%
' 3
 
0.3%
- 2
 
0.2%
2 1
 
0.1%
Other values (5) 5
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3314
63.5%
Hangul 1728
33.1%
CJK 161
 
3.1%
None 12
 
0.2%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
431
 
13.0%
e 247
 
7.5%
/ 194
 
5.9%
) 187
 
5.6%
( 187
 
5.6%
a 182
 
5.5%
i 162
 
4.9%
o 159
 
4.8%
n 158
 
4.8%
l 130
 
3.9%
Other values (52) 1277
38.5%
Hangul
ValueCountFrequency (%)
스 121
 
7.0%
국 103
 
6.0%
프 73
 
4.2%
랑 69
 
4.0%
일 58
 
3.4%
한 54
 
3.1%
리 52
 
3.0%
영 42
 
2.4%
본 42
 
2.4%
르 40
 
2.3%
Other values (228) 1074
62.2%
CJK
ValueCountFrequency (%)
藤 7
 
4.3%
助 6
 
3.7%
之 6
 
3.7%
三 4
 
2.5%
原 4
 
2.5%
伊 3
 
1.9%
內 3
 
1.9%
田 3
 
1.9%
野 3
 
1.9%
郞 2
 
1.2%
Other values (102) 120
74.5%
None
ValueCountFrequency (%)
ü 5
41.7%
é 4
33.3%
ç 2
 
16.7%
· 1
 
8.3%
Punctuation
ValueCountFrequency (%)
‘ 1
100.0%

FORMAT_MEDIUM
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
text/xml
242 
text/html
 
3

Length

Max length9
Median length8
Mean length8.0122449
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtext/html
2nd rowtext/html
3rd rowtext/html
4th rowtext/xml
5th rowtext/xml

Common Values

ValueCountFrequency (%)
text/xml 242
98.8%
text/html 3
 
1.2%

Length

2023-12-12T11:31:02.482982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:31:02.576758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
text/xml 242
98.8%
text/html 3
 
1.2%

TABLEOFCONTENTS
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing245
Missing (%)100.0%
Memory size2.3 KiB

ABSTRACT
Text

Distinct243
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T11:31:02.877132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1024
Median length356
Mean length316.54286
Min length33

Characters and Unicode

Total characters77553
Distinct characters997
Distinct categories16 ?
Distinct scripts5 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique241 ?
Unique (%)98.4%

Sample

1st row주제분류: 역사/지리_대한민국&#xD;키워드: 면앙정30영 혈포효무(穴浦曉霧),박행보,한국가사문학관,그림&#xD;초록: 석천 임억령의 한시인 면앙정 30영을 시제에 따라 박행보가 그림으로 형상화한 작품이다.
2nd row주제분류: 역사/지리_조선시대&#xD;키워드: 담양부 지도,이해섭,한국가사문학관,고지도&#xD;초록: 1700년대&amp;nbsp;담양부 지도
3rd row주제분류: 역사/지리_조선시대&#xD;키워드: 창평현 지도,이해섭,한국가사문학관,고지도&#xD;초록: 1700년대&amp;nbsp;창평현 지도
4th row이 지도의 좌측 상단에는 박영효가 쓴 소륭삼보(紹隆三寶)라는 제문이 있다. 이것은 왕권과 국토와 국민을 잘 보전하자는 뜻이다. 이 지도의 발문에 의하면 김옥균이 가져온 지도를 저본으로 제작하였다. 경위도를 그려 놓지는 않았지만 지도의 윤곽에 점을 찍어 놓아 참고하도록 하였다. 울릉도는 죽도(竹島)로 독도는 송도(松島)로 표기되어 있다. 독도는 지도의 동쪽 끝에 걸쳐 있는데, 지도에 나타내려고 애쓴 흔적이 보인다. &#13; 동북아역사재단 편&#13;
5th row드라마르셔는 1792년에 프랑스의 왕실지리학자 보곤디(Robert de Vaugondy)의 지도사업을 승계하였다. 그러한 측면에서 프랑스 왕실지리학자의 맥을 이었다고 볼 수 있다. 이 지도는 축척 약 1:2천만 지도로 보곤디가 이전에 그린 아시아 지도를 드라마르셔가 수정한 것이다. 아시아 주요 국가의 정치적 경계를 중심으로 기술하였는데, 당시로서는 매우 드물게 조선과 일본 본토, 사할린, 쿠릴 열도의 상대적 위치가 매우 정확하게 묘사되어 있다. 지도에서 동해 해역의 명칭은 한국해(MER DE COREE)로 표기했으며, 조선과 청의 경계는 당빌선의 형태를 따르고 있다. &#13; 동북아역사재단 편&#13;
ValueCountFrequency (%)
이 291
 
2.0%
동북아역사재단 192
 
1.3%
편&#13 191
 
1.3%
동해 187
 
1.3%
있다 180
 
1.2%
지도는 156
 
1.0%
해역의 145
 
1.0%
것이다 128
 
0.9%
de 121
 
0.8%
명칭은 110
 
0.7%
Other values (5332) 13183
88.6%
2023-12-12T11:31:03.346674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14402
 
18.6%
도 1674
 
2.2%
지 1342
 
1.7%
다 1318
 
1.7%
의 1263
 
1.6%
이 1244
 
1.6%
. 1216
 
1.6%
e 1164
 
1.5%
에 1040
 
1.3%
로 1017
 
1.3%
Other values (987) 51873
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43463
56.0%
Space Separator 14442
 
18.6%
Lowercase Letter 8846
 
11.4%
Other Punctuation 3615
 
4.7%
Uppercase Letter 2644
 
3.4%
Decimal Number 2223
 
2.9%
Open Punctuation 822
 
1.1%
Close Punctuation 820
 
1.1%
Control 510
 
0.7%
Dash Punctuation 104
 
0.1%
Other values (6) 64
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 1674
 
3.9%
지 1342
 
3.1%
다 1318
 
3.0%
의 1263
 
2.9%
이 1244
 
2.9%
에 1040
 
2.4%
로 1017
 
2.3%
는 1011
 
2.3%
해 936
 
2.2%
하 931
 
2.1%
Other values (882) 31687
72.9%
Lowercase Letter
ValueCountFrequency (%)
e 1164
13.2%
a 979
11.1%
o 789
 
8.9%
t 701
 
7.9%
r 665
 
7.5%
n 652
 
7.4%
i 609
 
6.9%
l 517
 
5.8%
s 439
 
5.0%
u 331
 
3.7%
Other values (20) 2000
22.6%
Uppercase Letter
ValueCountFrequency (%)
E 329
12.4%
A 246
 
9.3%
R 211
 
8.0%
C 209
 
7.9%
O 167
 
6.3%
M 164
 
6.2%
D 139
 
5.3%
S 139
 
5.3%
N 130
 
4.9%
G 107
 
4.0%
Other values (19) 803
30.4%
Other Punctuation
ValueCountFrequency (%)
. 1216
33.6%
, 765
21.2%
; 537
14.9%
& 533
14.7%
# 416
 
11.5%
: 58
 
1.6%
/ 36
 
1.0%
" 26
 
0.7%
' 20
 
0.6%
· 7
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 811
36.5%
3 475
21.4%
0 197
 
8.9%
7 168
 
7.6%
8 150
 
6.7%
9 105
 
4.7%
6 86
 
3.9%
5 82
 
3.7%
4 75
 
3.4%
2 74
 
3.3%
Math Symbol
ValueCountFrequency (%)
∼ 2
40.0%
> 1
20.0%
< 1
20.0%
＝ 1
20.0%
Space Separator
ValueCountFrequency (%)
14402
99.7%
　 28
 
0.2%
  12
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 704
85.6%
『 110
 
13.4%
「 8
 
1.0%
Close Punctuation
ValueCountFrequency (%)
) 702
85.6%
』 110
 
13.4%
」 8
 
1.0%
Control
ValueCountFrequency (%)
410
80.4%
100
 
19.6%
Dash Punctuation
ValueCountFrequency (%)
- 103
99.0%
– 1
 
1.0%
Final Punctuation
ValueCountFrequency (%)
’ 12
66.7%
” 6
33.3%
Initial Punctuation
ValueCountFrequency (%)
‘ 10
58.8%
“ 7
41.2%
Other Symbol
ValueCountFrequency (%)
㎝ 1
50.0%
㎞ 1
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 21
100.0%
Modifier Letter
ValueCountFrequency (%)
ー 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42389
54.7%
Common 22600
29.1%
Latin 11490
 
14.8%
Han 1062
 
1.4%
Katakana 12
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 1674
 
3.9%
지 1342
 
3.2%
다 1318
 
3.1%
의 1263
 
3.0%
이 1244
 
2.9%
에 1040
 
2.5%
로 1017
 
2.4%
는 1011
 
2.4%
해 936
 
2.2%
하 931
 
2.2%
Other values (630) 30613
72.2%
Han
ValueCountFrequency (%)
島 125
 
11.8%
陵 41
 
3.9%
山 39
 
3.7%
竹 37
 
3.5%
鬱 35
 
3.3%
于 34
 
3.2%
海 31
 
2.9%
松 29
 
2.7%
日 23
 
2.2%
大 20
 
1.9%
Other values (233) 648
61.0%
Latin
ValueCountFrequency (%)
e 1164
 
10.1%
a 979
 
8.5%
o 789
 
6.9%
t 701
 
6.1%
r 665
 
5.8%
n 652
 
5.7%
i 609
 
5.3%
l 517
 
4.5%
s 439
 
3.8%
u 331
 
2.9%
Other values (49) 4644
40.4%
Common
ValueCountFrequency (%)
14402
63.7%
. 1216
 
5.4%
1 811
 
3.6%
, 765
 
3.4%
( 704
 
3.1%
) 702
 
3.1%
; 537
 
2.4%
& 533
 
2.4%
3 475
 
2.1%
# 416
 
1.8%
Other values (36) 2039
 
9.0%
Katakana
ValueCountFrequency (%)
ル 3
25.0%
ヲ 2
16.7%
リ 1
 
8.3%
ヤ 1
 
8.3%
ノ 1
 
8.3%
コ 1
 
8.3%
ト 1
 
8.3%
ガ 1
 
8.3%
シ 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42389
54.7%
ASCII 33678
43.4%
CJK 1060
 
1.4%
None 367
 
0.5%
Punctuation 37
 
< 0.1%
Katakana 13
 
< 0.1%
Latin Ext Additional 3
 
< 0.1%
Math Operators 2
 
< 0.1%
CJK Ext A 2
 
< 0.1%
CJK Compat 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14402
42.8%
. 1216
 
3.6%
e 1164
 
3.5%
a 979
 
2.9%
1 811
 
2.4%
o 789
 
2.3%
, 765
 
2.3%
( 704
 
2.1%
) 702
 
2.1%
t 701
 
2.1%
Other values (70) 11445
34.0%
Hangul
ValueCountFrequency (%)
도 1674
 
3.9%
지 1342
 
3.2%
다 1318
 
3.1%
의 1263
 
3.0%
이 1244
 
2.9%
에 1040
 
2.5%
로 1017
 
2.4%
는 1011
 
2.4%
해 936
 
2.2%
하 931
 
2.2%
Other values (630) 30613
72.2%
CJK
ValueCountFrequency (%)
島 125
 
11.8%
陵 41
 
3.9%
山 39
 
3.7%
竹 37
 
3.5%
鬱 35
 
3.3%
于 34
 
3.2%
海 31
 
2.9%
松 29
 
2.7%
日 23
 
2.2%
大 20
 
1.9%
Other values (231) 646
60.9%
None
ValueCountFrequency (%)
『 110
30.0%
』 110
30.0%
é 67
18.3%
　 28
 
7.6%
  12
 
3.3%
」 8
 
2.2%
「 8
 
2.2%
É 7
 
1.9%
· 7
 
1.9%
ç 6
 
1.6%
Other values (4) 4
 
1.1%
Punctuation
ValueCountFrequency (%)
’ 12
32.4%
‘ 10
27.0%
“ 7
18.9%
” 6
16.2%
… 1
 
2.7%
– 1
 
2.7%
Katakana
ValueCountFrequency (%)
ル 3
23.1%
ヲ 2
15.4%
リ 1
 
7.7%
ヤ 1
 
7.7%
ノ 1
 
7.7%
コ 1
 
7.7%
ー 1
 
7.7%
ト 1
 
7.7%
ガ 1
 
7.7%
シ 1
 
7.7%
Latin Ext Additional
ValueCountFrequency (%)
Ḗ 3
100.0%
Math Operators
ValueCountFrequency (%)
∼ 2
100.0%
CJK Ext A
ValueCountFrequency (%)
㹨 1
50.0%
㭗 1
50.0%
CJK Compat
ValueCountFrequency (%)
㎝ 1
50.0%
㎞ 1
50.0%

ISPARTOF_ID
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing245
Missing (%)100.0%
Memory size2.3 KiB

ISPARTOF
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing245
Missing (%)100.0%
Memory size2.3 KiB

REQUIRES
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing245
Missing (%)100.0%
Memory size2.3 KiB

DATEEVENT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing245
Missing (%)100.0%
Memory size2.3 KiB

DOCCREATED
Date

MISSING 

Distinct2
Distinct (%)66.7%
Missing242
Missing (%)98.8%
Memory size2.0 KiB
Minimum2008-12-02 00:00:00
Maximum2009-01-27 00:00:00
2023-12-12T11:31:03.458908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:31:03.563179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

DOCISSUED
Text

MISSING 

Distinct119
Distinct (%)57.8%
Missing39
Missing (%)15.9%
Memory size2.0 KiB
2023-12-12T11:31:03.792789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2060
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)33.0%

Sample

1st row2001-01-01
2nd row1895-99-99
3rd row1778-99-99
4th row1750-99-99
5th row1848-99-99
ValueCountFrequency (%)
1750-99-99 11
 
5.3%
1894-99-99 6
 
2.9%
9999-99-99 5
 
2.4%
1892-99-99 4
 
1.9%
1778-99-99 4
 
1.9%
1760-99-99 4
 
1.9%
1780-99-99 4
 
1.9%
1749-99-99 4
 
1.9%
1904-99-99 4
 
1.9%
1794-99-99 4
 
1.9%
Other values (109) 156
75.7%
2023-12-12T11:31:04.204767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 914
44.4%
- 412
20.0%
1 233
 
11.3%
7 142
 
6.9%
8 113
 
5.5%
0 56
 
2.7%
4 50
 
2.4%
5 48
 
2.3%
6 41
 
2.0%
2 29
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1648
80.0%
Dash Punctuation 412
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 914
55.5%
1 233
 
14.1%
7 142
 
8.6%
8 113
 
6.9%
0 56
 
3.4%
4 50
 
3.0%
5 48
 
2.9%
6 41
 
2.5%
2 29
 
1.8%
3 22
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 412
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 914
44.4%
- 412
20.0%
1 233
 
11.3%
7 142
 
6.9%
8 113
 
5.5%
0 56
 
2.7%
4 50
 
2.4%
5 48
 
2.3%
6 41
 
2.0%
2 29
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 914
44.4%
- 412
20.0%
1 233
 
11.3%
7 142
 
6.9%
8 113
 
5.5%
0 56
 
2.7%
4 50
 
2.4%
5 48
 
2.3%
6 41
 
2.0%
2 29
 
1.4%

DATE_ISSUED
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2014-01-16 00:00:00
242 
1900-01-01 00:00:00
 
3

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1900-01-01 00:00:00
2nd row1900-01-01 00:00:00
3rd row1900-01-01 00:00:00
4th row2014-01-16 00:00:00
5th row2014-01-16 00:00:00

Common Values

ValueCountFrequency (%)
2014-01-16 00:00:00 242
98.8%
1900-01-01 00:00:00 3
 
1.2%

Length

2023-12-12T11:31:04.378681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:31:04.483825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00 245
50.0%
2014-01-16 242
49.4%
1900-01-01 3
 
0.6%

DATE_CREATED
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
1900-01-01 00:00:00
245 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1900-01-01 00:00:00
2nd row1900-01-01 00:00:00
3rd row1900-01-01 00:00:00
4th row1900-01-01 00:00:00
5th row1900-01-01 00:00:00

Common Values

ValueCountFrequency (%)
1900-01-01 00:00:00 245
100.0%

Length

2023-12-12T11:31:04.592401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:31:04.674725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1900-01-01 245
50.0%
00:00:00 245
50.0%

DATE_MODIFIED
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2015-09-18 00:00:00
242 
2012-12-01 00:00:00
 
3

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2012-12-01 00:00:00
2nd row2012-12-01 00:00:00
3rd row2012-12-01 00:00:00
4th row2015-09-18 00:00:00
5th row2015-09-18 00:00:00

Common Values

ValueCountFrequency (%)
2015-09-18 00:00:00 242
98.8%
2012-12-01 00:00:00 3
 
1.2%

Length

2023-12-12T11:31:04.779600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:31:04.862306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00 245
50.0%
2015-09-18 242
49.4%
2012-12-01 3
 
0.6%

URL
Text

UNIQUE 

Distinct245
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T11:31:05.068002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length183
Median length68
Mean length69.408163
Min length68

Characters and Unicode

Total characters17005
Distinct characters56
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

Unique245 ?
Unique (%)100.0%

Sample

1st row<url> <get>http://www.gasa.go.kr/WService/PavilionNRelic/Picture/DetailView.aspx?TopID=D&amp;SubID=04&amp;RelicID=R00000024&amp;Mode=100</get> </url>
2nd row<url> <get>http://www.gasa.go.kr/WService/PavilionNRelic/Picture/DetailView.aspx?TopID=D&amp;SubID=04&amp;RelicID=R00000192&amp;Mode=100</get> </url>
3rd row<url> <get>http://www.gasa.go.kr/WService/PavilionNRelic/Picture/DetailView.aspx?TopID=D&amp;SubID=04&amp;RelicID=R00000193&amp;Mode=100</get> </url>
4th row<url><get>http://contents.nahf.or.kr/id/NAHF.om_001_0010</get></url>
5th row<url><get>http://contents.nahf.or.kr/id/NAHF.om_001_0020</get></url>
ValueCountFrequency (%)
url 6
 
2.4%
url><get>http://contents.nahf.or.kr/id/nahf.om_003_0240</get></url 1
 
0.4%
url><get>http://contents.nahf.or.kr/id/nahf.om_003_0260</get></url 1
 
0.4%
url><get>http://contents.nahf.or.kr/id/nahf.om_003_0270</get></url 1
 
0.4%
url><get>http://contents.nahf.or.kr/id/nahf.om_003_0280</get></url 1
 
0.4%
url><get>http://contents.nahf.or.kr/id/nahf.om_003_0290</get></url 1
 
0.4%
url><get>http://contents.nahf.or.kr/id/nahf.om_003_0300</get></url 1
 
0.4%
url><get>http://contents.nahf.or.kr/id/nahf.om_003_0310</get></url 1
 
0.4%
url><get>http://contents.nahf.or.kr/id/nahf.om_003_0320</get></url 1
 
0.4%
url><get>http://contents.nahf.or.kr/id/nahf.om_003_0330</get></url 1
 
0.4%
Other values (236) 236
94.0%
2023-12-12T11:31:05.414782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 1476
 
8.7%
t 1470
 
8.6%
0 1057
 
6.2%
r 983
 
5.8%
< 980
 
5.8%
> 980
 
5.8%
. 980
 
5.8%
e 756
 
4.4%
o 738
 
4.3%
n 729
 
4.3%
Other values (46) 6856
40.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8958
52.7%
Other Punctuation 2722
 
16.0%
Math Symbol 1972
 
11.6%
Decimal Number 1733
 
10.2%
Uppercase Letter 1028
 
6.0%
Connector Punctuation 484
 
2.8%
Space Separator 108
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1470
16.4%
r 983
11.0%
e 756
 
8.4%
o 738
 
8.2%
n 729
 
8.1%
l 502
 
5.6%
g 496
 
5.5%
u 496
 
5.5%
h 487
 
5.4%
a 266
 
3.0%
Other values (12) 2035
22.7%
Uppercase Letter
ValueCountFrequency (%)
N 245
23.8%
F 242
23.5%
A 242
23.5%
H 242
23.5%
D 15
 
1.5%
I 9
 
0.9%
R 9
 
0.9%
P 6
 
0.6%
S 6
 
0.6%
W 3
 
0.3%
Other values (3) 9
 
0.9%
Decimal Number
ValueCountFrequency (%)
0 1057
61.0%
1 198
 
11.4%
3 105
 
6.1%
5 81
 
4.7%
2 79
 
4.6%
4 76
 
4.4%
6 35
 
2.0%
9 34
 
2.0%
7 34
 
2.0%
8 34
 
2.0%
Other Punctuation
ValueCountFrequency (%)
/ 1476
54.2%
. 980
36.0%
: 245
 
9.0%
& 9
 
0.3%
; 9
 
0.3%
? 3
 
0.1%
Math Symbol
ValueCountFrequency (%)
< 980
49.7%
> 980
49.7%
= 12
 
0.6%
Connector Punctuation
ValueCountFrequency (%)
_ 484
100.0%
Space Separator
ValueCountFrequency (%)
108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9986
58.7%
Common 7019
41.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1470
14.7%
r 983
 
9.8%
e 756
 
7.6%
o 738
 
7.4%
n 729
 
7.3%
l 502
 
5.0%
g 496
 
5.0%
u 496
 
5.0%
h 487
 
4.9%
a 266
 
2.7%
Other values (25) 3063
30.7%
Common
ValueCountFrequency (%)
/ 1476
21.0%
0 1057
15.1%
< 980
14.0%
> 980
14.0%
. 980
14.0%
_ 484
 
6.9%
: 245
 
3.5%
1 198
 
2.8%
108
 
1.5%
3 105
 
1.5%
Other values (11) 406
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 1476
 
8.7%
t 1470
 
8.6%
0 1057
 
6.2%
r 983
 
5.8%
< 980
 
5.8%
> 980
 
5.8%
. 980
 
5.8%
e 756
 
4.4%
o 738
 
4.3%
n 729
 
4.3%
Other values (46) 6856
40.3%

CREATORSORT
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
242 
이해섭
 
2
박행보
 
1

Length

Max length3
Median length2
Mean length2.0122449
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row박행보
2nd row이해섭
3rd row이해섭
4th row
5th row

Common Values

ValueCountFrequency (%)
242
98.8%
이해섭 2
 
0.8%
박행보 1
 
0.4%

Length

2023-12-12T11:31:05.553835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:31:05.652232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이해섭 2
66.7%
박행보 1
33.3%

DATESORT
Text

MISSING 

Distinct120
Distinct (%)57.7%
Missing37
Missing (%)15.1%
Memory size2.0 KiB
2023-12-12T11:31:05.861752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2080
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)32.7%

Sample

1st row2001-01-01
2nd row2009-01-27
3rd row2009-01-27
4th row1895-99-99
5th row1778-99-99
ValueCountFrequency (%)
1750-99-99 11
 
5.3%
1894-99-99 6
 
2.9%
9999-99-99 5
 
2.4%
1760-99-99 4
 
1.9%
1778-99-99 4
 
1.9%
1794-99-99 4
 
1.9%
1780-99-99 4
 
1.9%
1749-99-99 4
 
1.9%
1892-99-99 4
 
1.9%
1904-99-99 4
 
1.9%
Other values (110) 158
76.0%
2023-12-12T11:31:06.284270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 916
44.0%
- 416
20.0%
1 235
 
11.3%
7 144
 
6.9%
8 113
 
5.4%
0 62
 
3.0%
4 50
 
2.4%
5 48
 
2.3%
6 41
 
2.0%
2 33
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1664
80.0%
Dash Punctuation 416
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 916
55.0%
1 235
 
14.1%
7 144
 
8.7%
8 113
 
6.8%
0 62
 
3.7%
4 50
 
3.0%
5 48
 
2.9%
6 41
 
2.5%
2 33
 
2.0%
3 22
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 416
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2080
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 916
44.0%
- 416
20.0%
1 235
 
11.3%
7 144
 
6.9%
8 113
 
5.4%
0 62
 
3.0%
4 50
 
2.4%
5 48
 
2.3%
6 41
 
2.0%
2 33
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 916
44.0%
- 416
20.0%
1 235
 
11.3%
7 144
 
6.9%
8 113
 
5.4%
0 62
 
3.0%
4 50
 
2.4%
5 48
 
2.3%
6 41
 
2.0%
2 33
 
1.6%

Sample

URI_KHONMDCENTERSUBJECT_KHONDBINFOURI_KHDPMAINTITLEALTERNATIVEDOCSENDEREDITORAUTHORSUBJECT_KHON1SUBJECT_KHON2SUBJECT_KHDPTYPEUNITPUBLISHERFORMAT_MEDIUMTABLEOFCONTENTSABSTRACTISPARTOF_IDISPARTOFREQUIRESDATEEVENTDOCCREATEDDOCISSUEDDATE_ISSUEDDATE_CREATEDDATE_MODIFIEDURLCREATORSORTDATESORT
0KH.GASA.R000000024GASAKH.10.50.000한국가사문학R000000024면앙정30영 혈포효무(穴浦曉霧)穴浦曉霧<NA>박준규, 최한선박행보KH.10KH.10.50DF02<NA>2<NA>text/html<NA>주제분류: 역사/지리_대한민국&#xD;키워드: 면앙정30영 혈포효무(穴浦曉霧),박행보,한국가사문학관,그림&#xD;초록: 석천 임억령의 한시인 면앙정 30영을 시제에 따라 박행보가 그림으로 형상화한 작품이다.<NA><NA><NA><NA>2008-12-022001-01-011900-01-01 00:00:001900-01-01 00:00:002012-12-01 00:00:00<url> <get>http://www.gasa.go.kr/WService/PavilionNRelic/Picture/DetailView.aspx?TopID=D&amp;SubID=04&amp;RelicID=R00000024&amp;Mode=100</get> </url>박행보2001-01-01
1KH.GASA.R000000192GASAKH.10.50.000한국가사문학R000000192담양부 지도담양부 지도<NA>미상이해섭KH.10KH.10.50DF02<NA>2<NA>text/html<NA>주제분류: 역사/지리_조선시대&#xD;키워드: 담양부 지도,이해섭,한국가사문학관,고지도&#xD;초록: 1700년대&amp;nbsp;담양부 지도<NA><NA><NA><NA>2009-01-27<NA>1900-01-01 00:00:001900-01-01 00:00:002012-12-01 00:00:00<url> <get>http://www.gasa.go.kr/WService/PavilionNRelic/Picture/DetailView.aspx?TopID=D&amp;SubID=04&amp;RelicID=R00000192&amp;Mode=100</get> </url>이해섭2009-01-27
2KH.GASA.R000000193GASAKH.10.50.000한국가사문학R000000193창평현 지도창평현 지도<NA>미상이해섭KH.10KH.10.50DF02<NA>2<NA>text/html<NA>주제분류: 역사/지리_조선시대&#xD;키워드: 창평현 지도,이해섭,한국가사문학관,고지도&#xD;초록: 1700년대&amp;nbsp;창평현 지도<NA><NA><NA><NA>2009-01-27<NA>1900-01-01 00:00:001900-01-01 00:00:002012-12-01 00:00:00<url> <get>http://www.gasa.go.kr/WService/PavilionNRelic/Picture/DetailView.aspx?TopID=D&amp;SubID=04&amp;RelicID=R00000193&amp;Mode=100</get> </url>이해섭2009-01-27
3KH.NAHF.om_001_0010NAHFKH.10.51.000독도동해관련고지도om_001_0010조선여지도/朝鮮輿地圖<NA><NA><NA><NA>KH.10KH.10.51om<NA>2시미즈 미츠노리(淸水光憲)/일본text/xml<NA>이 지도의 좌측 상단에는 박영효가 쓴 소륭삼보(紹隆三寶)라는 제문이 있다. 이것은 왕권과 국토와 국민을 잘 보전하자는 뜻이다. 이 지도의 발문에 의하면 김옥균이 가져온 지도를 저본으로 제작하였다. 경위도를 그려 놓지는 않았지만 지도의 윤곽에 점을 찍어 놓아 참고하도록 하였다. 울릉도는 죽도(竹島)로 독도는 송도(松島)로 표기되어 있다. 독도는 지도의 동쪽 끝에 걸쳐 있는데, 지도에 나타내려고 애쓴 흔적이 보인다. &#13; 동북아역사재단 편&#13;<NA><NA><NA><NA><NA>1895-99-992014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_001_0010</get></url>1895-99-99
4KH.NAHF.om_001_0020NAHFKH.10.51.000독도동해관련고지도om_001_0020아시아 지역구분도/Asie divisée en principaux Etats, Empires &amp; Royaumes<NA><NA><NA><NA>KH.10KH.10.51om<NA>2샤를르 프랑스와 드라마르셔(Charles Françlos. Delamarche)/프랑스text/xml<NA>드라마르셔는 1792년에 프랑스의 왕실지리학자 보곤디(Robert de Vaugondy)의 지도사업을 승계하였다. 그러한 측면에서 프랑스 왕실지리학자의 맥을 이었다고 볼 수 있다. 이 지도는 축척 약 1:2천만 지도로 보곤디가 이전에 그린 아시아 지도를 드라마르셔가 수정한 것이다. 아시아 주요 국가의 정치적 경계를 중심으로 기술하였는데, 당시로서는 매우 드물게 조선과 일본 본토, 사할린, 쿠릴 열도의 상대적 위치가 매우 정확하게 묘사되어 있다. 지도에서 동해 해역의 명칭은 한국해(MER DE COREE)로 표기했으며, 조선과 청의 경계는 당빌선의 형태를 따르고 있다. &#13; 동북아역사재단 편&#13;<NA><NA><NA><NA><NA>1778-99-992014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_001_0020</get></url>1778-99-99
5KH.NAHF.om_001_0030NAHFKH.10.51.000독도동해관련고지도om_001_0030아시아 지도/Carte de l'Asie dressée sur les relations les plus nouvelles, principalement sur les cartes de Russie, de la Chine et de la Tartarie chinoise et divisée en ses empires et royaumes<NA><NA><NA><NA>KH.10KH.10.51om<NA>2보곤디(R. de Vaugondy)/프랑스text/xml<NA>프랑스의 왕실지리학자 보곤디의 『세계지도첩(Atlas Universel)』에 수록된 아시아 지도이다. 보곤디는 이 지도를 러시아와 중국, 만주 지역의 지도를 활용하여 제작하였다. 동해 해역의 명칭은 한국해(MER DE COREE)로 표기하였다. 그러나 지역에 대한 정보 부족으로 울릉도와 독도는 나타내지 않았다. 조선과 청나라의 경계는 당빌선을 채택하였다. 19세기 지도의 당빌선과 달리 녹둔도를 포함한 한반도 북동부 지역을 조선의 영토로 나타내었다.&#13; 동북아역사재단 편&#13;<NA><NA><NA><NA><NA>1750-99-992014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_001_0030</get></url>1750-99-99
6KH.NAHF.om_001_0040NAHFKH.10.51.000독도동해관련고지도om_001_0040동서지구만국전도/東西地球萬國全圖<NA><NA><NA><NA>KH.10KH.10.51om<NA>2구리하라 노부아키(栗原信晁)/일본text/xml<NA>프랑스에서 제작된 세계지도를 일본에서 입수하여 동반구와 서반구로 나누어 제작하였다. 상단에는 지도에 관한 발문이 있고 하단에 동반구와 서반구로 나누어 적도, 동지선, 하지선 등을 나타내었다. 동반구도는 교토(京都)를 중심으로 제작했으며, 조선의 윤곽은 명확하지 않다. 동해 해역의 명칭은 조선해(朝鮮海), 태평양 쪽에는 대일본해(大日本海)로 표기하였다. &#13; 동북아역사재단 편&#13;<NA><NA><NA><NA><NA>1848-99-992014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_001_0040</get></url>1848-99-99
7KH.NAHF.om_001_0050NAHFKH.10.51.000독도동해관련고지도om_001_0050시베리아, 또는 아시아 쪽 러시아, 중국 쪽 타타르, 엘뤼 타타르 및 일본 지도/Siberie ou russie asitique, Tartarie Chinoise Pays des Eluts et Isles de Japon<NA><NA><NA><NA>KH.10KH.10.51om<NA>2보곤디(Robert de Vaugondy)/프랑스text/xml<NA>프랑스의 왕실지리학자 보곤디가 1778년 그의 아틀라스 『Nouvel Atlas Portatif』에 삽입한 지도이다. 이 아틀라스는 일반 대중을 위한 보급판으로 제작되었기 때문에 수록된 지도의 표현 기법은 단순 명료하다. 홋카이도와 사할린의 형상은 왜곡되어 있지만, 조선의 영역은 압록강과 두만강을 훨씬 넘어 매우 넓게 나타내었다. 동해 해역의 명칭과 관련하여 보곤디는 원래 한국 쪽의 바다는 한국해, 일본의 동측은 일본해로 표기하는 것을 선호하였다. 그러나 이 지도에서는 한국해(MER DE COREE)라는 단일 명칭으로 표기하였다.&#13; 동북아역사재단 편&#13;<NA><NA><NA><NA><NA>1778-99-992014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_001_0050</get></url>1778-99-99
8KH.NAHF.om_001_0060NAHFKH.10.51.000독도동해관련고지도om_001_0060지리 공부를 위한 아시아 지도/L'Asie dressée pour l'étude de la géographie<NA><NA><NA><NA>KH.10KH.10.51om<NA>2브리옹 드라투르(M. Brion de la Tour)/프랑스text/xml<NA>이프랑스의 왕실지리학자 브리옹 드라투르가 1786년 일반인들의 지리공부를 위해 제작한 지도이다. 당시 러시아의 탐사 자료를 활용하여 베링의 명칭을 딴 섬을 표기하고, 감마랜드를 삭제하는 등 과학성 추구를 위해 노력하였다. 동해 해역의 명칭은 프랑스 왕실지리학자들의 전통에 따라 한국해(MER DE COREE)로 표기하였다. 조선과 청의 경계는 표시하지 않았으며, 조선 내부의 지명은 경기도(Kinkitao)만 기재하였다. 제주도는 지도상에 표시되어 있으나 울릉도는 나타나지 않았다.&#13; 동북아역사재단 편&#13;<NA><NA><NA><NA><NA>1786-99-992014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_001_0060</get></url>1786-99-99
9KH.NAHF.om_001_0070NAHFKH.10.51.000독도동해관련고지도om_001_0070중국과 일본 제국도/Imperio Chino Y JAPON<NA><NA><NA><NA>KH.10KH.10.51om<NA>2파블로 알라베른(PABLO ALABERN)/스페인text/xml<NA>지도에 동아시아에 대한 정보는 매우 정밀하다. 압록강과 두만강 이북 지역은 푸른색과 점선으로 경계를 표시하였는데, 경계선이 압록강 보다 훨씬 이북에 나타난다. 또한 변경 지역을 푸른색으로 표시한 것이 특징이다. 동아시아의 바다 명칭은 동지나해를 한국해(MAR DE COREA), 동해 해역을 일본해(MAR DEL JAPON)로 표기하였다. 그리고 한국 연안은 한국만(G.DE.COREA)으로 표기하였다. &#13; 동북아역사재단 편&#13;<NA><NA><NA><NA><NA>1830-99-992014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_001_0070</get></url>1830-99-99
URI_KHONMDCENTERSUBJECT_KHONDBINFOURI_KHDPMAINTITLEALTERNATIVEDOCSENDEREDITORAUTHORSUBJECT_KHON1SUBJECT_KHON2SUBJECT_KHDPTYPEUNITPUBLISHERFORMAT_MEDIUMTABLEOFCONTENTSABSTRACTISPARTOF_IDISPARTOFREQUIRESDATEEVENTDOCCREATEDDOCISSUEDDATE_ISSUEDDATE_CREATEDDATE_MODIFIEDURLCREATORSORTDATESORT
235KH.NAHF.om_005_0370NAHFKH.10.51.000독도동해관련고지도om_005_0370동관/東關팔도분도(八道分圖)<NA><NA><NA>KH.10KH.10.51om<NA>2한국text/xml<NA>강원도 전도로서, 지도 오른쪽 하단 울진(蔚珍) 건너편 바다에 울릉도(鬱陵島)를 표현하였다.<NA><NA><NA><NA><NA><NA>2014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_005_0370</get></url><NA>
236KH.NAHF.om_005_0380NAHFKH.10.51.000독도동해관련고지도om_005_0380강원도/江原道팔도지도(八道地圖)<NA><NA><NA>KH.10KH.10.51om<NA>2한국text/xml<NA>강원도 전도로서, 지도 오른쪽 하단 삼척(三陟)과 울진(蔚珍) 사이 건너편 바다에 울릉도(鬱陵島) 산도(山島 : 于山島의 오기)를 좌우로 그려넣었다. 울릉도 서쪽에는 다른 지도에서 주토굴(朱土窟)이라는 표기 대신 같은 의미로서 적토굴(赤土窟)로 표기되었으며 섬의 동쪽에는 적(笛), 남쪽에는 죽전(竹田)이 표기되었다.<NA><NA><NA><NA><NA><NA>2014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_005_0380</get></url><NA>
237KH.NAHF.om_005_0390NAHFKH.10.51.000독도동해관련고지도om_005_0390강원도/江原道팔도지도(八道地圖)<NA><NA><NA>KH.10KH.10.51om<NA>2한국text/xml<NA>강원도 전도로서, 지도 오른쪽 하단 삼척(三陟)과 울진(蔚珍) 사이 건너편 바다에 울릉도(鬱陵島) 우산도(于山島)를 좌우로 표현하였다. 울릉도에는 서쪽과 동쪽에 각각 주토굴(朱土窟), 적(笛)·죽전(竹田)이 표기되었다.<NA><NA><NA><NA><NA><NA>2014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_005_0390</get></url><NA>
238KH.NAHF.om_005_0400NAHFKH.10.51.000독도동해관련고지도om_005_0400강원도/江原道팔도지도(八道地圖)<NA><NA><NA>KH.10KH.10.51om<NA>2한국text/xml<NA>강원도 전도로서, 지도 오른쪽 중단 동해 가운데 고성(高城)과 삼척포(三陟浦) 사이에 울릉도(鬱陵島) 우산(于山) 두섬이 상하로 표현되어있다.&#13; 울릉도의 표현이 조선지도첩 강원도(om_000_0260) 처럼 타원형이 아닌 독특한 형태로 묘사되었다.<NA><NA><NA><NA><NA><NA>2014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_005_0400</get></url><NA>
239KH.NAHF.om_005_0410NAHFKH.10.51.000독도동해관련고지도om_005_0410강원도/江原道팔도지도(八道地圖)<NA><NA><NA>KH.10KH.10.51om<NA>2한국text/xml<NA>강원도 전도로서, 지도 오른쪽 하단에에 울릉도(鬱陵島)를 그려넣었으며, 그 동쪽에는 우산도(于山島)를 병기하였다. 울릉도 하단에 적혀있는 주기에는 "亦曰女于陵亦曰羽陵 自蔚珍淂風便二日到 一云自平海三陟順風一日半可至水路二千里"라고 적혀 있어, 울릉도의 다른 이름으로 우릉(于陵), 우릉(羽陵)이 있고, 울진에서는 2일, 평해·삼척에서는 하루반 정도의 걸리며 거리는 물길로 2천리 정도라고 하였다.<NA><NA><NA><NA><NA><NA>2014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_005_0410</get></url><NA>
240KH.NAHF.om_005_0420NAHFKH.10.51.000독도동해관련고지도om_005_0420강원도/江原道팔도지도(八道地圖)<NA><NA><NA>KH.10KH.10.51om<NA>2한국text/xml<NA>강원도 전도로서, 지도 오른쪽 하단 삼척(三陟) 건너편 바다에 울릉도(鬱陵島) 우산(于山)이라는 글씨가 쓰여 있다. 정상기의 동국지도 계열의 지도 중에서 울릉도와 우산도의 섬의 외곽이 그려지지 않고 글씨로만 남아 있는 유일한 사례이다. 특히 울릉도에 해당하는 위치에는 주왕굴(朱王窟 : 朱土窟의 오기), 죽전(竹田) 등 섬의 형태를 그리면 해당위치에 써있어야할 주기가 그대로 노출되어 있다.<NA><NA><NA><NA><NA><NA>2014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_005_0420</get></url><NA>
241KH.NAHF.om_005_0430NAHFKH.10.51.000독도동해관련고지도om_005_0430전도/全圖팔도지도(八道地圖)<NA><NA><NA>KH.10KH.10.51om<NA>2한국text/xml<NA>우리나라 전도로서, 지도의 오른쪽 주기가 적힌 부분 아래 큼지막하게 울릉도(鬱陵島)가 표기되었다.<NA><NA><NA><NA><NA><NA>2014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_005_0430</get></url><NA>
242KH.NAHF.om_005_0440NAHFKH.10.51.000독도동해관련고지도om_005_0440울릉도/鬱陵島해동지도(海東地圖)<NA><NA><NA>KH.10KH.10.51om<NA>2한국text/xml<NA>울릉도 지도로서, 울릉도의 동쪽 가가운 곳에 "所謂于山島"로 표기된 섬이 묘사되어 있다. 울릉도의 자연환경과 정박할 수 있는 곳(船泊可居)이 표기되었다. 또한 울릉도의 동쪽에는 "倭船倉可居 刻石立標"라는 주기와 함께 일본인의 출입을 통제하는 내용의 표식(刻石立標)이 있다.<NA><NA><NA><NA><NA>1750-99-992014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_005_0440</get></url>1750-99-99
243KH.NAHF.om_005_0450NAHFKH.10.51.000독도동해관련고지도om_005_0450대동총도/大東摠圖해동지도(海東地圖)<NA><NA><NA>KH.10KH.10.51om<NA>2한국text/xml<NA>우리나라 전도로서, 지도의 오른쪽 중단 삼척(三陟)의 건너편에 우산도(于山島) 울릉도(鬱陵島)가 표현되었다. 특히 우산도의 하단 주기에는 "倭船倉可居"라는 내용의 일본인의 출입사실이 적혀있다.<NA><NA><NA><NA><NA>1751-99-992014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_005_0450</get></url>1751-99-99
244KH.NAHF.om_005_0460NAHFKH.10.51.000독도동해관련고지도om_005_0460강원도/江原道해동지도(海東地圖)<NA><NA><NA>KH.10KH.10.51om<NA>2한국text/xml<NA>강원도 전도로서, 지도 오른쪽 하단에에 울릉도(鬱陵島)를 그려넣었으며, 그 동북쪽에는 우산도(于山島)를 병기하였다. 울릉도 내에는 주토굴(朱土窟), 중봉(中峰 : 성인봉) 40리·30리, 적(笛), 명릉(㭗陵)의 내용이 표기되었다. 울릉도 하단에 적혀있는 주기에는 "自蔚珍淂風便二日到"라고 적혀 있어, 울진에서는 2일이면 도착한다고 하였다.<NA><NA><NA><NA><NA><NA>2014-01-16 00:00:001900-01-01 00:00:002015-09-18 00:00:00<url><get>http://contents.nahf.or.kr/id/NAHF.om_005_0460</get></url><NA>