Overview

Dataset statistics

Number of variables13
Number of observations896
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory95.5 KiB
Average record size in memory109.1 B

Variable types

Text6
Numeric4
Categorical3

Dataset

Description독립기념관 독립운동 연구논문의 제목, 저자, 출판년도 등의 자료입니다.
Author독립기념관
URLhttps://www.data.go.kr/data/15067847/fileData.do

Alerts

비고 has constant value ""Constant
목차 is highly overall correlated with 수정일자High correlation
수정일자 is highly overall correlated with 페이지 and 4 other fieldsHigh correlation
페이지 is highly overall correlated with 수정일자High correlation
권집 is highly overall correlated with 출판년도 and 2 other fieldsHigh correlation
출판년도 is highly overall correlated with 권집 and 2 other fieldsHigh correlation
검색인덱스 is highly overall correlated with 권집 and 2 other fieldsHigh correlation
목차 is highly imbalanced (87.7%)Imbalance
수정일자 is highly imbalanced (62.9%)Imbalance
페이지 has 63 (7.0%) zerosZeros
검색인덱스 has 34 (3.8%) zerosZeros

Reproduction

Analysis started2023-12-12 00:15:30.302150
Analysis finished2023-12-12 00:15:33.928183
Duration3.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

제목
Text

Distinct832
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-12T09:15:34.191148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length107
Median length63
Mean length25.878348
Min length2

Characters and Unicode

Total characters23187
Distinct characters1223
Distinct categories15 ?
Distinct scripts6 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique825 ?
Unique (%)92.1%

Sample

1st row『신흥교우보』를 통해 본 신흥무관학교
2nd row1930년대 충북 지역 官制靑年團
3rd row『학해』를 통해 본 일제 말기 지성계의 단면
4th row일제시기 上海 고려인삼 상인들의 활동
5th row天津 朝鮮大獨立黨籌備會의 결성과 활동
ValueCountFrequency (%)
대한 43
 
1.0%
휘보 42
 
1.0%
독립운동 36
 
0.9%
35
 
0.8%
활동 33
 
0.8%
생애와 29
 
0.7%
성격 28
 
0.7%
중심으로 28
 
0.7%
연구 28
 
0.7%
그 26
 
0.6%
Other values (2521) 3826
92.1%
2023-12-12T09:15:34.772774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3284
 
14.2%
의 885
 
3.8%
( 758
 
3.3%
) 755
 
3.3%
동 473
 
2.0%
과 326
 
1.4%
한 325
 
1.4%
1 321
 
1.4%
운 321
 
1.4%
대 299
 
1.3%
Other values (1213) 15440
66.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16502
71.2%
Space Separator 3284
 
14.2%
Decimal Number 1062
 
4.6%
Open Punctuation 831
 
3.6%
Close Punctuation 828
 
3.6%
Other Punctuation 233
 
1.0%
Math Symbol 134
 
0.6%
Dash Punctuation 133
 
0.6%
Uppercase Letter 86
 
0.4%
Lowercase Letter 43
 
0.2%
Other values (5) 51
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 885
 
5.4%
동 473
 
2.9%
과 326
 
2.0%
한 325
 
2.0%
운 321
 
1.9%
대 299
 
1.8%
제 286
 
1.7%
국 277
 
1.7%
일 251
 
1.5%
사 235
 
1.4%
Other values (1126) 12824
77.7%
Uppercase Letter
ValueCountFrequency (%)
I 42
48.8%
S 6
 
7.0%
O 4
 
4.7%
A 4
 
4.7%
E 3
 
3.5%
B 3
 
3.5%
T 3
 
3.5%
K 2
 
2.3%
L 2
 
2.3%
H 2
 
2.3%
Other values (14) 15
 
17.4%
Lowercase Letter
ValueCountFrequency (%)
e 8
18.6%
r 5
11.6%
p 3
 
7.0%
t 3
 
7.0%
a 3
 
7.0%
h 3
 
7.0%
i 3
 
7.0%
s 2
 
4.7%
u 2
 
4.7%
o 2
 
4.7%
Other values (9) 9
20.9%
Decimal Number
ValueCountFrequency (%)
1 321
30.2%
9 211
19.9%
0 161
15.2%
3 112
 
10.5%
2 109
 
10.3%
4 53
 
5.0%
5 38
 
3.6%
8 23
 
2.2%
6 17
 
1.6%
7 17
 
1.6%
Other Punctuation
ValueCountFrequency (%)
· 81
34.8%
: 68
29.2%
' 34
14.6%
, 29
 
12.4%
. 17
 
7.3%
? 2
 
0.9%
* 1
 
0.4%
; 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 758
91.2%
「 30
 
3.6%
『 29
 
3.5%
[ 7
 
0.8%
{ 3
 
0.4%
《 2
 
0.2%
〈 2
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 755
91.2%
」 29
 
3.5%
』 29
 
3.5%
] 7
 
0.8%
〉 3
 
0.4%
} 3
 
0.4%
》 2
 
0.2%
Math Symbol
ValueCountFrequency (%)
< 51
38.1%
> 50
37.3%
~ 33
24.6%
Dash Punctuation
ValueCountFrequency (%)
- 130
97.7%
－ 3
 
2.3%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅰ 1
50.0%
Space Separator
ValueCountFrequency (%)
3284
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 17
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 16
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 12
100.0%
Other Symbol
ValueCountFrequency (%)
◆ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13104
56.5%
Common 6554
28.3%
Han 3392
 
14.6%
Latin 125
 
0.5%
Hiragana 6
 
< 0.1%
Cyrillic 6
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
韓 86
 
2.5%
動 85
 
2.5%
國 74
 
2.2%
民 68
 
2.0%
運 61
 
1.8%
日 61
 
1.8%
義 51
 
1.5%
立 50
 
1.5%
獨 45
 
1.3%
學 43
 
1.3%
Other values (692) 2768
81.6%
Hangul
ValueCountFrequency (%)
의 885
 
6.8%
동 473
 
3.6%
과 326
 
2.5%
한 325
 
2.5%
운 321
 
2.4%
대 299
 
2.3%
제 286
 
2.2%
국 277
 
2.1%
일 251
 
1.9%
사 235
 
1.8%
Other values (419) 9426
71.9%
Common
ValueCountFrequency (%)
3284
50.1%
( 758
 
11.6%
) 755
 
11.5%
1 321
 
4.9%
9 211
 
3.2%
0 161
 
2.5%
- 130
 
2.0%
3 112
 
1.7%
2 109
 
1.7%
· 81
 
1.2%
Other values (32) 632
 
9.6%
Latin
ValueCountFrequency (%)
I 42
33.6%
e 8
 
6.4%
S 6
 
4.8%
r 5
 
4.0%
O 4
 
3.2%
A 4
 
3.2%
E 3
 
2.4%
p 3
 
2.4%
B 3
 
2.4%
t 3
 
2.4%
Other values (29) 44
35.2%
Cyrillic
ValueCountFrequency (%)
Р 1
16.7%
Г 1
16.7%
А 1
16.7%
С 1
16.7%
П 1
16.7%
И 1
16.7%
Hiragana
ValueCountFrequency (%)
の 2
33.3%
と 1
16.7%
る 1
16.7%
み 1
16.7%
に 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13100
56.5%
ASCII 6430
27.7%
CJK 3283
 
14.2%
None 210
 
0.9%
CJK Compat Ideographs 109
 
0.5%
Punctuation 33
 
0.1%
Hiragana 6
 
< 0.1%
Cyrillic 6
 
< 0.1%
Compat Jamo 4
 
< 0.1%
Geometric Shapes 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3284
51.1%
( 758
 
11.8%
) 755
 
11.7%
1 321
 
5.0%
9 211
 
3.3%
0 161
 
2.5%
- 130
 
2.0%
3 112
 
1.7%
2 109
 
1.7%
: 68
 
1.1%
Other values (56) 521
 
8.1%
Hangul
ValueCountFrequency (%)
의 885
 
6.8%
동 473
 
3.6%
과 326
 
2.5%
한 325
 
2.5%
운 321
 
2.5%
대 299
 
2.3%
제 286
 
2.2%
국 277
 
2.1%
일 251
 
1.9%
사 235
 
1.8%
Other values (418) 9422
71.9%
CJK
ValueCountFrequency (%)
韓 86
 
2.6%
動 85
 
2.6%
國 74
 
2.3%
民 68
 
2.1%
運 61
 
1.9%
日 61
 
1.9%
義 51
 
1.6%
立 50
 
1.5%
獨 45
 
1.4%
學 43
 
1.3%
Other values (659) 2659
81.0%
None
ValueCountFrequency (%)
· 81
38.6%
「 30
 
14.3%
」 29
 
13.8%
』 29
 
13.8%
『 29
 
13.8%
〉 3
 
1.4%
－ 3
 
1.4%
《 2
 
1.0%
》 2
 
1.0%
〈 2
 
1.0%
CJK Compat Ideographs
ValueCountFrequency (%)
李 23
21.1%
臨 10
 
9.2%
歷 8
 
7.3%
領 6
 
5.5%
柳 6
 
5.5%
金 5
 
4.6%
麟 5
 
4.6%
論 5
 
4.6%
理 3
 
2.8%
聯 3
 
2.8%
Other values (23) 35
32.1%
Punctuation
ValueCountFrequency (%)
’ 17
51.5%
‘ 16
48.5%
Compat Jamo
ValueCountFrequency (%)
ㆍ 4
100.0%
Geometric Shapes
ValueCountFrequency (%)
◆ 4
100.0%
Hiragana
ValueCountFrequency (%)
の 2
33.3%
と 1
16.7%
る 1
16.7%
み 1
16.7%
に 1
16.7%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅰ 1
50.0%
Cyrillic
ValueCountFrequency (%)
Р 1
16.7%
Г 1
16.7%
А 1
16.7%
С 1
16.7%
П 1
16.7%
И 1
16.7%

저자
Text

Distinct297
Distinct (%)33.2%
Missing1
Missing (%)0.1%
Memory size7.1 KiB
2023-12-12T09:15:35.149431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length3
Mean length3.2960894
Min length1

Characters and Unicode

Total characters2950
Distinct characters266
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)19.3%

Sample

1st row김주용
2nd row주계운
3rd row정진아
4th row김광재
5th row양지선
ValueCountFrequency (%)
110
 
12.0%
한국독립운동사연구소 48
 
5.2%
박걸순 23
 
2.5%
이명화 20
 
2.2%
장세윤 16
 
1.7%
홍선표 14
 
1.5%
장석흥 13
 
1.4%
박민영 13
 
1.4%
이정은 13
 
1.4%
한시준 11
 
1.2%
Other values (307) 636
69.4%
2023-12-12T09:15:35.793636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 162
 
5.5%
- 110
 
3.7%
한 107
 
3.6%
이 103
 
3.5%
영 81
 
2.7%
박 72
 
2.4%
동 71
 
2.4%
윤 63
 
2.1%
국 60
 
2.0%
소 59
 
2.0%
Other values (256) 2062
69.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2776
94.1%
Dash Punctuation 110
 
3.7%
Space Separator 25
 
0.8%
Lowercase Letter 22
 
0.7%
Other Punctuation 5
 
0.2%
Open Punctuation 4
 
0.1%
Close Punctuation 4
 
0.1%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 162
 
5.8%
한 107
 
3.9%
이 103
 
3.7%
영 81
 
2.9%
박 72
 
2.6%
동 71
 
2.6%
윤 63
 
2.3%
국 60
 
2.2%
소 59
 
2.1%
구 57
 
2.1%
Other values (239) 1941
69.9%
Lowercase Letter
ValueCountFrequency (%)
n 4
18.2%
a 4
18.2%
r 4
18.2%
e 2
9.1%
m 2
9.1%
o 2
9.1%
d 2
9.1%
l 2
9.1%
Other Punctuation
ValueCountFrequency (%)
, 2
40.0%
· 2
40.0%
: 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
P 2
50.0%
B 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 110
100.0%
Space Separator
ValueCountFrequency (%)
25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2721
92.2%
Common 148
 
5.0%
Han 55
 
1.9%
Latin 26
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 162
 
6.0%
한 107
 
3.9%
이 103
 
3.8%
영 81
 
3.0%
박 72
 
2.6%
동 71
 
2.6%
윤 63
 
2.3%
국 60
 
2.2%
소 59
 
2.2%
구 57
 
2.1%
Other values (196) 1886
69.3%
Han
ValueCountFrequency (%)
村 3
 
5.5%
中 3
 
5.5%
瑾 2
 
3.6%
矯 2
 
3.6%
桂 2
 
3.6%
朴 2
 
3.6%
勝 2
 
3.6%
則 2
 
3.6%
政 2
 
3.6%
金 2
 
3.6%
Other values (33) 33
60.0%
Latin
ValueCountFrequency (%)
n 4
15.4%
a 4
15.4%
r 4
15.4%
e 2
7.7%
m 2
7.7%
P 2
7.7%
o 2
7.7%
d 2
7.7%
B 2
7.7%
l 2
7.7%
Common
ValueCountFrequency (%)
- 110
74.3%
25
 
16.9%
( 4
 
2.7%
) 4
 
2.7%
, 2
 
1.4%
· 2
 
1.4%
: 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2721
92.2%
ASCII 172
 
5.8%
CJK 55
 
1.9%
None 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 162
 
6.0%
한 107
 
3.9%
이 103
 
3.8%
영 81
 
3.0%
박 72
 
2.6%
동 71
 
2.6%
윤 63
 
2.3%
국 60
 
2.2%
소 59
 
2.2%
구 57
 
2.1%
Other values (196) 1886
69.3%
ASCII
ValueCountFrequency (%)
- 110
64.0%
25
 
14.5%
n 4
 
2.3%
a 4
 
2.3%
r 4
 
2.3%
( 4
 
2.3%
) 4
 
2.3%
, 2
 
1.2%
e 2
 
1.2%
m 2
 
1.2%
Other values (6) 11
 
6.4%
CJK
ValueCountFrequency (%)
村 3
 
5.5%
中 3
 
5.5%
瑾 2
 
3.6%
矯 2
 
3.6%
桂 2
 
3.6%
朴 2
 
3.6%
勝 2
 
3.6%
則 2
 
3.6%
政 2
 
3.6%
金 2
 
3.6%
Other values (33) 33
60.0%
None
ValueCountFrequency (%)
· 2
100.0%

페이지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct157
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.853795
Minimum0
Maximum578
Zeros63
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2023-12-12T09:15:36.023389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median9
Q334
95-th percentile310
Maximum578
Range578
Interquartile range (IQR)30

Descriptive statistics

Standard deviation99.887109
Coefficient of variation (CV)1.9642017
Kurtosis6.541711
Mean50.853795
Median Absolute Deviation (MAD)8
Skewness2.6582747
Sum45565
Variance9977.4345
MonotonicityNot monotonic
2023-12-12T09:15:36.201685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63
 
7.0%
1 59
 
6.6%
7 45
 
5.0%
4 45
 
5.0%
3 44
 
4.9%
2 44
 
4.9%
5 44
 
4.9%
6 44
 
4.9%
8 43
 
4.8%
9 35
 
3.9%
Other values (147) 430
48.0%
ValueCountFrequency (%)
0 63
7.0%
1 59
6.6%
2 44
4.9%
3 44
4.9%
4 45
5.0%
5 44
4.9%
6 44
4.9%
7 45
5.0%
8 43
4.8%
9 35
3.9%
ValueCountFrequency (%)
578 1
 
0.1%
551 1
 
0.1%
507 1
 
0.1%
491 1
 
0.1%
483 1
 
0.1%
463 1
 
0.1%
459 1
 
0.1%
443 1
 
0.1%
439 2
0.2%
435 3
0.3%

권집
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.608259
Minimum1
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2023-12-12T09:15:36.376372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median27
Q346
95-th percentile66
Maximum70
Range69
Interquartile range (IQR)35

Descriptive statistics

Standard deviation20.322525
Coefficient of variation (CV)0.68638028
Kurtosis-1.0924136
Mean29.608259
Median Absolute Deviation (MAD)17
Skewness0.35486599
Sum26529
Variance413.00503
MonotonicityNot monotonic
2023-12-12T09:15:36.567919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 25
 
2.8%
8 23
 
2.6%
3 23
 
2.6%
7 22
 
2.5%
2 22
 
2.5%
12 21
 
2.3%
11 21
 
2.3%
10 21
 
2.3%
6 20
 
2.2%
1 19
 
2.1%
Other values (60) 679
75.8%
ValueCountFrequency (%)
1 19
2.1%
2 22
2.5%
3 23
2.6%
4 18
2.0%
5 25
2.8%
6 20
2.2%
7 22
2.5%
8 23
2.6%
9 18
2.0%
10 21
2.3%
ValueCountFrequency (%)
70 7
0.8%
69 11
1.2%
68 10
1.1%
67 10
1.1%
66 8
0.9%
65 9
1.0%
64 9
1.0%
63 8
0.9%
62 8
0.9%
61 9
1.0%

설명문
Text

Distinct872
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-12T09:15:36.970941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length12
Mean length11.758929
Min length10

Characters and Unicode

Total characters10536
Distinct characters26
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

Unique848 ?
Unique (%)94.6%

Sample

1st row20120103.pdf
2nd row20120105.pdf
3rd row20120106.pdf
4th row20120107.pdf
5th row20120108.pdf
ValueCountFrequency (%)
199313.pdf 2
 
0.2%
20070111.pdf 2
 
0.2%
20070112.pdf 2
 
0.2%
20070104.pdf 2
 
0.2%
20070110.pdf 2
 
0.2%
20070105.pdf 2
 
0.2%
20070106.pdf 2
 
0.2%
199414.pdf 2
 
0.2%
199711.pdf 2
 
0.2%
20070109.pdf 2
 
0.2%
Other values (862) 876
97.8%
2023-12-12T09:15:37.523618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2167
20.6%
1 1328
12.6%
2 1022
9.7%
. 896
8.5%
p 892
8.5%
d 892
8.5%
f 892
8.5%
9 698
 
6.6%
3 315
 
3.0%
8 303
 
2.9%
Other values (16) 1131
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6672
63.3%
Lowercase Letter 2874
27.3%
Other Punctuation 896
 
8.5%
Connector Punctuation 65
 
0.6%
Dash Punctuation 29
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 892
31.0%
d 892
31.0%
f 892
31.0%
e 39
 
1.4%
s 39
 
1.4%
h 30
 
1.0%
r 26
 
0.9%
t 17
 
0.6%
a 13
 
0.5%
c 13
 
0.5%
Other values (3) 21
 
0.7%
Decimal Number
ValueCountFrequency (%)
0 2167
32.5%
1 1328
19.9%
2 1022
15.3%
9 698
 
10.5%
3 315
 
4.7%
8 303
 
4.5%
4 232
 
3.5%
7 223
 
3.3%
5 200
 
3.0%
6 184
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 896
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 65
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7662
72.7%
Latin 2874
 
27.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2167
28.3%
1 1328
17.3%
2 1022
13.3%
. 896
11.7%
9 698
 
9.1%
3 315
 
4.1%
8 303
 
4.0%
4 232
 
3.0%
7 223
 
2.9%
5 200
 
2.6%
Other values (3) 278
 
3.6%
Latin
ValueCountFrequency (%)
p 892
31.0%
d 892
31.0%
f 892
31.0%
e 39
 
1.4%
s 39
 
1.4%
h 30
 
1.0%
r 26
 
0.9%
t 17
 
0.6%
a 13
 
0.5%
c 13
 
0.5%
Other values (3) 21
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2167
20.6%
1 1328
12.6%
2 1022
9.7%
. 896
8.5%
p 892
8.5%
d 892
8.5%
f 892
8.5%
9 698
 
6.6%
3 315
 
3.0%
8 303
 
2.9%
Other values (16) 1131
10.7%

비고
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
-
896 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 896
100.0%

Length

2023-12-12T09:15:37.706576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:15:37.832722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
896
100.0%

저자(한문)
Text

Distinct98
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-12T09:15:38.119302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length1
Mean length1.4966518
Min length1

Characters and Unicode

Total characters1341
Distinct characters193
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)7.3%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
682
75.9%
張世胤 13
 
1.4%
李明花 12
 
1.3%
張錫興 11
 
1.2%
朴杰淳 10
 
1.1%
愼鏞廈 8
 
0.9%
洪善杓 7
 
0.8%
蔡永國 7
 
0.8%
趙凡來 6
 
0.7%
鄭濟愚 6
 
0.7%
Other values (90) 136
 
15.1%
2023-12-12T09:15:38.590751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 682
50.9%
金 34
 
2.5%
李 27
 
2.0%
張 24
 
1.8%
朴 17
 
1.3%
錫 17
 
1.3%
淳 15
 
1.1%
韓 14
 
1.0%
杰 14
 
1.0%
明 14
 
1.0%
Other values (183) 483
36.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 682
50.9%
Other Letter 657
49.0%
Space Separator 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
金 34
 
5.2%
李 27
 
4.1%
張 24
 
3.7%
朴 17
 
2.6%
錫 17
 
2.6%
淳 15
 
2.3%
韓 14
 
2.1%
杰 14
 
2.1%
明 14
 
2.1%
胤 13
 
2.0%
Other values (181) 468
71.2%
Dash Punctuation
ValueCountFrequency (%)
- 682
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 684
51.0%
Han 617
46.0%
Hangul 40
 
3.0%

Most frequent character per script

Han
ValueCountFrequency (%)
金 34
 
5.5%
李 27
 
4.4%
張 24
 
3.9%
朴 17
 
2.8%
錫 17
 
2.8%
淳 15
 
2.4%
韓 14
 
2.3%
杰 14
 
2.3%
明 14
 
2.3%
胤 13
 
2.1%
Other values (150) 428
69.4%
Hangul
ValueCountFrequency (%)
이 4
 
10.0%
김 3
 
7.5%
한 2
 
5.0%
현 2
 
5.0%
형 2
 
5.0%
동 2
 
5.0%
도 1
 
2.5%
연 1
 
2.5%
달 1
 
2.5%
용 1
 
2.5%
Other values (21) 21
52.5%
Common
ValueCountFrequency (%)
- 682
99.7%
2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 684
51.0%
CJK 575
42.9%
CJK Compat Ideographs 42
 
3.1%
Hangul 40
 
3.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 682
99.7%
2
 
0.3%
CJK
ValueCountFrequency (%)
金 34
 
5.9%
張 24
 
4.2%
朴 17
 
3.0%
錫 17
 
3.0%
淳 15
 
2.6%
韓 14
 
2.4%
杰 14
 
2.4%
明 14
 
2.4%
胤 13
 
2.3%
世 13
 
2.3%
Other values (145) 400
69.6%
CJK Compat Ideographs
ValueCountFrequency (%)
李 27
64.3%
柳 10
 
23.8%
烈 3
 
7.1%
梁 1
 
2.4%
廉 1
 
2.4%
Hangul
ValueCountFrequency (%)
이 4
 
10.0%
김 3
 
7.5%
한 2
 
5.0%
현 2
 
5.0%
형 2
 
5.0%
동 2
 
5.0%
도 1
 
2.5%
연 1
 
2.5%
달 1
 
2.5%
용 1
 
2.5%
Other values (21) 21
52.5%

목차
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
0
881 
1
 
15

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 (%)
0 881
98.3%
1 15
 
1.7%

Length

2023-12-12T09:15:38.753423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:15:38.867002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 881
98.3%
1 15
 
1.7%

출판년도
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.2154
Minimum1987
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2023-12-12T09:15:39.279370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1987
5-th percentile1989
Q11997
median2007
Q32013
95-th percentile2019
Maximum2020
Range33
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.7024692
Coefficient of variation (CV)0.0048386169
Kurtosis-1.1471164
Mean2005.2154
Median Absolute Deviation (MAD)8
Skewness-0.27306476
Sum1796673
Variance94.137908
MonotonicityNot monotonic
2023-12-12T09:15:39.425787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2010 57
 
6.4%
2013 45
 
5.0%
2000 43
 
4.8%
2016 42
 
4.7%
2012 38
 
4.2%
2019 37
 
4.1%
2017 34
 
3.8%
2018 34
 
3.8%
2015 33
 
3.7%
2004 30
 
3.3%
Other values (24) 503
56.1%
ValueCountFrequency (%)
1987 19
2.1%
1988 22
2.5%
1989 23
2.6%
1990 18
2.0%
1991 25
2.8%
1992 20
2.2%
1993 22
2.5%
1994 23
2.6%
1995 18
2.0%
1996 21
2.3%
ValueCountFrequency (%)
2020 18
 
2.0%
2019 37
4.1%
2018 34
3.8%
2017 34
3.8%
2016 42
4.7%
2015 33
3.7%
2014 19
2.1%
2013 45
5.0%
2012 38
4.2%
2011 22
2.5%

검색인덱스
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct863
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean433.6317
Minimum0
Maximum927
Zeros34
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2023-12-12T09:15:39.626320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.75
Q1191.75
median437.5
Q3661.25
95-th percentile871.25
Maximum927
Range927
Interquartile range (IQR)469.5

Descriptive statistics

Standard deviation273.49597
Coefficient of variation (CV)0.63071027
Kurtosis-1.1928021
Mean433.6317
Median Absolute Deviation (MAD)235
Skewness0.045116033
Sum388534
Variance74800.043
MonotonicityNot monotonic
2023-12-12T09:15:39.788913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34
 
3.8%
588 1
 
0.1%
25 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
17 1
 
0.1%
18 1
 
0.1%
19 1
 
0.1%
20 1
 
0.1%
21 1
 
0.1%
Other values (853) 853
95.2%
ValueCountFrequency (%)
0 34
3.8%
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%
ValueCountFrequency (%)
927 1
0.1%
926 1
0.1%
925 1
0.1%
924 1
0.1%
923 1
0.1%
922 1
0.1%
921 1
0.1%
911 1
0.1%
910 1
0.1%
909 1
0.1%

관리번호
Text

Distinct884
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-12T09:15:40.129012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.2946429
Min length6

Characters and Unicode

Total characters6536
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

Unique872 ?
Unique (%)97.3%

Sample

1st row20120103
2nd row20120105
3rd row20120106
4th row20120107
5th row20120108
ValueCountFrequency (%)
20070110 2
 
0.2%
20070107 2
 
0.2%
20070106 2
 
0.2%
20070112 2
 
0.2%
20070111 2
 
0.2%
20070109 2
 
0.2%
20070108 2
 
0.2%
20070105 2
 
0.2%
20070104 2
 
0.2%
20070102 2
 
0.2%
Other values (874) 876
97.8%
2023-12-12T09:15:40.599452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2184
33.4%
1 1308
20.0%
2 1016
15.5%
9 669
 
10.2%
8 282
 
4.3%
3 274
 
4.2%
4 213
 
3.3%
7 211
 
3.2%
6 178
 
2.7%
5 172
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6507
99.6%
Dash Punctuation 29
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2184
33.6%
1 1308
20.1%
2 1016
15.6%
9 669
 
10.3%
8 282
 
4.3%
3 274
 
4.2%
4 213
 
3.3%
7 211
 
3.2%
6 178
 
2.7%
5 172
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6536
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2184
33.4%
1 1308
20.0%
2 1016
15.5%
9 669
 
10.2%
8 282
 
4.3%
3 274
 
4.2%
4 213
 
3.3%
7 211
 
3.2%
6 178
 
2.7%
5 172
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2184
33.4%
1 1308
20.0%
2 1016
15.5%
9 669
 
10.2%
8 282
 
4.3%
3 274
 
4.2%
4 213
 
3.3%
7 211
 
3.2%
6 178
 
2.7%
5 172
 
2.6%

PDF파일
Text

Distinct266
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-12T09:15:40.833069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length1
Mean length6.1428571
Min length1

Characters and Unicode

Total characters5504
Distinct characters19
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

Unique253 ?
Unique (%)28.2%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
608
67.9%
paper_005_015.pdf 4
 
0.4%
paper_006_012.pdf 3
 
0.3%
paper_001_015.pdf 3
 
0.3%
paper_013_009.pdf 3
 
0.3%
paper_010_013.pdf 3
 
0.3%
paper_007_013.pdf 3
 
0.3%
paper_011_011.pdf 3
 
0.3%
paper_008_014.pdf 3
 
0.3%
paper_012_012.pdf 3
 
0.3%
Other values (256) 260
29.0%
2023-12-12T09:15:41.228347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 936
17.0%
p 864
15.7%
- 608
11.0%
_ 576
10.5%
1 322
 
5.9%
f 288
 
5.2%
d 288
 
5.2%
. 288
 
5.2%
r 288
 
5.2%
e 288
 
5.2%
Other values (9) 758
13.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2304
41.9%
Decimal Number 1728
31.4%
Dash Punctuation 608
 
11.0%
Connector Punctuation 576
 
10.5%
Other Punctuation 288
 
5.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 936
54.2%
1 322
 
18.6%
2 78
 
4.5%
3 72
 
4.2%
5 70
 
4.1%
4 61
 
3.5%
6 61
 
3.5%
7 47
 
2.7%
8 41
 
2.4%
9 40
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
p 864
37.5%
f 288
 
12.5%
d 288
 
12.5%
r 288
 
12.5%
e 288
 
12.5%
a 288
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 608
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 576
100.0%
Other Punctuation
ValueCountFrequency (%)
. 288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3200
58.1%
Latin 2304
41.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 936
29.2%
- 608
19.0%
_ 576
18.0%
1 322
 
10.1%
. 288
 
9.0%
2 78
 
2.4%
3 72
 
2.2%
5 70
 
2.2%
4 61
 
1.9%
6 61
 
1.9%
Other values (3) 128
 
4.0%
Latin
ValueCountFrequency (%)
p 864
37.5%
f 288
 
12.5%
d 288
 
12.5%
r 288
 
12.5%
e 288
 
12.5%
a 288
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 936
17.0%
p 864
15.7%
- 608
11.0%
_ 576
10.5%
1 322
 
5.9%
f 288
 
5.2%
d 288
 
5.2%
. 288
 
5.2%
r 288
 
5.2%
e 288
 
5.2%
Other values (9) 758
13.8%

수정일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2018-11-09 00:00:00
832 
<NA>
 
64

Length

Max length19
Median length19
Mean length17.928571
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-11-09 00:00:00
2nd row2018-11-09 00:00:00
3rd row2018-11-09 00:00:00
4th row2018-11-09 00:00:00
5th row2018-11-09 00:00:00

Common Values

ValueCountFrequency (%)
2018-11-09 00:00:00 832
92.9%
<NA> 64
 
7.1%

Length

2023-12-12T09:15:41.419175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:15:41.550736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-11-09 832
48.1%
00:00:00 832
48.1%
na 64
 
3.7%

Interactions

2023-12-12T09:15:33.146260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:31.663079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:32.213106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:32.670825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:33.269522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:31.791976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:32.331517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:32.814479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:33.379093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:31.948982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:32.446564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:32.923994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:33.481604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:32.087507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:32.547363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:33.041132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:15:41.627767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
페이지권집저자(한문)목차출판년도검색인덱스
페이지1.0000.6180.2840.0000.6540.566
권집0.6181.0000.4090.1910.9850.986
저자(한문)0.2840.4091.0000.0000.6140.507
목차0.0000.1910.0001.0000.1950.190
출판년도0.6540.9850.6140.1951.0000.981
검색인덱스0.5660.9860.5070.1900.9811.000
2023-12-12T09:15:41.751833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
목차수정일자
목차1.0001.000
수정일자1.0001.000
2023-12-12T09:15:41.844140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
페이지권집출판년도검색인덱스목차수정일자
페이지1.000-0.474-0.474-0.4830.0001.000
권집-0.4741.0001.0000.9320.1461.000
출판년도-0.4741.0001.0000.9320.1481.000
검색인덱스-0.4830.9320.9321.0000.1451.000
목차0.0000.1460.1480.1451.0001.000
수정일자1.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T09:15:33.657884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:15:33.864327image/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

제목저자페이지권집설명문비고저자(한문)목차출판년도검색인덱스관리번호PDF파일수정일자
0『신흥교우보』를 통해 본 신흥무관학교김주용34020120103.pdf--0201258820120103-2018-11-09 00:00:00
11930년대 충북 지역 官制靑年團주계운54020120105.pdf--0201259020120105-2018-11-09 00:00:00
2『학해』를 통해 본 일제 말기 지성계의 단면정진아64020120106.pdf--0201259120120106-2018-11-09 00:00:00
3일제시기 上海 고려인삼 상인들의 활동김광재74020120107.pdf--0201259220120107-2018-11-09 00:00:00
4天津 朝鮮大獨立黨籌備會의 결성과 활동양지선84020120108.pdf--0201259320120108-2018-11-09 00:00:00
51930~40년대 중국 화북지역 한인사회와 귀환 - 河北省·內蒙古·山西省 지역을 중심으로손염홍94020120109.pdf--0201259420120109-2018-11-09 00:00:00
6반식민 항의 암살 -미국인들, 한일관계에 직면하다Brandon Palmer104020120110.pdf--0201259520120110-2018-11-09 00:00:00
7「국민대표회의 출석원 서명부」해제반병률114020120111.pdf--0201259620120111-2018-11-09 00:00:00
81980년대 의식과 감성이 빚어낸 중국 관내지역 한국독립운동사, -김영범, 『혁명과 의열－한국독립운동사의 내면』-염인호124020120112.pdf--0201259720120112-2018-11-09 00:00:00
9휘보한국독립운동사연구소134020120113.pdf--0201259820120113-2018-11-09 00:00:00
제목저자페이지권집설명문비고저자(한문)목차출판년도검색인덱스관리번호PDF파일수정일자
886제4주제: 1930년대 독립운동의 특성김영범168199418.pdf--0199485199421paper_008_017.pdf2018-11-09 00:00:00
887제3주제: 1920년대 민족운동의 전개와 성격이균영248199417.pdf--0199486199420paper_008_016.pdf2018-11-09 00:00:00
888제2주제: 1910년대 독립운동의 동향과 그 특성윤경로188199416.pdf--0199487199419paper_008_015.pdf2018-11-09 00:00:00
889제1주제: 구한말 국권회복운동의 특성정제우168199415.pdf--0199488199418paper_008_014.pdf2018-11-09 00:00:00
890대주제(大主題) : 한국독립운동(韓國獨立運動)의 시기별(時期別) 특성(特性)-08199414.pdf--0199489199417paper_008_014.pdf2018-11-09 00:00:00
891이홍광연구(李紅光硏究)-항일유격대(抗日遊擊隊) 및 동북인민혁명군내(東北人民革命軍內) 한인지도자(韓人指導者)의 활동사례검토(活動事例檢討)장세윤368199413.pdf-張世胤0199491199415paper_008_013.pdf2018-11-09 00:00:00
892백산(白山) 안희제(安熙濟) 연구(硏究)이동언248199412.pdf-李東彦0199492199414paper_008_012.pdf2018-11-09 00:00:00
893일제하(日帝下) 선학원(禪學院)의 운영(運營)과 성격(性格)김광식328199411.pdf-金光植0199493199413paper_008_011.pdf2018-11-09 00:00:00
8941920년대(年代) 중반 남만지역독립군단(南滿地域獨立軍團)의 정비(整備)와 활동(活動)채영국268199410.pdf-蔡永國0199494199412paper_008_010.pdf2018-11-09 00:00:00
895흥사단속동임시위원부(興士團速東臨時委員部)와 도산(島山) 안창호(安昌浩)의 민족운동(民族運動)이명화268199409.pdf-李明花0199495199411paper_008_009.pdf2018-11-09 00:00:00