Overview

Dataset statistics

Number of variables11
Number of observations385
Missing cells385
Missing cells (%)9.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.3 KiB
Average record size in memory91.3 B

Variable types

Numeric2
Categorical3
Text4
DateTime1
Unsupported1

Dataset

Description국립암센터에서 19년도 9월까지 국립암센터홈페이지를 통해 개방하는 세미나 정보
Author국립암센터
URLhttps://www.data.go.kr/data/15049628/fileData.do

Alerts

SMR_ID is highly overall correlated with SMR_DAY and 3 other fieldsHigh correlation
SMR_DAY is highly overall correlated with SMR_ID and 2 other fieldsHigh correlation
SMR_WEEK is highly overall correlated with SMR_ID and 2 other fieldsHigh correlation
SMR_REGDATE is highly overall correlated with SMR_ID and 3 other fieldsHigh correlation
SMR_REGUSER is highly overall correlated with SMR_ID and 3 other fieldsHigh correlation
SMR_LINK has 385 (100.0%) missing valuesMissing
SMR_ID has unique valuesUnique
SMR_LINK is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 05:32:12.214216
Analysis finished2023-12-12 05:32:13.942315
Duration1.73 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SMR_ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct385
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193
Minimum1
Maximum385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-12T14:32:14.056865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20.2
Q197
median193
Q3289
95-th percentile365.8
Maximum385
Range384
Interquartile range (IQR)192

Descriptive statistics

Standard deviation111.28417
Coefficient of variation (CV)0.57660192
Kurtosis-1.2
Mean193
Median Absolute Deviation (MAD)96
Skewness0
Sum74305
Variance12384.167
MonotonicityNot monotonic
2023-12-12T14:32:14.234007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 1
 
0.3%
290 1
 
0.3%
264 1
 
0.3%
263 1
 
0.3%
262 1
 
0.3%
261 1
 
0.3%
260 1
 
0.3%
259 1
 
0.3%
258 1
 
0.3%
257 1
 
0.3%
Other values (375) 375
97.4%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
385 1
0.3%
384 1
0.3%
383 1
0.3%
382 1
0.3%
381 1
0.3%
380 1
0.3%
379 1
0.3%
378 1
0.3%
377 1
0.3%
376 1
0.3%

SMR_WEEK
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
FRI
241 
TUE
144 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFRI
2nd rowFRI
3rd rowTUE
4th rowTUE
5th rowTUE

Common Values

ValueCountFrequency (%)
FRI 241
62.6%
TUE 144
37.4%

Length

2023-12-12T14:32:14.393759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:32:14.520672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
fri 241
62.6%
tue 144
37.4%

SMR_REGDATE
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
12/05/24
57 
11/03/10
56 
11/03/09
50 
10/04/22
46 
10/04/16
35 
Other values (27)
141 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique6 ?
Unique (%)1.6%

Sample

1st row07/09/04
2nd row07/09/04
3rd row07/10/12
4th row07/10/12
5th row07/10/12

Common Values

ValueCountFrequency (%)
12/05/24 57
14.8%
11/03/10 56
14.5%
11/03/09 50
13.0%
10/04/22 46
11.9%
10/04/16 35
9.1%
07/09/04 26
 
6.8%
12/04/05 18
 
4.7%
07/10/12 10
 
2.6%
08/05/19 9
 
2.3%
11/04/13 7
 
1.8%
Other values (22) 71
18.4%

Length

2023-12-12T14:32:14.624383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12/05/24 57
14.8%
11/03/10 56
14.5%
11/03/09 50
13.0%
10/04/22 46
11.9%
10/04/16 35
9.1%
07/09/04 26
 
6.8%
12/04/05 18
 
4.7%
07/10/12 10
 
2.6%
08/05/19 9
 
2.3%
11/04/13 7
 
1.8%
Other values (22) 71
18.4%

SMR_REGUSER
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
박병민
180 
채희선
96 
최지애
52 
유정석
27 
전영민
23 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
박병민 180
46.8%
채희선 96
24.9%
최지애 52
 
13.5%
유정석 27
 
7.0%
전영민 23
 
6.0%
운영자 7
 
1.8%

Length

2023-12-12T14:32:14.723500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:32:14.826290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
박병민 180
46.8%
채희선 96
24.9%
최지애 52
 
13.5%
유정석 27
 
7.0%
전영민 23
 
6.0%
운영자 7
 
1.8%

SMR_TITLE
Text

Distinct383
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-12T14:32:15.129008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length179
Median length100
Mean length64.579221
Min length1

Characters and Unicode

Total characters24863
Distinct characters289
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique381 ?
Unique (%)99.0%

Sample

1st row암환자 삶의 질 향상 및 질적관리를 위한 근거중심의 맞춤형 중재프로그램 개발
2nd rowUtility of circulating cell-free nucleic acids in lung cancer patients
3rd rowImaging and Therapy of Cancer using Tumor Targeting Bacteria
4th rowIdentification of a positive feedback regulatory loop between p53 and XAF1
5th rowFinding Cancer Biomarkers Using Bioinformatics Approach
ValueCountFrequency (%)
of 198
 
5.7%
and 144
 
4.2%
in 140
 
4.0%
cancer 119
 
3.4%
the 85
 
2.5%
for 65
 
1.9%
cell 51
 
1.5%
cells 34
 
1.0%
a 34
 
1.0%
by 34
 
1.0%
Other values (1373) 2564
73.9%
2023-12-12T14:32:15.643837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3089
 
12.4%
e 2095
 
8.4%
n 1765
 
7.1%
i 1746
 
7.0%
a 1674
 
6.7%
o 1500
 
6.0%
t 1446
 
5.8%
r 1215
 
4.9%
s 1038
 
4.2%
c 950
 
3.8%
Other values (279) 8345
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18714
75.3%
Space Separator 3090
 
12.4%
Uppercase Letter 1763
 
7.1%
Other Letter 738
 
3.0%
Decimal Number 216
 
0.9%
Dash Punctuation 174
 
0.7%
Other Punctuation 127
 
0.5%
Close Punctuation 20
 
0.1%
Open Punctuation 20
 
0.1%
Final Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 35
 
4.7%
암 23
 
3.1%
기 18
 
2.4%
한 17
 
2.3%
이 17
 
2.3%
전 17
 
2.3%
에 16
 
2.2%
연 15
 
2.0%
과 14
 
1.9%
구 13
 
1.8%
Other values (198) 553
74.9%
Lowercase Letter
ValueCountFrequency (%)
e 2095
11.2%
n 1765
9.4%
i 1746
9.3%
a 1674
 
8.9%
o 1500
 
8.0%
t 1446
 
7.7%
r 1215
 
6.5%
s 1038
 
5.5%
c 950
 
5.1%
l 936
 
5.0%
Other values (18) 4349
23.2%
Uppercase Letter
ValueCountFrequency (%)
C 186
 
10.6%
A 169
 
9.6%
R 141
 
8.0%
T 139
 
7.9%
I 110
 
6.2%
S 110
 
6.2%
N 109
 
6.2%
M 100
 
5.7%
P 85
 
4.8%
D 84
 
4.8%
Other values (16) 530
30.1%
Decimal Number
ValueCountFrequency (%)
1 65
30.1%
2 39
18.1%
3 37
17.1%
5 22
 
10.2%
4 14
 
6.5%
0 11
 
5.1%
9 10
 
4.6%
7 9
 
4.2%
6 5
 
2.3%
8 4
 
1.9%
Other Punctuation
ValueCountFrequency (%)
: 42
33.1%
, 32
25.2%
/ 21
16.5%
& 9
 
7.1%
. 7
 
5.5%
; 6
 
4.7%
? 6
 
4.7%
' 3
 
2.4%
! 1
 
0.8%
Space Separator
ValueCountFrequency (%)
3089
> 99.9%
　 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 19
95.0%
] 1
 
5.0%
Open Punctuation
ValueCountFrequency (%)
( 19
95.0%
[ 1
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 174
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20475
82.4%
Common 3648
 
14.7%
Hangul 738
 
3.0%
Greek 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 35
 
4.7%
암 23
 
3.1%
기 18
 
2.4%
한 17
 
2.3%
이 17
 
2.3%
전 17
 
2.3%
에 16
 
2.2%
연 15
 
2.0%
과 14
 
1.9%
구 13
 
1.8%
Other values (198) 553
74.9%
Latin
ValueCountFrequency (%)
e 2095
 
10.2%
n 1765
 
8.6%
i 1746
 
8.5%
a 1674
 
8.2%
o 1500
 
7.3%
t 1446
 
7.1%
r 1215
 
5.9%
s 1038
 
5.1%
c 950
 
4.6%
l 936
 
4.6%
Other values (42) 6110
29.8%
Common
ValueCountFrequency (%)
3089
84.7%
- 174
 
4.8%
1 65
 
1.8%
: 42
 
1.2%
2 39
 
1.1%
3 37
 
1.0%
, 32
 
0.9%
5 22
 
0.6%
/ 21
 
0.6%
) 19
 
0.5%
Other values (17) 108
 
3.0%
Greek
ValueCountFrequency (%)
κ 1
50.0%
α 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24121
97.0%
Hangul 738
 
3.0%
None 3
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3089
12.8%
e 2095
 
8.7%
n 1765
 
7.3%
i 1746
 
7.2%
a 1674
 
6.9%
o 1500
 
6.2%
t 1446
 
6.0%
r 1215
 
5.0%
s 1038
 
4.3%
c 950
 
3.9%
Other values (67) 7603
31.5%
Hangul
ValueCountFrequency (%)
의 35
 
4.7%
암 23
 
3.1%
기 18
 
2.4%
한 17
 
2.3%
이 17
 
2.3%
전 17
 
2.3%
에 16
 
2.2%
연 15
 
2.0%
과 14
 
1.9%
구 13
 
1.8%
Other values (198) 553
74.9%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%
None
ValueCountFrequency (%)
　 1
33.3%
κ 1
33.3%
α 1
33.3%

SMR_DAY
Real number (ℝ)

HIGH CORRELATION 

Distinct383
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20093074
Minimum20031017
Maximum20120529
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-12T14:32:15.787435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20031017
5-th percentile20070421
Q120080520
median20091013
Q320110218
95-th percentile20120319
Maximum20120529
Range89512
Interquartile range (IQR)29698

Descriptive statistics

Standard deviation16239.835
Coefficient of variation (CV)0.00080823051
Kurtosis-0.65449684
Mean20093074
Median Absolute Deviation (MAD)10588
Skewness-0.13929209
Sum7.7358334 × 109
Variance2.6373225 × 108
MonotonicityNot monotonic
2023-12-12T14:32:15.936972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20070911 2
 
0.5%
20070904 2
 
0.5%
20070831 1
 
0.3%
20090929 1
 
0.3%
20091117 1
 
0.3%
20091110 1
 
0.3%
20091103 1
 
0.3%
20091027 1
 
0.3%
20091020 1
 
0.3%
20091013 1
 
0.3%
Other values (373) 373
96.9%
ValueCountFrequency (%)
20031017 1
0.3%
20061024 1
0.3%
20061031 1
0.3%
20061107 1
0.3%
20061114 1
0.3%
20061121 1
0.3%
20061128 1
0.3%
20070302 1
0.3%
20070309 1
0.3%
20070313 1
0.3%
ValueCountFrequency (%)
20120529 1
0.3%
20120525 1
0.3%
20120522 1
0.3%
20120518 1
0.3%
20120515 1
0.3%
20120511 1
0.3%
20120508 1
0.3%
20120504 1
0.3%
20120427 1
0.3%
20120424 1
0.3%

SMR_TIME
Date

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum2023-12-12 08:00:00
Maximum2023-12-12 13:00:00
2023-12-12T14:32:16.097682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:32:16.239757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

SMR_POSITION
Text

Distinct222
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-12T14:32:16.490563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length111
Median length84
Mean length11.467532
Min length1

Characters and Unicode

Total characters4415
Distinct characters215
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique167 ?
Unique (%)43.4%

Sample

1st row암관리사업부
2nd row폐암연구과
3rd row전남대학교 핵의학과
4th row고려대학교
5th row이화여자대학교
ValueCountFrequency (%)
서울대학교 58
 
8.4%
of 26
 
3.8%
의과대학 25
 
3.6%
university 19
 
2.8%
고려대학교 14
 
2.0%
생명과학과 14
 
2.0%
연세대학교 14
 
2.0%
약학대학 11
 
1.6%
성균관대학교 11
 
1.6%
kaist 11
 
1.6%
Other values (290) 487
70.6%
2023-12-12T14:32:16.967163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 330
 
7.5%
306
 
6.9%
대 264
 
6.0%
교 185
 
4.2%
과 183
 
4.1%
e 157
 
3.6%
i 138
 
3.1%
연 122
 
2.8%
n 120
 
2.7%
o 115
 
2.6%
Other values (205) 2495
56.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2495
56.5%
Lowercase Letter 1297
29.4%
Space Separator 306
 
6.9%
Uppercase Letter 276
 
6.3%
Other Punctuation 30
 
0.7%
Open Punctuation 3
 
0.1%
Close Punctuation 3
 
0.1%
Dash Punctuation 3
 
0.1%
Decimal Number 1
 
< 0.1%
Final Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 330
 
13.2%
대 264
 
10.6%
교 185
 
7.4%
과 183
 
7.3%
연 122
 
4.9%
구 102
 
4.1%
암 81
 
3.2%
울 76
 
3.0%
서 76
 
3.0%
의 64
 
2.6%
Other values (149) 1012
40.6%
Lowercase Letter
ValueCountFrequency (%)
e 157
12.1%
i 138
10.6%
n 120
9.3%
o 115
8.9%
a 112
8.6%
t 94
 
7.2%
r 90
 
6.9%
s 75
 
5.8%
l 65
 
5.0%
c 61
 
4.7%
Other values (13) 270
20.8%
Uppercase Letter
ValueCountFrequency (%)
C 33
12.0%
S 26
9.4%
I 24
 
8.7%
T 24
 
8.7%
U 23
 
8.3%
M 21
 
7.6%
K 20
 
7.2%
A 19
 
6.9%
H 13
 
4.7%
B 12
 
4.3%
Other values (12) 61
22.1%
Other Punctuation
ValueCountFrequency (%)
, 14
46.7%
. 8
26.7%
/ 4
 
13.3%
& 3
 
10.0%
？ 1
 
3.3%
Space Separator
ValueCountFrequency (%)
306
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2495
56.5%
Latin 1573
35.6%
Common 347
 
7.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 330
 
13.2%
대 264
 
10.6%
교 185
 
7.4%
과 183
 
7.3%
연 122
 
4.9%
구 102
 
4.1%
암 81
 
3.2%
울 76
 
3.0%
서 76
 
3.0%
의 64
 
2.6%
Other values (149) 1012
40.6%
Latin
ValueCountFrequency (%)
e 157
 
10.0%
i 138
 
8.8%
n 120
 
7.6%
o 115
 
7.3%
a 112
 
7.1%
t 94
 
6.0%
r 90
 
5.7%
s 75
 
4.8%
l 65
 
4.1%
c 61
 
3.9%
Other values (35) 546
34.7%
Common
ValueCountFrequency (%)
306
88.2%
, 14
 
4.0%
. 8
 
2.3%
/ 4
 
1.2%
( 3
 
0.9%
) 3
 
0.9%
& 3
 
0.9%
- 3
 
0.9%
1 1
 
0.3%
？ 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2495
56.5%
ASCII 1918
43.4%
None 1
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 330
 
13.2%
대 264
 
10.6%
교 185
 
7.4%
과 183
 
7.3%
연 122
 
4.9%
구 102
 
4.1%
암 81
 
3.2%
울 76
 
3.0%
서 76
 
3.0%
의 64
 
2.6%
Other values (149) 1012
40.6%
ASCII
ValueCountFrequency (%)
306
16.0%
e 157
 
8.2%
i 138
 
7.2%
n 120
 
6.3%
o 115
 
6.0%
a 112
 
5.8%
t 94
 
4.9%
r 90
 
4.7%
s 75
 
3.9%
l 65
 
3.4%
Other values (44) 646
33.7%
None
ValueCountFrequency (%)
？ 1
100.0%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%

SMR_SPEAKER
Text

Distinct328
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-12T14:32:17.420709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.4051948
Min length2

Characters and Unicode

Total characters1311
Distinct characters197
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)71.7%

Sample

1st row윤영호
2nd row윤경아
3rd row민정준
4th row지성길
5th row이상혁
ValueCountFrequency (%)
이현숙 3
 
0.7%
이혁준 3
 
0.7%
권영근 3
 
0.7%
지성길 3
 
0.7%
이윤실 3
 
0.7%
김재범 2
 
0.5%
차혁진 2
 
0.5%
설재홍 2
 
0.5%
김자은 2
 
0.5%
김윤기 2
 
0.5%
Other values (339) 387
93.9%
2023-12-12T14:32:18.061929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 75
 
5.7%
이 67
 
5.1%
정 33
 
2.5%
영 31
 
2.4%
27
 
2.1%
성 27
 
2.1%
박 26
 
2.0%
경 25
 
1.9%
호 24
 
1.8%
재 22
 
1.7%
Other values (187) 954
72.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1062
81.0%
Lowercase Letter 152
 
11.6%
Uppercase Letter 60
 
4.6%
Space Separator 27
 
2.1%
Other Punctuation 10
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 75
 
7.1%
이 67
 
6.3%
정 33
 
3.1%
영 31
 
2.9%
성 27
 
2.5%
박 26
 
2.4%
경 25
 
2.4%
호 24
 
2.3%
재 22
 
2.1%
윤 22
 
2.1%
Other values (145) 710
66.9%
Uppercase Letter
ValueCountFrequency (%)
M 7
11.7%
D 6
 
10.0%
S 5
 
8.3%
J 5
 
8.3%
Y 5
 
8.3%
A 4
 
6.7%
H 3
 
5.0%
N 3
 
5.0%
P 3
 
5.0%
R 2
 
3.3%
Other values (11) 17
28.3%
Lowercase Letter
ValueCountFrequency (%)
a 21
13.8%
e 18
11.8%
i 17
11.2%
n 13
 
8.6%
o 11
 
7.2%
r 9
 
5.9%
s 8
 
5.3%
h 7
 
4.6%
u 7
 
4.6%
l 7
 
4.6%
Other values (9) 34
22.4%
Space Separator
ValueCountFrequency (%)
27
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1062
81.0%
Latin 212
 
16.2%
Common 37
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 75
 
7.1%
이 67
 
6.3%
정 33
 
3.1%
영 31
 
2.9%
성 27
 
2.5%
박 26
 
2.4%
경 25
 
2.4%
호 24
 
2.3%
재 22
 
2.1%
윤 22
 
2.1%
Other values (145) 710
66.9%
Latin
ValueCountFrequency (%)
a 21
 
9.9%
e 18
 
8.5%
i 17
 
8.0%
n 13
 
6.1%
o 11
 
5.2%
r 9
 
4.2%
s 8
 
3.8%
h 7
 
3.3%
M 7
 
3.3%
u 7
 
3.3%
Other values (30) 94
44.3%
Common
ValueCountFrequency (%)
27
73.0%
. 10
 
27.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1062
81.0%
ASCII 249
 
19.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 75
 
7.1%
이 67
 
6.3%
정 33
 
3.1%
영 31
 
2.9%
성 27
 
2.5%
박 26
 
2.4%
경 25
 
2.4%
호 24
 
2.3%
재 22
 
2.1%
윤 22
 
2.1%
Other values (145) 710
66.9%
ASCII
ValueCountFrequency (%)
27
 
10.8%
a 21
 
8.4%
e 18
 
7.2%
i 17
 
6.8%
n 13
 
5.2%
o 11
 
4.4%
. 10
 
4.0%
r 9
 
3.6%
s 8
 
3.2%
h 7
 
2.8%
Other values (32) 108
43.4%

SMR_SENIOR
Text

Distinct102
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-12T14:32:18.376919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.8233766
Min length1

Characters and Unicode

Total characters1087
Distinct characters100
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

Unique40 ?
Unique (%)10.4%

Sample

1st row박은철
2nd row이진수
3rd row.
4th row.
5th row.
ValueCountFrequency (%)
95
24.7%
김수열 23
 
6.0%
김용연 21
 
5.5%
김인후 13
 
3.4%
홍경만 8
 
2.1%
김종헌 8
 
2.1%
박은철 7
 
1.8%
이연수 7
 
1.8%
이승훈 7
 
1.8%
이강현 6
 
1.6%
Other values (89) 190
49.4%
2023-12-12T14:32:18.914249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 220
20.2%
김 96
 
8.8%
이 60
 
5.5%
수 42
 
3.9%
용 30
 
2.8%
연 30
 
2.8%
박 28
 
2.6%
경 24
 
2.2%
호 23
 
2.1%
열 23
 
2.1%
Other values (90) 511
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 864
79.5%
Other Punctuation 221
 
20.3%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 96
 
11.1%
이 60
 
6.9%
수 42
 
4.9%
용 30
 
3.5%
연 30
 
3.5%
박 28
 
3.2%
경 24
 
2.8%
호 23
 
2.7%
열 23
 
2.7%
정 21
 
2.4%
Other values (87) 487
56.4%
Other Punctuation
ValueCountFrequency (%)
. 220
99.5%
/ 1
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 864
79.5%
Common 223
 
20.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 96
 
11.1%
이 60
 
6.9%
수 42
 
4.9%
용 30
 
3.5%
연 30
 
3.5%
박 28
 
3.2%
경 24
 
2.8%
호 23
 
2.7%
열 23
 
2.7%
정 21
 
2.4%
Other values (87) 487
56.4%
Common
ValueCountFrequency (%)
. 220
98.7%
- 2
 
0.9%
/ 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 864
79.5%
ASCII 223
 
20.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 220
98.7%
- 2
 
0.9%
/ 1
 
0.4%
Hangul
ValueCountFrequency (%)
김 96
 
11.1%
이 60
 
6.9%
수 42
 
4.9%
용 30
 
3.5%
연 30
 
3.5%
박 28
 
3.2%
경 24
 
2.8%
호 23
 
2.7%
열 23
 
2.7%
정 21
 
2.4%
Other values (87) 487
56.4%

SMR_LINK
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing385
Missing (%)100.0%
Memory size3.5 KiB

Interactions

2023-12-12T14:32:13.333601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:32:13.077824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:32:13.496948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:32:13.208243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:32:19.075212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SMR_IDSMR_WEEKSMR_REGDATESMR_REGUSERSMR_DAYSMR_TIME
SMR_ID1.0000.7640.9640.9000.8540.667
SMR_WEEK0.7641.0000.8070.7790.1130.913
SMR_REGDATE0.9640.8071.0000.9830.9500.924
SMR_REGUSER0.9000.7790.9831.0000.8510.823
SMR_DAY0.8540.1130.9500.8511.0000.758
SMR_TIME0.6670.9130.9240.8230.7581.000
2023-12-12T14:32:19.227028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SMR_WEEKSMR_REGDATESMR_REGUSER
SMR_WEEK1.0000.6420.581
SMR_REGDATE0.6421.0000.875
SMR_REGUSER0.5810.8751.000
2023-12-12T14:32:19.333119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SMR_IDSMR_DAYSMR_WEEKSMR_REGDATESMR_REGUSER
SMR_ID1.0000.9500.5960.7660.753
SMR_DAY0.9501.0000.1270.6930.708
SMR_WEEK0.5960.1271.0000.6420.581
SMR_REGDATE0.7660.6930.6421.0000.875
SMR_REGUSER0.7530.7080.5810.8751.000

Missing values

2023-12-12T14:32:13.651521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:32:13.858929image/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

SMR_IDSMR_WEEKSMR_REGDATESMR_REGUSERSMR_TITLESMR_DAYSMR_TIMESMR_POSITIONSMR_SPEAKERSMR_SENIORSMR_LINK
048FRI07/09/04채희선암환자 삶의 질 향상 및 질적관리를 위한 근거중심의 맞춤형 중재프로그램 개발2007083108:10암관리사업부윤영호박은철<NA>
149FRI07/09/04채희선Utility of circulating cell-free nucleic acids in lung cancer patients2007090708:10폐암연구과윤경아이진수<NA>
250TUE07/10/12채희선Imaging and Therapy of Cancer using Tumor Targeting Bacteria2007091808:00전남대학교 핵의학과민정준.<NA>
351TUE07/10/12채희선Identification of a positive feedback regulatory loop between p53 and XAF12007100208:00고려대학교지성길.<NA>
452TUE07/10/12채희선Finding Cancer Biomarkers Using Bioinformatics Approach2007100908:00이화여자대학교이상혁.<NA>
553TUE07/10/12채희선Inhibition of HSP27-Mediated Radio- and Chemoresistance by a Novel PKCdelta-V5 Heptapeptide2007101608:00방사선의학연구소이윤실.<NA>
654FRI07/10/12채희선Improving Her-2/neu detection method in Human Breast Cancer2007091408:10유방내분비암연구과이은숙박상윤<NA>
755FRI07/10/12채희선COX-2 regulates EGFR via Erk activation and causes gefitinib resistance2007092108:10폐암연구과표홍렬이건국<NA>
856FRI07/10/12채희선종양은행의 현황과 전망2007092808:10폐암연구과이건국표홍렬<NA>
957FRI07/10/12채희선Survivin Promoter-Driven Oncolytic Adenovirus for Cancer Gene Therapy2007100508:10비뇨생식기암연구과이상진정진수<NA>
SMR_IDSMR_WEEKSMR_REGDATESMR_REGUSERSMR_TITLESMR_DAYSMR_TIMESMR_POSITIONSMR_SPEAKERSMR_SENIORSMR_LINK
375376FRI12/05/24최지애Epithelial-mesenchymal transition of cancer cells regulated by p53 and Wnt signaling2012032308:00연대치대육종인김용연<NA>
376377FRI12/05/24최지애Genomic instability and Radiosensitivity2012033008:30자궁암연구과김주영이강현<NA>
377378FRI12/05/24최지애Glioma migration on the myelin2012040608:00특수암연구과박종배유헌<NA>
378379FRI12/05/24최지애Transcription termination by RNA polymerase II2012041308:00서울대김민규박은정<NA>
379380FRI12/05/24최지애Clonal Mesenchymal Stem Cells for the Treatment of Graft-Versus-Host Disease (GVHD)2012042008:30인하대송순욱엄현석<NA>
380381FRI12/05/24최지애ER Stress, GRASP, and Unconventional Trafficking2012042708:00연세의대 약리학이민구김용연<NA>
381382FRI12/05/24최지애종양은행의 운영 현황2012050408:00종양은행이건국한지연<NA>
382383FRI12/05/24최지애Cancer Therapy and Animal Models in NCC2012051108:00비뇨기생식기암연구과이상진이강현<NA>
383384FRI12/05/24최지애b2-spectrin: A way of communication from cytoskeleton to regulatory signal2012051808:00방사선의학연구과김상수심재갈<NA>
384385FRI12/05/24최지애암세포에서 유전자 카피수 증가와 항암제 민감성과의 관련성2012052508:30분자종양학연구과홍경만김수열<NA>