Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells6135
Missing cells (%)5.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory106.0 B

Variable types

Numeric2
Categorical1
Text9

Dataset

Description2018년부터 2022년까지 울산과학기술원이 보유한 논문 현황(논문제목, 저널명, Vol. 등)을 작성한 자료입니다.
Author울산과학기술원
URLhttps://www.data.go.kr/data/15034330/fileData.do

Alerts

유형 is highly imbalanced (80.4%)Imbalance
has 2654 (26.5%) missing valuesMissing
시작페이지 has 163 (1.6%) missing valuesMissing
종료페이지 has 2860 (28.6%) missing valuesMissing
DOI(디지털식별자) has 373 (3.7%) missing valuesMissing

Reproduction

Analysis started2023-12-12 16:03:01.353001
Analysis finished2023-12-12 16:03:04.931600
Duration3.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

게재년도
Real number (ℝ)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.5466
Minimum2008
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:03:05.014107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2012
Q12015
median2018
Q32020
95-th percentile2022
Maximum2022
Range14
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3066481
Coefficient of variation (CV)0.0016389451
Kurtosis-0.66311707
Mean2017.5466
Median Absolute Deviation (MAD)3
Skewness-0.46169929
Sum20175466
Variance10.933922
MonotonicityNot monotonic
2023-12-13T01:03:05.203018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2022 1234
12.3%
2021 1222
12.2%
2019 1081
10.8%
2018 1029
10.3%
2017 950
9.5%
2016 868
8.7%
2020 867
8.7%
2015 775
7.8%
2014 604
6.0%
2013 492
 
4.9%
Other values (5) 878
8.8%
ValueCountFrequency (%)
2008 3
 
< 0.1%
2009 70
 
0.7%
2010 160
 
1.6%
2011 254
 
2.5%
2012 391
3.9%
2013 492
4.9%
2014 604
6.0%
2015 775
7.8%
2016 868
8.7%
2017 950
9.5%
ValueCountFrequency (%)
2022 1234
12.3%
2021 1222
12.2%
2020 867
8.7%
2019 1081
10.8%
2018 1029
10.3%
2017 950
9.5%
2016 868
8.7%
2015 775
7.8%
2014 604
6.0%
2013 492
 
4.9%

게재월
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean6.5283208
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:03:05.382208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4463977
Coefficient of variation (CV)0.52791488
Kurtosis-1.2021524
Mean6.5283208
Median Absolute Deviation (MAD)3
Skewness-0.021052913
Sum65120
Variance11.877657
MonotonicityNot monotonic
2023-12-13T01:03:05.522075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 901
9.0%
8 869
8.7%
1 864
8.6%
10 864
8.6%
12 837
8.4%
9 833
8.3%
4 825
8.2%
11 808
8.1%
3 803
8.0%
5 801
8.0%
Other values (2) 1570
15.7%
ValueCountFrequency (%)
1 864
8.6%
2 775
7.8%
3 803
8.0%
4 825
8.2%
5 801
8.0%
6 901
9.0%
7 795
8.0%
8 869
8.7%
9 833
8.3%
10 864
8.6%
ValueCountFrequency (%)
12 837
8.4%
11 808
8.1%
10 864
8.6%
9 833
8.3%
8 869
8.7%
7 795
8.0%
6 901
9.0%
5 801
8.0%
4 825
8.2%
3 803
8.0%

유형
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Article
9696 
Review
 
304

Length

Max length7
Median length7
Mean length6.9696
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Article 9696
97.0%
Review 304
 
3.0%

Length

2023-12-13T01:03:05.658943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:03:05.764078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
article 9696
97.0%
review 304
 
3.0%
Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T01:03:06.063982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length369
Median length175
Mean length99.3803
Min length6

Characters and Unicode

Total characters993803
Distinct characters706
Distinct categories16 ?
Distinct scripts5 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9998 ?
Unique (%)> 99.9%

Sample

1st rowHigh Resolution Photoexcitation Measurements Exacerbate the Long-Standing Fe XVII Oscillator Strength Problem
2nd rowDesign for Sharing Emotional Touches during Phone Calls: A Quantitative Evaluation of Four Tactile Representations
3rd rowNi catalysts for dry methane reforming prepared by A-site exsolution on mesoporous defect spinel magnesium aluminate
4th rowMorphological and Optical Engineering for High-Performance Polymer Solar Cells
5th rowA multiple target chemosensor for the sequential fluorescence detection of Zn2+ and S2- and the colorimetric detection of Fe3+/2+ in aqueous media and living cells
ValueCountFrequency (%)
of 6781
 
5.4%
and 4042
 
3.2%
for 3228
 
2.6%
in 2840
 
2.2%
the 2451
 
1.9%
a 2313
 
1.8%
on 1491
 
1.2%
with 1391
 
1.1%
by 894
 
0.7%
using 854
 
0.7%
Other values (21505) 100112
79.2%
2023-12-13T01:03:06.548727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
116480
 
11.7%
e 86991
 
8.8%
i 71468
 
7.2%
o 66865
 
6.7%
a 65092
 
6.5%
n 64073
 
6.4%
t 61981
 
6.2%
r 55417
 
5.6%
s 44206
 
4.4%
l 40562
 
4.1%
Other values (696) 320668
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 781535
78.6%
Space Separator 116483
 
11.7%
Uppercase Letter 63473
 
6.4%
Dash Punctuation 10642
 
1.1%
Other Letter 10254
 
1.0%
Decimal Number 5308
 
0.5%
Other Punctuation 4086
 
0.4%
Close Punctuation 822
 
0.1%
Open Punctuation 822
 
0.1%
Math Symbol 260
 
< 0.1%
Other values (6) 118
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
366
 
3.6%
258
 
2.5%
237
 
2.3%
200
 
2.0%
164
 
1.6%
162
 
1.6%
129
 
1.3%
128
 
1.2%
126
 
1.2%
126
 
1.2%
Other values (563) 8358
81.5%
Lowercase Letter
ValueCountFrequency (%)
e 86991
11.1%
i 71468
 
9.1%
o 66865
 
8.6%
a 65092
 
8.3%
n 64073
 
8.2%
t 61981
 
7.9%
r 55417
 
7.1%
s 44206
 
5.7%
l 40562
 
5.2%
c 36005
 
4.6%
Other values (28) 188875
24.2%
Uppercase Letter
ValueCountFrequency (%)
S 6506
 
10.3%
C 5897
 
9.3%
A 4972
 
7.8%
P 4336
 
6.8%
E 3917
 
6.2%
M 3893
 
6.1%
T 3487
 
5.5%
D 3486
 
5.5%
I 3180
 
5.0%
O 3088
 
4.9%
Other values (19) 20711
32.6%
Other Punctuation
ValueCountFrequency (%)
: 1325
32.4%
, 1217
29.8%
/ 663
16.2%
. 430
 
10.5%
' 136
 
3.3%
? 81
 
2.0%
" 51
 
1.2%
% 49
 
1.2%
@ 43
 
1.1%
& 25
 
0.6%
Other values (6) 66
 
1.6%
Math Symbol
ValueCountFrequency (%)
+ 118
45.4%
> 47
 
18.1%
< 46
 
17.7%
= 37
 
14.2%
× 3
 
1.2%
2
 
0.8%
1
 
0.4%
1
 
0.4%
1
 
0.4%
1
 
0.4%
Other values (3) 3
 
1.2%
Decimal Number
ValueCountFrequency (%)
2 1458
27.5%
1 946
17.8%
3 788
14.8%
0 679
12.8%
5 384
 
7.2%
4 377
 
7.1%
6 206
 
3.9%
8 178
 
3.4%
7 158
 
3.0%
9 134
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 10575
99.4%
36
 
0.3%
27
 
0.3%
2
 
< 0.1%
2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 753
91.6%
] 57
 
6.9%
6
 
0.7%
} 4
 
0.5%
2
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 753
91.6%
[ 57
 
6.9%
6
 
0.7%
{ 4
 
0.5%
2
 
0.2%
Space Separator
ValueCountFrequency (%)
116480
> 99.9%
2
 
< 0.1%
  1
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
62
79.5%
16
 
20.5%
Initial Punctuation
ValueCountFrequency (%)
16
61.5%
10
38.5%
Other Symbol
ValueCountFrequency (%)
° 6
85.7%
1
 
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 844902
85.0%
Common 138541
 
13.9%
Hangul 10248
 
1.0%
Greek 106
 
< 0.1%
Han 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
366
 
3.6%
258
 
2.5%
237
 
2.3%
200
 
2.0%
164
 
1.6%
162
 
1.6%
129
 
1.3%
128
 
1.2%
126
 
1.2%
126
 
1.2%
Other values (557) 8352
81.5%
Common
ValueCountFrequency (%)
116480
84.1%
- 10575
 
7.6%
2 1458
 
1.1%
: 1325
 
1.0%
, 1217
 
0.9%
1 946
 
0.7%
3 788
 
0.6%
) 753
 
0.5%
( 753
 
0.5%
0 679
 
0.5%
Other values (56) 3567
 
2.6%
Latin
ValueCountFrequency (%)
e 86991
 
10.3%
i 71468
 
8.5%
o 66865
 
7.9%
a 65092
 
7.7%
n 64073
 
7.6%
t 61981
 
7.3%
r 55417
 
6.6%
s 44206
 
5.2%
l 40562
 
4.8%
c 36005
 
4.3%
Other values (44) 252242
29.9%
Greek
ValueCountFrequency (%)
β 33
31.1%
γ 21
19.8%
α 19
17.9%
δ 14
13.2%
μ 5
 
4.7%
π 4
 
3.8%
ω 2
 
1.9%
Δ 2
 
1.9%
θ 2
 
1.9%
κ 1
 
0.9%
Other values (3) 3
 
2.8%
Han
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 983212
98.9%
Hangul 10248
 
1.0%
Punctuation 186
 
< 0.1%
None 142
 
< 0.1%
Math Operators 7
 
< 0.1%
CJK 6
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116480
 
11.8%
e 86991
 
8.8%
i 71468
 
7.3%
o 66865
 
6.8%
a 65092
 
6.6%
n 64073
 
6.5%
t 61981
 
6.3%
r 55417
 
5.6%
s 44206
 
4.5%
l 40562
 
4.1%
Other values (82) 310077
31.5%
Hangul
ValueCountFrequency (%)
366
 
3.6%
258
 
2.5%
237
 
2.3%
200
 
2.0%
164
 
1.6%
162
 
1.6%
129
 
1.3%
128
 
1.2%
126
 
1.2%
126
 
1.2%
Other values (557) 8352
81.5%
Punctuation
ValueCountFrequency (%)
62
33.3%
36
19.4%
27
14.5%
16
 
8.6%
16
 
8.6%
13
 
7.0%
10
 
5.4%
2
 
1.1%
2
 
1.1%
2
 
1.1%
None
ValueCountFrequency (%)
β 33
23.2%
γ 21
14.8%
α 19
13.4%
δ 14
9.9%
° 6
 
4.2%
6
 
4.2%
6
 
4.2%
· 6
 
4.2%
μ 5
 
3.5%
π 4
 
2.8%
Other values (13) 22
15.5%
Math Operators
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Arrows
ValueCountFrequency (%)
1
100.0%
Distinct9459
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T01:03:06.956037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1024
Median length516
Mean length153.4907
Min length3

Characters and Unicode

Total characters1534907
Distinct characters290
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9193 ?
Unique (%)91.9%

Sample

1st rowKuhn, S[Kuehn, Steffen]; Shah, C[Shah, Chintan]; Lopez-Urrutia, JRC[Lopez-Urrutia, Jose R. Crespo]; Fujii, K[Fujii, Keisuke]; Steinbrugge, R[Steinbruegge, Rene]; Stierhof, J[Stierhof, Jakob]; Togawa, M[Togawa, Moto]; Harman, Z[Harman, Zoltan]; Oreshkina, NS[Oreshkina, Natalia S.]; Cheung, C[Cheung, Charles]; Kozlov, MG[Kozlov, Mikhail G.]; Porsev, SG[Porsev, Sergey G.]; Safronova, MS[Safronova, Marianna S.]; Berengut, JC[Berengut, Julian C.]; Rosner, M[Rosner, Michael]; Bissinger, M[Bissinger, Matthias]; Ballhausen, R[Ballhausen, Ralf]; Hell, N[Hell, Natalie]; Park, S[Park, SungNam]; Chung, M[Chung, Moses]; Hoesch, M[Hoesch, Moritz]; Seltmann, J[Seltmann, Joern]; Surzhykov, AS[Surzhykov, Andrey S.]; Yerokhin, VA[Yerokhin, Vladimir A.]; Wilms, J[Wilms, Joern]; Porter, FS[Porter, F. Scott]; Stohlker, T[Stoehlker, Thomas]; Keitel, CH[Keitel, Christoph H.]; Pfeifer, T[Pfeifer, Thomas]; Brown, GV[Brown, Gregory, V]; Leutenegger, MA[Leutenegger, Maurice A.]; Bernitt, S[Bernitt, Sven]
2nd rowPark, YW[Park, Young-Woo]; Bae, SH[Bae, Seok-Hyung]; Nam, TJ[Nam, Tek-Jin]
3rd rowCho, E[Cho, Eunkyung]; Lee, YH[Lee, Young-Hee]; Kim, H[Kim, Hyunjoung]; Jang, EJ[Jang, Eun Jeong]; Kwak, JH[Kwak, Ja Hun]; Lee, K[Lee, Kyubock]; Ko, CH[Ko, Chang Hyun]; Yoon, WL[Yoon, Wang Lai]
4th rowKo, SJ[Ko, Seo-Jin]; Heo, J[Heo, Jungwoo]; Lee, BH[Lee, Byoung Hoon]; Ha, SR[Ha, Su Ryong]; Bandyopadhyay, S[Bandyopadhyay, Sujoy]; Cho, HJ[Cho, Hong Joo]; Choi, H[Choi, Hyosung]; Kim, JY[Kim, Jin Young]
5th rowYun, JY[Yun, Jin Yeong]; Chae, JB[Chae, Ju Byeong]; Kim, M[Kim, Mingeun]; Lim, MH[Lim, Mi Hee]; Kim, C[Kim, Cheal]
ValueCountFrequency (%)
kim 8680
 
4.2%
lee 6310
 
3.0%
park 3563
 
1.7%
choi 1793
 
0.9%
jeong 1371
 
0.7%
cho 1295
 
0.6%
young 1276
 
0.6%
jung 1191
 
0.6%
1173
 
0.6%
s 1119
 
0.5%
Other values (32376) 179434
86.6%
2023-12-13T01:03:07.512570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
197351
 
12.9%
, 123068
 
8.0%
n 100113
 
6.5%
o 82915
 
5.4%
e 75419
 
4.9%
a 73165
 
4.8%
i 63627
 
4.1%
[ 62594
 
4.1%
] 62562
 
4.1%
; 52972
 
3.5%
Other values (280) 641121
41.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 699206
45.6%
Uppercase Letter 310904
20.3%
Space Separator 197365
 
12.9%
Other Punctuation 186621
 
12.2%
Open Punctuation 62596
 
4.1%
Close Punctuation 62564
 
4.1%
Dash Punctuation 11366
 
0.7%
Other Letter 4178
 
0.3%
Decimal Number 97
 
< 0.1%
Final Punctuation 7
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
297
 
7.1%
207
 
5.0%
160
 
3.8%
142
 
3.4%
122
 
2.9%
101
 
2.4%
93
 
2.2%
89
 
2.1%
84
 
2.0%
79
 
1.9%
Other values (182) 2804
67.1%
Lowercase Letter
ValueCountFrequency (%)
n 100113
14.3%
o 82915
11.9%
e 75419
10.8%
a 73165
10.5%
i 63627
9.1%
g 48857
7.0%
u 47991
6.9%
h 37499
 
5.4%
m 29864
 
4.3%
r 23713
 
3.4%
Other values (30) 116043
16.6%
Uppercase Letter
ValueCountFrequency (%)
J 37245
12.0%
S 36818
11.8%
K 35969
11.6%
H 29989
9.6%
Y 21034
 
6.8%
L 19235
 
6.2%
C 19177
 
6.2%
M 15715
 
5.1%
P 11612
 
3.7%
B 10841
 
3.5%
Other values (18) 73269
23.6%
Decimal Number
ValueCountFrequency (%)
8 23
23.7%
0 16
16.5%
2 16
16.5%
3 10
10.3%
1 8
 
8.2%
6 6
 
6.2%
7 6
 
6.2%
9 4
 
4.1%
5 4
 
4.1%
4 4
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 123068
65.9%
; 52972
28.4%
. 10430
 
5.6%
& 126
 
0.1%
# 24
 
< 0.1%
1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
197351
> 99.9%
9
 
< 0.1%
  3
 
< 0.1%
2
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
[ 62594
> 99.9%
( 2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
] 62562
> 99.9%
) 2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 11309
99.5%
57
 
0.5%
Final Punctuation
ValueCountFrequency (%)
7
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Math Symbol
ValueCountFrequency (%)
| 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1010111
65.8%
Common 520618
33.9%
Hangul 4178
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
297
 
7.1%
207
 
5.0%
160
 
3.8%
142
 
3.4%
122
 
2.9%
101
 
2.4%
93
 
2.2%
89
 
2.1%
84
 
2.0%
79
 
1.9%
Other values (182) 2804
67.1%
Latin
ValueCountFrequency (%)
n 100113
 
9.9%
o 82915
 
8.2%
e 75419
 
7.5%
a 73165
 
7.2%
i 63627
 
6.3%
g 48857
 
4.8%
u 47991
 
4.8%
h 37499
 
3.7%
J 37245
 
3.7%
S 36818
 
3.6%
Other values (59) 406462
40.2%
Common
ValueCountFrequency (%)
197351
37.9%
, 123068
23.6%
[ 62594
 
12.0%
] 62562
 
12.0%
; 52972
 
10.2%
- 11309
 
2.2%
. 10430
 
2.0%
& 126
 
< 0.1%
57
 
< 0.1%
# 24
 
< 0.1%
Other values (19) 125
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1530554
99.7%
Hangul 4178
 
0.3%
None 98
 
< 0.1%
Punctuation 76
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
197351
 
12.9%
, 123068
 
8.0%
n 100113
 
6.5%
o 82915
 
5.4%
e 75419
 
4.9%
a 73165
 
4.8%
i 63627
 
4.2%
[ 62594
 
4.1%
] 62562
 
4.1%
; 52972
 
3.5%
Other values (64) 636768
41.6%
Hangul
ValueCountFrequency (%)
297
 
7.1%
207
 
5.0%
160
 
3.8%
142
 
3.4%
122
 
2.9%
101
 
2.4%
93
 
2.2%
89
 
2.1%
84
 
2.0%
79
 
1.9%
Other values (182) 2804
67.1%
Punctuation
ValueCountFrequency (%)
57
75.0%
9
 
11.8%
7
 
9.2%
2
 
2.6%
1
 
1.3%
None
ValueCountFrequency (%)
í 15
15.3%
é 14
14.3%
á 12
12.2%
ü 8
8.2%
ö 8
8.2%
ł 7
7.1%
Ö 6
 
6.1%
ø 5
 
5.1%
Ø 4
 
4.1%
ć 4
 
4.1%
Other values (8) 15
15.3%
Number Forms
ValueCountFrequency (%)
1
100.0%
Distinct2237
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T01:03:07.826120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length125
Median length79
Mean length25.6395
Min length2

Characters and Unicode

Total characters256395
Distinct characters304
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1204 ?
Unique (%)12.0%

Sample

1st rowPHYSICAL REVIEW LETTERS
2nd rowARCHIVES OF DESIGN RESEARCH
3rd rowAPPLIED CATALYSIS A-GENERAL
4th rowACS APPLIED MATERIALS & INTERFACES
5th rowPHOTOCHEMICAL & PHOTOBIOLOGICAL SCIENCES
ValueCountFrequency (%)
of 2640
 
7.9%
journal 2355
 
7.1%
and 1439
 
4.3%
materials 1406
 
4.2%
chemistry 795
 
2.4%
science 666
 
2.0%
applied 604
 
1.8%
575
 
1.7%
acs 546
 
1.6%
energy 538
 
1.6%
Other values (1751) 21677
65.2%
2023-12-13T01:03:08.221263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 24867
 
9.7%
23290
 
9.1%
A 22584
 
8.8%
N 20417
 
8.0%
I 18190
 
7.1%
O 16575
 
6.5%
C 15654
 
6.1%
R 15593
 
6.1%
S 15592
 
6.1%
T 14285
 
5.6%
Other values (294) 69348
27.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 223986
87.4%
Space Separator 23290
 
9.1%
Lowercase Letter 4416
 
1.7%
Other Letter 3382
 
1.3%
Other Punctuation 668
 
0.3%
Dash Punctuation 607
 
0.2%
Decimal Number 28
 
< 0.1%
Open Punctuation 9
 
< 0.1%
Close Punctuation 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
325
 
9.6%
241
 
7.1%
195
 
5.8%
170
 
5.0%
152
 
4.5%
144
 
4.3%
123
 
3.6%
105
 
3.1%
103
 
3.0%
82
 
2.4%
Other values (226) 1742
51.5%
Uppercase Letter
ValueCountFrequency (%)
E 24867
11.1%
A 22584
10.1%
N 20417
9.1%
I 18190
 
8.1%
O 16575
 
7.4%
C 15654
 
7.0%
R 15593
 
7.0%
S 15592
 
7.0%
T 14285
 
6.4%
L 12337
 
5.5%
Other values (17) 47892
21.4%
Lowercase Letter
ValueCountFrequency (%)
e 495
11.2%
n 460
10.4%
o 448
10.1%
a 444
10.1%
i 354
 
8.0%
r 304
 
6.9%
t 269
 
6.1%
l 246
 
5.6%
c 241
 
5.5%
s 237
 
5.4%
Other values (14) 918
20.8%
Other Punctuation
ValueCountFrequency (%)
& 602
90.1%
; 29
 
4.3%
: 20
 
3.0%
, 9
 
1.3%
/ 5
 
0.7%
· 2
 
0.3%
. 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 21
75.0%
1 3
 
10.7%
0 1
 
3.6%
4 1
 
3.6%
9 1
 
3.6%
3 1
 
3.6%
Space Separator
ValueCountFrequency (%)
23290
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 607
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 228402
89.1%
Common 24611
 
9.6%
Hangul 3377
 
1.3%
Han 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
325
 
9.6%
241
 
7.1%
195
 
5.8%
170
 
5.0%
152
 
4.5%
144
 
4.3%
123
 
3.6%
105
 
3.1%
103
 
3.1%
82
 
2.4%
Other values (223) 1737
51.4%
Latin
ValueCountFrequency (%)
E 24867
10.9%
A 22584
9.9%
N 20417
 
8.9%
I 18190
 
8.0%
O 16575
 
7.3%
C 15654
 
6.9%
R 15593
 
6.8%
S 15592
 
6.8%
T 14285
 
6.3%
L 12337
 
5.4%
Other values (41) 52308
22.9%
Common
ValueCountFrequency (%)
23290
94.6%
- 607
 
2.5%
& 602
 
2.4%
; 29
 
0.1%
2 21
 
0.1%
: 20
 
0.1%
, 9
 
< 0.1%
( 9
 
< 0.1%
) 9
 
< 0.1%
/ 5
 
< 0.1%
Other values (7) 10
 
< 0.1%
Han
ValueCountFrequency (%)
2
40.0%
2
40.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 253007
98.7%
Hangul 3377
 
1.3%
None 6
 
< 0.1%
CJK 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 24867
 
9.8%
23290
 
9.2%
A 22584
 
8.9%
N 20417
 
8.1%
I 18190
 
7.2%
O 16575
 
6.6%
C 15654
 
6.2%
R 15593
 
6.2%
S 15592
 
6.2%
T 14285
 
5.6%
Other values (56) 65960
26.1%
Hangul
ValueCountFrequency (%)
325
 
9.6%
241
 
7.1%
195
 
5.8%
170
 
5.0%
152
 
4.5%
144
 
4.3%
123
 
3.6%
105
 
3.1%
103
 
3.1%
82
 
2.4%
Other values (223) 1737
51.4%
None
ValueCountFrequency (%)
É 4
66.7%
· 2
33.3%
CJK
ValueCountFrequency (%)
2
40.0%
2
40.0%
1
20.0%


Text

Distinct735
Distinct (%)7.4%
Missing42
Missing (%)0.4%
Memory size156.2 KiB
2023-12-13T01:03:08.572528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.0457923
Min length1

Characters and Unicode

Total characters20372
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

Unique243 ?
Unique (%)2.4%

Sample

1st row124
2nd row29
3rd row602
4th row11
5th row18
ValueCountFrequency (%)
8 318
 
3.2%
9 303
 
3.0%
10 298
 
3.0%
7 283
 
2.8%
11 278
 
2.8%
6 271
 
2.7%
12 259
 
2.6%
13 219
 
2.2%
5 217
 
2.2%
4 190
 
1.9%
Other values (725) 7322
73.5%
2023-12-13T01:03:09.113095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4392
21.6%
2 2912
14.3%
3 2237
11.0%
5 1966
9.7%
4 1955
9.6%
6 1544
 
7.6%
0 1383
 
6.8%
8 1360
 
6.7%
7 1332
 
6.5%
9 1290
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20371
> 99.9%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4392
21.6%
2 2912
14.3%
3 2237
11.0%
5 1966
9.7%
4 1955
9.6%
6 1544
 
7.6%
0 1383
 
6.8%
8 1360
 
6.7%
7 1332
 
6.5%
9 1290
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20372
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4392
21.6%
2 2912
14.3%
3 2237
11.0%
5 1966
9.7%
4 1955
9.6%
6 1544
 
7.6%
0 1383
 
6.8%
8 1360
 
6.7%
7 1332
 
6.5%
9 1290
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4392
21.6%
2 2912
14.3%
3 2237
11.0%
5 1966
9.7%
4 1955
9.6%
6 1544
 
7.6%
0 1383
 
6.8%
8 1360
 
6.7%
7 1332
 
6.5%
9 1290
 
6.3%


Text

MISSING 

Distinct164
Distinct (%)2.2%
Missing2654
Missing (%)26.5%
Memory size156.2 KiB
2023-12-13T01:03:09.469024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.4086578
Min length1

Characters and Unicode

Total characters10348
Distinct characters26
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

Unique87 ?
Unique (%)1.2%

Sample

1st row22
2nd row2
3rd row5
4th row1
5th row45
ValueCountFrequency (%)
1 911
 
12.4%
2 649
 
8.8%
3 601
 
8.2%
4 563
 
7.7%
5 443
 
6.0%
6 436
 
5.9%
7 302
 
4.1%
9 302
 
4.1%
10 291
 
4.0%
11 282
 
3.8%
Other values (154) 2566
34.9%
2023-12-13T01:03:09.978826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2838
27.4%
2 1690
16.3%
3 1176
11.4%
4 1134
 
11.0%
5 735
 
7.1%
6 678
 
6.6%
8 521
 
5.0%
7 513
 
5.0%
9 509
 
4.9%
0 499
 
4.8%
Other values (16) 55
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10293
99.5%
Lowercase Letter 22
 
0.2%
Uppercase Letter 22
 
0.2%
Dash Punctuation 11
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2838
27.6%
2 1690
16.4%
3 1176
11.4%
4 1134
 
11.0%
5 735
 
7.1%
6 678
 
6.6%
8 521
 
5.1%
7 513
 
5.0%
9 509
 
4.9%
0 499
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
B 6
27.3%
J 5
22.7%
M 5
22.7%
S 2
 
9.1%
N 1
 
4.5%
D 1
 
4.5%
I 1
 
4.5%
E 1
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
a 8
36.4%
n 5
22.7%
y 3
 
13.6%
u 2
 
9.1%
r 2
 
9.1%
o 1
 
4.5%
v 1
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10304
99.6%
Latin 44
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8
18.2%
B 6
13.6%
J 5
11.4%
n 5
11.4%
M 5
11.4%
y 3
 
6.8%
S 2
 
4.5%
u 2
 
4.5%
r 2
 
4.5%
N 1
 
2.3%
Other values (5) 5
11.4%
Common
ValueCountFrequency (%)
1 2838
27.5%
2 1690
16.4%
3 1176
11.4%
4 1134
 
11.0%
5 735
 
7.1%
6 678
 
6.6%
8 521
 
5.1%
7 513
 
5.0%
9 509
 
4.9%
0 499
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2838
27.4%
2 1690
16.3%
3 1176
11.4%
4 1134
 
11.0%
5 735
 
7.1%
6 678
 
6.6%
8 521
 
5.0%
7 513
 
5.0%
9 509
 
4.9%
0 499
 
4.8%
Other values (16) 55
 
0.5%

시작페이지
Text

MISSING 

Distinct6061
Distinct (%)61.6%
Missing163
Missing (%)1.6%
Memory size156.2 KiB
2023-12-13T01:03:10.394680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length3.9729592
Min length1

Characters and Unicode

Total characters39082
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

Unique4809 ?
Unique (%)48.9%

Sample

1st row225001
2nd row95
3rd row117694
4th row4705
5th row166
ValueCountFrequency (%)
1 235
 
2.4%
23 19
 
0.2%
25 19
 
0.2%
8 19
 
0.2%
53 19
 
0.2%
37 17
 
0.2%
63 16
 
0.2%
67 16
 
0.2%
50 16
 
0.2%
99 16
 
0.2%
Other values (6049) 9461
96.0%
2023-12-13T01:03:10.953664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7168
18.3%
2 4521
11.6%
0 4197
10.7%
3 3900
10.0%
4 3506
9.0%
5 3378
8.6%
7 3138
8.0%
6 3028
7.7%
8 2803
 
7.2%
9 2787
 
7.1%
Other values (46) 656
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38426
98.3%
Lowercase Letter 386
 
1.0%
Uppercase Letter 220
 
0.6%
Space Separator 24
 
0.1%
Dash Punctuation 17
 
< 0.1%
Math Symbol 5
 
< 0.1%
Other Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 206
53.4%
a 80
 
20.7%
b 27
 
7.0%
v 9
 
2.3%
c 7
 
1.8%
d 7
 
1.8%
y 6
 
1.6%
i 6
 
1.6%
m 4
 
1.0%
w 4
 
1.0%
Other values (13) 30
 
7.8%
Uppercase Letter
ValueCountFrequency (%)
A 40
18.2%
S 29
13.2%
E 22
10.0%
P 22
10.0%
L 18
8.2%
U 17
7.7%
N 17
7.7%
F 16
 
7.3%
G 8
 
3.6%
C 5
 
2.3%
Other values (9) 26
11.8%
Decimal Number
ValueCountFrequency (%)
1 7168
18.7%
2 4521
11.8%
0 4197
10.9%
3 3900
10.1%
4 3506
9.1%
5 3378
8.8%
7 3138
8.2%
6 3028
7.9%
8 2803
 
7.3%
9 2787
 
7.3%
Space Separator
ValueCountFrequency (%)
24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38476
98.4%
Latin 606
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 206
34.0%
a 80
 
13.2%
A 40
 
6.6%
S 29
 
4.8%
b 27
 
4.5%
E 22
 
3.6%
P 22
 
3.6%
L 18
 
3.0%
U 17
 
2.8%
N 17
 
2.8%
Other values (32) 128
21.1%
Common
ValueCountFrequency (%)
1 7168
18.6%
2 4521
11.8%
0 4197
10.9%
3 3900
10.1%
4 3506
9.1%
5 3378
8.8%
7 3138
8.2%
6 3028
7.9%
8 2803
 
7.3%
9 2787
 
7.2%
Other values (4) 50
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7168
18.3%
2 4521
11.6%
0 4197
10.7%
3 3900
10.0%
4 3506
9.0%
5 3378
8.6%
7 3138
8.0%
6 3028
7.7%
8 2803
 
7.2%
9 2787
 
7.1%
Other values (46) 656
 
1.7%

종료페이지
Text

MISSING 

Distinct3900
Distinct (%)54.6%
Missing2860
Missing (%)28.6%
Memory size156.2 KiB
2023-12-13T01:03:11.435216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.4530812
Min length1

Characters and Unicode

Total characters24655
Distinct characters26
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2773 ?
Unique (%)38.8%

Sample

1st row107
2nd row4711
3rd row176
4th row13778
5th row199
ValueCountFrequency (%)
8 29
 
0.4%
9 26
 
0.4%
6 25
 
0.4%
10 22
 
0.3%
7 18
 
0.3%
12 18
 
0.3%
5 17
 
0.2%
86 17
 
0.2%
85 17
 
0.2%
66 16
 
0.2%
Other values (3890) 6935
97.1%
2023-12-13T01:03:12.405538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4075
16.5%
2 2943
11.9%
3 2612
10.6%
4 2484
10.1%
5 2304
9.3%
6 2182
8.9%
7 2053
8.3%
8 1991
8.1%
0 1986
8.1%
9 1913
7.8%
Other values (16) 112
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24543
99.5%
Uppercase Letter 82
 
0.3%
Math Symbol 16
 
0.1%
Dash Punctuation 8
 
< 0.1%
Other Punctuation 5
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 25
30.5%
E 21
25.6%
F 12
14.6%
S 9
 
11.0%
H 3
 
3.7%
W 2
 
2.4%
B 2
 
2.4%
C 2
 
2.4%
D 2
 
2.4%
J 2
 
2.4%
Other values (2) 2
 
2.4%
Decimal Number
ValueCountFrequency (%)
1 4075
16.6%
2 2943
12.0%
3 2612
10.6%
4 2484
10.1%
5 2304
9.4%
6 2182
8.9%
7 2053
8.4%
8 1991
8.1%
0 1986
8.1%
9 1913
7.8%
Math Symbol
ValueCountFrequency (%)
+ 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%
Lowercase Letter
ValueCountFrequency (%)
i 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24572
99.7%
Latin 83
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4075
16.6%
2 2943
12.0%
3 2612
10.6%
4 2484
10.1%
5 2304
9.4%
6 2182
8.9%
7 2053
8.4%
8 1991
8.1%
0 1986
8.1%
9 1913
7.8%
Other values (3) 29
 
0.1%
Latin
ValueCountFrequency (%)
A 25
30.1%
E 21
25.3%
F 12
14.5%
S 9
 
10.8%
H 3
 
3.6%
W 2
 
2.4%
B 2
 
2.4%
C 2
 
2.4%
D 2
 
2.4%
J 2
 
2.4%
Other values (3) 3
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24655
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4075
16.5%
2 2943
11.9%
3 2612
10.6%
4 2484
10.1%
5 2304
9.3%
6 2182
8.9%
7 2053
8.3%
8 1991
8.1%
0 1986
8.1%
9 1913
7.8%
Other values (16) 112
 
0.5%
Distinct9626
Distinct (%)> 99.9%
Missing373
Missing (%)3.7%
Memory size156.2 KiB
2023-12-13T01:03:12.704621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length37
Mean length24.084346
Min length13

Characters and Unicode

Total characters231860
Distinct characters70
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9625 ?
Unique (%)> 99.9%

Sample

1st row10.1103/PhysRevLett.124.225001
2nd row10.15187/adr.2016.05.29.2.95
3rd row10.1016/j.apcata.2020.117694
4th row10.1021/acsami.8b16490
5th row10.1039/c8pp00408k
ValueCountFrequency (%)
10.1109/tcad.2021.3123178 2
 
< 0.1%
10 2
 
< 0.1%
10.1016/j.econmod.2022.106024 1
 
< 0.1%
10.1016/j.ijheatmasstransfer.2022.123229 1
 
< 0.1%
10.1002/elps.201000082 1
 
< 0.1%
10.1103/physrevlett.124.225001 1
 
< 0.1%
10.3389/fnhum.2017.00008 1
 
< 0.1%
10.1002/aenm.201401933 1
 
< 0.1%
10.1016/j.watres.2013.07.023 1
 
< 0.1%
10.3390/s18010154 1
 
< 0.1%
Other values (9619) 9619
99.9%
2023-12-13T01:03:13.163232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 40021
17.3%
1 38588
16.6%
. 25727
11.1%
2 16689
 
7.2%
/ 10196
 
4.4%
3 8881
 
3.8%
6 8140
 
3.5%
9 6735
 
2.9%
8 6535
 
2.8%
4 6464
 
2.8%
Other values (60) 63884
27.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 144074
62.1%
Lowercase Letter 44365
 
19.1%
Other Punctuation 35925
 
15.5%
Uppercase Letter 4290
 
1.9%
Dash Punctuation 3126
 
1.3%
Space Separator 34
 
< 0.1%
Connector Punctuation 16
 
< 0.1%
Open Punctuation 15
 
< 0.1%
Close Punctuation 15
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5272
11.9%
c 4955
11.2%
s 4376
9.9%
j 3842
 
8.7%
e 3744
 
8.4%
n 2699
 
6.1%
m 2630
 
5.9%
t 2186
 
4.9%
o 1982
 
4.5%
r 1788
 
4.0%
Other values (16) 10891
24.5%
Uppercase Letter
ValueCountFrequency (%)
C 539
12.6%
S 459
10.7%
T 443
10.3%
E 398
9.3%
A 305
 
7.1%
P 293
 
6.8%
M 246
 
5.7%
R 237
 
5.5%
J 230
 
5.4%
I 169
 
3.9%
Other values (16) 971
22.6%
Decimal Number
ValueCountFrequency (%)
0 40021
27.8%
1 38588
26.8%
2 16689
11.6%
3 8881
 
6.2%
6 8140
 
5.6%
9 6735
 
4.7%
8 6535
 
4.5%
4 6464
 
4.5%
5 6015
 
4.2%
7 6006
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 25727
71.6%
/ 10196
 
28.4%
: 2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 3126
100.0%
Space Separator
ValueCountFrequency (%)
34
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 183205
79.0%
Latin 48655
 
21.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5272
 
10.8%
c 4955
 
10.2%
s 4376
 
9.0%
j 3842
 
7.9%
e 3744
 
7.7%
n 2699
 
5.5%
m 2630
 
5.4%
t 2186
 
4.5%
o 1982
 
4.1%
r 1788
 
3.7%
Other values (42) 15181
31.2%
Common
ValueCountFrequency (%)
0 40021
21.8%
1 38588
21.1%
. 25727
14.0%
2 16689
9.1%
/ 10196
 
5.6%
3 8881
 
4.8%
6 8140
 
4.4%
9 6735
 
3.7%
8 6535
 
3.6%
4 6464
 
3.5%
Other values (8) 15229
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40021
17.3%
1 38588
16.6%
. 25727
11.1%
2 16689
 
7.2%
/ 10196
 
4.4%
3 8881
 
3.8%
6 8140
 
3.5%
9 6735
 
2.9%
8 6535
 
2.8%
4 6464
 
2.8%
Other values (60) 63884
27.6%
Distinct2104
Distinct (%)21.1%
Missing18
Missing (%)0.2%
Memory size156.2 KiB
2023-12-13T01:03:13.494083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.9994991
Min length8

Characters and Unicode

Total characters89833
Distinct characters14
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

Unique1061 ?
Unique (%)10.6%

Sample

1st row0031-9007
2nd row1226-8046
3rd row0926-860X
4th row1944-8244
5th row1474-905X
ValueCountFrequency (%)
1944-8244 198
 
2.0%
2045-2322 197
 
2.0%
2050-7488 190
 
1.9%
1936-0851 180
 
1.8%
0935-9648 149
 
1.5%
2041-1723 145
 
1.5%
1530-6984 108
 
1.1%
1616-301x 107
 
1.1%
1614-6832 106
 
1.1%
2040-3364 93
 
0.9%
Other values (2094) 8509
85.2%
2023-12-13T01:03:13.939387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12145
13.5%
- 9982
11.1%
1 9662
10.8%
2 9536
10.6%
4 7825
8.7%
3 7538
8.4%
6 6940
7.7%
9 6829
7.6%
5 6612
7.4%
8 6167
6.9%
Other values (4) 6597
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79038
88.0%
Dash Punctuation 9982
 
11.1%
Uppercase Letter 806
 
0.9%
Lowercase Letter 6
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12145
15.4%
1 9662
12.2%
2 9536
12.1%
4 7825
9.9%
3 7538
9.5%
6 6940
8.8%
9 6829
8.6%
5 6612
8.4%
8 6167
7.8%
7 5784
7.3%
Dash Punctuation
ValueCountFrequency (%)
- 9982
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 806
100.0%
Lowercase Letter
ValueCountFrequency (%)
x 6
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 89021
99.1%
Latin 812
 
0.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12145
13.6%
- 9982
11.2%
1 9662
10.9%
2 9536
10.7%
4 7825
8.8%
3 7538
8.5%
6 6940
7.8%
9 6829
7.7%
5 6612
7.4%
8 6167
6.9%
Other values (2) 5785
6.5%
Latin
ValueCountFrequency (%)
X 806
99.3%
x 6
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12145
13.5%
- 9982
11.1%
1 9662
10.8%
2 9536
10.6%
4 7825
8.7%
3 7538
8.4%
6 6940
7.7%
9 6829
7.6%
5 6612
7.4%
8 6167
6.9%
Other values (4) 6597
7.3%

Interactions

2023-12-13T01:03:04.068427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:03.807986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:04.194863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:03.941220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:03:14.034394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
게재년도게재월유형
게재년도1.0000.1320.071
게재월0.1321.0000.008
유형0.0710.0081.000
2023-12-13T01:03:14.131706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
게재년도게재월유형
게재년도1.000-0.0670.055
게재월-0.0671.0000.006
유형0.0550.0061.000

Missing values

2023-12-13T01:03:04.375124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:03:04.601100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T01:03:04.799794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

게재년도게재월유형논문제목저자명저널명시작페이지종료페이지DOI(디지털식별자)ISSN(국제표준연속간행물번호)
343320206ArticleHigh Resolution Photoexcitation Measurements Exacerbate the Long-Standing Fe XVII Oscillator Strength ProblemKuhn, S[Kuehn, Steffen]; Shah, C[Shah, Chintan]; Lopez-Urrutia, JRC[Lopez-Urrutia, Jose R. Crespo]; Fujii, K[Fujii, Keisuke]; Steinbrugge, R[Steinbruegge, Rene]; Stierhof, J[Stierhof, Jakob]; Togawa, M[Togawa, Moto]; Harman, Z[Harman, Zoltan]; Oreshkina, NS[Oreshkina, Natalia S.]; Cheung, C[Cheung, Charles]; Kozlov, MG[Kozlov, Mikhail G.]; Porsev, SG[Porsev, Sergey G.]; Safronova, MS[Safronova, Marianna S.]; Berengut, JC[Berengut, Julian C.]; Rosner, M[Rosner, Michael]; Bissinger, M[Bissinger, Matthias]; Ballhausen, R[Ballhausen, Ralf]; Hell, N[Hell, Natalie]; Park, S[Park, SungNam]; Chung, M[Chung, Moses]; Hoesch, M[Hoesch, Moritz]; Seltmann, J[Seltmann, Joern]; Surzhykov, AS[Surzhykov, Andrey S.]; Yerokhin, VA[Yerokhin, Vladimir A.]; Wilms, J[Wilms, Joern]; Porter, FS[Porter, F. Scott]; Stohlker, T[Stoehlker, Thomas]; Keitel, CH[Keitel, Christoph H.]; Pfeifer, T[Pfeifer, Thomas]; Brown, GV[Brown, Gregory, V]; Leutenegger, MA[Leutenegger, Maurice A.]; Bernitt, S[Bernitt, Sven]PHYSICAL REVIEW LETTERS12422225001<NA>10.1103/PhysRevLett.124.2250010031-9007
863420165ArticleDesign for Sharing Emotional Touches during Phone Calls: A Quantitative Evaluation of Four Tactile RepresentationsPark, YW[Park, Young-Woo]; Bae, SH[Bae, Seok-Hyung]; Nam, TJ[Nam, Tek-Jin]ARCHIVES OF DESIGN RESEARCH2929510710.15187/adr.2016.05.29.2.951226-8046
339820207ArticleNi catalysts for dry methane reforming prepared by A-site exsolution on mesoporous defect spinel magnesium aluminateCho, E[Cho, Eunkyung]; Lee, YH[Lee, Young-Hee]; Kim, H[Kim, Hyunjoung]; Jang, EJ[Jang, Eun Jeong]; Kwak, JH[Kwak, Ja Hun]; Lee, K[Lee, Kyubock]; Ko, CH[Ko, Chang Hyun]; Yoon, WL[Yoon, Wang Lai]APPLIED CATALYSIS A-GENERAL602<NA>117694<NA>10.1016/j.apcata.2020.1176940926-860X
524820192ArticleMorphological and Optical Engineering for High-Performance Polymer Solar CellsKo, SJ[Ko, Seo-Jin]; Heo, J[Heo, Jungwoo]; Lee, BH[Lee, Byoung Hoon]; Ha, SR[Ha, Su Ryong]; Bandyopadhyay, S[Bandyopadhyay, Sujoy]; Cho, HJ[Cho, Hong Joo]; Choi, H[Choi, Hyosung]; Kim, JY[Kim, Jin Young]ACS APPLIED MATERIALS & INTERFACES1154705471110.1021/acsami.8b164901944-8244
543220191ArticleA multiple target chemosensor for the sequential fluorescence detection of Zn2+ and S2- and the colorimetric detection of Fe3+/2+ in aqueous media and living cellsYun, JY[Yun, Jin Yeong]; Chae, JB[Chae, Ju Byeong]; Kim, M[Kim, Mingeun]; Lim, MH[Lim, Mi Hee]; Kim, C[Kim, Cheal]PHOTOCHEMICAL & PHOTOBIOLOGICAL SCIENCES18116617610.1039/c8pp00408k1474-905X
9306201511ArticlePhase transition-induced band edge engineering of BiVO4 to split pure water under visible lightJo, WJ[Jo, Won Jun]; Kang, HJ[Kang, Hyun Joon]; Kong, KJ[Kong, Ki-Jeong]; Lee, YS[Lee, Yun Seog]; Park, H[Park, Hunmin]; Lee, Y[Lee, Younghye]; Buonassisi, T[Buonassisi, Tonio]; Gleason, KK[Gleason, Karen K. ]; Lee, JS[Lee, Jae Sung]PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA11245137741377810.1073/pnas.15096741120027-8424
1160620129Article자아와 타자 사이: 고골 작품의 소러시아Yoon, S[Yoon, Saera]슬라브연구283177199<NA>1225-0406
106420224ArticleEncoding Enantiomeric Molecular Chiralities on Graphene Basal PlanesMeng, YQ[Meng, Yongqiang]; Fan, JBA[Fan, Jingbiao]; Wang, MH[Wang, Meihui]; Gong, WB[Gong, Wenbin]; Zhang, JP[Zhang, Jinping]; Ma, JP[Ma, Junpeng]; Mi, HY[Mi, Hongyu]; Huang, Y[Huang, Yan]; Yang, S[Yang, Shu]; Ruoff, RS[Ruoff, Rodney S.]; Geng, JX[Geng, Jianxin]ANGEWANDTE CHEMIE-INTERNATIONAL EDITION6115e202117815<NA>10.1002/anie.2021178151433-7851
89220225ArticleAll-sky search for gravitational wave emission from scalar boson clouds around spinning black holes in LIGO O3 dataAbbott, R.[Abbott, R.]; Kwak, K[Kwak, Kyujin]; The LIGO Scientific Collaborat[The LIGO Scientific Collaboration]; the Virgo Collaboration[the Virgo Collaboration]; the KAGRA Collaboration[ the KAGRA Collaboration]PHYSICAL REVIEW D10510102001<NA>10.1103/PhysRevD.105.1020012470-0010
887220168ArticleCRTC Potentiates Light-independent timeless Transcription to Sustain Circadian Rhythms in DrosophilaKim, M[Kim, Minkyung]; Lee, H[Lee, Hoyeon]; Hur, JH[Hur, Jin-Hoe]; Choe, J[Choe, Joonho]; Lim, C[Lim, Chunghun]SCIENTIFIC REPORTS6<NA>32113<NA>10.1038/srep321132045-2322
게재년도게재월유형논문제목저자명저널명시작페이지종료페이지DOI(디지털식별자)ISSN(국제표준연속간행물번호)
289720212ArticleEye Fixation-Related Potentials during Visual Search on Acquaintance and Newly-Learned FacesLee, S[Lee, Seungji]; Lee, D[Lee, Doyoung]; Gil, H[Gil, Hyunjae]; Oakley, I[Oakley, Ian]; Cho, YS[Cho, Yang Seok]; Kim, SP[Kim, Sung-Phil]BRAIN SCIENCES112218<NA>10.3390/brainsci110202182076-3425
72120227ArticleDevelopment of ensemble machine learning models for evaluating seismic demands of steel moment framesNguyen, HD[Nguyen, Hoang D.]; Kim, J[Kim, JunHee]; Shin, M[Shin, Myoungsu]STEEL AND COMPOSITE STRUCTURES441496310.12989/scs.2022.44.1.0491229-9367
12348200911ArticleLayer-by-Layer-Assembled Multilayer Films for Transcutaneous Drug and Vaccine DeliverySu, XF[Su, Xingfang]; Kim, BS[Kim, Byeong-Su]; Kim, SR[Kim, Sara R.]; Hammond, PT[Hammond, Paula T.]; Irvine, DJ[Irvine, Darrell J.]ACS NANO3113719372910.1021/nn900928u1936-0851
1222120108ArticleA Docking Model Based on Mass Spectrometric and Biochemical Data Describes Phage Packaging Motor IncorporationFu, CY[Fu, Chi-yu]; Uetrecht, C[Uetrecht, Charlotte]; Kang, S[Kang, Sebyung]; Morais, MC[Morais, Marc C.]; Heck, AJR[Heck, Albert J. R.]; Walter, MR[Walter, Mark R.]; Prevelige, PE[Prevelige, Peter E., Jr.]MOLECULAR & CELLULAR PROTEOMICS981764177310.1074/mcp.M900625-MCP2001535-9476
5573201811ArticleOptimal tuning of a Brownian information engine operating in a nonequilibrium steady statePaneru, G[Paneru, Govind]; Lee, DY[Lee, Dong Yun]; Park, JM[Park, Jong-Min]; Park, JT[Park, Jin Tae]; Noh, JD[Noh, Jae Dong]; Pak, HK[Pak, Hyuk Kyu]PHYSICAL REVIEW E98552119<NA>10.1103/PhysRevE.98.0521192470-0045
8125201611ArticleStrong Optical Dipole Force Exerted on Molecules Having Low Rotational TemperatureSun, XN[Sun, Xing Nan]; Jin, BG[Jin, Byung Gwun]; Kim, LY[Kim, Lee Yeong]; Kim, BJ[Kim, Bong Jun]; Zhao, BS[Zhao, Bum Suk]CHEMPHYSCHEM17223701370810.1002/cphc.2016008381439-4235
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775220171ArticleThe case for electron re-acceleration at galaxy cluster shocksvan Weeren, RJ[van Weeren, Reinout J.]; Andrade-Santos, F[Andrade-Santos, Felipe]; Dawson, WA[Dawson, William A.]; Golovich, N[Golovich, Nathan]; Lal, DV[Lal, Dharam V.]; Kang, H[Kang, Hyesung]; Ryu, D[Ryu, Dongsu]; Bruggen, M[Brueggen, Marcus]; Ogrean, GA[Ogrean, Georgiana A.]; Forman, WR[Forman, William R.]; Jones, C[Jones, Christine]; Placco, VM[Placco, Vinicius M.]; Santucci, RM[Santucci, Rafael M.]; Wittman, D[Wittman, David]; Jee, MJ[Jee, M. James]; Kraft, RP[Kraft, Ralph P.]; Sobral, D[Sobral, David]; Stroe, A[Stroe, Andra]; Fogarty, K[Fogarty, Kevin]NATURE ASTRONOMY1<NA>5<NA>10.1038/s41550-016-00052397-3366
86520226ArticleQuantile Autoencoder With Abnormality Accumulation for Anomaly Detection of Multivariate Sensor DataRyu, S[Ryu, Seunghyoung]; Yim, J[Yim, Jiyeon]; Seo, J[Seo, Junghoon]; Yu, Y[Yu, Yonggyun]; Seo, H[Seo, Hogeon]IEEE ACCESS10<NA>704287043910.1109/ACCESS.2022.31874262169-3536
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