Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells6
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory488.3 KiB
Average record size in memory50.0 B

Variable types

Numeric2
Text3

Dataset

Description한국기계연구원 전자도서관의 기계기술 전문도서 소장 목록 정보(제어번호, 서명, 접근주소, 저자, 출판년도 등)
URLhttps://www.data.go.kr/data/15050393/fileData.do

Alerts

제어번호 is highly overall correlated with 출판년도High correlation
출판년도 is highly overall correlated with 제어번호High correlation
제어번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:53:44.834675
Analysis finished2023-12-12 06:53:46.739532
Duration1.9 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

제어번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41939.095
Minimum29702
Maximum51744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:53:46.829983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29702
5-th percentile32534.95
Q134917.75
median43550.5
Q346433.25
95-th percentile51214.05
Maximum51744
Range22042
Interquartile range (IQR)11515.5

Descriptive statistics

Standard deviation6659.1685
Coefficient of variation (CV)0.15878188
Kurtosis-1.3190282
Mean41939.095
Median Absolute Deviation (MAD)6412
Skewness-0.17370101
Sum4.1939095 × 108
Variance44344525
MonotonicityNot monotonic
2023-12-12T15:53:46.990216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33463 1
 
< 0.1%
49931 1
 
< 0.1%
39822 1
 
< 0.1%
50237 1
 
< 0.1%
45919 1
 
< 0.1%
45019 1
 
< 0.1%
49711 1
 
< 0.1%
37588 1
 
< 0.1%
42468 1
 
< 0.1%
50138 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
29702 1
< 0.1%
29703 1
< 0.1%
29704 1
< 0.1%
29705 1
< 0.1%
29706 1
< 0.1%
29707 1
< 0.1%
29708 1
< 0.1%
29709 1
< 0.1%
29710 1
< 0.1%
29711 1
< 0.1%
ValueCountFrequency (%)
51744 1
< 0.1%
51743 1
< 0.1%
51742 1
< 0.1%
51741 1
< 0.1%
51740 1
< 0.1%
51739 1
< 0.1%
51738 1
< 0.1%
51737 1
< 0.1%
51736 1
< 0.1%
51735 1
< 0.1%

서명
Text

Distinct9727
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:53:47.378987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length400
Median length269
Mean length105.9769
Min length5

Characters and Unicode

Total characters1059769
Distinct characters116
Distinct categories16 ?
Distinct scripts6 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9458 ?
Unique (%)94.6%

Sample

1st rowDiscontinuous Systems [electronic resource] Lyapunov Analysis and Robust Synthesis under Uncertainty Conditions
2nd rowDesign and Implementation of Sigma Delta Modulators for Class D Audio Amplifiers using Differential Pairs [electronic resource] by Nuno Pereira, Nuno Paulino
3rd row4th EAI International Conference on Robotic Sensor Networks [electronic resource]
4th rowDistributed Control and Optimization of Networked Microgrids [electronic resource] : A Multi-Agent System Based Approach
5th rowREST: Advanced Research Topics and Practical Applications [electronic resource] edited by Cesare Pautasso, Erik Wilde, Rosa Alarcon
ValueCountFrequency (%)
electronic 9024
 
6.8%
resource 8989
 
6.8%
and 6590
 
5.0%
of 3626
 
2.7%
by 3196
 
2.4%
in 2340
 
1.8%
the 1936
 
1.5%
for 1834
 
1.4%
edited 1633
 
1.2%
1589
 
1.2%
Other values (18332) 91200
69.1%
2023-12-12T15:53:48.096749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
129722
 
12.2%
e 106918
 
10.1%
n 74892
 
7.1%
o 69723
 
6.6%
i 69498
 
6.6%
r 67405
 
6.4%
a 55842
 
5.3%
t 55350
 
5.2%
c 54335
 
5.1%
s 45576
 
4.3%
Other values (106) 330508
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 801085
75.6%
Space Separator 129722
 
12.2%
Uppercase Letter 84073
 
7.9%
Other Punctuation 12823
 
1.2%
Decimal Number 9749
 
0.9%
Open Punctuation 9407
 
0.9%
Close Punctuation 9406
 
0.9%
Dash Punctuation 3339
 
0.3%
Other Letter 73
 
< 0.1%
Final Punctuation 42
 
< 0.1%
Other values (6) 50
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 106918
13.3%
n 74892
9.3%
o 69723
8.7%
i 69498
8.7%
r 67405
8.4%
a 55842
 
7.0%
t 55350
 
6.9%
c 54335
 
6.8%
s 45576
 
5.7%
l 37806
 
4.7%
Other values (18) 163740
20.4%
Uppercase Letter
ValueCountFrequency (%)
S 9015
 
10.7%
C 8308
 
9.9%
A 7836
 
9.3%
M 7045
 
8.4%
P 5605
 
6.7%
I 5582
 
6.6%
T 4655
 
5.5%
E 4386
 
5.2%
D 4272
 
5.1%
R 3509
 
4.2%
Other values (17) 23860
28.4%
Other Letter
ValueCountFrequency (%)
큄 15
20.5%
짰 11
15.1%
챦 8
11.0%
첬 6
 
8.2%
창 3
 
4.1%
첩 3
 
4.1%
찾 3
 
4.1%
탑 3
 
4.1%
챔 3
 
4.1%
偈 3
 
4.1%
Other values (11) 15
20.5%
Other Punctuation
ValueCountFrequency (%)
, 8427
65.7%
. 2347
 
18.3%
: 1584
 
12.4%
' 138
 
1.1%
& 128
 
1.0%
/ 123
 
1.0%
" 36
 
0.3%
; 32
 
0.2%
＆ 3
 
< 0.1%
# 2
 
< 0.1%
Other values (2) 3
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 3266
33.5%
0 2170
22.3%
1 2170
22.3%
3 596
 
6.1%
4 439
 
4.5%
5 287
 
2.9%
9 274
 
2.8%
6 194
 
2.0%
8 179
 
1.8%
7 174
 
1.8%
Close Punctuation
ValueCountFrequency (%)
] 9004
95.7%
) 402
 
4.3%
Open Punctuation
ValueCountFrequency (%)
[ 9004
95.7%
( 403
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 3317
99.3%
― 22
 
0.7%
Final Punctuation
ValueCountFrequency (%)
’ 31
73.8%
” 11
 
26.2%
Math Symbol
ValueCountFrequency (%)
+ 20
87.0%
⇒ 3
 
13.0%
Initial Punctuation
ValueCountFrequency (%)
“ 11
78.6%
‘ 3
 
21.4%
Other Symbol
ValueCountFrequency (%)
® 6
85.7%
™ 1
 
14.3%
Space Separator
ValueCountFrequency (%)
129722
100.0%
Control
ValueCountFrequency (%)
 4
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%
Other Number
ValueCountFrequency (%)
² 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 885155
83.5%
Common 174538
 
16.5%
Hangul 68
 
< 0.1%
Han 5
 
< 0.1%
Cyrillic 2
 
< 0.1%
Greek 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 106918
12.1%
n 74892
 
8.5%
o 69723
 
7.9%
i 69498
 
7.9%
r 67405
 
7.6%
a 55842
 
6.3%
t 55350
 
6.3%
c 54335
 
6.1%
s 45576
 
5.1%
l 37806
 
4.3%
Other values (43) 247810
28.0%
Common
ValueCountFrequency (%)
129722
74.3%
] 9004
 
5.2%
[ 9004
 
5.2%
, 8427
 
4.8%
- 3317
 
1.9%
2 3266
 
1.9%
. 2347
 
1.3%
0 2170
 
1.2%
1 2170
 
1.2%
: 1584
 
0.9%
Other values (30) 3527
 
2.0%
Hangul
ValueCountFrequency (%)
큄 15
22.1%
짰 11
16.2%
챦 8
11.8%
첬 6
 
8.8%
창 3
 
4.4%
첩 3
 
4.4%
찾 3
 
4.4%
탑 3
 
4.4%
챔 3
 
4.4%
킁 2
 
2.9%
Other values (8) 11
16.2%
Han
ValueCountFrequency (%)
偈 3
60.0%
觀 1
 
20.0%
詮 1
 
20.0%
Cyrillic
ValueCountFrequency (%)
С 2
100.0%
Greek
ValueCountFrequency (%)
π 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1059599
> 99.9%
Punctuation 78
 
< 0.1%
Hangul 68
 
< 0.1%
None 13
 
< 0.1%
CJK 5
 
< 0.1%
Arrows 3
 
< 0.1%
Cyrillic 2
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
129722
 
12.2%
e 106918
 
10.1%
n 74892
 
7.1%
o 69723
 
6.6%
i 69498
 
6.6%
r 67405
 
6.4%
a 55842
 
5.3%
t 55350
 
5.2%
c 54335
 
5.1%
s 45576
 
4.3%
Other values (71) 330338
31.2%
Punctuation
ValueCountFrequency (%)
’ 31
39.7%
― 22
28.2%
“ 11
 
14.1%
” 11
 
14.1%
‘ 3
 
3.8%
Hangul
ValueCountFrequency (%)
큄 15
22.1%
짰 11
16.2%
챦 8
11.8%
첬 6
 
8.8%
창 3
 
4.4%
첩 3
 
4.4%
찾 3
 
4.4%
탑 3
 
4.4%
챔 3
 
4.4%
킁 2
 
2.9%
Other values (8) 11
16.2%
None
ValueCountFrequency (%)
® 6
46.2%
＆ 3
23.1%
ł 1
 
7.7%
´ 1
 
7.7%
π 1
 
7.7%
² 1
 
7.7%
CJK
ValueCountFrequency (%)
偈 3
60.0%
觀 1
 
20.0%
詮 1
 
20.0%
Arrows
ValueCountFrequency (%)
⇒ 3
100.0%
Cyrillic
ValueCountFrequency (%)
С 2
100.0%
Letterlike Symbols
ValueCountFrequency (%)
™ 1
100.0%

접근주소
Text

Distinct9714
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:53:48.434339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length222
Median length43
Mean length45.0644
Min length32

Characters and Unicode

Total characters450644
Distinct characters64
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

Unique9428 ?
Unique (%)94.3%

Sample

1st rowhttp://dx.doi.org/10.1007/978-1-84800-984-4
2nd rowhttp://dx.doi.org/10.1007/978-3-319-11638-9
3rd rowhttps://doi.org/10.1007/978-3-030-70451-3
4th rowhttps://doi.org/10.1007/978-3-030-95029-3
5th rowhttp://dx.doi.org/10.1007/978-1-4614-9299-3
ValueCountFrequency (%)
http://dx.doi.org/10.1007/978-1-4471-4742-8 2
 
< 0.1%
http://dx.doi.org/10.1007/978-1-4614-7400-5 2
 
< 0.1%
http://dx.doi.org/10.1007/978-1-4471-5140-1 2
 
< 0.1%
https://onlinelibrary.wiley.com/doi/book/10.1002/9781118926444 2
 
< 0.1%
http://dx.doi.org/10.1007/978-3-319-07884-7 2
 
< 0.1%
http://dx.doi.org/10.1002/9780470172513 2
 
< 0.1%
http://www.sciencedirect.com/science/book/9780080964461 2
 
< 0.1%
https://onlinelibrary.wiley.com/doi/book/10.1002/9783527820153 2
 
< 0.1%
http://dx.doi.org/10.1007/978-1-4614-7079-3 2
 
< 0.1%
http://dx.doi.org/10.1007/978-3-319-06596-0 2
 
< 0.1%
Other values (9704) 9980
99.8%
2023-12-12T15:53:48.957849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 42093
 
9.3%
0 38215
 
8.5%
1 31778
 
7.1%
- 28604
 
6.3%
. 26804
 
5.9%
7 24156
 
5.4%
o 23023
 
5.1%
t 21411
 
4.8%
9 18777
 
4.2%
8 17426
 
3.9%
Other values (54) 178357
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 178422
39.6%
Lowercase Letter 161483
35.8%
Other Punctuation 79350
17.6%
Dash Punctuation 28604
 
6.3%
Uppercase Letter 2564
 
0.6%
Math Symbol 219
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 23023
14.3%
t 21411
13.3%
d 15492
9.6%
i 14373
8.9%
r 11042
 
6.8%
p 10244
 
6.3%
h 10021
 
6.2%
g 8402
 
5.2%
c 8152
 
5.0%
e 8023
 
5.0%
Other values (14) 31300
19.4%
Uppercase Letter
ValueCountFrequency (%)
E 567
22.1%
B 391
15.2%
P 383
14.9%
A 180
 
7.0%
F 155
 
6.0%
C 147
 
5.7%
S 117
 
4.6%
R 96
 
3.7%
T 94
 
3.7%
O 93
 
3.6%
Other values (12) 341
13.3%
Decimal Number
ValueCountFrequency (%)
0 38215
21.4%
1 31778
17.8%
7 24156
13.5%
9 18777
10.5%
8 17426
9.8%
3 12621
 
7.1%
4 11038
 
6.2%
2 9657
 
5.4%
6 8386
 
4.7%
5 6368
 
3.6%
Other Punctuation
ValueCountFrequency (%)
/ 42093
53.0%
. 26804
33.8%
: 10000
 
12.6%
% 332
 
0.4%
& 121
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 28604
100.0%
Math Symbol
ValueCountFrequency (%)
= 219
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 286597
63.6%
Latin 164047
36.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 23023
14.0%
t 21411
13.1%
d 15492
9.4%
i 14373
8.8%
r 11042
 
6.7%
p 10244
 
6.2%
h 10021
 
6.1%
g 8402
 
5.1%
c 8152
 
5.0%
e 8023
 
4.9%
Other values (36) 33864
20.6%
Common
ValueCountFrequency (%)
/ 42093
14.7%
0 38215
13.3%
1 31778
11.1%
- 28604
10.0%
. 26804
9.4%
7 24156
8.4%
9 18777
6.6%
8 17426
6.1%
3 12621
 
4.4%
4 11038
 
3.9%
Other values (8) 35085
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 450644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 42093
 
9.3%
0 38215
 
8.5%
1 31778
 
7.1%
- 28604
 
6.3%
. 26804
 
5.9%
7 24156
 
5.4%
o 23023
 
5.1%
t 21411
 
4.8%
9 18777
 
4.2%
8 17426
 
3.9%
Other values (54) 178357
39.6%

저자
Text

Distinct7395
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:53:49.420256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length166
Median length102
Mean length15.733
Min length3

Characters and Unicode

Total characters157330
Distinct characters92
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6189 ?
Unique (%)61.9%

Sample

1st rowOrlov, Yury V
2nd rowPereira, Nuno
3rd rowMu, Shenglin
4th rowDing, Lei
5th rowPautasso, Cesare
ValueCountFrequency (%)
not 529
 
2.2%
offered 529
 
2.2%
a 308
 
1.3%
j 293
 
1.2%
m 244
 
1.0%
s 220
 
0.9%
r 200
 
0.8%
p 153
 
0.6%
c 148
 
0.6%
k 147
 
0.6%
Other values (9410) 21332
88.5%
2023-12-12T15:53:50.053997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 14301
 
9.1%
14104
 
9.0%
e 11195
 
7.1%
i 9808
 
6.2%
n 9439
 
6.0%
, 9370
 
6.0%
r 8568
 
5.4%
o 8476
 
5.4%
l 4978
 
3.2%
h 4872
 
3.1%
Other values (82) 62219
39.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 107961
68.6%
Uppercase Letter 24075
 
15.3%
Space Separator 14104
 
9.0%
Other Punctuation 10388
 
6.6%
Dash Punctuation 520
 
0.3%
Decimal Number 130
 
0.1%
Open Punctuation 63
 
< 0.1%
Close Punctuation 63
 
< 0.1%
Other Letter 24
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14301
13.2%
e 11195
10.4%
i 9808
 
9.1%
n 9439
 
8.7%
r 8568
 
7.9%
o 8476
 
7.9%
l 4978
 
4.6%
h 4872
 
4.5%
s 4845
 
4.5%
t 4674
 
4.3%
Other values (20) 26805
24.8%
Uppercase Letter
ValueCountFrequency (%)
S 2027
 
8.4%
M 1985
 
8.2%
A 1875
 
7.8%
C 1377
 
5.7%
J 1373
 
5.7%
R 1261
 
5.2%
K 1252
 
5.2%
B 1209
 
5.0%
P 1197
 
5.0%
N 1100
 
4.6%
Other values (17) 9419
39.1%
Other Letter
ValueCountFrequency (%)
큄 6
25.0%
첬 4
16.7%
책 3
12.5%
찾 2
 
8.3%
첩 1
 
4.2%
偈 1
 
4.2%
챦 1
 
4.2%
쩔 1
 
4.2%
챙 1
 
4.2%
탑 1
 
4.2%
Other values (3) 3
12.5%
Decimal Number
ValueCountFrequency (%)
0 49
37.7%
2 31
23.8%
1 22
16.9%
4 8
 
6.2%
9 5
 
3.8%
6 5
 
3.8%
3 4
 
3.1%
8 4
 
3.1%
5 1
 
0.8%
7 1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 9370
90.2%
. 956
 
9.2%
: 35
 
0.3%
' 23
 
0.2%
; 3
 
< 0.1%
& 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
14104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 520
100.0%
Open Punctuation
ValueCountFrequency (%)
( 63
100.0%
Close Punctuation
ValueCountFrequency (%)
) 63
100.0%
Control
ValueCountFrequency (%)
1
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 132036
83.9%
Common 25270
 
16.1%
Hangul 23
 
< 0.1%
Han 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14301
 
10.8%
e 11195
 
8.5%
i 9808
 
7.4%
n 9439
 
7.1%
r 8568
 
6.5%
o 8476
 
6.4%
l 4978
 
3.8%
h 4872
 
3.7%
s 4845
 
3.7%
t 4674
 
3.5%
Other values (47) 50880
38.5%
Common
ValueCountFrequency (%)
14104
55.8%
, 9370
37.1%
. 956
 
3.8%
- 520
 
2.1%
( 63
 
0.2%
) 63
 
0.2%
0 49
 
0.2%
: 35
 
0.1%
2 31
 
0.1%
' 23
 
0.1%
Other values (12) 56
 
0.2%
Hangul
ValueCountFrequency (%)
큄 6
26.1%
첬 4
17.4%
책 3
13.0%
찾 2
 
8.7%
첩 1
 
4.3%
챦 1
 
4.3%
쩔 1
 
4.3%
챙 1
 
4.3%
탑 1
 
4.3%
첫 1
 
4.3%
Other values (2) 2
 
8.7%
Han
ValueCountFrequency (%)
偈 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 157293
> 99.9%
Hangul 23
 
< 0.1%
None 12
 
< 0.1%
CJK 1
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 14301
 
9.1%
14104
 
9.0%
e 11195
 
7.1%
i 9808
 
6.2%
n 9439
 
6.0%
, 9370
 
6.0%
r 8568
 
5.4%
o 8476
 
5.4%
l 4978
 
3.2%
h 4872
 
3.1%
Other values (63) 62182
39.5%
None
ValueCountFrequency (%)
ł 6
50.0%
Ł 3
25.0%
ß 1
 
8.3%
ø 1
 
8.3%
ı 1
 
8.3%
Hangul
ValueCountFrequency (%)
큄 6
26.1%
첬 4
17.4%
책 3
13.0%
찾 2
 
8.7%
첩 1
 
4.3%
챦 1
 
4.3%
쩔 1
 
4.3%
챙 1
 
4.3%
탑 1
 
4.3%
첫 1
 
4.3%
Other values (2) 2
 
8.7%
CJK
ValueCountFrequency (%)
偈 1
100.0%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%

출판년도
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)0.5%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2013.119
Minimum1979
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:53:50.202302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1979
5-th percentile2003
Q12010
median2013
Q32015
95-th percentile2022
Maximum2023
Range44
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.9977442
Coefficient of variation (CV)0.0029793292
Kurtosis2.8271244
Mean2013.119
Median Absolute Deviation (MAD)2
Skewness-0.8508392
Sum20119111
Variance35.972935
MonotonicityNot monotonic
2023-12-12T15:53:50.352428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
2022 1850
18.5%
2013 1276
12.8%
2014 1129
11.3%
2015 1128
11.3%
2011 944
9.4%
2012 812
8.1%
2009 705
 
7.0%
2010 675
 
6.8%
2016 190
 
1.9%
2008 142
 
1.4%
Other values (35) 1143
11.4%
ValueCountFrequency (%)
1979 2
 
< 0.1%
1980 1
 
< 0.1%
1981 3
 
< 0.1%
1982 3
 
< 0.1%
1983 2
 
< 0.1%
1984 4
 
< 0.1%
1985 4
 
< 0.1%
1986 4
 
< 0.1%
1987 7
0.1%
1988 15
0.1%
ValueCountFrequency (%)
2023 1
 
< 0.1%
2022 1850
18.5%
2021 24
 
0.2%
2020 23
 
0.2%
2019 34
 
0.3%
2018 13
 
0.1%
2017 35
 
0.4%
2016 190
 
1.9%
2015 1128
11.3%
2014 1129
11.3%

Interactions

2023-12-12T15:53:46.282438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:46.069077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:46.399813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:46.164508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:53:50.441081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제어번호출판년도
제어번호1.0000.900
출판년도0.9001.000
2023-12-12T15:53:50.545976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제어번호출판년도
제어번호1.0000.901
출판년도0.9011.000

Missing values

2023-12-12T15:53:46.554963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:53:46.679433image/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

제어번호서명접근주소저자출판년도
118733463Discontinuous Systems [electronic resource] Lyapunov Analysis and Robust Synthesis under Uncertainty Conditionshttp://dx.doi.org/10.1007/978-1-84800-984-4Orlov, Yury V2009
725845806Design and Implementation of Sigma Delta Modulators for Class D Audio Amplifiers using Differential Pairs [electronic resource] by Nuno Pereira, Nuno Paulinohttp://dx.doi.org/10.1007/978-3-319-11638-9Pereira, Nuno2015
8887501064th EAI International Conference on Robotic Sensor Networks [electronic resource]https://doi.org/10.1007/978-3-030-70451-3Mu, Shenglin2022
1024851467Distributed Control and Optimization of Networked Microgrids [electronic resource] : A Multi-Agent System Based Approachhttps://doi.org/10.1007/978-3-030-95029-3Ding, Lei2022
620344705REST: Advanced Research Topics and Practical Applications [electronic resource] edited by Cesare Pautasso, Erik Wilde, Rosa Alarconhttp://dx.doi.org/10.1007/978-1-4614-9299-3Pautasso, Cesare2014
190634182High-Quality Visual Experience [electronic resource] Creation, Processing and Interactivity of High-Resolution and High-Dimensional Video Signalshttp://dx.doi.org/10.1007/978-3-642-12802-8Mrak, Marta2010
338436686Topics on the Dynamics of Civil Structures, Volume 1 [electronic resource] Proceedings of the 30th IMAC, A Conference on Structural Dynamics, 2012http://dx.doi.org/10.1007/978-1-4614-2413-0Caicedo, J.M2012
71132755Mobile and Wireless Communications Key Technologies and Future Applicationshttp://dx.doi.org/10.1049/PBBT009ENot offered2004
51332526Petroleum rock mechanics [electronic resource] drilling operations and well designhttp://www.sciencedirect.com/science/book/9780123855466Aadn, Bernt Sigve2011
833849302Design of piezo inkjet print heads [electronic resource] from acoustics to applications J. Frits Dijksmanhttps://onlinelibrary.wiley.com/doi/book/10.1002/9783527806874Dijksman, J. Frits2018
제어번호서명접근주소저자출판년도
139433670Axiomatic Fuzzy Set Theory and Its Applications [electronic resource]http://dx.doi.org/10.1007/978-3-642-00402-5Liu, Xiaodong2009
881150030Dry Mineral Processing [electronic resource]https://doi.org/10.1007/978-3-030-93750-8Chelgani, Saeed Chehreh2022
613544637Computational Electromagnetics [electronic resource] Recent Advances and Engineering Applications edited by Raj Mittrahttp://dx.doi.org/10.1007/978-1-4614-4382-7Mittra, Raj2014
970550924Advances in Design Engineering II [electronic resource] : Proceedings of the XXX International Congress INGEGRAF, 24-25 June, 2021, Valencia, Spainhttps://doi.org/10.1007/978-3-030-92426-3Cavas Martnez, Francisco2022
715345699Nonlinear Dynamics New Directions [electronic resource] Theoretical Aspects edited by Hernn Gonzlez-Aguilar, Edgardo Ugaldehttp://dx.doi.org/10.1007/978-3-319-09867-8Gonzlez-Aguilar, Hernn2015
322036285Preliminary Reconnaissance Report of the 2011 Tohoku-Chiho Taiheiyo-Oki Earthquake [electronic resource]http://dx.doi.org/10.1007/978-4-431-54097-7Architectural Institute of Japan2012
333036615Statistical Performance Analysis and Modeling Techniques for Nanometer VLSI Designs [electronic resource]http://dx.doi.org/10.1007/978-1-4614-0788-1Shen, Ruijing2012
957950798Advances in Mechanical Engineering [electronic resource] : Selected Contributions from the Conference “Modern Engineering: Science and Education”, Saint Petersburg, Russia, June 2021https://doi.org/10.1007/978-3-030-91553-7Evgrafov, Alexander N2022
862349842Advances in Communication, Devices and Networking [electronic resource] : Proceedings of ICCDN 2020https://doi.org/10.1007/978-981-16-2911-2Dhar, Sourav2022
932450543Technological Applications of Nanomaterials [electronic resource]https://doi.org/10.1007/978-3-030-86901-4Kopp Alves, Annelise2022