Overview

Dataset statistics

Number of variables6
Number of observations420
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.0 KiB
Average record size in memory51.3 B

Variable types

Text3
Numeric3

Dataset

Description한국국제교류재단이 해외에서의 한국학 진흥을 위해 설치한 해외대학의 한국학 교수직 설치정보에 관한 정보를 제공합니다.
Author한국국제교류재단
URLhttps://www.data.go.kr/data/3064822/fileData.do

Alerts

기관명(한글) has unique valuesUnique

Reproduction

Analysis started2024-03-14 23:38:56.729633
Analysis finished2024-03-14 23:39:00.134374
Duration3.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

국가명
Text

Distinct86
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2024-03-15T08:39:01.069401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length2.9547619
Min length2

Characters and Unicode

Total characters1241
Distinct characters119
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)8.3%

Sample

1st row과테말라
2nd row나이지리아
3rd row네덜란드
4th row네덜란드
5th row뉴질랜드
ValueCountFrequency (%)
미국 58
 
13.8%
중국 56
 
13.3%
일본 28
 
6.7%
베트남 25
 
5.9%
러시아 18
 
4.3%
프랑스 14
 
3.3%
태국 12
 
2.9%
독일 10
 
2.4%
몽골 9
 
2.1%
인도 9
 
2.1%
Other values (77) 182
43.2%
2024-03-15T08:39:02.326195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 133
 
10.7%
아 80
 
6.4%
스 60
 
4.8%
미 59
 
4.8%
중 56
 
4.5%
일 39
 
3.1%
트 33
 
2.7%
베 32
 
2.6%
시 31
 
2.5%
이 30
 
2.4%
Other values (109) 688
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1240
99.9%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 133
 
10.7%
아 80
 
6.5%
스 60
 
4.8%
미 59
 
4.8%
중 56
 
4.5%
일 39
 
3.1%
트 33
 
2.7%
베 32
 
2.6%
시 31
 
2.5%
이 30
 
2.4%
Other values (108) 687
55.4%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1240
99.9%
Common 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 133
 
10.7%
아 80
 
6.5%
스 60
 
4.8%
미 59
 
4.8%
중 56
 
4.5%
일 39
 
3.1%
트 33
 
2.7%
베 32
 
2.6%
시 31
 
2.5%
이 30
 
2.4%
Other values (108) 687
55.4%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1240
99.9%
ASCII 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 133
 
10.7%
아 80
 
6.5%
스 60
 
4.8%
미 59
 
4.8%
중 56
 
4.5%
일 39
 
3.1%
트 33
 
2.7%
베 32
 
2.6%
시 31
 
2.5%
이 30
 
2.4%
Other values (108) 687
55.4%
ASCII
ValueCountFrequency (%)
1
100.0%

기관명(한글)
Text

UNIQUE 

Distinct420
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2024-03-15T08:39:03.186348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length21
Mean length8.9095238
Min length4

Characters and Unicode

Total characters3742
Distinct characters410
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique420 ?
Unique (%)100.0%

Sample

1st row과테말라산카를로스대학교
2nd row칼라바르대학교
3rd row로테르담응용과학대학교
4th row흐로닝언대학교
5th row오클랜드공과대학교
ValueCountFrequency (%)
캘리포니아대학교 5
 
1.1%
대학교 3
 
0.6%
뉴욕주립대학교 3
 
0.6%
텍사스대학교 3
 
0.6%
길림대학교 2
 
0.4%
펜실베니아 2
 
0.4%
일리노이대학교 2
 
0.4%
송클라대학교 2
 
0.4%
산동대학교 2
 
0.4%
웨이하이분교 1
 
0.2%
Other values (439) 439
94.6%
2024-03-15T08:39:04.528472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 417
 
11.1%
교 400
 
10.7%
대 392
 
10.5%
스 89
 
2.4%
국 84
 
2.2%
리 70
 
1.9%
립 64
 
1.7%
아 59
 
1.6%
이 58
 
1.5%
르 53
 
1.4%
Other values (400) 2056
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3511
93.8%
Uppercase Letter 113
 
3.0%
Space Separator 44
 
1.2%
Open Punctuation 24
 
0.6%
Close Punctuation 24
 
0.6%
Dash Punctuation 12
 
0.3%
Lowercase Letter 9
 
0.2%
Decimal Number 4
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 417
 
11.9%
교 400
 
11.4%
대 392
 
11.2%
스 89
 
2.5%
국 84
 
2.4%
리 70
 
2.0%
립 64
 
1.8%
아 59
 
1.7%
이 58
 
1.7%
르 53
 
1.5%
Other values (366) 1825
52.0%
Uppercase Letter
ValueCountFrequency (%)
U 23
20.4%
S 12
10.6%
N 12
10.6%
I 9
 
8.0%
A 8
 
7.1%
T 7
 
6.2%
C 7
 
6.2%
L 6
 
5.3%
H 5
 
4.4%
V 3
 
2.7%
Other values (9) 21
18.6%
Lowercase Letter
ValueCountFrequency (%)
u 2
22.2%
t 2
22.2%
e 1
11.1%
g 1
11.1%
s 1
11.1%
i 1
11.1%
n 1
11.1%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
3 1
25.0%
5 1
25.0%
Space Separator
ValueCountFrequency (%)
44
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3511
93.8%
Latin 122
 
3.3%
Common 109
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 417
 
11.9%
교 400
 
11.4%
대 392
 
11.2%
스 89
 
2.5%
국 84
 
2.4%
리 70
 
2.0%
립 64
 
1.8%
아 59
 
1.7%
이 58
 
1.7%
르 53
 
1.5%
Other values (366) 1825
52.0%
Latin
ValueCountFrequency (%)
U 23
18.9%
S 12
 
9.8%
N 12
 
9.8%
I 9
 
7.4%
A 8
 
6.6%
T 7
 
5.7%
C 7
 
5.7%
L 6
 
4.9%
H 5
 
4.1%
V 3
 
2.5%
Other values (16) 30
24.6%
Common
ValueCountFrequency (%)
44
40.4%
( 24
22.0%
) 24
22.0%
- 12
 
11.0%
2 2
 
1.8%
3 1
 
0.9%
. 1
 
0.9%
5 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3511
93.8%
ASCII 231
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 417
 
11.9%
교 400
 
11.4%
대 392
 
11.2%
스 89
 
2.5%
국 84
 
2.4%
리 70
 
2.0%
립 64
 
1.8%
아 59
 
1.7%
이 58
 
1.7%
르 53
 
1.5%
Other values (366) 1825
52.0%
ASCII
ValueCountFrequency (%)
44
19.0%
( 24
10.4%
) 24
10.4%
U 23
10.0%
S 12
 
5.2%
N 12
 
5.2%
- 12
 
5.2%
I 9
 
3.9%
A 8
 
3.5%
T 7
 
3.0%
Other values (24) 56
24.2%

기관명(영문)
Text

Distinct419
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2024-03-15T08:39:05.994646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length105
Median length58
Mean length28.77619
Min length4

Characters and Unicode

Total characters12086
Distinct characters63
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

Unique418 ?
Unique (%)99.5%

Sample

1st rowUniversity of San Carlos of Guatemala
2nd rowUniversity of Calabar
3rd rowRotterdam University of Applied Sciences
4th rowUniversity of Groningen
5th rowAuckland University of Technology
ValueCountFrequency (%)
university 387
24.5%
of 200
 
12.7%
state 28
 
1.8%
national 27
 
1.7%
and 19
 
1.2%
international 18
 
1.1%
college 17
 
1.1%
studies 16
 
1.0%
institute 16
 
1.0%
technology 16
 
1.0%
Other values (587) 837
52.9%
2024-03-15T08:39:07.967009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 1294
 
10.7%
1161
 
9.6%
n 979
 
8.1%
e 894
 
7.4%
t 771
 
6.4%
a 757
 
6.3%
r 659
 
5.5%
o 649
 
5.4%
s 635
 
5.3%
y 479
 
4.0%
Other values (53) 3808
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9322
77.1%
Uppercase Letter 1475
 
12.2%
Space Separator 1161
 
9.6%
Close Punctuation 38
 
0.3%
Open Punctuation 38
 
0.3%
Other Punctuation 28
 
0.2%
Dash Punctuation 21
 
0.2%
Decimal Number 2
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1294
13.9%
n 979
10.5%
e 894
9.6%
t 771
8.3%
a 757
8.1%
r 659
 
7.1%
o 649
 
7.0%
s 635
 
6.8%
y 479
 
5.1%
v 412
 
4.4%
Other values (16) 1793
19.2%
Uppercase Letter
ValueCountFrequency (%)
U 435
29.5%
S 126
 
8.5%
C 110
 
7.5%
N 86
 
5.8%
T 73
 
4.9%
I 68
 
4.6%
M 66
 
4.5%
B 58
 
3.9%
A 57
 
3.9%
H 52
 
3.5%
Other values (16) 344
23.3%
Other Punctuation
ValueCountFrequency (%)
, 12
42.9%
. 11
39.3%
' 4
 
14.3%
? 1
 
3.6%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
3 1
50.0%
Space Separator
ValueCountFrequency (%)
1161
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10797
89.3%
Common 1289
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1294
 
12.0%
n 979
 
9.1%
e 894
 
8.3%
t 771
 
7.1%
a 757
 
7.0%
r 659
 
6.1%
o 649
 
6.0%
s 635
 
5.9%
y 479
 
4.4%
U 435
 
4.0%
Other values (42) 3245
30.1%
Common
ValueCountFrequency (%)
1161
90.1%
) 38
 
2.9%
( 38
 
2.9%
- 21
 
1.6%
, 12
 
0.9%
. 11
 
0.9%
' 4
 
0.3%
? 1
 
0.1%
1
 
0.1%
2 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1294
 
10.7%
1161
 
9.6%
n 979
 
8.1%
e 894
 
7.4%
t 771
 
6.4%
a 757
 
6.3%
r 659
 
5.5%
o 649
 
5.4%
s 635
 
5.3%
y 479
 
4.0%
Other values (53) 3808
31.5%

조사연도
Real number (ℝ)

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.3071
Minimum2018
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T08:39:08.345095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2018
Q12018
median2019
Q32020
95-th percentile2022
Maximum2023
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5658791
Coefficient of variation (CV)0.00077545368
Kurtosis-0.90828186
Mean2019.3071
Median Absolute Deviation (MAD)1
Skewness0.7858238
Sum848109
Variance2.4519775
MonotonicityNot monotonic
2024-03-15T08:39:08.699981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2018 209
49.8%
2022 84
20.0%
2020 78
 
18.6%
2019 46
 
11.0%
2021 2
 
0.5%
2023 1
 
0.2%
ValueCountFrequency (%)
2018 209
49.8%
2019 46
 
11.0%
2020 78
 
18.6%
2021 2
 
0.5%
2022 84
20.0%
2023 1
 
0.2%
ValueCountFrequency (%)
2023 1
 
0.2%
2022 84
20.0%
2021 2
 
0.5%
2020 78
 
18.6%
2019 46
 
11.0%
2018 209
49.8%

학과 개설연도
Real number (ℝ)

Distinct65
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.6357
Minimum1872
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T08:39:09.071750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1971.85
Q11997.75
median2007
Q32013
95-th percentile2018
Maximum2021
Range149
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation17.198078
Coefficient of variation (CV)0.0085877218
Kurtosis14.586715
Mean2002.6357
Median Absolute Deviation (MAD)7
Skewness-3.0456516
Sum841107
Variance295.7739
MonotonicityNot monotonic
2024-03-15T08:39:09.523313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2008 27
 
6.4%
2013 24
 
5.7%
2016 21
 
5.0%
2006 20
 
4.8%
2012 20
 
4.8%
2015 19
 
4.5%
2007 17
 
4.0%
2009 17
 
4.0%
2005 17
 
4.0%
2011 15
 
3.6%
Other values (55) 223
53.1%
ValueCountFrequency (%)
1872 1
0.2%
1887 1
0.2%
1925 1
0.2%
1928 1
0.2%
1944 1
0.2%
1945 1
0.2%
1950 2
0.5%
1951 1
0.2%
1952 1
0.2%
1954 1
0.2%
ValueCountFrequency (%)
2021 7
 
1.7%
2020 3
 
0.7%
2019 9
 
2.1%
2018 5
 
1.2%
2017 14
3.3%
2016 21
5.0%
2015 19
4.5%
2014 14
3.3%
2013 24
5.7%
2012 20
4.8%

교수 인원수
Real number (ℝ)

Distinct25
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9404762
Minimum0
Maximum36
Zeros4
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T08:39:09.971640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median4
Q37
95-th percentile14
Maximum36
Range36
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.5607833
Coefficient of variation (CV)0.9231465
Kurtosis8.0291567
Mean4.9404762
Median Absolute Deviation (MAD)3
Skewness2.2077052
Sum2075
Variance20.800744
MonotonicityNot monotonic
2024-03-15T08:39:10.368181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 102
24.3%
2 51
12.1%
4 44
10.5%
3 43
10.2%
5 31
 
7.4%
7 29
 
6.9%
6 27
 
6.4%
8 24
 
5.7%
9 14
 
3.3%
10 10
 
2.4%
Other values (15) 45
10.7%
ValueCountFrequency (%)
0 4
 
1.0%
1 102
24.3%
2 51
12.1%
3 43
10.2%
4 44
10.5%
5 31
 
7.4%
6 27
 
6.4%
7 29
 
6.9%
8 24
 
5.7%
9 14
 
3.3%
ValueCountFrequency (%)
36 1
 
0.2%
29 1
 
0.2%
25 1
 
0.2%
23 1
 
0.2%
21 1
 
0.2%
19 2
 
0.5%
18 1
 
0.2%
17 3
0.7%
16 3
0.7%
15 5
1.2%

Interactions

2024-03-15T08:38:59.011604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:38:57.367238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:38:58.208475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:38:59.248267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:38:57.627958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:38:58.612969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:38:59.521920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:38:57.945027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:38:58.802311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:39:10.630219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가명조사연도학과 개설연도교수 인원수
국가명1.0000.8450.0000.000
조사연도0.8451.0000.0180.139
학과 개설연도0.0000.0181.0000.133
교수 인원수0.0000.1390.1331.000
2024-03-15T08:39:10.921441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사연도학과 개설연도교수 인원수
조사연도1.0000.0920.056
학과 개설연도0.0921.000-0.313
교수 인원수0.056-0.3131.000

Missing values

2024-03-15T08:38:59.846153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:39:00.051369image/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

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2네덜란드로테르담응용과학대학교Rotterdam University of Applied Sciences201820131
3네덜란드흐로닝언대학교University of Groningen202020145
4뉴질랜드오클랜드공과대학교Auckland University of Technology201820161
5뉴질랜드오클랜드대학교University of Auckland201819895
6대만국립가오슝대학교National University of Kaohsiung201920084
7대만국립정치대학교National Chengchi University2018200017
8대만나가사키외국어대학교Nagasaki University of Foreign Studies202020096
9대만오사카대학교Osaka University202019635
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410필리핀세부기술공과대학교Cebu Technological University201820151
411필리핀아테네오데마닐라대학교Ateneo de Manila University2020201412
412필리핀필리핀아시아태평양대학교University of Asia and the Pacific201820062
413헝가리엘테대학교ELTE University202220081
414호주뉴사우스웨일스대학교(UNSW)University of New South Wales (UNSW)201819948
415호주맥쿼리대학교Macquarie University201820150
416호주사우스오스트레일리아대학교University of South Australia2018201612
417호주시드니대학교University of Sydney201819913
418호주퀸즐랜드대학교University of Queensland201819908
419호주호주국립대학교Australian National University201819954