Overview

Dataset statistics

Number of variables13
Number of observations150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.7 KiB
Average record size in memory106.9 B

Variable types

Numeric2
Categorical6
Text5

Dataset

Description우리 기관이 보유하고 있는 농림식품R&D 중분류 중 2020년 수의 R&D 과제정보 공개 농림식품RnD 관련 연구성과로 창출된 데이터를 제공합니다.
Author농림식품기술기획평가원
URLhttps://www.data.go.kr/data/15075438/fileData.do

Alerts

분류 has constant value ""Constant
총연구기간 종료일 is highly overall correlated with 총연구비 and 4 other fieldsHigh correlation
당해년연구기간 종료일 is highly overall correlated with 사업명 and 3 other fieldsHigh correlation
총연구기관 시작일 is highly overall correlated with 총연구비 and 4 other fieldsHigh correlation
당해년연구기간 시작일 is highly overall correlated with 사업명 and 3 other fieldsHigh correlation
총연구비 is highly overall correlated with 사업명 and 2 other fieldsHigh correlation
사업명 is highly overall correlated with 총연구비 and 4 other fieldsHigh correlation
당해년연구기간 종료일 is highly imbalanced (63.9%)Imbalance
번호 has unique valuesUnique
세부과제번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:15:04.832854
Analysis finished2023-12-12 18:15:06.782974
Duration1.95 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.5
Minimum1
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T03:15:06.896290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.45
Q138.25
median75.5
Q3112.75
95-th percentile142.55
Maximum150
Range149
Interquartile range (IQR)74.5

Descriptive statistics

Standard deviation43.445368
Coefficient of variation (CV)0.57543534
Kurtosis-1.2
Mean75.5
Median Absolute Deviation (MAD)37.5
Skewness0
Sum11325
Variance1887.5
MonotonicityStrictly increasing
2023-12-13T03:15:07.109519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
96 1
 
0.7%
98 1
 
0.7%
99 1
 
0.7%
100 1
 
0.7%
101 1
 
0.7%
102 1
 
0.7%
103 1
 
0.7%
104 1
 
0.7%
105 1
 
0.7%
Other values (140) 140
93.3%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
150 1
0.7%
149 1
0.7%
148 1
0.7%
147 1
0.7%
146 1
0.7%
145 1
0.7%
144 1
0.7%
143 1
0.7%
142 1
0.7%
141 1
0.7%

분류
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
수의
150 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수의 150
100.0%

Length

2023-12-13T03:15:07.257375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:15:07.362596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수의 150
100.0%

사업명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
검역방역기술
37 
동물의약품개발
36 
사회문제해결형 감염병대응기술개발
28 
확산방지 및 사후관리
20 
교육훈련
10 
Other values (7)
19 

Length

Max length17
Median length12
Mean length9.0933333
Min length4

Unique

Unique2 ?
Unique (%)1.3%

Sample

1st row확산방지 및 사후관리
2nd row검역방역기술
3rd row검역방역기술
4th row사회문제해결형 감염병대응기술개발
5th row확산방지 및 사후관리

Common Values

ValueCountFrequency (%)
검역방역기술 37
24.7%
동물의약품개발 36
24.0%
사회문제해결형 감염병대응기술개발 28
18.7%
확산방지 및 사후관리 20
13.3%
교육훈련 10
 
6.7%
국가연구개발성과후속지원 5
 
3.3%
진단예방기술 5
 
3.3%
산업기반연구 3
 
2.0%
생명자원 생산·관리기술 2
 
1.3%
에너지절감자재 2
 
1.3%
Other values (2) 2
 
1.3%

Length

2023-12-13T03:15:07.506405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
검역방역기술 37
16.8%
동물의약품개발 36
16.4%
사회문제해결형 28
12.7%
감염병대응기술개발 28
12.7%
확산방지 20
9.1%
및 20
9.1%
사후관리 20
9.1%
교육훈련 10
 
4.5%
국가연구개발성과후속지원 5
 
2.3%
진단예방기술 5
 
2.3%
Other values (6) 11
 
5.0%

총괄과제번호
Text

Distinct69
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T03:15:07.771257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1200
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

Unique17 ?
Unique (%)11.3%

Sample

1st row318047-3
2nd row318040-3
3rd row318040-3
4th row318070-3
5th row318047-3
ValueCountFrequency (%)
320005-4 10
 
6.7%
119081-5 4
 
2.7%
318069-3 4
 
2.7%
318041-3 3
 
2.0%
320071-2 3
 
2.0%
320054-2 3
 
2.0%
320063-2 3
 
2.0%
320062-2 3
 
2.0%
320064-2 3
 
2.0%
320059-2 3
 
2.0%
Other values (59) 111
74.0%
2023-12-13T03:15:08.180261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 239
19.9%
3 174
14.5%
2 169
14.1%
1 158
13.2%
- 150
12.5%
9 78
 
6.5%
8 65
 
5.4%
5 56
 
4.7%
6 42
 
3.5%
4 35
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1050
87.5%
Dash Punctuation 150
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 239
22.8%
3 174
16.6%
2 169
16.1%
1 158
15.0%
9 78
 
7.4%
8 65
 
6.2%
5 56
 
5.3%
6 42
 
4.0%
4 35
 
3.3%
7 34
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 239
19.9%
3 174
14.5%
2 169
14.1%
1 158
13.2%
- 150
12.5%
9 78
 
6.5%
8 65
 
5.4%
5 56
 
4.7%
6 42
 
3.5%
4 35
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 239
19.9%
3 174
14.5%
2 169
14.1%
1 158
13.2%
- 150
12.5%
9 78
 
6.5%
8 65
 
5.4%
5 56
 
4.7%
6 42
 
3.5%
4 35
 
2.9%

세부과제번호
Text

UNIQUE 

Distinct150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T03:15:08.472754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters2100
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique150 ?
Unique (%)100.0%

Sample

1st row318047033HD020
2nd row318040033SB010
3rd row318040033HD020
4th row318070033SB010
5th row318047033SB010
ValueCountFrequency (%)
318047033hd020 1
 
0.7%
320064021hd030 1
 
0.7%
320065021hd020 1
 
0.7%
320062021hd030 1
 
0.7%
320062021sb010 1
 
0.7%
320062021sb020 1
 
0.7%
120089021hd020 1
 
0.7%
120089021sb010 1
 
0.7%
320063021hd020 1
 
0.7%
118091033sb010 1
 
0.7%
Other values (140) 140
93.3%
2023-12-13T03:15:08.907547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 688
32.8%
1 295
14.0%
2 257
 
12.2%
3 239
 
11.4%
S 83
 
4.0%
B 83
 
4.0%
9 79
 
3.8%
H 67
 
3.2%
D 67
 
3.2%
8 66
 
3.1%
Other values (5) 176
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1799
85.7%
Uppercase Letter 300
 
14.3%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 688
38.2%
1 295
16.4%
2 257
 
14.3%
3 239
 
13.3%
9 79
 
4.4%
8 66
 
3.7%
5 58
 
3.2%
6 43
 
2.4%
4 39
 
2.2%
7 35
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
S 83
27.7%
B 83
27.7%
H 67
22.3%
D 67
22.3%
Lowercase Letter
ValueCountFrequency (%)
a 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1799
85.7%
Latin 301
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 688
38.2%
1 295
16.4%
2 257
 
14.3%
3 239
 
13.3%
9 79
 
4.4%
8 66
 
3.7%
5 58
 
3.2%
6 43
 
2.4%
4 39
 
2.2%
7 35
 
1.9%
Latin
ValueCountFrequency (%)
S 83
27.6%
B 83
27.6%
H 67
22.3%
D 67
22.3%
a 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 688
32.8%
1 295
14.0%
2 257
 
12.2%
3 239
 
11.4%
S 83
 
4.0%
B 83
 
4.0%
9 79
 
3.8%
H 67
 
3.2%
D 67
 
3.2%
8 66
 
3.1%
Other values (5) 176
 
8.4%

과제명
Text

Distinct142
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T03:15:09.364935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length86
Median length47.5
Mean length33.52
Min length12

Characters and Unicode

Total characters5028
Distinct characters349
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137 ?
Unique (%)91.3%

Sample

1st row종오리 초생추 품질이력 및 이동정보 관리시스템 구축
2nd row농장 단위 방역 강화를 위한 철새 위치추적기 활용 알림 서비스 앱 개발
3rd rowGIS 기반 통계분석 기술을 활용한 조류인플루엔자 확산예측 모형 구축
4th row질병 매개 닭진드기 방제 성분의 안전성 및 유효성 평가를 통한 신규한 방제법 및 유효성분 발굴
5th row종계장의 초생추 품질이력 및 이동정보 관리시스템 구축
ValueCountFrequency (%)
및 73
 
6.0%
개발 72
 
5.9%
위한 24
 
2.0%
연구 23
 
1.9%
백신 18
 
1.5%
구제역 17
 
1.4%
구축 16
 
1.3%
시스템 16
 
1.3%
바이러스 13
 
1.1%
이용한 13
 
1.1%
Other values (585) 934
76.6%
2023-12-13T03:15:10.071946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1080
 
21.5%
발 97
 
1.9%
개 87
 
1.7%
제 83
 
1.7%
및 73
 
1.5%
기 71
 
1.4%
구 71
 
1.4%
가 63
 
1.3%
용 58
 
1.2%
이 56
 
1.1%
Other values (339) 3289
65.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3575
71.1%
Space Separator 1080
 
21.5%
Uppercase Letter 176
 
3.5%
Lowercase Letter 87
 
1.7%
Decimal Number 27
 
0.5%
Close Punctuation 21
 
0.4%
Open Punctuation 21
 
0.4%
Other Punctuation 21
 
0.4%
Dash Punctuation 18
 
0.4%
Initial Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
발 97
 
2.7%
개 87
 
2.4%
제 83
 
2.3%
및 73
 
2.0%
기 71
 
2.0%
구 71
 
2.0%
가 63
 
1.8%
용 58
 
1.6%
이 56
 
1.6%
병 55
 
1.5%
Other values (289) 2861
80.0%
Lowercase Letter
ValueCountFrequency (%)
a 10
11.5%
e 8
 
9.2%
s 7
 
8.0%
n 7
 
8.0%
i 7
 
8.0%
u 7
 
8.0%
r 5
 
5.7%
l 5
 
5.7%
c 4
 
4.6%
d 4
 
4.6%
Other values (8) 23
26.4%
Uppercase Letter
ValueCountFrequency (%)
A 36
20.5%
I 29
16.5%
S 26
14.8%
F 18
10.2%
P 13
 
7.4%
H 9
 
5.1%
M 8
 
4.5%
G 7
 
4.0%
E 6
 
3.4%
V 5
 
2.8%
Other values (7) 19
10.8%
Decimal Number
ValueCountFrequency (%)
2 11
40.7%
1 9
33.3%
3 5
18.5%
5 1
 
3.7%
4 1
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 15
71.4%
/ 3
 
14.3%
. 2
 
9.5%
: 1
 
4.8%
Space Separator
ValueCountFrequency (%)
1080
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
” 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3575
71.1%
Common 1190
 
23.7%
Latin 263
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
발 97
 
2.7%
개 87
 
2.4%
제 83
 
2.3%
및 73
 
2.0%
기 71
 
2.0%
구 71
 
2.0%
가 63
 
1.8%
용 58
 
1.6%
이 56
 
1.6%
병 55
 
1.5%
Other values (289) 2861
80.0%
Latin
ValueCountFrequency (%)
A 36
 
13.7%
I 29
 
11.0%
S 26
 
9.9%
F 18
 
6.8%
P 13
 
4.9%
a 10
 
3.8%
H 9
 
3.4%
M 8
 
3.0%
e 8
 
3.0%
s 7
 
2.7%
Other values (25) 99
37.6%
Common
ValueCountFrequency (%)
1080
90.8%
) 21
 
1.8%
( 21
 
1.8%
- 18
 
1.5%
, 15
 
1.3%
2 11
 
0.9%
1 9
 
0.8%
3 5
 
0.4%
/ 3
 
0.3%
. 2
 
0.2%
Other values (5) 5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3571
71.0%
ASCII 1451
28.9%
Compat Jamo 4
 
0.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1080
74.4%
A 36
 
2.5%
I 29
 
2.0%
S 26
 
1.8%
) 21
 
1.4%
( 21
 
1.4%
- 18
 
1.2%
F 18
 
1.2%
, 15
 
1.0%
P 13
 
0.9%
Other values (38) 174
 
12.0%
Hangul
ValueCountFrequency (%)
발 97
 
2.7%
개 87
 
2.4%
제 83
 
2.3%
및 73
 
2.0%
기 71
 
2.0%
구 71
 
2.0%
가 63
 
1.8%
용 58
 
1.6%
이 56
 
1.6%
병 55
 
1.5%
Other values (288) 2857
80.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 4
100.0%
Punctuation
ValueCountFrequency (%)
“ 1
50.0%
” 1
50.0%

연구수행기관
Text

Distinct76
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T03:15:10.424546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length9.6
Min length3

Characters and Unicode

Total characters1440
Distinct characters171
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

Unique55 ?
Unique (%)36.7%

Sample

1st row바이오엔텍(주)
2nd row케이웨어(주)
3rd row강원대학교 산학협력단
4th row한국화학연구원
5th row(주)체리부로
ValueCountFrequency (%)
산학협력단 52
23.6%
전북대학교산학협력단 15
 
6.8%
주식회사 14
 
6.4%
서울대학교 11
 
5.0%
강원대학교 9
 
4.1%
건국대학교 9
 
4.1%
주)중앙백신연구소 6
 
2.7%
경상대학교 5
 
2.3%
충남대학교 5
 
2.3%
전남대학교 4
 
1.8%
Other values (72) 90
40.9%
2023-12-13T03:15:10.943309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 142
 
9.9%
산 74
 
5.1%
협 71
 
4.9%
단 71
 
4.9%
70
 
4.9%
대 70
 
4.9%
교 69
 
4.8%
력 69
 
4.8%
주 60
 
4.2%
) 46
 
3.2%
Other values (161) 698
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1276
88.6%
Space Separator 70
 
4.9%
Close Punctuation 46
 
3.2%
Open Punctuation 46
 
3.2%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 142
 
11.1%
산 74
 
5.8%
협 71
 
5.6%
단 71
 
5.6%
대 70
 
5.5%
교 69
 
5.4%
력 69
 
5.4%
주 60
 
4.7%
이 23
 
1.8%
북 20
 
1.6%
Other values (156) 607
47.6%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
K 1
50.0%
Space Separator
ValueCountFrequency (%)
70
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1276
88.6%
Common 162
 
11.2%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 142
 
11.1%
산 74
 
5.8%
협 71
 
5.6%
단 71
 
5.6%
대 70
 
5.5%
교 69
 
5.4%
력 69
 
5.4%
주 60
 
4.7%
이 23
 
1.8%
북 20
 
1.6%
Other values (156) 607
47.6%
Common
ValueCountFrequency (%)
70
43.2%
) 46
28.4%
( 46
28.4%
Latin
ValueCountFrequency (%)
T 1
50.0%
K 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1276
88.6%
ASCII 164
 
11.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 142
 
11.1%
산 74
 
5.8%
협 71
 
5.6%
단 71
 
5.6%
대 70
 
5.5%
교 69
 
5.4%
력 69
 
5.4%
주 60
 
4.7%
이 23
 
1.8%
북 20
 
1.6%
Other values (156) 607
47.6%
ASCII
ValueCountFrequency (%)
70
42.7%
) 46
28.0%
( 46
28.0%
T 1
 
0.6%
K 1
 
0.6%

주관기관
Text

Distinct76
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T03:15:11.242138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length9.6
Min length3

Characters and Unicode

Total characters1440
Distinct characters171
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

Unique55 ?
Unique (%)36.7%

Sample

1st row바이오엔텍(주)
2nd row케이웨어(주)
3rd row강원대학교 산학협력단
4th row한국화학연구원
5th row(주)체리부로
ValueCountFrequency (%)
산학협력단 52
23.6%
전북대학교산학협력단 15
 
6.8%
주식회사 14
 
6.4%
서울대학교 11
 
5.0%
강원대학교 9
 
4.1%
건국대학교 9
 
4.1%
주)중앙백신연구소 6
 
2.7%
경상대학교 5
 
2.3%
충남대학교 5
 
2.3%
전남대학교 4
 
1.8%
Other values (72) 90
40.9%
2023-12-13T03:15:11.708754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 142
 
9.9%
산 74
 
5.1%
협 71
 
4.9%
단 71
 
4.9%
70
 
4.9%
대 70
 
4.9%
교 69
 
4.8%
력 69
 
4.8%
주 60
 
4.2%
) 46
 
3.2%
Other values (161) 698
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1276
88.6%
Space Separator 70
 
4.9%
Close Punctuation 46
 
3.2%
Open Punctuation 46
 
3.2%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 142
 
11.1%
산 74
 
5.8%
협 71
 
5.6%
단 71
 
5.6%
대 70
 
5.5%
교 69
 
5.4%
력 69
 
5.4%
주 60
 
4.7%
이 23
 
1.8%
북 20
 
1.6%
Other values (156) 607
47.6%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
K 1
50.0%
Space Separator
ValueCountFrequency (%)
70
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1276
88.6%
Common 162
 
11.2%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 142
 
11.1%
산 74
 
5.8%
협 71
 
5.6%
단 71
 
5.6%
대 70
 
5.5%
교 69
 
5.4%
력 69
 
5.4%
주 60
 
4.7%
이 23
 
1.8%
북 20
 
1.6%
Other values (156) 607
47.6%
Common
ValueCountFrequency (%)
70
43.2%
) 46
28.4%
( 46
28.4%
Latin
ValueCountFrequency (%)
T 1
50.0%
K 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1276
88.6%
ASCII 164
 
11.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 142
 
11.1%
산 74
 
5.8%
협 71
 
5.6%
단 71
 
5.6%
대 70
 
5.5%
교 69
 
5.4%
력 69
 
5.4%
주 60
 
4.7%
이 23
 
1.8%
북 20
 
1.6%
Other values (156) 607
47.6%
ASCII
ValueCountFrequency (%)
70
42.7%
) 46
28.0%
( 46
28.0%
T 1
 
0.6%
K 1
 
0.6%

총연구기관 시작일
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2020-04-29
47 
2018-04-26
26 
2019-05-27
23 
2019-08-30
12 
2018-07-31
10 
Other values (10)
32 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique3 ?
Unique (%)2.0%

Sample

1st row2018-04-26
2nd row2018-04-26
3rd row2018-04-26
4th row2018-07-31
5th row2018-04-26

Common Values

ValueCountFrequency (%)
2020-04-29 47
31.3%
2018-04-26 26
17.3%
2019-05-27 23
15.3%
2019-08-30 12
 
8.0%
2018-07-31 10
 
6.7%
2020-01-31 10
 
6.7%
2018-11-15 7
 
4.7%
2019-05-10 3
 
2.0%
2020-05-29 3
 
2.0%
2020-01-29 2
 
1.3%
Other values (5) 7
 
4.7%

Length

2023-12-13T03:15:11.892965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-04-29 47
31.3%
2018-04-26 26
17.3%
2019-05-27 23
15.3%
2019-08-30 12
 
8.0%
2018-07-31 10
 
6.7%
2020-01-31 10
 
6.7%
2018-11-15 7
 
4.7%
2019-05-10 3
 
2.0%
2020-05-29 3
 
2.0%
2020-01-29 2
 
1.3%
Other values (5) 7
 
4.7%

총연구기간 종료일
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2020-12-31
60 
2021-12-31
60 
2024-01-30
10 
2021-08-14
7 
2023-12-31
 
4
Other values (5)
9 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)1.3%

Sample

1st row2020-12-31
2nd row2020-12-31
3rd row2020-12-31
4th row2020-12-31
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2020-12-31 60
40.0%
2021-12-31 60
40.0%
2024-01-30 10
 
6.7%
2021-08-14 7
 
4.7%
2023-12-31 4
 
2.7%
2021-01-09 3
 
2.0%
2022-01-28 2
 
1.3%
2021-04-19 2
 
1.3%
2022-01-31 1
 
0.7%
2023-02-28 1
 
0.7%

Length

2023-12-13T03:15:12.332566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:15:12.502270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 60
40.0%
2021-12-31 60
40.0%
2024-01-30 10
 
6.7%
2021-08-14 7
 
4.7%
2023-12-31 4
 
2.7%
2021-01-09 3
 
2.0%
2022-01-28 2
 
1.3%
2021-04-19 2
 
1.3%
2022-01-31 1
 
0.7%
2023-02-28 1
 
0.7%

당해년연구기간 시작일
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2020-01-01
71 
2020-04-29
47 
2020-01-31
10 
2020-08-15
 
7
2020-01-10
 
3
Other values (6)
12 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row2020-01-01
2nd row2020-01-01
3rd row2020-01-01
4th row2020-01-01
5th row2020-01-01

Common Values

ValueCountFrequency (%)
2020-01-01 71
47.3%
2020-04-29 47
31.3%
2020-01-31 10
 
6.7%
2020-08-15 7
 
4.7%
2020-01-10 3
 
2.0%
2020-05-29 3
 
2.0%
2020-03-01 2
 
1.3%
2020-01-29 2
 
1.3%
2020-04-20 2
 
1.3%
2020-04-01 2
 
1.3%

Length

2023-12-13T03:15:12.682913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-01-01 71
47.3%
2020-04-29 47
31.3%
2020-01-31 10
 
6.7%
2020-08-15 7
 
4.7%
2020-01-10 3
 
2.0%
2020-05-29 3
 
2.0%
2020-03-01 2
 
1.3%
2020-01-29 2
 
1.3%
2020-04-20 2
 
1.3%
2020-04-01 2
 
1.3%

당해년연구기간 종료일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2020-12-31
125 
2021-01-30
 
10
2021-08-14
 
7
2021-01-09
 
3
2021-01-28
 
2
Other values (2)
 
3

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row2020-12-31
2nd row2020-12-31
3rd row2020-12-31
4th row2020-12-31
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2020-12-31 125
83.3%
2021-01-30 10
 
6.7%
2021-08-14 7
 
4.7%
2021-01-09 3
 
2.0%
2021-01-28 2
 
1.3%
2021-04-19 2
 
1.3%
2021-01-31 1
 
0.7%

Length

2023-12-13T03:15:12.826539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:15:12.979078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 125
83.3%
2021-01-30 10
 
6.7%
2021-08-14 7
 
4.7%
2021-01-09 3
 
2.0%
2021-01-28 2
 
1.3%
2021-04-19 2
 
1.3%
2021-01-31 1
 
0.7%

총연구비
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4842439 × 108
Minimum20000000
Maximum2.13 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T03:15:13.131990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20000000
5-th percentile40000000
Q180000000
median1.2 × 108
Q31.6916675 × 108
95-th percentile3.1516715 × 108
Maximum2.13 × 109
Range2.11 × 109
Interquartile range (IQR)89166750

Descriptive statistics

Standard deviation1.8096315 × 108
Coefficient of variation (CV)1.2192278
Kurtosis97.548389
Mean1.4842439 × 108
Median Absolute Deviation (MAD)44500000
Skewness8.9891694
Sum2.2263659 × 1010
Variance3.2747662 × 1016
MonotonicityNot monotonic
2023-12-13T03:15:13.304050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000000 11
 
7.3%
150000000 8
 
5.3%
120000000 5
 
3.3%
90000000 4
 
2.7%
60000000 4
 
2.7%
50000000 4
 
2.7%
80000000 4
 
2.7%
85000000 3
 
2.0%
45000000 3
 
2.0%
98500000 3
 
2.0%
Other values (89) 101
67.3%
ValueCountFrequency (%)
20000000 1
 
0.7%
24000000 1
 
0.7%
25000000 2
1.3%
30000000 1
 
0.7%
34000000 1
 
0.7%
40000000 3
2.0%
42827000 1
 
0.7%
45000000 3
2.0%
49000000 1
 
0.7%
50000000 4
2.7%
ValueCountFrequency (%)
2130000000 1
0.7%
452625000 1
0.7%
370000000 1
0.7%
356000000 1
0.7%
337333000 1
0.7%
333400000 1
0.7%
322375000 1
0.7%
316667000 1
0.7%
313334000 1
0.7%
280000000 1
0.7%

Interactions

2023-12-13T03:15:06.190252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:15:05.948584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:15:06.322989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:15:06.072787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:15:13.443715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호사업명총괄과제번호연구수행기관주관기관총연구기관 시작일총연구기간 종료일당해년연구기간 시작일당해년연구기간 종료일총연구비
번호1.0000.6710.9810.5850.5850.8380.8210.7360.5510.084
사업명0.6711.0001.0000.7460.7460.9390.9030.8440.8780.871
총괄과제번호0.9811.0001.0000.9150.9151.0001.0001.0001.0000.685
연구수행기관0.5850.7460.9151.0001.0000.8000.8770.9220.9690.000
주관기관0.5850.7460.9151.0001.0000.8000.8770.9220.9690.000
총연구기관 시작일0.8380.9391.0000.8000.8001.0000.9970.9901.0000.785
총연구기간 종료일0.8210.9031.0000.8770.8770.9971.0000.9651.0000.776
당해년연구기간 시작일0.7360.8441.0000.9220.9220.9900.9651.0001.0000.000
당해년연구기간 종료일0.5510.8781.0000.9690.9691.0001.0001.0001.0000.174
총연구비0.0840.8710.6850.0000.0000.7850.7760.0000.1741.000
2023-12-13T03:15:13.605678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총연구기간 종료일사업명당해년연구기간 종료일총연구기관 시작일당해년연구기간 시작일
총연구기간 종료일1.0000.6770.9890.9290.854
사업명0.6771.0000.6760.7140.544
당해년연구기간 종료일0.9890.6761.0000.9720.986
총연구기관 시작일0.9290.7140.9721.0000.938
당해년연구기간 시작일0.8540.5440.9860.9381.000
2023-12-13T03:15:13.749485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호총연구비사업명총연구기관 시작일총연구기간 종료일당해년연구기간 시작일당해년연구기간 종료일
번호1.000-0.0530.3560.4920.3870.4240.315
총연구비-0.0531.0000.5620.5590.5800.0000.119
사업명0.3560.5621.0000.7140.6770.5440.676
총연구기관 시작일0.4920.5590.7141.0000.9290.9380.972
총연구기간 종료일0.3870.5800.6770.9291.0000.8540.989
당해년연구기간 시작일0.4240.0000.5440.9380.8541.0000.986
당해년연구기간 종료일0.3150.1190.6760.9720.9890.9861.000

Missing values

2023-12-13T03:15:06.481855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:15:06.698617image/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

번호분류사업명총괄과제번호세부과제번호과제명연구수행기관주관기관총연구기관 시작일총연구기간 종료일당해년연구기간 시작일당해년연구기간 종료일총연구비
01수의확산방지 및 사후관리318047-3318047033HD020종오리 초생추 품질이력 및 이동정보 관리시스템 구축바이오엔텍(주)바이오엔텍(주)2018-04-262020-12-312020-01-012020-12-3174000000
12수의검역방역기술318040-3318040033SB010농장 단위 방역 강화를 위한 철새 위치추적기 활용 알림 서비스 앱 개발케이웨어(주)케이웨어(주)2018-04-262020-12-312020-01-012020-12-31164000000
23수의검역방역기술318040-3318040033HD020GIS 기반 통계분석 기술을 활용한 조류인플루엔자 확산예측 모형 구축강원대학교 산학협력단강원대학교 산학협력단2018-04-262020-12-312020-01-012020-12-3150000000
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