Overview

Dataset statistics

Number of variables13
Number of observations128
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.4 KiB
Average record size in memory107.0 B

Variable types

Numeric2
Categorical6
Text5

Dataset

Description우리 기관이 보유하고 있는 농림식품R&D 중분류 중 수의 R&D 과제정보 공개 분류,사업명,총괄과제번호,세부과제번호,과제명,연구수행기관,주관기관,총연구기간 시작일,총연구기간 종료일,당해년도연구 시작일,당해년도연구 종료일,총연구비,연구내용요약 으로 구성
Author농림식품기술기획평가원
URLhttps://www.data.go.kr/data/15089673/fileData.do

Alerts

분류 has constant value ""Constant
총연구기간 종료일 is highly overall correlated with 총연구비 and 4 other fieldsHigh correlation
당해년연구기간 시작일 is highly overall correlated with 사업명 and 3 other fieldsHigh correlation
총연구기관 시작일 is highly overall correlated with 총연구비 and 4 other fieldsHigh correlation
번호 is highly overall correlated with 사업명High correlation
총연구비 is highly overall correlated with 사업명 and 2 other fieldsHigh correlation
사업명 is highly overall correlated with 번호 and 5 other fieldsHigh correlation
당해년연구기간 종료일 is highly overall correlated with 사업명 and 3 other fieldsHigh correlation
번호 has unique valuesUnique
세부과제번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:14:02.393121
Analysis finished2023-12-12 00:14:04.051806
Duration1.66 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct128
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.5
Minimum1
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T09:14:04.139498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.35
Q132.75
median64.5
Q396.25
95-th percentile121.65
Maximum128
Range127
Interquartile range (IQR)63.5

Descriptive statistics

Standard deviation37.094474
Coefficient of variation (CV)0.57510812
Kurtosis-1.2
Mean64.5
Median Absolute Deviation (MAD)32
Skewness0
Sum8256
Variance1376
MonotonicityStrictly increasing
2023-12-12T09:14:04.286282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
66 1
 
0.8%
96 1
 
0.8%
95 1
 
0.8%
94 1
 
0.8%
93 1
 
0.8%
92 1
 
0.8%
91 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
Other values (118) 118
92.2%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
128 1
0.8%
127 1
0.8%
126 1
0.8%
125 1
0.8%
124 1
0.8%
123 1
0.8%
122 1
0.8%
121 1
0.8%
120 1
0.8%
119 1
0.8%

분류
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
수의
128 

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 128
100.0%

Length

2023-12-12T09:14:04.428018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:14:04.554358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수의 128
100.0%

사업명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
동물의약품개발
56 
검역방역기술
33 
교육훈련
10 
사회문제해결형 감염병대응기술개발
9 
진단예방기술
 
5
Other values (6)
15 

Length

Max length17
Median length15
Mean length7.609375
Min length4

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
동물의약품개발 56
43.8%
검역방역기술 33
25.8%
교육훈련 10
 
7.8%
사회문제해결형 감염병대응기술개발 9
 
7.0%
진단예방기술 5
 
3.9%
내역사업명없음 3
 
2.3%
민간중심 R&D 사업화 지원 3
 
2.3%
산업기반연구 3
 
2.3%
유용 농생명자원 산업화 기술개발 3
 
2.3%
공공기술 사업화 촉진 2
 
1.6%

Length

2023-12-12T09:14:04.651943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동물의약품개발 56
35.2%
검역방역기술 33
20.8%
교육훈련 10
 
6.3%
사회문제해결형 9
 
5.7%
감염병대응기술개발 9
 
5.7%
사업화 5
 
3.1%
진단예방기술 5
 
3.1%
내역사업명없음 3
 
1.9%
민간중심 3
 
1.9%
r&d 3
 
1.9%
Other values (9) 23
14.5%

총괄과제번호
Text

Distinct55
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T09:14:04.867604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1024
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

Unique11 ?
Unique (%)8.6%

Sample

1st row120102-2
2nd row120102-2
3rd row121008-1
4th row121010-1
5th row121010-1
ValueCountFrequency (%)
320005-4 10
 
7.8%
321012-1 4
 
3.1%
321017-1 4
 
3.1%
119081-5 4
 
3.1%
121010-1 3
 
2.3%
120091-2 3
 
2.3%
320071-2 3
 
2.3%
321009-1 3
 
2.3%
321014-1 3
 
2.3%
321016-1 3
 
2.3%
Other values (45) 88
68.8%
2023-12-12T09:14:05.204014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 233
22.8%
1 192
18.8%
2 189
18.5%
- 128
12.5%
3 107
10.4%
5 37
 
3.6%
4 35
 
3.4%
9 30
 
2.9%
6 28
 
2.7%
8 24
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 896
87.5%
Dash Punctuation 128
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 233
26.0%
1 192
21.4%
2 189
21.1%
3 107
11.9%
5 37
 
4.1%
4 35
 
3.9%
9 30
 
3.3%
6 28
 
3.1%
8 24
 
2.7%
7 21
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 128
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1024
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 233
22.8%
1 192
18.8%
2 189
18.5%
- 128
12.5%
3 107
10.4%
5 37
 
3.6%
4 35
 
3.4%
9 30
 
2.9%
6 28
 
2.7%
8 24
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 233
22.8%
1 192
18.8%
2 189
18.5%
- 128
12.5%
3 107
10.4%
5 37
 
3.6%
4 35
 
3.4%
9 30
 
2.9%
6 28
 
2.7%
8 24
 
2.3%

세부과제번호
Text

UNIQUE 

Distinct128
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T09:14:05.430898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1792
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

Unique128 ?
Unique (%)100.0%

Sample

1st row120102022SB010
2nd row120102022HD030
3rd row121008011SB010
4th row121010011HD030
5th row121010011SB010
ValueCountFrequency (%)
120102022sb010 1
 
0.8%
120102022hd030 1
 
0.8%
321016011sb010 1
 
0.8%
321016011hd020 1
 
0.8%
321016011hd030 1
 
0.8%
321015011sb010 1
 
0.8%
321015011hd020 1
 
0.8%
321014011sb010 1
 
0.8%
321014011hd030 1
 
0.8%
321014011hd020 1
 
0.8%
Other values (118) 118
92.2%
2023-12-12T09:14:05.784491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 616
34.4%
1 300
16.7%
2 293
16.4%
3 138
 
7.7%
H 66
 
3.7%
D 66
 
3.7%
S 62
 
3.5%
B 62
 
3.5%
4 41
 
2.3%
5 38
 
2.1%
Other values (5) 110
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1535
85.7%
Uppercase Letter 256
 
14.3%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 616
40.1%
1 300
19.5%
2 293
19.1%
3 138
 
9.0%
4 41
 
2.7%
5 38
 
2.5%
9 31
 
2.0%
6 30
 
2.0%
8 25
 
1.6%
7 23
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
H 66
25.8%
D 66
25.8%
S 62
24.2%
B 62
24.2%
Lowercase Letter
ValueCountFrequency (%)
a 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1535
85.7%
Latin 257
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 616
40.1%
1 300
19.5%
2 293
19.1%
3 138
 
9.0%
4 41
 
2.7%
5 38
 
2.5%
9 31
 
2.0%
6 30
 
2.0%
8 25
 
1.6%
7 23
 
1.5%
Latin
ValueCountFrequency (%)
H 66
25.7%
D 66
25.7%
S 62
24.1%
B 62
24.1%
a 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 616
34.4%
1 300
16.7%
2 293
16.4%
3 138
 
7.7%
H 66
 
3.7%
D 66
 
3.7%
S 62
 
3.5%
B 62
 
3.5%
4 41
 
2.3%
5 38
 
2.1%
Other values (5) 110
 
6.1%

과제명
Text

Distinct109
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T09:14:06.164766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length73
Median length44
Mean length33.976562
Min length12

Characters and Unicode

Total characters4349
Distinct characters333
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)77.3%

Sample

1st row사물지능 기반 구제역 백신 부작용 제어 시스템 구축 연구
2nd row구제역 백신 부작용 제어를 위한 농장 내 영상 및 열화상 관제 시스템 구축
3rd row긴급방역용 식물 기반 아프리카 돼지열병(ASF)
4th row농장주의 효율적인 질병 인지를 위한 조기 예찰 시스템 개발
5th row농장주의 효율적인 질병 인지를 위한 조기 예찰 시스템 개발
ValueCountFrequency (%)
및 59
 
5.5%
개발 59
 
5.5%
위한 25
 
2.3%
연구 21
 
2.0%
바이러스 19
 
1.8%
백신 16
 
1.5%
시스템 16
 
1.5%
구축 15
 
1.4%
asf 15
 
1.4%
돼지 14
 
1.3%
Other values (458) 815
75.9%
2023-12-12T09:14:06.604004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
954
 
21.9%
발 80
 
1.8%
개 77
 
1.8%
이 66
 
1.5%
스 64
 
1.5%
및 59
 
1.4%
용 59
 
1.4%
지 58
 
1.3%
한 56
 
1.3%
구 52
 
1.2%
Other values (323) 2824
64.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3002
69.0%
Space Separator 954
 
21.9%
Uppercase Letter 173
 
4.0%
Lowercase Letter 97
 
2.2%
Decimal Number 41
 
0.9%
Open Punctuation 26
 
0.6%
Close Punctuation 26
 
0.6%
Other Punctuation 17
 
0.4%
Dash Punctuation 13
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
발 80
 
2.7%
개 77
 
2.6%
이 66
 
2.2%
스 64
 
2.1%
및 59
 
2.0%
용 59
 
2.0%
지 58
 
1.9%
한 56
 
1.9%
구 52
 
1.7%
병 52
 
1.7%
Other values (274) 2379
79.2%
Lowercase Letter
ValueCountFrequency (%)
a 16
16.5%
i 14
14.4%
d 7
 
7.2%
n 7
 
7.2%
t 7
 
7.2%
l 6
 
6.2%
s 5
 
5.2%
e 5
 
5.2%
k 4
 
4.1%
o 4
 
4.1%
Other values (9) 22
22.7%
Uppercase Letter
ValueCountFrequency (%)
S 34
19.7%
A 30
17.3%
F 27
15.6%
P 13
 
7.5%
V 10
 
5.8%
I 10
 
5.8%
D 7
 
4.0%
C 7
 
4.0%
G 6
 
3.5%
R 5
 
2.9%
Other values (8) 24
13.9%
Decimal Number
ValueCountFrequency (%)
1 16
39.0%
2 13
31.7%
3 10
24.4%
7 1
 
2.4%
9 1
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 10
58.8%
/ 6
35.3%
: 1
 
5.9%
Space Separator
ValueCountFrequency (%)
954
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3002
69.0%
Common 1077
 
24.8%
Latin 270
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
발 80
 
2.7%
개 77
 
2.6%
이 66
 
2.2%
스 64
 
2.1%
및 59
 
2.0%
용 59
 
2.0%
지 58
 
1.9%
한 56
 
1.9%
구 52
 
1.7%
병 52
 
1.7%
Other values (274) 2379
79.2%
Latin
ValueCountFrequency (%)
S 34
 
12.6%
A 30
 
11.1%
F 27
 
10.0%
a 16
 
5.9%
i 14
 
5.2%
P 13
 
4.8%
V 10
 
3.7%
I 10
 
3.7%
D 7
 
2.6%
d 7
 
2.6%
Other values (27) 102
37.8%
Common
ValueCountFrequency (%)
954
88.6%
( 26
 
2.4%
) 26
 
2.4%
1 16
 
1.5%
2 13
 
1.2%
- 13
 
1.2%
. 10
 
0.9%
3 10
 
0.9%
/ 6
 
0.6%
7 1
 
0.1%
Other values (2) 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2998
68.9%
ASCII 1347
31.0%
Compat Jamo 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
954
70.8%
S 34
 
2.5%
A 30
 
2.2%
F 27
 
2.0%
( 26
 
1.9%
) 26
 
1.9%
1 16
 
1.2%
a 16
 
1.2%
i 14
 
1.0%
2 13
 
1.0%
Other values (39) 191
 
14.2%
Hangul
ValueCountFrequency (%)
발 80
 
2.7%
개 77
 
2.6%
이 66
 
2.2%
스 64
 
2.1%
및 59
 
2.0%
용 59
 
2.0%
지 58
 
1.9%
한 56
 
1.9%
구 52
 
1.7%
병 52
 
1.7%
Other values (273) 2375
79.2%
Compat Jamo
ValueCountFrequency (%)
ㆍ 4
100.0%

연구수행기관
Text

Distinct72
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T09:14:06.828098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13.5
Mean length9.3515625
Min length3

Characters and Unicode

Total characters1197
Distinct characters164
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)40.6%

Sample

1st row인트플로우 주식회사
2nd row엔에이치네트웍스
3rd row(주)바이오앱
4th row건국대학교
5th row건국대학교
ValueCountFrequency (%)
산학협력단 35
19.2%
주식회사 16
 
8.8%
전북대학교산학협력단 15
 
8.2%
건국대학교 8
 
4.4%
강원대학교 7
 
3.8%
농림축산검역본부 6
 
3.3%
서울대학교 5
 
2.7%
경북대학교 5
 
2.7%
주)중앙백신연구소 5
 
2.7%
충북대학교 3
 
1.6%
Other values (65) 77
42.3%
2023-12-12T09:14:07.431335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 112
 
9.4%
산 63
 
5.3%
대 57
 
4.8%
단 56
 
4.7%
교 55
 
4.6%
협 54
 
4.5%
54
 
4.5%
주 54
 
4.5%
력 53
 
4.4%
) 34
 
2.8%
Other values (154) 605
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1075
89.8%
Space Separator 54
 
4.5%
Close Punctuation 34
 
2.8%
Open Punctuation 34
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 112
 
10.4%
산 63
 
5.9%
대 57
 
5.3%
단 56
 
5.2%
교 55
 
5.1%
협 54
 
5.0%
주 54
 
5.0%
력 53
 
4.9%
북 25
 
2.3%
전 18
 
1.7%
Other values (151) 528
49.1%
Space Separator
ValueCountFrequency (%)
54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1075
89.8%
Common 122
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 112
 
10.4%
산 63
 
5.9%
대 57
 
5.3%
단 56
 
5.2%
교 55
 
5.1%
협 54
 
5.0%
주 54
 
5.0%
력 53
 
4.9%
북 25
 
2.3%
전 18
 
1.7%
Other values (151) 528
49.1%
Common
ValueCountFrequency (%)
54
44.3%
) 34
27.9%
( 34
27.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1075
89.8%
ASCII 122
 
10.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 112
 
10.4%
산 63
 
5.9%
대 57
 
5.3%
단 56
 
5.2%
교 55
 
5.1%
협 54
 
5.0%
주 54
 
5.0%
력 53
 
4.9%
북 25
 
2.3%
전 18
 
1.7%
Other values (151) 528
49.1%
ASCII
ValueCountFrequency (%)
54
44.3%
) 34
27.9%
( 34
27.9%

주관기관
Text

Distinct72
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T09:14:07.697275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13.5
Mean length9.3515625
Min length3

Characters and Unicode

Total characters1197
Distinct characters164
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)40.6%

Sample

1st row인트플로우 주식회사
2nd row엔에이치네트웍스
3rd row(주)바이오앱
4th row건국대학교
5th row건국대학교
ValueCountFrequency (%)
산학협력단 35
19.2%
주식회사 16
 
8.8%
전북대학교산학협력단 15
 
8.2%
건국대학교 8
 
4.4%
강원대학교 7
 
3.8%
농림축산검역본부 6
 
3.3%
서울대학교 5
 
2.7%
경북대학교 5
 
2.7%
주)중앙백신연구소 5
 
2.7%
충북대학교 3
 
1.6%
Other values (65) 77
42.3%
2023-12-12T09:14:08.134835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 112
 
9.4%
산 63
 
5.3%
대 57
 
4.8%
단 56
 
4.7%
교 55
 
4.6%
협 54
 
4.5%
54
 
4.5%
주 54
 
4.5%
력 53
 
4.4%
) 34
 
2.8%
Other values (154) 605
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1075
89.8%
Space Separator 54
 
4.5%
Close Punctuation 34
 
2.8%
Open Punctuation 34
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 112
 
10.4%
산 63
 
5.9%
대 57
 
5.3%
단 56
 
5.2%
교 55
 
5.1%
협 54
 
5.0%
주 54
 
5.0%
력 53
 
4.9%
북 25
 
2.3%
전 18
 
1.7%
Other values (151) 528
49.1%
Space Separator
ValueCountFrequency (%)
54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1075
89.8%
Common 122
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 112
 
10.4%
산 63
 
5.9%
대 57
 
5.3%
단 56
 
5.2%
교 55
 
5.1%
협 54
 
5.0%
주 54
 
5.0%
력 53
 
4.9%
북 25
 
2.3%
전 18
 
1.7%
Other values (151) 528
49.1%
Common
ValueCountFrequency (%)
54
44.3%
) 34
27.9%
( 34
27.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1075
89.8%
ASCII 122
 
10.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 112
 
10.4%
산 63
 
5.9%
대 57
 
5.3%
단 56
 
5.2%
교 55
 
5.1%
협 54
 
5.0%
주 54
 
5.0%
력 53
 
4.9%
북 25
 
2.3%
전 18
 
1.7%
Other values (151) 528
49.1%
ASCII
ValueCountFrequency (%)
54
44.3%
) 34
27.9%
( 34
27.9%

총연구기관 시작일
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2021-04-01
52 
2020-04-29
45 
2020-01-31
10 
2019-08-30
6 
2019-05-27
 
4
Other values (5)
11 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row2020-05-29
2nd row2020-05-29
3rd row2021-04-01
4th row2021-04-01
5th row2021-04-01

Common Values

ValueCountFrequency (%)
2021-04-01 52
40.6%
2020-04-29 45
35.2%
2020-01-31 10
 
7.8%
2019-08-30 6
 
4.7%
2019-05-27 4
 
3.1%
2021-04-07 3
 
2.3%
2020-01-29 3
 
2.3%
2020-05-29 2
 
1.6%
2020-04-01 2
 
1.6%
2016-02-29 1
 
0.8%

Length

2023-12-12T09:14:08.273154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:14:08.394269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04-01 52
40.6%
2020-04-29 45
35.2%
2020-01-31 10
 
7.8%
2019-08-30 6
 
4.7%
2019-05-27 4
 
3.1%
2021-04-07 3
 
2.3%
2020-01-29 3
 
2.3%
2020-05-29 2
 
1.6%
2020-04-01 2
 
1.6%
2016-02-29 1
 
0.8%

총연구기간 종료일
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2021-12-31
55 
2022-03-31
44 
2024-01-30
10 
2023-12-31
9 
2024-12-31
 
3
Other values (3)
7 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row2021-12-31
2nd row2021-12-31
3rd row2022-03-31
4th row2022-03-31
5th row2022-03-31

Common Values

ValueCountFrequency (%)
2021-12-31 55
43.0%
2022-03-31 44
34.4%
2024-01-30 10
 
7.8%
2023-12-31 9
 
7.0%
2024-12-31 3
 
2.3%
2022-01-28 3
 
2.3%
2022-12-31 3
 
2.3%
2023-02-28 1
 
0.8%

Length

2023-12-12T09:14:08.557451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:14:08.700747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-12-31 55
43.0%
2022-03-31 44
34.4%
2024-01-30 10
 
7.8%
2023-12-31 9
 
7.0%
2024-12-31 3
 
2.3%
2022-01-28 3
 
2.3%
2022-12-31 3
 
2.3%
2023-02-28 1
 
0.8%

당해년연구기간 시작일
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2021-01-01
60 
2021-04-01
52 
2021-01-31
10 
2021-04-07
 
3
2021-01-29
 
3

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-01
2nd row2021-01-01
3rd row2021-04-01
4th row2021-04-01
5th row2021-04-01

Common Values

ValueCountFrequency (%)
2021-01-01 60
46.9%
2021-04-01 52
40.6%
2021-01-31 10
 
7.8%
2021-04-07 3
 
2.3%
2021-01-29 3
 
2.3%

Length

2023-12-12T09:14:08.834984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:14:08.946067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-01-01 60
46.9%
2021-04-01 52
40.6%
2021-01-31 10
 
7.8%
2021-04-07 3
 
2.3%
2021-01-29 3
 
2.3%

당해년연구기간 종료일
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2021-12-31
71 
2022-03-31
43 
2022-01-30
10 
2022-01-28
 
3
2022-03-01
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row2021-12-31
2nd row2021-12-31
3rd row2022-03-31
4th row2022-03-31
5th row2022-03-31

Common Values

ValueCountFrequency (%)
2021-12-31 71
55.5%
2022-03-31 43
33.6%
2022-01-30 10
 
7.8%
2022-01-28 3
 
2.3%
2022-03-01 1
 
0.8%

Length

2023-12-12T09:14:09.058833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:14:09.194215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-12-31 71
55.5%
2022-03-31 43
33.6%
2022-01-30 10
 
7.8%
2022-01-28 3
 
2.3%
2022-03-01 1
 
0.8%

총연구비
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7972864 × 108
Minimum25000000
Maximum2.13 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T09:14:09.337294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25000000
5-th percentile46750000
Q199427250
median1.4676 × 108
Q32 × 108
95-th percentile4.1290065 × 108
Maximum2.13 × 109
Range2.105 × 109
Interquartile range (IQR)1.0057275 × 108

Descriptive statistics

Standard deviation2.0596124 × 108
Coefficient of variation (CV)1.1459567
Kurtosis63.971859
Mean1.7972864 × 108
Median Absolute Deviation (MAD)53210500
Skewness7.0422311
Sum2.3005266 × 1010
Variance4.2420032 × 1016
MonotonicityNot monotonic
2023-12-12T09:14:09.474294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000000 11
 
8.6%
150000000 11
 
8.6%
200000000 5
 
3.9%
50000000 5
 
3.9%
134000000 3
 
2.3%
133400000 3
 
2.3%
66700000 3
 
2.3%
250000000 3
 
2.3%
130000000 3
 
2.3%
60000000 3
 
2.3%
Other values (71) 78
60.9%
ValueCountFrequency (%)
25000000 1
 
0.8%
30000000 1
 
0.8%
34000000 1
 
0.8%
40000000 2
 
1.6%
45000000 2
 
1.6%
50000000 5
3.9%
57173000 1
 
0.8%
60000000 3
2.3%
62000000 1
 
0.8%
63000000 1
 
0.8%
ValueCountFrequency (%)
2130000000 1
0.8%
730000000 1
0.8%
533340000 1
0.8%
500000000 1
0.8%
473000000 1
0.8%
452625000 1
0.8%
436001000 1
0.8%
370000000 1
0.8%
359334000 1
0.8%
350000000 1
0.8%

Interactions

2023-12-12T09:14:03.567900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:14:03.382543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:14:03.655075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:14:03.468267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:14:09.562347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호사업명총괄과제번호연구수행기관주관기관총연구기관 시작일총연구기간 종료일당해년연구기간 시작일당해년연구기간 종료일총연구비
번호1.0000.8430.9920.7090.7090.8240.6880.8190.7650.170
사업명0.8431.0001.0000.8120.8120.8950.9620.9490.8640.761
총괄과제번호0.9921.0001.0000.9260.9261.0001.0001.0000.9740.888
연구수행기관0.7090.8120.9261.0001.0000.0000.6030.8380.8840.540
주관기관0.7090.8120.9261.0001.0000.0000.6030.8380.8840.540
총연구기관 시작일0.8240.8951.0000.0000.0001.0000.9521.0000.9830.875
총연구기간 종료일0.6880.9621.0000.6030.6030.9521.0000.9840.9150.726
당해년연구기간 시작일0.8190.9491.0000.8380.8381.0000.9841.0000.9850.603
당해년연구기간 종료일0.7650.8640.9740.8840.8840.9830.9150.9851.0000.000
총연구비0.1700.7610.8880.5400.5400.8750.7260.6030.0001.000
2023-12-12T09:14:09.673611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업명총연구기간 종료일당해년연구기간 종료일당해년연구기간 시작일총연구기관 시작일
사업명1.0000.8760.6860.8560.661
총연구기간 종료일0.8761.0000.8470.9770.846
당해년연구기간 종료일0.6860.8471.0000.8230.798
당해년연구기간 시작일0.8560.9770.8231.0000.979
총연구기관 시작일0.6610.8460.7980.9791.000
2023-12-12T09:14:09.769444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호총연구비사업명총연구기관 시작일총연구기간 종료일당해년연구기간 시작일당해년연구기간 종료일
번호1.000-0.0360.5640.3900.4140.4670.412
총연구비-0.0361.0000.5360.5380.5520.2630.000
사업명0.5640.5361.0000.6610.8760.8560.686
총연구기관 시작일0.3900.5380.6611.0000.8460.9790.798
총연구기간 종료일0.4140.5520.8760.8461.0000.9770.847
당해년연구기간 시작일0.4670.2630.8560.9790.9771.0000.823
당해년연구기간 종료일0.4120.0000.6860.7980.8470.8231.000

Missing values

2023-12-12T09:14:03.776337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:14:03.964747image/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
01수의검역방역기술120102-2120102022SB010사물지능 기반 구제역 백신 부작용 제어 시스템 구축 연구인트플로우 주식회사인트플로우 주식회사2020-05-292021-12-312021-01-012021-12-31125829000
12수의검역방역기술120102-2120102022HD030구제역 백신 부작용 제어를 위한 농장 내 영상 및 열화상 관제 시스템 구축엔에이치네트웍스엔에이치네트웍스2020-05-292021-12-312021-01-012021-12-31131582000
23수의검역방역기술121008-1121008011SB010긴급방역용 식물 기반 아프리카 돼지열병(ASF)(주)바이오앱(주)바이오앱2021-04-012022-03-312021-04-012022-03-31533340000
34수의검역방역기술121010-1121010011HD030농장주의 효율적인 질병 인지를 위한 조기 예찰 시스템 개발건국대학교건국대학교2021-04-012022-03-312021-04-012022-03-31100000000
45수의검역방역기술121010-1121010011SB010농장주의 효율적인 질병 인지를 위한 조기 예찰 시스템 개발건국대학교건국대학교2021-04-012022-03-312021-04-012022-03-31236750000
56수의검역방역기술121010-1121010011HD020농장주의 효율적인 질병 인지를 위한 조기 예찰 시스템 개발(주)체리부로(주)체리부로2021-04-012022-03-312021-04-012022-03-31187084000
67수의검역방역기술320054-2320054022HD020GIS 기반 ASF 예찰 통합 상황판 : 원포인트 ASF 개발인플랩인플랩2020-04-292021-12-312021-01-012021-12-3197709000
78수의검역방역기술320054-2320054022SB010ASF 발생 예방 및 대응을 위한 양돈 농장간 네트워크 예찰 시스템 구축 연구인트플로우 주식회사인트플로우 주식회사2020-04-292021-12-312021-01-012021-12-31201118000
89수의검역방역기술320054-2320054022HD030ASF 발생 예방과 확산방지를 위한 ASF 대처 방안 및 방역 전략 연구전남대학교 산학협력단전남대학교 산학협력단2020-04-292021-12-312021-01-012021-12-3157173000
910수의검역방역기술320055-2320055022HD020ASF 전파모형 설계를 위한 공간 자료 구축과 수리모델개발강원대학교 산학협력단강원대학교 산학협력단2020-04-292021-12-312021-01-012021-12-31158000000
번호분류사업명총괄과제번호세부과제번호과제명연구수행기관주관기관총연구기관 시작일총연구기간 종료일당해년연구기간 시작일당해년연구기간 종료일총연구비
118119수의산업기반연구120011-2120011022SB010한우에서의 OPU 유래 이식가능 수정란생산 전문 인력양성(주)라트바이오(주)라트바이오2020-01-292022-01-282021-01-292022-01-28134000000
119120수의연구지원716002-7716002076SB110방제기술 체계화 및 전문가양성 프로그램 개발전북대학교산학협력단전북대학교산학협력단2016-02-292023-02-282021-01-012021-12-312130000000
120121수의유용 농생명자원 산업화 기술개발121040-2121040021SB010두릅 및 귤피 혼합추출물을 이용한 간건강에 유용한 반려동물용 헬스케어 제품 개발(주)벡스퍼트(주)벡스퍼트2021-04-012022-12-312021-04-012021-12-31134000000
121122수의유용 농생명자원 산업화 기술개발121040-2121040021HD020두릅 및 귤피 혼합추출물을 이용한 간건강에 유용한 반려동물용 헬스케어 제품 개발전북대학교산학협력단전북대학교산학협력단2021-04-012022-12-312021-04-012021-12-3195000000
122123수의유용 농생명자원 산업화 기술개발121040-2121040021HD030두릅 및 귤피 혼합추출물을 이용한 간건강에 유용한 반려동물용 헬스케어 제품 개발주식회사 차온주식회사 차온2021-04-012022-12-312021-04-012021-12-3140000000
123124수의진단예방기술320060-2320060022SB010현장검사 및 실험실검사기법 구축과 평가 및 진단결과 관리시스템 구축전북대학교산학협력단전북대학교산학협력단2020-04-292021-12-312021-01-012021-12-31231000000
124125수의진단예방기술320060-2320060022HD020pen-side PCR 검사체계 구축(주)제넷바이오(주)제넷바이오2020-04-292021-12-312021-01-012021-12-31183667000
125126수의진단예방기술320071-2320071022SB010돈군 구강액 이용 ASF 진단기법 개발연세대학교 원주산학협력단연세대학교 원주산학협력단2020-04-292021-12-312021-01-012021-12-31217000000
126127수의진단예방기술320071-2320071022HD020돈군 구강액 이용 ASF 진단기법 현장평가충청북도동물위생시험소중부지소충청북도동물위생시험소중부지소2020-04-292021-12-312021-01-012021-12-3140000000
127128수의진단예방기술320071-2320071022HD030돈군 구강액용 ASFV 진단키트 제작(주)파이지노믹스(주)파이지노믹스2020-04-292021-12-312021-01-012021-12-3166700000