Overview

Dataset statistics

Number of variables13
Number of observations437
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.4 KiB
Average record size in memory106.3 B

Variable types

Numeric2
Categorical6
Text5

Dataset

Description우리 기관이 보유하고 있는 농림식품R&D 중분류 중 농림식품 R&D 식품 과제정보 공개 농림식품RnD 관련 연구성과로 창출된 데이터를 제공합니다.
Author농림식품기술기획평가원
URLhttps://www.data.go.kr/data/15025753/fileData.do

Alerts

분류 has constant value ""Constant
사업명 is highly overall correlated with 총연구기간 시작일 and 3 other fieldsHigh correlation
당해년도연구종료일 is highly overall correlated with 사업명 and 3 other fieldsHigh correlation
총연구기간 시작일 is highly overall correlated with 번호 and 4 other fieldsHigh correlation
당해년도연구시작일 is highly overall correlated with 사업명 and 3 other fieldsHigh correlation
총연구기간 종료일 is highly overall correlated with 사업명 and 3 other fieldsHigh correlation
번호 is highly overall correlated with 총연구기간 시작일High correlation
번호 has unique valuesUnique
세부과제번호 has unique valuesUnique
총연구비 has 10 (2.3%) zerosZeros

Reproduction

Analysis started2023-12-12 13:06:33.682972
Analysis finished2023-12-12 13:06:35.247995
Duration1.57 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct437
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219
Minimum1
Maximum437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T22:06:35.330605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22.8
Q1110
median219
Q3328
95-th percentile415.2
Maximum437
Range436
Interquartile range (IQR)218

Descriptive statistics

Standard deviation126.29529
Coefficient of variation (CV)0.57669082
Kurtosis-1.2
Mean219
Median Absolute Deviation (MAD)109
Skewness0
Sum95703
Variance15950.5
MonotonicityStrictly increasing
2023-12-12T22:06:35.465149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
289 1
 
0.2%
300 1
 
0.2%
299 1
 
0.2%
298 1
 
0.2%
297 1
 
0.2%
296 1
 
0.2%
295 1
 
0.2%
294 1
 
0.2%
293 1
 
0.2%
Other values (427) 427
97.7%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
437 1
0.2%
436 1
0.2%
435 1
0.2%
434 1
0.2%
433 1
0.2%
432 1
0.2%
431 1
0.2%
430 1
0.2%
429 1
0.2%
428 1
0.2%

분류
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
식품
437 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품 437
100.0%

Length

2023-12-12T22:06:35.602357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:06:35.703146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품 437
100.0%

사업명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
고부가가치식품기술개발
200 
미래형혁신식품기술개발
83 
농식품연구성과후속지원
30 
기술사업화지원
24 
수출전략기술개발
24 
Other values (6)
76 

Length

Max length24
Median length11
Mean length11.491991
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
고부가가치식품기술개발 200
45.8%
미래형혁신식품기술개발 83
19.0%
농식품연구성과후속지원 30
 
6.9%
기술사업화지원 24
 
5.5%
수출전략기술개발 24
 
5.5%
포스트게놈 신산업육성을 위한 다부처유전체사업 20
 
4.6%
농생명산업기술개발 15
 
3.4%
농식품수출비즈니스전략모델구축 14
 
3.2%
농림축산식품 연구센터 지원 13
 
3.0%
농축산물안전생산유통관리기술개발 13
 
3.0%

Length

2023-12-12T22:06:35.858506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고부가가치식품기술개발 200
38.2%
미래형혁신식품기술개발 83
15.9%
농식품연구성과후속지원 30
 
5.7%
기술사업화지원 24
 
4.6%
수출전략기술개발 24
 
4.6%
포스트게놈 20
 
3.8%
신산업육성을 20
 
3.8%
위한 20
 
3.8%
다부처유전체사업 20
 
3.8%
농생명산업기술개발 15
 
2.9%
Other values (6) 67
 
12.8%

총괄과제번호
Text

Distinct152
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-12-12T22:06:36.193237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3496
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

Unique29 ?
Unique (%)6.6%

Sample

1st row117051-3
2nd row117053-3
3rd row318027-4
4th row119048-1
5th row118048-3
ValueCountFrequency (%)
617074-5 8
 
1.8%
714001-7 7
 
1.6%
617068-5 7
 
1.6%
617067-5 7
 
1.6%
318027-4 7
 
1.6%
318090-3 7
 
1.6%
710012-3 6
 
1.4%
118011-3 6
 
1.4%
319090-3 5
 
1.1%
918003-4 5
 
1.1%
Other values (142) 372
85.1%
2023-12-12T22:06:36.691305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 906
25.9%
0 500
14.3%
3 466
13.3%
- 437
12.5%
9 280
 
8.0%
8 214
 
6.1%
7 212
 
6.1%
2 155
 
4.4%
4 142
 
4.1%
5 94
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3059
87.5%
Dash Punctuation 437
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 906
29.6%
0 500
16.3%
3 466
15.2%
9 280
 
9.2%
8 214
 
7.0%
7 212
 
6.9%
2 155
 
5.1%
4 142
 
4.6%
5 94
 
3.1%
6 90
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 437
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3496
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 906
25.9%
0 500
14.3%
3 466
13.3%
- 437
12.5%
9 280
 
8.0%
8 214
 
6.1%
7 212
 
6.1%
2 155
 
4.4%
4 142
 
4.1%
5 94
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 906
25.9%
0 500
14.3%
3 466
13.3%
- 437
12.5%
9 280
 
8.0%
8 214
 
6.1%
7 212
 
6.1%
2 155
 
4.4%
4 142
 
4.1%
5 94
 
2.7%

세부과제번호
Text

UNIQUE 

Distinct437
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-12-12T22:06:36.914596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters6118
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique437 ?
Unique (%)100.0%

Sample

1st row117051033SB010
2nd row117053033SB010
3rd row318027042HD050
4th row119048011SB010
5th row118048032SB010
ValueCountFrequency (%)
117051033sb010 1
 
0.2%
617074053hd220 1
 
0.2%
119028031sb010 1
 
0.2%
119017031sb020 1
 
0.2%
119019021sb010 1
 
0.2%
119028031hd040 1
 
0.2%
319049031hd050 1
 
0.2%
119027031hd030 1
 
0.2%
319045031hd020 1
 
0.2%
714001076sb320 1
 
0.2%
Other values (427) 427
97.7%
2023-12-12T22:06:37.228953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1720
28.1%
1 1358
22.2%
3 670
 
11.0%
2 385
 
6.3%
9 280
 
4.6%
8 214
 
3.5%
7 212
 
3.5%
H 206
 
3.4%
D 206
 
3.4%
4 188
 
3.1%
Other values (6) 679
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5244
85.7%
Uppercase Letter 874
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1720
32.8%
1 1358
25.9%
3 670
 
12.8%
2 385
 
7.3%
9 280
 
5.3%
8 214
 
4.1%
7 212
 
4.0%
4 188
 
3.6%
5 116
 
2.2%
6 101
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
H 206
23.6%
D 206
23.6%
S 175
20.0%
B 175
20.0%
W 56
 
6.4%
T 56
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 5244
85.7%
Latin 874
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1720
32.8%
1 1358
25.9%
3 670
 
12.8%
2 385
 
7.3%
9 280
 
5.3%
8 214
 
4.1%
7 212
 
4.0%
4 188
 
3.6%
5 116
 
2.2%
6 101
 
1.9%
Latin
ValueCountFrequency (%)
H 206
23.6%
D 206
23.6%
S 175
20.0%
B 175
20.0%
W 56
 
6.4%
T 56
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1720
28.1%
1 1358
22.2%
3 670
 
11.0%
2 385
 
6.3%
9 280
 
4.6%
8 214
 
3.5%
7 212
 
3.5%
H 206
 
3.4%
D 206
 
3.4%
4 188
 
3.1%
Other values (6) 679
 
11.1%

과제명
Text

Distinct411
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-12-12T22:06:37.473698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length82
Median length51
Mean length33.06865
Min length12

Characters and Unicode

Total characters14451
Distinct characters502
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

Unique388 ?
Unique (%)88.8%

Sample

1st row원발성 및 이차성 골다공증 개선 효과를 갖는 기능성프로바이오틱스 제품 개발
2nd row개똥쑥 추출물 대량생산 공정 확립과 전임상 기능성연구 및 인체적용시험을 통한 효능 확인
3rd row인지기능 및 불안, 우울 인체시험
4th row아로니아를 활용한 천연발효빵 및 발효 콩포트 개발
5th row곡류 저장 안전성 향상을 위한 천연 유래 방충 유효 성분 선발 및 효과 검증
ValueCountFrequency (%)
및 292
 
7.9%
개발 202
 
5.5%
이용한 69
 
1.9%
활용한 59
 
1.6%
기능성 39
 
1.1%
개선 39
 
1.1%
연구 37
 
1.0%
위한 36
 
1.0%
소재 35
 
1.0%
기술 34
 
0.9%
Other values (1374) 2832
77.1%
2023-12-12T22:06:37.865920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3247
 
22.5%
발 336
 
2.3%
개 322
 
2.2%
기 295
 
2.0%
및 293
 
2.0%
품 258
 
1.8%
용 255
 
1.8%
한 241
 
1.7%
화 221
 
1.5%
성 197
 
1.4%
Other values (492) 8786
60.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10645
73.7%
Space Separator 3247
 
22.5%
Lowercase Letter 315
 
2.2%
Uppercase Letter 87
 
0.6%
Other Punctuation 66
 
0.5%
Close Punctuation 34
 
0.2%
Open Punctuation 34
 
0.2%
Decimal Number 15
 
0.1%
Dash Punctuation 6
 
< 0.1%
Initial Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
발 336
 
3.2%
개 322
 
3.0%
기 295
 
2.8%
및 293
 
2.8%
품 258
 
2.4%
용 255
 
2.4%
한 241
 
2.3%
화 221
 
2.1%
성 197
 
1.9%
의 190
 
1.8%
Other values (430) 8037
75.5%
Lowercase Letter
ValueCountFrequency (%)
i 45
14.3%
o 32
10.2%
n 27
 
8.6%
e 25
 
7.9%
a 21
 
6.7%
t 21
 
6.7%
c 18
 
5.7%
l 17
 
5.4%
s 16
 
5.1%
r 15
 
4.8%
Other values (13) 78
24.8%
Uppercase Letter
ValueCountFrequency (%)
R 11
12.6%
M 9
10.3%
P 9
10.3%
H 9
10.3%
C 7
8.0%
K 7
8.0%
G 6
 
6.9%
N 5
 
5.7%
S 5
 
5.7%
I 4
 
4.6%
Other values (9) 15
17.2%
Decimal Number
ValueCountFrequency (%)
2 6
40.0%
1 3
20.0%
0 2
 
13.3%
3 1
 
6.7%
8 1
 
6.7%
9 1
 
6.7%
4 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 37
56.1%
· 13
 
19.7%
/ 10
 
15.2%
. 4
 
6.1%
% 2
 
3.0%
Close Punctuation
ValueCountFrequency (%)
) 33
97.1%
] 1
 
2.9%
Open Punctuation
ValueCountFrequency (%)
( 33
97.1%
[ 1
 
2.9%
Space Separator
ValueCountFrequency (%)
3247
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
” 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10645
73.7%
Common 3404
 
23.6%
Latin 402
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
발 336
 
3.2%
개 322
 
3.0%
기 295
 
2.8%
및 293
 
2.8%
품 258
 
2.4%
용 255
 
2.4%
한 241
 
2.3%
화 221
 
2.1%
성 197
 
1.9%
의 190
 
1.8%
Other values (430) 8037
75.5%
Latin
ValueCountFrequency (%)
i 45
 
11.2%
o 32
 
8.0%
n 27
 
6.7%
e 25
 
6.2%
a 21
 
5.2%
t 21
 
5.2%
c 18
 
4.5%
l 17
 
4.2%
s 16
 
4.0%
r 15
 
3.7%
Other values (32) 165
41.0%
Common
ValueCountFrequency (%)
3247
95.4%
, 37
 
1.1%
) 33
 
1.0%
( 33
 
1.0%
· 13
 
0.4%
/ 10
 
0.3%
2 6
 
0.2%
- 6
 
0.2%
. 4
 
0.1%
1 3
 
0.1%
Other values (10) 12
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10645
73.7%
ASCII 3791
 
26.2%
None 13
 
0.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3247
85.7%
i 45
 
1.2%
, 37
 
1.0%
) 33
 
0.9%
( 33
 
0.9%
o 32
 
0.8%
n 27
 
0.7%
e 25
 
0.7%
a 21
 
0.6%
t 21
 
0.6%
Other values (49) 270
 
7.1%
Hangul
ValueCountFrequency (%)
발 336
 
3.2%
개 322
 
3.0%
기 295
 
2.8%
및 293
 
2.8%
품 258
 
2.4%
용 255
 
2.4%
한 241
 
2.3%
화 221
 
2.1%
성 197
 
1.9%
의 190
 
1.8%
Other values (430) 8037
75.5%
None
ValueCountFrequency (%)
· 13
100.0%
Punctuation
ValueCountFrequency (%)
“ 1
50.0%
” 1
50.0%

연구수행기관
Text

Distinct289
Distinct (%)66.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-12-12T22:06:38.092366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length9.8878719
Min length2

Characters and Unicode

Total characters4321
Distinct characters315
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique225 ?
Unique (%)51.5%

Sample

1st row종근당바이오
2nd row(주)성균바이오텍
3rd row경희의료원
4th row(주)올라이스
5th row하이포스알앤씨
ValueCountFrequency (%)
산학협력단 113
 
18.3%
주식회사 23
 
3.7%
한국식품연구원 20
 
3.2%
건국대학교 13
 
2.1%
서울대학교 12
 
1.9%
농업회사법인 11
 
1.8%
경희대학교산학협력단(국제캠퍼스 9
 
1.5%
중앙대학교 8
 
1.3%
단국대학교 7
 
1.1%
천안캠퍼스 7
 
1.1%
Other values (293) 394
63.9%
2023-12-12T22:06:38.470725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 344
 
8.0%
대 183
 
4.2%
산 181
 
4.2%
180
 
4.2%
단 174
 
4.0%
교 173
 
4.0%
주 167
 
3.9%
협 167
 
3.9%
력 158
 
3.7%
) 142
 
3.3%
Other values (305) 2452
56.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3852
89.1%
Space Separator 180
 
4.2%
Close Punctuation 142
 
3.3%
Open Punctuation 142
 
3.3%
Uppercase Letter 4
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 344
 
8.9%
대 183
 
4.8%
산 181
 
4.7%
단 174
 
4.5%
교 173
 
4.5%
주 167
 
4.3%
협 167
 
4.3%
력 158
 
4.1%
국 83
 
2.2%
식 82
 
2.1%
Other values (297) 2140
55.6%
Uppercase Letter
ValueCountFrequency (%)
G 1
25.0%
R 1
25.0%
F 1
25.0%
B 1
25.0%
Space Separator
ValueCountFrequency (%)
180
100.0%
Close Punctuation
ValueCountFrequency (%)
) 142
100.0%
Open Punctuation
ValueCountFrequency (%)
( 142
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3852
89.1%
Common 465
 
10.8%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 344
 
8.9%
대 183
 
4.8%
산 181
 
4.7%
단 174
 
4.5%
교 173
 
4.5%
주 167
 
4.3%
협 167
 
4.3%
력 158
 
4.1%
국 83
 
2.2%
식 82
 
2.1%
Other values (297) 2140
55.6%
Common
ValueCountFrequency (%)
180
38.7%
) 142
30.5%
( 142
30.5%
& 1
 
0.2%
Latin
ValueCountFrequency (%)
G 1
25.0%
R 1
25.0%
F 1
25.0%
B 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3852
89.1%
ASCII 469
 
10.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 344
 
8.9%
대 183
 
4.8%
산 181
 
4.7%
단 174
 
4.5%
교 173
 
4.5%
주 167
 
4.3%
협 167
 
4.3%
력 158
 
4.1%
국 83
 
2.2%
식 82
 
2.1%
Other values (297) 2140
55.6%
ASCII
ValueCountFrequency (%)
180
38.4%
) 142
30.3%
( 142
30.3%
G 1
 
0.2%
R 1
 
0.2%
F 1
 
0.2%
& 1
 
0.2%
B 1
 
0.2%

주관기관
Text

Distinct132
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-12-12T22:06:38.705211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length8.9565217
Min length2

Characters and Unicode

Total characters3914
Distinct characters240
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)4.6%

Sample

1st row종근당바이오
2nd row(주)성균바이오텍
3rd row(주)헬릭스미스
4th row(주)올라이스
5th row하이포스알앤씨
ValueCountFrequency (%)
산학협력단 56
 
9.7%
주식회사 35
 
6.0%
대상(주 14
 
2.4%
한국식품연구원 14
 
2.4%
농업회사법인 11
 
1.9%
서울우유협동조합 10
 
1.7%
서울대학교 10
 
1.7%
사단)한국 8
 
1.4%
농식품 8
 
1.4%
미래 8
 
1.4%
Other values (132) 406
70.0%
2023-12-12T22:06:39.416266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 255
 
6.5%
( 216
 
5.5%
) 216
 
5.5%
학 176
 
4.5%
143
 
3.7%
대 111
 
2.8%
식 108
 
2.8%
산 99
 
2.5%
단 93
 
2.4%
협 89
 
2.3%
Other values (230) 2408
61.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3331
85.1%
Open Punctuation 216
 
5.5%
Close Punctuation 216
 
5.5%
Space Separator 143
 
3.7%
Uppercase Letter 6
 
0.2%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 255
 
7.7%
학 176
 
5.3%
대 111
 
3.3%
식 108
 
3.2%
산 99
 
3.0%
단 93
 
2.8%
협 89
 
2.7%
사 83
 
2.5%
교 82
 
2.5%
스 75
 
2.3%
Other values (222) 2160
64.8%
Uppercase Letter
ValueCountFrequency (%)
F 2
33.3%
B 2
33.3%
G 1
16.7%
R 1
16.7%
Open Punctuation
ValueCountFrequency (%)
( 216
100.0%
Close Punctuation
ValueCountFrequency (%)
) 216
100.0%
Space Separator
ValueCountFrequency (%)
143
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3331
85.1%
Common 577
 
14.7%
Latin 6
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 255
 
7.7%
학 176
 
5.3%
대 111
 
3.3%
식 108
 
3.2%
산 99
 
3.0%
단 93
 
2.8%
협 89
 
2.7%
사 83
 
2.5%
교 82
 
2.5%
스 75
 
2.3%
Other values (222) 2160
64.8%
Common
ValueCountFrequency (%)
( 216
37.4%
) 216
37.4%
143
24.8%
& 2
 
0.3%
Latin
ValueCountFrequency (%)
F 2
33.3%
B 2
33.3%
G 1
16.7%
R 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3331
85.1%
ASCII 583
 
14.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 255
 
7.7%
학 176
 
5.3%
대 111
 
3.3%
식 108
 
3.2%
산 99
 
3.0%
단 93
 
2.8%
협 89
 
2.7%
사 83
 
2.5%
교 82
 
2.5%
스 75
 
2.3%
Other values (222) 2160
64.8%
ASCII
ValueCountFrequency (%)
( 216
37.0%
) 216
37.0%
143
24.5%
& 2
 
0.3%
F 2
 
0.3%
B 2
 
0.3%
G 1
 
0.2%
R 1
 
0.2%

총연구기간 시작일
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2019-05-20
90 
2017-06-15
60 
2018-04-30
54 
2019-12-02
25 
2019-05-10
20 
Other values (32)
188 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique7 ?
Unique (%)1.6%

Sample

1st row2017-06-15
2nd row2017-06-15
3rd row2018-04-30
4th row2019-06-05
5th row2018-04-30

Common Values

ValueCountFrequency (%)
2019-05-20 90
20.6%
2017-06-15 60
13.7%
2018-04-30 54
12.4%
2019-12-02 25
 
5.7%
2019-05-10 20
 
4.6%
2017-10-18 20
 
4.6%
2018-11-09 17
 
3.9%
2018-04-25 15
 
3.4%
2017-08-30 15
 
3.4%
2019-11-01 13
 
3.0%
Other values (27) 108
24.7%

Length

2023-12-12T22:06:39.568295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-05-20 90
20.6%
2017-06-15 60
13.7%
2018-04-30 54
12.4%
2019-12-02 25
 
5.7%
2019-05-10 20
 
4.6%
2017-10-18 20
 
4.6%
2018-11-09 17
 
3.9%
2018-04-25 15
 
3.4%
2017-08-30 15
 
3.4%
2019-11-01 13
 
3.0%
Other values (27) 108
24.7%

총연구기간 종료일
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2021-12-31
115 
2019-12-31
89 
2020-12-31
73 
2020-12-01
25 
2020-10-31
23 
Other values (21)
112 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique3 ?
Unique (%)0.7%

Sample

1st row2019-12-31
2nd row2019-12-31
3rd row2021-12-31
4th row2020-06-04
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2021-12-31 115
26.3%
2019-12-31 89
20.4%
2020-12-31 73
16.7%
2020-12-01 25
 
5.7%
2020-10-31 23
 
5.3%
2021-11-08 17
 
3.9%
2020-05-09 15
 
3.4%
2020-06-04 10
 
2.3%
2019-07-23 9
 
2.1%
2021-09-17 7
 
1.6%
Other values (16) 54
12.4%

Length

2023-12-12T22:06:39.699898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-12-31 115
26.3%
2019-12-31 89
20.4%
2020-12-31 73
16.7%
2020-12-01 25
 
5.7%
2020-10-31 23
 
5.3%
2021-11-08 17
 
3.9%
2020-05-09 15
 
3.4%
2020-06-04 10
 
2.3%
2019-07-23 9
 
2.1%
2021-09-17 7
 
1.6%
Other values (16) 54
12.4%

당해년도연구시작일
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2019-01-01
178 
2019-05-20
90 
2019-05-01
29 
2019-12-02
24 
2019-11-01
22 
Other values (18)
94 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique4 ?
Unique (%)0.9%

Sample

1st row2019-01-01
2nd row2019-01-01
3rd row2019-01-01
4th row2019-06-05
5th row2019-01-01

Common Values

ValueCountFrequency (%)
2019-01-01 178
40.7%
2019-05-20 90
20.6%
2019-05-01 29
 
6.6%
2019-12-02 24
 
5.5%
2019-11-01 22
 
5.0%
2019-05-10 21
 
4.8%
2019-11-09 17
 
3.9%
2019-06-05 10
 
2.3%
2019-05-18 7
 
1.6%
2019-08-01 6
 
1.4%
Other values (13) 33
 
7.6%

Length

2023-12-12T22:06:39.804292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-01-01 178
40.7%
2019-05-20 90
20.6%
2019-05-01 29
 
6.6%
2019-12-02 24
 
5.5%
2019-11-01 22
 
5.0%
2019-05-10 21
 
4.8%
2019-11-09 17
 
3.9%
2019-06-05 10
 
2.3%
2019-05-18 7
 
1.6%
2019-08-01 6
 
1.4%
Other values (13) 33
 
7.6%

당해년도연구종료일
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2019-12-31
246 
2020-10-31
 
24
2020-12-01
 
24
2020-11-08
 
17
2020-05-09
 
15
Other values (19)
111 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st row2019-12-31
2nd row2019-12-31
3rd row2019-12-31
4th row2020-06-04
5th row2019-12-31

Common Values

ValueCountFrequency (%)
2019-12-31 246
56.3%
2020-10-31 24
 
5.5%
2020-12-01 24
 
5.5%
2020-11-08 17
 
3.9%
2020-05-09 15
 
3.4%
2020-02-29 14
 
3.2%
2020-03-31 10
 
2.3%
2020-06-04 10
 
2.3%
2019-07-23 9
 
2.1%
2020-02-28 8
 
1.8%
Other values (14) 60
 
13.7%

Length

2023-12-12T22:06:39.903980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-12-31 246
56.3%
2020-10-31 24
 
5.5%
2020-12-01 24
 
5.5%
2020-11-08 17
 
3.9%
2020-05-09 15
 
3.4%
2020-02-29 14
 
3.2%
2020-03-31 10
 
2.3%
2020-06-04 10
 
2.3%
2019-07-23 9
 
2.1%
2020-02-28 8
 
1.8%
Other values (14) 60
 
13.7%

총연구비
Real number (ℝ)

ZEROS 

Distinct215
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0658936 × 108
Minimum0
Maximum5.4 × 108
Zeros10
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T22:06:40.023191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20000000
Q147000000
median75000000
Q31.3 × 108
95-th percentile3.2722 × 108
Maximum5.4 × 108
Range5.4 × 108
Interquartile range (IQR)83000000

Descriptive statistics

Standard deviation94990259
Coefficient of variation (CV)0.89117955
Kurtosis4.5792731
Mean1.0658936 × 108
Median Absolute Deviation (MAD)35000000
Skewness2.0184431
Sum4.6579551 × 1010
Variance9.0231493 × 1015
MonotonicityNot monotonic
2023-12-12T22:06:40.184792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000000 19
 
4.3%
60000000 18
 
4.1%
70000000 17
 
3.9%
100000000 15
 
3.4%
50000000 14
 
3.2%
20000000 14
 
3.2%
80000000 10
 
2.3%
0 10
 
2.3%
30000000 7
 
1.6%
55000000 6
 
1.4%
Other values (205) 307
70.3%
ValueCountFrequency (%)
0 10
2.3%
5000000 2
 
0.5%
10000000 4
 
0.9%
15000000 1
 
0.2%
20000000 14
3.2%
21000000 1
 
0.2%
21574000 1
 
0.2%
22000000 1
 
0.2%
23500000 1
 
0.2%
25000000 5
 
1.1%
ValueCountFrequency (%)
540000000 1
0.2%
532000000 1
0.2%
526141000 1
0.2%
500000000 1
0.2%
467000000 1
0.2%
463000000 1
0.2%
451250000 1
0.2%
433750000 1
0.2%
390000000 2
0.5%
386667000 1
0.2%

Interactions

2023-12-12T22:06:34.787890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:06:34.562045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:06:34.863712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:06:34.687524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:06:40.289845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호사업명총연구기간 시작일총연구기간 종료일당해년도연구시작일당해년도연구종료일총연구비
번호1.0000.8090.8730.7830.7890.7660.387
사업명0.8091.0000.9920.9410.9330.9410.374
총연구기간 시작일0.8730.9921.0000.9880.9960.9900.532
총연구기간 종료일0.7830.9410.9881.0000.9870.9950.461
당해년도연구시작일0.7890.9330.9960.9871.0000.9880.366
당해년도연구종료일0.7660.9410.9900.9950.9881.0000.434
총연구비0.3870.3740.5320.4610.3660.4341.000
2023-12-12T22:06:40.432496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업명당해년도연구종료일총연구기간 시작일당해년도연구시작일총연구기간 종료일
사업명1.0000.7150.9060.6960.708
당해년도연구종료일0.7151.0000.8160.8380.921
총연구기간 시작일0.9060.8161.0000.9040.792
당해년도연구시작일0.6960.8380.9041.0000.826
총연구기간 종료일0.7080.9210.7920.8261.000
2023-12-12T22:06:40.537445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호총연구비사업명총연구기간 시작일총연구기간 종료일당해년도연구시작일당해년도연구종료일
번호1.000-0.2880.3740.5230.4150.4310.400
총연구비-0.2881.0000.1230.2080.1820.1410.171
사업명0.3740.1231.0000.9060.7080.6960.715
총연구기간 시작일0.5230.2080.9061.0000.7920.9040.816
총연구기간 종료일0.4150.1820.7080.7921.0000.8260.921
당해년도연구시작일0.4310.1410.6960.9040.8261.0000.838
당해년도연구종료일0.4000.1710.7150.8160.9210.8381.000

Missing values

2023-12-12T22:06:34.982068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:06:35.169151image/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

번호분류사업명총괄과제번호세부과제번호과제명연구수행기관주관기관총연구기간 시작일총연구기간 종료일당해년도연구시작일당해년도연구종료일총연구비
01식품고부가가치식품기술개발117051-3117051033SB010원발성 및 이차성 골다공증 개선 효과를 갖는 기능성프로바이오틱스 제품 개발종근당바이오종근당바이오2017-06-152019-12-312019-01-012019-12-31166000000
12식품고부가가치식품기술개발117053-3117053033SB010개똥쑥 추출물 대량생산 공정 확립과 전임상 기능성연구 및 인체적용시험을 통한 효능 확인(주)성균바이오텍(주)성균바이오텍2017-06-152019-12-312019-01-012019-12-31307000000
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45식품고부가가치식품기술개발118048-3118048032SB010곡류 저장 안전성 향상을 위한 천연 유래 방충 유효 성분 선발 및 효과 검증하이포스알앤씨하이포스알앤씨2018-04-302020-12-312019-01-012019-12-31150000000
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67식품기술사업화지원817032-3817032033SB010스팀조리 호환성 향상 및 전용푸드 개발(주)우리식품(주)우리식품2017-08-302019-12-312019-01-012019-12-31345000000
78식품수출전략기술개발315049-5315049055HD050고려인삼의 염증조절용 소재를 활용한 제품개발 및 상품화금산덕원인삼약초영농조합법인(재)금산국제인삼약초연구소2015-08-142020-08-132019-04-142020-08-13183000000
89식품농생명산업기술개발317001-3317001033SB010소포장 쇠고기 냉장 포장육의 품질 유지능 향상을 위한 패키징기술 및 포장재 사업화(주)태우그린푸드(주)태우그린푸드2017-04-212019-12-312019-01-012019-12-31180000000
910식품농생명산업기술개발117041-3117041033SB010산수유복합추출물을 이용한 여성갱년기 증상 개선용 식의약 소재 및 제품 개발주식회사 나인비주식회사 나인비2019-12-312020-06-302019-01-012019-12-31320000000
번호분류사업명총괄과제번호세부과제번호과제명연구수행기관주관기관총연구기간 시작일총연구기간 종료일당해년도연구시작일당해년도연구종료일총연구비
427428식품고부가가치식품기술개발319117-1319117011HD020생쌀발효 증류식 소주의 상품화 1술도가제주바당한국농수산대학2019-12-022020-12-012019-12-022020-12-0137500000
428429식품고부가가치식품기술개발119114-1119114011HD040체험용 식초제품 개발 및 사업화(주)올라이스베리앤바이오식품연구소2019-12-022020-12-012019-12-022020-12-0138666000
429430식품고부가가치식품기술개발319117-1319117011HD050생쌀발효 증류식 소주의 상품화 4순성왕매실영농조합한국농수산대학2019-12-012020-11-302019-12-012020-11-3037500000
430431식품고부가가치식품기술개발119111-1119111011SB010유용성분 고 함유 동충하초 대량생산 및 이를 이용한 복합기능성 건강식품소재 개발 및 제품화경희대학교산학협력단(국제캠퍼스)경희대학교산학협력단(국제캠퍼스)2019-11-012020-10-312019-11-012020-10-31200000000
431432식품농식품수출비즈니스전략모델구축319090-3319090031WT021베트남 현지 환경에 적합한 포장 디자인 및 기술개발김수일포장연구소(주)한국식품연구원2019-08-012022-01-312019-08-012020-04-3030000000
432433식품고부가가치식품기술개발119114-1119114011HD020보리 및 복분자 혼합발효 식초 음료 사업화신토복분자영농조합법인베리앤바이오식품연구소2019-12-022020-12-012019-12-022020-12-0138667000
433434식품고부가가치식품기술개발119114-1119114011HD030보리와 아로니아 혼합발효 식초음료 개발 및 사업화고창베리촌영농조합법인베리앤바이오식품연구소2019-12-022020-12-012019-12-022020-12-0138667000
434435식품고부가가치식품기술개발319117-1319117011SB010국산원료와 미생물을 이용한 소규모 쌀 증류식 소주 제조공정 표준화한국농수산대학한국농수산대학2019-12-022020-12-012019-12-022020-12-0150000000
435436식품고부가가치식품기술개발319117-1319117011HD030생쌀발효 증류식 소주의 상품화 2자연과사람들한국농수산대학2019-12-022020-12-012019-12-022020-12-0137500000
436437식품농축산물안전생산유통관리기술개발318081-2318081022SB010인삼을 활용한 신규 제형·제품 개발세종대학교산학협력단세종대학교산학협력단2018-09-102020-09-092019-09-102020-09-09130000000