Overview

Dataset statistics

Number of variables7
Number of observations550
Missing cells139
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.7 KiB
Average record size in memory57.2 B

Variable types

Numeric1
Text5
Categorical1

Dataset

Description민법 제32조에 근거하여 농림축산식품부장관이 허가한 비영리 사단법인 및 재단법인의 구분, 법인명, 설립일자, 설립목적, 소재지 등 현황 을 제공 합니다.
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220217000000002037

Alerts

번호 has 138 (25.1%) missing valuesMissing

Reproduction

Analysis started2023-12-11 03:30:43.382103
Analysis finished2023-12-11 03:30:44.497970
Duration1.12 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

MISSING 

Distinct412
Distinct (%)100.0%
Missing138
Missing (%)25.1%
Infinite0
Infinite (%)0.0%
Mean209.11408
Minimum1
Maximum416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-12-11T12:30:44.563428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21.55
Q1105.75
median209.5
Q3313.25
95-th percentile395.45
Maximum416
Range415
Interquartile range (IQR)207.5

Descriptive statistics

Standard deviation120.14776
Coefficient of variation (CV)0.57455604
Kurtosis-1.1966775
Mean209.11408
Median Absolute Deviation (MAD)104
Skewness-0.0038425982
Sum86155
Variance14435.483
MonotonicityNot monotonic
2023-12-11T12:30:44.695903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301 1
 
0.2%
289 1
 
0.2%
288 1
 
0.2%
287 1
 
0.2%
286 1
 
0.2%
285 1
 
0.2%
284 1
 
0.2%
283 1
 
0.2%
282 1
 
0.2%
281 1
 
0.2%
Other values (402) 402
73.1%
(Missing) 138
 
25.1%
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 (%)
416 1
0.2%
415 1
0.2%
414 1
0.2%
413 1
0.2%
412 1
0.2%
411 1
0.2%
410 1
0.2%
409 1
0.2%
408 1
0.2%
407 1
0.2%
Distinct548
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2023-12-11T12:30:45.101247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.9418182
Min length1

Characters and Unicode

Total characters1618
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique546 ?
Unique (%)99.3%

Sample

1st row1
2nd row2
3rd row3
4th row5
5th row8
ValueCountFrequency (%)
588 2
 
0.4%
44 2
 
0.4%
558 1
 
0.2%
1 1
 
0.2%
535 1
 
0.2%
536 1
 
0.2%
537 1
 
0.2%
538 1
 
0.2%
541 1
 
0.2%
542 1
 
0.2%
Other values (538) 538
97.8%
2023-12-11T12:30:45.608205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 200
12.4%
5 197
12.2%
3 192
11.9%
2 187
11.6%
1 185
11.4%
4 179
11.1%
7 156
9.6%
9 108
6.7%
8 103
6.4%
0 101
6.2%
Other values (4) 10
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1608
99.4%
Dash Punctuation 4
 
0.2%
Lowercase Letter 4
 
0.2%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 200
12.4%
5 197
12.3%
3 192
11.9%
2 187
11.6%
1 185
11.5%
4 179
11.1%
7 156
9.7%
9 108
6.7%
8 103
6.4%
0 101
6.3%
Lowercase Letter
ValueCountFrequency (%)
a 2
50.0%
n 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1612
99.6%
Latin 6
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
6 200
12.4%
5 197
12.2%
3 192
11.9%
2 187
11.6%
1 185
11.5%
4 179
11.1%
7 156
9.7%
9 108
6.7%
8 103
6.4%
0 101
6.3%
Latin
ValueCountFrequency (%)
J 2
33.3%
a 2
33.3%
n 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1618
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 200
12.4%
5 197
12.2%
3 192
11.9%
2 187
11.6%
1 185
11.4%
4 179
11.1%
7 156
9.6%
9 108
6.7%
8 103
6.4%
0 101
6.2%
Other values (4) 10
 
0.6%
Distinct548
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2023-12-11T12:30:45.847989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length23
Mean length9.5672727
Min length3

Characters and Unicode

Total characters5262
Distinct characters393
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique546 ?
Unique (%)99.3%

Sample

1st row대한잠사회
2nd row대한곡물협회
3rd row한국제분협회
4th rowFAO한국협회
5th row한국사료협회
ValueCountFrequency (%)
8
 
1.4%
한국향토음식진흥원 2
 
0.3%
한국작물보호협회 2
 
0.3%
한국원예문화협회 1
 
0.2%
한국뚱딴지협회 1
 
0.2%
한국식품발전협회 1
 
0.2%
한국농경문화원 1
 
0.2%
우리술세계화연구회 1
 
0.2%
한국조리협회 1
 
0.2%
농식품.농어촌특별포럼 1
 
0.2%
Other values (562) 562
96.7%
2023-12-11T12:30:46.259738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
405
 
7.7%
402
 
7.6%
381
 
7.2%
257
 
4.9%
181
 
3.4%
139
 
2.6%
125
 
2.4%
112
 
2.1%
99
 
1.9%
94
 
1.8%
Other values (383) 3067
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4993
94.9%
Close Punctuation 78
 
1.5%
Open Punctuation 78
 
1.5%
Uppercase Letter 43
 
0.8%
Space Separator 32
 
0.6%
Lowercase Letter 21
 
0.4%
Other Punctuation 14
 
0.3%
Decimal Number 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
405
 
8.1%
402
 
8.1%
381
 
7.6%
257
 
5.1%
181
 
3.6%
139
 
2.8%
125
 
2.5%
112
 
2.2%
99
 
2.0%
94
 
1.9%
Other values (350) 2798
56.0%
Uppercase Letter
ValueCountFrequency (%)
C 7
16.3%
A 7
16.3%
P 5
11.6%
B 4
9.3%
G 4
9.3%
O 4
9.3%
I 3
7.0%
F 2
 
4.7%
L 2
 
4.7%
T 2
 
4.7%
Other values (3) 3
7.0%
Lowercase Letter
ValueCountFrequency (%)
o 4
19.0%
i 3
14.3%
e 3
14.3%
a 2
9.5%
f 2
9.5%
s 2
9.5%
n 1
 
4.8%
t 1
 
4.8%
k 1
 
4.8%
r 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
· 5
35.7%
. 5
35.7%
, 2
 
14.3%
? 1
 
7.1%
& 1
 
7.1%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Open Punctuation
ValueCountFrequency (%)
( 78
100.0%
Space Separator
ValueCountFrequency (%)
32
100.0%
Decimal Number
ValueCountFrequency (%)
6 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4992
94.9%
Common 205
 
3.9%
Latin 64
 
1.2%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
405
 
8.1%
402
 
8.1%
381
 
7.6%
257
 
5.1%
181
 
3.6%
139
 
2.8%
125
 
2.5%
112
 
2.2%
99
 
2.0%
94
 
1.9%
Other values (349) 2797
56.0%
Latin
ValueCountFrequency (%)
C 7
 
10.9%
A 7
 
10.9%
P 5
 
7.8%
o 4
 
6.2%
B 4
 
6.2%
G 4
 
6.2%
O 4
 
6.2%
I 3
 
4.7%
i 3
 
4.7%
e 3
 
4.7%
Other values (14) 20
31.2%
Common
ValueCountFrequency (%)
) 78
38.0%
( 78
38.0%
32
15.6%
· 5
 
2.4%
. 5
 
2.4%
6 3
 
1.5%
, 2
 
1.0%
? 1
 
0.5%
& 1
 
0.5%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4992
94.9%
ASCII 264
 
5.0%
None 5
 
0.1%
CJK 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
405
 
8.1%
402
 
8.1%
381
 
7.6%
257
 
5.1%
181
 
3.6%
139
 
2.8%
125
 
2.5%
112
 
2.2%
99
 
2.0%
94
 
1.9%
Other values (349) 2797
56.0%
ASCII
ValueCountFrequency (%)
) 78
29.5%
( 78
29.5%
32
12.1%
C 7
 
2.7%
A 7
 
2.7%
P 5
 
1.9%
. 5
 
1.9%
o 4
 
1.5%
B 4
 
1.5%
G 4
 
1.5%
Other values (22) 40
15.2%
None
ValueCountFrequency (%)
· 5
100.0%
CJK
ValueCountFrequency (%)
1
100.0%
Distinct523
Distinct (%)95.3%
Missing1
Missing (%)0.2%
Memory size4.4 KiB
2023-12-11T12:30:46.626424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.0364299
Min length2

Characters and Unicode

Total characters1667
Distinct characters188
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)91.1%

Sample

1st row윤장근
2nd row전병기
3rd row이희상
4th row유병린
5th row이양희
ValueCountFrequency (%)
황민영 3
 
0.5%
주형로 3
 
0.5%
이상무 3
 
0.5%
김동수 2
 
0.4%
이정찬 2
 
0.4%
윤숙자 2
 
0.4%
이상규 2
 
0.4%
김진필 2
 
0.4%
김순자 2
 
0.4%
김명규 2
 
0.4%
Other values (517) 530
95.8%
2023-12-11T12:30:47.101670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
99
 
5.9%
98
 
5.9%
55
 
3.3%
39
 
2.3%
36
 
2.2%
35
 
2.1%
32
 
1.9%
30
 
1.8%
26
 
1.6%
25
 
1.5%
Other values (178) 1192
71.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1656
99.3%
Space Separator 7
 
0.4%
Other Punctuation 2
 
0.1%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
99
 
6.0%
98
 
5.9%
55
 
3.3%
39
 
2.4%
36
 
2.2%
35
 
2.1%
32
 
1.9%
30
 
1.8%
26
 
1.6%
25
 
1.5%
Other values (174) 1181
71.3%
Space Separator
ValueCountFrequency (%)
7
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1656
99.3%
Common 11
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
99
 
6.0%
98
 
5.9%
55
 
3.3%
39
 
2.4%
36
 
2.2%
35
 
2.1%
32
 
1.9%
30
 
1.8%
26
 
1.6%
25
 
1.5%
Other values (174) 1181
71.3%
Common
ValueCountFrequency (%)
7
63.6%
, 2
 
18.2%
( 1
 
9.1%
) 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1656
99.3%
ASCII 11
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
99
 
6.0%
98
 
5.9%
55
 
3.3%
39
 
2.4%
36
 
2.2%
35
 
2.1%
32
 
1.9%
30
 
1.8%
26
 
1.6%
25
 
1.5%
Other values (174) 1181
71.3%
ASCII
ValueCountFrequency (%)
7
63.6%
, 2
 
18.2%
( 1
 
9.1%
) 1
 
9.1%
Distinct497
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2023-12-11T12:30:47.354873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length11
Mean length9.5054545
Min length7

Characters and Unicode

Total characters5228
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique452 ?
Unique (%)82.2%

Sample

1st row1946.07.12.
2nd row1954.02.12.
3rd row1955.12.31
4th row1957.12.10
5th row1961. 7. 3
ValueCountFrequency (%)
7 20
 
2.8%
2002 14
 
2.0%
2013 13
 
1.8%
4 13
 
1.8%
1999 11
 
1.6%
8 11
 
1.6%
6 9
 
1.3%
3 6
 
0.8%
1 6
 
0.8%
2 6
 
0.8%
Other values (491) 597
84.6%
2023-12-11T12:30:47.737880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1131
21.6%
1 874
16.7%
2 866
16.6%
0 839
16.0%
9 361
 
6.9%
3 200
 
3.8%
7 172
 
3.3%
4 160
 
3.1%
6 157
 
3.0%
5 156
 
3.0%
Other values (4) 312
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3939
75.3%
Other Punctuation 1131
 
21.6%
Space Separator 156
 
3.0%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 874
22.2%
2 866
22.0%
0 839
21.3%
9 361
9.2%
3 200
 
5.1%
7 172
 
4.4%
4 160
 
4.1%
6 157
 
4.0%
5 156
 
4.0%
8 154
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 1131
100.0%
Space Separator
ValueCountFrequency (%)
156
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5228
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1131
21.6%
1 874
16.7%
2 866
16.6%
0 839
16.0%
9 361
 
6.9%
3 200
 
3.8%
7 172
 
3.3%
4 160
 
3.1%
6 157
 
3.0%
5 156
 
3.0%
Other values (4) 312
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1131
21.6%
1 874
16.7%
2 866
16.6%
0 839
16.0%
9 361
 
6.9%
3 200
 
3.8%
7 172
 
3.3%
4 160
 
3.1%
6 157
 
3.0%
5 156
 
3.0%
Other values (4) 312
 
6.0%
Distinct548
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2023-12-11T12:30:48.011206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length364
Median length138
Mean length78.638182
Min length12

Characters and Unicode

Total characters43251
Distinct characters598
Distinct categories15 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique546 ?
Unique (%)99.3%

Sample

1st row양잠, 상묘, 잠종, 제사업 등 잠사업 종사회원에 대한 지원지도, 기술보급 및 개발, 홍보, 조사연구와 잠사관련 민간행사주관 및 대정부 위임사업 수행 등을 통해 회원의 복리증진과 잠사업의 진흥발전에 기여함
2nd row양곡도정업과 양곡 보관업의 건전한 발전과 국가양곡정책 수행에 기여
3rd row제분공업의 건전한 발전과 국책수행 협조
4th row우리나라와 UN_FAO상호간의 유기적인 연결 및 우방제국과의 기술 ·자료 교환 추진
5th row배합사료 제조업의 선진국 과학기술 향상과 사료가공업 및 축산진흥에 기여
ValueCountFrequency (%)
518
 
5.3%
기여 167
 
1.7%
위한 112
 
1.1%
83
 
0.8%
통해 81
 
0.8%
통한 75
 
0.8%
통하여 73
 
0.7%
관한 73
 
0.7%
도모 73
 
0.7%
발전에 67
 
0.7%
Other values (4279) 8497
86.5%
2023-12-11T12:30:48.530883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9407
 
21.7%
1057
 
2.4%
773
 
1.8%
750
 
1.7%
744
 
1.7%
, 739
 
1.7%
676
 
1.6%
661
 
1.5%
600
 
1.4%
578
 
1.3%
Other values (588) 27266
63.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32487
75.1%
Space Separator 9407
 
21.7%
Other Punctuation 1038
 
2.4%
Uppercase Letter 104
 
0.2%
Lowercase Letter 73
 
0.2%
Close Punctuation 34
 
0.1%
Open Punctuation 32
 
0.1%
Decimal Number 29
 
0.1%
Modifier Symbol 14
 
< 0.1%
Other Symbol 10
 
< 0.1%
Other values (5) 23
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1057
 
3.3%
773
 
2.4%
750
 
2.3%
744
 
2.3%
676
 
2.1%
661
 
2.0%
600
 
1.8%
578
 
1.8%
573
 
1.8%
568
 
1.7%
Other values (522) 25507
78.5%
Uppercase Letter
ValueCountFrequency (%)
A 17
16.3%
G 13
12.5%
P 12
11.5%
T 11
10.6%
I 7
 
6.7%
D 7
 
6.7%
O 6
 
5.8%
C 4
 
3.8%
N 4
 
3.8%
W 4
 
3.8%
Other values (7) 19
18.3%
Lowercase Letter
ValueCountFrequency (%)
n 11
15.1%
i 10
13.7%
a 8
11.0%
r 8
11.0%
o 7
9.6%
t 6
8.2%
e 6
8.2%
g 4
 
5.5%
s 3
 
4.1%
c 3
 
4.1%
Other values (4) 7
9.6%
Other Punctuation
ValueCountFrequency (%)
, 739
71.2%
· 132
 
12.7%
? 80
 
7.7%
. 75
 
7.2%
* 4
 
0.4%
& 3
 
0.3%
' 2
 
0.2%
# 1
 
0.1%
; 1
 
0.1%
: 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 9
31.0%
2 8
27.6%
6 5
17.2%
8 3
 
10.3%
3 2
 
6.9%
4 1
 
3.4%
5 1
 
3.4%
Other Number
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Close Punctuation
ValueCountFrequency (%)
) 32
94.1%
2
 
5.9%
Open Punctuation
ValueCountFrequency (%)
( 30
93.8%
2
 
6.2%
Initial Punctuation
ValueCountFrequency (%)
5
62.5%
3
37.5%
Final Punctuation
ValueCountFrequency (%)
4
57.1%
3
42.9%
Space Separator
ValueCountFrequency (%)
9407
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 14
100.0%
Other Symbol
ValueCountFrequency (%)
10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32485
75.1%
Common 10587
 
24.5%
Latin 177
 
0.4%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1057
 
3.3%
773
 
2.4%
750
 
2.3%
744
 
2.3%
676
 
2.1%
661
 
2.0%
600
 
1.8%
578
 
1.8%
573
 
1.8%
568
 
1.7%
Other values (520) 25505
78.5%
Common
ValueCountFrequency (%)
9407
88.9%
, 739
 
7.0%
· 132
 
1.2%
? 80
 
0.8%
. 75
 
0.7%
) 32
 
0.3%
( 30
 
0.3%
` 14
 
0.1%
10
 
0.1%
1 9
 
0.1%
Other values (25) 59
 
0.6%
Latin
ValueCountFrequency (%)
A 17
 
9.6%
G 13
 
7.3%
P 12
 
6.8%
n 11
 
6.2%
T 11
 
6.2%
i 10
 
5.6%
a 8
 
4.5%
r 8
 
4.5%
I 7
 
4.0%
D 7
 
4.0%
Other values (21) 73
41.2%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32479
75.1%
ASCII 10598
 
24.5%
None 136
 
0.3%
Punctuation 15
 
< 0.1%
Geometric Shapes 10
 
< 0.1%
Compat Jamo 6
 
< 0.1%
Enclosed Alphanum 5
 
< 0.1%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9407
88.8%
, 739
 
7.0%
? 80
 
0.8%
. 75
 
0.7%
) 32
 
0.3%
( 30
 
0.3%
A 17
 
0.2%
` 14
 
0.1%
G 13
 
0.1%
P 12
 
0.1%
Other values (43) 179
 
1.7%
Hangul
ValueCountFrequency (%)
1057
 
3.3%
773
 
2.4%
750
 
2.3%
744
 
2.3%
676
 
2.1%
661
 
2.0%
600
 
1.8%
578
 
1.8%
573
 
1.8%
568
 
1.7%
Other values (518) 25499
78.5%
None
ValueCountFrequency (%)
· 132
97.1%
2
 
1.5%
2
 
1.5%
Geometric Shapes
ValueCountFrequency (%)
10
100.0%
Compat Jamo
ValueCountFrequency (%)
5
83.3%
1
 
16.7%
Punctuation
ValueCountFrequency (%)
5
33.3%
4
26.7%
3
20.0%
3
20.0%
Enclosed Alphanum
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%
Distinct39
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
원예산업과
40 
외식산업진흥과
39 
원예경영과
39 
축산경영과
38 
친환경농업과
 
33
Other values (34)
361 

Length

Max length12
Median length5
Mean length5.7872727
Min length5

Unique

Unique6 ?
Unique (%)1.1%

Sample

1st row종자생명산업과
2nd row식량정책과
3rd row식량정책과
4th row국제협력총괄과
5th row친환경축산팀

Common Values

ValueCountFrequency (%)
원예산업과 40
 
7.3%
외식산업진흥과 39
 
7.1%
원예경영과 39
 
7.1%
축산경영과 38
 
6.9%
친환경농업과 33
 
6.0%
축산정책과 33
 
6.0%
유통정책과 27
 
4.9%
식품산업진흥과 27
 
4.9%
식생활소비정책과 24
 
4.4%
농촌산업과 23
 
4.2%
Other values (29) 227
41.3%

Length

2023-12-11T12:30:48.704692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
원예산업과 40
 
7.3%
외식산업진흥과 39
 
7.1%
원예경영과 39
 
7.1%
축산경영과 38
 
6.9%
친환경농업과 33
 
6.0%
축산정책과 33
 
6.0%
유통정책과 27
 
4.9%
식품산업진흥과 27
 
4.9%
농촌산업과 24
 
4.4%
식생활소비정책과 24
 
4.4%
Other values (28) 226
41.1%

Interactions

2023-12-11T12:30:44.111538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:30:48.814514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지도감독과(정부조직개편)
번호1.0000.390
지도감독과(정부조직개편)0.3901.000
2023-12-11T12:30:48.905725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지도감독과(정부조직개편)
번호1.0000.146
지도감독과(정부조직개편)0.1461.000

Missing values

2023-12-11T12:30:44.225069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:30:44.353543image/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.
2023-12-11T12:30:44.458304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

번호허가번호단체명대표자설립일자설립목적지도감독과(정부조직개편)
011대한잠사회윤장근1946.07.12.양잠, 상묘, 잠종, 제사업 등 잠사업 종사회원에 대한 지원지도, 기술보급 및 개발, 홍보, 조사연구와 잠사관련 민간행사주관 및 대정부 위임사업 수행 등을 통해 회원의 복리증진과 잠사업의 진흥발전에 기여함종자생명산업과
182대한곡물협회전병기1954.02.12.양곡도정업과 양곡 보관업의 건전한 발전과 국가양곡정책 수행에 기여식량정책과
2133한국제분협회이희상1955.12.31제분공업의 건전한 발전과 국책수행 협조식량정책과
345FAO한국협회유병린1957.12.10우리나라와 UN_FAO상호간의 유기적인 연결 및 우방제국과의 기술 ·자료 교환 추진국제협력총괄과
458한국사료협회이양희1961. 7. 3배합사료 제조업의 선진국 과학기술 향상과 사료가공업 및 축산진흥에 기여친환경축산팀
569한국잠종협회강경모1961.12. 6잠종업의 개량발전과 회원 복리증진을 도모함으로써 잠사업의 진흥에 기여함을 목적으로 한다종자생명산업과
6710한국상묘협회조상현1961.11.21상묘 생산의 개량발전과 회원의 복리증진을 도모하므로써 잠사업의 진흥에 기여종자생명산업과
7<NA>18한국양곡가공협회김진경, 이범락1966. 7.11양곡가공업의 획기적인 발전과 국가식량 증산시책에 기여식량정책과
8920한국농공학회손재권1957. 1.26농업공학에 관한 학문연구와 기술발전을 통하여 국가발전과 농업인의 복지 향상에 기여하고 회원상호간의 친목과 권익신장을 목적으로 한다.농업기반과
91021한국식생활개발연구회안승춘1969. 2.15식생활 및 영양개선 계몽 보급 운동을 전개하고 국가 양곡정책 수행 협조농촌복지여성과
번호허가번호단체명대표자설립일자설립목적지도감독과(정부조직개편)
540<NA>749한국승용마생산자협회이영윤2016.02.23.국내 사육중인 모든 품종의 승용마를 전문적인 생산 및 육성을 통해 안전하고 우수한 승용마를 공급하여 말산업의 활성화와 공익에 기여하고 생산농가의 소득증대와 회원 상호간 친목과 발전을 도모축산정책과
541<NA>751(사)전국도시농업시민협의회김진덕2016.04.15시민주도형 도시농업, 자원순환의 생태적 삶, 더불어 사는 공동체를 핵심가치로 하여 도시의 생태적 변화와 농민과 함께하는 도시농업 공동체를 만들어 가기 위한 도시농업활성화와 단체 간의 교류와 협력을 목적종자생명산업과
542<NA>752(사)향토자원발전협회김원규2016.04.28농어촌의 다양한 향토자원들을 활용하여 부가가치 향상을 위한 농업인의 창업을 지원하고, 농업경쟁력 강화를 위한 전 주기적 활동의 조사, 연구, 교육 등을 지원함으로써 농가소득 향상 및 농업,농촌 발전에 공헌농촌산업과
543<NA>753(사)종자사랑실천운동연합이경자2016.5.11종자산업이 고부가가치 산업으로서 재조명 받고, 미래직업을 창출할 수 있는 분야로 급부상함에 따라 농업인, 도시민, 청소녕들에게 종자의 중요성을 알리고, 참여 중심의 종자 교육 컨텐츠 연구, 개발, 보급 등을 통한 종자산업 교육과 홍보를 목적으로 함종자생명산업과
544<NA>755(사)한국농어촌자원봉사개발원조구형2016.5.24.농어촌자원봉사 활동이 인간성을 회복하고 더불어 사는 가치를 증진시키며 건강하고 풍요로운 이상 사회를 만들어갈 수 있다는 모토를 기반으로, 농어촌자원봉사자의 참여 확대 및 농어촌자원봉사지도자 교육을 통한 현장 관리능력을 가진 지도자 개발·육성, 농어촌체험과 자원봉사를 접목할 수 있는 농어촌자원봉사센터 운영 모형 개발을 통한 도·농 간 균형발전 기회 마련농촌정책과
545<NA>756(사)전국농축수산물 직거래장터협회박종구2016.5.27.농산물을 적거래 장터를 이용하여 판매함으로써 농업인의 소득향상유통정책과
546<NA>757(사)전국6차산업인증사업자협회고태훈2016.7.8.전국 6차산업 인증사업자를 대상으로 회원 상호간의 네트워크를 통한 협조체계를 구축하고, 농업&#8228;농촌의 발전, 농촌경제 활성화를 통하여 농업인과 농촌주민의 소득증대 및 국민경제의 발전에 이바지함농촌산업과
547<NA>758(사)힐링외식채움진흥원하재만2016.7.12.힐링외식산업 조리전문인 유소년 미각조리 교육전문가 등 힐링음식개발 전문가를 양성하여 고령화시대에 국민 삶의 질을 높이는데 이바지하고, 외식산업발전을 통한 국가성장에도 기여외식산업진흥과
548<NA>759(사)한국경마미디어연합회최우섭2016.7.25.경마 관련단체 간 협력 및 협력적 경쟁관계의 구축과 올바른 경마 관련 데이터의 수집 및 정보화를 통하여 경마 이용자의 알권리 충족 등 경마산업의 진흥 및 발전에 기여축산정책과
549<NA>760(사)한국시장도매인정산조합이구복2016.09.08.시장도매인제 시장의 정산창구로써의 기능과 역할을 수행하여 산지 출하대금의 안정성을 보장하여 출하자에게 신뢰와 만족도를 높이고 시장도매인제 거래의 투명성 및 효율성 제고을 통해 농수산물 도매시장의 활성화에 기여함을 목적으로 하며, 조합원의 권익 및 복리증진에 기여유통정책과