Overview

Dataset statistics

Number of variables10
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory86.6 B

Variable types

Categorical5
Text3
Numeric2

Dataset

Description부산광역시농업교육정보_20240122
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3036705

Alerts

담당자 is highly overall correlated with 실행계획서 과제단위 and 2 other fieldsHigh correlation
연락처 is highly overall correlated with 실행계획서 과제단위 and 2 other fieldsHigh correlation
담당부서 is highly overall correlated with 구분 and 3 other fieldsHigh correlation
구분 is highly overall correlated with 실행계획서 과제단위 and 1 other fieldsHigh correlation
실행계획서 과제단위 is highly overall correlated with 구분 and 3 other fieldsHigh correlation
교육과정명 has unique valuesUnique

Reproduction

Analysis started2024-03-13 13:18:41.339607
Analysis finished2024-03-13 13:18:42.540819
Duration1.2 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size364.0 B
시민농업
17 
농업기술
11 
농어기술
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)3.4%

Sample

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

Common Values

ValueCountFrequency (%)
시민농업 17
58.6%
농업기술 11
37.9%
농어기술 1
 
3.4%

Length

2024-03-13T22:18:42.621451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:42.739841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시민농업 17
58.6%
농업기술 11
37.9%
농어기술 1
 
3.4%

실행계획서 과제단위
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Memory size364.0 B
농촌자원 교육
6 
도시농업 수요 맞춤형 교육
5 
도시농업전문인력 양성기관 교육
4 
농업인대학
2 
여성농업인 교육
2 
Other values (9)
10 

Length

Max length16
Median length13
Mean length10.551724
Min length5

Unique

Unique8 ?
Unique (%)27.6%

Sample

1st row새해농업인실용교육
2nd row농업인대학
3rd row농업인대학
4th row곤충산업인력 양성교육
5th row품목별 전문교육

Common Values

ValueCountFrequency (%)
농촌자원 교육 6
20.7%
도시농업 수요 맞춤형 교육 5
17.2%
도시농업전문인력 양성기관 교육 4
13.8%
농업인대학 2
 
6.9%
여성농업인 교육 2
 
6.9%
치유·반려농업 교육 2
 
6.9%
새해농업인실용교육 1
 
3.4%
곤충산업인력 양성교육 1
 
3.4%
품목별 전문교육 1
 
3.4%
농산물 우수관리(GAP) 교육 1
 
3.4%
Other values (4) 4
13.8%

Length

2024-03-13T22:18:42.860443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
교육 23
31.5%
농촌자원 6
 
8.2%
도시농업 5
 
6.8%
수요 5
 
6.8%
맞춤형 5
 
6.8%
도시농업전문인력 4
 
5.5%
양성기관 4
 
5.5%
치유·반려농업 2
 
2.7%
여성농업인 2
 
2.7%
농업인대학 2
 
2.7%
Other values (15) 15
20.5%

교육과정명
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-03-13T22:18:43.073274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length14
Mean length12.103448
Min length8

Characters and Unicode

Total characters351
Distinct characters113
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

Unique29 ?
Unique (%)100.0%

Sample

1st row새해농업인실용교육
2nd row부산 명품토마토 생산과정
3rd row토마토대학 후속과정
4th row곤충산업인력 양성교육
5th row품목별 전문교육
ValueCountFrequency (%)
교육 9
 
11.5%
프로그램 3
 
3.8%
부산 2
 
2.6%
도시농업 2
 
2.6%
양성과정 2
 
2.6%
식품가공 2
 
2.6%
이용 2
 
2.6%
쌀 2
 
2.6%
우리 2
 
2.6%
여성농업인 2
 
2.6%
Other values (50) 50
64.1%
2024-03-13T22:18:43.435065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49
 
14.0%
교 23
 
6.6%
육 21
 
6.0%
농 15
 
4.3%
업 13
 
3.7%
정 9
 
2.6%
과 8
 
2.3%
인 8
 
2.3%
전 7
 
2.0%
가 7
 
2.0%
Other values (103) 191
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 292
83.2%
Space Separator 49
 
14.0%
Close Punctuation 3
 
0.9%
Open Punctuation 3
 
0.9%
Uppercase Letter 3
 
0.9%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 23
 
7.9%
육 21
 
7.2%
농 15
 
5.1%
업 13
 
4.5%
정 9
 
3.1%
과 8
 
2.7%
인 8
 
2.7%
전 7
 
2.4%
가 7
 
2.4%
성 6
 
2.1%
Other values (96) 175
59.9%
Uppercase Letter
ValueCountFrequency (%)
P 1
33.3%
A 1
33.3%
G 1
33.3%
Space Separator
ValueCountFrequency (%)
49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 292
83.2%
Common 56
 
16.0%
Latin 3
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 23
 
7.9%
육 21
 
7.2%
농 15
 
5.1%
업 13
 
4.5%
정 9
 
3.1%
과 8
 
2.7%
인 8
 
2.7%
전 7
 
2.4%
가 7
 
2.4%
성 6
 
2.1%
Other values (96) 175
59.9%
Common
ValueCountFrequency (%)
49
87.5%
) 3
 
5.4%
( 3
 
5.4%
& 1
 
1.8%
Latin
ValueCountFrequency (%)
P 1
33.3%
A 1
33.3%
G 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 292
83.2%
ASCII 59
 
16.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49
83.1%
) 3
 
5.1%
( 3
 
5.1%
& 1
 
1.7%
P 1
 
1.7%
A 1
 
1.7%
G 1
 
1.7%
Hangul
ValueCountFrequency (%)
교 23
 
7.9%
육 21
 
7.2%
농 15
 
5.1%
업 13
 
4.5%
정 9
 
3.1%
과 8
 
2.7%
인 8
 
2.7%
전 7
 
2.4%
가 7
 
2.4%
성 6
 
2.1%
Other values (96) 175
59.9%

교육대상
Text

Distinct17
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-03-13T22:18:43.604598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length4.4827586
Min length2

Characters and Unicode

Total characters130
Distinct characters44
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

Unique13 ?
Unique (%)44.8%

Sample

1st row농업인
2nd row농업인
3rd row농업인
4th row농업인(예비포함)
5th row농업인
ValueCountFrequency (%)
농업인 8
25.8%
일반인 6
19.4%
전문가,일반인 2
 
6.5%
여성농업인 2
 
6.5%
교사 1
 
3.2%
학교 1
 
3.2%
특수계층 1
 
3.2%
청소년 1
 
3.2%
어린이 1
 
3.2%
활동가(고급 1
 
3.2%
Other values (7) 7
22.6%
2024-03-13T22:18:43.930596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 24
18.5%
농 12
 
9.2%
업 12
 
9.2%
일 10
 
7.7%
반 10
 
7.7%
가 4
 
3.1%
) 4
 
3.1%
( 4
 
3.1%
문 3
 
2.3%
전 3
 
2.3%
Other values (34) 44
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 115
88.5%
Close Punctuation 4
 
3.1%
Open Punctuation 4
 
3.1%
Uppercase Letter 3
 
2.3%
Other Punctuation 2
 
1.5%
Space Separator 2
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 24
20.9%
농 12
 
10.4%
업 12
 
10.4%
일 10
 
8.7%
반 10
 
8.7%
가 4
 
3.5%
문 3
 
2.6%
전 3
 
2.6%
급 3
 
2.6%
여 2
 
1.7%
Other values (27) 32
27.8%
Uppercase Letter
ValueCountFrequency (%)
P 1
33.3%
A 1
33.3%
G 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 115
88.5%
Common 12
 
9.2%
Latin 3
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 24
20.9%
농 12
 
10.4%
업 12
 
10.4%
일 10
 
8.7%
반 10
 
8.7%
가 4
 
3.5%
문 3
 
2.6%
전 3
 
2.6%
급 3
 
2.6%
여 2
 
1.7%
Other values (27) 32
27.8%
Common
ValueCountFrequency (%)
) 4
33.3%
( 4
33.3%
, 2
16.7%
2
16.7%
Latin
ValueCountFrequency (%)
P 1
33.3%
A 1
33.3%
G 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 115
88.5%
ASCII 15
 
11.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 24
20.9%
농 12
 
10.4%
업 12
 
10.4%
일 10
 
8.7%
반 10
 
8.7%
가 4
 
3.5%
문 3
 
2.6%
전 3
 
2.6%
급 3
 
2.6%
여 2
 
1.7%
Other values (27) 32
27.8%
ASCII
ValueCountFrequency (%)
) 4
26.7%
( 4
26.7%
, 2
13.3%
2
13.3%
P 1
 
6.7%
A 1
 
6.7%
G 1
 
6.7%

교육시기(월)
Text

Distinct20
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2024-03-13T22:18:44.170069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length28
Mean length20.241379
Min length2

Characters and Unicode

Total characters587
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)55.2%

Sample

1st row01
2nd row04,05,06,07,08,09,10,101
3rd row05,06,07,08,09
4th row02,03,04,05,06,07,08,09,10
5th row02,03,04,05,06,07,08,09,10,101,102
ValueCountFrequency (%)
04,05,06,07,08,09,10,101 5
16.1%
02,03,04,05,06,07,08,09,10,101 3
 
9.7%
09,10,101 3
 
9.7%
01,02,03,04,05,06,07,08,09,10,101 3
 
9.7%
03,04,05,06,07,08 2
 
6.5%
03,04,05,06,07,08,09,10 1
 
3.2%
08,09,10,101 1
 
3.2%
07,08,09 1
 
3.2%
07,08 1
 
3.2%
03,04,05,06 1
 
3.2%
Other values (10) 10
32.3%
2024-03-13T22:18:44.521790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 198
33.7%
, 169
28.8%
1 64
 
10.9%
8 23
 
3.9%
6 22
 
3.7%
9 22
 
3.7%
5 21
 
3.6%
7 21
 
3.6%
4 19
 
3.2%
3 14
 
2.4%
Other values (2) 14
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 416
70.9%
Other Punctuation 169
28.8%
Space Separator 2
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 198
47.6%
1 64
 
15.4%
8 23
 
5.5%
6 22
 
5.3%
9 22
 
5.3%
5 21
 
5.0%
7 21
 
5.0%
4 19
 
4.6%
3 14
 
3.4%
2 12
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 169
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 587
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 198
33.7%
, 169
28.8%
1 64
 
10.9%
8 23
 
3.9%
6 22
 
3.7%
9 22
 
3.7%
5 21
 
3.6%
7 21
 
3.6%
4 19
 
3.2%
3 14
 
2.4%
Other values (2) 14
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 587
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 198
33.7%
, 169
28.8%
1 64
 
10.9%
8 23
 
3.9%
6 22
 
3.7%
9 22
 
3.7%
5 21
 
3.6%
7 21
 
3.6%
4 19
 
3.2%
3 14
 
2.4%
Other values (2) 14
 
2.4%

교육횟수
Real number (ℝ)

Distinct14
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3103448
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-03-13T22:18:44.686481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.4
Q14
median6
Q312
95-th percentile23
Maximum30
Range29
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.5218892
Coefficient of variation (CV)0.80790662
Kurtosis0.8757251
Mean9.3103448
Median Absolute Deviation (MAD)4
Skewness1.2214412
Sum270
Variance56.578818
MonotonicityNot monotonic
2024-03-13T22:18:44.794009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
5 6
20.7%
10 4
13.8%
20 3
10.3%
3 3
10.3%
4 2
 
6.9%
1 2
 
6.9%
6 2
 
6.9%
25 1
 
3.4%
12 1
 
3.4%
2 1
 
3.4%
Other values (4) 4
13.8%
ValueCountFrequency (%)
1 2
 
6.9%
2 1
 
3.4%
3 3
10.3%
4 2
 
6.9%
5 6
20.7%
6 2
 
6.9%
10 4
13.8%
11 1
 
3.4%
12 1
 
3.4%
14 1
 
3.4%
ValueCountFrequency (%)
30 1
 
3.4%
25 1
 
3.4%
20 3
10.3%
15 1
 
3.4%
14 1
 
3.4%
12 1
 
3.4%
11 1
 
3.4%
10 4
13.8%
6 2
 
6.9%
5 6
20.7%

교육인원(명)
Real number (ℝ)

Distinct14
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.2069
Minimum20
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-03-13T22:18:44.911373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile30
Q130
median120
Q3220
95-th percentile330
Maximum400
Range380
Interquartile range (IQR)190

Descriptive statistics

Standard deviation116.14073
Coefficient of variation (CV)0.79435879
Kurtosis-0.88358053
Mean146.2069
Median Absolute Deviation (MAD)90
Skewness0.60408473
Sum4240
Variance13488.67
MonotonicityNot monotonic
2024-03-13T22:18:45.031405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
30 8
27.6%
200 3
 
10.3%
300 3
 
10.3%
80 2
 
6.9%
150 2
 
6.9%
120 2
 
6.9%
280 2
 
6.9%
220 1
 
3.4%
400 1
 
3.4%
20 1
 
3.4%
Other values (4) 4
13.8%
ValueCountFrequency (%)
20 1
 
3.4%
30 8
27.6%
50 1
 
3.4%
60 1
 
3.4%
80 2
 
6.9%
120 2
 
6.9%
140 1
 
3.4%
150 2
 
6.9%
200 3
 
10.3%
220 1
 
3.4%
ValueCountFrequency (%)
400 1
 
3.4%
350 1
 
3.4%
300 3
10.3%
280 2
6.9%
220 1
 
3.4%
200 3
10.3%
150 2
6.9%
140 1
 
3.4%
120 2
6.9%
80 2
6.9%

담당부서
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
시민농업팀
17 
인재양성팀
12 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시민농업팀 17
58.6%
인재양성팀 12
41.4%

Length

2024-03-13T22:18:45.161752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:18:45.259244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시민농업팀 17
58.6%
인재양성팀 12
41.4%

담당자
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
전순배
5 
김혜경
4 
이화정
3 
정희철
3 
류한수
3 
Other values (6)
11 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)6.9%

Sample

1st row오순필
2nd row이화정
3rd row이화정
4th row전순배
5th row조지영

Common Values

ValueCountFrequency (%)
전순배 5
17.2%
김혜경 4
13.8%
이화정 3
10.3%
정희철 3
10.3%
류한수 3
10.3%
김수남 3
10.3%
오순필 2
 
6.9%
이재순 2
 
6.9%
홍창우 2
 
6.9%
조지영 1
 
3.4%

Length

2024-03-13T22:18:45.366464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전순배 5
17.2%
김혜경 4
13.8%
이화정 3
10.3%
정희철 3
10.3%
류한수 3
10.3%
김수남 3
10.3%
오순필 2
 
6.9%
이재순 2
 
6.9%
홍창우 2
 
6.9%
조지영 1
 
3.4%

연락처
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
051-970-3763
5 
051-970-3746
4 
051-970-3765
3 
051-970-3745
3 
051-970-3741
3 
Other values (6)
11 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique2 ?
Unique (%)6.9%

Sample

1st row051-970-3762
2nd row051-970-3765
3rd row051-970-3765
4th row051-970-3763
5th row051-970-3761

Common Values

ValueCountFrequency (%)
051-970-3763 5
17.2%
051-970-3746 4
13.8%
051-970-3765 3
10.3%
051-970-3745 3
10.3%
051-970-3741 3
10.3%
051-970-3744 3
10.3%
051-970-3762 2
 
6.9%
051-970-3742 2
 
6.9%
051-970-3743 2
 
6.9%
051-970-3761 1
 
3.4%

Length

2024-03-13T22:18:45.483239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
051-970-3763 5
17.2%
051-970-3746 4
13.8%
051-970-3765 3
10.3%
051-970-3745 3
10.3%
051-970-3741 3
10.3%
051-970-3744 3
10.3%
051-970-3762 2
 
6.9%
051-970-3742 2
 
6.9%
051-970-3743 2
 
6.9%
051-970-3761 1
 
3.4%

Interactions

2024-03-13T22:18:42.054293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:41.836465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:42.138058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:18:41.936110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:18:45.569712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분실행계획서 과제단위교육과정명교육대상교육시기(월)교육횟수교육인원(명)담당부서담당자연락처
구분1.0001.0001.0001.0000.9630.0000.0001.0000.7270.727
실행계획서 과제단위1.0001.0001.0000.7730.9470.6790.7701.0000.9160.916
교육과정명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
교육대상1.0000.7731.0001.0000.9350.7730.0001.0000.0000.000
교육시기(월)0.9630.9471.0000.9351.0000.0000.7850.9420.8960.896
교육횟수0.0000.6791.0000.7730.0001.0000.6220.0000.0000.000
교육인원(명)0.0000.7701.0000.0000.7850.6221.0000.3510.6060.606
담당부서1.0001.0001.0001.0000.9420.0000.3511.0001.0001.000
담당자0.7270.9161.0000.0000.8960.0000.6061.0001.0001.000
연락처0.7270.9161.0000.0000.8960.0000.6061.0001.0001.000
2024-03-13T22:18:46.011335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
담당자실행계획서 과제단위연락처담당부서구분
담당자1.0000.6181.0000.8160.472
실행계획서 과제단위0.6181.0000.6180.7450.760
연락처1.0000.6181.0000.8160.472
담당부서0.8160.7450.8161.0000.981
구분0.4720.7600.4720.9811.000
2024-03-13T22:18:46.122167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교육횟수교육인원(명)구분실행계획서 과제단위담당부서담당자연락처
교육횟수1.0000.0680.0000.2260.0000.0000.000
교육인원(명)0.0681.0000.0000.3730.2010.2740.274
구분0.0000.0001.0000.7600.9810.4720.472
실행계획서 과제단위0.2260.3730.7601.0000.7450.6180.618
담당부서0.0000.2010.9810.7451.0000.8160.816
담당자0.0000.2740.4720.6180.8161.0001.000
연락처0.0000.2740.4720.6180.8161.0001.000

Missing values

2024-03-13T22:18:42.314209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:18:42.482319image/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

구분실행계획서 과제단위교육과정명교육대상교육시기(월)교육횟수교육인원(명)담당부서담당자연락처
0농업기술새해농업인실용교육새해농업인실용교육농업인014220인재양성팀오순필051-970-3762
1농업기술농업인대학부산 명품토마토 생산과정농업인04,05,06,07,08,09,10,1012530인재양성팀이화정051-970-3765
2농업기술농업인대학토마토대학 후속과정농업인05,06,07,08,09530인재양성팀이화정051-970-3765
3농업기술곤충산업인력 양성교육곤충산업인력 양성교육농업인(예비포함)02,03,04,05,06,07,08,09,102030인재양성팀전순배051-970-3763
4농업기술품목별 전문교육품목별 전문교육농업인02,03,04,05,06,07,08,09,10,101,10212400인재양성팀조지영051-970-3761
5농업기술농산물 우수관리(GAP) 교육농산물 우수관리(GAP) 교육GAP인증 농업인06,08280인재양성팀전순배051-970-3763
6농어기술친환경 농업 교육친환경 인증관련 교육친환경인증 농업인02120인재양성팀전순배051-970-3763
7농업기술여성농업인 교육여성농업인 역량개발 교육여성농업인05,0611350인재양성팀전순배051-970-3763
8농업기술여성농업인 교육여성농업인 리더십 향상교육여성농업인10130인재양성팀전순배051-970-3763
9농업기술신규농업인 교육신규농업인 교육예비농업인03,04,05,06,07,08, 09,10,1013060인재양성팀오순필051-970-3762
구분실행계획서 과제단위교육과정명교육대상교육시기(월)교육횟수교육인원(명)담당부서담당자연락처
19시민농업도시농업 수요 맞춤형 교육교육기부 진로체험 교육청소년09,10,1015120시민농업팀김수남051-970-3744
20시민농업도시농업 수요 맞춤형 교육도시농업 특별교육일반인04,05,06,07,08,09,10,1016300시민농업팀류한수051-970-3741
21시민농업치유·반려농업 교육치유농업 프로그램특수계층04,05,06,07,08,09,10,1015200시민농업팀이재순051-970-3742
22시민농업치유·반려농업 교육반려농업 프로그램일반인04,05,06,07,08,09,10,1013300시민농업팀이재순051-970-3742
23시민농업농촌자원 교육전통식문화 계승 교육전문가,일반인01,02,03,04,05,06,07,08,09,10,10115280시민농업팀김혜경051-970-3746
24시민농업농촌자원 교육로컬푸드 활성화 교육일반인01,02,03,04,05,06,07,08,09,10,1016150시민농업팀김혜경051-970-3746
25시민농업농촌자원 교육부산 농특산물 가공 교육전문가,일반인02,03,04,05,06,07,08,09,10,10114280시민농업팀홍창우051-970-3743
26시민농업농촌자원 교육우리 쌀 이용 식품가공 기술교육(전문과정)전문가02,03,04,05,06,07,08,09,10,10110200시민농업팀김혜경051-970-3746
27시민농업농촌자원 교육우리 쌀 이용 식품가공 기술교육(일반과정)일반인02,03,04,05,06,07,08,09,10,10110300시민농업팀홍창우051-970-3743
28시민농업농촌자원 교육전통생활 기술교육일반인01,02,03,04,05,06,07,08,09,10,101380시민농업팀김혜경051-970-3746