Overview

Dataset statistics

Number of variables4
Number of observations7460
Missing cells9
Missing cells (%)< 0.1%
Duplicate rows247
Duplicate rows (%)3.3%
Total size in memory233.2 KiB
Average record size in memory32.0 B

Variable types

Text2
Categorical1
DateTime1

Dataset

Description강사가 수행했던 교육 과정명, 교육 내용 등의 정보를 관리하는 공공데이터입니다. 과정명, 교육분과명, 교육일, 교육목적을 제공합니다.
Author농촌진흥청
URLhttps://www.data.go.kr/data/15041716/fileData.do

Alerts

Dataset has 247 (3.3%) duplicate rowsDuplicates
교육분과명 is highly imbalanced (55.6%)Imbalance

Reproduction

Analysis started2024-04-20 23:52:16.909947
Analysis finished2024-04-20 23:52:18.452414
Duration1.54 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

과정명
Text

Distinct516
Distinct (%)6.9%
Missing1
Missing (%)< 0.1%
Memory size58.4 KiB
2024-04-21T08:52:19.408684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length38
Mean length8.6329267
Min length1

Characters and Unicode

Total characters64393
Distinct characters289
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)0.9%

Sample

1st row농업비즈니스모델개선(경기도김포시)-5차
2nd row농업비즈니스모델개선(경기도김포시)-5차
3rd row농업비즈니스모델개선(전남보성군)-6차
4th row농업비즈니스모델개선(전남보성군)-6차
5th row농업인비즈니스모델개선(경기도여주군)-7차
ValueCountFrequency (%)
1기 461
 
3.8%
2기 404
 
3.3%
265
 
2.2%
gap 201
 
1.6%
전통주제조 191
 
1.6%
원예치료 160
 
1.3%
농촌지도기초 151
 
1.2%
실용화 136
 
1.1%
새해영농 135
 
1.1%
공통과목 130
 
1.1%
Other values (448) 9982
81.7%
2024-04-21T08:52:20.752598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5230
 
8.1%
기 3752
 
5.8%
농 2869
 
4.5%
( 1535
 
2.4%
) 1522
 
2.4%
업 1258
 
2.0%
원 1075
 
1.7%
관 996
 
1.5%
전 987
 
1.5%
지 953
 
1.5%
Other values (279) 44216
68.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51585
80.1%
Space Separator 5230
 
8.1%
Decimal Number 2198
 
3.4%
Open Punctuation 1535
 
2.4%
Close Punctuation 1522
 
2.4%
Uppercase Letter 1475
 
2.3%
Other Punctuation 550
 
0.9%
Dash Punctuation 214
 
0.3%
Lowercase Letter 45
 
0.1%
Letter Number 39
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 3752
 
7.3%
농 2869
 
5.6%
업 1258
 
2.4%
원 1075
 
2.1%
관 996
 
1.9%
전 987
 
1.9%
지 953
 
1.8%
리 930
 
1.8%
도 880
 
1.7%
촌 869
 
1.7%
Other values (246) 37016
71.8%
Decimal Number
ValueCountFrequency (%)
2 805
36.6%
1 749
34.1%
3 175
 
8.0%
0 138
 
6.3%
4 130
 
5.9%
9 75
 
3.4%
5 60
 
2.7%
6 36
 
1.6%
7 15
 
0.7%
8 15
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
P 473
32.1%
A 469
31.8%
G 423
28.7%
C 52
 
3.5%
H 23
 
1.6%
O 9
 
0.6%
E 8
 
0.5%
T 7
 
0.5%
J 7
 
0.5%
R 4
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/ 254
46.2%
, 188
34.2%
. 61
 
11.1%
: 25
 
4.5%
． 14
 
2.5%
· 8
 
1.5%
Letter Number
ValueCountFrequency (%)
Ⅱ 27
69.2%
Ⅰ 12
30.8%
Space Separator
ValueCountFrequency (%)
5230
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1535
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1522
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 214
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 51585
80.1%
Common 11249
 
17.5%
Latin 1559
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 3752
 
7.3%
농 2869
 
5.6%
업 1258
 
2.4%
원 1075
 
2.1%
관 996
 
1.9%
전 987
 
1.9%
지 953
 
1.8%
리 930
 
1.8%
도 880
 
1.7%
촌 869
 
1.7%
Other values (246) 37016
71.8%
Common
ValueCountFrequency (%)
5230
46.5%
( 1535
 
13.6%
) 1522
 
13.5%
2 805
 
7.2%
1 749
 
6.7%
/ 254
 
2.3%
- 214
 
1.9%
, 188
 
1.7%
3 175
 
1.6%
0 138
 
1.2%
Other values (10) 439
 
3.9%
Latin
ValueCountFrequency (%)
P 473
30.3%
A 469
30.1%
G 423
27.1%
C 52
 
3.3%
e 45
 
2.9%
Ⅱ 27
 
1.7%
H 23
 
1.5%
Ⅰ 12
 
0.8%
O 9
 
0.6%
E 8
 
0.5%
Other values (3) 18
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 51585
80.1%
ASCII 12747
 
19.8%
Number Forms 39
 
0.1%
None 22
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5230
41.0%
( 1535
 
12.0%
) 1522
 
11.9%
2 805
 
6.3%
1 749
 
5.9%
P 473
 
3.7%
A 469
 
3.7%
G 423
 
3.3%
/ 254
 
2.0%
- 214
 
1.7%
Other values (19) 1073
 
8.4%
Hangul
ValueCountFrequency (%)
기 3752
 
7.3%
농 2869
 
5.6%
업 1258
 
2.4%
원 1075
 
2.1%
관 996
 
1.9%
전 987
 
1.9%
지 953
 
1.8%
리 930
 
1.8%
도 880
 
1.7%
촌 869
 
1.7%
Other values (246) 37016
71.8%
Number Forms
ValueCountFrequency (%)
Ⅱ 27
69.2%
Ⅰ 12
30.8%
None
ValueCountFrequency (%)
． 14
63.6%
· 8
36.4%

교육분과명
Categorical

IMBALANCE 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.4 KiB
농촌진흥공무원전문교육
5420 
특별교육
630 
농업인교육
579 
소비자교육
 
290
농업인 교육
 
258
Other values (5)
 
283

Length

Max length11
Median length11
Mean length9.3605898
Min length4

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
농촌진흥공무원전문교육 5420
72.7%
특별교육 630
 
8.4%
농업인교육 579
 
7.8%
소비자교육 290
 
3.9%
농업인 교육 258
 
3.5%
소비자 교육 250
 
3.4%
농업인실용기술교육 30
 
0.4%
소비자 녹색기술 교육 1
 
< 0.1%
소비자녹색기술교육 1
 
< 0.1%
엘리트귀농대학 1
 
< 0.1%

Length

2024-04-21T08:52:21.191064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T08:52:21.563024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농촌진흥공무원전문교육 5420
68.0%
특별교육 630
 
7.9%
농업인교육 579
 
7.3%
교육 509
 
6.4%
소비자교육 290
 
3.6%
농업인 258
 
3.2%
소비자 251
 
3.1%
농업인실용기술교육 30
 
0.4%
녹색기술 1
 
< 0.1%
소비자녹색기술교육 1
 
< 0.1%

교육일
Date

Distinct585
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size58.4 KiB
Minimum2003-01-01 00:00:00
Maximum2012-02-24 00:00:00
2024-04-21T08:52:21.956239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T08:52:22.365809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

교육목적
Text

Distinct4834
Distinct (%)64.9%
Missing8
Missing (%)0.1%
Memory size58.4 KiB
2024-04-21T08:52:23.429655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length148
Median length100
Mean length14.067767
Min length1

Characters and Unicode

Total characters104833
Distinct characters726
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3623 ?
Unique (%)48.6%

Sample

1st row어서오십시오 강소농교육1번지 농촌인적자원개발센터/강소농 비즈니스개선교육 성공적으로 참가
2nd rowCEO의 다짐과 수련/약속하기
3rd row어서오십시오 강소농교육1번지 농촌인적자원개발센터/강소농 비즈니스모델개선교육 성공적으로 참가
4th rowCEO의 다짐과 수련/약속하기
5th row어서오십시오 강소농교육1번지 농촌인적자원개발센터/강소농 비즈니스모델개선교육 성공적으로 참가
ValueCountFrequency (%)
및 1276
 
5.0%
695
 
2.7%
실습 542
 
2.1%
관리 203
 
0.8%
위한 192
 
0.7%
gap 167
 
0.7%
농산물 151
 
0.6%
추진방향 139
 
0.5%
기술 135
 
0.5%
방향 133
 
0.5%
Other values (5842) 21988
85.8%
2024-04-21T08:52:25.141634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18607
 
17.7%
기 2181
 
2.1%
농 2178
 
2.1%
의 1646
 
1.6%
리 1529
 
1.5%
사 1501
 
1.4%
업 1354
 
1.3%
과 1301
 
1.2%
및 1280
 
1.2%
관 1213
 
1.2%
Other values (716) 72043
68.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79828
76.1%
Space Separator 18607
 
17.7%
Uppercase Letter 1742
 
1.7%
Other Punctuation 1708
 
1.6%
Open Punctuation 851
 
0.8%
Close Punctuation 850
 
0.8%
Lowercase Letter 492
 
0.5%
Dash Punctuation 381
 
0.4%
Decimal Number 299
 
0.3%
Letter Number 66
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 2181
 
2.7%
농 2178
 
2.7%
의 1646
 
2.1%
리 1529
 
1.9%
사 1501
 
1.9%
업 1354
 
1.7%
과 1301
 
1.6%
및 1280
 
1.6%
관 1213
 
1.5%
방 1180
 
1.5%
Other values (632) 64465
80.8%
Uppercase Letter
ValueCountFrequency (%)
A 411
23.6%
P 404
23.2%
G 349
20.0%
C 104
 
6.0%
H 64
 
3.7%
T 64
 
3.7%
S 60
 
3.4%
R 47
 
2.7%
F 40
 
2.3%
E 30
 
1.7%
Other values (14) 169
9.7%
Lowercase Letter
ValueCountFrequency (%)
e 75
15.2%
i 69
14.0%
n 39
 
7.9%
a 36
 
7.3%
r 31
 
6.3%
o 31
 
6.3%
t 29
 
5.9%
m 22
 
4.5%
d 21
 
4.3%
l 20
 
4.1%
Other values (13) 119
24.2%
Other Punctuation
ValueCountFrequency (%)
, 692
40.5%
/ 588
34.4%
. 225
 
13.2%
: 134
 
7.8%
& 23
 
1.3%
· 15
 
0.9%
' 9
 
0.5%
? 8
 
0.5%
* 8
 
0.5%
" 4
 
0.2%
Other values (2) 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 80
26.8%
2 72
24.1%
0 47
15.7%
4 40
13.4%
3 22
 
7.4%
5 15
 
5.0%
8 9
 
3.0%
7 6
 
2.0%
6 5
 
1.7%
9 3
 
1.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 33
50.0%
Ⅰ 28
42.4%
ⅱ 2
 
3.0%
Ⅲ 2
 
3.0%
ⅰ 1
 
1.5%
Other Symbol
ValueCountFrequency (%)
○ 3
33.3%
㈜ 3
33.3%
◎ 2
22.2%
● 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 850
99.9%
「 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 849
99.9%
」 1
 
0.1%
Space Separator
ValueCountFrequency (%)
18607
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 381
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79831
76.2%
Common 22702
 
21.7%
Latin 2300
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 2181
 
2.7%
농 2178
 
2.7%
의 1646
 
2.1%
리 1529
 
1.9%
사 1501
 
1.9%
업 1354
 
1.7%
과 1301
 
1.6%
및 1280
 
1.6%
관 1213
 
1.5%
방 1180
 
1.5%
Other values (633) 64468
80.8%
Latin
ValueCountFrequency (%)
A 411
17.9%
P 404
17.6%
G 349
15.2%
C 104
 
4.5%
e 75
 
3.3%
i 69
 
3.0%
H 64
 
2.8%
T 64
 
2.8%
S 60
 
2.6%
R 47
 
2.0%
Other values (42) 653
28.4%
Common
ValueCountFrequency (%)
18607
82.0%
( 850
 
3.7%
) 849
 
3.7%
, 692
 
3.0%
/ 588
 
2.6%
- 381
 
1.7%
. 225
 
1.0%
: 134
 
0.6%
1 80
 
0.4%
2 72
 
0.3%
Other values (21) 224
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79827
76.1%
ASCII 24912
 
23.8%
Number Forms 66
 
0.1%
None 21
 
< 0.1%
Geometric Shapes 6
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18607
74.7%
( 850
 
3.4%
) 849
 
3.4%
, 692
 
2.8%
/ 588
 
2.4%
A 411
 
1.6%
P 404
 
1.6%
- 381
 
1.5%
G 349
 
1.4%
. 225
 
0.9%
Other values (61) 1556
 
6.2%
Hangul
ValueCountFrequency (%)
기 2181
 
2.7%
농 2178
 
2.7%
의 1646
 
2.1%
리 1529
 
1.9%
사 1501
 
1.9%
업 1354
 
1.7%
과 1301
 
1.6%
및 1280
 
1.6%
관 1213
 
1.5%
방 1180
 
1.5%
Other values (631) 64464
80.8%
Number Forms
ValueCountFrequency (%)
Ⅱ 33
50.0%
Ⅰ 28
42.4%
ⅱ 2
 
3.0%
Ⅲ 2
 
3.0%
ⅰ 1
 
1.5%
None
ValueCountFrequency (%)
· 15
71.4%
㈜ 3
 
14.3%
． 1
 
4.8%
」 1
 
4.8%
「 1
 
4.8%
Geometric Shapes
ValueCountFrequency (%)
○ 3
50.0%
◎ 2
33.3%
● 1
 
16.7%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

Missing values

2024-04-21T08:52:17.766174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T08:52:18.051247image/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.
2024-04-21T08:52:18.315518image/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

과정명교육분과명교육일교육목적
0농업비즈니스모델개선(경기도김포시)-5차농업인실용기술교육2011-07-07어서오십시오 강소농교육1번지 농촌인적자원개발센터/강소농 비즈니스개선교육 성공적으로 참가
1농업비즈니스모델개선(경기도김포시)-5차농업인실용기술교육2011-07-08CEO의 다짐과 수련/약속하기
2농업비즈니스모델개선(전남보성군)-6차농업인실용기술교육2011-07-19어서오십시오 강소농교육1번지 농촌인적자원개발센터/강소농 비즈니스모델개선교육 성공적으로 참가
3농업비즈니스모델개선(전남보성군)-6차농업인실용기술교육2011-07-20CEO의 다짐과 수련/약속하기
4농업인비즈니스모델개선(경기도여주군)-7차농업인실용기술교육2011-07-28어서오십시오 강소농교육1번지 농촌인적자원개발센터/강소농 비즈니스모델개선교육 성공적으로 참가
5농업비즈니스모델개선(경기도여주군)-7차농업인실용기술교육2011-07-29CEO의 다짐과 수련/약속하기
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7농업비즈니스모델개선(전북부안군)-8차농업인실용기술교육2011-08-05CEO의 다짐과 수련/약속하기
8농업비즈니스모델개선(경기도남양주시)-9차농업인실용기술교육2011-08-09어서오십시오 강소농교육1번지 농촌인적자원개발센터/강소농 비즈니스모델교육 성공적으로 참가
9소비자 단체 임원 친농업 마인드향상소비자 녹색기술 교육2010-11-09로컬푸드의 이해와 실천
과정명교육분과명교육일교육목적
7450과수정지전정반농촌진흥공무원전문교육2005-01-01최근과수 정지전정(토론)
7451과수정지전정반농촌진흥공무원전문교육2005-01-01사과나무 정지전정기술
7452과수정지전정반농촌진흥공무원전문교육2005-01-01복숭아나무 정지전정기술
7453과수정지전정반농촌진흥공무원전문교육2005-01-01단감나무 정지전정기술
7454과수정지전정반농촌진흥공무원전문교육2005-01-01배나무 정지전정기술
7455과수정지전정반농촌진흥공무원전문교육2005-01-01포도나무 정지전정기술
7456과수정지전정반농촌진흥공무원전문교육2005-01-01자두나무 정지전정기술
7457과수정지전정반농촌진흥공무원전문교육2005-01-01현장실습
7458과수정지전정반농촌진흥공무원전문교육2005-01-01과실생산동향 및 기술보급방향
7459과수정지전정반농촌진흥공무원전문교육2005-01-01농촌진흥사업발전방향

Duplicate rows

Most frequently occurring

과정명교육분과명교육일교육목적# duplicates
33공통과목농촌진흥공무원전문교육2006-01-01농촌진흥사업 추진방향16
27공통과목농촌진흥공무원전문교육2005-01-01농촌진흥사업 발전방향14
38공통과목농촌진흥공무원전문교육2006-01-01입교인사11
36공통과목농촌진흥공무원전문교육2006-01-01시장정책 브랜드마케팅과 지역연합8
30공통과목농촌진흥공무원전문교육2005-01-01입교인사 및 특강7
32공통과목농촌진흥공무원전문교육2006-01-01농촌진흥기관 발전을 위한 개혁과제 추진전략7
205중견농업기계농촌진흥공무원전문교육2003-01-01포장기계학 및 농산기계학7
26공통과목농촌진흥공무원전문교육2005-01-01농업공직자의 사명6
142인공수정 1기농업인 교육2009-04-22(실습) 인공수정Ⅱ (-소 이용)6
68농업비전아카데미(버섯과정)소비자교육2007-04-24현장학습5