Overview

Dataset statistics

Number of variables12
Number of observations1211
Missing cells6033
Missing cells (%)41.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory118.4 KiB
Average record size in memory100.1 B

Variable types

Text3
Categorical2
Numeric1
Boolean1
Unsupported3
DateTime2

Dataset

Description농축산물소득자료는 기대 가능한 소득액을 작목별, 지역별로 분석한 단순 평균개념의 통계자료가 아니라 영농설계나 경영개선 연구지도를 위한 지표로 이용될 수 있도록 분석한 자료이자 효율적 농장경영을 위한 설계와 진단 등, 개별농가의 경영실적 비교와 경영계획 수립 등에 참고자료로 활용
Author농촌진흥청
URLhttps://www.data.go.kr/data/15069669/fileData.do

Alerts

코드타입명 is highly overall correlated with 코드타입 and 1 other fieldsHigh correlation
사용유무 is highly overall correlated with 코드타입 and 1 other fieldsHigh correlation
코드타입 is highly overall correlated with 코드타입명 and 1 other fieldsHigh correlation
코드타입 is highly imbalanced (56.5%)Imbalance
코드타입명 is highly imbalanced (56.5%)Imbalance
사용유무 is highly imbalanced (93.1%)Imbalance
비고 has 1211 (100.0%) missing valuesMissing
등록자ID has 1211 (100.0%) missing valuesMissing
등록일 has 1203 (99.3%) missing valuesMissing
수정자ID has 1211 (100.0%) missing valuesMissing
수정일 has 1196 (98.8%) missing valuesMissing
통합코드 has unique valuesUnique
비고 is an unsupported type, check if it needs cleaning or further analysisUnsupported
등록자ID is an unsupported type, check if it needs cleaning or further analysisUnsupported
수정자ID is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 23:47:26.132637
Analysis finished2023-12-11 23:47:27.139677
Duration1.01 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통합코드
Text

UNIQUE 

Distinct1211
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
2023-12-12T08:47:27.411669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length8.6630884
Min length3

Characters and Unicode

Total characters10491
Distinct characters32
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

Unique1211 ?
Unique (%)100.0%

Sample

1st rowABO0100
2nd rowABO0101
3rd rowABO0102
4th rowABO0103
5th rowABO0104
ValueCountFrequency (%)
abo0100 1
 
0.1%
q0402a000 1
 
0.1%
q04020700 1
 
0.1%
q04020600 1
 
0.1%
q04020500 1
 
0.1%
q04020400 1
 
0.1%
q04020300 1
 
0.1%
q04020200 1
 
0.1%
q04020100 1
 
0.1%
q04020000 1
 
0.1%
Other values (1201) 1201
99.2%
2023-12-12T08:47:27.920898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4544
43.3%
1 1282
 
12.2%
4 1106
 
10.5%
Q 669
 
6.4%
2 521
 
5.0%
3 461
 
4.4%
P 374
 
3.6%
5 224
 
2.1%
6 223
 
2.1%
8 160
 
1.5%
Other values (22) 927
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8798
83.9%
Uppercase Letter 1693
 
16.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Q 669
39.5%
P 374
22.1%
A 144
 
8.5%
B 98
 
5.8%
N 81
 
4.8%
O 70
 
4.1%
E 54
 
3.2%
M 50
 
3.0%
Z 24
 
1.4%
C 21
 
1.2%
Other values (12) 108
 
6.4%
Decimal Number
ValueCountFrequency (%)
0 4544
51.6%
1 1282
 
14.6%
4 1106
 
12.6%
2 521
 
5.9%
3 461
 
5.2%
5 224
 
2.5%
6 223
 
2.5%
8 160
 
1.8%
7 147
 
1.7%
9 130
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 8798
83.9%
Latin 1693
 
16.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
Q 669
39.5%
P 374
22.1%
A 144
 
8.5%
B 98
 
5.8%
N 81
 
4.8%
O 70
 
4.1%
E 54
 
3.2%
M 50
 
3.0%
Z 24
 
1.4%
C 21
 
1.2%
Other values (12) 108
 
6.4%
Common
ValueCountFrequency (%)
0 4544
51.6%
1 1282
 
14.6%
4 1106
 
12.6%
2 521
 
5.9%
3 461
 
5.2%
5 224
 
2.5%
6 223
 
2.5%
8 160
 
1.8%
7 147
 
1.7%
9 130
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4544
43.3%
1 1282
 
12.2%
4 1106
 
10.5%
Q 669
 
6.4%
2 521
 
5.0%
3 461
 
4.4%
P 374
 
3.6%
5 224
 
2.1%
6 223
 
2.1%
8 160
 
1.5%
Other values (22) 927
 
8.8%

코드타입
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct20
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
Q
669 
P
374 
M
 
50
N
 
18
K
 
13
Other values (15)
87 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
Q 669
55.2%
P 374
30.9%
M 50
 
4.1%
N 18
 
1.5%
K 13
 
1.1%
O 13
 
1.1%
A 12
 
1.0%
L 12
 
1.0%
B 8
 
0.7%
D 7
 
0.6%
Other values (10) 35
 
2.9%

Length

2023-12-12T08:47:28.070235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
q 669
55.2%
p 374
30.9%
m 50
 
4.1%
n 18
 
1.5%
k 13
 
1.1%
o 13
 
1.1%
a 12
 
1.0%
l 12
 
1.0%
b 8
 
0.7%
j 7
 
0.6%
Other values (10) 35
 
2.9%

코드타입명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct20
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
농진청_홈페이지_소득분석항목코드
669 
농진청_홈페이지_작목코드
374 
조사내역코드
 
50
농진청_홈페이지_시도코드
 
18
입력작목유형
 
13
Other values (15)
87 

Length

Max length17
Median length17
Mean length14.444261
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
농진청_홈페이지_소득분석항목코드 669
55.2%
농진청_홈페이지_작목코드 374
30.9%
조사내역코드 50
 
4.1%
농진청_홈페이지_시도코드 18
 
1.5%
입력작목유형 13
 
1.1%
농진청_홈페이지_통계작목유형코드 13
 
1.1%
비료종류 12
 
1.0%
통계작목유형 12
 
1.0%
사용권한 8
 
0.7%
소득분석표시코드타입 7
 
0.6%
Other values (10) 35
 
2.9%

Length

2023-12-12T08:47:28.193233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
농진청_홈페이지_소득분석항목코드 669
55.1%
농진청_홈페이지_작목코드 374
30.8%
조사내역코드 50
 
4.1%
농진청_홈페이지_시도코드 18
 
1.5%
입력작목유형 13
 
1.1%
농진청_홈페이지_통계작목유형코드 13
 
1.1%
비료종류 12
 
1.0%
통계작목유형 12
 
1.0%
사용권한 8
 
0.7%
이력관리 7
 
0.6%
Other values (11) 39
 
3.2%

공통코드
Text

Distinct1170
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
2023-12-12T08:47:28.472617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length7.6630884
Min length2

Characters and Unicode

Total characters9280
Distinct characters25
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

Unique1130 ?
Unique (%)93.3%

Sample

1st rowBO0100
2nd rowBO0101
3rd rowBO0102
4th rowBO0103
5th rowBO0104
ValueCountFrequency (%)
01030000 3
 
0.2%
01080100 2
 
0.2%
01080000 2
 
0.2%
01060000 2
 
0.2%
01040000 2
 
0.2%
01030200 2
 
0.2%
01030100 2
 
0.2%
01020000 2
 
0.2%
04010000 2
 
0.2%
01050000 2
 
0.2%
Other values (1160) 1190
98.3%
2023-12-12T08:47:28.885503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4544
49.0%
1 1283
 
13.8%
4 1105
 
11.9%
2 520
 
5.6%
3 461
 
5.0%
6 224
 
2.4%
5 224
 
2.4%
8 160
 
1.7%
7 147
 
1.6%
A 132
 
1.4%
Other values (15) 480
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8798
94.8%
Uppercase Letter 482
 
5.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 132
27.4%
B 90
18.7%
N 63
13.1%
O 57
11.8%
E 51
 
10.6%
Z 24
 
5.0%
C 18
 
3.7%
I 16
 
3.3%
T 12
 
2.5%
S 12
 
2.5%
Other values (5) 7
 
1.5%
Decimal Number
ValueCountFrequency (%)
0 4544
51.6%
1 1283
 
14.6%
4 1105
 
12.6%
2 520
 
5.9%
3 461
 
5.2%
6 224
 
2.5%
5 224
 
2.5%
8 160
 
1.8%
7 147
 
1.7%
9 130
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 8798
94.8%
Latin 482
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 132
27.4%
B 90
18.7%
N 63
13.1%
O 57
11.8%
E 51
 
10.6%
Z 24
 
5.0%
C 18
 
3.7%
I 16
 
3.3%
T 12
 
2.5%
S 12
 
2.5%
Other values (5) 7
 
1.5%
Common
ValueCountFrequency (%)
0 4544
51.6%
1 1283
 
14.6%
4 1105
 
12.6%
2 520
 
5.9%
3 461
 
5.2%
6 224
 
2.5%
5 224
 
2.5%
8 160
 
1.8%
7 147
 
1.7%
9 130
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4544
49.0%
1 1283
 
13.8%
4 1105
 
11.9%
2 520
 
5.6%
3 461
 
5.0%
6 224
 
2.4%
5 224
 
2.4%
8 160
 
1.7%
7 147
 
1.6%
A 132
 
1.4%
Other values (15) 480
 
5.2%

공통코드명
Text

Distinct1069
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
2023-12-12T08:47:29.173779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length5.4863749
Min length1

Characters and Unicode

Total characters6644
Distinct characters396
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique986 ?
Unique (%)81.4%

Sample

1st row비종
2nd row요소
3rd row유안
4th row용성인비
5th row염화칼리
ValueCountFrequency (%)
기타 10
 
0.8%
유제 7
 
0.6%
입제 7
 
0.6%
여 5
 
0.4%
양잠 5
 
0.4%
남 5
 
0.4%
분제 5
 
0.4%
임차료 5
 
0.4%
과수 4
 
0.3%
춘 4
 
0.3%
Other values (1060) 1173
95.4%
2023-12-12T08:47:29.598886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 300
 
4.5%
) 300
 
4.5%
가 210
 
3.2%
수 205
 
3.1%
자 162
 
2.4%
비 140
 
2.1%
재 136
 
2.0%
정 129
 
1.9%
매 127
 
1.9%
소 127
 
1.9%
Other values (386) 4808
72.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5449
82.0%
Decimal Number 458
 
6.9%
Open Punctuation 300
 
4.5%
Close Punctuation 300
 
4.5%
Other Punctuation 42
 
0.6%
Connector Punctuation 35
 
0.5%
Space Separator 20
 
0.3%
Uppercase Letter 18
 
0.3%
Lowercase Letter 15
 
0.2%
Other Symbol 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 210
 
3.9%
수 205
 
3.8%
자 162
 
3.0%
비 140
 
2.6%
재 136
 
2.5%
정 129
 
2.4%
매 127
 
2.3%
소 127
 
2.3%
부 126
 
2.3%
류 123
 
2.3%
Other values (352) 3964
72.7%
Uppercase Letter
ValueCountFrequency (%)
P 6
33.3%
C 2
 
11.1%
D 2
 
11.1%
I 1
 
5.6%
S 1
 
5.6%
A 1
 
5.6%
K 1
 
5.6%
N 1
 
5.6%
V 1
 
5.6%
H 1
 
5.6%
Decimal Number
ValueCountFrequency (%)
8 114
24.9%
1 91
19.9%
2 59
12.9%
7 57
12.4%
0 54
11.8%
6 23
 
5.0%
4 20
 
4.4%
5 14
 
3.1%
3 14
 
3.1%
9 12
 
2.6%
Other Punctuation
ValueCountFrequency (%)
/ 32
76.2%
. 7
 
16.7%
, 2
 
4.8%
% 1
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
a 11
73.3%
t 2
 
13.3%
s 1
 
6.7%
e 1
 
6.7%
Open Punctuation
ValueCountFrequency (%)
( 300
100.0%
Close Punctuation
ValueCountFrequency (%)
) 300
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 35
100.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5441
81.9%
Common 1162
 
17.5%
Latin 33
 
0.5%
Han 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 210
 
3.9%
수 205
 
3.8%
자 162
 
3.0%
비 140
 
2.6%
재 136
 
2.5%
정 129
 
2.4%
매 127
 
2.3%
소 127
 
2.3%
부 126
 
2.3%
류 123
 
2.3%
Other values (348) 3956
72.7%
Common
ValueCountFrequency (%)
( 300
25.8%
) 300
25.8%
8 114
 
9.8%
1 91
 
7.8%
2 59
 
5.1%
7 57
 
4.9%
0 54
 
4.6%
_ 35
 
3.0%
/ 32
 
2.8%
6 23
 
2.0%
Other values (9) 97
 
8.3%
Latin
ValueCountFrequency (%)
a 11
33.3%
P 6
18.2%
t 2
 
6.1%
C 2
 
6.1%
D 2
 
6.1%
I 1
 
3.0%
S 1
 
3.0%
A 1
 
3.0%
K 1
 
3.0%
N 1
 
3.0%
Other values (5) 5
15.2%
Han
ValueCountFrequency (%)
理 2
25.0%
利 2
25.0%
修 2
25.0%
水 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5441
81.9%
ASCII 1188
 
17.9%
CJK 8
 
0.1%
CJK Compat 7
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 300
25.3%
) 300
25.3%
8 114
 
9.6%
1 91
 
7.7%
2 59
 
5.0%
7 57
 
4.8%
0 54
 
4.5%
_ 35
 
2.9%
/ 32
 
2.7%
6 23
 
1.9%
Other values (23) 123
10.4%
Hangul
ValueCountFrequency (%)
가 210
 
3.9%
수 205
 
3.8%
자 162
 
3.0%
비 140
 
2.6%
재 136
 
2.5%
정 129
 
2.4%
매 127
 
2.3%
소 127
 
2.3%
부 126
 
2.3%
류 123
 
2.3%
Other values (348) 3956
72.7%
CJK Compat
ValueCountFrequency (%)
㎡ 7
100.0%
CJK
ValueCountFrequency (%)
理 2
25.0%
利 2
25.0%
修 2
25.0%
水 2
25.0%

정렬순서
Real number (ℝ)

Distinct670
Distinct (%)55.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean244.76116
Minimum0
Maximum669
Zeros5
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2023-12-12T08:47:29.734073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q168.25
median219.5
Q3370.75
95-th percentile608.55
Maximum669
Range669
Interquartile range (IQR)302.5

Descriptive statistics

Standard deviation193.13568
Coefficient of variation (CV)0.78907813
Kurtosis-0.8434337
Mean244.76116
Median Absolute Deviation (MAD)151.5
Skewness0.49335263
Sum296161
Variance37301.39
MonotonicityNot monotonic
2023-12-12T08:47:29.875967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 20
 
1.7%
2 17
 
1.4%
3 15
 
1.2%
4 13
 
1.1%
5 12
 
1.0%
6 11
 
0.9%
7 11
 
0.9%
8 9
 
0.7%
9 8
 
0.7%
12 8
 
0.7%
Other values (660) 1086
89.7%
ValueCountFrequency (%)
0 5
 
0.4%
1 20
1.7%
2 17
1.4%
3 15
1.2%
4 13
1.1%
5 12
1.0%
6 11
0.9%
7 11
0.9%
8 9
0.7%
9 8
 
0.7%
ValueCountFrequency (%)
669 1
0.1%
668 1
0.1%
667 1
0.1%
666 1
0.1%
665 1
0.1%
664 1
0.1%
663 1
0.1%
662 1
0.1%
661 1
0.1%
660 1
0.1%

사용유무
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
True
1201 
False
 
10
ValueCountFrequency (%)
True 1201
99.2%
False 10
 
0.8%
2023-12-12T08:47:29.982625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

비고
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1211
Missing (%)100.0%
Memory size10.8 KiB

등록자ID
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1211
Missing (%)100.0%
Memory size10.8 KiB

등록일
Date

MISSING 

Distinct4
Distinct (%)50.0%
Missing1203
Missing (%)99.3%
Memory size9.6 KiB
Minimum2009-01-27 13:09:50
Maximum2013-11-01 09:55:45
2023-12-12T08:47:30.054564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:47:30.161621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

수정자ID
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1211
Missing (%)100.0%
Memory size10.8 KiB

수정일
Date

MISSING 

Distinct11
Distinct (%)73.3%
Missing1196
Missing (%)98.8%
Memory size9.6 KiB
Minimum2009-01-27 13:09:50
Maximum2014-05-23 09:15:15
2023-12-12T08:47:30.257136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:47:30.353644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)

Interactions

2023-12-12T08:47:26.676345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:47:30.436358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코드타입코드타입명정렬순서사용유무등록일수정일
코드타입1.0001.0000.6260.9971.0001.000
코드타입명1.0001.0000.6260.9971.0001.000
정렬순서0.6260.6261.0000.175NaNNaN
사용유무0.9970.9970.1751.000NaN1.000
등록일1.0001.000NaNNaN1.0001.000
수정일1.0001.000NaN1.0001.0001.000
2023-12-12T08:47:30.531072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코드타입명사용유무코드타입
코드타입명1.0000.9441.000
사용유무0.9441.0000.944
코드타입1.0000.9441.000
2023-12-12T08:47:30.605596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정렬순서코드타입코드타입명사용유무
정렬순서1.0000.2460.2460.134
코드타입0.2461.0001.0000.944
코드타입명0.2461.0001.0000.944
사용유무0.1340.9440.9441.000

Missing values

2023-12-12T08:47:26.806470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:47:26.970488image/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.
2023-12-12T08:47:27.086374image/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

통합코드코드타입코드타입명공통코드공통코드명정렬순서사용유무비고등록자ID등록일수정자ID수정일
0ABO0100A비료종류BO0100비종1Y<NA><NA><NA><NA><NA>
1ABO0101A비료종류BO0101요소2Y<NA><NA><NA><NA><NA>
2ABO0102A비료종류BO0102유안3Y<NA><NA><NA><NA><NA>
3ABO0103A비료종류BO0103용성인비4Y<NA><NA><NA><NA><NA>
4ABO0104A비료종류BO0104염화칼리5Y<NA><NA><NA><NA><NA>
5ABO0105A비료종류BO0105붕소6Y<NA><NA><NA><NA><NA>
6ABO0106A비료종류BO0106농용석회7Y<NA><NA><NA><NA><NA>
7ABO0107A비료종류BO0107규산질8Y<NA><NA><NA><NA><NA>
8ABO0108A비료종류BO0108복합비료9Y<NA><NA><NA><NA><NA>
9SBO0200S감가상각구분BO0200감가상각1Y<NA><NA><NA><NA><NA>
통합코드코드타입코드타입명공통코드공통코드명정렬순서사용유무비고등록자ID등록일수정자ID수정일
1201P10100002P농진청_홈페이지_작목코드10100002노지브로콜리191Y<NA><NA><NA><NA><NA>
1202P11010006P농진청_홈페이지_작목코드11010006월동무199Y<NA><NA><NA><NA><NA>
1203P12010004P농진청_홈페이지_작목코드11010006양파(조생)208Y<NA><NA><NA><NA><NA>
1204P17030001P농진청_홈페이지_작목코드17030001새송이버섯283Y<NA><NA><NA><NA><NA>
1205P18000002P농진청_홈페이지_작목코드18000002인삼6년근290Y<NA><NA><NA><NA><NA>
1206R00RTBDOGUN_SUB00구분없음0Y<NA><NA>2009-01-27 13:09:50<NA>2009-01-27 13:09:50
1207R01RTBDOGUN_SUB01공통0Y<NA><NA>2009-01-27 13:09:50<NA>2009-01-27 13:09:50
1208R02RTBDOGUN_SUB02평야0Y<NA><NA>2009-01-27 13:09:50<NA>2009-01-27 13:09:50
1209R03RTBDOGUN_SUB03중간0Y<NA><NA>2009-01-27 13:09:50<NA>2009-01-27 13:09:50
1210R04RTBDOGUN_SUB04산간0Y<NA><NA>2009-01-27 13:09:50<NA>2009-01-27 13:09:50