Overview

Dataset statistics

Number of variables18
Number of observations25
Missing cells2
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory159.3 B

Variable types

Text7
Numeric10
DateTime1

Dataset

Description농기계 임대 정보 현황(제공표준)
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=7PV1MDI984K6DC3IL5R421552415&infSeq=1

Alerts

트랙터및작업기보유대수 is highly overall correlated with 땅속작물수확기보유대수 and 1 other fieldsHigh correlation
경운기및작업기보유대수 is highly overall correlated with 관리기및작업기보유대수 and 2 other fieldsHigh correlation
관리기및작업기보유대수 is highly overall correlated with 경운기및작업기보유대수 and 1 other fieldsHigh correlation
땅속작물수확기보유대수 is highly overall correlated with 트랙터및작업기보유대수 and 3 other fieldsHigh correlation
탈곡기및정선작업기보유대수 is highly overall correlated with 트랙터및작업기보유대수 and 2 other fieldsHigh correlation
자주형파종기보유대수 is highly overall correlated with 이앙작업기보유대수High correlation
이앙작업기보유대수 is highly overall correlated with 자주형파종기보유대수 and 1 other fieldsHigh correlation
벼수확및운반작업기보유대수 is highly overall correlated with 이앙작업기보유대수High correlation
기타임대농기계보유정보 has 2 (8.0%) missing valuesMissing
사업소명 has unique valuesUnique
사업소전화번호 has unique valuesUnique
소재지도로명주소 has unique valuesUnique
소재지지번주소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
경운기및작업기보유대수 has 2 (8.0%) zerosZeros
자주형파종기보유대수 has 12 (48.0%) zerosZeros
이앙작업기보유대수 has 14 (56.0%) zerosZeros
벼수확및운반작업기보유대수 has 11 (44.0%) zerosZeros

Reproduction

Analysis started2024-05-10 21:09:45.515960
Analysis finished2024-05-10 21:10:14.997581
Duration29.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업소명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-05-10T21:10:15.335636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length15.24
Min length4

Characters and Unicode

Total characters381
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row양평군농업기술센터 본소
2nd row수원시 농업기술센터 농기계 임대사업소
3rd row안성시농기계임대사업소 동부분소
4th row이천시 농기계임대사업소
5th row이천시 농기계임대사업소(남부분소)
ValueCountFrequency (%)
본소 4
 
7.4%
안성시농기계임대사업소 4
 
7.4%
농기계임대사업소 4
 
7.4%
농업기계 3
 
5.6%
농업기술센터 3
 
5.6%
파주시농기계임대사업소 2
 
3.7%
임대사업소 2
 
3.7%
평택시농업기술센터 2
 
3.7%
이천시 2
 
3.7%
연천군 2
 
3.7%
Other values (26) 26
48.1%
2024-05-10T21:10:16.258614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32
 
8.4%
32
 
8.4%
31
 
8.1%
30
 
7.9%
29
 
7.6%
22
 
5.8%
21
 
5.5%
20
 
5.2%
20
 
5.2%
19
 
5.0%
Other values (40) 125
32.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 344
90.3%
Space Separator 29
 
7.6%
Open Punctuation 4
 
1.0%
Close Punctuation 4
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
32
 
9.3%
32
 
9.3%
31
 
9.0%
30
 
8.7%
22
 
6.4%
21
 
6.1%
20
 
5.8%
20
 
5.8%
19
 
5.5%
8
 
2.3%
Other values (37) 109
31.7%
Space Separator
ValueCountFrequency (%)
29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 344
90.3%
Common 37
 
9.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
32
 
9.3%
32
 
9.3%
31
 
9.0%
30
 
8.7%
22
 
6.4%
21
 
6.1%
20
 
5.8%
20
 
5.8%
19
 
5.5%
8
 
2.3%
Other values (37) 109
31.7%
Common
ValueCountFrequency (%)
29
78.4%
( 4
 
10.8%
) 4
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 344
90.3%
ASCII 37
 
9.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
32
 
9.3%
32
 
9.3%
31
 
9.0%
30
 
8.7%
22
 
6.4%
21
 
6.1%
20
 
5.8%
20
 
5.8%
19
 
5.5%
8
 
2.3%
Other values (37) 109
31.7%
ASCII
ValueCountFrequency (%)
29
78.4%
( 4
 
10.8%
) 4
 
10.8%
Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-05-10T21:10:16.716852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.24
Min length12

Characters and Unicode

Total characters306
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

Unique25 ?
Unique (%)100.0%

Sample

1st row031-770-3591
2nd row031-228-2564
3rd row031-678-3035
4th row031-644-4139
5th row031-645-3499
ValueCountFrequency (%)
031-770-3591 1
 
4.0%
031-678-0711 1
 
4.0%
031-8024-7483 1
 
4.0%
031-8024-4585 1
 
4.0%
031-580-4791 1
 
4.0%
031-8082-6151 1
 
4.0%
031-887-3714 1
 
4.0%
031-538-3766 1
 
4.0%
031-538-3787 1
 
4.0%
031-839-4265 1
 
4.0%
Other values (15) 15
60.0%
2024-05-10T21:10:17.918202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 50
16.3%
3 45
14.7%
0 41
13.4%
1 37
12.1%
8 24
7.8%
4 24
7.8%
5 22
7.2%
7 19
 
6.2%
6 18
 
5.9%
2 14
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 256
83.7%
Dash Punctuation 50
 
16.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 45
17.6%
0 41
16.0%
1 37
14.5%
8 24
9.4%
4 24
9.4%
5 22
8.6%
7 19
7.4%
6 18
 
7.0%
2 14
 
5.5%
9 12
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 306
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 50
16.3%
3 45
14.7%
0 41
13.4%
1 37
12.1%
8 24
7.8%
4 24
7.8%
5 22
7.2%
7 19
 
6.2%
6 18
 
5.9%
2 14
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 50
16.3%
3 45
14.7%
0 41
13.4%
1 37
12.1%
8 24
7.8%
4 24
7.8%
5 22
7.2%
7 19
 
6.2%
6 18
 
5.9%
2 14
 
4.6%
Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-05-10T21:10:18.470993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length23
Mean length21.4
Min length14

Characters and Unicode

Total characters535
Distinct characters110
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row경기도 양평군 양평읍 농업기술센터길 37
2nd row경기도 수원시 권선구 온정로45
3rd row경기도 안성시 죽산면 상삼로 95
4th row경기도 이천시 모가면 사실로 720
5th row경기도 이천시 장호원읍 서동대로8759번길 97-80
ValueCountFrequency (%)
경기도 25
 
20.2%
안성시 4
 
3.2%
파주시 2
 
1.6%
포천시 2
 
1.6%
연천군 2
 
1.6%
평택시 2
 
1.6%
이천시 2
 
1.6%
서운면 1
 
0.8%
523-34 1
 
0.8%
11-88 1
 
0.8%
Other values (82) 82
66.1%
2024-05-10T21:10:19.416385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
99
 
18.5%
26
 
4.9%
25
 
4.7%
25
 
4.7%
21
 
3.9%
21
 
3.9%
5 15
 
2.8%
2 15
 
2.8%
15
 
2.8%
1 13
 
2.4%
Other values (100) 260
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 326
60.9%
Space Separator 99
 
18.5%
Decimal Number 99
 
18.5%
Dash Punctuation 11
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
26
 
8.0%
25
 
7.7%
25
 
7.7%
21
 
6.4%
21
 
6.4%
15
 
4.6%
9
 
2.8%
8
 
2.5%
8
 
2.5%
8
 
2.5%
Other values (88) 160
49.1%
Decimal Number
ValueCountFrequency (%)
5 15
15.2%
2 15
15.2%
1 13
13.1%
3 11
11.1%
4 9
9.1%
0 9
9.1%
8 8
8.1%
7 7
7.1%
9 7
7.1%
6 5
 
5.1%
Space Separator
ValueCountFrequency (%)
99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 326
60.9%
Common 209
39.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
26
 
8.0%
25
 
7.7%
25
 
7.7%
21
 
6.4%
21
 
6.4%
15
 
4.6%
9
 
2.8%
8
 
2.5%
8
 
2.5%
8
 
2.5%
Other values (88) 160
49.1%
Common
ValueCountFrequency (%)
99
47.4%
5 15
 
7.2%
2 15
 
7.2%
1 13
 
6.2%
- 11
 
5.3%
3 11
 
5.3%
4 9
 
4.3%
0 9
 
4.3%
8 8
 
3.8%
7 7
 
3.3%
Other values (2) 12
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 326
60.9%
ASCII 209
39.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
99
47.4%
5 15
 
7.2%
2 15
 
7.2%
1 13
 
6.2%
- 11
 
5.3%
3 11
 
5.3%
4 9
 
4.3%
0 9
 
4.3%
8 8
 
3.8%
7 7
 
3.3%
Other values (2) 12
 
5.7%
Hangul
ValueCountFrequency (%)
26
 
8.0%
25
 
7.7%
25
 
7.7%
21
 
6.4%
21
 
6.4%
15
 
4.6%
9
 
2.8%
8
 
2.5%
8
 
2.5%
8
 
2.5%
Other values (88) 160
49.1%
Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-05-10T21:10:19.951780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length21
Min length15

Characters and Unicode

Total characters525
Distinct characters101
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row경기도 양평군 양평읍 공흥리 1-1
2nd row경기도 수원시 권선구 오목천동 40
3rd row경기도 안성시 죽산면 두현리 456-6번지
4th row경기도 이천시 모가면 어농리 615-7
5th row경기도 이천시 장호원읍 진암리 328-5
ValueCountFrequency (%)
경기도 25
 
20.2%
안성시 4
 
3.2%
파주시 2
 
1.6%
포천시 2
 
1.6%
이천시 2
 
1.6%
평택시 2
 
1.6%
연천군 2
 
1.6%
서운면 1
 
0.8%
송산리 1
 
0.8%
647-1 1
 
0.8%
Other values (82) 82
66.1%
2024-05-10T21:10:20.908864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
99
18.9%
26
 
5.0%
26
 
5.0%
25
 
4.8%
21
 
4.0%
21
 
4.0%
- 20
 
3.8%
15
 
2.9%
5 13
 
2.5%
1 12
 
2.3%
Other values (91) 247
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 318
60.6%
Space Separator 99
 
18.9%
Decimal Number 88
 
16.8%
Dash Punctuation 20
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
26
 
8.2%
26
 
8.2%
25
 
7.9%
21
 
6.6%
21
 
6.6%
15
 
4.7%
10
 
3.1%
9
 
2.8%
9
 
2.8%
8
 
2.5%
Other values (79) 148
46.5%
Decimal Number
ValueCountFrequency (%)
5 13
14.8%
1 12
13.6%
2 11
12.5%
8 10
11.4%
7 9
10.2%
4 7
8.0%
3 7
8.0%
6 7
8.0%
9 7
8.0%
0 5
 
5.7%
Space Separator
ValueCountFrequency (%)
99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 318
60.6%
Common 207
39.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
26
 
8.2%
26
 
8.2%
25
 
7.9%
21
 
6.6%
21
 
6.6%
15
 
4.7%
10
 
3.1%
9
 
2.8%
9
 
2.8%
8
 
2.5%
Other values (79) 148
46.5%
Common
ValueCountFrequency (%)
99
47.8%
- 20
 
9.7%
5 13
 
6.3%
1 12
 
5.8%
2 11
 
5.3%
8 10
 
4.8%
7 9
 
4.3%
4 7
 
3.4%
3 7
 
3.4%
6 7
 
3.4%
Other values (2) 12
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 318
60.6%
ASCII 207
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
99
47.8%
- 20
 
9.7%
5 13
 
6.3%
1 12
 
5.8%
2 11
 
5.3%
8 10
 
4.8%
7 9
 
4.3%
4 7
 
3.4%
3 7
 
3.4%
6 7
 
3.4%
Other values (2) 12
 
5.8%
Hangul
ValueCountFrequency (%)
26
 
8.2%
26
 
8.2%
25
 
7.9%
21
 
6.6%
21
 
6.6%
15
 
4.7%
10
 
3.1%
9
 
2.8%
9
 
2.8%
8
 
2.5%
Other values (79) 148
46.5%

위도
Real number (ℝ)

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.470109
Minimum36.943708
Maximum38.10002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-10T21:10:21.296507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.943708
5-th percentile37.016364
Q137.114528
median37.431946
Q337.843323
95-th percentile38.075136
Maximum38.10002
Range1.1563121
Interquartile range (IQR)0.72879471

Descriptive statistics

Standard deviation0.39105368
Coefficient of variation (CV)0.010436417
Kurtosis-1.5197092
Mean37.470109
Median Absolute Deviation (MAD)0.33559686
Skewness0.253222
Sum936.75272
Variance0.15292298
MonotonicityNot monotonic
2024-05-10T21:10:21.695505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
37.50924203 1
 
4.0%
37.25012337 1
 
4.0%
37.43194566 1
 
4.0%
37.0963488 1
 
4.0%
37.014605071 1
 
4.0%
37.845968 1
 
4.0%
37.84332265 1
 
4.0%
37.2537575 1
 
4.0%
38.10002011 1
 
4.0%
37.9232987 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
36.943708 1
4.0%
37.014605071 1
4.0%
37.02339983 1
4.0%
37.048953 1
4.0%
37.08079265 1
4.0%
37.0963488 1
4.0%
37.11452794 1
4.0%
37.12032373 1
4.0%
37.17229221 1
4.0%
37.17591657 1
4.0%
ValueCountFrequency (%)
38.10002011 1
4.0%
38.08457871 1
4.0%
38.03736705 1
4.0%
37.93534778 1
4.0%
37.9232987 1
4.0%
37.845968 1
4.0%
37.84332265 1
4.0%
37.76515582 1
4.0%
37.68294393 1
4.0%
37.64977718 1
4.0%

경도
Real number (ℝ)

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.16609
Minimum126.64077
Maximum127.63777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-10T21:10:22.209920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.64077
5-th percentile126.81008
Q1126.96317
median127.19989
Q3127.29928
95-th percentile127.59164
Maximum127.63777
Range0.9970009
Interquartile range (IQR)0.3361142

Descriptive statistics

Standard deviation0.2649028
Coefficient of variation (CV)0.0020831245
Kurtosis-0.71088922
Mean127.16609
Median Absolute Deviation (MAD)0.21873088
Skewness0.018598058
Sum3179.1523
Variance0.070173496
MonotonicityNot monotonic
2024-05-10T21:10:22.528356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
127.5132942 1
 
4.0%
126.9631655 1
 
4.0%
127.2343488 1
 
4.0%
127.0809798574 1
 
4.0%
126.9811593157 1
 
4.0%
127.498648 1
 
4.0%
126.9980277 1
 
4.0%
127.6377736 1
 
4.0%
127.2770214 1
 
4.0%
127.2252129 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
126.6407727 1
4.0%
126.7962227 1
4.0%
126.8655254 1
4.0%
126.8700851 1
4.0%
126.879548 1
4.0%
126.9412994 1
4.0%
126.9631655 1
4.0%
126.9811593157 1
4.0%
126.9980277 1
4.0%
127.0779438 1
4.0%
ValueCountFrequency (%)
127.6377736 1
4.0%
127.6112318 1
4.0%
127.5132942 1
4.0%
127.498648 1
4.0%
127.4402069 1
4.0%
127.4019946 1
4.0%
127.2992797 1
4.0%
127.2868005 1
4.0%
127.2770214 1
4.0%
127.234857 1
4.0%

트랙터및작업기보유대수
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.84
Minimum3
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-10T21:10:22.829362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile21.8
Q150
median67
Q3102
95-th percentile160.6
Maximum247
Range244
Interquartile range (IQR)52

Descriptive statistics

Standard deviation56.41563
Coefficient of variation (CV)0.68934055
Kurtosis1.624528
Mean81.84
Median Absolute Deviation (MAD)28
Skewness1.2299665
Sum2046
Variance3182.7233
MonotonicityNot monotonic
2024-05-10T21:10:23.124722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
57 2
 
8.0%
87 1
 
4.0%
56 1
 
4.0%
26 1
 
4.0%
70 1
 
4.0%
161 1
 
4.0%
136 1
 
4.0%
247 1
 
4.0%
137 1
 
4.0%
36 1
 
4.0%
Other values (14) 14
56.0%
ValueCountFrequency (%)
3 1
4.0%
21 1
4.0%
25 1
4.0%
26 1
4.0%
36 1
4.0%
39 1
4.0%
50 1
4.0%
54 1
4.0%
56 1
4.0%
57 2
8.0%
ValueCountFrequency (%)
247 1
4.0%
161 1
4.0%
159 1
4.0%
158 1
4.0%
137 1
4.0%
136 1
4.0%
102 1
4.0%
87 1
4.0%
84 1
4.0%
77 1
4.0%

경운기및작업기보유대수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.64
Minimum0
Maximum75
Zeros2
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-10T21:10:23.469780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q14
median6
Q314
95-th percentile30.2
Maximum75
Range75
Interquartile range (IQR)10

Descriptive statistics

Standard deviation15.416117
Coefficient of variation (CV)1.3244087
Kurtosis12.132366
Mean11.64
Median Absolute Deviation (MAD)5
Skewness3.174718
Sum291
Variance237.65667
MonotonicityNot monotonic
2024-05-10T21:10:23.838952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4 3
12.0%
6 3
12.0%
14 2
 
8.0%
5 2
 
8.0%
0 2
 
8.0%
1 2
 
8.0%
27 1
 
4.0%
20 1
 
4.0%
13 1
 
4.0%
31 1
 
4.0%
Other values (7) 7
28.0%
ValueCountFrequency (%)
0 2
8.0%
1 2
8.0%
2 1
 
4.0%
4 3
12.0%
5 2
8.0%
6 3
12.0%
7 1
 
4.0%
8 1
 
4.0%
11 1
 
4.0%
12 1
 
4.0%
ValueCountFrequency (%)
75 1
4.0%
31 1
4.0%
27 1
4.0%
20 1
4.0%
15 1
4.0%
14 2
8.0%
13 1
4.0%
12 1
4.0%
11 1
4.0%
8 1
4.0%

관리기및작업기보유대수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.84
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-10T21:10:24.202508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile14
Q122
median30
Q334
95-th percentile44.2
Maximum49
Range36
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.5336247
Coefficient of variation (CV)0.33056951
Kurtosis-0.37025146
Mean28.84
Median Absolute Deviation (MAD)6
Skewness0.099053895
Sum721
Variance90.89
MonotonicityNot monotonic
2024-05-10T21:10:24.569930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
32 3
 
12.0%
30 2
 
8.0%
22 2
 
8.0%
14 2
 
8.0%
26 1
 
4.0%
33 1
 
4.0%
17 1
 
4.0%
36 1
 
4.0%
39 1
 
4.0%
18 1
 
4.0%
Other values (10) 10
40.0%
ValueCountFrequency (%)
13 1
4.0%
14 2
8.0%
17 1
4.0%
18 1
4.0%
22 2
8.0%
24 1
4.0%
25 1
4.0%
26 1
4.0%
27 1
4.0%
30 2
8.0%
ValueCountFrequency (%)
49 1
 
4.0%
45 1
 
4.0%
41 1
 
4.0%
39 1
 
4.0%
36 1
 
4.0%
35 1
 
4.0%
34 1
 
4.0%
33 1
 
4.0%
32 3
12.0%
31 1
 
4.0%

땅속작물수확기보유대수
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.6
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-10T21:10:24.942235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median12
Q316
95-th percentile43.2
Maximum50
Range49
Interquartile range (IQR)8

Descriptive statistics

Standard deviation12.365948
Coefficient of variation (CV)0.84698273
Kurtosis2.8727672
Mean14.6
Median Absolute Deviation (MAD)4
Skewness1.7531357
Sum365
Variance152.91667
MonotonicityNot monotonic
2024-05-10T21:10:25.313147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
9 3
12.0%
12 3
12.0%
8 2
 
8.0%
20 2
 
8.0%
2 2
 
8.0%
10 2
 
8.0%
15 2
 
8.0%
5 2
 
8.0%
21 1
 
4.0%
1 1
 
4.0%
Other values (5) 5
20.0%
ValueCountFrequency (%)
1 1
 
4.0%
2 2
8.0%
5 2
8.0%
8 2
8.0%
9 3
12.0%
10 2
8.0%
12 3
12.0%
13 1
 
4.0%
15 2
8.0%
16 1
 
4.0%
ValueCountFrequency (%)
50 1
 
4.0%
45 1
 
4.0%
36 1
 
4.0%
21 1
 
4.0%
20 2
8.0%
16 1
 
4.0%
15 2
8.0%
13 1
 
4.0%
12 3
12.0%
10 2
8.0%

탈곡기및정선작업기보유대수
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25
Minimum2
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-10T21:10:25.775584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.2
Q112
median24
Q335
95-th percentile53.4
Maximum58
Range56
Interquartile range (IQR)23

Descriptive statistics

Standard deviation15.564382
Coefficient of variation (CV)0.6225753
Kurtosis-0.51780051
Mean25
Median Absolute Deviation (MAD)12
Skewness0.43970108
Sum625
Variance242.25
MonotonicityNot monotonic
2024-05-10T21:10:26.190392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10 3
 
12.0%
17 2
 
8.0%
34 2
 
8.0%
30 1
 
4.0%
36 1
 
4.0%
24 1
 
4.0%
43 1
 
4.0%
32 1
 
4.0%
38 1
 
4.0%
56 1
 
4.0%
Other values (11) 11
44.0%
ValueCountFrequency (%)
2 1
 
4.0%
4 1
 
4.0%
5 1
 
4.0%
10 3
12.0%
12 1
 
4.0%
15 1
 
4.0%
16 1
 
4.0%
17 2
8.0%
18 1
 
4.0%
24 1
 
4.0%
ValueCountFrequency (%)
58 1
4.0%
56 1
4.0%
43 1
4.0%
40 1
4.0%
38 1
4.0%
36 1
4.0%
35 1
4.0%
34 2
8.0%
32 1
4.0%
30 1
4.0%

자주형파종기보유대수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.56
Minimum0
Maximum31
Zeros12
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-10T21:10:26.526308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile29.4
Maximum31
Range31
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.396634
Coefficient of variation (CV)1.5848528
Kurtosis0.82049563
Mean6.56
Median Absolute Deviation (MAD)1
Skewness1.4944419
Sum164
Variance108.09
MonotonicityNot monotonic
2024-05-10T21:10:26.907153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 12
48.0%
1 2
 
8.0%
20 2
 
8.0%
2 2
 
8.0%
12 1
 
4.0%
3 1
 
4.0%
27 1
 
4.0%
6 1
 
4.0%
31 1
 
4.0%
9 1
 
4.0%
ValueCountFrequency (%)
0 12
48.0%
1 2
 
8.0%
2 2
 
8.0%
3 1
 
4.0%
6 1
 
4.0%
9 1
 
4.0%
12 1
 
4.0%
20 2
 
8.0%
27 1
 
4.0%
30 1
 
4.0%
ValueCountFrequency (%)
31 1
4.0%
30 1
4.0%
27 1
4.0%
20 2
8.0%
12 1
4.0%
9 1
4.0%
6 1
4.0%
3 1
4.0%
2 2
8.0%
1 2
8.0%

이앙작업기보유대수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.92
Minimum0
Maximum29
Zeros14
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-10T21:10:27.438926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile15.8
Maximum29
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.2509769
Coefficient of variation (CV)1.849739
Kurtosis5.3092542
Mean3.92
Median Absolute Deviation (MAD)0
Skewness2.300993
Sum98
Variance52.576667
MonotonicityNot monotonic
2024-05-10T21:10:27.887049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 14
56.0%
4 2
 
8.0%
1 2
 
8.0%
15 2
 
8.0%
5 1
 
4.0%
16 1
 
4.0%
6 1
 
4.0%
2 1
 
4.0%
29 1
 
4.0%
ValueCountFrequency (%)
0 14
56.0%
1 2
 
8.0%
2 1
 
4.0%
4 2
 
8.0%
5 1
 
4.0%
6 1
 
4.0%
15 2
 
8.0%
16 1
 
4.0%
29 1
 
4.0%
ValueCountFrequency (%)
29 1
 
4.0%
16 1
 
4.0%
15 2
 
8.0%
6 1
 
4.0%
5 1
 
4.0%
4 2
 
8.0%
2 1
 
4.0%
1 2
 
8.0%
0 14
56.0%

벼수확및운반작업기보유대수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4
Minimum0
Maximum20
Zeros11
Zeros (%)44.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-10T21:10:28.271568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile18.2
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.8949131
Coefficient of variation (CV)1.733798
Kurtosis3.6410451
Mean3.4
Median Absolute Deviation (MAD)1
Skewness2.0927477
Sum85
Variance34.75
MonotonicityNot monotonic
2024-05-10T21:10:28.772379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 11
44.0%
1 5
20.0%
2 2
 
8.0%
20 2
 
8.0%
10 1
 
4.0%
11 1
 
4.0%
8 1
 
4.0%
4 1
 
4.0%
3 1
 
4.0%
ValueCountFrequency (%)
0 11
44.0%
1 5
20.0%
2 2
 
8.0%
3 1
 
4.0%
4 1
 
4.0%
8 1
 
4.0%
10 1
 
4.0%
11 1
 
4.0%
20 2
 
8.0%
ValueCountFrequency (%)
20 2
 
8.0%
11 1
 
4.0%
10 1
 
4.0%
8 1
 
4.0%
4 1
 
4.0%
3 1
 
4.0%
2 2
 
8.0%
1 5
20.0%
0 11
44.0%
Distinct23
Distinct (%)100.0%
Missing2
Missing (%)8.0%
Memory size332.0 B
2024-05-10T21:10:29.373729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length393
Median length140
Mean length113.47826
Min length2

Characters and Unicode

Total characters2610
Distinct characters188
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row인력파종기(1)+동력분무기(1)+파이프성형기(1)+스키드로더(1)+농용굴착기(2)+고구마수확기(2)+감자수확기(2)+휴립피복기(1)+돌수집기(1)
2nd row동력살분무기(8)+파종기(10)+동력이식기(12)+육묘잎절단기(5)+동력예취기(5)+동력파쇄기(2)+농용굴착기(4)+퇴비살포기(2)+기타(16)
3rd row53
4th row39
5th row농용파이프밴딩성형기(1)+동력살분무기(6)+논두렁제초기(1)+동력퇴비살포기(2)+승용예초기(3)+인력비닐피복기(5)+인력파종기(10)+자동전동가위(6)+주행형동력분무기(3)+지주설치기(5)+콩예취기(4)
ValueCountFrequency (%)
2
 
5.1%
인력파종기(1)+동력분무기(1)+파이프성형기(1)+스키드로더(1)+농용굴착기(2)+고구마수확기(2)+감자수확기(2)+휴립피복기(1)+돌수집기(1 1
 
2.6%
세척기(1 1
 
2.6%
12 1
 
2.6%
승용예초기(14)+광역살포기(2)+굴삭기(8)+동력분무기(26)+로더(1)+운반차(1)+이식기(2)+인삼파종기(2)+비닐피복기(15)+잔가지파쇄기(5)+콩참깨수확기(9)+파이프정형기(5)+인력파종기(25 1
 
2.6%
콩콤바인(4)+농용굴삭기(4)+승용관리기(3)+승용관리기용작업기 1
 
2.6%
6대 1
 
2.6%
스키드로우더 1
 
2.6%
2대+볍씨파종기(4)+곡물적재함(1)+동력분무기(2)동력살분무기(2)+미스트기(6)+전정가위(2)+콩예취기(2)+동력이식기(5)+동력운반차(1)+중경제초기(2)+승용예취기(2)+목재파쇄기(자주형)(7)+보리방아(1)+색체선별기(1)+논물꼬베토기(1)+땅콜탈피기(1)+인력피복기(7)+동력제초기(2)동력이식기(5)+동력운반차(1)+중경제초기(2)+승용예취기(2)+목재파쇄기(자주형)(7)+보리방아(1)+색체선별기(1)+논물꼬베토기(1)+땅콜탈피기(1)+인력피복기(7)+동력제초기(2)+적심기(2)고구마수확기(2)+고구마순제초기(동력)(2 1
 
2.6%
박피기(1)+굴삭기(7)+스키드로더(2)+동력예초기(4)+스피드스프레이어(1)+동력퇴비살포기(2)+잔가지파쇄기(6)+콩예초기(5)+육묘상자운반기(2)+농산물제피기(1)+이식기(5)+진압기(1)+엔진브로워(2)+고소작업차(1)+대파파종기(1)+육묘상자 1
 
2.6%
Other values (28) 28
71.8%
2024-05-10T21:10:30.418396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 280
 
10.7%
) 280
 
10.7%
+ 248
 
9.5%
237
 
9.1%
1 93
 
3.6%
2 82
 
3.1%
71
 
2.7%
66
 
2.5%
51
 
2.0%
44
 
1.7%
Other values (178) 1158
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1486
56.9%
Decimal Number 296
 
11.3%
Open Punctuation 280
 
10.7%
Close Punctuation 280
 
10.7%
Math Symbol 248
 
9.5%
Space Separator 16
 
0.6%
Uppercase Letter 2
 
0.1%
Connector Punctuation 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
237
 
15.9%
71
 
4.8%
66
 
4.4%
51
 
3.4%
44
 
3.0%
33
 
2.2%
29
 
2.0%
24
 
1.6%
23
 
1.5%
23
 
1.5%
Other values (161) 885
59.6%
Decimal Number
ValueCountFrequency (%)
1 93
31.4%
2 82
27.7%
5 29
 
9.8%
3 27
 
9.1%
4 21
 
7.1%
6 12
 
4.1%
7 10
 
3.4%
8 9
 
3.0%
9 7
 
2.4%
0 6
 
2.0%
Open Punctuation
ValueCountFrequency (%)
( 280
100.0%
Close Punctuation
ValueCountFrequency (%)
) 280
100.0%
Math Symbol
ValueCountFrequency (%)
+ 248
100.0%
Space Separator
ValueCountFrequency (%)
16
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1486
56.9%
Common 1122
43.0%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
237
 
15.9%
71
 
4.8%
66
 
4.4%
51
 
3.4%
44
 
3.0%
33
 
2.2%
29
 
2.0%
24
 
1.6%
23
 
1.5%
23
 
1.5%
Other values (161) 885
59.6%
Common
ValueCountFrequency (%)
( 280
25.0%
) 280
25.0%
+ 248
22.1%
1 93
 
8.3%
2 82
 
7.3%
5 29
 
2.6%
3 27
 
2.4%
4 21
 
1.9%
16
 
1.4%
6 12
 
1.1%
Other values (6) 34
 
3.0%
Latin
ValueCountFrequency (%)
S 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1486
56.9%
ASCII 1124
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 280
24.9%
) 280
24.9%
+ 248
22.1%
1 93
 
8.3%
2 82
 
7.3%
5 29
 
2.6%
3 27
 
2.4%
4 21
 
1.9%
16
 
1.4%
6 12
 
1.1%
Other values (7) 36
 
3.2%
Hangul
ValueCountFrequency (%)
237
 
15.9%
71
 
4.8%
66
 
4.4%
51
 
3.4%
44
 
3.0%
33
 
2.2%
29
 
2.0%
24
 
1.6%
23
 
1.5%
23
 
1.5%
Other values (161) 885
59.6%
Distinct20
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-05-10T21:10:30.816426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.24
Min length12

Characters and Unicode

Total characters306
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

Unique17 ?
Unique (%)68.0%

Sample

1st row031-770-3592
2nd row031-228-2564
3rd row031-678-3031
4th row031-644-4139
5th row031-645-3499
ValueCountFrequency (%)
031-678-3031 4
 
16.0%
031-538-3753 2
 
8.0%
031-839-4201 2
 
8.0%
031-940-4509 1
 
4.0%
031-770-3592 1
 
4.0%
031-940-5263 1
 
4.0%
031-8024-7483 1
 
4.0%
031-8024-4585 1
 
4.0%
031-580-4791 1
 
4.0%
031-8082-6151 1
 
4.0%
Other values (10) 10
40.0%
2024-05-10T21:10:31.660436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 51
16.7%
- 50
16.3%
0 43
14.1%
1 39
12.7%
4 25
8.2%
8 22
7.2%
5 20
 
6.5%
7 15
 
4.9%
2 15
 
4.9%
6 14
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 256
83.7%
Dash Punctuation 50
 
16.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 51
19.9%
0 43
16.8%
1 39
15.2%
4 25
9.8%
8 22
8.6%
5 20
 
7.8%
7 15
 
5.9%
2 15
 
5.9%
6 14
 
5.5%
9 12
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 306
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 51
16.7%
- 50
16.3%
0 43
14.1%
1 39
12.7%
4 25
8.2%
8 22
7.2%
5 20
 
6.5%
7 15
 
4.9%
2 15
 
4.9%
6 14
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 51
16.7%
- 50
16.3%
0 43
14.1%
1 39
12.7%
4 25
8.2%
8 22
7.2%
5 20
 
6.5%
7 15
 
4.9%
2 15
 
4.9%
6 14
 
4.6%
Distinct17
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-05-10T21:10:32.042482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length11.16
Min length7

Characters and Unicode

Total characters279
Distinct characters40
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)44.0%

Sample

1st row경기도 양평군청
2nd row경기도 수원시청
3rd row경기도 안성시농업기술센터
4th row경기도 이천시 연구개발과
5th row경기도 이천시 연구개발과
ValueCountFrequency (%)
경기도 20
37.7%
농업기술센터 5
 
9.4%
안성시농업기술센터 4
 
7.5%
이천시 2
 
3.8%
연구개발과 2
 
3.8%
평택시농업기술센터 2
 
3.8%
포천시청 2
 
3.8%
파주시농업기술센터 2
 
3.8%
연천군청 2
 
3.8%
농업정책과 1
 
1.9%
Other values (11) 11
20.8%
2024-05-10T21:10:32.860329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35
12.5%
28
 
10.0%
21
 
7.5%
20
 
7.2%
20
 
7.2%
16
 
5.7%
16
 
5.7%
15
 
5.4%
15
 
5.4%
15
 
5.4%
Other values (30) 78
28.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 251
90.0%
Space Separator 28
 
10.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
35
13.9%
21
 
8.4%
20
 
8.0%
20
 
8.0%
16
 
6.4%
16
 
6.4%
15
 
6.0%
15
 
6.0%
15
 
6.0%
10
 
4.0%
Other values (29) 68
27.1%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 251
90.0%
Common 28
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
35
13.9%
21
 
8.4%
20
 
8.0%
20
 
8.0%
16
 
6.4%
16
 
6.4%
15
 
6.0%
15
 
6.0%
15
 
6.0%
10
 
4.0%
Other values (29) 68
27.1%
Common
ValueCountFrequency (%)
28
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 251
90.0%
ASCII 28
 
10.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
35
13.9%
21
 
8.4%
20
 
8.0%
20
 
8.0%
16
 
6.4%
16
 
6.4%
15
 
6.0%
15
 
6.0%
15
 
6.0%
10
 
4.0%
Other values (29) 68
27.1%
ASCII
ValueCountFrequency (%)
28
100.0%
Distinct17
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
Minimum2022-10-31 00:00:00
Maximum2024-02-15 00:00:00
2024-05-10T21:10:33.176498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:33.480162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)

Interactions

2024-05-10T21:10:11.246625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:47.471294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:49.917819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:52.582046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:55.357774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:57.970734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:00.419100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:03.084393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:05.634889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:08.480033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:11.503472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:47.690043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:50.164477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:52.848460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:55.603236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:58.212341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:00.682153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:03.304560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:05.892300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:08.754392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:11.767317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:47.914439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:50.409003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:53.098915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:55.815987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:58.478863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:00.909904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:03.530730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:06.116027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:09.053057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:12.100030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:48.126748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:50.655450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:53.343857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:56.018171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:58.732087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:01.151881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:03.783210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:06.312159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:09.336124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:12.370390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:48.387458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:50.915392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:53.614555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:56.267237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:59.009369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:01.406191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:04.061066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:06.519338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:09.637999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:12.649372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:48.651169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:51.152665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:53.935560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:56.620045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:59.244234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:01.949363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:04.325802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:06.772430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:09.896525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:12.921363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:48.895754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:51.479574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:54.185151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:56.902761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:59.488322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:02.139685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:04.577527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:07.238866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:10.171979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:13.202498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:49.148129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:51.848144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:54.452362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:57.159970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:59.697957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:02.375690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:04.780210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:07.621659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:10.430791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:13.469096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:49.398707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:52.130239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:54.711412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:57.426170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:59.933917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:02.580765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:05.017314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:07.877200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:10.730667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:13.721101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:49.655136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:52.349276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:55.033764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:09:57.682417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:00.193376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:02.818099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:05.325557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:08.187174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:10:10.974751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T21:10:33.781538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업소명사업소전화번호소재지도로명주소소재지지번주소위도경도트랙터및작업기보유대수경운기및작업기보유대수관리기및작업기보유대수땅속작물수확기보유대수탈곡기및정선작업기보유대수자주형파종기보유대수이앙작업기보유대수벼수확및운반작업기보유대수기타임대농기계보유정보관리기관전화번호관리기관명데이터기준일자
사업소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사업소전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0001.0001.0000.0000.0000.6570.0000.4370.0000.1710.5990.6951.0000.9410.8570.857
경도1.0001.0001.0001.0000.0001.0000.0000.0000.0000.0000.0000.5750.7000.0001.0000.7970.0000.000
트랙터및작업기보유대수1.0001.0001.0001.0000.0000.0001.0000.1980.7570.6940.4450.7650.4570.4601.0000.9630.8770.877
경운기및작업기보유대수1.0001.0001.0001.0000.6570.0000.1981.0000.8010.8780.8670.0000.5980.9631.0000.9200.8150.815
관리기및작업기보유대수1.0001.0001.0001.0000.0000.0000.7570.8011.0000.4890.6440.5310.5530.6551.0000.6370.4060.406
땅속작물수확기보유대수1.0001.0001.0001.0000.4370.0000.6940.8780.4891.0000.7440.1400.3590.6501.0000.9710.9020.902
탈곡기및정선작업기보유대수1.0001.0001.0001.0000.0000.0000.4450.8670.6440.7441.0000.1720.0000.8151.0000.8670.6960.696
자주형파종기보유대수1.0001.0001.0001.0000.1710.5750.7650.0000.5310.1400.1721.0000.7200.4491.0000.9080.8190.819
이앙작업기보유대수1.0001.0001.0001.0000.5990.7000.4570.5980.5530.3590.0000.7201.0000.7701.0000.9940.9740.974
벼수확및운반작업기보유대수1.0001.0001.0001.0000.6950.0000.4600.9630.6550.6500.8150.4490.7701.0001.0000.9210.7730.773
기타임대농기계보유정보1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
관리기관전화번호1.0001.0001.0001.0000.9410.7970.9630.9200.6370.9710.8670.9080.9940.9211.0001.0001.0001.000
관리기관명1.0001.0001.0001.0000.8570.0000.8770.8150.4060.9020.6960.8190.9740.7731.0001.0001.0001.000
데이터기준일자1.0001.0001.0001.0000.8570.0000.8770.8150.4060.9020.6960.8190.9740.7731.0001.0001.0001.000
2024-05-10T21:10:34.234558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도트랙터및작업기보유대수경운기및작업기보유대수관리기및작업기보유대수땅속작물수확기보유대수탈곡기및정선작업기보유대수자주형파종기보유대수이앙작업기보유대수벼수확및운반작업기보유대수
위도1.000-0.2680.093-0.099-0.052-0.2760.1410.3600.2460.271
경도-0.2681.000-0.167-0.1330.2500.007-0.2860.3630.102-0.118
트랙터및작업기보유대수0.093-0.1671.0000.4990.4800.6800.8340.2450.2950.259
경운기및작업기보유대수-0.099-0.1330.4991.0000.5090.8140.5850.1640.1960.240
관리기및작업기보유대수-0.0520.2500.4800.5091.0000.5470.4630.2970.3830.130
땅속작물수확기보유대수-0.2760.0070.6800.8140.5471.0000.6420.1060.083-0.010
탈곡기및정선작업기보유대수0.141-0.2860.8340.5850.4630.6421.0000.2490.2440.203
자주형파종기보유대수0.3600.3630.2450.1640.2970.1060.2491.0000.7130.310
이앙작업기보유대수0.2460.1020.2950.1960.3830.0830.2440.7131.0000.553
벼수확및운반작업기보유대수0.271-0.1180.2590.2400.130-0.0100.2030.3100.5531.000

Missing values

2024-05-10T21:10:14.144745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T21:10:14.774962image/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

사업소명사업소전화번호소재지도로명주소소재지지번주소위도경도트랙터및작업기보유대수경운기및작업기보유대수관리기및작업기보유대수땅속작물수확기보유대수탈곡기및정선작업기보유대수자주형파종기보유대수이앙작업기보유대수벼수확및운반작업기보유대수기타임대농기계보유정보관리기관전화번호관리기관명데이터기준일자
0양평군농업기술센터 본소031-770-3591경기도 양평군 양평읍 농업기술센터길 37경기도 양평군 양평읍 공흥리 1-137.509242127.5132948715342130142<NA>031-770-3592경기도 양평군청2023-05-23
1수원시 농업기술센터 농기계 임대사업소031-228-2564경기도 수원시 권선구 온정로45경기도 수원시 권선구 오목천동 4037.250123126.963166321315011인력파종기(1)+동력분무기(1)+파이프성형기(1)+스키드로더(1)+농용굴착기(2)+고구마수확기(2)+감자수확기(2)+휴립피복기(1)+돌수집기(1)031-228-2564경기도 수원시청2023-05-12
2안성시농기계임대사업소 동부분소031-678-3035경기도 안성시 죽산면 상삼로 95경기도 안성시 죽산면 두현리 456-6번지37.080793127.40199554535915000동력살분무기(8)+파종기(10)+동력이식기(12)+육묘잎절단기(5)+동력예취기(5)+동력파쇄기(2)+농용굴착기(4)+퇴비살포기(2)+기타(16)031-678-3031경기도 안성시농업기술센터2023-03-21
3이천시 농기계임대사업소031-644-4139경기도 이천시 모가면 사실로 720경기도 이천시 모가면 어농리 615-737.175917127.44020757122212172001053031-644-4139경기도 이천시 연구개발과2023-07-07
4이천시 농기계임대사업소(남부분소)031-645-3499경기도 이천시 장호원읍 서동대로8759번길 97-80경기도 이천시 장호원읍 진암리 328-537.120324127.61123225414810121039031-645-3499경기도 이천시 연구개발과2023-07-07
5용인시 농업기술센터 농업기계임대사업소031-324-4074경기도 용인시 처인구 원삼면 농촌파크로 80경기도 용인시 처인구 원삼면 사암리 858-137.172292127.29928848412010000농용파이프밴딩성형기(1)+동력살분무기(6)+논두렁제초기(1)+동력퇴비살포기(2)+승용예초기(3)+인력비닐피복기(5)+인력파종기(10)+자동전동가위(6)+주행형동력분무기(3)+지주설치기(5)+콩예취기(4)031-324-4074용인시 농업기술센터2023-12-04
6고양시 농업기계 임대사업소031-8075-4284경기도 고양시 덕양구 고양대로 1695경기도 고양시 덕양구 원흥동 471-10번지37.649777126.8700852101422001광역방제기(1)+SS분무기(1)+축산세트장비(1)+인력파종기(6)+적재사다리(6)031-8075-4284경기도 고양시청2023-11-23
7화성시농기계임대사업소031-5189-3612경기도 화성시 팔탄면 버들로 1613경기도 화성시 팔탄면 매곡리 172-9번지37.114528126.87954815975455058252<NA>031-5189-3612경기도 화성시농업기술센터2023-05-18
8남양주시농업기계은행031-590-4558경기도 남양주시 진건읍 사릉로 234-46경기도 남양주시 진건읍 사능리 92-1번지37.649127.1970237312521631611자주식베일러 등(67)031-590-4564경기도 남양주시농업기술센터2023-05-22
9김포시농기계임대사업소031-5186-4356경기도 김포시 통진읍 하성로 58-155경기도 김포시 통진읍 수참리 37337.682944126.640773158143220402760농산물선별기(5)+육묘잎자르기(1)+농업용고소작업차(1)+굴삭기(농업용굴삭기)(5)+농업용동력운반차(1)+동력분무기(2)+동력배토기(5)+동력살분무기(5)+동력살포기(4)+예취기(2)+농산물제피기(2)+육묘상자세척기(2)+육묘용파종기용품(2)+비닐피복기(9)+퇴비살포기(동력퇴비살포기)(3)+쑥쑥이(지주대뽑기)(2)+농산물순제거기(1)+콩예취기(2)+곡물선별기(2)+탈망기(3)+파렛트 랩피복기(랩피복기)(1)+농용파이프벤딩성형기(3)+휴대용자동전동가위(4)+동력이식기(15)+동력구굴기(2)+동력항타기(2)+다목적유압테스터기(1)+엔진발전기(2)+오일여과장치(1)+유압호수 압착테스터기(1)+전기용접기(1)+동력제초기_논둑용(2)+동력제초기-원판형(3)+동력중격제초기(4)+동력파쇄기(9)+콤바인(3)031-5186-4356경기도 김포시청 농업기술센터2023-05-16
사업소명사업소전화번호소재지도로명주소소재지지번주소위도경도트랙터및작업기보유대수경운기및작업기보유대수관리기및작업기보유대수땅속작물수확기보유대수탈곡기및정선작업기보유대수자주형파종기보유대수이앙작업기보유대수벼수확및운반작업기보유대수기타임대농기계보유정보관리기관전화번호관리기관명데이터기준일자
15연천군 농업기계 임대사업소(연천본소)031-839-4261경기도 연천군 연천읍 연천로42번길 42경기도 연천군 연천읍 동막리 598-238.084579127.07794477632836100승용관리기(4)+비료살포기(1)+휴립피복기(1)+로터베이터(1)+콩파종기(3)+제초기(1)+농용굴삭기(3)+농용로우더(1)+동력분무기(2)+동력살분무기(4)+스피드스프레이어 퇴비살포기(2)+콩예취기(3)+휴대용전동가위(5)+승용예취기(3)+파이프밴딩성형기(1)+비닐피복기(인력형)(4)+채소이식기(2)+잔가지파쇄기(2)+사료절단기(1)+콩파종기(인력형)(18)031-839-4201경기도 연천군청 농업기술센터2023-06-27
16연천군 농업기계 임대사업소(서부지소)031-839-4265경기도 연천군 미산면 청정로 800경기도 연천군 미산면 아미리 763-838.037367126.941299676241034001승용관리기(3)+비료살포기(1)+휴립피복기(1)+로터베이터(1)+콩파종기(3)+제초기(1)+농용굴삭기(2)+농용로우더(1)+동력분무기(2)+동력살분무기(2)+스피드스프레이어 퇴비살포기(2)+콩예취기(3)+휴대용전동가위(6)+승용예취기(2)+파이프밴딩성형기(1)+비닐피복기(인력형)(2)+채소이식기(2)+잔가지파쇄기(1)+사료절단기(1)+콩파종기(인력형)(9)+양파파종기(1)+자주식옥수수수확기(1)+농용고소작업차(1)+방제기(주행형)(1)+양파이식기(2)+양파전엽기(1)031-839-4201경기도 연천군청 농업기술센터2023-06-27
17포천시 농기계임대사업소(본소)031-538-3787경기도 포천시 신북면 틀못이길 11-88경기도 포천시 신북면 기지리 647-137.923299127.22521350274915292015824031-538-3753경기도 포천시청2022-10-31
18영북분소031-538-3766경기도 포천시 영북면 호국로 4147-21경기도 포천시 영북면 자일리 845-638.10002127.277021361185460012031-538-3753경기도 포천시청2022-10-31
19여주시농업기술센터 농기계임대은행031-887-3714경기도 여주시 농산로 71경기도 여주시 상거동 5-537.253757127.637774137113945563120승용예초기(14)+광역살포기(2)+굴삭기(8)+동력분무기(26)+로더(1)+운반차(1)+이식기(2)+인삼파종기(2)+비닐피복기(15)+잔가지파쇄기(5)+콩참깨수확기(9)+파이프정형기(5)+인력파종기(25)031-887-3714경기도 여주시청2023-08-01
20양주시농기계대여은행031-8082-6151경기도 양주시 은현면 그루고개로221번길 264-21경기도 양주시 은현면 도하리 68237.843323126.9980282471430163891520콩콤바인(4)+농용굴삭기(4)+승용관리기(3)+승용관리기용작업기 6대 +스키드로우더 2대+볍씨파종기(4)+곡물적재함(1)+동력분무기(2)동력살분무기(2)+미스트기(6)+전정가위(2)+콩예취기(2)+동력이식기(5)+동력운반차(1)+중경제초기(2)+승용예취기(2)+목재파쇄기(자주형)(7)+보리방아(1)+색체선별기(1)+논물꼬베토기(1)+땅콜탈피기(1)+인력피복기(7)+동력제초기(2)동력이식기(5)+동력운반차(1)+중경제초기(2)+승용예취기(2)+목재파쇄기(자주형)(7)+보리방아(1)+색체선별기(1)+논물꼬베토기(1)+땅콜탈피기(1)+인력피복기(7)+동력제초기(2)+적심기(2)고구마수확기(2)+고구마순제초기(동력)(2)031-8082-6151경기도 양주시청 농업정책과2023-06-09
21가평군농업기술센터 농기계임대사업소031-580-4791경기도 가평군 가평읍 아랫마장길 59경기도 가평군 가평읍 승안리 100번지37.845968127.498648136036532302920박피기(1)+굴삭기(7)+스키드로더(2)+동력예초기(4)+스피드스프레이어(1)+동력퇴비살포기(2)+잔가지파쇄기(6)+콩예초기(5)+육묘상자운반기(2)+농산물제피기(1)+이식기(5)+진압기(1)+엔진브로워(2)+고소작업차(1)+대파파종기(1)+육묘상자 세척기(1)031-580-4791경기도 가평군2024-02-15
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