Overview

Dataset statistics

Number of variables11
Number of observations380
Missing cells0
Missing cells (%)0.0%
Duplicate rows9
Duplicate rows (%)2.4%
Total size in memory34.6 KiB
Average record size in memory93.3 B

Variable types

Categorical4
Text2
DateTime1
Numeric4

Dataset

Description국립종자원 정부보급종 못자리점검 현황에 대한 데이터로 년산,지원명,작물명,품종명,단지명,파종일,파종(모판수),키다리병발생(모판수),폐기(모판수),이앙가능(모판수),점검월일 등의 항목을 제공합니다
Author농림축산식품부 국립종자원
URLhttps://www.data.go.kr/data/15066068/fileData.do

Alerts

작물명 has constant value ""Constant
Dataset has 9 (2.4%) duplicate rowsDuplicates
파종(모판수) is highly overall correlated with 이앙가능(모판수)High correlation
이앙가능(모판수) is highly overall correlated with 파종(모판수)High correlation
지원명 is highly overall correlated with 품종명High correlation
품종명 is highly overall correlated with 지원명High correlation
키다리병발생(모판수) has 317 (83.4%) zerosZeros
폐기(모판수) has 300 (78.9%) zerosZeros

Reproduction

Analysis started2023-12-12 19:40:47.086592
Analysis finished2023-12-12 19:40:49.625670
Duration2.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년산
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2021
147 
2020
121 
2022
112 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2021 147
38.7%
2020 121
31.8%
2022 112
29.5%

Length

2023-12-13T04:40:49.707900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:40:49.842441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 147
38.7%
2020 121
31.8%
2022 112
29.5%

지원명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
전북지원
86 
충남지원
68 
경북지원
57 
경남지원
53 
강원지원
43 
Other values (2)
73 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전북지원 86
22.6%
충남지원 68
17.9%
경북지원 57
15.0%
경남지원 53
13.9%
강원지원 43
11.3%
전남지원 39
10.3%
충북지원 34
 
8.9%

Length

2023-12-13T04:40:49.960928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:40:50.120513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북지원 86
22.6%
충남지원 68
17.9%
경북지원 57
15.0%
경남지원 53
13.9%
강원지원 43
11.3%
전남지원 39
10.3%
충북지원 34
 
8.9%

작물명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
벼
380 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
벼 380
100.0%

Length

2023-12-13T04:40:50.341738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:40:50.472007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
벼 380
100.0%

품종명
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
삼광벼
65 
신동진벼
57 
일품벼
37 
오대벼
28 
새청무
23 
Other values (25)
170 

Length

Max length4
Median length3
Mean length3.3473684
Min length2

Unique

Unique5 ?
Unique (%)1.3%

Sample

1st row오대벼
2nd row추청벼
3rd row추청벼
4th row추청벼
5th row추청벼

Common Values

ValueCountFrequency (%)
삼광벼 65
17.1%
신동진벼 57
15.0%
일품벼 37
9.7%
오대벼 28
 
7.4%
새청무 23
 
6.1%
영호진미 23
 
6.1%
새일미벼 20
 
5.3%
친들벼 20
 
5.3%
동진찰벼 18
 
4.7%
해담쌀 17
 
4.5%
Other values (20) 72
18.9%

Length

2023-12-13T04:40:50.628340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
삼광벼 65
17.1%
신동진벼 57
15.0%
일품벼 37
9.7%
오대벼 28
 
7.4%
새청무 23
 
6.1%
영호진미 23
 
6.1%
새일미벼 20
 
5.3%
친들벼 20
 
5.3%
동진찰벼 18
 
4.7%
해담쌀 17
 
4.5%
Other values (20) 72
18.9%

단지명
Text

Distinct138
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-13T04:40:51.012078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.4605263
Min length2

Characters and Unicode

Total characters935
Distinct characters129
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)6.3%

Sample

1st row명 도
2nd row율 리
3rd row모 산
4th row비 석
5th row행 정
ValueCountFrequency (%)
후동 6
 
1.4%
신전 6
 
1.4%
대죽 6
 
1.4%
동 6
 
1.4%
신덕 6
 
1.4%
돈포 5
 
1.2%
방아 5
 
1.2%
학수 4
 
1.0%
왕태 4
 
1.0%
철원 4
 
1.0%
Other values (139) 362
87.4%
2023-12-13T04:40:51.418581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
68
 
7.3%
산 30
 
3.2%
동 30
 
3.2%
포 29
 
3.1%
신 25
 
2.7%
장 23
 
2.5%
두 18
 
1.9%
농 18
 
1.9%
성 17
 
1.8%
원 16
 
1.7%
Other values (119) 661
70.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 831
88.9%
Space Separator 68
 
7.3%
Open Punctuation 12
 
1.3%
Close Punctuation 12
 
1.3%
Decimal Number 12
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 30
 
3.6%
동 30
 
3.6%
포 29
 
3.5%
신 25
 
3.0%
장 23
 
2.8%
두 18
 
2.2%
농 18
 
2.2%
성 17
 
2.0%
원 16
 
1.9%
곡 16
 
1.9%
Other values (114) 609
73.3%
Decimal Number
ValueCountFrequency (%)
2 6
50.0%
1 6
50.0%
Space Separator
ValueCountFrequency (%)
68
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 831
88.9%
Common 104
 
11.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 30
 
3.6%
동 30
 
3.6%
포 29
 
3.5%
신 25
 
3.0%
장 23
 
2.8%
두 18
 
2.2%
농 18
 
2.2%
성 17
 
2.0%
원 16
 
1.9%
곡 16
 
1.9%
Other values (114) 609
73.3%
Common
ValueCountFrequency (%)
68
65.4%
( 12
 
11.5%
) 12
 
11.5%
2 6
 
5.8%
1 6
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 831
88.9%
ASCII 104
 
11.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
68
65.4%
( 12
 
11.5%
) 12
 
11.5%
2 6
 
5.8%
1 6
 
5.8%
Hangul
ValueCountFrequency (%)
산 30
 
3.6%
동 30
 
3.6%
포 29
 
3.5%
신 25
 
3.0%
장 23
 
2.8%
두 18
 
2.2%
농 18
 
2.2%
성 17
 
2.0%
원 16
 
1.9%
곡 16
 
1.9%
Other values (114) 609
73.3%

파종일
Date

Distinct118
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum2020-04-10 00:00:00
Maximum2022-05-30 00:00:00
2023-12-13T04:40:51.546736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:51.936728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

파종(모판수)
Real number (ℝ)

HIGH CORRELATION 

Distinct362
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7590.8553
Minimum835
Maximum25304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-13T04:40:52.064161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum835
5-th percentile2969.05
Q15009.25
median7469
Q39493.25
95-th percentile13313.1
Maximum25304
Range24469
Interquartile range (IQR)4484

Descriptive statistics

Standard deviation3534.5904
Coefficient of variation (CV)0.46563797
Kurtosis4.1836731
Mean7590.8553
Median Absolute Deviation (MAD)2272
Skewness1.3194225
Sum2884525
Variance12493329
MonotonicityNot monotonic
2023-12-13T04:40:52.186297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3480 3
 
0.8%
4828 2
 
0.5%
7468 2
 
0.5%
4001 2
 
0.5%
8260 2
 
0.5%
4476 2
 
0.5%
9650 2
 
0.5%
5053 2
 
0.5%
4630 2
 
0.5%
10350 2
 
0.5%
Other values (352) 359
94.5%
ValueCountFrequency (%)
835 1
0.3%
978 1
0.3%
1057 1
0.3%
1300 1
0.3%
1371 1
0.3%
1733 1
0.3%
1750 1
0.3%
2065 1
0.3%
2103 1
0.3%
2130 1
0.3%
ValueCountFrequency (%)
25304 1
0.3%
24420 2
0.5%
21373 1
0.3%
21006 1
0.3%
18095 1
0.3%
16251 1
0.3%
15609 1
0.3%
15541 1
0.3%
14970 1
0.3%
14860 1
0.3%

키다리병발생(모판수)
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8368421
Minimum0
Maximum1337
Zeros317
Zeros (%)83.4%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-13T04:40:52.308036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile21.1
Maximum1337
Range1337
Interquartile range (IQR)0

Descriptive statistics

Standard deviation72.257125
Coefficient of variation (CV)9.2201838
Kurtosis304.70499
Mean7.8368421
Median Absolute Deviation (MAD)0
Skewness16.777017
Sum2978
Variance5221.092
MonotonicityNot monotonic
2023-12-13T04:40:52.436707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 317
83.4%
2 9
 
2.4%
1 7
 
1.8%
3 6
 
1.6%
4 4
 
1.1%
27 3
 
0.8%
8 3
 
0.8%
10 3
 
0.8%
25 3
 
0.8%
7 3
 
0.8%
Other values (20) 22
 
5.8%
ValueCountFrequency (%)
0 317
83.4%
1 7
 
1.8%
2 9
 
2.4%
3 6
 
1.6%
4 4
 
1.1%
6 2
 
0.5%
7 3
 
0.8%
8 3
 
0.8%
10 3
 
0.8%
12 1
 
0.3%
ValueCountFrequency (%)
1337 1
 
0.3%
255 1
 
0.3%
240 1
 
0.3%
170 1
 
0.3%
165 1
 
0.3%
135 1
 
0.3%
60 1
 
0.3%
48 1
 
0.3%
47 1
 
0.3%
27 3
0.8%

폐기(모판수)
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.184211
Minimum0
Maximum972
Zeros300
Zeros (%)78.9%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-13T04:40:52.573194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile70.6
Maximum972
Range972
Interquartile range (IQR)0

Descriptive statistics

Standard deviation68.612427
Coefficient of variation (CV)4.5186694
Kurtosis106.36338
Mean15.184211
Median Absolute Deviation (MAD)0
Skewness8.9681277
Sum5770
Variance4707.6652
MonotonicityNot monotonic
2023-12-13T04:40:52.701120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 300
78.9%
5 8
 
2.1%
1 5
 
1.3%
2 5
 
1.3%
30 4
 
1.1%
36 3
 
0.8%
25 3
 
0.8%
15 3
 
0.8%
20 3
 
0.8%
279 2
 
0.5%
Other values (35) 44
 
11.6%
ValueCountFrequency (%)
0 300
78.9%
1 5
 
1.3%
2 5
 
1.3%
4 1
 
0.3%
5 8
 
2.1%
6 2
 
0.5%
7 2
 
0.5%
10 2
 
0.5%
12 2
 
0.5%
13 2
 
0.5%
ValueCountFrequency (%)
972 1
0.3%
398 1
0.3%
370 1
0.3%
306 1
0.3%
302 1
0.3%
279 2
0.5%
237 1
0.3%
200 1
0.3%
170 1
0.3%
140 2
0.5%

이앙가능(모판수)
Real number (ℝ)

HIGH CORRELATION 

Distinct363
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7575.6711
Minimum835
Maximum25304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-13T04:40:52.824304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum835
5-th percentile2969.05
Q15005.5
median7469
Q39454
95-th percentile13312.85
Maximum25304
Range24469
Interquartile range (IQR)4448.5

Descriptive statistics

Standard deviation3532.7661
Coefficient of variation (CV)0.46633046
Kurtosis4.2183222
Mean7575.6711
Median Absolute Deviation (MAD)2256.5
Skewness1.3293912
Sum2878755
Variance12480437
MonotonicityNot monotonic
2023-12-13T04:40:52.969150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3480 3
 
0.8%
4828 2
 
0.5%
7468 2
 
0.5%
4001 2
 
0.5%
4476 2
 
0.5%
8260 2
 
0.5%
5053 2
 
0.5%
9650 2
 
0.5%
10553 2
 
0.5%
24420 2
 
0.5%
Other values (353) 359
94.5%
ValueCountFrequency (%)
835 1
0.3%
978 1
0.3%
1055 1
0.3%
1300 1
0.3%
1371 1
0.3%
1733 1
0.3%
1750 1
0.3%
2065 1
0.3%
2103 1
0.3%
2125 1
0.3%
ValueCountFrequency (%)
25304 1
0.3%
24420 2
0.5%
21373 1
0.3%
21006 1
0.3%
18095 1
0.3%
16251 1
0.3%
15609 1
0.3%
15541 1
0.3%
14970 1
0.3%
14760 1
0.3%

점검월일
Text

Distinct74
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-13T04:40:53.170818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3800
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

Unique20 ?
Unique (%)5.3%

Sample

1st row2020-05-11
2nd row2020-05-13
3rd row2020-05-18
4th row2020-05-12
5th row2020-05-12
ValueCountFrequency (%)
2021-06-18 28
 
7.4%
2021-05-12 24
 
6.3%
2020-05-12 17
 
4.5%
2021-05-18 14
 
3.7%
2021-05-26 13
 
3.4%
2022-05-12 12
 
3.2%
2022-05-11 12
 
3.2%
2020-05-11 12
 
3.2%
2021-05-10 11
 
2.9%
2020-05-13 11
 
2.9%
Other values (64) 226
59.5%
2023-12-13T04:40:53.469086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1031
27.1%
0 958
25.2%
- 760
20.0%
1 426
11.2%
5 327
 
8.6%
6 87
 
2.3%
8 71
 
1.9%
4 53
 
1.4%
3 45
 
1.2%
9 27
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3040
80.0%
Dash Punctuation 760
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1031
33.9%
0 958
31.5%
1 426
14.0%
5 327
 
10.8%
6 87
 
2.9%
8 71
 
2.3%
4 53
 
1.7%
3 45
 
1.5%
9 27
 
0.9%
7 15
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1031
27.1%
0 958
25.2%
- 760
20.0%
1 426
11.2%
5 327
 
8.6%
6 87
 
2.3%
8 71
 
1.9%
4 53
 
1.4%
3 45
 
1.2%
9 27
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1031
27.1%
0 958
25.2%
- 760
20.0%
1 426
11.2%
5 327
 
8.6%
6 87
 
2.3%
8 71
 
1.9%
4 53
 
1.4%
3 45
 
1.2%
9 27
 
0.7%

Interactions

2023-12-13T04:40:48.890464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:47.629393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:48.005806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:48.487912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:48.988253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:47.708360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:48.141184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:48.593354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:49.095109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:47.800500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:48.261714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:48.692373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:49.196769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:47.899263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:48.382026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:40:48.798374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:40:53.558981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년산지원명품종명파종(모판수)키다리병발생(모판수)폐기(모판수)이앙가능(모판수)점검월일
년산1.0000.2230.0000.0490.1530.0000.0311.000
지원명0.2231.0000.9580.3850.1260.2080.3900.947
품종명0.0000.9581.0000.6130.4490.0000.6170.889
파종(모판수)0.0490.3850.6131.0000.1310.0001.0000.000
키다리병발생(모판수)0.1530.1260.4490.1311.0000.5880.1380.000
폐기(모판수)0.0000.2080.0000.0000.5881.0000.0000.000
이앙가능(모판수)0.0310.3900.6171.0000.1380.0001.0000.000
점검월일1.0000.9470.8890.0000.0000.0000.0001.000
2023-12-13T04:40:53.655869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년산지원명품종명
년산1.0000.1520.000
지원명0.1521.0000.785
품종명0.0000.7851.000
2023-12-13T04:40:53.732937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
파종(모판수)키다리병발생(모판수)폐기(모판수)이앙가능(모판수)년산지원명품종명
파종(모판수)1.0000.1280.0741.0000.0000.2040.234
키다리병발생(모판수)0.1281.0000.2110.1280.0460.0840.225
폐기(모판수)0.0740.2111.0000.0610.0000.1250.000
이앙가능(모판수)1.0000.1280.0611.0000.0000.2070.237
년산0.0000.0460.0000.0001.0000.1520.000
지원명0.2040.0840.1250.2070.1521.0000.785
품종명0.2340.2250.0000.2370.0000.7851.000

Missing values

2023-12-13T04:40:49.345364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:40:49.532613image/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

년산지원명작물명품종명단지명파종일파종(모판수)키다리병발생(모판수)폐기(모판수)이앙가능(모판수)점검월일
02020충북지원벼오대벼명 도2020-04-2248280048282020-05-11
12020충북지원벼추청벼율 리2020-05-1678520078522020-05-13
22020충북지원벼추청벼모 산2020-05-2550420050422020-05-18
32020충북지원벼추청벼비 석2020-04-1765220065222020-05-12
42020충북지원벼추청벼행 정2020-04-2089270089272020-05-12
52020충북지원벼추청벼소 태2020-04-1654810054812020-05-13
62020충북지원벼삼광벼논 강2020-04-2535100035102020-05-13
72020충북지원벼삼광벼탑 평2020-04-2873390073392020-05-11
82020충북지원벼삼광벼추 동2020-04-2555300055302020-05-11
92020충북지원벼삼광벼창 전2020-04-1749700049702020-05-18
년산지원명작물명품종명단지명파종일파종(모판수)키다리병발생(모판수)폐기(모판수)이앙가능(모판수)점검월일
3702022강원지원벼삼광벼좌운2022-04-1992700092702022-05-11
3712021강원지원벼오대벼감두2021-04-1896500096502021-04-26
3722021강원지원벼오대벼철원2021-04-121055300105532021-04-19
3732021강원지원벼오대벼화지2021-04-122442000244202021-04-19
3742021강원지원벼오대벼내포2021-04-121168000116802021-04-19
3752021강원지원벼운광벼학수2021-04-1981600081602021-05-03
3762021강원지원벼오륜벼사천2021-05-2533500033502021-05-12
3772021강원지원벼대안벼후동2021-04-1729700029702021-05-10
3782021강원지원벼삼광벼후동2021-04-1834800034802021-05-10
3792021강원지원벼삼광벼좌운2021-04-2074680074682021-05-10

Duplicate rows

Most frequently occurring

년산지원명작물명품종명단지명파종일파종(모판수)키다리병발생(모판수)폐기(모판수)이앙가능(모판수)점검월일# duplicates
02021강원지원벼대안벼후동2021-04-1729700029702021-05-102
12021강원지원벼삼광벼좌운2021-04-2074680074682021-05-102
22021강원지원벼삼광벼후동2021-04-1834800034802021-05-102
32021강원지원벼오대벼감두2021-04-1896500096502021-04-262
42021강원지원벼오대벼내포2021-04-121168000116802021-04-192
52021강원지원벼오대벼철원2021-04-121055300105532021-04-192
62021강원지원벼오대벼화지2021-04-122442000244202021-04-192
72021강원지원벼오륜벼사천2021-05-2533500033502021-05-122
82021강원지원벼운광벼학수2021-04-1981600081602021-05-032