Overview

Dataset statistics

Number of variables11
Number of observations434
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.0 KiB
Average record size in memory94.3 B

Variable types

Categorical5
Text1
Numeric5

Dataset

Description국립종자원 정부보급종 개별신청 내역에 대한 데이터로 년산, 지원명, 작물, 품종명, 춘추기, 추가공급잔량, 공급계획량_1, 공급계획량_2, 개별신청량_기본1, 개별신청량_기본2, 구분 등의 항목을 제공합니다.
Author농림축산식품부 국립종자원
URLhttps://www.data.go.kr/data/15066229/fileData.do

Alerts

춘추기 has constant value ""Constant
추가공급잔량 is highly overall correlated with 공급계획량_2 and 1 other fieldsHigh correlation
공급계획량_1 is highly overall correlated with 개별신청량_기본1High correlation
공급계획량_2 is highly overall correlated with 추가공급잔량 and 1 other fieldsHigh correlation
개별신청량_기본1 is highly overall correlated with 공급계획량_1High correlation
개별신청량_기본2 is highly overall correlated with 추가공급잔량 and 1 other fieldsHigh correlation
추가공급잔량 has 178 (41.0%) zerosZeros
공급계획량_1 has 331 (76.3%) zerosZeros
공급계획량_2 has 20 (4.6%) zerosZeros
개별신청량_기본1 has 332 (76.5%) zerosZeros
개별신청량_기본2 has 31 (7.1%) zerosZeros

Reproduction

Analysis started2023-12-12 06:55:38.912922
Analysis finished2023-12-12 06:55:42.830385
Duration3.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년산
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2020
151 
2021
144 
2022
139 

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 (%)
2020 151
34.8%
2021 144
33.2%
2022 139
32.0%

Length

2023-12-12T15:55:42.909092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:55:43.042858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 151
34.8%
2021 144
33.2%
2022 139
32.0%

지원명
Categorical

Distinct8
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
전남지원
91 
전북지원
81 
경남지원
67 
충남지원
58 
강원지원
39 
Other values (3)
98 

Length

Max length7
Median length4
Mean length4.2419355
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경북지원
2nd row경기종자관리소
3rd row충북지원
4th row전남지원
5th row전남지원

Common Values

ValueCountFrequency (%)
전남지원 91
21.0%
전북지원 81
18.7%
경남지원 67
15.4%
충남지원 58
13.4%
강원지원 39
9.0%
경기종자관리소 35
 
8.1%
경북지원 32
 
7.4%
충북지원 31
 
7.1%

Length

2023-12-12T15:55:43.194585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:55:43.347094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전남지원 91
21.0%
전북지원 81
18.7%
경남지원 67
15.4%
충남지원 58
13.4%
강원지원 39
9.0%
경기종자관리소 35
 
8.1%
경북지원 32
 
7.4%
충북지원 31
 
7.1%

작물
Categorical

Distinct13
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
벼
225 
콩
86 
보리
49 
밀
 
21
보리(비축)
 
15
Other values (8)
38 

Length

Max length6
Median length1
Mean length1.5599078
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
벼 225
51.8%
콩 86
 
19.8%
보리 49
 
11.3%
밀 21
 
4.8%
보리(비축) 15
 
3.5%
벼(비축) 10
 
2.3%
팥 8
 
1.8%
보리(춘파) 6
 
1.4%
호밀 4
 
0.9%
밀(비축) 4
 
0.9%
Other values (3) 6
 
1.4%

Length

2023-12-12T15:55:43.506553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
벼 225
51.8%
콩 86
 
19.8%
보리 49
 
11.3%
밀 21
 
4.8%
보리(비축 15
 
3.5%
벼(비축 10
 
2.3%
팥 8
 
1.8%
보리(춘파 6
 
1.4%
호밀 4
 
0.9%
밀(비축 4
 
0.9%
Other values (3) 6
 
1.4%

품종명
Text

Distinct70
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-12-12T15:55:43.755625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.5898618
Min length2

Characters and Unicode

Total characters1558
Distinct characters83
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

Unique9 ?
Unique (%)2.1%

Sample

1st row일품벼
2nd row대안벼
3rd row진수미
4th row일미벼
5th row일미벼
ValueCountFrequency (%)
대원콩 30
 
6.9%
삼광벼 29
 
6.7%
새일미벼 16
 
3.7%
흰찰쌀보리 13
 
3.0%
해담쌀 13
 
3.0%
선풍콩 13
 
3.0%
추청벼 13
 
3.0%
백옥찰벼 12
 
2.8%
신동진벼 11
 
2.5%
해품벼 11
 
2.5%
Other values (60) 273
62.9%
2023-12-12T15:55:44.129914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
벼 163
 
10.5%
리 97
 
6.2%
콩 87
 
5.6%
보 72
 
4.6%
쌀 55
 
3.5%
대 53
 
3.4%
찰 53
 
3.4%
새 50
 
3.2%
광 46
 
3.0%
미 46
 
3.0%
Other values (73) 836
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1528
98.1%
Decimal Number 18
 
1.2%
Open Punctuation 6
 
0.4%
Close Punctuation 6
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
벼 163
 
10.7%
리 97
 
6.3%
콩 87
 
5.7%
보 72
 
4.7%
쌀 55
 
3.6%
대 53
 
3.5%
찰 53
 
3.5%
새 50
 
3.3%
광 46
 
3.0%
미 46
 
3.0%
Other values (70) 806
52.7%
Decimal Number
ValueCountFrequency (%)
1 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1528
98.1%
Common 30
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
벼 163
 
10.7%
리 97
 
6.3%
콩 87
 
5.7%
보 72
 
4.7%
쌀 55
 
3.6%
대 53
 
3.5%
찰 53
 
3.5%
새 50
 
3.3%
광 46
 
3.0%
미 46
 
3.0%
Other values (70) 806
52.7%
Common
ValueCountFrequency (%)
1 18
60.0%
( 6
 
20.0%
) 6
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1528
98.1%
ASCII 30
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
벼 163
 
10.7%
리 97
 
6.3%
콩 87
 
5.7%
보 72
 
4.7%
쌀 55
 
3.6%
대 53
 
3.5%
찰 53
 
3.5%
새 50
 
3.3%
광 46
 
3.0%
미 46
 
3.0%
Other values (70) 806
52.7%
ASCII
ValueCountFrequency (%)
1 18
60.0%
( 6
 
20.0%
) 6
 
20.0%

춘추기
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
전체
434 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 434
100.0%

Length

2023-12-12T15:55:44.266501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:55:44.394181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 434
100.0%

추가공급잔량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct208
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11297.917
Minimum0
Maximum529760
Zeros178
Zeros (%)41.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T15:55:44.528163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median205
Q36740
95-th percentile60020
Maximum529760
Range529760
Interquartile range (IQR)6740

Descriptive statistics

Standard deviation40194.737
Coefficient of variation (CV)3.5577122
Kurtosis100.76368
Mean11297.917
Median Absolute Deviation (MAD)205
Skewness8.9334091
Sum4903296
Variance1.6156169 × 109
MonotonicityNot monotonic
2023-12-12T15:55:44.711594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 178
41.0%
20 6
 
1.4%
40 6
 
1.4%
100 5
 
1.2%
10000 4
 
0.9%
200 4
 
0.9%
400 3
 
0.7%
1800 3
 
0.7%
1200 3
 
0.7%
280 3
 
0.7%
Other values (198) 219
50.5%
ValueCountFrequency (%)
0 178
41.0%
5 1
 
0.2%
10 2
 
0.5%
20 6
 
1.4%
40 6
 
1.4%
55 1
 
0.2%
60 2
 
0.5%
80 2
 
0.5%
95 1
 
0.2%
100 5
 
1.2%
ValueCountFrequency (%)
529760 1
0.2%
461340 1
0.2%
162160 1
0.2%
160760 1
0.2%
148100 1
0.2%
146960 1
0.2%
122720 1
0.2%
119980 1
0.2%
113860 1
0.2%
109220 1
0.2%

공급계획량_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.55991
Minimum0
Maximum14540
Zeros331
Zeros (%)76.3%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T15:55:44.840195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile714
Maximum14540
Range14540
Interquartile range (IQR)0

Descriptive statistics

Standard deviation920.32976
Coefficient of variation (CV)5.0137842
Kurtosis144.95885
Mean183.55991
Median Absolute Deviation (MAD)0
Skewness10.596099
Sum79665
Variance847006.87
MonotonicityNot monotonic
2023-12-12T15:55:44.986741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 331
76.3%
40 7
 
1.6%
100 5
 
1.2%
20 5
 
1.2%
120 4
 
0.9%
280 3
 
0.7%
30 3
 
0.7%
240 3
 
0.7%
215 3
 
0.7%
440 2
 
0.5%
Other values (62) 68
 
15.7%
ValueCountFrequency (%)
0 331
76.3%
5 1
 
0.2%
10 2
 
0.5%
20 5
 
1.2%
25 1
 
0.2%
30 3
 
0.7%
35 1
 
0.2%
40 7
 
1.6%
60 2
 
0.5%
70 1
 
0.2%
ValueCountFrequency (%)
14540 1
0.2%
6280 1
0.2%
5440 1
0.2%
4220 1
0.2%
3600 1
0.2%
3315 1
0.2%
2900 1
0.2%
2780 1
0.2%
2440 1
0.2%
2340 2
0.5%

공급계획량_2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct295
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15354.288
Minimum0
Maximum529760
Zeros20
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T15:55:45.168514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q1285
median2080
Q310143.75
95-th percentile78394
Maximum529760
Range529760
Interquartile range (IQR)9858.75

Descriptive statistics

Standard deviation43532.694
Coefficient of variation (CV)2.8352141
Kurtosis71.350374
Mean15354.288
Median Absolute Deviation (MAD)2000
Skewness7.2566279
Sum6663761
Variance1.8950954 × 109
MonotonicityNot monotonic
2023-12-12T15:55:45.306549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
4.6%
200 11
 
2.5%
120 9
 
2.1%
20 9
 
2.1%
40 8
 
1.8%
80 8
 
1.8%
100 8
 
1.8%
600 4
 
0.9%
300 4
 
0.9%
10000 4
 
0.9%
Other values (285) 349
80.4%
ValueCountFrequency (%)
0 20
4.6%
5 1
 
0.2%
10 4
 
0.9%
20 9
2.1%
30 2
 
0.5%
40 8
 
1.8%
50 3
 
0.7%
55 1
 
0.2%
60 2
 
0.5%
80 8
 
1.8%
ValueCountFrequency (%)
529760 1
0.2%
461340 1
0.2%
200000 1
0.2%
161860 1
0.2%
160760 1
0.2%
159300 1
0.2%
158640 1
0.2%
148100 1
0.2%
144180 1
0.2%
123100 1
0.2%

개별신청량_기본1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.23733
Minimum0
Maximum14540
Zeros332
Zeros (%)76.5%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T15:55:45.439334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile701
Maximum14540
Range14540
Interquartile range (IQR)0

Descriptive statistics

Standard deviation919.46782
Coefficient of variation (CV)5.0179068
Kurtosis145.47951
Mean183.23733
Median Absolute Deviation (MAD)0
Skewness10.616784
Sum79525
Variance845421.08
MonotonicityNot monotonic
2023-12-12T15:55:45.608183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 332
76.5%
40 7
 
1.6%
100 5
 
1.2%
20 4
 
0.9%
120 4
 
0.9%
280 3
 
0.7%
30 3
 
0.7%
215 3
 
0.7%
240 3
 
0.7%
60 2
 
0.5%
Other values (62) 68
 
15.7%
ValueCountFrequency (%)
0 332
76.5%
5 1
 
0.2%
10 2
 
0.5%
20 4
 
0.9%
25 1
 
0.2%
30 3
 
0.7%
35 1
 
0.2%
40 7
 
1.6%
60 2
 
0.5%
70 1
 
0.2%
ValueCountFrequency (%)
14540 1
0.2%
6280 1
0.2%
5440 1
0.2%
4220 1
0.2%
3500 1
0.2%
3315 1
0.2%
2900 1
0.2%
2780 1
0.2%
2440 1
0.2%
2340 2
0.5%

개별신청량_기본2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct275
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7498.6636
Minimum0
Maximum159300
Zeros31
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T15:55:45.773493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1200
median1485
Q36695
95-th percentile31832
Maximum159300
Range159300
Interquartile range (IQR)6495

Descriptive statistics

Standard deviation16941.554
Coefficient of variation (CV)2.2592764
Kurtosis27.601269
Mean7498.6636
Median Absolute Deviation (MAD)1435
Skewness4.6693618
Sum3254420
Variance2.8701625 × 108
MonotonicityNot monotonic
2023-12-12T15:55:45.987994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
7.1%
200 12
 
2.8%
120 11
 
2.5%
100 9
 
2.1%
20 8
 
1.8%
80 7
 
1.6%
40 7
 
1.6%
220 5
 
1.2%
300 5
 
1.2%
160 4
 
0.9%
Other values (265) 335
77.2%
ValueCountFrequency (%)
0 31
7.1%
5 1
 
0.2%
10 4
 
0.9%
20 8
 
1.8%
30 2
 
0.5%
40 7
 
1.6%
50 3
 
0.7%
55 1
 
0.2%
60 2
 
0.5%
80 7
 
1.6%
ValueCountFrequency (%)
159300 1
0.2%
117640 1
0.2%
108240 1
0.2%
104120 1
0.2%
98680 1
0.2%
84680 1
0.2%
79680 1
0.2%
74405 1
0.2%
69580 1
0.2%
68660 1
0.2%

구분
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
보급종
251 
원종
165 
원원종
 
18

Length

Max length3
Median length3
Mean length2.6198157
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row보급종
2nd row보급종
3rd row보급종
4th row보급종
5th row원종

Common Values

ValueCountFrequency (%)
보급종 251
57.8%
원종 165
38.0%
원원종 18
 
4.1%

Length

2023-12-12T15:55:46.171387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:55:46.277848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보급종 251
57.8%
원종 165
38.0%
원원종 18
 
4.1%

Interactions

2023-12-12T15:55:41.591206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:39.405129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:39.921224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:40.494692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:41.020641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:41.685179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:39.482976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:40.030781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:40.595753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:41.137498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:41.831917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:39.569932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:40.176958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:40.692457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:41.241129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:41.979537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:39.669144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:40.271334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:40.781254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:41.345466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:42.100130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:39.806305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:40.368639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:40.896278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:41.448725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:55:46.358580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년산지원명작물품종명추가공급잔량공급계획량_1공급계획량_2개별신청량_기본1개별신청량_기본2구분
년산1.0000.0000.1120.0000.0000.0000.0000.0000.1670.000
지원명0.0001.0000.3260.9060.1700.0600.1080.0600.0000.271
작물0.1120.3261.0000.9530.0000.1030.0000.1030.0000.418
품종명0.0000.9060.9531.0000.0000.0000.0000.0000.0000.494
추가공급잔량0.0000.1700.0000.0001.0000.0000.9910.0000.5740.235
공급계획량_10.0000.0600.1030.0000.0001.0000.0561.0000.5620.000
공급계획량_20.0000.1080.0000.0000.9910.0561.0000.0560.6830.328
개별신청량_기본10.0000.0600.1030.0000.0001.0000.0561.0000.5620.000
개별신청량_기본20.1670.0000.0000.0000.5740.5620.6830.5621.0000.416
구분0.0000.2710.4180.4940.2350.0000.3280.0000.4161.000
2023-12-12T15:55:46.525205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
작물지원명년산구분
작물1.0000.1520.0620.258
지원명0.1521.0000.0000.177
년산0.0620.0001.0000.000
구분0.2580.1770.0001.000
2023-12-12T15:55:46.663629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
추가공급잔량공급계획량_1공급계획량_2개별신청량_기본1개별신청량_기본2년산지원명작물구분
추가공급잔량1.0000.1990.5810.1950.5190.0000.0940.0000.099
공급계획량_10.1991.0000.0630.9960.1350.0000.0320.0500.000
공급계획량_20.5810.0631.0000.0610.9090.0000.0600.0000.143
개별신청량_기본10.1950.9960.0611.0000.1330.0000.0320.0500.000
개별신청량_기본20.5190.1350.9090.1331.0000.0730.0000.0000.200
년산0.0000.0000.0000.0000.0731.0000.0000.0620.000
지원명0.0940.0320.0600.0320.0000.0001.0000.1520.177
작물0.0000.0500.0000.0500.0000.0620.1521.0000.258
구분0.0990.0000.1430.0000.2000.0000.1770.2581.000

Missing values

2023-12-12T15:55:42.267978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:55:42.757326image/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

년산지원명작물품종명춘추기추가공급잔량공급계획량_1공급계획량_2개별신청량_기본1개별신청량_기본2구분
02020경북지원벼일품벼전체0082660079680보급종
12020경기종자관리소벼대안벼전체600020598004540보급종
22020충북지원벼진수미전체774000774000360보급종
32020전남지원벼일미벼전체6562006926004200보급종
42020전남지원벼일미벼전체00229000원종
52020강원지원벼오대벼전체0806700806700보급종
62020충북지원벼오대벼전체001200120원종
72020강원지원벼오대벼전체00178001780원종
82020경기종자관리소벼추청벼전체30000360026400350022700보급종
92020충북지원벼추청벼전체1138600113860013080보급종
년산지원명작물품종명춘추기추가공급잔량공급계획량_1공급계획량_2개별신청량_기본1개별신청량_기본2구분
4242022전남지원벼(비축)새청무전체20002000200원원종
4252022경남지원벼(비축)영진전체27800278002780보급종
4262022전북지원콩(비축)선풍콩전체001100110원원종
4272022전북지원보리(비축)흰찰쌀보리전체002000200원종
4282022전남지원보리(비축)흰찰쌀보리전체00460002080원종
4292022전남지원보리(비축)큰알보리1호전체00558001560원종
4302022전북지원밀(비축)금강밀전체31400314003140보급종
4312022전남지원밀(비축)금강밀전체002000180보급종
4322022전남지원밀(비축)백강밀전체00826001560보급종
4332022전북지원호밀(비축)곡우전체003000300원종