Overview

Dataset statistics

Number of variables14
Number of observations81
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 KiB
Average record size in memory122.6 B

Variable types

Categorical5
Numeric6
DateTime1
Boolean1
Text1

Dataset

Description국립종자원 정부보급종 포장검사 현황에 대한 데이터로 년산,지원명,작물명,차수,검사면적,합격면적,합격비율,불합격(계),불합격(종자원),불합격(자체),진척율,보고서날짜,보고완료여부,검사일자 등의 항목을 제공합니다.
Author농림축산식품부 국립종자원
URLhttps://www.data.go.kr/data/15066224/fileData.do

Alerts

차수 has constant value ""Constant
진척율 has constant value ""Constant
보고완료여부 has constant value ""Constant
검사면적 is highly overall correlated with 합격면적High correlation
합격면적 is highly overall correlated with 검사면적High correlation
합격비율 is highly overall correlated with 불합격(계) and 2 other fieldsHigh correlation
불합격(계) is highly overall correlated with 합격비율 and 2 other fieldsHigh correlation
불합격(종자원) is highly overall correlated with 합격비율 and 1 other fieldsHigh correlation
불합격(자체) is highly overall correlated with 합격비율 and 1 other fieldsHigh correlation
불합격(계) has 19 (23.5%) zerosZeros
불합격(종자원) has 32 (39.5%) zerosZeros
불합격(자체) has 36 (44.4%) zerosZeros

Reproduction

Analysis started2023-12-12 10:48:55.018709
Analysis finished2023-12-12 10:49:00.756008
Duration5.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년산
Categorical

Distinct3
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size780.0 B
2020
27 
2021
27 
2022
27 

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 27
33.3%
2021 27
33.3%
2022 27
33.3%

Length

2023-12-12T19:49:00.858044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:49:01.012216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 27
33.3%
2021 27
33.3%
2022 27
33.3%

지원명
Categorical

Distinct8
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Memory size780.0 B
전북지원
16 
충남지원
12 
전남지원
12 
경남지원
12 
경북지원
10 
Other values (3)
19 

Length

Max length7
Median length4
Mean length4.037037
Min length4

Unique

Unique1 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
전북지원 16
19.8%
충남지원 12
14.8%
전남지원 12
14.8%
경남지원 12
14.8%
경북지원 10
12.3%
충북지원 9
11.1%
강원지원 9
11.1%
경기종자관리소 1
 
1.2%

Length

2023-12-12T19:49:01.174058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:49:01.338045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북지원 16
19.8%
충남지원 12
14.8%
전남지원 12
14.8%
경남지원 12
14.8%
경북지원 10
12.3%
충북지원 9
11.1%
강원지원 9
11.1%
경기종자관리소 1
 
1.2%

작물명
Categorical

Distinct6
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size780.0 B
벼
21 
콩
21 
보리
19 
밀
12 
호밀
4 

Length

Max length2
Median length1
Mean length1.2839506
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
벼 21
25.9%
콩 21
25.9%
보리 19
23.5%
밀 12
14.8%
호밀 4
 
4.9%
팥 4
 
4.9%

Length

2023-12-12T19:49:01.530568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:49:01.692911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
벼 21
25.9%
콩 21
25.9%
보리 19
23.5%
밀 12
14.8%
호밀 4
 
4.9%
팥 4
 
4.9%

차수
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size780.0 B
0
81 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 81
100.0%

Length

2023-12-12T19:49:01.845726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:49:02.022160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 81
100.0%

검사면적
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29260.63
Minimum580
Maximum164800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T19:49:02.147160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum580
5-th percentile1220
Q14500
median12000
Q321430
95-th percentile137480
Maximum164800
Range164220
Interquartile range (IQR)16930

Descriptive statistics

Standard deviation42461.391
Coefficient of variation (CV)1.4511441
Kurtosis2.5698434
Mean29260.63
Median Absolute Deviation (MAD)8040
Skewness1.9217665
Sum2370111
Variance1.8029698 × 109
MonotonicityNot monotonic
2023-12-12T19:49:02.324486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4500 3
 
3.7%
1000 3
 
3.7%
9000 3
 
3.7%
5000 2
 
2.5%
1610 2
 
2.5%
2600 2
 
2.5%
11000 2
 
2.5%
13200 2
 
2.5%
137480 1
 
1.2%
36970 1
 
1.2%
Other values (60) 60
74.1%
ValueCountFrequency (%)
580 1
 
1.2%
1000 3
3.7%
1220 1
 
1.2%
1610 2
2.5%
1830 1
 
1.2%
1940 1
 
1.2%
1970 1
 
1.2%
2450 1
 
1.2%
2600 2
2.5%
3080 1
 
1.2%
ValueCountFrequency (%)
164800 1
1.2%
151904 1
1.2%
143183 1
1.2%
142982 1
1.2%
137480 1
1.2%
131019 1
1.2%
129550 1
1.2%
123776 1
1.2%
80635 1
1.2%
78520 1
1.2%

합격면적
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34709.827
Minimum580
Maximum243794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T19:49:02.858654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum580
5-th percentile1205
Q13998
median11786
Q321395
95-th percentile194130
Maximum243794
Range243214
Interquartile range (IQR)17397

Descriptive statistics

Standard deviation57671.745
Coefficient of variation (CV)1.6615394
Kurtosis4.4934314
Mean34709.827
Median Absolute Deviation (MAD)8086
Skewness2.3153465
Sum2811496
Variance3.3260302 × 109
MonotonicityNot monotonic
2023-12-12T19:49:03.051531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2600 2
 
2.5%
11000 2
 
2.5%
1000 2
 
2.5%
2232 1
 
1.2%
12380 1
 
1.2%
4235 1
 
1.2%
205438 1
 
1.2%
13200 1
 
1.2%
36450 1
 
1.2%
28304 1
 
1.2%
Other values (68) 68
84.0%
ValueCountFrequency (%)
580 1
1.2%
989 1
1.2%
1000 2
2.5%
1205 1
1.2%
1319 1
1.2%
1578 1
1.2%
1824 1
1.2%
1847 1
1.2%
1874 1
1.2%
2232 1
1.2%
ValueCountFrequency (%)
243794 1
1.2%
225966 1
1.2%
205438 1
1.2%
196113 1
1.2%
194130 1
1.2%
181643 1
1.2%
142963 1
1.2%
142510 1
1.2%
109764 1
1.2%
82908 1
1.2%

합격비율
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.308642
Minimum72.3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T19:49:03.204589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum72.3
5-th percentile85.8
Q197.6
median99
Q3100
95-th percentile100
Maximum100
Range27.7
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation4.9577767
Coefficient of variation (CV)0.050948986
Kurtosis10.353416
Mean97.308642
Median Absolute Deviation (MAD)1
Skewness-3.0716336
Sum7882
Variance24.579549
MonotonicityNot monotonic
2023-12-12T19:49:03.370218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
100.0 21
25.9%
99.8 6
 
7.4%
98.4 5
 
6.2%
98.0 3
 
3.7%
99.9 3
 
3.7%
99.5 3
 
3.7%
96.5 2
 
2.5%
98.9 2
 
2.5%
99.3 2
 
2.5%
97.2 2
 
2.5%
Other values (29) 32
39.5%
ValueCountFrequency (%)
72.3 1
1.2%
79.4 1
1.2%
81.9 1
1.2%
84.7 1
1.2%
85.8 1
1.2%
88.9 1
1.2%
91.0 1
1.2%
91.1 1
1.2%
92.6 1
1.2%
93.3 1
1.2%
ValueCountFrequency (%)
100.0 21
25.9%
99.9 3
 
3.7%
99.8 6
 
7.4%
99.7 2
 
2.5%
99.5 3
 
3.7%
99.4 1
 
1.2%
99.3 2
 
2.5%
99.2 1
 
1.2%
99.1 1
 
1.2%
99.0 1
 
1.2%

불합격(계)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean342.11111
Minimum0
Maximum3124
Zeros19
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T19:49:03.542607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median133
Q3378
95-th percentile1497
Maximum3124
Range3124
Interquartile range (IQR)372

Descriptive statistics

Standard deviation550.14714
Coefficient of variation (CV)1.6080949
Kurtosis8.7950366
Mean342.11111
Median Absolute Deviation (MAD)133
Skewness2.727228
Sum27711
Variance302661.87
MonotonicityNot monotonic
2023-12-12T19:49:03.724619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
23.5%
32 3
 
3.7%
110 2
 
2.5%
765 1
 
1.2%
133 1
 
1.2%
291 1
 
1.2%
42 1
 
1.2%
123 1
 
1.2%
572 1
 
1.2%
469 1
 
1.2%
Other values (50) 50
61.7%
ValueCountFrequency (%)
0 19
23.5%
3 1
 
1.2%
6 1
 
1.2%
11 1
 
1.2%
15 1
 
1.2%
19 1
 
1.2%
20 1
 
1.2%
32 3
 
3.7%
35 1
 
1.2%
42 1
 
1.2%
ValueCountFrequency (%)
3124 1
1.2%
2004 1
1.2%
1893 1
1.2%
1532 1
1.2%
1497 1
1.2%
1469 1
1.2%
1309 1
1.2%
1200 1
1.2%
1062 1
1.2%
925 1
1.2%

불합격(종자원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.79012
Minimum0
Maximum3020
Zeros32
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T19:49:03.940134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q3174
95-th percentile1200
Maximum3020
Range3020
Interquartile range (IQR)174

Descriptive statistics

Standard deviation477.77646
Coefficient of variation (CV)2.2452943
Kurtosis16.733361
Mean212.79012
Median Absolute Deviation (MAD)20
Skewness3.7837892
Sum17236
Variance228270.34
MonotonicityNot monotonic
2023-12-12T19:49:04.158830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 32
39.5%
3 3
 
3.7%
71 2
 
2.5%
110 2
 
2.5%
304 1
 
1.2%
520 1
 
1.2%
22 1
 
1.2%
33 1
 
1.2%
455 1
 
1.2%
469 1
 
1.2%
Other values (36) 36
44.4%
ValueCountFrequency (%)
0 32
39.5%
3 3
 
3.7%
5 1
 
1.2%
6 1
 
1.2%
10 1
 
1.2%
11 1
 
1.2%
15 1
 
1.2%
20 1
 
1.2%
22 1
 
1.2%
30 1
 
1.2%
ValueCountFrequency (%)
3020 1
1.2%
1893 1
1.2%
1532 1
1.2%
1402 1
1.2%
1200 1
1.2%
1078 1
1.2%
673 1
1.2%
520 1
1.2%
469 1
1.2%
455 1
1.2%

불합격(자체)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)50.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.32099
Minimum0
Maximum1700
Zeros36
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-12T19:49:04.332522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q3152
95-th percentile595
Maximum1700
Range1700
Interquartile range (IQR)152

Descriptive statistics

Standard deviation265.22838
Coefficient of variation (CV)2.0509307
Kurtosis17.216253
Mean129.32099
Median Absolute Deviation (MAD)20
Skewness3.760565
Sum10475
Variance70346.096
MonotonicityNot monotonic
2023-12-12T19:49:04.551594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 36
44.4%
32 3
 
3.7%
46 2
 
2.5%
20 2
 
2.5%
10 2
 
2.5%
218 1
 
1.2%
1112 1
 
1.2%
214 1
 
1.2%
276 1
 
1.2%
90 1
 
1.2%
Other values (31) 31
38.3%
ValueCountFrequency (%)
0 36
44.4%
10 2
 
2.5%
19 1
 
1.2%
20 2
 
2.5%
25 1
 
1.2%
32 3
 
3.7%
35 1
 
1.2%
46 2
 
2.5%
56 1
 
1.2%
61 1
 
1.2%
ValueCountFrequency (%)
1700 1
1.2%
1112 1
1.2%
795 1
1.2%
777 1
1.2%
595 1
1.2%
418 1
1.2%
391 1
1.2%
367 1
1.2%
300 1
1.2%
298 1
1.2%

진척율
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size780.0 B
100
81 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 81
100.0%

Length

2023-12-12T19:49:04.743436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:49:04.881851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100 81
100.0%

보고서날짜
Date

Distinct50
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Memory size780.0 B
Minimum2020-05-04 00:00:00
Maximum2023-07-11 00:00:00
2023-12-12T19:49:05.029534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:49:05.220051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

보고완료여부
Boolean

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size213.0 B
True
81 
ValueCountFrequency (%)
True 81
100.0%
2023-12-12T19:49:05.387475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

검사일자
Text

Distinct62
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-12T19:49:05.668748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.8518519
Min length6

Characters and Unicode

Total characters798
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)58.0%

Sample

1st row2020-05-15
2nd row2020-09-23
3rd row2020-04-21
4th row2020-08-12
5th row2020-09-25
ValueCountFrequency (%)
2022-04-29 4
 
5.1%
2022-08-04 3
 
3.8%
2021-05-03 2
 
2.6%
2021-04-28 2
 
2.6%
2021-08-05 2
 
2.6%
2022-04-27 2
 
2.6%
2021-05-04 2
 
2.6%
2020-08-14 2
 
2.6%
2020-09-09 2
 
2.6%
2021-10-08 2
 
2.6%
Other values (51) 55
70.5%
2023-12-12T19:49:06.236231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 221
27.7%
0 214
26.8%
- 156
19.5%
1 48
 
6.0%
8 35
 
4.4%
4 33
 
4.1%
9 20
 
2.5%
5 19
 
2.4%
18
 
2.3%
7 17
 
2.1%
Other values (2) 17
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 624
78.2%
Dash Punctuation 156
 
19.5%
Space Separator 18
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 221
35.4%
0 214
34.3%
1 48
 
7.7%
8 35
 
5.6%
4 33
 
5.3%
9 20
 
3.2%
5 19
 
3.0%
7 17
 
2.7%
3 12
 
1.9%
6 5
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 156
100.0%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 221
27.7%
0 214
26.8%
- 156
19.5%
1 48
 
6.0%
8 35
 
4.4%
4 33
 
4.1%
9 20
 
2.5%
5 19
 
2.4%
18
 
2.3%
7 17
 
2.1%
Other values (2) 17
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 221
27.7%
0 214
26.8%
- 156
19.5%
1 48
 
6.0%
8 35
 
4.4%
4 33
 
4.1%
9 20
 
2.5%
5 19
 
2.4%
18
 
2.3%
7 17
 
2.1%
Other values (2) 17
 
2.1%

Interactions

2023-12-12T19:48:59.476881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:55.723771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:56.457285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:57.254748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:57.977112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:58.632829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:59.619813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:55.839931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:56.631255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:57.371902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:58.090875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:58.763508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:59.755357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:55.949578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:56.732078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:57.499265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:58.198323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:58.879367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:59.906052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:56.065861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:56.848148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:57.617123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:58.302278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:59.012830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:49:00.040263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:56.182723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:56.973643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:57.734411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:58.395765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:59.165937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:49:00.169718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:56.335872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:57.121670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:57.846155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:58.510914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:48:59.330249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:49:06.386703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년산지원명작물명검사면적합격면적합격비율불합격(계)불합격(종자원)불합격(자체)보고서날짜검사일자
년산1.0000.0000.0000.0000.0000.4680.1940.1400.2001.0001.000
지원명0.0001.0000.2320.5890.2670.4380.0000.0000.0000.9730.889
작물명0.0000.2321.0000.5120.5200.1290.0000.0000.0000.0000.606
검사면적0.0000.5890.5121.0000.9150.0000.5510.8440.4300.8830.978
합격면적0.0000.2670.5200.9151.0000.0000.6670.8270.5590.7850.957
합격비율0.4680.4380.1290.0000.0001.0000.6020.5580.6590.0000.000
불합격(계)0.1940.0000.0000.5510.6670.6021.0000.8550.8620.4910.889
불합격(종자원)0.1400.0000.0000.8440.8270.5580.8551.0000.0000.6430.924
불합격(자체)0.2000.0000.0000.4300.5590.6590.8620.0001.0000.0000.463
보고서날짜1.0000.9730.0000.8830.7850.0000.4910.6430.0001.0000.982
검사일자1.0000.8890.6060.9780.9570.0000.8890.9240.4630.9821.000
2023-12-12T19:49:06.568247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
작물명년산지원명
작물명1.0000.0000.125
년산0.0001.0000.000
지원명0.1250.0001.000
2023-12-12T19:49:06.685060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검사면적합격면적합격비율불합격(계)불합격(종자원)불합격(자체)년산지원명작물명
검사면적1.0000.9950.1570.3850.3490.1180.0000.2290.310
합격면적0.9951.0000.1840.3590.3280.1190.0000.1300.283
합격비율0.1570.1841.000-0.768-0.572-0.6320.2220.2280.052
불합격(계)0.3850.359-0.7681.0000.8140.6250.1250.0000.000
불합격(종자원)0.3490.328-0.5720.8141.0000.2030.0790.0000.000
불합격(자체)0.1180.119-0.6320.6250.2031.0000.1290.0000.000
년산0.0000.0000.2220.1250.0790.1291.0000.0000.000
지원명0.2290.1300.2280.0000.0000.0000.0001.0000.125
작물명0.3100.2830.0520.0000.0000.0000.0000.1251.000

Missing values

2023-12-12T19:49:00.372912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:49:00.653141image/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

년산지원명작물명차수검사면적합격면적합격비율불합격(계)불합격(종자원)불합격(자체)진척율보고서날짜보고완료여부검사일자
02020경기종자관리소보리02450223291.121802181002020-05-19Y2020-05-15
12020충북지원벼02278022780100.00001002020-07-08Y2020-09-23
22020충북지원보리0100098998.9111101002023-06-17Y2020-04-21
32020충북지원콩0132401297498.026602661002020-08-12Y2020-08-12
42020충남지원벼0736007353899.9620621002020-07-10Y2020-09-25
52020충남지원보리08820843795.7383852981002020-06-02Y2020-05-11
62020충남지원콩09000885498.414631431002020-08-07Y2020-08-04
72020충남지원밀0580580100.00001002020-06-02Y2020-04-28
82020전북지원벼07155071550100.00001002020-07-22Y2020-09-09
92020전북지원보리0214302139599.8350351002020-05-04Y2020-04-23
년산지원명작물명차수검사면적합격면적합격비율불합격(계)불합격(종자원)불합격(자체)진척율보고서날짜보고완료여부검사일자
712022경북지원벼013101919611399.9242174681002022-09-23Y2022-09-21
722022경북지원보리01610157898.0320321002022-09-15Y2022-05-02
732022경북지원콩0166001632798.4273253201002022-09-15Y2022-08-04
742022경남지원벼0806358290897.6200430417001002022-09-15Y2022-09-06
752022경남지원보리030803080100.00001002022-07-07Y2022-04-25
762022경남지원콩04500436096.914001401002022-08-09Y2022-08-04
772022경남지원밀09350924098.811011001002022-07-07Y2022-04-27
782022강원지원벼0576935739899.529529501002022-09-05Y2022-09-08
792022강원지원콩01100011000100.00001002022-08-10Y2022-08-02
802022강원지원팥026002600100.00001002022-08-10Y2022-07-29