Overview

Dataset statistics

Number of variables14
Number of observations530
Missing cells220
Missing cells (%)3.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.8 KiB
Average record size in memory123.2 B

Variable types

Numeric8
Categorical6

Dataset

Description국립종자원 정부보급종 정선 곡온 현황 순서,지원명,부서명,작업일자,작업시간,빈번호,작물명,저장량,1번,2번,3번,4번,5번,6번 등의 항목을 포합합니다.
Author농림축산식품부 국립종자원
URLhttps://www.data.go.kr/data/15066293/fileData.do

Alerts

지원명 has constant value ""Constant
부서명 has constant value ""Constant
작업시간 has constant value ""Constant
5번 has constant value ""Constant
6번 has constant value ""Constant
순서 is highly overall correlated with 작업일자High correlation
작업일자 is highly overall correlated with 순서High correlation
빈번호 is highly overall correlated with 작물명High correlation
저장량 is highly overall correlated with 작물명High correlation
1번 is highly overall correlated with 2번 and 3 other fieldsHigh correlation
2번 is highly overall correlated with 1번 and 3 other fieldsHigh correlation
3번 is highly overall correlated with 1번 and 3 other fieldsHigh correlation
4번 is highly overall correlated with 1번 and 3 other fieldsHigh correlation
작물명 is highly overall correlated with 빈번호 and 5 other fieldsHigh correlation
저장량 has 220 (41.5%) missing valuesMissing
순서 has unique valuesUnique
저장량 has 67 (12.6%) zerosZeros
1번 has 221 (41.7%) zerosZeros
2번 has 221 (41.7%) zerosZeros
3번 has 221 (41.7%) zerosZeros
4번 has 222 (41.9%) zerosZeros

Reproduction

Analysis started2023-12-12 13:53:23.193924
Analysis finished2023-12-12 13:53:31.902777
Duration8.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순서
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct530
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26481.5
Minimum26217
Maximum26746
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-12-12T22:53:31.983680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26217
5-th percentile26243.45
Q126349.25
median26481.5
Q326613.75
95-th percentile26719.55
Maximum26746
Range529
Interquartile range (IQR)264.5

Descriptive statistics

Standard deviation153.14209
Coefficient of variation (CV)0.005782984
Kurtosis-1.2
Mean26481.5
Median Absolute Deviation (MAD)132.5
Skewness0
Sum14035195
Variance23452.5
MonotonicityStrictly increasing
2023-12-12T22:53:32.140483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26217 1
 
0.2%
26566 1
 
0.2%
26580 1
 
0.2%
26579 1
 
0.2%
26578 1
 
0.2%
26577 1
 
0.2%
26576 1
 
0.2%
26575 1
 
0.2%
26574 1
 
0.2%
26573 1
 
0.2%
Other values (520) 520
98.1%
ValueCountFrequency (%)
26217 1
0.2%
26218 1
0.2%
26219 1
0.2%
26220 1
0.2%
26221 1
0.2%
26222 1
0.2%
26223 1
0.2%
26224 1
0.2%
26225 1
0.2%
26226 1
0.2%
ValueCountFrequency (%)
26746 1
0.2%
26745 1
0.2%
26744 1
0.2%
26743 1
0.2%
26742 1
0.2%
26741 1
0.2%
26740 1
0.2%
26739 1
0.2%
26738 1
0.2%
26737 1
0.2%

지원명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
전남지원
530 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전남지원 530
100.0%

Length

2023-12-12T22:53:32.291456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:53:32.404305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전남지원 530
100.0%

부서명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
함평
530 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
함평 530
100.0%

Length

2023-12-12T22:53:32.497960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:53:32.593015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
함평 530
100.0%

작업일자
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20160110
Minimum20160104
Maximum20160115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-12-12T22:53:32.712089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20160104
5-th percentile20160104
Q120160106
median20160110
Q320160113
95-th percentile20160115
Maximum20160115
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.7784835
Coefficient of variation (CV)1.8742376 × 10-7
Kurtosis-1.5064762
Mean20160110
Median Absolute Deviation (MAD)3.5
Skewness0
Sum1.0684858 × 1010
Variance14.276938
MonotonicityIncreasing
2023-12-12T22:53:32.836531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
20160104 53
10.0%
20160105 53
10.0%
20160106 53
10.0%
20160107 53
10.0%
20160108 53
10.0%
20160111 53
10.0%
20160112 53
10.0%
20160113 53
10.0%
20160114 53
10.0%
20160115 53
10.0%
ValueCountFrequency (%)
20160104 53
10.0%
20160105 53
10.0%
20160106 53
10.0%
20160107 53
10.0%
20160108 53
10.0%
20160111 53
10.0%
20160112 53
10.0%
20160113 53
10.0%
20160114 53
10.0%
20160115 53
10.0%
ValueCountFrequency (%)
20160115 53
10.0%
20160114 53
10.0%
20160113 53
10.0%
20160112 53
10.0%
20160111 53
10.0%
20160108 53
10.0%
20160107 53
10.0%
20160106 53
10.0%
20160105 53
10.0%
20160104 53
10.0%

작업시간
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
0
530 

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 (%)
0 530
100.0%

Length

2023-12-12T22:53:32.968939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:53:33.050482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 530
100.0%

빈번호
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-12-12T22:53:33.167463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median27
Q340
95-th percentile51
Maximum53
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.31151
Coefficient of variation (CV)0.56709297
Kurtosis-1.2008542
Mean27
Median Absolute Deviation (MAD)13
Skewness0
Sum14310
Variance234.44234
MonotonicityNot monotonic
2023-12-12T22:53:33.298145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10
 
1.9%
41 10
 
1.9%
30 10
 
1.9%
31 10
 
1.9%
32 10
 
1.9%
33 10
 
1.9%
34 10
 
1.9%
35 10
 
1.9%
36 10
 
1.9%
37 10
 
1.9%
Other values (43) 430
81.1%
ValueCountFrequency (%)
1 10
1.9%
2 10
1.9%
3 10
1.9%
4 10
1.9%
5 10
1.9%
6 10
1.9%
7 10
1.9%
8 10
1.9%
9 10
1.9%
10 10
1.9%
ValueCountFrequency (%)
53 10
1.9%
52 10
1.9%
51 10
1.9%
50 10
1.9%
49 10
1.9%
48 10
1.9%
47 10
1.9%
46 10
1.9%
45 10
1.9%
44 10
1.9%

작물명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
기타
287 
새누리벼
123 
황금누리
70 
새일미벼
30 
운광벼
 
20

Length

Max length4
Median length2
Mean length2.8792453
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 287
54.2%
새누리벼 123
23.2%
황금누리 70
 
13.2%
새일미벼 30
 
5.7%
운광벼 20
 
3.8%

Length

2023-12-12T22:53:33.429985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:53:33.540870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 287
54.2%
새누리벼 123
23.2%
황금누리 70
 
13.2%
새일미벼 30
 
5.7%
운광벼 20
 
3.8%

저장량
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct13
Distinct (%)4.2%
Missing220
Missing (%)41.5%
Infinite0
Infinite (%)0.0%
Mean73.967742
Minimum0
Maximum150
Zeros67
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-12-12T22:53:33.646815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q160
median100
Q3100
95-th percentile100
Maximum150
Range150
Interquartile range (IQR)40

Descriptive statistics

Standard deviation41.216913
Coefficient of variation (CV)0.55722822
Kurtosis-0.54475536
Mean73.967742
Median Absolute Deviation (MAD)0
Skewness-1.117768
Sum22930
Variance1698.8339
MonotonicityNot monotonic
2023-12-12T22:53:33.743770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 190
35.8%
0 67
 
12.6%
90 10
 
1.9%
60 10
 
1.9%
76 10
 
1.9%
94 10
 
1.9%
70 3
 
0.6%
20 3
 
0.6%
80 2
 
0.4%
50 2
 
0.4%
Other values (3) 3
 
0.6%
(Missing) 220
41.5%
ValueCountFrequency (%)
0 67
12.6%
10 1
 
0.2%
20 3
 
0.6%
40 1
 
0.2%
50 2
 
0.4%
60 10
 
1.9%
70 3
 
0.6%
76 10
 
1.9%
80 2
 
0.4%
90 10
 
1.9%
ValueCountFrequency (%)
150 1
 
0.2%
100 190
35.8%
94 10
 
1.9%
90 10
 
1.9%
80 2
 
0.4%
76 10
 
1.9%
70 3
 
0.6%
60 10
 
1.9%
50 2
 
0.4%
40 1
 
0.2%

1번
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct99
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9043396
Minimum0
Maximum17.3
Zeros221
Zeros (%)41.7%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-12-12T22:53:33.886843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.4
Q36.5
95-th percentile10.5
Maximum17.3
Range17.3
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation3.8089786
Coefficient of variation (CV)0.97557562
Kurtosis-0.84893701
Mean3.9043396
Median Absolute Deviation (MAD)4.4
Skewness0.4425671
Sum2069.3
Variance14.508318
MonotonicityNot monotonic
2023-12-12T22:53:34.039528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 221
41.7%
5.1 14
 
2.6%
4.4 12
 
2.3%
6.3 11
 
2.1%
6.1 10
 
1.9%
5.7 8
 
1.5%
6.6 8
 
1.5%
5.4 8
 
1.5%
5.0 8
 
1.5%
4.7 7
 
1.3%
Other values (89) 223
42.1%
ValueCountFrequency (%)
0.0 221
41.7%
1.4 1
 
0.2%
1.8 1
 
0.2%
2.1 1
 
0.2%
2.3 2
 
0.4%
2.4 1
 
0.2%
2.8 2
 
0.4%
2.9 1
 
0.2%
3.0 2
 
0.4%
3.1 2
 
0.4%
ValueCountFrequency (%)
17.3 1
0.2%
14.6 1
0.2%
14.2 1
0.2%
12.9 1
0.2%
12.0 2
0.4%
11.9 1
0.2%
11.8 1
0.2%
11.7 1
0.2%
11.6 1
0.2%
11.5 1
0.2%

2번
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct103
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3273585
Minimum0
Maximum17.5
Zeros221
Zeros (%)41.7%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-12-12T22:53:34.188440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.35
Q312.9
95-th percentile15.355
Maximum17.5
Range17.5
Interquartile range (IQR)12.9

Descriptive statistics

Standard deviation6.20284
Coefficient of variation (CV)0.98032061
Kurtosis-1.6843108
Mean6.3273585
Median Absolute Deviation (MAD)4.35
Skewness0.2127754
Sum3353.5
Variance38.475224
MonotonicityNot monotonic
2023-12-12T22:53:34.348057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 221
41.7%
9.6 10
 
1.9%
9.8 9
 
1.7%
13.3 9
 
1.7%
12.5 9
 
1.7%
9.9 8
 
1.5%
15.5 8
 
1.5%
12.9 7
 
1.3%
13.5 7
 
1.3%
13.7 7
 
1.3%
Other values (93) 235
44.3%
ValueCountFrequency (%)
0.0 221
41.7%
1.6 1
 
0.2%
1.7 1
 
0.2%
2.4 3
 
0.6%
2.5 1
 
0.2%
2.7 2
 
0.4%
2.8 3
 
0.6%
2.9 2
 
0.4%
3.0 1
 
0.2%
3.1 1
 
0.2%
ValueCountFrequency (%)
17.5 1
 
0.2%
16.6 1
 
0.2%
16.4 1
 
0.2%
16.3 1
 
0.2%
16.2 2
0.4%
16.1 3
0.6%
16.0 2
0.4%
15.9 2
0.4%
15.8 2
0.4%
15.7 2
0.4%

3번
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct111
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0339623
Minimum0
Maximum18.8
Zeros221
Zeros (%)41.7%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-12-12T22:53:34.494558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.1
Q313.7
95-th percentile16.8
Maximum18.8
Range18.8
Interquartile range (IQR)13.7

Descriptive statistics

Standard deviation6.8211016
Coefficient of variation (CV)0.96973815
Kurtosis-1.6741336
Mean7.0339623
Median Absolute Deviation (MAD)5.1
Skewness0.19347266
Sum3728
Variance46.527427
MonotonicityNot monotonic
2023-12-12T22:53:34.657574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 221
41.7%
14.8 10
 
1.9%
12.5 9
 
1.7%
16.8 9
 
1.7%
13.3 8
 
1.5%
10.0 8
 
1.5%
13.1 7
 
1.3%
13.2 7
 
1.3%
14.7 7
 
1.3%
13.5 6
 
1.1%
Other values (101) 238
44.9%
ValueCountFrequency (%)
0.0 221
41.7%
1.6 1
 
0.2%
1.7 1
 
0.2%
2.3 1
 
0.2%
2.7 1
 
0.2%
3.1 1
 
0.2%
3.5 3
 
0.6%
3.7 5
 
0.9%
3.8 2
 
0.4%
3.9 2
 
0.4%
ValueCountFrequency (%)
18.8 1
 
0.2%
18.6 1
 
0.2%
18.5 1
 
0.2%
18.3 1
 
0.2%
18.2 1
 
0.2%
17.8 1
 
0.2%
17.7 3
0.6%
17.6 2
0.4%
17.4 4
0.8%
17.2 2
0.4%

4번
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct127
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.805283
Minimum-1.2
Maximum18
Zeros222
Zeros (%)41.9%
Negative3
Negative (%)0.6%
Memory size4.8 KiB
2023-12-12T22:53:34.849558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.2
5-th percentile0
Q10
median4.2
Q311.875
95-th percentile17
Maximum18
Range19.2
Interquartile range (IQR)11.875

Descriptive statistics

Standard deviation6.1531103
Coefficient of variation (CV)1.0599156
Kurtosis-1.2620844
Mean5.805283
Median Absolute Deviation (MAD)4.2
Skewness0.51420097
Sum3076.8
Variance37.860766
MonotonicityNot monotonic
2023-12-12T22:53:35.077799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 222
41.9%
4.4 8
 
1.5%
15.5 8
 
1.5%
11.2 8
 
1.5%
12.5 7
 
1.3%
5.1 7
 
1.3%
11.1 7
 
1.3%
12.4 6
 
1.1%
12.3 6
 
1.1%
5.6 6
 
1.1%
Other values (117) 245
46.2%
ValueCountFrequency (%)
-1.2 1
 
0.2%
-0.4 1
 
0.2%
-0.1 1
 
0.2%
0.0 222
41.9%
0.2 1
 
0.2%
0.3 1
 
0.2%
0.4 1
 
0.2%
1.5 1
 
0.2%
1.7 1
 
0.2%
1.8 1
 
0.2%
ValueCountFrequency (%)
18.0 1
 
0.2%
17.9 3
0.6%
17.8 2
 
0.4%
17.7 2
 
0.4%
17.6 2
 
0.4%
17.5 1
 
0.2%
17.4 3
0.6%
17.3 4
0.8%
17.2 4
0.8%
17.0 6
1.1%

5번
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
0
530 

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 (%)
0 530
100.0%

Length

2023-12-12T22:53:35.219640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:53:35.306948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 530
100.0%

6번
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
0
530 

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 (%)
0 530
100.0%

Length

2023-12-12T22:53:35.405732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:53:35.517025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 530
100.0%

Interactions

2023-12-12T22:53:30.581376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:23.685295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:24.640867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:25.520616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:26.397917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:27.376472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:28.358044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:29.274052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:30.707431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:23.798929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:24.741815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:25.609330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:26.489942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:27.515542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:28.456926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:29.393462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:30.831784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:23.917392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:24.839546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:25.704205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:26.591455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:27.640414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:28.566817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:29.522868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:30.941955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:24.040201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:24.926831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:25.799731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:26.764094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:27.761279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:28.666405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:29.636039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:31.063378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:24.152526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:25.064313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:25.901918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:26.883922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:27.884862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:28.779792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:29.760486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:31.170755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:24.278604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:25.206233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:26.026395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:27.021246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:27.998744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:28.904004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:29.892783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:31.301871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:24.398706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:25.319851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:26.171092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:27.128354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:28.109819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:29.031227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:30.041576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:31.418301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:24.521035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:25.429392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:26.278406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:27.246892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:28.242975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:29.150335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:30.141859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:53:35.596173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순서작업일자빈번호작물명저장량1번2번3번4번
순서1.0001.0000.0000.0000.0000.0000.0000.0000.000
작업일자1.0001.0000.0000.0000.0000.0000.0000.0000.000
빈번호0.0000.0001.0000.9450.5580.7340.8060.8310.849
작물명0.0000.0000.9451.0000.7760.8420.8590.8940.878
저장량0.0000.0000.5580.7761.0000.5880.6410.6240.599
1번0.0000.0000.7340.8420.5881.0000.8800.8930.842
2번0.0000.0000.8060.8590.6410.8801.0000.9500.905
3번0.0000.0000.8310.8940.6240.8930.9501.0000.934
4번0.0000.0000.8490.8780.5990.8420.9050.9341.000
2023-12-12T22:53:36.060694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순서작업일자빈번호저장량1번2번3번4번작물명
순서1.0000.9950.100-0.124-0.085-0.047-0.028-0.0380.000
작업일자0.9951.0000.000-0.112-0.113-0.077-0.060-0.0710.000
빈번호0.1000.0001.000-0.1310.2720.2950.3120.3230.704
저장량-0.124-0.112-0.1311.0000.2170.2060.2780.3000.617
1번-0.085-0.1130.2720.2171.0000.9570.8980.8810.502
2번-0.047-0.0770.2950.2060.9571.0000.9490.8800.520
3번-0.028-0.0600.3120.2780.8980.9491.0000.8880.570
4번-0.038-0.0710.3230.3000.8810.8800.8881.0000.550
작물명0.0000.0000.7040.6170.5020.5200.5700.5501.000

Missing values

2023-12-12T22:53:31.582226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:53:31.809578image/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

순서지원명부서명작업일자작업시간빈번호작물명저장량1번2번3번4번5번6번
026217전남지원함평2016010401기타<NA>0.00.00.00.000
126218전남지원함평2016010402기타<NA>0.00.00.00.000
226219전남지원함평2016010403기타<NA>0.00.00.00.000
326220전남지원함평2016010404기타<NA>0.00.00.00.000
426221전남지원함평2016010405기타<NA>0.00.00.00.000
526222전남지원함평2016010406기타<NA>0.00.00.00.000
626223전남지원함평2016010407기타<NA>0.00.00.00.000
726224전남지원함평2016010408기타<NA>0.00.00.00.000
826225전남지원함평2016010409기타<NA>0.00.00.00.000
926226전남지원함평20160104010기타<NA>0.00.00.00.000
순서지원명부서명작업일자작업시간빈번호작물명저장량1번2번3번4번5번6번
52026737전남지원함평20160115044새누리벼504.411.317.43.200
52126738전남지원함평20160115045기타04.74.65.75.900
52226739전남지원함평20160115046기타05.05.54.44.600
52326740전남지원함평20160115047기타03.34.45.05.400
52426741전남지원함평20160115048기타<NA>0.00.00.00.000
52526742전남지원함평20160115049기타<NA>0.00.00.00.000
52626743전남지원함평20160115050기타<NA>0.00.00.00.000
52726744전남지원함평20160115051기타04.43.33.53.700
52826745전남지원함평20160115052기타02.83.23.53.700
52926746전남지원함평20160115053기타00.00.00.00.000