Overview

Dataset statistics

Number of variables13
Number of observations57
Missing cells400
Missing cells (%)54.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory118.3 B

Variable types

Text1
Numeric12

Dataset

Description국립종자원에서 생산한 벼 정부 보급종 품종별 생산량(kg)
Author국립종자원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220725000000002290

Alerts

2010년산 is highly overall correlated with 2011년산 and 8 other fieldsHigh correlation
2011년산 is highly overall correlated with 2010년산 and 8 other fieldsHigh correlation
2012년산 is highly overall correlated with 2010년산 and 8 other fieldsHigh correlation
2013년산 is highly overall correlated with 2010년산 and 6 other fieldsHigh correlation
2014년산 is highly overall correlated with 2010년산 and 8 other fieldsHigh correlation
2015년산 is highly overall correlated with 2010년산 and 8 other fieldsHigh correlation
2016년산 is highly overall correlated with 2010년산 and 9 other fieldsHigh correlation
2017년산 is highly overall correlated with 2010년산 and 10 other fieldsHigh correlation
2018년산 is highly overall correlated with 2010년산 and 9 other fieldsHigh correlation
2019년산 is highly overall correlated with 2010년산 and 8 other fieldsHigh correlation
2020년산 is highly overall correlated with 2017년산 and 3 other fieldsHigh correlation
2021년산 is highly overall correlated with 2014년산 and 5 other fieldsHigh correlation
2010년산 has 32 (56.1%) missing valuesMissing
2011년산 has 33 (57.9%) missing valuesMissing
2012년산 has 35 (61.4%) missing valuesMissing
2013년산 has 34 (59.6%) missing valuesMissing
2014년산 has 33 (57.9%) missing valuesMissing
2015년산 has 35 (61.4%) missing valuesMissing
2016년산 has 37 (64.9%) missing valuesMissing
2017년산 has 33 (57.9%) missing valuesMissing
2018년산 has 35 (61.4%) missing valuesMissing
2019년산 has 33 (57.9%) missing valuesMissing
2020년산 has 30 (52.6%) missing valuesMissing
2021년산 has 30 (52.6%) missing valuesMissing
품종 has unique valuesUnique

Reproduction

Analysis started2023-12-11 03:06:58.677622
Analysis finished2023-12-11 03:07:16.688381
Duration18.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품종
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
2023-12-11T12:07:16.835910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.2982456
Min length2

Characters and Unicode

Total characters188
Distinct characters70
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

Unique57 ?
Unique (%)100.0%

Sample

1st row고시히카리
2nd row남평벼
3rd row대보벼
4th row대안벼
5th row동안벼
ValueCountFrequency (%)
고시히카리 1
 
1.8%
오대벼 1
 
1.8%
온누리 1
 
1.8%
운광벼 1
 
1.8%
일미벼 1
 
1.8%
일품벼 1
 
1.8%
조명1호 1
 
1.8%
조평벼 1
 
1.8%
주남벼 1
 
1.8%
진수미 1
 
1.8%
Other values (47) 47
82.5%
2023-12-11T12:07:17.167393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
벼 39
20.7%
호 9
 
4.8%
미 9
 
4.8%
진 8
 
4.3%
동 6
 
3.2%
찰 5
 
2.7%
새 5
 
2.7%
품 5
 
2.7%
화 4
 
2.1%
청 4
 
2.1%
Other values (60) 94
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 184
97.9%
Decimal Number 4
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
벼 39
21.2%
호 9
 
4.9%
미 9
 
4.9%
진 8
 
4.3%
동 6
 
3.3%
찰 5
 
2.7%
새 5
 
2.7%
품 5
 
2.7%
화 4
 
2.2%
청 4
 
2.2%
Other values (58) 90
48.9%
Decimal Number
ValueCountFrequency (%)
1 3
75.0%
2 1
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 184
97.9%
Common 4
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
벼 39
21.2%
호 9
 
4.9%
미 9
 
4.9%
진 8
 
4.3%
동 6
 
3.3%
찰 5
 
2.7%
새 5
 
2.7%
품 5
 
2.7%
화 4
 
2.2%
청 4
 
2.2%
Other values (58) 90
48.9%
Common
ValueCountFrequency (%)
1 3
75.0%
2 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 184
97.9%
ASCII 4
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
벼 39
21.2%
호 9
 
4.9%
미 9
 
4.9%
진 8
 
4.3%
동 6
 
3.3%
찰 5
 
2.7%
새 5
 
2.7%
품 5
 
2.7%
화 4
 
2.2%
청 4
 
2.2%
Other values (58) 90
48.9%
ASCII
ValueCountFrequency (%)
1 3
75.0%
2 1
 
25.0%

2010년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)100.0%
Missing32
Missing (%)56.1%
Infinite0
Infinite (%)0.0%
Mean1096184.2
Minimum33000
Maximum4456180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T12:07:17.290614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33000
5-th percentile103934.4
Q1235630
median924283
Q31379132
95-th percentile2773070
Maximum4456180
Range4423180
Interquartile range (IQR)1143502

Descriptive statistics

Standard deviation1017056.7
Coefficient of variation (CV)0.92781552
Kurtosis3.9472901
Mean1096184.2
Median Absolute Deviation (MAD)655717
Skewness1.695912
Sum27404604
Variance1.0344043 × 1012
MonotonicityNot monotonic
2023-12-11T12:07:17.395474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
924283 1
 
1.8%
1265070 1
 
1.8%
225409 1
 
1.8%
33000 1
 
1.8%
2139950 1
 
1.8%
141640 1
 
1.8%
878318 1
 
1.8%
4456180 1
 
1.8%
898250 1
 
1.8%
1686220 1
 
1.8%
Other values (15) 15
26.3%
(Missing) 32
56.1%
ValueCountFrequency (%)
33000 1
1.8%
97318 1
1.8%
130400 1
1.8%
135000 1
1.8%
141640 1
1.8%
225409 1
1.8%
235630 1
1.8%
312000 1
1.8%
602600 1
1.8%
644481 1
1.8%
ValueCountFrequency (%)
4456180 1
1.8%
2931350 1
1.8%
2139950 1
1.8%
1967120 1
1.8%
1686220 1
1.8%
1580000 1
1.8%
1379132 1
1.8%
1292360 1
1.8%
1265070 1
1.8%
1224280 1
1.8%

2011년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing33
Missing (%)57.9%
Infinite0
Infinite (%)0.0%
Mean963769.75
Minimum23400
Maximum3913000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T12:07:17.501674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23400
5-th percentile33600
Q1162239.75
median678625
Q31464224.8
95-th percentile2606863.5
Maximum3913000
Range3889600
Interquartile range (IQR)1301985

Descriptive statistics

Standard deviation980133.17
Coefficient of variation (CV)1.0169786
Kurtosis2.2512417
Mean963769.75
Median Absolute Deviation (MAD)555125
Skewness1.433578
Sum23130474
Variance9.6066103 × 1011
MonotonicityNot monotonic
2023-12-11T12:07:17.628962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1186614 1
 
1.8%
2652983 1
 
1.8%
170653 1
 
1.8%
30000 1
 
1.8%
741559 1
 
1.8%
54000 1
 
1.8%
23400 1
 
1.8%
687890 1
 
1.8%
3913000 1
 
1.8%
1460690 1
 
1.8%
Other values (14) 14
24.6%
(Missing) 33
57.9%
ValueCountFrequency (%)
23400 1
1.8%
30000 1
1.8%
54000 1
1.8%
97440 1
1.8%
110000 1
1.8%
137000 1
1.8%
170653 1
1.8%
218100 1
1.8%
248660 1
1.8%
559489 1
1.8%
ValueCountFrequency (%)
3913000 1
1.8%
2652983 1
1.8%
2345520 1
1.8%
1750600 1
1.8%
1595880 1
1.8%
1474829 1
1.8%
1460690 1
1.8%
1218480 1
1.8%
1186614 1
1.8%
1178567 1
1.8%

2012년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing35
Missing (%)61.4%
Infinite0
Infinite (%)0.0%
Mean1129036.8
Minimum30000
Maximum3993060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T12:07:17.749126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30000
5-th percentile56203.75
Q1217025
median780045
Q31469708.5
95-th percentile3835413.2
Maximum3993060
Range3963060
Interquartile range (IQR)1252683.5

Descriptive statistics

Standard deviation1167987.4
Coefficient of variation (CV)1.034499
Kurtosis1.3226276
Mean1129036.8
Median Absolute Deviation (MAD)679147.5
Skewness1.3523181
Sum24838809
Variance1.3641945 × 1012
MonotonicityNot monotonic
2023-12-11T12:07:17.901068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1450835 1
 
1.8%
1804463 1
 
1.8%
75887 1
 
1.8%
30000 1
 
1.8%
55982 1
 
1.8%
268100 1
 
1.8%
893200 1
 
1.8%
3993060 1
 
1.8%
1940900 1
 
1.8%
1470428 1
 
1.8%
Other values (12) 12
 
21.1%
(Missing) 35
61.4%
ValueCountFrequency (%)
30000 1
1.8%
55982 1
1.8%
60417 1
1.8%
75887 1
1.8%
162525 1
1.8%
200000 1
1.8%
268100 1
1.8%
284000 1
1.8%
503564 1
1.8%
580000 1
1.8%
ValueCountFrequency (%)
3993060 1
1.8%
3894635 1
1.8%
2710200 1
1.8%
1940900 1
1.8%
1804463 1
1.8%
1470428 1
1.8%
1467550 1
1.8%
1450835 1
1.8%
1262333 1
1.8%
1063840 1
1.8%

2013년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing34
Missing (%)59.6%
Infinite0
Infinite (%)0.0%
Mean1146196.7
Minimum30675
Maximum5604105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T12:07:18.017572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30675
5-th percentile67412.5
Q1281285.5
median533756
Q31795220
95-th percentile3531216.1
Maximum5604105
Range5573430
Interquartile range (IQR)1513934.5

Descriptive statistics

Standard deviation1359489
Coefficient of variation (CV)1.186087
Kurtosis4.3845372
Mean1146196.7
Median Absolute Deviation (MAD)439856
Skewness1.964834
Sum26362524
Variance1.8482104 × 1012
MonotonicityNot monotonic
2023-12-11T12:07:18.137803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
533756 1
 
1.8%
1982000 1
 
1.8%
30675 1
 
1.8%
66015 1
 
1.8%
380900 1
 
1.8%
354403 1
 
1.8%
3663129 1
 
1.8%
2112100 1
 
1.8%
1518065 1
 
1.8%
1608440 1
 
1.8%
Other values (13) 13
 
22.8%
(Missing) 34
59.6%
ValueCountFrequency (%)
30675 1
1.8%
66015 1
1.8%
79990 1
1.8%
93900 1
1.8%
98178 1
1.8%
238571 1
1.8%
324000 1
1.8%
354403 1
1.8%
380900 1
1.8%
458164 1
1.8%
ValueCountFrequency (%)
5604105 1
1.8%
3663129 1
1.8%
2344000 1
1.8%
2112100 1
1.8%
2054000 1
1.8%
1982000 1
1.8%
1608440 1
1.8%
1518065 1
1.8%
955600 1
1.8%
754000 1
1.8%

2014년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing33
Missing (%)57.9%
Infinite0
Infinite (%)0.0%
Mean1120458.3
Minimum43770
Maximum6429600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T12:07:18.246062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43770
5-th percentile65750
Q1210825
median523745
Q31845096.2
95-th percentile3423555.7
Maximum6429600
Range6385830
Interquartile range (IQR)1634271.2

Descriptive statistics

Standard deviation1471470.7
Coefficient of variation (CV)1.3132757
Kurtosis6.7628583
Mean1120458.3
Median Absolute Deviation (MAD)369345
Skewness2.3876775
Sum26891000
Variance2.1652261 × 1012
MonotonicityNot monotonic
2023-12-11T12:07:18.358386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
168800 1
 
1.8%
2020000 1
 
1.8%
65000 1
 
1.8%
475000 1
 
1.8%
224600 1
 
1.8%
3598500 1
 
1.8%
43770 1
 
1.8%
2113000 1
 
1.8%
629740 1
 
1.8%
1786795 1
 
1.8%
Other values (14) 14
24.6%
(Missing) 33
57.9%
ValueCountFrequency (%)
43770 1
1.8%
65000 1
1.8%
70000 1
1.8%
140000 1
1.8%
168800 1
1.8%
210000 1
1.8%
211100 1
1.8%
224600 1
1.8%
254000 1
1.8%
385000 1
1.8%
ValueCountFrequency (%)
6429600 1
1.8%
3598500 1
1.8%
2432205 1
1.8%
2113000 1
1.8%
2060000 1
1.8%
2020000 1
1.8%
1786795 1
1.8%
990000 1
1.8%
792400 1
1.8%
744000 1
1.8%

2015년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)95.5%
Missing35
Missing (%)61.4%
Infinite0
Infinite (%)0.0%
Mean1255704.5
Minimum73000
Maximum7189430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T12:07:18.467891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum73000
5-th percentile91050
Q1205558.25
median669403
Q31799000
95-th percentile3239560
Maximum7189430
Range7116430
Interquartile range (IQR)1593441.8

Descriptive statistics

Standard deviation1619298.1
Coefficient of variation (CV)1.2895534
Kurtosis8.263061
Mean1255704.5
Median Absolute Deviation (MAD)541403
Skewness2.5691136
Sum27625500
Variance2.6221263 × 1012
MonotonicityNot monotonic
2023-12-11T12:07:18.603648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
340000 2
 
3.5%
460806 1
 
1.8%
878000 1
 
1.8%
1032587 1
 
1.8%
90000 1
 
1.8%
3280800 1
 
1.8%
2132000 1
 
1.8%
228233 1
 
1.8%
1790000 1
 
1.8%
1064194 1
 
1.8%
Other values (11) 11
 
19.3%
(Missing) 35
61.4%
ValueCountFrequency (%)
73000 1
1.8%
90000 1
1.8%
111000 1
1.8%
145000 1
1.8%
188000 1
1.8%
198000 1
1.8%
228233 1
1.8%
308000 1
1.8%
340000 2
3.5%
460806 1
1.8%
ValueCountFrequency (%)
7189430 1
1.8%
3280800 1
1.8%
2456000 1
1.8%
2207950 1
1.8%
2132000 1
1.8%
1802000 1
1.8%
1790000 1
1.8%
1310500 1
1.8%
1064194 1
1.8%
1032587 1
1.8%

2016년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)100.0%
Missing37
Missing (%)64.9%
Infinite0
Infinite (%)0.0%
Mean1099109.9
Minimum12840
Maximum5502782
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T12:07:18.717427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12840
5-th percentile72000.3
Q1200930.25
median355743
Q31256620.5
95-th percentile3610593.9
Maximum5502782
Range5489942
Interquartile range (IQR)1055690.2

Descriptive statistics

Standard deviation1465095.1
Coefficient of variation (CV)1.3329833
Kurtosis3.2695297
Mean1099109.9
Median Absolute Deviation (MAD)274186
Skewness1.8660847
Sum21982197
Variance2.1465036 × 1012
MonotonicityNot monotonic
2023-12-11T12:07:18.836282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
921379 1
 
1.8%
226427 1
 
1.8%
3511005 1
 
1.8%
284184 1
 
1.8%
2708780 1
 
1.8%
1686285 1
 
1.8%
88000 1
 
1.8%
1099300 1
 
1.8%
292512 1
 
1.8%
421813 1
 
1.8%
Other values (10) 10
 
17.5%
(Missing) 37
64.9%
ValueCountFrequency (%)
12840 1
1.8%
75114 1
1.8%
88000 1
1.8%
117364 1
1.8%
124440 1
1.8%
226427 1
1.8%
239809 1
1.8%
284184 1
1.8%
291337 1
1.8%
292512 1
1.8%
ValueCountFrequency (%)
5502782 1
1.8%
3511005 1
1.8%
2846453 1
1.8%
2708780 1
1.8%
1686285 1
1.8%
1113399 1
1.8%
1099300 1
1.8%
921379 1
1.8%
421813 1
1.8%
418974 1
1.8%

2017년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing33
Missing (%)57.9%
Infinite0
Infinite (%)0.0%
Mean1132917.8
Minimum31449
Maximum4466047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T12:07:18.981592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31449
5-th percentile33900
Q186555.75
median467705
Q31505650
95-th percentile4038406.8
Maximum4466047
Range4434598
Interquartile range (IQR)1419094.2

Descriptive statistics

Standard deviation1439489.4
Coefficient of variation (CV)1.2706035
Kurtosis0.29578974
Mean1132917.8
Median Absolute Deviation (MAD)427925
Skewness1.2973982
Sum27190028
Variance2.0721297 × 1012
MonotonicityNot monotonic
2023-12-11T12:07:19.146017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1067057 1
 
1.8%
39000 1
 
1.8%
33000 1
 
1.8%
254854 1
 
1.8%
56000 1
 
1.8%
500410 1
 
1.8%
3261328 1
 
1.8%
133400 1
 
1.8%
2431000 1
 
1.8%
1197200 1
 
1.8%
Other values (14) 14
24.6%
(Missing) 33
57.9%
ValueCountFrequency (%)
31449 1
1.8%
33000 1
1.8%
39000 1
1.8%
40560 1
1.8%
56000 1
1.8%
82910 1
1.8%
87771 1
1.8%
110000 1
1.8%
133400 1
1.8%
254854 1
1.8%
ValueCountFrequency (%)
4466047 1
1.8%
4102728 1
1.8%
3673920 1
1.8%
3261328 1
1.8%
2575122 1
1.8%
2431000 1
1.8%
1197200 1
1.8%
1067057 1
1.8%
921600 1
1.8%
756972 1
1.8%

2018년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing35
Missing (%)61.4%
Infinite0
Infinite (%)0.0%
Mean1066713.3
Minimum65686
Maximum4554646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T12:07:19.262581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum65686
5-th percentile82179.05
Q1204678.75
median526511.5
Q31293544.8
95-th percentile4071939.1
Maximum4554646
Range4488960
Interquartile range (IQR)1088866

Descriptive statistics

Standard deviation1310433.4
Coefficient of variation (CV)1.2284776
Kurtosis2.0329548
Mean1066713.3
Median Absolute Deviation (MAD)368357
Skewness1.6885612
Sum23467692
Variance1.7172356 × 1012
MonotonicityNot monotonic
2023-12-11T12:07:19.382672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1433715 1
 
1.8%
611701 1
 
1.8%
136320 1
 
1.8%
550000 1
 
1.8%
2607783 1
 
1.8%
218715 1
 
1.8%
2063870 1
 
1.8%
413000 1
 
1.8%
80716 1
 
1.8%
498425 1
 
1.8%
Other values (12) 12
 
21.1%
(Missing) 35
61.4%
ValueCountFrequency (%)
65686 1
1.8%
80716 1
1.8%
109977 1
1.8%
124500 1
1.8%
136320 1
1.8%
200000 1
1.8%
218715 1
1.8%
241115 1
1.8%
413000 1
1.8%
498425 1
1.8%
ValueCountFrequency (%)
4554646 1
1.8%
4149000 1
1.8%
2607783 1
1.8%
2571966 1
1.8%
2063870 1
1.8%
1433715 1
1.8%
873034 1
1.8%
842500 1
1.8%
618000 1
1.8%
611701 1
1.8%

2019년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing33
Missing (%)57.9%
Infinite0
Infinite (%)0.0%
Mean925528.17
Minimum33296
Maximum4263202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T12:07:19.500725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33296
5-th percentile67859.65
Q1206805
median412143
Q31105460
95-th percentile3624971.5
Maximum4263202
Range4229906
Interquartile range (IQR)898655

Descriptive statistics

Standard deviation1182870.3
Coefficient of variation (CV)1.278049
Kurtosis2.6455672
Mean925528.17
Median Absolute Deviation (MAD)296143
Skewness1.8318941
Sum22212676
Variance1.3991822 × 1012
MonotonicityNot monotonic
2023-12-11T12:07:19.664480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
220000 1
 
1.8%
33296 1
 
1.8%
111000 1
 
1.8%
261000 1
 
1.8%
458422 1
 
1.8%
170220 1
 
1.8%
660000 1
 
1.8%
2512557 1
 
1.8%
121000 1
 
1.8%
2476780 1
 
1.8%
Other values (14) 14
24.6%
(Missing) 33
57.9%
ValueCountFrequency (%)
33296 1
1.8%
67129 1
1.8%
72000 1
1.8%
111000 1
1.8%
121000 1
1.8%
170220 1
1.8%
219000 1
1.8%
220000 1
1.8%
231920 1
1.8%
261000 1
1.8%
ValueCountFrequency (%)
4263202 1
1.8%
3821280 1
1.8%
2512557 1
1.8%
2476780 1
1.8%
1476566 1
1.8%
1118840 1
1.8%
1101000 1
1.8%
1036000 1
1.8%
660000 1
1.8%
647360 1
1.8%

2020년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)100.0%
Missing30
Missing (%)52.6%
Infinite0
Infinite (%)0.0%
Mean814501.11
Minimum33204
Maximum3721313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T12:07:19.775726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33204
5-th percentile63829.7
Q1110405
median298490
Q31102500
95-th percentile3137055.3
Maximum3721313
Range3688109
Interquartile range (IQR)992095

Descriptive statistics

Standard deviation1023967.2
Coefficient of variation (CV)1.257171
Kurtosis2.6533499
Mean814501.11
Median Absolute Deviation (MAD)226100
Skewness1.7797747
Sum21991530
Variance1.0485088 × 1012
MonotonicityNot monotonic
2023-12-11T12:07:19.924808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
361927 1
 
1.8%
85188 1
 
1.8%
293732 1
 
1.8%
528644 1
 
1.8%
1486436 1
 
1.8%
2161851 1
 
1.8%
243385 1
 
1.8%
96234 1
 
1.8%
72390 1
 
1.8%
189000 1
 
1.8%
Other values (17) 17
29.8%
(Missing) 30
52.6%
ValueCountFrequency (%)
33204 1
1.8%
60161 1
1.8%
72390 1
1.8%
85188 1
1.8%
96234 1
1.8%
103996 1
1.8%
110000 1
1.8%
110810 1
1.8%
189000 1
1.8%
243385 1
1.8%
ValueCountFrequency (%)
3721313 1
1.8%
3555000 1
1.8%
2161851 1
1.8%
1932790 1
1.8%
1822147 1
1.8%
1486436 1
1.8%
1107000 1
1.8%
1098000 1
1.8%
866862 1
1.8%
721320 1
1.8%

2021년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)100.0%
Missing30
Missing (%)52.6%
Infinite0
Infinite (%)0.0%
Mean875228.59
Minimum28567
Maximum3660000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T12:07:20.078083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28567
5-th percentile34839.1
Q1109500
median352017
Q31129174
95-th percentile3411489.4
Maximum3660000
Range3631433
Interquartile range (IQR)1019674

Descriptive statistics

Standard deviation1132434.7
Coefficient of variation (CV)1.2938731
Kurtosis1.2587679
Mean875228.59
Median Absolute Deviation (MAD)291017
Skewness1.5581382
Sum23631172
Variance1.2824084 × 1012
MonotonicityNot monotonic
2023-12-11T12:07:20.233186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
28567 1
 
1.8%
139000 1
 
1.8%
357690 1
 
1.8%
523117 1
 
1.8%
67710 1
 
1.8%
1177000 1
 
1.8%
1778025 1
 
1.8%
352017 1
 
1.8%
145000 1
 
1.8%
3189000 1
 
1.8%
Other values (17) 17
29.8%
(Missing) 30
52.6%
ValueCountFrequency (%)
28567 1
1.8%
34531 1
1.8%
35558 1
1.8%
61000 1
1.8%
67710 1
1.8%
99429 1
1.8%
109000 1
1.8%
110000 1
1.8%
135000 1
1.8%
139000 1
1.8%
ValueCountFrequency (%)
3660000 1
1.8%
3506842 1
1.8%
3189000 1
1.8%
2850000 1
1.8%
1778025 1
1.8%
1188000 1
1.8%
1177000 1
1.8%
1081348 1
1.8%
974000 1
1.8%
930282 1
1.8%

Interactions

2023-12-11T12:07:14.720966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:59.193754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:00.517935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:03.208773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:04.814799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:06.222362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:07.449148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:08.791262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:10.306578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:11.445239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:12.434216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:13.491353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:14.828382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:59.319353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:00.638759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:03.326593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:04.904353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:06.318620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:07.527675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:08.926737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:10.381853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:11.518757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:12.513221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:13.564149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:14.933079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:59.451419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:00.804389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:03.462013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:05.018903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:06.446867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:07.618774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:09.051530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:10.480740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:11.641071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:12.604740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:13.696721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:15.048648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:59.574453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:00.919898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:03.628429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:05.126147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:06.548101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:07.746077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:09.161376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:10.566115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:11.730459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:12.718588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:13.819555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:15.137584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:59.677610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:01.126502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:03.774197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:05.268942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:06.640569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:07.851560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:09.268759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:10.650995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:11.797828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:12.804290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:13.916840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:15.237237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:59.762019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:01.421896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:03.944550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:05.403934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:06.734601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:07.958843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:09.382769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:10.758414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:11.870480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:12.892805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:14.039692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:15.340883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:59.848071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:01.676747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:04.078151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:05.549471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:06.827169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:08.079088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:09.503777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:10.857479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:11.961072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:12.983937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:14.139208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:15.435725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:06:59.953582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:01.969404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:04.191745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:05.647993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:06.923887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:08.197913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:09.886083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:10.961184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:12.047464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:13.070041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:14.253222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:15.768154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:00.080347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:02.259993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:04.317322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:05.745737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:07.009493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:08.301013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:09.989501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:11.065264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:12.119815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:13.152433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:14.345755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:15.874890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:00.191423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:02.443049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:04.458722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:05.840487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:07.114210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:08.420367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:10.067473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:11.150059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:12.195899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:13.223114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:14.439040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:16.000137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:00.290725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:02.553606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:04.583373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:05.940316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:07.209530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:08.533371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:10.138496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:11.249474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:12.278341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:13.299962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:14.530339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:16.118369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:00.407879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:03.061861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:04.707361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:06.070172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:07.341775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:08.657593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:10.224691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:11.354433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:12.356674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:13.400430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:14.628780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:07:20.334799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품종2010년산2011년산2012년산2013년산2014년산2015년산2016년산2017년산2018년산2019년산2020년산2021년산
품종1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2010년산1.0001.0000.7760.9130.9240.5290.6260.8180.9010.5720.6820.2660.647
2011년산1.0000.7761.0000.8430.8090.9050.8930.8580.7440.7510.0000.5320.689
2012년산1.0000.9130.8431.0000.9410.6530.5740.8930.8650.5710.4850.2310.884
2013년산1.0000.9240.8090.9411.0000.9560.8980.9670.9520.6180.7560.6350.796
2014년산1.0000.5290.9050.6530.9561.0000.9720.9400.8070.6960.4680.6300.904
2015년산1.0000.6260.8930.5740.8980.9721.0000.9230.8440.8490.6730.7820.932
2016년산1.0000.8180.8580.8930.9670.9400.9231.0000.8250.7410.6560.3060.822
2017년산1.0000.9010.7440.8650.9520.8070.8440.8251.0000.7470.9240.8250.844
2018년산1.0000.5720.7510.5710.6180.6960.8490.7410.7471.0000.7350.7550.839
2019년산1.0000.6820.0000.4850.7560.4680.6730.6560.9240.7351.0000.9780.911
2020년산1.0000.2660.5320.2310.6350.6300.7820.3060.8250.7550.9781.0000.914
2021년산1.0000.6470.6890.8840.7960.9040.9320.8220.8440.8390.9110.9141.000
2023-12-11T12:07:20.521594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2010년산2011년산2012년산2013년산2014년산2015년산2016년산2017년산2018년산2019년산2020년산2021년산
2010년산1.0000.8480.9140.8650.7760.6130.7090.8600.6610.6500.4360.464
2011년산0.8481.0000.9650.9280.8740.7230.7910.9160.6480.5310.3000.391
2012년산0.9140.9651.0000.9650.9070.7840.8640.9210.6210.5000.1980.345
2013년산0.8650.9280.9651.0000.9770.8760.8210.9000.4770.4350.1870.300
2014년산0.7760.8740.9070.9771.0000.8690.8160.8700.5140.4970.4430.524
2015년산0.6130.7230.7840.8760.8691.0000.7350.8090.6950.5220.4440.492
2016년산0.7090.7910.8640.8210.8160.7351.0000.8790.6970.5670.4180.635
2017년산0.8600.9160.9210.9000.8700.8090.8791.0000.8660.7430.5850.635
2018년산0.6610.6480.6210.4770.5140.6950.6970.8661.0000.8560.7890.796
2019년산0.6500.5310.5000.4350.4970.5220.5670.7430.8561.0000.9620.879
2020년산0.4360.3000.1980.1870.4430.4440.4180.5850.7890.9621.0000.891
2021년산0.4640.3910.3450.3000.5240.4920.6350.6350.7960.8790.8911.000

Missing values

2023-12-11T12:07:16.292948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:07:16.441840image/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.
2023-12-11T12:07:16.577400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

품종2010년산2011년산2012년산2013년산2014년산2015년산2016년산2017년산2018년산2019년산2020년산2021년산
0고시히카리312000137000200000238571254000340000421813435000503023481240298490344056
1남평벼924283559489503564533756547490<NA><NA><NA><NA><NA><NA><NA>
2대보벼<NA><NA><NA><NA>21110013105001284040560<NA><NA><NA><NA>
3대안벼6444816057605800005044405000004608067511435800024111536586436192728567
4동안벼<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
5동진1호137913211785671262333604093<NA><NA><NA><NA><NA><NA><NA><NA>
6동진2호13500097440<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
7동진벼<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
8동진찰벼6026006693606668907540007440008780001113399921600842500110100010980001188000
9말그미97318<NA><NA>98178<NA><NA><NA><NA><NA><NA><NA><NA>
품종2010년산2011년산2012년산2013년산2014년산2015년산2016년산2017년산2018년산2019년산2020년산2021년산
47해담쌀<NA><NA><NA><NA><NA><NA><NA><NA><NA>458422528644523117
48해품벼<NA><NA><NA><NA><NA><NA><NA>254854611701261000293732357690
49현품벼<NA><NA><NA><NA><NA><NA><NA><NA><NA>11100085188139000
50호반벼<NA>23400<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
51호평벼14164054000559826601565000<NA><NA>33000<NA><NA><NA><NA>
52호품벼2139950741559<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
53화선찰벼33000300003000030675<NA><NA><NA>39000<NA>33296<NA><NA>
54화성벼<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
55화영벼22540917065375887<NA><NA><NA><NA><NA><NA><NA><NA><NA>
56황금누리126507026529831804463198200020200001032587<NA><NA><NA><NA><NA><NA>