Overview

Dataset statistics

Number of variables14
Number of observations60
Missing cells471
Missing cells (%)56.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 KiB
Average record size in memory127.2 B

Variable types

Text1
Numeric13

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 9 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 10 other fieldsHigh correlation
2019년산 is highly overall correlated with 2010년산 and 9 other fieldsHigh correlation
2020년산 is highly overall correlated with 2017년산 and 4 other fieldsHigh correlation
2021년산 is highly overall correlated with 2014년산 and 6 other fieldsHigh correlation
2022년산 is highly overall correlated with 2010년산 and 4 other fieldsHigh correlation
2010년산 has 35 (58.3%) missing valuesMissing
2011년산 has 36 (60.0%) missing valuesMissing
2012년산 has 38 (63.3%) missing valuesMissing
2013년산 has 37 (61.7%) missing valuesMissing
2014년산 has 36 (60.0%) missing valuesMissing
2015년산 has 38 (63.3%) missing valuesMissing
2016년산 has 40 (66.7%) missing valuesMissing
2017년산 has 36 (60.0%) missing valuesMissing
2018년산 has 38 (63.3%) missing valuesMissing
2019년산 has 36 (60.0%) missing valuesMissing
2020년산 has 33 (55.0%) missing valuesMissing
2021년산 has 33 (55.0%) missing valuesMissing
2022년산 has 35 (58.3%) missing valuesMissing
품종 has unique valuesUnique

Reproduction

Analysis started2023-12-11 03:07:22.244621
Analysis finished2023-12-11 03:07:42.613795
Duration20.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품종
Text

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-11T12:07:42.794716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.2666667
Min length2

Characters and Unicode

Total characters196
Distinct characters72
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

Unique60 ?
Unique (%)100.0%

Sample

1st row고시히카리
2nd row남평벼
3rd row대보벼
4th row대안벼
5th row동안벼
ValueCountFrequency (%)
고시히카리 1
 
1.7%
남평벼 1
 
1.7%
추청벼 1
 
1.7%
오륜벼 1
 
1.7%
온누리 1
 
1.7%
운광벼 1
 
1.7%
일미벼 1
 
1.7%
일품벼 1
 
1.7%
조명1호 1
 
1.7%
조영 1
 
1.7%
Other values (50) 50
83.3%
2023-12-11T12:07:43.160971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
20.4%
10
 
5.1%
9
 
4.6%
8
 
4.1%
6
 
3.1%
5
 
2.6%
5
 
2.6%
5
 
2.6%
5
 
2.6%
5
 
2.6%
Other values (62) 98
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 192
98.0%
Decimal Number 4
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
40
20.8%
10
 
5.2%
9
 
4.7%
8
 
4.2%
6
 
3.1%
5
 
2.6%
5
 
2.6%
5
 
2.6%
5
 
2.6%
5
 
2.6%
Other values (60) 94
49.0%
Decimal Number
ValueCountFrequency (%)
1 3
75.0%
2 1
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 192
98.0%
Common 4
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
40
20.8%
10
 
5.2%
9
 
4.7%
8
 
4.2%
6
 
3.1%
5
 
2.6%
5
 
2.6%
5
 
2.6%
5
 
2.6%
5
 
2.6%
Other values (60) 94
49.0%
Common
ValueCountFrequency (%)
1 3
75.0%
2 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 192
98.0%
ASCII 4
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
40
20.8%
10
 
5.2%
9
 
4.7%
8
 
4.2%
6
 
3.1%
5
 
2.6%
5
 
2.6%
5
 
2.6%
5
 
2.6%
5
 
2.6%
Other values (60) 94
49.0%
ASCII
ValueCountFrequency (%)
1 3
75.0%
2 1
 
25.0%

2010년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)100.0%
Missing35
Missing (%)58.3%
Infinite0
Infinite (%)0.0%
Mean1096184.2
Minimum33000
Maximum4456180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-11T12:07:43.273310image/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
2023-12-11T12:07:43.382524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
924283 1
 
1.7%
1265070 1
 
1.7%
225409 1
 
1.7%
33000 1
 
1.7%
2139950 1
 
1.7%
141640 1
 
1.7%
878318 1
 
1.7%
4456180 1
 
1.7%
898250 1
 
1.7%
1686220 1
 
1.7%
Other values (15) 15
25.0%
(Missing) 35
58.3%
ValueCountFrequency (%)
33000 1
1.7%
97318 1
1.7%
130400 1
1.7%
135000 1
1.7%
141640 1
1.7%
225409 1
1.7%
235630 1
1.7%
312000 1
1.7%
602600 1
1.7%
644481 1
1.7%
ValueCountFrequency (%)
4456180 1
1.7%
2931350 1
1.7%
2139950 1
1.7%
1967120 1
1.7%
1686220 1
1.7%
1580000 1
1.7%
1379132 1
1.7%
1292360 1
1.7%
1265070 1
1.7%
1224280 1
1.7%

2011년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing36
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean963769.75
Minimum23400
Maximum3913000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-11T12:07:43.507952image/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
2023-12-11T12:07:43.634011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1186614 1
 
1.7%
2652983 1
 
1.7%
170653 1
 
1.7%
30000 1
 
1.7%
741559 1
 
1.7%
54000 1
 
1.7%
23400 1
 
1.7%
687890 1
 
1.7%
3913000 1
 
1.7%
1460690 1
 
1.7%
Other values (14) 14
 
23.3%
(Missing) 36
60.0%
ValueCountFrequency (%)
23400 1
1.7%
30000 1
1.7%
54000 1
1.7%
97440 1
1.7%
110000 1
1.7%
137000 1
1.7%
170653 1
1.7%
218100 1
1.7%
248660 1
1.7%
559489 1
1.7%
ValueCountFrequency (%)
3913000 1
1.7%
2652983 1
1.7%
2345520 1
1.7%
1750600 1
1.7%
1595880 1
1.7%
1474829 1
1.7%
1460690 1
1.7%
1218480 1
1.7%
1186614 1
1.7%
1178567 1
1.7%

2012년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing38
Missing (%)63.3%
Infinite0
Infinite (%)0.0%
Mean1129036.8
Minimum30000
Maximum3993060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-11T12:07:43.743798image/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
2023-12-11T12:07:43.849332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1450835 1
 
1.7%
1804463 1
 
1.7%
75887 1
 
1.7%
30000 1
 
1.7%
55982 1
 
1.7%
268100 1
 
1.7%
893200 1
 
1.7%
3993060 1
 
1.7%
1940900 1
 
1.7%
1470428 1
 
1.7%
Other values (12) 12
 
20.0%
(Missing) 38
63.3%
ValueCountFrequency (%)
30000 1
1.7%
55982 1
1.7%
60417 1
1.7%
75887 1
1.7%
162525 1
1.7%
200000 1
1.7%
268100 1
1.7%
284000 1
1.7%
503564 1
1.7%
580000 1
1.7%
ValueCountFrequency (%)
3993060 1
1.7%
3894635 1
1.7%
2710200 1
1.7%
1940900 1
1.7%
1804463 1
1.7%
1470428 1
1.7%
1467550 1
1.7%
1450835 1
1.7%
1262333 1
1.7%
1063840 1
1.7%

2013년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing37
Missing (%)61.7%
Infinite0
Infinite (%)0.0%
Mean1146196.7
Minimum30675
Maximum5604105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-11T12:07:43.953589image/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
2023-12-11T12:07:44.051238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
533756 1
 
1.7%
1982000 1
 
1.7%
30675 1
 
1.7%
66015 1
 
1.7%
380900 1
 
1.7%
354403 1
 
1.7%
3663129 1
 
1.7%
2112100 1
 
1.7%
1518065 1
 
1.7%
1608440 1
 
1.7%
Other values (13) 13
 
21.7%
(Missing) 37
61.7%
ValueCountFrequency (%)
30675 1
1.7%
66015 1
1.7%
79990 1
1.7%
93900 1
1.7%
98178 1
1.7%
238571 1
1.7%
324000 1
1.7%
354403 1
1.7%
380900 1
1.7%
458164 1
1.7%
ValueCountFrequency (%)
5604105 1
1.7%
3663129 1
1.7%
2344000 1
1.7%
2112100 1
1.7%
2054000 1
1.7%
1982000 1
1.7%
1608440 1
1.7%
1518065 1
1.7%
955600 1
1.7%
754000 1
1.7%

2014년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing36
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean1120458.3
Minimum43770
Maximum6429600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-11T12:07:44.157673image/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
2023-12-11T12:07:44.262031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
168800 1
 
1.7%
2020000 1
 
1.7%
65000 1
 
1.7%
475000 1
 
1.7%
224600 1
 
1.7%
3598500 1
 
1.7%
43770 1
 
1.7%
2113000 1
 
1.7%
629740 1
 
1.7%
1786795 1
 
1.7%
Other values (14) 14
 
23.3%
(Missing) 36
60.0%
ValueCountFrequency (%)
43770 1
1.7%
65000 1
1.7%
70000 1
1.7%
140000 1
1.7%
168800 1
1.7%
210000 1
1.7%
211100 1
1.7%
224600 1
1.7%
254000 1
1.7%
385000 1
1.7%
ValueCountFrequency (%)
6429600 1
1.7%
3598500 1
1.7%
2432205 1
1.7%
2113000 1
1.7%
2060000 1
1.7%
2020000 1
1.7%
1786795 1
1.7%
990000 1
1.7%
792400 1
1.7%
744000 1
1.7%

2015년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)95.5%
Missing38
Missing (%)63.3%
Infinite0
Infinite (%)0.0%
Mean1255704.5
Minimum73000
Maximum7189430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-11T12:07:44.440559image/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
2023-12-11T12:07:44.573770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
340000 2
 
3.3%
460806 1
 
1.7%
878000 1
 
1.7%
1032587 1
 
1.7%
90000 1
 
1.7%
3280800 1
 
1.7%
2132000 1
 
1.7%
228233 1
 
1.7%
1790000 1
 
1.7%
1064194 1
 
1.7%
Other values (11) 11
 
18.3%
(Missing) 38
63.3%
ValueCountFrequency (%)
73000 1
1.7%
90000 1
1.7%
111000 1
1.7%
145000 1
1.7%
188000 1
1.7%
198000 1
1.7%
228233 1
1.7%
308000 1
1.7%
340000 2
3.3%
460806 1
1.7%
ValueCountFrequency (%)
7189430 1
1.7%
3280800 1
1.7%
2456000 1
1.7%
2207950 1
1.7%
2132000 1
1.7%
1802000 1
1.7%
1790000 1
1.7%
1310500 1
1.7%
1064194 1
1.7%
1032587 1
1.7%

2016년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)100.0%
Missing40
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean1099109.9
Minimum12840
Maximum5502782
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-11T12:07:44.673019image/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
2023-12-11T12:07:44.765048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
921379 1
 
1.7%
226427 1
 
1.7%
3511005 1
 
1.7%
284184 1
 
1.7%
2708780 1
 
1.7%
1686285 1
 
1.7%
88000 1
 
1.7%
1099300 1
 
1.7%
292512 1
 
1.7%
421813 1
 
1.7%
Other values (10) 10
 
16.7%
(Missing) 40
66.7%
ValueCountFrequency (%)
12840 1
1.7%
75114 1
1.7%
88000 1
1.7%
117364 1
1.7%
124440 1
1.7%
226427 1
1.7%
239809 1
1.7%
284184 1
1.7%
291337 1
1.7%
292512 1
1.7%
ValueCountFrequency (%)
5502782 1
1.7%
3511005 1
1.7%
2846453 1
1.7%
2708780 1
1.7%
1686285 1
1.7%
1113399 1
1.7%
1099300 1
1.7%
921379 1
1.7%
421813 1
1.7%
418974 1
1.7%

2017년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing36
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean1132917.8
Minimum31449
Maximum4466047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-11T12:07:44.873089image/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
2023-12-11T12:07:45.013847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1067057 1
 
1.7%
39000 1
 
1.7%
33000 1
 
1.7%
254854 1
 
1.7%
56000 1
 
1.7%
500410 1
 
1.7%
3261328 1
 
1.7%
133400 1
 
1.7%
2431000 1
 
1.7%
1197200 1
 
1.7%
Other values (14) 14
 
23.3%
(Missing) 36
60.0%
ValueCountFrequency (%)
31449 1
1.7%
33000 1
1.7%
39000 1
1.7%
40560 1
1.7%
56000 1
1.7%
82910 1
1.7%
87771 1
1.7%
110000 1
1.7%
133400 1
1.7%
254854 1
1.7%
ValueCountFrequency (%)
4466047 1
1.7%
4102728 1
1.7%
3673920 1
1.7%
3261328 1
1.7%
2575122 1
1.7%
2431000 1
1.7%
1197200 1
1.7%
1067057 1
1.7%
921600 1
1.7%
756972 1
1.7%

2018년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing38
Missing (%)63.3%
Infinite0
Infinite (%)0.0%
Mean1066713.3
Minimum65686
Maximum4554646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-11T12:07:45.137149image/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
2023-12-11T12:07:45.243897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1433715 1
 
1.7%
611701 1
 
1.7%
136320 1
 
1.7%
550000 1
 
1.7%
2607783 1
 
1.7%
218715 1
 
1.7%
2063870 1
 
1.7%
413000 1
 
1.7%
80716 1
 
1.7%
498425 1
 
1.7%
Other values (12) 12
 
20.0%
(Missing) 38
63.3%
ValueCountFrequency (%)
65686 1
1.7%
80716 1
1.7%
109977 1
1.7%
124500 1
1.7%
136320 1
1.7%
200000 1
1.7%
218715 1
1.7%
241115 1
1.7%
413000 1
1.7%
498425 1
1.7%
ValueCountFrequency (%)
4554646 1
1.7%
4149000 1
1.7%
2607783 1
1.7%
2571966 1
1.7%
2063870 1
1.7%
1433715 1
1.7%
873034 1
1.7%
842500 1
1.7%
618000 1
1.7%
611701 1
1.7%

2019년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing36
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean925528.17
Minimum33296
Maximum4263202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-11T12:07:45.350382image/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
2023-12-11T12:07:45.480170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
220000 1
 
1.7%
33296 1
 
1.7%
111000 1
 
1.7%
261000 1
 
1.7%
458422 1
 
1.7%
170220 1
 
1.7%
660000 1
 
1.7%
2512557 1
 
1.7%
121000 1
 
1.7%
2476780 1
 
1.7%
Other values (14) 14
 
23.3%
(Missing) 36
60.0%
ValueCountFrequency (%)
33296 1
1.7%
67129 1
1.7%
72000 1
1.7%
111000 1
1.7%
121000 1
1.7%
170220 1
1.7%
219000 1
1.7%
220000 1
1.7%
231920 1
1.7%
261000 1
1.7%
ValueCountFrequency (%)
4263202 1
1.7%
3821280 1
1.7%
2512557 1
1.7%
2476780 1
1.7%
1476566 1
1.7%
1118840 1
1.7%
1101000 1
1.7%
1036000 1
1.7%
660000 1
1.7%
647360 1
1.7%

2020년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)100.0%
Missing33
Missing (%)55.0%
Infinite0
Infinite (%)0.0%
Mean814501.11
Minimum33204
Maximum3721313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-11T12:07:45.613063image/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
2023-12-11T12:07:45.757129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
361927 1
 
1.7%
85188 1
 
1.7%
293732 1
 
1.7%
528644 1
 
1.7%
1486436 1
 
1.7%
2161851 1
 
1.7%
243385 1
 
1.7%
96234 1
 
1.7%
72390 1
 
1.7%
189000 1
 
1.7%
Other values (17) 17
28.3%
(Missing) 33
55.0%
ValueCountFrequency (%)
33204 1
1.7%
60161 1
1.7%
72390 1
1.7%
85188 1
1.7%
96234 1
1.7%
103996 1
1.7%
110000 1
1.7%
110810 1
1.7%
189000 1
1.7%
243385 1
1.7%
ValueCountFrequency (%)
3721313 1
1.7%
3555000 1
1.7%
2161851 1
1.7%
1932790 1
1.7%
1822147 1
1.7%
1486436 1
1.7%
1107000 1
1.7%
1098000 1
1.7%
866862 1
1.7%
721320 1
1.7%

2021년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)100.0%
Missing33
Missing (%)55.0%
Infinite0
Infinite (%)0.0%
Mean875228.59
Minimum28567
Maximum3660000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-11T12:07:45.895283image/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
2023-12-11T12:07:46.037635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
28567 1
 
1.7%
139000 1
 
1.7%
357690 1
 
1.7%
523117 1
 
1.7%
67710 1
 
1.7%
1177000 1
 
1.7%
1778025 1
 
1.7%
352017 1
 
1.7%
145000 1
 
1.7%
3189000 1
 
1.7%
Other values (17) 17
28.3%
(Missing) 33
55.0%
ValueCountFrequency (%)
28567 1
1.7%
34531 1
1.7%
35558 1
1.7%
61000 1
1.7%
67710 1
1.7%
99429 1
1.7%
109000 1
1.7%
110000 1
1.7%
135000 1
1.7%
139000 1
1.7%
ValueCountFrequency (%)
3660000 1
1.7%
3506842 1
1.7%
3189000 1
1.7%
2850000 1
1.7%
1778025 1
1.7%
1188000 1
1.7%
1177000 1
1.7%
1081348 1
1.7%
974000 1
1.7%
930282 1
1.7%

2022년산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)96.0%
Missing35
Missing (%)58.3%
Infinite0
Infinite (%)0.0%
Mean860274.8
Minimum42405
Maximum3330100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-11T12:07:46.169532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42405
5-th percentile73437.6
Q1110000
median539363
Q31064190
95-th percentile2876089.2
Maximum3330100
Range3287695
Interquartile range (IQR)954190

Descriptive statistics

Standard deviation992600.8
Coefficient of variation (CV)1.1538183
Kurtosis1.0035128
Mean860274.8
Median Absolute Deviation (MAD)430363
Skewness1.4503927
Sum21506870
Variance9.8525635 × 1011
MonotonicityNot monotonic
2023-12-11T12:07:46.285111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
110000 2
 
3.3%
1146249 1
 
1.7%
296200 1
 
1.7%
539363 1
 
1.7%
1396588 1
 
1.7%
1064190 1
 
1.7%
1031088 1
 
1.7%
77824 1
 
1.7%
2517742 1
 
1.7%
72341 1
 
1.7%
Other values (14) 14
 
23.3%
(Missing) 35
58.3%
ValueCountFrequency (%)
42405 1
1.7%
72341 1
1.7%
77824 1
1.7%
94910 1
1.7%
109000 1
1.7%
110000 2
3.3%
174000 1
1.7%
217328 1
1.7%
265867 1
1.7%
276367 1
1.7%
ValueCountFrequency (%)
3330100 1
1.7%
2886764 1
1.7%
2833390 1
1.7%
2517742 1
1.7%
1396588 1
1.7%
1146249 1
1.7%
1064190 1
1.7%
1031088 1
1.7%
925700 1
1.7%
863559 1
1.7%

Interactions

2023-12-11T12:07:40.428162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:23.085967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:24.410171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:25.787403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:27.270698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:28.762496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.580158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.126677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.403029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.781554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:36.400900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:37.625658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:38.980348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:40.504780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:23.178597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:24.528369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:25.906751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:27.377576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:28.853511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.688326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.214355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.496224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.872324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:36.482543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:37.722600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:39.063345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:40.611184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:23.294760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:24.638512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:26.021991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:27.512591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:28.958676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.827221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.318850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.594641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.969997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:36.584576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:37.833697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:39.243376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:40.702365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:23.405610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:24.759246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:26.128709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:27.626836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:29.050873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.966231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.413332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.688178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:35.102352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:36.674677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:37.969484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:39.371036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:40.794296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:23.500068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:24.871783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:26.239086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:27.736096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:29.450233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.092187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.496983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.783604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:35.209464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:36.757484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:38.073809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:39.470327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:40.939898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:23.592737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:24.997077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:26.343172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:27.851559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:29.596242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.196040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.596770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.892784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:35.314559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:36.847478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:38.182142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:39.565624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:41.055586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:23.703165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:25.112900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:26.487621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:28.006086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:29.736772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.327587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.691368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.993233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:35.456626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:36.945039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:38.292710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:39.666444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:41.135876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:23.799855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:25.195916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:26.606205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:28.145042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:29.846819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.458407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.783494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.105754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:35.560390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:37.031113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:38.401453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:39.783776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:41.231273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:23.895412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:25.272988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:26.707497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:28.233283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:29.956621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.563266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.878925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.212426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:35.643448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:37.114223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:38.490009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:39.870391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:41.336193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:23.975315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:25.352537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:26.822228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:28.320055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.078927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.669313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.956672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.342990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:35.721097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:37.217623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:38.586315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:39.948292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:41.428383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:24.072015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:25.450731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:26.935906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:28.433617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.205158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.794560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.042966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.436310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:35.806611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:37.306694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:38.673134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:40.052297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:41.536447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:24.187336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:25.552930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:27.049370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:28.546647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.341353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.916841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.159306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.545815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:35.929853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:37.406030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:38.773691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:40.199264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:41.635785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:24.303098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:25.677687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:27.168945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:28.658343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.468225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.031853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.294602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.664935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:36.053268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:37.535800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:38.886225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:40.319981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:07:46.379202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품종2010년산2011년산2012년산2013년산2014년산2015년산2016년산2017년산2018년산2019년산2020년산2021년산2022년산
품종1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2010년산1.0001.0000.7760.9130.9240.5290.6260.8180.9010.5720.6820.2660.6470.655
2011년산1.0000.7761.0000.8430.8090.9050.8930.8580.7440.7510.0000.5320.6890.787
2012년산1.0000.9130.8431.0000.9410.6530.5740.8930.8650.5710.4850.2310.8840.478
2013년산1.0000.9240.8090.9411.0000.9560.8980.9670.9520.6180.7560.6350.7960.573
2014년산1.0000.5290.9050.6530.9561.0000.9720.9400.8070.6960.4680.6300.9040.770
2015년산1.0000.6260.8930.5740.8980.9721.0000.9230.8440.8490.6730.7820.9320.920
2016년산1.0000.8180.8580.8930.9670.9400.9231.0000.8250.7410.6560.3060.8220.685
2017년산1.0000.9010.7440.8650.9520.8070.8440.8251.0000.7470.9240.8250.8440.748
2018년산1.0000.5720.7510.5710.6180.6960.8490.7410.7471.0000.7350.7550.8390.793
2019년산1.0000.6820.0000.4850.7560.4680.6730.6560.9240.7351.0000.9780.9110.870
2020년산1.0000.2660.5320.2310.6350.6300.7820.3060.8250.7550.9781.0000.9140.910
2021년산1.0000.6470.6890.8840.7960.9040.9320.8220.8440.8390.9110.9141.0000.978
2022년산1.0000.6550.7870.4780.5730.7700.9200.6850.7480.7930.8700.9100.9781.000
2023-12-11T12:07:46.559589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2010년산2011년산2012년산2013년산2014년산2015년산2016년산2017년산2018년산2019년산2020년산2021년산2022년산
2010년산1.0000.8480.9140.8650.7760.6130.7090.8600.6610.6500.4360.4640.600
2011년산0.8481.0000.9650.9280.8740.7230.7910.9160.6480.5310.3000.3910.367
2012년산0.9140.9651.0000.9650.9070.7840.8640.9210.6210.5000.1980.3450.236
2013년산0.8650.9280.9651.0000.9770.8760.8210.9000.4770.4350.1870.3000.224
2014년산0.7760.8740.9070.9771.0000.8690.8160.8700.5140.4970.4430.5240.287
2015년산0.6130.7230.7840.8760.8691.0000.7350.8090.6950.5220.4440.4920.371
2016년산0.7090.7910.8640.8210.8160.7351.0000.8790.6970.5670.4180.6350.376
2017년산0.8600.9160.9210.9000.8700.8090.8791.0000.8660.7430.5850.6350.465
2018년산0.6610.6480.6210.4770.5140.6950.6970.8661.0000.8560.7890.7960.703
2019년산0.6500.5310.5000.4350.4970.5220.5670.7430.8561.0000.9620.8790.912
2020년산0.4360.3000.1980.1870.4430.4440.4180.5850.7890.9621.0000.8910.843
2021년산0.4640.3910.3450.3000.5240.4920.6350.6350.7960.8790.8911.0000.919
2022년산0.6000.3670.2360.2240.2870.3710.3760.4650.7030.9120.8430.9191.000

Missing values

2023-12-11T12:07:41.834076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:07:42.238983image/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.
2023-12-11T12:07:42.472565image/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

품종2010년산2011년산2012년산2013년산2014년산2015년산2016년산2017년산2018년산2019년산2020년산2021년산2022년산
0고시히카리312000137000200000238571254000340000421813435000503023481240298490344056276367
1남평벼924283559489503564533756547490<NA><NA><NA><NA><NA><NA><NA><NA>
2대보벼<NA><NA><NA><NA>21110013105001284040560<NA><NA><NA><NA><NA>
3대안벼6444816057605800005044405000004608067511435800024111536586436192728567<NA>
4동안벼<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
5동진1호137913211785671262333604093<NA><NA><NA><NA><NA><NA><NA><NA><NA>
6동진2호13500097440<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
7동진벼<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
8동진찰벼6026006693606668907540007440008780001113399921600842500110100010980001188000925700
9말그미97318<NA><NA>98178<NA><NA><NA><NA><NA><NA><NA><NA><NA>
품종2010년산2011년산2012년산2013년산2014년산2015년산2016년산2017년산2018년산2019년산2020년산2021년산2022년산
50해담쌀<NA><NA><NA><NA><NA><NA><NA><NA><NA>458422528644523117539363
51해품벼<NA><NA><NA><NA><NA><NA><NA>254854611701261000293732357690296200
52현품벼<NA><NA><NA><NA><NA><NA><NA><NA><NA>11100085188139000<NA>
53호반벼<NA>23400<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54호평벼14164054000559826601565000<NA><NA>33000<NA><NA><NA><NA><NA>
55호품벼2139950741559<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
56화선찰벼33000300003000030675<NA><NA><NA>39000<NA>33296<NA><NA><NA>
57화성벼<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
58화영벼22540917065375887<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
59황금누리126507026529831804463198200020200001032587<NA><NA><NA><NA><NA><NA><NA>