Overview

Dataset statistics

Number of variables10
Number of observations450
Missing cells318
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.8 KiB
Average record size in memory88.3 B

Variable types

Numeric8
Categorical2

Dataset

Description제주특별자치도 서귀포시 감귤생산정보에 관한 데이터로 년도별,행정구역별,품종별,감귤생산정보,극조생, 조생, 보통, 조기출하, 월동, 시설 만감류, 노지 만감류,농가수, 면적, 생산량등의 정보를 제공합니다.(*무게는 톤, 면적은 ha)
URLhttps://www.data.go.kr/data/15048184/fileData.do

Alerts

노지온주(극조생) is highly overall correlated with 노지온주(조생) and 6 other fieldsHigh correlation
노지온주(조생) is highly overall correlated with 노지온주(극조생) and 5 other fieldsHigh correlation
노지온주(보통) is highly overall correlated with 노지온주(극조생) and 5 other fieldsHigh correlation
하우스감귤(조기출하) is highly overall correlated with 노지온주(극조생) and 5 other fieldsHigh correlation
비가림(월동)감귤 is highly overall correlated with 노지온주(극조생) and 5 other fieldsHigh correlation
만감류(시설) is highly overall correlated with 노지온주(극조생) and 5 other fieldsHigh correlation
만감류(노지) is highly overall correlated with 노지온주(극조생) and 5 other fieldsHigh correlation
구분 is highly overall correlated with 노지온주(극조생)High correlation
노지온주(극조생) has 5 (1.1%) missing valuesMissing
노지온주(보통) has 120 (26.7%) missing valuesMissing
하우스감귤(조기출하) has 62 (13.8%) missing valuesMissing
비가림(월동)감귤 has 42 (9.3%) missing valuesMissing
만감류(노지) has 84 (18.7%) missing valuesMissing
하우스감귤(조기출하) has 9 (2.0%) zerosZeros
비가림(월동)감귤 has 7 (1.6%) zerosZeros
만감류(노지) has 5 (1.1%) zerosZeros

Reproduction

Analysis started2023-12-12 14:54:59.703136
Analysis finished2023-12-12 14:55:07.736900
Duration8.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct10
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.5
Minimum2012
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-12T23:55:07.791344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12014
median2016.5
Q32019
95-th percentile2021
Maximum2021
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8754781
Coefficient of variation (CV)0.0014259747
Kurtosis-1.224502
Mean2016.5
Median Absolute Deviation (MAD)2.5
Skewness0
Sum907425
Variance8.2683742
MonotonicityIncreasing
2023-12-12T23:55:07.919984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2012 45
10.0%
2013 45
10.0%
2014 45
10.0%
2015 45
10.0%
2016 45
10.0%
2017 45
10.0%
2018 45
10.0%
2019 45
10.0%
2020 45
10.0%
2021 45
10.0%
ValueCountFrequency (%)
2012 45
10.0%
2013 45
10.0%
2014 45
10.0%
2015 45
10.0%
2016 45
10.0%
2017 45
10.0%
2018 45
10.0%
2019 45
10.0%
2020 45
10.0%
2021 45
10.0%
ValueCountFrequency (%)
2021 45
10.0%
2020 45
10.0%
2019 45
10.0%
2018 45
10.0%
2017 45
10.0%
2016 45
10.0%
2015 45
10.0%
2014 45
10.0%
2013 45
10.0%
2012 45
10.0%

읍면동
Categorical

Distinct16
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
대정읍
 
30
남원읍
 
30
성산읍
 
30
안덕면
 
30
표선면
 
30
Other values (11)
300 

Length

Max length4
Median length3
Mean length3.0066667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대정읍
2nd row대정읍
3rd row대정읍
4th row남원읍
5th row남원읍

Common Values

ValueCountFrequency (%)
대정읍 30
 
6.7%
남원읍 30
 
6.7%
성산읍 30
 
6.7%
안덕면 30
 
6.7%
표선면 30
 
6.7%
송산동 30
 
6.7%
천지동 30
 
6.7%
영천동 30
 
6.7%
동홍동 30
 
6.7%
서홍동 30
 
6.7%
Other values (6) 150
33.3%

Length

2023-12-12T23:55:08.042133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대정읍 30
 
6.7%
남원읍 30
 
6.7%
성산읍 30
 
6.7%
안덕면 30
 
6.7%
표선면 30
 
6.7%
송산동 30
 
6.7%
천지동 30
 
6.7%
영천동 30
 
6.7%
동홍동 30
 
6.7%
서홍동 30
 
6.7%
Other values (5) 150
33.3%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
농가수
150 
면적
150 
생산량
150 

Length

Max length3
Median length3
Mean length2.6666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row농가수
2nd row면적
3rd row생산량
4th row농가수
5th row면적

Common Values

ValueCountFrequency (%)
농가수 150
33.3%
면적 150
33.3%
생산량 150
33.3%

Length

2023-12-12T23:55:08.173505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:55:08.292421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농가수 150
33.3%
면적 150
33.3%
생산량 150
33.3%

노지온주(극조생)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct327
Distinct (%)73.5%
Missing5
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean647.56
Minimum0.2
Maximum9163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-12T23:55:08.420502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile9
Q139
median100
Q3620
95-th percentile2895.8
Maximum9163
Range9162.8
Interquartile range (IQR)581

Descriptive statistics

Standard deviation1267.3024
Coefficient of variation (CV)1.9570425
Kurtosis13.869135
Mean647.56
Median Absolute Deviation (MAD)79
Skewness3.3313081
Sum288164.2
Variance1606055.4
MonotonicityNot monotonic
2023-12-12T23:55:08.866601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.0 13
 
2.9%
39.0 9
 
2.0%
36.0 7
 
1.6%
10.0 6
 
1.3%
50.0 5
 
1.1%
52.0 5
 
1.1%
3.0 5
 
1.1%
73.0 5
 
1.1%
29.0 4
 
0.9%
158.0 3
 
0.7%
Other values (317) 383
85.1%
(Missing) 5
 
1.1%
ValueCountFrequency (%)
0.2 1
 
0.2%
1.0 2
 
0.4%
2.0 3
0.7%
2.2 1
 
0.2%
2.5 1
 
0.2%
2.9 1
 
0.2%
3.0 5
1.1%
3.4 1
 
0.2%
3.6 1
 
0.2%
4.0 1
 
0.2%
ValueCountFrequency (%)
9163.0 1
0.2%
8460.6 1
0.2%
7990.0 1
0.2%
7287.0 1
0.2%
7110.0 1
0.2%
5382.7 1
0.2%
5291.0 1
0.2%
5021.0 1
0.2%
4688.0 1
0.2%
4492.0 1
0.2%

노지온주(조생)
Real number (ℝ)

HIGH CORRELATION 

Distinct421
Distinct (%)94.0%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean7175.4681
Minimum0.4
Maximum141747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-12T23:55:08.995673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile80.55
Q1391.7
median762.7
Q36702
95-th percentile27818.5
Maximum141747
Range141746.6
Interquartile range (IQR)6310.3

Descriptive statistics

Standard deviation17045.219
Coefficient of variation (CV)2.3754854
Kurtosis28.796471
Mean7175.4681
Median Absolute Deviation (MAD)546.5
Skewness4.9321928
Sum3214609.7
Variance2.9053951 × 108
MonotonicityNot monotonic
2023-12-12T23:55:09.154995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.0 4
 
0.9%
318.0 4
 
0.9%
431.0 3
 
0.7%
8.0 3
 
0.7%
246.7 2
 
0.4%
488.0 2
 
0.4%
1101.0 2
 
0.4%
127.0 2
 
0.4%
435.4 2
 
0.4%
397.0 2
 
0.4%
Other values (411) 422
93.8%
ValueCountFrequency (%)
0.4 1
0.2%
1.0 1
0.2%
1.3 1
0.2%
2.0 1
0.2%
4.0 1
0.2%
5.7 1
0.2%
5.8 1
0.2%
6.0 1
0.2%
7.0 1
0.2%
7.4 1
0.2%
ValueCountFrequency (%)
141747.0 1
0.2%
126021.0 1
0.2%
123087.0 1
0.2%
122287.0 1
0.2%
100909.0 1
0.2%
98652.0 1
0.2%
90340.0 1
0.2%
87269.9 1
0.2%
86682.0 1
0.2%
72321.0 1
0.2%

노지온주(보통)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct166
Distinct (%)50.3%
Missing120
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean252.73758
Minimum0
Maximum10320
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-12T23:55:09.322781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16.325
median19
Q3146.75
95-th percentile830.65
Maximum10320
Range10320
Interquartile range (IQR)140.425

Descriptive statistics

Standard deviation1018.589
Coefficient of variation (CV)4.030224
Kurtosis55.137954
Mean252.73758
Median Absolute Deviation (MAD)16
Skewness7.1812232
Sum83403.4
Variance1037523.6
MonotonicityNot monotonic
2023-12-12T23:55:09.541934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 15
 
3.3%
6.0 13
 
2.9%
9.0 13
 
2.9%
11.0 12
 
2.7%
4.0 11
 
2.4%
20.0 10
 
2.2%
3.0 10
 
2.2%
10.0 9
 
2.0%
19.0 7
 
1.6%
13.0 6
 
1.3%
Other values (156) 224
49.8%
(Missing) 120
26.7%
ValueCountFrequency (%)
0.0 2
 
0.4%
0.2 1
 
0.2%
0.5 3
 
0.7%
1.0 15
3.3%
1.2 1
 
0.2%
1.3 1
 
0.2%
1.5 1
 
0.2%
1.7 1
 
0.2%
2.0 6
 
1.3%
2.4 1
 
0.2%
ValueCountFrequency (%)
10320.0 1
0.2%
8118.0 1
0.2%
7005.0 1
0.2%
6589.0 1
0.2%
6555.0 1
0.2%
5657.0 1
0.2%
1552.0 1
0.2%
1139.0 1
0.2%
1109.0 1
0.2%
1104.0 1
0.2%

하우스감귤(조기출하)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct210
Distinct (%)54.1%
Missing62
Missing (%)13.8%
Infinite0
Infinite (%)0.0%
Mean594.42552
Minimum0
Maximum18371
Zeros9
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-12T23:55:09.715698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16.675
median24
Q3221.675
95-th percentile1730.25
Maximum18371
Range18371
Interquartile range (IQR)215

Descriptive statistics

Standard deviation2416.6296
Coefficient of variation (CV)4.0654876
Kurtosis34.77843
Mean594.42552
Median Absolute Deviation (MAD)22
Skewness5.8906983
Sum230637.1
Variance5840098.4
MonotonicityNot monotonic
2023-12-12T23:55:09.893166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 14
 
3.1%
1.0 13
 
2.9%
8.0 12
 
2.7%
2.0 9
 
2.0%
13.0 9
 
2.0%
0.0 9
 
2.0%
6.0 8
 
1.8%
4.0 8
 
1.8%
14.0 7
 
1.6%
7.0 6
 
1.3%
Other values (200) 293
65.1%
(Missing) 62
 
13.8%
ValueCountFrequency (%)
0.0 9
2.0%
0.3 3
 
0.7%
0.5 2
 
0.4%
0.7 1
 
0.2%
0.9 1
 
0.2%
1.0 13
2.9%
1.1 2
 
0.4%
1.2 4
 
0.9%
1.5 1
 
0.2%
1.6 1
 
0.2%
ValueCountFrequency (%)
18371.0 1
0.2%
17642.0 1
0.2%
16600.0 1
0.2%
16179.0 1
0.2%
15411.0 1
0.2%
15260.0 1
0.2%
13999.0 1
0.2%
13211.0 1
0.2%
12269.0 1
0.2%
10951.0 1
0.2%

비가림(월동)감귤
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct284
Distinct (%)69.6%
Missing42
Missing (%)9.3%
Infinite0
Infinite (%)0.0%
Mean655.49412
Minimum0
Maximum16259
Zeros7
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-12T23:55:10.069343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q117
median58.4
Q3381.775
95-th percentile2107.75
Maximum16259
Range16259
Interquartile range (IQR)364.775

Descriptive statistics

Standard deviation2216.1988
Coefficient of variation (CV)3.3809591
Kurtosis33.952945
Mean655.49412
Median Absolute Deviation (MAD)52.5
Skewness5.7385819
Sum267441.6
Variance4911537
MonotonicityNot monotonic
2023-12-12T23:55:10.241461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0 8
 
1.8%
10.0 7
 
1.6%
6.0 7
 
1.6%
18.0 7
 
1.6%
0.0 7
 
1.6%
2.0 6
 
1.3%
13.0 5
 
1.1%
32.0 5
 
1.1%
12.0 5
 
1.1%
26.0 5
 
1.1%
Other values (274) 346
76.9%
(Missing) 42
 
9.3%
ValueCountFrequency (%)
0.0 7
1.6%
0.2 2
 
0.4%
0.6 1
 
0.2%
1.0 4
0.9%
1.1 1
 
0.2%
1.3 1
 
0.2%
1.4 1
 
0.2%
2.0 6
1.3%
2.2 2
 
0.4%
2.9 1
 
0.2%
ValueCountFrequency (%)
16259.0 1
0.2%
15877.0 1
0.2%
15538.0 1
0.2%
15285.0 1
0.2%
15283.0 1
0.2%
15139.0 1
0.2%
12797.0 1
0.2%
12677.0 1
0.2%
10788.0 1
0.2%
9668.0 1
0.2%

만감류(시설)
Real number (ℝ)

HIGH CORRELATION 

Distinct384
Distinct (%)85.9%
Missing3
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1470.8443
Minimum1.8
Maximum35044.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-12T23:55:10.409468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile17.06
Q171.35
median217
Q31156
95-th percentile6020.6
Maximum35044.6
Range35042.8
Interquartile range (IQR)1084.65

Descriptive statistics

Standard deviation3813.8114
Coefficient of variation (CV)2.5929402
Kurtosis30.288993
Mean1470.8443
Median Absolute Deviation (MAD)183
Skewness5.1094111
Sum657467.4
Variance14545157
MonotonicityNot monotonic
2023-12-12T23:55:10.578491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.0 5
 
1.1%
121.0 4
 
0.9%
54.0 4
 
0.9%
92.0 3
 
0.7%
100.0 3
 
0.7%
51.0 3
 
0.7%
147.0 3
 
0.7%
151.0 3
 
0.7%
29.0 3
 
0.7%
8.0 3
 
0.7%
Other values (374) 413
91.8%
ValueCountFrequency (%)
1.8 1
 
0.2%
2.0 2
0.4%
2.1 1
 
0.2%
2.7 2
0.4%
3.0 2
0.4%
6.0 1
 
0.2%
6.3 1
 
0.2%
7.0 1
 
0.2%
8.0 3
0.7%
9.0 1
 
0.2%
ValueCountFrequency (%)
35044.6 1
0.2%
27380.0 1
0.2%
25566.0 1
0.2%
22338.0 1
0.2%
21553.0 1
0.2%
21311.0 1
0.2%
21150.9 1
0.2%
20601.0 1
0.2%
20060.0 1
0.2%
18346.0 1
0.2%

만감류(노지)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct188
Distinct (%)51.4%
Missing84
Missing (%)18.7%
Infinite0
Infinite (%)0.0%
Mean74.821311
Minimum0
Maximum939
Zeros5
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-12T23:55:10.730552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.325
Q19.2
median22
Q378
95-th percentile349
Maximum939
Range939
Interquartile range (IQR)68.8

Descriptive statistics

Standard deviation135.4526
Coefficient of variation (CV)1.8103479
Kurtosis13.837499
Mean74.821311
Median Absolute Deviation (MAD)18
Skewness3.4687231
Sum27384.6
Variance18347.407
MonotonicityNot monotonic
2023-12-12T23:55:10.885508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.0 16
 
3.6%
5.0 14
 
3.1%
12.0 10
 
2.2%
14.0 10
 
2.2%
1.0 8
 
1.8%
4.0 8
 
1.8%
6.0 8
 
1.8%
18.0 6
 
1.3%
54.0 6
 
1.3%
2.0 5
 
1.1%
Other values (178) 275
61.1%
(Missing) 84
 
18.7%
ValueCountFrequency (%)
0.0 5
1.1%
0.5 1
 
0.2%
0.6 1
 
0.2%
0.8 2
 
0.4%
1.0 8
1.8%
1.2 1
 
0.2%
1.3 1
 
0.2%
1.4 2
 
0.4%
2.0 5
1.1%
2.2 1
 
0.2%
ValueCountFrequency (%)
939.0 1
0.2%
847.0 1
0.2%
808.0 1
0.2%
802.0 1
0.2%
594.0 1
0.2%
578.0 1
0.2%
570.3 1
0.2%
568.0 1
0.2%
541.0 1
0.2%
525.0 1
0.2%

Interactions

2023-12-12T23:55:06.522340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:00.249489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:01.103694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:01.998995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:03.207324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:03.921490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:04.729787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:05.501896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:06.636030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:00.374123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:01.194301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:02.117399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:03.301193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:04.013380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:04.834555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:05.593538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:06.729068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:00.496301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:01.282650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:02.212525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:03.386922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:04.111227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:04.912696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:05.727928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:06.844668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:00.591726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:01.417136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:02.330566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:03.468827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:04.233291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:05.007932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:05.842203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:06.924560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:00.693687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:01.510791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:02.442263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:03.544964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:04.329784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:05.094928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:05.949639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:07.017460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:00.805032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:01.639024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:02.863691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:03.635470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:04.442117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:05.195861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:06.073801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:07.109802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:00.918023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:01.760493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:02.990541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:03.738469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:04.548106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:05.297527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:06.270913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:07.209979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:01.022699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:01.875679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:03.120394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:03.843189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:04.642533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:05.402092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:55:06.404280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:55:11.009259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도읍면동구분노지온주(극조생)노지온주(조생)노지온주(보통)하우스감귤(조기출하)비가림(월동)감귤만감류(시설)만감류(노지)
연도1.0000.0000.0000.0000.0000.0700.0000.0000.0000.000
읍면동0.0001.0000.0000.4560.5580.2020.4940.4320.6420.440
구분0.0000.0001.0000.8190.5770.2950.2750.3840.4080.685
노지온주(극조생)0.0000.4560.8191.0000.8540.7840.8120.8150.7830.859
노지온주(조생)0.0000.5580.5770.8541.0000.8800.9120.7650.9340.799
노지온주(보통)0.0700.2020.2950.7840.8801.0000.8310.9160.9270.911
하우스감귤(조기출하)0.0000.4940.2750.8120.9120.8311.0000.9170.8550.761
비가림(월동)감귤0.0000.4320.3840.8150.7650.9160.9171.0000.8470.720
만감류(시설)0.0000.6420.4080.7830.9340.9270.8550.8471.0000.738
만감류(노지)0.0000.4400.6850.8590.7990.9110.7610.7200.7381.000
2023-12-12T23:55:11.167642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분읍면동
구분1.0000.000
읍면동0.0001.000
2023-12-12T23:55:11.272816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도노지온주(극조생)노지온주(조생)노지온주(보통)하우스감귤(조기출하)비가림(월동)감귤만감류(시설)만감류(노지)읍면동구분
연도1.0000.187-0.066-0.2470.040-0.0730.1600.2050.0000.000
노지온주(극조생)0.1871.0000.8750.6960.7800.8050.8990.7960.2050.524
노지온주(조생)-0.0660.8751.0000.8150.7750.8520.8940.7770.2260.441
노지온주(보통)-0.2470.6960.8151.0000.6910.8320.7430.6400.0960.127
하우스감귤(조기출하)0.0400.7800.7750.6911.0000.7410.8590.7310.2490.191
비가림(월동)감귤-0.0730.8050.8520.8320.7411.0000.8530.7100.2110.278
만감류(시설)0.1600.8990.8940.7430.8590.8531.0000.7990.2770.283
만감류(노지)0.2050.7960.7770.6400.7310.7100.7991.0000.1960.391
읍면동0.0000.2050.2260.0960.2490.2110.2770.1961.0000.000
구분0.0000.5240.4410.1270.1910.2780.2830.3910.0001.000

Missing values

2023-12-12T23:55:07.325307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:55:07.479283image/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.
2023-12-12T23:55:07.643082image/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

연도읍면동구분노지온주(극조생)노지온주(조생)노지온주(보통)하우스감귤(조기출하)비가림(월동)감귤만감류(시설)만감류(노지)
02012대정읍농가수50.0727.03.03.092.0201.017.0
12012대정읍면적38.8505.01.00.950.068.23.9
22012대정읍생산량1067.015691.029.055.01575.01910.078.0
32012남원읍농가수180.04017.0233.0489.0390.01129.0126.0
42012남원읍면적171.33527.3197.9153.5416.7546.134.4
52012남원읍생산량5021.0122287.06555.013211.015285.018346.0500.0
62012성산읍농가수73.01558.025.05.0111.0136.054.0
72012성산읍면적40.0837.020.02.045.343.710.5
82012성산읍생산량1244.029769.0740.0114.01653.0965.0132.0
92012안덕면농가수128.01239.020.028.0112.0109.033.0
연도읍면동구분노지온주(극조생)노지온주(조생)노지온주(보통)하우스감귤(조기출하)비가림(월동)감귤만감류(시설)만감류(노지)
4402021대륜동생산량1260.714813.7<NA>280.096.72170.554.4
4412021대천동농가수108.6591.3<NA>23.0305.0881.044.0
4422021대천동면적102.9473.4<NA>10.789.4244.238.3
4432021대천동생산량2731.118622.1<NA>716.02836.48922.7330.5
4442021중문동농가수200.51107.0<NA>94.022.0372.053.0
4452021중문동면적86.5531.9<NA>25.03.9103.522.1
4462021중문동생산량2482.721284.8<NA>1675.0130.73542.7151.3
4472021예래동농가수224.6530.4<NA>10.059.0286.010.0
4482021예래동면적186.4334.1<NA>3.216.271.72.4
4492021예래동생산량5382.713025.1<NA>215.0494.02331.718.0