Overview

Dataset statistics

Number of variables9
Number of observations2641
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory198.7 KiB
Average record size in memory77.0 B

Variable types

Numeric5
Categorical3
Text1

Dataset

Description2010년 이후 노지채소 계약재배 현황 자료
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20210930000000001623

Alerts

계약농가수 is highly overall correlated with 계약금액(천원) and 1 other fieldsHigh correlation
계약물량(kg) is highly overall correlated with 계약금액(천원) and 1 other fieldsHigh correlation
계약금액(천원) is highly overall correlated with 계약농가수 and 2 other fieldsHigh correlation
계약면적(m2) is highly overall correlated with 계약농가수 and 2 other fieldsHigh correlation
계약면적(m2) is highly skewed (γ1 = 50.2991133)Skewed

Reproduction

Analysis started2023-12-11 03:10:06.541608
Analysis finished2023-12-11 03:10:10.764238
Duration4.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct11
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.0288
Minimum2010
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-12-11T12:10:10.842285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12012
median2015
Q32018
95-th percentile2020
Maximum2020
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1078964
Coefficient of variation (CV)0.0015423583
Kurtosis-1.1915012
Mean2015.0288
Median Absolute Deviation (MAD)3
Skewness0.011952333
Sum5321691
Variance9.6590201
MonotonicityIncreasing
2023-12-11T12:10:10.985881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2014 260
9.8%
2013 256
9.7%
2017 246
9.3%
2011 244
9.2%
2016 243
9.2%
2012 240
9.1%
2015 238
9.0%
2018 238
9.0%
2020 236
8.9%
2019 235
8.9%
ValueCountFrequency (%)
2010 205
7.8%
2011 244
9.2%
2012 240
9.1%
2013 256
9.7%
2014 260
9.8%
2015 238
9.0%
2016 243
9.2%
2017 246
9.3%
2018 238
9.0%
2019 235
8.9%
ValueCountFrequency (%)
2020 236
8.9%
2019 235
8.9%
2018 238
9.0%
2017 246
9.3%
2016 243
9.2%
2015 238
9.0%
2014 260
9.8%
2013 256
9.7%
2012 240
9.1%
2011 244
9.2%

시도
Categorical

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size20.8 KiB
전남
554 
강원
387 
전북
363 
경북
302 
충남
247 
Other values (7)
788 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row강원
2nd row강원
3rd row전남
4th row전남
5th row전남

Common Values

ValueCountFrequency (%)
전남 554
21.0%
강원 387
14.7%
전북 363
13.7%
경북 302
11.4%
충남 247
9.4%
경남 244
9.2%
충북 237
9.0%
경기 123
 
4.7%
제주 122
 
4.6%
대구 37
 
1.4%
Other values (2) 25
 
0.9%

Length

2023-12-11T12:10:11.120639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전남 554
21.0%
강원 387
14.7%
전북 363
13.7%
경북 302
11.4%
충남 247
9.4%
경남 244
9.2%
충북 237
9.0%
경기 123
 
4.7%
제주 122
 
4.6%
대구 37
 
1.4%
Other values (2) 25
 
0.9%

시군
Text

Distinct95
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size20.8 KiB
2023-12-11T12:10:11.517304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0825445
Min length2

Characters and Unicode

Total characters8141
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.3%

Sample

1st row평창군
2nd row평창군
3rd row직할
4th row나주시
5th row목포신안시군
ValueCountFrequency (%)
직할 185
 
7.0%
부안군 99
 
3.7%
해남군 85
 
3.2%
정선군 83
 
3.1%
목포신안시군 80
 
3.0%
고창군 74
 
2.8%
제주시 70
 
2.7%
안동시 66
 
2.5%
거창군 65
 
2.5%
진도군 60
 
2.3%
Other values (85) 1774
67.2%
2023-12-11T12:10:12.098232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1652
20.3%
921
 
11.3%
373
 
4.6%
351
 
4.3%
282
 
3.5%
245
 
3.0%
185
 
2.3%
185
 
2.3%
178
 
2.2%
166
 
2.0%
Other values (82) 3603
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8141
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1652
20.3%
921
 
11.3%
373
 
4.6%
351
 
4.3%
282
 
3.5%
245
 
3.0%
185
 
2.3%
185
 
2.3%
178
 
2.2%
166
 
2.0%
Other values (82) 3603
44.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8141
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1652
20.3%
921
 
11.3%
373
 
4.6%
351
 
4.3%
282
 
3.5%
245
 
3.0%
185
 
2.3%
185
 
2.3%
178
 
2.2%
166
 
2.0%
Other values (82) 3603
44.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8141
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1652
20.3%
921
 
11.3%
373
 
4.6%
351
 
4.3%
282
 
3.5%
245
 
3.0%
185
 
2.3%
185
 
2.3%
178
 
2.2%
166
 
2.0%
Other values (82) 3603
44.3%

품목
Categorical

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size20.8 KiB
배추
681 
양파
482 
건고추
378 
마늘
344 
321 
Other values (4)
435 

Length

Max length3
Median length2
Mean length2.0639909
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row배추
2nd row감자
3rd row마늘
4th row양파
5th row대파

Common Values

ValueCountFrequency (%)
배추 681
25.8%
양파 482
18.3%
건고추 378
14.3%
마늘 344
13.0%
321
12.2%
감자 145
 
5.5%
대파 145
 
5.5%
홍고추 112
 
4.2%
당근 33
 
1.2%

Length

2023-12-11T12:10:12.329634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:10:12.862301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
배추 681
25.8%
양파 482
18.3%
건고추 378
14.3%
마늘 344
13.0%
321
12.2%
감자 145
 
5.5%
대파 145
 
5.5%
홍고추 112
 
4.2%
당근 33
 
1.2%

사업방식
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.8 KiB
매취
1690 
수탁
951 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
매취 1690
64.0%
수탁 951
36.0%

Length

2023-12-11T12:10:13.068660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:10:13.189134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
매취 1690
64.0%
수탁 951
36.0%

계약농가수
Real number (ℝ)

HIGH CORRELATION 

Distinct586
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163.02234
Minimum1
Maximum3597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-12-11T12:10:13.325131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median31
Q3133
95-th percentile902
Maximum3597
Range3596
Interquartile range (IQR)125

Descriptive statistics

Standard deviation349.73766
Coefficient of variation (CV)2.1453358
Kurtosis20.885322
Mean163.02234
Median Absolute Deviation (MAD)28
Skewness4.0375882
Sum430542
Variance122316.43
MonotonicityNot monotonic
2023-12-11T12:10:13.532295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 140
 
5.3%
2 117
 
4.4%
3 104
 
3.9%
4 85
 
3.2%
5 67
 
2.5%
6 62
 
2.3%
8 53
 
2.0%
7 47
 
1.8%
11 46
 
1.7%
9 46
 
1.7%
Other values (576) 1874
71.0%
ValueCountFrequency (%)
1 140
5.3%
2 117
4.4%
3 104
3.9%
4 85
3.2%
5 67
2.5%
6 62
2.3%
7 47
 
1.8%
8 53
 
2.0%
9 46
 
1.7%
10 26
 
1.0%
ValueCountFrequency (%)
3597 1
< 0.1%
3092 1
< 0.1%
2932 1
< 0.1%
2856 1
< 0.1%
2777 1
< 0.1%
2746 1
< 0.1%
2678 1
< 0.1%
2638 1
< 0.1%
2615 1
< 0.1%
2503 1
< 0.1%

계약물량(kg)
Real number (ℝ)

HIGH CORRELATION 

Distinct2442
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2461642.4
Minimum840
Maximum1.822 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-12-11T12:10:13.717052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum840
5-th percentile22080
Q1162215
median633334
Q31959596
95-th percentile10791450
Maximum1.822 × 108
Range1.8219916 × 108
Interquartile range (IQR)1797381

Descriptive statistics

Standard deviation7243260.8
Coefficient of variation (CV)2.9424505
Kurtosis223.29045
Mean2461642.4
Median Absolute Deviation (MAD)547144
Skewness12.013136
Sum6.5011975 × 109
Variance5.2464827 × 1013
MonotonicityNot monotonic
2023-12-11T12:10:13.932775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000.0 19
 
0.7%
200000.0 13
 
0.5%
400000.0 11
 
0.4%
150000.0 8
 
0.3%
500000.0 8
 
0.3%
250000.0 7
 
0.3%
300000.0 7
 
0.3%
1000000.0 7
 
0.3%
120000.0 7
 
0.3%
10000.0 6
 
0.2%
Other values (2432) 2548
96.5%
ValueCountFrequency (%)
840.0 1
< 0.1%
1164.0 1
< 0.1%
1800.0 1
< 0.1%
2064.0 1
< 0.1%
2112.0 1
< 0.1%
2236.0 1
< 0.1%
2484.6 1
< 0.1%
4200.0 1
< 0.1%
4500.0 1
< 0.1%
5000.0 2
0.1%
ValueCountFrequency (%)
182200000.0 1
< 0.1%
126145672.0 1
< 0.1%
124431592.0 1
< 0.1%
93107490.0 1
< 0.1%
74874872.0 1
< 0.1%
56170379.0 1
< 0.1%
48139838.0 1
< 0.1%
46263144.0 1
< 0.1%
44855180.0 1
< 0.1%
42338647.0 1
< 0.1%

계약금액(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct2496
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1433601.1
Minimum3428.1
Maximum57635944
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-12-11T12:10:14.126295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3428.1
5-th percentile34640.52
Q1125000
median349074
Q31126580
95-th percentile6716129.2
Maximum57635944
Range57632516
Interquartile range (IQR)1001580

Descriptive statistics

Standard deviation3714165.6
Coefficient of variation (CV)2.5907943
Kurtosis79.211931
Mean1433601.1
Median Absolute Deviation (MAD)273999.6
Skewness7.4608291
Sum3.7861406 × 109
Variance1.3795026 × 1013
MonotonicityNot monotonic
2023-12-11T12:10:14.344506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000.0 17
 
0.6%
120000.0 11
 
0.4%
200000.0 11
 
0.4%
90000.0 8
 
0.3%
30000.0 6
 
0.2%
80000.0 5
 
0.2%
45000.0 5
 
0.2%
20000.0 5
 
0.2%
400000.0 4
 
0.2%
180000.0 4
 
0.2%
Other values (2486) 2565
97.1%
ValueCountFrequency (%)
3428.1 1
< 0.1%
4892.25 1
< 0.1%
5000.0 1
< 0.1%
5085.99 1
< 0.1%
5311.14 1
< 0.1%
5646.16 1
< 0.1%
5886.9 1
< 0.1%
6069.93 1
< 0.1%
6300.0 1
< 0.1%
6500.0 1
< 0.1%
ValueCountFrequency (%)
57635944.0 1
< 0.1%
51953647.1 1
< 0.1%
49761947.2 1
< 0.1%
49449853.9 1
< 0.1%
47831703.9 1
< 0.1%
41999720.0 1
< 0.1%
37843701.6 1
< 0.1%
28336880.0 1
< 0.1%
27996480.0 1
< 0.1%
27932247.0 1
< 0.1%

계약면적(m2)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2571
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1325085.3
Minimum925
Maximum1.3822654 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2023-12-11T12:10:14.549775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum925
5-th percentile17850
Q179120
median215878
Q3673689
95-th percentile3153417
Maximum1.3822654 × 109
Range1.3822645 × 109
Interquartile range (IQR)594569

Descriptive statistics

Standard deviation27082357
Coefficient of variation (CV)20.438199
Kurtosis2563.6286
Mean1325085.3
Median Absolute Deviation (MAD)174310
Skewness50.299113
Sum3.4995503 × 109
Variance7.3345408 × 1014
MonotonicityNot monotonic
2023-12-11T12:10:14.745128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000 6
 
0.2%
30000 5
 
0.2%
26446 4
 
0.2%
15000 4
 
0.2%
57750 4
 
0.2%
200000 3
 
0.1%
12000 3
 
0.1%
20000 3
 
0.1%
12500 3
 
0.1%
38017 2
 
0.1%
Other values (2561) 2604
98.6%
ValueCountFrequency (%)
925 1
< 0.1%
2500 1
< 0.1%
2778 1
< 0.1%
3088 1
< 0.1%
3334 1
< 0.1%
3488 1
< 0.1%
4000 1
< 0.1%
4500 1
< 0.1%
4628 1
< 0.1%
4653 1
< 0.1%
ValueCountFrequency (%)
1382265400 1
< 0.1%
105758023 1
< 0.1%
101929624 1
< 0.1%
38607612 1
< 0.1%
18550837 1
< 0.1%
18298799 1
< 0.1%
15074193 1
< 0.1%
14155426 1
< 0.1%
13838037 1
< 0.1%
13692278 1
< 0.1%

Interactions

2023-12-11T12:10:09.947056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:10:07.360334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:10:08.036032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:10:08.743832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:10:09.391538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:10:10.074326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/<